Search Results (1,034)

Integrated energy systems (IES) are widely recognized as a key pathway toward carbon neutrality, enabling the coupling and coordinated optimization of electricity, heat, gas, and cooling. This review provides a structured, technology-oriented overview of IES based on a unified five-subsystem framework (production, conversion, transmission, storage, and consumption). It systematically covers: (1) renewable energy utilization—solar, wind, and geothermal—supported by a global spatial distribution map and representative top-performing commercial products; (2) energy cascade utilization, where combined heat and power/combined cooling, heating and power (CHP/CCHP) raises overall efficiency from approximately 35–40% to 70–90%; (3) multi-form energy storage—electrical, electrochemical, chemical, thermal, and mechanical—distinguishing short-term balancing (e.g., lithium-ion (Li-ion), flywheels, supercapacitors, with 85–95% round-trip efficiency) from long-duration and seasonal applications (e.g., pumped hydro, hydrogen/power-to-gas (P2G), redox flow batteries); and (4) forecasting, collaborative optimization, and the bidirectional integration of IES with smart grids and grid modernization. A strategic strengths, weaknesses, opportunities, and threats–Political, Economic, Sociological, Technological, Legal, and Environmental (SWOT–PESTLE) analysis is further presented to position IES within the global energy transition. The review highlights that IES and grid innovation are mutually enabling, and that realizing the full carbon-neutrality potential of IES requires coordinated progress in standardization, digitalization, long-duration storage, and cross-sector policy alignment. Full article

Ultra-fast electric vehicle (EV) charging systems are among the most demanding converter-dominated applications due to their high power levels, wide battery-voltage range, strict thermal constraints, and the need for adaptive charging control. Conventional design and tuning approaches often rely on fixed control policies and computationally expensive iterative optimization, which limits their ability to address nonlinear multi-objective trade-offs across the full charging envelope. This paper proposes a hybrid AI–quantum co-design framework for a SiC-based dual active bridge (DAB) converter intended for ultra-fast EV charging applications. The proposed approach combines a physical converter model, an AI surrogate-learning layer for rapid prediction of converter performance, and a quantum-assisted optimization layer for multi-objective exploration of design and control variables. To demonstrate the framework, a representative modular 350 kW ultra-fast charging case study is considered, implemented by four parallel 87.5 kW SiC-based DAB modules and including converter-level optimization and adaptive charging-policy refinement. The revised manuscript introduces a complete system schematic, an explicit DAB converter topology, a clarified methodological workflow, and a simulation-based proof-of-concept evaluation. Representative results indicate improved design-space exploration and more balanced trade-offs between efficiency, thermal stress, ripple, and dynamic response compared with a conventional baseline tuning approach. Although the study does not claim hardware-level quantum advantage, it provides a structured and practically interpretable computational framework for intelligent co-design of high-power charging converters. Full article

Embedding large language model (LLM) coordinators in production electronic systems, connected vehicles, multi-robot fabrics, IoT control loops, telecommunications orchestration, demands a pre-delivery filter stage that preserves ethical guarantees under adversarial influence at deployment scale. We present a constitutional governance layer that filters compiled influence policies before they reach a heterogeneous population of grounded LLM agents whose hybrid decision model combines a game-theoretic base probability with an LLM-evaluated narrative shift attenuated by per-agent resistance. Four experiments on a Barabási–Albert scale-free network of 30 agents powered by Llama-3.3-70B-Instruct show that the filter holds an Ethical Cooperation Score (ECS) of $0.176$ (multi-seed mean $0.163$, 95% confidence interval (CI) $[0.150,0.174]$) against an unconstrained baseline of $\mathrm{ECS}=0$, enforced by a hard integrity gate ($1.000$ vs. $0.000$). We surface an autonomy paradox in which unconstrained agents resist manipulation more forcefully ($0.856$ vs. $0.728$) yet collapse to $\mathrm{ECS}=0$, establishing that system-level integrity cannot be delegated to agent-level defence. The advantage is monotonic in resistance ($+0.174$ to $+0.183$), seed-stable (Cliff’s $\delta =1.0$, complete separation), topology- and backbone-invariant across five contemporary LLMs, robust to alternative ECS formulations, and reproduces at N = 100. Against constitutional artificial intelligence (CAI) critique-revise and LlamaGuard-style safety-classifier baselines, the framework matches the integrity floor and adds a measurable margin on the secondary risk surface (burst timing, composite manipulation risk). The filter runs at 0.78 μs/call ($\approx 1.3\times {10}^6$ decisions/s/core), supporting always-on deployment as a stateless, model-agnostic component of LLM agent pipelines in adversarially contested electronic systems. Full article

This article examines remittances not only as financial transfers but also as datafied political objects shaped by measurement, modelling and presentation infrastructures. Using the UK–Romania corridor, we compare observed personal remittance receipts published by the National Bank of Romania (NBR) with model-based bilateral estimates associated with World Bank/KNOMAD data. The article develops an analytical framework that links quantification, metric power, algorithmic governmentality, hybrid media circulation and emerging bottom-up social policies. It then shows how nominal values, real values at constant 2021 prices, year-by-year changes, moving-average smoothing, employment-scaled scenarios and transfer-balance indicators generate different representations of diaspora contribution, welfare substitution and national economic performance. Rather than assigning final authority to one dataset, the article demonstrates how calculation and presentation choices become communicative interventions. The conclusion emphasises methodological transparency and the need to connect remittance statistics to both political communication and community-level welfare practices. Full article

Deep learning-powered Street View Imagery (SVI) analytics provides a critical mechanism for smart city perception within the framework of Sustainable Development Goal 11 (SDG 11), effectively bridging the gap left by traditional remote sensing in fine-grained street-level observation. Over the years, deep learning-based semantic segmentation of urban streetscapes has become the dominant paradigm. However, when scaling to megacity measurements, current research faces the dual bottlenecks of “computational redundancy” and the “geographical domain shift” caused by the blind application of pre-trained models based on Western datasets. To address these challenges, this study is the first to systematically quantify the performance trade-off between Multi-Task Learning (MTL) and Single-Task Learning (STL) in megacity scenarios. Using this as a baseline, we constructed and validated a “low-computation, high-robustness” framework for streetscape semantic perception and spatial measurement. Relying on an integrated ResNeXt101-FPN MTL architecture and an ultra-low-cost fine-tuning strategy to overcome geographical domain shift, we extracted and analyzed the spatial heterogeneity of five core semantic elements—vegetation, sky, building, road, and vehicle—across the road network within Beijing’s 5th Ring Road. The results indicate the following: (1) We explicitly defined the computation-accuracy trade-off of MTL and STL in megacity perception. While utilizing only 1/5 of the parameters of STL, the MTL framework achieved a 5.34-fold increase in inference speed with a negligible 0.1% loss in overall mean Intersection over Union (mIoU); however, a 27.13% decrease in boundary segmentation accuracy was observed. (2) We established a low-cost, localized correction paradigm to overcome domain shift. Utilizing a minimal annotation cost (only 200 local images) significantly improved cross-domain adaptability, boosting the overall mIoU by 8.92% and significantly mitigating the geographical domain shift problem. (3) Multi-dimensional measurement and spatial analysis revealed a significant spatial decoupling pattern in Beijing’s streetscapes. The visual proportion of vegetation exhibited a pronounced “north-high, south-low” spatial differentiation, whereas built environment elements (e.g., building and road) displayed a typical “center-periphery” concentric gradient. This objectively reflects the spatial inequality of urban street greenery resources and the monocentric development characteristics of the built environment. The proposed framework therefore serves as a low-cost, AI-driven computational paradigm for smart city perception in resource-constrained regions. Furthermore, the revealed spatial heterogeneity offers data-driven insights for formulating sustainable urban renewal policies aligned with SDG 11. Full article

Digital literacy (DL) is a cornerstone of sustainable digital transformation in education, yet its systemic cultivation among pre-service teachers in resource-constrained regions remains critically under-theorized. This study develops and validates a contextualized DL measurement framework grounded in the “triple identity” of pre-service teachers as citizens, learners, and future educators. Employing a multiphase mixed-methods design, this systemic framework was refined via Delphi consultation and rigorously validated through exploratory (n = 287) and confirmatory factor analyses (n = 1462) across 24 universities in Southwest China. The newly validated framework, comprising five core dimensions and 41 specific indicators, demonstrates robust psychometric properties and systemic structural validity. Findings reveal a moderately high overall DL (M = 3.44) but highlight a significant “skills-awareness” gap, where operational proficiency exceeds conceptual cognition, particularly in comparison with findings reported in other Chinese regions. Hierarchical regression indicated that structural variables added substantial explanatory power beyond individual demographic characteristics, with institution type emerging as the strongest predictor and urban-rural background making an additional significant contribution. These results underscore the paradox between DL’s inherent malleability and the rigid constraints of structural inequality. The study offers a validated measurement tool and an evidence-informed roadmap for policy interventions aimed at bridging the digital divide in underdeveloped teacher education systems, advocating for a shift from technical training toward systemic equity. Full article

Long-term decarbonization of urban residential buildings in southern China depends on the joint evolution of building stock, end-use efficiency, and electricity carbon intensity. This study develops a dynamic stock-energy-carbon framework for urban residential buildings in China’s hot-summer warm-winter region from 2010 to 2060, using Guangdong, Guangxi, Fujian, and Hainan as case provinces. The model links demographic and housing-space change with stock survival, retrofit of the base-year stock, cohort-specific performance levels for post-2022 new construction, and time-varying provincial grid emission factors. EnergyPlus simulations of seven high-rise residential archetypes show that nearly zero-energy performance reduces province-level EUI by 19.2–26.5% relative to the baseline, with cooling-load reductions forming the dominant part of the improvement in the warmer provinces. Across coupled demand-side scenarios, stricter new-build performance standards reduce 2026–2060 cumulative operational energy by 5.3–10.1% relative to the conservative demand-side setting, while increasing retrofit intensity provides a smaller but consistent additional reduction. Carbon outcomes are more sensitive to electricity-sector assumptions: under the main demand-side setting, moving from the conservative to the accelerated grid pathway advances the operational-carbon peak by 8–15 years across the four provinces and lowers 2060 residual emissions by about 71%. A comparison with available observed provincial household-electricity statistics is added as a plausibility check; it confirms the relevant order of magnitude but also indicates that absolute demand estimates should be interpreted cautiously because of boundary and EUI-representation differences. These results suggest that demand-side efficiency policies must be coordinated with rapid provincial power-sector decarbonization if the residential sector in Hot-Summer Warm-Winter Region is to reach earlier carbon peaks and lower residual operational emissions. Full article

Conventional truck emission accounting methods based on average activity levels and static emission factors are increasingly inadequate for dynamic regulation and policy comparison at high spatiotemporal resolution. This review synthesizes recent progress in dynamic truck emission estimation from four perspectives: multi-source data support, key feature extraction, physics-constrained emission modeling, and governance-oriented applications. The literature was collected from Web of Science Core Collection and ScienceDirect for the period 2014–2026, supplemented by backward reference checking, and was analyzed through a progressive framework linking data, features, models, and governance tasks. Unlike previous reviews that usually discuss emission inventories, conventional emission models, or data-driven prediction methods separately, this review highlights an integrated governance-oriented chain that connects multi-source data fusion, mechanism-related feature construction, physics-constrained modeling, and environmental management applications. Existing studies suggest that multi-source data, including GPS trajectories, on-board diagnostics (OBDs), on-board monitoring (OBM), portable emissions measurement system (PEMS) measurements, traffic flow monitoring, and road network attributes, provide an important basis for representing real-world operating processes. Meanwhile, key features have expanded from surface-level variables such as vehicle velocity to mechanism-related factors, including payload, road grade, engine operating conditions, vehicle-specific power, and roadway context. Truck emission modeling has also evolved from unconstrained or weakly constrained approaches toward frameworks that place greater emphasis on physical consistency, interpretability, and result credibility. In parallel, application scenarios have extended from emission quantification to high-emission vehicle identification, dynamic inventory development, hotspot detection, policy comparison, and transport optimization. These developments can support policymakers, transportation planners, and environmental agencies in moving from aggregate emission accounting toward targeted and process-based truck emission governance. Current research, however, still faces challenges related to data consistency, model generalizability, uncertainty propagation, and real-time application. Future work should focus on standardized datasets, hybrid AI–physics modeling frameworks, uncertainty-aware validation, real-time deployment in intelligent transportation systems, and improved links between dynamic estimation and practical environmental management. Full article

Inclusion has become a central concept in disability policy, education, and welfare state reform, yet its practical implementation remains ambivalent. While inclusion is promoted as a rights-based ideal grounded in equality, it can also function as an administrative label that obscures persistent exclusion. Drawing on critical disability studies, this article analyses inclusion as a contested, power-laden concept and develops a three-stage framework—access, participation, and agency—to distinguish formal inclusion from substantive belonging and influence. The framework is applied to key domains of disabled people’s lives—education, housing, service systems, working life, crises, and digitalised everyday life—showing how ableist norms, managerial governance, and institutional logics can reproduce exclusion within ‘inclusive’ reforms, including forms of transformed institutionalisation. The article argues that meaningful inclusion requires dismantling ableist norms, addressing structural power relations, resourcing supports, and strengthening disabled people’s agency in decision-making. Full article

As the core pillar of international trade, the global shipping industry has seen its carbon and pollutant emissions become a key challenge in global environmental governance. Statistics indicate that ship carbon emissions account for 3% of the world’s total anthropogenic CO₂ emissions, while contributing 20% of global NO_x and 12% of SO₂ emissions, posing a serious threat to coastal ecosystems and public health. In response to the International Maritime Organization (IMO) “Net Zero Framework” and national green shipping policies, the transformation of ship power systems toward low-carbon and zero-carbon operation has become an inevitable trend. This paper systematically reviews the research progress and application status of green energy materials for ships, focusing on the working principles, technical characteristics, and engineering application cases of solar photovoltaic (PV) materials, wind energy utilization technologies, fuel cell materials, and alternative clean energy fuels (e.g., liquefied natural gas (LNG), methanol, and hydrogen energy). It also discusses the integration mode and optimization strategy of multi-energy hybrid power systems. The research findings show that solar photovoltaic technology has achieved large-scale application in coastal ships; hydrogen fuel cells are suitable for long-range ocean navigation scenarios due to their high energy density; LNG and methanol have become the current mainstream alternative fuels, relying on mature infrastructure; and hybrid energy systems can significantly improve power supply reliability and emission reduction efficiency through multi-energy complementarity. Finally, aiming at the existing bottlenecks (e.g., cost, energy storage, and safety) of various technologies, future development directions are proposed. This study provides a reference for the technological breakthrough and engineering practice of green energy power systems for ships and contributes to the realization of the “carbon neutrality” goal in the global shipping industry. Full article

Background: The negative impact of adverse childhood experiences (ACEs) on child development is documented. The parent–child relationship protects against ACEs and improves healthy child development, playing a crucial role in preventing and mitigating ACEs by strengthening parental resilience. However, there is a gap in the literature on our understanding of parental resilience’s impact on the parent–child relationship within the social–ecological model (SEM) (i.e., intra- and interpersonal, community, and societal levels). Objective: This study explores parents’ perspectives on parental resilience as a protective factor for preventing and mitigating ACEs at every level of the SEM. Method: This study uses a thematic analysis approach for qualitative research. In-depth individual interviews (n = 21) were conducted with members of a parent support group (PSG) (85% female) based in a community-based organization serving families. Demographic information and ACE scores were collected for each participant to describe the sample. Results: Key findings highlighted parents’ perspectives on improved resilience through self-regulation and social support following participation in PSGs, conceptualized as an inter-level construct within the SEM mechanism due to its influence on parents’ well-being, traversing SEM levels. Under Theme 1: The Many Faces of Parental Resilience, Theme 3: The Power of Close Relationships, Theme 4: Community Resources as a Buffer, and Theme 7: Change Through a Policy Lens: “Anything that protects them,” parents expressed a strong desire for ACE prevention and mitigation strategies and called for systemic policy change to combat ACEs. Conclusions: Parental resilience perceptions are valuable and hold promise to inform the future institutionalization of a multi-level parent resilience-focused framework, which will aid in ACE prevention and mitigation. Full article

This concept paper advances stigma power as a central analytical mechanism for understanding how patriarchy, capitalism, white supremacy, and cis-heteronormativity operate with particular intensity against sex workers. Integrating Link and Phelan’s stigma power with Bourdieu’s symbolic violence and Foucauldian productive power, the framework theorises stigma as a mechanism institutionalised through law and enforced by institutions, which produces measurable consequences that include violence, exclusion, and health harms. Analysing the intersecting axes of gender, sexuality, race, migration, and class across three qualitative studies (SWMH, SEXHUM, VICSW), the article demonstrates why labour-rights reforms, including decriminalisation, are necessary but insufficient. Dismantling stigma requires not only removing sanctions but actively contesting the actors exercising stigma power and interrupting the stabilising mechanisms that reproduce it. This requires policy that acknowledges stigma’s existence whilst working to dismantle it, rather than eliding its reality through liberal mainstreaming or strengthening it through criminalisation or rescue frameworks. The framework explains why decriminalisation is associated with better access to rights and health; why all criminalisation including the so-called Swedish model correlates with increased violence; why stigma persists under optimal legal conditions; and how intersecting marginalisations produce differential vulnerability. Policy implications emphasise pairing decriminalisation with peer-led anti-stigma work, institutional reform, migrant rights, and funded support for sex worker self-organisation. Full article

The rapid digitalisation of the agri-food sector has generated unprecedented volumes of farm and value chain data, but also highly fragmented data ecosystems and asymmetric power relations between farmers, technology providers, and public authorities. In response, the European Union has developed a comprehensive data governance architecture—including the Data Governance Act, the Data Act, the GDPR and the EU Code of Conduct on Agricultural Data Sharing—and is building a Common European Agricultural Data Space (CEADS). This article examines that governance framework and explores its implications for the agri-food sector in Spain. Through a qualitative legal policy review, we map the regulatory landscape, analyse five major European and Spanish initiatives (CEADS/AgriDataSpace, AgData, Agdatahub, RegenAg-X, and DADS), and use Spain as a national case study. A multi-level actor model (meta-governance, data originators, transformation intermediaries, and data users) structures the comparative analysis. On this basis, six design principles for responsible agri-food data spaces are identified: clarity of use cases, inclusive multi-stakeholder governance, data life cycle mapping, privacy and sovereignty by design, a fair economic model, and regulatory compliance as a trust factor. The article identifies open research questions on anonymisation of georeferenced data, data sovereignty, and equitable value distribution, and outlines an agenda for future empirical and legal research. Full article

Rapid urbanization and stringent ecological protection policies in China have reshaped spatial competition among urban, agricultural, and ecological spaces. However, existing studies often overlook how this competition evolves across different slope structures. To address this, this study establishes a fine-scale analytical framework using H3 hexagonal grids and slope spectrum analysis to investigate slope structure evolution and spatial competition patterns from 1990 to 2023. The results reveal a distinct topographic stratification: urban space dominates low-slope regions (<6°) but exhibits a pervasive “upslope expansion” trend, with its average slope increasing from $1.81°$ to $2.07°$, equivalent to an annualized increase of approximately $0.008°{\mathrm{yr}}^{-1}$; agricultural space characterizes the transition zones (6–$15°$), showing an “upslope migration” in the Southeastern Hills associated with urban expansion pressure in low-slope areas; and ecological space functions as a stable barrier in steep terrains (>15°) but faces encroachment in transition zones. Furthermore, cluster analysis identifies significant regional heterogeneity aligned with China’s macro-topography, including “low-slope agglomeration” in the Eastern Plains, “interwoven upslope” patterns in the Southern Hilly Regions, and ecological dominance in the Western Highlands. Association analysis using GeoDetector and Multiscale Geographically Weighted Regression (MGWR) indicates that competition intensity is most strongly associated with human activity factors, especially human footprint and nighttime lights ($q>0.29$), which show the highest explanatory power among the examined factor groups. The interaction between human activity and elevation further shows relatively high explanatory power ($q=0.41$), suggesting that spatial competition is more pronounced where intensive human activities overlap with topographic constraints. Crucially, this study challenges the traditional flat-projection planning model. We propose a transition to “three-dimensional topographic regulation,” advocating differentiated management strategies—such as strict “slope redlines” for urban-agricultural transition zones—to mitigate intensifying spatial conflicts in complex terrains and safeguard agricultural sustainability. Full article

International and regional decarbonisation policies are accelerating the deployment of hybrid electric propulsion systems (HEPSs) in short-sea and coastal trades, yet most existing design studies focus on ferries or tugs, rely on stylised duty cycles, and treat battery degradation only superficially. This paper proposes an integrated, data-driven framework for the design and dynamic performance optimisation of a diesel–battery HEPS for a coastal general cargo vessel operating on short-sea routes. A multi-year automatic identification system (AIS) and logbook data are processed to derive route-specific, time-resolved operating profiles, which drive a DC-based hybrid propulsion model comprising diesel generator sets, propulsion motors and a lithium-ion battery energy storage system (ESS). A degradation-aware ESS model is embedded in a life-cycle cost (LCC) formulation that explicitly accounts for battery replacement timing and residual value. The hybrid design problem is cast as a bi-level optimisation: an upper level determines engine rating and ESS capacity to minimise LCC, while fuel savings and emissions are evaluated as key parallel performance indicators, while a lower level uses dynamic programming to compute optimal power split trajectories under state-of-charge, C-rate and power constraints. A surrogate-assisted global search with Kriging and Expected Improvement is employed to manage the computational burden of repeated lower-level optimisations. Case-study results for representative coastal routes show that the optimised hybrid configurations achieve fuel savings of 16–21%, CO₂ reductions of 17–20%, and LCC reductions of 8–14% relative to a conventional mechanical baseline, outperforming a rule-based hybrid design. Sensitivity analyses with varying fuel prices and ESS costs confirm the robustness of the proposed framework and highlight the importance of explicitly coupling degradation-aware ESS. Full article