Search Results (491)

Carbon sequestration and oxygen release (CSOR) are core regulating functions of terrestrial ecosystems. However, regional assessments often fail to (i) separate scale-driven high supply from per-area efficiency, (ii) detect structural instability and degradation risk from long-term trajectories, and (iii) provide evidence that is comparable across units for management prioritization. Using Minnesota, USA, we integrated satellite-derived net primary productivity (NPP; 1998–2021) with a Quantity–Intensity–Structure (Q–I–S) framework to quantify CSOR, detect trends and change points (Mann–Kendall and Pettitt tests), map spatial clustering and degradation risk (Exploratory Spatial Data Analysis, ESDA), and attribute natural and human drivers (principal component regression and GeoDetector). CSOR increased overall from 1998 to 2021, with a marked shift around 2013 from a slight, variable decline to sustained recovery. Spatially, CSOR showed a persistent north–south gradient, with higher and improving services in northern Minnesota and lower, more degraded services in the south; persistent degradation was concentrated in a central high-risk belt. The Q–I–S framework also revealed inconsistencies between total supply and condition, identifying high-supply yet degrading areas and low-supply areas with recovery potential that are not evident from the totals alone. Climate variables primarily controlled CSOR quantity and structure, whereas human factors more strongly influenced intensity; the interactions of the two further shaped observed patterns. These results provide an interpretable and transferable basis for diagnosing degradation and prioritizing restoration under long-term environmental change. Full article

Accurate, subject-specific estimation of cervical muscle forces is a critical prerequisite for advancing spinal biomechanics and clinical diagnostics. However, this task remains challenging due to substantial inter-individual anatomical variability and the invasiveness of direct measurement techniques. In this study, we propose a novel data-driven biomechanical framework that addresses these limitations by integrating massive-scale personalized musculoskeletal simulations with an efficient Feedforward Neural Network (FNN) model. We generated an unprecedented dataset comprising one million personalized OpenSim cervical models, systematically varying key anthropometric parameters (neck length, shoulder width, head mass) to robustly capture human morphological diversity. A random subset was selected for inverse dynamics simulations to establish a comprehensive, physics-based training dataset. Subsequently, an FNN was trained to learn a robust, nonlinear mapping from non-invasive kinematic and anthropometric inputs to the forces of 72 cervical muscles. The model’s accuracy was validated on a test set, achieving a coefficient of determination (R²) exceeding 0.95 for all 72 muscle forces. This approach effectively transforms a computationally intensive biomechanical problem into a rapid tool. Additionally, the framework incorporates a functional assessment module that evaluates motion deficits by comparing observed head trajectories against a simulated idealized motion envelope. Validation using data from a healthy subject and a patient with restricted mobility demonstrated the framework’s ability to accurately track muscle force trends and precisely identify regions of functional limitations. This methodology offers a scalable and clinically translatable solution for personalized cervical muscle evaluation, supporting targeted rehabilitation and injury risk assessment based on readily obtainable sensor data. Full article

Electric vehicles (EVs) rely on a battery pack as their primary energy source, making it a critical component for their operation. To guarantee safe and correct functioning, a Battery Management System (BMS) is employed, which uses variables such as State of Charge (SOC) to set charge/discharge limits and to monitor pack health. In this article, we propose a Multilayer Perceptron (MLP) network to estimate the SOC of a 14.8 V battery pack installed in a robotic vacuum cleaner. Both offline and online (real-time) tests were conducted under continuous load and with rest intervals. The MLP’s output is compared against two commonly used approaches: NARX (Nonlinear Autoregressive Exogenous) and CNN (Convolutional Neural Network). Performance is evaluated via statistical metrics, Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE), and we also assess computational cost using Operational Intensity. Finally, we map these results onto a Roofline Model to predict how the MLP would perform on an automotive-grade microcontroller unit (MCU). A generalization analysis is performed using Transfer Learning and optimization using MLP–Kalman. The best performers are the MLP–Kalman network, which achieved an RMSE of approximately 13% relative to the true SOC, and NARX, which achieved approximately 12%. The computational cost of both is very close, making it particularly suitable for use in BMS. Full article

Currently, wireless sensor networks (WSNs) have been mutually applied to environmental monitoring and industrial control due to their low-cost and low-energy sensor nodes. However, WSNs are composed of a large number of energy-limited sensor nodes, which requires balancing the relationship among energy consumption, transmission delay, and network lifetime simultaneously to avoid the formation of energy holes. In nature, gregarious herbivores, such as the white-bearded wildebeest on the African savanna, employ a “fast-transit and selective-dwell” strategy when searching for water; they cross low-value regions quickly and prolong their stay in nutrient-rich pastures, thereby minimizing energy cost while maximizing nutrient gain. Ants, meanwhile, dynamically evaluate the “energy-to-reward” ratio of a path through pheromone concentration and its evaporation rate, achieving globally optimal foraging. Inspired by these two complementary biological mechanisms, our study proposes a novel ACO-conceptualized optimization model formulated via mixedinteger linear programming (MILP). By mapping the pheromone intensity and evaporation rate into the MILP energy constraints and cost functions, the model integrates discrete decision-making (path selection) and continuous variables (dwell time) by dynamic path planning and energy optimization of mobile sink, constituting multi-objective optimization. Firstly, we can achieve flexible trade-offs between multiple objectives such as data transmission delay and energy consumption balance through adjustable weight coefficients of the MILP model. Secondly, the method transforms complex path planning and scheduling problems into deterministic optimization models with theoretical global optimality guarantees. Finally, experimental results show that the model can effectively optimize network performance, significantly improve energy efficiency, while ensuring real-time performance and extended network lifetime. Full article

The article analyses the direction and intensity of changes occurring in agriculture in mountain areas in Europe between 2000 and 2022. For the calculations, the ESA CCI Land Cover global land-use map set was used. This dataset was established by the European Space Agency (ESA) through the classification of satellite images from sources (MERIS, AVHRR, SPOT, PROBA, and Sentinel-3). In the next step, the organisational features and economic performance of farms located in mountain areas of the European Union were determined for the period 2004–2022. For this purpose, data from the European Farms Accountancy Data Network (FADN-FSDN) were used. Subsequently, using Poland as a case study, the capacity of mountain agriculture to implement key environmental interventions under the Common Agricultural Policy (CAP) 2023–2027 was assessed. The results highlight the varying directions and intensity of organisational changes occurring in mountain agriculture across Europe. They also show that farms can operate successfully in these areas, although their economic situation varies between EU countries. The findings indicate the need for further adaptation of CAP instruments to better reflect the ecological and economic conditions of mountain areas. Strengthening support mechanisms for these regions within the current and future CAP is of crucial importance for protecting biodiversity, promoting sustainable land use, and maintaining the socio-environmental functions of rural mountain landscapes. Our study highlights that the CAP for mountain farms should be targeted, long-term, and compensatory, so as to compensate for the naturally unfavorable farming conditions and support their multifunctional role. The most important assumptions of CAP for mountain farms are a fair system of compensatory payments (LFA/ANCs), support for local and high-quality production, income diversification, and investments adapted to mountain conditions. Full article

Tea’s chemical composition is influenced by cultivar, harvest maturity, and growing environment; however, processing remains the dominant factor shaping final quality. Despite the diversity of Taiwanese native teas, systematic comparisons of functional components across multiple manufacturing stages remain limited. In this study, nine representative Taiwanese teas were evaluated at four key processing stages—green tea (G), enzymatic fermentation (oxidative fermentation, F), semi-finished tea prior to roasting (S), and completed tea (C)—to clarify how enzymatic oxidation, rolling, and roasting alter major bioactive constituents. Green-tea-stage samples exhibited clear cultivar-dependent profiles: large-leaf cultivars contained higher catechins and gallic acid, whereas bud-rich small-leaf teas showed elevated caffeine and amino acids, with amino acids further enhanced at higher elevations. Fermentation intensity governed the major chemical transitions, including catechin depletion, gallic acid formation, accumulation of early stage catechin-derived paired oxidative polymerization compounds (POPCs), and pronounced increases in theasinensins in heavily fermented teas. L-theanine decreased most markedly in teas subjected to prolonged withering. Roasting further reduced amino acids but had minimal influence on caffeine, while rolling effects varied by tea type. Overall, this study provides the first stage-resolved chemical map of Taiwanese native teas, offering practical insights for optimizing processing strategies to enhance functional phytochemical profiles. Full article

The increasing complexity and digitization of scientific research require Electronic Laboratory Notebooks (ELNs) that are adaptable, sustainable, and compliant across heterogeneous laboratory environments. In response to the limitations of proprietary, inflexible, and cost-intensive ELN solutions, this study systematically derives comprehensive requirements and proposes a modular solution concept for self-configurable ELNs that is explicitly platform-agnostic and broadly accessible. The methodological approach combines a structured requirements analysis with a modular architectural design, followed by theoretical validation through stepwise implementation walkthroughs on Microsoft SharePoint and Google Workspace. These walkthroughs demonstrate the feasibility of deploying self-configurable ELN modules using widely available low-code/no-code tools and native platform extensibility mechanisms. Based on a rigorous literature-driven analysis, key requirements, including modularity, usability, regulatory compliance, interoperability, scalability, auditability, and cost efficiency, are explicitly mapped to concrete architectural features within the proposed framework. The results show that essential ELN functionalities can, in principle, be realized across diverse digital platforms, enabling researchers and local administrators to independently assemble, configure, and adapt ELNs to their specific operational and regulatory contexts. Beyond technical feasibility, the proposed approach fundamentally democratizes ELN deployment and substantially mitigates vendor lock-in by leveraging existing digital infrastructures. Identified limitations, particularly with respect to advanced workflow orchestration and real-time data integration, delineate clear directions for future development. Overall, this work provides a systematic theoretical validation of a modular, self-configurable ELN concept, establishing it as a robust, scalable, and future-ready foundation for digital laboratory infrastructures. Full article

By regulating gene expression to maintain homeostasis and enabling rapid responses to environmental change, epigenetic mechanisms can provide valuable insights into how populations respond to external pressures. Here, we examined genome-wide DNA methylation in natural populations of the mayfly Andesiops torrens from a semi-arid watershed of northern Chile exposed to intense climatic and anthropogenic stress. We analyzed 285 individuals from 30 sites using methylRAD sequencing and assembled a draft reference genome to map methylated loci and determine their associated gene functions. Discriminant analyses of principal components revealed a methylation structure among sampling sites, identifying five groups, and the coexistence within localities of individuals with distinct methylation profiles. Non-CpG methylRAD loci accounted for most methylation divergence, consistent with environmental effects. The five groups shared a broad functional spectrum dominated by regulatory processes related to cellular processes, gene regulation, morphogenesis, neurogenesis, and metabolism, and formed a continuum from core cellular regulation in small groups to more integrated developmental and adaptive stress-related control in larger groups. While the drivers of these patterns remain to be clarified, our study suggests that DNA methylation contributes to local responses in A. torrens and also reveals the potential of DNA methylation analyses as an initial approach for exploring ecological pressures in natural populations. Full article

Microbial fermentation stands as the foundational technology in modern biorefineries, yet its industrial scalability is critically constrained by product inhibition, prohibitive downstream separation costs, and substrate inhibition. Metal–organic frameworks (MOFs) offer a tunable material platform to address these challenges through rational design of pore size, shape, and chemical functionality. This review systematically chronicles the evolution of MOF applications in biomass fermentation across four generations, demonstrating a synergistic mapping where the core fermentation challenges—product toxicity, substrate toxicity, and separation energy intensity—align with the inherent MOF advantages of high adsorption capacity, programmable selectivity, and tunable functionality. The applications progress from first-generation passive adsorbents for in situ product removal, to second-generation protective agents for mitigating inhibitors, and third-generation immobilization scaffolds enabling continuous processing. The fourth-generation systems transcend passive scaffolding to position MOFs as active metabolic partners in microbe-MOF hybrids, driving cofactor regeneration and tandem biocatalysis. By synthesizing diverse research streams, ranging from defect engineering to artificial symbiosis, including defect engineering strategies, this review establishes critical design principles for the rational integration of programmable materials in next-generation biorefineries. Full article

This study compared CO₂ emissions during a WLTP (Worldwide Harmonized Light-Duty Vehicles Test Procedure) test performed on a chassis dynamometer for the same flex-fuel vehicle, fuelled sequentially with E10 gasoline and E85 fuel. Based on the test data, a CO₂ emissions map was created, describing its dependence on speed and acceleration. The use of a 3D surface enabled the visualisation of the whole dynamics of emissions as a function of engine load in the WLTP cycle, including the identification of distinct emission peaks in areas of high positive acceleration. Analysis of the emission surface enabled the identification of structural differences between the fuels. For E85, more pronounced emission increases are observed in areas of intense acceleration, a consequence of the higher fuel demand resulting from the lower calorific value of bioethanol. In steady-state and moderate-load driving, CO₂ emissions for both fuels are similar. The results confirm that the main differences between E10 and E85 are not simply a shift in emission levels per se, but stem from variations in engine load during the dynamic cycle. Although E85 emits measurable CO₂ emissions, its carbon is not of fossil origin, highlighting the importance of biofuels in the context of greenhouse gas emission reduction strategies and the pursuit of climate neutrality. The presented methodology, combining chassis dynamometer tests with analysis of the speed-acceleration emission map, provides a tool for clearly identifying emission zones and can serve as a basis for further optimisation of engine control strategies and assessing the impact of fuel composition on emissions under dynamic conditions. Full article

Urban river floodplains function not only as zones for flood regulation and ecological buffering but have increasingly been utilized as multifunctional spaces that support leisure, waterfront, and cultural activities. However, overlapping hydraulic and geomorphic factors such as channel meandering, vegetation distribution, and flood-induced flow redistribution have amplified environmental risks, including recurrent erosion deposition, vegetation disturbance, and infrastructure damage, yet quantitative assessment frameworks remain limited. This study systematically evaluates the environmental safety of an urban floodplain by estimating vegetation variability using Sentinel-2 derived NDVI time series and deriving SEDI and TEDI through FaSTMECH two-dimensional hydraulic modeling. NDVI response cases were identified for different rainfall intensities, and interpolation-based hazard maps were generated using spatial cross-validation. Results show that the left bank exhibits higher vegetation variability, indicating strong sensitivity to hydrological fluctuations, while outer meander bends repeatedly display elevated SEDI and TEDI values, revealing concentrated structural vulnerability. Integrated analyses across rainfall conditions indicate that overall safety remains high; however, low-safety zones expand in the upstream meander and several outer bends as rainfall intensity increases. Full article

This paper introduces a novel framework for sensory representation of brain imaging data, combining deep learning-based segmentation with multimodal visual and auditory outputs. Structural magnetic resonance imaging (MRI) predictions are converted into color-coded maps and stereophonic/MIDI sonifications, enabling intuitive interpretation of cortical activation patterns. High-precision U-Net models efficiently generate these outputs, supporting clinical decision-making, cognitive research, and creative applications. Spatial, intensity, and anomalous features are encoded into perceivable visual and auditory cues, facilitating early detection and introducing the concept of “auditory biomarkers” for potential pathological identification. Despite current limitations, including dataset size, absence of clinical validation, and heuristic-based sonification, the pipeline demonstrates technical feasibility and robustness. Future work will focus on clinical user studies, the application of functional MRI (fMRI) time-series for dynamic sonification, and the integration of real-time emotional feedback in cinematic contexts. This multisensory approach offers a promising avenue for enhancing the interpretability of complex neuroimaging data across medical, research, and artistic domains. Full article

Iceberg detection in Arctic sea-ice environments is essential for navigation safety and climate monitoring, yet remains challenging due to observational and environmental constraints. The scarcity of labelled data, limited optical coverage caused by cloud and polar night conditions, and the small, irregular signatures of icebergs in synthetic aperture radar (SAR) imagery make automated detection difficult. This study evaluates the environmental feasibility of applying a modern deep learning model for iceberg detection within land-fast sea ice. We adapt a YOLOv8 convolutional neural network within the Dual Polarisation Intensity Ratio Anomaly Detector (iDPolRAD) framework using dual-polarised Sentinel-1 SAR imagery from the Franz Josef Land region, validated against Sentinel-2 optical data. A total of 2344 icebergs were manually labelled to generate the training dataset. Results demonstrate that the network is capable of detecting icebergs embedded in fast ice with promising precision under highly constrained data conditions (precision = 0.81; recall = 0.68; F1 = 0.74; mAP = 0.78). These findings indicate that deep learning can function effectively within the physical and observational limitations of current Arctic monitoring, establishing a foundation for future large-scale applications once broader datasets become available. Full article

With the rapid development of the automotive industry, terminals—as critical components of wiring harnesses—play a pivotal role in ensuring the reliability and stability of signal transmission. At present, terminal crimping quality inspection (TCQI) primarily relies on manual visual examination, which suffers from low efficiency, high labor intensity, and susceptibility to missed detections. To address these challenges, this study proposes an improved YOLOv5-based model, TCQI-YOLOv5, designed to achieve efficient and accurate automatic detection of terminal crimping quality. In the feature extraction module, the model integrates the C2f structure, FasterNet module, and Efficient Multi-scale Attention (EMA) attention mechanism, enhancing its capability to identify small targets and subtle defects. Moreover, the SIOU loss function is employed to replace the traditional IOU, thereby improving the localization accuracy of predicted bounding boxes. Experimental results demonstrate that TCQI-YOLOv5 significantly improves recognition ccuracy for difficult-to-detect defects such as shallow insulation crimps, achieving a mean average precision (mAP) of 98.3%, outperforming comparative models. Furthermore, the detection speed meets the requirements of real-time industrial applications, indicating strong potential for practical deployment. Full article

Reducing reliance on private vehicles, optimizing public spaces, and adopting low-carbon, energy-efficient practices are essential strategies for advancing sustainable urban development. This study investigates user perceptions and spatial experiences at Hakata Station in Fukuoka, Japan, by analyzing online reviews collected over 1 year. The results indicate that: (1) Using TF–IDF vectorization and K-means clustering (K = 5), five major semantic themes were identified, and a chi-square test (χ²(16) = 632.00, p < 0.001) confirmed their strong correspondence with the station’s five functional zones. This revealed a cognitive mapping effect between users’ semantic structures and spatial functions. (2) Six environmental psychology indicators—Wayfinding Usability, Crowding Density, Seating and Rest Availability, Functional Convenience, Environmental Quality, and Information Legibility—were established. Logistic regression showed that only Functional Convenience significantly predicted positive sentiment (OR = 31.6, p = 0.05), underscoring the emotional influence of smooth circulation and well-integrated commercial facilities. (3) Process-intensive areas exhibited emotional accumulation and cognitive strain, while restorative zones reduced mental fatigue; moderate spatial concealment enhanced exploration, and a shared social atmosphere fostered belongingness. The findings elucidate the psychological correspondence between semantic structures and spatial functions, providing user-centered indicators for urban node design that promote comfort, accessibility, and urban sustainability. Full article