Intelligent movement tracking performing arts has become a paradigm shift in research, where computer intelligence is applied to creative performance. Conventional methods of motion analysis, most of which rely on manual observation, marker tracking systems, or single sensory modes, are incapable of tracking the subtleties, fluidity, and stylistic diversity of dances, theatre and performance. The recent innovations in artificial intelligence, multimodal sensing, and real-time analytics provide new opportunities to measure expressive movement with an unprecedented accuracy. This paper suggests an all-encompassing design that is based on optical cameras, inertial measurement units, depth sensors, and wearable devices and combines them with cutting-edge machine learning algorithms, including CNN-based pose estimators, graph convolution networks, and transformers. The system architecture has the focus of multimodal fusion, through which it is possible to consider the concurrent perception of visual, inertial, acoustic, and biomechanical signals to gain deeper insights into human movement. The processes of live performance environments, strong annotation of expressive and stylistic features, and deep learning architecture design based on the dynamics of performing arts are developed as a methodological pipeline. They have been applied to choreography analysis, automated assessment of movement-quality, intelligent systems of teaching dance and acting, and performance optimization using biomechanical feedback.

As a key supporting structure of photovoltaic (PV) systems, the technological development of PV mounts directly impacts their power generation efficiency, stability, and service life. This paper systematically reviews the current research status and development trends of PV mounts, based on an analysis of literature from databases such as Scopus, Web of Science, and Engineering Village, covering publications from 2000 to 2023. Our review focuses on the characteristics and application scenarios of main structural types, including fixed mounts, single-axis and dual-axis tracking mounts, and flexible mounts. It also examines the performance and research progress of related materials, including metals, fiber-reinforced polymers (FRP), and novel composites. The analysis indicates that, under favorable climatic conditions (e.g., high irradiance regions), single-axis and dual-axis tracking systems can enhance power generation efficiency by 15-30% and over 30%, respectively, compared to fixed-tilt systems. Flexible mounts demonstrate notable adaptability to complex terrains, while composite material mounts can achieve weight reductions exceeding 50% compared to traditional steel, alongside superior corrosion resistance. Future development directions for PV mounts are identified as intelligent tracking, lightweight design, environmentally sustainable materials, and adaptation to diversified application scenarios. However, this review also identifies critical research gaps, including the need for robust wind-induced vibration control and long-term durability data for flexible systems, as well as comprehensive lifecycle assessments (LCA) and recycling strategies for novel composites. By synthesizing these findings and highlighting these specific gaps, this study provides a structured reference and clear direction for future innovative development and engineering applications in PV mount technology.

In modern times, magnetic resonance imaging-based brain tumor detection has gained significant attention due to its ability to provide high-quality images across various spatial resolutions, which improves diagnostic accuracy. Despite that, imbalanced classes and poor-quality images are the main challenges that complicate training models. Besides, many researchers have established various methods to address these aforementioned issues, but have suffered from interpretability issues and poor accuracy due to the lack of sufficient information. Therefore, this research proposes an automated intelligent tracking scout optimization-based shuffle attention-enabled deep convolutional neural network (ISADCN) model for accurate brain tumor detection. The proposed model integrates shuffle attention and the intelligent tracking scout optimization (ITSO) algorithm, which highlights proper semantic features and adjusts the hyperparameters to improve accuracy in brain tumor detection. In line with this, the proposed model integrates an ITSO-optimized ResUNet to enable accurate and adaptive segmentation of tumor regions. Additionally, the proposed model addresses interpretation issues and overcomes computational complexity, also quite significant for accurately detecting brain tumors in real-world scenarios. On top of that, the ISADCN model greatly achieves an overall accuracy of 96.84%, a positive predictive value of 97.91%, and a negative predictive value of 95.97% when compared with other state-of-the-art methods.

MH-T2TA: a multiple-hypothesis algorithm for multi-sensor track-to-track association with an intelligent track score

This research examines how Saudi Arabia might achieve Vision 2030’s broad goals while meeting the demands of significant events, such as the 2034 FIFA World Cup, by leveraging high-tech logistics hubs. It examines how supply chain resilience may be enhanced by artificial intelligence (AI) through safety stock, cross-docking for speedy delivery, and intelligent tracking. Using a mixed-methods approach that includes expert interviews, case studies, and performance measurements, the study illustrates how innovations boost productivity, reduce delays, and ensure a smooth flow of commodities. Beyond operations, the study explains how logistics innovation creates avenues for entrepreneurship, supporting start-ups in sustainable transport technology and AI logistics solutions. The study claims that the use of AI, crossdocking, and high-capacity logistics nodes enhances Saudi Arabia’s ability to host international logistics events and solidifies its standing as a developing center for supply chain innovation and entrepreneurship.

The primary challenge in active object tracking (AOT) lies in maintaining robust and accurate tracking performance in the complex physical scenarios. Existing end-to-end frameworks based on deep learning and reinforcement learning often struggle with high computational costs, data dependency, and limited generalization, hindering their performance in practical applications. Although embodied intelligence (EI) is promising to enable agents to learn from physical interactions, it cannot tackle severe anomalies happened in the complex scenarios. In order to address this issue, here we propose a novel embodied learning method, called the Cognitive Embodied Learning (CEL), which is inspired by the dual decision-making system of the human brain. The CEL can dynamically switch between normal tracking and anomaly handling modes, supported by specialized modules including the anomaly cognition module, the rule reasoning module, and the anomaly elimination module. Moreover, we further introduce the categorical objective function to address function non-measurability and data confusion caused by severe anomalies. Extensive unmanned aerial vehicle anomaly active target tracking experiments in both simulated and real-world scenarios demonstrate the superior performance of our method. Compared to the state-of-the-art methods, the CEL achieves a 361.4% increase in the success rate and a 54.4% improvement of the task completion efficiency, which highlights the potential of CEL to advance the field of AOT and open new avenues for more robust and intelligent tracking systems in the challenging environments.

Intelligent tracking control of IT2 fuzzy systems with dynamic event-triggering via neural networks method

The increasing complexity of global supply chains necessitates advanced logistics information tracking and security mechanisms to ensure operational efficiency and reliability. Traditional logistics tracking models, which primarily rely on static routing and heuristic-based estimations, often fail to adapt to dynamic transportation networks and unforeseen disruptions. Moreover, existing methods struggle to achieve real-time accuracy while maintaining security and scalability. We propose an innovative logistics tracking and security optimization framework that integrates blockchain technology with an Adaptive Spatiotemporal Tracking Network (ASTN) and a Proactive Logistics Optimization Strategy (PLOS). Our approach leverages blockchain’s decentralized and tamper-proof architecture to enhance data integrity and security while employing ASTN’s deep learning-driven tracking mechanism for precise real-time shipment monitoring. PLOS dynamically optimizes routing and resource allocation through predictive analytics, ensuring resilience against disruptions. By combining these advanced technologies, our framework mitigates the risks associated with fraud, cyberattacks, and data manipulation, significantly improving the transparency and reliability of supply chain operations. Comparative analysis with conventional logistics models reveals that our approach not only streamlines supply chain workflows but also minimizes inefficiencies caused by outdated tracking mechanisms. The scalability of our framework allows it to be seamlessly integrated into various industries, including pharmaceuticals, perishable goods, and high-value asset transportation. These findings highlight the potential of integrating blockchain with intelligent tracking models to establish a more efficient, secure, and adaptive supply chain management system, ultimately transforming global logistics into a smarter and more resilient network.

Surface defects in ballastless tracks pose potential risks to the safety and stability of high-speed railway operations. However, the complex detection environment at night often leads to low computational efficiency and detection accuracy in structural damage assessment. To address these issues, this article proposes a lightweight detection and localization method for surface damage in ballastless tracks, deployed on an intelligent track inspection vehicle. The method enhances the network model’s ability to extract shallow and deep damage features under nighttime conditions, while mitigating the harmful gradient effects caused by low-quality samples. Besides, the method uses photoelectric encoding technology to achieve precise mileage localization of surface defects. Compared to the You Only Look Once (YOLO)v7 model, the YOLO-Track model achieves a precision of 98.2%, reflecting a 7.68% improvement, and reduces the parameter size by 33.80%, down to 23.05M. The YOLO-Track model with a dynamic nonmonotonic mechanism loss function achieves a mean average precision @0.75 of 81.4%, surpassing the traditional complete intersection over union loss function model by 13.06%. Finally, the effectiveness of the proposed method is validated through field tests.

Accurate track segment association plays an important role in modern sensor data processing systems to ensure the temporal and spatial consistency of target information. Traditional methods face a series of challenges in association accuracy when handling complex scenarios involving short tracks or multi-target intersections. This study proposes an intelligent association method that includes a multi-dimensional track data preprocessing algorithm and the characteristic-aware attention long short-term memory (CA-LSTM) network. The algorithm can segment and temporally align track segments containing multi-dimensional characteristics. The CA-LSTM model is built to perform track segment association and has two basic parts. One part focuses on the target characteristic dimension and utilizes the separation and importance evaluation of physical characteristics to make association decisions. The other part focuses on the time dimension, matching the application scenarios of short, medium and long tracks by obtaining the temporal characteristics of different time spans. The method is verified on a multi-source track association dataset. Experimental results show that association accuracy rate is 85.19% for short-range track segments and 96.97% for long-range track segments. Compared with the typical traditional method LSTM, this method has a 9.89% improvement in accuracy on short tracks.

Optimizing glucose control is one of the primary goals of diabetes management. This study assessed the feasibility and accuracy of a remote real-time continuous glucose monitoring system (RT-CGMS) integrated with intelligent tracking in diabetic dogs. Seven Beagle dogs were monitored using interstitial sensors across different configurations: adhesive only, adhesive with protective garments, and garments combined with an innovative glucose monitoring approach for remote transmission. Sensor wear time was slightly longer with garments (8.2 ± 6.7 vs. 5.8 ± 3.1 days; P > 0.05). Valid data acquisition was significantly higher in the remote-monitoring group [95 (84, 96)] compared to Group 1 [67 (47, 78)] and Group 2 [76 (64, 80), P < 0.001 for both]. A strong correlation was found between RT-CGMS and PBGM measurements (r = 0.904). Calibration improved accuracy at glucose levels ≥ 5.5 mmol/L, reducing MARD from 28.5 to 14.5% and increasing Bland-Altman agreement from 48 to 67%. However, MAD slightly increased in the < 5.5 mmol/L range (2.2 to 2.7 mmol/L). Frequent hyperglycemia, high variability, and glucose excursions were observed. In conclusion, RT-CGMS with intelligent tracking improved data continuity and accuracy in diabetic dogs. Future research should focus on improving the system’s sensitivity under hypoglycemic conditions and exploring its broader applications, including its role in enhancing in-hospital glucose management, utilizing big data to facilitate online diagnostics and offline follow-up care, providing guidance for daily glucose stabilization, enabling personalized veterinary services, and offering subscription-based health reports for pet owners.

As the global demand for sustainable development and green finance continues to increase, traditional banks have gradually exposed problems such as limited monitoring dimensions and delayed data response in environmental risk identification, green business promotion, and performance evaluation. To solve the above bottlenecks, this paper integrates artificial intelligence and Internet of Things technologies to build a green financial intelligent perception and control system. By applying an AI credit approval model, a real-time environmental data collection mechanism based on the Internet of Things, and a green performance intelligent tracking system, refined dynamic control of key indicators such as the proportion of green credit, environmental risk levels, and customer green satisfaction can be achieved. The experimental results show that the AI deep learning model performs best in green loan risk prediction, with an AUC (Area Under the Curve) value of 0.91, which is significantly higher than the 0.72 of the traditional credit scoring model and the 0.84 of the ESG enhanced model, indicating that the model has a stronger ability to distinguish between high-risk and low-risk loans. In the case of the simulated policy incentive bank, the initial green business accounts for 18.0%. Under the simulated policy incentive, the bank's green business accounts for 33.6%, an increase of 15.6%. This result shows that policy incentives have a significant promoting effect on underdeveloped banks.

This review provides a comprehensive and multidisciplinary overview of recent advancements in solar tracking systems (STSs) aimed at improving the efficiency and adaptability of photovoltaic (PV) technologies. The study systematically classifies solar trackers based on tracking axes (fixed, single-axis, and dual-axis), drive mechanisms (active, passive, semi-passive, manual, and chronological), and control strategies (open-loop, closed-loop, hybrid, and AI-based). Fixed-tilt PV systems serve as a baseline, with single-axis trackers achieving 20–35% higher energy yield, and dual-axis trackers offering energy gains ranging from 30% to 45% depending on geographic and climatic conditions. In particular, dual-axis systems outperform others in high-latitude and equatorial regions due to their ability to follow both azimuth and elevation angles throughout the year. Sensor technologies such as LDRs, UV sensors, and fiber-optic sensors are compared in terms of precision and environmental adaptability, while microcontroller platforms—including Arduino, ATmega, and PLC-based controllers—are evaluated for their scalability and application scope. Intelligent tracking systems, especially those leveraging machine learning and predictive analytics, demonstrate additional energy gains up to 7.83% under cloudy conditions compared to conventional algorithms. The review also emphasizes adaptive tracking strategies for backtracking, high-latitude conditions, and cloudy weather, alongside emerging applications in agrivoltaics, where solar tracking not only enhances energy capture but also improves shading control, crop productivity, and rainwater distribution. The findings underscore the importance of selecting appropriate tracking strategies based on site-specific factors, economic constraints, and climatic conditions, while highlighting the central role of solar tracking technologies in achieving greater solar penetration and supporting global sustainability goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action).

This paper aims to present a hybrid steering control method combining the self-guidance capability of a wheelset and fuzzy logic controller (FLC), which were applied to our new micro-autonomous railway inspection vehicle, enhancing the vehicle’s stability. The vehicle features intelligent inspection systems and a suspension system with variable damping capability that uses smart magnetorheological fluid to control vertical oscillations. A mathematical model of the steering dynamic system was developed based on the vehicle’s unique structure. Two simulation models of the vehicle were built on Simpack and Simulink to evaluate the lateral dynamic capability of the wheelset, applying Hertzian normal theory and Kalker’s linear theory. The hybrid steering control was designed to adjust the torque differential of the two front-wheel drive motors of the vehicle to keep the vehicle centered on the track during operation. The control simulation results show that this hybrid control system has better performance than an uncontrolled vehicle, effectively keeps the car on the track centerline with deviation below 10% under working conditions, and takes advantage of the natural self-guiding force of the wheelset. In conclusion, the proposed hybrid steering system controller demonstrates stable and efficient operation and meets the working requirements of intelligent track inspection systems installed on vehicles.

近年来，男性不育患者数量持续上升，精子质量的快速、精准评估已成为生殖医学领域的重要挑战。然而，传统精子分析方法在分类效率、客观性和成本效益方面存在明显局限，难以满足临床需求。人工智能（artificial ntelligence, AI）技术的引入为该领域提供了创新解决方案。本文系统综述了AI在男性不育诊疗中的应用进展，重点关注其在精子浓度、活力、形态学及脱氧核糖核苷酸碎片指数（DNA fragmentation index, DFI）检测等参数的评估，以及非梗阻性无精症（non-obstructive azoospermia, NOA）治疗中的作用。研究表明，基于卷积神经网络（convolutional neural network, CNN）等深度学习模型在精子浓度和活力评估中表现出较高的准确性和效率，尤其在精子形态学分析方面已得到多项验证，显著提升了分析的客观性和临床实用性。然而，在DFI检测领域，由于缺乏高分辨率成像技术支持，相关研究仍较有限，仅少数模型展现出潜在应用价值。此外，AI辅助图像识别技术在睾丸精子提取术中的应用显著提高了精子检出率，为NOA患者的治疗提供了新突破。本文还探讨了自然语言处理技术在患者预问诊和随访管理中的应用，如自动化数据采集和智能随访系统，展现了AI在优化诊疗流程方面的潜力。未来，随着高质量数据集的积累、算法优化及成像技术的进步，AI有望实现多维度精子参数综合评估，并在男性不育的精准诊疗中发挥更重要的作用。

Design of Fire-extinguishing Car with Intelligent Tracking and Obstacle Avoidance

Upon returning to the depot, rail transit vehicles require necessary maintenance. The working condition of train maintenance personnel directly impacts the safety of both staff and equipment. Therefore, effective monitoring and control of activities within train roof access platforms are essential. Traditional manual monitoring demands substantial manpower and is prone to human error, whereas machine vision–based intelligent monitoring offers a promising alternative, reducing the dispatch control center (DCC) workload while enhancing safety management. Our intelligent monitoring approach involves three key steps: train maintenance personnel identification, tracking of maintenance activities to generate movement trajectories, and analysis of movement patterns to detect anomalous behavior. This study primarily addresses the challenges of personnel identification and process tracking. In the scenario of train maintenance, facial recognition is limited by posture variations, making direct video tracking impractical. Pedestrian reidentification (Re‐ID) also struggles with posture and attire changes. To address these issues, we propose a hybrid approach: facial recognition confirms personnel identity upon entry, followed by pedestrian feature extraction for Re‐ID‐based tracking throughout the maintenance process. To handle occlusion, we designed a Re‐ID method based on body part recognition, segmenting features into head–shoulder, body, arm, and leg components, with higher weights assigned to visible parts. This method achieved improved mean average precision (mAP) and Rank‐1 values of 87.6% and 95.7%, respectively, on the Market1501 dataset. A tracking and monitoring system was developed, effectively identifying and tracking maintenance activities, demonstrating a strong practical value. Furthermore, this work lays the groundwork for future research into trajectory‐based abnormal behavior detection.

This paper introduces the design and implementation of an intelligent tracking car based on STM32 micro-controller, aiming to provide a low-cost and efficient solution to deal with the challenge of "last twenty meters" distribution in the logistics industry. The system integrates a variety of sensors to realize autonomous movement in complex environment, with intelligent obstacle avoidance, tracking and Bluetooth control functions. By optimizing the hardware cost and simplifying the design, the project has demonstrated significant application value in the field of intelligent logistics, especially the practical application potential in short-distance automatic distribution.

Abstract. This paper discusses the control problem of intelligent tracking vehicle in complex dynamic environment, and puts forward a solution combining Bayesian optimization algorithm and PID control. Traditional PID control faces many challenges when dealing with time-varying and nonlinear systems, but the operating environment of intelligent tracking vehicles is complex and changeable, and it is difficult to adjust parameters in real time to adapt to different operating conditions. Therefore, this paper introduces Bayesian optimization algorithm to predict the optimal parameter combination of PID controller by constructing hyperparameter optimization model, thus effectively improving the control performance and robustness of the system.

Magnetic microrobots, designed to navigate the complex environments of the human body, show promise for minimally invasive diagnosis and treatment. However, their clinical adoption faces hurdles such as biocompatibility, precise control, and intelligent tracking. Here a novel formulation (referred to water-stable magnetic lipiodol micro-droplets, MLMD), integrating clinically approved lipiodol, gelatin, and superparamagnetic iron oxide nanoparticles (SPION) with a fundamental understanding of the structure-property relationships is presented. This formulation demonstrates multiple improved properties including flowability, shape adaptability, efficient drug loading, and compatibility with digital subtraction angiography (DSA) imaging in both in vitro and in vivo experiments. This enables the MLMD as a versatile tool for image-guided therapy, supported by a close-looped magnetic navigation system featuring artificial intelligence (AI)-driven visual feedback for autonomous control. The system effectively performs navigational tasks, including pinpointing specific locations of MLMD, recognizing and avoiding obstacles, mapping and following predetermined paths, and utilizing magnetic fields for precise motion planning to achieve visual drug delivery. The MLMD combines magnetic actuation with an AI-directed close-looped navigation, offering a transformative platform for targeted therapeutic delivery.