Search Results (115)

The exponential growth of unstructured medical data, particularly clinical notes and diagnostic reports, presents mounting challenges for healthcare knowledge extraction and utilization. This study introduces the Digital Medical Catalog (DMC), a framework that automates the conversion of clinical narratives into an auditable, semantically structured knowledge base. The framework combines BioClinicalBERT embeddings, c-TF-IDF statistical grounding, and semantic clustering, enabling high-fidelity classification (Macro F1 = 0.877 ± 0.012), traceable topic labeling, and temporal trend analysis. By demonstrating that semantic representation methods, reinforced with statistical grounding, are essential for large-scale medical text processing, this work establishes a foundation for privacy-preserving data governance and real-time intelligence within modern healthcare infrastructures. Full article

The Xinqiao Cu-S polymetallic deposit is located in the Tongling ore concentration area of the Middle-Lower Yangtze River metallogenic belt. The orebodies consist of skarn orebodies and stratiform sulfide orebodies, but the genetic link between them remains controversial. In this study, magnetite was used as a proxy to systematically constrain the hydrothermal evolution from the intrusion to the contact zone and further to the stratiform orebodies. A representative drill hole (E603) was logged, and samples were systematically collected from the Jitou pluton outward to the contact zone. Composite samples from the 8–28 m interval were crushed and prepared as resin mounts for integrated TIMA automated mineralogy, BSE textural observation, and in situ LA-ICP-MS trace element analysis. Five types of magnetite (Mt1 to Mt5) were systematically identified. Mt1 occurs as inclusions within feldspar in the quartz monzodiorite. It exhibits typical magmatic magnetite characteristics and contains grid-like ilmenite exsolution, indicating crystallization during the late magmatic stage. Mt2 is distributed in the interstices of magmatic minerals, commonly showing hematitization and replacement of ilmenite exsolution lamellae by titanite. Its trace element geochemistry displays magmatic–hydrothermal transitional features. Mt3–Mt5 in the skarn and stratiform orebodies are paragenetic with retrograde alteration minerals (e.g., epidote, chlorite, and actinolite) and sulfides, and are characterized by low Ti, Al, and V contents and high Mg, Mn, and Sn contents, indicating a hydrothermal origin. From Mt3 to Mt5, (Ti + V) and (Al + Mn) decrease, while Zn and Mn increase, accompanied by a decrease in the (Si + Al)/(Mg + Mn) ratio. This reflects a trend of decreasing fluid temperature and progressively enhanced wall-rock buffering. The Mg-in-magnetite geothermometer yields relatively consistent results for Mt1–Mt3, but anomalously high temperatures for Mt4–Mt5. This suggests that the elevated Mg activity in the fluid, caused by reaction with carbonate wall rocks, can significantly influence the calculated temperatures. Therefore, this geothermometer should be used cautiously for magnetite in the outer skarn zone and interpreted in combination with other temperature constraints. The textures, paragenetic mineral assemblages, and trace element characteristics of magnetite collectively reveal a continuous mineralization process linking the skarn and stratiform orebodies at Xinqiao, providing robust mineralogical and geochemical evidence for the contribution of Yanshanian magmatic–hydrothermal activity to the stratiform mineralization. Full article

Forest fires are a major concern due to their significant impact on the environment, economy, and wildlife habitats. Efficient early detection systems can significantly mitigate their devastating effects. This paper provides a comprehensive review of forest fire detection (FFD) techniques and traces their evolution from basic lookout-based methods to sophisticated remote sensing technologies, including recent Internet of Things (IoT)- and Unmanned Aerial Vehicle (UAV)-based sensor network systems. Historical methods, characterized primarily by human surveillance and basic electronic sensors, laid the foundation for modern techniques. Recently, there has been a noticeable shift toward ground-based sensors, automated camera systems, aerial surveillance using drones and aircraft, and satellite imaging. Moreover, the rise of Artificial Intelligence (AI), Machine Learning (ML), and the IoT introduces a new era of advanced detection capabilities. These detection systems are being actively deployed in wildfire-prone regions, where early alerts have proven critical in minimizing damage and aiding rapid response. All FFD techniques follow a common path of data collection, pre-processing, data compression, transmission, and post-processing. Providing sufficient power to complete these tasks is also an important area of research. Recent research focuses on image compression techniques, data transmission, the application of ML and AI at edge nodes and servers, and the minimization of energy consumption, among other emerging directions. However, to build a sustainable FFD model, proper sensor deployment is essential. Sensors can be either fixed at specific geographic locations or attached to UAVs. In some cases, a combination of fixed and UAV-mounted sensors may be used. Careful planning of sensor deployment is essential for the success of the model. Moreover, ensuring adequate energy supply for both ground-based and UAV-based sensors is important. Replacing sensor batteries or recharging UAVs in remote areas is highly challenging, particularly in the absence of an operator. Hence, future FFD systems must prioritize not only detection accuracy but also long-term energy autonomy and strategic sensor placement. Integrating renewable energy sources, optimizing data processing, and ensuring minimal human intervention will be key to developing truly sustainable and scalable solutions. This review aims to guide researchers and developers in designing next-generation FFD systems aligned with practical field demands and environmental resilience. Full article

Collaborative robots (cobots) can work cooperatively alongside humans, while contributing to task automation in industries such as manufacturing. Designed with enhanced safety features, cobots can safely assist a range of users, including those with no previous robotics experience. Despite the human-centric design of cobots, programming them can be challenging for novice operators, who may lack the skills and understanding of robotics. If left with a choice between major worker upskilling or replacement and investing in expensive and complex precision cobot positioning and object-detection systems, business owners may be reluctant to embrace cobot ownership. Furthermore, if a cobot’s primary intended tasks were simple Pick-and-Place operations, the tenuous return on investment, compared to retaining current manual processes, could make cobot adoption financially impracticable. This paper proposes a low-cost cobot control system (LCCS), an intuitive cobot solution for Pick-and-Place tasks, designed for novice cobot operators. Off-the-shelf vision-based positioning solutions, priced at around $US20,000, are typically designed to be assigned to a single cobot. The LCCS comprises a Raspberry Pi, a standard USB webcam and ArUco fiducial markers, which can easily be incorporated into a multi-cobot operation, with a combined total hardware cost of around $US100. The system scales simply and economically to support an expanding operation and it is easy to use It allows a user to specify a target pick location by positioning a portable localisation scanner upon an object to be grasped by the cobot end-effector. The scanner’s integrated webcam captures the location and orientation perspective from ArUco markers affixed to predefined positions outside the cobot workspace. By pressing a switch mounted on the scanner, the user relays the captured information, converted to 3D coordinates, to the cobot controller. Finally, the cobot’s integrated processor calculates the corresponding pose using inverse kinematics, which allows the cobot to move to the target position. Subsequent actions can be pre-programmed as required, as part of the initial system configuration. Preliminary testing indicates that the proposed system provides accurate and repeatable localisation information, with a mean positional error below 3.5 mm and a mean standard deviation less than 1.8. With a hardware investment just 0.3% of the UR5e purchase price, an easy to use, customisable, and easily scalable vision-based Pick-and-Place localisation system for cobots can be implemented. It has the potential to be a reliable and robust system that significantly lowers cobot operation barriers for novice operators by alleviating the programming requirement. By reducing the reliance on experienced programmers in a production environment, cobot tasks could be deployed more rapidly and with greater flexibility. Full article

Compared to the façades of student multi-story dormitories, flat horizontal roofs offer greater freedom in shaping the layout, orientation, horizontal inclination, and geometry of photovoltaic installations (PVI). The large number of parameters defining the geometric and physical characteristics of PVI necessitates the development of a method to support the optimization of energy renovation processes. To facilitate this innovative method, several automation and optimization procedures were implemented into a specialized computer application developed within the Rhino/Grasshopper graphical programming environment. The method’s algorithm allows for the definition of an initial parametric qualitative model of each rooftop installation. This model is configured through multiple iterative computer simulations aimed at identifying various discrete optimal qualitative models. The implemented optimizing condition concerns the amount of energy produced and relates to the variability of energy prices as well as the costs of purchasing and mounting the PVI. The optimizing procedure involves replacing a specific portion of grid energy with electricity produced by the PVI. The parameters describing variability include the geometric and physical properties, as well as the orientation of the PVI. In the second step, the algorithm optimizes the desired payback period and investment costs. The obtained results fill a gap in the field of multi-parameter optimizing methods for the energy renovation of student dormitories. Full article

This study presents a novel integrated manufacturing approach that combines fused deposition modeling (FDM) 3D printing with in situ electrospinning to fabricate hierarchical composite structures composed of polylactic acid (PLA) reinforced with polyacrylonitrile (PAN) nanofibers. A mounting fixture was employed to enable layer-by-layer nanofiber deposition directly onto printed PLA layers in a continuous automated process, eliminating the need for prefabricated electrospun nanofiber mats. The influences of nozzle temperature (210–230 °C) and electrospinning time (5–15 min per layer) on mechanical, thermal, and morphological properties were systematically investigated. Optimal performance was achieved at an FDM nozzle temperature of 220 °C with 5 min of electrospinning time (sample E1), showing a 36.5% increase in tensile strength (71 MPa), a 33.3% increase in Young’s modulus (2.8 GPa), and a 62.0% increase in flexural strength (128 MPa) compared with the neat PLA. This enhancement resulted from the complete infiltration of molten PLA into the thin nanofiber mats, creating true fiber–matrix integration. Excessive nanofiber content (15 min ES) caused a 36.5% reduction in strength due to delamination and incomplete infiltration. Thermal analysis revealed a decrease in glass transition temperature (1.2 °C) and onset of thermal degradation (5.3–15.2 °C) with nanofiber integration. Fracture morphology confirmed that to achieve optimal properties, it was critical to balance the nanofiber reinforcement content with the depth of infiltration, as excessive content created poorly bonded interleaved layers. This integrated fabrication platform enables the production of lightweight hierarchical composites with multiscale, custom-made reinforcement for applications in biomedical scaffolds, protective equipment, and structural components. Full article

Background: The clinical research laboratory plays a pivotal role in the execution of clinical studies. The accurate and consistent registration of patients is dependent on the competent use of laboratory equipment and manual techniques by technicians, ensuring the reliability of the data collected. To support these activities, the Groupe de Recherche Action en Santé (GRAS) has been registered with the College of American Pathologists (CAP) and the Clinical Laboratories Services (CLS) in Johannesburg, South Africa, for external proficiency testing (EPT) of its laboratory, as part of our commitment to quality assurance. The following report details the performance achievements over the past five years. Methods: Proficiency testing (PT) samples are dispatched to GRAS Lab three times a year (quarterly) and the results are generally returned within two to three weeks. In the field of parasitology, challenge specimens were prepared as follows: thick and thin blood films were stained with Giemsa and mounted with strips to protect them for multiple uses. Photographs, also known as whole slide images (WSIs), were also taken. For the biochemistry and haematology tests, a set of five samples were received for processing. All evaluations were carried out in accordance with the GRAS laboratory’s internal procedures. Results: The CAP laboratory’s performance in terms of the diagnosis of malaria and other blood parasites from 2020 to 2024 was 97.3% accurate (ranging from 93.33% to 100%), with 93.33%, 100%, 100%, 93.33% and 100% achieved in 2020, 2021, 2022, 2023 and 2024, respectively. The number of microscopists evaluated annually has been subject to variation according to operational staff at the time of evaluation. A total of 31 microscopists were enrolled in the CLS PT scheme, of which 73.9% were classified as ‘experts’ and 19.2% as ‘reference’ microscopists. In the field of haematology, the PT demonstrated 100% accuracy over the four-year study period. This outcome is indicative of the high-performance levels exhibited by the automated systems under scrutiny and the comparable nature of the data produced by these systems. The same trend was observed in the biochemistry PT results, with an overall score of 92.12%, ranging from 78% to 100%. Conclusions: Proficiency testing has been shown to be an effective tool for quality assurance in laboratories, helping to ensure the accuracy of malaria and other blood parasite diagnoses made by microscopists, as well as the results generated by automated systems. It has been instrumental in assisting laboratories in identifying issues related to test design and performance. Full article

Accurate vibration measurement is crucial for maintaining the performance, reliability, and safety of automated manufacturing environments. Abnormal vibrations caused by faults in gears or bearings can degrade positional accuracy, reduce productivity, and, over time, significantly impair production efficiency and product quality. Such vibrations may also disrupt supply chains, cause financial losses, and pose safety risks to workers through collisions, falling objects, or other operational hazards. Conventional vibration measurement techniques, such as wired accelerometers and strain gauges, are typically limited to a few discrete measurement points. Achieving multi-point measurements requires numerous sensors, which increases installation complexity, wiring constraints, and setup time, making the process both time-consuming and costly. The integration of high-frame-rate (HFR) cameras with Digital Image Correlation (DIC) enables non-contact, multi-point, full-field vibration measurement of robot manipulators, effectively addressing these limitations. In this study, HFR cameras were employed to perform non-contact, full-field vibration measurements of industrial robots. The HFR camera recorded the robot’s vibrations at 1000 frames per second (fps), and the resulting video was decomposed into individual frames according to the frame rate. Each frame, with a resolution of 1920 × 1080 pixels, was divided into 128 × 128 pixel blocks with a 64-pixel stride, yielding 435 sub-images. This setup effectively simulates the operation of 435 virtual vibration sensors. By applying mask processing to these sub-images, eight key points representing critical robot components were selected for multi-point DIC displacement measurements, enabling effective assessment of vibration distribution and real-time vibration visualization across the entire manipulator. This approach allows simultaneous capture of displacements across all robot components without the need for physical sensors. The transfer function is defined in the frequency domain as the ratio between the output displacement of each robot component and the input excitation applied by the shaker mounted on the end-effector. The frequency–domain transfer functions were computed for multiple robot components, enabling accurate and full-field vibration analysis during operation. Full article

This article focuses on the design, development and optimization of a mechanical system with the aim of increasing the efficiency of the production process. The article describes the issues involved in the production of molds used for EPS (Expanded Polystyrene) and EPP (Expanded Polypropylene) materials, specifically the assembly of mold nozzles. Currently, the assembly of nozzles is performed manually, and the proposed solution aims to automate this process using software and robotics. The solution involves scanning the mounting holes and then modifying the mold model in Siemens NX, based on which a trajectory is generated in the virtual environment of RoboDK software. Communication between Siemens NX and RoboDK software is implemented via a Python algorithm using NXOpen and RoboDK API (Application Programming Interface) libraries. The proposed tool has flexible settings and is not dependent on a robotic arm or tool. The result is a prototype software tool for offline programming of automated assembly, which is adapted to different hole layouts, allowing its use in small-batch production in the future. The proposed tool has flexible settings and is not dependent on a specific robotic arm or tool. The solution was validated through comprehensive simulation testing in the RoboDK environment, demonstrating significant potential for time reduction and process optimization. Full article

This study presents a fully automated, real-time robotic stacking system based on deep learning-driven visual perception, designed to optimize classification and handling tasks on industrial production lines. The proposed system integrates a YOLOv5s-based object detection algorithm with an ABB IRB6640 robotic arm via a programmable logic controller and the Profinet communication protocol. Using a camera mounted above a conveyor belt and a Python-based interface, 13 different types of industrial bags were classified and sorted. The trained model achieved a high validation performance with an mAP@0.5 score of 0.99 and demonstrated 99.08% classification accuracy in initial field tests. Following environmental and mechanical optimizations, such as adjustments to lighting, camera angle, and cylinder alignment, the system reached 100% operational accuracy during real-world applications involving 9600 packages over five days. With an average cycle time of 10–11 s, the system supports a processing capacity of up to six items per minute, exhibiting robustness, adaptability, and real-time performance. This integration of computer vision, robotics, and industrial automation offers a scalable solution for future smart manufacturing applications. Full article

In this article, we present LaserCAD, an open-source, script-based software toolkit for the design and visualization of optical setups based on parametric ray tracing. Unlike conventional commercial tools, which focus on complex lens optimization and offer dense GUIs with extensive parameters, LaserCAD is tailored for fast, intuitive modeling of laser beam paths and opto-mechanical assemblies with minimal setup overhead. Written in Python, it allows users to describe optical systems in a language close to geometrical optics, using simple commands with sensible defaults for most parameters. Optical elements can be automatically positioned including the required mounts. As a graphical backend, FreeCAD renders 3D models of all components for interactive visualization and post-processing. LaserCAD supports integration with other simulation tools and can automate the creation of alignment aids for 3D printing. This makes it especially suitable for rapid prototyping and lab-ready designs. Full article

This study evaluated and integrated automatic pathology equipment and a collaborative robot to create a fully autonomous workflow. We selected the Gemini AS Automated Slide Stainer, ClearVue Coverslipper, and Pannoramic 1000 digital slide scanner, controlled by a UR5e robotic arm. To perform essential clinical laboratory tasks, we determined that the robotic arm, in combination with a custom manipulator, requires 9 degrees of freedom—5 from the robot and 4 from the manufactured manipulator. The patented manipulator is equipped with a camera, LED lighting, and three specialized grippers for object detection and precise handling of equipment doors, magazines, and slides. It is designed to mount onto a standardized robot flange interface (ISO 9409-1-50-4-M6), making it mechanically compatible with various robot arms. A minimum of 24 distinct laboratory tasks were defined for the training of the robotic arm. This autonomous workflow mitigates labor shortages and accelerates diagnostic processes by offloading repetitive tasks, thereby improving efficiency in pathology laboratories. Full article

This paper presents the development of a modular and low-cost IoT (Internet of Things) system for remote monitoring of essential parameters during wine fermentation, designed for small and medium-sized wineries—Winnie. The system combines distributed embedded sensing units with centralized colorimetric analysis and real-time data transmission to a remote server. Barrel-mounted devices measure wine and cellar parameters (temperature, humidity, and CO₂ concentration), while a central hub performs colorimetric SO₂ analysis using an RGB color sensor and automated fluid handling. Communication between the Barrel and Hub device relies on the RS-485 protocol, providing robustness in harsh winery conditions. All measurements are securely transferred via Wi-Fi. A hash-based integrity check ensures continuous and reliable data collection. The modular design, simple installation, and user-friendly web interface make the system accessible to winemakers. This technology provides a scalable method for digitalizing conventional winemaking processes by reducing the cost and complexity of wine quality monitoring. Full article

This paper presents the design and implementation of an autonomous mobile robot system able to deliver objects from one location to another with minimal hardware requirements. Unlike most existing systems, our robot uses only a single camera—mounted on its robotic arm—to guide both its movements and the pick-and-place process. The robot detects target signs and objects, automatically navigates to desired locations, and accurately grasps and delivers items without the need for complex sensor arrays or multiple cameras. The main innovation of this work is a unified visual control strategy that coordinates both the vehicle and the robotic arm through homography-based visual servoing. Our experimental results demonstrate that the system can reliably locate, pick up, and place objects, achieving a high success rate in real-world tests. This approach offers a simple yet effective solution for object delivery tasks and lays the groundwork for practical, cost-efficient mobile robots in automation and logistics. Full article

To increase the safety and efficiency of underground mining processes, it is important to advance automation. An important part of that is to achieve autonomous material loading using load–haul–dump (LHD) machines. To be able to autonomously load material from a muck pile, it is crucial to first detect and characterize it in terms of spatial configuration and geometry. Currently, the technologies available on the market that do not require an operator at the stope are only applicable in specific mine layouts or use 2D camera images of the surroundings that can be observed from a control room for teleoperation. However, due to missing depth information, estimating distances is difficult. This work presents a novel approach to muck pile detection developed as part of the EU-funded Next Generation Carbon Neutral Pilots for Smart Intelligent Mining Systems (NEXGEN SIMS) project. It uses a stereo camera mounted on an LHD to gather three-dimensional data of the surroundings. By applying a topological algorithm, a muck pile can be located and its overall shape determined. This system can detect and segment muck piles while driving towards them at full speed. The detected position and shape of the muck pile can then be used to determine an optimal attack point for the machine. This sensor solution was then integrated into a complete system for autonomous loading with an LHD. In two different underground mines, it was tested and demonstrated that the machines were able to reliably load material without human intervention. Full article