Search Results (217)

To address the limited environmental perception capability and the difficulty of achieving consistent and efficient representation of the workspace feasible domain caused by high dust concentration, uneven illumination, and enclosed spaces in underground coal mines, this paper proposes a digital spatial construction and representation method for underground environments by integrating RGB-D depth vision with ORB-SLAM3. First, a ChArUco calibration board with embedded ArUco markers is adopted to perform high-precision calibration of the RGB-D camera, improving the reliability of geometric parameters under weak-texture and non-uniform lighting conditions. On this basis, a “dense–sparse cooperative” OAK-DenseMapper Pro module is further developed; the module improves point-cloud generation using a mathematical projection model, and combines enhanced stereo matching with multi-stage depth filtering to achieve high-quality dense point-cloud reconstruction from RGB-D observations. The dense point cloud is then converted into a probabilistic octree occupancy map, where voxel-wise incremental updates are performed for observed space while unknown regions are retained, enabling a memory-efficient and scalable 3D feasible-space representation. Experiments are conducted in multiple representative coal-mine tunnel scenarios; compared with the original ORB-SLAM3, the number of points in dense mapping increases by approximately 38% on average; in trajectory evaluation on the TUM dataset, the root mean square error, mean error, and median error of the absolute pose error are reduced by 7.7%, 7.1%, and 10%, respectively; after converting the dense point cloud to an octree, the map memory footprint is only about 0.5% of the original point cloud, with a single conversion time of approximately 0.75 s. The experimental results demonstrate that, while ensuring accuracy, the proposed method achieves real-time, efficient, and consistent representation of the 3D feasible domain in complex underground environments, providing a reliable digital spatial foundation for path planning, safe obstacle avoidance, and autonomous operation. Full article

Accurate path planning was the prerequisite for autonomous navigation of agricultural vehicles. An Unmanned Aerial Vehicle (UAV)-based coverage path planning was developed in this research for automating guidance of agricultural vehicles and reducing the operator maneuver in the creation of navigation maps. High-resolution orthophoto maps of the field were constructed by using low-altitude UAV photogrammetry to obtain spatial information. Travel paths and working paths were automatically generated from anchor points selected by the operator under the image coordinate domain. The navigation path for unmanned agricultural vehicles was generated by Mercator projection-based conversion for the anchor pixel coordinates into latitude and longitude geographic coordinates. A Graphical User Interface (GUI) was developed for path generation, visualization, and performance evaluation, through which the proposed path planning method was implemented for autonomous agricultural vehicle navigation. Calculation accuracy tests demonstrated the mean planar coordinate error was 2.23 cm and the maximum error was 3.37 cm for path planning. Field tests showed that lateral navigation errors remained within ±5.5 cm for the unmanned high-clearance sprayer, which indicated that the developed UAV-based coverage path planning method was feasible and featured high accuracy. It provided an effective solution for achieving fully autonomous agricultural vehicle operations. Full article

Maintenance of building fire protection facilities is crucial for preventing fires and safeguarding lives and property; the standardization and timeliness of these activities directly determine operational reliability. However, as fire-safety requirements escalate, manually drafting maintenance work orders remains inefficient and prone to omissions. Furthermore, regulatory documents in this domain are inherently complex, and annotated resources are scarce, hampering the digitalization of fire-safety management. To address these challenges, this paper presents an LLM-based method for automatically generating maintenance work orders for building fire protection facilities. The proposed approach integrates a domain-specific knowledge base and incorporates the FS-RAG (Fire Services–Retrieval-Augmented Generation) framework to enhance both the accuracy and practical usability of generated work orders. First, we construct a lightweight domain knowledge base, FSKB (Fire Services Knowledge Base), derived from extensive maintenance regulations, capturing key elements such as equipment types, components, maintenance actions, and frequencies. Second, we design an FS-RAG framework that leverages retrieval-augmented generation to extract critical information from regulations and fuse it with the knowledge base, ensuring high accuracy and operational feasibility. Multi-round evaluations across stages B0–B4 validate the effectiveness of our method. Results indicate significant improvements over traditional approaches: the line-level compliance rate reaches 97.3% (an increase of 5.7% over B1 and 30.4% over B0), and the F1 score achieves 90.42% (an increase of 12.62% over B1 and 29.87% over B0). 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

Precise identification of early peanut leaf spot is strategically significant for safeguarding oilseed supplies and reducing pesticide reliance. However, general-purpose detectors face severe domain adaptation bottlenecks in unstructured field environments due to small feature dissipation, physical occlusion, and class imbalance. To address this, this study constructs a dataset spanning two phenological cycles and proposes POD-YOLO, a physics-aware and dynamics-optimized lightweight framework. Anchored on the YOLOv10n architecture and adhering to a “data-centric” philosophy, the framework optimizes the parameter convergence path via a synergistic “Augmentation-Loss-Optimization” mechanism: (1) Input Stage: A Physical Domain Reconstruction (PDR) module is introduced to simulate physical occlusion, blocking shortcut learning and constructing a robust feature space; (2) Loss Stage: A Loss Manifold Reshaping (LMR) mechanism is established utilizing dual-branch constraints to suppress background gradients and enhance small target localization; and (3) Optimization Stage: A Decoupled Dynamic Scheduling (DDS) strategy is implemented, integrating AdamW with cosine annealing to ensure smooth convergence on small-sample data. Experimental results demonstrate that POD-YOLO achieves a 9.7% precision gain over the baseline and 83.08% recall, all while maintaining a low computational cost of 8.4 GFLOPs. This study validates the feasibility of exploiting the potential of lightweight architectures through optimization dynamics, offering an efficient paradigm for edge-based intelligent plant protection. Full article

Voltage source converter-based–high voltage direct current (VSC-HVDC) systems exhibit strong nonlinear characteristics that dominate their dynamic behavior under large disturbances, making large-signal stability assessment essential for secure operation. This paper proposes a large-signal stability analysis framework for VSC-HVDC systems. The framework combines a unified Takagi–Sugeno (T–S) fuzzy model with a model-free predictive control (MFPC) scheme to enlarge the estimated domain of attraction (DOA) and bring it closer to the true stability region. The global nonlinear dynamics are captured by integrating local linear sub-models corresponding to different operating regions into a single T–S fuzzy representation. A Lyapunov function is then constructed, and associated linear matrix inequality (LMI) conditions are derived to certify large-signal stability and estimate the DOA. To further reduce the conservatism of the LMI-based iterative search, we embed a genetic-algorithm-based optimizer into the model-free predictive controller. The optimizer guides the improved LMI iteration paths and enhances the DOA estimation. Simulation studies in MATLAB 2023b/Simulink on a benchmark VSC-HVDC system confirm the feasibility of the proposed approach and show a less conservative DOA estimate compared with conventional methods. Full article

Background/Objectives: Nonunion bone healing results from a critical size defect that fails to bridge a bone injury to produce bony union. Novel approaches are critical for refining therapy in clinically challenging bone injuries, but the complex and coordinated nature of fracture callus tissue development requires study outside of the simple closed murine fracture model. Methods: We have utilized a three-dimensional printing approach to develop a scaffold construct with layers designed to sequentially release small molecule therapy within the tissues of a murine endochondral segmental defect to augment different mechanisms of fracture repair during critical stages of nonunion bone healing. Initially, a sonic hedgehog (SHH) agonist is released from a fibrin layer to promote chondrogenesis. A prolyl-hydroxylase domain (PHD)2 inhibitor is subsequently released from a β-tricalcium phosphate (β-TCP) layer to promote hypoxia-inducible factor (HIF)-1α regulation of angiogenesis. This sequential approach to therapy delivery is assisted by the inclusion of bone marrow stromal cells (BMSCs) to increase the cell substrate available for the small molecule therapy. Results: Immunohistochemistry of fracture callus tissue revealed increased expression of PTCH1 and HIF1α, targets of hedgehog and hypoxia signaling pathways, respectively, in the SAG21k/IOX2-treated mice compared to vehicle control. MicroCT and histology analyses showed increased bone in the fracture callus of mice that received therapy compared to control vehicle scaffolds. Conclusions: While our findings establish feasibility for the use of BMSCs and small molecules in the fibrin gel/β-TCP scaffolds to promote new bone formation for segmental defect healing, further optimization of these approaches is required to develop a fracture callus capable of completing bony union in a large defect. Full article

The construction sector is central to achieving the objectives of the European Green Deal (EGD). While existing research on transition risks predominantly focuses on project- or firm-level challenges, less is known about the transition risks implied by high-level EU policy documents. This study addresses this gap by systematically analysing 101 EGD-related policy and guidance documents published between 2019 and February 2025. A mixed human–AI content analysis approach was applied, combining human expert manual coding with automated validation using large language models (Kimi K2 and GLM 4.6). The final dataset contains 2752 coded risk references organised into eight main categories and twenty-six subcategories. Results show that transition risks are most frequently associated with environmental, economic, and legislative domains, with Climate Change Impact, Cost of Transition, Pollution, Investment Risks, and Implementation Variability emerging as the most prominent risks across the corpus. Technological and social risks appear less frequently but highlight important systemic and contextual vulnerabilities. Overall, analysis of the EGD policy texts reveals the green transition as being constrained not only by environmental pressures but also by financial feasibility and execution capacity. The study provides a structured, policy-level risk profile of the EGD and demonstrates the value of hybrid human–LLM analysis for large-scale policy content analysis and interpretation. These insights support policymakers and industry stakeholders to anticipate structural uncertainties that may affect the construction sector’s transition toward a low-carbon, circular economy. Full article

Background/Objectives: The present exploratory study evaluates the feasibility and psychological effects of a structured Qigong intervention implemented in an Italian university setting. Qigong is a traditional Chinese mind–body practice combining gentle movements, breathwork, and mindful attention, aimed at enhancing mind–body integration and interoceptive awareness. Methods: A total of 332 undergraduate students voluntarily enrolled in a 12-week Qigong program. The intervention was based on Neidan Qigong and integrated both static and dynamic exercises. Psychological functioning was assessed through several self-report measures evaluating a range of constructs, including mindfulness (FFMQ), interoceptive ability (MAIA), perceived stress (PSS), depression, anxiety, and stress (BDI; DASS-21; STAI Y), emotion regulation (DERS), alexithymia (TAS), and sleep quality (PSQI). Results: A total of 114 students completed the intervention. The protocol was well received by participants and demonstrated high feasibility in the academic context, with good attendance rates and overall engagement. Preliminary findings indicate consistent improvements across several psychological domains. Conclusions: The results suggest that Qigong may be associated with improvements in mental health and well-being in young adults and may represent a promising, low-cost intervention. The findings should be interpreted as preliminary. Further research using controlled and methodologically rigorous designs is needed to assess the stability of these effects over time, incorporate physiological measures, and clarify the specific therapeutic contribution of spontaneous movement within Qigong practice. Full article

Aiming at the receiving-end power grid frequency security after HVDC blocking events, this paper proposes a hybrid model and data-driven optimization method of emergency load shedding (ELS). Firstly, a decision-making model of ELS considering multiple dynamic security constraints, e.g., frequency and rotor angle, is constructed. Then, the particle swarm optimization (PSO) algorithm is improved by integrating with symmetric opposite learning and Tent chaotic mapping to obtain high-quality ELS schemes. Finally, to boost the optimization efficiency, a data-driven dynamic security assessment model based on the hybrid neural network is constructed and introduced into the solution process of PSO. To ensure the feasibility of the final ELS scheme, the model-driven time-domain simulation method is adopted for validation. The effectiveness of the proposed ELS optimization method is verified on a receiving-end power grid with multi-infeed HVDC lines. It can obtain a high-quality and feasible ELS scheme within 0.7 s. Full article

Hazardous gas leaks are a major trigger of chemical incidents. If not handled in time, they can easily lead to secondary disasters such as fires and explosions. In recent years, with the construction of hazardous chemical monitoring and early-warning systems in China, large volumes of field operating data from flammable and toxic gas sensors have been accumulated, providing a data foundation for leak-pattern studies grounded in real-world scenarios. In this study, 56 leak samples verified by site feedback were selected. Time-aware interpolation and Z-score normalization were used for preprocessing, and time-series features—including standard deviation of first differences, autocorrelation coefficients, and frequency-domain energy—were extracted. Leak patterns were then identified using two unsupervised approaches: K-Means clustering and a 1D-CNN autoencoder. Results show that K-Means effectively distinguishes macro-patterns such as sustained leaks, instantaneous leaks, fluctuating leaks, and interrupted leaks, while the autoencoder demonstrates stronger capability in extracting temporal features, revealing leak evolution and transition characteristics. The two methods are complementary and together provide a viable route to developing an end-to-end model for leak scenario identification and risk discrimination. This work not only verifies the feasibility of conducting leak-pattern recognition using real GDS data but also offers technical guidance for the intelligent upgrading of hazardous chemical monitoring and early-warning systems. Full article

The current mainstream methods for online detection of transformers all have shortcomings such as low sensitivity and susceptibility to interference from the testing environment. Aiming at the shortcomings of the existing online detection methods for transformer winding deformation in terms of feature sensitivity and diagnostic accuracy, this paper proposes a fault intelligent diagnosis method based on high sensitivity multimodal feature fusion. First, the winding deformation experiment is designed for typical fault data, which is obtained to extract multiple frequency and time domain response features and construct a multidimensional feature library. Subsequently, principal component analysis is used to evaluate the sensitivity of each feature to different faults and establish a highly sensitive multimodal feature system. On this basis, a TCN-BiGRU-PHA diagnostic model combining time convolutional network, bidirectional gated loop unit and attention mechanism is constructed to realize accurate identification of winding deformation faults. The experimental results show that the method has higher recognition accuracy under multiple types of faults, which provides feasible ideas and methodological support for realizing online intelligent monitoring of transformer winding deformation. Full article

Against the backdrop of growing demand for rapid soil testing technologies in precision agriculture, this study proposes a detection method based on pyrolysis-electronic nose and machine olfaction signal analysis to achieve precise measurement of key soil nutrients. An electronic nose system comprising 10 metal oxide semiconductor gas sensors was constructed to collect response signals from 112 black soil samples undergoing pyrolysis at 400 °C. By extracting time-domain and frequency-domain features from sensor responses, an initial dataset of 180 features was constructed. A novel feature fusion method combining Pearson correlation coefficients (PCC) with recursive feature elimination cross-validation (RFECV) was proposed to optimize the feature space, enhance representational power, and select key sensitive features. In predicting soil organic matter (SOM), total nitrogen (TN), available potassium (AK), and available phosphorus (AP) content, we compared support vector machines (SVM), support vector machine-random forest models (SVM-RF), and particle swarm optimization-enhanced support vector machine-random forest models (PSO-SVM-RF). Results indicate that PSO-SVM-RF demonstrated optimal performance across all nutrient predictions, achieving a coefficient of determination (R²) of 0.94 for SOM and TN, with a performance-to-bias ratio (RPD) exceeding 3.8. For AK and AP, R² improved to 0.78 and 0.74, respectively. Compared to the SVM model, the root mean square error (RMSE) decreased by 25.4% and 21.6% for AK and AP, respectively, with RPD values approaching the practical threshold of 2.0. This study validated the feasibility and application potential of combining electronic nose technology with a time-frequency domain feature fusion strategy for precise quantitative analysis of soil nutrients, providing a new approach for soil fertility assessment in precision agriculture. Full article

Collagen-based biomaterials are widely used, but their relatively rapid biodegradation can limit functional duration. Such collagen constructs are widely used as barrier membranes in guided tissue and bone regeneration, where controlled degradation is essential for maintaining function. Although conventional crosslinking methods extend stability, they may introduce cytotoxicity, alter mechanical behavior, or hinder tissue integration. This study evaluated whether incorporating L-serine, a polar amino acid capable of hydrogen bonding, could modulate collagen structure and slow degradation without chemical crosslinking. L-Serine was selected because its hydroxyl-containing side chain can engage in biocompatible, hydrogen-bond–mediated interactions that offer a mild, non-crosslinking means of stabilizing collagen. Collagen scaffolds, prepared by incorporating L-serine into a collagen hydrogel followed by drying, were produced with 0–40 wt% L-serine and characterized using X-ray diffraction, Fourier-transform infrared spectroscopy, circular dichroism, and scanning electron microscopy. In vivo degradation was assessed in a subcutaneous mouse model comparing unmodified collagen, collagen containing 40 wt% L-serine, and a commercially available bilayer porcine collagen membrane (Bio-Gide^®, composed of type I and III collagen), with residual area quantified by serial sonography and histological evaluation. Low-to-moderate L-serine incorporation preserved triple-helical features, while 40 wt% led to crystalline domain formation and β-sheet enrichment. L-serine–treated collagen exhibited significantly greater residual area (2.70 ± 1.45 mm²) than unmodified collagen (0.37 ± 0.22 mm², p < 0.05), although Bio-Gide^® remained the most persistent (5.64 ± 2.76 mm²). These findings demonstrate that L-serine incorporation can modulate collagen structure and degradation kinetics through a simple, aqueous, and non-crosslinking approach. The results provide preliminary feasibility data supporting amino acid–assisted tuning of collagen resorption properties and justify further evaluation using membrane-specific fabrication and application-relevant testing. Full article

Distinguishing subcutaneous adipose tissue (SAT) from intermuscular adipose tissue (IMAT) is clinically important because IMAT infiltration is strongly associated with age-related functional decline, sarcopenia, diabetes, cardiovascular disease, and obesity. Current assessments rely on MRI or CT, which are stationary, costly, and labor-intensive. Portable ultrasound-based solutions could enable broader, proactive screening. This model study investigated the feasibility of differentially assessing SAT and IMAT using features extracted from propagating ultrasound signals. Twenty-five phantoms were constructed using gelatin as a muscle-mimicking matrix and oil as the SAT and IMAT compartments, arranged to provide gradual variations in fat fractions ranging from 0% to 50%. Ultrasound measurements were collected at 0.8 MHz and 2.2 MHz, and multiple evaluation criteria were computed, including ultrasound velocity and parameters derived from the signal intensity. Classification domains were then generated from intersecting decision rules associated with these criteria. In parallel, artificial neural networks (ANN/LSTM) were trained and tested on identical phantom subsets to evaluate data-driven classification performance. Both the rule-based and ANN/LSTM approaches achieved diagnostically meaningful separation of SAT and IMAT. The aim of this work was to perform an experimental proof-of-concept study on idealized tissue models to demonstrate that ultrasound measurements can reliably differentiate SAT and IMAT, supporting the development of future screening devices. Full article