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Unravelling the Impact of Ideological Diversity on Stock Returns Amidst Uncertainty
Get PDFData Availability Statement:
Data sharing is not applicable to this article as no new data was created or analysed in this study.JEL Classification(s): G34, M14.Recently, the Covid-19 uncertainties have raised interest in identifying factors that influence firms’ resilience. Existing Covid-19 research primarily focused on market reactions and lockdown impacts, overlooking the influence of ideological diversity of firms’ directors on resilience. To address this gap, we examine personal contributions to the US Republican or Democratic parties by 11,741 directors from 328 S&P 500 firms, revealing their political ideologies. Our findings highlight that firms with diverse boards experience milder stock return declines during the Covid-19 outbreak, indicating a positive link between ideological diversity and firm performance. This study presents evidence of the significant impact of ideological diversity in corporate boardrooms, showcasing how it affects firms’ resilience during times of extreme market uncertainty. Our findings emphasise the importance of revisiting the theories to explain the ideological diversity in shaping strategies to respond to uncertainty during unpredictable times. Based on social psychological theory alongside agency theory, the findings provide clear indications to practitioners that during future uncertainties, the ideological diversity of the board should be considered to optimise the board's potential to improve performance
A Model-Driven Architecture Approach for Recovering Microservice Architectures: Defining and Evaluating MiSAR
Get PDFData availability - Data & code available on GitHub: https://github.com/MicroServiceArchitectureRecovery/misarContext:
Microservice architecture is an architectural style in modern software systems, characterized by small, independent services called microservices. This architecture is ideal to facilitate rapid feature deployment. However, it presents a challenge for software engineers, who often lack a comprehensive architectural view due to the distributed nature and complex interdependencies of microservices.
Objective:
This paper presents a Model Driven Architecture approach for MicroService Architecture Recovery called MiSAR. Building on previous work that defined a Platform Independent Metamodel, this study seeks to extend this metamodel, introduce a Platform Specific Metamodel, and establish mapping rules. The goal is to enable the semi-automatic recovery of architectural models for microservice systems.
Methods:
An empirical study was conducted on nine microservice systems to define MiSAR’s artefacts and support semiautomatic recovery of architectural models. These artefacts are then implemented and used to semi-automatically recover the architectures of three systems. The effectiveness of MiSAR is evaluated based on metrics such as recall, precision, and F-measure, to assess the recovered models against actual architectures. We also compared the recovered architectural models with the ones documented by the developers.
Results:
The study identified key requirements for the Platform Independent Metamodel to support comprehensive microservice architecture recovery, leading to an incremental extension of the MiSAR Platform Independent Metamodel. Mapping rules were established to effectively transform Platform Specific Models into Platform Independent ones. Furthermore, MiSAR was successfully implemented to recover architecture models. An evaluation using three systems demonstrated that MiSAR could recover architectural models with a high degree of completeness and correctness when compared with the actual architecture.
Conclusion:
The MiSAR artefacts, including the extended Platform Independent Metamodel and mapping rules, effectively produce expressive architectural models of microservice systems. Systems confirmed MiSAR’s ability to semi-automatically recover accurate architectural models, providing a holistic view often missing in current software engineering practices
Next leap in the sustainable transport revolution: Identifying gaps and proposing solutions for hydrogen mobility
Get PDFAmid escalating global climate concerns, the reliance of the transportation sector on high-carbon fossil fuels urgently demands sustainable alternatives. Hydrogen has emerged as a potent solution because of its zero-emission usage, but its overall impact hinges on its full life cycle, which this review comprehensively examines. This article delves into the environmental, economic, and safety dimensions of hydrogen as an alternative fuel by systematically reviewing the life cycle assessment (LCA) literature across the production, storage, delivery, and usage phases, with a focus on electrolysis and natural gas reforming methods, among others. A key insight from this study is the critical importance of considering the entire delivery system holistically rather than isolating the delivery phase. Many studies have overlooked two important aspects: first, the distribution of hydrogen as a product itself is often underemphasized; second, the integration of storage and delivery (the “storage-delivery nexus”) is crucial since separating them can lead to misleading conclusions about cost and emissions. For example, while certain delivery methods may appear cost-effective, their associated storage processes (such as hydrogenation and dehydrogenation in liquid organic hydrogen carrier systems) can have significant emission impacts. To address these gaps, this study introduces a novel “surface-level” LCA framework to enhance the assessment of the environmental impacts of hydrogen, promoting a more integrated understanding of the storage-delivery system. This framework aims to provide more accurate insights into hydrogen's life cycle, thereby facilitating better-informed policy-making and technological advancements. This study underscores the imperative for robust policy support, public engagement, and continuous innovation to overcome these barriers, advocating for strategic initiatives that bolster the sustainability and adoption of hydrogen mobility, particularly in hydrogen fuel cell vehicles (HFCVs)
Trends and persistence in the number of hot days: some multi-country evidence
No full textJEL Code: C22; Q54.Data availability:
Data are available from the authors upon request.This paper uses fractional integration methods to obtain comprehensive evidence on the evolution of the number of hot days, defined as those with temperatures above 35 °C, in 54 countries from various regions of the world over the period from 1950 to 2022. The variable analysed is a key indicator of global warming, and the chosen modelling approach is most informative about the behaviour of the series as it provides evidence on the possible presence of time trends, on whether or not mean reversion occurs, and on the degree of persistence. In brief, the findings indicate the presence of considerable heterogeneity among the countries studied and highlight the importance of tailored climate policies based on both global and local factors.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. Luis A. Gil-Alana gratefully acknowledges financial support from the project from ‘Ministerio de Ciencia, Innovación y Universidades`Agencia Estatal de Investigación’ (AEI) Spain and `Fondo Europeo de Desarrollo Regional’ (FEDER), Grant PID2023-149516NB-I00/AEI/https://doi.org/10.13039/501100011033/ FEDER, UE funded by MCIN/AEI/https://doi.org/10.13039/501100011033, and also from an internal Project of the Universidad Francisco de Vitoria
Everyday norms have become more permissive over time and vary across cultures
Get PDFData availability:
All data and materials generated and/or analyzed in this study, including the raw data underlying the figures and tables, are available at OSF (https://osf.io/sh4rb/, https://doi.org/10.17605/OSF.IO/SH4RB).Code availability:
The R syntax for all analyses are available at OSF (https://osf.io/sh4rb/, https://doi.org/10.17605/OSF.IO/SH4RB).Every social situation that people encounter in their daily lives comes with a set of unwritten rules about what behavior is considered appropriate or inappropriate. These everyday norms can vary across societies: some societies may have more permissive norms in general or for certain behaviors, or for certain behaviors in specific situations. In a preregistered survey of 25,422 participants across 90 societies, we map societal differences in 150 everyday norms and show that they can be explained by how societies prioritize individualizing moral foundations such as care and liberty versus binding moral foundations such as purity. Specifically, societies with more individualistic morality tend to have more permissive norms in general (greater liberty) and especially for behaviors deemed vulgar (less purity), but they exhibit less permissive norms for behaviors perceived to have negative consequences in specific situations (greater care). By comparing our data with available data collected twenty years ago, we find a global pattern of change toward more permissive norms overall but less permissive norms for the most vulgar and inconsiderate behaviors. This study explains how social norms vary across behaviors, situations, societies, and time.Funders that supported this research include the Knut and Alice Wallenberg Foundation (grant no. 2022.0191; Pontus Strimling), the Higher School of Economics Basic Research Program (Ekaterina Nastina and Natalia Soboleva), NextGenerationEU (contract no. BG-RRP-2.004-0007-С01; Kristina Stoyanova), the John Templeton Foundation (grant no. 62631; Robert M. Ross), the Australian Research Council (grant no. DP180102384; Robert M. Ross), the Youth Innovation Promotion Association, Chinese Academy of Sciences (grant no. 2023095; Junhui Wu), Open University of Israel (grant no. 48766; Ravit Nussinson), JSPS KAKENHI (grant no. JP21K02983; Toko Kiyonari); Narodowe Centrum Nauki (grant no. 2019/35/B/HS6/01421; Katarzyna Growiec), National Research and Development Fund (grant no. NKFIH-OTKA-K 135963; Marta Fulop), Australian National University (Samantha K. Stanley), the Swedish Research Council (grant no. 2023-01306; Giulia Andrighetto), Shota Rustaveli National Science Foundation (grant no. FR-22-15319; Vladimer Gamsakhurdia), The Science Fund of the Republic of Serbia (grant no. 7744418; Bojana M. Dinić), CIS—Centro de Investigação e Intervenção Social (CIS-ISCTE) through funds allocated by the Portuguese Foundation for Science and Technology (FCT) (grant no. UIDB/03125/2020, https://doi.org/10.54499/UIDB/03125/2020: Ricardo B. Rodrigues), Czech Science Foundation (grant no. GA23-06170S; Sylvie Graf and Martina Hřebíčková), the Ministry of Science, Technological Development and Innovations of the Republic of Serbia (contract no. 451-03-66/2024-03; Ivana Pedović), Linköping University (Maria Luisa Mendes Teixeira). Open access funding provided by Mälardalen University
Joint User Association and Beamforming Design for ISAC Networks With Large Language Models
Get PDFIntegrated sensing and communication (ISAC) has been envisioned to play a more important role in future wireless networks. However, the design of ISAC networks is challenging, especially when there are multiple communication and sensing (C&S) nodes and multiple sensing targets. We investigate a multi-base station (BS) ISAC network in which multiple BSs equipped with multiple antennas simultaneously provide C&S services for multiple ground communication users (CUs) and targets. To enhance the overall performance of C&S, we formulate a joint user association (UA) and multi-BS transmit beamforming optimization problem with the objective of maximizing the total sum rate of all CUs while ensuring both the minimum target detection and parameter estimation requirements in terms of the radar signal-to-noise ratio (SNR) and the Cramér-Rao bound (CRB), respectively. To efficiently solve the highly non-convex mixed integer nonlinear programming (MINLP) optimization problem, we propose an alternating optimization (AO)-based algorithm that decomposes the problem into two sub-problems, i.e., UA optimization and multi-BS transmit beamforming optimization. Inspired by the huge potential of large language models (LLMs) for prediction and inference, we propose a unified framework integrating LLMs with convex-based optimization methods to benefit from the theoretical rigor and convergence guarantees of convex-based methods, and the adaptability and flexibility of LLMs. First, we propose a comprehensive design of prompt engineering based on in-context, few-shot, chain of thought, and self-reflection techniques to guide LLMs in solving the binary integer programming UA optimization problem. Second, we utilize convex-based optimization methods to handle the non-convex beamforming optimization problem based on fractional programming (FP), majorization minimization (MM), and the alternating direction method of multipliers (ADMM) with an optimized UA from LLMs. Numerical results demonstrate..10.13039/501100003725-Swedish Research Council (VR) Project entitled “Coding for Large-scale Distributed Machine Learning” (Grant Number: 2021-04772);
Swedish Agency for Innovation Systems (VINNOVA), Project “Integrating Large AI Models into 6G networks” (Grant Number: 2024-02435);
joint KTH-NTU collaboration project, entitled “Intelligent Joint Radar Communications with Millimeter Wave”
Horizon Europe COVER project (Grant Number: 101086228);
10.13039/100014013-UK Research and Innovation (Grant Number: EP/Y028031/1);
Royal Society Industry Fellow scheme (Grant Number: IF\R2\23200104);
Federal Ministry for Research, Technology and Space (BMFTR) in Germany in the program of “Souverän. Digital. Vernetzt.” joint project 6G-RIC (Grant Number: 16KISK023);
Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through projects SFB 1483 (Grant Number: 442419336);
>EmpkinS;
Horizon Europe Marie Skodowska-Curie Actions (MSCA)-UNITE (Grant Number: 101129618);
National Research Foundation of Korea (NRF) Grant funded by the Korean Government (MSIT) (Grant Number: 2021R1A2C2007638)
The burden of injuries in Nepal: findings from the NIHR Global Health Research Group
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Quantitative data are available on reasonable request. Data associated with the studies summarised in this synopsis paper are included in the published articles and the related supplementary information. All data requests should be submitted to the corresponding author for consideration.This synopsis was published based on current knowledge at the time and date of publication. NIHR is committed to being inclusive and will continually monitor best practice and guidance in relation to terminology and language to ensure that we remain relevant to our stakeholders.Background:
Injuries cause significant harm and may lead to disability yet are largely preventable. Understanding the epidemiology and determinants of injury in any given context is an essential step towards effective prevention. In Nepal, surveys suggest that injuries on the road, at home and at work are a problem, but in the absence of injury surveillance, robust death registration or police records, the true burden is unclear. For those who are injured, access to prehospital care is variable.
Objectives:
To understand the epidemiology of injuries.
To identify potentially modifiable risk factors to inform the development of prevention interventions.
To build capacity and capability for injury prevention research.
Design:
Observational, secondary data analysis and qualitative methods were used. We worked with communities, practitioners and stakeholders to identify potential participants, develop study protocols and disseminate findings.
Setting:
Nepal.
Participants:
Patients, communities and road users, health system practitioners and managers, professionals (e.g. police, engineers, journalists) and local and national decision-makers.
Main outcome measures:
Epidemiological evidence of the burden of injuries, evidence to inform future intervention development.
Data sources:
Participants, health services, police and information in the public domain.
Review methods:
Reviews were conducted systematically with evidence synthesised narratively.
Results:
The Nepal Injury Research Centre was established and a cadre of researchers trained. Three researchers and our data manager completed Master’s degree courses, and all researchers developed their skills by leading at least one project from protocol development through to publication. A review of publications reporting injuries indicated that existing epidemiological evidence mostly arose from case series at high risk of bias. A review of existing legislation showed policy gaps and incomplete implementation or enforcement. Surveillance studies and a household survey showed the high burden of injuries at home, at work and on the roads, and the neglected issue of suicide. Previously unreported inequalities by age, sex, ethnic group and income level were identified. Existing health, police and death registration data systems are at high risk of under-reporting and misclassification. Road traffic injury emerged as a major concern: road users fear being injured as pedestrians, passengers or drivers; the economic burden of road injuries has increased threefold over 8 years; and potentially modifiable risk factors were identified. The provision of first-response services is highly variable, and the public and practitioners are fearful of prosecution in the event of poor outcomes. We found it is feasible to train the traffic police in first response and for them to use their skills at traffic collisions. Research priorities for suicide prevention were identified.
Limitations:
Studies were limited by the quality of the data available through existing systems, with data often incomplete or poorly coded. Our studies were largely conducted in one district with topography typical of many areas of Nepal. However, our findings may not be generalisable to all districts.
Conclusions:
Our programme identified the inequitable and significant burden of injuries in Nepal. There is the potential to develop existing legislation and health and transport systems to reduce the incidence and consequences of injury.
Future work:
Research should focus on interventions to reduce injury risk on the roads and at home/work, to develop the first-response system and standardise care and to strengthen injury data systems.This synopsis presents independent research funded by the National Institute for Health and Care Research (NIHR) Global Health Research (GHR) programme as award number 16/137/49
Mutual Trust in Judicial Cooperation in Criminal Matters
No full text1. Mutual trust has been acclaimed by the Court of Justice of the European Union (CJEU) as a ‘the raison d’être of the European Union and the creation of an area of freedom, security and justice’, signifying the fundamental significance of it (Joined cases C-411/10 and C-493/10, NS, para. 83). The principle of mutual trust is a sophisticated constitutional principle of EU law, governing the judicial cooperation in criminal matters as well as other policy areas such as the Common European Asylum System, judicial cooperation in civil matters, and police cooperation..
The Effect of Ergometer Cycling and Visual Foraging on Brain Function: A Pilot Study
Get PDFData Availability: All relevant data are within the manuscript and its Supporting Information files.Supporting information is available online at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0336642#sec021 .Dual-task training comprising cognitive and physical components may enhance cognitive function, and increased prefrontal cortex activation may underpin these improvements. The aim of this pilot study was to examine the effects of cycling and visual foraging on executive function (EF). Twenty-seven participants (mean age 25.44 ± 4.31 years) completed four lab-based sessions, one in which their aerobic capacity (O2max) and baseline EF scores assessed were determined, and three randomized experimental conditions: ergometer cycling (EC), visual foraging (VF) and both combined (EC + VF). Participants’ EF performance was assessed at baseline, and pre-and post- intervention using the 2-Back task (working memory), the Flanker Task (inhibitory control), and the Wisconsin Card Sorting Task (WCST; task switching). Functional near-infrared spectroscopy (fNIRS) and eye-tracking data were collected throughout each condition. Affective state was assessed via the Affect Grid. Repeated measures ANCOVAs, incorporating baseline EF task scores as covariates, revealed condition x time x covariate interactions for the Flanker task only; task performance of participants with poorer baseline scores improved more profoundly in the EC condition. Subjective arousal and prefrontal cortex (PFC) activation were higher in both cycling conditions relative to VF; hence, ergometer cycling, rather than visual foraging, might be the more impactful intervention in these regards. However, these elevations were not associated with EF enhancements; near-ceiling effects in EF task performance may explain this. The EC condition elicited greater energetic investment than the EC + VF condition; possibly because the secondary VF task distracted from the cycling exercise. PFC activation was only correlated with gaze fixations during the EC + VF condition, potentially reflecting concurrent increases in supply of, and demand for, oxygen during the combined condition.The author(s) received no specific funding for this work
A Transfer-Learning-Assisted Role-Differentiated Approach for Industrial Outlier Detection under Label Noise
Get PDFIn this paper, a novel role-differentiated learning with noisy label (RD-LNL) approach is proposed for industrial outlier detection. A leader-follower-inspired sample selection (LFSS) strategy is introduced to choose relatively "clean samples" for establishing a robust outlier detector against label noise. Specifically, a pre-trained deep learning model is employed as the leader network to guide the training of two follower deep learning models via a joint training manner, where a selection metric is designed to facilitate sample selection by leveraging both the training dynamics and the prediction discrepancy among the models. To further enhance the possibility of selecting potential clean samples, an adaptive selection scheme is put forward to adaptively adjust the clean sample selection ratio throughout the model training process by making full use of the loss characteristics of the samples. The proposed outlier detection approach is exploited in a real-world industrial outlier detection task with application to wire arc additive manufacturing (WAAM). Experimental results demonstrate the effectiveness of the developed RD-LNL approach for WAAM outlier detection in terms of detection accuracy
Data sources:
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