Search Results (1,289)

With the increasing integration of mobile technologies into manufacturing automation environments, the effective visualisation of data on small-screen devices has emerged as an important consideration. This study investigates the usability and readability of common visualisation types (bar charts, line charts, and tables) on mobile devices, comparing different interface designs and interaction methods. Using a within-subject experimental design with 11 participants, we evaluated two primary approaches for handling large visualisations on mobile screens: segmented (cutting) displays versus continuous (dragging) displays. Results indicate that segmented displays generally improve task completion time and reduce mental workload for bar charts and tables. In contrast, line charts exhibit more complex patterns that depend on the size of the data. These findings provide practical guidelines for designing responsive data visualisations optimised for mobile interfaces. Full article

The advancement of smart grid technologies necessitates the integration of cutting-edge computational methods to enhance predictive energy optimization. This study proposes a multi-faceted approach by incorporating (1) Deep Reinforcement Learning (DRL) agents trained using data from digital twins (DTs) to optimize energy consumption in real time, (2) Physics-Informed Neural Networks (PINNs) to seamlessly embed physical laws within the optimization process, ensuring model accuracy and interpretability, and (3) blockchain (BC) technology to facilitate secure and transparent communication across the smart grid infrastructure. The model was trained and validated using comprehensive datasets, including smart meter energy consumption data, renewable energy outputs, dynamic pricing, and user preferences collected from IoT devices. The proposed framework achieved superior predictive performance with a Mean Absolute Error (MAE) of 0.237 kWh, Root Mean Square Error (RMSE) of 0.298 kWh, and an R-squared (R²) value of 0.978, indicating a 97.8% explanation of data variance. Classification metrics further demonstrated the model’s robustness, achieving 97.7% accuracy, 97.8% precision, 97.6% recall, and an F1 Score of 97.7%. Comparative analysis with traditional models like Linear Regression, Random Forest, SVM, LSTM, and XGBoost revealed the superior accuracy and real-time adaptability of the proposed method. In addition to enhancing energy efficiency, the model reduced energy costs by 35%, maintained a 96% user comfort index, and increased renewable energy utilization to 40%. This study demonstrates the transformative potential of integrating PINNs, DT, and blockchain technologies to optimize energy consumption in smart grids, paving the way for sustainable, secure, and efficient energy management systems. Full article

With the rapid advancement of artificial intelligence technology, the widespread application of deep learning in computer vision is driving the transformation of agricultural pest detection and control toward greater intelligence and precision. This paper systematically reviews the evolution of agricultural pest detection and control technologies, with a special focus on the effectiveness of deep-learning-based image recognition methods for pest identification, as well as their integrated applications in drone-based remote sensing, spectral imaging, and Internet of Things sensor systems. Through multimodal data fusion and dynamic prediction, artificial intelligence has significantly improved the response times and accuracy of pest monitoring. On the control side, the development of intelligent prediction and early-warning systems, precision pesticide-application technologies, and smart equipment has advanced the goals of eco-friendly pest management and ecological regulation. However, challenges such as high data-annotation costs, limited model generalization, and constrained computing power on edge devices remain. Moving forward, further exploration of cutting-edge approaches such as self-supervised learning, federated learning, and digital twins will be essential to build more efficient and reliable intelligent control systems, providing robust technical support for sustainable agricultural development. Full article

As stroke is becoming more frequent nowadays, cutting edge rehabilitation approaches are required to recover upper limb functionalities and to support patients during daily activities. Recently, focus has moved to robotic rehabilitation; however, therapeutic devices are still highly expensive, making rehabilitation not easily affordable. Moreover, devices are not easily accepted by patients, who can refuse to use them due to not feeling comfortable. The presented work proposes the exploitation of a virtual prototype of the human–robot ecosystem for the study and analysis of patient–robot interactions, enabling their simulation-based investigation in multiple scenarios. For the accomplishment of this task, the Dynamics of Multi-physical Systems platform, previously presented by the authors, is further developed to enable the integration of biomechanical models of the human body with mechatronics models of robotic devices for motion assistance, as well as with PID-based control strategies. The work begins with (1) a description of the background; hence, the current state of the art and purpose of the study; (2) the platform is then presented and the system is formalized, first from a general side and then (3) in the application-specific scenario. (4) The use case is described, presenting a controlled gym weightlifting exercise supported by an exoskeleton and the results are analyzed in a final paragraph (5). Full article

Three-dimensional bulk fin-type field-effect transistors (FinFETs) have been the dominant devices since the sub-22 nm technology node. Electrical characteristics of scaled devices suffer from different process variation effects. Owing to the trapping and de-trapping of charge carriers, random interface traps (RITs) degrade device characteristics, and, to study this effect, this work investigates the impact of RITs on the DC/AC/RF characteristic fluctuations of FinFETs. Under high gate bias, the device screening effect suppresses large fluctuations induced by RITs. In relation to different densities of interface traps (D_it), fluctuations of short-channel effects, including potential barriers and current densities, are analyzed. Bulk FinFETs exhibit entirely different variability, despite having the same number of RITs. Potential barriers are significantly altered when devices with RITs are located near the source end. An analysis and a discussion of RIT-fluctuated gate capacitances, transconductances, cut-off, and 3-dB frequencies are provided. Under high D_it conditions, we observe ~146% variation in off-state current, ~26% in threshold voltage, and large fluctuations of ~107% and ~131% in gain and cut-off frequency, respectively. The effects of the random position of RITs on both AC and RF characteristic fluctuations are also discussed and designed in three different scenarios. Across all densities of interface traps, the device with RITs near the drain end exhibits relatively minimal fluctuations in gate capacitance, voltage gain, cut-off, and 3-dB frequencies. Full article

Accurate and efficient pest monitoring in complex rice field environments is vital for food security. Existing detection methods often struggle with small targets and high computational redundancy, limiting deployment on resource-constrained edge devices. To address these issues, we propose YOLO-RP, a lightweight and efficient rice pest detection method based on YOLO11n. YOLO-RP reduces model complexity while maintaining detection accuracy. The model removes the redundant P5 detection head and introduces a high-resolution P2 head to enhance small-object detection. A lightweight partial convolution detection head (LPCHead) decouples task branches and shares feature extraction, reducing redundancy and boosting performance. The re-parameterizable DBELCSP module strengthens feature representation and robustness while cutting parameters and computation. Wavelet pooling preserves essential edge and texture information during downsampling, improving accuracy under complex backgrounds. Experiments show that YOLO-RP achieves a precision of 90.62%, recall of 87.38%, mAP@0.5 of 90.99%, and mAP@0.5:0.95 of 63.84%, while reducing parameters, GFLOPs, and model size by 61.3%, 50.8%, and 49.1% to 1.00 M, 3.1, and 2.8 MB. Cross-dataset tests on Common Rice Pests (Philippines), IP102, and Pest24 confirm strong robustness and generalization. On NVIDIA Jetson Nano, YOLO-RP attains 20.8 FPS—66.4% faster than the baseline—validating its potential for edge deployment. These results indicate that YOLO-RP provides an effective solution for real-time rice pest detection in complex, resource-limited environments. Full article

Eye tracking, a fundamental process in gaze analysis, involves measuring the point of gaze or eye motion. It is crucial in numerous applications, including human–computer interaction (HCI), education, health care, and virtual reality. This study delves into eye-tracking concepts, terminology, performance parameters, applications, and techniques, focusing on modern and efficient approaches such as video-oculography (VOG)-based systems, deep learning models for gaze estimation, wearable and cost-effective devices, and integration with virtual/augmented reality and assistive technologies. These contemporary methods, prevalent for over two decades, significantly contribute to developing cutting-edge eye-tracking applications. The findings underscore the significance of diverse eye-tracking techniques in advancing eye-tracking applications. They leverage machine learning to glean insights from existing data, enhance decision-making, and minimize the need for manual calibration during tracking. Furthermore, the study explores and recommends strategies to address limitations/challenges inherent in specific eye-tracking methods and applications. Finally, the study outlines future directions for leveraging eye tracking across various developed applications, highlighting its potential to continue evolving and enriching user experiences. Full article

Background: Hearing loss results from diverse biological insults along the auditory pathway, including sensory hair cell death, neural degeneration, and central auditory processing deficits. Implantable auditory neuroprostheses, such as cochlear and brainstem implants, aim to restore hearing by directly stimulating neural structures. Advances in neurobiology and device technology underpin the development of more sophisticated implants tailored to the biological complexity of auditory dysfunction. Aim: This narrative review of reviews aims to map the integration of artificial intelligence (AI) in auditory neuroprosthetics, analyzing recent research trends, key thematic areas, and the opportunities and challenges of AI-enhanced devices. By synthesizing biological and computational perspectives, it seeks to guide future interdisciplinary efforts toward more adaptive and biologically informed hearing restoration solutions. Methods: This narrative review analyzed recent literature reviews from PubMed and Scopus (last 5 years), focusing on AI integration with auditory neuroprosthetics and related biological processes. Emphasis was placed on studies linking AI innovations to neural plasticity and device–nerve interactions, excluding purely computational works. The ANDJ (a standard narrative review checklist) checklist guided a transparent, rigorous narrative approach suited to this interdisciplinary, rapidly evolving field. Results and discussion: Eighteen recent review articles were analyzed, highlighting significant advancements in the integration of artificial intelligence with auditory neuroprosthetics, particularly cochlear implants. Established areas include predictive modeling, biologically inspired signal processing, and AI-assisted surgical planning, while emerging fields such as multisensory augmentation and remote care remain underexplored. Key limitations involve fragmented biological datasets, lack of standardized biomarkers, and regulatory challenges related to algorithm transparency and clinical application. This review emphasizes the urgent need for AI frameworks that deeply integrate biological and clinical insights, expanding focus beyond cochlear implants to other neuroprosthetic devices. To complement this overview, a targeted analysis of recent cutting-edge studies was also conducted, starting from the emerging gaps to capture the latest technological and biological innovations shaping the field. These findings guide future research toward more biologically meaningful, ethical, and clinically impactful solutions. Conclusions: This narrative review highlights progress in integrating AI with auditory neuroprosthetics, emphasizing the importance of biological foundations and interdisciplinary approaches. It also recognizes ongoing challenges such as data limitations and the need for clear ethical frameworks. Collaboration across fields is vital to foster innovation and improve patient care. Full article

This paper focuses on the scoring-wheel cutting process for circular structures of OLED display panels, conducting in-depth research through an experiment–analysis–optimization system. Based on the Taguchi experimental design, a three-factor, five-level experiment is conducted, with the blade wheel angle (A), cutting speed (B), and pressure (C) set as influencing factors, and the scratch depth (h), width (w), median crack depth (l), and transverse crack width (d) set as evaluation indicators. The experiments are carried out using a self-developed dicing-wheel cutting device, and the morphology, roughness, and hardness of the cutting surface and cross-section are characterized by means of ultra-depth-of-field microscopy, laser confocal microscopy, microhardness tester, and other equipment. The research shows that the order of factors affecting the cutting quality is as follows: A > C > B. Through the analysis of morphology and crack characteristics, it is determined that the optimal parameter combination is a dicing wheel angle of 130°, a cutting speed of 20 mm/s, and a pressure of 11 N. The verification results indicate that this combination can reduce surface roughness, stabilize hardness, and realize efficient and precise processing of special-shaped structures in OLED display panels, providing strong theoretical and technical support for industrial process optimization. Full article

Dental caries remains one of the most prevalent global diseases, and the early detection of occlusal lesions is critical because demineralization often begins deep within pits and fissures where conventional visual–tactile or radiographic inspection cannot detect it. SmarTooth, a newly introduced fluorescence device that irradiates enamel with a 655 nm laser and records the reflected intensity, may provide more objective, quantitative diagnoses. This study assessed its diagnostic performance against the International Caries Detection and Assessment System (ICDAS). We examined 1421 occlusal surfaces from 153 adults, scored each surface with ICDAS codes 0–4, and recorded SmarTooth peak values. Spearman’s rank correlation was used to test the association between codes and peak values; one-way ANOVA with Tukey’s post hoc was used to compare mean values across codes; and sensitivity, specificity, and the area under the receiver operating characteristic curve (AUROC) were calculated at three diagnostic thresholds: D1 (0 vs. 1–4), D2 (0–2 vs. 3–4), and D3 (0–3 vs. 4). The SmarTooth values rose with lesion severity and correlated moderately with ICDAS (r = 0.495, p < 0.001). The AUROC ranged from 0.69 to 0.82, with the best accuracy observed at D2 (cut-off: 7.0; AUC: 0.82; sensitivity: 78.3%; specificity: 77.4%). These findings suggest that SmarTooth can complement ICDAS scoring as an adjunctive tool, potentially enhancing diagnostic accuracy and supporting early intervention for occlusal caries in general dental practice. Full article

The use of quick and non-invasive techniques for detecting chronic kidney disease (CKD) in patients with type 2 diabetes mellitus is desirable and has recently garnered attention. One of these techniques is the evaluation of nephropathy risk based on electrochemical skin conductance (ESC) measured with a SUDOSCAN device. This paper aims to evaluate the possibility of using SUDOSCANs in chronic kidney disease prediction in diabetic patients and to investigate the relationships between clinical characteristics and SUDOSCAN parameters. The number of patients with type 2 diabetes included in this study was 254. Clinical metabolic characteristics like glycated hemoglobin, total and LDL cholesterol, triglyceride, blood pressure, and creatinine were determined along with body mass index, diabetes duration, and age. The estimated glomerular filtration rate (EGFR) was calculated and patients were grouped into three CKD stages based on EGFR values. Electrochemical skin conductance in hands and feet was determined with a SUDOSCAN device. The results showed that patients with symptomatic CKD (S2 and 3) presented lower ESC values, along with lower EFGRs and higher creatinine levels. A significant positive but weak correlation (p < 0.05) was observed between SUDOSCAN nephropathy risk and EGFR. The general linear model indicated that the SUDOSCAN nephropathy risk score could be used in CKD diagnosis only if considering age, diabetes duration, and body mass index. The area under the curve (AUC) of the receiver operating characteristic (ROC) analysis revealed the moderate possibility of using the SUDOSCAN nephropathy risk score to predict CKD, since it was 0.61 (p < 0.01, 95% CI 0.54–0.68), but only if the other factors mentioned above are included. Based on the cut-off value of 59.50 identified, patients were grouped (values above and below cut-off), and the results showed that patients with a SUDOSCAN nephropathy risk score of <59.50 have lower SUDOSCAN-ESC values measured in their hands and feet, lower EGFR and higher creatinine levels. These results indicated the possibility of using SUDOSCAN as a supporting tool to identify CKD if it is correlated with other factors like age, diabetes duration, and body mass index. This is important for medical progress regarding the use of novel non-invasive technologies in identifying CKD associated with type 2 diabetes. Full article

This paper presents an innovative and low-cost approach to the dispensing of multiple liquids on a microfluidic chip with the aim of dispensing liquids in a controlled sequence. The project focused on the design and development of a microfluidic liquid dispensing system that is an integral part of the Lab-on-Chip (LOC). Liquids are often dispensed into LOCs through blisters, syringes, or electric microfluidic pumps, but these can be impractical for Point-of-Care (POC) settings, especially in remote areas. Additionally, incorrect volumes of biochemical reagents and the introduction of reagents outside the sequence can distort the results of the diagnosis. The process undertaken involved designing and 3D printing prototypes of the dispensing system, along with laser cutting and manufacturing the Polymethyl Methacrylate (PMMA) LOC devices intended for receiving the liquids. The proposed novel low-cost dispensing system uses manually operated actuators and cams to disperse metered fluids sequentially to minimise end-user errors at POC settings. Full article

This paper deals with an optimal operation method for surge protective devices (SPDs) to calculate the maximum continuous operating voltage (U_C) and the voltage protection level (U_P) by considering the sum of the voltage protection level and the dielectric continuous voltage limit of surge protective devices in order to effectively protect energy storage system (ESS) from switching and lightning surges. This paper also implements a test device for SPDs in ESSs based on the concept of a lightning electromagnetic surge protection measurement system (LPMS) by combining an SPD coordinated with spatial shielding with an ESS configuration. Here, the test device for the SPD in the ESS is composed of a power distribution unit (PDU), uninterruptible power supply (UPS), and a lightning electromagnetic pulse (LEMP) protection device, which combines two units of SPDs and disconnection switches (DSs) connected in parallel with two units of main circuit breakers (MCBs) and noise cut transformers (NCTs) connected in series. From the test results based on the proposed optimal operation method and test device, it is clear that the residual voltage with a third-class combination waveform can be kept within 1.5 kV of the surge voltage limit in all test scenarios, and it is confirmed that the proposed test device for SPDs can protect ESSs from switching and lightning surges. Therefore, it is confirmed that the SPD tested using the proposed method can effectively reduce switching and lightning surges, while the existing SPDs installed in ESS sites cannot protect ESSs from such surges. Full article

We examined the effect of SiO₂ passivation on the parameters of mesa heterostructure InAs/InAsSbP photodiodes with a spectral responsivity 50% cut off at 3.5 µm at 295 K, specific to the InAs absorber layer. The R₀A product was found to increase by 30% after passivation of the devices of 113 µm in diameter, up to 0.9 Ωcm², while for those with a diameter of 1.13 mm, R₀A of 2.2 Ωcm² was achieved, with a value of D* > 3 × 10⁹ cmHz^1/2/W at the peak of the spectrum, 1 kHz, 0 V bias, 295 K. To the best of our knowledge, this is the highest R₀A value at room temperature reported to date for a photodiode with an InAs absorber. Full article

Several approaches to producing lab-on-chip (LOC) devices have been developed in the last 20 years, including laser cutting of acrylic sheets and laminating them with adhesive films. While this route allows for rapid manufacture of devices, it cannot be scaled up beyond a couple of prototypes. For mass production of 3D LOC devices, injection molding is required, but mold manufacturing can be very costly. In this work we briefly report laser cutting parameters and lamination approaches, as well as 3D-printed injection mold inserts that allow one to produce LOC prototypes in facilities that have limited resources. This allows these facilities to transition from a couple of demonstrators to more than 100 devices in a short time and with limited costs. Full article