Shared premotor resources underlie dual-task interference between speech and balance.

To address inter-center workload imbalance and low resource utilization in multi-center home healthcare systems, this study proposes a bilevel optimization approach for routing and scheduling that enables cross-center resource sharing. First, we formulate a weighted single-objective model that jointly accounts for travel time, service costs, penalties for time-window violations, and workload-imbalance penalties, thereby providing a unified representation of multi-criteria trade-offs while incorporating skill-matching and labor-compliance constraints. Second, we develop a bilevel evolutionary framework, termed the Bilevel Evolutionary Planning Framework for Multi-Center Routing and Scheduling with Resource Sharing (BEPSF). At the upper level, a genetic algorithm (GA) determines client-to-center assignments and performs global workload regulation. At the lower level, a hybrid GAPSO strategy is employed to solve the within-center routing and scheduling of multi-skilled staff, integrating a time-window-driven feasibility repair mechanism with an elitist preservation strategy to enhance solution accuracy and robustness. Computational experiments on 18 standardized benchmark instances demonstrate that BEPSF significantly outperforms the GA-GA and DE-DE baselines in both solution quality and stability, achieving average improvements of approximately 31% and 63%, respectively, and attaining 0% deviation from the reference optimum on small-scale instances. Sensitivity and scenario analyses further reveal that maximum working hours, time-window tolerance, and break intervals substantially affect the cost composition. After enabling cross-center resource sharing, the total system cost is generally reduced by 20-50%, with a maximum reduction of 66%.

The current English education resource sharing platform has fragmented resource organization, insufficient granularity in user behavior modeling, and weak responsiveness of the scheduling feedback mechanism, resulting in limited resource aggregation accuracy, targeted recommendation ranking, and system distribution stability. Based on cloud computing architecture, this paper proposes a three-layer optimization strategy that integrates nested semantic tensor modeling, path attention sorting, and edge feedback scheduling control. This paper first uses multimodal tensors to semantically map and restructure resource content to unify resource expression; secondly, combines user behavior vectors with task path attention mechanism to construct a priority recommendation sequence to enhance the consistency of individual recommendations; finally, introduces load-aware index graph and minimum rescheduling cost function to realize dynamic resource path scheduling based on state feedback. Experiments show that the average response time of static load-aware scheduling is 229ms when the number of concurrent requests is 400, the resource matching accuracy of the fusion optimization model is 94.0%, and the node load variance under peak impact of the load level of this strategy is 30.1. This strategy achieves a systematic improvement in resource organization, service recommendation, and scheduling control based on the cloud computing architecture, enhances the platform's sharing efficiency and operational stability, and provides solid support for the efficient integration of lifelong learning and intelligent English education resources.

The article examines the theoretical and applied aspects of the risks of misstatement of information related to intangible assets and the peculiarities of their consideration in the audit of financial statements of agricultural enterprises. It is substantiated that in the context of the digital transformation of the economy, the implementation of innovative agricultural technologies, the development of production management software systems, and the use of licenses, patents, plant variety rights, and other intellectual property objects, intangible assets acquire strategic importance in ensuring the competitiveness of agricultural businesses. It is determined that the accounting and auditing of intangible assets are associated with an increased risk of material misstatement of financial information. The main sources of such risks include the complexity of asset identification, uncertainty in determining methods for measuring their initial and fair value, difficulties in documentary confirmation of ownership rights, errors in amortization calculations, impairment risks, as well as potential fraud. Special attention is paid to the industry-specific features of agricultural enterprises, in particular the impact of production seasonality, dependence on natural and climatic conditions, rapid technological development, and the high share of intellectual resources in the structure of assets. International approaches to auditing intangible assets in accordance with IAS 38 and modern digital methods for detecting anomalies in accounting records are analyzed. A comprehensive approach to minimizing the risks of misstatement is proposed, which includes improving accounting policies, strengthening internal control, regular revaluation of assets, automation of accounting processes, and the application of risk-oriented audit procedures. Proposals for the use of Deep Learning techniques in the accounting and auditing of intangible assets of agricultural enterprises are developed. It is proven that effective auditing of intangible assets enhances the reliability of financial reporting, reduces financial and reputational risks, and ensures the sustainable development of agricultural enterprises.

1.0 Abstract In today’s world, even though technology is growing very fast, many NGOs, donors, volunteers, and people who need help still struggle to connect with each other properly. There is no single platform where everyone can come together, and because of this, many problems occur such as food wastage, improper distribution of resources like clothes and study materials, and lack of clarity in social service activities. To solve this problem, the project Community Connect is designed as a web-based platform that connects all these people in one place. Through this system, donors can give resources and also track them, NGOs can manage and approve requests, volunteers can handle the collection and delivery work, and administrators can monitor everything smoothly. The platform includes useful features like role-based access, real-time tracking, location-based NGO search, and a dashboard to show the overall impact. It is developed using simple technologies like HTML, CSS, JavaScript, and backend tools such as Node.js or Django, along with databases like MySQL or MongoDB. By making the entire process more organized and transparent, this platform helps in reducing wastage, improving coordination, and making social service more effective. 2.0 Keywords Web Development, Social Welfare, Resource Sharing, NGO System, Community Platform, Public Service

Effective sharing of local resources, tools, everyday goods, and skills can change daily life. However, current peer-to-peer (P2P) systems, the majority of the time, struggle with user engagement, supply-demand mismatch, and a lack of trust. This paper surveys these models and introduces a mobile-centric sharing app using Flutter and Firebase. The design features user authentication, location-based discovery, and an in-app negotiation module with a rating system for reliability. Prototype tests show responsive interactions averaging under two seconds. Role-based access control and encrypted data exchanges address security concerns. This survey demonstrates how intelligent local platforms can optimize assets, minimize waste, and enhance community resilience.

The random number generation capabilities of the GNU/Linux operating system are subject to certain limitations. As of Linux version 5.6, /dev/random operates in a non-blocking manner and, as such, no longer satisfies the criteria for a True Random Number Generator (TRNG). While dedicated quantum random number generator (QRNG) hardware is the preferred source of unpredictable entropy, it is often expensive and difficult to deploy in virtualized/cloud environments and IoT (Internet of Things) devices. Furthermore, hardware RNG integration typically requires cryptographic applications to adhere to vendor-specific APIs. This paper proposes a user-space integration approach for a shared , potentially remote QRNG device. We develop a QRNG service on top of D-Bus, a ubiquitous interprocess communication framework. It serves as an interface for applications to retrieve true random numbers. Communication with the remote QRNG device occurs over mutually authenticated TLS 1.3 channels, protected by post-quantum cryptography (PQC) algorithms. We show, as a proof-of-concept, how the proposed D-Bus service can be integrated into the OpenSSL 3 cryptographic library, demonstrating the use of TRNG in a wide range of Linux applications. Our approach is resistant to entropy starvation attacks, supports sharing a QRNG across host and virtualized environments, requires no kernel-level or system-wide modifications, supports mixing multiple sources of entropy, and configuration of post-processing. It provides applications with a TRNG interface suitable for information-theoretically secure (ITS) use cases.

Abstract - The rapid increase in urban consumption has led to inefficient utilization of resources, contributing to sustainability challenges and economic inefficiencies. This paper presents a Hyperlocal Circular Economy Platform that facilitates resource sharing within trusted community networks. The proposed system integrates a dual-layer trust mechanism and artificial intelligence models for automated item categorization and price prediction. The trust model combines behavioral metrics and experience-based scoring to ensure reliability and encourage user participation. The platform is implemented using a microservices architecture comprising a React-based frontend, a Spring Boot backend, and a FastAPI-based AI service. Experimental evaluation demonstrates strong predictive performance, with the price model achieving an R² score of 0.96 and low error margins. However, observations highlight challenges in real-world generalization, emphasizing the need for robust dataset diversity. The system effectively promotes sustainable consumption while maintaining trust and usability in community-driven environments. Key Words: Hyperlocal Platform Circular Economy, Trust Score, Artificial Intelligence, Machine Learning, Natural Language Processing, Resource Sharing, Microservices Architecture, Sustainable Systems, XGBoost

The proposed EU Multiannual Financial Framework (2028–2034) reshapes rural development by consolidating instruments into a Single Fund and recentralising strategic decisions. Although presented as simplification, the reform risks reinforcing existing biases toward urban, high‑productivity regions. Drawing on rural proofing concepts, EU comparative analyses, and fine‑grained Italian municipal data, the article shows that 2014–2020 Cohesion Policy allocated only a limited share of resources to rural areas, while the EAFRD remained the main—though uneven—source of support. The reform also gives rise concerns about resources and institutional responsibilities for broader rural measures, including diversification, infrastructure, services, and funding for LEADER and cooperation initiatives. Key recommendations include: (i) a mandatory rural chapter in NRPPs; (ii) explicit rural safeguards in Single Fund rules; (iii) clearer rural typologies and better data systems; (iv) stronger multi‑level governance with real local participation; and (v) dedicated resources for capacity‑building and co‑design

Abstract We study a model encompassing economic, epidemiological, and environmental domains, which feature reciprocal interactions. The economy is described by an overlapping generations model in which productivity and agents’ preferences are affected by the epidemiological situation. The evolution of an epidemic is represented by a susceptible–infected–susceptible model, in which disease spread depends on the pollution level and can be reduced through government expenditure. The pollution level increases during the production process and can be reduced by allocating resources to its abatement. Resources are collected through the capital taxation and the regulator must decide how to share them between healthcare and environmental protection. For the resulting model, we show the possible existence of a unique steady state, either characterized by the presence of epidemics or disease-free. We study the comparative statics with respect to the policy parameter that governs the share of resources devoted to improving the epidemiological situation relative to environmental protection. We investigate the emergence of dynamics that do not converge to equilibrium, including complex and quasi-periodic trajectories.

ABSTRACT This study examines how Reddit users engaged with the racial narratives of HBO’s Watchmen (2019) and Lovecraft Country (2020), two television series that reimagine historical racial trauma. Drawing on multistep flow theory and critical technocultural discourse analysis (CTDA), we analyze 3,879 Reddit comments using topic modeling and critical discourse analysis. We identify three dynamic social roles – advocates, adversaries, and adaptives – and explore how users move between them in response to racial discourse. Findings reveal how Reddit’s pseudonymous affordances shape role fluidity, opinion leadership, and moral engagement. While adversaries minimized or rejected the shows’ portrayals of racism and violence as exaggerated, advocates shared standpoint experiences and resources to challenge these claims. Adaptive users shifted perspectives over time, demonstrating how online publics can foster critical racial learning. This research highlights how popular culture and participatory platforms intersect in shaping collective meaning-making around race and historical memory.

Abstract: In today’s rapidly growing digital world, community welfare activities still face major challenges due to the lack of structured and centralized systems. Although many people are willing to help others, there is often a gap between those who need support and those who can provide it. Existing methods such as social media posts, phone calls, and informal communication channels are widely used, but they are unorganized, difficult to track, and unreliable when the number of requests increases. These limitations lead to delayed responses, duplication of efforts, lack of transparency, and reduced trust among users. This paper presents CommunityConnect, a multi-role community support and welfare management platform designed to address these real-world challenges. The system provides a centralized digital environment where users, NGOs, volunteers, donors, and administrators can interact in a structured and coordinated manner. The core idea of the platform is to treat every welfare request not as a simple submission, but as a complete process that moves through multiple stages such as request creation, verification, acceptance or rejection, volunteer assignment, execution, tracking, and final closure. This workflow-based approach ensures that each request is properly managed, monitored, and completed with accountability. The platform is developed using modern web technologies to ensure performance, scalability, and usability. The frontend is built using React with Vite, providing a responsive and user-friendly interface. The backend is implemented using Node.js and Express, which handle business logic, request processing, and API communication. A MySQL database is used for structured data storage, enabling efficient management of users, requests, assignments, and system records. Secure authentication is implemented through email-based OTP verification and JWT-based session management, ensuring that only authorized users can access the system. Additional features such as media uploads, role-based access control, and integrated communication further enhance system functionality. The design and development of CommunityConnect are strongly supported by existing research in areas such as digital volunteering, nonprofit technology adoption, and civic engagement platforms. Previous studies highlight that digital platforms can improve participation and coordination, but most of them focus on specific aspects such as volunteer management or donation systems. They lack integration, workflow tracking, and multi-stakeholder coordination. CommunityConnect addresses this research gap by combining multiple functionalities into a single unified platform, enabling better coordination, transparency, and efficiency. The system has been successfully implemented and deployed on a live environment, demonstrating its real-world applicability. The results show that the platform is capable of improving communication between stakeholders, reducing response delays, increasing user participation, and ensuring better utilization of resources. By providing a structured and transparent workflow, CommunityConnect enhances trust and accountability in community welfare systems. Thus, CommunityConnect is not only a technical solution but also a socially impactful platform that leverages digital technology to strengthen community support mechanisms and improve the overall effectiveness of welfare activities in modern society. Keywords: Community Welfare, Digital Platform, Workflow Management, NGO Coordination, Volunteer Management, Role-Based Access Control, Request Lifecycle, Web Application, Transparency, Resource Sharing

Global inland waters trophic status from space observation: scientific advances and future challenges.

Abstract As the UK power system transitions towards a greater share of variable renewable energy resources, the system becomes more weather dependent. During winter, the most challenging conditions occur when low wind speeds are accompanied by cold temperatures, driving increased electricity demand and reduced wind power generation. To understand the risks posed by such days, the magnitude and likelihood of these events is assessed, using a large climate model ensemble combined with simple empirical relationships to quantify electricity demand net of wind power (DnW) from meteorological variables. It is demonstrated that the annual probability of experiencing a winter day with higher demand net wind than observed is approximately 1% (1 in 103 years) under the current climate. This represents a 10-fold decrease in probability since the 1960s and is primarily associated with a similar reduction in the probability of an unprecedented cold winter day (a 10-fold decrease to 1.3% each year under present climate). The probability of an unprecedented low-wind day is low (0.4% each year) and has remained largely unchanged over the same period. The large sample size also highlights the meteorological conditions driving these extreme high DnW days. It is found that all high DnW days are associated with low-wind conditions (i.e., very low wind generation is essential for high DnW to occur) but the severity of the DnW event is strongly influenced by the temperature (i.e., the level of demand dictates the magnitude of the DnW). Synoptically, this is associated with a reversal of the climatological pressure gradient over the North Atlantic. The most severe DnW days occur when high pressure is located over Greenland and extends to the UK, leading to a weak but cold northerly airflow over the UK.

Next Generation EU and industrial transformation: Evidence from Spain

Many countries use geographical funding formulae to distribute public funds for health care to local planning areas in proportion to need. In England, these aim to distribute resources in proportion to all healthcare needs regardless as to whether these are currently met or unmet. The National Health Service also has an additional objective to allocate resources to reduce health inequalities (i.e. differences in health between socioeconomic groups). Adjusting for unmet needs could help achieve this second objective, if a greater proportion of needs are unmet in disadvantaged socioeconomic groups with poorer health compared to more advantaged socioeconomic groups. Alternatively, if there are greater unmet needs for relatively expensive conditions that tend to affect older age groups (e.g. cancer), this could lead to a greater proportion of needs being unmet in more advantaged socioeconomic groups, who will tend to be older due to greater life expectancy. Adjusting for unmet needs would then lead to allocation of a greater share of resources to these more affluent populations with better health, potentially increasing health inequalities. It is, however, unclear how met and unmet healthcare needs should be measured in these formulae and how better accounting for unmet needs influences health inequalities. We outline a framework for estimating the relative need in geographical healthcare resource allocation and show how the distribution of needed resources between local health planning areas in England changes when accounting for unmet needs due to underdiagnosis for 11 long-term conditions. We derive a synthetic data set for all people aged ≥30 years in England, in 2018, including age, sex, socioeconomic deprivation, region, local health planning area and whether people have diagnosed or undiagnosed long-term conditions. We calculated the annual primary and secondary care costs for each condition using linked electronic healthcare record data, then estimated needed expenditure for each health planning area for two scenarios: (1) when only accounting for diagnosed cases and (2) including all cases (diagnosed and undiagnosed). We examine how the distribution of need between places changes between these scenarios and the consequences of this for health inequalities. Based on the estimates of underdiagnosis used, areas with the lowest overall needs tended to have a greater proportion of their needs unmet. Adjusting resource allocation by accounting for these unmet needs due to underdiagnosis would move resources from areas with the highest level of needs to areas with lower overall needs. Moving to this 'fair share distribution' would tend to benefit less deprived areas more than more deprived areas, potentially widening health inequalities. We show how accounting for unmet needs due to underdiagnosis in allocating resources could widen health differences between more and less deprived areas when underdiagnosis and treatment costs increase with age. Further research is needed to confirm our provisional estimates, but we provide a useful framework for improving assessments of relative need for healthcare resource allocation. Alternative approaches are likely to be needed where resource allocation policy additionally aims to reduce health inequalities. This article presents independent research funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme as award number NIHR130258.

The rapid increase in the use of computerized systems in supply chains has revealed a significant number of cybersecurity weaknesses, particularly where large enterprises interact with their small-scale vendors. This paper examines the gaps and weaknesses in cybersecurity integration across different supply chain partners. Through qualitative analysis of existing literature, case studies, and industry frameworks, the research identifies the limitations of traditional compliance-based approaches, such as the fundamental restraints faced by small vendors, limited budgets, technical expertise, and cybersecurity awareness. The study proposes a tiered, partnership-oriented framework that reframes cybersecurity integration from punitive compliance requirements to collaborative support mechanisms for large enterprises and small-scale vendors. Key findings propose that successful integration requires (1) risk-based vendor categorization, (2) proportionate security requirements, (3) shared resource models, and (4) continuous relationship management. The paper concludes that the ability to withstand, recover from, or adapt to cyber-attacks and system failures in modern supply chains depends on transforming vendor relationships from transactional compliance to strategic partnerships, with large firms assuming greater responsibility for capability building across their extended digital ecosystem. Practical recommendations include developing scalable assessment tools, creating cybersecurity knowledge sharing platforms, and establishing clear governance structures that balance security requirements with vendor sustainability.

Introduction: This study investigates how Islamic Social Capital humanizes digitally controlled gig work systems characterized by algorithmic surveillance and performance monitoring. In many platform-based labor environments, worker sustainability is often explained through technological efficiency and performance metrics, while relational and moral dimensions remain underexplored. Drawing on the Job Demands–Resources framework and digital labor theory, this research aims to examine the direct, mediating, and moderating mechanisms linking Islamic Social Capital, Psychological Safety, Collaborative Resource Sharing, Algorithmic Management Intensity, and Social Sustainability Performance among gig workers operating under algorithmic management systems. Methods: A quantitative survey was conducted with 237 app-based gig workers. Data were analyzed using structural equation modeling to test the proposed structural relationships, including mediation and moderation effects. Results: Islamic Social Capital significantly enhances Psychological Safety, Collaborative Resource Sharing, and Social Sustainability Performance. Psychological Safety and Collaborative Resource Sharing partially mediate the relationship between Islamic Social Capital and Social Sustainability Performance, with psychological pathways showing stronger effects. Algorithmic Management Intensity strengthens the positive relationship between Islamic Social Capital and Psychological Safety but does not significantly moderate collaborative behaviors. Conclusion and suggestion: Sustainable performance in digitally governed gig ecosystems depends not only on technological systems but also on morally embedded relational capital. Platform governance strategies should integrate transparency, fairness, and community-based relational infrastructures to enhance psychological security and long-term sustainability.

The Student Collaboration Portal is a web-based platform designed to enhance communication, collaboration, and productivity among students by providing features such as real-time chat, group discussions, file sharing, and project collaboration similar to modern messaging applications. It enables students to interact efficiently, share resources, and work together seamlessly on academic projects within a centralized environment. The system is developed using React.js for the frontend, FastAPI for the backend, and MySQL for database management, ensuring a scalable and responsive architecture. With functionalities like real-time messaging, media sharing, notifications, and group management, the platform improves user engagement and teamwork, demonstrating how digital collaboration tools can significantly enhance student productivity and communication in an academic setting.

Forest villages in Türkiye, similar to those in many other countries, constitute some of the most low-income and low-access communities. This situation has caused more migration from forest villages to cities. The aim of this study is to reveal the most important factors affecting rural development in Bursa, to rank the villages according to their development levels, to derive a comprehensive, applicable, and generalizable development index (DI), and to determine the variables that best explain the differences in development levels. The study also aims to evaluate the subject in terms of forestry policy and to develop suggestions within this scope to eliminate development differences between villages and ensure rural development. Therefore, in order to achieve the purpose of this study and to provide concrete data for the strategies and policies to be developed; various combined and complementary analyses, such as factor analysis, Geographic Information Systems (GIS), regression analysis, and discriminant analysis, were used. In this study, as a result of factor analysis conducted with 52 variables, approximately 72% of the factors affecting rural development in forest villages were explained by three factors: (1) population characteristics and infrastructure status, (2) location of the forest village and number of employees, (3) climatological factors and income status . Based on these three factors, index coefficients were developed at the village level using variables representing the factor dimensions, and villages and districts were ranked according to their level of development using these coefficients. Furthermore, the suitability of this ranking was tested using discriminant analysis based on 52 variables. According to the results of the discriminant analysis, the success rate of the ranking based on DI was found to be 93.2%. Allocating a higher share of resources to underdeveloped villages arguably would support sustainable development of the region. Although forestry work plays an important role in the livelihoods of villagers in underdeveloped villages, forestry-related livelihoods alone are not sufficient.”