Search Results (4,268)

This paper introduces AIDE, an active inference-driven evaluation framework designed to provide a unified and theoretically grounded approach for analyzing sequential textual data. AIDE formulates the evaluation problem as variational inference in a latent dynamical system, enabling joint treatment of representation, temporal structure, and predictive reasoning. The framework integrates (i) a representation and augmentation module based on variational learning and contrastive semantic encoding, (ii) a parametric state–space model that captures the evolution of latent states and supports probabilistic forecasting, and (iii) a policy-selection mechanism that minimizes the expected free energy, guiding a latent diffusion generator to produce coherent and interpretable evaluation outputs. This formulation yields a principled pipeline linking evidence accumulation, latent-state inference, and policy-driven generative reporting. Experimental studies demonstrate that AIDE provides stable inference, coherent predictions, and consistent evaluation behavior across heterogeneous textual sequences. The proposed framework offers a general probabilistic foundation for dynamic evaluation tasks and contributes a structured methodology for integrating representation learning, dynamical modeling, and generative mechanisms within a single variational paradigm. Full article

The rapid proliferation of Internet of Things (IoT) devices has increased the attack surface for cyber threats. Traditional intrusion detection systems often struggle to keep pace with novel or evolving threats. This study proposes an end-to-end generative AI-based intrusion detection and response pipeline designed for automated threat mitigation in smart home IoT environments. It leverages a Variational Autoencoder (VAE) trained on benign traffic to flag anomalies, a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model to classify anomalies into five attack categories (C&C, DDoS, Okiru, PortScan, and benign), and Grok3—a large language model—to generate tailored countermeasure recommendations. Using the Aposemat IoT-23 dataset, the VAE model achieves a recall of 0.999 and a precision of 0.961 for anomaly detection. The BERT model achieves an overall accuracy of 99.90% with per-class F1 scores exceeding 0.99. End-to-end prototype simulation involving 10,000 network traffic samples demonstrate a 98% accuracy in identifying cyber attacks and generating countermeasures to mitigate them. The pipeline integrates generative models for improved detection and automated security policy formulation in IoT settings, enhancing detection and enabling quicker and actionable security responses to mitigate cyber threats targeting smart home environments. Full article

The pursuit of low-carbon economic development represents an inherent requirement for implementing the Sustainable Development Goals (SDGs) and serves as a vital support for advancing SDG 7, SDG 9, and SDG 13. Drawing on provincial data from China (2006–2023), this research investigates how digital-real convergence influences low-carbon economic development. The results demonstrate a positive contribution of this convergence to growth in the low-carbon economy, and it proves to be superior to models reliant solely on either digital-digital or real-real convergence. A notable finding is the considerable regional variation in the effect. It is strong in both eastern and western parts of the country, which stands in sharp contrast to central China, where the effect is statistically insignificant or negative. Identified as underlying mechanisms are the agglomeration of innovative talent and the accumulation of innovative capital. Additionally, a single-threshold effect of urbanization level is identified, indicating that the positive impact strengthens only after urbanization surpasses a critical value. Furthermore, digital-real convergence not only enhances local low-carbon development but also generates positive spillover effects on neighboring regions. Thus, to fully advance the SDGs, policy formulation and implementation must account for regional heterogeneity, prioritize the elevation of urbanization levels, enhance cross-regional collaboration, and amplify the enabling role of digital-real integration. Full article

Background: Pediatric patients often receive medicines manipulated from adult formulations due to a lack of age-appropriate products. While such practices are clinically routine, they may reflect deeper systemic deficiencies in pediatric pharmacotherapy. Objective: This scoping review aimed to map the prevalence, definitions, and types of pediatric drug manipulation and to conceptualize manipulation as an indicator of structural gaps in formulation science, regulation, and access. Methods: A systematic search of PubMed (January 2014–July 2024) included 10 studies reporting the frequency of drug manipulation in children aged ≤18 years. Eligible studies were synthesized narratively according to PRISMA-ScR guidelines. Results: Ten studies from nine countries were included, reporting manipulation frequencies ranging from 6.4% to 62% of all drug administrations and up to 60% at the patient level. Manipulated formulations most commonly included oral solid doses, altered through dispersing, splitting, or crushing. Definitions and methodologies varied considerably. The findings revealed five recurring structural gaps: limited pediatric formulations, inconsistent regulatory implementation, lack of standardized definitions and guidance, insufficient evidence on manipulation safety, and inequitable access across regions. Conclusion: Manipulation of finished dosage forms for use in children is a widespread, measurable phenomenon reflecting systemic inadequacies in formulation development, regulation, and access. Recognizing manipulation as a structural indicator may guide policy, innovation, and equitable pediatric pharmacotherapy worldwide. Full article

This study involves the construction of a dynamic evolutionary game model involving three key participants, including the Group Purchasing Organization (GPO), medical institutions, and pharmaceutical suppliers, while comprehensively considering critical factors such as benefit compensation, bad debt risk, and fiscal costs. The model characterizes the strategy evolution of each participant under bounded rationality and imitation learning mechanisms. Based on the replicator dynamics equations, the evolutionary trajectories and equilibrium conditions of the three parties’ strategies are systematically derived. The Jacobian matrix is then used to analyze the local stability of eight boundary equilibria and potential internal mixed equilibria. Furthermore, to capture the optimal adjustment process of the compensation mechanism, the GPO’s compensation level is introduced into an optimal control framework. A controlled evolutionary system is formulated, and the dynamic optimal relationship between compensation intensity and system state is described using the Hamilton–Jacobi–Bellman (HJB) equation. Through analytical linearization and numerical simulations, the optimal feedback compensation law and its closed-loop evolutionary trajectory are obtained, allowing for a comparative analysis between the “fixed compensation” and “optimal compensation” scenarios. The results reveal that an appropriately designed dynamic compensation mechanism can significantly enhance system cooperation stability and overall social welfare. This provides a quantitative theoretical foundation and methodological tool for the refined design and dynamic regulation of pharmaceutical group purchasing policies. Full article

Inland LNG-fuelled liner shipping is emerging as a significant trend, yet limited refueling infrastructure presents operational challenges. The complexity of inland navigation requires frequent speed adjustments to meet scheduled arrivals, which directly affects fuel consumption and refueling strategies. Additionally, imbalances in domestic and foreign trade container flows further increase operating costs for liner shipping companies. Given estimated weekly demands, considering navigational restrictions such as water depth and bridge clearance, as well as streamflow velocity, port time windows, empty container repositioning, port selection, speed adjustment, and uncertain fuel consumption, two novel models based on empty container arc variables and node variables are formulated, aiming to maximize voyage profit. These models are extended from divisible demand to indivisible demand cases. The explicit expression for the maximum fuel consumption under the worst-case speed deviation is derived, and an external linear approximation algorithm is proposed to linearize the nonlinear models while controlling approximation errors. Furthermore, the NP-hardness of the problem, the strict equivalence of the two modeling approaches, and the solution properties are proved. A case study of LNG-fuelled liner shipping on the Yangtze River shows the following: (1) for divisible demand, both models achieve optimal solutions within seconds, while for indivisible demand, the node-variable model outperforms the arc-variable model; (2) tactical strategies should be flexibly adjusted based on seasonal water depth, fuel prices, carbon taxes, speed deviations, and expected lock passage times; and (3) increasing fuel prices and carbon taxes generally reduce port calls and sailing speeds, suggesting that stricter fuel price and carbon tax policies can support the transition to green shipping. This study provides both theoretical guidance and managerial insights, supporting shipping companies in optimizing operations and promoting the development of sustainable inland shipping. Full article

With the rise of computation-intensive and latency-sensitive applications in the Internet of Vehicles (IoV), vehicles face increasing computational pressure. Computation offloading has become a key strategy for enhancing processing capabilities. Meanwhile, growing IoV data traffic raises security and reliability concerns. Existing blockchain-based solutions secure data transmission but overlook added delay and energy costs, increasing overall system cost. To address this issue, a blockchain-assisted computation offloading strategy based on Twin Delayed Deterministic Policy Gradient (TD3) (B-COTD) is proposed. Specifically, the offloading strategy selection is formulated as a multi-objective optimization problem considering latency, energy consumption, and blockchain costs, with the Delegated Byzantine Fault Tolerance (DBFT) algorithm ensuring the security of the offloading process. The TD3 algorithm solves this optimization problem, achieving efficient task offloading. Extensive experiments show that B-COTD improves overall performance, with the total system cost reduced by approximately 23.89% on average and the offloading success rate increased by about 11.02%. Full article

This article outlines the development of an inter-scale analytical tool designed to evaluate urban, intermediate, and domestic spaces from a gender perspective. Framed within feminist urbanism and ecofeminist theory, the study addresses the need to foster inclusive and equitable environments by incorporating gender-sensitive criteria into spatial planning processes. The methodology employed consists of a six-stage process: (1) a review of the existing literature; (2) the definition of scales of approach; (3) the formulation of indicators; (4) the establishment of evaluation criteria; (5) the design of data collection instruments; and (6) the refinement of the tool through field testing. The tool uses both qualitative and quantitative indicators across three spatial scales—neighbourhood, inter-block, and housing—organised into dimensions such as safety, accessibility, diversity, vitality, and representativeness. The evaluation process employs direct observation, graphic analysis, interviews, and participatory focus groups to provide a nuanced and multidimensional understanding of the built environment. The results confirm that both urban and domestic spaces have historically been designed from an androcentric perspective. They also highlight the potential of using gender-based evaluations to identify spatial inequalities and guide transformative interventions. The tool is replicable, adaptable, and scalable, and can therefore offer a robust framework for future research and public policy-making aimed at fostering gender equity in urban contexts. Full article

This study analyses the operational efficiency of urban loading and unloading zones (LUZs) by applying queuing theory without waiting (Erlang B model) and incorporating weighted occupancy time as a fundamental metric. Six scenarios were evaluated in an urban block in Zaragoza, Spain: three using field data obtained through real world observation and three simulated. The system’s performance was compared under conditions of free access with a model that strictly enforces the municipal ordinance for Urban Goods Distribution, restricting access to authorized vehicles and maximum dwell times. The objective of this study is to evaluate the operational performance of different LUZ configurations, assessing how real versus regulation-compliant usage affects system capacity, estimated loss rates, and the spatial temporal productivity of the zones. The M/M/1/1 model in Kendall notation is suitable for representing this type of queuing-free urban environment, and weighted occupancy time proves to be a robust indicator for evaluating the performance of heterogeneous zones. The scenario assessment confirms that the sizing of these zones is correct if their proper use is guaranteed. The study concludes with recommendations and best practices for city governance in formulating urban policies aimed at developing more efficient and sustainable logistics to control land use in the LUZ. Full article

The Yellow River Basin serves as China’s core food security zone and a vital ecological barrier. However, while the “three plots of land” reform has revitalized land resources, it has also exerted complex effects on the allocation of grain production factors. Scientifically assessing the actual impacts of this policy reform on food security and identifying optimization pathways has become a critical issue for safeguarding national food security. Using panel data from 101 county-level administrative units in the Yellow River Basin covering 2010–2023, this study employs a difference-in-differences model and a moderation effect model to systematically evaluate the impact of the “three plots of land” reform policy on food security. By introducing new-type urbanization and agricultural modernization as moderating variables, it further reveals the regional heterogeneity of the policy’s operational mechanisms. The study finds that (1) the “three plots of land” reform policy significantly enhances food security levels, (2) both new-type urbanization and agricultural modernization positively amplify policy effects through moderation mechanisms, and (3) regional heterogeneity tests considering geographical location and climate conditions reveal a spatial gradient pattern of “midstream > downstream > upstream” in policy effects, clarifying the logic of regional heterogeneity. Accordingly, the “three plots of land” reform policy in the Yellow River Basin should be deepened by formulating differentiated policies based on regional heterogeneity. A moderation mechanism should be established where agricultural modernization and new urbanization synergistically support food security, comprehensively enhancing food security safeguarding capabilities. Full article

Microplastics are very small particles of plastics, usually smaller than 5 mm. Microplastic pollution has emerged as a rising and challenging issue worldwide, posing serious threats to aquatic and terrestrial ecosystems and human health. Because of global demand and frequent use in daily routines, including clothing, packaging, and household items, the production of plastic is increasing annually. This study provides a comprehensive overview of the source, classification (based on shape, color, polymer), transportation, and impact of microplastic pollution. Depending upon size, mass, and density, microplastics can be transported to the environment via air and water. However, microplastics can be inhaled and ingested by humans, causing various health issues; for example, aquatic organisms like small fish ingest microplastics, which accumulate through the food chain and end up in the human body. This can lead to physiological harm, including inflammation, digestion tract obstruction, biomagnification throughout the food chain, and reproductive failure. This study further highlighted initiatives taken by government agencies to address plastic and microplastic pollution across India; for example, The Ministry of Environment Forest and Climate Change (MoEFCC) has formulated and amended the Plastic Waste Management (PWM) rules, Mission LiFE (LiFEStyle for Environment) launched campaigns such as “Say No to Single Use Plastic” and “One Nation, One Mission: End Plastic Pollution” to create awareness at the grassroot level, and institutions like the Food Safety and Standards Authority of India (FSSAI) have initiated a project to detect microplastics in food products. In addition, the National Green Tribunal (NGT) has instructed the Central Pollution Control Board (CPCB) to actively take measures to address microplastic pollution across Indian cities, focusing on key parameters like air, water, food, and humans. This study presents several recommendations, including detection and removal techniques (conventional, advanced, and removal); strengthening legislative policies such as Extended Producer Responsibility (EPR); research collaboration and monitoring with institutions such as CSIR-IITR, ICAR-CIFT, and BITS-Pilani; integrating EPR and Material Recovery Facilities (MRF) to develop a circular economy model; and mass awareness through government initiatives like the Swachh Bharat and Smart City programs to foster long-term behavioral change. Full article

Real estate inventory dynamics exhibit distinct temporal patterns and spatial heterogeneity, and precise identification of these trends serves as a prerequisite for effective policy formulation. Research on the spatiotemporal evolution patterns and influencing factors of real estate inventory holds significant academic and practical value. By employing ESDA, the Boston Matrix, and geographically weighted regression models to analyze 2017–2022 data from 287 Chinese cities, this study reveals a cyclical shift in China’s real estate inventory management—from “destocking” to “restocking”. The underlying drivers have transitioned from policy-led interventions to fundamentals-driven factors, including population dynamics, income levels, and market expectations. China’s real estate inventory and its changes exhibit significant spatiotemporal differentiation and spatial agglomeration patterns, demonstrating a spatial structure characterized by “multiple clustered highlands with peripheral lowlands” led by urban agglomerations. The influencing mechanism of China’s real estate inventory constitutes a complex system shaped by three key dimensions: macro-level drivers, regional differentiation, and structural contradictions. Policymakers should reorient destocking policies from “short-term stimulus” to “long-term coordination”, from “industrial policy” to “spatial policy”, and from addressing market “symptoms” to tackling “root causes”. This study argues that effective destocking policies constitute a systematic engineering challenge, demanding policymakers demonstrate profound analytical depth. They must move beyond simplistic sales metrics and perform multi-dimensional evaluations encompassing economic geography, demographic trends, fiscal systems, and land supply mechanisms. This paradigm shift from “symptom management” to “root cause resolution” and “systemic regulation” is essential for achieving sustainable real estate market development. Full article

The Paraguayan spatial planning system is analyzed through its legal framework, institutional structure, and implementation mechanisms, placing it within the Latin American context marked by fragmented governance and institutional inequality. Based on a review of laws and planning instruments at the national, departmental, and municipal levels, this study examines the system’s evolution, with particular focus on the period from the consolidation of the constitutional framework to the formulation of recent policies promoting sustainable development, decentralization, and democratic decision-making. The findings show a process of partial institutionalization, where norms and methodologies advance more rapidly than operational and financial capacities, resulting in uneven implementation across regions. Ongoing challenges include regulatory fragmentation, overlapping responsibilities, and weak multilevel coordination. Enhancing institutional coherence, prioritizing planning instruments, and strengthening subnational technical capacities are key to achieving a coherent and equitable spatial planning system that integrates international cooperation and translates sustainability and equity principles into practical dimensions of territorial governance. Full article

Cardiovascular disease (CVD), diabetes, and obesity pose major public health challenges in the United Arab Emirates (UAE), contributing substantially to morbidity, mortality, and healthcare expenditure. Despite progress in expanding access and service delivery, Health Quality Management (HQM) practices remain constrained. This study represents one of the first comprehensive, co-productive efforts to evaluate and strengthen HQM for CVD, diabetes and obesity in the UAE. Using a sequential, multi-phase design, it integrates evidence synthesis with the active engagement of interest groups to bridge gaps between research, policy, and practice. Phase 1 involves a scoping review to establish an evidence base on existing HQM practices and system-level challenges. Phase 2 conducts mapping and interviews with health professionals, policymakers, and patients to capture contextual insights. Phase 3 synthesizes findings to identify critical gaps, opportunities, and emerging research questions that can guide future inquiry. Phase 4 convenes consultative and consensus-building workshops to co-produce actionable recommendations and facilitate knowledge translation and exchange among health authorities, academic institutions, and other interest groups. Guided by the Institute of Medicine’s quality domains, the Donabedian model, and WHO quality indicators, this study situates HQM within the UAE’s ongoing shift toward value-based healthcare. The expected outcomes include the identification of key barriers to and facilitators of effective HQM, the formulation of context-specific recommendations to strengthen performance and coordination, production of knowledge translation outputs and the generation of new research priorities, thus contributing to achieving UAE Vision 2031 and global NCD targets. Full article

The electric vehicle (EV) battery supply chain plays a critical role in promoting sustainable transportation and tackling scarce resources, environmental costs, and supply chain vulnerabilities. The current study aims to conduct an extensive literature review of the EV battery supply chain given its importance for developing sustainable and efficient EVs. Using keyword co-occurrence and article co-citation analyses, this study analyses more than 681 publications from 2005 to 2024 and sourced from the Scopus database. Findings show that the number of articles increased considerably after 2020, which can be attributed to the global focus on decarbonization, electromobility, and circular economy practices. The review identifies important themes such as sustainability challenges, critical materials management, reverse logistics, and policy-driven frameworks for closed-loop supply chains. The findings from this study highlight a multidimensional approach where the integration of technologies, innovative policies, and collaborative actions can contribute to the resilience and sustainability of EV battery supply chains. It offers practical insights for stakeholders, strategic directions to maximize EV battery lifecycle management, and outlines the pathways to reach carbon neutrality in the transportation sector. By identifying the intellectual structure of this emerging field, the study contributes to academic discourse and informs the formulation of practical strategies to advance sustainable mobility. Full article