Search Results (699)

Community multi-building energy management is a key approach for reducing carbon emissions from the building sector and alleviating peak grid pressure. However, load coupling among buildings and coordinated energy-storage operation make control-policy design highly challenging. To address the limitation of the standard multi-agent proximal policy optimization (MAPPO) algorithm, in which the centralized critic simply concatenates building observations and therefore struggles to model inter-building interactions, this paper proposes an improved MAPPO algorithm with a multi-head-attention-enhanced centralized critic, referred to as a multi-head-attention MAPPO (MHA-MAPPO). Without changing the decentralized execution framework, the proposed method improves the critic network in three aspects. First, a dual-branch gated embedding module is designed to adaptively fuse local building observations and global interaction information. Second, an interaction-attention path is constructed to explicitly capture pairwise dependencies among buildings through multi-head attention. Third, a context-attention path is introduced to extract high-level community-level global features by means of learnable query vectors. These improvements enable the critic to estimate the joint-state value more accurately and provide more reliable advantage estimates for all agents. Experiments in the CityLearn environment show that, compared with the original MAPPO, MHA-MAPPO improves the mean evaluation reward by approximately 19.2%, reduces the reward standard deviation by one order of magnitude, and decreases peak net load and total net load by approximately 15.4% and 35.5%, respectively. The results verify the effectiveness of multi-head attention for coordinated multi-building scheduling. The proposed method provides a useful reference for improving multi-agent reinforcement learning algorithms in community energy management. Full article

Background/Objectives: Patient- and family-initiated abuse of nurses is widely recognized as workplace violence, but it also raises a distinct professional-regulatory question: when may a nurse end or restrict an established therapeutic relationship without creating an allegation of abandonment or discriminatory denial of care? This perspective focuses on Alberta and examines whether the province’s nursing regulator provides a comprehensive, nurse-specific, all-registrant standard comparable in clarity to Alberta’s physician standard. The concern is not the absence of every form of protection but the absence of a clearly defined regulatory pathway for all Alberta RNs and NPs. Methods: Publicly available legal, occupational health and safety, regulatory, legal-risk and scholarly materials were purposively selected where they addressed relationship termination, discontinuing or declining care, workplace violence, immediate safety risk, abandonment, documentation, continuity of care or patient safeguards. Results: The College of Physicians and Surgeons of Alberta standard provides a clearly defined regulatory pathway, including immediate discharge where a patient poses a safety risk, is abusive or fails to respect professional boundaries. Alberta nursing materials contain important elements but do not yet constitute a dedicated Alberta RN/NP standard applicable across office-based, community, virtual, home care and independent nursing practice. Interprovincial nursing standards demonstrate feasibility, operational detail, emerging professional consensus and potential templates for policy transfer; however, they do not bind the CRNA or create an Alberta regulatory benchmark for complaint review. Conclusions: Alberta should adopt a nurse-specific standard for ending or restricting abusive patient- and family-initiated relationships. Such a standard should include ordinary and urgent safety pathways, prohibited grounds, documentation requirements, continuity safeguards, employer integration and practical templates. Nurse protection and patient protection are mutually reinforcing regulatory objectives. Full article

Digital policy has become an important instrument for promoting sustainable agricultural modernization. However, its effectiveness depends on the strategic responses of the government, agricultural operators, and farmers. This study develops a theoretical framework to examine how digital policy affects sustainable agricultural modernization through multi-agent interaction. Specifically, it constructs a three-party evolutionary game model and a Stackelberg game model to analyze strategy evolution under different implementation costs, subsidies, and penalties, as well as the government’s first-mover role in subsidy design. The results show that digital policy does not promote sustainable agricultural modernization through a simple linear pathway. Instead, it operates by reshaping the incentive structures of agricultural operators and farmers. Lower government implementation costs increase the likelihood of active policy implementation, while subsidies for agricultural operators and farmers strengthen their willingness to adopt digital tools, engage in standardized production, and participate in digital agricultural activities. However, the marginal effect of subsidies weakens as participation and digitalization increase, indicating that unlimited subsidy expansion may reduce policy efficiency and increase fiscal pressure. This study contributes to the literature by linking digital policy design, multi-agent strategic interaction, and sustainable agricultural modernization within a unified theoretical framework. It highlights that effective digital agricultural policy requires incentive compatibility, fiscal sustainability, inclusive participation, and adaptive governance, rather than reliance solely on digital technology investment or subsidy expansion. Full article

The increasing frequency of extreme weather events, particularly hailstorms, driven by climate change, poses growing threats to the resilience, environmental sustainability, and long-term performance of photovoltaic (PV) systems. This study evaluates the environmental impacts of a 12 kWp rooftop PV installation in Brescia, northern Italy, through a comparative Life Cycle Assessment (LCA) of three system configurations: a standard unprotected system (Scenario A), one equipped with a retractable polycarbonate hail-protection panel with automated weather-sensor activation (Scenario B), and one using thicker reinforced front-glass modules (Scenario C). The analysis follows a cradle-to-gate plus operational maintenance phase (30-year horizon, excluding end-of-life) system boundary and employs the ReCiPe 2016 Midpoint (H) methodology across 18 environmental impact categories. A novel integration of the Social Discount Rate (SDR) to the LCA framework—constituting a Discounted LCA (D-LCA)—incorporates both temporal discounting and risk dimensions into the environmental evaluation. A structured PESTEL-based risk taxonomy is applied to derive scenario-specific SDRs, with the Environmental risk category as the key differentiator between configurations. The static LCA identifies Scenario A as the lowest-impact option, while the D-LCA framework reverses this ranking: Scenario C achieves the highest Net Present Value of Emissions, followed by Scenario A. A negative NPV-E for Scenario B reflects the temporal cost of a large, front-loaded construction debt rather than absolute environmental harm. D-LCA framework should be interpreted as a complement to the full 18-category static LCIA profile, not a replacement. These results demonstrate that risk-informed D-LCA provides a more policy-relevant environmental sustainability assessment than static LCA for long-lived energy infrastructure subject to climate-driven operational risks. Full article

Break-bulk cargo operations are characterized by high variability and complex resource synchronization, yet they have received limited research attention compared to containerized logistics. This paper proposes an integrated lean-informed simulation framework for evaluating vessel service time (VST) in multipurpose terminals handling break-bulk cargo. The framework sequences three analytical stages: Value Stream Mapping paired with Ohno’s waste taxonomy to diagnose non-value-adding activities, a discrete-event simulation model built in Simio to quantify their impact on VST, and Sobol sensitivity analysis to decompose the remaining variability across operational factors. Demonstrated at DP World Lirquén, a multipurpose terminal in Chile, the lean diagnostic identified 101 min of waste per cycle across waiting, motion, and overproduction categories. Scenario evaluation showed that eliminating shift-transition delays and standardizing load composition reduced VST by 14.3% and 10.6%, respectively, without capital investment. The sensitivity decomposition revealed that warehouse machinery composition, particularly the interaction between equipment types, dominates VST variability, while truck fleet size operates as an independent factor. These findings demonstrate that coordination-related policy interventions outperform incremental resource additions. More specifically, machinery allocation must be optimized jointly rather than by equipment type in isolation. Full article

AI-enabled infrastructure systems increasingly govern access to emergency services, disaster relief, and utility restoration, yet they routinely produce inequitable outcomes even when allocation algorithms apply procedurally neutral rules. The standard explanation locates the cause inside the algorithm. This paper argues instead that inequity arises from the interaction between the algorithm and the physical environment in which it operates: network topology, resource locations, and demand distribution jointly constrain what any policy can achieve, and when those constraints are sufficiently binding, ethical infeasibility is structural rather than algorithmic. We introduce a constraint-based formulation that embeds ethical requirements into the feasible region, and a hierarchical Irreducible Infeasible Subsystem (IIS) procedure that attributes infeasibility to rule design, algorithmic choice, or physical infrastructure. We further establish the Structural Infeasibility Theorem, deriving closed-form bounds on inter-group disparity across all feasible policies. The framework was applied to zone-decomposable infrastructure allocation problems generally, with a metropolitan ambulance-dispatch system serving as a concrete instantiation. The study delivers four findings. First, the minimum-service violation may not be caused by the allocation algorithm itself; rather, it may arise from the physical layout of the infrastructure. Second, the observed efficiency–equity trade-off may not be an unavoidable feature of equitable allocation, but may instead reflect the difficulty of achieving equity within an underbuilt system. Third, before new infrastructure is added, improvements in equity may represent harm redistribution rather than harm reduction. Fourth, the IIS certificate can be translated into a concrete capital-investment requirement, showing what physical change may be needed to restore ethical feasibility. Full article

Buildings account for nearly 30% of global energy-related carbon emissions. In rapidly developing economies, the operational phase of buildings represents a major and growing source of emissions. However, emission pathways in hot-summer-cold-winter (HSCW) regions remain understudied. This study analyzes carbon emission peaks and influencing factors in the operational phase of existing civilian buildings in Anhui Province. It integrates energy balance tables, the LEAP model, carbon emission factors, and the STIRPAT model. The energy balance table method disaggregates building energy consumption into urban, rural residential and public sectors. It adjusts for transportation energy by deducting specific proportions of gasoline and diesel from industrial, commercial, and residential sectors. Heating energy calculations are simplified because the region has a HSCW climate with limited centralized heating. The LEAP model projects emissions under four scenarios from 2020 to 2060. The STIRPAT model with ridge regression reveals that the permanent population and energy structure negatively influence residential emissions with elasticities of −2.646 and −1.465, respectively. This finding is consistent with the province’s energy transition, where coal use dropped from 28.48% in 2005 to 0.45% in 2020 and electricity use rose from 39.86% to 59.01%. In contrast, per capita GDP, building area, and energy intensity show positive effects. For public buildings, tertiary industry added value and energy structure are key determinants. Scenario analysis identifies the blueprint scenario as optimal, with residential emissions peaking at 34.29 million tons in 2025 and declining to 9.19 million tons by 2060 through measures such as 10% building retrofits by 2025, 75% energy-saving standards for new constructions, 50% retrofits by 2060, and renewable energy integration with building electrification, outperforming the baseline scenario that peaks in 2036 at 49.46 million tons and other intermediate scenarios. The study underscores that energy structure optimization significantly decouples energy consumption from emissions, offering actionable pathways for dual carbon goals through policy synergies in building efficiency, population management, and clean energy adoption to foster sustainable development and the construction industry’s low-carbon transition. Full article

Background: Cardiac telemetry monitoring represents an important aspect of in-hospital patient safety in both telemetry and critical care settings. Despite technological advancements, telemetry effectiveness may be diminished due to systemic failures including operational processes, instructional policies, and human factors. Alarm fatigue, recognized by the Joint Commission as a leading contributor to serious patient harm, lies at the forefront of these failures. Objective: This narrative review utilized and synthesized sources indexed through PubMed, PubMed Central, MEDLINE, Web of Science, Google Scholar, Directory of Open Access Journals (DOAJ), and Scopus to illustrate the factors involved in hospital related monitoring failures. We purport that alarm fatigue and telemetry monitoring failures are the result of complex systemic failures comprising technological and human failures. Through this narrative, we propose an evidence-based framework known as the Safer Telemetry Architecture (STA) to pinpoint redundancies and promote closed-loop communication regarding alarm management. Conclusions: Monitored in-hospital environments represent a key area of preventable morbidity and mortality due to systemic design flaws. Our STA framework addresses such flaws via improvements in nurse-driven protocols, alarm routing, mandatory coverage standards for backup, and increased performance auditing. Systemic improvements via such a framework may represent an important institutional strategy for hospitals with cardiac monitoring, but requires further prospective validation. Managing redundancies in alerts and sounds, improving backup and nursing telemetry protocols, and promoting closed or continuous loops targeting alarm response times and telemetry utilization are key to effectively improving patient safety. Full article

Urban and regional resilience has gained increasing relevance as cities and territories concentrate larger shares of population, economic activity, and exposure to various shocks. This study proposes an integrated framework to evaluate and compare territorial resilience across Northern Portugal, combining quantitative data from 42 indicators spanning five resilience dimensions. Municipal values for each indicator were classified into quintiles, converted into a standardized ranking scale from 1 to 5, and aggregated through GIS spatial operations to produce composite regional resilience maps. The results indicate that Northern Portugal displays moderate resilience, with pronounced spatial disparities. More urbanized and coastal municipalities tend to exhibit higher resilience levels than inland territories. Infrastructural resilience emerges as the weakest, while social resilience achieves the highest performance. By highlighting spatial inequalities and the dimensions requiring targeted intervention, this study offers actionable insights to support evidence-based policies aimed at strengthening territorial resilience in Northern Portugal. Full article

Background/Aims: The increasing complexity of disasters requires effective integration of nurses into Civil Defense (CD) systems. Despite their crucial role, the competencies needed to operate within these multi-agency frameworks remain fragmented and insufficiently defined. The main aim of the study was to map nursing competencies for disaster response within the CD context, identifying essential skills, contextual variations, and barriers to application. Methods: A scoping review was conducted following the JBI methodology and reported according to PRISMA-ScR guidelines. Major databases (PubMed, CINAHL, Scopus, Embase) were searched without time limits, resulting in the inclusion of 27 studies published between 2011 and 2025. Results: 12 core competency domains were identified. Clinical care was the most cited competency (70% of studies), followed by communication (63%), leadership (60%), triage (48%), and psychosocial support (48%). The lack of specific training emerged as the primary individual barrier (44%), while the absence of standardized curricula was the leading systemic obstacle (41%). Competency requirements varied significantly based on the hazard type and organizational setting. Conclusions: Disaster nursing is emerging as an essential specialized field in response to the increasing frequency of climate-related events and global conflicts. There is an urgent need to move beyond purely clinical training to integrate “organizational literacy” and psychological resilience, harmonizing educational pathways with national CD policies and competency-based disaster preparedness programs. Full article

Accurate forecasting of hourly electricity consumption is critical for smart grid management, energy market operations, national policy planning, and—particularly for import-dependent economies such as Türkiye—energy security. This study presents, to the best of the authors’ knowledge, the first systematic benchmark of four state-of-the-art time series architectures—TimesNet, PatchTST, iTransformer, and Temporal Fusion Transformer (TFT)—conducted specifically on a national-scale Turkish multivariate energy dataset from the Energy Exchange Istanbul (EPİAŞ), covering 72,322 hourly observations across 15 generation, consumption, and market-clearing price variables from January 2018 to April 2026. While benchmark studies of Transformer-based architectures exist on general time-series datasets, no prior work has applied this specific combination of architectures to the EPİAŞ dataset under unified experimental conditions with an explicit energy-security interpretation. All models were trained under standardized preprocessing (StandardScaler), a 24 h lookback window, and systematic hyperparameter optimization. Experimental results demonstrate that iTransformer achieves the best predictive performance (MAE = 521.34 MWh, RMSE = 748.12 MWh, R² = 0.9881, MAPE = 1.34%), followed by TFT (R² = 0.9863) and PatchTST (R² = 0.9844). TimesNet, while the most computationally efficient, achieves an R² of 0.9791. Beyond predictive benchmarking, this study situates the findings within Türkiye’s energy security agenda: the dataset captures fossil fuel dependency, the growing share of domestic renewables, and market-clearing price dynamics shaped by geopolitical shocks, including the Russo–Ukrainian war and evolving EU–Türkiye energy relations. Comprehensive analysis of model architectures, attention mechanisms, temporal feature importance, and computational efficiency is provided. These findings establish a rigorous baseline for deploying modern sequence models in large-scale, real-time national energy forecasting systems that serve both market-efficiency and strategic-energy-autonomy objectives. The results specifically highlight how high-fidelity forecasting can serve as a risk-mitigation tool against geopolitical supply disruptions by quantifying the impact of domestic renewable integration. Full article

E-commerce has reshaped short-term financial management by altering transaction speed, payment structures, and supply chain coordination. This study examines how large publicly listed e-commerce firms, viewed as complex digital business systems, adjusted their working capital policies during and after the COVID-19 shock. The sample is based on the 100 largest e-commerce companies worldwide by market capitalization, as reported by CompaniesMarketCap (February 2026), and is reduced to 76 firms from 23 countries due to data availability, yielding 802 firm-year observations. Firm-level data are obtained from LSEG Datastream, while macroeconomic variables are sourced from the World Bank. The analysis distinguishes between two dimensions of working capital: flow-based operational adjustment, measured by the cash conversion cycle (CCC), and stock-based balance-sheet adjustment, captured by net working capital relative to total assets (WC/TA). Fixed-effects models with firm-clustered standard errors are employed. The results indicate a substantial contraction of the CCC during the pandemic, followed by partial persistence of that contraction rather than a return to pre-pandemic norms. In contrast, WC/TA remains broadly stable during the crisis but declines in the post-pandemic period, suggesting a delayed balance-sheet adjustment. Business-model heterogeneity is not statistically significant, which may reflect a common system-level response across e-commerce firm types. Leverage and supply-chain pressures are associated with working capital intensity (WC/TA), while inflation shapes operate cycle duration (CCC). The findings are consistent with a two-stage adaptive response to systemic disruption. Full article

LIFE REVIVE aims to restore ecological status and ecosystem services in the Lima and Vouga river basins (NW Iberian Peninsula), where hydromorphological alteration and hydropower-driven flow regulation are major causes of water bodies failing to reach Good Ecological Status under the EU WFD. The project targets key pressures such as longitudinal fragmentation by weirs and dams, artificial flow regimes, degradation of spawning substrates, and the spread of invasive aquatic plants, which strongly affect fish communities, including sea lamprey, salmonids, and other diadromous species. Technically, the project combines barrier removal or eco-adaptation, nature-like fish passes, and spawning-habitat renaturalisation with optimized environmental flow regimes (EFR) downstream of important hydropower systems, explicitly accounting for present and future hydroclimatic scenarios. Multi-scale ecohydrological modelling (species distribution models, habitat suitability models, GLM/GAM approaches) will quantify fish–flow–habitat relationships and support the definition of operational EFR guidelines that balance ecological requirements with hydropower and agricultural constraints through joint work with the main Portuguese hydropower operator, EDP. Impact evaluation is structured around a rigorous BACI monitoring design in intervention and control tributaries, using standard WFD biological indices for fish and aquatic/riparian vegetation, hydromorphological indices (HQA, HMS, RHS), and project-specific Key Performance Indicators for water quality, biodiversity, and habitat. Expected outcomes include the restoration of at least 51 km of rivers towards free-flowing conditions, reduced hydromorphological pressure in more than 20 km of heavily modified river stretches, and measurable increases in the distribution and abundance of fish species and native vegetation. A strong communication and capacity-building programme underpins public engagement, while a decision matrix for barrier prioritization, technical workshops, and pilot replications in additional basins (e.g., Alva, Mouro, Deva, and Tea in Galicia) are designed to maximize transferability, policy uptake, and long-term sustainability of the solutions beyond the project lifetime. Full article

With the growing global population, intensifying resource constraints, and deepening climate change impacts, agriculture faces dual challenges of ensuring food security and advancing sustainable development. Artificial intelligence (AI) has emerged as a transformative technology, penetrating the entire crop production chain and offering innovative solutions to traditional agricultural bottlenecks. This paper systematically reviews AI applications in five core domains: biotic stress monitoring, soil health management, precision operation, supply chain optimization, and climate-resilient agriculture. It further categorizes and analyzes four key technical pathways—deep learning, sensor fusion, data-driven methods, and hybrid modeling—while critically examining major challenges across data, technology, implementation, and ethics/policy dimensions. Future directions are discussed from technological innovation, scenario expansion, implementation guarantees, and sustainability orientation. Research findings show that AI has achieved technical validation in pest/disease detection, soil parameter modeling, and intelligent spraying, with accuracy exceeding 85% in some cases. However, regional data bias, insufficient model generalization, and the digital divide still hinder large-scale deployment. Moving forward, coordinated efforts in technological innovation and policy support are required to promote inclusive, standardized, and sustainable AI applications in crop production. Full article

Mass-based circularity indicators, such as ISO 59020:2024, quantify material recovery as a share of total throughput but do not account for chemical composition or functional performance, as a consequence of their sector-agnostic design. In copper metallurgical systems, trace tramp elements (e.g., As, Sb, Bi, Fe, Sn, Ni) present in WEEE-derived scrap, anode slimes, and refinery residues can significantly reduce electrical conductivity. Even at nominal purities of ≥99.7 wt.% Cu, conductivity may drop to 85.0–88.0% IACS, as illustrated by selected reported cases—a level of functional degradation that remains undetected by mass-based accounting. Analysis of Grade A cathode standards (EN 1978:2022, LME Cu-CATH-1, ASTM B115-10:2021) shows that impurity limits as low as 2 ppm (Bi) constrain the achievable share of secondary feed in closed-loop recycling. For a specific flash-smelting–refinery configuration, modeling indicates that secondary feed shares above approximately 30% may lead to impurity accumulation beyond the stated specification constraints unless low-impurity primary copper is introduced. This study introduces the Quality-Adjusted Circularity Indicator (QACI), a conceptual, specification-constrained indicator framework that applies a dilution factor f_dil derived from a binary blending mass balance to adjust ISO 59020:2024 inflow-based circularity indicators using a feed-composition blending constraint anchored to Grade A specification limits. The QACI functions as a feed-composition screening indicator operating at the anode blending stage and does not represent a correction of the full electrorefining system. Parametric scenario analysis across six stylized impurity configurations shows that, at identical mass-based circularity (C_mass = 25%), the QACI ranges from 7.1% to 25.0%. This corresponds to a 1.3- to 3.5-fold difference between the mass-based and quality-adjusted indicator values under the stated feed-composition assumptions, illustrating the potential overestimation introduced when feed-quality constraints are not considered. This ratio quantifies the divergence between two indicator values under stylized conditions and should not be interpreted as a directly measured fold-difference in actual loop-closure performance. Positioned within the ISO 59020:2024 Annex C complementary method space, the QACI is positioned as a first-order screening approach of existing circularity metrics that may inform future research discussion of quality-differentiated approaches in EU secondary metals policy. Full article