Search Results (3,697)

The transition toward sustainable and resilient new-type power systems requires robust diagnostic frameworks for terminal power supply units to ensure continuous grid stability. To ensure the resilience of modern power systems, this paper proposes a multi-source domain deep Transfer Learning method for the abnormal condition diagnosis of low-voltage distribution nodes within a cloud-edge collaborative framework. This approach integrates feature selection based on the Categorical Boosting (CatBoost) algorithm with a hybrid architecture combining a Convolutional Neural Network (CNN) and a Residual Network (ResNet). Additionally, it utilizes a multi-loss adaptation strategy consisting of Multi-Kernel Maximum Mean Difference (MK-MMD), Local Maximum Mean Difference (LMMD), and Mean Squared Error (MSE) to effectively bridge domain gaps and ensure diagnostic consistency. By balancing global commonality with local adaptation, the framework optimizes resource efficiency, reducing collaborative training time by 19.3%. Experimental results confirm that the method effectively prevents equipment failure, achieving diagnostic accuracies of 98.29% for low-voltage anomalies and 88.96% for three-phase imbalance conditions. Full article

Shearers and roadheaders are critical equipment in coal mining and roadway excavation, where the rock-breaking performance of cutting picks directly influences operational efficiency and economic outcomes. Complex geological conditions, such as hard coal seams and embedded inclusions like gangue or pyrite nodules, pose significant challenges to cutting efficiency and tool wear. This study presents a numerical investigation into the rotational cutting process of a single pick in heterogeneous coal seams using the Smoothed Particle Hydrodynamics (SPH) method integrated with a mixed failure model. The model combines the Drucker–Prager criterion for shear failure and the Grady–Kipp damage model for tensile failure, enabling accurate simulation of crack initiation, propagation, and coalescence without requiring explicit fracture treatments. Simulations reveal that cutting depth significantly influences the failure mode: shallow depths promote tensile crack-induced spallation of hard nodules under compressive stress, while deeper cuts lead to shear-dominated failure. The cutting pick exhibits periodic force fluctuations corresponding to stages of compressive-shear crack initiation, propagation, and spallation. The results provide deep insights into pick–rock interaction mechanisms and offer a reliable computational tool for optimizing cutting parameters and improving mining equipment design under complex geological conditions. A key finding is the identification of a critical transition in failure mechanism from tensile-dominated spallation to shear-driven fragmentation with increasing cutting depth, which provides a theoretical basis for practitioners to select optimal cutting parameters that minimize tool wear and energy consumption in field operations. Full article

Image-Based Overlay (IBO) equipment leverages optical reflection imaging principles, combined with focusing and alignment strategies to measure overlay marks. Among all measurement steps, the focal plane measurement of marks exerts the most critical impact on overlay accuracy, while the time consumed by focal plane detection directly determines the overall measurement throughput. To address the trade-off between accuracy and efficiency in advanced process nodes, this paper proposes an integrated optimization strategy encompassing optical hardware design and software algorithms. The hardware solution adopts a dual-wavelength, dual-detector architecture: optimal imaging wavelengths are selected independently for the previous-layer and current-layer marks, ensuring each layer achieves ideal imaging conditions without mutual interference. The software strategy employs a deep learning framework to simultaneously predict and adjust the horizontal position (alignment) and vertical defocus number of measured marks in real time with high precision, thereby securing the optimal imaging posture. By synergizing hardware-based optimal imaging conditions and software-based posture adjustment, this method effectively mitigates the impact of background noise and system aberrations, ultimately improving both the accuracy and efficiency of overlay measurement. Full article

Background/Objectives: Cell-free DNA (cfDNA) is a valuable biomarker for cancer diagnosis and therapy monitoring; however, its low abundance and fragmented nature present major challenges for reliable isolation, particularly from limited plasma volumes. Here, we report the development and evaluation of a novel magnetically assisted microfluidic chip with a three-inlet design for efficient cfDNA extraction from small-volume plasma samples. Methods: The platform enables controlled infusion of plasma, lysis buffer, and magnetic nanoparticle suspensions at defined flow rates. An external magnetic field selectively captures cfDNA-bound nanoparticles while efficiently removing background impurities. Results: Direct comparison with two in vitro diagnostic (IVD)-certified commercial cfDNA extraction kits showed that the microfluidic system achieved comparable cfDNA yields at standard plasma volumes and superior performance at reduced input volumes. High DNA purity and integrity were confirmed by quantitative PCR amplification of a housekeeping gene and clinically relevant targets. The complete workflow required approximately 9 min, used minimal equipment, reduced contamination risk, and enabled rapid processing with future potential for parallel multi-chip configurations. Conclusions: These findings establish the proposed microfluidic platform as a rapid, reproducible, and scalable alternative to conventional cfDNA extraction methods. By significantly improving recovery efficiency from small plasma volumes, the system enhances the clinical feasibility of liquid biopsy applications in cancer diagnostics and precision medicine. Full article

A hybrid AI and LLM-enabled architecture is presented for real-time decision support in industrial batch processes, where supervision still relies heavily on human operators and ad hoc SCADA logic. Unlike algorithmic contributions proposing novel AI methods, this work addresses the practical integration and deployment challenges arising when applying existing AI techniques to safety-critical industrial environments with legacy PLC/SCADA infrastructure and real-time constraints. The framework combines deterministic rule-based agents, fuzzy and statistical enrichment, and large language models (LLMs) to support monitoring, diagnostic interpretation, preventive maintenance planning, and operator interaction with minimal manual intervention. High-frequency sensor streams are collected into rolling buffers per active process instance; deterministic agents compute enriched variables, discrete supervisory states, and rule-based alarms, while an LLM-driven analytics agent answers free-form operator queries over the same enriched datasets through a conversational interface. The architecture is instantiated and deployed in the Clean-in-Place (CIP) system of an industrial beverage plant and evaluated following a case study design aimed at demonstrating architectural feasibility and diagnostic behavior under realistic operating regimes rather than statistical generalization. Three representative multi-stage CIP executions—purposively selected from 24 runs monitored during a six-month deployment—span nominal baseline, preventive-warning, and diagnostic-alert conditions. The study quantifies stage-specification compliance, state-to-specification consistency, and temporal stability of supervisory states, and performs spot-check audits of numerical consistency between language-based summaries and enriched logs. Results in the evaluated CIP deployment show high time within specification in sanitizing stages (100% compliance across the evaluated runs), coherent and mostly stable supervisory states in variable alkaline conditions (state-specification consistency ${\Gamma }_s\ge 0.98$), and data-grounded conversational diagnostics in real time (median numerical error below 3% in audited samples), without altering the existing CIP control logic. These findings suggest that the architecture can be transferred to other industrial cleaning and batch operations by reconfiguring process-specific rules and ontologies, though empirical validation in other process types remains future work. The contribution lies in demonstrating how to bridge the gap between AI theory and industrial practice through careful system architecture, data transformation pipelines, and integration patterns that enable reliable AI-enhanced decision support in production environments, offering a practical path toward AI-assisted process supervision with explainable conversational interfaces that support preventive maintenance decision-making and equipment health monitoring. Full article

Decades ago, the introduction of GC-MS marked a significant advancement in primary plant metabolite studies. Here, in our review, we will delve into critical aspects of the workflow, spanning the selection of an analytical platform, sample preparation, analytical acquisition, and data processing and interpretation. The exceptional separation capabilities of GC, characterized by remarkable chromatographic resolution, render it ideal for analysis of the complex plant metabolome, including the separation of isomeric compounds. The diversity of analytical platforms allows the investigation of plant metabolomes using targeted and non-targeted approaches. GC-MS, equipped with efficient extraction methods and reliable derivatization protocols for semi- and non-volatile compounds, enables qualitative and quantitative analysis of these molecules. The stability of derivatives forms the foundation for the robustness and reproducibility of GC-MS methods, and their mass spectra provide characteristic fragments for confident identification and sensitive quantification of individual metabolites. There has been key progress in the advancement of GC-MS approaches to studying plant metabolism. However, the presence of artifacts during GC-MS analysis, particularly during derivatization, is a challenge that requires careful validations, which frequently necessitate additional investigations. The feasible solutions that were achieved to overcome the limitations in GC-MS-based studies are a particular focus of the present discussion. Full article

In this paper, an explainable decision-making and guidance integration method is developed based on dynamic Bayesian network and the optimized control method. The proposed method can be applied for the autonomous decision-making and guidance in the game of attacking and defending of unmanned combat aerial vehicles in close air combat. Firstly, the target maneuver recognition and target trajectory prediction are carried out according to the target information detected by the sensor. Then, a dynamic Bayesian network model for close combat decision is established by combining space occupancy situation and equipment performance information with target maneuver identification results. The decision model realizes the intelligent selection of the optimization index function of the maneuver. The optimal control constrained gradient method is adopted to realize the optimal calculation of the unmanned combat aerial vehicle occupancy guidance quantity by considering the constraint of unmanned combat aerial vehicle flight performance. The simulation results of several typical close air combat show that the proposed method can realize rationalized autonomous decision-making and space occupancy guidance of unmanned combat aerial vehicles, overcome the solidification of mobile action mode by traditional methods, and has better real-time performance and optimization performance. Full article

For mine ventilation-on-demand (VOD) scenarios, conventional joint optimisation of airflow augmentation and energy saving in mine ventilation systems is often constrained in practical engineering applications by shrinkage of the feasible region, limited adjustable resistance margins, and strongly multi-modal objective functions. These factors tend to result in low solution efficiency, pronounced sensitivity to initial values and insufficient solution robustness. In response to these challenges, a two-layer intelligent evolutionary optimisation framework, termed ES–Hybrid JADE with Competitive Niching, is developed in this study. In the outer layer, four classes of evolutionary algorithms—CMAES, DE, ES, and GA—are comparatively assessed over 50 repeated test runs, with a combined ranking based on convergence speed and solution quality adopted as the evaluation metric. ES, with a rank_mean of 2.0, is ultimately selected as the global hyper-parameter self-adaptive regulator. In the inner layer, four algorithms—COBYLA, JADE, PSO and TPE—are compared. The results indicate that JADE achieves the best overall performance in terms of terminal objective value, multi-dimensional performance trade-offs and robustness across random seeds. Furthermore, all four inner-layer algorithms attain feasible solutions with a success rate of 1.0 under the prescribed constraints, thereby ensuring that the entire optimisation process remains within the feasible domain. The proposed framework is applied to an exhaust-type dual-fan ventilation system in a coal mine in Shaanxi Province as an engineering case study. By integrating GA-based automatic ventilation network drawing (longest-path/connected-path) with roadway sensitivity analysis and maximum resistance increment assessment, two solution schemes—direct optimisation and composite optimisation—are constructed and compared. The results show that, within the airflow augmentation interval [0.40, 0.55], the two schemes are essentially equivalent in terms of the optimal augmentation effect, whereas the computation time of the composite optimisation scheme is reduced significantly from approximately 29 min to about 13 s, and a set of multi-modal elite solutions can be provided to support dispatch and decision-making. Under global constraints, a maximum achievable airflow increment of approximately 0.66 m³·s⁻¹ is obtained for branch 10, and optimal dual-branch and triple-branch cooperative augmentation combinations, together with the corresponding power projections, are further derived. To the best of our knowledge, prior VOD airflow-augmentation studies have not combined feasibility-region contraction (via sensitivity- and resistance-margin gating) with a two-layer ES-tuned JADE optimiser equipped with Competitive Niching to output multiple feasible optima. This work provides new insight that the constrained airflow-augmentation problem is intrinsically multimodal, and that retaining multiple basins of attraction yields dispatch-ready elite solutions while achieving orders-of-magnitude runtime reduction through prediction-based constraints. The study demonstrates that the proposed two-layer intelligent evolutionary framework combines fast convergence with high solution stability under strict feasibility constraints, and can be employed as an engineering algorithmic core for energy-efficiency co-ordination in mine VOD control. Full article

Waste electrical and electronic equipment (WEEE) is a rapidly growing waste stream rich in precious metals, with gold in particular being concentrated in printed circuit boards and other high-value components. Historically, industrial recycling has relied on pyrometallurgy and non-selective hydrometallurgical leaching. These recovery routes have major drawbacks, including high energy demand, corrosion, the use of toxic reagents, and the complexity of pregnant leach solutions, which complicate downstream gold recovery. This review aims to synthesize recent advances in selective gold recovery from WEEE using a speciation-driven approach. Mechanical pretreatment and physical beneficiation methods are critically assessed as processes for concentrating gold and reducing the amount of material sent to downstream hydrometallurgical leaching. Different lixiviants, from conventional cyanide to halide-based, as well as greener chemistries such as thiosulfate and thiourea, are assessed for gold dissolution from the WEEE stream. Assessment of different extraction methods, including sorbents, ion exchange resins, solvent/ionic liquid, direct reduction/precipitation, and electrochemical recovery, is conducted. The review concludes with guidelines for potential process integration and highlights the need for scalable, reusable lixiviants and sorbent materials validated under realistic multi-metal conditions in real WEEE leachate. Full article

Peracetic acid (PAA) has strong biocidal activity against bacteria, fungi, and spores, even with short contact times. PAA-mediated sterilization is therefore an attractive method for sterilization of growth media that have heat-labile components or when polymer-based equipment is used. However, residual PAA and co-existing hydrogen peroxide (H₂O₂) can inhibit the growth of cultivated species, necessitating a fast and reliable quenching strategy that does not require rinsing. In contrast to Fe–EDTA-based catalytic decomposition that is strongly influenced by pH, buffers, and organic nitrogen, we demonstrate a fundamentally different, stoichiometric quenching strategy using sodium dithionite that enables instantaneous and selective removal of PAA. Na₂S₂O₄ preferentially reduced PAA over H₂O₂ in a 0.03% PAA solution and achieved complete PAA reduction within 5 s, independent of pH and in the presence of nitrogen compounds. By adjusting the Na₂S₂O₄ dose, PAA could be selectively removed while allowing a small fraction of H₂O₂ to remain. When applied to the cultivation of Euglena gracilis, which tolerates low levels of H₂O₂, the PAA–Na₂S₂O₄-treated medium resulted in greater cell growth and higher paramylon production than autoclaved medium. Full article

To improve the fire safety performance of fire protection renovation projects for existing public buildings, this paper systematically sorts out and analyzes relevant research studies, accident reports, and fire protection renovation codes and guidelines. It constructs a fire performance evaluation system for such projects, including 4 first-level indicators—”Building Characteristics”, “Building Fire Protection and Rescue”, “Fire Facilities and Equipment”, and “Heating, Ventilation, Air Conditioning (HVAC) and Electrical Systems”—and 19 second-level indicators such as “Building Usage Function”. The subjective–objective combined weighting method of Analytic Hierarchy Process (AHP)-CRITIC is adopted to determine the weights of indicators at all levels. Four high-weight second-level indicators are selected as core remediation objects: average fire load density, floor layout, automatic fire alarm and linkage control system, and electrical systems. Meanwhile, the evaluation system is converted into a Bayesian Network model, with an empirical verification analysis carried out on a shopping mall in Chaoyang District, Beijing, as a case study. Results show that the approach of combining partial codes with the rectification of high-weight indicators can reduce the fire occurrence probability of the mall from 78%, before renovation, to 24%. Therefore, the constructed evaluation system and Bayesian Network model can realize the accurate quantification of fire risks, provide scientific and feasible technical schemes for the fire protection renovation of existing public buildings, and lay a foundation for enriching and improving fire protection assessment theories. Full article

Health systems face pressure to strengthen resilience against supply chain disruptions while maintaining cost-effective service delivery. This mixed-methods study describes a pilot project that integrated 3D printing services into a Canadian provincial health authority. Quantitative data were derived from internal clinical engineering work orders, where a scenario-based economic analysis compared original equipment manufacturer (OEM) procurement with modelled 3D-printed parts. Using conservative assumptions, selected non-electronic structural parts were assigned a fixed unit cost. Qualitative data were collected from two focus groups with clinical engineers and other end-users. Results from an exploratory scenario-based economic analysis suggest that substituting selected structurally simple clinical engineering parts with 3D-printed alternatives would be associated with modelled cost impacts ranging from a 67.4% net increase (OEM prices halved and 3D-printing costs doubled) to a 69.6% cost reduction (OEM prices increased by 10% and 3D-printing costs decreased by 20%). Demand changes affected absolute savings but not the percent difference (58.1% under ±50% quantity changes), and a pessimistic procurement scenario (OEM prices decreased by 30% and 3D-printing costs increased by 50%) reduced savings to 10.3%. Focus groups highlighted perceived benefits and implementation challenges associated with integrating additive manufacturing. Implementation was facilitated through an outsourcing model, which was perceived to shift certain responsibilities and risk-management functions to the vendor. Long-term adoption will require clearer communication and targeted education. This pilot study suggests that, under constrained regulatory scope and scenario-based assumptions, additive manufacturing may contribute to supply chain resilience and may be associated with modelled cost advantages for selected low-risk components. Full article

In the context of manufacturing logistics, this study sheds light on the difficult task of concurrently optimizing cost, time, influence on sustainability, and spatial efficiency. Specifically, this addresses the integrated challenge of material handling equipment selection and facility space allocation, a crucial decision-making domain where conventional single-objective methodologies frequently overlook vital considerations. While recent research predominantly relies on meta-heuristics and simulation-based solution methodologies, they do not guarantee a global optimum solution space. To effectively address this multifaceted decision environment, a Mixed-Integer Linear Programming (MILP) model is developed and resolved utilizing two distinct scalarization methodologies: the conventional ϵ-constraint method and the augmented ϵ-constraint method (AUGMECON2). The comparative analysis indicates that although both methods effectively identify the Pareto front, the AUGMECON2 approach offers a more robust assurance of solution efficiency by incorporating slack variables. The results illustrate a convex trade-off between capital expenditure and operational flow time, indicating that substantial reductions in time necessitate strategic investments in higher-capacity equipment fleets. Furthermore, the analysis underscores a significant conflict between achieving extreme operational efficiency and adhering to facility design standards, as reducing time or energy consumption beyond a specific point requires deviations from optimal space allocation policies. Ultimately, a “Best Compromise Solution” is determined that harmonizes near-optimal operational efficiency with strict compliance to spatial constraints, providing a resilient framework for sustainable manufacturing logistical planning. Full article

The rapid proliferation of connected vehicles equipped with both Vehicle-to-Vehicle (V2V) sidelink and cellular interfaces creates new opportunities for real-time vehicular applications, yet achieving ultra-reliable communication without prohibitive cellular costs remains challenging. This paper addresses reliable inter-vehicle video streaming for safety-critical applications such as See-Through for Passing and Obstructed View Assist, which require stringent Service Level Objectives (SLOs) of 50 ms latency with 99% reliability. Through measurements in Seoul urban environments, we characterize the complementary nature of V2V and Vehicle-to-Network-to-Vehicle (V2N2V) paths: V2V provides ultra-low latency (mean 2.99 ms) but imperfect reliability (95.77%), while V2N2V achieves perfect reliability but exhibits high latency variability ($P_{99}$: 120.33 ms in centralized routing) that violates target SLOs. We propose a hybrid framework that exploits V2V as the primary path while selectively retransmitting only lost packets via V2N2V. The key innovation is a dual loss detection mechanism combining gap-based and timeout-based triggers leveraging Real-Time Protocol (RTP) headers for both immediate response and comprehensive coverage. Trace-driven simulation demonstrates that the proposed framework achieves a 99.96% packet reception rate and 99.71% frame playback ratio, approaching lossless transmission while maintaining cellular utilization at only 5.54%, which is merely 0.84 percentage points above the V2V loss rate. This represents a $7\times$ cost reduction versus PLR Switching (4.2 GB vs. 28 GB monthly) while reducing video stalls by $10\times$. These results demonstrate that packet-level selective redundancy enables cost-effective ultra-reliable V2X communication at scale. Full article

Background/Objectives: The COVID-19 pandemic exposed global weaknesses in healthcare preparedness and highlighted the pivotal role of the World Health Organization (WHO) in coordinating responses and issuing technical guidance. Among these, the document “Rational use of personal protective equipment (PPE) for COVID-19 and considerations during severe shortages” (December 2020) aimed to standardize PPE use amid global scarcity. This study assessed the awareness, implementation, and perceived usefulness of this WHO guidance among Polish healthcare personnel and evaluated discrepancies between the WHO expectations and workplace realities. Methods: A cross-sectional, anonymous online survey was conducted between July and September 2025 among employees of 243 randomly selected healthcare facilities in Poland (constituting 20% of all hospitals). The original 24-item questionnaire covered the demographics, awareness and implementation of the WHO PPE guidelines, and perceptions of their effectiveness during and after the pandemic. Data were analyzed descriptively. Results: A total of 542 healthcare workers participated, predominantly nurses (56.8%) and physicians (12.2%), with 86.8% being female and 59.3% having over 20 years of experience. Most respondents (76.5%) reported familiarity with the WHO PPE document, and 63.1% confirmed its implementation in their institutions. Over two-thirds (68.0%) reported that the guidelines improved their sense of safety at work. The main barriers to implementation included staff shortages (52.9%) and insufficient local guidance (20.6%). In 2025, 52.3% continue to apply the WHO recommendations, and 70.8% believe they remain relevant in current practice. However, 80.2% indicated that the WHO guidance should be more closely adapted to local conditions. Conclusions: The WHO PPE guidance was widely recognized and reported as implemented by respondents from participating healthcare facilities, contributing to improved preparedness. Nonetheless, limited institutional support and inadequate local adaptation reduced implementation effectiveness. Future WHO recommendations should better align with national healthcare contexts to enhance preparedness for future crises. Full article