Search Results (8,880)

Regenerative aluminum melting serves as a core process in recycled aluminum production. In the regenerative aluminum melting process, the furnace temperature is a key variable which affects product performance and energy costs. The extreme in-furnace temperature necessitates sensors equipped with protective jackets, which increases measurement costs and severely compromises real-time monitoring capability. Accordingly, accurate furnace temperature prediction is highly valuable for regenerative aluminum melting. In regenerative aluminum melting furnaces, periodic burner nozzle commutation and frequent material charging and discharging lead to complex and time-varying operating conditions, posing considerable challenges to high-precision furnace temperature prediction. To address these issues, a condition-adaptive multi-scale convolution-enhanced Transformer (CA-MC-Transformer) model is proposed for furnace temperature prediction. Firstly, an agglomerative hierarchical clustering algorithm based on the weighted dynamic time warping (WDTW) distance is designed to perform unsupervised clustering on historical process data, thereby extracting physically interpretable prior labels for macroscopic operating conditions. Secondly, multi-scale dilated causal convolutions are utilized to capture local dynamic features at diverse temporal resolutions. A soft attention mechanism is further introduced to dynamically assign fusion weights to condition embeddings and local features, enabling condition-adaptive feature reconstruction. Finally, the fused adaptive features are fed into an encoder-only Transformer network to capture the global long-range temporal dependencies and achieve accurate furnace temperature prediction. Comparative experiments conducted on real operational datasets from an aluminum plant verify that the proposed method effectively eliminates the inherent tracking lag of conventional deep learning models, and substantially improves prediction accuracy and anti-noise robustness under complex and variable operating conditions. Full article

Drosophila melanogaster shares approximately 75% of human disease-related genes and possesses sophisticated genetic toolkits, including GAL4/UAS, CRISPR-Cas9, and RNA interference (RNAi), making it a rapid, cost-effective, and genetically tractable in vivo platform for natural products (NPs) discovery. This review systematically summarizes the modeling strategies, pathological mechanisms, and therapeutic applications of Drosophila models for six major human diseases, including type 2 diabetes, nephrolithiasis, inflammatory bowel disease, cancer, Alzheimer’s disease, and Parkinson’s disease. Cross-disease analysis identifies five evolutionarily conserved signaling networks—IIS/PI3K/Akt/FOXO, JNK/JAK/STAT, Nrf2/Keap1, mTOR/TORC1, and IMD/Toll—as common molecular targets of bioactive NPs, providing a unified mechanistic framework for understanding their multi-target pharmacological activities and broad therapeutic potential. Critically, we propose a generalizable integrated stepwise pipeline: high-throughput fly screening of crude extracts, bioassay-guided isolation of active monomers, genetic mechanistic dissection via RNAi and mutant rescue, and layered validation in human cells and selective mammalian models. This pipeline addresses key challenges in NPs research, including the identification of bioactive constituents and mechanistic validation, while improving screening efficiency and translational potential. Overall, this review establishes a multi-disease-applicable framework linking disease modeling, conserved signaling mechanisms, and translational pharmacology, providing practical guidance for future mechanism-driven NP discovery and preclinical development using Drosophila. By leveraging Drosophila genetics to bridge evolutionary conservation and human pathology, this framework offers a powerful, paradigm-shifting strategy to accelerate mechanism-driven NP discovery and preclinical development. Full article

Background: Rabies is an almost universally fatal disease for which post-exposure prophylaxis (PEP), including passive immunization (PI) with rabies immunoglobulin or monoclonal antibodies, remains the only effective intervention. Despite established clinical guidelines, real-world PI utilization remains suboptimal. This study aimed to investigate the prevalence and determinants of PI hesitancy among previously unvaccinated patients with Category III rabies exposure in China. Methods: This multicenter cross-sectional questionnaire study was conducted at 12 rabies PEP clinics or emergency departments in 12 provinces of mainland China. The analysis included 585 previously unvaccinated adults with Category III rabies exposure who initially hesitated to receive or declined PI. The questionnaire collected sociodemographic characteristics, exposure profiles, awareness of the early protection gap, reasons for initial PI hesitancy or refusal, the decision to receive PI during the same clinical encounter, and PI product selection. Multivariable logistic regression identified factors associated with PI treatment decisions, with results reported as adjusted odds ratios (aORs); reason-specific models used the Benjamini–Hochberg false discovery rate correction. All inferential analyses were restricted to adults. Results: Among 5068 eligible previously unvaccinated patients with Category III rabies exposure, 2866 (57%) initially hesitated to receive or declined recommended PI. Of 617 questionnaire respondents, 32 minors were excluded, leaving 585 adults for all inferential analyses. The leading reasons for initial hesitancy were lack of understanding of the immediate protective role of PI (199/585, 34%), belief that vaccination alone was sufficient (194/585, 33%), and high cost (178/585, 30%). During the same clinical encounter, 320/585 adults (55%) chose to receive PI. Awareness of the early protection gap (aOR 2.63, 95% CI 1.75–3.98, p < 0.001) and bachelor’s degree or higher (aOR 1.61, 95% CI 1.07–2.43, p = 0.023) were positively associated with the decision to receive PI, whereas head/face wounds were negatively associated (aOR 0.53, 95% CI 0.30–0.92, p = 0.026). In reason-specific models, lack of knowledge about immediate protection was associated with higher odds of choosing PI (aOR 2.43, 95% CI 1.62–3.68, BH-FDR p < 0.001), whereas high cost was associated with lower odds (aOR 0.41, 95% CI 0.27–0.62, BH-FDR p < 0.001). Conclusion: Hesitancy toward and refusal of PI were observed among previously unvaccinated adults with Category III rabies exposure, yet initial hesitation or refusal is not irreversible. Early protection gap awareness was positively associated with PI choice, whereas cost-related concern was negatively associated. Clear explanation of protection timing and attention to cost-related barriers may support informed PI treatment decisions. Full article

Timely and accurate defect classification is critical for ensuring product quality and safety in industrial inspection scenarios. The widespread deployment of Internet of Things (IoT) devices equipped with sensing, computing, and communication capabilities has promoted the development of AI-enabled industrial applications. However, conventional AI approaches typically rely on centralized data collection and processing, which become impractical in real-world IoT environments due to growing privacy concerns and constrained device resources. To address these challenges, this paper proposes a communication-efficient adaptive federated learning algorithm for heterogeneous defect classification tasks. The proposed approach jointly accelerates the training process through three mechanisms: (i) adaptive local updates that balance communication and computation overheads; (ii) parameter compression that trades off communication cost against model accuracy; (iii) joint bandwidth and computation-power allocation that optimizes per-round communication and computation time across participating devices. We further analyze the joint effects of these three mechanisms and provide a convergence analysis. Extensive simulations show that the proposed method achieves competitive classification accuracy while reducing single-round training time by up to 70%. Full article

This study investigates machine learning techniques for predicting the behavior of proton exchange membrane electrolyzers, which are vital for sustainable hydrogen production. This work addresses these challenges by integrating artificial neural networks to develop predictive models capable of capturing the performance of proton exchange membrane electrolyzers with high accuracy. This research utilizes a multi-layer perceptron network architecture, optimized through rigorous data preprocessing, parameter tuning, and error minimization strategies. The dataset used was based on published PEME numerical simulation datasets and encompasses key performance indicators, including stack voltage, water transport, and electrochemical reactions. The trained artificial neural networks models achieved mean squared error values of 3.66 × 10⁻⁵ and 9.75 × 10⁻⁶, with correlation coefficients of 0.99996 and 0.99958, demonstrating near-perfect predictive accuracy. A comparative benchmarking study against alternative regression algorithms revealed that the proposed MLP models significantly outperformed Gradient Boosting and Random Forest by several orders of magnitude, thereby establishing a higher level of persuasiveness and reliability for the developed framework. Average deviation rates of 0.11% and −0.01% further validated model reliability. The novelty of this work lies in its comprehensive approach, which goes beyond isolated metrics by addressing interactions across system parameters. This integrated framework enables enhanced prediction, control, and optimization of proton exchange membrane electrolyzer’s performance, setting a new benchmark for leveraging machine learning in hydrogen energy systems. These findings pave the way for scalable, cost-effective solutions to improve proton exchange membrane electrolyzers’ efficiency and operational reliability. Full article

Walnuts are commercially important tree nuts whose moisture content (MC) influences quality, shelf life, and post-harvest processing. This study evaluated the potential of low-cost acoustic sensing combined with machine learning for non-destructive MC prediction. Sixty in-shell walnuts were subjected to controlled drying at 40 °C for 26 h, with acoustic recordings and physical measurements collected every two hours. Acoustic signals were processed using Wavelet Soft Threshold Denoising (WSTD), Short-Time Fourier Transform (STFT), and Variational Mode Decomposition (VMD), and features were extracted from the resulting signals. Predictive models included generalized linear models (GLM), random forests (RF), gradient boosting machines (GBM), and Partial Least Squares (PLS) approaches. Following grouped walnut-level validation, the highest MC prediction performance was achieved by the model combining dimensional and acoustic descriptors (RF: R² = 0.836; GBM: R² = 0.826), while the model combining drying time and acoustic descriptors achieved moderate predictive performance (RF: R² = 0.762; GBM: R² = 0.760). Overall, the results provide proof-of-concept evidence that acoustic descriptors may complement physical measurements for non-destructive walnut moisture-content prediction. However, substantially larger independent datasets collected across multiple cultivars, production batches, acquisition conditions, and external validation studies will be required before practical industrial implementation can be considered. Full article

Continuous carbon anode roasting in industrial parks requires a stable high-temperature heat supply and remains highly dependent on grid electricity and natural gas. However, existing energy-system studies rarely coordinate hydrogen production and storage, volumetric hydrogen blending, and temperature-graded waste-heat recovery under continuous production constraints. To address this gap, this study proposes an electricity–heat–gas–hydrogen integrated energy system for carbon anode industrial parks and develops a 24 h multi-objective scheduling model. The model coordinates heat demands at different temperature levels with hourly electricity and hydrogen flows, using surplus photovoltaic power to produce hydrogen for later high-load periods. The selected scheme achieves a daily volumetric hydrogen-blending ratio of 10.79%, with an operating cost of 82,985.75 CNY and carbon emissions of 54,371.53 kg. Relative to an otherwise equivalent non-hydrogen configuration, hydrogen coupling provides additional reductions of 7.2% in operating cost and 2.3% in carbon emissions. Compared with a basic conventional configuration, operating cost and carbon emissions decrease by 27.9% and 26.0%, respectively. Cascade recovery also increases the daily waste-heat utilization rate by approximately 30 percentage points. These results show that the proposed scheduling framework can coordinate hydrogen utilization and graded waste-heat recovery while maintaining continuous carbon anode production. Full article

The aim of the study is to analyze primary statistics on the use of intellectual property in agriculture in modern Russia, as well as to identify the relationships between human capital, the supply of research labor, and the growth of intellectual capital in agriculture and its intensity relative to agricultural output. To conduct the study, a sample of 34 regions of the Russian Federation for the period from 2017 to 2024 was analyzed. By using count model estimation, it was demonstrated that both economic incentives and human capital have a significant impact on the use of breeding achievements in agriculture. However, this impact of economic incentives is valid only for the extensive expansion of the applied objects of intellectual property. The level of human capital in the region consistently stimulates the growth and intensity of innovation activity to the scale of the agricultural industry. At the same time, the increase in the cost of researcher labor has a negative effect on the number of breeding achievements used or their intensity in agricultural output. The authors hope that this paper will contribute to the literature examining complex, multifactorial influences on regional agricultural production. Full article

Natural gas production from mature fields is progressively shifting toward low-rate wells operating near their economic limit, creating challenges for long-term asset management. This study proposes an integrated stochastic decision-support framework combining Arps decline curve analysis, a calibrated Schwartz Type-I mean-reverting jump-diffusion model, Monte Carlo simulation, and Bellman dynamic programming to optimize marginal dry gas well management. The framework evaluates pipeline commercialization and a hybrid strategy integrating on-site electricity generation, while incorporating monetized environmental externalities associated with CO₂ emissions from gas combustion and potential post-abandonment CH₄ emissions. Application to the Mareógrafo 100 well in Mexico shows that the environmentally adjusted Bellman policy yields a mean NPV of USD 34.84 thousand, exceeding the comparable pipeline-only and hybrid strategies. Internalizing environmental costs reduces the mean optimal NPV by 46.6% relative to the economic-only formulation, while the mean abandonment time is approximately 200 days. Sensitivity analysis identifies electricity price, natural gas price, and pipeline distance as the dominant profitability drivers. The proposed framework provides a transferable methodology for jointly evaluating commercialization, environmental externalities, and abandonment decisions in mature dry gas fields under uncertainty. Full article

Amid increasing upstream raw-material disruptions and battery supply uncertainty, NEV manufacturers may obtain emergency supply from vertically integrated competitors after a disruption, but must incur technology conversion costs. We develop a differentiated Cournot duopoly model with an outsourcing manufacturer and an integrated manufacturer to compare equilibrium, profit, and welfare outcomes with and without emergency procurement cooperation. The results show that cooperation has a clear feasibility boundary jointly determined by market potential, relative costs, and conversion costs, giving rise to competition-only, cooperation-only, and coopetition regimes. The cooperation option reshapes pre-disruption quantity decisions: the outsourcing manufacturer increases regular procurement, while the integrated manufacturer reduces its own-brand output by a larger amount. Within the cooperation region, higher conversion costs continuously reduce the outsourcing manufacturer’s profit, whereas the integrated manufacturer’s profit can be U-shaped. Greater product substitutability makes cooperation more fragile and widens the divergence between private profit incentives and supply-chain resilience. Consumer surplus and social welfare may also move in different directions, depending on the outsourcing manufacturer’s benchmark market share and conversion cost. A Nash-bargaining extension confirms the robustness of the activation condition and pre-disruption quantity-adjustment mechanism, while reducing emergency markups and improving consumer surplus. Full article

Morocco’s National Green Hydrogen Roadmap targets large-scale hydrogen exports, yet the offshore wind and tidal resources of the Atlantic Sahara coast remain underexplored, and single-resource electrolysis plants suffer from low, variable electrolyser utilisation. This study presents a reproducible, model-based techno-economic assessment of a 560 MW hybrid offshore wind–tidal hub at Dakhla that produces hydrogen by proton exchange membrane (PEM) electrolysis and exports it as liquid hydrogen (LH₂) to Jorf Lasfar. The assessment is entirely theoretical: it couples reanalysis-based resource characterisation, harmonic tidal modelling, hourly dispatch, and discounted levelised cost of hydrogen (LCOH) analysis, and does not include experimental or in situ measurements. The hybrid plant reaches a 45.5% capacity factor and produces 36,781 t of hydrogen per year at 60% electrolyser utilisation. The 2025 base-case production LCOH is 7.53 USD/kg (10.04 USD/kg delivered), falling to 4.45 USD/kg under a 2030 learning scenario that approaches the national 2–4 USD/kg target band. Because the wind and tidal resources are almost uncorrelated, hybridisation firms the supply and reduces electrolyser cycling rather than adding bulk energy; capacity factor and electrolyser-specific energy consumption are the dominant cost drivers. This work provides the first integrated wind–tidal hydrogen assessment for the Moroccan Atlantic coast and a transparent platform for future optimisation. Full article

China’s publishing industry continues to expand across production, warehousing, circulation, and export. Book quality inspection affects product grading, logistics sorting, customer satisfaction, and the international image of Chinese publishing. However, these defects are often small, irregular, and low-contrast, and their visual similarity to paper textures and printed content complicates automated classification and localization. To address this problem, this study proposes Local-detail Modelling and Edge-enhancement YOLO (LME-YOLO), a book defect detection model based on YOLO11n for quality inspection in China’s publishing logistics. On a dataset of 632 book images, LME-YOLO achieved a Precision of 0.829, Recall of 0.777, mAP50 of 0.818, and mAP50–95 of 0.465. The model contains 2.64 M parameters and requires 6.6 GFLOPs. Among the detectors compared in this study, LME-YOLO achieved the highest mAP50 and mAP50–95. Compared with YOLO11n, its mAP50 and mAP50–95 increased by 0.015 and 0.006, respectively, with increases of 0.05 M parameters and 0.2 GFLOPs. Compared with YOLOv8, its mAP50 and mAP50–95 increased by 0.017 and 0.005, respectively, while the parameter count and computational cost decreased by 0.37 M and 1.6 GFLOPs. Full article

As the market of perishable goods in China continues to expand, reducing quality loss during transportation has become an urgent issue for the industry. Multimodal transport, as a key approach to optimizing the transportation structure and lowering logistics costs, has been increasingly adopted in practice. However, multimodal transport involves multiple transfers, and continuous vibration from transportation equipment throughout the transport process, together with impacts during transfer operations, can easily increase the loss of perishable goods, making it highly significant in practice to consider vibration loss during transportation in route planning. Since different transportation equipment generates different levels of vibration acceleration, this study considers the vibration losses caused by road, rail, and air transport. A bi-objective route optimization model for fresh produce multimodal transport is established, aiming to minimize total cost while maximizing product quality satisfaction. A hybrid algorithm combining an improved Strength Pareto Evolutionary Algorithm and a multi-objective adaptive large neighborhood search algorithm is designed to solve the model. The effectiveness of the model and algorithm is verified through case analysis, followed by sensitivity analysis on different time-sensitive factors of vibration damage and vibration acceleration. The results show that, compared with the multi-objective adaptive large neighborhood search algorithm and the non-dominated sorting genetic algorithm, the proposed algorithm can obtain solutions with lower total costs or higher product quality satisfaction. The minimum total cost of the multimodal transport scheme obtained by the proposed algorithm is reduced by 0.27% and 2.4%, respectively, compared with the multi-objective adaptive large neighborhood search algorithm and the non-dominated sorting genetic algorithm, while the maximum satisfaction is increased by 2.1% and 0.35%, respectively. Full article

Certified seed purity is a prerequisite for sustaining the agronomic performance and commercial value of Khao Dawk Mali 105 (KDML-105), Thailand’s premium aromatic rice; however, conventional inspection methods are destructive, labour-intensive, and poorly suited to high-throughput operations. This study developed a non-destructive purity inspection system integrating five-band MSI acquired using a MicaSense RedEdge-MX sensor with three machine learning classifiers: Extra Trees (ET), Random Forest (RF), and Support Vector Machine (SVM). The system was evaluated systematically across four illumination levels (360.90–13,188.46 lx) to discriminate KDML-105 from three morphologically similar contaminating varieties: Chainat-1, RD-6, and RD-15. Two-way ANOVA confirmed that classifier type was the dominant performance determinant (η² = 0.847). Under optimal illumination (L4, 13,188.46 lx), ET achieved the highest accuracy (88.8%), recall (86.6%), and F1-score (88.4%) with a training time of 0.098 s. External validation confirmed model generalisability: KDML-105 seeds were identified with 90.6% accuracy and 95.6% recall, while Chainat-1 and RD-6 yielded accuracies of 85.1% and 90.6%, respectively; RD-15 remained challenging (62.9%; 67.4%) owing to spectral proximity to KDML-105. These findings establish MSI combined with ET classification as a viable, cost-effective approach for automated seed purity screening in certified rice production. Full article

As China’s dominant urban housing form, gated residential communities deploy layered spatial access controls governing who may enter and move through neighbourhood space. While neighbourhood social sustainability has attracted substantial scholarly attention, how micro-spatial governance arrangements affect the inclusiveness of residential built environments toward essential service workers remains underexplored. Drawing on Lefebvre’s theory of the production of space, this study uses food delivery riders—who navigate gated community access controls dozens of times daily—as an analytical lens to evaluate how neighbourhood spatial governance shapes social inclusiveness. Vignette-based survey data from 445 riders across 157 cities in 27 Chinese provinces were analysed using structural equation modeling. Results show that cumulative anxiety from procedural delays, detours, and elevator waiting is the dominant pathway through which spatial governance undermines riders’ perceived spatial inclusion—operationalised through occupational dignity indicators—with elevator-based spatial stratification identified as a concrete, low-cost intervention target within the spatial-channeling mechanism. Technology-mediated access partially mitigates face-to-face exclusion but leaves underlying spatial inequalities intact. Demographic invariance across all tested variables confirms that diminished inclusion arises from the governance regime itself. The findings position neighbourhood spatial governance as a measurable dimension of urban social sustainability—one concretely testable through the experience of essential service workers—and identify low-cost built-environment interventions for inclusive residential design. While the study focuses on riders as a single user group, the analytical framework is transferable to evaluating how residential built environments accommodate diverse non-resident populations. Full article