Search Results (129)

Background/Objectives: Childhood metabolic dysregulation exerts a profound influence on the development of obesity and metabolic diseases. The human gut microbiota, with highly personalized characteristics, plays an important role in host metabolism. However, the dynamics of gut microbial features during this developmental phase are still unclear. This longitudinal observational study collected 204 fecal samples and 153 blood samples from 51 children (aged 8.90 ± 0.78 years) at four timepoints over 52 weeks, aiming to identify dynamic changes in individual gut microbiota and underlying mechanistic interactions that predict measures of pediatric metabolic health. Methods: Fecal samples were subjected to 16S rRNA gene amplicon sequencing and short-chain fatty acid quantification. Serum samples were analyzed for biochemical tests. Dietary intake, physical activity, clinical phenotypes, early-life factors, and fecal characteristics were further assessed. Results: In the results, the fecal microbiota dynamics exhibit inter-individual variation among children, allowing classification into high- and low-stability subgroups based on intra-individual β-diversity variability. Children with low-stability microbiota had adverse blood lipid profiles (p < 0.05). Compared to the high-stability group, the low-stability microbiota demonstrated significant association with low dietary fiber and highly variable amino acid consumption (|r| > 0.3, q < 0.05). Low-stability microbiota exhibited marked fluctuations in Phocaeicola vulgatus, which was strongly linked to both blood triglycerides and lipoprotein(a) levels, as well as dietary fiber and amino acid intake. Baseline depletion of P. vulgatus and Faecalibacterium duncaniae, combined with the children’s physiological status, lifestyle behaviors, and early-life factors, predicted microbial stability classification (AUROC = 0.93). Conclusions: These findings suggested that the variation in the gut microbiota dynamics could be considered as a possible complementary biomarker to understand the individualized responses within dietary interventions aimed at improving metabolic health in childhood. Further well-designed intervention study is needed to define these observational associations. Full article

Accurate quantification of photosynthetically active radiation absorbed by chlorophyll (${\mathit{fAPAR}}_{\mathit{Chl}\mathit{a}}$) and the corresponding fluorescence quantum efficiency (FQE) is critical for understanding vegetation productivity. In this study, we investigate the retrieval of pigment-specific effective absorbance and Sun-Induced Chlorophyll Fluorescence (SIF) using both airborne hyperspectral imagery (HyPlant) and ground-based field spectroscopy (FloX) over a well-irrigated alfalfa field in northeastern Spain. Spectral unmixing techniques, including Constrained Least Squares (CLS), Potential Function (POT), and Bilinear (BIL) models, were applied to disentangle pigment and background contributions. The CLS approach was identified as the most robust, balancing reconstruction accuracy with physical plausibility. We derived ${\mathit{fAPAR}}_{\mathit{Chl}\mathit{a}}$ from the abundance-weighted pigment absorbance and combined it with spectrally-integrated SIF to calculate FQE. Comparisons between airborne and ground-based measurements revealed strong agreement, highlighting the potential of this combined methodology. The study demonstrates the applicability of advanced spectral unmixing frameworks for both airborne and proximal sensing data, providing a reliable baseline for photosynthetic efficiency in a healthy crop and establishing a foundation for future stress detection studies. Full article

Background: Maintaining physical activity (PA) is vital for older people, particularly those with frailty and mobility limitations. Wearable activity trackers and digital feedback tools show promise for encouraging PA, but their feasibility and acceptability in aged care remain underexplored. This study evaluated the feasibility and acceptability of using wearable and mobile devices for step tracking and examined the usability of three interfaces (Fitbit, mobile app, and website) for reviewing PA progress in aged care. Methods: This is a user experience and feasibility study that does not involve objective physical activity quantification or device performance analysis. It is a mixed-methods feasibility study conducted with 14 participants aged ≥65 years from residential and community aged care services in metropolitan and regional New South Wales, Australia. Participants used a Fitbit Inspire 3 linked to a study website and a mobile phone step-counting app to monitor their steps across the three interfaces for four weeks. Feasibility was evaluated through enrolment and retention, and acceptability through a facilitator-led survey. Quantitative items on usability, comfort, motivation and device preference were summarised descriptively; open-ended responses were analysed thematically to identify user experiences, benefits, and barriers. Results: Step tracking was feasible, with 82% enrolment and 93% retention. Participants preferred the Fitbit over the mobile phone or website due to its ease of use, visibility and more enjoyable experience. Step tracking increased awareness of PA and supported confidence to move more. Participants valued reminders, rewards and opportunities for social sharing. Reported barriers included illness, usability challenges and occasional technical issues. Conclusions: Wearable step trackers show promise for supporting PA among older people receiving aged care. Despite the small sample and short follow-up, strong acceptability signals suggest that simple digital tools could enhance the reach and sustainability of mobility-promoting interventions into aged care systems. Future studies should examine long-term adherence, usability across diverse mobility and cognitive needs, and conditions for successful scale-up. Full article

Sandy beach resilience faces growing threats from extreme events and intensified human activity. Deep Learning (DL) has emerged as a powerful tool in coastal research, offering strengths in spatial feature extraction, nonlinear sequence modeling, acceleration of physical processes, and integration of multi-source data. This review frames resilience in three technical dimensions—resistance, recovery, and adaptation—and examines DL applications across three domains: first, monitoring and forecasting external forcing, including typhoon tracks and storm surge peak values; second, modeling and simulating beach processes, from rapid hydrodynamic forecasting to medium- and long-term shoreline evolution, and high-resolution sediment transport forecasting; and third, management and decision support, where DL methods and multi-scenario generation expand governance options, and interpretable features with uncertainty quantification enhance risk communication and policy adoption. DL complements traditional models by shortening the “observation–model–decision” cycle, expanding scenario analysis, and improving governance transparency. Challenges remain in cross-domain generalization, robustness in extreme scenarios, and data governance. This review confirms DL’s potential as a technology stack for enhancing sandy beach resilience and provides a methodological foundation for future research. Full article

Urban densification intensifies the heat island effect, threatening ecological security. Green spaces, as crucial spatial elements in regulating the urban thermal environment, remain poorly understood in terms of their morphological characteristics and regulatory mechanisms, with a lack of systematic quantification and recognition of diurnal variations. This study, focusing on Shanghai’s main urban area, constructs physiological, physical, and morphological variables of green spaces based on high-resolution remote sensing data and the MSPA landscape morphology analysis framework. By integrating machine learning models with the SHAP interpretation algorithm, it analyses the influence mechanism of green spaces on Land Surface Temperature (LST) and its non-linear characteristics from the perspective of diurnal variation. The results indicate the following: (1) Green spaces exhibit pronounced diurnal variation in LST influence. Daytime cooling is primarily driven by vegetation cover, vegetation activity, and surface albedo through evapotranspiration and shading; night-time cooling depends on soil moisture and green space spatial structure and is achieved via thermal storage-radiative heat dissipation and cold air transport. (2) Green space indicators exhibit pronounced nonlinearity and threshold effects on LST. Optimal cooling efficiency occurs under moderate vegetation activity and moderate humidity conditions, whereas extreme high humidity or high vegetation activity may induce heat retention effects. (3) Day–night thermal regulation mechanisms differ markedly. Daytime cooling primarily depends on vegetation transpiration and shading to suppress surface warming; night-time cooling is dominated by soil thermal storage release, longwave radiation dissipation, and ventilation transport, enabling cold air to diffuse across the city and establishing a stable, three-dimensional nocturnal cooling effect. This study systematically reveals the distinct diurnal cooling mechanisms of high-density urban green spaces, providing theoretical support for refined urban thermal environment management. Full article

Modern semantic segmentation models require extensive pixel-level annotations, creating a significant barrier to practical deployment as labeling a single image can take hours of human effort. Active learning offers a promising way to reduce annotation costs through intelligent sample selection. However, existing methods rely on poorly calibrated confidence estimates, making uncertainty quantification unreliable. We introduce Conformal Risk Controlled Active Learning (CRC-AL), a novel framework that provides statistical guarantees on uncertainty quantification for semantic segmentation, in contrast to heuristic approaches. CRC-AL calibrates class-specific thresholds via conformal risk control, transforming softmax outputs into multi-class prediction sets with formal guarantees. From these sets, our approach derives complementary uncertainty representations: risk maps highlighting uncertain regions and class co-occurrence embeddings capturing semantic confusions. A physics-inspired selection algorithm leverages these representations with a barycenter-based distance metric that balances uncertainty and diversity. Experiments on Cityscapes and PascalVOC2012 show CRC-AL consistently outperforms baseline methods, achieving 95% of fully supervised performance with only 30% of labeled data, making semantic segmentation more practical under limited annotation budgets. Full article

In modern power systems, distributed generation (DG) clusters such as wind and solar resources are increasingly being integrated into active distribution networks through DG cluster control, which enhances the economic efficiency and adaptability of the DGs. However, cyber attacks on cyber–physical systems (CPS) may disable control links within the DG cluster, leading to the loss of control over slave DGs and resulting in power deficits, thereby threatening system stability. Existing CPS security assessment methods have limited capacity to capture cross-domain propagation effects caused by cyber attacks and lack a comprehensive evaluation framework from the attacker’s perspective. This paper establishes a CPS system model and control–communication framework and then analyzes the cyber–physical interaction characteristics under DG cluster control. A logical model of cyber attack strategies targeting DG cluster inverters is proposed. Based on the control topology and master–slave logic, a probabilistic failure model for DG cluster control is developed. By considering power deficits at cluster point of common coupling (PCC) and results in internal network of the DG cluster, a physical consequence quantification method is introduced. Finally, a cyber risk assessment method is proposed for DG cluster control under cyber attacks. Simulation results validate the effectiveness of the proposed method. Full article

Background: Elite athletes are frequently subjected to high-intensity training regimens, which can result in cumulative physical stress, overtraining, and potential health risks. Monitoring autonomic responses to such load is essential for optimizing performance and preventing maladaptation. Objective: The present study aimed to assess changes in autonomic regulation immediately and two hours after training in athletes, using an integrated framework (combining time- and frequency-domain HRV indices with nonlinear and recurrence quantification analysis). It was investigated how repeated assessments over a 4-month period can reveal cumulative effects and identify athletes at risk. Special attention was paid to identifying signs of excessive fatigue, autonomic imbalance, and cardiovascular stress. Methods: Holter ECGs of 12 athletes (mean age 21 ± 2.22 years; males, athletes participating in competitions) over a 4-month period were recorded before, immediately after, and two hours after high-intensity training, with HRV calculated from 5-min segments. Metrics included HRV and recurrent quantitative analysis. Statistical comparisons were made between the pre-, post-, and recovery phases to quantify autonomic changes (repeated-measures ANOVA for comparisons across the three states, paired t-tests for direct two-state contrasts, post hoc analyses with Holm–Bonferroni corrections, and effect size estimates η²). Results: Immediately after training, significant decreases in SDNN (↓ 35%), RMSSD (↓ 40%), and pNN50 (↓ 55%), accompanied by increases in LF/HF (↑ 32%), were observed. DFA α1 and Recurrence Rate increased, indicating reduced complexity and more structured patterns of RR intervals. After two hours of recovery, partial normalization was observed; however, RMSSD (−18% vs. baseline) and HF (−21% vs. baseline) remained suppressed, suggesting incomplete recovery of parasympathetic activity. Indications of overtraining and cardiac risk were found in three athletes. Conclusion: High-intensity training in elite athletes induces pronounced acute autonomic changes and incomplete short-term recovery, potentially increasing fatigue and cardiovascular workload. Longitudinal repeated testing highlights differences between well-adapted, fatigued, and at-risk athletes. These findings highlight the need for individualized recovery strategies and ongoing monitoring to optimize adaptation and minimize the risk of overtraining and health complications. Full article

Wearable sensors provide objective, continuous, and non-invasive quantification of physical activity, with step count serving as one of the most intuitive measures. However, significant gait alterations in individuals with neurological conditions limit the accuracy of step-counting algorithms trained on able-bodied individuals. Therefore, this study investigates the accuracy of step counting during activities of daily living (ADL) in a neurological population. Seven individuals with neurological conditions wore seven accelerometers while performing ADL for 30 min. Step events manually annotated from video served as ground truth. An optimal sensing and analysis configuration for machine learning algorithm development (sensor location, filter range, window length, and regressor type) was identified and compared to existing algorithms developed for able-bodied individuals. The most accurate configuration includes a waist-worn sensor, a 0.5–3 Hz bandpass filter, a 5 s window, and gradient boosting regression. The corresponding algorithm showed a significantly lower error rate compared to existing algorithms trained on able-bodied data. Notably, all algorithms undercounted steps. This study identified an optimal sensing and analysis configuration for machine learning-based step counting in a neurological population and highlights the limitations of applying able-bodied-trained algorithms. Future research should focus on developing accurate and robust step-counting algorithms tailored to individuals with neurological conditions. Full article

Foreign object intrusion in railway perimeter areas poses significant risks to train operation safety. To address the limitation of current visual detection technologies that overly focus on target identification while lacking quantitative risk assessment, this paper proposes a railway intrusion risk quantification method integrating track semantic segmentation and spatiotemporal features. An improved BiSeNetV2 network is employed to accurately extract track regions, while physical-constrained risk zones are constructed based on railway structure gauge standards. The lateral spatial distance of intruding objects is precisely calculated using track gauge prior knowledge. A lightweight detection architecture is designed, adopting ShuffleNetV2 as the backbone to reduce computational complexity, with an incorporated Dilated Transformer module to enhance global context awareness and sparse feature extraction, significantly improving detection accuracy for small-scale objects. The comprehensive risk assessment formula integrates object category weights, lateral risk coefficients in intrusion zones, longitudinal distance decay factors, and dynamic velocity compensation. Experimental results demonstrate that the proposed method achieves 84.9% mean average precision (mAP) on our proprietary dataset, outperforming baseline models by 3.3%. By combining lateral distance detection with multidimensional risk indicators, the method enables quantitative intrusion risk assessment and graded early warning, providing data-driven decision support for active train protection systems and substantially enhancing intelligent safety protection capabilities. Full article

Solvents represent the quiet majority in biomolecular systems, yet modeling their influence with both speed and ri:gor remains a central challenge. This study maps the state of the art in implicit solvent theory and practice, spanning classical continuum electrostatics (PB/GB; DelPhi, APBS), modern nonpolar and cavity/dispersion treatments, and quantum–continuum models (PCM, COSMO/COSMO-RS, SMx/SMD). We highlight where these methods excel and where they falter, namely, around ion specificity, heterogeneous interfaces, entropic effects, and parameter sensitivity. We then spotlight two fast-moving frontiers that raise both accuracy and throughput: machine learning-augmented approaches that serve as PB-accurate surrogates, learn solvent-averaged potentials for MD, or supply residual corrections to GB/PB baselines, and quantum-centric workflows that couple continuum solvation methods, such as IEF-PCM, to sampling on real quantum hardware, pointing toward realistic solution-phase electronic structures at emerging scales. Applications across protein–ligand binding, nucleic acids, and intrinsically disordered proteins illustrate how implicit models enable rapid hypothesis testing, large design sweeps, and long-time sampling. Our perspective argues for hybridization as a best practice, meaning continuum cores refined by improved physics, such as multipolar water, ML correctors with uncertainty quantification and active learning, and quantum–continuum modules for chemically demanding steps. Full article

The integration of large-scale wind power clusters significantly reduces the inertia level of the power system, increasing the risk of frequency instability. Accurately assessing the equivalent virtual inertia of wind farms is critical for grid stability. Addressing the dual bottlenecks in existing inertia assessment methods, where physics-based modeling requires full control transparency and data-driven approaches lack interpretability for inertia response analysis, thus failing to reconcile commercial confidentiality constraints with analytical needs, this paper proposes a symbolic regression framework for inertia evaluation in doubly fed wind farms with limited control information constraints. First, a dynamic model for the inertia response of DFIG wind farms is established, and a mathematical expression for the equivalent virtual inertia time constant under different control strategies is derived. Based on this, a nonlinear function library reflecting frequency-active power dynamic is constructed, and a symbolic regression model representing the system’s inertia response characteristics is established by correlating operational data. Then, sparse relaxation optimization is applied to identify unknown parameters, allowing for the quantification of the wind farm’s equivalent virtual inertia. Finally, the effectiveness of the proposed method is validated in an IEEE three-machine nine-bus system containing a doubly fed wind power cluster. Case studies show that the proposed method can fully utilize prior model knowledge and operational data to accurately assess the system’s inertia level with low computational complexity. Full article

Wind farm performance monitoring has traditionally relied on deterministic models, such as power curves or machine learning approaches, which often fail to account for farm-wide behavior and the uncertainty quantification necessary for the reliable detection of underperformance. To overcome these limitations, we propose a probabilistic methodology for turbine-level active power prediction and uncertainty estimation using high-frequency SCADA data and farm-wide autoregressive information. The method leverages a Stochastic Variational Gaussian Process with a Linear Model of Coregionalization, incorporating physical models like manufacturer power curves as mean functions and enabling flexible modeling of active power and its associated variance. The approach was validated on a wind farm in the Belgian North Sea comprising over 40 turbines, using only 15 days of data for training. The results demonstrate that the proposed method improves predictive accuracy over the manufacturer’s power curve, achieving a reduction in error measurements of around 1%. Improvements of around 5% were seen in dominant wind directions (200°–300°) using 2 and 3 Latent GPs, with similar improvements observed on the test set. The model also successfully reconstructs wake effects, with Energy Ratio estimates closely matching SCADA-derived values, and provides meaningful uncertainty estimates and posterior turbine correlations. These results demonstrate that the methodology enables interpretable, data-efficient, and uncertainty-aware turbine-level power predictions, suitable for advanced wind farm monitoring and control applications, enabling a more sensitive underperformance detection. Full article

(1) Background: Older people ageing in place alone with functional limitations experience several difficulties in daily life, potentially hampering their social participation. This in turn could impact their perceived loneliness. This paper aims to investigate these issues based on findings from the IN-AGE (“Inclusive ageing in place”) study carried out in 2019 in Italy. (2) Methods: The focus of this paper is on the Marche region (Central Italy), where 40 qualitative/semi-structured interviews with seniors were administered in both urban and rural sites. A content analysis was carried out, in addition to some quantification of statements. (3) Results: Older people are mainly involved in receiving/making visits, lunches/dinners with family members and friends, religious functions, walking, and watching television (TV). Overall, the more active seniors are those living in rural sites, with lower physical impairments, and with lower perceived loneliness, even though in some cases, a reverse pattern emerged. The results also indicate some different nuances regarding urban and rural sites. (4) Conclusions: Despite the fact that this exploratory study did not have a representative sample of the target population, and that only general considerations can be drawn from results, these findings can offer some insights to policymakers who aim to develop adequate interventions supporting the social participation of older people with functional limitations ageing in place alone. This can also potentially reduce the perceived loneliness, while taking into consideration the urban–rural context. Full article

Blackleg disease poses a major threat to global canola production. The resistance gene Rlm1, corresponding to the avirulence gene AvrLm1 in the pathogen Leptosphaeria maculans, has been widely used to mitigate the impact of the disease. To investigate the biochemical basis of Rlm1-mediated resistance against blackleg, we conducted an LC-MS–based analysis of a susceptible Topas double haploid (DH) line and its isogenic Rlm1-carrying resistant counterpart for metabolomic profiles during the infection process. Samples were labeled with ¹²C- and ¹³C for LC-MS analyses to enhance both chemical and physical properties of metabolites for improved quantification and detection sensitivity. Resistant plants showed early and sustained accumulation of several defense metabolites, notably pipecolic acid (PA, up to 326-fold), salicylic acid (SA), and gentisic acid (GA) in L. maculans-inoculated Topas–Rlm1 plants compared to mock-inoculated Topas–Rlm1 controls (adjusted p < 0.05), indicating activation of lysine degradation and hormonal defense pathways. Elevated glucosinolates (GLS), γ-aminobutyric acid (GABA), and melatonin precursors may further contribute to antimicrobial defense and cell-wall reinforcement. In contrast, flavonoid and phenylpropanoid pathways were down-regulated, suggesting metabolic reallocation during resistance. Exogenous application of PA, SA, GA, ferulic acid, and piperonylic acid (a known inhibitor of the phenylpropanoid pathway in plants) significantly reduced infection in susceptible canola varieties, validating their defense roles against blackleg. These results offer new insights into Rlm1-mediated resistance and support metabolic targets for breeding durable blackleg resistance in canola. Full article