Search Results (332)

Due to the stroke-protective effects of dietary fiber and anthocyanin together with the synergistic interaction, we hypothesized that the functional drink containing the anthocyanins and dietary fiber-enriched functional ingredient from banana and germinated black Jasmine rice (BR) should protect against ischemic stroke. BR at doses of 300, 600, and 900 mg/kg body weight (BW) was orally given to male Wistar rats weighing 290–350 g once daily for 21 days, and they were subjected to ischemic reperfusion injury induced by temporary occlusion of the middle cerebral artery (MCAO/IR) for 90 min. The treatment was prolonged for 21 days after MCAO/IR. They were assessed for brain infarction volume, neuron density, Nrf2, MDA, and catalase in the cortex together with serum TNF-α and IL-6. Lactobacillus and Bifidobacterium spp. in feces were also assessed. Our results showed that BR improved the increase in brain infarcted volume, MDA, TNF-α, and IL-6 and the decrease in neuron density, Nrf2, catalase, and both bacteria spp. induced by MCAO/IR. These data suggest the stroke-protective effect of the novel functional drink, and the action may involve the improvement of Nrf2, oxidative stress, inflammation, and the amount of Lactobacillus and Bifidobacterium spp. Full article

Work-related musculoskeletal disorders (WMSDs) are a leading concern in industrial ergonomics, often stemming from sustained non-neutral postures and repetitive tasks. This paper presents a vision-based framework for real-time, frame-level ergonomic risk classification using a lightweight Vision Transformer (ViT). The proposed system operates directly on raw RGB images without requiring skeleton reconstruction, joint angle estimation, or image segmentation. A single ViT model simultaneously classifies eight anatomical regions, enabling efficient multi-label posture assessment. Training is supervised using a multimodal dataset acquired from synchronized RGB video and full-body inertial motion capture, with ergonomic risk labels derived from RULA scores computed on joint kinematics. The system is validated on realistic, simulated industrial tasks that include common challenges such as occlusion and posture variability. Experimental results show that the ViT model achieves state-of-the-art performance, with F1-scores exceeding 0.99 and AUC values above 0.996 across all regions. Compared to previous CNN-based system, the proposed model improves classification accuracy and generalizability while reducing complexity and enabling real-time inference on edge devices. These findings demonstrate the model’s potential for unobtrusive, scalable ergonomic risk monitoring in real-world manufacturing environments. Full article

The integration of artificial intelligence (AI) and advanced digital workflows is revolutionising full-arch implant dentistry, particularly for geriatric patients with edentulous and atrophic arches, for whom achieving both prosthetic passivity and optimal aesthetic outcomes is critical. This narrative review evaluates current challenges in intraoral scanning accuracy—such as scan distortion, angular deviation, and cross-arch misalignment—and presents how innovations like the Medit SmartX AI-guided workflow and the Scan Ladder system can significantly enhance precision in implant position registration. These technologies mitigate stitching errors by using real-time scan body recognition and auxiliary geometric references, yielding mean RMS trueness values as low as 11–13 µm, comparable to dedicated photogrammetry systems. AI-driven prosthetic design further aligns implant-supported restorations with facial symmetry and smile aesthetics, prioritising predictable midline and occlusal plane control. Early clinical data indicate that such tools can reduce prosthetic misfits to under 20 µm and lower complication rates related to passive fit, while shortening scan times by up to 30% compared to conventional workflows. This is especially valuable for elderly individuals who may not tolerate multiple lengthy adjustments. Additionally, emerging AI applications in design automation, scan validation, and patient-specific workflow adaptation continue to evolve, supporting more efficient and personalised digital prosthodontics. In summary, AI-enhanced scanning and prosthetic workflows do not merely meet functional demands but also elevate aesthetic standards in complex full-arch rehabilitations. The synergy of AI and digital dentistry presents a transformative opportunity to consistently deliver superior precision, passivity, and facial harmony for edentulous implant patients. Full article

Occlusion remains a major challenge in person re-identification, as it often leads to incomplete or misleading visual cues. To address this issue, we propose a dual-branch occlusion-aware network (DOAN), which explicitly and implicitly enhances the model’s capability to perceive and handle occlusions. The proposed DOAN framework comprises two synergistic branches. In the first branch, we introduce an Occlusion-Aware Semantic Attention (OASA) module to extract semantic part features, incorporating a parallel channel and spatial attention (PCSA) block to precisely distinguish between pedestrian body regions and occlusion noise. We also generate occlusion-aware parsing labels by combining external human parsing annotations with occluder masks, providing structural supervision to guide the model in focusing on visible regions. In the second branch, we develop an occlusion-aware recovery (OAR) module that reconstructs occluded pedestrians to their original, unoccluded form, enabling the model to recover missing semantic information and enhance occlusion robustness. Extensive experiments on occluded, partial, and holistic benchmark datasets demonstrate that DOAN consistently outperforms existing state-of-the-art methods. Full article

Reconstructing accurate human body shapes from clothed images remains a challenge due to occlusion by garments and limitations of the existing methods. Traditional parametric models often require minimal clothing and involve high computational costs. To address these issues, we propose a lightweight algorithm that predicts body shape from clothed RGB images by leveraging pose estimation. Our method simultaneously extracts major joint positions and body features to reconstruct complete 3D body shapes, even in regions hidden by clothing or obscured from view. This approach enables real-time, non-invasive body modeling suitable for practical applications. Full article

Quadruped robots have shown significant potential in disaster relief applications, where they have to navigate complex terrains for search and rescue or reconnaissance operations. However, their deployment is hindered by limited adaptability in highly uncertain environments, especially when relying solely on vision-based sensors like cameras or LiDAR, which are susceptible to occlusions, poor lighting, and environmental interference. To address these limitations, this paper proposes a novel sensor-enhanced hierarchical switching model predictive control (MPC) framework that integrates proprioceptive sensing with a bi-level hybrid dynamic model. Unlike existing methods that either rely on handcrafted controllers or deep learning-based control pipelines, our approach introduces three core innovations: (1) a situation-aware, bi-level hybrid dynamic modeling strategy that hierarchically combines single-body rigid dynamics with distributed multi-body dynamics for modeling agility and scalability; (2) a three-layer hybrid control framework, including a terrain-aware switching MPC layer, a distributed torque controller, and a fast PD control loop for enhanced robustness during contact transitions; and (3) a multi-IMU-based proprioceptive feedback mechanism for terrain classification and adaptive gait control under sensor-occluded or GPS-denied environments. Together, these components form a unified and computationally efficient control scheme that addresses practical challenges such as limited onboard processing, unstructured terrain, and environmental uncertainty. A series of experimental results demonstrate that the proposed method outperforms existing vision- and learning-based controllers in terms of stability, adaptability, and control efficiency during high-speed locomotion over irregular terrain. Full article

Estimating the 3D pose of a human body from monocular images is crucial for computer vision applications, but the technique remains challenging due to depth ambiguity and self-occlusion. Traditional methods often suffer from insufficient prior knowledge and weak constraints, resulting in inaccurate 3D keypoint estimation. In this paper, we propose a method for whole-body 3D pose estimation based on a Transformer architecture, integrating body mass distribution and center of gravity constraints. The method maps the pose to the center of gravity position using the anatomical mass ratio of the human body and computes the segment-level center of gravity using the moment synthesis method. A combined loss function is designed to enforce consistency between the predicted keypoints and the center of gravity position, as well as the invariance of limb length. Extensive experiments on the Human 3.6M WholeBody dataset demonstrate that the proposed method achieves state-of-the-art performance, with a whole-body mean joint position error (MPJPE) of 44.49 mm, which is 60.4% lower than the previous Large Simple Baseline method. Notably, it reduces the body part keypoints’ MPJPE from 112.6 to 40.41, showcasing the enhanced robustness and effectiveness to occluded scenes. This study highlights the effectiveness of integrating physical constraints into deep learning frameworks for accurate 3D pose estimation. Full article

Background and Clinical Significance: The incidence of persistent ductus arteriosus (PDA) in preterm infants is the highest and depends on their birth weight (BW) and respiratory condition after birth. Previously, after the unsuccessful drug treatment, surgical ligation was the primary treatment option. However, according to clinical studies, the Amplatzer Piccolo Occluder was approved for PDA closure for patients ≥700 g. In our country, percutaneous PDA embolization has not been performed yet. Case Presentation: We present three premature infants with hemodynamically significant patent ductus arteriosus (hsPDA) in whom percutaneous occlusion was performed using the Amplatzer Piccolo Occluder (APO). The average gestational week (GW) was 27 ± 1, while body weight was 1030 ± 60 g. All patients had respiratory deterioration, with dilatation of the left heart chambers, and renal failure. The second developed a severe form of broncho-pulmonary dysplasia. Transthoracic echocardiography (TTE) examinations revealed a hemodynamically significant PDA (LA/Ao 1.8–2.2) and medical closure was unsuccessfully carried out. Due to the hemodynamically significant PDA maintenance in all neonates, transvenous PDA closure was performed using the APO (APO 9-PDAP-04-02-L, 9-PDAP-04-04-L, 9-PDAP-05-054L, respectively). The entire devices, with both retention discs, are implanted within the duct. TTE pointed out adequate device position without descending aorta, left pulmonary artery obstruction, residual shunt, and reverse remodelling of the left ventricle and left atrium. The first newborn was weaned from mechanical ventilation three days after the procedure and discharged three weeks after. The second patient was extubated 2 weeks after the procedure, and even the severe BPD, X-ray showed improvement. The third patient’s renal failure completely resolved, weaned from inotropic drug support and mechanical ventilation. Conclusions: Due to a significantly lower complication rate than surgical ligation, we will strive to make percutaneous PDA occlusion a new standard for treatment in newborns, especially preterm newborns, in our country. Full article

This article presents an in-depth examination of the technical and cultural dimensions of singing practices within the traditional music of sub-Saharan Africa. Utilizing an extensive body of theoretical and ethnomusicological research, comparative transcription, and culturally situated observation, it presents a comprehensive framework for understanding the significance of the human voice in various performance contexts. The study revolves around a tripartite model—auditory clarity, ambiguous auditory clarity, and occlusion—that delineates the varying levels of audibility of vocal lines amidst intricate instrumental arrangements. The article examines case studies from West, East, and Southern Africa, highlighting essential vocal techniques such as straight tone, nasal resonance, ululation, and controlled (or delayed) vibrato. It underscores the complex interplay between language, melody, and rhythm in tonal languages. The analysis delves into the influence of sound reinforcement technologies on vocal presence and cultural authenticity, positing that PA systems have the capacity to either enhance or disrupt the equilibrium between traditional aesthetics and modern requirements. This research is firmly rooted in a blend of African and Western theoretical frameworks, drawing upon the contributions of Nketia, Agawu, Chernoff, and Kubik. It proposes a nuanced methodology that integrates technical analysis with cultural significance. It posits that singing in African traditional music transcends mere expression, serving as a vessel for collective memory, identity, and the socio-musical framework. The article concludes by emphasizing the enduring strength and flexibility of African vocal traditions, illustrating their capacity for evolution while preserving fundamental communicative and artistic values. Full article

Presented study evaluates and compares two deep learning models, i.e., YOLOv8n and Faster R-CNN, for automated detection of date fruits in natural orchard environments. Both models were trained and tested using a publicly available annotated dataset. YOLO, a single-stage detector, achieved a mAP@0.5 of 0.942 with a training time of approximately 2 h. It demonstrated strong generalization, especially in simpler conditions, and is well-suited for real-time applications due to its speed and lower computational requirements. Faster R-CNN, a two-stage detector using a ResNet-50 backbone, reached comparable accuracy (mAP@0.5 = 0.94) with slightly higher precision and recall. However, its training required significantly more time (approximately 19 h) and resources. Deep learning metrics analysis confirmed both models performed reliably, with YOLO favoring inference speed and Faster R-CNN offering improved robustness under occlusion and variable lighting. Practical recommendations are provided for model selection based on application needs—YOLO for mobile or field robotics and Faster R-CNN for high-accuracy offline tasks. Additional conclusions highlight the benefits of GPU acceleration and high-resolution inputs. The study contributes to the growing body of research on AI deployment in precision agriculture and provides insights into the development of intelligent harvesting and crop monitoring systems. Full article

Cyber situational awareness systems are increasingly used for creating cyber common operating pictures for cybersecurity analysis and education. However, these systems face data occlusion and convolution issues due to the burgeoning complexity, dimensionality, and heterogeneity of cybersecurity data, which damages cyber situational awareness of end-users. Moreover, conventional forms of human–computer interactions, such as mouse and keyboard, increase the mental effort and cognitive load of cybersecurity practitioners when analyzing cyber situations of large-scale infrastructures. Therefore, immersive technologies, such as virtual reality, augmented reality, and mixed reality, are employed in the cybersecurity realm to create intuitive, engaging, and interactive cyber common operating pictures. Immersive cyber situational awareness (ICSA) systems provide several unique visualization techniques and interaction features for the perception, comprehension, and projection of cyber situational awareness. However, there has been no attempt to comprehensively investigate and classify the existing state of the art in the use of immersive technologies for cyber situational awareness. Therefore, in this paper, we have gathered, analyzed, and synthesized the existing body of knowledge on ICSA systems. In particular, our survey has identified visualization and interaction techniques, evaluation mechanisms, and different levels of cyber situational awareness (i.e., perception, comprehension, and projection) for ICSA systems. Consequently, our survey has enabled us to propose (i) a reference framework for designing and analyzing ICSA systems by mapping immersive visualization and interaction techniques to the different levels of ICSA; (ii) future research directions for advancing the state of the art on ICSA systems; and (iii) an in-depth analysis of the industrial implications of ICSA systems to enhance cybersecurity operations. Full article

Background/Objectives: Temporomandibular disorder (TMDs) patients often present limited mouth opening (LMO). A key diagnostic cutoff is a mouth opening threshold >40 mm. However, this universal cutoff may not accurately reflect gender anatomical variations. This study investigates gender-specific differences in maximum mouth opening (MMO) to propose revised diagnostic criteria for LMO. Methods: A five-year prospective study was conducted from 1 August 2019 to 1 May 2024 in a Portuguese TMDs department. The patients’ gender, MMO, and LMO complaints with clinical validation were recorded. Statistical analyses, including Generalized Additive Models (GAMs) and Generalized Linear Models (GLMs), assessed the relationship between MMO and LMO, with gender-stratified comparisons. Results: In this study 1045 patients were included. The median (accompanied by the interquartile range [25th percentile–75th percentile]) MMO was lower in females (40 mm [34–45]) than in males (44 mm [40–48]). Patients presenting LMO complaints exhibited significantly reduced MMO values compared to those without LMO complaints (p < 0.001). Gender-specific thresholds emerged: for females, LMO was observed when MMO was ≤35 mm, while in males, LMO symptoms appeared when MMO was ≤38 mm. A “gray zone” of diagnostic uncertainty was identified between 36 and 37 mm for females and 38 and 42 mm for males. Conclusions: In this study we observed the gold standard cutoff for diagnosing MMO in female should be <35mm and for male <38mm. These findings suggest that a single LMO threshold does not account for gender-related anatomical differences, potentially leading to underdiagnosis in females and misclassification in males. Revising diagnostic criteria to incorporate gender-specific thresholds could enhance accuracy, improve early diagnosis, and promote personalized treatment strategies for TMDs patients. Further research incorporating additional variables such as age, dental occlusion, craniofacial structure, and body mass index is recommended to refine these diagnostic guidelines. Full article

Accurately estimating 3D human pose and body shape from a single monocular image remains challenging, especially under poor lighting or occlusions. Traditional RGB-based methods struggle in such conditions, whereas single-pixel imaging (SPI) in the Near-Infrared (NIR) spectrum offers a robust alternative. NIR penetrates clothing and adapts to illumination changes, enhancing body shape and pose estimation. This work explores an SPI camera (850–1550 nm) with Time-of-Flight (TOF) technology for human detection in low-light conditions. SPI-derived point clouds are processed using a Vision Transformer (ViT) to align poses with a predefined SMPL-X model. A self-supervised PointNet++ network estimates global rotation, translation, body shape, and pose, enabling precise 3D human mesh reconstruction. Laboratory experiments simulating night-time conditions validate NIR-SPI’s potential for real-world applications, including human detection in rescue missions. Full article

Nucleopolyhedroviruses of lepidopteran larvae (Alphabaculovirus, Baculoviridae) form the basis for effective and highly selective biological insecticides for the control of caterpillar pests of greenhouse and field crops and forests. Horizontal transmission is usually achieved following the release of large quantities of viral occlusion bodies (OBs) from virus-killed insects. In the present review, I examine the evidence for productive midgut infection in different host species and the resulting transmission through the release of OBs in the feces (frass) of the host. This has been a neglected aspect of virus transmission since it was initially studied over six decades ago. The different host–virus pathosystems vary markedly in the quantity of OBs released in feces and in their ability to contaminate the host’s food plant. The release of fecal OBs tends to increase over time as the infection progresses. Although based on a small number of studies, the prevalence of transmission of fecal inoculum is comparable with that of recognized alternative routes for transmission and dissemination, such as cannibalism and interactions with predators and parasitoids. Finally, I outline a series of predictions that would affect the importance of OBs in feces as a source of inoculum in the environment and which could form the basis for future lines of research. Full article

Human Pose Estimation (HPE) remains challenging due to scale variation, occlusion, and high computational costs. Standard methods often struggle to capture detailed spatial information when keypoints are obscured, and they typically rely on computationally expensive deconvolution layers for upsampling, making them inefficient for real-time or resource-constrained scenarios. We propose AMFACPose (Attentive Multi-scale Features with Adaptive Context PoseResNet) to address these limitations. Specifically, our architecture incorporates Coordinate Convolution 2D (CoordConv2d) to retain explicit spatial context, alleviating the loss of coordinate information in conventional convolutions. To reduce computational overhead while maintaining accuracy, we utilize Depthwise Separable Convolutions (DSCs), separating spatial and pointwise operations. At the core of our approach is an Adaptive Feature Pyramid Network (AFPN), which replaces costly deconvolution-based upsampling by efficiently aggregating multi-scale features to handle diverse human poses and body sizes. We further introduce Dual-Gate Context Blocks (DGCBs) that refine global context to manage partial occlusions and cluttered backgrounds. The model integrates Squeeze-and-Excitation (SE) blocks and the Spatial–Channel Refinement Module (SCRM) to emphasize the most informative feature channels and spatial regions, which is particularly beneficial for occluded or overlapping keypoints. For precise keypoint localization, we replace dense heatmap predictions with coordinate classification using Multi-Layer Perceptron (MLP) heads. Experiments on the COCO and CrowdPose datasets demonstrate that AMFACPose surpasses the existing 2D HPE methods in both accuracy and computational efficiency. Moreover, our implementation on edge devices achieves real-time performance while preserving high accuracy, confirming the suitability of AMFACPose for resource-constrained pose estimation in both benchmark and real-world environments. Full article