Search Results (6,956)

The incorporation of the Internet of Things (IoT) in indoor agricultural systems has become an essential tool for monitoring and analyzing environmental variables, contributing to more efficient decision-making. This article presents the design and implementation of an IoT-based digital agriculture system applied to a Plant Factory (PF) for hydroponic vegetable cultivation using the Nutrient Film Technique (NFT). The objective of this study was to develop a system capable of effectively monitoring and controlling the environmental variables that directly influence the microclimate of a closed agricultural environment. The proposed system integrates a four-layer IoT architecture based on a MODBUS RS-485 communication bus, which allows for continuous data acquisition and the operation of multiple sensors and controlled devices. Additionally, user-oriented tools such as a human–machine interface (HMI), a web application, a mobile application and an automatic alert module were incorporated, enhancing accessibility and remote supervision. Experimental results showed stable control performance of ambient temperature (TA), relative humidity (RH), photoperiod, and photosynthetic photon flux density (PPFD), along with continuous monitoring of CO₂ concentration. A 30-day validation experiment using Swiss chard (Beta vulgaris L. var. cicla) under controlled conditions was conducted. The results showed progressive plant development, with leaf area increasing from 15.17 cm² to 690.39 cm², plant height from 7 cm to 31 cm, fresh weight from 23 g to 171 g, and the number of leaves from 9 to 20. These results support the functional validity of the proposed system as a reliable platform for environmental monitoring and control in controlled-environment agriculture. Full article

Zinc oxide (ZnO) is currently one of the most significant wide-bandgap semiconductor materials, attracting extensive research across diverse fields including materials science, chemistry, physics, medicine, electronics, and power engineering. Its exceptional properties, such as high optical transparency, high electron mobility, chemical stability, and compatibility with low-cost fabrication techniques, have established ZnO as a versatile material with immense application potential. A critical application for ZnO is its role as a transparent conducting oxide (TCO) in modern optoelectronic and photovoltaic devices, as well as in sensors, transparent electronics, and spintronics. To meet the requirements of these advanced applications, precise control over the structural, optical, and electrical properties of ZnO thin films is essential. This is effectively achieved through the selection of specific synthesis methods and intentional modification techniques, such as doping. This review provides a comprehensive overview of the synthesis and modification of ZnO thin films, with a particular focus on how various dopants influence their fundamental characteristics. The work discusses a range of deposition techniques, including physical vapor deposition (PVD), chemical vapor deposition (CVD), atomic layer deposition (ALD), sol–gel methods, spray pyrolysis, and other solution-based approaches. The novelty of this review lies in its comparative analysis of different doping strategies combined with various thin-film deposition techniques, highlighting how specific synthesis routes influence dopant incorporation and ultimately determine functional properties. Furthermore, recent advances in tailoring ZnO thin films are summarized, alongside the identification of key challenges and future research directions. Ultimately, this work aims to provide researchers with a systematic perspective on the synthesis–structure–property relationships in doped ZnO thin films to support the development of optimized materials for next-generation electronic and optoelectronic devices. This review, thus, serves as a comprehensive reference for researchers and engineers seeking to optimize the functionality of ZnO-based thin films for emerging technological applications. Full article

Background: Telehealth has expanded access to care for people with chronic diseases, but little is known about how patients in Jordan become activated, motivated, and ready to use these services, particularly during early adoption. Aim: To explore how patients with chronic diseases in Jordan describe their initial activation, readiness, and experiences with telehealth services. Methods: This exploratory qualitative study used interviews with 14 purposively selected adults with chronic diseases from three hospitals in Jordan. Data was analyzed using Braun and Clarke’s six-step thematic analysis. Results: Four interrelated themes emerged. First, patients valued telehealth for preserving independence and ensuring continuity of care, particularly by reducing reliance on family members for transportation to health facilities. Second, readiness was shaped by geography, mobility, and finances. Although telehealth reduced transport costs and lost wages, patients still had to pay for devices and internet access, creating an economic paradox for poorer patients. Third, participation was supported by families but hindered by low digital literacy, platform changes, and unstable internet connectivity. Fourth, trust in telehealth was conditional and depended on patients’ perceptions of convenience and responsiveness. Conclusions: Readiness to use telehealth was relational, structural, experiential, and conditional rather than purely individual. Patients with chronic diseases in Jordan need hybrid care models that engage families and leverage affordable digital technologies to support sustained telehealth use for disease management. Full article

Dissemination and conservation of cultural heritage have been challenged by continued accessibility in museums, where traditional information delivery systems are at times ineffective in terms if interaction with visitors. The current paper investigates RumiArt IA, a mobile application, to identify cultural objects in real-time, remaining fully in the scope of this line of research without relying on internet connectivity. The system, which is developed based on the Rumiñahui Museum and Cultural Center, Ecuador, uses transfer learning in the MobileNetV2 architecture with INT8 post-training quantization to identify 21 cultural artifacts spread across six thematic rooms. The experiment involved building a dataset of 36,000 images under diverse lighting conditions, viewing angles, and distances; furthermore, artificial transformations were explicitly crafted to simulate real museum conditions such as glass reflections and non-frontal capture angles. Quantization was used to reduce each model to 775 KB as compared with the 2.4 MB, with accuracy loss not reaching more than 0.5 percent (DKL < 0.05). Assessment of 9450 validation images yielded a general accuracy of 92.2%, with an inference time of 63 ms on current devices with a high throughput and 215 ms on mid-range hardware from 2020. Practical validation involving 50 visitors of the museum showed a success rate of 93.7%, with average user satisfaction at 8.5/10 and 87%, indicating they would recommend the application. An in-depth error study of the most difficult room (88.3% accuracy) indicated that 47% of the errors were due to the angles of the camera, which blocked out distinguishing features, and 22% were caused by display case reflections and the shadows of the visitors. These results indicate that end-to-end machine learning can provide consistent cultural heritage recognition in resource-constrained settings but its efficiency is susceptible to physical capture factors that cannot be resolved by data augmentation. Offline mode and low memory footprint (less than 90 MB when loaded on six models) of the system are especially relevant to application in situations where there is no guarantee of cloud connectivity. Full article

Background/Objectives: In several contexts, Dried Sample Spot Devices (DSSDs) offer a convenient and safe alternative for sampling, storage, and shipment, allowing the transport and storage of biological samples at room temperature, reducing shipment costs and improving access to diagnostics in faraway sites. This can be pivotal for the use of the therapeutic drug monitoring of anti-HIV treatment: therefore, this study aimed to develop and validate a UHPLC–MS/MS method for the simultaneous quantification of 12 antiretroviral drugs, including the recently introduced long-acting agents, in Dry Plasma Spots (DPSs). Methods: First, 100 µL of plasma sample and 100 µL of internal standard solution were spotted on each DSSD. After complete drying, DPSs were added with an acidifying solution (ammonium acetate buffer pH 4), and then, each sample underwent extraction with hexane-dichloromethane 50:50 (v/v). After tumbling, the organic phase was evaporated and reconstituted for injection. An Acquity UPLC HSS T3 1.8 µm, 2.1 × 150 mm column at 50 °C enabled separation, performed using H₂O + F.A. 0.05% (phase A) and ACN + F.A. 0.05% (phase B) as the mobile phase in gradient elution mode, for a total run time of 15 min. Results: The method was validated over the clinically relevant concentration ranges. For all quality control levels, accuracies ranged from 98.2% to 114.1%, and intra-day and inter-day RSD values ranged from 2.7% to 9.7% and 5.2% to 13.9%, respectively. All analytes demonstrated satisfactory short- and long-term stability in DPSs, confirming the suitability of shipment and storage at room temperature. Conclusions: The method demonstrated robustness and reproducibility in accordance with FDA and EMA guidelines. It ensures satisfactory accuracy and rapid analysis, supporting its application in clinical practice, including for monitoring the newest long-acting drugs. Full article

Skin cancer represents a significant public health challenge, necessitating early detection and timely treatment for optimal management. Timely and accurate evaluation of skin lesions is crucial, as delays can lead to more severe outcomes. However, identifying skin lesions accurately can be challenging due to differences in color, shape, and the various types of imaging equipment used for diagnosis. While recent studies have demonstrated the potential of ensemble convolutional neural networks (CNNs) for early diagnosis of skin disorders, these models are often too large and inefficient for processing contextual information. Although lightweight networks like MobileNetV3 and EfficientNet have been developed to reduce parameters and enable deep neural networks on mobile devices, their performance is limited by inadequate feature representation depth. To mitigate these limitations, we propose a new hybrid attention dual-stream deep learning model for skin lesion detection. Our model uses one training process to preprocess the images and splits the task into two branches. Each branch extracts different features using multi-stage and multi-branch attention techniques, improving the model’s ability to detect skin lesions accurately. The first branch processes the original image using a convolutional layer integrated with three novel attention modules: Enhanced Separable Depthwise Convolution (SCAttn), stage attention, and branch attention. The second branch utilizes Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance the input image, improving local contrast and revealing finer details. The integration of CLAHE with SCAttn modules leverages enhanced local contrast to capture more nuanced features while maintaining computational efficiency. A classification module receives the concatenated hierarchical characteristics that were taken from both branches. Utilizing the PAD2020 and ISIC 2019 datasets, we assessed the proposed model and obtained an accuracy rate of 98.59% for PAD2020, surpassing the state-of-the-art performance by 2%, and stable performance accuracy for the ISIC 2019 dataset. This illustrates how well the model can integrate several attention mechanisms and feature enhancement methods, providing a reliable and effective means of detecting skin cancer. Full article

When ground transportation is disrupted by natural disasters, airdropped rescue vehicles require energy-absorbing cushioning devices to prevent landing impact damage. Thin-walled circular tubes are preferred for their high energy absorption capacity and structural efficiency. However, to reduce platform force fluctuations and decrease residual stroke after compression, thereby avoiding unbalanced loading and ensuring post-landing mobility, slots are introduced into the tube wall, which renders the mean crushing force (MCF) difficult to predict accurately using conventional methods. To address this issue, this paper proposes a physics–data-driven method for predicting the energy absorption characteristics of slotted thin-walled circular tubes. The engineering scenario is introduced, followed by comparative validation via drop weight tests and impact simulations to obtain a sample set via design of experiments (DOE). A multi-layer perceptron (MLP) neural network then augments the samples to generate a dataset. Dimensional analysis yields candidate MCF prediction equations, whose forms and coefficients are determined via a physics–data-driven approach. Weighted graph encoding transforms the equation-solving problem into a graph optimization problem to reduce the computational complexity, and an improved differential evolution (DE) algorithm with a dual-adaptive mutation operator (DSADE) adjusts the parameters and accelerates convergence. The resulting MCF prediction formula, combined with drop test requirements as the optimization objective, achieves a simulation relative error below 5%. These parameters also satisfy engineering requirements in actual airdrop tests, confirming the method’s effectiveness in predicting the energy absorption characteristics of slotted thin-walled tubes. Full article

The fast drainage of surface water from road pavements is essential to ensure both driving safety and adequate infrastructure service life. For close-graded asphalt mixtures, surface runoff relies on sufficient longitudinal and transverse slopes that convey water toward hydraulic drainage devices. However, construction defects, surface distress, or inadequate placement of drainage systems may compromise this process and reduce pavement durability. When water infiltrates beneath the wearing course and saturates the underlying layers, heavy traffic loads can accelerate deterioration through erosion, pumping, interlayer delamination, and subgrade overstress. This work investigates the joint use of Ground Penetrating Radar (GPR) and Mobile Laser Scanning (MLS) to evaluate drainage deficiencies and detect signs of layer delamination in bituminous pavements. A highway section in Salerno (Italy) was selected as a case study due to known hydraulic-related issues. MLS data were used to reconstruct pavement geometry and model surface runoff patterns, while GPR surveys assessed the condition of the bonding between asphalt and base layers. The results revealed ineffective runoff management and identified multiple areas affected by delamination, confirming a relationship between surface drainage behaviour and subsurface damage. These findings highlight the broader potential of the integrated GPR–MLS framework as a scalable and transferable approach for proactive drainage assessment and structural monitoring in pavement management practices. Full article

Background: Myocardial ischemia (MI) is a major cause of morbidity and mortality worldwide and requires timely and reliable detection. Although Spin-Exchange Relaxation-Free (SERF) magnetocardiography (MCG) provides femtotesla-level sensitivity for identifying non-linear cardiac repolarization anomalies, its clinical deployment is currently impeded by the computational bottlenecks inherent to portable edge platforms. Methods: We propose a “Sensor-to-Image” Edge-AI framework that links quantum sensing with computer vision. Single-channel SERF-MCG signals from a large cohort of 2118 subjects (1135 Healthy, 983 Ischemia) were transformed into phase-space images using three distinct encoding modalities: Recurrence Plots (RP), Gramian Angular Summation Fields (GASF), and Markov Transition Fields (MTF). These visual representations were subsequently analyzed by a streamlined MobileNetV3-Small architecture, optimized for low-latency inference. To maximize diagnostic precision, an adaptive weighted fusion mechanism was engineered to combine the chaotic specificity captured by RP with the morphological sensitivity of GASF through a validation-optimized fixed global weighting strategy. Results: In our experiments, the fusion model achieved an Area Under the Curve (AUC) of 0.865, which was higher than the 1D-CNN baseline (AUC 0.857) and the single-modality models. Notably, the fusion strategy significantly elevated sensitivity to 88.3% while maintaining a specificity of 66.5%. Although specificity is moderate, this trade-off prioritizes high sensitivity to minimize false negatives in pre-hospital screening scenarios. The average inference time was 4.7 ms per sample on a standard CPU, suggesting suitability for real-time Point-of-Care (PoC) scenarios under further on-device validation. Conclusions: The results suggest that multi-view phase-space fusion can capture subtle spatio-temporal changes associated with ischemia. The proposed lightweight framework may support the development of portable SERF-MCG systems with embedded AI screening. Full article

While direct current (DC) ice-melting is currently adopted for some transmission lines, its application to 10 kV distribution transformers—often located in remote and rugged terrain—presents significant operational challenges. Disconnecting these transformers prior to ice-melting is a complex procedure that incurs substantial labor, material, and financial costs. Leaving transformers connected risks DC current flowing into idle windings, potentially causing damage. Furthermore, existing mobile DC ice-melting power supplies are bulky and impose stringent transportation requirements, rendering them unsuitable for use on mountain roads. To overcome these limitations, this paper proposes a compact, lightweight variable-frequency ice-melting device. The operating principle and output characteristics of the variable-frequency method are investigated in detail. Using Simulink, system modeling and simulation analyses are performed to obtain the voltage and current output characteristics, along with harmonic spectra. Simulation results demonstrate that the proposed device achieves significant miniaturization compared with conventional solutions: within the typical parameter range of conventional devices, the volume can be reduced by 44–58% and the weight by 43–52%. In addition, the selected LC filter parameters (L = 10.39 mH, C = 86.62 μF) represent an optimized compromise solution that effectively suppresses input harmonics while maintaining the output current total harmonic distortion (THD) within an acceptable limit of 3.6%. Experimental results further validate the feasibility of the variable-frequency ice-melting current. Based on a matrix converter topology, the proposed device enables flexible adjustment of the output melting voltage and frequency, exhibits excellent low-frequency performance and dynamic response, and maintains low output harmonic content—fully meeting the application requirements for variable-frequency ice-melting. The key novelty lies in a compact matrix-converter-based de-icing device with systematic low-frequency performance analysis, offering superior portability and adaptability over traditional DC solutions. Full article

Pesticide residues in food and agricultural products continue to constitute a significant concern for food safety, particularly when rapid decision-making is required across production and supply chains. Although chromatographic methods such as GC-MS and LC-MS/MS remain essential for confirmatory analysis, their dependence on central laboratories limits their applicability for field screening. Consequently, portable biosensor-based detection platforms have attracted increasing attention as rapid screening tools. This review synthesizes 26 peer-reviewed studies published between 2010 and 2025 on portable biosensor-based screening tools for pesticide detection in food and agricultural matrices, including electrochemical sensors, immunoassays, aptamer-based systems, paper-based lateral flow devices, and smartphone-assisted platforms. Given the heterogeneity of analytes, sensing mechanisms, and study designs, a narrative synthesis approach was applied. Overall, the evidence suggests a shift from laboratory-centered detection toward field-deployable technologies that may support preliminary screening within food safety monitoring frameworks. Paper-based lateral flow assays are widely reported as deployable formats, while electrochemical and affinity-based platforms are often positioned as intermediate solutions for mobile or semi-controlled testing environments. However, most platforms remain at the proof-of-concept or early validation stage, and challenges related to matrix interference, long-term stability, reproducibility, standardization, and large-scale implementation persist. This review highlights the potential role of portable biosensor technologies as complementary tools within tiered food safety monitoring systems and outlines key priorities for further development before wider regulatory integration can be considered. Full article

Accurate fruit localization and efficient harvesting are key challenges for agricultural robots, especially in dynamic orchard environments, where platform vibration, fruit occlusion, and computational resource limitations of embedded devices significantly impact system performance. To address these issues, this paper proposes a lightweight “fruit detection + harvesting” framework. First, by integrating MobileNetV4 and Triplet Attention mechanisms, an improved YOLOv8n network is designed, with the improved YOLOv8n Precision reaching 98.148% and FPS reaching 30 FPS on Jetson Nano, achieving a good balance between detection accuracy and computational efficiency suitable for edge deployment. Second, a strawberry three-dimensional coordinate reconstruction method based on weighted 3D centroid reconstruction is proposed, utilizing depth bias adjustment coefficients to improve spatial accuracy. Third, to address localization errors caused by vibration and platform motion, a dynamic compensation and temporal fusion strategy based on an Inertial Measurement Unit (IMU) is proposed. The rotation matrix estimated from IMU data is first used to correct camera pose variations. Then, an adaptive sliding window is employed to smooth the coordinate sequence. Finally, an Extended Kalman Filter (EKF) is applied to further refine the fused results by incorporating temporal dynamics, ensuring that the reconstructed three-dimensional coordinates in the robotic arm reference frame achieve higher stability and continuity. Experimental results in orchard scenarios show that compared with traditional methods, the system has higher localization accuracy, stronger robustness to dynamic disturbances, and higher harvesting efficiency. This work provides a practical and deployable solution for advancing intelligent fruit-harvesting robots. Full article

Background/Objectives: The uprighting of mesially tipped mandibular second molars following first molar loss is a complex surgical and orthodontic challenge. Conventional methods often result in reciprocal anchorage loss. Mini-implants (MIs) have emerged as essential temporary anchorage devices (TADs) that provide absolute anchorage and enable more predictable tooth movements. Methods: Numerical simulations were performed to evaluate the forces required for mandibular second molar uprighting under two conditions: first, only with the second molar present, and second, with both the second and the third molars present. Although the periodontal ligament exhibits nonlinear and viscoelastic behavior in vivo, a linear elastic approximation was adopted to allow for a reliable evaluation of comparative stress distribution and initial displacement patterns within the scope of this exploratory biomechanical study. Stress distribution in the roots, periodontal ligament, and alveolar bone was assessed for each scenario. Two three-dimensional (3D) models of the left mandibular segment were created from scans of a human mandible and its teeth. The first model included the canine, the first and second premolars, and the second molar. A second model additionally incorporated the third molar. A retromolar MI was placed in both models. Molar uprighting was simulated using a spring connecting the implant to a button bonded on the mesial surface of the second molar. A force of 200 g was applied because in clinical orthodontic practice, forces that exceed approximately 2 N may cause pain or undesirable tooth mobility. Displacements along the X, Y, and Z axes, as well as regions of peak stress, were analyzed. Results: Model 1 showed maximum displacements at the furcation/mid-root, distal root apex, and distal crown, with von Mises stresses of 0.470 to 0.371 MPa. In Model 2, peak displacements occurred at the mesial root and crown, with stresses of 0.185 and 0.149 MPa, respectively. The magnitude of displacements was in the order of 10⁻⁵ mm. Such values represent initial mechanical responses rather than clinically observable tooth movements. However, the differences between models (e.g., the stress reduction) are expected to be clinically meaningful. Conclusions: Since clinical measurements regarding the stress distribution on teeth and surrounding tissues during orthodontic molar uprighting movements are impossible to perform, the finite element method (FEM) can offer insight into these aspects. The presence of the third molar significantly modulates the biomechanics of second molar uprighting via retromolar MIs. When the third molar is present, the second molar exhibits a reduced tendency for deformation during distalization, although this leads to a slower displacement. This FEM provides biomechanical insights but does not support direct clinical decision-making. The present findings should be viewed as theoretical biomechanical tendencies that require confirmation through clinical, experimental, and longitudinal studies before translation into clinical practice. Full article

Population aging increases the demand for different assistive devices enabling independent mobility and safe vehicle boarding. This paper presents the design and development of a universal lifting platform intended to support the legs of people with reduced mobility during vehicle entry. The device was designed to be independent of a specific vehicle and to be powered by vehicle standard 12 Volt current. A CAD model of the proposed device was modeled in SolidWorks 2017 and validated through analytical calculations and finite element simulations. Based on the calculation results, a functional prototype was manufactured and tested under real operating conditions, confirming the feasibility and usability of the proposed solution. The presented platform provides a low-cost, lightweight and vehicle-independent assistive device, supporting controlled and safe leg transfer without the need for vehicle modification or homologation. Full article

In response to the pressing need for miniaturized MEMS microphones in wearable technology and mobile devices, and to surmount the technical limitations inherent in conventional piezoelectric microphones, which typically depend on enlarging chip dimensions or decreasing stiffness to attain low resonance frequencies, this study introduces a novel piezoelectric MEMS microphone (PMM) design predicated on a flexibility-enhanced rotating structure. The proposed design utilizes an aluminum scandium nitride (Al_0.8Sc_0.2N) piezoelectric thin film with 20% scandium doping and incorporates four equivalent sensing units formed by four curved cutting lines centrally located on the chip. This configuration employs a nested arrangement of four cantilever beams to substantially increase vibration compliance, thereby effectively lowering the natural frequency without altering the chip’s external size. Three-dimensional finite element simulations reveal that, relative to traditional triangular cantilever beam architectures, the flexibility-enhanced rotating structure reduces the natural frequency from 15.6 kHz to 13.49 kHz while enhancing sensitivity from −44.6 dB to −40 dB. The device was fabricated via a comprehensive microfabrication process and subsequently characterized within a standardized acoustic testing environment. Experimental results indicate that the microphone attains a sensitivity of −43.84 dB at 1 kHz and exhibits a first resonance frequency of 13.5 kHz, closely aligning with simulation predictions. Furthermore, the signal-to-noise ratio (SNR) reaches 58.3 dB across the full range of human-audible frequencies. By leveraging the flexibility-enhanced rotating structure, this work achieves an optimal compromise between elevated sensitivity and reduced resonance frequency within a compact form factor, thereby offering a viable technical solution for the advancement of high-performance miniature acoustic sensors. Full article