Search Results (3,698)

Conductive hydrogels (CHs) have attracted significant attention in the fields of flexible electronics, human–machine interaction, and electronic skin (e-skin) due to their self-adhesiveness, environmental stability, and multi-stimuli responsiveness. However, integrating these diverse functionalities into a single conductive hydrogel system remains a challenge. In this study, polyvinyl alcohol (PVA) and polyacrylamide (PAM) were used as the dual-network matrix, lithium chloride and MXene were added, and a simple immersion strategy was adopted to synthesize a multifunctional MXene-based conductive hydrogel in a glycerol/water (1:1) binary solvent system. A subsequent investigation was then conducted on the hydrogel. The prepared PVA/PAM/LiCl/MXene hydrogel exhibits excellent tensile properties (~1700%), high electrical conductivity (1.6 S/m), and good self-healing ability. Furthermore, it possesses multimodal sensing performance, including humidity sensitivity (sensitivity of −1.09/% RH), temperature responsiveness (heating sensitivity of 2.2 and cooling sensitivity of 1.5), and fast pressure response/recovery times (220 ms/230 ms). In addition, the hydrogel has successfully achieved real-time monitoring of human joint movements (elbow and knee bending) and physiological signals (pulse, breathing), as well as enabled monitoring of spatial pressure distribution via a 3 × 3 sensor array. The performance and versatility of this hydrogel make it a promising candidate for next-generation flexible sensors, which can be applied in the fields of human health monitoring, electronic skin, and human–machine interaction. Full article

Background: Rowing is a highly demanding endurance sport, requiring simultaneous work of approximately 70% of the body’s muscle mass and the combined contribution of aerobic and anaerobic energy systems. Objective: This study aimed to analyze the cardiorespiratory responses and performance characteristics of elite junior male and female rowers during maximal effort over 2000 m on a rowing ergometer. Methods: Fifteen junior rowers (six males aged 15–17 and nine females aged 15–18) participated in the study. Anthropometric data (body height, weight, and body surface area) were recorded. All participants performed a maximal 2000 m test on a Concept2 D-model ergometer. Throughout the test, oxygen uptake (VO₂), carbon dioxide production (VCO₂), heart rate, and ventilation parameters were continuously measured. Performance and physiological data were analyzed in three intensity zones, defined by ventilatory thresholds (VT1–VT3), as well as at peak exercise. Results: Significant anthropometric differences were observed between genders. In terms of performance, males completed the 2000 m test significantly faster than females (208.83 ± 87.66 s vs. 333.78 ± 97.51 s, p = 0.0253). Relative VO₂ at peak exercise was higher in males (58.73 ± 5.25 mL·kg⁻¹·min⁻¹) than females (48.32 ± 6.09 mL·kg⁻¹·min⁻¹, p = 0.0046). In most cardiorespiratory parameters, males outperformed females significantly, except for heart rate and ventilatory equivalents. Ranking analysis revealed that higher VO₂max values were generally associated with a better placement in both genders, though this relationship was not perfectly linear. Performance time was negatively correlated with VO₂Peak (r = −0.8286; p < 0.001), rVO₂Peak (r = −0.6781; p < 0.01), and O₂P_Peak (r = −0.7729; p < 0.01). Conclusions: The findings confirm significant gender differences in anthropometric and cardiorespiratory characteristics of elite junior rowers and reinforce VO₂max as a key determinant of performance. Yet, deviations from a direct VO₂max–rank correlation highlight the influence of tactical, psychological, and biomechanical factors. Future research should provide practical recommendations for monitoring performance and tailoring training to optimize adaptation and long-term athlete development. Full article

Cross-country (XC) skiing imposes high physiological demands under hypoxic conditions at altitude. Lactate thresholds such as Onset Blood Lactate Accumulation at 4 mmol/L (OBLA4) and lactate plus 1 mmol/L above baseline (Bsln+1.0) are crucial for tracking performance. This study investigates physiological responses in junior XC skiers under normoxic and hypoxic conditions before (PreCs) and after (PosCs) the competitive season. Nine national-level XC skiers performed a Graded Exercise Test (GXT) on a treadmill using roller skis under both normoxic and hypoxic conditions in PreCS and PosCS. Heart rate, slope (treadmill inclination), and lactate thresholds (Bsln+1.0 and OBLA4) were measured. Significant differences were found between PreCs and PosCs under hypoxia for maximum heart rate (p < 0.05). Estimated slopes at Bsln+1.0 and OBLA4 were lower under hypoxia compared to normoxia in PreCs (p = 0.005, d = −1.29 for Bsln+1.0 and p = 0.013, d = −1.06 for OBLA4). In PosCs, a lower impairment effect of hypoxia exposure under slope at OBLA4 was found (p = 0.02, d = −0.95). Positive correlations were found between heart rate and slope for Bsln+1.0 and OBLA4 in PreCs under normoxia and hypoxia, becoming stronger at PosCs, especially under hypoxia. Delta values showed that the higher the slope at Bsln+1.0 and OBLA 4 under normoxia was, the greater the decrease between normoxia and hypoxia was. Physiological changes in junior XC skiers after training and competition in normoxic and hypoxic conditions highlight the importance of hypoxic environments for assessing and monitoring performance throughout the season. Full article

The green macroalgae Ulva Linnaeus, 1753, also known as sea lettuce, is one of the most ecologically and economically significant algal genera. Its representatives occur in marine, brackish, and freshwater environments worldwide and show high adaptability, rapid growth, and marked biochemical diversity. These traits support their ecological roles in nutrient cycling, primary productivity, and habitat provision, and they also explain their growing relevance to the blue bioeconomy. This review summarizes current knowledge of Ulva biodiversity, taxonomy, and physiology, and evaluates applications in food, feed, bioremediation, biofuel, pharmaceuticals, and biomaterials. Particular attention is given to molecular approaches that resolve taxonomic difficulties and to biochemical profiles that determine nutritional value and industrial potential. This review also considers risks and limitations. Ulva species can act as hyperaccumulators of heavy metals, microplastics, and organic pollutants, which creates safety concerns for food and feed uses and highlights the necessity of strict monitoring and quality control. Technical and economic barriers restrict large-scale use in energy and material production. By presenting both opportunities and constraints, this review stresses the dual role of Ulva as a promising bioresource and a potential ecological risk. Future research must integrate molecular genetics, physiology, and applied studies to support sustainable utilization and ensure safe contributions of Ulva to biodiversity assessment, environmental management, and bioeconomic development. Full article

Emotion recognition using electroencephalogram (EEG) signals has gained significant attention due to its potential applications in human–computer interaction (HCI), brain computer interfaces (BCIs), mental health monitoring, etc. Although deep learning (DL) techniques have shown impressive performance in this domain, they often require large datasets and high computational resources and offer limited interpretability, limiting their practical deployment. To address these issues, this paper presents a novel frequency-driven ensemble framework for electroencephalogram-based emotion recognition (FREQ-EER), an ensemble of lightweight machine learning (ML) classifiers with a frequency-based data augmentation strategy tailored for effective emotion recognition in low-data EEG scenarios. Our work focuses on the targeted analysis of specific EEG frequency bands and brain regions, enabling a deeper understanding of how distinct neural components contribute to the emotional states. To validate the robustness of the proposed FREQ-EER, the widely recognized DEAP (database for emotion analysis using physiological signals) dataset, SEED (SJTU emotion EEG dataset), and GAMEEMO (database for an emotion recognition system based on EEG signals and various computer games) were considered for the experiment. On the DEAP dataset, classification accuracies of up to 96% for specific emotion classes were achieved, while on the SEED and GAMEEMO, it maintained 97.04% and 98.6% overall accuracies, respectively, with nearly perfect AUC values confirming the frameworks efficiency, interpretability, and generalizability. Full article

Background: Basketball is characterized as a high-intensity, intermittent sport that places considerable demands on the cardiorespiratory, neuromuscular, and mechanical systems. These physiological requirements are modulated by contextual variables and the athlete’s stage of biological maturation, both of which significantly influence physical fitness outcomes. Consequently, it is imperative to employ age- and development-specific assessment protocols. Objectives: This study aimed to evaluate the differences in physical fitness across competitive categories and to explore the interrelationships among the various physical assessment tests. Twenty-four male players (U14 = 12; U16 = 12) participated in this research. Methods: Athletes were monitored using WIMUPRO inertial measurement units and completed the SBAFIT test battery to evaluate physical fitness parameters. Statistical analyses included both inferential and correlational approaches, with effect sizes calculated for all relevant variables. The independent variable was the competitive age category of the players. Results: The results indicated notable differences in physical performance between developmental groups, primarily attributed to biological maturation. Significant disparities were observed in measures of aerobic capacity, linear speed, agility, and centripetal force. Conclusions: The comparative nature of this study across developmental categories offers novel insights and practical implications for talent development and training optimization. Full article

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, with ischemic heart disease (IHD) being the most prevalent and deadly subtype. The growing burden of IHD underscores the urgent need for effective early detection methods that are scalable and non-invasive. Heart Rate Variability (HRV), a non-invasive physiological marker influenced by the autonomic nervous system (ANS), has shown clinical relevance in predicting adverse cardiac events. This study presents a photoplethysmography (PPG)-based Zhurek IoT device, a custom-developed Internet of Things (IoT) device for non-invasive HRV monitoring. The platform’s effectiveness was evaluated using HRV metrics from electrocardiography (ECG) and PPG signals, with machine learning (ML) models applied to the task of early IHD risk detection. ML classifiers were trained on HRV features, and the Random Forest (RF) model achieved the highest classification accuracy of 90.82%, precision of 92.11%, and recall of 91.00% when tested on real data. The model demonstrated excellent discriminative ability with an area under the ROC curve (AUC) of 0.98, reaching a sensitivity of 88% and specificity of 100% at its optimal threshold. The preliminary results suggest that data collected with the “Zhurek” IoT devices are promising for the further development of ML models for IHD risk detection. This study aimed to address the limitations of previous work, such as small datasets and a lack of validation, by utilizing real and synthetically augmented data (conditional tabular GAN (CTGAN)), as well as multi-sensor input (ECG and PPG). The findings of this pilot study can serve as a starting point for developing scalable, remote, and cost-effective screening systems. The further integration of wearable devices and intelligent algorithms is a promising direction for improving routine monitoring and advancing preventative cardiology. 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

Hydrogen bonding, a prevalent molecular interaction in nature, is crucial in biological and chemical processes. The emergence of single-molecule techniques has enhanced our microscopic understanding of hydrogen bonding. However, it is still challenging to track the dynamic behaviour of hydrogen bonding in solution, particularly under physiological conditions where interactions are significantly weakened. Here, we present a nanoscale-confined, functionalised quantum mechanical tunnelling (QMT) probe that enables continuous monitoring of electrical fingerprints of single-molecule hydrogen bonding interactions for over tens of minutes in diverse solvents, including polar physiological solutions, which reveal reproducible multi-level conductance distributions. Moreover, the functionalised QMT probes have successfully discriminated between L(+)- and D(−)-tartaric acid enantiomers by resolving the conductance difference. This work uncovers dynamic single-molecule hydrogen bonding processes within confined nanoscale spaces under physiological conditions, establishing a new paradigm for probing molecular hydrogen-bonding networks in supramolecular chemistry and biology. Full article

Cyber coercion, where legitimate users are forced to perform actions under duress, poses a serious insider threat to modern organizations, especially to critical infrastructure. Traditional security controls and monitoring tools struggle to distinguish coerced actions from normal user actions. In this paper, we propose a cyber coercion detection system that analyzes a user’s activity using an integrated large language model (LLM) to evaluate contextual cues from user commands or actions and current policies and procedures. If the LLM indicates coercion, behavioral methods, such as keystroke dynamics and mouse usage patterns, and physiological signals such as heart rate are analyzed to detect stress or anomalies indicative of duress. Experimental results show that the LLM-assisted multimodal approach shows potential in detecting coercive activity with and without detected coercive communication, where multimodal biometrics assist the confidence of the LLM in cases in which it does not detect coercive communication. The proposed system may add a critical detection capability against coercion-based cyber-attacks, providing early warning signals that could inform defensive responses before damage occurs. Full article

We propose an edge-enhanced federated learning framework for real-time itinerary optimization in elderly oriented adventure tourism, addressing the critical need for adaptive scheduling that balances activity intensity with health constraints. The system integrates lightweight convolutional neural networks with a priority-based scheduling algorithm, processing participant profiles and real-time biometric data through a decentralized computation model to enable dynamic adjustments. A modified Hungarian algorithm incorporates physical exertion scores, temporal proximity weights, and health risk factors, then optimizes activity assignments while respecting physiological recovery requirements. The federated learning architecture operates across distributed edge nodes, preserving data privacy through localized model training and periodic global aggregation. Furthermore, the framework interfaces with transportation systems and medical monitoring infrastructure, automatically triggering itinerary modifications when vital sign anomalies exceed adaptive thresholds. Implemented on NVIDIA Jetson AGX Orin modules, the system achieves 300 ms end-to-end latency for real-time schedule updates, meeting stringent safety requirements for elderly participants. The proposed method demonstrates significant improvements over conventional itinerary planners through its edge computing efficiency and personalized adaptation capabilities, particularly in handling the latency-sensitive demands of intensive tourism scenarios. Experimental results show robust performance across diverse participant profiles and activity types, confirming the system’s practical viability for real-world deployment in elderly adventure tourism operations. Full article

Background: Physical conditioning is essential to meet the operational demands of military environments. However, high-intensity exercise provokes muscle microinjuries resulting in exercise-induced muscle damage. This condition is typically monitored using serum biomarkers such as creatine kinase (CK), myoglobin (MYO), and lactate dehydrogenase (LDH). Nevertheless, individual variability and genetic factors complicate the interpretation. In this context, the rs1815739 variant (ACTN3), the most common variant related to exercise phenotypes, hypothetically could interfere with the muscle physiological response. This study aimed to evaluate the kinetics of serum biomarkers during a high-intensity activity and their potential association with rs1815739 polymorphism. Materials and Methods: 32 male cadets were selected during the Army Paratrooper Course. Serum was obtained at six distinct moments while they performed regular course tests and recovery time. Borg scale was assessed in 2 moments (~11 and ~17). Results: Serum levels of CK, CK-MB, MYO, and LDH significantly increase after exercise, proportionally to Borg’s level, following the applicability of longitudinal studies to understand biomarker levels in response to exercise. R allele carriers (ACTN3) were only slightly associated with greater levels of MYO and CK, mainly in relative kinetic levels, and especially at moments of greater physical demand/recovery. Although the ACTN3 was slightly related to different biomarker levels in our investigation, the success or healthiness in military activities is multifactorial and does not depend only on interindividual variability or physical capacity. Conclusions: Monitoring biomarkers and multiple genomic regions can generate more efficient exercise-related phenotype interventions. 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

This research introduces a hybrid framework that integrates stochastic modeling and machine learning for predicting postprandial glucose levels in individuals with Type 1 Diabetes (T1D). The primary aim is to enhance the accuracy of glucose predictions by merging a biophysical Glucose–Insulin–Meal (GIM) model with advanced machine learning techniques. This framework is tailored to utilize the Kaggle BRIST1D dataset, which comprises real-world data from continuous glucose monitoring (CGM), insulin administration, and meal intake records. The methodology employs the GIM model as a physiological prior to generate simulated glucose and insulin trajectories, which are then utilized as input features for the machine learning (ML) component. For this component, the study leverages the Light Gradient Boosting Machine (LightGBM) due to its efficiency and strong performance with tabular data, while Long Short-Term Memory (LSTM) networks are applied to capture temporal dependencies. Additionally, Bayesian regression is integrated to assess prediction uncertainty. A key advancement of this research is the transition from a deterministic GIM formulation to a stochastic differential equation (SDE) framework, which allows the model to represent the probabilistic range of physiological responses and improves uncertainty management when working with real-world data. The findings reveal that this hybrid methodology enhances both the precision and applicability of glucose predictions by integrating the physiological insights of Glucose Interaction Models (GIM) with the flexibility of data-driven machine learning techniques to accommodate real-world variability. This innovative framework facilitates the creation of robust, transparent, and personalized decision-support systems aimed at improving diabetes management. Full article

Imaging in controlled agriculture helps maximize plant growth by saving labor and optimizing resources. By monitoring specific plant traits, growers can prevent crop losses by correcting environmental conditions that lead to physiological disorders like leaf tipburn. This study aimed to identify morphometric and spectral markers for the early detection of tipburn in two Romaine lettuce (Lactuca sativa) cultivars (‘Chicarita’ and ‘Dragoon’) using an image-based system with color and multispectral cameras. By monitoring tipburn in treatments using melatonin, lettuce cultivars, and with and without supplemental lighting, we enhanced our system’s accuracy for high-resolution tipburn symptom identification. Canopy geometrical features varied between cultivars, with the more susceptible cultivar exhibiting higher compactness and extent values across time, regardless of lighting conditions. These traits were further used to compare simple linear, logistic, least absolute shrinkage and selection operator (LASSO) regression, and random forest models for predicting leaf fresh and dry weight. Random forest regression outperformed simpler models, reducing the percentage error for leaf fresh weight from ~34% (LASSO) to ~13% (RMSE: 34.14 g to 17.32 g). For leaf dry weight, the percentage error decreased from ~20% to ~12%, with an explained variance increase to 94%. Vegetation indices exhibited cultivar-specific responses to supplemental lighting. ‘Dragoon’ consistently had higher red-edge chlorophyll index (CIrededge), enhanced vegetation index, and normalized difference vegetation index values than ‘Chicarita’. Additionally, ‘Dragoon’ showed a distinct temporal trend in the photochemical reflectance index, which increased under supplemental lighting. This study highlights the potential of morphometric and spectral traits for early detection of tipburn susceptibility, optimizing cultivar-specific environmental management, and improving the accuracy of predictive modeling strategies. Full article