Search Results (4,456)

Inefficient chromium (III)–collagen cross-linking during leather tanning generates solid waste and effluents containing residual chromium, raising environmental and health concerns. Biological strategies are increasingly popular for tannery waste treatment, but the microbial communities involved in leather degradation remain poorly understood. This study did not seek to evaluate leather disintegration according to standardised compostability criteria, but to establish a thermophilic composting system suitable for characterising leather-associated microbial communities, biofilm formation on leather and isolating cultivable strains. Composting assays were carried out at two scales, in which wet blue leather was mixed with organic compost under self-heating thermophilic conditions. Temperature was monitored, and mass loss and changes in leather structure were determined by gravimetry and scanning electron microscopy. Bacterial and fungal communities in compost with and without leather were analysed using high-throughput amplicon sequencing. Thermophilic consortia dominated by Firmicutes, Actinobacteria and Ascomycota were established, and several bacterial isolates and a filamentous fungus were recovered. Together, these results provide a first basis for understanding the communities and strains associated with chromium-tanned leather during thermophilic composting, supporting future searches for microorganisms and enzymes of interest for biological strategies to manage chromium-tanned leather waste. Full article

The expansion force generated by lithium-ion batteries during charge–discharge cycles is a key indicator of their structural safety and health. Recently, flexible pressure-sensing technologies have emerged as promising solutions for in situ swelling monitoring, owing to their high flexibility, sensitivity and integration capability. This review provides a systematic summary of progress in this field. Firstly, we discuss the mechanisms of battery swelling and the principles of conventional measurement methods. It then compares their accuracy, dynamic response and environmental adaptability. Subsequently, the main flexible pressure-sensing mechanisms are categorized, including piezoresistive, capacitive, piezoelectric and triboelectric types, and their material designs, structural configurations and sensing behaviors are discussed. Building on this, we examine integration strategies for flexible pressure sensors in battery systems. It covers surface-mounted and embedded approaches at the cell level, as well as array-based and distributed schemes at the module level. A comparative analysis highlights the differences in installation constraints and monitoring capabilities between these approaches. Additionally, this section also summarizes the characteristics of swelling signals and recent advances in data processing techniques, including AI-assisted feature extraction, fault detection and health state correlation. Despite their promise, challenges such as long-term material stability and signal interference remain. Future research is expected to focus on high-performance sensing materials, multimodal sensing fusion and intelligent data processing, with the aim of further advancing the integration of flexible sensing technologies into battery management systems and enhancing early warning and safety protection capabilities. Full article

Background: The continuous emergence of SARS-CoV-2 variants represents a major public health concern. Next-generation sequencing (NGS) enables genomic surveillance, facilitating the detection and monitoring of mutations that impact viral evolution. Methods: In this study, full-length SARS-CoV-2 genomes were analyzed between February 2022 and March 2024 as part of routine genomic surveillance conducted in Verona, Italy. Mutations in the envelope (E), membrane (M), and nucleocapsid (N) structural proteins were investigated. Only substitutions with a total prevalence of greater than 1% in the study dataset were considered. Results: A total of 178 mutations were identified across the three proteins (E: 16; M: 33; N: 129), of which 18 met the inclusion threshold (E: 3; M: 5; N: 10). Mutations were classified according to temporal dynamics as fixed, emerging, or transient. Throughout the study period, fixed mutations were consistently prevalent, emerging mutations appeared later but persisted with an ascending trend, while transient mutations displayed a single frequency peak before disappearing. Several mutations were reported with potential structural or functional relevance based on the existing literature, while others remain of unknown significance. Conclusions: The mutational patterns detected in this study broadly reflect global evolutionary trends of SARS-CoV-2. These findings emphasize the importance of continued genomic surveillance and underline the need for integrated experimental approaches to clarify the biological and epidemiological impact of poorly characterized mutations. Full article

The prompt and precise detection of road damage is vital for effective infrastructure management, forming the foundation for intelligent transportation systems and cost-effective pavement maintenance. While current convolutional neural network (CNN)-based methodologies have made progress, they are fundamentally limited by treating damages as independent, isolated entities, thereby ignoring the intrinsic spatial symmetry and topological organization inherent in complex damage patterns like alligator cracking. This conceptual asymmetry in modeling leads to two major deficiencies: “context blindness,” which overlooks essential structural interrelations, and “temporal inconsistency” in video analysis, resulting in unstable, flickering predictions. To address this, we propose a Spatio-Temporal Graph Mamba You-Only-Look-Once (STG-Mamba-YOLO) network, a novel architecture that introduces a symmetry-informed, hierarchical reasoning process. Our approach explicitly models and integrates contextual dependencies across three levels to restore a holistic and consistent structural representation. First, at the pixel level, a Mamba state-space model within the YOLO backbone enhances the modeling of long-range spatial dependencies, capturing the elongated symmetry of linear cracks. Second, at the object level, an intra-frame damage Graph Network enables explicit reasoning over the topological symmetry among damage candidates, effectively reducing false positives by leveraging their relational structure. Third, at the sequence level, a Temporal Graph Mamba module tracks the evolution of this damage graph, enforcing temporal symmetry across frames to ensure stable, non-flickering results in video streams. Comprehensive evaluations on multiple public benchmarks demonstrate that our method outperforms existing state-of-the-art approaches. STG-Mamba-YOLO shows significant advantages in identifying intricate damage topologies while ensuring robust temporal stability, thereby validating the effectiveness of our symmetry-guided, multi-level contextual fusion paradigm for structural health monitoring. Full article

Background/Objectives: Kidney transplantation remains the most effective treatment for patients with end-stage kidney disease, increasing both survival and quality of life. There are concerns regarding the long-term outcomes of donors in developing countries, as kidney transplants are predominantly performed from living donors. This study was conducted to evaluate the long-term clinical outcomes of living kidney donors, with a particular focus on kidney and cardiovascular health. Methods: We retrospectively reviewed the records of 232 individuals who underwent donor nephrectomy between January 2011 and November 2022. Cardiovascular events, mortality, chronic kidney disease, hypertension, and newly onset diabetes were assessed. Estimated glomerular filtration rate (eGFR) values were employed to monitor kidney function over time. Results: Living kidney donors were monitored for a median of 6 years (IQR: 4–9 years). During the follow-up period, 18.9% of donors experienced a decline in eGFR to below 60 mL/min/1.73 m²; however, none progressed to end-stage kidney disease. Of the cohort, 20 (8.6%) had newly onset proteinuria and none had proteinuria before transplantation. Although there were no recorded deaths from cardiovascular causes, 4.3% of donors experienced major adverse cardiac events. 12.3% of donors had newly diagnosed hypertension following transplantation, and 20.2% of donors had hypertension overall. Lower baseline eGFR, treated as a continuous variable in the logistic regression model, was independently associated with a higher likelihood of post-donation eGFR < 60 mL/min/1.73 m² (OR: 0.91; 95% CI: 0.88–0.94; p < 0.001). Post donation proteinuria (OR: 6.61; 95% CI: 1.98–22.07, p: 0.002) was also identified as independent risk factors for decline in eGFR to below 60 mL/min/1.73 m². Diabetes mellitus was found to be a significant predictor of newly onset hypertension. Conclusions: A considerable percentage of the donors experienced gradual deterioration in kidney function, even though none of them developed kidney failure necessitating dialysis. The prevalence of obesity and chronic kidney disease was higher post-donation compared to the general population, indicating the need for structured long-term monitoring. Full article

High-fidelity numerical models are widely used to study the behavior of complex structures in Structural Health Monitoring (SHM), but their high computational cost limits their use in stochastic settings such as fleet-level applications. In practice, fleets of engineering assets show natural variability due to differences in loading, materials, and manufacturing, making them inherently stochastic. To address these challenges, this work develops a probabilistic surrogate model based on conditional variational autoencoders (CVAEs). The CVAE is trained to reconstruct the high-dimensional boundary response field of a critical structural region while explicitly conditioning on operational and structural parameters. By learning a latent probabilistic representation, the model explains the behavior of all individual members of a homogeneous population. Synthetic training and testing data are generated using a finite element model together with an aerodynamic panel model of a UAV. Results show that the CVAE can efficiently reproduce the spatial and stochastic features of the system response, providing accurate approximations at a fraction of the computational cost of high-fidelity simulations. Full article

Harmful substances in food and agricultural environments pose significant risks to human health, necessitating the development of sensitive detection technologies. Electrochemical sensors are ideal for rapid monitoring because of their low cost, high efficiency, and portability. Recently developed laser-induced graphene (LIG)-based electrochemical sensors have demonstrated exceptional potential owing to the unique structural properties and outstanding electrochemical performance of LIG. In this review, the key factors influencing the LIG material characteristics during fabrication are discussed. Then, LIG-based electrochemical sensors are systematically categorized as pristine LIG and nanomaterial-functionalized, biomaterial-modified, and polymer-functionalized electrochemical sensors, and their application in the detection of functional components, additives, and agrochemicals in food products, and the detection of environmental pollutants, is comprehensively analyzed. Finally, the current challenges and the directions for future development are discussed. Full article

Distributed fiber-optic sensing (DFOS) with intentionally spaced mechanical fixity points was experimentally evaluated for the structural health monitoring (SHM) of reinforced concrete (RC) members. A full-scale four-point bending test was conducted on a 12 m RC beam (400 × 400 mm) instrumented with a single-mode DFOS cable incorporating internal anchors at 2 m intervals and bonded externally with structural epoxy. Brillouin time-domain analysis (BOTDA) provided distributed strain measurements at approximately 0.5 m spatial resolution, with all cables calibrated to ±15,000 µε. Under stepwise monotonic loading, the system captured smooth, repeatable strain baselines and clearly resolved localized tensile peaks associated with crack initiation and propagation. Long-gauge averages exhibited a near-linear load–strain response (R² ≈ 0.99) consistent with discrete foil and vibrating-wire strain gauges. Even after cracking, the DFOS signal remained continuous, while some discrete sensors showed saturation or scatter. Temperature compensation via a parallel fiber ensured thermally stable interpretation during load holds. The fixed-point configuration mitigated local debonding effects and yielded unbiased long-gauge strain data suitable for assessing serviceability and differential settlement. Overall, the results confirm the suitability of fixed-point DFOS as a durable, SHM-ready sensing approach for RC foundation elements and as a dense data source for emerging digital-twin frameworks. Full article

The present work focuses on the inverse identification of loads acting on wing-like geometries, through strain measurements. These loads are considered quasi-static and considered acting at discrete stations across the span of the wing. A demonstrative case study is investigated, focusing on a complex composite structure, an Unmanned Aerial Vehicle (UAV) fin. To achieve this, a high-fidelity Finite Element model is developed, with “virtual” strain data generated through simulations. The technical challenge of optimal sensor placement is addressed with D-optimal designs, which promise sensor networks (sensor locations and strain components) that produce minimal uncertainty propagation from strain measurements to load estimates. These designs are obtained through the implementation of Genetic Algorithms. Different sensor networks with an increasing number of sensors are evaluated in order to identify a further reduction in epistemic uncertainty posed by the problem’s ill-conditioned nature. Full article

The European catfish (Silurus glanis, Linnaeus 1758), commonly known as the wels catfish, is one of the largest freshwater fish in Europe and an ecologically and economically important species in both natural ecosystems and aquaculture. Its broad native distribution, together with the rapid growth of farming practices, increases concerns about pathogen dissemination and their potential impact on biodiversity, animal health, and potential risks to human healthcare. This review is based on a structured literature search following PRISMA recommendations for narrative reviews and summarizes current knowledge on the main pathogen groups affecting S. glanis—viruses (ranaviruses, alloherpesviruses), bacteria (Aeromonas spp., Edwardsiella spp.), protozoan and metazoan parasites (Ichthyophthirius multifiliis, Thaparocleidus spp., Eustrongylides spp., Contracaecum larvae), and oomycetes (Saprolegnia spp., Branchiomyces spp.). Within the One Health approach, particular attention is given to zoonotic pathogens such as Aeromonas spp., Edwardsiella tarda, and helminths like Eustrongylides and Contracaecum, which may cause risks to human health through contaminated water or consumption of raw or undercooked fish. The review integrates findings from field surveys, regional case studies such as those from the Danube basin, and data from the authors’ doctoral research. Because the wels catfish is increasingly cultivated and serves as an apex predator in natural habitats, its effective disease management is critical for both aquaculture and wild populations, and also for the food chains at all. Strengthened surveillance, health monitoring, and biosecurity measures are essential preventing the introduction and spread of pathogens into new hosts and habitats. Through the underlining of major catfish pathogen groups, this review highlights key challenges within the One Health approach and underscores the need for integrated health monitoring, biosecurity, and environmental management strategies. Full article

The sustainability of apiculture within Mediterranean forest ecosystems is contingent upon the extent and health of melliferous tree habitats. This study outlines a five-year initiative (2020–2024) aimed at mapping and monitoring four principal honey-producing tree species—pine (Pinus halepensis and Pinus nigra), Greek fir (Abies cephalonica), oak (Quercus ithaburensis subsp. macrolepis), and chestnut (Castanea sativa)—across Evia, Greece. This is achieved through the utilization of high-resolution Sentinel-2 satellite imagery in conjunction with a hierarchical deep learning framework. Distinct from prior vegetation mapping endeavors, this research introduces an innovative application of a hierarchical framework for species-level semantic segmentation of apicultural flora, employing a U-Net convolutional neural network to capture fine-scale spatial and temporal dynamics. The proposed framework first stratifies forests into broadleaf and coniferous types using Copernicus DLT data, and subsequently applies two specialized U-Net models trained on Sentinel-2 NDVI time series and DEM-derived topographic variables to (i) discriminate pine from fir within coniferous forests and (ii) distinguish oak from chestnut within broadleaf stands. This hierarchical decomposition reduces spectral confusion among structurally similar species and enables fine-scale semantic segmentation of apicultural flora. Our hierarchical framework achieves 92.1% overall accuracy, significantly outperforming traditional multiclass approaches (89.5%) and classical ML methods (76.9%). The results demonstrate the framework’s efficacy in accurately delineating species distributions, quantifying the ecological and economic impacts of the catastrophic 2021 forest fires, and projecting long-term habitat recovery trajectories. The integration of a novel hierarchical approach with Deep Learning-driven monitoring of climate- and disturbance-driven changes in honey-producing habitats marks a significant step towards more effective assessment and management of four major beekeeping tree species. These findings highlight the significance of such methodologies in guiding conservation, restoration, and adaptive management strategies, ultimately supporting resilient apiculture and safeguarding ecosystem services in fire-prone Mediterranean landscapes. Full article

Population aging is increasing dementia care demand. We present an audio-driven monitoring pipeline that operates either on mobile phones, microcontroller nodes, or smart television sets. The system combines audio signal processing with AI tools for structured interpretation. Preprocessing includes voice activity detection, speaker diarization, automatic speech recognition for dialogs, and speech-emotion recognition. An audio classifier detects home-care–relevant events (cough, cane taps, thuds, knocks, and speech). A large language model integrates transcripts, acoustic features, and a consented household knowledge base to produce a daily caregiver report covering orientation/disorientation (person, place, and time), delusion themes, agitation events, health proxies, and safety flags (e.g., exit seeking and falling). The pipeline targets real-time monitoring in homes and facilities, and it is an adjunct to caregiving, not a diagnostic device. Evaluation focuses on human-in-the-loop review, various audio/speech modalities, and the ability of AI to integrate information and reason. Intended users are low-income households in remote settings where in-person caregiving cannot be secured, enabling remote monitoring support for older adults with dementia. Full article

Background/Objectives: The university stage is a critical period for consolidating dietary habits that influence future health. University canteens therefore play a key role in providing menus aligned with nutritional recommendations. As menu composition shapes students’ access to healthy food, its evaluation also has equity implications. This study aimed to apply a newly designed questionnaire—the COMUNI questionnaire—intended to provide a rapid, user-friendly, and transferable method for evaluating the dietary quality of lunch menus offered in university canteens. Methods: Two versions of the 13-item COMUNI questionnaire were developed: COMUNI-1 for single-option menus and COMUNI-2 for menus offering multiple first- and second-course choices. The tool evaluates the frequency of key food groups, the availability of water and wholegrain bread, and the variety of foods and culinary techniques. To test the questionnaire, it was applied to 34 menu templates from university residences, colleges, and cafeterias. Results: 85.3% of menus showed deficient dietary quality, and 14.7% were rated as improvable; none achieved an optimal score. Menus managed by catering companies obtained significantly higher scores than those under direct management. Most frequently shortcomings included insufficient offerings of vegetables, legumes, fish, and wholegrain bread, alongside a frequent presence of refined carbohydrate sources and fried or ultra-processed foods. Conclusions: Universities should incorporate adherence to dietary recommendations as a key criterion in food-service procurement. The COMUNI questionnaire provides a simple and operational tool for assessing menu quality, supporting both diagnosis and monitoring of university food-service, once formally validated. Its use may also help identify structural disparities in access to healthy foods across campus settings, supporting more equitable food-service policies. Full article

In recent years, polymer-mediated signal amplification has drawn wide attention in bioelectronic sensing. With the rapid progress of biosensing and flexible electronics, polymers with excellent electron–ion transport properties, tunable molecular structures, and good biocompatibility have become essential materials for enhancing detection sensitivity and interfacial stability. However, current sensing systems still face challenges such as signal attenuation, surface fouling, and multi-component interference in complex biological environments, limiting their use in medical diagnosis and environmental monitoring. This review summarizes the progress of conductive polymers, molecularly imprinted polymers, hydrogels, and composite polymers in medical diagnosis, food safety, and environmental monitoring, focusing on their signal amplification mechanisms and structural optimization strategies in electronic transport regulation, molecular recognition enhancement, and antifouling interface design. Overall, polymers improve detection performance through interfacial electronic reconstruction and multidimensional synergistic amplification, offering new ideas for developing highly sensitive, stable, and intelligent biosensors. In the future, polymer-based amplification systems are expected to expand in multi-parameter integrated detection, long-term wearable monitoring, and in situ analysis of complex samples, providing new approaches to precision medicine and sustainable environmental health monitoring. Full article

The Inverse Finite Element Method (iFEM) is a valuable tool for reconstructing displacement fields from strain measurements, making it ideal for structural health monitoring. Traditional iFEM approaches are deterministic and typically require dense sensor networks for accurate results. However, practical constraints—such as limited sensor placement and cost—call for robust extrapolation techniques to estimate strain in non-instrumented regions. This paper proposes a stochastic 1D iFEM framework that integrates uncertainty quantification into the strain extrapolation process. By assigning confidence weights to extrapolated values, the method enhances the reliability of displacement reconstruction in sparsely instrumented structures. The approach is validated through numerical and experimental studies, demonstrating improved accuracy and robustness compared to traditional interpolation methods, even under varying loading conditions. The results confirm the method’s suitability for real-world applications in aerospace, civil, and naval engineering, particularly when direct strain measurements are limited. Full article