The introduction of network science approaches into public transport research has seen great advances in the past 15 years. However, it has become apparent that monolayer networks are often not sufficient to model and analyse real-world systems in sufficient detail. In the last decade, the theory of multilayer networks has proven to be an invaluable tool in various disciplines, including transport. Multilayer networks consist of layers of networks that are coupled among themselves. This enables modelling of complex systems with heterogeneous elements and relations between them. Although there is a body of work in public transport research that uses multilayer networks, the related literature is scattered, lacking unified terminology and agreed-upon approaches. We posit that there is vast uncovered potential in using multilayer network approaches to public transport modelling, planning, and operations. We first present the basic formalisms of multilayer networks with a focus on how they (may) relate to public transport networks. We then provide a systematic review of the literature on multilayer networks in public transport research. We identify and taxonomise ways in which public transport systems are modelled as multilayer networks. Based on the survey and drawing from the state and history of network science in public transport research as well as multilayer approaches across other application domains, we propose a research agenda for multilayer public transport networks for the upcoming decade(s).
Abstract Government of India aims at making the Indian Banks internationally competitive. In the wake of intense competition and changing global and national business environment, the efficiency issues have emerged as an important pillar of success in the Indian banking sector. Therefore, it is an essential task to comprehend the efficiency levels of the overall Indian banking sector and also across different ownership structures (viz. Public, Private and Foreign). The present study endeavors to carry out an assessment of intra-sector efficiency in the Indian banking sector based on a cross-sectional data of 66 banks for the year 2015-16. The authors employ directional distance function based meta-frontier DEA approach and the results reveal that the Indian banking sector is 73.44% efficient. It also confirms the existence of different production functions across different ownership structures of the industry. Among the different ownership structures, the group frontier of foreign banks coincides with the meta-frontier. The group frontier of private sector banks is the second closest to the meta-frontier and public sector banks are found to be the laggards in the overall industry. The study gains special significance in the backdrop of the recommendations floated by the Reserve Bank of India and Ministry of Finance (Government of India) to consolidate the public sector banks in order to retain fewer but healthier banks. The finding of the study fully support these recommendations and affirms that consolidation in the industry will bring positive synergies and will lead to the enhancement of efficiency levels in the industry.
This study examines the direct and indirect impacts of perceived overqualification (POQ) on turnover intentions (TI) among faculty members in Saudi private universities, with job crafting (JC) serving as a mediating variable. Utilizing Conservation of Resources (COR) theory, the study asserts that overqualified professors, experiencing a disparity between their qualifications and job responsibilities, may undertake proactive job designing to restore autonomy, purpose, and engagement. A quantitative study design was employed, gathering data from 378 faculty members via a structured survey. Structural equation modeling (SEM) was utilized to evaluate the proposed correlations. The findings indicate that POQ exerts a substantial beneficial influence on job crafting and a small direct impact on turnover intentions. Work crafting adversely predicts turnover intention, signifying its contribution to enhancing work fit and diminishing faculty members. Mediation research indicates partial mediation, implying that job crafting mitigates certain adverse impacts of POQ but does not completely eradicate them. The research enhances Person-Job Fit Theory by illustrating how job design can convert apparent misalignment into significant work experiences. Theoretical and practical consequences are examined, highlighting the necessity for university administrators to advocate for job crafting techniques within comprehensive personnel management and retention efforts. Constraints and avenues for subsequent research are also indicated.
Summary: The circular economy is vital for sustainability, yet its resilience to unexpected socio-economic shocks is not well understood. This study explores the impact of one of the major global disruptive events, the COVID-19 pandemic, on the circular economy by focusing on copper recycling. Using transaction-level data from a waste trading platform and causal inference methods, we evaluated how the pandemic disrupted copper scrap supply and transactions. The findings indicate significant and enduring negative effects, including reduced trading volumes, prices, and material diversity. The disruption was uneven across sectors: labor-intensive industries were most seriously affected, while technology-intensive and capital-intensive sectors demonstrated greater resilience. To enhance recovery and strengthen the resilience of a circular economy, we recommend coordinating policy and market signals, incentivizing resilience-enhancing practices, and balancing efficiency, sustainability, and resilience goals. By mitigating adverse effects from unexpected disruptions, these strategies aim to foster a more resilient circular economy.
Since the beginning of the 21st century, with the widespread use of the internet and the development of new technologies, the need for a fundamental technological change in public and private institutions has emerged. Especially in public institutions, the digital transformation process is critical to provide faster and more effective services to citizens, as well as to strengthen principles such as transparency and accountability. Furthermore, digital transformation plays a crucial role in developing sustainable public policies by optimising the use of public resources. Thus, academic studies on digital transformation have been increasing over the years. This study conducts a bibliometric analysis of research on digital transformation within the social sciences. With the RStudio-based Biblioshiny program, 4,563 articles published in the Web of Science database between 2002 and 2024 were included in the analysis. Because of the analysis, it was seen that China, Italy, Germany, Russia and the United Kingdom were the countries with the highest number of publications. Chinese researchers are active and pioneering in the literature. It is thought that the bibliometric analysis of studies in the field of digital transformation will contribute to the creation of strategic road maps that emphasise the importance of this transformation for public institutions and organisations. This study presents a general literature review on digital transformation in social sciences and serves as a resource for researchers who want to conduct studies on the subject in the future.
Abstract Ecological restoration has become a critical tool for mitigating ecosystem degradation and enhancing ecological health. Effective restoration efforts require regionalisation through identifying of social-ecological system (SES) that integrate socioeconomic and ecological characteristics. However, methodological gaps in clarifying interdimensional interplay often hinder coastal restoration planning. This study develops a spatial characterisation framework to identify coastal SESs for targeted ecological restoration in Zhejiang Province, China. Integrating socioeconomic data and land-marine ecological variables across 28 coastal counties, we employed principal component analysis and hierarchical clustering to delineate nine distinct SESs exhibiting significant heterogeneity: northern clusters (SES1/7/9) show high socioeconomic performance but suboptimal environmental indicators; southern systems (SES2/8) feature lower development but superior environmental conditions; central zones (SES5/6) demonstrate moderate socioeconomic-environmental profiles; while island systems (SES3/4) display low population density, high aging rates, and unique biogeophysical traits. Precise SES categorization enabled identification of primary degradation pressures and formulation tailored restoration strategies within each SES in Zhejiang. Critically, transcending administrative boundaries is essential to accommodate intra-city diversity and cross-city unity, given prevalent transboundary SESs and intra-city variations. We propose four management implications: ecosystem service conservation, pressure-specific interventions, cross-sector governance mechanisms, and participatory restoration incentivization. This framework establishes a transferable approach for sustainable coastal SES restoration management.
Hasti Narimanzadeh, Arash Badie-Modiri, Iuliia Smirnova
et al.
Affective polarization and political sorting drive public antagonism around issues at the science-policy nexus. Looking at the COVID-19 period, we study cross-domain spillover of incivility and contentiousness in public engagements with climate change and public health on Twitter and Reddit. We find strong evidence of the signatures of affective polarization surrounding COVID-19 spilling into the climate change domain. Across different social media systems, COVID-19 content is associated with incivility and contentiousness in climate discussions. These patterns of increased antagonism were responsive to pandemic events that made the link between science and public policy more salient. The observed spillover activated along pre-pandemic political cleavages, specifically anti-internationalist populist beliefs, that linked climate policy opposition to vaccine hesitancy. Our findings show how affective polarization in public engagement with science becomes entrenched across science policy domains.
Sharing sensitive time series data in domains such as finance, healthcare, and energy consumption, such as patient records or investment accounts, is often restricted due to privacy concerns. Privacy-aware synthetic time series generation addresses this challenge by enforcing noise during training, inherently introducing a trade-off between privacy and utility. In many cases, sensitive sequences is correlated with publicly available, non-sensitive contextual metadata (e.g., household electricity consumption may be influenced by weather conditions and electricity prices). However, existing privacy-aware data generation methods often overlook this opportunity, resulting in suboptimal privacy-utility trade-offs. In this paper, we present Pub2Priv, a novel framework for generating private time series data by leveraging heterogeneous public knowledge. Our model employs a self-attention mechanism to encode public data into temporal and feature embeddings, which serve as conditional inputs for a diffusion model to generate synthetic private sequences. Additionally, we introduce a practical metric to assess privacy by evaluating the identifiability of the synthetic data. Experimental results show that Pub2Priv consistently outperforms state-of-the-art benchmarks in improving the privacy-utility trade-off across finance, energy, and commodity trading domains.
Jiaxin Pei, Dustin Wright, Isabelle Augenstein
et al.
Effectively engaging the public with science is vital for fostering trust and understanding in our scientific community. Yet, with an ever-growing volume of information, science communicators struggle to anticipate how audiences will perceive and interact with scientific news. In this paper, we introduce a computational framework that models public perception across twelve dimensions, such as newsworthiness, importance, and surprisingness. Using this framework, we create a large-scale science news perception dataset with 10,489 annotations from 2,101 participants from diverse US and UK populations, providing valuable insights into public responses to scientific information across domains. We further develop NLP models that predict public perception scores with a strong performance. Leveraging the dataset and model, we examine public perception of science from two perspectives: (1) Perception as an outcome: What factors affect the public perception of scientific information? (2) Perception as a predictor: Can we use the estimated perceptions to predict public engagement with science? We find that individuals' frequency of science news consumption is the driver of perception, whereas demographic factors exert minimal influence. More importantly, through a large-scale analysis and carefully designed natural experiment on Reddit, we demonstrate that the estimated public perception of scientific information has direct connections with the final engagement pattern. Posts with more positive perception scores receive significantly more comments and upvotes, which is consistent across different scientific information and for the same science, but are framed differently. Overall, this research underscores the importance of nuanced perception modeling in science communication, offering new pathways to predict public interest and engagement with scientific content.
Infanti, A. C., & Crawford, B. J. (2009). Critical Tax Theory: An Introduction. Cambridge University Press, 356 pp., $64.00 (paperback), ISBN: 978-0-521-87904-1.
IntroductionGlobally, one in five individuals faces unemployment, which substantially increases their risk of developing mental disorders. Understanding the relationship between unemployment and specific mental health outcomes is crucial for formulating effective policy interventions.MethodsThis study examines the relationship between unemployment and mental disorders across 201 countries from 1970 to 2020. Using a fixed-effects model, we analyze the impact of unemployment on various mental health outcomes, including anxiety, depression, bipolar disorder, drug use, and eating disorders, with a focus on demographic variations.ResultsThe analysis reveals a significant positive association between unemployment and mental disorders, particularly anxiety, depression, and bipolar disorder. Moreover, distinct patterns emerge, linking unemployment to higher rates of drug use and eating disorders in specific demographics.DiscussionThese findings underscore the critical interplay between socio-economic factors and mental health, highlighting the need for proactive strategies to address the dual burden of unemployment and mental health disorders. Targeted interventions, such as employment support programs and accessible mental health services, are essential to improve global mental health outcomes. These initiatives can also alleviate the economic burden of unemployment by boosting workforce participation and productivity. Long-term economic gains may offset the increased healthcare expenditures associated with mental health support.
Summary: Quantum secret sharing (QSS) represents the fusion of quantum mechanics principles with secret information sharing, allowing a sender to distribute a secret among receivers for collective recovery. This paper introduces the concept of quantum anonymous secret sharing (QASS) to enhance the practicality of such protocols. We propose a QASS protocol leveraging W states, ensuring both recover-security and anonymity of shared secrets. Our protocol undergoes rigorous evaluation verifying their accuracy and fortifying their security against scenarios involving the active adversary. Additionally, acknowledging the imperfections inherent in real-world communication channels, we conduct a comprehensive analysis of protocol security and efficacy in noisy quantum networks. Our investigations reveal that W states exhibit good performance in mitigating noise interference, making them apt for practical applications.
Abstract Malaysia's excessive energy consumption has led to the depletion of traditional energy reserves such as oil and natural gas. Although Malaysia has implemented multiple policies to achieve sustainable national energy development, the current results are unsatisfactory. As of 2022, only 2% of the country's electricity supply comes from renewable energy, which accounts for less than 30% of the energy structure. Malaysia must ensure energy security and diversified energy supply while ensuring sustainable energy development. This article uses the fuzzy multi-criteria decision-making(MCDM) method based on cumulative prospect theory to help decision-makers choose the most suitable renewable energy for sustainable development in Malaysia from four dimensions of technology, economy, society, and environment. The results show that solar power is the most suitable renewable energy for sustainable development, followed by biomass, wind, and hydropower, but the optimal alternative is sensitive to the prospect parameters. Finally, it was analyzed that efficiency, payback period, employment creation, and carbon dioxide (CO2) emissions are the most critical factors affecting the development of renewable energy in Malaysia under the four dimensions. Reasonable suggestions are proposed from policy review, green finance, public awareness, engineering education, and future energy. This research provides insightful information that can help Malaysian decision-makers scientifically formulate Sustainable development paths for renewable energy, analyze the problems encountered in the current stage of renewable energy development, and provide recommendations for Malaysia's future renewable energy transition and sustainable development.
Cassandra Crowe, Belinda Middleweek, Laura Ryan
et al.
We surveyed Australian finance professionals and tested whether there are statistically significant differences in promotional propensity according to gender identity. The findings indicate men and women are equally likely to ask for promotion, however, 'gifted advancements' account for the higher statistical frequency of promotions among men. These gender-based differences in behaviors have been overlooked in existing research on promotion. We call for a standardized framework for the development of promotion policies to address this industry-wide problem.
In recent years, Large Language Models (LLMs) like ChatGPT have seen considerable advancements and have been applied in diverse fields. Built on the Transformer architecture, these models are trained on extensive datasets, enabling them to understand and generate human language effectively. In the financial domain, the deployment of LLMs is gaining momentum. These models are being utilized for automating financial report generation, forecasting market trends, analyzing investor sentiment, and offering personalized financial advice. Leveraging their natural language processing capabilities, LLMs can distill key insights from vast financial data, aiding institutions in making informed investment choices and enhancing both operational efficiency and customer satisfaction. In this study, we provide a comprehensive overview of the emerging integration of LLMs into various financial tasks. Additionally, we conducted holistic tests on multiple financial tasks through the combination of natural language instructions. Our findings show that GPT-4 effectively follow prompt instructions across various financial tasks. This survey and evaluation of LLMs in the financial domain aim to deepen the understanding of LLMs' current role in finance for both financial practitioners and LLM researchers, identify new research and application prospects, and highlight how these technologies can be leveraged to solve practical challenges in the finance industry.
Piotr Mironowicz, Akshata Shenoy H., Antonio Mandarino
et al.
Machine learning and quantum machine learning (QML) have gained significant importance, as they offer powerful tools for tackling complex computational problems across various domains. This work gives an extensive overview of QML uses in quantitative finance, an important discipline in the financial industry. We examine the connection between quantum computing and machine learning in financial applications, spanning a range of use cases including fraud detection, underwriting, Value at Risk, stock market prediction, portfolio optimization, and option pricing by overviewing the corpus of literature concerning various financial subdomains.