Search Results (1,098)

This paper addresses the problem of loss of control during flight caused by failures of flight control surfaces. It presents a study of an emergency thrust control system based on linear-quadratic control with integral action. The research encompasses an analysis of thrust modulation control characteristics, a review of existing control systems, and a detailed description of the development process, including the research platform configuration, identification of the aircraft state-space model, control law design, integration of system components within the MATLAB and Simulink environment, and software-in-the-loop testing conducted in the X-Plane 11 flight simulator using a Boeing 757-200 model. The study also investigates the issue of control channel cross-coupling and its impact on simultaneous control of the aircraft’s longitudinal and lateral dynamics. The simulation results demonstrate that the proposed emergency system provides adequate controllability, with settling times of approximately 12 s for achieving a flight path angle setpoint of +5°, and 13 s for attaining a maximum (limited) roll angle of 20°, achieved in separate manoeuvres. Furthermore, simulated landing attempts suggest that the system could potentially enable successful landings at approach speeds significantly higher than standard recommendations. However, further investigation is required to address decoupling of control channels, ensure system stability, and evaluate control performance across a broader range of aircraft configurations. Full article

The aim of the study is to carry out a technical assessment of a Hungarian baths complex, which is a major tourist center with approximately 180,000 visitors per year. The bath complex had been partially closed. Following the partial closure of the spa, a comprehensive survey was carried out, identifying four main problem areas: operational difficulties with the thermal and cold-water wells, outdated water treatment technology, structural damage to the swimming pool and general mechanical deficiencies. Based on these investigations, recommendations were made for a safe and sustainable reopening of the spa, such as the reactivation of the geothermal system, the installation of modern filtration and dosing systems, and the application of energy-efficient and intelligent technologies. Based on the recommendations, the safe, economical, and sustainable reopening of the spa can be achieved, while also providing guidance for the modernization of other spa complexes. A separate section presents detailed development proposals, such as restarting the geothermal system, applying modern water treatment technologies and intelligent control systems, renovating the pool structure, and modernizing the mechanical and electrical systems. These proposals contribute to the modernization of the spa infrastructure and can also provide guidance for solving technical problems in other similar facilities. Full article

Semantic change detection technology based on remote sensing data holds significant importance for urban and rural planning decisions and the monitoring of ground objects. However, simple convolutional networks are limited by the receptive field, cannot fully capture detailed semantic information, and cannot effectively perceive subtle changes and constrain edge information. Therefore, a dynamic graph reasoning network with layer-by-layer semantic decomposition for semantic change detection in remote sensing data is developed in response to these limitations. This network aims to understand and perceive subtle changes in the semantic content of remote sensing data from the image pixel level. On the one hand, low-level semantic information and cross-scale spatial local feature details are obtained by dividing subspaces and decomposing convolutional layers with significant kernel expansion. Semantic selection aggregation is used to enhance the characterization of global and contextual semantics. Meanwhile, the initial multi-scale local spatial semantics are screened and re-aggregated to improve the characterization of significant features. On the other hand, at the encoding stage, the weight-sharing approach is employed to align the positions of ground objects in the change area and generate more comprehensive encoding information. Meanwhile, the dynamic graph reasoning module is used to decode the encoded semantics layer by layer to investigate the hidden associations between pixels in the neighborhood. In addition, the edge constraint module is used to constrain boundary pixels and reduce semantic ambiguity. The weighted loss function supervises and optimizes each module separately to enable the network to acquire the optimal feature representation. Finally, experimental results on three open-source datasets, such as SECOND, HIUSD, and Landsat-SCD, show that the proposed method achieves good performance, with an SCD score reaching 35.65%, 98.33%, and 67.29%, respectively. Full article

Currently, most studies evaluating storm surges are conducted at the provincial level, and there is a lack of detailed research focusing on cities. This paper focuses on the urban scale, using some fine-scale data of coastal areas obtained through remote sensing images. This research is based on the Hazard–Exposure–Vulnerability (H-E-V) framework and PPRR (Prevention, Preparedness, Response, and Recovery) crisis management theory. It focuses on 52 Chinese coastal cities as the research subject. The evaluation system for the disaster response capabilities of Chinese coastal cities was constructed based on three aspects: the stability of the disaster-incubating environment (S), the risk of disaster-causing factors (R), and the vulnerability of disaster-bearing bodies (V). The significance of this study is that the storm surge capability of China’s coastal cities can be analyzed based on the results of the evaluation, and the evaluation model can be used to identify its deficiencies. In this paper, these storm surge disaster response capabilities of coastal cities were scored using the entropy weighted TOPSIS method and the weight rank sum ratio (WRSR), and the results were also analyzed. The results indicate that Wenzhou has the best comprehensive disaster response capability, while Yancheng has the worst. Moreover, Tianjin, Ningde, and Shenzhen performed well in the three aspects of vulnerability of disaster-bearing bodies, risk of disaster-causing factors, and stability of disaster-incubating environment separately. On the contrary, Dandong (tied with Qinzhou), Jiaxing, and Chaozhou performed poorly in the above three areas. Full article

Facial expression plays an important role in human–computer interaction and affective computing. However, existing expression recognition methods cannot effectively capture multi-scale structural details contained in facial expressions, leading to a decline in recognition accuracy. Inspired by the multi-scale processing mechanism of the biological visual system, this paper proposes a wavelet-guided global–local feature aggregation network (WGGLFA) for facial expression recognition (FER). Our WGGLFA network consists of three main modules: the scale-aware expansion (SAE) module, which combines dilated convolution and wavelet transform to capture multi-scale contextual features; the structured local feature aggregation (SLFA) module based on facial keypoints to extract structured local features; and the expression-guided region refinement (ExGR) module, which enhances features from high-response expression areas to improve the collaborative modeling between local details and key expression regions. All three modules utilize the spatial frequency locality of the wavelet transform to achieve high-/low-frequency feature separation, thereby enhancing fine-grained expression representation under frequency domain guidance. Experimental results show that our WGGLFA achieves accuracies of 90.32%, 91.24%, and 71.90% on the RAF-DB, FERPlus, and FED-RO datasets, respectively, demonstrating that our WGGLFA is effective and has more capability of robustness and generalization than state-of-the-art (SOTA) expression recognition methods. Full article

The transition to clean and renewable energy sources is critically dependent on efficient hydrogen production technologies. This review surveys recent advances in photocatalytic water splitting, focusing on MNb₂O₆ nanomaterials, which have emerged as promising photocatalysts due to their tunable band structures, chemical robustness, and tailored morphologies. The objectives of this work are to (i) encompass the current synthesis strategies for MNb₂O₆ compounds; (ii) assess their structural, electronic, and optical properties in relation to photocatalytic performance; and (iii) elucidate the mechanisms underpinning enhanced hydrogen evolution. Main data collection methods include a literature review of experimental studies reporting bandgap measurements, structural analyses, and hydrogen production metrics for various MNb₂O₆ compositions—especially those incorporating transition metals such as Mn, Cu, Ni, and Co. Novelty stems from systematically detailing the relationships between synthesis routes (hydrothermal, solvothermal, electrospinning, etc.), crystallographic features, conductivity type, and bandgap tuning in these materials, as well as by benchmarking their performance against more conventional photocatalyst systems. Key findings indicate that MnNb₂O₆, CuNb₂O₆, and certain engineered heterostructures (e.g., with g-C₃N₄ or TiO₂) display significant visible-light-driven hydrogen evolution, achieving hydrogen production rates up to 146 mmol h⁻¹ g⁻¹ in composite systems. The review spotlights trends in heterojunction design, defect engineering, co-catalyst integration, and the extension of light absorption into the visible range, all contributing to improved charge separation and catalytic longevity. However, significant challenges remain in realizing the full potential of the broader MNb₂O₆ family, particularly regarding efficiency, scalability, and long-term stability. The insights synthesized here serve as a guide for future experimental investigations and materials design, advancing the deployment of MNb₂O₆-based photocatalysts for large-scale, sustainable hydrogen production. Full article

The rise of virtual production has created an urgent need for both efficient and high-fidelity 3D face generation schemes for cinema and immersive media, but existing methods are often limited by lighting–geometry coupling, multi-view dependency, and insufficient artistic quality. To address this, this study proposes a cross-modal 3D face generation framework based on single-view semantic masks. It utilizes Swin Transformer for multi-level feature extraction and combines with NeRF for illumination decoupled rendering. We utilize physical rendering equations to explicitly separate surface reflectance from ambient lighting to achieve robust adaptation to complex lighting variations. In addition, to address geometric errors across illumination scenes, we construct geometric a priori constraint networks by mapping 2D facial features to 3D parameter space as regular terms with the help of semantic masks. On the CelebAMask-HQ dataset, this method achieves a leading score of SSIM = 0.892 (37.6% improvement from baseline) with FID = 40.6. The generated faces excel in symmetry and detail fidelity with realism and aesthetic scores of 8/10 and 7/10, respectively, in a perceptual evaluation with 1000 viewers. By combining physical-level illumination decoupling with semantic geometry a priori, this paper establishes a quantifiable feedback mechanism between objective metrics and human aesthetic evaluation, providing a new paradigm for aesthetic quality assessment of AI-generated content. Full article

A novel strategy has been developed for extracting value-added resources from iron-lean, high-alumina- and -silica-containing red muds (RMs). With little or no recycling, such RMs are generally destined for waste dumps. Detailed results are presented on the carbothermic reduction of 100% RM (29.3 wt.% Fe₂O₃, 22.2 wt.% Al₂O₃, 20.0 wt.% SiO₂, 1.2 wt.% CaO, 12.2 wt.% Na₂O) and its 2:1 blends with Fe₂O₃ and red mill scale (MS). Synthetic graphite was used as the reductant. Carbothermic reduction of RM and blends was carried out in a Tamman resistance furnace at 1650 °C for 20 min in an Ar atmosphere. Reduction residues were characterized using scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS), elemental mapping and X-ray diffraction (XRD). Small amounts of Fe₃Si alloys, alumina, SiC and other oxide-based residuals were detected in the carbothermic residue of 100% RM. A number of large metallic droplets of Fe–Si alloys were observed for RM/Fe₂O₃ blends; no aluminium was detected in these metallic droplets. A clear segregation of alumina was observed as a separate phase. For the RM/red MS blends, a number of metallic Fe–Si droplets were seen embedded in an alumina matrix in the form of a cermet. This study has shown the regeneration of alumina and the formation of alumina-based cermets, Fe–Si alloys and SiC during carbothermic reduction of RM and its blends. This innovative recycling strategy could be used for extracting value-added resources from iron-lean RMs, thereby enhancing process productivity, cost-effectiveness of alumina regeneration, waste utilization and sustainable developments in the field. Full article

Smoke appears earlier than flames, so image-based fire monitoring techniques mainly focus on the detection of smoke, which is regarded as one of the effective strategies for preventing the spread of initial fires that eventually evolve into serious fires. Smoke monitoring in mine fires faces serious challenges: the underground environment is complex, with smoke and backgrounds being highly integrated and visual features being blurred, which makes it difficult for existing image-based monitoring techniques to meet the actual needs in terms of accuracy and robustness. The conventional ground-based methods are directly used in the underground with a high rate of missed detection and false detection. Aiming at the core problems of mixed target and background information and high boundary uncertainty in smoke images, this paper, inspired by the principle of information entropy, proposes a method for recognizing smoke from mine fires by integrating entropy-enhanced image processing and improved YOLOv8. Firstly, according to the entropy change characteristics of spatio-temporal information brought by smoke diffusion movement, based on spatio-temporal entropy separation, an equidistant frame image differential fusion method is proposed, which effectively suppresses the low entropy background noise, enhances the detail clarity of the high entropy smoke region, and significantly improves the image signal-to-noise ratio. Further, in order to cope with the variable scale and complex texture (high information entropy) of the smoke target, an improvement mechanism based on entropy-constrained feature focusing is introduced on the basis of the YOLOv8m model, so as to more effectively capture and distinguish the rich detailed features and uncertain information of the smoke region, realizing the balanced and accurate detection of large and small smoke targets. The experiments show that the comprehensive performance of the proposed method is significantly better than the baseline model and similar algorithms, and it can meet the demand of real-time detection. Compared with YOLOv9m, YOLOv10n, and YOLOv11n, although there is a decrease in inference speed, the accuracy, recall, average detection accuracy mAP (50), and mAP (50–95) performance metrics are all substantially improved. The precision and robustness of smoke recognition in complex mine scenarios are effectively improved. Full article

Frequent corn leaf diseases and pests pose serious threats to agricultural production. Traditional manual detection methods suffer from significant limitations in both performance and efficiency. To address this, the present paper proposes a novel biotic condition screening (BCS) model for the detection of corn leaf diseases and pests, called BCS_YOLO, based on the You Only Look Once version 11n (YOLOv11n). The proposed model enables accurate detection and classification of various corn leaf pathologies and pest infestations under challenging agricultural field conditions. It achieves this thanks to three key newly designed modules—a Self-Perception Coordinated Global Attention (SPCGA) module, a High/Low-Frequency Feature Enhancement (HLFFE) module, and a Local Attention Enhancement (LAE) module. The SPCGA module improves the model’s ability to perceive fine-grained targets by fusing multiple attention mechanisms. The HLFFE module adopts a frequency domain separation strategy to strengthen edge delineation and structural detail representation in affected areas. The LAE module effectively improves the model’s discrimination ability between targets and backgrounds through local importance calculation and intensity adjustment mechanisms. Conducted experiments show that BCS_YOLO achieves 78.4%, 73.7%, 76.0%, and 82.0% in $precision$, recall, $F1score$, and $mAP@50$, respectively, representing corresponding improvements of 3.0%, 3.3%, 3.2%, and 4.6% compared to the baseline model (YOLOv11n), while also outperforming the mainstream object detection models. In summary, the proposed BCS_YOLO model provides a practical and scalable solution for efficient detection of corn leaf diseases and pests in complex smart-agriculture scenarios, demonstrating significant theoretical and application value. Full article

Cavitation-induced degradation of metallic materials presents a significant challenge for engineers and users of equipment operating with high-velocity fluids. For any metallic material, the mechanical strength and ductility characteristics are controlled by the mobility of dislocations and their interaction with other defects in the crystal lattice (such as dissolved foreign atoms, grain boundaries, phase separation surfaces, etc.). The increase in mechanical properties, and consequently the resistance to cavitation erosion, is possible through the application of heat treatments and cold plastic deformation processes. These factors induce a series of hardening mechanisms that create structural barriers limiting the mobility of dislocations. Cavitation tests involve exposing a specimen to repeated short-duration erosion cycles, followed by mass loss measurements and surface morphology examinations using optical microscopy and scanning electron microscopy (SEM). The results obtained allow for a detailed study of the actual wear processes affecting the tested material and provide a solid foundation for understanding the degradation mechanism. The tested material is the Ni-based alloy INCONEL 625, subjected to stress-relief annealing heat treatment. Experiments were conducted using an ultrasonic vibratory device operating at a frequency of 20 kHz and an amplitude of 50 µm. Microstructural analyses showed that slip bands formed due to shock wave impacts serve as preferential sites for fatigue failure of the material. Material removal occurs along these slip bands, and microjets result in pits with sizes of several micrometers. Full article

In order to solve the problem of small and dense targets in drone aerial images, a small target detection model based on dual branches and adaptive feature fusion is proposed. The model first constructs a small target detection framework with dual branches to improve the detection accuracy while reducing the number of parameters. Secondly, the model introduces semantic and detail injection (SDI) in the neck network and embeds bidirectional adaptive feature fusion in the detection head to innovate and optimize the feature fusion mechanism, achieve the full interaction of deep and shallow information, enhance the feature representation of small targets, and overcome the problem of scale inconsistency. Finally, in order to focus on the target area more accurately, we introduce the large separable kernel attention mechanism into the convolutional layer to provide it with a richer and more comprehensive feature representation, which significantly improves the detection accuracy of targets of different scales. The experimental results show that the model algorithm performs well in the VisDrone2019 dataset. Compared with the original model, the mAP50 of this model increases by 20.9%, the mAP50–95 increases by 23.7%, and the total number of parameters decreases by 61.3%, making it more suitable for drones. Full article

Insects exhibit diverse ecological characteristics, but species identification is challenging due to high morphological similarity. Traditional methods require genitalia dissection, which damages specimens and flattens three-dimensional structures, potentially losing key morphological details. In this study, we evaluate the utility of Micro-CT (Computed Tomography) as a non-destructive alternative for species identification by comparing genitalia structures obtained through Micro-CT with those obtained through traditional dissection. Micro-CT enabled three-dimensional reconstructions of male genitalia and aedeagus, providing detailed views from multiple angles without physical damage. The aedeagus was also virtually separated in a digital environment, further enhancing morphological analysis. Using this approach, we identified three new species, Casmara fulvacorona sp. nov. from Sumatra, C. falcatussica sp. nov. and C. fuscatulipa sp. nov. from Taiwan, based on genitalia characteristics. In addition, we provide a checklist of all Casmara Walker, 1863 species reported to date, including these newly described species, to confirm and clarify the distribution of this genus. Our results demonstrate that the additional use of Micro-CT in insect species identification can provide a scientific basis for reviewing and increasing confidence in species identification based on genital dissection. Full article

In recent years, there has been an increasing transition from 1D point-based to 3D point-cloud-based data acquisition for monitoring applications and deformation analysis tasks. Previously, many studies relied on point-to-point measurements using total stations to assess structural deformation. However, the introduction of terrestrial laser scanning (TLS) has commenced a new era in data capture with a high level of efficiency and flexibility for data collection and post processing. Thus, a robust understanding of both data acquisition and processing techniques is required to guarantee high-quality deliverables to geometrically separate the measurement uncertainty and movements. TLS is highly demanding in capturing detailed 3D point coordinates of a scene within either short- or long-range scanning. Although various studies have examined scanner misalignments under controlled conditions within the short range of observation (scanner calibration), there remains a knowledge gap in understanding and characterizing errors related to long-range scanning (scanning calibration). Furthermore, limited information on manufacturer-oriented calibration tests highlights the motivation for designing a user-oriented calibration test. This research focused on investigating four primary sources of error in the generic error model of TLS. These were categorized into four geometries: instrumental imperfections related to the scanner itself, atmospheric effects that impact the laser beam, scanning geometry concerning the setup and varying incidence angles during scanning, and object and surface characteristics affecting the overall data accuracy. This study presents previous findings of TLS calibration relevant to the four error sources and mitigation strategies and identified current challenges that can be implemented as potential research directions. Full article

The increasing incidence of oil contamination in many aquatic ecosystems, particularly in oil-rich regions such as Qatar, poses significant threats to marine life and human activities. Our study addresses the critical need for effective and eco-friendly oil-water separation techniques, focusing on developing graphene and chitosan-based three-dimensional (3D) polymeric sponges. These materials have demonstrated potential due to their high porosity and surface area, which can be enhanced through surface treatment to improve hydrophobicity and oleophilicity. This study introduces a new technique dependent on the optimization of the graphene oxide (GO) concentration within the composite sponge to achieve a superior oil uptake capacity (51.4 g oil/g sponge at 3% GO), and the detailed characterization of the material’s performance in separating heavy oil-water emulsions. Our study seeks to answer key questions regarding the performance of these modified sponges and their scalability for industrial applications. This research directly aligns with Qatar’s environmental goals and develops sustainable oil-water separation technologies. It addresses the pressing challenges of oil spills, ultimately contributing to improved marine ecosystem protection and efficient resource recovery. Full article