Search Results (10,348)

A novel electrochemical sensing platform based on a glassy carbon electrode (GCE) modified with a juglone/mesoporous carbon composite (JUG-MC/GCE) was developed for the ultrasensitive determination of melatonin (MEL) in pharmaceutical formulations and dietary supplements. Incorporation of juglone and mesoporous carbon within the coating deposited on the glassy carbon electrode, resulted in enhanced charge-transfer characteristics at the electrode–electrolyte interface. Under optimized differential pulse voltammetry (DPV) conditions in a 0.1 mol L⁻¹ McIlvaine buffer (pH 2.8), the proposed sensor demonstrated an exceptional electrocatalytic response for melatonin oxidation via a two-electron, one-proton irreversible pathway under kinetic control. The analytical performance reveal multiple linear calibration intervals with an ultra-low limit of detection (LOD) of 0.21 µg L⁻¹ (0.9 nM). The sensor manifested robust operational repeatability (RSD ≤ 1.8%), long-term storage stability, and satisfactory selectivity toward melatonin in the presence of common tablet excipients and selected potential interferents. The practical utility of the JUG-MC/GCE platform was successfully validated through the direct quantification of melatonin in commercial tablets, capsules, and complex jelly matrices, confirming its suitability for melatonin determination in real samples. Full article

Metals are widely used in various types of structural applications such as the automotive, aerospace and construction industries. However, their service life is limited due to the various loads they experience during operation. Specifically, cyclic loading can lead to the early fatigue failure of these structures. Therefore, early detection of fatigue deformation is essential to prevent catastrophic failures. In this study, an automated electrical resistance data acquisition system was developed using LabVIEW to obtain measurements from a Keithley 6221 current source for fatigue damage detection. The results showed an increase in electrical resistivity after the application of cyclic loading. It was observed that electrical resistivity increased after each set of loading cycles, with an average increase of 7.38%, a stress level of 260 MPa (high-cycle fatigue), and a 6.5% increase after the application of 25,000 cycles at 165 MPa (low-cycle fatigue). Scanning Transmission Electron Microscopy (S/TEM) was used for microstructural investigation as a proof of concept for the high-cycle fatigue sample interrupted after 25,000 cycles to analyse the modification in dislocation structures as well as a qualitative increment in the dislocation density with respect to the initial microstructural state of the as-machined sample. Such a modification in dislocation structures as well as an increment in dislocation density corroborates the findings proposed by electrical resistivity measurement. The results demonstrated that electrical resistivity measurement provides a promising non-destructive approach for the early detection of fatigue damage in metallic materials. Full article

Photovoltaic (PV) technology is now one of the main options for expanding the power of low-carbon electricity generation. However, in practical operation, PV systems still face several persistent difficulties, including the variability of solar irradiance, gradual performance degradation, fault occurrence, suboptimal control, and the growing complexity of grid-connected operation. These issues explain why artificial intelligence (AI) has become increasingly relevant in PV research, not only as a prediction tool, but also to improve monitoring, control, diagnosis, and decision-making. This review investigates the applications of AI in the major stages of the PV system lifecycle: solar resource assessment, power forecasting, fault detection, condition monitoring, system sizing, maximum power point tracking (MPPT), and grid integration. Rather than treating these applications as separate research topics, the review attempts to connect them through the common factors that determine their practical value: data quality, sensing configuration, model complexity, physical operating conditions, and deployment constraints. The reviewed studies indicate that AI-based MPPT methods can achieve tracking efficiencies close to 99%, while recent forecasting models, particularly LSTM, CNN–LSTM, and transformer-based architectures, can reduce prediction errors under changing weather conditions. At the same time, PV fault detection is moving beyond electroluminescence image classification toward more practical multimodal strategies that combine infrared thermography, RGB and drone imagery, electrical measurements, and SCADA/IoT data. Nevertheless, the progress reported in the literature should be interpreted with caution. Many proposed models are still evaluated on limited or non-standardized datasets, and their performance may decrease when they are transferred to different PV technologies, climates, fault severities, or operating conditions. Other recurring limitations include class imbalance, high computational cost, weak generalization, and the limited interpretability of deep-learning models. For this reason, hybrid neural networks, explainable AI, physics-informed learning, edge-AI, federated learning, and quantum machine learning are discussed as possible directions for making AI-based PV solutions more reliable and deployable. This review aims to critically synthesize recent advances and remaining gaps in order to support the practical integration of AI into efficient, reliable, and sustainable PV systems. Full article

Software vulnerability detection plays a critical role in improving software quality, system reliability, and security assurance. Prompt-based adaptation offers a lightweight alternative for vulnerability detection in low-resource Common Weakness Enumeration (CWE) settings, where labeled target-domain data are limited and model fine-tuning can be costly or unstable. This paper proposes an Integrated-Gradient-Guided Prompt Optimization (IGPO) method that uses attribution feedback to guide large language models in revising prompts for low-resource cross-CWE vulnerability detection. IGPO keeps the vulnerability detector fixed, evaluates the current prompt on target validation data, identifies false-positive and false-negative cases, computes Integrated Gradients (IG) for misclassified functions, aggregates token-level attributions into line-level feedback, and uses a large language model to diagnose error patterns and optimize the prompt. Experiments on C/C++ functions from PrimeVul, DiverseVul, and BigVul across 14 CWE categories show that, on positive-transfer source–target pairs, IGPO improves the average F1 score from 0.6780 to 0.7267 and outperforms the zero-shot baseline on 94.97% of them. These results position IGPO as an attribution-informed prompt adaptation framework that improves cross-CWE vulnerability detection without updating the detector, while highlighting the importance of backbone suitability under negative transfer. This method is particularly useful for security researchers and practitioners who need to adapt vulnerability detectors to new CWE categories with limited labeled data. Full article

Cyberattacks represent a serious threat for the security of network-based systems and are responsible for large economic losses every year. In this context, physical unclonable function (PUF)-based authentication can provide access to the network resources to legitimate users only, thus preventing unauthorized accesses. On the other hand, transient disturbances (e.g., noise, temperature and power supply variations) and permanent faults can lead to erroneous PUF responses, resulting in failed authentication and reduced network availability for legitimate users. Error-correcting codes have been proposed in the literature to improve PUF reliability. However, they typically require significant costs in terms of processing power and area overhead, meaning they are often unsuitable for resource-constrained devices, such as low-cost microcontrollers and FPGAs. In this paper, we have investigated strategies based on the use of different kinds of error-detecting and error-correcting codes, as well as their possible combination, with limited requirements in terms of processing power and no need for helper data. These strategies have been evaluated using both a synthetic PUF dataset and a real PUF dataset. The results show that the strategy based on a checksum error-detecting code achieves a good performance in terms of network availability, i.e., an error probability in the order of 10⁻³ (3.69 × 10⁻²) when the error on the PUF response (without any ECC) is 12.89% (55.04%), with a low data overhead (1.56% of the PUF challenge size), but it is effective only in the presence of transient disturbances. Instead, the strategy combining the checksum and the Hamming codes provides even higher network availability, i.e., an error probability in the order of 10⁻⁴ (1.6 × 10⁻³) when the error on the PUF response (without any ECC) is 12.89% (55.04%), at the cost of a slightly higher data overhead (7.81% of the PUF challenge size), while also enabling the capability to correct erroneous PUF responses caused by both disturbances and permanent faults. Full article

Robot-assisted surgery has significantly expanded the possibilities of minimally invasive procedures by providing enhanced dexterity and visualization. However, the lack of direct haptic feedback still limits the surgeon’s ability to accurately assess instrument–tissue interactions, motivating the need for automatic intraoperative assistance systems. During vesicourethral anastomosis (VUA) in robot-assisted radical prostatectomy (RARP), accurate engagement of the bladder and urethral mucosa is essential to ensure proper tissue approximation and watertight closure. Nevertheless, automatic identification of fine-grained needle–tissue interactions during this phase remains largely unexplored. In this work, we propose a proof-of-concept framework for vision-based needle–tissue interaction analysis in RARP endoscopic videos, combining semantic segmentation, geometric proximity analysis, and motion coherence estimation to identify biomechanically plausible interaction events. Two independent transformer-based models were fine-tuned for semantic segmentation of the mucosal tissue and the surgical needle using a patient-level split of six real-world RARP procedures, comprising four procedures for training, one for validation, and one for independent testing. The models achieved Dice scores of 0.837 and 0.774, respectively. The segmentation outputs were subsequently used to drive a motion-aware interaction analysis pipeline, combining geometric proximity estimation between the needle endpoint and the mucosal tissue with optical-flow motion coherence analysis. The proposed interaction framework was evaluated on an independent test set, achieving a specificity of 0.933 and a recall of 0.667. An ablation study further demonstrated the complementary contribution of geometric proximity and motion coherence cues for needle–tissue interaction detection. Although limited by the retrospective nature and size of the dataset, this study introduces a low-latency, end-to-end framework for interaction-aware surgical scene understanding during RARP. The proposed approach represents an initial step toward the development of context-aware intraoperative guidance systems for robotic urologic surgery. Full article

Accurate crack segmentation based on optical sensor imagery is of paramount importance for infrastructure health monitoring and disaster early warning. However, in complex natural scenes, cracks typically exhibit characteristics such as being thin and elongated, multi-branched, and having low contrast. Existing segmentation models struggle to balance low computational overhead with the simultaneous modeling of global topological continuity and the precise extraction of local details. To overcome the aforementioned limitations, we introduce a Structure-Aware Global–Local Mamba (SGL-Mamba). Firstly, the SGL-Mamba Block is designed in the encoding stage, which significantly enhances the joint modeling capacity for continuous topological structures and edge textures through the synergy of a parallel directional scanning mechanism and a local perception branch. Secondly, we design a High–Low Frequency Separation Enhancement (HLFSE) module to reconstruct the skip connections. This module leverages frequency decoupling to adaptively suppress high-frequency background noise and alleviate the semantic gap. Finally, in the decoding stage, the Deformable Large Kernel Attention (D-LKA) is integrated, utilizing a dynamic spatial receptive field to precisely adapt to irregular crack orientations. Extensive experiments on three public datasets (Crack500, DeepCrack, and CrackMap) demonstrate that SGL-Mamba outperforms other state-of-the-art (SOTA) methods, achieving an F1 score (F1) of 0.7982 ± 0.0038 and an mIoU of 0.8011 ± 0.0036 on the Crack500 dataset. While ensuring lightweight architecture and high computational efficiency, the proposed method provides an effective and practical solution for automatic crack detection. Full article

Botrytis cinerea, the causal agent of gray mold or Botrytis rot on many vegetable and fruit crops worldwide, is ranked among the main limiting factors for strawberry production, being able to strongly compromise the quality, shelf life, and commercial value of fruit. UV-C is considered an environmentally friendly strategy for postharvest protection for several commodities. In this regard, this research evaluated the performance of short-wavelength UV-C radiation in reducing postharvest Botrytis decay, extending or maintaining the shelf life and quality of strawberry fruit, and detecting possible negative effects. The topic is of growing interest, as it falls within the strategic priorities of so-called “Green Technologies” to pursue a sustainable agriculture and safe food supply by minimizing fungicide use as much as possible. UV-C exposure (at a rate of 1428 J m⁻²) by means of a UV-C lamp (15 W) at a source distance of 25 cm for just 1 min provided significant reductions (from 41 up to almost 80%) depending on the artificial or natural decay pressure of gray mold caused by B. cinerea on strawberry fruit without phytotoxic effects. Analyses performed under both refrigerated and room-temperature storage conditions at different day intervals showed that postharvest UV-C does not significantly affect key chemical parameters, including the ascorbic acid content, total sugars, pH, and titratable acidity of strawberry fruit. Thus, UV-C exposure for a brief time, especially if combined with a low temperature, can be effectively considered for sustainable protection of strawberry fruit at the postharvest stage, providing a theoretical basis for further elucidation of the action modes for this and other targets, and potential large-scale application. Full article

Early detection of colorectal cancer (CRC) is a major determinant of patient prognosis, as survival strongly depends on disease stage at diagnosis. Despite advances in screening programs, a significant proportion of CRC cases are still diagnosed at advanced stages, underscoring the need for improved early detection strategies. Most sporadic CRCs arise through the adenoma–carcinoma sequence over 10 to 15 years, providing a window for the detection of premalignant lesions, such as advanced adenomas. Current screening approaches are based on colonoscopy or its combination with stool-based tests. Although colonoscopy is the gold standard, it is an invasive technique with high associated costs and limited patient compliance. Stool-based tests are non-invasive and more widely accepted but lack specificity and sufficient sensitivity for detecting premalignant lesions. In this context, liquid biopsies have emerged as a promising minimally invasive alternative for identifying tumor-derived biomarkers in biological fluids such as blood or stool. Small non-coding RNAs (sncRNAs), and particularly microRNAs (miRNAs), have gained considerable attention as non-invasive biomarkers for their highly stability, resistance to handling conditions, and reliable quantification even in low-input samples. Single miRNAs and miRNA signatures detected in biofluids and combined with clinical parameters have shown promise for CRC detection. However, their utility for detecting advanced adenomas remains insufficiently characterized. Further validation in large, independent cohorts and standardization of analytical methods are required before their clinical implementation. Despite these challenges, sncRNA-based liquid biopsies represent a promising approach for improving early detection of CRC and, consequently, its prognosis. Full article

Ovarian cancer (OC) remains the most lethal gynecological malignancy, mostly due to its frequent diagnosis at advanced stages, early peritoneal dissemination, ascites formation, and limited sensitivity of currently available approaches for early detection. Extracellular vesicles (EVs), particularly exosomes, mediate intercellular communication through the transfer of proteins, lipids, metabolites, and nucleic acids. In OC, EV-associated protein profiles reflect both tumor-cell-intrinsic programs and the complex interactions between malignant cells and the peritoneal microenvironment. This review summarizes current evidence regarding the involvement of exosomal proteins in OC progression, with particular emphasis on epithelial–mesenchymal transition, mesothelial reprogramming, extracellular matrix remodeling, angiogenesis, immune suppression, peritoneal dissemination, and platinum resistance. Mechanistic studies indicate that exosomal proteins, including CD44, the integrin α5β1/asparaginyl endopeptidase complex, annexin A2, low-density lipoprotein receptor-related protein 1, and programmed death-ligand 1, can directly contribute to metastatic niche formation and tumor progression. In parallel, proteomic studies of plasma-, serum-, ascites-, peritoneal-fluid-, and uterine-lavage-derived EVs have identified candidate liquid-biopsy biomarkers, including MUC1, EpCAM, FOLR1, integrins, complement- and coagulation-related proteins, and proteins associated with treatment resistance. To integrate the biological significance of proteins reported in OC-associated exosomes, we additionally performed protein–protein interaction and functional enrichment analyses. These analyses revealed interconnected protein groups associated with cell adhesion, oxidative stress adaptation, secretory remodeling, lipid metabolism, extracellular matrix organization, and inflammatory signaling. Taken together, the available evidence supports exosomal proteome profiling as a promising approach for investigating OC dissemination and developing minimally invasive diagnostic and prognostic tools. However, standardized EV isolation, quantitative proteomics, functional validation, and independent clinical cohorts remain essential for translation into clinical practice. Full article

Medicanes are mesoscale cyclones that develop over the Mediterranean Sea and display tropical-like cyclone characteristics, including a warm core, spiral cloud organization, and deep convection over warm sea surfaces. Since their structure and position can change rapidly on short lead times before coastal impact, robust near-real-time tracking algorithms are essential for timely warning and operational decision support. To advance this research direction, this work introduces the Deep Learning Medicane Tracking (DeMeTrA) Algorithm, an end-to-end deep learning framework for medicane detection and rotation-center localization from SEVIRI Rapid Scan Airmass RGB imagery. The proposed methodology consists of a three-stage VideoMAE v2 architecture encompassing the following: (i) self-supervised domain specialization on unlabeled satellite image sequences, (ii) supervised binary classification of cyclone versus non-cyclone events, and (iii) supervised coordinate regression for rotation-center tracking. The training corpus spans several time windows of Meteosat Second-Generation observations across the Mediterranean basin, with ground-truth annotations derived from a consensus cyclone-track reference. On event-based splits, cyclone detection reaches 91% balanced accuracy on a balanced validation set and 89% on an unbalanced test set representative of operational conditions. The tracking results show generally low localization errors (mostly below 20 km), with limited outliers in the most complex cases. These findings support the use of Transformer-based video models for operational medicane monitoring and establish a baseline for future developments. Full article

Carbon nanotube (CNT)-based biosensors have emerged as promising platforms for non-invasive biofluid analysis because of their high electrical conductivity, large surface area, tunable optical properties, and versatile surface chemistry, enabling miniaturized, flexible sensing devices. Sweat, saliva, tears, and urine are increasingly recognized as attractive alternatives to blood for point-of-care diagnostics because they enable repeated, non-invasive sampling while containing clinically relevant metabolites, electrolytes, proteins, hormones, nucleic acids, pathogens, and other biomarkers. However, the low abundance of many analytes, matrix complexity, biofouling, and biofluid-specific variability present significant analytical challenges. This review critically examines the different CNT-based sensor architectures, and their recent advances in non-invasive analysis of sweat, saliva, tears, and urine. It integrates sensor architecture, biofluid-specific analytical challenges, sample-validation level, and translational readiness within a single comparative framework. Representative applications are discussed for metabolic monitoring, renal health assessment, infectious disease testing, and other clinically relevant uses. Beyond clinical diagnostics, emerging non-clinical applications, including drug-of-abuse detection, forensic body-fluid identification, and occupational or environmental exposure assessment, are also highlighted. Finally, we discuss key barriers limiting real-world translation of CNT biosensors, including material reproducibility issues, biofouling, physiological interpretation of biofluid biomarkers, scalable manufacturing, and long-term operational stability, and outline future strategies to advance these platforms toward robust, reliable, and widely deployable biosensing technologies. Full article

Although incremental capacity (IC) curve analysis is promising for early internal short circuit (ISC) detection, its diagnostic accuracy degrades significantly across a wide resistance range, especially for low-resistance events dominated by leakage currents. To overcome this limitation, we propose a threshold-adaptive ISC diagnostic framework that dynamically integrates quantitative resistance calculation (for high resistance, R ≥ 100 Ω) with Gaussian process regression (GPR)-based leakage current analysis (for low resistance, R < 100 Ω). Validated on 20 Ah LiFePO₄ batteries, this approach achieves <6% error for 100–300 Ω and <8% error for <100 Ω (after GPR correction), demonstrating robust, implementation-ready solutions for real-world battery safety monitoring. Full article

Human-induced mined-out areas are widely distributed across alpine regions, making reliable deformation detection particularly challenging. However, widespread low coherence and seasonal variations in snow cover cause severe spatiotemporal decorrelation and phase-unwrapping errors, substantially limiting the applicability of conventional InSAR techniques in these regions. To address these limitations, this study proposes an SCA-constrained seasonal partitioning Stacking-InSAR method based on the relationship between snow cover and interferometric coherence. The proposed method was applied to the Songhu Iron Mine in Xinjiang, China. Snow cover area (SCA) was first extracted from high-resolution Planet optical imagery and subsequently used to partition the interferometric network by season, thereby selecting high-quality summer interferometric pairs. This screening procedure reduced the interferometric network from 590 pairs to 168 high-quality pairs. The subsequent Stacking-InSAR results clearly delineated a spatially coherent surface subsidence zone within the mined-out area. The maximum line-of-sight (LOS) subsidence rate derived from the Stacking-InSAR analysis reached −132.31 mm/year. Overall, the proposed optical SCA-constrained seasonal partitioning strategy enabled Stacking-InSAR to identify a distinct subsidence funnel in an alpine region characterized by low coherence while maintaining high interferometric quality. The proposed method provides valuable technical and data support for deformation monitoring and geological hazard early warning in complex alpine environments. Full article

Sustainable coastal environmental management relies on continuous assessment of sediment quality. In the Saudi Arabian Red Sea, such sediments function as both sinks and potential secondary sources of heavy metals (HMs) originating from natural processes and anthropogenic activities. The present study investigated the concentrations of HMs in surface sediments from Jeddah and Rabigh. Contamination levels, as well as possible natural and anthropogenic inputs and potential risks to human health through ingestion, dermal contact, and inhalation pathways, were evaluated. The results indicated that Fe is the most abundant metal at both sites (1570.40 mg/kg at Jeddah and 1804.11 mg/kg at Rabigh). Cu, Ni, and Zn are present in the sediments at concentrations ranging between 3.12 and 4.49 mg/kg, while Cd and Pb remain below the detection limits in all samples. Generally, the contamination index values indicated low levels in both regions. The evaluation of human health risks identified ingestion as the primary exposure pathway. Non-carcinogenic risk levels were within safe limits for both adults and children. However, the carcinogenic risk assessment of nickel (Ni) indicated that all values fall within the acceptable range (10⁻⁶–10⁻⁴), although children consistently showed higher risks than adults. Despite the overall low levels of pollution, the moderate enrichment of Cu, Ni, and Zn in Jeddah and Cu in Rabigh, along with the higher non-carcinogenic risks to children at the Rabigh site, highlights the need for continuous environmental monitoring and further investigation to support the sustainable management of coastal ecosystems. Full article