Search Results (5,952)

The increasing use of UAVs has reshaped urban logistics, enabling sustainable alternatives to traditional deliveries. To address critical issues inherent in the system, the proposed study presents the design and evaluation of an innovative unmanned aerial vehicle (UAV) prototype that integrates advanced electronic components and artificial intelligence (AI), with the aim of reducing environmental impact and enabling autonomous navigation in complex urban environments. The UAV platform incorporates brushless DC motors, high-density LiPo batteries and perovskite solar cells to improve energy efficiency and increase flight range. The Deep Q-Network (DQN) allocates energy and selects reference points in the presence of wind and payload disturbances, while an integrated sensor system monitors motor vibration/temperature and charge status to prevent failures. In urban canyon and field scenarios (wind from 0 to 8 m/s; payload from 0.35 to 0.55 kg), the system reduces energy consumption by up to 18%, increases area coverage by 12% for the same charge, and maintains structural safety factors > 1.5 under gust loading. The approach combines sustainable materials, efficient propulsion, and real-time AI-based navigation for energy-conscious flight planning. A hybrid methodology, combining experimental design principles with finite-element-based structural modelling and AI-enhanced monitoring, has been applied to ensure structural health awareness. The study implements proven edge-AI sensor fusion architectures, balancing portability and telemonitoring with an integrated low-power design. The results confirm a reduction in energy consumption and CO₂ emissions compared to traditional delivery vehicles, confirming that the proposed system represents a scalable and intelligent solution for last-mile delivery, contributing to climate resilience and urban sustainability. The findings position the proposed UAV as a scalable reference model for integrating AI-driven navigation and renewable energy systems in sustainable logistics. Full article

Optimal hydration is crucial for maintaining health and athletic performance in young soccer players. This requires constant monitoring by medical and sports teams during training sessions and competitions. Objective: The purpose of this study was to examine hydration status based on variations in body weight, fluid intake, and urine specific gravity during three training sessions and a FIFA competition in elite U-17 youth soccer players, national team members. Methods: Twenty-one elite soccer players, aged 17.2 ± 0.29 years, with a body weight of 72.1 ± 6.95 kg and a height of 1.80 ± 0.05 m, participated in the study. To determine hydration status, percentage weight loss, fluid intake, and urine density were measured during three training sessions and one FIFA-level competition. Results: Differences in body weight were observed in two of the training sessions, with greater variation in the competition (3.5% of BW, p < 0.001). Significant differences were found between weight losses in training sessions vs. matches. An increase in initial weight was associated with lower urine density. Regression coefficients showed that differences in body weight can predict urine density during training and competition (p < 0.05). A decrease in final body weight could be a valid indicator as a predictor of higher urinary density. Full article

Intestinal microbiota populations are constantly shaped by both intrinsic and extrinsic factors, including diet, environment, and host genetics. As a result, understanding how to assess, monitor, and exploit microbiome–host interplay remains an active area of investigation, especially in aquaculture. In this study, we analyzed the taxonomic structure and functional potential of the intestinal microbiota of European sea bass and rainbow trout, incorporating gilthead sea bream as a final reference. The results showed that the identified core microbiota (40 taxa for sea bass and 20 for trout) held a central role in community organization, despite taxonomic variability, and exhibited a predominant number of positive connections (>60% for both species) with the rest of the microbial community in a Bayesian network. From a functional perspective, core-associated bacterial clusters (75% for sea bass and 81% for sea bream) accounted for the majority of predicted metabolic pathways (core contribution: >75% in sea bass and >87% in trout), particularly those involved in carbohydrate, amino acid, and vitamin metabolism. Comparative analysis across ecological phenotypes highlighted distinct microbial biomarkers, with genera such as Vibrio, Pseudoalteromonas, and Paracoccus enriched in saltwater species (Dicentrarchus labrax and Sparus aurata) and Mycoplasma and Clostridium in freshwater (Oncorhynchus mykiss). Overall, this study underscores the value of integrating taxonomic, functional, and network-based approaches as practical tools to monitor intestinal health status, assess welfare, and guide the development of more sustainable production strategies in aquaculture. Full article

Based on monitoring data from 2019 to 2024 at 270 typical surface drinking water sources (SDWS) in the Pearl River Basin (PRB), the occurrence and health risks of metal and metalloid pollutants (MMPs) were analyzed from a large watershed scale and long-term evolution. The results indicated that the overall pollution status of 8 MMPs (As, Cd, Pb, Mn, Sb, Ni, Ba, V) were at a low level and the concentrations of Cd, Pb, Ni, Ba, and V exhibited downward trends from 2019 to 2024. The distribution of MMPs exhibited significant regional differences with the main influencing factors including geological conditions, industrial activities, and urban development. River-type drinking water sources might be more affected by pollution from human activities such as industrial wastewater discharge, and the concentration levels of MMPs were generally higher than those in lake-type drinking water sources. Monte Carlo simulation revealed that 33.08% and 12.90% of total carcinogenic risks (TCR) exceeded the threshold of 10⁻⁶ for adults and children, respectively. Ba and Ni were the main contributors to the TCR, while As posed a certain non-carcinogenic risk to children. Sensitivity analysis indicated that concentrations of As and Ba were the main factors contributing to health risks. Although highly stringent water pollution control and a water resource protection policy have been implemented, it is still suggested to strengthen the control of As, Ba, and Ni in industrial-intensive areas and river-type water sources in the PRB. Full article

As climate extremes increasingly threaten global food security, precision tools for early detection of crop stress have become vital, particularly for root crops such as potato (Solanum tuberosum L.) and sweet potato (Ipomoea batatas L. Lam.), which are especially susceptible to environmental stressors throughout their life cycles. In this study, plants were monitored from the initial onset of seasonal stressors, including spring drought, heat, and episodes of excessive rainfall, through to harvest, capturing the full range of physiological and biochemical responses under seasonal, simulated conditions in greenhouses. The spectral data were obtained from regions of interest (ROIs) of each cultivar’s leaves, with over 3000 data points extracted per cultivar; these data were subsequently used for model development. A comprehensive classification framework was established by employing machine learning models, Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Partial Least Squares-Discriminant Analysis (PLS-DA), to detect stress across various growth stages. Furthermore, severity levels were objectively defined using photoreflectance indices and principal component analysis (PCA) data visualizations, which enabled consistent and reliable classification of stress responses in both individual cultivars and combined datasets. All models achieved high classification accuracy (90–98%) on independent test sets. The application of the Successive Projections Algorithm (SPA) for variable selection significantly reduced the number of wavelengths required for robust stress classification, with SPA-PLS-DA models maintaining high accuracy (90–96%) using only a subset of informative bands. Furthermore, SPA-PLS-DA-based chemical imaging enabled spatial mapping of stress severity within plant tissues, providing early, non-invasive insights into physiological and biochemical status. These findings highlight the potential of integrating hyperspectral imaging and machine learning for precise, real-time crop monitoring, thereby contributing to sustainable agricultural management and reduced yield losses. Full article

Modern military operations place significant physiological and cognitive demands on soldiers, necessitating innovative strategies to monitor and optimize health and performance. This narrative review examines the role of continuous physiological state monitoring and precision health strategies to enhance soldier resilience and operational readiness. Advanced wearable biosensors were analyzed for their ability to measure vital physiological parameters—such as heart-rate variability, core temperature, hydration status, and biochemical markers—in real-time operational scenarios. Emerging technological solutions, including AI-driven analytics and edge computing, facilitate rapid data interpretation and predictive health assessments. Results indicate that real-time physiological feedback significantly enhances early detection and prevention of conditions like exertional heat illness and musculoskeletal injuries, reducing medical attrition and improving combat effectiveness. However, ethical challenges related to data privacy, informed consent, and secure data management highlight the necessity for robust governance frameworks and stringent security protocols. Personalized training regimens and rehabilitation programs informed by monitoring data demonstrate potential for substantial performance optimization and sustained force readiness. In conclusion, integrating precision health strategies into military operations offers clear advantages in soldier health and operational effectiveness, contingent upon careful management of ethical considerations and data security. Full article

Traumatic brain injury (TBI) remains a major global health problem, contributing significantly to morbidity and mortality worldwide. Despite advances in understanding its complex pathophysiology, current therapeutic strategies are insufficient in addressing the long-term cognitive, emotional, and neurological impairments. While the primary mechanical injury is immediate and unavoidable, the secondary phase involves a cascade of biological processes leading to neuroinflammation, blood–brain barrier (BBB) disruption, and systemic immune activation. The heterogeneity of patient responses underscores the urgent need for reliable biomarkers and targeted interventions. Emerging evidence highlights the gut–brain axis as a critical modulator of the secondary phase, with microbiota-derived extracellular vesicles (MEVs) representing a promising avenue for both diagnosis and therapy. MEVs can cross the intestinal barrier and BBB, carrying biomolecules that influence neuronal survival, synaptic plasticity, and inflammatory signaling. These properties make MEVs promising biomarkers for early detection, severity classification, and prognosis in TBI, while also offering therapeutic potential through modulation of neuroinflammation and promotion of neural repair. MEV-based strategies could enable tailored interventions based on the individual’s microbiome profile, immune status, and injury characteristics. The integration of multi-omics with artificial intelligence is expected to fully unlock the diagnostic and therapeutic potential of MEVs. These approaches can identify molecular subtypes, predict outcomes, and facilitate real-time clinical decision-making. By bridging microbiology, neuroscience, and precision medicine, MEVs hold transformative potential to advance TBI diagnosis, monitoring, and treatment. This review also identifies key research gaps and proposes future directions for MEVs in precision diagnostics and gut microbiota-based therapeutics in neurotrauma care. Full article

True wireless stereo (TWS) earbuds are evolving into multifunctional wearable devices, offering opportunities not only for audio streaming but also for health-related applications. A fundamental requirement for such devices is the ability to accurately detect whether they are being worn, yet conventional proximity sensors remain limited in both reliability and functionality. This work explores the use of photoplethysmography (PPG) sensors, which are widely applied in heart rate and blood oxygen monitoring, as an alternative solution for wearing detection. A PPG sensor was embedded into a TWS prototype to capture blood flow changes, and the wearing status was classified in real time using a lightweight k-nearest neighbor (k-NN) algorithm on an edge AI processor. Experimental evaluation showed that incorporating a validity check enhanced classification performance, achieving F1 scores above 0.95 across all wearing conditions. These results indicate that PPG-based sensing can serve as a robust alternative to proximity sensors and expand the capabilities of TWS devices. Full article

Background: Right heart failure (RHF) in dogs is marked by pathological fluid redistribution and extracellular fluid (ECF) accumulation, which intensifies cardiac work-load and disrupts systemic homeostasis. This study aimed to validate the clinical utility of phase angle (PhA), a key biomarker derived from bioelectrical impedance analysis (BIA), as a non-invasive and real-time indicator of fluid distribution abnormalities in canine RHF. PhA reflects cellular integrity and fluid balance, making it a promising tool for detecting ECF accumulation, one of the hallmark features of RHF. Additionally, the study assessed the feasibility and clinical applicability of the InBody M20 device in veterinary cardiology, supporting its potential role in monitoring and managing fluid-related complications in dogs with RHF. Methods: A total of 110 canine patients presenting to the Tokyo University of Agriculture and Technology Veterinary Hospital were enrolled and categorized into three groups: right-sided heart failure (RHF), left-sided heart failure (LHF), and healthy controls. Phase angle (PhA) was measured using the InBody M20 device, and plasma osmolality (OSM) was also assessed. Additionally, the effects of body weight and age on PhA values were analyzed to account for potential confounding factors. Results: Dogs in the RHF group exhibited significantly lower phase angle (PhA) values and higher plasma osmolality (OSM) compared to those in the LHF and control groups. A strong positive correlation was observed between PhA and OSM (r = 0.9211, p < 0.0001). Additionally, PhA measured at 5 kHz demonstrated a significant negative correlation with body weight (r = –0.4536, p = 0.0007), while PhA at 50 kHz showed a significant negative correlation with age (r = –0.3219, p = 0.0176). Conclusions: PhA is a reliable and non-invasive biomarker for assessing extracellular fluid accumulation and diagnosing right heart failure in dogs. Its strong correlation with plasma osmolality, as well as its associations with body weight and age, highlights its clinical relevance for comprehensive fluid status evaluation. The findings support the feasibility and applicability of using the InBody M20 device in veterinary cardiology to monitor and manage fluid-related complications in canine patients. Full article

Accurate rice yield prediction is essential for optimizing water management and supporting decision-making in agricultural systems, particularly in arid environments where irrigation efficiency is critical. This study assessed five machine learning algorithms—Multiple Linear Regression (MLR), Support Vector Regression (SVR, linear and RBF), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—for plot-scale rice yield estimation using Sentinel-2 vegetation indices (VIs) during the 2022 and 2023 seasons in the Chancay–Lambayeque Valley, Peru. VIs sensitive to canopy vigor, water status, and structure were derived in Google Earth Engine and optimized via Sequential Forward Selection to identify the most relevant predictors per phenological stage. Models were trained and validated against field yields using leave-one-out cross-validation (LOOCV). Intermediate stages (Flowering, Milk, Dough) yielded the strongest relationships, with water-sensitive indices (NDMI, MSI) consistently ranked as key predictors. MLR and PLSR achieved the highest generalization (R²_CV up to 0.68; RMSE_CV ≈ 1.3 t ha⁻¹), while RF and XGBoost showed high training accuracy but lower validation performance, indicating overfitting. Model accuracy decreased in 2023 due to climatic variability and limited satellite observations. Findings confirm that Sentinel-2–based VI modeling offers a cost-effective, scalable alternative to UAV data for operational rice yield monitoring, supporting water resource management and decision-making in data-scarce agricultural regions. Full article

Cells of the innate immune system, particularly natural killer (NK) cells, serve as the first line of defense against tumor development and play a critical role in antitumor immunity. Characterizing the immune cell pool and its functional state is essential for understanding immunotherapy mechanisms and identifying key cellular players. However, defining NK cell populations in mice, the primary model for cancer immunotherapy, is challenging due to strain-specific marker variability and the absence of a universal NK cell marker, such as human CD56. This study evaluates surface markers of NK and other peripheral blood immune cells in both humans and mice, associating these markers with specific functional profiles. Bioinformatic approaches are employed to visualize these markers, enabling rapid immunoprofiling. We explore the translational relevance of these markers in assessing immunotherapy efficacy, including their gene associations, ligand interactions, and interspecies variations. Markers compatible with rapid flow-cytometry-based detection are prioritized to streamline experimental workflows. We propose a standardized immunoprofiling strategy for monitoring systemic immune status and evaluating the effectiveness of immunotherapy in preclinical and clinical settings. This approach facilitates the design of preclinical studies that aim to identify predictive biomarkers for immunotherapy outcomes by monitoring immune status. Full article

Background: Gorlin syndrome (GS) is a rare autosomal dominant disorder, associated with pathogenic PTCH1 or SUFU variants, predisposing to tumors such as basal cell carcinoma, medulloblastoma (MB), odontogenic keratocyst, and, rarely, cardiac fibroma (CF). MB occurs in ~5% of GS cases, typically in early childhood, while CF appears in 1–3%. Their coexistence in childhood is extremely rare. This report describes a pediatric GS case with synchronous MB and CF, focusing on the management priorities between oncologic and cardiac interventions. Methods: A 15-year follow-up is reported for a girl diagnosed at 22 months with desmoplastic/nodular MB and left ventricular CF. GS diagnosis was based on clinical features, imaging, and confirmation of a pathogenic PTCH1 variant (c.3306+1G>T). A literature narrative review on CF in GS was also conducted. Results: MB gross total resection was followed by chemotherapy, during which ventricular tachycardia episodes occurred, managed with cardioversion and antiarrhythmics. Given the favorable prognosis of early-treated MB in GS, oncologic therapy was prioritized. Cardiac status was monitored with ECG, Holter, echocardiography, and cardiac MRI. An adapted AIEOP protocol minimized cardiotoxicity. CF was managed conservatively, with no further arrhythmias and preserved ventricular function throughout 15 years. MB has not recurred. Conclusions: In GS patients with concurrent MB and CF, prioritizing MB treatment and adopting a conservative, closely monitored approach to CF can yield excellent long-term outcomes. In children with MB, especially syndromic forms, routine echocardiography is recommended to detect CF. This case underscores the value of multidisciplinary care in managing complex GS presentations. Full article

Temporal lobe epilepsy (TLE) is a debilitating disorder that affects millions of people worldwide and is difficult to treat with medicines. There has been little progress in the development of novel therapies for these patients because of the lack of suitable animal models. Current rodent models of TLE use chemoconvulsants or electrical stimulation to induce status epilepticus, which evolves into chronic epilepsy with spontaneous recurring seizures. These models have face validity in human TLE as they share similarities with seizure onset in the hippocampus, EEG patterns, tonic–clonic convulsions behavior, and hippocampal sclerosis. Unfortunately, seizure frequencies are so variable that they hinder drug testing. The ideal model for screening epilepsy therapies would have spontaneous seizure frequencies that are greater than two per day, little-to-no seizure-free days, and would maintain these features for more than 4 weeks. This study describes a series of improvements to the mouse pilocarpine TLE model. First, a pharmacokinetic model was developed to guide pilocarpine dosing. Second, induction was combined with EEG monitoring, allowing for real-time monitoring of pilocarpine-induced EEG discharges and electrographic seizures that precede behavioral manifestations. Third, strains of mice were identified that withstand pilocarpine-induced status epilepticus and reliably develop spontaneous recurring seizures. The pilocarpine model was improved by lowering mortality and increasing the fraction of mice that developed spontaneous seizures and had seizure frequencies that are amenable to drug screening. Future studies are required to identify the ideal mouse strain for drug screening and validate the response to known anti-epileptic drugs. Full article

The present study investigated factors influencing cybersecurity in railway critical infrastructure by identifying relevant factors and criteria and then prioritizing them in order of importance. To address the lack of multi-criteria analysis in previous studies on this topic, the present study applied the analytical hierarchy process to identify factors and criteria influencing cybersecurity and then selected the top 70% of influencing criteria to serve as a reference for railway cybersecurity project management. A total of 25 valid expert questionnaires were collected for weight vector analysis, revealing that the influencing criteria in the top 70% were inability to monitor train occupancy in track sections (locations); inability of controllers to issue commands to safety control systems; inability to provide drivers with information on upcoming signals, block status, and train occupancy; failure to automatically apply brakes when the train exceeds the speed limit; increased risk of catastrophic accidents due to power system security vulnerabilities; and inability of the dispatching system to automatically track train numbers. Full article

Integrating human–robot collaboration (HRC) into construction sites has significantly enhanced efficiency and quality. However, it also introduces new or intensifies existing risks as it brings in new entities, relationships, and construction activities. Safety remains the top priority and a persistent concern in HRC systems. However, the current literature on human–robot collaboration safety (HRCS) is vast yet fragmented, and a systematic exploration of its status and research trends in the construction context is still lacking. This paper explores advances in HRCS over the past two decades through a mixed quantitative and qualitative analysis method. Initially, 287 related articles were identified by keyword-searching in Scopus, followed by bibliometric analysis using CiteSpace to uncover the knowledge structure and track emerging research trends. Subsequently, a qualitative discussion highlights achievements in HRCS across five dimensions: (1) optimization of remote intelligent machinery; (2) hazard analysis and risk assessment in HRCS; (3) digital twin for safety monitoring; (4) cognitive and psychological impacts; (5) organizational management perspective. This study quantitatively maps the scientific landscape of HRCS at a macro level and qualitatively identifies key research areas. It provides a comprehensive foundation for understanding the evolution of HRCS and exploring future research directions and applications. Full article