Search Results (2,309)

Under intensifying climate change and anthropogenic pressures, extreme low-flow events increasingly jeopardize water security in the Yellow River water supply region. This study develops the Inter-basin Multi-source Water Joint Allocation and Interconnected Routes Regulation System (IMWA-IRRS) to optimize spatiotemporal allocation of multi-source water and simulate topological relationships in complex water networks. The model integrates system dynamics simulation with multi-objective optimization, validated through multi-criteria calibration using three performance indicators: correlation coefficient (R), Nash-Sutcliffe Efficiency (E_ns), and percent bias (PBIAS). Application results demonstrated exceptional predictive performance in the study area: Monthly runoff simulations at four hydrological stations yielded R > 0.98 and E_ns > 0.98 between simulated and observed data during both calibration and validation periods, with |PBIAS| < 10%; human-impacted runoff simulations at four hydrological stations achieved R > 0.8 between simulated and observed values, accompanied by PBIAS within ±10%; sectoral water consumption across the Yellow River Basin exhibited PBIAS < 5%, while source-specific water supply simulations maintained PBIAS generally within 10%. Comparative analysis revealed the IMWA-IRRS model achieves simulation performance comparable to the WEAP model for natural runoff, human-impacted runoff, water consumption, and water supply dynamics in the Yellow River Basin. The 2035 water allocation scheme for Yellow River water supply region projects total water supply of 59.691 billion m³ with an unmet water demand of 3.462 billion m³ under 75% low-flow conditions and 58.746 billion m³ with 4.407 billion m³ unmet demand under 95% low-flow conditions. Limited coverage of the South-to-North Water Diversion Project’s Middle and Eastern Routes constrains water supply security, necessitating future expansion of their service areas to leverage inter-route complementarity while implementing demand-side management strategies. Collectively, the IMWA-IRRS model provides a robust decision-support tool for refined water resources management in complex inter-basin diversion systems. Full article

Background: The emergence of artificial intelligence (AI) has triggered a global transformation, with the healthcare sector experiencing significant disruption and innovation. In current public health practice, AI is being deployed to power various aspects of public functions, including the assessment and monitoring of health, surveillance and disease control, health promotion and education, policy development and planning, health protection and regulation, prevention services, workforce development, community engagement and partnerships, emergency preparedness and response, and evaluation and research. Nevertheless, its use in leadership and management, such as in change management, process development and integration, problem solving, and decision-making, is still evolving. Aim: This study proposes the adoption of the Public Health AI Framework to ensure that inclusive data are used in AI development, the right policies are deployed, and appropriate partnerships are developed, with human-relevant resources trained to maximize AI potential. Implications: AI holds immense potential to reshape public health by enabling personalized interventions, democratizing access to actionable data, supporting rapid and effective crisis response, advancing equity in health outcomes, promoting ethical and participatory public health practices, and strengthening environmental health and climate resilience. Achieving this goal will require a deliberate and proactive leadership vision, where public health leaders move beyond passive adoption to collaborate with AI specialists to co-create, co-design, co-develop, and co-deploy tools and resources tailored to the unique needs of public health practice. Call to action: Public health professionals can co-innovate in shaping AI evolution to ensure equitable, ethical, and value-based public health. Full article

Smart city infrastructures are evolving from centralized cloud systems to distributed Cyber-Physical Systems of Systems (CPSoS), requiring integration across heterogeneous administrative domains. This work presents a flexible, modular, multi-domain architecture for automated orchestration and management of IoT services across heterogeneous environments. It relies on a recursive federation model, where autonomous local domains manage their own resources while higher-level components coordinate cross-domain operations. Interoperability is achieved through standardized interfaces using TM Forum Open APIs and ETSI NGSI-LD, while a Secure Integration Fabric enables secure, policy-based coordination across public and private domains. The architecture is validated in a real-world Smart Waste Management pilot, demonstrating support for flexible workflows, cross-platform collaboration, real-time decision-making, and avoidance of vendor lock-in. Experimental results show that dynamic, context-driven service orchestration improves scalability, interoperability, and resource efficiency compared to static deployments. Full article

The Syrian conflict has created one of the largest displacement crises of the twenty-first century, disproportionately affecting adolescent girls. Syrian girls have been primarily portrayed as victims of war or “the lost generation”, neglecting the plurality of their experiences. Building on Bandura’s social cognitive theory, Giddens’ structuration theory, Kabeer’s empowerment framework, and Mahmood’s modalities of agency, this study examines how Syrian refugee adolescent girls in Lebanon enact agency within contexts of forced displacement and how structural factors shape these processes. We conducted a secondary analysis of 293 first-person narratives from Syrian girls and mothers collected in 2016 using Cognitive Edge’s SenseMaker^®. Thematic analysis revealed seven structural barriers—restricted access to education, economic insecurity, inadequate infrastructure/living conditions, limited healthcare, gender and social norms, xenophobia, and lack of legal status—as well as key enablers including community services, parental support, and peer networks. Girls expressed agency through seven interconnected processes: awareness/acknowledgement of barriers, emotional navigation, resource identification, decision-making, future planning, reflection, and action execution. These processes were adaptive and recursive, highlighting that agency during displacement is dynamic, relational, and conditioned by structural forces. These findings inform approaches that both reduce structural barriers and enable refugee girls’ agency. Full article

In the big data era, understanding and influencing consumer behavior in digital marketing increasingly relies on large-scale data and AI-driven analytics. This narrative, concept-driven review examines how big data technologies and machine learning reshape consumer behavior analysis across key decision-making areas. After outlining the theoretical foundations of consumer behavior in digital settings and the main data and AI capabilities available to marketers, this paper discusses five application domains: personalized marketing and recommender systems, dynamic pricing, customer relationship management, data-driven product development and fraud detection. For each domain, it highlights how algorithmic models affect targeting, prediction, consumer experience and perceived fairness. This review then turns to synthetic data as a privacy-oriented way to support model development, experimentation and scenario analysis, and to dark data as a largely underused source of behavioral insight in the form of logs, service interactions and other unstructured records. A discussion section integrates these strands, outlines implications for digital marketing practice and identifies research needs related to validation, governance and consumer trust. Finally, this paper sketches future directions, including deeper integration of AI in real-time decision systems, increased use of edge computing, stronger consumer participation in data use, clearer ethical frameworks and exploratory work on quantum methods. Full article

Background: With the growing emphasis on full-process disease management, efficient post-discharge care has become increasingly critical. Although prior studies have examined follow-up services, resource allocation, and facility location in primary healthcare, model-based optimization of collaborative frameworks between comprehensive hospitals and primary care systems remains limited. Methods: We study a cooperative community hospital selection problem involving contractual cooperation, patient engagement, and follow-up resource allocation. A multi-objective mixed-integer programming model is developed to maximize patient accessibility and minimize total hospital costs, and an NSGA-II-based heuristic is proposed for solution generation. A real-world case study using data from a comprehensive hospital in Chengdu, China, is conducted. Results: The proposed approach produces a Pareto set that quantifies the accessibility–cost trade-off and reveals a knee region with diminishing returns: moderate expansion of cooperating providers substantially improves accessibility, whereas further expansion yields limited additional gains while increasing hospital cost. Sensitivity analyses indicate that cost-related parameters and follow-up frequencies are key drivers of the trade-off. Conclusions: The proposed optimization framework serves as an implementable decision aid for designing hospital–primary care collaboration for post-discharge follow-up: it supports partner selection and capacity planning and indicates levers to improve performance. Full article

Against the backdrop of carbon quota trading policies and Energy Performance Contracting (EPC), Energy Service Companies (ESCOs) engage in supply chain emission reduction via embedded low-carbon services. However, the impact mechanism of their financing mode selection on emission reduction efficiency and economic benefits has not been fully revealed, and there is a lack of support from a systematic theoretical and engineering design framework. Therefore, this study innovatively constructs a multi-agent Stackelberg game model with bank financing, green bond financing, and internal factoring financing. We incorporate the embedding degree, emission reduction cost coefficient, and financing mode selection into a unified analysis framework. The research findings are as follows: (1) There is a significant positive linear relationship between supply chain profit and the embedding degree. In contrast, the profit of ESCOs shows an inverted “U-shaped” change trend. Moreover, there is a sustainable cooperation threshold for each of the three financing modes. (2) Green bond financing can significantly increase the overall emission reduction rate of the industrial supply chain in high-embedding-degree scenarios. However, due to emission reduction investment cost pressure, ESCOs tend to choose bank financing. (3) The dynamic change of the emission reduction investment cost coefficient will trigger a reversal effect on the financing preferences of the supply chain and ESCOs. This study unveils the internal mechanism of multi-party decision-making in the low-carbon industrial supply chain and is supported by cross-country institutional evidence and comparative case-based analysis, providing a scientific basis and engineering design guidance for optimizing ESCO financing strategies, crafting incentive contracts, and enhancing government subsidy policies. Full article

Post-COVID-19, it is widely reported that the attendance rates of higher education students have not recovered to pre-COVID-19 numbers. Initial internal investigations in the Department of Mathematics and Statistics at Maynooth University suggested that factors relating to the cost of living, commuting, and working were impacting students’ ability to attend university. In order to establish the degree to which these issues were influencing student attendance at lectures, tutorials, and with the academic support of mathematics at Maynooth University, we conducted an in-depth survey of first-year service mathematics students. This paper focuses on the qualitative experiences and perspectives of the 415 students who participated in this study. Using reflective thematic analysis, we identified two dominant themes across the survey responses: the weight of the ‘financial burdens’ that students were experiencing, and frustration with the ‘poor infrastructure’ that they encountered. As a result, a further three themes of students being ‘time poor’, feeling forced to make difficult ‘decisions’, and ‘missing out’ on academic and social life were also prevalent. These findings reveal the complex and systemic challenges facing students in their day-to-day efforts to attend university, and they emphasise the urgent need for both institutional specific measures and coordinated government policies to tackle these issues. Full article

Growing pressures on water resources, exacerbated by climate change, resource depletion, and population growth, underline the need for sustainable and energy-efficient wastewater management. Wastewater treatment plants (WWTPs) are among the most energy-intensive elements of the Integrated Water Service, and their environmental performance depends on infrastructure design, resource availability, and treatment configuration. Improving resource efficiency while reducing energy demand and environmental impacts is therefore a priority for water utilities seeking innovative decision-support tools. Within the national project “WATERGY—Energy Efficiency of the Integrated Water Service”, this study proposes a life-cycle-based framework to assess the sustainability of technological interventions in WWTPs. A comparative gate-to-grave Life Cycle Assessment (LCA) was applied to the municipal WWTP of Potenza (Southern Italy). Three sludge End-of-Life Scenarios were assessed: the current landfill-based configuration, an enhanced oxygenation–nitrification setup, and anaerobic digestion with biogas-based cogeneration. Compared to the current scenario, anaerobic digestion with cogeneration reduces Global Warming Potential by 17% and decreases freshwater ecotoxicity by approximately 30%. Compost production shows the highest reduction in ecotoxicity (−51%) but increases fossil resource depletion and acidification due to higher energy demand. Overall, energy recovery pathways, particularly anaerobic digestion with cogeneration, provide the most balanced environmental benefits, supporting more sustainable WWTP operation within the Integrated Water Service. Full article

Emergency Medical Services (EMS) require timely and equitable ambulance allocation supported by accurate demand estimation. In our prior work, we developed a statistical forecasting module based on Overall Smoothed Average Demand (OSAD) and Average Maximum (AMX) to estimate proportional EMS demand across spatial zones. Although this approach was interpretable and computationally efficient, it was limited in modeling nonlinear spatiotemporal dependencies and adapting to dynamic demand variations. This paper presents a unified deep learning-based EMS planning framework that integrates spatiotemporal demand forecasting with adaptive ambulance allocation. Specifically, the statistical OSAD/AMX estimators are replaced by graph-based spatiotemporal forecasting models capable of capturing spatial interactions and temporal dynamics. The predicted demand is then incorporated into a reinforcement learning-based allocator that dynamically optimizes ambulance placement under fairness, coverage, and operational constraints. Experiments conducted on real-world EMS datasets demonstrate that the proposed end-to-end framework not only improves demand forecasting accuracy but also translates these improvements into tangible operational benefits, including enhanced equity in resource distribution and reduced response distance. Compared with traditional statistical and heuristic-based baselines, the proposed approach provides a more adaptive and decision-aware solution for EMS planning. Full article

Public passenger transport systems increasingly face the challenge of balancing economic efficiency with ecological sustainability, reflecting both policy objectives and passenger expectations. This study examines passenger perceptions of the economic and environmental aspects of public transport services and the factors influencing these perceptions, primarily based on survey data collected in Slovakia. The Slovak dataset was analysed using contingency analysis, namely Chi-square tests of independence, contingency coefficients, and sign scheme, and C5.0 decision tree classification models to identify key determinant of behavioural and attitudinal outcomes. In addition, descriptive comparisons with a complementary Polish sample are provided to illustrate potential differences in preference patterns across national contexts, without formal statistical inference. The results identify key socio-demographic and behavioural factors influencing passenger perceptions and usage patterns in Slovakia, while the complementary Polish sample is used to provide contextual descriptive comparison without formal testing. The study enhances scientific understanding of public transport by exploring the interaction between economic efficiency and ecological sustainability of transport services and provides practical recommendations for the strategic management of transport companies, especially in service modernisation, marketing communication, and support for sustainable mobility. The findings are relevant not only to Slovakia but also to broader European discussions on integrating economic and environmental dimensions into public transport development. Full article

Background/Objectives: The incorporation of peer support within mental health services has shown benefits for service users’ recovery and engagement, yet implementation is often hindered by role ambiguity and limited institutional recognition. The aim of this study is to explore the experiences of workers in a programme that provides peer support within a personal assistance model. The focus is on how they perceive the shaping of their professional role and their integration within care teams, rather than on evaluating service outcomes or effectiveness. Methods: An interpretive qualitative methodology with an exploratory approach was used. The study was conducted in a single organisational setting and focused on the self-reported experiences of personal assistants. Fieldwork was conducted in 2025 with ten personal assistants. Data were obtained through individual semi-structured interviews and one focus group with the same participants. A thematic content analysis combining inductive and deductive coding strategies was conducted using MAXQDA (version 24.11). Results: Findings indicate that the Personal Assistant role was perceived as reducing some of the ambiguity commonly associated with peer support, due to a clearer contractual framework and a more explicit delineation of functions. However, tensions persisted in relation to its hybrid professional identity, experiences of task overload, and ongoing gaps in coordination with traditional professional roles. Key facilitators included institutional support, accessible coordination, a supportive culture of care, and informal peer networks. Perceived benefits were reported for service users, including increased trust, hope, and autonomy, as well as for assistants, who described enhanced professional purpose and progress in their own recovery, alongside risks of emotional strain. Conclusions: Analysing the perspective of participants, the personal assistance model may represent a promising framework for the professionalisation of peer support through functional clarity, continuous supervision, and recognition of experiential knowledge. Further progress requires strengthening internal communication, expanding training opportunities, and enhancing the structural participation of personal assistants in decision-making. The study contributes an exploratory qualitative perspective to the growing literature on integrating lived-experience professionals into mental health services. Full article

The integration of a user-side energy storage system (ESS) faces notable economic challenges, including high upfront investment, uncertainty in quantifying battery degradation, and fragmented ancillary service revenue streams, which hinder large-scale deployment. Conventional configuration studies often handle capacity planning and operational scheduling at different stages, complicating consistent life-cycle valuation under degradation and multi-service participation. This paper proposes a life-cycle multi-service co-optimization model (LC-MSCOM) to jointly determine ESS power–energy ratings and operating strategies. A unified revenue framework quantifies stacked revenues from time-of-use arbitrage, demand charge management, demand response, and renewable energy accommodation, while depth of discharge (DoD)-related lifetime loss is converted into an equivalent degradation cost and embedded in the optimization. The model is validated on a modified IEEE benchmark system using real generation and load data. Results show that LC-MSCOM increases net present value (NPV) by 26.8% and reduces discounted payback period (DPP) by 12.7% relative to conventional benchmarks, and sensitivity analyses confirm robustness under discount-rate, inflation-rate, and tariff uncertainties. By coordinating ESS dispatch with distribution network operating limits (nodal power balance, voltage bounds, and branch ampacity constraints), the framework provides practical, investment-oriented decision support for user-side ESS deployment. Full article

This paper discusses digital capability and its potential translation into sustainable organizational performance through the integration of sustainable management accounting and high-quality managerial decision-making. Even though previous studies acknowledge the importance of digital technologies and sustainability practices, the current literature mostly analyzes them separately, and no empirical models explain how digital capability can be translated into sustainability outcomes through internal decision-making and accounting processes. To fill this gap, this paper constructs and proves a Digital-Enabled Sustainability Management Accounting (DSMA) framework based on Dynamic Capabilities Theory, the Knowledge-Based View, and the Technology–Organization–Environment framework. Based on Survey data from 667 respondents in the financial services industry and Partial Least Squares Structural Equation Modeling (PLS-SEM), the results indicate that digital capability can significantly contribute to sustainability performance by increasing accounting integration and decision quality. Although technological readiness enhances such relations, it does so with only a low degree of influence, meaning it has a supportive rather than a transformative effect. The research is valuable because it contributes to sustainability theory, provides a solid empirical database that is understudied, and has practical implications for organizations striving to implement digitally enabled sustainability initiatives. Full article

Within the cloud manufacturing paradigm, Cloud Manufacturing Service Composition (CMSC) is a core technology for intelligent resource orchestration in Cloud Manufacturing Platforms (CMP). However, existing research faces critical limitations in real-world CMP operations: single-task-centric optimization ignores resource sharing/competition among coexisting manufacturing tasks (MTs), causing performance degradation and resource “starvation”; traditional heuristics require full re-execution for new scenarios, failing to support real-time online decision-making; single-agent reinforcement learning (RL) lacks mechanisms to balance global efficiency and inter-task fairness, suffering from inherent fairness defects. To address these challenges, this paper proposes a fairness-aware multi-task CMSC method based on Multi-Agent Reinforcement Learning (MARL) under the Centralized Training with Decentralized Execution (CTDE) framework, targeting the symmetry-breaking issue of uneven resource allocation among MTs and aiming to achieve symmetry restoration by restoring relative balance in resource acquisition. The method constructs a multi-task CMSC model that captures real-world resource sharing/competition among concurrent MTs, and integrates a centralized global coordination agent into the MARL framework (with independent task agents per MT) to dynamically regulate resource selection probabilities, overcoming single-agent fairness defects while preserving distributed autonomy. Additionally, a two-layer attention mechanism is introduced—task-level self-attention for intra-task subtask correlations and global state self-attention for critical resource features—enabling precise synergy between local task characteristics and global resource states. Experiments verify that the proposed method significantly enhances inter-task fairness while maintaining superior global Quality of Service (QoS), demonstrating its effectiveness in balancing efficiency and fairness for dynamic multi-task CMSC. Full article