Search Results (4,110)

In freeform optical metrology, wavefront fitting over non-circular apertures is hindered by the loss of Zernike polynomial orthogonality and severe sampling grid distortion inherent in standard conformal mappings. To address the resulting numerical instability and fitting bias, we propose a unified framework curve-shortening flow (CSF)-guided progressive quasi-conformal mapping (CSF-QCM), which integrates geometric boundary evolution with topology-aware parameterization. CSF-QCM first smooths complex boundaries via curve-shortening flow, then solves a sparse Laplacian system for harmonic interior coordinates, thereby establishing a stable diffeomorphism between physical and canonical domains. For doubly connected apertures, it preserves topology by computing the conformal modulus via Dirichlet energy minimization and simultaneously mapping both boundaries. Benchmarked against state-of-the-art methods (e.g., Fornberg, Schwarz–Christoffel, and Ricci flow) on representative irregular apertures, CSF-QCM suppresses area distortion and restores discrete orthogonality of the Zernike basis, reducing the Gram matrix condition number from >900 to <8. This enables high-precision reconstruction with RMS residuals as low as $3\times {10}^{-3}\lambda$ and up to 92% lower fitting errors than baselines. The framework provides a unified, computationally efficient, and numerically stable solution for wavefront reconstruction in complex off-axis and freeform optical systems. Full article

The dairy industry requires effective non-thermal processing strategies capable of ensuring microbial safety while preserving the nutritional and bioactive quality of milk. This study describes a novel milk decontamination approach based on selective ionic removal by dialysis, resulting in a controlled reduction in ionic strength. Milk deionization significantly reduced the microbial load in raw bovine milk to levels comparable to those achieved by conventional thermal pasteurization, while largely preserving its physicochemical composition. Ionic depletion enhanced the antimicrobial effectiveness of endogenous milk components; this effect was abolished when native salt concentrations were maintained, highlighting the key role of ionic modulation in microbial control. Major milk constituents, including proteins, fat, and solids-not-fat, were not substantially affected by deionization, whereas low-molecular-weight solutes such as lactose and urea were partially removed. Deionized milk also exhibited improved stability during refrigerated storage, as evidenced by delayed acidification compared with raw and pasteurized milk. Overall, these results demonstrate that milk deionization represents a feasible proof-of-concept non-thermal alternative to pasteurization based on ionic modulation, with potential applications in dairy processing and human milk preservation, where maintenance of bioactive components is particularly desirable. Full article

Against the backdrop of accelerating urbanisation in China, the urban-rural divide continues to widen, while cave dwellings along the Yellow River have been largely abandoned, facing the challenge of cultural erosion. This study breaks from conventional conservation approaches by empirically exploring the viability of living heritage in promoting sustainable rural revitalisation and integrated urban-rural development. Employing participatory action research, it engaged multiple stakeholders—including villagers, returning migrants, and urban designers—across 60 villages in the middle reaches of the Yellow River. This collaboration catalysed a “collective-centred” adaptive reuse model, generating multifaceted solutions. The case of Fangshan County’s transformation into a cultural ecosystem demonstrates how this model simultaneously fosters endogenous social cohesion, attracts tourism resources and investment, while disseminating traditional culture. Quantitative analysis using the Yao Dong Living Heritage Sensitivity Index (Y-LHSI) and Living Heritage Transmission Index (Y-LHI) indicates that the efficacy of collective action is a decisive factor, revealing an inverted U-shaped relationship between economic development and cultural preservation. The findings further propose that living heritage regeneration should be reconceptualised from a purely technical restoration task into a viable social design pathway fostering mutually beneficial urban-rural symbiosis. It presents a replicable “Yao Dong Solution” integrating cultural sustainability, community resilience, and inclusive economic development, offering insights for achieving sustainable development goals in similar contexts across China and globally. Full article

Freeze-thaw (F-T) cycles in seasonally frozen regions induce progressive volumetric strains leading to degradation of soils’ mechanical properties and performance of earthen infrastructure. Conventional chemical stabilization techniques often are not adaptive to cyclic thermal stresses and do not address the fundamental phase changes of porous media, underscoring the need for sustainable alternatives. This study explores the potential of extracellular polymeric substances (EPS) produced by the psychrophilic bacterium Polaromonas hydrogenivorans as a bio-mediated soil treatment to enhance freeze-thaw durability. Two EPS formulations were examined—EPS 1 (high ice-binding activity) and EPS 2 (low ice-binding activity)—to evaluate their effectiveness in improving volumetric stability and thawing strength of silty soil subjected to ten F-T cycles. Tests were conducted at four moisture contents (12%, 18%, 24%, and 30%) and three EPS concentrations (3, 10, and 20 g/L). Volumetric strain measurements quantified freezing expansion and thawing contraction, while unconfined compressive strength assessed post-thaw mechanical integrity. The untreated soils exhibited maximum net volumetric strains (γ_Net) of 5.62% and only marginal strength recovery after ten F-T cycles. In contrast, EPS 1 at 20 g/L mitigated volumetric changes across all moisture contents and increased compressive strength to 191.2 kPa. EPS 2 yielded moderate improvements, reducing γ_Net to 0.98% and enhancing strength to 183.9 kPa at 30% moisture. Lower EPS concentrations (3 and 10 g/L) partially mitigated volumetric strain, with performance strongly dependent on moisture content. These results demonstrate that psychrophilic EPS, particularly EPS 1, effectively suppresses ice formation within soil pores and preserves mechanical structure, offering a sustainable, high-performance solution for stabilizing frost-susceptible soils in cold-regions. Full article

The increasing demand for air travel has intensified the need for more efficient air traffic management (ATM) solutions. One of the key challenges in this domain is the optimal sectorization of airspace to ensure balanced controller workload and operational efficiency. Traditional airspace sectors, typically static and based on historical flow patterns, often fail to adapt to evolving traffic complexity, resulting in imbalanced workload distribution and reduced system performance. This study introduces a novel methodology for optimizing ATC sector geometries based on air traffic complexity indicators, aiming to enhance the balance of operational workload across sectors. The proposed optimization is formulated in the horizontal plane using a two-dimensional cell-based airspace representation. A graph-partitioning optimization model with spatial and operational constraints is applied, along with a refinement step using adjacent-cell pairs to improve geometric coherence. Tested on real data from Madrid North ACC, the model achieved significant complexity balancing while preserving sector shapes in a real-world case study based on a Spanish ACC. This work provides a methodological basis to support static and dynamic airspace design and has the potential to enhance ATC efficiency through data-driven optimization. Full article

Population aging is driving the need for unobtrusive, continuous monitoring solutions in residential care environments. Radio-frequency (RF)-based technologies such as Ultra-Wideband (UWB) and millimeter-wave (mmWave) radar are particularly attractive for providing detailed information on presence and movement while preserving privacy. Building on a UWB–mmWave localization system deployed in a senior living residence, this paper focuses on the data-processing methodology for extracting quantitative mobility indicators from long-term indoor monitoring data. The system combines a device-free mmWave radar setup in bedrooms and bathrooms with a tag-based UWB positioning system in common areas. For mmWave data, an adaptive short-term average/long-term average (STA/LTA) detector operating on an aggregated, normalized radar energy signal is used to classify micro- and macromovements into bedroom occupancy and non-sedentary activity episodes. For UWB data, a partially constrained Kalman filter with a nearly constant velocity dynamics model and floor-plan information yields smoothed trajectories, from which daily gait- and mobility-related metrics are derived. The approach is illustrated using one-day samples from three users as a proof of concept. The proposed methodology provides individualized indicators of bedroom occupancy, sedentary behavior, and mobility in shared spaces, supporting the feasibility of combined UWB and mmWave radar sensing for longitudinal routine analysis in real-world elderly care environments. Full article

The study addresses the assessment of environmental sustainability in agriculture, noting that the existing scientific literature has predominantly focused on negative environmental impacts, particularly greenhouse gas emissions from the livestock sector. It argues that a comprehensive evaluation of farming systems should go beyond impact-based metrics and instead compare the demand and supply of natural capital, using appropriate methodologies such as the ecological footprint (EF). Accordingly, the objective of the study is to analyze the environmental sustainability of fattening pig farming systems in Italy by applying the EF to compare a virtuous case-study farm (located in Umbria, 72.4 ha of utilized agricultural area, and 1960 pigs per year) with a representative sample of ninety-four specialized pig-fattening farms drawn from the Italian FADN 2023 database. The results show the following marked differences between the two systems: the case study exhibits a positive ecological balance (EB = +50.1 gha; IEP = +0.69 gha/ha), while the FADN sample displays, on average, a negative ecological balance (EB = −167.6 gha) and a strongly negative sustainability index (IEP = −3.84 gha/ha). These findings indicate that, in a sector characterized by generalized environmental unsustainability, the preservation of natural capital can be achieved not only through low-impact technical solutions, but also by addressing structural factors (e.g., livestock density per unit area and the presence of non-productive land uses). Overall, the study demonstrates that sustainability assessment requires explicitly comparing natural capital demand and supply, rather than merely quantifying emissions. Full article

This paper proposes a Federated Multi-Agent Deep Reinforcement Learning (FMADRL) framework to enhance the resilience of highway service area microgrids against extreme weather events. The method integrates Generative Adversarial Networks with Monte Carlo simulations to generate high-fidelity weather scenarios, enabling privacy-preserving collaborative optimization across distributed microgrids. A multi-objective approach using the Ripple-Spreading Algorithm yields balanced solutions for economic efficiency, reliability, and response speed. Large-scale simulations demonstrate significant improvements: the proposed method achieves an 88.3 score on the comprehensive system resilience metric, reduces the average fault recovery time from 46.6 min to 8.4 min, lowers annual operating costs by 69.3%, equivalent to 536,945.1 USD, and achieves annual carbon emissions reductions of 285 Mg. This approach provides an innovative solution for enhancing the resilience of distributed microgrids during extreme weather events. Full article

Accurate registration of optical and synthetic aperture radar (SAR) images is a fundamental prerequisite for multi-source remote sensing data fusion and analysis. However, due to the substantial differences in imaging mechanisms, optical–SAR image pairs often exhibit significant radiometric discrepancies and spatially varying geometric inconsistencies, which severely limit the robustness of traditional feature or region-based registration methods in cross-modal scenarios. To address these challenges, this paper proposes an end-to-end Optical–SAR Registration Network (OSR-Net) based on multi-constraint joint optimization. The proposed framework explicitly decouples cross-modal feature alignment and geometric correction, enabling robust registration under large appearance variation. Specifically, a multi-modal feature extraction module constructs a shared high-level representation, while a multi-scale channel attention mechanism adaptively enhances cross-modal feature consistency. A multi-scale affine transformation prediction module provides a coarse-to-fine geometric initialization, which stabilizes parameter estimation under complex imaging conditions. Furthermore, an improved spatial transformer network is introduced to perform structure-preserving geometric refinement, mitigating spatial distortion induced by modality discrepancies. In addition, a multi-constraint loss formulation is designed to jointly enforce geometric accuracy, structural consistency, and physical plausibility. By employing a dynamic weighting strategy, the optimization process progressively shifts from global alignment to local structural refinement, effectively preventing degenerate solutions and improving robustness. Extensive experiments on public optical–SAR datasets demonstrate that the proposed method achieves accurate and stable registration across diverse scenes, providing a reliable geometric foundation for subsequent multi-source remote sensing data fusion. Full article

This study presents an Adaptive PID controller designed to enhance temperature stability and energy performance in household refrigerator systems subject to non-stationary disturbances. Classical PID control is limited by fixed gains and the assumption of linear time-invariant dynamics, which is frequently violated by door opening, load variation, and compressor cycling. To address this issue, the proposed approach introduces a Laplace-distribution-based adaptive gain function L(t) that adjusts controller sensitivity according to the statistical rarity of the composite temperature error. The method preserves the conventional PID control structure while introducing a lightweight gain-scaling mechanism suitable for embedded implementation. Experimental validation using a commercial two-compartment refrigerator demonstrated substantial improvements in performance compared with a classical PID controller. The Adaptive PID achieved reduced temperature deviations in both compartments, significantly smoother compressor and fan actuation, and a 4.6% reduction in total energy consumption under an identical disturbance schedule. These results confirm that the proposed controller provides a practical, embedded-friendly solution that improves thermal regulation, actuator longevity, and energy efficiency under the tested disturbance schedule representative of typical household usage. Full article

This study presents a comprehensive structural optimization workflow for a railway motor bogie frame, aimed at developing an innovative and lightweight design compliant with the reference European standards. The methodology integrates a two-stage topology optimization process, supported by an extensive numerical simulation campaign and a dedicated sensitivity analysis to identify the most critical load scenarios. In the first optimization stage, a global evaluation of the frame performance revealed that increasing the number of optimization parameters leads to a rise of approximately 50% in solver iterations. Symmetry constraints proved essential for simplifying both the optimization and the subsequent geometric reconstruction. The minimum feasible feature dimension strongly affected the final solution, modifying the material distribution and enabling a mass reduction of about 18%. The second optimization stage, focused on the cross beams, highlighted the relevance of manufacturing constraints in guiding the solver toward practical configurations. Static and fatigue assessments confirmed stress distributions consistent with the original frame, providing designers with a reliable basis for future material upgrades. Finally, the dynamic analysis showed a first natural frequency above 60 Hz, with variations in the first eigenvalue within 1% and preservation of the local flexural mode shape, ensuring full compatibility with the original frame interfaces and enabling seamless replacement with the optimized configuration. Full article

Low-salt cured fish is prone to deterioration due to lipid oxidation and microbial proliferation during refrigeration. Chlorogenic acid (CGA), with excellent antioxidant and antimicrobial activity, is a promising candidate for the preservation of cured fish. However, its instability in the presence of environmental factors significantly confines its direct application. In this research, CGA was encapsulated in liposomes and utilized as a dip-coating for cured fish. The effects of varying concentrations of CGA-loaded liposomes (L-CGA) coating on the physicochemical quality and microbial diversity of cured fish were rigorously compared to those treated with CGA solutions, blank liposomes, and distilled water throughout 32 days’ storage at 4 °C. The results showed that L-CGA exhibited a higher lipid oxidation-inhibiting capacity (generation of hydroperoxides and their secondary oxidation products) than the corresponding free CGA at fixed concentrations. Furthermore, the liposomal formulation showed significantly enhanced inhibitory activity against dominant spoilage-associated bacterial genera (e.g., Staphylococcus, Macrococcus, and Rothia), with the L-CGA loaded at 800 mg/L of CGA showing optimal effectiveness. This enhanced preservation effect can be attributed to the protective and controlled release properties of the liposomes, which facilitate improved preservation outcomes for CGA. These findings demonstrate that L-CGA could be used as a promising preservative for low-salt cured fish or some similar products. Full article

The growing global reliance on solar photovoltaic (PV) systems requires robust, automated inspection techniques to ensure reliability and efficiency. Thermal infrared (IR) imaging is widely used for detecting PV faults; however, accurate classification remains challenging due to severe class imbalance, low thermal contrast, and high inter-class visual similarity among fault types. This study proposes a hierarchical deep learning framework for thermal PV fault classification, integrating a multi-class dataset-balancing strategy to enhance representational efficiency. The proposed framework consists of two major components: (i) a hierarchical two-stage classification scheme that mitigates data imbalance and leverages limited labeled data for improved fault discrimination; and (ii) a contrast-preserving MixUp augmentation technique designed explicitly for low-contrast thermal imagery, improving minority fault class recognition and overall robustness. Comprehensive experiments were conducted on benchmark 8-class thermal PV datasets using nine deep network architectures. Dataset refactoring decisions are validated through quantitative inter-class distance analysis using multiple complementary metrics. Results demonstrate that the proposed hierarchical SlantNet model achieves the best trade-off between accuracy and computational efficiency, achieving an F1-Efficiency Index of 337.6 and processing 42,072 images per second on a GPU, over twice the efficiency of conventional approaches. Comparatively, the Swin-T Transformer attained the highest classification accuracy of 89.48% and F1 score of 80.50%, while SlantNet achieved 86.15% accuracy and 73.03% F1 score with substantially higher inference speed, highlighting its real-time potential. Ablation studies on augmentation and regularization strategies confirm that the proposed techniques significantly improve minority class detection without compromising overall performance, with detailed per-class precision, recall, and F1 analysis. The proposed framework delivers a high-accuracy, low-latency, and edge-deployable solution for automated PV inspection, facilitating seamless integration into operational PV plants for real-time fault diagnosis. Full article

Early fault vibration signals of rolling bearings are non-stationary and nonlinear, with weak fault signatures easily masked by noise. Traditional denoising methods (e.g., wavelet thresholding, empirical mode decomposition (EMD)) struggle to accurately extract effective features. Although variational mode decomposition (VMD) overcomes mode mixing, its core parameters rely on empirical selection, making it prone to local optima and limiting its denoising performance. To address this critical issue, this study aims to propose a hybrid model with adaptive parameter optimization and efficient denoising capabilities, enhancing the signal-to-noise ratio (SNR) and feature discriminability of early fault signals in rolling bearings. The novelty of this work is reflected in three aspects: (1) An improved edge strategy whale optimization algorithm (IEWOA) is proposed, incorporating six enhancements to balance global exploration and local exploitation. Using the minimum average envelope entropy as the objective function, the IEWOA achieves adaptive global optimization of VMD parameters. (2) A hybrid framework of “IEWOA-VMD + dataset-specific wavelet thresholding for secondary denoising” is constructed. The optimized VMD first decomposes signals to separate noise and effective components, followed by secondary denoising, ensuring both adaptable signal decomposition and precise denoising. (3) Comprehensive validation is conducted across five models using two public datasets (Case Western Reserve University (CWRU) and Paderborn Universität (PU)). Key findings demonstrate that the proposed method achieves a root-mean-square error (RMSE) as low as 0.00013–0.00041 and a Normalized Cross-Correlation (NCC) of 0.9689–0.9798, significantly outperforming EEMD, traditional VMD, and VMD optimized by single algorithms. The model effectively suppresses noise interference, preserves the fundamental and harmonic components of fault features, and exhibits strong robustness under different loads and fault types. This work provides an efficient and reliable signal preprocessing solution for early fault diagnosis of rolling bearings. Full article

Hierarchical asynchronous federated learning (HAFL) accommodates more real networking and ensures practical communications and efficient aggregations. However, existing HAFL schemes still face challenges in balancing privacy-preserving and robustness. Malicious training nodes may infer the privacy of other training nodes or poison the global model, thereby damaging the system’s robustness. To address these issues, we propose a secure hierarchical asynchronous federated learning (SHAFL) framework. SHAFL organizes training nodes into multiple groups based on their respective gateways. Within each group, the training nodes prevent inference attacks from the gateways and committee nodes via a mask–DP exchange protocol and employ homomorphic encryption (HE) to prevent collusion attacks from other training nodes. Compared with conventional solutions, SHAFL uses noise that can be eliminated to reduce the impact of noise on the global model’s performance, while employing a shuffle model and subsampling to enhance the local model’s privacy-preserving level. At global model aggregation, SHAFL considers both model accuracy and communication delay, effectively reducing the impact of malicious and stale models on system performance. Theoretical analysis and experimental evaluations demonstrate that SHAFL outperforms state-of-the-art solutions in terms of convergence, security, robustness, and privacy-preserving capabilities. Full article