Search Results (7,577)

Based on sample data from A-share listed heavy polluters from 2012 to 2021, this paper adopts the double-difference method to explore the influence of the construction of national pilot zone for ecological conservation on the ESG performance of heavily polluting enterprises. Following several robustness tests, this study argues that the ESG performance of heavy-polluting companies is significantly enhanced by the construction of the national pilot zone for ecological conservation. Specifically, the construction of the pilot zone enhances the ESG performance of heavy polluters by easing financing constraints. The enhancing effect of the construction of the pilot zone on the ESG performance of heavy polluters is more prominent in terms of strengthening social responsibility and optimizing governance structure. Additionally, improving heavily polluting enterprises’ ESG performance is demonstrated to effectively enhance their financial performance. The facilitating effect of the construction of the pilot zone on ESG performance is more obvious among state-owned enterprises, enterprises with high media attention, enterprises established at a late stage, and enterprises with high-quality environmental information disclosure. This study offers an empirical foundation for the government to develop policies regarding the establishment of pilot zones and for heavily polluting enterprises to enhance their ESG performance. Full article

The pilot phase of a research study is essential for refining methodological and theoretical aspects before a full-scale launch. Using participatory action research with youth and sector partners, this study tested the design and implementation of a longitudinal research project, focusing on four key areas: recruitment strategies, survey design, incentive strategies, and participant engagement and retention. The study compares two recruitment messages, assessed survey clarity and completion rates, tested financial and non-financial incentives, and evaluated participants’ willingness to share contact information and LinkedIn profiles. Data were collected through surveys (n = 91) and focus groups (n = 11) with young people aged 15–29 from across Canada who completed an RBC Future Launch-funded program. Findings indicated that branding and messaging in recruitment emails influenced response rates. Despite concerns about survey length, 97% of participants completed it, with most finishing within 15 min. Among the incentives offered, a CAD 10 payment resulted in the highest response rate. Additionally, both the CAD 10 incentive and the LinkedIn Learning licenses increased participants’ willingness to share LinkedIn profiles. The pilot study provided valuable insights into optimizing recruitment, survey design, and incentive structures for a longitudinal study. These findings provide insights for improving participant engagement and retention in research studies, as well as a co-creation approach to research design. Full article

This study investigates whether and how the complexity of derivative use influences the stock price crash risk in China’s capital market, a critical question given the growing use of derivatives in emerging economies where governance structures and disclosure standards vary widely. While prior research has examined the binary effects of derivative usage, limited attention has been paid to the multidimensional complexity of such instruments and its informational consequences. Using a novel hand-collected dataset of annual reports from Chinese A-share-listed firms between 2010 and 2023, we develop and implement new indicators that capture both the economic complexity (diversity and scale) and accounting complexity (reporting dispersion and fair-value hierarchy) of derivative use. Our analysis shows that higher complexity is associated with a significantly lower likelihood of stock price crashes. This effect is especially pronounced in non-state-owned firms and those with weaker internal-control systems, suggesting that derivative complexity can enhance information transparency and serve as a substitute for other governance mechanisms. These findings challenge the conventional view that complexity necessarily increases opacity and highlight the importance of disclosure quality and institutional context in shaping the market consequences of financial innovation. Full article

With the exponential growth of user-generated content on video-sharing platforms, the challenge of facilitating efficient searching and browsing of videos has garnered significant attention. To enhance users’ ability to swiftly locate and review pertinent videos, the creation of concise and informative video summaries has become increasingly important. Video-llama is an effective tool for generating video summarization, but it cannot effectively unify and optimize the modeling of temporal and spatial features and requires a lot of computational resources and time. Therefore, we propose MiLoRA-ViSum to more efficiently capture complex temporal dynamics and spatial relationships inherent in video data and to control the number of parameters for training. By extending traditional Low-Rank Adaptation (LoRA) into a sophisticated mixture-of-experts paradigm, MiLoRA-ViSum incorporates a dual temporal–spatial adaptation mechanism tailored specifically for video summarization tasks. This approach dynamically integrates specialized LoRA experts, each fine-tuned to address distinct temporal or spatial dimensions. Extensive evaluations of the VideoXum and ActivityNet datasets demonstrate that MiLoRA-ViSum achieves the best summarization performance compared to state-of-the-art models, while maintaining significantly lower computational costs. The proposed mixture-of-experts strategy, combined with the dual adaptation mechanism, highlights the model’s potential to enhance video summarization capabilities, particularly in large-scale applications requiring both efficiency and precision. Full article

Background: Screening for substance use disorder (SUD) is a critical step to address the ongoing opioid crisis in the U.S., but fewer than 10% of people at risk screen. Technology may play a role in substantially increasing screening by making screening accessible through artificially intelligent (AI) chatbots. Methods: This was a single-arm mixed-methods pilot study to establish the system usability of an AI chatbot delivering information about substances, substance use disorder, and treatment options, and implementing self-screening for anxiety, depression, and substance use disorder. Participants were asked to engage with the AI chatbot for seven days and could self-select to screen. Results: Of the 92 participants enrolled, 91 engaged with the system at least once, and 29 (32%) completed at least one screener. Those who screened were given a referral if they exhibited moderate or severe anxiety, depression, and/or SUD. Over three-quarters (83%) of those screened received a referral for treatment, and 50% of those referred made an appointment for care. Users indicated that they found the system helpful and informative, and they felt comfortable screening. Conclusions: While other AI systems that share information about mental health and substance use exist, we know of no other AI chatbot that is being deployed specifically to facilitate SUD screening and referral. The system we describe here shows potential to support self-screening. Users generally find the system acceptable to use. AI technology may allow for improved access to SUD screening and treatment referrals, a critical step in responding to the opioid crisis. Full article

The gearbox, as an important transmission component in wind turbines, connects the blades to the generator and is responsible for converting wind energy into mechanical energy and transmitting it to the generator. Its ability to reduce vibrations directly affects the operational lifespan of the wind turbine. When designing the gearbox’s elastic support structure, it is essential to evaluate how the design parameters influence various performance metrics. Neural networks offer a powerful means of capturing and interpreting the intricate associations linking structural parameters with performance metrics. However, conventional neural networks are usually optimized for a single task, failing to fully account for task differences and shared information. This can lead to task conflicts or insufficient feature modeling, which in turn affects the learning efficiency of inter-task correlations. Furthermore, physical experiments are costly and provide limited training, making it difficult to meet the large-scale dataset requirements for neural network training. To address the high cost and limited scalability of traditional physical testing for gearbox rubber damping structures, in this study, we propose a low-cost performance prediction method that replaces expensive experiments with simulation-driven dataset generation. An optimal Latin hypercube sampling technique is employed to generate high-quality data at minimal cost. On this basis, a multi-task prediction model called multi-gate mixture-of-experts with LSTM (PLE-LSTM) is constructed. The adaptive gating mechanism, hierarchical nonlinear transformation, and effective capture of temporal dynamics in the LSTM significantly enhance the model’s ability to model complex nonlinear patterns. During training, a dynamic weighting strategy named GradNorm is utilized to counteract issues like the early stabilization in multi-task loss convergence and the uneven minimization of loss values. Finally, ablation experiments conducted on different datasets validate the effectiveness of this approach, with experimental results demonstrating its success. Full article

Background/Objective: Salinity is a growing problem affecting a large portion of global agricultural land, particularly in areas where water resources are scarce. The objective of this study was to provide physiological and molecular information on salt-tolerant citrus rootstocks to mitigate the detrimental effects of salinity on citriculture. Methods: Ten accessions belonging to eight Citrus species and four to Poncirus trifoliata Raf. were tested for salinity tolerance (0 and 15 mM NaCl for 1 year) in terms of vegetative and Cl⁻ tissue distribution traits. In addition, most accessions were evaluated for leaf Na⁺ and other cations. Results: All salt tolerant accessions tended to restrict the leaf Cl⁻ content, although in a lower degree than the Cleopatra mandarin. However, differences in their ability to restrict leaf [Na⁺] were evident, contributing to a classification of trifoliate and sour orange accessions that matched their genotypic grouping based on allele sharing at a marker targeting candidate gene coding for the NPF5.9 transporter within LCL-6 quantitative trait locus. Conclusions: Our markers targeting LCl-6 candidate genes coding for NPF5.9, PIP2.1, and CHX20 (citrus GmSALT3 ortholog) could be efficient tools for managing the detected salt tolerance diversity in terms of both Cl⁻ and Na⁺ homeostasis in rootstock breeding programs derived from these species, in addition to Citrus reshni. Full article

Background/Objectives:Breast reconstruction (BR) following mastectomy plays a critical role in post-cancer care by offering both physical and psychological benefits. Despite advancements in techniques and shared decision-making (SDM), BR uptake remains inconsistent. This systematic review aims to synthesize evidence on objective (medical and socioeconomic) and subjective (psychological and personal) factors influencing BR decision-making among women undergoing mastectomy for breast cancer. Methods: A systematic search was conducted across PubMed, ScienceDirect, OVID, and Google Scholar, identifying peer-reviewed studies published between January 2013 and 25 July 2024. Eligible studies examined determinants of BR decisions in women undergoing therapeutic mastectomy, excluding perspectives of non-patient stakeholders and post-decision outcomes. The risk of bias and study quality were assessed using the Quality Appraisal for Diverse Studies (QuADS) tool. This review was registered in PROSPERO (CRD42023456198) and followed PRISMA guidelines. Results: Twenty-seven studies comprising 994,528 participants across 16 countries met the inclusion criteria. The objective factors included age, comorbidities, insurance coverage, physician recommendations, and healthcare access. The subjective factors encompassed body image concerns, self-esteem, fear of recurrence, and emotional readiness. Younger age, private insurance, and active physician counseling were associated with increased BR uptake, while older age, lack of information, and financial or logistical barriers reduced uptake. Regional disparities were noted across healthcare systems. Conclusions: BR decisions are influenced by complex, interrelated clinical, psychological, and systemic factors. Integrating SDM tools, enhancing patient education, and addressing healthcare inequities are essential for supporting informed and equitable BR decision-making. Future research should prioritize longitudinal studies and policy interventions to improve access to and patient satisfaction with BR outcomes. Full article

Teacher collaboration, particularly through collaborative lesson planning, plays a key role in fostering professional development and teaching skills. This study investigates the effectiveness of the proposed Shared Collaborative Lesson Planning (SCLP) procedure in enhancing the professional development of prospective teachers through the lens of socially shared regulation of learning (SSRL). Twenty-four prospective information and technology teachers participated in two rounds of SCLP activities. Using a cyclical framework, the prospective teachers engaged in collaborative tasks supported by an online learning platform and other computer-supported collaborative learning (CSCL) tools. Detailed descriptions from observations, recordings, questionnaires, and interviews revealed that the SCLP procedure can help facilitate structured collaboration, improve professional skills, and encourage adaptive regulatory behaviors among the prospective teachers. The integration of CSCL tools further enhanced the effectiveness of the collaborative lesson planning process. However, challenges related to technical proficiency and emotional regulation were identified. The study concludes that the SCLP procedure offers a promising approach for teacher education by fostering a collaborative learning environment and promoting regulatory behaviors. Future research should explore the long-term impacts and adaptability across diverse educational contexts to strengthen teacher education programs or courses. Full article

In the digital era, social networks play a crucial role in promoting university cultural events and enhancing accessibility, interaction, and student engagement. This study examines the impact of the communication strategies employed by the West University of Timisoara (UVT) to promote the initiative “At UVT, Culture is Capital” within the framework of Timisoara 2023—European Capital of Culture. This research employs a mixed methodology, combining a quantitative analysis of the performance of Facebook and Instagram posts with a qualitative examination of communication strategies. The results reveal that, despite Generation Z’s preference for visual platforms like TikTok and Instagram, Facebook continues to be an effective channel for promoting university cultural events. This effectiveness stems from trust in the information shared and the strengthened community surrounding the university. Key factors contributing to the campaign’s success included the prominence of guest speakers, high-quality visual and textual content, and the organization of interactive giveaway campaigns. There was also a notable increase in student participation in events and an enhanced sense of belonging to the university community. The findings suggest incorporating more dynamic media formats, such as reels and stories, to boost engagement and explore emerging platforms favored by younger audiences. This research contributes to understanding how universities can effectively leverage social media to promote cultural events and foster a more engaged academic community. Full article

Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques. Full article

This study investigates the interplay between cellular senescence and inflammation in human umbilical vein endothelial cells (HUVECs). We employed RNA sequencing to analyze gene expression changes in HUVECs subjected to replicative- or radiation-stress-induced senescence, and we compared these profiles with those of cells under acute or chronic TNFα-mediated inflammation. Our findings reveal that both senescence types exhibited significant upregulation of genes associated with epithelial- (or endothelial) mesenchymal transition (EMT) and inflammatory pathways, indicating a shared molecular response. Notably, chronic inflammation led to a pronounced EMT signature, while acute inflammation primarily activated classical inflammatory responses. Experimental validation confirmed reduced proliferation and increased secretion of pro-inflammatory cytokines (IL-6 and IL-8) in senescent and chronically inflamed cells and substantiated the upregulation of EMT marker genes. Additionally, we observed impaired wound healing capacity in senescent and chronically inflamed cells, highlighting the functional consequences of these cellular states. Our study underscores the critical role of inflammation in exacerbating senescence-related changes, contributing to the understanding of age-related cardiovascular pathologies. These insights may inform future therapeutic strategies aimed at mitigating the effects of aging and inflammation on endothelial function and cardiovascular health. Full article

Family violence is a social issue that impacts families and communities every day in Canada and around the world. As family violence rates continue to increase there is an urgent need for cross-sectoral collaboration to codesign social work and social service systems, in partnership with those experiencing family violence. This article will share learnings from a two-year community-based participatory research study that worked alongside survivors and witnesses of family violence, community partners from diverse social service agencies, and researchers, to understand experiences of family violence in racialized communities in Peel region, Ontario, Canada. An intersectional-trauma-informed approach guided the work that included establishing a community advisory board, hiring peer research assistants, Photovoice, and holding a knowledge exchange event (KEE) with survivors and witnesses of family violence, researchers, and community partners to rapidly generate ideas for intervention areas through a 25/10 crowdsourcing activity and codesign preliminary solutions through a mini hackathon. Key findings from the photovoice highlighted systemic failures and gaps experienced by those facing family violence. As we shifted into ideation, this preliminary focus on systems solidified and top ideas identified included barrier-free, culturally aware provision of services ranging from mental health supports, safe housing, financial independence, and accessing wrap-around services. Our work concluded with the collaborative development of preliminary solutions to these ideas and emphasized the need for cross-sectoral partnerships and lived experience engagement to change systems. Centering the voices of those who have experienced FV in system-level change and advocacy is necessary to ensure services and supports meet the needs of service users. Full article

Stock returns are a critical aspect of investment decisions, and understanding whether climate change commitment influences stock returns is essential for informed decision-making. This paper investigates the impact of climate change commitment on the stock returns in the GCC countries for non-financial companies during the period of 2010–2023. The sample consisted of a total of 285 companies collected using the Refinitiv Eikon platform. The developed model was estimated using panel GMM regression. The results suggested that when companies reported their climate change commitment, it was appreciated by high demand for their stock and in turn caused the stock return to be higher. In addition, profitability and growth affect stock returns significantly positively, and this implies that investors concentrate on whether the company has higher profits and better growth prospects to demand more shares, and this affects the share prices positively. In addition, the size of a company has been found to affect the stock return positively, and this suggests that investors in the GCC feel confident in demanding the shares of large companies. Moreover, the results showed that leverage significantly negatively affects stock return, and this implies that investors interpret the position of highly leveraged companies to be bad due to worries that companies will not be able to service their loans. Such results might help investors to formulate their investment strategies and select their shares based on significant determinants. Finally, our results hold based on the reported robustness of results. Full article

Decision making in modern supply chain management relies heavily on data-driven decision support. Companies show a growing interest in building insights not only on data from within the company’s own boundaries, but also from collaborators and other actors in the market. While the topic of data and information sharing has been the focus of previous works, there has been a lack of studies focusing on practical implementations in the supply chain domain. Our aim is to conduct a technical feasibility study of data sharing in supply chain management. We analyze the requirements for cross-company data sharing in supply chains, and discuss existing technologies that enable such collaboration. We apply a distributed analytics framework that has already been implemented in the healthcare domain to a simulated use case of key performance indicator (KPI) exchange between supply chain actors. We find that the application is able to compute and exchange KPIs from the simulated companies’ datasets without requiring centralization of the databases. Furthermore, we find that the framework supports integration of data quality assessment and privacy preservation mechanisms. The application thus yields promising results with regard to technical feasibility. Factors that may facilitate scalability are discussed as directions for future research. Full article