Using ChatGPT to annotate research output metadata to improve interdisciplinary understanding of research

Nietzsche is concerned with what he calls ‘affirmation of life’, or ‘saying Yes to life’. This article examines attitudes or processes that Nietzsche describes as ‘affirmation’ or ‘Yes-saying’ (Bejahung, Jasagen). Nietzsche often speaks of something other than an individual as the locus of affirmation. Surveying Nietzsche’s uses from the period of Daybreak onwards, we find Bejahung, Jasagen and cognates with a variety of grammatical subjects, referring to human individuals, cultural products and practices such as art forms and value-systems, and sub-personal items such as instincts and drives. This raises the question how he conceives the attitude or process of ‘Yes-saying’. Taking a distinction made by Ken Gemes, between ‘naïve affirmation’ and ‘reflective affirmation’, the article argues that Nietzsche gives priority to affirmation that is the direct expression of instincts or drives in action. However, many drives or instincts are culturally acquired, for Nietzsche. Dispositions to action, feeling and thought can become drives or instincts through cultural transmission, and operate outside conscious control to influence culture in turn. The notion that a drive ‘in us’ says Yes to life and the notion that a surrounding culture says Yes to life do not conflict but are two sides of the same coin.

AI Summary

This article examines Nietzsche's concept of 'affirmation of life' or 'saying Yes to life' and the various entities, including individuals and cultural products, that can embody this affirmation. It explores the distinction between 'naïve affirmation' and 'reflective affirmation' and argues that Nietzsche prioritizes affirmation rooted in instincts or drives.

AI Analysis

The study delves into Nietzsche's complex ideas about affirming life, shedding light on how both individual instincts and cultural mechanisms can contribute to a positive embrace of existence.

Using Law to Fight Terror seeks to enhance the effectiveness of our current counterterrorism approaches through the investigation of different legal tools and frameworks. The volume will explore the use of law in combating the threat posed by both non-state and state-sponsored terrorism, with contributions drawn from interdisciplinary scholars and practitioners providing critical analysis of current methods, and proposing new and innovative approaches to using legal tools and structures. The ultimate aim of this project is to produce a volume that develops, disseminates, and helps implement effective policy alternatives to the current overly militaristic response to the terrorist threat – an urgent domestic and foreign policy challenge.

AI Summary

The research explores using legal tools to fight terrorism from both non-state and state-sponsored sources.

AI Analysis

The research aims to develop new, effective policy alternatives to the current militaristic response to terrorism.

This previously unpublished chronicle from the mid-fifteenth century covers the English wars in France from 1415 to 1429. It is highly unusual in that it was written by two soldiers, Peter Basset and Christopher Hanson.William Worcester, secretary to the English commander Sir John Fastolf, also had a hand in it, and it was specifically written for Sir John. The content is unusual, as it includes many lists of individuals serving in the war, and records their presence at battles, naming more than 700 in all. Over half these individuals are French or Scottish, so it would seem that the authors had a particularly detailed knowledge of French military participation. The narrative is important for the English campaigns in Maine in the 1420s in which Fast olf was heavily involved and which otherwise receive little attention in chronicles written on either side of the Channel. The progress of the war is well mapped, with 230 place names mentioned.The chronicle was extensively used in the 16th century by several heralds and by Edward Hall. As a result, it had an influence on Shakespeare.The death of the earl of Salisbury at Orleans in ‘Henry VI Part I’ follows the chronicle closely. The ‘Mirror for Magistrates’ Salisbury narrative is also derived from the chronicle. Another point of interest is that the chronicle is by a scribe who can be identified, and proves to be the only known fifteenth-century account of the war written in England in French,which adds an important linguistic dimension to its study.

AI Summary

A previously unpublished chronicle written by English soldiers Peter Basset and Christopher Hanson, along with William Worcester, covering English wars in France from 1415 to 1429, providing detailed lists of individuals involved in battles.

AI Analysis

This chronicle sheds light on the English campaigns in France during the 15th century, provides unique insights into the French military participation, and influenced works by heralds, Edward Hall, and even Shakespeare.

Henry VIII's wives, his watershed break with Rome, Mary's 'bloody' persecution of Protestants and Elizabeth's fearless reign have been immortalised in history books and the public consciousness. This book widens the scope of established historiography by examining the dynamics of Tudor power and assessing where power really lay. By considering the roles of the monarch, church and individuals it sheds a fascinating light on the study of government in 16th century England.

Addressing different aspects of how Tudor England was governed, the twelve chapters discuss who participated in that government, and the extent of their power and governance. Paying close attention to the scholars who have shaped perceptions of major Tudor political figures, this book re-situates the dynamics of Tudor power and its historiography.

AI Summary

This book explores the dynamics of power in Tudor England by examining the roles of the monarch, church, and individuals. It discusses who governed Tudor England and the extent of their power.

AI Analysis

The research delves into the established historiography of Tudor England, shedding light on where power truly resided during this transformative period in English history.

This is the first academic text that considers the design of the 1970s domestic interior beyond the kitsch, emphasising the significance of the home, including its furnishings and décor, as barometer and testing ground for wider socio-cultural, historical and political concerns. Focusing on the home as a site of interplay between the private and the public, the book considers the ways in which the period extends notions of being 'being modern'

AI Summary

This research explores the design of domestic interiors in the US and UK during the 1970s, highlighting their importance as reflections of societal, historical, and political issues.

AI Analysis

The research sheds light on how home decor and furnishings can serve as indicators of wider cultural trends and social dynamics.

The CCRA is a statutory requirement under the Climate Change Act, and must be completed every five years. The first CCRA was laid before Parliament in January 2012, the second (CCRA2) in January 2017, and the next is due by January 2022. As in CCRA2, the Government report will be draw on an independent report prepared by the Committee on Climate Change (CCC), written by July 2021.

AI Summary

The research discusses the 3rd UK Climate Change Risk Assessment (CCRA3) Evidence Report, which is required by law every five years and is due by January 2022.

AI Analysis

The report is important for understanding the risks associated with climate change in the UK and planning for its impacts.

Dataset supports the PhD thesis: 'High Temperature Secondary Lithium-ion Batteries Operating Between 25 deg C and 150 deg C' by Wright.

AI Summary

Dataset supporting PhD thesis on high temperature secondary lithium-ion batteries operating between 25°C and 150°C.

AI Analysis

The dataset provides valuable information for the development of batteries that can operate effectively at high temperatures.

Research data for the paper: Christopher Bayliss, Christine S.M. Currie, Julia A. Bennell, Antonio Martinez-Sykora, 'Queue-constrained packing: A vehicle ferry case study' in European Journal of Operational Research. Contains a text file describing the data and two zipped directories. The directory "testInstances_QueueConstrainedPacking" contains ferry dimensions, vehicle mixes, vehicle dimensions, vehicle type quantities, yard queue width information for each of the 100 test instance in the three classes of instances. The input values and their names are specified in each file, that is they are self contained. The data is organised with a single file for each individual instance. The directory "bestMetaheuristicParameters" contains full details of the metaheuristic parameters that worked best for each individual test instance. This directory contains two files, one corresponding to the metaheuristic that utilised the SOPE packing encoder, and one corresponding to the metaheuristic that utilised the GPE packing encoder. Within each file there is a line for each individual test instance, information that is specified in the file to ensure that the file is self contained.

AI Summary

Research data on queue-constrained packing for a vehicle ferry case study including test instances and metaheuristic parameters.

AI Analysis

The dataset provides valuable information for optimizing vehicle ferry loading, which can lead to more efficient transportation systems.

An overview over nanomembrane-based nanomechanical photonic metamaterials will be given, from the demonstration of a temperature-actuated metamaterial in 2011 to recent breakthroughs. These include the optical detection of thermal motion in nanomechanical metamaterials, and demonstrations of nanobolometers with record-breaking spatial resolution, electrogyration a million times stronger than in natural materials, nonlinear asymmetric transmission and an optically bistable device at microwatt power levels.

AI Summary

The research discusses nanomembrane-based nanomechanical photonic metamaterials, focusing on their optical properties and recent advancements in the field.

AI Analysis

The research showcases various breakthroughs in nanomechanical photonic metamaterials, such as optical detection of thermal motion, record-breaking spatial resolution in nanobolometers, and optically bistable devices operating at very low power levels.

This paper explores the engagement of home-based businesses in digital trading, measured as proportion of their sales from buying and selling services and products online of all their sales. Findings are drawn from a sample of 994 Small- and Medium-Sized Businesses that are members of the Federation of Small Businesses in Scotland. Multivariate findings show that home-based businesses are associated with high proportions of online sales supporting the view of home-based businesses as ‘online’ businesses. However, quantitatively, the overall transformational effects of digital technologies on the nature and processes of entrepreneurship are rather small as the vast majority of home-based businesses, like SMEs that are not home-based, trade offline. Online business models represent a very small proportion of the home-based business sector. Home-based businesses in rural areas do not make greater use of e-commerce. The findings add to the critical literature on the transformative nature of digital entrepreneurship and the emerging home-based business literature that question whether the role of digital technologies and online marketplaces for home-based businesses are being exaggerated, particularly in rural economies.

AI Summary

The research examines how home-based businesses in Scotland engage in digital trading, finding that they have high proportions of online sales but overall, most still trade offline.

AI Analysis

The study challenges the perception that home-based businesses are heavily reliant on online sales, especially in rural areas, adding to discussions about the impact of digital technologies on entrepreneurship.

Supply chains are customarily associated with multiple interconnected risks originated from supply side, demand side, or from the unanticipated background uncertainties faced by a firm. Also, effective functioning of supply chain hinges on sourcing decisions of inputs (raw materials). Therefore, there is a striking need to analyse the risk preference of the decision maker while going for optimal sourcing decision under varying degree of interconnected supply chain risks. This study addresses this issue by analysing the comparative static effects under interconnected supply chain risks for a risk averse decision-maker, manufacturing and selling products in a regulated market under perfect competition. The decision-maker faces not only supply-side risk (due to random input material prices) but also interconnected risks arising out of background risk (setup costs risk) and demand-side risk (output prices risk). With preferences defined over the mean and standard deviation of the uncertain final profit, this study illustrates the effects of the changes in the pairwise correlations between the three above mentioned risks on the optimum input choice of the manufacturer. To contextualise this study, an India-based generic drug manufacturer cum seller has been considered as a case in the parametric example of our model. Adaptation of the mean–variance framework helps obtaining all the results in terms of the relative trade-off between risk and return, with simple yet intuitive interpretations.

AI Summary

The research examines how decision makers in supply chains account for interconnected risks when making sourcing decisions for optimal outcomes.

AI Analysis

This research addresses the complex issue of managing risks in supply chains, using a real-world case study to illustrate the effects of interconnected risks on decision-making processes.

Understanding and predicting the effects of wind loading on a structure is necessary for safe, effective, and economical engineering design. Wind tunnel techniques often provide data that is not sufficient for the structural engineer. With increasing advances in computational capabilities, Computational Fluid Dynamics techniques have recently become feasible to complement experiments. Of these, one of the most effective is Large-Eddy Simulation (LES). The application of LES to analyse wind loading, and aeroelastic effects on structures are only a recent venture in the field. This paper reviews the progress made over the last few decades for the analysis of wind flow around slender structures, and the more recent analysis incorporating the effects of freestream turbulence. First, a review of the literature is carried out for generating freestream turbulence approaches, of which many are used for the analysis of surface pressures on an isolated object. Subsequently, a review is made on wind tunnel experiment and LES for aeroelastic analysis of bridge sections. The recent advances in the understanding of turbulence effects on the aerostatic responses are summarised. Finally, the future of LES and its relationship with wind tunnel experiment for wind loading analysis are discussed.

AI Summary

The research reviews the use of Large-Eddy Simulation (LES) in analyzing wind loading on structures, focusing on wind flow around slender structures and the effects of freestream turbulence.

AI Analysis

The study highlights the importance of advancing computational techniques like LES to complement wind tunnel experiments for more accurate analysis of wind loading on structures, which is crucial for engineering design.

This dataset supports the MPhil thesis: Li Zhu (2019) 'Dynamic topology optimization of plate for vibration suppression', University of Southampton This dataset contains all the simulation code and data used to generate the figures included in the main text.

AI Summary

Dataset containing simulation code and data used in a thesis on dynamic topology optimization for vibration suppression.

AI Analysis

The dataset provides valuable resources for understanding and replicating the research on dynamic topology optimization for vibration suppression.

We study the properties of Dutch auctions in an independent private value setting, where bidders face uncertainty over the type distribution of their opponents and evaluate their payoffs by the worst case from a set of probabilistic scenarios. In contrast to static auction formats, participants in the Dutch auction gradually learn about the valuations of other bidders. We show that the transmitted information can lead to changes in the worst-case distribution and thereby shift a bidder's payoff maximizing exit price over time. We characterise the equilibrium bidding function in this environment and show that the arriving information leads bidders to exit earlier at higher prices. As a result, the Dutch auction systematically generates more revenue than the first-price auction.

AI Summary

The research explores how Dutch auctions, where participants gradually learn about other bidders' valuations, can lead to higher revenue compared to first-price auctions.

AI Analysis

The study investigates how information sharing during Dutch auctions can influence bidders to exit earlier at higher prices, ultimately generating more revenue than other auction formats.

Supporting data for Chapter 6 in thesis Fragment-Based Energy Models and Machine Learning Methods for the Computational Study of Organic Molecular Crystals. University of Southampton, 2020

AI Summary

The research provides supporting data for a thesis chapter on predicting the crystal structures of organic molecular cocrystals.

AI Analysis

This research is valuable for understanding how different molecules can interact and form stable crystal structures.

At crucial junctures in the development of his concept of ‘authenticity’, Heidegger discusses at length Aristotle’s concept of ‘phronêsis’; and there is a widely-held suspicion that those discussions shape that development. The present paper examines that suspicion in the light of an apparent tension in Aristotle’s texts between understanding phronêsis as a perceptual capacity and understanding it as a deliberative capacity. Bronwyn Finnigan has argued that some influential, recent Heideggerian scholarship on this topic emphasises the perceptual and downplays the deliberative, and there is evidence in Heidegger’s texts that might suggest he does too. The present paper, however, offers an alternative to this perceptually-focused reading, which I call ‘the all-things-considered judgment reading’. It understands the exercise of phronêsis, and the authenticity which Heidegger models upon it, as deliberative feats, accommodates the evidence thought to support the perceptually-focused reading, and avoids philosophical objections that the latter reading’s understanding invites.

AI Summary

The research discusses Heidegger's interpretation of Aristotle's concept of 'phronêsis' and the debates surrounding whether it is about perception or deliberation.

AI Analysis

This research delves into a critical aspect of Heidegger's exploration of authenticity and sheds light on the philosophical implications of his interpretation of Aristotle's concept.

Background: over 13,000 new cases of non-Hodgkin’s lymphoma (NHL) are diagnosed in the UK, with approximately 4,900 attributable deaths each year. Diffuse Large B-cell Lymphoma (DLBCL) is the most common NHL comprising one third of adult NHL cases. R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, prednisolone) is accepted as the international standard first-line regimen, but improvement in first line treatment is needed. Dysregulated B-cell receptor (BCR) signalling has been identified as a feature of DLBCL. Inhibition of Bruton’s tyrosine kinase (Btk), downstream of the BCR has proven efficacious in other B-cell malignancies and in combination with R-CHOP. The second generation Btk inhibitor, acalabrutinib, may have improved target potency and specificity, and therefore better efficacy and tolerability.

Methods: ACCEPT is an open-label non-randomised Phase Ib/II trial testing the addition of acalabrutinib to conventional R-CHOP therapy. ACCEPT incorporates an initial 6+6 modified Phase I design of up to 24 participants followed by 15 participant single arm Phase II expansion cohort in treatment naive patients with histologically confirmed DLBCL expressing CD20. Participants are recruited from UK secondary care sites. Phase I will establish the recommended Phase II dose (RP2D, primary endpoint) of acalabrutinib in combination with R-CHOP. Phase II will gain additional information on safety and efficacy on the RP2D. The primary endpoints of Phase II are overall response rate and toxicity profile. Secondary endpoints include duration of response (progression-free survival and overall survival OS) in relation to cell of origin. Analyses are not powered for formal statistical comparisons; descriptive statistics will describe rates of toxicity, efficacy and translational endpoints.

Discussion: ACCEPT will provide evidence for whether acalabrutinib in combination with R-CHOP is safe and biologically effective prior to future Phase II/III trials in patients with previously untreated CD20 positive DLBCL.

Trial registration: EudraCT Number: 2015-003213-18 (issued 16 July 2015); ISRCTN13626902 (registered 07 March 2017).

AI Summary

The research is about testing a new drug, acalabrutinib, in combination with standard treatment for Diffuse Large B-cell Lymphoma (DLBCL).

AI Analysis

The study aims to see if adding acalabrutinib to the regular treatment is safe and effective for patients with DLBCL, potentially improving outcomes for this type of cancer.

The institutional form and conception of Higher Education have changed through the growth of mass higher education, which in many national systems now operates on market logics. Drawing on theories of credentialism, this article provides a critical analysis of the inter-relationship between massification and marketization and examines a range of consequences this has for institutional relations and dynamics. A central feature of credential inflation in mass systems has been the growing competition for scarce status goods and the reproduction of structural inequalities in accessing sought-after occupational outcomes. The policy context of marketization has concurrently reinforced the pressures on institutions to fulfil the promise held by governments, employers and graduates of enhancing human capital and Higher Education institutions’ economic value. Accompanying New Public Management policy levers have further established institutional conditions based on competitive accountability and performative evaluation. We show how these pressures are manifested in new forms of instrumental rationality that valorize the commodification of academic credentials, and relatedly, studentship and academic scholarship. We finally consider the possible ways forward in appraising the goals of HE beyond credential inflation.

AI Summary

The research examines the impact of the massification and marketization of higher education on the competition for status and access to desirable job outcomes.

AI Analysis

The research sheds light on how modern higher education systems prioritize economic value and competitive accountability, leading to a commodification of academic credentials and reinforcing structural inequalities in society.

Dataset supports chapter 3 of the thesis - Biogeographic processes determining the distributions of European bats across spatial scales: The role of biotic interactions and habitat preferences

AI Summary

The dataset supports a study on the factors influencing the distributions of European bats.

AI Analysis

The dataset provides valuable information on how biotic interactions and habitat preferences affect the distribution of bats in Europe.

Inflammatory markers including C-reactive protein (CRP) and procalcitonin (PCT) have been shown to be useful biomarkers to improve triage speed and prevent the inappropriate use of antibiotics for infections such as pneumonia. Here, we present a novel and exciting solution to guide the administration of antibiotic treatment via rapid, semi-quantitative and multiplexed detection of CRP and PCT using an advanced lateral flow device (LFD) designed to have multiple parallel flow-paths, produced via the precise laser-based partitioning of the single flow-path of a standard LFD. Each flow-path within this multiplexed LFD has a unique detection capability which permits tailored detection of CRP within a predefined cut-off range (20 μg/mL - 100 μg/mL) and PCT above a pre-defined threshold (0.5 ng/mL). We demonstrate the use of this LFD in the successful detection of CRP and PCT semi-quantitatively within spiked human serum samples. This multiplexed near-patient assay has potential for development into a rapid triage and treatment of patients with suspected pneumonia.

AI Summary

Detection of inflammatory biomarkers CRP and PCT using a laser-patterned multiplexed lateral flow device for rapid antibiotic treatment guidance.

AI Analysis

The research introduces a new method for quickly and semi-quantitatively detecting key biomarkers to aid in antibiotic administration, which could enhance patient care for pneumonia.

This dataset contains data from my PhD thesis which investigates the impact of adolescent dysmenorrhea on health-related quality of life. The dataset contains: 1. Qualtiative Data Set: Adolescent's Interviews This data is within a folder titled 'Adolescent Interviews' which contains 19 anonymised transcripts from adolescents. The aim of this study was to explore the experiences of adolescents living with dysmenorrhea. 2. Qualtiative Data Set: Mother's Interviews This data is within a folder titled 'Mother's Interviews' which contains 18 anonymised transcripts from mothers of adolescents experiencing dysmneorrhea. The aim of this study was to explore maternal perceptions of adolescent dysmenorrhea. 3. Quantitative Survey Data A quantiative data set from a survey which collected data from 333 adolescent girls aged 13-18 years old. The file is titled 'Quantitative Data Set'. The data is presented in excel. This has been exported from SPSS. The data was exported with the values instead of codes. Therefore, the data set should be easy to read as the values assigned are simply answers to the survey questions. The aim of this survey study was to investigate the psychosocial predictors of qualtiy of life outcomes among adolescent girls.

AI Summary

The dataset contains interviews and survey data related to how dysmenorrhea affects the health-related quality of life of adolescent girls.

AI Analysis

This research provides insights into the experiences of adolescents with dysmenorrhea and aims to understand the impact of psychosocial factors on their quality of life.

Real-world multilabel data are high dimensional, and directly using them for label distribution learning (LDL) will incur extensive computational costs. We propose a multilabel distribution learning algorithm based on multioutput regression through manifold learning, referred to as MDLRML. By exploiting smooth, similar spaces' information provided by the samples' manifold learning and LDL, we link the two spaces' manifolds. This facilitates using the topological relationship of the manifolds in the feature space to guide the manifold construction of the label space. The smoothest regression function is used to fit the manifold data, and a locally constrained multioutput regression is designed to improve the data's local fitting. Based on the regression results, we enhance the logical labels into the label distributions, thereby mining and revealing the label's hidden information regarding importance or significance. Extensive experimental results using real-world multilabel datasets show that the proposed MDLRML algorithm significantly improves the multilabel distribution learning accuracy and efficiency over several existing state-of-the-art schemes.

AI Summary

A new algorithm, MDLRML, is proposed for multilabel distribution learning by combining multi-output regression and manifold learning to improve accuracy and efficiency in handling high-dimensional data.

AI Analysis

The MDLRML algorithm enhances label distribution learning by linking spaces through manifold learning, significantly improving accuracy and efficiency over existing schemes when dealing with real-world multilabel datasets.

A key characteristic of biological systems is the ability to update the memory by learning new knowledge and removing out-of-date knowledge so that intelligent decision can be made based on the relevant knowledge acquired in the memory. Inspired by this fundamental biological principle, this article proposes a multi-output selective ensemble regression (SER) for online identification of multi-output nonlinear time-varying industrial processes. Specifically, an adaptive local learning approach is developed to automatically identify and encode a newly emerging process state by fitting a local multi-output linear model based on the multi-output hypothesis testing. This growth strategy ensures a highly diverse and independent local model set. The online modeling is constructed as a multi-output SER predictor by optimizing the combining weights of the selected local multi-output models based on a probability metric. An effective pruning strategy is also developed to remove the unwanted out-of-date local multi-output linear models in order to achieve low online computational complexity without scarifying the prediction accuracy. A simulated two-output process and two real-world identification problems are used to demonstrate the effectiveness of the proposed multi-output SER over a range of benchmark schemes for real-time identification of multi-output nonlinear and nonstationary processes, in terms of both online identification accuracy and computational complexity.

AI Summary

The research proposes a method inspired by biological systems to identify nonlinear and nonstationary industrial processes using a selective ensemble regression approach.

AI Analysis

The proposed approach utilizes a biological principle for online identification of industrial processes, showing improved accuracy and low computational complexity.

In contemporary international remanufacturing supply chains, whether an original equipment manufacturer (OEM) engages in remanufacturing operations or outsources to a third-party remanufacturer (TPR) is influenced by tax and tariff regulations. This study develops a two-stage game model for the decision-making of an OEM from an exporting country showing that the optimal remanufacturing model is significantly affected by the tax and tariff regulations of the importing country and more particularly, the difference between sales tax on remanufactured products and the unit product import tariffs on new products. The model selections for the OEM and the importing country align when this difference is close to zero. This paper is one of the few examining the impact of tax and tariff regulations on outsourcing decisions in remanufacturing contexts, which is largely neglected in the extant literature but has become increasingly important, especially with recent development trends of deglobalization (e.g., Brexit, the US–China trade war, and various sanctions). The significance of this study is threefold: the work makes novel theoretical contributions to the decision-making game model with tax and tariff constructs taken into consideration, has practical implementations for optimizing the strategic business deployment of OEMs, and has implications for consideration of policy and social welfare by policy makers of the destination country.

AI Summary

This study explores how tax and tariff regulations influence whether a company chooses to do remanufacturing operations in-house or outsource to a third party.

AI Analysis

The research sheds light on the importance of tax and tariff regulations in outsourcing decisions within remanufacturing supply chains, providing valuable insights for businesses and policy makers.

Dataset supports the thesis 'Structural Studies into πˑˑˑπ Interactions and their Cooperativity Effect on the Spin Crossover Behaviour of a Novel Series of Naphthalimide Compounds' by Zhang.

AI Summary

Studying how certain interactions impact the behavior of a new group of compounds.

AI Analysis

The research explores how the way molecules interact affects their properties.

This paper comprehensively examines the performance of a host of popular variables to predict Bitcoin returns. We show that time-series momentum, economic policy uncertainty, and financial uncertainty outperform other predictors in all in-sample, out-of-sample, and asset allocation tests. Bitcoin returns have no exposure to common stock and bond market factors but rather are affected by Bitcoin-specific and external uncertainty factors.

AI Summary

The research looked at different factors to predict Bitcoin returns and found that momentum, economic policy uncertainty, and financial uncertainty were the most effective predictors.

AI Analysis

This study is significant because it shows how certain factors can be used to predict Bitcoin returns, which are influenced by unique Bitcoin-related and external uncertainties rather than traditional market factors.

We relate the low-energy expansions of world-sheet integrals in genus-one amplitudes of open- and closed-string states. The respective expansion coefficients are elliptic multiple zeta values (eMZVs) in the open-string case and non-holomorphic modular forms dubbed 'modular graph forms (MGFs)' for closed strings. By inspecting the differential equations and degeneration limits of suitable generating series of genus-one integrals, we identify formal substitution rules mapping the eMZVs of open strings to the MGFs of closed strings. Based on the properties of these rules, we refer to them as an elliptic single-valued map which generalizes the genus-zero notion of a single-valued map acting on MZVs seen in tree-level relations between the open and closed string.

AI Summary

The research explores a connection between open and closed-string states in string theory at genus one by analyzing world-sheet integrals.

AI Analysis

The study uncovers a formal mapping between elliptic multiple zeta values of open strings and modular graph forms of closed strings, extending the understanding of relationships between different string states.

Background: while the likelihood of identifyingconstitutional breast cancer-associated BRCA1, BRCA2and TP53 pathogenic variants (PVs) increases with earlierdiagnosis age, little is known about the correlation withage at diagnosis in other predisposition genes. Here,we assessed the contribution of known breast cancerassociated genes to very early onset disease.

Methods: sequencing of BRCA1, BRCA2, TP53 andCHEK2 c.1100delC was undertaken in women withbreast cancer diagnosed ≤30 years. Those testingnegative were screened for PVs in a minimum of eightadditional breast cancer-associated genes. Rates ofPVs were compared with cases ≤30 years from theProspective study of Outcomes in Sporadic vs Hereditarybreast cancer (POSH) study.

Results: testing 379 women with breast cancer aged≤30 years identified 75 PVs (19.7%) in BRCA1, 35(9.2%) in BRCA2, 22 (5.8%) in TP53 and 2 (0.5%)CHEK2 c.1100delC. Extended screening of 184 PVnegative women only identified eight additionalactionable PVs. BRCA1/2 PVs were more common inwomen aged 26–30 years than in younger women(p=0.0083) although the younger age group had ratesmore similar to those in the POSH cohort. Out of 26women with ductal carcinoma in situ (DCIS) alone,most were high-grade and 11/26 (42.3%) had a PV(TP53=6, BRCA2=2, BRCA1=2, PALB2=1). This PV yieldis similar to the 61 (48.8%) BRCA1/2 PVs identified in125 women with triple-negative breast cancer. The POSHcohort specifically excluded pure DCIS which may explainlower TP53 PV rates in this group (1.7%).

Conclusion: the rates of BRCA1, BRCA2 and TP53PVs are high in very early onset breast cancer, withlimited benefit from testing of additional breast cancerassociated genes.

AI Summary

Study assessed the likelihood of detecting pathogenic variants in breast cancer genes in women diagnosed with breast cancer at age ≤30, finding high rates of BRCA1, BRCA2, and TP53 variants.

AI Analysis

This research is important because it shows that women with very early onset breast cancer have a high likelihood of having detectable pathogenic variants in well-known breast cancer genes, particularly BRCA1, BRCA2, and TP53.

This chapter investigates the development of luxury retailing in the airport from its origins at Shannon airport in 1947, to the establishment of specialist duty-free retailers, and, up to the present-day airport luxury brand stores. The evolution of luxury retailing in the airport has followed the growth in air travel resulting from globalization, the privatization and deregulation of air transport and the rise of low-cost airlines. Airport luxury retailing serves a resilient global market and it is an important source of sales for luxury brand companies. Moreover, luxury brands have recognized the commercial significance and public relations value of an international airport location where they deploy a sophisticated visual language to promote their global brand identity. The distribution of luxury goods in international airports through luxury brand stores and multi-brand duty-free outlets is investigated and consideration is given to the future of luxury in the airport.

Keywords: Luxury, Luxury Brands, Air Travel, International Airport, Duty-free, Travel Retail

AI Summary

The research explores the history and development of luxury retail stores in airports, highlighting their importance for luxury brands and the global market.

AI Analysis

The presence of luxury retail stores in airports and their impact on sales and brand recognition showcases the evolving landscape of luxury retail in a globalized world.

Although often associated with the preservation of traditional craft skills, luxury is simultaneously linked to creativity and innovation in terms of new and technologically advanced goods. This chapter identifies how the timelessness of craftsmanship relates to creativity and innovation in the production of luxury branded goods from bespoke shoes and silk scarves to luxury cars. Drawing on relevant academic literature from the fields of craft, creativity, and innovation, together with the exploration of examples derived from secondary sources, the chapter sheds light on the connections and tensions between these three attributes of present-day luxury branded goods. The analysis presented provides insight into the varied and often complex connections between craft, creativity and innovation in the context of the production of luxury branded goods.

AI Summary

The chapter discusses the relationship between tradition, creativity, and innovation in the production of luxury goods.

AI Analysis

The research highlights how luxury brands balance traditional craftsmanship with new technologies to create innovative products.

Simple, transparent rules are often frowned upon while complex, black-box models are seen as holding greater promise. Yet in quickly changing situations, simple rules can protect against overfitting and adapt quickly. We show that the surprisingly simple recency heuristic forecasts more accurately than Google Flu Trends (GFT) which used big data analytics and a black-box algorithm. This heuristic predicts that “this week's proportion of flu-related doctor visits equals the proportion from the most recent week.” It is based on psychological theory of how people deal with rapidly changing situations. Other theory-inspired heuristics have outperformed big data models in predicting outcomes, such as U.S. presidential elections, or other uncertain events, such as consumer purchases, patient hospitalizations, and terrorist attacks. Heuristics are transparent, clearly communicating the underlying rationale for their predictions. We advocate taking into account psychological principles that have evolved over millennia and using these as a benchmark when testing big data models.

AI Summary

The study compares the accuracy of a simple rule based on psychological theory with a complex model using big data analytics for forecasting influenza incidence.

AI Analysis

The research shows that a simple heuristic based on psychological theory outperformed a complex big data model in predicting influenza incidence, highlighting the potential effectiveness of transparent and easy-to-understand rules in forecasting rapidly changing situations.

The COVID-19 pandemic imposed restrictive measures on dentistry in different regions of the world, ranging from stoppage of care to only permission for urgent and emergency dental services. Thus, new biosafety guidelines for resuming activities, whether in single dental offices, large clinics or dental education activities, are urgently required. In this sense, herein, guidelines that incorporate common points of the main protocols found in the literature for the resumption of dental activities at their different levels, whether in the scope of care or education, are presented. Furthermore, we present the incorporation of measures that allow an increase in the level of biosafety, such as the control of the dental team, the inclusion in the history of conjunctivitis as a possible alert for COVID-19, and the use of the pulse oximeter to assess the risk of silent hypoxemia, which may indicate a complication of COVID-19. In addition, new perspectives for directing research and innovation for biosafety in dentistry are discussed.

AI Summary

New dental guidelines incorporating biosafety measures for resuming activities during the COVID-19 pandemic are presented, including protocols for dental care and education, team control, conjunctivitis alert, and the use of pulse oximeters.

AI Analysis

This research provides important guidelines to ensure the safety of dental practices amidst the ongoing pandemic, offering innovative measures to reduce the risk of COVID-19 transmission in dental settings.

Anxiety disorders are the most common psychiatric disorder in children and young people. They can be prevented in those at risk, but families do not always take up opportunities to participate in prevention programmes. This qualitative study aimed to understand what families with children who were at prospective risk of anxiety disorders perceived to be the barriers to access to targeted anxiety prevention programmes, and to explore what would help facilitate access.
We used Information Power to determine our sample size, and individually interviewed seven young people (14-17 years) who had anxiety disorders, and their mothers, each of whom had pre-natal anxiety disorders. We transcribed all interviews and thematically analysed them to identify perceived barriers and facilitators to targeted anxiety prevention programmes.
Perceived potential barriers to access included possible negative consequences of anxiety prevention, difficulties in identifying anxiety as a problem, and concerns about how professions would respond to raising concerns about anxiety. Possible facilitators included promoting awareness of anxiety prevention programmes, and involvement of schools in promotion and delivery of prevention.
Our findings illustrate that implementation of targeted anxiety prevention could be improved through i) the provision of tools for parents to recognize anxiety in their children as a problem, ii) promotion of awareness, as well as delivery, of anxiety prevention via schools, and iii) the involvement of parents and possibly adolescents in the intervention programme, but not younger children.

AI Summary

The study explored reasons why families at risk of anxiety disorders may not participate in prevention programs, and identified ways to make access easier.

AI Analysis

The research highlights important barriers and facilitators to targeted anxiety prevention programs, providing insights that could improve the effectiveness of such interventions.

This article enhances the discrete singular convolution method for free vibration analysis of non-uniform thin beams with variability in their geometrical and material properties such as thickness, specific volume (inverse of density) and Young’s modulus. The discrete singular convolution method solves the differential equation of motion of a structure with a high accuracy using a small number of discretisation points. The method uses polynomial chaos expansion to express these variabilities simulating uncertainty in a closed form. Non-uniformity is locally provided by changing the cross section and Young’s modulus of the beam along its length. In this context, firstly natural frequencies of deterministic uniform and non-uniform beams are predicted via the discrete singular convolution. These results are compared with finite element calculations and analytical solutions (if available) for the purpose of verification. Next, the uncertainty of the beam because of geometrical and material variabilities is modelled in a global manner by polynomial chaos expansion to predict probability distribution functions of the natural frequencies. Monte Carlo simulations are then performed for validation purpose. Results show that the proposed algorithm of the discrete singular convolution with polynomial chaos expansion is very accurate and also efficient, regarding computation cost, in handling non-uniform beams having material and geometrical variabilities. Therefore, it promises that it can be reliably applied to more complex structures having uncertain parameters.

AI Summary

The research improves a method for analyzing the vibrations of uncertain non-uniform beams using polynomial chaos expansion.

AI Analysis

The study offers a precise and efficient technique for predicting the behavior of beams with varying geometric and material properties, which can be valuable for analyzing complex structures with uncertain parameters.

This article discusses the intensification of research performance demands in UK universities in relation to the complex terrain of academic identity formation. It considers whether a demand for academic researchers to produce and evidence economic and societal impact–in the rewards game of the UK’s performance-based research funding system, the Research Excellence Framework (REF)–influences their self-concept as ‘engaged researchers’. While a designation of being REF impactful may be considered constitutive to a researcher’s sense of self-worth and advantageous to their professional and institutional profile, a consultation of researchers included within REF2014 impact case studies challenges these assumptions. Instead, respondents are found to complain of identity dispossession and exploitation by their universities where their public contributions are appropriated for positional gain. Their testimony confirms the prevalence of a culture of ‘competitive accountability’ across UK universities which is with a systemic insatiability for ‘scholarly distinction’, causing the privileging of appearance in rationalisations of publicly funded research. Using the theoretical insights of Guy Debord and Erving Goffman it is argued that REF impact elucidates the UK higher education sector as a ‘society of the Spectacle’ that subjugates ‘authentic’ versions of the academic Self. However, REF-impact is also seen to provide an opportunity for cultural detournément and a means to elicit and concurrently invert ‘simulations’ of research praxis’, thus enabling the assertion or ‘front-staging’ of perceived and idealised academic identities.

AI Summary

The research examines how the pressure to show economic and societal impact in UK universities affects academic identity.

AI Analysis

The study challenges assumptions about the benefits of meeting impact demands, revealing how researchers feel exploited and lose their sense of self within a culture of competitive accountability.

Chris Janaway argues that Schopenhauer's theory of negation of the will is problematic: How can you will not to will? If will is the basis of all reality, who would remain to experience the satisfaction that negation of the will supposedly generates? Janaway argues that negation of the will is best thought of as negation specifically of the will to life, and that this is compatible with the existence of other kinds of willing. Will to life is egoistic willing; and the negation of this kind of willing is consistent with nonegoistic willing and, in particular, moral action. This more constrained interpretation of the doctrine of negation of the will not only makes more sense of the text when Schopenhauer distinguishes between self- and other-directed willing; it helps clarify Schopenhauer’s account of the relation between virtue and holiness. The morally righteous person has other-directed desires at least some of the time, but not necessarily all of the time, while the saint no longer has any self-directed desires at all. Finally, Janaway shows that this interpretation of negation of the will has the virtue of bringing Schopenhauer closer to the Buddhist models he cites in support of his theory.

AI Summary

Schopenhauer's theory of negation of the will is reinterpreted as negation of the will to life, allowing for other kinds of willing like moral action.

AI Analysis

This research challenges traditional views on Schopenhauer's concept of will and offers a new perspective that aligns his ideas with Buddhist philosophy.

Ubiquitin-specific protease (USP7), also known as Herpesvirus-associated ubiquitin-specific protease (HAUSP), is a deubiquitinase. There has been significant recent attention on USP7 following the discovery that USP7 is a key regulator of the p53-MDM2 pathway. The USP7 protein is 130 kDa in size and has multiple domains which bind to a diverse set of proteins. These interactions mediate key developmental and homeostatic processes including the cell cycle, immune response, and modulation of transcription factor and epigenetic regulator activity and localization. USP7 also promotes carcinogenesis through aberrant activation of the Wnt signalling pathway and stabilization of HIF-1α. These findings have shown that USP7 may induce tumour progression and be a therapeutic target. Together with interest in developing USP7 as a target, several studies have defined new protein interactions and the regulatory networks within which USP7 functions. In this review, we focus on the protein interactions of USP7 that are most important for its cancer-associated roles.

AI Summary

USP7 protein, a deubiquitinase, has key interactions promoting tumorigenesis through modulation of various cellular processes and pathways.

AI Analysis

Understanding the USP7 protein interaction network sheds light on its crucial role in cancer and potential as a therapeutic target.

We propose a novel probabilistic label enhancement algorithm, called PLEA, to solve challenging label distribution learning (LDL) for multi-label classification problems. We adopt the well-known maximum entropy model based label distribution learner. However, unlike the existing LDL algorithms based on the maximum entropy model, we propose to use manifold learning to enhance the label distribution learner. Specifically, the supervised information in the label manifold is utilized in the feature manifold space construction to improve the accuracy of feature extraction, while dramatically reducing the feature dimension. Then the robust linear regression is employed to estimate the label distributions associated with the extracted reduced-dimension features. Using the enhanced reduced-dimension features and their associated estimated label distributions in the maximum entropy model, the unknown true label distributions can be estimated more accurately, while imposing considerably lower computational complexity. We evaluate the proposed PLEA method on a wide-range artificial and high-dimensional real-world datasets. Experimental results obtained demonstrate that our proposed PLEA method has advantages in LDL accuracy and runtime performance, compared to the latest multi-label LDL approaches. The results also show that our PLEA compares favourably with the state-of-the-arts multi-label learning algorithms for classification tasks.

AI Summary

The research introduces a new algorithm, PLEA, that enhances label distribution learning for multi-label classification through manifold learning. This algorithm improves accuracy and reduces computational complexity.

AI Analysis

The algorithm proposed in this research, PLEA, offers improved accuracy and lower computational complexity compared to existing methods for multi-label classification tasks.

Objective:To determine the relationship between BC, specifically low skeletal muscle mass (sarcopenia) and poor muscle quality (myosteatosis) and outcomes in emergency laparotomy patients.Background:Emergency laparotomy has one of the highest morbidity and mortality rates of all surgical interventions. BC objectively identifies patients at risk of adverse outcomes in elective cancer cohorts, however, evidence is lacking in emergency surgery.Methods:An observational cohort study of patients undergoing emergency laparotomy at ten English hospitals was performed. BC analyses were performed at the third lumbar vertebrae level using preoperative computed tomography images to quantify skeletal muscle index (SMI) and skeletal muscle radiation attenuation (SM-RA). Sex-specific SMI and SM-RA were determined, with the lower tertile splits defining sarcopenia (low SMI) and myosteatosis (low SM-RA). Accuracy of mortality risk prediction, incorporating SMI and SM-RA variables into risk models was assessed with regression modeling.Results:Six hundred ten patients were included. Sarcopenia and myosteatosis were both associated with increased risk of morbidity (52.1% vs 45.1%, P = 0.028; 57.5% vs 42.6%, P = 0.014), 30-day (9.5% vs 3.6%, P = 0.010; 14.9% vs 3.4%, P < 0.001), and 1-year mortality (27.4% vs 11.5%, P < 0.001; 29.7% vs 12.5%, P < 0.001). Risk-Adjusted 30-day mortality was significantly increased by sarcopenia [OR 2.56 (95% CI 1.12-5.84), P = 0.026] and myosteatosis [OR 4.26 (2.01-9.06), P < 0.001], similarly at 1-year [OR 2.66 (95% CI 1.57-4.52), P < 0.001; OR2.08 (95%CI 1.26-3.41), P = 0.004]. BC data increased discrimination of an existing mortality risk-prediction model (AUC 0.838, 95% CI 0.835-0.84).Conclusion:Sarcopenia and myosteatosis are associated with increased adverse outcomes in emergency laparotomy patients.

AI Summary

The study investigated how low muscle mass and poor muscle quality are related to outcomes in emergency laparotomy patients.

AI Analysis

The research found that both sarcopenia (low muscle mass) and myosteatosis (poor muscle quality) are linked to higher risks of complications and mortality in emergency laparotomy patients, highlighting the importance of assessing muscle health in these individuals.

The fundamental understanding of how Cancer initiates, persists and then progresses is evolving. High-resolution technologies, including single-cell mutation and gene expression measurements, are now attainable, providing an ever-increasing insight into the molecular details. However, this higher resolution has shown that somatic mutation theory itself cannot explain the extraordinary resistance of cancer to extinction. There is a need for a more Systems-based framework of understanding cancer complexity, which in particular explains the regulation of gene expression during cell-fate decisions. Cancer displays a series of paradoxes. Here we attempt to approach them from the view-point of adaptive exploration of gene regulatory networks at the edge of order and chaos, where cell-fate is changed by oscillations between alternative regulators of cellular senescence and reprogramming operating through self-organisation. On this background, the role of polyploidy in accessing the phylogenetically pre-programmed “oncofetal attractor” state, related to unicellularity, and the de-selection of unsuitable variants at the brink of cell survival is highlighted. The concepts of the embryological and atavistic theory of cancer, cancer cell “life-cycle”, and cancer aneuploidy paradox are dissected under this lense. Finally, we challenge researchers to consider that cancer “defects” are mostly the adaptation tools of survival programs that have arisen during evolution and are intrinsic of cancer. Recognition of these features should help in the development of more successful anti-cancer treatments.

AI Summary

The research explores the paradoxes of cancer survival by investigating gene regulation and cell-fate decisions at the edge of order and chaos.

AI Analysis

The study challenges the traditional view of cancer as simply genetic mutations and highlights the adaptive nature of cancer cells in resisting extinction.

Underpinning data for PhD thesis exploring relationships between Pilates teachers and clients with persistent low back pain, including: - transcripts pseudonymised with direct identifiers removed - fieldnotes pseudonymised with direct identifiers removed - analytical coding - blank consent forms, questionnaires, participant information sheets - interview topic guides Dataset is available on request after 9/2/2022. http://library.soton.ac.uk/datarequest Due to ethical concerns, supporting data cannot be made publicly available. Bona fide researchers, subject to registration and ethical approval may request supporting data via University of Southampton repository.

AI Summary

Data supporting a thesis exploring relationships between Pilates instructors and clients with persistent low back pain includes pseudonymised transcripts, fieldnotes, and analytical coding.

AI Analysis

The dataset is not publicly available due to ethical reasons but can be requested by registered researchers with ethical approval after 9/2/2022.

This paper presents some optimization approaches for redox flow batteries, which are promising energy storage devices due to their potential of providing long-duration storage at low cost. In order to compete with alternative energy storage technologies (e.g. lithium-ion batteries), flow battery optimization will need to focus on increased round-trip efficiency and ensuring outstanding flow battery durability, while utilizing low-cost material for critical electrolyte and stack components.

AI Summary

This paper discusses how to optimize redox flow batteries for efficient and durable energy storage using low-cost materials.

AI Analysis

Redox flow batteries have the potential to offer long-duration storage at a low cost, making their optimization crucial for competing with other energy storage technologies like lithium-ion batteries.

Compulsory voting involves a legal obligation to vote (or attend the polls), but we might instead require those who do not to pay a charge, without any legal obligation for them to do so. This nonpunitive price creates an incentive for all citizens to participate and prevents free riding, but permits nonvoting and avoids condemning nonvoters. Thus, this proposal delivers what at least some advocates and opponents of compulsory voting want. Moreover, considering this possibility helps to clarify the disagreement over compulsory voting. Those who wish to reject this proposal need further arguments for their respective positions.

AI Summary

The research discusses using a nonpunitive price as an alternative to compulsory voting to encourage citizens to vote.

AI Analysis

The study offers a new perspective on increasing voter turnout without enforcing compulsory voting.

This dataset supports the thesis entitled 'X-ray Absorption Spectroscopy and Electrochemical Studies of Pt-Sn Electrocatalysts'.

AI Summary

The research involves studying the properties of Pt-Sn electrocatalysts using X-ray absorption spectroscopy and electrochemical methods.

AI Analysis

The research provides valuable insights into the behavior of Pt-Sn electrocatalysts, which are important for fuel cell reactions.

Code supporting Doctoral Thesis "Flying Doughnuts: Space-Time non-Separable Electromagnetic Pulses" Awarded University of Southampton 2021. A python implementation of the TERMITES-MAZE method for the spatio-temporal characterization of ultrashort cylindrical vector electromagnetic pulses. The first part implements the TERMITES algorithm for the characterization of linearly polarized pulses with uniform spectral properties at their center. The second part implements the analysis steps required to characterize cylindrical vector pulses. An example with simulated data is included in the example.py file.

AI Summary

A Python module called TERMITES-MAZE has been developed to characterize ultrashort cylindrical vector electromagnetic pulses.

AI Analysis

The module supports a doctoral thesis from the University of Southampton, offering a novel method for analyzing these complex pulses in both linearly polarized and cylindrical vector forms.

As rational agents, we are governed by reasons. The fact that there’s beer at the pub might be a reason to go there and a reason to believe you’ll enjoy it. As this example illustrates, there are reasons for both action and for belief. There are also many other responses for which there seem to be reasons – for example, desire, regret, admiration, and blame. This diversity raises questions about how reasons for different responses relate to each other. Might certain such reasons be more fundamental than others? Should certain reasons and not others be treated as paradigmatic? At least implicitly, many philosophers treat reasons for action as the fundamental or paradigmatic case. In contrast, this paper articulates and defends an alternative approach, on which reasons for attitudes are fundamental, and reasons for action are both derivative and, in certain ways, idiosyncratic. After outlining this approach, we focus on defending its most contentious thesis, that reasons for action are fundamentally reasons for intention. We offer two arguments for this thesis, which turn on central roles of reasons: that reasons can be responded to, and that reasons can feature as premises of good reasoning. We then examine objections to the thesis and argue that none succeed. We conclude by sketching some ways in which our approach is significant for theorising about reasons.

AI Summary

The research examines if reasons for attitudes are more fundamental than reasons for actions, defending the idea that reasons for intentions are fundamental.

AI Analysis

The research challenges the common view that reasons for action are the most important, proposing a new perspective that reasons for attitudes, specifically intentions, are the fundamental reasons.

Dominant normative theories of armed conflict orientate themselves around the ultimate goal of peace. Yet the deployment of these theories in the international sphere appears to have failed in advancing toward this goal. In this paper, we argue that one major reason for this failure is these theories’ use of essentially contested concepts—that is, concepts whose internally complex character results in no principled way of adjudicating between rival interpretations of them. This renders the theories susceptible to manipulation by international actors who are able to pursue bellicose policies under the cover of nominally pacific frameworks, and we show how this happened historically in a case study of the Korean War of 1950–1953. In order to better serve the goals of peace, we suggest, the rules of war should be reframed to simpler, but more restrictive, normative principles.

A response was published by Lonneke Peperkamp under the title “Restraining the fox: Minimalism in the ethics of war and peace” (2021) (https://doi.org/10.1177/17550882211034704)

AI Summary

The research argues that using complex ethical concepts in theories of armed conflict makes them susceptible to exploitation, as shown by a case study of the Korean War, and suggests simplifying the rules of war for better peace goals.

AI Analysis

This research explores how the complexity of ethical concepts in theories of armed conflict can be manipulated by international actors, with implications for reframing the rules of war for promoting peace.

Small island states are much more likely to have democratic regimes than large continental states. This trend also holds across Africa, where the five island states with populations of 1.5 million or less are all rated at least ‘partly free’ by Freedom House. In this article we explore what it is about being a small island state that might explain this trend. Building on studies from other small island states, we find that the interaction between the two contextual factors is key to explaining their diversion from mainland trends in the African context. Specifically, ‘smallness’ leads to closer links between citizens and politicians in addition to more effective service delivery, while ‘islandness’ promotes community cohesion and provides a buffer against instability and conflict in neighbouring states. This results in a positive feedback loop that guards against authoritarian excess. Our focus on population size and geography thus adds to the existing studies of the contextual drivers of African democratisation.

AI Summary

Small island states in Africa tend to have democratic regimes, which is different from mainland trends. The article explores how factors like closeness between citizens and politicians, effective service delivery, community cohesion, and geographical isolation contribute to democratic development in small island states.

AI Analysis

This research sheds light on why small island states in Africa have more democratic regimes compared to larger continental states, providing insights into the unique contextual factors that influence democratic development in these regions.

Finding a conceptually informed and substantive means of understanding the value of higher education (HE) remains a challenging but crucial issue in the context of continued market-orientated policies. This article offers a way forward and posits that formal approaches to measuring the value of HE can only have currency if engaging in longer-term and sustainable notions of value given that many of the benefits of HE are manifest in less tangible, non-immediate and non-monetary outcomes. Capability perspectives are drawn upon better to capture the more developmental and longer-term value potentiality of a university education. We explicitly refer to three key spheres of value pertaining to personal, social and economic milieus that may be derived from HE. This approach moves beyond the utilitarian gain approach to value popularised in recent HE policy, in particular the Teaching Excellence and Student Outcomes Framework. Instead, it brings into play the significance of agency and selfhood as key value dimensions, and a broader conception of working life. The implications for engaging with future measurements of the value of HE are also discussed.

AI Summary

The research discusses the importance of understanding the value of higher education beyond immediate and monetary outcomes, drawing on capability perspectives to explore its long-term benefits in various aspects of life.

AI Analysis

This research explores a broader and more meaningful way of measuring the value of higher education, highlighting the importance of considering personal, social, and economic benefits over time rather than solely focusing on immediate gains.

The classical problem setup of iterative learning control (ILC) is to enforce tracking of a reference profile specified at all time points in the fixed task duration. The removal of the time specification releases significant design freedom in how the path is followed but has not been fully exploited in the literature. This article unlocks this extra design freedom by formulating the ILC task description to handle repeated path-following tasks, e.g., welding and laser cutting, which aim at following a given ``spatial'' path defined in the output space without any temporal information. The general ILC problem is reformulated for ILC design with the inclusion of an additional performance index, and the class of piecewise linear paths is characterized for the reformulated problem setup. A two-stage design framework is proposed to solve the characterized problem and yields a comprehensive algorithm based on an ILC update and a gradient projection update. This algorithm is verified on a gantry robot experimental platform to demonstrate its practical efficacy and robustness against model uncertainty.

AI Summary

This research explores using iterative learning control (ILC) to improve path following tasks without specific time constraints by optimizing performance.

AI Analysis

The study introduces a new approach to ILC that allows for more flexibility in path following tasks and demonstrates its effectiveness on a real-world robot platform.

We study a coordination game among agents in a network. The agents choose whether to take action (e.g., adopting a new technology) in an uncertain environment that yields increasing value in the actions of neighbors. We develop an algorithm that fully partitions the network into communities (coordination sets) within which agents have the same propensity to adopt. Our main finding is that a novel measure of network connectedness, which we term “social connectedness”, determines the propensity to adopt for each agent. Social connectedness captures both the number of links each agent has within her community (interconnectedness) as well as the number of links she has with members of other communities who have a higher propensity to adopt (embeddedness). There is a single coordination set if and only if the network is balanced —that is, the average degree of each subnetwork is no larger than the average degree of the network. Finally, we demonstrate that contagion is localized within coordination sets, such that a shock to an agent uniformly affects this agent and all members of her coordination set but has no impact on the other agents in the network.

AI Summary

The research analyzes how social connections influence the adoption of new technologies in a network of agents, finding that the level of "social connectedness" determines each agent's willingness to adopt. It also shows that contagion effects are localized within specific groups of connected agents.

AI Analysis

The study presents a new algorithm for partitioning network communities based on agents' adoption propensities, shedding light on the role of social interactions in decision-making processes.

During the COVID-19 outbreak, many people rose to the occasion by engaging in volunteerism and health work. We conducted two nationwide surveys in the United States ( n  = 2931) and China ( n  = 2793) assessing volunteers' and health workers' levels of mental distress and happiness. In spite of data being collected at different phases of the COVID-19 outbreak and across two different cultures, the results converged. Volunteers and health workers reported higher mental distress (e.g., depression, anxiety, somatization) than the comparison group. However, volunteers and health workers also reported more happiness than the comparison group. More importantly, in a follow-up in China ( n  = 1914) one month later, health workers still reported heightened happiness but were no longer more distressed than the comparison group. The changes in distress were partially mediated by happiness at the first time point, pointing to the potential role of happiness in coping with distress. In sum, the emotional landscape of volunteers and health workers was complicated-they experienced higher distress but also higher happiness than comparison groups. Future research would do well to include longer follow-up periods to examine how experiencing happiness during highly stressful situations predicts mental health over time.

Supplementary Information: The online version of this article (doi:10.1007/s40167-021-00100-1) contains supplementary material, which is available to authorized users.

AI Summary

Volunteers and health workers in the US and China experienced higher mental distress and happiness during the COVID-19 pandemic.

AI Analysis

This research highlights the complex emotional experiences of volunteers and health workers during the pandemic, showing how they reported both higher distress and happiness compared to non-volunteers.

Background: Risk-adjusted cancer screening and prevention is a promising and continuously emerging option for improving cancer prevention. It is driven by increasing knowledge of risk factors and the ability to determine them for individual risk prediction. However, there is a knowledge gap between evidence of increased risk and evidence of the effectiveness and efficiency of clinical preventive interventions based on increased risk. This gap is, in particular, aggravated by the extensive availability of genetic risk factor diagnostics, since the question of appropriate preventive measures immediately arises when an increased risk is identified. However, collecting proof of effective preventive measures, ideally by prospective randomized preventive studies, typically requires very long periods of time, while the knowledge about an increased risk immediately creates a high demand for action. Summary: Therefore, we propose a risk-adjusted prevention concept that is based on the best current evidence making needed and appropriate preventive measures available, and which is constantly evaluated through outcome evaluation, and continuously improved based on these results. We further discuss the structural and procedural requirements as well as legal and socioeconomical aspects relevant for the implementation of this concept.

AI Summary

The research proposes a risk-adjusted cancer screening and prevention approach based on identifying individual risk factors for improving preventive measures.

AI Analysis

This research suggests a more personalized approach to cancer prevention by utilizing risk factors to tailor screening and prevention strategies.

This article engages with Meg Russell and Ruxandra Serban's (2021) argument that the Westminster model is 'a concept stretched beyond repair' that deserves 'to be retired'. We examine the logic, theory and methods that led to such a powerful, potent and provocative argument. We suggest their approach may have inadvertently 'muddied' an already muddled concept. We assess the implications of 'muddying' for their conclusion that the Westminster model is, in essence, a dead concept in need of a decent funeral. We suggest the concept is 'stretched but not snapped' by developing a simple four-perspective broadening of the analytical lens. This approach aids understanding about what the concept covers, how it is operationalized and why it remains useful in comparative research.

AI Summary

The article responds to an argument about retiring the Westminster model by examining its logic, theory, and methods and proposing a different perspective to understand its continued relevance in research.

AI Analysis

The research challenges the idea of retiring the Westminster model and offers a new perspective on its usefulness in comparative research.

We explore the impact of the COVID-19 pandemic on the term structure of interest rates. Using data from developed and emerging countries, we demonstrate that the expansion of the disease significantly affects sovereign bond markets. The growth of confirmed cases significantly widens the term spreads of government bonds. The effect is independent of government policy and monetary responses to COVID-19 and robust to many considerations.

AI Summary

The research examines how the COVID-19 pandemic affected interest rates in different countries by studying government bond markets.

AI Analysis

The study shows that as the number of COVID-19 cases rose, it had a significant impact on the term spreads of government bonds, regardless of government policies or monetary responses.

Paradoxes generate tensions and contradictions in organizations. In this paper, we contribute to the paradox literature by developing a complex systems approach to how organizational members experience tensions generated by the strategic intent paradox. Specifically, we focus on the unfolding dynamics of vicious cycles experienced by organizations dealing with paradox. Drawing on a case study of a design firm, we demonstrate how a vicious cycle forms through feedback loops and develops dynamic stability over time. On the basis of our findings, we develop a micro-level understanding of vicious cycles, which incorporates defence mechanisms at staff and senior management levels. Our main contribution is a theoretical model of unfolding dynamics of vicious cycles. Our model shows the importance of (1) feedback loops that underpin a vicious cycle and (2) importance of circular causality, reinforcing cycles, and micro-mechanisms in theorizing vicious cycles.

AI Summary

The research explores how organizations experience tensions generated by paradoxes, specifically focusing on vicious cycles in a design firm.

AI Analysis

The research provides insights into how organizations deal with paradoxes and the development of theoretical models explaining the dynamics of vicious cycles in organizational contexts.

The term Hyksos commonly refers to the foreign dynasty that inhabited and held power in Egypt during the Second Intermediate Period, circa 1650–1550 BCE. The later historian Manetho described the Hyksos as invading foreigners, and the view persisted until the modern period. Recent research has integrated archaeological, artistic and textual evidence, revealing the Hyksos origin and presence in Egypt more complex than previously envisioned.
To investigate the provenance of the so-called Hyksos, human remains from Tell el-Dab’a, the ancient Hyksos capital of Avaris, were analyzed using ASUDAS dental nonmetric traits. An intra- and inter-site biodistance analysis was conducted on individuals from Tell el-Dab’a (n=92) and other contemporary sites in the Near East (n=285). Two statistical tests were selected, mean measure of divergence (MMD) and Gower distance analysis.
The archaeological and biological evidence suggest occupation continuation spanning from the end of the Middle Kingdom through the Hyksos Dynasty, offering further evidence contra Manetho, describing a sudden invasion. The inter-site analysis supports the archaeological finds from Tell el-Dab’a, suggesting not only commodities but people as well made their way to Tell el-Dab’a.

AI Summary

The research investigates the origin of the Hyksos, a foreign dynasty in ancient Egypt, by analyzing dental traits of human remains from their capital, Avaris.

AI Analysis

The study challenges the traditional view of the Hyksos as invading foreigners and reveals a more complex understanding of their origin and presence in Egypt.

Dataset supports the University of Southampton Doctoral thesis entitled 'Molecular Dynamics Simulations of Complex Bacterial Membranes' Awarded 2021.

AI Summary

Dataset for molecular dynamics simulations of complex bacterial membranes in support of a doctoral thesis from the University of Southampton awarded in 2021.

AI Analysis

The dataset provides valuable information for understanding the behavior of complex bacterial membranes through molecular dynamics simulations.

Declining graduate labour markets, perceived devaluing of degree qualifications, and intense focus on graduate employment metrics have increased pressure on universities to enhance their students’ employability. Formal curricula developments have been accompanied by co-curricular and extra-curricular offerings intended to enhance students’ career readiness and emerging graduate profiles. Using survey data from undergraduate and postgraduate students in an Australian and UK university (N = 352), multivariate techniques examined participation in employability-related activities and students’ perceptions of their importance for their employability. Data revealed differential participation based on student profile characteristics and the type of activity undertaken. Overall, these were valued for boosting a range of employability-related facets, including networks, learning about future career and profile attractiveness to employers. The more aligned these activities were to intended career outcomes, the more importance they were ascribed. The implications of these findings for enhancing employability-related offerings in universities are discussed.

AI Summary

The research looked at how work experience, extra-curricular activities, and university-based activities impact student employability.

AI Analysis

The study found that students value activities aligned with their career goals for enhancing their employability.

BACKGROUND: Transcriptomic changes in patients who respond clinically to biological therapies may identify responses in other tissues or diseases.

OBJECTIVE: We sought to determine whether a disease signature identified in atopic dermatitis (AD) is seen in adults with severe asthma and whether a transcriptomic signature for patients with AD who respond clinically to anti-IL-22 (fezakinumab [FZ]) is enriched in severe asthma.

METHODS: An AD disease signature was obtained from analysis of differentially expressed genes between AD lesional and nonlesional skin biopsies. Differentially expressed genes from lesional skin from therapeutic superresponders before and after 12 weeks of FZ treatment defined the FZ-response signature. Gene set variation analysis was used to produce enrichment scores of AD and FZ-response signatures in the Unbiased Biomarkers for the Prediction of Respiratory Disease Outcomes asthma cohort.

RESULTS: The AD disease signature (112 upregulated genes) encompassing inflammatory, T-cell, T H2, and T H17/T H22 pathways was enriched in the blood and sputum of patients with asthma with increasing severity. Patients with asthma with sputum neutrophilia and mixed granulocyte phenotypes were the most enriched (P < .05). The FZ-response signature (296 downregulated genes) was enriched in asthmatic blood (P < .05) and particularly in neutrophilic and mixed granulocytic sputum (P < .05). These data were confirmed in sputum of the Airway Disease Endotyping for Personalized Therapeutics cohort. IL-22 mRNA across tissues did not correlate with FZ-response enrichment scores, but this response signature correlated with T H22/IL-22 pathways.

CONCLUSIONS: The FZ-response signature in AD identifies severe neutrophilic asthmatic patients as potential responders to FZ therapy. This approach will help identify patients for future asthma clinical trials of drugs used successfully in other chronic diseases.

AI Summary

The research studied whether a disease signature in atopic dermatitis is present in severe asthma patients and if a transcriptomic signature for atopic dermatitis patients responding to an anti-IL-22 treatment can also be found in severe asthma patients.

AI Analysis

The study found that the anti-IL-22 treatment response signature in atopic dermatitis can help identify severe neutrophilic asthmatic patients who may respond well to the same treatment, offering a potential new approach to personalized asthma therapy.

Objective: To describe the population of patients with cleft lip and/or palate (CL/P) in terms of cleft phenotypes, gender, age, ethnic group, family history, clinical presentation (syndromic vs nonsyndromic), some environmental and behavioral factors, and some clinical features. Design: Descriptive retrospective study. Setting: Patients attending the genetics counseling practice in Operation Smile Foundation, Bogotá, Colombia, for over 8 years. Participants: No screening was conducted. All patients requiring clinical genetics assessment in Operation Smile Foundation were included in the study. Results: Left cleft lip and palate (CLP) and nonsyndromic forms were the most frequent types of malformations in this population. Psychomotor retardation and heart disease were the most frequent comorbidities in these patients. A low proportion of mothers exposed to passive smoking during pregnancy was observed and low birth weight accounted for an important number of cases. Aarskog, velocardiofacial, and orofaciodigital syndromes were the most frequent syndromic forms of CLP in this population. Conclusions: In this study, the most frequent type of CL/P was the nonsyndromic complete left CLP. Aarskog, velocardiofacial, and orofaciodigital syndromes were the most frequent syndromic forms of CL/P in this population.

AI Summary

Study describing 2069 patients with cleft lip and/or palate in Colombia from Operation Smile Foundation, including cleft phenotypes, comorbidities, and syndromic forms.

AI Analysis

The study provides valuable insights into the characteristics and prevalence of orofacial clefts in Colombia, highlighting important clinical features and syndromes associated with the condition.

In this paper, direct numerical simulations are performed for low-speed flows past a NACA0012 aerofoil at high incidence angles. The aim is to investigate the significance of quadrupole noise generated due to separated shear layers, in comparison to dipole noise emanating from the aerofoil surface. The two different noise components (dipole and quadrupole) are calculated by using the Ffowcs Williams & Hawkings method in two different approaches: One with a solid surface and another with a permeable surface. The quadrupole noise is then estimated approximately by taking the relative difference between the two. The current study provides detailed comparisons between the quadrupole and dipole noise components at various observer locations in a wide range of frequencies. The comparisons are also made in terms of Mach number scaling, which differs significantly from theoretical predictions and changes rapidly with frequency. Additionally, pre-, near- A nd full-stall conditions are cross-examined, which reveals significant differences in the quadrupole contributions, including changes in the major source locations and frequencies. It is found that the inclusion of the quadrupole sources gives rise to the predicted noise power level at all frequencies (varying between 2 and 10 dB for an observer above the aerofoil) compared to the dipole-only case. The quadrupole contribution is far from negligible even at the low subsonic speeds (Mach 0.3 and 0.4) when aerofoil stall occurs.

AI Summary

The research investigates the quadrupole noise generated by a NACA0012 aerofoil in low-speed flows at high incidence angles compared to dipole noise.

AI Analysis

The study shows that quadrupole noise contribution from separated shear layers is significant, challenging previous theoretical predictions, and plays a crucial role in the overall noise generation at low subsonic speeds during aerofoil stall conditions.

Background: Currently, there are no effective methods for assessing hepatic inflammation without resorting to histological examination of liver tissue obtained by biopsy. T2-weighted images (T2WI) are routinely obtained from liver magnetic resonance imaging (MRI) scan sequences. We aimed to establish a radiomics signature based on T2WI (T2-RS) for assessment of hepatic inflammation in people with nonalcoholic fatty liver disease (NAFLD). Methods: A total of 203 individuals with biopsy-confirmed NAFLD from two independent Chinese cohorts with liver MRI examination were enrolled in this study. The hepatic inflammatory activity score (IAS) was calculated by the unweighted sum of the histologic scores for lobular inflammation and ballooning. One thousand and thirty-two radiomics features were extracted from the localized region of interest (ROI) in the right liver lobe of T2WI and, subsequently, selected by minimum redundancy maximum relevance and least absolute shrinkage and selection operator (LASSO) methods. The T2-RS was calculated by adding the selected features weighted by their coefficients. Results: Eighteen radiomics features from Laplacian of Gaussian, wavelet, and original images were selected for establishing T2-RS. The T2-RS value differed significantly between groups with increasing grades of hepatic inflammation (P<0.01). The T2-RS yielded an area under the receiver operating characteristic (ROC) curve (AUROC) of 0.80 [95% confidence interval (CI): 0.71–0.89] for predicting hepatic inflammation in the training cohort with excellent calibration. The AUROCs of T2-RS in the internal cohort and external validation cohorts were 0.77 (0.61–0.93) and 0.75 (0.63–0.84), respectively. Conclusions: The T2-RS derived from radiomics analysis of T2WI shows promising utility for predicting hepatic inflammation in individuals with NAFLD. Keywords: Nonalcoholic fatty liver disease (NAFLD); inflammation activity; radiomics; magnetic resonance imaging (MRI)

AI Summary

A radiomics signature based on T2-weighted imaging accurately predicts hepatic inflammation in individuals with nonalcoholic fatty liver disease.

AI Analysis

The research developed a novel method to assess hepatic inflammation without the need for invasive biopsy, providing a non-invasive and potentially more efficient alternative for monitoring liver health in patients with NAFLD.

The overwhelming spatio-temporal nature of the spread of the ongoing Covid-19 pandemic demands urgent attention of data analysts and model developers. Modelling results obtained from analytical tool development are essential to understand the ongoing pandemic dynamics with a view to helping the public and policy makers. The pandemic has generated data on a huge number of interesting statistics such as the number of new cases, hospitalisations and deaths in many spatio-temporal resolutions for the analysts to investigate. The multivariate nature of these data sets, along with the inherent spatio-temporal dependencies, poses new challenges for modellers. This article proposes a two-stage hierarchical Bayesian model as a joint bivariate model for the number of cases and deaths observed weekly for the different local authority administrative regions in England. An adaptive model is proposed for the weekly Covid-19 death rates as part of the joint bivariate model. The adaptive model is able to detect possible step changes in death rates in neighbouring areas. The joint model is also used to evaluate the effects of several socio-economic and environmental covariates on the rates of cases and deaths. Inclusion of these covariates points to the presence of a north-south divide in both the case and death rates. Nitrogen dioxide, the only air pollution measure used in the model, is seen to be significantly positively associated with the number cases, even in the presence of the spatio-temporal random effects taking care of spatio-temporal
dependencies present in the data. The proposed models provide excellent fits to the observed data and are seen to perform well for predicting the location specific number of deaths a week in advance. The structure of the models is very general and the same framework can be used for modelling other areally aggregated temporal statistics of the pandemics, e.g. the rate of hospitalisation.

AI Summary

The research proposes a two-stage hierarchical Bayesian model to map Covid-19 cases and deaths in different regions of England, incorporating socio-economic and environmental factors.

AI Analysis

The research offers a sophisticated statistical method to understand and predict the spread of Covid-19, highlighting the impact of socio-economic and environmental factors on case and death rates, which could inform public health policies.

To date, no studies have assessed lateral abdominal muscles’ (LAM) elasticity and thickness in relation to hip and groin symptoms in any population. The objectives were to a) assess the relationship between LAM ultrasound measurements (elasticity and thickness) and self-reported subscales of the Copenhagen Hip and Groin Outcome Score (HAGOS) and b) compare LAM elasticity and thickness between asymptomatic and symptomatic sides. Shear modulus and thickness of the oblique external (OE), internal (OI) and transversus abdominis (TrA) muscles in 25 young soccer players were assessed at rest and during isometric contraction using ultrasound shear wave elastography. HAGOS subscales were used to assess self-reported hip/groin problems. There was a significant (p < 0.05) moderate correlation between allometric scaled OI resting thickness (mean of right and left) and the Activities of Daily Living (r = 0.40), Sport (r = 0.57) and Quality of Life (QOL) (r = 0.41) HAGOS subscales. Also, a moderate significant correlation was found between allometric-scaled TrA resting thickness and the QOL subscale (r = 0.47). Moderate correlations were found between resting OI shear modulus and the QOL (r = 0.44), between right TrA shear modulus during contraction and Symptoms (r =72 0.57), and between the left TrA shear modulus during contraction and Physical Activity (r = 0.41) subscales. No differences were found between the symptomatic and asymptomatic side in thickness and elasticity measurements among soccer players with unilateral symptoms (p >0.05). The relationships found between LAM and hip/groin problems in youth male soccer players indicate that muscles are thinner and more elastic (less stiff) in more symptomatic athletes.

AI Summary

The study looked at the relationship between lateral abdominal muscles' elasticity and thickness with self-reported hip and groin symptoms in youth soccer players.

AI Analysis

The research found that there are correlations between the thickness and elasticity of certain abdominal muscles and the severity of hip and groin issues in young male soccer players, suggesting a potential link between muscle characteristics and symptoms.

Background
Disrespect and abuse during childbirth can result in fear of childbirth. Consequently, women may be discouraged to seek care, increasing the likelihood for women to choose elective cesarean section in order to avoid humiliation, postnatal depression and even maternal mortality. This study investigates the causes underlying mistreatment of women during childbirth by health care providers in India, where evidence of disrespect and abuse has been reported.

Methods
Qualitative research was undertaken involving 34 in-depth interviews with midwifery and nursing leaders from India who represent administration, advocacy, education, regulation, research and service provision at state and national levels. Data are analysed thematically with NVivo12. The analysis added value by bringing an international perspective from interviews with midwifery leaders from Switzerland and the United Kingdom.

Findings
The factors leading to disrespect and abuse of women relate to characteristics of both women and their midwives. Relevant woman-related attributes include her age, gender, physical appearance and education, extending to the social environment including her social status, family support, culture of abuse, myths around childbirth and sex-based discrimination. Midwife-related factors include gender, workload, medical hierarchy, bullying and powerlessness.

Discussion
The intersectionality of factors associated with mistreatment during childbirth operate at individual, infrastructural, social and policy levels for both the women and nurse-midwives, and these factors could exacerbate existing gender-based inequalities. Maternal health policies should address the complex interplay of these factors to ensure a positive birthing experience for women in India.

Conclusion
Maternal health interventions could improve by integrating women-centred protocols and monitoring measures to ensure respectful and dignified care during childbirth.

AI Summary

Study investigates reasons behind mistreatment of women during childbirth by health care providers in India.

AI Analysis

Understanding factors contributing to disrespect and abuse during childbirth can help improve maternal health policies and interventions in India.

Obtaining high-resolution energy consumption data from a large, representative sample of homes is critical for research, but low response rates, sample bias and high recruitment costs form substantial barriers. The widespread installation of smart meters offers a novel route to access such data, but in countries like Great Britain (GB) consent is required from each household; a real barrier to large-scale sampling. In this paper we show how certain study design choices can impact the response rate for energy studies requesting access to half-hourly smart meter data and (optional) survey completion. We used a randomised control trial (RCT) with a 3×2×2 factorial design; 3 (including none) incentive groups ×2 message content/structures ×2 ‘push-to-web’ treatment groups. Up to 4 mailings (letters) were sent to 18,000 addresses, recruiting 1711 participants (9.5% response rate) in England and Wales. The most effective strategy offered a conditional £5 voucher and postal response options in multiple mailings (compared to only once in the push-to-web approach, although at the expense of far fewer online signups). Motivational headlines and message structure were also found to be influential. Reminders increased response but a 4^th mailing was not cost effective. Our results and recommendations can be used to help future energy studies to achieve greater response rates and improved representation. UK-based researchers can apply to use our longitudinal smart meter and contextual datasets.

AI Summary

The research focused on increasing response rates for energy studies by testing different study design choices.

AI Analysis

The research highlights effective strategies to improve response rates for energy studies using smart meters, which could benefit future research designs in the field.

Raw data (text files) underpinning the thesis entitled "The role of water in the electrochemical response of platinum". The data was plotted and analysed in OriginLab 2019 as detailed in the thesis.

AI Summary

Raw data supporting the thesis on the role of water in the electrochemical response of platinum was plotted and analyzed in OriginLab 2019.

AI Analysis

The research investigated how water affects platinum's electrochemical behavior.

The bias of growth opportunity (BGO), measured as the difference between market and fundamental values of a firm's growth opportunity, has an ability to predict future stock returns. In the portfolio sort, downward-biased BGO firms earn higher returns than upward-biased BGO firms, which is unexplained by the common asset pricing models. Cross-sectional regression results also confirm BGO's power in predicting stock returns. To explain the anomaly, we show that the BGO premium is more pronounced when investor sentiment is high or when limits-to-arbitrage is severe, which suggests that the BGO is more likely to capture behavioral biases than systematic risk.

AI Summary

The study examines the bias of growth opportunity (BGO) in predicting stock returns based on market and fundamental values of a firm's growth opportunity.

AI Analysis

The research shows that BGO can predict future stock returns, with downward-biased BGO firms earning higher returns than upward-biased ones, even when common asset pricing models cannot explain this phenomenon.

Card transaction fraud is a growing problem affecting card holders worldwide. Financial institutions increasingly rely upon data-driven methods for developing fraud detection systems, which are able to automatically detect and block fraudulent transactions. From a machine learning perspective, the task of detecting fraudulent transactions is a binary classification problem. Classification models are commonly trained and evaluated in terms of statistical performance measures, such as likelihood and AUC, respectively. These measures, however, do not take into account the actual business objective, which is to minimize the financial losses due to fraud. Fraud detection is to be acknowledged as an instance-dependent cost-sensitive classification problem, where the costs due to misclassification vary between instances, and requiring adapted approaches for learning a classification model. In this article, an instance-dependent threshold is derived, based on the instance-dependent cost matrix for transfer fraud detection, that allows for making the optimal cost-based decision for each
transaction. Two novel classifiers are presented, based on lasso-regularized logistic regression and gradient tree boosting, which directly minimize the proposed instance-dependent cost measure when learning a classification model. The proposed methods are implemented in the R packages cslogit and csboost, and compared against state-of-the-art methods on a publicly available data set from the machine learning competition website Kaggle and a proprietary card transaction data set. The results of the experiments highlight the potential of reducing fraud losses by adopting the proposed methods.

AI Summary

This research explores ways to improve fraud detection in card transactions by considering the varying costs of misclassification.

AI Analysis

The study introduces novel classifiers that take into account instance-dependent costs to optimize decision-making for detecting transfer fraud, potentially reducing financial losses due to fraudulent transactions.

There have been few attempts to conduct highly controlled laboratory experiments to isolate how wear propagates on metal artefacts with differing metallurgy during simulated use. This reflects a lack of appreciation for the underlying structure of materials within the production of reference datasets for metalwork wear analysis. Here, we present the use of a drop tower (Instron CEAST 9350) to reconstruct use on replica Bronze Age palstave axes with archaeologically relevant microstructures. The development, form, and properties of surface wear at the cutting edge were sequentially analysed by low-power microscopy (digital), high-power microscopy (Scanning Electron Microscope), and microhardness indentation. Major deformations of the blade were documented by photography. This intensive approach reveals the impact of abrasive wear associated with sharpening and use, as well as the frequency and morphology of larger deformations generated by repeated impact—all of which, we demonstrate, can be overtly modified by subtle differences in metallurgy.

AI Summary

The research tested replica Bronze Age palstave axe blades in a laboratory to understand how wear occurs on metal objects with different metallurgy during use.

AI Analysis

The research presents a detailed analysis of how materials structure and metallurgy affect the wear on ancient metal objects, shedding light on how they were used and maintained in the past.

For Kant, the human cognitive faculty has two sub-faculties: sensibility and the understanding. Each has pure forms which are necessary to us as humans: space and time for sensibility; the categories for the understanding. But Kant is careful to leave open the possibility of there being creatures like us, with both sensibility and understanding, who nevertheless have different pure forms of sensibility. They would be finite rational beings and discursive cognizers. But they would not be human. And this raises a question about the pure forms of the understanding. Does Kant leave open the possibility of discursive cognizers who have different categories? Even if other discursive cognizers might not sense like us, must they at least think like us? We argue that textual and systematic considerations do not determine the answers to these questions and examine whether Kant thinks that the issue cannot be decided. Consideration of his wider views on the nature and limits of our knowledge of mind shows that Kant could indeed remain neutral on the issue but that the exact form his neutrality can take is subject to unexpected constraints. The result would be an important difference between what Kant says about discursive cognizers with other forms of sensibility and what he is in a position to say about discursive cognizers with other forms of understanding. Kantian humility here takes on a distinctive character.

AI Summary

The research explores Kant's idea of pure forms in human cognition and raises the question of whether there could be creatures with different pure forms of understanding.

AI Analysis

The research challenges traditional views on human cognition and raises thought-provoking questions about the nature of understanding in different beings.

Explores the practices in African businesses and their interactions with sustainable Global Value Chains Contains a selection of conceptual discussions on sustainability in global value chains as well as empirical case studies from various African countries and diverse industries Identifies challenges and barriers to the implementation of sustainable principles in African companies.

AI Summary

The research examines how African businesses engage with sustainable global value chains, discussing challenges and barriers.

AI Analysis

The study highlights the importance of sustainable practices in African companies and their integration into global value chains.

We develop and make available a new Cryptocurrency Uncertainty Index (UCRY) based on news coverage. Our UCRY Index captures two types of the uncertainty: cryptocurrency price uncertainty (UCRY Price) and cryptocurrency policy uncertainty (UCRY Policy). We show that the constructed index has distinct movements around major events in cryptocurrency space. We suggest that this index captures uncertainty beyond Bitcoin, and can be used for academic, policy, and practice-driven research.

AI Summary

A new Cryptocurrency Uncertainty Index (UCRY) based on news coverage tracks uncertainty in cryptocurrency prices and policies, showing distinct movements around major events in the cryptocurrency space.

AI Analysis

The UCRY Index provides a way to measure and analyze uncertainty in the cryptocurrency market beyond just Bitcoin, which can be useful for academic, policy, and practical research purposes.

This essay concerns two historical films both released in 2018 to widespread critical attention (if not in both cases acclaim). Guadagnino’s Suspiria (2018) remake revisits the cult horror about a dance academy in the Black Forest by relocating the action to a politically divided Berlin in the 1970s. Cold War (Pawlikowski, 2018) tells the original story of a romance between a singer and a musicologist in the years 1949 to 1964 and across their European exile and their eventual return to Communist Poland. Completely different in plot and style, the two films have one important thing in common: they offer beauty as a form of historical understanding. Beauty provides both a goal and a sensibility for the films’ performer protagonists and their directorial styles alike. Identifiable in both films as an appeal to deep feeling and formal perfection, beauty is neither merely formalistic nor uniquely individual. Instead, it shows the delicacy of artistic feeling to be shaped by the dynamics of an often ugly historical reality. As these films beautify, so they historicize: by being about dance and song, love and sensuality, they are also about genocide and terror, totalitarianism and persecution. Their common story of beauty endangered by brutality places them within a longer tradition that looks at Europe’s past aesthetically, as it also expresses a historically determined position with regards to the European present.

AI Summary

This essay discusses two historical films from 2018, Suspiria and Cold War, highlighting how they use beauty to provide historical insight.

AI Analysis

The research analyzes how the films Suspiria and Cold War utilize beauty as a means to convey historical understanding, intertwining aesthetics with the harsh realities of Europe's past and present.

Deep learning-aided optical orthogonal frequency division multiplexing (O-OFDM) is proposed for intensity modulated direct detection transmissions, which is termed as OOFDMNet. In particular, O-OFDMNet employs deep neural networks (DNNs) for converting a complex-valued signal into a non-negative signal in the time-domain at the transmitter and vice versa at the receiver. The associated frequency-domain signal processing remains the same as in conventional radio frequency (RF) OFDM. As a result, our scheme achieves the same spectral efficiency as the RF scheme, which has never been attained by the existing O-OFDM schemes, because they have relied on the Hermitian symmetry of the spectral-domain signal to guarantee that the time-domain signal becomes real-valued. We show that O-OFDMNet can be viewed as an autoencoder architecture, which can be trained in an end-to-end manner in order to simultaneously improve both the bit error ratio (BER) and the peak-to-average power ratio (PAPR) for transmission over both additive white Gaussian noise and frequency-selective channels. Furthermore, we intrinsically integrate a soft-decision aided channel decoder with our O-OFDMNet and investigate its coded performance relying on both convolutional and polar codes. The simulation results show that our scheme improves both the uncoded and coded BER as well as a reducing the PAPR compared to the benchmarks at the cost of a moderate additional DNN complexity. Furthermore, our scheme is capable of approaching the throughput of RF-OFDM, which is notably higher than that of conventional O-OFDM. Finally, our complexity analysis shows that O-OFDMNet is suitable for real-time operation.

AI Summary

Deep learning is used to improve the performance of optical communication systems, allowing them to achieve similar throughput as traditional radio frequency systems.

AI Analysis

The research presents a novel approach using deep learning to significantly enhance the efficiency of optical communication systems, bringing them closer to the performance of radio frequency systems.

Like many other countries, the United Kingdom (UK) produces a national consumer price index (CPI) to measure inflation. Presently, CPI measures are not produced for regions within the UK. It is believed that, using only available data sources, a regional CPI would not be precise or reliable enough as an official statistic, primarily because the regional partitioning of the data makes sample sizes too small. We investigate this claim by producing experimental regional CPIs using publicly available price data, and deriving expenditure weights from the Living Costs and Food survey. We detail the methods and challenges of developing a regional CPI and evaluate its reliability. We then assess whether model-based methods such as smoothing and small area estimation significantly improve the measures. We find that a regional CPI can be produced with available data sources, however it appears to be excessively volatile over time, mainly due to the weights. Smoothing and small area estimation improve the reliability of the regional CPI series to some extent but they remain too volatile for regional policy use. This research provides a valuable framework for the development of a more viable regional CPI measure for the UK in the future.

AI Summary

The research investigates producing experimental regional consumer price indices for the United Kingdom using available data sources and model-based expenditure weights, finding that while a regional CPI can be produced, it is excessively volatile over time.

AI Analysis

This study explores the challenges and methods of generating regional consumer price indices in the UK, providing insight into the potential for developing a more reliable measure in the future.

Turning the wealth of health and social data into insights to promote better public health, while enabling more effective personalized care, is critically important for society. In particular, social determinants of health have a significant impact on individual health, well-being, and inequalities in health. However, concerns around accessing and processing such sensitive data, and linking different datasets, involve significant challenges, not least to demonstrate trustworthiness to all stakeholders. Emerging datatrust services provide an opportunity to address key barriers to health and social care data linkage schemes, specifically a loss of control experienced by data providers, including the difficulty to maintain a remote reidentification risk over time, and the challenge of establishing and maintaining a social license. Datatrust services are a sociotechnical evolution that advances databases and data management systems, and brings together stakeholder-sensitive data governance mechanisms with data services to create a trusted research environment. In this article, we explore the requirements for datatrust services, a proposed implementation—the Social Data Foundation, and an illustrative test case. Moving forward, such an approach would help incentivize, accelerate, and join up the sharing of regulated data, and the use of generated outputs safely amongst stakeholders, including healthcare providers, social care providers, researchers, public health authorities, and citizens.

AI Summary

The research explores the use of datatrust services, specifically the Social Data Foundation, to address challenges in linking health and social care data for better public health and personalized care.

AI Analysis

The study introduces a new model that could revolutionize how sensitive health and social data is managed and shared, with potential benefits for individuals, institutions, and society as a whole.

We consider what genealogical links, kinship and sociality are promised through the marketing of assisted reproductive technologies (ARTs). Using a mixed method of formal analysis of Facebook's algorithmic architectures and textual analysis of twenty-eight adverts for egg donation drawn from the Facebook Ad Library, we analyse the ways in which the figure of the ‘fertile woman’ is constituted both within the text and at the level of Facebook's targeted advertising systems. We critically examine the ways in which ART clinics address those women whose eggs they wish to harvest and exchange, in combination with the ways in which Facebook's architecture identifies, and sorts those women deemed of ‘relevance’ to the commercial ART industry. We find that women variously appear in these adverts as empowered consumers, generous girlfriends, potential mothers and essentialised bodies who provide free-floating eggs. The genealogical and fertility possibility offered through ART is represented with banal ambiguity wherein potentially disruptive forms of biogenetic relatedness and arrangements of kinship are derisked by an overarching narrative of simplicity and sameness which excludes men, messy genealogies and explicitly queer forms of kinship. This rationalisation is supported by the simplicity and certainty of the Facebook targeted advertising algorithm which produces a coherent audience and interpellates users as fertile subjects whose choices are both biologically determined and only available through clinical intervention.

AI Summary

The research explores how assisted reproductive technologies are marketed on Facebook and how they shape perceptions of female fertility and kinship.

AI Analysis

The study uncovers how ART clinics and Facebook advertising create a narrative of simplicity and exclusion of queer kinship structures, highlighting the influence of targeted advertising on perceptions of fertility and social relationships.

Purpose: This article provides empirical validation of the Graduate Capital Model, adopted at a UK Russell Group University as a tool to analyse and support the career preparedness of both undergraduates and postgraduate students. An overview of employability capitals and how the development of these will potentially result in positive employment outcomes is explored. We describe the development of a psychometric tool “the Graduate Capital Scale” that seeks to operationalize these capitals. We then draw on data to establish the factor structure, reliability and validity of the tool. Design/methodology/approach: This paper introduces a new psychometric instrument, called the “Graduate Capital Scale”; this self-reflective tool aligns closely with the five capitals within the Graduate Capital Model (Tomlinson, 2017) and has been designed for higher education students to self-assess their confidence in transitioning to the graduate labour market. Findings: Based on a sample of 1,501 students across data collection waves, the findings from the psychometric scale show good factor reliability and validity for the constructs central to the overarching Graduate Capital Model. Within each of the component of the model, high factors loading emerged for a range of scale items, including subject-related skills, social networking, perceived job market fit and engagement with extra-curricula activities. Few gender differences emerged across the constructs. Research limitations/implications: The research was confined to a specific English university comprised of mainly academically high-achieving and higher socio-economic students. However, there is significant scope for the model and related scale tool to be applied to diverse student groups given its wholistic nature. Practical implications: The scale has considerable potential to be incorporated into careers practices and also embedded into course programmes as it aligns with a range of related learning outcomes. There is significant scope for this approach to complement a range of pedagogical and practical career interventions, including: self-reflective tools within tutorials; measures of learning gain for specific interventions such as careers coaching and mentoring; and as a personal reflective tool in careers guidance. Social implications: The approach developed through this employability tool has scope to be used for diverse graduate groups, including those with lower levels of career confidence, preparedness and insight and including those from lower socio-economic backgrounds. Originality/value: This paper has introduced and demonstrated the validity of a practical careers and employability development tool that has significant practical applicability for students, graduates and practitioners. Moreover, this scale supports a pre-existing conceptually driven model and has demonstrated a clear alignment between theory and practice in the area of graduate employability.

AI Summary

The research validates a tool called the Graduate Capital Scale aimed at assessing the career preparedness of university students.

AI Analysis

The Graduate Capital Scale offers a valuable method for students to evaluate their readiness for the job market, showcasing a clear link between theory and practice in graduate employability.

We investigate the use of two or more linked lists, for both population size estimation and the relationship between variables appearing on all or only some lists. This relationship is usually not fully known because some individuals appear in only some lists, and some are not in any list. These two problems have been solved simultaneously using the EM algorithm. We extend this approach to estimate the size of the indigenous Māori population in New Zealand, leading to several innovations: (1) the approach is extended to four lists (including the population census), where the reporting of Māori status differs between registers; (2) some individuals in one or more lists have missing ethnicity, and we adapt the approach to handle this additional missingness; (3) some lists cover subsets of the population by design. We discuss under which assumptions such structural undercoverage can be ignored and provide a general result; (4) we treat the Māori indicator in each list as a variable measured with error, and embed a latent class model in the multiple system estimation to estimate the population size of a latent variable, interpreted as the true Māori status. Finally, we discuss estimating the Māori population size from administrative data only. Supplementary materials for our article are available online.

AI Summary

Investigating the use of linked lists for estimating population size and relationships between variables with missing values and measurement errors.

AI Analysis

The research extends an approach to estimate the Maori population in New Zealand using multiple linked lists and addresses issues like missing ethnicity and measurement errors, providing innovative solutions for accurate population estimation.

According to Pure Russellianism, if David believes that Hesperus is a planet is true, (2) David believes that Phosphorus is a planet   is also true. It is also usually thought, by friends and foes of Pure Russellianism alike, that on it, when (1) and (2) are true,  (3) David does not believe that Phosphorus is a planet  cannot but be false and because of this, many departed from Pure Russellianism. In this paper, I will show that by relying on the very explanation Pure Russellianism is usually combined with to account for sentences such as (1) and (2) and by acknowledging the well-attested linguistic phenomenon of metalinguistic negation, even if (1) and (2) are true, it is not the case that on Pure Russellianism (3) cannot but be false.

AI Summary

Pure Russellianism is a philosophical viewpoint about belief, with the paper arguing that a certain type of belief scenario does not have to lead to a specific contradictory conclusion.

AI Analysis

The research challenges a common assumption about Pure Russellianism by introducing the concept of metalinguistic negation to show how a scenario can have a different interpretation within this philosophical framework.

Institutional settings in emerging markets are often plagued by state actors exploiting the vulnerability of resource-constrained small and medium sized enterprises (SMEs). Whilst we know a great deal about how large firms use non-market strategies (NMS) to navigate such institutional spaces, current knowledge of such strategies in connection with SMEs remains limited. Using in-depth interview data from a wide range of actors in Russia, we reveal the predatory behavior of state actors and how, in response, SMEs develop NMS to respond to fluctuating institutional conditions. We underline four forms of institutional predatory behaviors comprising shifting the rules of the game; privatizing power; selectively using/abusing laws; and normalizing informalities. In turn, we identify how SMEs variously adopt NMS to tackle these predatory strategies; namely deflection, alliance, concealment and internationalization. We highlight how SMEs learn to navigate, and ultimately to overcome, dysfunctional and fragile institutional conditions of emerging markets through the pursuit of particular NMS.

AI Summary

The research explores how small businesses in emerging markets use strategies to deal with challenges posed by corrupt institutional environments, focusing on tactics such as forming alliances and expanding internationally.

AI Analysis

The study reveals how small firms adapt to predatory behavior from state actors in difficult institutional contexts, shedding light on their innovative responses to such challenges.

Solitary sexual activity is a free, safe, and accessible way to experience sexual pleasure. Despite these advantages, research on masturbation in later life is highly understudied. Using data from a cross-sectional probability-based survey of 3816 European adults (mean age 67 years; range 60–75 years), we explored several sociodemographic, health, attitudinal, and sexual behavioral factors associated with reported masturbation frequency. Across all countries, between 41% and 65% of men and 27% and 40% of women reported any masturbation in the preceding month. Satisfaction with sexual activity and attitudes related to disapproval of sex without love were significant predictors of reported masturbation in almost all countries and in both genders. Age, education, self-perceived health, and depression were for the most part predictive of men’s reported masturbation, but not women’s. Generally, those believing sex is beneficial to older people were more likely to masturbate, while less permissive attitudes decreased the likelihood of reporting masturbation. To improve healthy sexual aging, misinformation about masturbation and sexual attitudes in older people need to be addressed.

AI Summary

The study looked at masturbation habits in older adults in four European countries and found that between 27% to 65% of men and 27% to 40% of women reported engaging in masturbation in the previous month, with factors such as satisfaction with sexual activity and attitudes towards sex without love influencing frequency.

AI Analysis

Understanding the prevalence and factors associated with masturbation in older adults can help address misinformation and improve healthy sexual aging in this population.

Penetration of open- and closed-ended model piles into intact chalk, a soft calcareous rock, was investigated using microfocus X-ray computed tomography (XCT). Three-dimensional images of the specimens showed that the piles crushed and densified the chalk in their path, creating a crushed chalk annulus around the shaft, a region of compressed destructured chalk below the tip, and fractures across cemented regions of the specimen. Laser-diffraction particle-size analyses of the crushed chalk annulus after exhumation showed limited difference with laboratory-remoulded chalk, which suggested thorough de-cementation. Installation stresses and XCT-derived densities were paired using a simplified cylindrical cavity expansion solution to estimate effective radial stress–void ratio states at the pile tip during penetration. More complex numerical solutions could not be applied using the available data. This approach posed significant problems, as it could not suitably incorporate hardening and non-linear stiffness behaviours of chalk during pile penetration, nor account for the creation of discontinuities. However, effective radial stress–void ratio estimates were found to converge with the reconstituted critical state line of the material at high stresses and low void ratios. This partially supported the use of a critical state framework to characterise pile penetration in chalk, as proposed in recent literature.

AI Summary

X-ray computed tomography was used to study how model piles penetrate soft calcareous rock, showing chalk crushing and densification around the shaft, fractures in cemented regions, and the possibility of characterizing the pile penetration process using a critical state framework.

AI Analysis

This study used advanced imaging techniques to better understand the behavior of piles in soft calcareous rock, with potential implications for construction and geotechnical engineering.