Search Results (943)

Traumatic brain injury (TBI) initiates a cascade of molecular and cellular reactions leading to long-term disturbances of neuronal and glial homeostasis. One of the key mechanisms of secondary injury is a pathological increase in intracellular Ca²⁺ concentration. Parvalbumin (PV) plays an important role in the regulation of Ca²⁺ homeostasis in neurons. In turn, connexin 43 (Cx43) is the principal protein of astrocytic gap junctions (GJs), which ensure neuroglial communication. The spatiotemporal changes in these proteins and the mechanisms of their interaction after TBI remain insufficiently studied. In the present study, a comprehensive analysis of the expression, localization, and spatial organization of PV and Cx43 in the cerebral cortex following TBI was performed. In intact tissue, PV was localized predominantly in neurons, whereas Cx43 formed typical punctate structures of astrocytic GJs. Twenty-four hours after TBI, a sharp activation of PV with pronounced nuclear translocation was observed against the background of a catastrophic decrease in Cx43 expression, accompanied by a reduction in the number of NeuN⁺ neurons and signs of apoptosis. However, after 7 days, a mirror-opposite effect was detected, characterized by decreased PV expression and increased Cx43 levels with its aggregation into cluster-like structures, as well as partial restoration of NeuN immunoreactivity. In addition, molecular dynamics simulations demonstrated that the stability of the PV–Cx43 complex is determined by the presence of Ca²⁺ and physiological pH, whereas acidosis and Ca²⁺ overload destabilize their interaction. Taken together, these results reveal a phase-dependent mirror-opposite pattern of PV and Cx43 expression and localization and emphasize the key role of Ca²⁺- and pH-dependent neuroglial interactions in TBI. Full article

Papillary thyroid cancer (PTC) is the most common endocrine malignancy with especially high incidence in Middle Eastern populations. While classical hereditary syndromes explain a minority of cases, the broader germline landscape of non-syndromic PTC remains unclear. whole-exome sequencing was performed on 245 unselected Saudi PTC patients to identify germline pathogenic or likely pathogenic variants (PVs/LPVs) in cancer predisposition genes. Clinical and molecular characteristics, and family history were integrated to assess phenotypic correlations. Eleven patients (4.5%) harbored germline PVs/LPVs in cancer susceptibility genes including STK11, TP53, BRCA1, BRCA2, FANCA, SLX4, RAD50, MSH6, POLD1 and NF1. Four patients (36.4%) carried PVs/LPVs in canonical FA pathway genes; this increased to five patients (45.5%) when RAD50 was included. Two unrelated patients harbored the same STK11 variant (p.R304Q) without classical Peutz–Jeghers syndrome features. A TP53 hotspot mutation (p.R175H) was identified in a patient with a personal history of gastric cancer, a malignancy associated with Li–Fraumeni syndrome. Notably, the BRCA1 PV detected matches a known Saudi founder mutation in hereditary breast cancer, now observed in PTC. Most germline positive cases lacked syndromic manifestations, underscoring limitations of phenotype or family history-driven genetic testing strategies. These findings suggest that a small subset of non-syndromic PTC cases may carry germline PVs/LPVs in cancer predisposition genes, highlighting the need for broader genetic screening frameworks. Unbiased whole-exome analysis in unselected cohorts can uncover under-recognized genetic risk and guide screening strategies to address the unique hereditary landscape of thyroid cancer in underrepresented populations. Full article

Soiling of solar panels has a considerable impact on the performance of photo voltaic (PV) systems, emphasizing the importance of developing reliable decision support tools for solar panel cleaning. Although recent convolutional neural network (CNN)-based models, including lightweight architectures such as SolPowNet, have demonstrated high classification accuracy, their performance can be sensitive to dataset variability and domain shifts encountered in real-world PV environments. Motivated by the lightweight design philosophy of SolPowNet, this paper proposes a hybrid and ensemble-based intelligent cleaning decision framework that integrates classical image processing, machine learning, and deep learning techniques. The proposed approach combines physically interpretable handcrafted texture and sharpness features classified using a Random Forest model with a pretrained MobileNetV3-Small CNN through a conservative OR-based ensemble fusion strategy. In addition, a probability-driven Soiling Index (SI) is introduced to translate classification confidence into actionable cleaning decisions, including no cleaning, light cleaning, and full cleaning. Experimental results on multiple PV image datasets demonstrate that, under domain-shift conditions where individual models may experience performance degradation, the proposed ensemble framework achieves an accuracy of up to 85.93% and attains a dusty-panel detection rate of 0.90 on the unseen dataset. On the in-distribution evaluation, the proposed OR-ensemble achieves an average accuracy of 0.9663 ± 0.0177 with dusty recall of 0.9896 ± 0.0104 over repeated stratified runs. Importantly, the conservative fusion strategy minimizes high-risk false negative cases while avoiding excessive misclassification of clean panels. Overall, the proposed framework offers a robust, scalable, and deployment-ready solution for intelligent PV cleaning decision support, advancing CNN-based soiling detection toward practical and risk-aware operation and maintenance systems. Full article

The growing reliance on renewable energy sources, particularly solar photovoltaics (PVs), requires intelligent management strategies to address challenges of intermittency, storage, and efficiency in autonomous microgrids. This review investigates IoT-based solutions for energy optimization, focusing on hardware platforms, communication protocols, and intelligent control strategies that enhance the reliability and autonomy of PV-powered systems. This review follows a structured methodological protocol including predefined research questions, database selection, screening criteria, and systematic categorization of studies of IoT-enabled solar microgrid applications, relying on peer-reviewed journal articles, reputable conference proceedings, and scholarly works published between 2020 and 2025. The focus centers on microcontroller-based platforms (e.g., Arduino, ESP32, NodeMCU, TTGO LoRa32) and Single-Board Computers (SBCs) (e.g., Raspberry Pi), alongside the integration of optimization algorithms with Machine Learning (ML) and Neural Network (NN) approaches. Results highlight that lightweight microcontrollers offer cost-effective monitoring, ESP32 and NodeMCU balance real-time analytics with energy efficiency, Raspberry Pi supports edge-level AI processing, and LoRa enables scalable long-range communication for remote PV systems. Furthermore, optimization algorithms (PSO, WOA-SA) and neural models (ANN, LSTM, CNN–LSTM) are explored as methods to improve forecasting accuracy, fault detection, and demand-side management. Conclusions indicate that IoT-based architectures significantly improve energy efficiency, support predictive maintenance, and enable scalable deployment of autonomous solar microgrids. The study emphasizes the necessity of hybrid IoT architectures, combining edge and cloud intelligence, to balance computational complexity, power constraints, and cybersecurity requirements. These findings provide practical insights into designing robust, cost-effective, and scalable IoT-enabled PV microgrids that contribute to decentralized and sustainable energy transitions. Full article

Background/Objectives: Diabetic neuropathy (DN), a common complication of type 2 diabetes mellitus, manifests as peripheral nerve dysfunction with symptoms such as fatigue. Although exercise effectively reduces fatigue in neuropathy patients, precise detection methods are crucial to elucidate the role of rehabilitation. Accordingly, this study aimed to evaluate fatigue in DN patients using a multimodal approach (clinical and instrumental) and to compare the efficacy of aerobic versus resistance training on fatigue parameters. Methods: Eligible DN inpatients admitted for rehabilitation at the Neuromotor Rehabilitation Unit of the IRCCS ICS Maugeri Institute of Montescano (PV) were enrolled. Inclusion criteria included age between 65 and 85 years and confirmation via the Michigan Neuropathy Screening Instrument (anamnestic section: ≥7; clinical section: ≥2.5). Patients with confounding orthopedic, neurologic, or unstable cardiopulmonary/diabetic conditions were excluded. Overall, 36 participants were randomized into two groups: 17 underwent aerobic training (treadmill), while 19 received resistance training (elastic bands), both as supplements to a standard rehabilitation program. Assessments at baseline and post-training comprised clinical measures (Borg CR10 scale, Functional Independence Measure (FIM) total and subitems, Six-Minute Walk Test (6MWT), fasting blood glucose) and instrumental evaluations (sEMG of the tibialis anterior muscle to analyze conduction velocity intercept, slope, and changes). Results: All patients completed the protocol without dropout or adverse events. Both groups demonstrated significant improvements in FIM scores and post-exercise perceived exertion over time. Instrumental sEMG analysis confirmed a physiological fatigue trend manifested as conduction velocity reduction, yet revealed no significant differences between groups. Conclusions: Multimodal assessment provides an effective means to characterize fatigue in DN patients. Both aerobic and resistance modalities enhance functional independence and fatigue perception. Its early identification enables clinicians to tailor rehabilitation strategies to overcome exercise barriers. Full article

This study compares uniform and stratified sampling strategies applied to hourly solar irradiance signals. The analysis examines how each approach affects signal reconstruction, anomaly detection, and dynamic PV modelling. Using PCHIP interpolation and error metrics such as RMSE and MAE, results show that uniform sampling yields lower global reconstruction error (26.64 W/m² vs. 32.98 W/m²), while stratified sampling captures instantaneous peaks more accurately under high-variability conditions. Stratified sampling also improves anomaly identification due to its more representative temporal distribution. These findings highlight a practical trade-off between minimizing average error and preserving extreme events, providing guidance for PV estimation, forecasting, and discrete-time control applications. Full article

This paper presents the development of Electrohouse, a 3D educational simulator used for illustrating the electricity consumption of a household in the presence of a photovoltaic (PV) system designed to teach users how to efficiently manage electrical equipment from an energy perspective. The paper addresses elements of energy system modeling, human–computer interaction and educational visualization. The application connects electricity consumption graphs with practical appliance controls, providing a comprehensive view of kilowatt-hour usage with an intuitive interface. The software offers two consumption scenarios, with one for 28 days and one for 30 days. Furthermore, the household displays the integration of a photovoltaic solar panel for direct energy production, with the system simulating an actual meter by deducting the generated current from the accumulated consumption. Relevant for sustainability, especially in the fields of energy education, the project incorporates the creation of a prototype of a night-time home surveillance robot designed for intruder detection and control. This study contributes to the global framework of Sustainable Development Goals (SDGs) adopted by the United Nations. The simulator supports SDG 7 (Affordable and Clean Energy) by promoting awareness of photovoltaic integration with household energy optimization and SDG 4 (Quality Education) by providing an interactive digital learning environment that improves energy literacy with sustainability-oriented skills. Full article

Low-voltage ride-through (LVRT) capability is essential for grid-connected photovoltaic (PV) systems, especially as rising renewable integration challenges grid stability during voltage disturbances. Existing LVRT methods often target isolated control functions, leading to limited system resilience. This paper presents a unified control strategy integrating DC-link voltage regulation, reactive power injection, and overvoltage mitigation using a coordinated fuzzy logic framework. The proposed architecture employs a cascaded control structure comprising an outer voltage loop and an inner current loop with feed-forward decoupling, synchronized via a Synchronous Reference Frame Phase-Locked Loop (SRF-PLL). At its core is a dual-input, single-output Fuzzy Logic Controller (FLC), featuring optimized membership functions and dynamic rule-based logic to manage multiple control objectives during grid faults. The proposed FLC-based unified LVRT controller for grid-tied PV system was implemented and validated for both symmetrical and asymmetrical fault conditions in MATLAB/Simulink 2023b platform. The proposed FLC-based LVRT controller achieves voltage sag compensation of 97.02% and 98.4% for symmetrical and asymmetrical faults, respectively, outperforming conventional PI control, which achieves 94.02% and 96.5%. The system maintains a stable DC-link voltage of 800 V and delivers up to 78% reactive power support during faults. Fault detection and recovery are completed within 200 ms, complying with Bangladesh grid code requirements. This integrated fuzzy logic approach offers a significant advancement for enhancing grid stability in high-renewable environments and supports reliable renewable utilization, and more sustainable grid operation in developing regions. Full article

As the global energy landscape shifts toward sustainability, microgrids incorporating Photovoltaic (PV) generation and Battery Energy Storage Systems (BESS) are becoming essential in commercial and industrial facilities. This research tackles the challenge of maintaining uninterrupted power supply to sensitive loads when grid disturbances occur. We propose a novel loss-of-mains detection method capable of identifying grid faults in under 3 milliseconds—well within the 10-millisecond threshold required for critical equipment to ride through the transition without disruption. Building on this fast detection, we develop inverter control strategies that enable a smooth transfer from grid-following to grid-forming operation while limiting transient overvoltage and overcurrent. Additionally, a coordinated operating sequence is introduced to ensure grid code compliance and proper management of distributed energy resources throughout the islanding process. The complete approach is validated experimentally using a dedicated prototype and a Power-Hardware-in-the-Loop (P-HIL) microgrid demonstrator, confirming its effectiveness and advancing the technology readiness level toward real-world deployment. Full article

Photovoltaic (PV) systems nowadays represent an essential component of renewable energy production. However, undetected faults often compromise their reliability, leading to significant energy losses and high maintenance costs. Existing deep learning approaches for PV fault diagnosis have achieved high accuracy, but they require massive, labeled datasets and high computational resources, which make them unsuitable for real-time applications. This paper proposes a lightweight, self-supervised hybrid learning framework for real-time PV fault diagnosis to address these limitations. First, the dataset is split into training, testing, and validation subsets. Thereafter, weighted class calculation steps are performed to overcome the issue of imbalance in the data. Then, a self-supervised pre-training phase is established to enable the encoder to produce effective internal representations prior to the implementation of a supervised fine-tuning classifier, characterized as a lightweight feed-forward network (Dense–Dropout–Dense Softmax), which will be trained using categorical cross-entropy and fault-type labels. Finally, a supervised fine-tuning stage is employed based on the pre-trained hybrid CNN–transformer encoder to perform PV fault classification. The experimental results indicate that the proposed approach outperforms existing models by achieving an overall accuracy of 99.8%, a recall of 99.6%, and an outstanding specificity of 100%. The confusion matrix demonstrates that classification is excellent on all operating types. Runtime analysis indicates that the model processes each sample in 2.78 ms and requires 0.07 MB to store weights of 19,429 parameters, confirming its suitability for real-time deployment. These findings highlight that using a hybrid CNN–Transformer encoder with self-supervised learning can improve fault detection and classification performance while significantly reducing inference time, making it an effective and efficient solution for intelligent PV system monitoring. Full article

Lymphatic filariasis (LF) is a mosquito-borne neglected tropical disease that causes substantial morbidity and social exclusion. Global efforts under the World Health Organization’s Global Programme to Eliminate Lymphatic Filariasis have markedly reduced prevalence, and several Pacific Island Countries and Territories (PICTs) have achieved elimination of the disease as a public health problem. However, post-validation surveillance (PVS), essential for detecting resurgence and enabling early response, has rarely been implemented, and barriers to its delivery remain poorly understood. We used two complementary qualitative approaches to identify systemic barriers and enablers to LF PVS in PICTs. First, we conducted a Nominal Group Technique followed by a structured expert elicitation involving program managers and technical staff. Data were analysed thematically and triangulated across sources. Participants identified 70 challenges which were consolidated into ten thematic domains. Pertinent barriers relate to limited leadership understanding of LF and surveillance options, inconsistent technical and financial support, and a lack of context-appropriate operational guidance. Additional challenges included limited field-ready diagnostics, procurement delays, the absence of formal mandates, and low community engagement. Enablers included embedding PVS within existing health services, leveraging trusted community networks, strengthening regional frameworks, and co-developing practical tools with countries. Sustaining LF elimination in the Pacific will require political commitment, regional collaboration, and integrated, programmatic approaches informed by recent PVS experience. Full article

Electroluminescence (EL) imaging is widely used for non-destructive inspection of photovoltaic (PV) cells; however, the low contrast of grayscale EL images limits the performance of automated defect detection methods. This manuscript proposes a defect-aware EL image classification framework that enhances defect visibility through local contrast enhancement and physically motivated RGB false-color mapping. Instead of simple channel replication, grayscale intensities are segmented into defect-related ranges and encoded to emphasize cracks, inactive regions, healthy silicon emission, and conductive pathways. The approach is evaluated on the public ELPV benchmark dataset proposing ResNet–50, EfficientNet–B0, and EfficientNet–B3 architectures at two input resolutions. The proposed representation consistently improves defect discrimination and achieves a maximum classification accuracy, outperforming previously reported CNN-based results on the same dataset. Notably, comparable accuracy is obtained at lower resolution, significantly reducing computational cost and inference time, which supports deployment with cheaper sensors and faster inspection pipelines. Class imbalance is addressed using focal loss, class weighting, and threshold calibration without artificial resampling, preserving realistic operating conditions. The results confirm that combining defect-aware RGB representation with resolution-efficient learning provides an accurate and computationally practical solution for EL-based PV defect detection. Full article

Given the rising global demand for environmentally sustainable energy sources, solar photovoltaic (PV) power generation has emerged as a pivotal component of the energy transition. In PV systems, power conversion efficiency is degraded and operational lifespan reduced due to the presence of defective modules. Consequently, achieving accurate and efficient defect detection during PV module manufacturing is critical to ensuring product quality and reliability. To address this challenge, we propose MambaVSS-YOLOv11n, an electroluminescence (EL) image-based multi-defect detection method for PV modules. Our study utilizes a dataset containing six types of defects—Broken Gate, Cold Solder Joint, Black Spot, Scratch, Microcrack, and Suction Mark—to construct 692 labeled EL images of defective PV modules. The model integrates the Vision State Space (VSS) module from Mamba and optimizes the C3k2 Bottleneck structure to enhance fine-grained feature extraction, while employing Space-to-Depth Convolutional (SPD-Conv) Layer for downsampling to improve computational efficiency. Additionally, to address YOLOv11n’s limited generalization capability for small objects and complex backgrounds, we adopt the Inner Mask Distance Penalized Intersection over the Union (Inner-MDPIoU) loss function, which enhances detection accuracy and mitigates the impact of low-quality samples. Experimental results demonstrate that compared to YOLOv11n, MambaVSS-YOLOv11n reduces the number of parameters by 18.1%, while improving mAP@0.5 to 0.869 and mAP@0.5:0.95 to 0.637. This achieves model lightweighting while enhancing detection performance. These findings indicate that the model is well-suited for real-time defect detection in PV module production lines, providing PV manufacturers with a lightweight yet accurate and reliable solution for PV module defect inspection. Full article

Siberian sturgeon (Acipenser baerii Brandt, 1869), characterized by its rapid mass accumulation and high survival rate under industrial breeding conditions, is one of the most promising aquacultural species. This research aimed to study the growth and development of farmed Siberian sturgeon (Acipenser baerii Brandt, 1869) to improve breeding programs. This research was conducted at the Federal Research Center for Animal Husbandry named after Academy Member L.K. Ernst and focused on the Lena population broodstock of Siberian sturgeon of the April 2022 generation (n = 98), grown in a recirculating aquaculture system (RAS). The experiment took into account body weight (W, g) and eleven morphological measurements: L—absolute length (cm); LR—fish body length increase (cm/day); l—commercial length (cm); L2—fork length (cm); HL—head length (cm); PV—pectoventral distance (cm); VA—ventroanal distance (cm); pl1—peduncle length (cm); H—body height (cm); h—peduncle height (cm); SC—body thickness (cm); GC—body circumference (cm); and Cc—peduncle circumference (cm). These measurements were taken from the same sample of fish at five different time points, all belonging to the same generation and approximately the same age. Measurements were taken every 3 to 9 months: 1 y (group G1), 1 y. 5 m. (group G2), 2 y. 2 m. (group G3), 2 y. 5 m. (group G4), 3 y. 2 m. (group G5), and 3 y. 5 m. (group G6). To evaluate the rate of growth and development, relative speed of growth (SGR) and relative speed of lengthening (SLR) during the observation period were determined. To characterize the fish’s exterior, we evaluated Fulton’s condition factor (KF) and the leanness index (Q). With increasing age, there was a significant (p < 0.01) decline in both SGR (from 0.454 to 0.065 g%/day) and SLR (from 0.132 to 0.028 cm%/day), which reflects changes in the fish’s physiological processes tied to the transition from the growth phase to the puberty phase. Relatively large variability was observed in body weight (C_v = 19.7–30.4%) compared to morphological measurements (C_v = 5.7–14.9%). Correlations between morphological measurements and the body weight of the fish varied from low to high (r = 0.22–0.97). Equations that allow for very precise (coefficient of determination R² = 0.800–0.933) estimation of the fish’s body weight based on morphological measurements were developed. The most preferable predictors were measurements of H (R² = 0.931), SC (R² = 0.933), and L2 (R² = 0.930). These morphological measurements are promising candidates for future development of contactless live weight detection using computer vision and machine learning algorithms. The study of live weight conjugacy at different ages showed that the best time to use this measurement to select fish for reproduction is at the age of 2 y. 2 m. or older. Acquired data can be used for the development and improvement of programs for the selection and breeding of Siberian sturgeon grown in a recirculating aquaculture system. Full article

Potato virus X (PVX) and potato virus Y (PVY) are major pathogens that threaten seed potato quality and yield. To improve the efficiency of field screening, we developed monovalent PVX, monovalent PVY, and bivalent PVX/PVY nanozyme strips using Fe₃O₄ nanozymes as labels in a double-antibody sandwich lateral flow immunochromatographic assay. Western blot analysis demonstrated that four monoclonal antibodies (PVX 2, PVX 6, PVY 2, and PVY 5) specifically recognized their corresponding viral coat proteins. Specificity testing showed that the nanozyme strips reacted only with the target viruses and did not cross-react with other common potato viruses, including Potato virus A (PVA), Potato virus M (PVM), Potato virus S (PVS), and Potato leafroll virus (PLRV). The PVX nanozyme strip detected PVX-positive extracts diluted up to 10³-fold, the PVY nanozyme strip up to 10⁴-fold, and the bivalent strip detected PVX/PVY co-infected samples diluted up to 10³-fold. In addition, detection results by strips from 12 samples of plantlets in vitro were fully consistent with RT-PCR. These nanozyme strips provide rapid, simple, specific, and sensitive methods that can be stored at ambient temperature, enabling field surveys, warehouse screening, and on-site testing and supporting early detection of potato virus diseases. Full article