Search Results (2,346)

Background: Multi-objective optimization (MOO) is widely used in engineering design and energy systems to represent trade-offs through Pareto fronts. Yet practical deployment requires moving from a non-dominated set to an implementable preferred design, and this decision step is often treated implicitly. Many studies equate decision support with improved Pareto front generation or visualization, while decision-maker preferences are assumed, weakly specified, or not elicited from stakeholders. Methods: A two-phase scoping evidence synthesis with PRISMA-informed reporting was adopted to map the literature and synthesize explicit Pareto-front decision-support mechanisms. Phase 1 produced a broad evidence map of how Pareto-front decision support is framed and clustered studies by primary contribution, while Phase 2 conducted a focused synthesis of explicit Pareto-front decision-support methods using refined searches in Scopus and SpringerLink. Results: Phase 1 mapped 46 studies; only 10 reported an explicit reproducible Pareto front solution-selection mechanism. Phase 2 included 17 studies and identified four method families: post hoc scoring and ranking, compromise aggregation, interactive preference-guided exploration, and preference elicitation and learning. Conclusions: The literature remains dominated by Pareto front generation and exploration rather than reproducible final solution selection; future work should strengthen preference elicitation, transparency, sensitivity analysis, and uncertainty-aware recommendation stability. Full article

China’s steel decarbonization is a key sustainability challenge because cleaner production routes must be evaluated not only by their mitigation potential, but also by their implications for industrial continuity, cost affordability, resource security, and transition manageability. This study develops a national-scale soft-linked sustainability assessment framework that translates policy-conditioned macro signals into a multi-period, multi-objective optimization model of steelmaking-route transition from 2025 to 2050. Three policy environments are examined: carbon-control pressure, electricity-cost support for electrified routes, and their combined application. The model evaluates route portfolios by cumulative system cost, emissions, and transition adjustment intensity, linking mitigation with affordability and implementation feasibility. Results show that policy environments do not shift pathways uniformly; instead, they reshape the feasible trade-off frontier and alter which route combinations emerge as plausible compromise solutions. Across scenarios, scrap-based electric arc furnace steelmaking (Scrap-EAF) becomes the central medium-term route, while blast furnace–basic oxygen furnace steelmaking (BF-BOF) contracts but remains residual. Hydrogen-based direct reduced iron–electric arc furnace steelmaking (H₂-DRI-EAF) expands under favorable conditions, but does not become dominant by 2050 under the baseline national-scale parameterization. Overall, this study contributes to sustainability-oriented industrial transition analysis by showing how policy-conditioned environments reshape route feasibility, transition sequencing, affordability–mitigation trade-offs, and the practical manageability of China’s steel-sector decarbonization. Full article

Low-carbon scheduling of regional integrated energy systems (RIES) based only on energy-balance models may overlook the physical operating limits of distribution, gas, and heating networks, resulting in a gap between scheduling outcomes and actual operating boundaries. To address this issue, this paper proposes a framework integrating bi-objective scheduling, three-network posterior verification, and closed-loop repair. A mixed-integer linear programming model is first formulated with operating cost and carbon emissions as the two objectives, and an adaptive ε-constraint strategy is used to improve the characterization of the compromise region on the Pareto front. Posterior verification models are then established for the distribution, gas, and heating networks to assess the physical feasibility of representative solutions. When infeasibility is detected, a boundary-shrinking repair mechanism is triggered to iteratively update the scheduling boundaries. Case results show that the adaptive refined strategy improves the resolution of the compromise region by 3.2 times with only a 20.4% increase in computational time. Compared with the cost-optimal solution, the carbon-optimal solution reduces carbon emissions but increases peak purchased electricity from 7.333 MW to 11.1 MW, further tightening the lower-voltage margin of the distribution network. The results show that posterior physical verification and closed-loop repair provide additional support for evaluating and improving the engineering feasibility of RIES scheduling solutions. Full article

Balancing small object detection performance with model lightweighting remains a critical challenge in the remote sensing domain. To address the massive computational and parameter overhead of existing algorithms, we propose the hybrid scale dynamic detection transformer (HSD-DETR). This lightweight detector incorporates four core innovations to effectively enhance feature extraction for small objects. First, to reduce costs without compromising performance, we design a hybrid convolution and selective scanning fusion (HCSS-Fusion) module to reconstruct the backbone, combining local convolution with global linear scanning. Second, to preserve fine-grained information, we introduce a space-to-depth mixer (SPDMixer) to achieve pixel-level lossless downsampling. Third, to mitigate background interference and enhance small object representation, we develop a dynamic sparse adaptive intra-scale feature interaction (DSAIFI) module, employing a gating mechanism to dynamically select informative spatial tokens. Finally, to improve the localization precision for small objects, we propose the rational-focal minimum point distance intersection over union (RF-MPDIoU) loss, utilizing a non-linear mapping to dynamically modulate sample weights. Experimental results on public benchmarks confirmed that, compared to mainstream models, HSD-DETR achieves highly competitive accuracy while significantly reducing parameter scale and theoretical computational complexity. Ultimately, this research provides a lightweight and robust algorithmic solution for the field of remote sensing object detection. Full article

Steel surface quality critically determines the service safety and structural reliability of industrial products. Defects such as cracks, inclusions, patches, pitting, rolled-in scale, and scratches severely compromise product safety, making accurate and efficient detection a key step in quality control. However, the native A2C2f module in YOLOv13 exhibits insufficient multi-scale feature extraction for tiny defects and weak robustness under complex industrial backgrounds, hindering the detection of these six defect types. To address these gaps, we propose a multi-scale denoising enhanced module, A2C2f-MSDE, which constructs a multi-scale multi-kernel fusion branch (MSKF) with learnable adaptive weights, integrates a lightweight SEL channel attention and a DE denoising module, and employs a dual learnable residual scaling structure, while preserving the original multi-scale fusion architecture. We embed A2C2f-MSDE into the YOLOv13 backbone, perform ablation studies to verify each component’s contribution, compare it with mainstream advanced detectors on the public NEU-DET dataset, and conduct generalization tests on the GC10-DET dataset. Experiments on NEU-DET show that the improved YOLOv13n achieves mAP50-95 of 0.454 (9.4% relative gain over baseline, absolute gain 0.039), with mAP50 and mAP75 reaching 0.774 and 0.466, at an inference speed of 555 FPS, respectively, outperforming the compared mainstream models. On GC10-DET, mAP50 reaches 0.704, comparable to the baseline, maintaining stable overall detection capability, while mAP75 and mAP50-95 improve by 0.033 and 0.019, verifying the module’s performance advantages under high localization accuracy requirements and its cross-dataset generalization ability. The proposed module effectively balances detection accuracy and lightweight characteristics, providing a high-performance solution for industrial steel defect detection. Full article

Driven by global climate change and carbon reduction targets, nuclear energy has gained increasing prominence as a clean baseload power source. Enhancing the energy efficiency of key equipment in nuclear power plants is essential for achieving a low-carbon transition. This study addresses the trade-off between separation efficiency and pressure drop under multi-parameter coupling in hooked corrugated plate separators by proposing a multi-objective optimization strategy that integrates automated numerical simulation with data-driven optimization. An automated CFD framework was developed to efficiently generate a comprehensive dataset covering inlet velocity, droplet diameter, plate spacing, and hook length. A multilayer perceptron (MLP) surrogate model was then constructed, achieving high predictive accuracy with coefficients of determination (R²) of 0.95 for separation efficiency and 0.91 for pressure drop. Using the trained surrogate model, the NSGA-II algorithm was employed for multi-objective optimization, and the TOPSIS method was applied to identify the optimal compromise solutions. The results show that for representative droplet diameters of 5, 10, and 15 μm, the optimized structures improve separation efficiency by 25.71–29.14%. The integrated automated CFD–surrogate model–multi-objective optimization framework established in this study provides an efficient and generalizable approach for the design of gas–liquid separation equipment, contributing to energy consumption reduction in nuclear and process industries and supporting the realization of global carbon neutrality goals. Full article

Background/Objectives: Wrist cock-up orthoses are standard for work-related musculoskeletal disorders, yet consensus is lacking on whether commercial orthoses (COs) or custom-made thermoplastic orthoses (THs) better preserve function. While COs offer availability, THs provide a superior anatomical fit. This study evaluated dexterity and satisfaction in healthy female employees to establish a functional baseline for preventive strategies. Methods: Healthy female office workers with no prior musculoskeletal or neurological conditions participated in this randomized cross-over study. Manual dexterity was assessed at baseline and after each of two consecutive workdays, during which participants wore, in a randomized order, either a CO or a TH made by an expert physiotherapist. Outcome measures included the Functional Dexterity Test (FDT), recording time and errors, and the Client Satisfaction with Device (CSD-It) scale. Results: Twenty right-handed women (mean age 45.6 ± 11 years) participated. A significant difference in FDT completion times across conditions (χ² = 12.6, p = 0.002) was found. While both orthoses slowed performance compared to baseline (p < 0.01), the CO allowed for faster dexterity than the TH (p < 0.01). No differences were found in error rates. Regarding satisfaction, the CO achieved significantly better CSD-It scores than the TH (p = 0.0047), despite 60% of users reporting increased skin temperature with the CO. Final preferences were nearly evenly split (55% CO vs. 45% TH). Conclusions: Both orthoses impact manual dexterity without compromising precision. While the CO offered better execution speed and overall satisfaction, the TH version was preferred for prolonged skin tolerability. Selection should be individualized, balancing mechanical efficiency with the superior fit of custom-fabricated solutions in office environments. Full article

This study addresses the challenge of prioritizing Virtual Reality (VR) and Augmented Reality (AR) applications in Product Lifecycle Management (PLM) under multiple conflicting criteria. A comprehensive fuzzy Multi-Criteria Decision-Making (FMCDM) framework is proposed to support robust and unbiased decision-making. The methodology integrates multiple objective weighting techniques, including Entropy, Criteria Importance Through Intercriteria Correlation (CRITIC), Method based on the Removal Effects of Criteria (MEREC), and Standard Deviation, which are aggregated using the Bonferroni operator to obtain balanced criterion weights. The Fuzzy Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method is employed as the primary ranking approach, supported by comparative methods such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VIšekriterijumsko KOmpromisno Rangiranje (VIKOR), Evaluation based on Distance from Average Solution (EDAS), Weighted Aggregated Sum Product Assessment (WASPAS), and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) for validation. The results indicate that Virtual Reality Digital Prototyping and Design Review (A3) is the most preferred alternative, achieving the highest utility value (0.95267), followed by Augmented Reality-Assisted Assembly and Inspection Guidance (A1) and Augmented Reality-Supported Maintenance and Operator Training (A4). A high Stability Index of 0.9133 confirms robustness, and sensitivity analysis shows stable rankings. The framework provides a reliable and scalable decision-support system for smart manufacturing. Full article

Rectangular shield tunnels demonstrate significant advantages in underground space utilization due to their optimal cross-section efficiency and enhanced spatial functionality. Furthermore, their shallow overburden construction capability minimizes environmental impact and preserves subsurface resources. However, compared with circular shield tunnels, rectangular configurations exhibit greater susceptibility to longitudinal differential torsional deformation under asymmetric external loading. This deformation mechanism may induce excessive stresses in segments and connecting bolts, potentially causing joint offsets at tunnel rings that compromise structural integrity. This paper proposes a computational method for determining the longitudinal equivalent torsional stiffness of rectangular shield tunnels under combined compression–bending–torsion loading based on an equivalent continuum model. The proposed novel theoretical solutions were systematically validated against numerical simulations through comparative analysis. Parametric studies revealed that the effective ratio of longitudinal torsional stiffness increases proportionally with segment width-to-height ratio and bolt quantity while exhibiting inverse correlations with segment thickness and bolt equivalent shear length. The effective ratio of longitudinal torsional stiffness is directly correlated with compression–torsion ratios and bending–torsion ratios, with different load combinations significantly influencing torsional performance. Consequently, design optimizations incorporating increased bolt pre-tension forces or pre-stressed segment structures are proposed to improve torsional performance in rectangular shield tunneling systems. Full article

To address the power quality deterioration caused by the large-scale integration of grid-following (GFL) electric vehicle charging stations, this paper proposes a comprehensive assessment method based on relative entropy distance fusion weighting and a dynamic ideal solution VIKOR algorithm. First, a multi-dimensional power quality evaluation system is constructed, focusing on key indicators such as voltage deviation, frequency deviation, three-phase imbalance, and harmonic distortion, to accommodate the operational characteristics of vehicle-to-grid (V2G) under grid-following and grid-forming (GFM) interaction scenarios. Building on this, the three-scale analytic hierarchy process (AHP) is employed to determine subjective weights, while the divergence-maximized entropy weight method is used to derive objective weights. The relative entropy distance model is then applied to achieve adaptive fusion of subjective and objective weights, resulting in an optimal combined weighting. Subsequently, a dynamic ideal solution mechanism is introduced into the VIKOR algorithm, where the range of the ideal solution is adjusted based on the indicator weights to enhance the discrimination of key indicators. By comprehensively calculating the group utility value, individual regret value, and compromise evaluation index, accurate ranking and performance assessment of different mitigation schemes are achieved. Using measured data from a vehicle-grid interaction demonstration base for analysis, the results demonstrate that the proposed method can effectively quantify the actual effects of various mitigation schemes, providing decision-making support for power grid safety and stability under high penetration of renewable energy and converter-interfaced generation. Full article

Triangular mesh is one of the most widely used representations for 3D surfaces. However, high-resolution mesh models often contain a large number of triangles, leading to significant burdens in storage, transmission, and real-time rendering. Mesh simplification aims to reduce model complexity while preserving geometric fidelity and structural features. Classical methods, such as quadric error metrics (QEM), rely solely on local geometric errors, making them difficult to distinguish between redundant regions and structurally important features, often resulting in feature loss and topological degradation. To address these limitations, this study proposes a structure-aware triangular mesh simplification framework based on graph neural networks (GNNs)-guided QEM. GNNs are employed as a structural importance estimator to predict geometric saliencies of mesh edges. The predicted importances are incorporated into the classical QEM edge collapse cost through a soft modulation mechanism. Furthermore, a geometry-saliency-driven dynamic cost modulation strategy is designed, enabling the simplification process to prioritize critical features in early stages and gradually transition to global error minimization in later stages, without compromising the geometric optimality of QEM. In terms of model design, hybrid structural representation GNNs are constructed by integrating spectral geometry and a dual-branch architecture. Laplacian positional encoding is introduced to capture global topological information, while 1-hop and 2-hop message passing branches enable multi-scale representation of complex geometric structures. In addition, a staged inference strategy is adopted to dynamically update graph structural features during simplification, effectively mitigating topological drift. Experimental results on the TOSCA dataset demonstrate that the proposed method achieves stable performance across various simplification ratios. It consistently outperforms FQMS and QEM in terms of geometric error (\({P_{CD}}\)) and normal consistency (\({P_{NE}}\)). For structural preservation (\({P_{LE}}\)), the method shows advantages, with win-rates generally exceeding 90%. Moreover, it significantly improves the preservation of local geometric details at low to moderate simplification ratios. In summary, the proposed method effectively enhances local structural preservation while maintaining global geometric topology, providing an interpretable and practical solution for integrating learning-based structural awareness with classical geometric optimization in mesh simplification. Full article

Urban water-ecological governance in the Yellow River Basin is shifting from a single administratively dominated model toward a polycentric collaborative system. However, ambiguous responsibilities and overlapping tasks among governments, enterprises, and society often lead to governance conflicts, reduced coordination efficiency, and growing risks to regional ecological security. To address this challenge, this study develops a multi-actor governance analysis framework integrating deep learning, knowledge graphs, and complex network optimization. Stakeholder demands are extracted from multi-source data using a BERT-BiLSTM-CRF model, including policy documents, enterprise reports, and public discourse, and are then organized into a knowledge graph for water-ecological governance. A Relational Graph Attention Network (R-GAT) is subsequently used to transform the knowledge graph into a signed weighted network, enabling the measurement of conflict intensity and the identification of key conflict nodes across governance scenarios. Based on multi-objective optimization, a Pareto frontier is constructed to balance conflict tension, fairness, and governance efficiency, from which a compromise solution for responsibility weighting is identified. An empirical case study of a typical city in the Yellow River Basin shows that the proposed framework can identify core conflict nodes and provide quantitative support for conflict mitigation and coordination adjustment. The findings offer a quantitative reference for institutional innovation and evidence-based decision-making in urban water-ecological governance. Full article

The evaluation of inefficient industrial land is crucial for sustainable urban renewal, yet conventional methods are often compromised by applying a single uniform set of evaluation criteria that ignore local contextual patterns. We introduce a novel, pattern-sensitive framework that identifies distinct inefficiency patterns by interrelationships between evaluation indicators and land performance and calibrates expert-derived weights with data-driven insights. Using public access data for Xiaoshan District, Hangzhou, we establish an evaluation system via the Analytic Hierarchy Process (AHP). Subsequently, a novel iterative clustering method partitions parcels into segments sharing the same inefficiency pattern. Within each segment, a random forest model learns the local interrelationships from the data. This machine-learned information is then used to optimize the initial AHP weights, creating a unique evaluation system for each identified pattern. Results demonstrate that our optimization framework achieves Pearson correlations of 0.66–0.82 with ground-truth inefficiency across four identified patterns, outperforming traditional AHP-based models. Temporal validation on 2023 data confirms robustness of weights optimized on 2022 data, maintaining significant positive correlations (Pearson’s r = 0.58–0.66) with ground-truth inefficiency across all segments. By synergizing expert knowledge with machine learning, this study provides an accurate tool to formulate targeted urban renewal strategies that move beyond one-size-fits-all solutions. Full article

Tomato leaf diseases pose significant threats to crop yield and food security. However, in real-world cultivation environments, factors such as fluctuating illumination, varying leaf occlusion, and ambiguous lesion morphology often compromise detection accuracy. This paper presents the Gradient-aware Bidirectional Retentive Detection Transformer (GBR-DETR), a model designed for high-precision, real-time disease detection. This model is composed of two network structures and a retentive feature aggregation module: (1) a Multi-scale Gradient-Aware Transfer Network (MGAT-Net) is designed to encode gradient information through the Sobel operator, thereby enhancing the localization stability for small and blurry lesions; (2) a Bidirectional Context Pyramid Network (BCPN) is proposed to enable bidirectional interactions among multi-level features through a top-down and a bottom-up pathway, thereby generating multi-scale lesion features and bridging cross-scale semantic gaps; and (3) a Retentive Feature Aggregation Module (RFAM) is used to suppress background noise and establish global feature correlations, thereby enhancing the overall representation capability for lesion recognition. Experiments on the Multi-scenario Tomato Leaf Disease (M-TLD) dataset show that GBR-DETR yields gains of 3.12, 4.88, and 3.41 percentage points in mAP_50–95, mAP₅₀, and mAP₇₅, respectively, over the baseline RT-DETR, while also outperforming representative DETR-based and CNN-based detectors. The model demonstrates robust generalization on the PlantDoc cross-domain benchmark, achieving a 2.11% improvement in mAP₅₀ over the baseline. Deployed on the NVIDIA Jetson Orin Nano with TensorRT FP16, it achieves 54 ms latency, enabling real-time disease monitoring on edge devices. This solution provides effective technical support for real-time disease monitoring in smart agriculture. Full article

Selecting sustainable third-party logistics providers (3PLPs) is essential for enhancing competitiveness and sustainability performance. However, conventional approaches often fail to adequately capture sustainability dimensions and manage uncertainty arising from hesitant and incomplete information. To address these limitations, this study proposes a novel decision-making framework that extends the Triple Bottom Line by incorporating a technical dimension. The model integrates Dempster–Shafer theory for uncertainty modeling, Deng entropy for objective criteria weighting, Murphy’s combination rule for improved evidence aggregation, and the Combined Compromise Solution method for ranking. The core novelty lies in the unified integration of these techniques to simultaneously address uncertainty within a comprehensive evaluation framework. A case study, along with sensitivity and comparative analyses, demonstrates the effectiveness and robustness of the proposed approach, providing a reliable tool for sustainable 3PLP selection. Full article