Search Results (1,393)

This paper presents a Vibration-based Track Anomaly Detection (VTAD) system designed for real-time monitoring of urban tram infrastructure. The novelty of VTAD is that it converts existing public transport vehicles into distributed mobile sensor platforms, eliminating the need for specialized diagnostic trains. The system integrates low-cost micro-electro-mechanical system (MEMS) accelerometers, Global Positioning System (GPS) modules, and Espressif 32-bit microcontrollers (ESP32) with wireless data transmission via Message Queuing Telemetry Transport (MQTT), enabling scalable and continuous condition monitoring. A stringent ±6σ statistical threshold was applied to vertical vibration signals, minimizing false alarms while preserving sensitivity to critical faults. Field tests conducted on multiple tram routes in Zagreb, Croatia, confirmed that the VTAD system can reliably detect and locate anomalies with meter-level accuracy, validated by repeated measurements. These results show that VTAD provides a cost-effective, scalable, and operationally validated predictive maintenance solution that supports integration into intelligent transportation systems and smart city infrastructure. Full article

In response to the dual challenge of global agricultural greening and digital transformation, it is imperative for agricultural colleges and universities in China to restructure talent cultivation models to support the development of sustainable and intelligent agriculture. This study combines literature analysis, case studies, and questionnaire surveys to identify misalignments between the current agricultural education system and industry needs. Focusing on educational objectives, curricula, practical training, and faculty expertise, the authors propose a novel four-dimensional collaborative cultivation model, “Objectives–Curriculum–Practice–Faculty”. This model centers on interdisciplinary course clusters (e.g., agricultural artificial intelligence and blockchain traceability), industry–academia-integrated training platforms (e.g., smart agriculture innovation centers), and a Dynamic Adjustment Mechanism (DCAM). To support the implementation of this model, this study advances policy recommendations from three perspectives. First, governments should accelerate reforms by providing special funding support and formulating legislation on industry–academia integration. Second, universities must establish early-warning response mechanisms. Third, enterprises must participate in developing education on ecosystems. This paper establishes both a theoretical framework and a practical pathway to transform agricultural education, offering significant referential value for global agricultural institutions adapting to technological revolutions. Full article

Learning from Demonstration (LfD) enables robots to acquire manipulation skills by observing human actions. However, existing methods often face challenges such as high computational cost, limited generalizability, and a loss of key interaction details. This study presents a compact representation for interaction recognition in LfD that encodes human–object interactions using 2D wrist trajectories and 3D object poses. A lightweight extraction pipeline combines MediaPipe-based wrist tracking with FoundationPose-based 6-DoF object estimation to obtain these trajectories directly from RGB-D video without specialized sensors or heavy preprocessing. Experiments on the GRAB and FPHA datasets show that the representation effectively captures task-relevant interactions, achieving 94.6% accuracy on GRAB and 96.0% on FPHA with well-calibrated probability predictions. Both Bidirectional Long Short-Term Memory (Bi-LSTM) with attention and Transformer architectures deliver consistent performance, confirming robustness and generalizability. The method achieves sub-second inference, a memory footprint under 1 GB, and reliable operation on both GPU and CPU platforms, enabling deployment on edge devices such as NVIDIA Jetson. By bridging pose-based and object-centric paradigms, this approach offers a compact and efficient foundation for scalable robot learning while preserving essential spatiotemporal dynamics. Full article

The increasing complexity of mechanical manufacturing demands intelligent, integrated solutions to maintain high levels of precision, efficiency, and traceability. While ERP systems provide centralized management for core business functions, they often fall short in addressing operational-level workflows on the shopfloor. This paper presents the development and implementation of GIP (Gestão Integrada de Produção—Integrated Production Management), a custom software solution designed to bridge this gap for a small-to-medium enterprise (SME) specializing in precision mechanical components. GIP automates manual tasks such as technical drawing validation, file management, and part tracking, significantly reducing approval times and human error while enhancing traceability through unique DataMatrix part marking and centralized data logging. Developed with a modular, user-centered design using C# and SQL Server, the system integrates seamlessly with existing ERP infrastructure, following Industry 4.0 principles. Its deployment resulted in quantifiable improvements in productivity, data security, interdepartmental communication, and project delivery times. The success of GIP underscores the benefits of complementing ERP platforms with task-specific tools tailored to real user workflows. This approach aligns with smart manufacturing trends such as digital threads and digital twins, laying the groundwork for future enhancements in predictive maintenance and real-time analytics. GIP demonstrates how agile, scalable digital tools can drive competitiveness in modern industrial environments. Full article

The development of automated systems for real-time material evaluation is becoming increasingly critical for structural engineering applications, infrastructure diagnostics and advanced material research. This work introduces a novel, fully automated nonlinear acoustics monitoring platform that employs Bulk Wave excitation in combination with non-contact Laser Doppler Vibrometry (LDV) detection to continuously assess the microstructural evolution of cement-based composites. Unlike conventional approaches—such as ultrasonic velocity measurements or compressive strength tests—which lack sensitivity to early-stage changes and also require manual operation, the proposed system enables unsupervised, high-precision monitoring of the material by leveraging the second and third harmonic generation (β₂, β₃) as nonlinear indicators of internal material changes. A specialized LabVIEW-based software manages excitation control, signal acquisition, frequency-domain analysis, and real-time feedback. As an initial step, the system’s stability, linearity, and measurement reliability were validated on metallic samples, and verified through long-duration experiments. Subsequently, the system was used to monitor hydration in cement-based specimens with varying water-to-cement and carbon nanotube (CNT) reinforcement ratios, thereby demonstrating its capability to resolve subtle nonlinear responses. The results highlight the system’s enhanced sensitivity, repeatability, and scalability, demonstrating that it as a powerful tool for structural health monitoring, smart infrastructure, and predictive maintenance applications. Full article

Background/Objective: Healthcare accessibility remains a critical challenge in medically underserved regions, particularly for specialized care such as trauma treatment and hyperbaric oxygen therapy (HBOT). This study aims to develop and empirically evaluate the Dr. LINK platform, a remote collaborative care system designed to bridge healthcare gaps in geographically isolated or resource-limited areas through real-time interdisciplinary medical collaboration. Methods: Dr. LINK platform employs a SaaS-based infrastructure with Zero Trust security architecture, supporting structured data exchange, automated notifications, and dynamic consultation transfer. Patients completed a modified Telehealth Usability Questionnaire on a 7-point Likert scale, evaluating usefulness, ease of use, interface quality, interaction quality, reliability, and overall satisfaction. Results: Dr. LINK successfully facilitated real-time collaborative consultations for emergency medicine and HBOT, supporting multiple concurrent consultations while maintaining data security and system performance. Overall usability scores were high (mean 6.71–6.83/7), with HBOT patients consistently reporting higher satisfaction across all domains. The platform enabled timely, structured, and coordinated care, reducing unnecessary patient transfers and enhancing multidisciplinary decision-making. Conclusions: Dr. LINK represents a significant advancement in addressing healthcare disparities by enabling structured, secure, and scalable remote collaborative care. The platform effectively overcomes geographic and infrastructural barriers, providing a practical framework for future telemedicine implementations in specialized care domains. Continued refinement and evaluation will be essential to fully realize its potential in transforming healthcare delivery models toward greater equity and accessibility. Full article

This study investigates how software developers discuss usability on Stack Overflow through an analysis of posts from 2008 to 2024. Despite recognizing the importance of usability for software success, there is a limited amount of research on developer engagement with usability topics. Using mixed methods that combine quantitative metric analysis and qualitative content review, we examine temporal trends, comparative engagement patterns across eight non-functional requirements, and programming context-specific usability issues. Our findings show a significant decrease in usability posts since 2010, contrasting with other non-functional requirements, such as performance and security. Despite this decline, usability posts exhibit high resolution efficiency, achieving the highest answer and acceptance rates among all topics, suggesting that the community is highly effective at resolving these specialized questions. We identify distinctive platform-specific usability concerns: web development prioritizes responsive layouts and form design; desktop applications emphasize keyboard navigation and complex controls; and mobile development focuses on touch interactions and screen constraints. These patterns indicate a transformation in the sharing of usability knowledge, reflecting the maturation of the field, its integration into frameworks, and the migration to specialized communities. This first longitudinal analysis of usability discussions on Stack Overflow provides insights into developer engagement with usability and highlights opportunities for integrating usability guidance into technical contexts. Full article

Real-time gait analysis is essential for the development of responsive and reliable motorized prosthetics. Deploying advanced deep learning models on resource-constrained embedded systems, however, remains a major challenge. This proof-of-concept study presents a TinyML-based approach for knee joint angle prediction using convolutional neural networks (CNNs) trained on inertial measurement unit (IMU) signals. Gait data were acquired from four healthy participants performing multiple stride types, and data augmentation strategies were applied to enhance model robustness. Multi-objective optimization was employed to balance accuracy and computational efficiency, yielding specialized CNN architectures tailored for short, natural, and long strides. A lightweight classifier enabled real-time selection of the appropriate specialized model. The proposed framework achieved an average RMSE of 2.05°, representing a performance gain of more than 35% compared to a generalist baseline, while maintaining reduced inference latency (16.8 ms) on a $40 embedded platform (Sipeed MaixBit with Kendryte K210). These findings demonstrate the feasibility of deploying compact and specialized deep learning models on low-cost hardware, enabling affordable prosthetic solutions with real-time responsiveness. This work contributes to advancing intelligent assistive technologies by combining efficient model design, hardware-aware optimization, and clinically relevant gait prediction performance. Full article

Metal–organic frameworks (MOFs) have been increasingly recognized as promising platforms for enzyme modulation, owing to their tunable porosity, high surface area, and versatile chemical functionality. In this review, the potential of MOFs for the inhibition and modulation of protein kinases and phosphatases—key regulators of cellular signaling and disease progression—is examined. The structural fundamentals of MOFs are outlined, followed by a discussion of common synthesis strategies, including solvothermal, microwave-assisted, sonochemical, and mechanochemical methods. Emphasis is placed on how synthesis conditions influence critical features such as particle size, crystallinity, surface chemistry, and functional group accessibility, all of which impact biological performance. Four primary mechanisms of MOF–enzyme interaction are discussed: surface adsorption, active site coordination, catalytic mimicry, and allosteric modulation. Each mechanism is linked to distinct physicochemical parameters, including pore size, surface charge, and metal node identity. Special focus is given to biologically relevant metal centers such as Zr⁴⁺, Ce⁴⁺, Cu²⁺, Fe³⁺, and Ti⁴⁺, which have been shown to contribute to both MOF stability and enzymatic inhibition through Lewis acid or redox-mediated mechanisms. Recent in vitro studies are reviewed, in which MOFs demonstrated selective inhibition of disease-relevant enzymes with minimal cytotoxicity. Despite these advancements, several limitations have been identified, including scalability challenges, limited physiological stability, and potential off-target effects. Strategies such as post-synthetic modification, green synthesis, and biomimetic surface functionalization are being explored to overcome these barriers. Through an integration of materials science, coordination chemistry, and molecular biology, this review aims to provide a comprehensive perspective on the rational design of MOFs for targeted enzyme inhibition in therapeutic contexts. Full article

This paper presents a novel continuous-wave Doppler RADAR system for real-time speed measurement of moving objects, implemented using software-defined radio (SDR). Unlike traditional high-cost solutions typically found in research centers or specialized laboratories, this prototype offers a low-cost, compact, and easily deployable platform that lowers the entry barrier for experimentation and research. Operating within the 70 MHz–6 GHz range, SDR enables highly flexible signal processing; in this implementation, a 5.5 GHz carrier is selected to improve the detection precision by exploiting its reduced bandwidth for more accurate observation of frequency shifts. The carrier is modulated with a 2 kHz signal, and Doppler frequency deviations induced by object motion are processed to calculate velocity. Using a Welch spectral estimator, the system effectively reduces noise and extracts the Doppler frequency with high reliability. The prototype achieves speed measurements up to 196.36 km/h with approximately 2% error in the 0–100 km/h range, confirming its suitability for road traffic monitoring. A key innovation of this work is its single-antenna cross-polarized configuration, which simplifies hardware requirements while maintaining measurement accuracy. Furthermore, the system’s portability and open-access design make it ideal for in-vehicle applications, enabling direct deployment for automotive testing, driver-assistance research, and educational demonstrations. All design files and implementation details are openly shared, eliminating patent restrictions and encouraging adoption in low-resource academic and research environments. Full article

3D object detection and tracking technology are increasingly being adopted in unmanned ground vehicles, as robust perception systems significantly improve the obstacle avoidance performance of a UGV. However, most existing algorithms depend heavily on computationally intensive point cloud neural networks, rendering them unsuitable for resource-constrained platforms. In this work, we propose an efficient 3D object detection and tracking method specially designed for deployment on low-cost vehicle platforms. For the detection phase, our method integrates an image-based 2D detector with data fusion techniques to coarsely extract object point clouds, followed by an unsupervised learning approach to isolate objects from noisy point cloud data. For the tracking process, we propose a multi-target tracking algorithm based on multi-level data association. This method introduces an additional data association step to handle targets that fail in 3D detection, thereby effectively reducing the impact of detection errors on tracking performance. Moreover, our method enhances association precision between detection outputs and existing trajectories through the integration of 2D and 3D information, thereby further mitigating the adverse effects of detection inaccuracies. By adopting unsupervised learning as an alternative to complex neural networks, our approach demonstrates strong compatibility with both low-resolution LiDAR and GPU-free computing platforms. Experiments on the KITTI benchmark demonstrate that our tracking framework achieves significant computational efficiency gains while maintaining detection accuracy. Furthermore, experimental evaluations on the real-world UGV platform demonstrated the deployment feasibility of our approach. Full article

Glioblastoma (GB) is one of the most aggressive and treatment-resistant cancers affecting the central nervous system (CNS), predominantly in adults. Despite significant advancements in this field, GB treatment still relies primarily on conventional approaches, including surgical resection, radiotherapy, and chemotherapy, which, due to its complex pathological characteristics, resistance mechanisms, and restrictive nature of the blood–brain barrier (BBB) and blood–brain tumor barrier (BBTB), remain of limited efficacy. In this context, the development of innovative therapeutic strategies able to overcome these barriers, induce cancer cell death, and improve patient prognosis is crucial. Recently, nanoparticle platforms and focused ultrasounds seem to be promising approaches for cancer treatment. Nanoparticles enable targeting and controlled release, whilst focused ultrasounds enhance tissue permeation, increasing drug accumulation in a specific organ. However, nanoparticles can suffer from synthesis complexity, long-term biocompatibility and accumulation in the body with consequent toxicity, whereas focused ultrasounds require specialized equipment and can potentially cause thermal damage, hemorrhage, or cavitation injury. Cyclodextrins (CYDs) possess good properties and represent a versatile and safer alternative able to improve drug stability, solubility, and bioavailability, and depending on the type, dose, and administration route, can reduce local and systemic toxicity. Thus, CYDs emerge as promising novel excipients in GB treatment. Despite these advantages, CYD complexes suffer from receptor specificity, reducing their potential in precision medicine. By combining CYD complexes with polymeric or lipidic platforms, the advantages of CYD safety and drug solubilization together with their specific targeting can be obtained, thus enhancing selectivity and maximizing efficacy while minimizing recurrence and systemic toxicity. This review provides a comprehensive overview of GB pathology, conventional treatments, and emerging CYD-based strategies aimed at enhancing drug delivery and therapeutic efficacy. Full article

The emergence of mobile private networks (MPNs) has enabled tailored communication solutions for industries, enterprises, and specialized applications, fostering improved control, security, and flexibility. With the rapid advancements in software-defined radio (SDR) platforms, implementing MPNs using cost-effective and versatile hardware has become increasingly feasible. Analyzing the radio coverage of such networks is critical for optimizing performance, ensuring reliable connectivity, and addressing site-specific challenges in deployment. This paper investigates the radio coverage of a 4G MPN implemented using as radio front-end an SDR platform from the Universal Software Radio Peripheral (USRP) family and the srsRAN-4G open-source software suite. Using the HTZ Communication software as simulation tool and field-test measurements performed using an off-the-shelf mobile phone as user equipment (UE), an analysis is made to evaluate the accuracy of various propagation models in predicting network coverage, in several different frequency bands. The results provide valuable insights into the design and deployment of MPNs, highlighting the importance of accurate coverage estimation in achieving robust and efficient network operation. Full article

Quantum dots (QDs) have emerged as promising nanomaterials in cancer therapeutics owing to their tunable optical properties, versatile surface functionalization, and potential for simultaneous imaging and drug delivery. This review focuses on targeted quantum dots (TQDs), highlighting their role in overcoming the limitations of passive drug delivery strategies, such as poor specificity, high systemic toxicity, and limited therapeutic efficacy. We begin by outlining the fundamentals of QDs, including their types, heterostructures, and biomedical formulations. Recent advances in tailoring QD physicochemical properties to the cancer microenvironment are discussed, with emphasis on routes of administration and targeting strategies. The review critically examines different molecular targeting approaches—such as folate receptors, transferrin receptors, aptamers, antibodies, peptides, and hyaluronic acid—used to enhance therapeutic precision. Furthermore, we summarize progress in TQD-based combination therapies, including chemotherapy–photodynamic therapy, photothermal therapy, radiotherapy, and multimodal platforms that integrate therapy with imaging. Special attention is given to the role of QDs in theranostic, hydrogels, nanocomposites, and hybrid systems that enable controlled drug release and real-time monitoring. Despite significant advancements, challenges remain regarding biocompatibility, safety, and regulatory approval. Overall, this review provides an integrative perspective on the design, functionalization, and biomedical applications of TQDs, underscoring their potential to improve cancer treatment outcomes through enhanced specificity, reduced side effects, and multifunctional theranostic capabilities. Highlight of novelty: This review uniquely emphasizes the latest advances in targeted quantum dots (TQDs), particularly in surface functionalization, hybrid nanostructures, biodistribution, and multimodal theranostic applications, providing an updated perspective that extends beyond conventional QD-based cancer therapies. Full article

Megakaryocytes (MKs) have traditionally been viewed as terminal hematopoietic cells responsible solely for platelet production. However, recent advances in imaging and single-cell transcriptomics have revealed substantial heterogeneity among MK populations and diverse functions beyond thrombopoiesis. MKs actively participate in innate and adaptive immunity, modulate the hematopoietic stem cell (HSC) niche, and adapt to physiological and pathological stimuli. Located in distinct anatomical sites such as bone marrow and lung, MKs exhibit compartment-specific specializations that enable them to serve as critical integrators of hemostatic, immune, and regenerative processes. Experimental models using human pluripotent stem cells and inducible MKs have enhanced mechanistic insights, while innovative bioreactor platforms and xenotransplantation strategies advance translational applications in platelet production and therapy. Furthermore, immune MK subsets derived from pluripotent stem cells show promising therapeutic potential for modulating inflammation and autoimmune diseases. Continued exploration of MK diversity, tissue-specific roles, and intercellular communication will unlock new opportunities for leveraging MK plasticity in regenerative medicine, immunotherapy, and hematologic disorders, repositioning these versatile cells as central players in systemic homeostasis and defense. Full article