Search Results (6,553)

In 2024 and 2025, researchers investigated the breeding ecology of the Twite Linaria flavirostris in riparian shrubland habitats at an elevation of 3400 m in the northeastern Qinghai–Tibet Plateau. This species lays eggs from late June to mid-July, capitalizing on the region’s brief warm season. Nests are typically open-cup structures built in Hippophae spp. shrubs. The population predominantly exhibits monogamous mating, with a mean clutch size of 4.7 ± 0.49 (3~5). Incubation is performed solely by the female and lasts 11.52 ± 1.65 days. Both parents provision the nestlings, and the nestling period lasts 12.43 ± 2.39 days. Morphological measurements of nestling body mass and external organs all fit well to the Logistic growth curve equation. By fledging, tarsus length and bill length reach over 90% of adult values, conferring substantial terrestrial mobility. However, flight-related feathers, primaries and rectrices, remain markedly underdeveloped compared to adults, resulting in extremely poor flight capability; further post-fledging development is thus required. Based on reproductive outcomes from this single breeding season, a total of 121 eggs were laid, of which 81 successfully hatched, and ultimately 79 fledglings survived to leave the nest. The overall hatching success was 66.94%, fledging success (among hatchlings) was 97.53%, and overall offspring survival (from eggs to fledglings) was 65.29%. The apparent nesting success rate was 76.0%, based on a total of 50 nests monitored over two years. Daily nest survival rates were estimated using Mayfield’s method and program MARK, resulting in nest success probabilities of 0.587 and 0.219, respectively. Comparing populations across different geographic regions, the results indicate that Twites breeding in environments with higher levels of environmental stress produce smaller clutch sizes and larger eggs, and exhibit a prolonged nestling period. This life-history strategy likely represents an evolutionary adaptation to spatially variable environmental conditions. Full article

Riverine systems in tropical deltaic environments are increasingly exposed to hydrological variability driven by climate change, sea level rise, and extreme precipitation. In Nigeria’s Niger Delta, recurrent flooding and environmental degradation are intensifying pressures on freshwater ecosystems and dependent communities. This study examines hydrological stressors in riverine settlements of Bayelsa State and explores associated socio-ecological responses. Using an exploratory qualitative design, data were collected from 51 women residing in highly vulnerable riverine communities through 24 in-depth interviews and three focus group discussions. Thematic analysis identified prolonged flooding, riverbank erosion, salinity intrusion, water quality deterioration, and oil pollution, as key drivers of declining fisheries, reduced agricultural productivity, and household water insecurity. These stressors have prompted relocation, livelihood diversification, and reliance on indigenous adaptation practices. The study recommends: (1) installation of community-based flood early warning systems; (2) routine monitoring of surface water quality and salinity; (3) enforcement of oil spill remediation and pollution control measures; (4) rehabilitation of wetlands and natural drainage channels; and (5) targeted support for climate-resilient livelihoods such as aquaculture and elevated farming systems. These measures are critical for sustaining freshwater ecosystems and strengthening resilience in vulnerable deltaic communities. Full article

Continuous monitoring of livestock vocalizations offers a non-invasive tool for welfare assessment, but deploying current deep learning models in resource-constrained farm environments remains challenging due to high computational demands. This study proposes a feature-based machine learning pipeline optimized for edge computing to classify caprine vocalizations. Using the VOCAPRA dataset, which comprises 4147 labeled caprine vocalizations categorized into eight distinct welfare states and contexts, a hybrid feature extraction framework was applied to derive 156 spectral, temporal, and bioacoustic descriptors. Dimensionality reduction and a comprehensive comparative screening of 18 algorithms identified the CatBoost Classifier and a Multilayer Perceptron (MLP) as the optimal models. The CatBoost ensemble achieved a robust accuracy of 85.2%, while the optimized MLP reached 87.2% overall accuracy. An edge deployment benchmark revealed that the MLP was the best candidate with for real-time application, featuring a memory footprint of just 0.639 MB and near-instantaneous inference speeds of under 0.005 milliseconds per sample. Furthermore, feature importance and SHAP analyses revealed that mel-frequency cepstral coefficients heavily drove model decisions, particularly for identifying extreme physical distress and maternal reunion. The proposed methodology achieves competitive classification performance while dramatically reducing pre-processing and computational loads compared to image-based deep learning approaches, demonstrating the viability of lightweight-model, energy-efficient, real-time bioacoustic monitoring for precision livestock farming. Full article

Urban areas in subtropical regions are increasingly exposed to heat stress as climate change intensifies extreme heat events. In high-density cities, urban renewal is widely implemented to upgrade aging building stock, yet its potential role as a passive heat adaptation strategy remains insufficiently understood, particularly for projects below environmental impact assessment thresholds. This study examines how urban renewal influences neighborhood-scale microclimates through a comparative analysis of six residential renewal cases using computational fluid dynamics (CFD) simulations. Pre- and post-renewal scenarios are evaluated to assess changes in wind environment and thermal conditions, with a particular focus on the spatial extent and distance–decay characteristics of renewal-induced effects. The results reveal a consistent distance–decay pattern of microclimate responses across all cases. The influence of urban renewal is strongest within 0–50 m, remains detectable up to approximately 100 m, and diminishes substantially beyond 100–150 m, indicating a clear neighborhood-scale impact radius. Ventilation performance improves systematically following renewal, while thermal responses are more heterogeneous. Localized cooling of up to 1.5 °C is observed in selected cases, whereas others exhibit negligible temperature change despite enhanced airflow. These findings demonstrate that improved ventilation alone does not guarantee thermal mitigation. Instead, thermal outcomes depend on the interaction between airflow, solar exposure, and surface thermal properties. Urban renewal can therefore function as a form of passive heat adaptation when morphological changes are coordinated with shading and surface design strategies. By quantifying the spatial limits of renewal-induced microclimate effects, this study provides empirical evidence for integrating microclimate considerations into neighborhood-scale planning. The identified influence radius offers a practical reference for climate-responsive urban renewal, particularly in high-density subtropical cities where incremental redevelopment plays a dominant role. Full article

Reliable and timely estimation of air pollution exposure at high spatial and temporal resolution remains challenging in complex urban environments, where pollutant concentrations vary due to traffic emissions, urban morphology, and meteorological conditions. This study presents a physics-informed machine learning framework for near-real-time estimation of NO₂ concentrations at fine spatial scales. The approach combines a limited set of steady-state computational fluid dynamics (CFD) simulations with operational meteorological and air-quality data. CFD simulations under specific wind directions are first used to characterize site-specific dispersion patterns. These outputs are then scaled using hourly meteorological observations to generate physics-based concentration descriptors. A machine learning predictor, implemented using Random Forest and Extreme Gradient Boosting, is trained to refine these estimates by incorporating additional environmental and observational features. The method is applied to a 1 km × 1 km urban district in Antwerp, Belgium, within the FAIRMODE intercomparison framework. Validation against measurements from 105 passive samples collected over one month shows substantial improvement compared to standalone dispersion modeling, with coefficients of determination up to R² = 0.965 and reduced bias across locations. These findings demonstrate that integrating physical modeling with machine learning enables accurate and computationally efficient high-resolution exposure assessment in urban settings. Full article

Background/Objectives: High-altitude exposure represents a complex psychophysiological stressor involving hypoxia, physical effort, sleep disruption and psychological strain. The endocannabinoid system (ECS) plays a key role in stress regulation, yet its epigenetic modulation under extreme environmental conditions remains poorly characterized. This pilot and exploratory study investigated DNA methylation and descriptive microRNA (miRNA) expression patterns of CNR1 and FAAH genes, and their associations with mood and anxiety outcomes, in trekkers exposed to Himalayan high altitude. Methods: Twenty-one healthy lowlanders completed a longitudinal expedition from 2860 m to 5050 m. Psychometric measures (SVARAD, BDI, SAS, SHAPS) and saliva samples were collected at baseline (T0) and at high altitude (T1). DNA methylation of CNR1 and FAAH regulatory regions was quantified by pyrosequencing. Exosomal miRNAs targeting these genes were profiled using qRT-PCR, on pooled samples; results are presented descriptively. Results: DNA methylation analysis revealed heterogeneous, sex-specific epigenetic patterns following high-altitude exposure. A significant increase in CNR1 promoter methylation at CpG4 was observed in males at T1, whereas methylation remained largely stable in females. Descriptive miRNA expression data showed bidirectional differences between groups, consistent with context-dependent stress regulation. Convergent directional patterns between miR-23b-3p expression and CNR1 methylation in males were observed. However, given the descriptive nature of the miRNA data, this observation is purely exploratory and requires replication before any mechanistic conclusions can be drawn. Psychometrically, participants showed a mild mood decline without overt clinical symptoms. Sex-specific differences in the relationship between CNR1 methylation and psychometric outcomes were observed and warrant further investigation in adequately powered cohorts. Conclusions: These preliminary findings suggest that CNR1 epigenetic regulation warrants further investigation as a potential indicator of stress adaptation and psychological responses and underscore the need to consider sex differences when evaluating resilience and vulnerability to extreme environments. Full article

Addressing the challenges of extreme sample scarcity, complex underwater optical environments, and significant variations in lesion scales in real-world aquaculture, this paper proposes a small-sample grass carp disease recognition method, namely Swin Transformer with Supervised Contrastive Learning (ST-SCL), integrating dual-stream data augmentation and supervised contrastive learning. First, a frequency-spatial dual-stream augmentation strategy is constructed. In the frequency domain, the Amplitude-Mix technique is introduced to simulate diverse lighting and turbidity styles by mixing background amplitude spectra, thereby enhancing environmental generalization. In the spatial domain, a pathology-mask-guided instance-level Copy-Paste strategy is employed to directionally expand scarce lesion samples and address data imbalance. Second, the Swin Transformer is adopted as the backbone network, leveraging its hierarchical shifted window attention mechanism to effectively capture multi-scale features, balancing the detection of tiny parasites and extensive superficial ulcerations. Finally, supervised contrastive learning is incorporated to maximize intra-class compactness and minimize inter-class separability within the feature space, effectively reducing overfitting inherent to small-sample learning. Experimental results demonstrate that the proposed method achieves a macro-average F1-score of 95.86% across six disease categories. Compared with mainstream models such as ResNet and ConvNeXt, the ST-SCL exhibits notable performance improvements and enhanced robustness in small-sample scenarios, offering a promising technical path for precise fish disease diagnosis in complex aquatic environments. Full article

Recent advances in edge computing and Tiny Machine Learning (TinyML) have enabled the deployment of artificial intelligence models directly on microcontrollers with extremely limited computational and memory resources. In this context, this work presents the design, implementation, and validation of a real-time cardiac arrhythmia classification system based on a quantized one-dimensional convolutional neural network (1D-CNN), deployed on an 8-bit Arduino UNO microcontroller. The proposed system integrates end-to-end processing, including ECG signal acquisition using a low-cost AD8232 analog front-end, signal preprocessing, heartbeat segmentation, classification, and real-time visualization on an OLED display. The model was trained and evaluated using the MIT-BIH Arrhythmia Database, considering a reduced three-class problem (Normal, Ventricular, and Supraventricular) to meet the constraints of ultra-low-cost hardware deployment. Under benchmark conditions, the quantized model achieved an accuracy of 97.6%, with a memory footprint below 24 KB and an average inference time of 200 ms per heartbeat, enabling real-time operation on a resource-constrained microcontroller. Real-time experiments were conducted using signals acquired from healthy volunteers to validate system functionality, although no annotated ground truth was available for these recordings, and therefore no diagnostic performance was derived from them. The results demonstrate the feasibility of deploying lightweight deep learning models on ultra-constrained embedded systems using the TinyML paradigm, implemented using TensorFlow 2.15 and TensorFlow Lite. This work should be interpreted as a proof-of-concept platform that highlights the trade-off between classification performance and hardware limitations, providing a foundation for future development of low-cost cardiac monitoring technologies in resource-limited environments. Full article

The durability and serviceability of steel-fiber-reinforced concrete (SFRC) tunnel linings in cold regions are significantly challenged by repeated freeze–thaw actions, making the accurate prediction of frost resistance a critical engineering problem. Although extensive research has been conducted on the freeze–thaw characteristics of concrete, the existing empirical and mechanism-based models remain limited in capturing the complex nonlinear interactions among mixture proportions, steel fiber characteristics, and environmental conditions. Therefore, a data-driven prediction framework based on machine learning was developed in this study. A database containing 277 groups of standardized SFRC freeze–thaw test results was established, incorporating key variables including mixture design parameters, fiber properties, and freeze–thaw cycle conditions. Four machine-learning models, namely, support vector regression, back-propagation neural network, gradient boosting, and extreme gradient boosting (XGB), were constructed and systematically compared. Model accuracy was assessed using MAE, MAPE, MSE, RMSE, and R². The results demonstrate that all models can reflect the nonlinear relationship between the input variables and mass loss rate, while the XGB model exhibits superior predictive performance with a testing R² of 0.91, representing an improvement of approximately 3–28% compared with other models. Meanwhile, the prediction errors are reduced significantly, with RMSE and MAE decreased by about 19–58% and 22–65%, respectively. The proposed approach provides an improved and reliable tool for predicting frost resistance and supports the durability design and optimization of SFRC tunnel linings in severe cold-region environments. Full article

Threat modelling is a methodology employed for identifying and analysing threats and applicable mitigations for web applications, mobile applications, infrastructure, and environments including smart home environments. Threat modelling starts with a tabletop exercise to identify threats. It provides extremely important insights into what can go wrong if certain events or a series of events take place. The identification of these events is critical to ensuring the right mitigation strategies are applied. Threat modelling also helps to identify security controls that may be assumed to provide required security, but, in reality, may not be addressing the existing and applicable threat(s). Existing literature, in the public domain and in academia, discusses threat materialisation for smart homes; however, entry points for a threat to materialise and exploit these vulnerabilities are not explored and a dedicated threat model for smart home environments is currently unavailable. Whilst threats can be mitigated by smart home device manufacturers, there are also mitigations that need to be applied by smart home owners who are both technology-aware and technology-unaware. In this paper, we propose a structured, domain-specific threat modelling methodology for smart home environments. The methodology models threats from a smart home owner’s perspective, identifies entry points and the mitigations that need to be implemented by a smart home owner. It also acknowledges that the attack surface expands and contracts and is not constant; which is addressed by applying zero-trust principles. Full article

Aerobic exercise capacity, best quantified by maximal oxygen uptake (VO_2max), varies between individuals and is dependent on cardiac output (CO) and oxygen uptake in the periphery (a-vO₂ diff). Environmental stressors like hypoxia, microgravity, and heat negatively impact these parameters, thereby reducing aerobic exercise capacity. However, in response to acute and chronic exposures to these environments, compensatory processes serve to counteract reductions in VO_2max. In hypoxic environments, reduced oxygen partial pressure (PO₂) leads to hypoxic pulmonary vasoconstriction (HPV) and a diffusion limitation at the level of the lungs and skeletal muscle, resulting in a reduction in VO_2max. Microgravity environments reduce VO_2max through cardiac and skeletal muscle deconditioning, as well as reductions in plasma volume (PV), resulting in an increase in sympathetic nerve activity through baroreceptor-mediated pathways. In heat stress environments, increases in skin perfusion upon acute exposure hinder exercise performance, whereas compensatory PV expansion mitigates further decreases in VO_2max. As humans are increasingly exposed to austere environments and environmental extremes, it is critical to understand how these environments impact cardiovascular exercise physiology so that effective strategies and protocols ensuring proper aerobic functioning may be implemented. Full article

Exposure to high-altitude hypoxia leads to complex physiological and molecular adaptations, particularly in skeletal muscle. MicroRNAs (miRNAs), including muscle-enriched (myomiRNAs) and hypoxia-responsive (hypoxamiRNAs), play critical roles in regulating these responses. We investigated miRNA expression changes in the skeletal muscle of healthy, non-smoking Italian adults (mean age 36.7 ± 12.4 years) participating in the Himalayan expedition “Lobuche Peak—Pyramid Exploration & Physiology” conducted in the Sagaramāthā (Mount Everest) National Park, Nepal. The peak overnight stay altitude was ≈5000 m at the Pyramid International Laboratory—Observatory. Muscle biopsies were taken before and after the expedition from Vastus lateralis, at one-third of the distance from the upper margin of the rotula to the anterior superior iliac spine. Small RNA sequencing was used to profile differentially expressed miRNAs. Several miRNAs were differentially expressed (exploratory analysis), suggesting potential involvement in hypoxia-related adaptation. These encompass both canonical myomiRNAs (e.g., miR-206, miR-486-5p) and hypoxamiRNAs (e.g., miR-378a-5p, miR-199a-3p, let-7b-5p). In enrichment analysis, we found several connections between miRNAs and pathways that may play a role in physiological regeneration or differentiation in muscle cells. Among functions, focal adhesion (p-value = 0.001), regulation of actin cytoskeleton (p-value = 0.026), Rap-1 (p-value = 0.007), cAMP (p-value = 0.017), MAPK (p-value = 0.019), and Hippo (p-value = <0.001) signaling pathways were predicted to be the most targeted. These findings provide preliminary insights into physiological adaptation, requiring confirmation in larger and controlled cohorts. Full article

Extreme rainfall poses an increasing threat to mountainous transportation systems by frequently triggering landslides along road corridors. Most existing studies focus on long-term landslide susceptibility, whereas event-scale assessments remain limited, particularly in road environments. This study develops an event-scale framework for assessing landslide risk along mountain roads under extreme rainfall conditions, using the July 2023 “23·7” rainfall event in Mentougou District, Beijing, as a case study. A Random Forest model was constructed by integrating multi-source geospatial data with an event-specific inventory of 8930 landslides. The model achieved high predictive performance, with ROC–AUC values of 0.9187 and 0.9166 for the validation and test datasets, respectively. Feature importance analysis further indicates that landslide occurrence is controlled by the combined effects of rainfall, terrain conditions, vegetation cover, and anthropogenic disturbance, with rainfall acting as the primary trigger. High-risk road segments are mainly concentrated in the southeastern part of the study area, showing clear spatial clustering. These results highlight the value of event-scale analysis and demonstrate the effectiveness of the road-oriented framework for identifying hazardous segments under extreme rainfall conditions. The proposed approach provides practical support for landslide monitoring, risk mitigation, and resilient management of mountainous transportation infrastructure. Full article

Securing Industrial IoT (IIoT) network environments remains a significant challenge due to the increasing complexity of interconnected sensors, actuators, gateways, and control systems, which are frequent targets of cyberattacks. These threats can lead to operational disruptions, financial losses, and safety risks. This paper proposes an efficient multi-stage intrusion detection framework based on an enhanced Extreme Gradient Boosting (XGBoost) model for IIoT environments. The proposed framework integrates data preprocessing, class imbalance handling, hyperparameter optimization, probability calibration, and class-specific decision thresholds within a unified pipeline. In addition, calibrated probability outputs are utilized as continuous indicators of prediction confidence, enabling more reliable and risk-aware decision-making. The hierarchical multi-stage design decomposes the detection task into progressively refined classification levels, improving discrimination among complex and overlapping attack categories. The framework is evaluated using the Edge-IIoTset benchmark dataset, which reflects realistic IIoT network traffic under both normal and malicious conditions. Experimental results demonstrate that the proposed approach achieved significant performance improvements, including up to 21% increase in recall and 15% improvement in macro F1 score compared to the baseline models. Furthermore, the model exhibits low inference latency and supports efficient deployment in time-sensitive IIoT monitoring scenarios. These results indicate that the proposed framework provides an effective and scalable solution for multi-class cyber threat detection in IIoT networks. Full article

Rapid urbanization and complex topography complicate Urban Heat Island (UHI) spatio-temporal dynamics. Traditional models and coarse-resolution imagery often fail to capture fine-scale, spatially non-stationary seasonal driving mechanisms. This study investigates the multi-dimensional drivers of surface thermal dynamics in Kunming, a typical low-latitude plateau city, using seasonal median LST composite (2018–2025). Integrating eXtreme Gradient Boosting (XGBoost) with eXplainable Artificial Intelligence (XAI) models decoupled the nonlinear impacts of these drivers. Results reveal a seasonal thermal dichotomy: Summer exhibits the most intense UHI effect with extreme peak temperatures, while Spring presents an anomaly where natural and vegetated Local Climate Zones (LCZs) show pronounced warming. SHapley Additive exPlanations (SHAP) analysis identified a seasonal rotation: anthropogenic and structural factors dominate Summer and Autumn warming, whereas natural and topographic regulators govern Spring and Winter. GeoShapley deconstruction demonstrated strong spatial non-stationarity. Building-density warming is amplified in poorly ventilated urban cores, and fragmented vegetation’s cooling is offset by anthropogenic heat during peak summer. This study provides new insights into the seasonal drivers of urban thermal environments in plateau cities. Full article