Search Results (560)

High-precision three-dimensional geological modeling of mining faces is crucial for intelligent coal mining and disaster prevention. Accurate spatial interpolation is essential for building high-quality models. This study focuses on the 25214 workface of the Hongliulin coal mine, addressing challenges in interpolating terrain elevation, stratum thickness, and coal seam thickness data. We evaluate eight interpolation methods (four kriging methods, an inverse distance weighting method, and three radial basis function methods) for terrain and stratum thickness, and nine methods (including the Bayesian Maximum Entropy method) for coal seam thickness, using cross-validation to assess their accuracy. Research results indicate that for terrain elevation data with dense and evenly distributed sampling points, linear kriging achieves the highest accuracy (MAE = 1.01 m, RMSE = 1.20 m). For the optimal interpolation methods of five layers of thickness data with sparse sampling points, the results are as follows: Q₄, spherical kriging (MAE = 2.13 m, RMSE = 2.83 m); N₂b, IDW (p = 2), MAE = 2.08 m, RMSE = 2.44 m; J₂y³, RS-RBF (MAE = 0.89 m, RMSE = 1.05 m); J₂y², TPS-RBF (MAE = 1.96 m, RMSE = 2.25 m); J₂y¹, HS-RBF (MAE = 2.36 m, RMSE = 2.71 m). A method for accurately delineating the zero line of strata thickness by assigning negative values to virtual thickness in areas of missing strata has been proposed. For coal seam thickness data with uncertain data (from channel wave exploration), a soft-hard data fusion interpolation method based on Bayesian Maximum Entropy has been introduced, and its interpolation results (MAE = 0.64 m, RMSE = 0.66 m) significantly outperform those of eight other interpolation algorithms. Using the optimal interpolation methods for terrain, strata, and coal seams, we construct a high-precision three-dimensional geological model of the workface, which provides reliable support for intelligent coal mining. Full article

Power generation is an important part of air vehicle energy management when developing long-endurance and reusable hypersonic aircraft. In order to utilize an air turbine power generation system on board, fuel-based rotating cooling has been researched to cool the turbine’s rotor blades. For fuel-cooling air turbines, each blade corresponds to a separate cooling channel. All the separate cooling channels cross together and form a distributary cavity and a confluence cavity in the center of the disk. In order to determine the flow characteristics in the distributary and confluence cavities, computational fluid dynamics (CFD) simulations using the shear–stress–transport turbulence model were carried out under the conditions of different rotating speeds and different mass flow rates. The results showed great differences between non-rotating flow and rotating flow conditions in the distributary and confluence cavities. The flow in the distributary and confluence cavities has rotational velocity, with obvious layering distribution regularity. Moreover, a high-speed rotational flow surface is formed in the confluence cavity of the original structure, due to the combined functions of centrifugal force, inertia, and the Coriolis force. Great pressure loss occurs when fluid passes through the high-speed rotational flow surface. This pressure loss increases with the increase in rotating speed and mass flow rate. Finally, four structures were compared, and an optimal structure with a separated outlet channel was identified as the best structure to eliminate this great pressure loss. Full article

The expansion of aquaculture necessitates innovative disease detection methods to ensure sustainable production. Fish diseases caused by bacteria, viruses, fungi, and parasites result in significant economic losses and threaten food security. Traditional detection methods are labor-intensive and time-consuming, emphasizing the need for automated approaches. This study investigates the application of convolutional neural networks (CNNs) for classifying freshwater fish diseases. Such CNNs offer an efficient and automated solution for fish disease detection, reducing the burden on aquatic health experts and enabling timely interventions to mitigate economic losses. A dataset of 2444 images was used across seven classes—bacterial red disease, bacterial Aeromoniasis disease, bacterial gill disease, fungal disease, parasitic diseases, white tail disease, and healthy fish. The CNNs model incorporates convolutional layers for feature extraction, max-pooling for down-sampling, dense layers for classification, and dropout for regularization. Categorical cross-entropy loss and the Adam optimizer were used over 50 epochs, with continuous training and validation performance monitoring. The results indicated that the model achieved an accuracy of 99.71% and a test loss of 0.0119. This study highlights the transformative potential of artificial intelligence in aquaculture for enhancing food security. Full article

There has been no feasible approach for predicting weight loss after bariatric surgery. This study develops wide and deep learning (WAD), a predictive and explainable artificial intelligence for weight loss after sleeve gastrectomy with medical image and non-image data, such as electronic medical records (EMRs). Prospective data came from 42 patients with sleeve gastrectomy at a university hospital. They were followed for one year after surgery. The dependent variable consisted of three categories: minimal, moderate, and significant change groups, classified based on postoperative percentage total weight loss (%TWL) in body mass index. A pair of 100 images and their non-image data came from each patient, with 4200 pairs from 42 patients in total. A WAD model was trained and tested with 3200 and 1000 pairs, respectively. Here, the WAD model combined a convolutional neural network (CNN) for image data and a linear layer for non-image data (EMR). The study population included 42 patients, with a mean age of 36.6 years (standard deviation SD 11.0) and a female proportion of 58% (26/45). On average, %TWL was 19.1 (SD 2.8), 27.3 (SD 2.2), and 35.1 (SD 4.7) for the minimal, moderate, and significant change groups, respectively. The corresponding accuracy outcomes were 61%, 100%, and 75% for the minimal, moderate, and significant change groups (average 71%). When the minimal and moderate change groups were combined, the accuracy was 100% for the combined group and 75% for the significant change group, with an overall average accuracy of 88%. Baseline HOMA2-B, insulin, and vitamin B12 were major predictors of %TWL. The optimal region of interest for predicting %TWL was found to be the entire cross-section above the diaphragm. In conclusion, WAD is an effective predictive and explainable artificial intelligence for weight loss following sleeve gastrectomy with image and non-image data. The most important predictors of postoperative weight loss were identified as baseline HOMA2-B, insulin, and vitamin B12 levels, while the key region of interest (ROI) in abdominal CT imaging was the entire cross-section located above the diaphragm. Full article

A novel composite biomass aerogel adsorbent (HSA) was prepared by dual physical and chemical cross-linking using sodium alginate (SA) as an organic biomass template and hydroxyapatite nanowires (HAPNWs) as an inorganic biomass skeleton. The structure of the HSA was characterized by scanning electron microscopy (SEM), X-ray powder diffractometry (XRD), Fourier transformed infrared spectroscopy (FTIR), and stress testing. One-factor experiments were conducted focusing on adsorption conditions at a Pb ion concentration of 300 mg/L, and the adsorption conditions were optimized using the response surface method. The optimal conditions obtained by numerical optimization using Design-Expert 13 were as follows: pH of 7.23, adsorption temperature of 35.42 °C, and adsorption time of 1050.73 min; the optimal adsorption capacity was 278.874 mg/g. To further reveal the adsorption mechanism of HSA, its adsorption model and kinetics were analyzed. Adsorption was most consistent with the Langmuir isothermal adsorption model, while the kinetics were most consistent with the pseudo-secondary kinetic model. R² reached 0.9986, indicating a mono-molecular layer of adsorption by heat, while the main adsorption mechanism was chemisorption. Full article

Side-scan sonar and multi-beam echo sounder (MBES) are the most widely used underwater surveying tools in marine mapping today. The MBES offers high accuracy in depth measurement but is limited by low imaging resolution due to beam density constraints. Conversely, side-scan sonar provides high-resolution backscatter intensity images but lacks precise positional information and often suffers from distortions. Thus, MBES and side-scan images complement each other in depth accuracy and imaging resolution. To obtain high-quality seafloor topography images in practice, matching between MBES and side-scan images is necessary. However, due to the significant differences in content and resolution between MBES depth images and side-scan backscatter images, they represent a typical example of heterogeneous images, making feature matching difficult with traditional image matching methods. To address this issue, this paper proposes a feature matching network based on the LoFTR algorithm, utilizing the intermediate layers of the ResNet-50 network to extract shared features between the two types of images. By leveraging self-attention and cross-attention mechanisms, the features of the MBES and side-scan images are combined, and a similarity matrix of the two modalities is calculated to achieve mutual matching. Experimental results show that, compared to traditional methods, the proposed model exhibits greater robustness to noise interference and effectively reduces noise. It also overcomes challenges, such as large nonlinear differences, significant geometric distortions, and high matching difficulty between the MBES and side-scan images, significantly improving the optimized image matching results. The matching error RMSE has been reduced to within six pixels, enabling the accurate matching of multi-beam and side-scan images. Full article

Electro-Fenton chemical mechanical polishing primarily regulates the generation of hydroxyl radicals (·OH) via the Fenton reaction through an applied electric field, which subsequently influences the formation and removal of the oxide layer on the workpiece surface, thereby impacting the overall polishing quality and rate. This study employs Pin–Disk friction and wear experiments to investigate the material removal behavior of single-crystal GaN during electro-Fenton chemical mechanical polishing. Utilizing a range of analytical techniques, including coefficient of friction (COF) curves, surface morphology assessments, cross-sectional analysis, and power spectral density (PSD) measurements on the workpiece surface, we examine the influence of abrasives, polishing pads, polishing pressure, and other parameters on the electro-Fenton chemical–mechanical material removal process. Furthermore, this research provides preliminary insights into the synergistic removal mechanisms associated with the electro-Fenton chemical–mechanical action in single-crystal GaN. The experimental results indicate that optimal mechanical removal occurs when using a W0.5 diamond at a concentration of 1.5 wt% combined with a urethane pad (SH-Q13K-600) under a pressure of 0.2242 MPa. Full article

During natural disasters such as earthquakes, fires, or landslides, the timely passage of special vehicles (primarily oversized vehicles) is crucial for successful emergency rescue operations. Efficient route planning algorithms capable of handling large-scale road networks are essential to facilitate this. This paper focuses on the rapid dispatch of special vehicles to their destinations within large-scale national road networks during emergency rescue operations. Using China’s national road network as a case study, a dual-layer road network data model was proposed to separate high-grade expressways from low-grade ordinary roadways to optimize data storage and access. A two-level spatial grid framework is also introduced to efficiently segment, extract, and store road network data. An improved algorithm constrained by a shortest-route planning objective function is proposed to improve route planning efficiency. This algorithm optimizes data access by loading high-grade road network data into memory once and only loading the necessary grid segments of low-grade road network data during route planning. The objective function incorporates constraints such as bridge weight and tunnel height limitations to ensure the safe passage of special vehicles. A parallelized bidirectional Dijkstra algorithm was proposed to further accelerate route planning. This approach simultaneously searches for optimal routes from both the starting and ending points, significantly improving efficiency for large-scale, cross-regional route planning. Experimental results demonstrate that our improved road network model and algorithm reduce search time by 1.69 times compared to conventional methods. The parallelized bidirectional Dijkstra algorithm further accelerates route planning by a factor of 3.75, achieving comparable performance to commercial software. The proposed road network model, route planning algorithm, and related findings offer valuable insights for optimizing emergency rescue operations and ensuring cost-effective resource allocation. Full article

Chemical sensors, relying on electrical conductance changes in a gas-sensitive material due to the surrounding gas, have the (dis-)advantage of reacting with multiple target gases and humidity. In this work, we report CMOS-integrated SnO₂ thin film-based gas sensors, which are functionalized with mono-, bi-, and trimetallic nanoparticles (NPs) to optimize the sensor performance. The spray pyrolysis technology was used to deposit the metal oxide sensing layer on top of a CMOS-fabricated micro-hotplate (µhp), and magnetron sputtering inert-gas condensation was employed to functionalize the sensing layer with metallic NPs, Ag-, Pd-, and Ru-NPs, and all combinations thereof were used as catalysts to improve the sensor response to carbon monoxide and to suppress the cross-sensitivity toward humidity. The focus of this work is the detection of toxic carbon monoxide and a specific hydrocarbon mixture (HC_mix) in a concentration range of 5–50 ppm at different temperatures and humidity levels. The use of CMOS chips ensures low-power, integrated sensors, ready to apply in cell phones, watches, etc., for air quality-monitoring purposes. Full article

Dielectric materials have gained traction for their energy-storage capacitive and electrically insulating properties as sensors and in smart surface technologies such as in In-Mold Electronics (IME). IME is a disruptive technology that involves environmentally protected electronics in plastic thermoformed and molded structures. The use of IME in a human–machine interface (HMI) provides a favorable experience to the users and helps reduce production costs due to a smaller list of parts and lower material costs. A few functional components that are compatible with one another are crucial to the final product’s properties in the IME structure. Of these components, the dielectric layers are an important component in the smart surface industry, providing insulation for the prevention of leakage currents in multilayered printed structures and capacitance sensing on the surface of specially designed shapes in IME. Advanced dielectric materials are non-conductive materials that impend and polarize electron movements within the material, store electrical energy, and reduce the flow of electric current with exceptional thermal stability. The selection of a suitable dielectric ink is an integral stage in the planning of the IME smart touch surface. The ink medium, solvent, and surface tension determine the printability, adhesion, print quality, and the respective reaction with the bottom and top conductive traces. The sequence in which the components are deposited and the heating processes in subsequent thermoforming and injection molding are other critical factors. In this study, various commercially available dielectric layers were each printed in two to four consecutive layers with a mesh thickness of 50–60 µm or 110–120 µm, acting as an insulator between conductive silver traces overlaid onto a polycarbonate substrate. Elemental mapping and optical analysis on the cross-section were conducted to determine the compatibility and the adhesion of the dielectric layers on the conductive traces and polycarbonate substrate. The final selection was based on the functionality, reliability, repeatability, time-stability, thickness, total processing time, appearance, and cross-sectional analysis results. The chosen candidate was then placed through the final product design, circuitry design, and plastic thermoforming process. In summary, this study will provide a general guideline to optimize the selection of dielectric inks for in-mold electronics applications. Full article

With the continuous advancement of 6G technology, SAGINs provide seamless coverage and efficient connectivity for future communications by integrating terrestrial, aerial, and satellite networks. Unmanned aerial vehicles (UAVs), owing to their high maneuverability and flexibility, have emerged as a critical component of the aerial layer in SAGINs. In this paper, we systematically review the key technologies, applications, and challenges of UAV-assisted SAGINs. First, the hierarchical architecture of SAGINs and their dynamic heterogeneous characteristics are elaborated on, and this is followed by an in-depth discussion of UAV communication. Subsequently, the core technologies of UAV-assisted SAGINs are comprehensively analyzed across five dimensions—routing protocols, security control, path planning, resource management, and UAV deployment—highlighting the progress and limitations of existing research. In terms of applications, UAV-assisted SAGINs demonstrate significant potential in disaster recovery, remote network coverage, smart cities, and agricultural monitoring. However, their practical deployment still faces challenges such as dynamic topology management, cross-layer protocol adaptation, energy-efficiency optimization, and security threats. Finally, we summarize the applications and challenges of UAV-assisted SAGINs and provide prospects for future research directions. Full article

A novel atmospheric pollutant transport quantification model (APTQM) has been developed to analyze and quantify cross-regional air pollutant transport pathways and fluxes. The model integrates high-resolution numerical simulations, Geographic Information System (GIS) capabilities, and advanced statistical evaluation metrics with boundary pixel decomposition methods to effectively characterize complex pollutant transport dynamics while ensuring computational efficiency. To evaluate its performance, the model was applied to a representative winter pollution event in Beijing in December 2021, using fine particulate matter (PM_2.5) as the target pollutant. The results underscore the model’s capability to accurately capture spatial and temporal variations in pollutant dispersion, effectively identify major transport pathways, and quantify the contributions of inter-regional sources. Cross-validation with established methods reveals strong spatial and temporal correlations, further substantiating its accuracy. APTQM demonstrates unique strengths in resolving dynamic transport processes within the boundary layer, particularly in scenarios involving complex cross-regional pollutant exchanges. However, the model’s reliance on a simplified chemical framework constrains its applicability to pollutants significantly influenced by secondary chemical transformations, such as ozone and nitrate. Consequently, APTQM is currently optimized for the quantification of primary pollutant transport rather than modeling complex atmospheric chemical processes. Overall, this study presents APTQM as a reliable and computationally efficient tool for quantifying inter-regional air pollutant transport, offering critical insights to support regional air quality management and policy development. Full article

Chitosan is a positively charged natural polymer with several properties conducive to wound-healing applications, such as biodegradability, structural integrity, hydrophilicity, adhesiveness to tissue, and bacteriostatic potential. Along with other mechanical properties, some of the properties discussed in this review are antibacterial properties, mucoadhesive properties, biocompatibility, high fluid absorption capacity, and anti-inflammatory response. Chitosan forms stable complexes with oppositely charged polymers, arising from electrostatic interactions between (+) amino groups of chitosan and (−) groups of other polymers. These polyelectrolyte complexes (PECs) can be manufactured using various materials and methods, which brings a diversity of formulations and properties that can be optimized for specific wound healing as well as other applications. For example, chitosan-based PEC can be made into dressings/films, hydrogels, and membranes. There are various pros and cons associated with manufacturing the dressings; for instance, a layer-by-layer casting technique can optimize the nanoparticle release and affect the mechanical strength due to the formation of a heterostructure. Furthermore, chitosan’s molecular weight and degree of deacetylation, as well as the nature of the negatively charged biomaterial with which it is cross-linked, are major factors that govern the mechanical properties and biodegradation kinetics of the PEC dressing. The use of chitosan in wound care products is forecasted to drive the growth of the global chitosan market, which is expected to increase by approximately 14.3% within the next decade. This growth is driven by products such as chitoderm-containing ointments, which provide scaffolding for skin cell regeneration. Despite significant advancements, there remains a critical gap in translating chitosan-based biomaterials from research to clinical applications. Full article

Forest fires pose a severe threat to ecological environments and the safety of human lives and property, making real-time forest fire monitoring crucial. This study addresses challenges in forest fire image object detection, including small fire targets, sparse smoke, and difficulties in feature extraction, by proposing TFNet, a Transformer-based multi-scale feature fusion detection network. TFNet integrates several components: SRModule, CG-MSFF Encoder, Decoder and Head, and WIOU Loss. The SRModule employs a multi-branch structure to learn diverse feature representations of forest fire images, utilizing 1 × 1 convolutions to generate redundant feature maps and enhance feature diversity. The CG-MSFF Encoder introduces a context-guided attention mechanism combined with adaptive feature fusion (AFF), enabling effective multi-scale feature fusion by reweighting features across layers and extracting both local and global representations. The Decoder and Head refine the output by iteratively optimizing target queries using self- and cross-attention, improving detection accuracy. Additionally, the WIOU Loss assigns varying weights to the IoU metric for predicted versus ground truth boxes, thereby balancing positive and negative samples and improving localization accuracy. Experimental results on two publicly available datasets, D-Fire and M4SFWD, demonstrate that TFNet outperforms comparative models in terms of precision, recall, F1-Score, mAP50, and mAP50–95. Specifically, on the D-Fire dataset, TFNet achieved metrics of 81.6% precision, 74.8% recall, an F1-Score of 78.1%, mAP50 of 81.2%, and mAP50–95 of 46.8%. On the M4SFWD dataset, these metrics improved to 86.6% precision, 83.3% recall, an F1-Score of 84.9%, mAP50 of 89.2%, and mAP50–95 of 52.2%. The proposed TFNet offers technical support for developing efficient and practical forest fire monitoring systems. Full article

Knowledge graphs have shown great potential in alleviating the data sparsity problem in recommendation systems. However, existing graph-attention-based recommendation methods primarily focus on user–item–entity interactions, overlooking potential relationships between users while introducing noisy entities and redundant high-order information. To address these challenges, this paper proposes a graph-attention-based recommendation method that enhances user features using knowledge graphs (KGAEUF). This method models user relationships through collaborative propagation, links entities via similar user entities, and filters highly relevant entities from both user–entity and user–relation perspectives to reduce noise interference. In multi-layer propagation, a distance-aware weight allocation mechanism is introduced to optimize high-order information aggregation. Experimental results demonstrate that KGAEUF outperforms existing methods on AUC and F1 metrics on the Last.FM and Book-Crossing datasets, validating the model’s effectiveness. Full article