Search Results (311)

Artificial intelligence (AI), as an emerging interdisciplinary field dedicated to simulating and extending human intelligence, is increasingly integrating into the domain of infectious disease medicine with unprecedented depth and breadth. This narrative review is based on a systematic literature search in databases such as PubMed and Web of Science for relevant studies published between 2018 and 2025, with the aim of synthesizing the current landscape. It demonstrates transformative potential, particularly in the realm of diagnostic assistance. Confronting global challenges such as pandemic control, emerging infectious diseases, and antimicrobial resistance, AI technologies offer innovative solutions to these pressing issues. Leveraging its robust capabilities in data mining, pattern recognition, and predictive analytics, AI enhances diagnostic efficiency and accuracy, enables real-time monitoring, and facilitates the early detection and intervention of outbreaks. This narrative review systematically examines the application scenarios of AI within infectious disease diagnostics, based on an analysis of recent literature. It highlights significant technological advances and demonstrated practical outcomes related to high-throughput sequencing (HTS) for pathogen surveillance, AI-driven analysis of digital and radiological images, and AI-enhanced point-of-care testing (POCT). Simultaneously, the review critically analyzes the key challenges and limitations hindering the clinical translation of current AI-based diagnostic technologies. These obstacles include data scarcity and quality constraints, limitations in model generalizability, economic and administrative burdens, as well as regulatory and integration barriers. By synthesizing existing research findings and cataloging essential data resources, this review aims to establish a valuable reference framework to guide future in-depth research, from model development and data sourcing to clinical validation and standardization of AI-assisted infectious disease diagnostics. Full article

Autonomous landing capability is crucial for fully autonomous UAV flight. Currently, most solutions use either color imaging from a camera pointed down, lidar sensors, dedicated landing spots, beacons, or a combination of these approaches. Classical strategies can be limited by either no color data when lidar is used, limited obstacle perception when only color imaging is used, a low field of view from a single RGB-D sensor, or the requirement for the landing spot to be prepared in advance. In this paper, a new approach is proposed where an RGB-D camera mounted on a gimbal is used. The gimbal is actively actuated to counteract the limited field of view while color images and depth information are provided by the RGB-D camera. Furthermore, a combined UAV-and-gimbal-motion strategy is proposed to counteract the low maximum range of depth perception to provide static obstacle detection and avoidance, while preserving safe operating conditions for low-altitude flight, near potential obstacles. The system is developed using a PX4 flight stack, CubeOrange flight controller, and Jetson nano onboard computer. The system was flight-tested in simulation conditions and statically tested on a real vehicle. Results show the correctness of the system architecture and possibility of deployment in real conditions. Full article

Crack monitoring in concrete structures is essential to maintaining structural integrity. Therefore, this paper proposes a mobile ground robot equipped with a 2-DoF manipulator and stereo vision sensors for autonomous crack monitoring and mapping. To facilitate crack detection over large areas, a 2-DoF motorized manipulator providing linear and rotational motions, with a stereo vision sensor mounted on the end effector, was deployed. In combination with a manual rotation plate, this configuration enhances accessibility and expands the field of view for crack monitoring. Another stereo vision sensor, mounted at the front of the robot, was used to acquire point cloud data of the surrounding environment, enabling tasks such as SLAM (simultaneous localization and mapping), path planning and following, and obstacle avoidance. Cracks are detected and segmented using the deep learning algorithms YOLO (You Only Look Once) v6-s and SFNet (Semantic Flow Network), respectively. To enhance the performance of crack segmentation, synthetic image generation and preprocessing techniques, including cropping and scaling, were applied. The dimensions of cracks are calculated using point clouds filtered with the median absolute deviation method. To validate the performance of the proposed crack-monitoring and mapping method with the robot system, indoor experimental tests were performed. The experimental results confirmed that, in cases of divided imaging, the crack propagation direction was predicted, enabling robotic manipulation and division-point calculation. Subsequently, total crack length and width were calculated by combining reconstructed 3D point clouds from multiple frames, with a maximum relative error of 1%. Full article

Pedestrian detection, as a core function of an intelligent vision system, plays a key role in obstacle avoidance during driverless navigation, intelligent traffic monitoring, and other fields. In this paper, we optimize the YOLO11 detection algorithm to solve the problem of insufficient accuracy of pedestrian detection in complex scenes. The C3K2-lighter module is constructed by replacing the Bottleneck in the C3K2 module with the FasterNet Block, which significantly enhances feature extraction for long-distance pedestrians in dense scenes. In addition, it incorporates the Triplet Attention Module to establish correlations between local features and the global context, thereby effectively mitigating omission problems caused by occlusion. The Variable Focus Loss Function (VFL) is additionally introduced to optimize target classification by quantifying the variance in features between the predicted frame and the ground-truth frame. The improved model, YOLO11-Improved, achieves a synergistic optimization of detection accuracy and computational efficiency, increasing the AP value by 3.7% and the precision by 2.8% and reducing the parameter volume by 0.5 M while maintaining real-time performance. Full article

Induction motors (IMs) are the backbone of modern industry. Despite their robustness and reliability, they are prone to a range of problems that can result in periods of inactivity, diminished operational efficiency, and potential safety risks. Rapid identification and assessment of faults is important to maintain efficient motors operation and avoid serious malfunctions. The paper offers a comprehensive analysis of the existing body of knowledge in IMs’ faults detection, highlighting areas of deficiency and obstacles. Our review is built according to the IMs diagnosis process, presenting for each step of this process several approaches. Finally, we discuss the effectiveness of each fault classification approach in addressing data-driven challenges such as high-dimensionality, class imbalance, nonlinearity, noise, and overfitting. This paper highlights the rising transition to data-driven strategies, with deep learning increasingly taking center stage in tackling the complex challenges of fault diagnosis. It underscores the significant impact of these advancements on the field, actively facilitating future research into intelligent, real-time condition monitoring systems. Full article

Glaucoma remains a leading cause of irreversible blindness. We reviewed more than 150 peer-reviewed studies (January 2019–July 2025) that applied artificial or augmented intelligence (AI/AuI) to glaucoma care. Deep learning systems analyzing fundus photographs or OCT volumes routinely achieved area-under-the-curve values around 0.95 and matched—or exceeded—subspecialists in prospective tests. Sequence-aware models detected visual field worsening up to 1.7 years earlier than conventional linear trends, while a baseline multimodal network integrating OCT, visual field, and clinical data predicted the need for incisional surgery with AUROC 0.92. Offline smartphone triage in community clinics reached sensitivities near 94% and specificities between 86% and 94%, illustrating feasibility in low-resource settings. Large language models answered glaucoma case questions with specialist-level accuracy but still require human oversight. Key obstacles include algorithmic bias, workflow integration, and compliance with emerging regulations, such as the EU AI Act and FDA GMLP. With rigorous validation, bias auditing, and transparent change control, AI/AuI can augment—rather than replace—clinician expertise, enabling earlier intervention, tailored therapy, and more equitable access to glaucoma care worldwide. Full article

The white cane is a reliable and often-used assistive aid; however, it does not protect against obstacles at the head level. We designed and built an ultrasonic-based obstacle detector with a limited detection field in front of the head. The detector is located on the chest and can be mounted on backpack straps or around the neck. We have performed testing with 74 blind people and their instructors. Blind people used the device for three to four weeks in their regular lives, and instructors tested it by themselves or with their clients. The testing showed that individualization by the type of mounting is helpful. The needed detection distance depends on the situation and the speed of movement. In total, 70% of the users were satisfied with the distance options 80 cm, 110 cm, and 140 cm. 81% of the testers were satisfied, or somewhat satisfied, with the sliding switches to control. It is simple, and its position (setting) can be detected by touch. The testers see the benefit of using the device, especially in unknown environments (outdoor and indoor), primarily because of the increased safety by movement (64%) or the feeling of security (41%). Full article

Laser-induced graphene (LIG) is a high-quality graphene material produced by laser scribing. It has garnered significant attention as a solution to various growing global concerns, such as biological threats, energy scarcity, and environmental contamination due to its high conductivity, tunable surface chemistry, and ease of synthesis from a variety of carbonaceous substrates. This review provides a survey of recent advances in LIG applications for energy storage, heavy metal adsorption, water purification, and antimicrobial materials. As a part of this, we discuss the most recent research efforts to develop LIG as (1) sensors to detect heavy metals at ultralow detection limits, (2) as membranes capable of salt and bacteria rejection, and (3) antimicrobial materials capable of bacterial inactivation efficiencies of up to 99.998%. Additionally, due to its wide surface area, electrochemical stability, and rapid charge conduction, we report on the current body of literature that showcases the potential of LIG within energy storage applications (e.g., batteries and supercapacitors). All in all, this critical review highlights the findings and promise of LIG as an emerging next-generation material for integrated biomedical, energy, and environmental technologies and identifies the key knowledge gaps and technological obstacles that currently hinder the full-scale implementation of LIG in each field. Full article

Background: Tuberculosis (TB) remains a primary global health concern, despite the widespread availability of effective chemotherapeutic interventions. The emergence and dissemination of drug-resistant strains of Mycobacterium tuberculosis, particularly those exhibiting resistance to rifampicin, present significant obstacles to the success of TB control programs. Consequently, there is an urgent need for rapid, sensitive, and specific molecular diagnostic tools to inform timely clinical decision-making and reduce the transmission of disease. Loop-mediated isothermal amplification (LAMP) has gained attention as a promising alternative to conventional polymerase chain reaction (PCR) techniques. This method, which facilitates DNA amplification under constant temperature conditions, offers advantages including high specificity, rapid turnaround time, and operational simplicity—features that render it especially suitable for implementation in resource-limited settings. Methods: In this study, a LAMP assay targeting the rpoB gene was developed, with particular focus on detecting the codon 531 C→T mutation associated with rifampicin resistance. A set of four to six primers was designed to recognize six distinct regions of the target sequence. Allele-specific amplification was achieved by incorporating a deliberate single nucleotide mismatch at the 3′ terminus of the B2 primer to enable precise discrimination between wild-type and mutant alleles. The assay was conducted at an optimized temperature of 61 °C for 60 min, followed by visual detection using a lateral flow dipstick (LFD) within five minutes. Results: The LAMP-LFD assay demonstrated 100% concordance with drug susceptibility testing (DST) and DNA sequencing. No cross-reactivity with wild-type strains was observed, underscoring the assay’s high specificity. Conclusions: This platform offers a robust, field-deployable solution for detecting the codon 531 C→T mutation associated with rifampicin resistance in low-resource settings. Full article

With the widespread application of unmanned aerial vehicle technology in search and detection, express delivery and other fields, the requirements for unmanned aerial vehicle dynamic area coverage algorithms has become higher. For an unknown dynamic environment, an improved Dual-Attention Mechanism Double Deep Q-network area coverage algorithm is proposed in this paper. Firstly, a dual-channel attention mechanism is designed to deal with flight environment information. It can extract and fuse the features of the local obstacle information and full-area coverage information. Then, based on the traditional Double Deep Q-network algorithm, an adaptive exploration decay strategy and a coverage reward function are designed based on the real-time area coverage rate to meet the requirement of a low repeated coverage rate. The proposed algorithm can avoid dynamic obstacles and achieve global coverage under low repeated coverage rate conditions. Finally, with Python 3.12 and PyTorch 2.2.1 environment as the training platform, the simulation results show that, compared with the Soft Actor–Critic algorithm, the Double Deep Q-network algorithm, and the Attention Mechanism Double Deep Q-network algorithm, the proposed algorithm in this paper can complete the area coverage task in a dynamic and complex environment with a lower repeated coverage rate and higher coverage efficiency. Full article

Seed-level disease detection in soybeans presents significant challenges, including small-sample limitations, spectral interference, and dense occlusions, which are less pronounced in leaf-level analysis. To overcome these obstacles, we propose YOLOv8-ECCI, an enhanced algorithm based on YOLOv8 for high-precision identification of purple spot disease directly on soybean seeds. Experimental results demonstrate that YOLOv8-ECCI substantially outperforms the baseline YOLOv8n model, achieving significant gains of +5.7% precision, +6.5% recall, +8.0% mAP@0.5, and +7.1% mAP@0.5:0.95. Crucially, the model exhibits superior generalization capability, validated through rigorous cross-dataset testing on the African Wildlife dataset, where it surpasses conventional methods by +6.0% precision and +2.9% mAP@0.5. These results confirm that YOLOv8-ECCI effectively addresses the critical challenges in seed-level pathology, providing a robust and accurate solution for practical in-field agricultural disease detection and quality control. Full article

Pineapple harvesting in natural orchard environments faces challenges such as high occlusion rates caused by foliage and the need for complex spatial planning to guide robotic arm movement in cluttered terrains. This study proposes an innovative visual detection model, Yolov10n-SSE, which integrates split convolution (SPConv), squeeze-and-excitation (SE) attention, and efficient multi-scale attention (EMA) modules. These improvements enhance detection accuracy while reducing computational complexity. The proposed model achieves notable performance gains in precision (93.8%), recall (84.9%), and mAP (91.8%). Additionally, a dimensionality-reduction strategy transforms 3D path planning into a more efficient 2D image-space task using point clouds from a depth camera. Combining the artificial potential field (APF) method with an improved RRT* algorithm mitigates randomness, ensures obstacle avoidance, and reduces computation time. Experimental validation demonstrates the superior stability of this approach and its generation of collision-free paths, while robotic arm simulation in ROS confirms real-world feasibility. This integrated approach to detection and path planning provides a scalable technical solution for automated pineapple harvesting, addressing key bottlenecks in agricultural robotics and fostering advancements in fruit-picking automation. Full article

Tactile pavings (Tenji Blocks) are prone to wear, obstruction, and improper installation, posing significant safety risks for visually impaired pedestrians. This system incorporates a lightweight YOLOv8 (You Only Look Once version 8) model for real-time detection using a fisheye camera to maximize field-of-view coverage, which is highly advantageous for low-altitude UAV navigation in complex urban settings. To enable lightweight deployment, a novel Lightweight Shared Detail Enhanced Oriented Bounding Box (LSDE-OBB) head module is proposed. The design rationale of LSDE-OBB leverages the consistent structural patterns of tactile pavements, enabling parameter sharing within the detection head as an effective optimization strategy without significant accuracy compromise. The feature extraction module is further optimized using StarBlock to reduce computational complexity and model size. Integrated Contextual Anchor Attention (CAA) captures long-range spatial dependencies and refines critical feature representations, achieving an optimal speed–precision balance. The framework demonstrates a 25.13% parameter reduction (2.308 M vs. 3.083 M), 46.29% lower GFLOPs, and achieves 11.97% ${\mathrm{mAP}}_{50:95}$ on tactile paving datasets, enabling real-time edge deployment. Validated through public/custom datasets and actual UAV flights, the system realizes robust tactile paving detection and stable navigation in complex urban environments via hierarchical control algorithms for dynamic trajectory planning and obstacle avoidance, providing an efficient and scalable platform for automated infrastructure inspection. Full article

Due to the complex spatial structure, dense tree distribution, and narrow passages in orchard environments, traditional path planning algorithms often struggle with large path deviations, frequent turning, and reduced navigational safety. In order to overcome these challenges, this paper proposes a hybrid path planning algorithm based on improved D* Lite for narrow forest orchard environments. The proposed approach enhances path feasibility and improves the robustness of the navigation system. The algorithm begins by constructing a 2D grid map reflecting the orchard layout and inflates the tree regions to create safety buffers for reliable path planning. For global path planning, an enhanced D* Lite algorithm is used with a cost function that jointly considers centerline proximity, turning angle smoothness, and directional consistency. This guides the path to remain close to the orchard row centerline, improving structural adaptability and path rationality. Narrow passages along the initial path are detected, and local replanning is performed using a Hybrid A* algorithm that accounts for the kinematic constraints of a differential tracked robot. This generates curvature-continuous and directionally stable segments that replace the original narrow-path portions. Finally, a gradient descent method is applied to smooth the overall path, improving trajectory continuity and execution stability. Field experiments in representative orchard environments demonstrate that the proposed hybrid algorithm significantly outperforms traditional D* Lite and KD* Lite-B methods in terms of path accuracy and navigational safety. The average deviation from the centerline is only 0.06 m, representing reductions of 75.55% and 38.27% compared to traditional D* Lite and KD* Lite-B, respectively, thereby enabling high-precision centerline tracking. Moreover, the number of hazardous nodes, defined as path points near obstacles, was reduced to five, marking decreases of 92.86% and 68.75%, respectively, and substantially enhancing navigation safety. These results confirm the method’s strong applicability in complex, constrained orchard environments and its potential as a foundation for efficient, safe, and fully autonomous agricultural robot operation. Full article

This paper presents the design and implementation of a SLAM-based online mapping and autonomous trajectory execution system for the Nimbulus-e, a concept vehicle designed for agile maneuvering in confined spaces. The Nimbulus-e uses individual steer-by-wire corner modules with in-wheel motors at all four corners. The associated eight joint variables serve as control inputs, allowing precise trajectory following. These control inputs can be derived from the vehicle’s trajectory using nonholonomic constraints. A LiDAR sensor is used to map the environment and detect obstacles. The system processes LiDAR data in real time, continuously updating the environment map and enabling localization within the environment. The inclusion of vehicle odometry data significantly reduces computation time and improves accuracy compared to a purely visual approach. The A* and Hybrid A* algorithms are used for trajectory planning and optimization, ensuring smooth vehicle movement. The implementation is validated through both full vehicle simulations using an ADAMS Car—MATLAB co-simulation and a scaled physical prototype, demonstrating the effectiveness of the system in navigating complex environments. This work contributes to the field of autonomous systems by demonstrating the potential of combining advanced sensor technologies with innovative control algorithms to achieve reliable and efficient navigation. Future developments will focus on improving the robustness of the system by implementing a robust closed-loop controller and exploring additional applications in dense urban traffic and agricultural operations. Full article