Search Results (147)

Safety hazards in coal mines are characterized by significant spatiotemporal heterogeneity, dynamic evolution, and multi-actor coupling. Traditional safety management models, which center on periodic inspections and accident rectification, struggle to achieve proactive risk identification and full-process control. To address this issue, this paper proposes a multi-scale, collaborative “four-in-one” safety management framework oriented toward the hazard lifecycle, based on the spatiotemporal evolution patterns of safety hazards. This framework integrates systems safety theory with the safety philosophy of socio-technical systems, viewing safety hazards as an evolutionary process shaped by the combined effects of spatial exposure, human behavior, organizational management, and dynamic states. It establishes a comprehensive safety governance system comprising precise risk identification, active personnel participation, closed-loop accountability governance, and intelligent dynamic feedback. By integrating the “Area–Point–Number” risk classification method; the “Two-way Risk Purchasing” incentive mechanism; the “Six-level, Six-step, and Three-chain” closed-loop management model; and the Hazard Alert System, the framework achieves the coordinated optimization of risk identification, hazard management, and information feedback. Application validation based on safety hazard data from a coal mine between 2017 and 2020 demonstrates that this method enhances the ability to identify potential risks and effectively reduces major hazard types, such as management deficiencies, unsafe behaviors, and unsafe conditions. The research findings indicate that this framework overcomes the limitations of traditional safety management—such as a single-entity approach, static inspections, and passive responses—and facilitates a shift in coal mine safety governance from hazard control to risk prevention and from manual, experience-based management to intelligent, collaborative decision-making, thereby providing a new theoretical approach for enhancing the safety resilience of complex coal mine production systems. Full article

GaP and ZnTe semiconductor crystals are numerically investigated for terahertz (THz) generation driven by ultrafast 10.6 µm CO₂ laser pumping. At this wavelength, low-order multiphoton absorption is effectively suppressed, enabling the study of intrinsic performance limits governed by nonlinear optical effects, material dispersion, and THz absorption. A one-plus-one-dimensional (1+1D) frequency domain propagation model is employed, including optical rectification, cascaded nonlinear interactions, self-phase modulation, second-harmonic generation, and pulse-front-tilt-related dispersion. The simulations reveal markedly different behavior in the two crystals. In ZnTe, the large effective nonlinear coefficient enables conversion efficiencies exceeding 1% while maintaining good THz pulse quality. In GaP, strong self-phase modulation and nonlinear pulse compression can enhance the predicted efficiency but at the cost of waveform distortion and increased damage risk. MV/cm-level THz electric fields are predicted in both materials; however, ZnTe provides a more favorable compromise between conversion efficiency and pulse quality. The results provide practical guidelines for optimizing CO₂-laser-driven semiconductor THz sources. Full article

Reliable cervical cytology classification remains difficult when rare diagnostic categories are represented by only a few labeled examples and exhibit substantial morphological overlap with neighboring classes. This study introduces HardNegRect, a lightweight inductive prototype-rectification module designed for low-support classification among fixed cervical cytology categories. The method constructs a class-specific hard-negative reference from the most similar competing prototypes and uses this inter-class context to predict a bounded, gated residual correction to each support-derived prototype. Because rectification depends exclusively on support information, the approach preserves independent query processing and avoids transductive access to the test distribution. HardNegRect was evaluated on two public cervical cytology benchmarks using common fold assignments, support sizes, held-out query sets, and draw-level metric aggregation for frozen-feature, metric-based, and optimization-based comparators. The study also includes a controlled component ablation study, a neighborhood-sensitivity analysis, and an additional-seed stability analysis. On Mendeley LBC, the clearest benefit occurred in the one-shot setting, where HardNegRect achieved a Macro-F1 of $0.9862\pm 0.0062$ and an SCC F1 of $0.9655\pm 0.0216$. On SIPaKMeD, the default $K_{\mathrm{hn}}=2$ configuration achieved Macro-F1 values of $0.9596\pm 0.0052$, $0.9626\pm 0.0048$, and $0.9616\pm 0.0048$ for $K=1,3,10$, respectively, numerically exceeding the strongest comparator mean at each support size. The controlled component ablation study associates the additional one-shot gain on Mendeley LBC with inter-class prototype correction rather than with embedding transformation alone. Overall, HardNegRect provides a lightweight, parameter-efficient, and geometry-aware extension to prototype-based low-support cytology classification, while patient-grouped, source-grouped, repeated-seed, and multi-center validation remain necessary before clinical generalization can be established. Full article

Heat pumps are core equipment for efficient low-grade thermal energy utilization and low-carbon transformation of the energy structure, offering significant energy-saving potential in building heating and industrial waste heat recovery. This paper reviews the research progress and technical challenges of compression, absorption, and adsorption heat pumps as well as nanofluid-enhanced heat transfer technology and elastocaloric heat pump systems. Air source heat pumps can delay frosting through variable frequency, heat storage, and waste heat recovery. However, accurate prediction models for performance degradation under extreme cold conditions are lacking. Although ground source and water source heat pumps exhibit significant energy efficiency advantages, ground source systems may suffer from performance degradation due to underground thermal imbalance. The application of water source systems is strictly constrained by water resource conditions. Driven by low-grade waste heat, absorption heat pumps employing traditional working pairs suffer from crystallization, corrosion, or high rectification energy consumption. The COP of a single-effect cycle under 80~100 °C waste heat is only 1.2~1.9, while hybrid cycles can reach approximately 3.2 at 120~150 °C. Although adsorption heat pumps achieve significantly improved performance under continuous heat recovery cycles, the full-scale power density of novel adsorbents such as metal–organic frameworks is inferior to the power density of traditional silica gel. Moreover, under off-design conditions, the performance drops by 23~48% compared to theoretical values. Nanofluids can enhance heat transfer, but the long-term effects of particle agglomeration at high temperatures on pump power consumption and system compatibility remain to be systematically evaluated. Elastocaloric heat pump systems can achieve refrigerant-free cooling, but current prototypes still cannot compete with traditional vapor compression systems in long-cycle fatigue reliability and power density. Current heat pump technologies generally face challenges such as insufficient adaptability to extreme conditions, bottlenecks in working fluids and materials, and a lack of long-term validation. Future research must construct a multi-source coupling optimization system, address common problems in working fluids and materials, promote long-term validation and kilowatt-level prototype demonstrations, and drive the large-scale deployment and engineering application of heat pump technology toward high efficiency, intelligence, and high reliability. Full article

Tesla valves are passive hydraulic devices capable of producing directional flow resistance without moving components, making them attractive for applications in microfluidics, thermal systems, and high-reliability hydraulic circuits. Despite extensive experimental and numerical studies, an analytical formulation capable of describing the hydrodynamic behavior of Tesla valves under varying operating and geometric conditions remains limited. In this work, a comprehensive analytical model is developed to describe the pressure losses, flow redistribution, and diodicity behavior of Tesla valves through a physics-based formulation derived from conservation laws, dimensional analysis, and inertial scaling principles. The proposed model incorporates the influence of Reynolds number, flow partition, geometric ratios, branch inclination angle, and number of diode stages within a unified nonlinear framework. A closed structural equation is obtained that relates hydraulic losses and directional asymmetry to the internal geometry of the valve. The formulation reveals the existence of geometric and energetic constraints governing rectification efficiency, including bounds associated with stage number, channel scaling, and angular momentum exchange. The results show that Tesla valve performance emerges from a delicate balance between inertial amplification and dissipative mechanisms, providing an analytical framework for the design and optimization of Tesla-type hydraulic systems across multiple scales. Full article

Tesla valves have emerged as promising passive flow-regulation devices for sustainable water systems because they provide directional flow control without moving parts, external energy input, or complex maintenance requirements. This review systematically examines the fundamental mechanisms, structural evolution, and engineering applications of Tesla valves in water-related systems. The underlying rectification behavior is analyzed from the perspectives of flow separation, recirculation, jet interaction, vortex evolution, and mechanism switching under varying hydraulic conditions. Recent advances in geometric optimization, multistage configurations, three-dimensional architectures, topology optimization, and data-driven design approaches are summarized to illustrate the transition from classical Tesla geometries to next-generation passive flow-control structures. Current applications in microfluidic systems, water-quality monitoring, thermo-hydraulic devices, pressure-regulation networks, and hydraulic safety enhancement are critically reviewed. The analysis indicates that Tesla-valve performance is governed by coupled interactions among geometry, flow regime, fluid properties, and operating conditions, while multifunctional designs increasingly integrate flow regulation, mixing enhancement, heat transfer, and pressure management. Finally, key challenges related to performance standardization, realistic operating conditions, manufacturability, and system-level integration are discussed. Tesla valves are expected to play an increasingly important role in intelligent and energy-efficient water infrastructure, supporting the development of next-generation sustainable water and fluid-management systems. Full article

Self-powered monitoring systems capable of scavenging ambient mechanical energy are a highly desirable solution to eliminate the reliance on batteries and grid power in remote and distributed atmospheric sensing networks. However, the widespread adoption of such systems is severely hindered by the insufficient output power density of current energy harvesters, which struggle to simultaneously drive environmental sensors, data acquisition units, and wireless transmission modules. In this work, we report a highly integrated hybrid power generation system that couples a triboelectric nanogenerator (TENG) and an electromagnetic generator (EMG) to efficiently harvest low-frequency mechanical energy from the surroundings. Through systematic structural optimization and synergistic matching of the two transduction mechanisms, the device achieves an outstanding volumetric power density of 129.9 W·m⁻³, which represents one of the highest values ever reported for hybrid nanogenerators targeting self-powered environmental applications. The output characteristics of both the TENG and EMG units under varying load impedances are thoroughly characterized, revealing the optimal operating points for maximum power extraction. A tailored power management module, consisting of rectification, energy storage, and regulation circuits, is designed to convert the irregular alternating output into a stable direct-current supply. To demonstrate the practical viability of the system, we construct a complete self-powered atmospheric environment monitoring node, which integrates multiple environmental sensors, a data acquisition module, and a wireless transmission module. Driven exclusively by the hybrid TENG–EMG generator under ambient mechanical excitation, the node successfully performs real-time sensing, signal processing, and remote data communication without any external power input. This work not only provides a record-high power density among hybrid generators for environmental monitoring, but also establishes a feasible pathway toward maintenance-free, widely distributed, and truly autonomous atmospheric sensing networks. The presented strategy of maximizing volumetric power density through hybrid design and impedance engineering can be readily extended to other self-powered systems. Full article

The rapid expansion of wireless connectivity has led to vast amounts of radio-frequency (RF) energy being continuously radiated into the environment, much of which is dissipated due to severe propagation losses. Recycling this otherwise wasted RF energy is, therefore, a critical enabler for energy-efficient and sustainable wireless systems. RF energy harvesting nodes and passive backscatter communication devices provide promising solutions by enabling battery-less or low-maintenance operation for future green networks. However, both paradigms suffer from fundamental limitations, including restricted communication range, near–far effects, and insufficient harvested energy at extended distances. This review examines how cooperative relays can address these challenges by harvesting ambient RF energy and assisting both information transfer and power delivery. From a communication perspective, we review cooperative backscatter communication and harvest-then-transmit (HTT) protocols, highlighting how multi-hop relaying significantly extends coverage and improves throughput for energy-constrained devices. Particular emphasis is placed on tag-to-tag (T2T) backscatter systems, relay-assisted architectures, decode-and-forward and amplify-and-forward protocols, and optimal multi-access time allocation strategies that mitigate the doubly near–far problem in passive networks. From an energy-transfer perspective, the review is structured around three pillars: wireless power transfer (WPT), multi-hop energy transfer (MET), and integrated charging-and-sensing frameworks. We discuss relay deployment and placement optimisation, UAV-enabled mobile energy relays, waveform and beam-forming design, and the transition from idealised linear harvesting models to practical nonlinear rectification models. Key practical constraints, such as regulatory limits, safety compliance, self-interference, protocol overhead, synchronisation, and imperfect channel knowledge, are systematically reviewed. The paper concludes by identifying the scalability limits of multi-hop cooperative systems, outlining how the joint optimisation of energy relaying and cooperative communication enables RF energy recycling for sustainable, low-carbon wireless networks and highlighting open challenges and future research directions. Full article

Freeform lenses introduce spatially varying and asymmetric distortions that cannot be reliably modeled by conventional calibration frameworks. This work presents a calibration approach that represents the distortion field using a bicubic B-spline surface and integrates it directly into parameter estimation. To address instability caused by uneven sampling, an adaptive knot placement strategy guided by distortion-aware density estimation is introduced, together with a regularized control-point formulation. These components enable stable optimization under non-uniform and sparse observations. Experiments on synthetic and real datasets show that the proposed method consistently achieves sub-pixel reprojection accuracy (0.39–0.80 pixels on synthetic data and 0.24 pixels on real data) and improves geometric rectification quality compared with representative parametric and non-parametric approaches. The results indicate that continuous distortion modeling with adaptive spatial parameterization provides a reliable solution for calibrating complex lens systems. Full article

The growing expansion of the Internet of Things and wireless sensor networks has created an urgent demand for compact and reliable radio frequency energy-harvesting circuits. This study introduces the design, simulation and extensive performance of a high-efficiency single band radio frequency detection system optimized for 1.8 GHz operation. The detector is realized on a Rogers RO4003C substrate and employs the SMS7630-079LF Schottky diode, selected for its excellent detection capability and economic viability. The introduction of this filtering stage effectively suppresses undesired harmonic components produced during the rectification process, thereby improving the sensitivity and overall power conversion efficiency of the system. The circuit shows a sensitivity of 1.8 mV for every dBm through its simulation tests. The system shows increased sensitivity to 2.2 mV/dBm because of the band stop filter implementation. The system reaches its peak power conversion efficiency of 65.28% at a 1.5 kΩ load, which makes it suitable for applications that require low-power energy harvesting. These combined attributes establish the developed 1.8 GHz detector as a strong candidate for next-generation energy harvesting modules, self-powered sensor networks and intelligent embedded computing platforms within the expanding domain of the Internet of Things. Full article

In large-scale state-owned enterprise audit and compliance tasks, ensuring that similar violations receive consistent disciplinary decisions is essential for procedural fairness and institutional credibility. However, existing retrieval methods face three major challenges: lexical matching methods fail to recognize semantically equivalent violation descriptions, general-purpose semantic encoders lack knowledge of inspection-specific terminology and regulatory distinctions, and retrieved precedents are often not directly transformed into actionable disciplinary references. To address these problems, this paper proposes InspectCL, a domain-enhanced contrastive learning and Retrieval-Augmented Generation framework for similar case retrieval, validated on audit data from a provincial power grid company. First, to provide task-specific supervision that is unavailable in existing benchmarks, we construct InspectCase, a de-identified dataset of 4200 audit and compliance cases across 12 violation categories, with expert-validated positive pairs and hard negative pairs. Second, to overcome the weak domain awareness of generic encoders, we design a domain-enhanced contrastive learning model. Specifically, terminology-masking augmentation improves robustness to specialized inspection expressions, regulatory semantic injection incorporates disciplinary rules to distinguish factually similar but legally different cases, and hierarchical contrastive optimization strengthens both case-level similarity learning and category-level boundary separation. Third, to convert retrieved precedents into practical decision support, the Top-K similar cases are used as evidence for a large language model to generate structured disciplinary recommendation summaries, including violation classification, penalty references, applicable regulations, and rectification measures. Experimental results on InspectCase show that InspectCL substantially outperforms BM25, BERT-base, SimCSE, and Legal-BERT baselines, achieving 56.9% ± 0.7% Recall@5 and an 87.6% ± 0.4% Penalty Consistency Score (PCS). These results demonstrate that the proposed problem-driven modules jointly improve semantic retrieval accuracy and disciplinary decision consistency, offering a practical reference for similar power-grid audit scenarios, with broader applicability to be validated in future cross-domain studies. Full article

The ability to continuously learn over time by incorporating new information while holding onto previously acquired expertise is known as incremental learning (IL). Although this concept is fundamental to human learning, existing machine learning techniques have a significant propensity to forget prior experience by overwriting previously learned patterns from classes. The continuous learning of new information in K-nearest neighbor (KNN) with lazy learning strategies compounds to loss of old knowledge upon learning new information and stability-plasticity dilemma. The change in new data points and data distributions in unforeseen ways impacts KNN’s ability to adapt to changes in class label distribution, leading to concept drift. This experiment models a hybrid 3WDKNN-based incremental learning algorithm (ILA) designed for application in a heterogeneous and dynamically changing environment. This model addresses the limitations of KNN by overcoming computational costs and inefficiencies associated with loss of information in classes, while facilitating incremental learning to attain high predictive accuracy in crop yield datasets. The algorithm employs weighted voting to identify optimal assigned classes for the nearest neighbor and uses memory reconstruction strategy for class incremental learning until the memory is full without forgetting. Using weighted voting for the best assigned classes for the nearest neighbor, the algorithm uses a local mean vector to determine the best distances for the shortest-term incremental learning to achieve the highest performance accuracy in a concept drift environment. The hybrid 3WDKNN_ILA was developed and evaluated alongside advanced algorithms within the same dataset context. The model improves performance in incremental learning contexts by utilizing current concepts and minimizing errors on both current and recent data to avoid parameterization. The model achieves optimal efficient incremental learning by mitigating intentional loss and minimizing errors associated with valuable class information derived from aggregated mean values through class rectification and transfer. The hybrid model achieves the best efficient performance accuracy in all the tested weighted averages of 200W, 500W, and 1000W with tested set K values of 5, 9, and 13K. This hybrid model demonstrates performance accuracy of 97% at a value of 13K, whereas 3WD_KNN achieves 96% at 9K, HoKNN attains 89% at 13K, and 1IKNN reaches 88% at 9K accuracy, respectively. The integrated novelty in the hybrid 3WDKNN_ILA proves superior in terms of computational efficiency, accuracy, and high-level incremental performance and learning in comparison with other tested models of algorithms. Full article

In modern intelligent food processing, the unpredictable variability in pomelo orientation on high-speed conveyors poses a significant challenge to automated grading and precision peeling operations. To address this, a deep learning-based method is proposed for the real-time detection of pomelo posture. Firstly, a pomelo posture dataset was constructed to support model training and validation. Secondly, to balance the extraction of posture features from uniform fruits with the low-power constraints of edge deployment, a domain-specific architectural optimization is presented. Building on the YOLOv8n framework, the proposed model synergistically integrates specialized modules. A lightweight GhostHGNetV2 foundation is utilized to significantly reduce computational redundancy while maintaining the resolution required to detect key anatomical landmarks. To overcome spatial confusion and capture multi-scale global appearance information, a multi-path coordinate attention (MPCA) module is introduced. Furthermore, the SlimNeck architecture and VoVGSCSP module streamline multi-scale feature fusion via one-time aggregation, effectively preventing computational bottlenecks. This design optimizes the computational efficiency of the model while maintaining detection accuracy. Experimental results demonstrate that compared with the baseline YOLOv8n model, the proposed method increased the mAP50 accuracy by 3.67% while reducing parameter count and computational load by 17.5% and 23.3%, respectively. Additionally, it achieved a processing speed of 19.3 FPS on the Jetson Orin Nano 6G edge platform. This research provides a critical technical foundation for the recognition of pomelo posture, enabling subsequent orientation rectification and fostering the development of streamlined, automated pomelo processing lines. Full article

Traffic flow forecasting provides essential support for the construction of smart transportation systems. Despite the superiority of the ASTGCN, which uses an attention mechanism to capture spatio-temporal correlations, it lacks an explicit physical interpretation and thus falls into a more general category known for its lack of such interpretation. As a result, in the presence of sparse or unstable congestion, these data-driven models often violate conservation laws and may generate “physical anomalies” or other logically impossible states. To close the gap of data-driven expressiveness and physical consistency, we propose the congestion-aware physics-guided STGCN (CAP-STGCN). This framework builds a synergistic model that achieves intrinsic coupling between the macroscopic traffic flow kinematics (fundamental diagram) and the spatio-temporal learning process. That is to say, under the model’s solution-space constraining effect, its motion space is bound on a feasible manifold. In terms of kinematics, it restricts consistency in the flow, density and speed. Concurrently, to address slow convergence under long-tailed distributions due to a lack of training samples, such as when there are fewer users or higher-quality items, a dynamic congestion-rectification mechanism is introduced. The aforementioned mechanism redefines the optimization landscape by prioritizing hard-to-predict saturation occurrences. Experiments show that, compared with other models, CAP-STGCN achieves higher prediction accuracy; more importantly, it is free of physical anomalies during inference and can be directly used in practice. Full article

Most existing deep learning models for remote sensing scene classification (RSSC) adopt an offline learning paradigm, where all classes are jointly optimized on fixed-class datasets. In dynamic real-world scenarios with streaming data and emerging classes, such paradigms are inherently prone to catastrophic forgetting when models are incrementally trained on new data. Recently, a growing number of class-incremental learning (CIL) methods have been proposed to tackle these issues, some of which achieve promising performance by rehearsing training data from previous tasks. However, implementing such strategy in real-world scenarios is often challenging, as the requirement to store historical data frequently conflicts with strict memory constraints and data privacy protocols. To address these challenges, we propose a novel memory-efficient feature-replay CIL framework (FR-CIL) for RSSC that retains compact feature embeddings, rather than raw images, as exemplars for previously learned classes. Specifically, a progressive multi-scale feature enhancement (PMFE) module is proposed to alleviate representation ambiguity. It adopts a progressive construction scheme to enable fine-grained and interactive feature enhancement, thereby improving the model’s representation capability for remote sensing scenes. Then, a specialized feature calibration network (FCN) is trained in a transductive learning paradigm with manifold consistency regularization to adapt stored feature descriptors to the updated feature space, thereby effectively compensating for feature space drift and enabling a unified classifier. Following feature calibration, a bias rectification (BR) strategy is employed to mitigate prediction bias by exclusively optimizing the classifier on a balanced exemplar set. As a result, this memory-efficient CIL framework not only addresses data privacy concerns but also mitigates representation drift and classifier bias. Extensive experiments on public datasets demonstrate the effectiveness and robustness of the proposed method. Notably, FR-CIL outperforms the leading state-of-the-art CIL methods in mean accuracy by margins of 3.75%, 3.09%, and 2.82% on the six-task AID, seven-task RSI-CB256, and nine-task NWPU-45 datasets, respectively. At the same time, it reduces memory storage requirements by over 94.7%, highlighting its strong potential for real-world RSSC applications under strict memory constraints. Full article