Search Results (6,359)

Objective: This study investigates the optimization of surface EMG (sEMG) classification for seven transtibial movements using short analysis windows (64 ms) suitable for real-time control of below-knee prostheses. Methods: We systematically evaluated feature engineering strategies, dimensionality reduction techniques, and classification approaches using linear Support Vector Machines on four-channel sEMG data from the transtibial region. We compared amplitude-based versus derivative-based time-domain features, integrated frequency-domain features, and implemented multi-window majority voting with 50% overlap. Results: Evaluated across nine subjects (four male, five female), the optimized system achieves a population-level accuracy of $70.16\%\pm 7.09\%$ with multi-window majority voting (per-subject range: 60.71–78.57%), with voting consistently improving accuracy over single-window classification by $+7.06$% on average. We demonstrate that PCA provides zero benefit for linear classifiers when all features are retained. Documented failed approaches include adaptive windowing and spectral entropy features. Conclusion: Careful feature engineering combining time-domain (MAV2, RMS, VAR, MAX, LOG, IEMG) and frequency-domain features (MPF, MF, band powers) with multi-window voting substantially recovers accuracy losses from aggressive window reduction while maintaining sub-100 ms latency suitable for prosthetic control. This work provides a validated methodology across multiple subjects for optimizing EMG classification latency–accuracy trade-offs, demonstrates that PCA is unnecessary for linear classifiers with well-engineered features, and documents negative results to guide future prosthetic control research. Full article

With a modest estimate of 11 million workers and high greenhouse gas emissions, the Indian brick sector is a relevant study for understanding how low-carbon energy transition impacts justice for the society, environment, and livelihoods. This empirical article provides an analysis of the ongoing policy-driven energy efficiency transition and justice trade-offs and benefits in the brick production sector in the state of Bihar. The transition is explored in a larger framework of power relations and vulnerability to determine whether the policies enable or challenge transformative justice for the labour force, nature and future generations. Present policies focus on regulations and financial incentives relevant for entrepreneurs with pre-existing skills, network and financial resources. Further, present policy narratives lack attention to mechanisms that reproduce the socio-economic inequality of the brick labour force, and implications for balancing different livelihood and environmental objectives. We conclude that the findings emphasise the need for integrating a wider variety of social dimensions and relevant support schemes to overcome inequality barriers and safeguard the environment for future generations. Full article

Outer rotor permanent magnet synchronous motors (ORPMSMs) are widely used in drone and aircraft propulsion due to their high power density. However, conventional arc-shaped designs involve an inherent trade-off between efficiency and torque ripple. Increasing the arc curvature improves the sinusoidal air gap flux density and reduces torque ripple, but it also increases rotor eddy current loss due to larger flux variations, thereby degrading efficiency. This paper investigates the effects of stator and rotor geometries on rotor eddy current loss and torque ripple in ORPMSMs. To address this trade-off, arc- and rectangular-shaped rotor and stator pole shoes are combined to form four design candidates. Their electromagnetic performance is evaluated using finite element analysis. Based on this comparison, a configuration with rectangular rotor and stator pole shoes is selected as the initial design and further optimized using a multi-objective genetic algorithm to simultaneously improve efficiency and torque ripple. The optimized design demonstrates significant improvements, achieving reductions of 56.67% in peak-to-peak torque ripple and 46.89% in rotor eddy current loss compared to the initial design. Full article

Smart hydrogel systems with stimuli-responsive properties are increasingly being investigated in combination with advanced additive manufacturing techniques for targeted drug delivery and wound healing in regenerative medicine; however, their clinical translation remains limited by challenges related to material performance, design complexity, and manufacturing scalability. This review analyzes recent developments in smart hydrogel design and 4D-printed scaffolds, with emphasis on programmable and stimuli-responsive architectures. The literature is selectively evaluated based on relevance to (i) hydrogel structure–property relationships, (ii) 3D/4D printing strategies, and (iii) demonstrated performance in drug delivery and wound healing applications. The analysis highlights design approaches enabling spatiotemporal control of drug release and dynamic scaffold behavior, while also examining how fabrication methods influence functional outcomes. Major limitations are critically assessed, including issues of reproducibility, mechanical stability, long-term performance, and the gap between experimental studies and clinical application. Challenges in defining and implementing 4D printing in biomedical contexts are discussed as well. Overall, this review identifies current design trade-offs, outlines priorities for improving reliability and translational potential, and synthesizes emerging trends in 3D and 4D printed hydrogel scaffolds for precision drug delivery and regenerative wound therapy. Full article

Recent discussions on the future of European Union governance highlight a growing emphasis on integrated policy frameworks that align agricultural, territorial, and socio-economic development objectives within unified strategic planning systems. One of the proposed innovations for the next EU programming period is the introduction of National and Regional Partnership Plans (NRPPs), which aim to coordinate several EU funding instruments within a single national planning framework. This article explores stakeholder perspectives on the development of integrated policy planning in this context. The analysis is guided by analytical propositions derived from the literature on policy integration and multi-level governance, focusing on how stakeholder interpretations influence strategic priority alignment, perceived policy trade-offs, and governance coordination capacity. The study is based on a qualitative focus group discussion involving policy stakeholders, researchers, and institutional representatives in Lithuania. Using thematic analysis, the study examines how stakeholders interpret integrated planning concepts, identify strategic priorities, and assess governance challenges associated with policy integration. The findings reveal three key issues shaping stakeholder perspectives. First, conceptual ambiguity surrounding strategic priorities such as competitiveness, regional vitality, and sustainability may complicate policy coordination. Second, perceived conflicts between economic competitiveness and environmental sustainability may be less pronounced than often assumed. Third, the implementation of integrated policy frameworks requires stronger governance capacity, including improved cross-ministerial coordination and shared monitoring systems. The article contributes to research on policy integration and multi-level governance by providing empirical evidence on how policy actors interpret integrated strategic planning frameworks and how these interpretations shape perceptions of governance capacity, policy trade-offs, and stakeholder participation in EU funding reforms. Full article

With the rapid development of new energy vehicles, fast and reliable prediction of power battery collision damage has become increasingly important. Traditional finite-element analysis is computationally expensive and difficult to deploy for rapid prediction under varying conditions. Although learning-based methods are faster, they usually rely on single-fidelity data: high-fidelity data is accurate but scarce and costly, while low-fidelity data is abundant but less reliable. Existing multi-fidelity methods alleviate this issue, yet often suffer from imbalanced sample allocation and weak cross-fidelity modeling. Moreover, current adaptive sampling strategies cannot dynamically determine the appropriate fidelity for different regions of the design space. To address these challenges, we propose HNGP-LCA, a multi-fidelity active learning framework for battery pack collision damage prediction. Our method consists of two components: (1) an Ensemble Nested Gaussian Process module that integrates single-layer and double-layer nested Gaussian process regression to better capture high–low fidelity correlations; and (2) a Location Information Cost-aware Active Learning strategy that leverages positional information to reconstruct expected improvement under different fidelities, enabling dynamic fidelity selection during sampling. Experiments on multiple synthetic benchmarks and a real battery pack engineering case demonstrate that HNGP-LCA achieves a better trade-off among accuracy, efficiency, and cost than strong baselines such as NARCO and MFBO. In the engineering case, it improves prediction accuracy by 0.6% over NARCO and 1.29% over MFBO, while reducing dependence on expensive high-fidelity data. These results show that HNGP-LCA provides an effective and practical solution for battery collision damage prediction. Full article

AI infrastructure is entering a constraint-dominated regime in which power access, cooling, water conditions, reliability, and financing jointly shape cost, sustainability, and operational risk. Yet the metrics used to evaluate these systems remain fragmented across facility engineering, compute/workload performance, and economic or risk analysis, with definitions that often sit at different layers and under different boundaries. This fragmentation weakens cross-layer reasoning and makes decision-traceable trade-off analysis difficult. This paper proposes a structured, decision-oriented measurement architecture for AI infrastructure metrics. The framework combines a 6 × 3 taxonomy, which organizes metrics across six layers and three semantic domains, with a procedural workflow built around a problem card, variable registry, minimality gate record, activated-cell map, boundary log, metric ledger, and a results sheet with case-pack manifest. Within this protocol, the Metric Propagation Graph is used as a case-specific dependency representation for tracing decision-facing metrics back to minimal boundary-consistent inputs. It is introduced as a traceability layer within the framework rather than as a stand-alone graph-theoretic method. The paper is illustrated through one fully worked case and one scoped portability illustration. The first is a fully worked large-load planning case for the Northern Virginia data-center corridor within PJM’s Dominion zone, showing that a boundary-consistent integrated metric can reverse the ranking obtained under a simpler screening view. The second is a scoped portability illustration for hourly matching under dual Scope 2 boundaries. Its purpose is not to provide a second full empirical validation, but to show how the same dossier logic, boundary discipline, and traceable metric construction transfer to a distinct decision setting. Full article

The widespread adoption of mobile devices has rendered mobile browsers critical repositories of sensitive personal and organizational data, making their analysis a cornerstone of modern digital forensics. This paper presents a systematic empirical evaluation of the forensic recoverability and interpretability of data from popular mobile browsers (Chrome, Firefox, Tor, DuckDuckGo, and Brave) on authentic Android 13 devices. By utilizing a rooted environment to bypass application sandboxing, we introduce a standardized scoring framework to quantify and compare the residual digital footprints left across diverse usage scenarios, including standard browsing, manual data deletion, and private/incognito modes. The study details a hybrid acquisition methodology that integrates persistent storage analysis with custom volatile memory extraction routines to capture ephemeral process data. Through a suite of controlled, realistic scenarios—encompassing form filling, virtual transactions, and anti-forensic activities—the results demonstrate that significant portions of user activity remained recoverable within the tested and evaluated experimental environment and browser configurations despite aggressive privacy-enhancing measures. Our findings reveal that while private modes effectively minimize the persistent filesystem footprint, volatile memory remains a fertile source of cleartext credentials and session identifiers. This recovery is particularly pronounced in Chromium-based browsers, whereas privacy-centric alternatives like Tor exhibit higher forensic resilience. Ultimately, this research underscores the importance of volatile memory acquisition in mobile investigations and provides an experimental systematic approach for evaluating the trade-offs between browser usability and forensic traceability in contemporary Android environments, demonstrating potential applicability to subsequent Android iterations. Full article

As China’s urbanization transitions from incremental expansion to stock-based renewal, industrial land redevelopment has become a key pathway for promoting high-quality urban development. However, existing studies mostly assess redevelopment potential from a single dimension and lack a systematic framework integrating ecological function (E), spatial structure (S), economic conditions (C), and building foundations (B). Taking the built-up area of Xi’an as a case study, this study adopts a functional–structural optimization perspective and constructs a four-dimensional ESCB assessment framework based on 13 indicators covering ecological function, spatial structure, economic conditions, and building foundations. GIS-based spatial quantification, MiniBatchKMeans clustering, and the XGBoost algorithm were employed to identify the redevelopment potential of industrial land, while SHAP analysis was used to interpret indicator contributions and determine the core influencing factors. The results show that industrial land in the study area can be classified into four types: vitality–density dominant, transport–scale coordinated, scale–facility lagging, and topography–vegetation sensitive, with significant differences in spatial distribution and indicator characteristics. The interpretable machine learning model further identifies road network density, block-level economic vitality, and land-use suitability as the three principal drivers of redevelopment potential, among which road network density plays the most critical role. By integrating clustering analysis with interpretable machine learning, the ESCB framework effectively reveals the synergies and trade-offs among multidimensional indicators and provides differentiated and precise support for industrial land redevelopment strategies. Full article

Energy-aware Adaptive Cruise Control (Eco-ACC) has become an essential approach for enhancing the energy efficiency of electric vehicles while ensuring safe and comfortable driving. This paper presents a systematic review, following the PRISMA methodology, of 60 recent studies published between 2021 and 2025. The review provides a structured analysis of control strategies, validation approaches, computational demands, and battery-related considerations in Eco-ACC systems. The results indicate that Model Predictive Control (MPC) remains the most widely adopted technique (41.7%), primarily due to its ability to handle system constraints and address multi-objective optimization problems. Reinforcement Learning (RL) approaches (33.3%) are increasingly explored for their capability to adapt to uncertain and dynamic driving conditions. In addition, hybrid MPC–AI methods (16.7%) show strong potential for balancing optimal control performance with real-time implementation requirements. A key observation is the clear imbalance in validation practices: more than 73% of the studies rely on simulation-based evaluation, whereas only 10% include real-world experiments, revealing a pronounced simulation-to-reality (sim2real) gap. Furthermore, two critical research gaps are identified. First, the computational energy paradox highlights the trade-off between improved control performance and increased computational cost. Second, battery-aware control remains insufficiently addressed, as most existing methods overlook long-term battery degradation effects. Based on these findings, this review proposes a deployment-oriented research framework that prioritizes hybrid control architectures, real-time feasibility, and robust validation strategies, including Hardware-in-the-Loop and field testing. The presented insights aim to support the development of practical and energy-efficient Eco-ACC systems suitable for real-world deployment in next-generation electric vehicles. Full article

With the formal inclusion of the shipping industry in the European Union Emissions Trading System (EU ETS), the speed optimization of ocean-going container ships must simultaneously balance operating costs, incorporating carbon emission costs and shipper satisfaction with transportation timeliness. Taking ocean-going container liner routes as the research object, this paper establishes a ship navigation resistance model based on meteorological and hydrological conditions, and constructs a route segmentation mechanism and a ship fuel consumption model on this basis. The spatially differentiated carbon accounting rules of the EU ETS are introduced, a fuzzy membership function is adopted to quantify shipper satisfaction, and a Q-learning-based solution algorithm for ship speed optimization that balances operating costs and shipper satisfaction is designed. Numerical experiments on a 20,150 Twenty-foot Equivalent Unit (TEU) container ship demonstrate that the proposed framework reduces total operating costs by 5.56%, EU ETS carbon compliance costs by 18.72%, and total voyage carbon emissions by 11.01% compared with the conventional constant-speed strategy. Meanwhile, the algorithm can spontaneously form an optimal speed strategy adapted to meteorological conditions and policy rules. Through parameter sensitivity analysis, this paper further extracts management implications for liner-operating companies. Full article

Carbon is an essential component in the regulation of climate systems through the global biogeochemical cycle. However, changes in land use/land cover (LULC) have reduced the capacity of terrestrial ecosystems like watershed to store carbon. This shows the need for a policy framework that balances conservative objectives with agricultural demands, as watersheds are required to support carbon storage and food production. Previous studies have generally assessed carbon dynamics or LULC change separately, with limited integration of policy-driven scenarios. Therefore, this study aimed to conduct multi-scenario carbon storage modeling to evaluate LULC protection strategies in the Cimanuk Watershed, Indonesia, an area experiencing significant LULC pressures. The method used consisted of Support Vector Machine (SVM)–Markov, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Geodetector, and Getis-Ord Gi*. A total of four scenarios were used to project LULC and carbon storage in 2042, which included Business as Usual (BAU), Paddy Field Protection (PFP), Forest Protection (FOP), and Paddy Field and Forest Protection (PFFOP). The results showed that forest area declined by 39,400 ha between 2015 and 2025, thereby reducing carbon storage. The PFFOP scenario was identified as the most viable, combining the protection of paddy fields and forests to balance agricultural production and carbon sequestration. Among the factors analyzed, slope exerted the greatest influence on carbon storage. Spatial cluster analysis showed that carbon hotspots were predominantly located in the upper Cimanuk sub-watershed. These results offered valuable insights into scenario-based sustainable watershed management to optimize carbon storage and maintain agricultural function. Furthermore, the proposed framework showed promising potential for application in other tropical watersheds, serving as a reference for decision-makers in sustainable watershed management. Full article

Hybrid robotic manufacturing systems integrating additive and subtractive processes enable fabrication of complex, high-value components but are typically executed sequentially, resulting in long cycle times. Concurrent execution of Directed Energy Deposition (DED) and milling promises productivity gains but introduces coupled thermal, mechanical and spatial interactions that challenge conventional process planning. This work addresses the methodological problem of planning milling operations in the presence of an ongoing DED process. The concurrent planning task is formulated as a mixed-integer, nonlinear, multi-objective optimisation problem capturing sequencing and orientation decisions, cutting parameters and enabling temporal coupling to the deposition trajectory. A hierarchical, surrogate-assisted optimisation framework is proposed, combining unified decision-variable encoding, deterministic decoding and staged feasibility enforcement to ensure robotic executability. Disturbance mechanisms such as thermal interaction, particulate interference and pose-dependent dynamic compatibility are incorporated as modular objective abstractions, enabling systematic trade-offs between machining productivity and preservation of deposition process integrity. The proposed framework is demonstrated on a representative case study, enabling analysis of the interaction between spatial sequencing, temporal feasibility and disturbance-aware optimisation. The case study provides a controlled instantiation and illustrates its application to concurrent additive–subtractive planning under explicitly modelled temporal and disturbance constraints. Full article

Housing acquisition decisions play a strategic role in shaping urban habitability and long-term sustainability, as they directly influence the quality of the built environment and users’ well-being. From an architectural and urban perspective, housing selection can be understood as an assessment of urban habitat quality, in which economic, spatial, social, environmental, and risk-related dimensions interact to define the conditions of livability. This study proposes a multi-objective decision-support framework that integrates evolutionary optimization algorithms (NSGA-II and MOEA/D) with multi-criteria decision analysis (TOPSIS) to support sustainable housing decisions. The model simultaneously considers four conflicting objectives: minimizing acquisition cost, minimizing spatial accessibility and disutility from essential services, maximizing socio-spatial safety and long-term habitat value, and minimizing environmental and territorial risk. A real-world case study in the Tampico metropolitan area demonstrates how the proposed approach generates Pareto-optimal housing alternatives that explicitly reveal trade-offs between habitability dimensions. The resulting non-dominated solutions are subsequently ranked using TOPSIS to reflect user-centered preferences and facilitate transparent decision-making. The results show that the proposed framework effectively operationalizes the concept of urban habitat quality through an explainable, customizable computational tool, thereby contributing to sustainable urban development, resilience, and informed housing choices. This research supports the technological enablement of habitat assessment and aligns with the objectives of SDG 11: Sustainable Cities and Communities, offering a replicable methodology for urban and architectural decision-making contexts. Full article

Jicama (Pachyrhizus erosus L.) is a tropical tuber crop that has potential not only as a food source but also as a natural active ingredient in the cosmetics industry. This study aims to evaluate the phenology of tuber development and the content of primary and secondary metabolites of two jicama accessions (Bogor and Kebumen) at three tuber ages (3, 4, and 5 months). The parameters observed included tuber weight, starch yield, total soluble solids (TSS), total titratable acidity (TTA), vitamin C, total phenols, total flavonoids, and antioxidant activity (% inhibition). For data analysis, we used the T-test to compare differences between accessions. The results showed that tuber weight and starch yield increased significantly up to 5 months of age, while secondary metabolite content (phenols, flavonoids, antioxidant activity) was higher in young tubers (3–4 months). This study shows a trade-off between productivity (starch and vitamin C) and bioactive metabolite content (phenols, flavonoids, antioxidants) as the tubers age. The Bogor accession has a more stable vitamin C content, phenol levels, and antioxidant activity, while the Kebumen accession shows higher flavonoid levels in young tubers. The optimal tuber age and accession recommended to obtain a balance between productivity and secondary metabolite content is the Bogor accession at 4 months of age. This supports the potential use of jicama in the cosmetics industry as a brightening agent (vitamin C), humectant (sugar), anti-aging agent (phenols, flavonoids), and base ingredient for natural starch-based formulations. This study provides the first integrated evaluation of tuber phenology, primary metabolites, and secondary metabolite dynamics of two Indonesian jicama accessions in relation to cosmetic functionality. The results highlight a clear trade-off between productivity and bioactive compound accumulation, offering a scientific basis for selecting optimal harvest age and accession for cosmetic raw materials This study provides the first integrated evaluation of tuber phenology, primary metabolites, and secondary metabolite dynamics of two Indonesian jicama accessions in relation to cosmetic functionality. The results highlight a clear trade-off between productivity and bioactive compound accumulation, offering a scientific basis for selecting the optimal harvest age and accession for cosmetic raw materials. Full article