Search Results (271)

The detection of blood by human remains detection (HRD) canines and blood detection dogs (BDDs) is crucial to both search and rescue (SAR) and crime scene investigation. They can be used to find both missing persons and to detect otherwise undetectable blood evidence at crime scenes. An added level of difficulty with training occurs as blood volatile organic compounds (VOCs) are drastically affected by time. Previous studies have shown this, with a focus on a longer timescale (weeks/months). Little data exists on the changes in the first 48 h, the most crucial time in SAR, something this study aims to rectify. Data was collected using headspace solid-phase microextraction/gas chromatography–mass spectrometry, which was then analyzed using chemometrics and confirmed with canine trials. The results of the laboratory analysis indicated that there were multiple, distinct odor profiles between the 1 h and 2-week time windows—namely, the fresh, intermediate, and aged stages of decomposition. The noted changes in the odor profiles were validated with HRD canine trials. Canines had difficulty detecting the fresh blood (1–2 h old) and had the greatest detection rate for the aged blood (34–36 h old). Both the chemical analysis and canine behavior data displayed a clear change in the odor profile within the first 48 h. This information will assist SAR, HRD, and BBD training to ensure they train on all distinct odor profiles. Full article

This study addresses the impact of pandemics, economic limitations, and physical constraints on physical pilgrimage by proposing and evaluating a culturally sensitive, ritual-oriented virtual Boudhanath Stupa environment. Using user-centered design (UCD) and Behavior-Driven Development (BDD), the project created interactive ritual nodes on a Minecraft–VR platform, combining spatial configuration, symbolic elements, and exploratory freedom to move beyond static representation toward participatory engagement. A mixed-methods evaluation with 50 participants from diverse backgrounds and 2 Tibetan Buddhist experts showed positive feedback for aesthetic experience (M = 4.36) and user control (M = 4.62). Despite its non-photorealistic style, the environment was able to evoke a strong sense of presence and was recognized by experts as a “digital Dharma gate” suitable for younger audiences and those unable to travel to sacred sites. Limitations include a small sample size, a short evaluation period, and a lack of social interaction features. Future development will enhance guidance and feedback, expand narratives, support community co-creation, and introduce multi-user functions, providing a scalable framework for virtual religious cultural heritage. Full article

With the wide application of battery energy storage systems (BESSs) in DC microgrids, BESSs are facing increasingly severe cyber threats, among which, false data injection attacks (FDIAs) seriously undermine the accuracy of battery state estimation by tampering with sensor measurement data. To address this problem, this paper proposes an improved generative adversarial network (WGAN-GP)-based detection and defence method for FDIAs in battery energy storage systems. Firstly, a more perfect FDIA model is constructed based on the comprehensive consideration of the dual objectives of circumventing the bad data detection (BDD) system of microgrid and triggering the effective deviation of the system operating state quantity; subsequently, the WGAN-GP network architecture introducing the gradient penalty term is designed to achieve the efficient detection of the attack based on the anomalous scores output from the discriminator, and the generator reconstructs the tampered measurement data. Finally, the state prediction after repair is completed based on Gaussian process regression. The experimental results show that the proposed method achieves more than 92.9% detection accuracy in multiple attack modes, and the maximum reconstruction error is only 0.13547 V. The overall performance is significantly better than that of the traditional detection and restoration methods, and it provides an effective technical guarantee for the safe and stable operation of the battery energy storage system. Full article

Efficient navigation of Automated Guided Vehicles (AGVs) in dynamic warehouse environments requires real-time and accurate path segmentation algorithms. However, traditional semantic segmentation models suffer from excessive parameters and high computational costs, limiting their deployment on resource-constrained embedded platforms. A lightweight image segmentation algorithm is proposed, built on an improved ESPNet-C architecture, combining Spatial Group-wise Enhance (SGE) and Efficient Channel Attention (ECA) with a dual-branch upsampling decoder. On our custom warehouse dataset, the model attains 90.5% Miou with 0.425 M parameters and runs at ~160 FPS, reducing parameters by ×116–×136 and computational costs by 70–92% in comparison with DeepLabV3+. The proposed model improves boundary coherence by 22% under uneven lighting and achieves 90.2% Miou on the public BDD100K benchmark, demonstrating strong generalization beyond warehouse data. These results highlight its suitability as a real-time visual perception module for AGV navigation in resource-constrained environments and offer practical guidance for designing lightweight semantic segmentation models for embedded applications. Full article

Smart grids (SGs) are becoming increasingly complex with the integration of communication, protection, and automation technologies. However, this digital transformation has introduced new vulnerabilities, especially false data injection attacks (FDIAs) and Denial of Service (DoS) attacks. FDIAs can subtly corrupt measurement data, misleading operators without triggering traditional bad data detection (BDD) methods in state estimation (SE), while DoS attacks disrupt the availability of sensor data, affecting grid observability. This paper presents a deep learning-based framework for detecting and localizing FDIAs, including under DoS conditions. A hybrid CNN, Transformer, and BiLSTM model captures spatial, global, and temporal correlations to forecast measurements and detect anomalies using a threshold-based approach. For further detection and localization, a Multi-layer Perceptron (MLP) model maps forecast errors to the compromised sensor locations, effectively complementing or replacing BDD methods. Unlike conventional SE, the approach is fully data-driven and does not require knowledge of grid topology. Experimental evaluation on IEEE 14–bus and 118–bus systems demonstrates strong performance for the FDIA condition, including precision of 0.9985, recall of 0.9980, and row-wise accuracy (RACC) of 0.9670 under simultaneous FDIA and DoS conditions. Furthermore, the proposed method outperforms existing machine learning models, showcasing its potential for real-time cybersecurity and situational awareness in modern SGs. Full article

The presence of acetaminophen (ACTP) in aquatic environments has become a significant concern due to its environmental persistence and the potential formation of toxic transformation products. This study systematically compares the performance of three electrochemical advanced oxidation processes (EAOPs), electro-oxidation (EO), electro-Fenton (EF), and solar photo-electro-Fenton (SPEF), for the degradation and mineralization of ACTP in aqueous media using boron-doped diamond (BDD) electrodes. Reactions were conducted under varying operational parameters, including current densities (15–60 mA cm⁻²), initial ACTP concentrations (10–30 mg L⁻¹), and Fe²⁺ dosages. In the SPEF system, natural sunlight was utilized as the source of UV-A irradiation (30–35 W m⁻²). Among the evaluated processes, SPEF exhibited the highest degradation efficiency, achieving up to 97% ACTP removal and 78% chemical oxygen demand (COD) reduction within 90 min. High-performance liquid chromatography (HPLC) analysis identified phenol and catechol as major intermediates, suggesting a degradation pathway involving hydroxylation, aromatic ring cleavage, and subsequent oxidation into low-molecular-weight carboxylic acids. Kinetic modeling revealed pseudo-first-order behavior, with a maximum rate constant of 0.0865 min⁻¹ under optimized conditions determined via Box–Behnken experimental design. Additionally, SPEF demonstrated enhanced energy efficiency (~0.052 kWh gCOD⁻¹) and improved oxidant regeneration under solar radiation, highlighting its potential as an environmentally friendly and cost-effective alternative for pharmaceutical wastewater treatment. These results support the implementation of SPEF as a sustainable strategy for mitigating the environmental impact of emerging contaminants, especially in regions with high solar availability and limited technological resources. Full article

On-road vision-based systems rely on object detection to ensure vehicle safety and efficiency, making it an essential component of autonomous driving. Deep learning methods show high performance; however, they often require special hardware due to their large sizes and computational complexity, which makes real-time deployment on edge devices expensive. This study proposes lightweight object detection models based on the YOLOv5s architecture, known for its speed and accuracy. The models integrate advanced channel attention strategies, specifically the ECA module and SE attention blocks, to enhance feature selection while minimizing computational overhead. Four models were developed and trained on the KITTI dataset. The models were analyzed using key evaluation metrics to assess their effectiveness in real-time autonomous driving scenarios, including precision, recall, and mean average precision (mAP). BaseECAx2 emerged as the most efficient model for edge devices, achieving the lowest GFLOPs (13) and smallest model size (9.1 MB) without sacrificing performance. The BaseSE-ECA model demonstrated outstanding accuracy in vehicle detection, reaching a precision of 96.69% and an mAP of 98.4%, making it ideal for high-precision autonomous driving scenarios. We also assessed the models’ robustness in more challenging environments by training and testing them on the BDD-100K dataset. While the models exhibited reduced performance in complex scenarios involving low-light conditions and motion blur, this evaluation highlights potential areas for improvement in challenging real-world driving conditions. This study bridges the gap between affordability and performance, presenting lightweight, cost-effective solutions for integration into real-time autonomous vehicle systems. Full article

In intelligent vehicular networks, the accuracy of semantic segmentation in road scenes is crucial for vehicle-mounted artificial intelligence to achieve environmental perception, decision support, and safety control. Although deep learning methods have made significant progress, two main challenges remain: first, the difficulty in balancing global and local features leads to blurred object boundaries and misclassification; second, conventional convolutions have limited ability to perceive irregular objects, causing information loss and affecting segmentation accuracy. To address these issues, this paper proposes a global–local collaborative attention module and a spider web convolution module. The former enhances feature representation through bidirectional feature interaction and dynamic weight allocation, reducing false positives and missed detections. The latter introduces an asymmetric sampling topology and six-directional receptive field paths to effectively improve the recognition of irregular objects. Experiments on the Cityscapes, CamVid, and BDD100K datasets, collected using vehicle-mounted cameras, demonstrate that the proposed method performs excellently across multiple evaluation metrics, including mIoU, mRecall, mPrecision, and mAccuracy. Comparative experiments with classical segmentation networks, attention mechanisms, and convolution modules validate the effectiveness of the proposed approach. The proposed method demonstrates outstanding performance in sensor-based semantic segmentation tasks and is well-suited for environmental perception systems in autonomous driving. Full article

The electrooxidation (EO) of three important environmental contaminants, anticorrosive 1H-benzotriazole (BTA), plasticizer dibutyl phthalate (DBP), and non-ionic surfactant Triton X-100 (tert-octylphenoxy[poly(ethoxy)] ethanol, t-OPPE), was studied as a possible means to improve their elimination from wastewaters, which are an important emission source. EO was performed in a batch reactor with a boron-doped diamond (BDD) anode and a stainless steel cathode. Different supporting electrolytes were tested: NaCl, H₂SO₄, and Na₂SO₄. Results were analysed from the point of their efficacy in terms of degradation rate, kinetics, energy consumption, and transformation products. The highest degradation rate, shortest half-life, and lowest energy consumption was observed in the electrolyte H₂SO₄, followed by Na₂SO₄ with only slightly less favourable characteristics. In both cases, degradation was probably due to the formation of persulphate or sulphate radicals. Transformation products (TPs) were studied mainly in the sulphate media and several oxidation products were identified with all three contaminants, while some evidence of progressive degradation, e.g., ring-opening products, was observed only with t-OPPE. The possible reasons for the lack of further degradation in BTA and DBP are too short of an EO treatment time and perhaps a lack of detection due to unsuitable analytical methods for more polar TPs. Results demonstrate that BDD-based EO is a robust method for the efficient removal of structurally diverse organic contaminants, making it a promising candidate for advanced water treatment technologies. Full article

Histamine sensing that uses enzymatic reactions is the most common form of testing due to its selectivity for histamine. However, enzymes are difficult to store for long periods of time, and the inactivation of enzymes decreases the reliability of the results. In this study, we developed a novel, quick, and easily operated histamine sensing technique that takes advantage of the histamine redox reaction and does not require enzyme-based processes. Because the redox potential of histamine is relatively high, we used a boron-doped diamond (BDD) electrode that has a wide potential window. At pH 8.4, which is between the acidity constant of histamine and the isoelectric point of histidine, it was found that an oxygen-terminated BDD surface successfully detected histamine, both selectively and exclusively. Measurements of the sensor’s responses to extracts from fish meat samples that contained histamine at various concentrations revealed that the sensor responds linearly to the histamine concentration, thus allowing it to be used as a calibration curve. The sensor was used to measure histamine in another fish meat sample treated as an unknown sample, and the response was fitted to the calibration curve to perform an inverse estimation. When estimated in this way, the histamine concentration matched the certified value within the range of error. A more detailed examination showed that the sensor response was little affected by the histidine concentration in the sample. The detection limit was 20.9 ppm, and the linear response range was 0–150 ppm. This confirms that this sensing method can be used to measure standard histamine concentrations. Full article

This study evaluates the efficiency of sub-stoichiometric Ti₄O₇ titanium oxide anodes for the electrochemical degradation of glyphosate, a persistent herbicide classified as a probable carcinogen by the World Health Organization. After optimizing the process operating parameters (pH and current density), the mineralization efficiency and fate of degradation by-products of the treated solution were determined using a total organic carbon (TOC) analyzer and HPLC/MS, respectively. The results showed that at pH = 3, glyphosate degradation and mineralization are enhanced by the increased generation of hydroxyl radicals (^●OH) at the anode surface. A current density of 14 ${\mathrm{mA} \mathrm{cm}}^{-2}$ enables complete glyphosate removal with 77.8% mineralization. Compared with boron-doped diamond (BDD), ${\mathrm{Ti}}_4{\mathrm{O}}_7$ shows close performance for treatment of a concentrated glyphosate solution (0.41 mM), obtained after nanofiltration of a synthetic ionic solution (0.1 mM glyphosate), carried out using an NF-270 membrane at a conversion rate (Y) of 80%. At 10${ \mathrm{mA} \mathrm{cm}}^{-2}$ for 8 h, ${\mathrm{Ti}}_4{\mathrm{O}}_7$ achieved 81.3% mineralization with an energy consumption of 6.09 ${\mathrm{kWh} \mathrm{g}}^{-1}$ TOC, compared with 90.5% for BDD at 5.48 ${\mathrm{kWh} \mathrm{g}}^{-1}$ TOC. Despite a slight yield gap, ${\mathrm{Ti}}_4{\mathrm{O}}_7$ demonstrates notable efficiency under demanding conditions, suggesting its potential as a cost-effective alternative to BDD for glyphosate electro-oxidation. Full article

Image segmentation is a foundation for autonomous driving frameworks that empower vehicles to explore and navigate their surrounding environment. It gives a fundamental setting to the dynamic cycles by dividing the image into significant parts like streets, vehicles, walkers, and traffic signs. Precise segmentation ensures safe navigation and the avoidance of collisions, while following the rules of traffic is very critical for seamless operation in self-driving cars. The most recent deep learning-based image segmentation models have demonstrated impressive performance in structured environments, yet they often fall short when applied to the complex and unpredictable conditions encountered in autonomous driving. This study proposes an Adaptive Ensemble Attention (AEA) mechanism within a Generative Adversarial Network architecture to deal with dynamic and complex driving conditions. The AEA integrates the features of self, spatial, and channel attention adaptively and powerfully changes the amount of each contribution as per input and context-oriented relevance. It does this by allowing the discriminator network in GAN to evaluate the segmentation mask created by the generator. This explains the difference between real and fake masks by considering a concatenated pair of an original image and its mask. The adversarial training will prompt the generator, via the discriminator, to mask out the image in such a way that the output aligns with the expected ground truth and is also very realistic. The exchange of information between the generator and discriminator improves the quality of the segmentation. In order to check the accuracy of the proposed method, the three widely used datasets BDD100K, Cityscapes, and KITTI were selected to calculate average IoU, where the value obtained was 89.46%, 89.02%, and 88.13% respectively. These outcomes emphasize the model’s effectiveness and consistency. Overall, it achieved a remarkable accuracy of 98.94% and AUC of 98.4%, indicating strong enhancements compared to the State-of-the-art (SOTA) models. Full article

Background. Cognitive-behavioral theories suggest that obsessions in obsessive-compulsive disorder (OCD) develop from maladaptive misinterpretations and coping strategies of unwanted intrusive thoughts (UITs). Models of Body Dysmorphic Disorder (BDD) and Illness Anxiety Disorder (IAD) propose that these symptoms stem from similar misinterpretations of common UITs relating to perceived defects in appearance and illness. This study examines whether maladaptive misinterpretations and control strategies leading to the escalation of obsessional UITs to OCD symptoms also have a similar effect on the development of BDD and IAD. More specifically, we examined whether misinterpretations and neutralizing responses mediate the associations between the frequency of disorder-specific UITs and symptoms of these disorders. Method. A total of 625 non-clinical participants from four countries completed the Questionnaire of Unpleasant Intrusive Thoughts (QUIT) that assesses OCD, BDD and IAD-related UITs and their associated misinterpretations and neutralizing strategies, as well as self-report measures of OCD, BDD, and IAD symptoms. Parallel multiple mediation models were conducted. Results. The frequency of OCD, BDD and IAD-related UITs predicted symptoms of each disorder. Dysfunctional appraisals and neutralizing behaviors mediated the associations between disorder-specific UITs and symptoms in OCD and IAD. The IAD model accounted for a smaller proportion of variance than the OCD model. No mediating effects were found for BDD symptoms. Conclusions. Experiencing disturbing UITs is a transdiagnostic risk factor of OCD, BDD and IAD, and is associated with symptoms of these disorders. Maladaptive interpretation of UITs and neutralizing strategies should be specific targets in the assessment and treatment of OCD and IAD. The absence of mediation effects for BDD could be due to the limitations observed on the self-report used to assess BDD symptoms and/or the low relevance of the misinterpretations and control strategies assessed by the QUIT, which are more typically endorsed by individuals with OCD. Full article

Special vehicle detection in intelligent surveillance, emergency rescue, and reconnaissance faces significant challenges in accuracy and robustness under complex environments, necessitating advanced detection algorithms for critical applications. This paper proposes HSF-DETR (Hypergraph Spatial Feature DETR), integrating four innovative modules: a Cascaded Spatial Feature Network (CSFNet) backbone with Cross-Efficient Convolutional Gating (CECG) for enhanced long-range detection through hybrid state-space modeling; a Hypergraph-Enhanced Spatial Feature Modulation (HyperSFM) network utilizing hypergraph structures for high-order feature correlations and adaptive multi-scale fusion; a Dual-Domain Feature Encoder (DDFE) combining Bipolar Efficient Attention (BEA) and Frequency-Enhanced Feed-Forward Network (FEFFN) for precise feature weight allocation; and a Spatial-Channel Fusion Upsampling Block (SCFUB) improving feature fidelity through depth-wise separable convolution and channel shift mixing. Experiments conducted on a self-built special vehicle dataset containing 2388 images demonstrate that HSF-DETR achieves mAP50 and mAP50-95 of 96.6% and 70.6%, respectively, representing improvements of 3.1% and 4.6% over baseline RT-DETR while maintaining computational efficiency at 59.7 GFLOPs and 18.07 M parameters. Cross-domain validation on VisDrone2019 and BDD100K datasets confirms the method’s generalization capability and robustness across diverse scenarios, establishing HSF-DETR as an effective solution for special vehicle detection in complex environments. Full article

Background/Objectives: Temporomandibular disorders (TMDs) are a group of musculoskeletal and neuromuscular conditions involving the temporomandibular joint (TMJ), masticatory muscles, and related anatomical structures. Although magnetic resonance imaging (MRI) is considered a noninvasive and highly informative imaging modality for assessing TMJ soft tissues, few studies have examined how TMJ structural features observed on MRI findings relate to oral function and craniofacial morphology in female patients with malocclusion. To investigate the associations among TMJ structural features, oral function, and craniofacial morphology in female patients with malocclusion, using MRI findings interpreted in conjunction with a preliminary assessment based on selected components of the DC/TMDs Axis I protocol. Methods: A total of 120 female patients (mean age: 27.3 ± 10.9 years) underwent clinical examination based on DC/TMDs Axis I and MRI-based structural characterization of the TMJ. Based on the structural features identified by MRI, patients were classified into four groups for comparison: osteoarthritis (OA), bilateral disk displacement (BDD), unilateral disk displacement (UDD), and a group with Osseous Change/Disk Displacement negative (OC/DD (−)). Occlusal contact area, occlusal force, masticatory efficiency, tongue pressure, and lip pressure were measured. Lateral cephalometric analysis assessed skeletal and dental patterns. Results: OA group exhibited significantly reduced occlusal contact area (p < 0.0083, η² = 0.12) and occlusal force (p < 0.0083, η² = 0.14) compared to the OC/DD (−) group. Cephalometric analysis revealed that both OA and BDD groups had significantly larger ANB angles (OA: 5.7°, BDD: 5.2°, OC/DD (−): 3.7°; p < 0.0083, η² = 0.21) and FMA angles (OA: 32.4°, BDD: 31.8°, OC/DD (−): 29.0°; p < 0.0083, η² = 0.17) compared to the OC/DD (−) group. No significant differences were observed in masticatory efficiency, tongue pressure, or lip pressure. Conclusions: TMJ structural abnormalities detected via MRI, especially osteoarthritis, are associated with diminished oral function and skeletal Class II and high-angle features in female patients with malocclusion. Although orthodontic treatment is not intended to manage TMDs, MRI-based structural characterization—when clinically appropriate—may aid in treatment planning by identifying underlying joint conditions. Full article