Search Results (7,121)

This paper presents Aerokinesis, an IoT-based software–hardware system for intuitive gesture-driven control of quadcopter unmanned aerial vehicles (UAVs), developed within the Robot Operating System 2 (ROS2) framework. The proposed system addresses the challenge of providing an accessible human–drone interaction interface for operators in scenarios where traditional remote controllers are impractical or unavailable. The architecture comprises two hierarchical control levels: (1) high-level discrete command control utilizing a fully connected neural network classifier for static gesture recognition, and (2) low-level continuous flight control based on three-dimensional hand keypoint analysis from a depth camera. The gesture classification module achieves an accuracy exceeding 99% using a multi-layer perceptron trained on MediaPipe-extracted hand landmarks. For continuous control, we propose a novel approach that computes Euler angles (roll, pitch, yaw) and throttle from 3D hand pose estimation, enabling intuitive four-degree-of-freedom quadcopter manipulation. A hybrid signal filtering pipeline ensures robust control signal generation while maintaining real-time responsiveness. Comparative user studies demonstrate that gesture-based control reduces task completion time by 52.6% for beginners compared to conventional remote controllers. The results confirm the viability of vision-based gesture interfaces for IoT-enabled UAV applications. Full article

Objective: We aimed to study acquired esotropia in adults and its risk factors, compile treatments performed and describe surgical technique used, with a novel indication. Methods: We conducted a retrospective study of patients with insidious distant esotropia along with distant horizontal diplopia (angles 2–30 PD with wide fusion amplitude): Refractively emmetropic, moderately myopic and mildly hyperopic. No systemic alterations. Results: 30 cases were included, average age: 38.13 ± 14.95. Mean time elapsed from the onset of symptoms to surgical treatment was 22.52. Mean spherical equivalent is −3.19 ± 2.83. Mean preoperative horizontal deviation was 18.58 ± 5.45 PD in distant vision and 5.48 ± 8.35 PD in close vision (p < 0.001). 100% of cases reported diplopia in distance vision. 20% required prismatic treatment (<10 PD) and 80% surgical (>10 PD) by lateral rectus resection, with an average of 4.82 ± 1.23 mm. Sensory result was successful in 100% of the cases and motor in 75%. Conclusions: We are facing a new type of acquired esotropia in adults that can be individualized by its clinical and therapeutic characteristics. Our prismatic and surgical treatment has been successful. Full article

Plasmonics and metasurfaces (MSs) have emerged as two of the most influential platforms for manipulating light at the nanoscale, each offering complementary strengths that challenge the limits of conventional optical design. Plasmonics enables extreme subwavelength field confinement, ultrafast light–matter interaction, and strong optical nonlinearities, while MSs provide versatile and compact control over phase, amplitude, polarization, and dispersion through planar, nanostructured interfaces. Recent advances in materials, nanofabrication, and device engineering are increasingly enabling these technologies to be combined within unified planar and hybrid optical platforms. This review surveys the physical principles, material strategies, and device architectures that underpin plasmonic, MS, and hybrid plasmonic–dielectric systems, with an emphasis on interface-mediated optical functionality rather than long-range guided-wave propagation. Key developments in modulators, detectors, nanolasers, metalenses, beam steering devices, and programmable optical surfaces are discussed, highlighting how hybrid designs can leverage strong field localization alongside low-loss wavefront control. System-level challenges including optical loss, thermal management, dispersion engineering, and large-area fabrication are critically examined. Looking forward, plasmonic and MS technologies are poised to define a new generation of flat, multifunctional, and programmable optical systems. Applications spanning imaging, sensing, communications, augmented and virtual reality, and optical information processing illustrate the transformative potential of these platforms. By consolidating recent progress and outlining future directions, this review provides a coherent perspective on how plasmonics and MSs are reshaping the design space of next-generation planar optical hardware. Full article

Background/Objectives: Accurate identification of dental malocclusions from routine clinical photographs can be time-consuming and subject to interobserver variability. A YOLOv11-based deep learning approach is presented and evaluated for automatic malocclusion detection on routine intraoral photographs, testing the hypothesis that training on a structured annotation protocol enables reliable detection of multiple clinically relevant malocclusions. Methods: An anonymized dataset of 5854 intraoral photographs (frontal occlusion; right/left buccal; maxillary/mandibular occlusal) was labeled according to standardized instructions derived from the Index of Orthodontic Treatment Need (IOTN) A total of 17 clinically relevant classes were annotated with bounding boxes. Due to an insufficient number of examples, two malocclusions (transposition and non-occlusion) were excluded from our quantitative analysis. A YOLOv11 model was trained with augmented data and evaluated on a held-out test set using mean average precision at IoU 0.5 (mAP50), macro precision (macro-P), and macro recall (macro-R). Results: Across 15 analyzed classes, the model achieved 87.8% mAP50, 76.9% macro-P, and 86.1% macro-R. The highest per-class AP₅₀ was observed for Deep bite (98.8%), Diastema (97.9%), Angle Class II canine (97.5%), Anterior open bite (92.8%), Midline shift (91.8%), Angle Class II molar (91.1%), Spacing (91%), and Crowding (90.1%). Moderate performance included Anterior crossbite (88.3%), Angle Class III molar (87.4%), Head bite (82.7%), and Posterior open bite (80.2%). Lower values were seen for Angle Class III canine (76%), Posterior crossbite (75.6%), and Big overjet (75.3%). Precision–recall trends indicate earlier precision drop-off for posterior/transverse classes and comparatively more missed detections in Posterior crossbite, whereas Big overjet exhibited more false positives at the chosen threshold. Conclusion: A YOLOv11-based deep learning system can accurately detect several clinically salient malocclusions on routine intraoral photographs, supporting efficient screening and standardized documentation. Performance gaps align with limited examples and visualization constraints in posterior regions. Larger, multi-center datasets, protocol standardization, quantitative metrics, and multimodal inputs may further improve robustness. Full article

Vitamin A in the form of 11-cis-retinaldehyde is the chromophore essential to vision. Thus, deficiencies in vitamin A necessitate the implementation of vitamin A supplementation. Moreover, some vitamin A is lost from the visual cycle due to random reactions that generate diretinaldehyde (bisretinoid) molecules; the latter are photoreactive and contribute to retinal disease. Here, we measured the systemic and ocular uptake of vitamin A along with bisretinoid as a function of vitamin A availability when supplied in the diet or by weekly i.p. injection in light- and dark-reared mice. Retinyl palmitate delivered as an i.p. bolus served to elevate plasma ROL but an associated increase in ocular 11-cisRAL was not observed in light- or dark-reared mice. In dark-reared mice, 11-cisRAL was more abundant when retinyl palmitate was provided in chow versus weekly i.p. injection; moreover, by the latter route, retinyl acetate was more effective. Conversely in dark-reared mice given retinyl palmitate by weekly i.p. injection versus chow, ocular atRAL was elevated. Liver atRE was elevated by increased retinyl palmitate in chow; the latter also favored elevated 11-cisRAL in dark-reared mice. In cyclic light-reared mice, ocular stores of atRE were increased by i.p. retinyl palmitate. With dark-rearing, there was no difference in bisretinoid (A2E) with retinyl palmitate in chow, nor by weekly i.p. injection; notably, bisretinoid levels were lower in cyclic light-reared mice due to photooxidative loss. In summary, light modulates the ocular retinoid, plasma atROL does not predict ocular levels of retinoid or bisretinoid and atRAL is elevated with sustained darkness. Full article

This article presents the Intelligent Monitoring System (IMS), an AI-assisted, low-latency surveillance platform designed for defense environments. The study addresses the need for real-time autonomous situational awareness by integrating high-speed video transmission with advanced computer vision analytics in constrained network settings. The IMS employs a hybrid transmission architecture based on RTSP for ingestion and WHEP/WebRTC for distribution, orchestrated via MediaMTX, with the objective of achieving end-to-end latencies below one second. The methodology includes a comparative evaluation of video streaming protocols (JPEG-over-WebSocket, HLS, WebRTC, etc.) and AI frameworks, alongside the modular architectural design and prolonged experimental validation. The detection module integrates YOLOv11 models fine-tuned on the VisDrone dataset to optimize performance for small objects, aerial views, and dense scenes. Experimental results, obtained through over 300 h of operational tests using IP cameras and aerial platforms, confirmed the stability and performance of the chosen architecture, maintaining latencies close to 500 ms. The YOLOv11 family was adopted as the primary detection framework, providing an effective trade-off between accuracy and inference performance in real-time scenarios. The YOLOv11n model was trained and validated on a Tesla T4 GPU, and YOLOv11m will be validated on the target platform in subsequent experiments. The findings demonstrate the technical viability and operational relevance of the IMS as a core component for autonomous surveillance systems in defense, satisfying strict requirements for speed, stability, and robust detection of vehicles and pedestrians. Full article

Hypoxic eye diseases represent a pivotal yet often underappreciated contributor to the onset and progression of many retinal disorders. When hypoxia persists or exceeds the tissue’s compensatory capacity, it triggers pathological retinal neovascularization, blood–retinal barrier disruption, and neuronal apoptosis, ultimately resulting in irreversible visual impairment. Connexins (Cxs) form gap junction channels and hemichannels and regulate retinal cell proliferation, differentiation, and survival, thereby playing a central regulatory role in the pathogenesis of hypoxic ocular diseases. In addition to gap junctions, Cx hemichannels promote transmission of molecules between intra- and extracellular environments, further influencing retinal homeostasis under hypoxic stress. This review synthesizes recent progress in understanding connexins in localized and systemic hypoxic eye diseases. We focus on the molecular mechanisms underlying the development and progression of hypoxia-induced ocular pathology, with particular emphasis on the emerging potential of Cxs as novel therapeutic targets for hypoxic ocular diseases. Following a systematic literature search, the electronic databases PubMed and EMBASE were consulted, with the search deadline set at December 2025. The search terms employed were as follows: hypoxia, connexin, gap junctions, hemichannels. Full article

The study establishes a comprehensive mathematical modeling framework for solar-driven hydrogen production by integrating a triple-diode photovoltaic (PV) model, an alkaline electrolyzer, and a hydrogen turbine (H₂T), subsequently using hybrid power utilization to optimize hydrogen output. The Triple-Diode Model (TDM) accurately reproduces the electrical performance of a 144-cell photovoltaic module under standard test conditions (STC), enabling precise calculations of hourly maximum power point outputs based on real-world conditions of global horizontal irradiance and ambient temperature. The photovoltaic system produced 1.07 MWh during the summer months (May to September 2025), which was sent straight to the alkaline electrolyzer. The electrolyzer, using Specific Energy Consumption (SEC)-based formulations and Faraday’s law, produced 22.6 kg of green hydrogen and used around 203 L of water. The generated hydrogen was later utilized to power a hydrogen turbine (H₂T), producing 414.6 kWh, which was then integrated with photovoltaic power to create a hybrid renewable energy source. This hybrid design increased hydrogen production to 31.4 kg, indicating a substantial improvement in renewable hydrogen output. All photovoltaic, electrolyzer, and turbine models were integrated into a cohesive MATLAB R2024b framework, allowing for an exhaustive depiction of system dynamics. The findings validate that the amalgamation of H₂T with photovoltaic-driven electrolysis may significantly improve both renewable energy and hydrogen production. This research aligns with Saudi Vision 2030 and global clean-energy initiatives, including the Paris Agreement, to tackle climate change and its negative impacts. An integrated green hydrogen system, informed by this study’s findings, could significantly improve energy sustainability, strengthen production reliability, and augment hydrogen output, fully aligning with economical, technical, and environmental objectives. Full article

The field of study on intimate partner violence has long been characterized by a bitter debate between the following two opposing theoretical and ideological positions on the nature of the phenomenon: the first is typical of the feminist perspective and considers IPV as an expression of gender-based violence; the second is typical—among others—of the attachment-based perspective and maintains that IPV would be a neutral form of violence with respect to gender. The aim of this contribution is to try to show how it is possible to make a more heuristically fruitful comparison between these two antagonistic perspectives, shifting the focus on the conceptual frameworks that underlie them and on their two different corresponding key explanatory concepts as follows: on the one hand, gender-based power on which the feminist perspective hinges, and on the other, love and love-related emotional dynamics on which the attachment-based perspective focuses. Finally, we will argue how these two key explanatory concepts can be kept combined in a sort of binocular vision and integrated into a more complex “power-and-love” explanatory framework. To this end, we will refer to a systemic approach to IPV, in particular to the contribution of Virginia Goldner, who proposes a model based on the close interconnection between power dynamics and love-related dynamics in the genesis and perpetuation of male violence in heterosexual intimate relationships. Full article

This paper presents a low-cost, fully on-premise Edge Artificial Intelligence (AI) system designed to support real-time pest and disease detection in open-field chili pepper cultivation. The proposed architecture integrates AI-Thinker ESP32-CAM module (ESP32-CAM) image acquisition nodes (“Sticks”) with a Raspberry Pi 5–based edge server (“Module”), forming a plug-and-play Internet of Things (IoT) pipeline that enables autonomous operation upon simple power-up, making it suitable for aging farmers and resource-limited environments. A Leaf-First 2-Stage vision model was developed by combining YOLOv8n-based leaf detection with a lightweight ResNet-18 classifier to improve the diagnostic accuracy for small lesions commonly occurring in dense pepper foliage. To address network instability, which is a major challenge in open-field agriculture, the system adopted a dual-protocol communication design using Hyper Text Transfer Protocol (HTTP) for Joint Photographic Experts Group (JPEG) transmission and Message Queuing Telemetry Transport (MQTT) for event-driven feedback, enhanced by Redis-based asynchronous buffering and state recovery. Deployment-oriented experiments under controlled conditions demonstrated an average end-to-end latency of 0.86 s from image capture to Light Emitting Diode (LED) alert, validating the system’s suitability for real-time decision support in crop management. Compared to heavier models (e.g., YOLOv11 and ResNet-50), the lightweight architecture reduced the computational cost by more than 60%, with minimal loss in detection accuracy. This study highlights the practical feasibility of resource-constrained Edge AI systems for open-field smart farming by emphasizing system-level integration, robustness, and real-time operability, and provides a deployment-oriented framework for future extension to other crops. Full article

Cardiac arrest is a life-threatening emergency requiring immediate intervention, with bystander-initiated Cardiopulmonary resuscitation (CPR) being critical for survival, especially in out-of-hospital situations where medical help is often delayed. Given that over 70% of out-of-hospital cases occur in private residences, there is a growing imperative to provide widespread CPR training to the public. However, conventional instructor-led CPR training faces inherent limitations regarding spatiotemporal constraints and the lack of personalized feedback. To address these issues, this paper proposes an AI-integrated XR-based CPR training system designed as an advanced auxiliary tool for skill acquisition. The system integrates vision-based pose estimation with multimodal sensor data to assess the trainee’s posture and compression metrics in accordance with Korean regional CPR guidelines. Moreover, it utilizes a Large Language Model to evaluate verbal protocols, including requesting an emergency call that aligns with the guidelines. Experimental validation of the proof-of-concept reveals a verbal evaluation accuracy of 88% and a speech recognition accuracy of approximately 95%. Furthermore, the optimized concurrent architecture provides a real-time response latency under 0.5 s, and the automated marker-based tracking ensures precise spatial registration without manual calibration. These results confirm the technical feasibility of the system as a complementary solution for basic life support education. Full article

In recent years, despite the continuous improvement of China’s construction safety management systems and the adoption of advanced technologies, safety accidents remain frequent. This shift highlights the growing importance of human factors in construction safety. As the main labor force, the new generation of construction workers differs significantly from previous generations in values and motivation, reducing the effectiveness of traditional safety management models. This study investigates the direct effect of transformational leadership on the safety behavior of new-generation construction workers. Using survey data collected from construction enterprises in Guangdong Province, China, and applying structural equation modeling (SEM), the results reveal that transformational leadership has a significant positive impact on safety behavior. All four dimensions—idealized influence, inspirational motivation, idealized influence (charisma) and individualized consideration—positively influence both safety compliance and participation, with inspirational motivation exerting the strongest effect (β = 0.509 for compliance; β = 0.446 for participation). These findings indicate that leaders who articulate a compelling shared vision can effectively internalize safety norms and motivate proactive safety participation. This study enriches theoretical understanding of safety leadership mechanisms and provides practical guidance for construction enterprises to enhance safety performance through cultivating transformational leadership among managers. Full article

Prolonged +Gz acceleration during high-performance flight exposes pilots to the risk of G-induced loss of consciousness (G-LOC), a dangerous condition that compromises operational safety. To enable early detection without intrusive sensors, we present a vision-based warning system that analyzes facial action units (AUs) as physiological indicators of impending G-LOC. Our approach combines computer vision with physiological modeling to capture subtle facial microexpressions associated with cerebral hypoxia using widely available RGB cameras. We propose a novel Graph Convolution-Augmented Vision Transformer (GC-ViT) network architecture that effectively captures dynamic AU variations in pilots under G-LOC conditions by integrating global context modeling with vision Transformer. The proposed framework integrates a vision–semantics collaborative Transformer for robust AU feature extraction, where EfficientNet-based spatiotemporal modeling is enhanced by Transformer attention mechanisms to maintain recognition accuracy under high-G stress. Building upon this, we develop a graph-based physiological model that dynamically tracks interactions between critical AUs during G-LOC progression by learning the characteristic patterns of AU co-activation during centrifugal training. Experimental validation on centrifuge training datasets demonstrates strong performance, achieving an AUC-ROC of 0.898 and an AP score of 0.96, confirming the system’s ability to reliably identify characteristic patterns of AU co-activation during G-LOC events. Overall, this contact-free system offers an interpretable solution for rapid G-LOC detection, or as a complementary enhancement to existing aeromedical monitoring technologies. The non-invasive design demonstrates significant potential for improving safety in aerospace physiology applications without requiring modifications to current cockpit or centrifuge setups. Full article

Forest surveying and inspection face significant challenges due to unstructured environments, variable terrain conditions, and the high costs of manual data collection. Although mobile robotics and artificial intelligence offer promising solutions, reliable autonomous navigation in forest, terrain-aware path planning, and tree parameter estimation remain open challenges. In this paper, we present the results of the AI4FOREST project, which addresses these issues through three main contributions. First, we develop an autonomous mobile robot, integrating SLAM-based navigation, 3D point cloud reconstruction, and a vision-based deep learning architecture to enable tree detection and diameter estimation. This system demonstrates the feasibility of generating a digital twin of forest while operating autonomously. Second, to overcome the limitations of classical navigation approaches in heterogeneous natural terrains, we introduce a machine learning-based surrogate model of wheel–soil interaction, trained on a large synthetic dataset derived from classical terramechanics. Compared to purely geometric planners, the proposed model enables realistic dynamics simulation and improves navigation robustness by accounting for terrain–vehicle interactions. Finally, we investigate the impact of point cloud density on the accuracy of forest parameter estimation, identifying the minimum sampling requirements needed to extract tree diameters and heights. This analysis provides support to balance sensor performance, robot speed, and operational costs. Overall, the AI4FOREST project advances the state of the art in autonomous forest monitoring by jointly addressing SLAM-based mapping, terrain-aware navigation, and tree parameter estimation. Full article

A laser tracker is an essential tool in the field of precise geometric measurement. Its fundamental operating idea is a dual-axis rotating device that propels the laser beam to continuously align and measure the attitude of a collaborating target. Such systems provide numerous benefits, including a broad measuring range, high precision, outstanding real-time performance, and ease of use. To solve the issue of low beam recovery efficiency in typical laser trackers, this research offers a two-dimensional laser tracking system that incorporates a machine vision module. The system uses a unique off-axis optical design in which the distance measuring and laser tracking paths are independent, decreasing the system’s dependency on optical coaxiality and mechanical processing precision. A tracking head error calibration method based on singular value decomposition (SVD) is introduced, using optical axis point cloud data obtained from experiments on various components for geometric fitting. A complete prototype system was constructed and subjected to accuracy testing. Experimental results show that the proposed system achieves a relative positioning accuracy of less than 0.2 mm (spatial root mean square error (RMSE) = 0.189 mm) at the maximum working distance of 1.5 m, providing an effective solution for the design of high-precision laser tracking systems. Full article