Search Results (931)

Population aging has increased the need for public open spaces that older adults can use safely, comfortably, and confidently. In many urban parks and community squares, however, resting facilities are still designed as standardized street furniture, with cold materials, insufficient hand support, limited wheelchair-inclusive space, and weak support for everyday social interaction. This study examines age-friendly public facilities as micro-scale spatial elements that shape sitting, standing, staying, communication, and willingness to remain in small urban open spaces. Drawing on field observation, behavioral analysis, semi-structured interviews, and a multi-criteria design-evaluation process, the study identifies older adults’ key facility-use needs and translates them into design indicators and alternative facility schemes. The results show that physical support and inclusive spatial use are the most important design priorities. Standing-up assistance, sitting-posture support, perceived structural stability, and age-appropriate dimensional adaptation were more influential than purely decorative or auxiliary functions. Among the three alternative schemes, the modular pergola system performed best because it combined stable hand support, independent seating, an age-friendly interactive table, shaded resting space, wheelchair-inclusive layout, and wood-based sensory comfort. The sensitivity analysis further confirmed that this scheme maintained a stable advantage under most weight-adjustment conditions. The findings suggest that age-friendly public facility design should move beyond the improvement of individual furniture products and instead integrate bodily support, spatial accessibility, social interaction, material comfort, and environmental pattern quality. This study provides a design-decision framework for improving the inclusiveness, accessibility, and health-supportive capacity of urban public open spaces for older adults. Full article

This paper examines how inflow-state discretisation affects wake-loss and annual energy production (AEP) estimates for offshore wind farms. A reproducible workflow is presented for constructing weighted inflow-state ensembles from long-term offshore wind datasets using empirical wind-speed–direction occurrence frequencies. Hub-height wind speeds are reconstructed from multi-level wind data using a time-varying power–law shear exponent, after which the wind climatology is discretised using configurable directional sectors and wind-speed bins. The methodology was evaluated using both a controlled synthetic wind dataset and offshore climatological datasets processed through the same inflow-state and wake-modelling workflow. The analysis quantified how directional resolution, wind-speed bin width, and sector-mean inflow representations affect predicted turbine power, wake loss, and AEP relative to empirical reference cases. For the synthetic dataset, replacing the within-sector wind-speed distribution with a single sector-mean wind speed produced an annual power difference of 12.58%, with seasonal differences ranging from 6.66% in JJA to 13.91% in DJF. Offshore wake-model calculations showed the same overall behaviour. Reducing the empirical inflow-state ensemble from 1593 to 416 retained states changed annual AEP by only 0.03% and wake loss by $-0.03$ percentage points, whereas the sector-mean inflow representation increased predicted AEP by 18.40% and wake loss by 5.13 percentage points relative to the empirical reference case. The results show that preserving the within-sector wind-speed distribution has a larger influence on predicted wake loss and AEP than moderate reductions in retained state count or directional resolution for the datasets and layouts considered here. Empirical inflow-state ensembles using 36 directional sectors together with 1 $\mathrm{m}$${\mathrm{s}}^{-1}$ or 2 $\mathrm{m}$${\mathrm{s}}^{-1}$ wind-speed bins remained within 0.03% of the higher-resolution annual AEP reference while reducing the number of retained inflow states by approximately 74%, with a corresponding reduction in the number of wake-model evaluations required. Full article

Underground gas control is a critical link in coal mine safety production, and drilling robots serve as the core equipment for gas extraction drilling operations. However, the autonomous locomotion technology of coal mine drilling robots has long been constrained by the unstructured underground environment and the limitations of existing navigation schemes, which restrict their intelligent application. To address this bottleneck, this paper conducts systematic research on key autonomous navigation technologies for coal mine drilling robots operating in narrow underground working faces, focusing on their practical operational requirements. First, a hardware scheme complying with coal mine safety standards is selected, the hardware structure and sensor layout are optimized via digital modeling, and the software interface and data interface format of the navigation system are designed. Second, an innovative 3D point cloud-based offline obstacle avoidance algorithm is proposed, which integrates a terrain analysis module, a local path planning method with maximum arrival probability, a Bézier curve-based trajectory library generation strategy, and a trajectory index construction method. Finally, simulation experiments, ground-simulated roadway field tests, and underground coal mine field experiments are performed to validate the proposed system. Experimental results demonstrate that the constructed autonomous navigation system enables smooth and safe autonomous locomotion and fixed-point parking of drilling robots, with an average parking error lower than 0.17 m, and can effectively avoid obstacles in complex environments. This research provides crucial technical support for the intelligent advancement of coal mine drilling robots. Full article

This paper develops a theoretical model to analyze the protective effect and nonlinear mechanism of preferential rules of origin (ROOs) on regional supply chains amid global value chain restructuring and rising regional supply chain security demands. Supported by numerical simulations and a triple difference-in-differences (DDD) empirical approach based on the China–ASEAN Free Trade Agreement (CAFTA), the findings reveal a nonlinear, inverted U-shaped relationship between ROO stringency and supply chain stability—exhibiting a typical Laffer curve characteristic. Moderate restrictions significantly promote intra-regional intermediate goods procurement and stabilize regional supply chain layout, while excessively stringent rules raise enterprise compliance costs and restrain integration. These findings carry important implications for regional economic resilience and sustainable development. While our empirical analysis focuses on economic resilience (measured through regional procurement stability), we discuss how well-designed ROO may also support broader sustainability goals, including contributions to SDG 8 (Decent Work and Economic Growth) and SDG 17 (Partnerships for the Goals) through more stable and inclusive regional production networks. The study highlights the need for careful calibration of ROO stringency to balance protective effects with compliance costs in pursuit of both resilient and sustainable regional trade governance. Full article

Against the backdrop of deep digital economic integration, the synergistic agglomeration of new quality productive forces (NQPFs) and digital transformation (DT) has become a key engine for regional high-quality development. Based on data from 31 Chinese provinces during 2011–2023, this study measured the synergistic level of NQPF and DT. Using a modified gravity model, we convert attribute data into relational data and analyze driving mechanisms via social network analysis and quadratic assignment procedures. The results show that the synergistic agglomeration network presents club convergence rather than homogeneous dispersion, forming a structure comprising “polar-core absorption, hub transmission, hinterland integration, and peripheral marginalization.” Eastern regions act as net beneficiaries; Guangdong, Fujian, and other hubs become net-spillover brokers; central and western regions achieve element equilibrium, yet traditional industrial bases face a widening digital divide. Targeted policy implications are proposed. This study provides references for breaking regional digital barriers and optimizing the spatial layout of high-quality development. Full article

Accurate performance prediction for large offshore wind turbines requires a principled treatment of uncertainty in both the wind resource and the rotor design parameters. In the present work, we develop a surrogate-based, multi-level uncertainty quantification (UQ) framework coupling a physics-based Blade Element Momentum (BEM) solver with a spectral Polynomial Chaos Expansion (PCE) surrogate that replaces the expensive Monte Carlo loop and apply it to the IEA 15 MW offshore reference wind turbine. The framework is completed by Sobol variance-based global sensitivity analysis. The contribution is methodological rather than algorithmic: although each individual ingredient (PCE, Sobol, BEM, and Jensen) is well established, their joint deployment in a single, internally consistent, end-to-end probabilistic workflow that simultaneously delivers (i) aerodynamic–structural UQ with analytical Sobol ranking, (ii) a like-for-like cross-comparison of three reference turbines, (iii) a quantitative leading-edge icing degradation study, and (iv) a farm-level wake-steering optimization on the same IEA 15 MW reference rotor yields a unified probabilistic envelope from which manufacturing tolerances, cold-climate investment thresholds, and farm-layout/control trade-offs can be read off consistently. Five input parameters are treated as random variables: hub-height wind speed (Weibull, k = 2.2, c = 9.8 m/s), air density, blade chord length, twist angle, and rotor speed. A degree-4 sparse PCE is built by non-intrusive spectral projection using N = 5000 Sobol quasi-random realizations, which allows the Sobol indices to be recovered analytically from the expansion coefficients at essentially no extra cost. Three parallel engineering studies complement the core UQ analysis: (A) a head-to-head comparison of the NREL 5 MW, DTU 10 MW, and IEA 15 MW reference turbines; (B) a quantitative assessment of leading-edge ice accretion at four severity levels; and (C) a Jensen-based wake optimization for a 25-turbine offshore array with static wake steering. The main results are as follows: the turbine reaches C_p,max = 0.480 at λ_opt = 8.51, and an annual energy production (AEP) of 71,261 MWh/year (PCE: 70,840 ± 2,140 MWh/year, 95% CI). Wind speed emerges as the dominant driver of C_p variance (S₁ = 0.412), followed by blade twist (0.198) and chord (0.143). Severe icing (30 kg/m) reduces C_p by 18.2% and increases the blade-root Damage Equivalent Load (DEL) by 18.5%. For the array, the optimal spacing (s_x = 8D, s_y = 6D) gives a farm efficiency of 89.6% and 1296 GWh/year, and a 15° wake-steering offset adds a further +3.2% to farm AEP. Compared with plain Monte Carlo, the sparse PCE delivers the same statistics with about 36% fewer model evaluations and a relative error below 0.8%. Full article

At present, research on the long-term stability of multi-cavern coordinated injection–production operations for salt cavern compressed air energy storage (CAES) remains limited. Large-capacity energy storage utilizing multiple interconnected salt caverns has become an inevitable development trend for modern CAES power stations, highlighting the necessity and importance of stability evaluation and design optimization for underground salt cavern storage clusters. Based on the Anning 350 MW CAES demonstration project, this paper takes the abandoned salt caverns of the project as research objects. A three-dimensional geological and cavern model is established using the FLAC3D numerical simulation method, and stability analysis is carried out under static conditions and three long-term gas injection and production scenarios (the pressure conditions are provided by ground-based equipment). The characteristics of the plastic zone, displacement, stress distribution, and volume shrinkage of the caverns are systematically investigated. The results show that under static conditions, the internal pressure significantly controls the development of the plastic zone, and the caverns are generally stable at pressures above 4 MPa. During long-term operation, the plastic zones of each cavern gradually expand, displacements accumulate continuously, and stresses tend to stabilize after an initial accumulation period. After 30 years of operation, no through-going plastic zones appear in any cavern, and all volume shrinkage rates are below 30%. Among the three cases, Case 1 exhibits the best stability, while enhanced monitoring is required for local high-stress regions in Case 3. This study verifies that the salt cavern development for the Anning CAES project is safe and controllable during long-term operation. The layout spacing of caverns is reasonably designed and fully satisfies the stability requirements of salt cavern CAES power stations. The research results can provide a technical guarantee for the construction of the first CAES power station in Yunnan Province and also offer a reliable reference for the design and construction of similar multi-cavity salt cavern CAES projects. Full article

Wind farm layout optimization (WFLO) plays a key role in reducing wake effect energy losses and increasing annual energy production (AEP). This paper proposes a double-stage optimization approach that incorporates staggered grid-based optimization with coordinate-based local optimization. In the first stage, staggered grid-based optimization is performed to determine optimal turbine locations within predefined grid boundaries. In the second stage, turbine positions are locally optimized within bounded regions to improve AEP efficiently without extending the search across the entire wind farm. The modified electric charged particle optimization (MECPO) algorithm is applied to evaluate five optimization approaches, including two double-stage and three single-stage approaches. The framework is tested on a wind farm covering an area of 2000 m by 2000 m with 20 turbines under single-direction, uniform multi-directional, and spatially varying wind conditions. The proposed double-stage optimization approach achieves comparable or improved net AEP while significantly reducing computational cost across different wind conditions. The method provides up to 0.36% improvement in net AEP, reduces wake losses by up to 6.84%, and decreases computational time by up to 90% compared with the coordinate-based approach. These results confirm that the proposed approach significantly enhances computational efficiency while maintaining comparable energy performance. The findings indicate that integrating staggered grid-based optimization with coordinate-based local optimization provides an effective balance between solution quality and computational efficiency, offering a practical and scalable approach for WFLO. Full article

As a clean and renewable energy source, wind energy offers lower development and utilization costs than solar energy, making it the most promising renewable option. However, the carbon footprint of offshore wind power and its external impacts on cross-regional carbon emissions have not been investigated sufficiently. Using the provinces of Guangdong and Jiangsu as case studies, this study employs socioeconomic and environmental statistical data. It applies the environmentally extended multi-regional input–output (EE-MRIO) method to quantify cross-regional environmental spillover effects associated with offshore wind power development. The findings show that China’s power structure has been continuously optimized, with offshore winds achieving leapfrog growth since 2010. Through a “local consumption” model, offshore wind power in Guangdong and Jiangsu has effectively replaced coal-fired generation, substantially reducing carbon emissions locally and in neighboring areas. Jiangsu has reduced CO₂ emissions by 16.72 million tons annually, and Guangdong by about 7.23 million tons annually. Furthermore, offshore wind development drives the green transformation of upstream industries (e.g., steel, non-ferrous metals, and chemicals). It extends carbon-reduction benefits to resource-rich regions such as the Northwest and North China. As major manufacturing hubs, both provinces lowered the embodied carbon intensity of their export products by using clean electricity, thereby indirectly reducing the national carbon footprint through cross-regional trade. This study offers scientific insights to help policymakers optimize offshore wind layouts, facilitate coordinated regional emission reductions, and advance sustainable energy transitions. Full article

Promoting the high-quality development of farmers’ specialized cooperatives and narrowing regional development gaps is critical for advancing China’s rural revitalization strategy. Based on provincial panel data covering 30 Chinese regions from 2015 to 2023, this paper constructs a five-dimensional evaluation index system including standardized operation, operational performance, service scope, driving effect, and industrial upgrading, and adopts the entropy weight method to quantify the comprehensive development level of cooperatives. By combining spatial autocorrelation, kernel density estimation, the Dagum Gini coefficient and the Geodetector model, this paper explores the spatio-temporal evolution, regional disparities and multi-factor coupled driving mechanism of cooperative development. The main findings are as follows: (1) While the total quantity of cooperatives keeps expanding nationwide, their overall development level presents an evolutionary feature of declining first and then rising; industrial upgrading gradually becomes a new growth engine, whereas operational performance and driving effect slip downward. (2) The spatial layout of cooperatives maintains a typical pyramid structure; high-value agglomeration shifts from the Yangtze River Delta to southeast coastal regions, and low-value clusters are persistently concentrated in Northeast China. (3) The overall Dagum Gini coefficient reflects widening-then-shrinking regional gaps, and intra-eastern provincial differences constitute the primary source of nationwide spatial divergence. (4) Household consumption and rural labor force stock serve as core driving factors; regional economic development, agricultural production efficiency, rural human capital and land resource allocation form a coupled driving system, and all explanatory variables show mutual enhancement effects without offsetting interactions. Targeted policy suggestions are put forward to realize balanced and high-quality development of farmers’ specialized cooperatives across China. Full article

Timely fish disease monitoring is an important requirement for sustainable aquaculture because disease outbreaks can reduce survival, increase treatment inputs, and destabilise production. In aquaponic systems, fish health is also linked to nutrient cycling and the stability of integrated fish–vegetable production, making automated fish-health perception a potentially useful component of resource-efficient farming. Existing classification and detection methods can identify disease categories or approximate lesion locations, but they provide limited information about lesion area, boundary shape, and severity-related spatial extent. This study presents a deep learning framework for pixel-level fish lesion segmentation to support sustainable aquaculture health monitoring, with aquaponic systems considered as a potential application context. The framework combines lesion-preserving frequency augmentation (LPFA), confidence-guided large-kernel encoding (CGLE), and confidence-filtered decoder refinement (CFDR). LPFA expands lesion appearance variation during training while retaining the main lesion layout. CGLE uses coarse prediction confidence to allocate broader contextual modelling to uncertain encoder regions, and CFDR applies selective decoder correction to low-confidence regions. A public freshwater fish disease dataset is reformulated into a dense prediction task with 1750 raw images from seven image-level categories, including six disease categories and one normal healthy category. The images are divided into training, validation, and test subsets at an 8:1:1 ratio, and controlled augmentation strategies are applied online rather than being used to create a larger static dataset. Across five random-seed runs, the proposed method achieves $82.6\pm 0.3$% mIoU, $90.9\pm 0.2$% mDice, and $73.5\pm 0.4$% Boundary IoU. Relative to TransUNet, the mean mIoU rises from $78.4\pm 0.4$% to $82.6\pm 0.3$%, and Boundary IoU rises from $68.8\pm 0.5$% to $73.5\pm 0.4$%, with paired bootstrap testing supporting the stability of the improvement. These results indicate its potential as a lesion-quantification decision-support component for smart and sustainable fish-production systems. Full article

Rain gauge networks are the core infrastructure for hydrological and water resource monitoring, flood control and disaster mitigation early warning, and water resource planning and regulation. The rationality of their layout directly determines the accuracy, representativeness, and economy of regional precipitation data acquisition. Considering that information entropy can accurately characterize the spatial distribution law and information complexity of rainfall, and spatiotemporal deep learning models have strong capabilities in fitting spatiotemporal features, this paper couples mutual information entropy with a spatiotemporal deep learning model and proposes a novel optimal layout method for rain gauge networks. Daily observed rainfall data from 50 ground-based rain gauges in the upper reaches of the Tuojiang River during 2015–2024, as well as the PERSIANN-CCS remote sensing precipitation product for the same period, were used in the study. A CNN-LSTM spatiotemporal deep learning model integrating spatial features and temporal dependence was constructed, coupled with the mutual information entropy index, and the GA-PSO hybrid optimization algorithm was applied for solution. The superiority of the proposed method was verified by comparison with the calculation results of the traditional mutual information entropy-based greedy optimization algorithm. The results show that the hybrid optimization algorithm driven by the spatiotemporal deep learning model coupled with mutual information entropy is significantly superior to the comparison algorithm in terms of the rationality of the station network structure, the ability to characterize spatial rainfall distribution, the control of average relative error, and the improvement of total information entropy. After optimization, the number of rain gauges in the upper reaches of the Tuojiang River can be reduced from 50 to 25. While greatly reducing the number of stations, the optimized network can still relatively accurately reflect the spatiotemporal characteristics of rainfall in the basin, which can provide a theoretical basis and technical support for the optimal layout of basin rain gauge networks and water resource management. Full article

Transitioning toward efficient and digital industrial design requires preliminary tools that support early decision-making in plant layout studies. This qualitative and exploratory study analyzes an Artificial Intelligence (AI)-assisted and Augmented Reality (AR)-supported workflow within the Systematic Layout Planning (SLP) framework for the preliminary spatial evaluation of a cosmetic emulsion production plant. The study was developed as a case study based on a previously reported layout for obtaining cosmetic emulsions from Amazonian oils. A top-view layout was examined through structured prompts aligned with SLP criteria, including product journey, activity relationships, relational diagrams, and space requirements. ChatGPT was used only as a qualitative reasoning assistant, without optimization, prediction, mathematical modeling, or algorithmic functions. After the AI-assisted review, the refined layout was represented in three dimensions and visualized through AR in a real environment. The results identified potential improvements related to operational flow, traceability, critical area relationships, and spatial organization. AR-assisted visualization provided preliminary visual evidence of compatibility between the refined layout and the selected site, supporting an early review of circulation, access, and volumetric behavior. The sequential integration of SLP, AI, and AR is proposed as an exploratory workflow for early-stage layout evaluation, pending future quantitative validation studies and expert technical review. Full article

This paper presents a rule-based LVS-driven methodology for parasitic RC extraction from CMOS layouts for post-layout SPICE simulation. The proposed approach operates directly within foundry-qualified rule environments, ensuring consistency with Process Design Kits (PDKs) and enabling seamless integration with existing design and verification flows without requiring field-solver execution during the production extraction flow. The methodology provides a generalized framework for deriving electrical parameters from layout geometries and is applicable to interconnects, contacts, vias, and gate structures in multilayer CMOS technologies. By decomposing conductive regions into directional components and applying geometric and Boolean operations, the method captures the impact of layout topology and process-dependent features on circuit-level behavior. In addition, a model-order reduction technique based on π-equivalent representations is introduced to simplify the resulting networks while preserving timing accuracy. This enables the scalable simulation of complex layouts with reduced computational overhead. The proposed framework supports layout optimization, variability-aware design, and process-technology co-design, particularly for mature and advanced planar nodes. The methodology is evaluated using register-file layout test cases and post-layout SPICE simulations. The results show that the proposed rule-based extraction and RC-merging flow preserve timing behavior while reducing netlist complexity. Full article

Multiplication is a fundamental operation in digital signal processing, embedded computing, and nanoscale arithmetic data paths, where area, delay, and energy efficiency are critical design constraints. However, nanoscale multiplier design is challenged by high interconnect complexity, frequent wire crossings, clock-zone synchronization issues, and the rapid growth of area and latency with operand size. Quantum-dot cellular automata (QCA) technology offers a promising post-CMOS platform for compact arithmetic circuit realization through field-coupled computation and transistor-free switching. This paper presents a single-layer QCA-based Dadda Tree Multiplier (DTM) using layout-aware integration of compact half-adder, full adder, XOR, and carry-skip adder modules. The proposed design emphasizes partial-product compression, routing compactness, clock-aware organization, and area-efficient final accumulation. Functional verification is performed using QCADesigner 2.0.3, while energy-related behavior is evaluated using QCADesigner-E under the conventional QCA simulation framework. The proposed DTM consists of 4282 cells and occupies 6.14 μm². Compared with a recent compact QCA multiplier baseline, the proposed architecture reduces cell count by 59.12% and occupies area by 39.80%, while maintaining competitive clocking latency. These results indicate that layout-aware integration of arithmetic modules can substantially improve the area efficiency of QCA-based multipliers, making the proposed design a compact arithmetic core for future nanoscale embedded and signal-processing systems. Full article