Pediatric type 1 diabetes (T1D) is a lifelong condition requiring meticulous glucose management to prevent acute and chronic complications. Conventional management of diabetic patients does not allow for continuous monitoring of glucose trends, and can place patients at risk for hypo- and hyperglycemia. Continuous glucose monitors (CGMs) have emerged as a mainstay for pediatric diabetic care and are continuing to advance treatment by providing real-time blood glucose (BG) data, with trend analysis aided by machine learning (ML) algorithms. These predictive analytics serve to prevent against dangerous BG variations in the perioperative environment for fasted children undergoing surgical stress. Integration of CGM data into electronic health records (EHR) is essential, as it establishes a foundation for future technologic interfaces with artificial intelligence (AI). Challenges in perioperative CGM implementation include equitable device access, protection of patient privacy and data accuracy, ensuring institution of standardized protocols, and financing the cumbersome healthcare costs associated with staff training and technology platforms. This paper advocates for implementation of CGM data into the EHR utilizing multiple facets of AI/ML algorithms.

Grape cluster compactness is a key trait that influences fruit quality, yield, and disease susceptibility. Understanding the genetic basis of this trait is essential for optimizing vineyard management and improving grapevine cultivars. In this study, we performed quantitative trait locus (QTL) mapping to identify genomic regions associated with cluster architecture and yield components in a bi-parental population derived from Vitis vinifera cv. Riesling × Cabernet Sauvignon. A total of 138 full-sibling progeny were evaluated over two growing seasons at Oakville, Napa Valley, California. Traditional yield-related traits were measured, including cluster number, total cluster weight, and average cluster weight. Additionally, an image-based phenotyping pipeline leveraging the foundation model Segment Anything Model (SAM) was employed to segment individual berries, measure their size and shape, and compute cluster compactness with minimal manual intervention. Trait correlations revealed that compact clusters tended to have a higher berry count but smaller berry size, highlighting the role of compactness in modulating cluster structure. Heritability estimates varied across traits, with berry dimensions and compactness displaying moderate to high heritability, indicating strong genetic control. Two parental linkage maps were constructed using a pseudo-test cross strategy. QTL mapping identified multiple loci associated with cluster architecture and yield components, with several stable QTLs detected across both years, with marker effects ranging from 7.6% to 22.1%. Notably, a QTL for cluster compactness was found in both seasons on chromosome 1 in Cabernet Sauvignon. Other stable QTLs were associated with berry size (chromosomes 6 and 17) and berry count (chromosome 5 in Cabernet Sauvignon and chromosome 7 in Riesling). Additional QTLs were detected in a single year, reflecting the influence of environmental variation. Our findings provide valuable insights into the application of foundation models requiring no prior training and minimal intervention for high-quality segmentation and enhance our understanding of the genetic architecture of cluster compactness and yield traits. The genomic regions identified in this study offer promising targets for breeding programs aimed at improving grape quality and disease resistance.

ABSTRACT Aim As anthropogenic activities continue, species are exposed to climate change and rapid urbanisation that alter their distribution; however, the relative contributions of climate and anthropogenic influence on species distribution are unknown for most species. We use an environmentally sensitive salamander genus ( Eurycea ) that occupies urban and forested habitats to test the relative importance of temperature‐, precipitation‐ and urbanisation‐related variables before placing these results into a phylogenetic context. We aim to test the impact of climate and urbanisation in driving the distribution of the genus while evaluating patterns of niche conservatism. Location Nearctic. Taxon Eurycea (Plethodontidae, Lungless Salamanders). Methods We developed MAXENT niche models for 13 Eurycea species using bioclimatic and urbanisation‐related variables. We assessed the importance of these environmental variables through permutation importance and compared response curves to determine niche overlap. Phylogenetic analyses tested for evolutionary constraints on species responses to environmental factors. Results Climatic variables were the primary drivers of Eurycea distributions, while urbanisation‐related variables had lower overall importance. Phylogenetic analyses revealed that responses to urbanisation‐related factors, specifically impervious surface and human population density, exhibited significant phylogenetic signal, indicating a stronger evolutionary constraint on responses to urbanisation than to climate. While climatic variables showed limited phylogenetic conservatism, niche overlap analyses demonstrated that more closely related species had greater similarity in ecological responses to urbanisation than to climate. Evolutionary history influenced species' ecological tolerances, with some environmental responses more conserved than expected under Brownian motion. Main Conclusions Our findings highlight the role of evolutionary history in shaping Eurycea responses to environmental variation. While climatic factors predominantly influence broadscale distributions, urbanisation‐related responses are more evolutionarily conserved across the genus. These results suggest that past evolutionary trajectories may constrain species' capacity to adapt to novel anthropogenic stressors, underscoring the importance of incorporating phylogenetic perspectives in conservation strategies for Eurycea and other evolutionarily constrained taxa.

The use of isotopic sequence allowing a longitudinal life tracking of an individual (isobiography), by taking a series of isotope measurements on dentine sections and estimating the age of the individual at their formation, provides a means of tracing dietary and environmental variations during childhood. This approach is based on the use of standards for estimating the age at which teeth are formed. By using a dual mathematical model, linear and a generalised additive model, and by testing two standards commonly used in biological anthropology to estimate dental age, we have characterised the isobiography of 4 Neolithic individuals from France. Our study shows the importance of the choice of mathematical model and standard in age estimates. Depending on the choices made, there can be gaps of several years between the estimates, underscoring the difficulty and precautions that need to be taken when making inferences on social ages. The statistical processing protocol developed can be re-used or adapted for new studies.

Parental cooperation is not self-evident, as conflicts often arise over individual contributions. Evolutionary game theory suggests this conflict may be resolved through negotiation, where parents adjust their care level based on their partner's contribution. However, mathematical negotiation models typically predict low parental cooperation. As these models are not dynamically explicit and mostly neglect stochasticity, we employ individual-based simulations to investigate how parental negotiation strategies evolve and shape care patterns. Our results differ markedly from earlier analytical predictions. Parental negotiation strategies readily evolve, resulting in four alternative care patterns: uniparental care, sex-biased care and two types of egalitarian biparental care. Effective cooperation evolves regularly but, contrary to common expectations, always relies on a Tit-for-Tat strategy rather than parental compensation. Our study underscores that diverse cooperative patterns in animals can emerge from sex-specific negotiation strategies, even in the absence of initial sex roles and environmental variation.

Summary Lianas, or woody climbers, are an important component of many tropical forests with fast growth responses to both natural environmental variations and human disturbances. Still, studies on their growth rate are rare to nonexistent. We sampled 22 specimens of Dalbergia frutescens , a liana that produces annual semi-porous growth rings in a secondary Atlantic Rainforest fragment in the megacity of São Paulo, Brazil. This forest fragment went through an intensive management of an exotic and invasive palm species, Archontophoenix cunninghamiana , that resulted in the removal of ca. 14 000 individuals in 2011 and 2012. We assessed the impacts of this management program on the growth of D. frutescens by measuring the tree-ring area in cross sections. Their ages ranged from 6 to 23 years old, and all samples had confluent rings. The occurrence of the confluent rings was not associated with any particular year, indicating that their formation is an intrinsic characteristic of this species. Despite the confluent rings, we successfully built a tree-ring basal area increment chronology spanning from 1995 to 2018. A piecewise regression indicated a growth release in 2014, right after the management of the palm trees. This study demonstrates the potential of tree-ring analysis in lianas as a valuable tool for detecting significant environmental changes and understanding the responses of lianas to natural and anthropogenic disturbances. These findings contribute to the broader understanding of tropical forest dynamics and the role of lianas in tracking ecological changes over time.

Heart Rate Variability (HRV) is a crucial biomarker in health monitoring and disease management. Radio sensing has emerged as a promising contactless alternative, addressing the limitations of conventional contact-based techniques. However, a major challenge of existing approaches is their poor generalization performance in real-world deployments. This arises from the inherent sensitivity of radio signals to environmental variations, causing intrinsic shifts in signal distribution and representation. As a result, current methods struggle to adapt to different deployment conditions, leading to performance degradation in real-world applications where complex environments are unavoidable. In this paper, we systematically analyze the generalization challenge posed by environmental variations from the perspective of statistical signal modeling and formulate it as an estimation problem under a global environmental distribution. Inspired by the law of large numbers, we assume that assembling a sufficiently large number of environment-variant samples allows the empirical risk to approximate the true risk, thereby yielding a robust estimator. Accordingly, we propose a novel Massive Radio Sensing framework that leverages massive environment-variant signal sampling and a structured deep learning optimization strategy to statistically derive a robust HRV estimator. We evaluate our method across a broad spectrum of real-world deployment scenarios. Specifically, testing on 30 participants across 32 application-oriented environments—including home and workplace settings—demonstrates a 29.7% improvement in performance compared to the current state-of-the-art method. To further assess clinical generalizability, we evaluate our method on 130 inpatients across 8 distinct hospital environments, achieving a 41.7% performance improvement. These results highlight the effectiveness of our approach and its strong potential for real-world deployment in radio-based HRV monitoring.

Objective: This study aims to examine the relationship between atopic dermatitis (AD), one of the most common dermatological conditions in children, and environmental factors, including meteorological variables and air pollution. Methods: This retrospective cross-sectional study analyzed the medical records of 21,407 pediatric patients aged 0 to 18 years who presented to the city hospital in Agri, Turkey, between 2020 and 2024. Admission dates were matched with meteorological data (wind speed, atmospheric pressure, humidity, temperature) and air pollution indicators (PM10, SO2, NO2, NOx, NO, O3). Statistical analyses included t-tests, correlation analyses, binary logistic regression, and a CHAID decision tree model. Results: AD accounted for 10.1% of all dermatology-related visits. AD admissions increased particularly during the first half of the year and were significantly associated with higher O3 levels, whereas increased PM10 levels were associated with a lower likelihood of AD admissions. Logistic regression showed that age, sex, semiannual period, atmospheric pressure, PM10, and O3 were significant predictors of AD. The decision tree model identified age, period, and O3 as the strongest discriminating variables for AD. Conclusion: AD was found to be more sensitive to environmental and seasonal variations compared with other dermatitis types. In particular, elevated ozone levels and temporal factors played a notable role in increasing AD presentations. These findings may inform environmental risk management and preventive strategies for children with AD.

Abstract This work presents a measurement framework for in situ corrosion monitoring of pipelines using phase-matched harmonic guided waves. We introduce a weighted principal component analysis calibration that accounts for sensor amplitude and frequency response as well as guided wave attenuation, which suppresses environmental and operational variations and enables consistent extraction of a second harmonic index (SHI) from three receivers (R2, R3, R4) under stepped excitations from 100 to 250 kHz. Measurement performance is quantified against a physical reference, the average outer diameter (AOD). We compute the Pearson correlation between SHI and AOD and, for comparison, between a linear indicator (LI) and AOD across a phase-matched band (about 135 to 150 kHz) and a non-matched band (about 165 to 180 kHz). SHI tracks the three corrosion stages (formation, development, stabilization) and shows stronger, more consistent agreement with AOD than LI, with a maximum correlation coefficient of 0.9168 over the entire monitoring cycle. The phase-matched band yields higher correlations, especially at R2 and R4, indicating improved sensitivity to cumulative damage. Comparisons between full cycle and development stage analyses indicate that SHI maintains sensitivity throughout the monitoring period. The proposed calibration and index methodology addresses key measurement challenges, including sensor response, propagation loss, and temporal drift, and provides a general framework that could be applied to practical guided-wave monitoring applications, with further validation needed in engineering applications.

ABSTRACT Aim Understanding how diverse communities respond to environmental fluctuations is a central challenge in ecology. Here, we assessed how communities responded to environmental variation over the past 23 years and evaluated the extent to which these responses can be associated with taxonomic relationships, as well as biological and ecological species traits. Location Central portion of the Humboldt Upwelling Ecosystem, with 22 rocky shore survey sites spanning 8° of latitude (28° S–36° S). Time Period 2000–2023. Major Taxa Studied Intertidal zone communities. Methods We used joint species distribution models to integrate quantitative survey data, satellite sea surface temperature (SST) as a proxy for environmental conditions, taxonomic information and species biological traits along a latitudinal gradient with heterogeneous thermal conditions. Results Our proxy for environmental variation revealed weak and non‐significant SST cooling at central sites, whereas sites near 30° S showed slight warming trends. Taxonomic relationships and individual species traits were weakly associated with their collective responses to SST variability. However, we identified a consistent increase in the occurrence of both macroalgal and invertebrate species across the region. The occurrence of macroalgal species was more sensitive to SST variation than invertebrates, with responses shifting from positive at equatorward sites to increasingly negative at poleward sites. Patterns of species co‐occurrence were strongly dependent on spatial scale, particularly among invertebrates. Main Conclusions Species occurrences increased across the region, but these responses were not significantly associated with taxonomic relatedness or with easily assigned species traits. This pattern likely indicates comparatively low niche conservatism within these communities in relation to SST responses, while other structuring processes—such as species interactions—are not well captured by the traits examined. As other studies have detected slight cooling trends over the past two decades, our results suggest that the lack of community‐wide reorganisation reflects the absence of a clear environmental driver.

Microplastics in the Southern Brazilian estuary: Interactions between environment, morphology, and seasonal variation

A ratiometric fluorescent nanoprobe for formaldehyde imaging in Arabidopsis thaliana.

Methyl-free meso-thiazole-substituted red-shifted BODIPY-based molecular rotor for fluorescence imaging viscosity in mitochondria.

The hepatitis C virus (HCV) is recognized as a significant global public health concern due to its complex transmission dynamics and long-term health consequences. In this study, a stochastic delay differential model was examined to enhance the understanding of HCV transmission. Time delays were incorporated into the mathematical model to represent incubation periods, while stochastic perturbations were introduced to reflect random environmental and demographic variations. The model was formulated to account for key compartments, including susceptible individuals, acute and chronic infections, and recovered individuals, along with disease-induced mortality and progression rates. By representing delays associated with incubation periods and asymptomatic stages, the model was used to explore HCV transmission mechanisms under stochastic influences. Additionally, strategies for disease control through vaccination and treatment were investigated. Stochastic fluctuations were included to capture uncertainties arising from environmental and demographic factors. Using stochastic Lyapunov functional techniques, the existence of a unique global solution was established, and conditions for disease extinction, persistence, and the existence of stationary distributions were derived. Optimal control strategies were developed with the goal of minimizing infection prevalence and associated intervention costs, focusing on measures such as treatment, public health education, and vaccination. The optimal control trajectories under the influence of delays and stochastic effects were determined by applying Pontryagin's Maximum Principle (PMP), thereby ensuring practical relevance. Numerical simulations were conducted to demonstrate the effects of time delays and stochastic variables on HCV dynamics and to highlight the effectiveness of the proposed control strategies. Overall, this work provides important insights into the interplay between stochastic processes, time delays, and optimal interventions, offering a comprehensive framework for the effective management and potential eradication of HCV epidemics.

This paper proposes a high-efficiency maximum power point tracking (MPPT) algorithm based on a variable step size control technique for standalone hybrid solar-wind energy systems. Unlike conventional approaches that utilize separate MPPT controllers for photovoltaic (PV) and wind systems, the proposed method integrates a single adaptive control strategy that simultaneously optimizes power extraction from both renewable sources. The algorithm dynamically adjusts the step size according to environmental variations, improving convergence speed and tracking accuracy. The system is modeled in MATLAB/Simulink, incorporating a 500 W solar PV system and a 560 W wind turbine, both interfaced through traditional boost converters. To validate the performance, simulations are conducted under varying solar irradiance levels (600 W/m², 800 W/m², and 1000 W/m²) and wind speeds (8 m/s, 10 m/s, and 12 m/s). Results indicate that the PV output power increases from 288.8 W to 513 W with rising irradiance, while the wind output improves from 301.4 W to 439.3 W with increasing wind speed. The combined hybrid system achieves total output powers of 557.35 W, 691.74 W, and 807.12 W across three operating intervals. These findings confirm that the proposed variable step size MPPT algorithm significantly enhances energy harvesting efficiency and system performance under dynamic environmental conditions.

Structural Damage Detection is an area that is becoming increasingly important as structure age and become more prone to failure. Early identification of these changes can lead to significant cost savings and potential damage reduction. Conventional data-driven methods typically require large datasets from both damaged and undamaged structural states, which can be difficult or even impossible to collect in real-world situations. Meanwhile, purely model-based techniques often face challenges in accounting for real-time environmental variations and the complexities of structural behavior. To address this limitation, the proposed methodology in this paper employs a hybrid system that utilizes structural models to generate training data for various structural scenarios, using a methodology based on the concepts of Generative Adversarial Networks to find the optimal excitation parameters for the model, aiming to produce response levels as close as possible to those obtained experimentally. This data serves as input for training algorithms to classify the structural condition based on the frequency information of temporal acceleration signals. The results show that the neural-based computational learning techniques are able to achieve efficiency rates above 99% in damage localization and almost 97% in severity estimation over 2 min-long experiments on a four-story lab-scale shear building.

ABSTRACT Forest ecosystems play a critical role in the global carbon cycle. As a significant terrestrial carbon sink, plantations exhibit carbon stock patterns that are shaped by tree species composition, stand structure, and environmental conditions. Here, we investigated typical plantation types in the Mufu Mountain, Hubei Province. Total carbon stock and its distribution across different stand types were quantified by establishing permanent monitoring plots and conducting tree surveys, applying general biomass models to estimate biomass, and employing elemental analysis to measure soil carbon content. Our results indicated that total carbon stock ranged from 37,452.54 to 184,909.38 kg/ha among six forest subplots in the Mufu Mountain. Broadleaf and coniferous stands accumulated substantially more carbon than Phyllostachys edulis (Carrière) J. Houz. forests. Higher soil temperature, illuminance, and increased shrub cover promoted carbon accumulation in trees and shrubs. In contrast, multiple environmental factors regulated carbon stock in herbaceous plants, litter, and soil organic matter, demonstrating clear carbon pool‐specific effects. Our findings clarify key environmental drivers of carbon dynamics in subtropical plantations, and based on these results, we propose concrete management strategies including the selection of high‐carbon stock tree species, maintenance of understory shrub layers, and implementation of strategic canopy thinning to enhance forest carbon sequestration.

This article examines the automation of drainage systems in the mining industry, emphasizing the urgent need to modernize this critical component of mining infrastructure. The proposed system is based on open-source hardware and software, making it suitable for use by designers working in small enterprises as well as for educational and training purposes. Drainage systems are used to pump water from underground mines to maintain safe working conditions and prevent flooding. The presented solution enables automatic control of pumps based on parameters such as current water level, pipeline pressure, actuator positions, and the presence of a cooling water flow. The developed system provides a convenient platform for experimentation with large-scale mining control applications, such as water drainage systems. The introduction of automation in the management of drainage processes significantly enhances their efficiency and reliability by reducing the need for continuous human supervision and minimizing the risk of operational errors. The architecture of a typical automated drainage system is described, comprising water level sensors, programmable logic controllers (PLC), and monitoring and control systems (SCADA). Modern automated drainage systems in the mining sector utilize PLCs to provide reliable and flexible process control. The automation of pumping stations with PLCs requires adherence to specific operational principles to ensure safe and fault-free equipment performance. One of the innovative solutions in this category is the Arduino Opta, which combines industrial-grade reliability with open-source flexibility. The article describes the system’s operational algorithm and its ability to respond in real time to variations in the hydrogeological environment. The advantages of automation are analyzed, including reduced human intervention, improved operational safety, and increased efficiency.

This study presents a comprehensive simulation-based approach to predict the lifecycle of tires utilized in reach stackers, which operate under a variety of challenging conditions and heavy loads. By employing advanced vehicle force simulations, we systematically assess tire wear across a range of operational parameters, including container load, vehicle speed, turning radius, and friction coefficient. We also explore the effects of environmental variations, such as changes in friction coefficients, alongside critical tire properties like Poisson’s ratio. Our results indicate that strategic adjustments—such as reducing vehicle speed by 35%, increasing the turning radius by 100%, decreasing container load by 25%, enhancing friction coefficient by 20%, raising Poisson’s ratio by 25%, and boosting tire tread density by 42%—can yield significant reductions in tire wear, quantified as 11%, 10%, 26%, 21%, 7%, and 13%, respectively. Notably, our analysis reveals that dynamic wear is predominantly driven by container load, with a 25% reduction in load leading to a remarkable 21% decrease in wear. These insights pave the way for the design of tires specifically tailored to meet the demanding requirements of reach stackers, while also optimizing maintenance strategies and prolonging tire lifespan.

Introduction Artificial intelligence (AI) has been widely used to detect faults and failures in photovoltaic (PV) systems, particularly those that conventional protection devices fail to identify. However, previous AI-based approaches still face major limitations, including neglecting critical detection conditions, relying on large and complex datasets, and lacking simultaneous and accurate multi-fault detection and classification. Methods To address these challenges, a novel PV fault detection framework is proposed by combining a fuzzy logic (FL) system with a particle swarm optimization (PSO) algorithm. An initial dataset is generated from the current–voltage (I–V) curve of a PV array. Manhattan distance (MD) and Chebyshev distance (CD) features are extracted from the I–V characteristics. A wide set of machine-learning classifiers is evaluated, and the FL system nominates the most reliable models based on mean accuracy, F1-score, and standard deviation. PSO is then used to determine the optimal subset of classifiers and to assign optimized weights for ensemble prediction. Several output-combining techniques are also examined to obtain the most accurate final classification. Results Model verification is performed using a dataset that includes normal operation as well as line-to-line (LL), open-circuit (OC), and degradation (DEG) faults under various environmental (irradiance, temperature) and electrical (mismatch, impedance) conditions. The proposed FL+PSO-based model achieves outstanding accuracy in detecting and classifying multiple PV faults and outperforms recent state-of-the-art approaches. Discussion The integration of distance-based feature extraction, fuzzy-driven classifier selection, and PSO-optimized weighting significantly enhances robustness and reduces sensitivity to environmental variations. These improvements enable reliable multi-fault detection even when fault signatures closely resemble normal conditions. Conclusion The proposed FL and PSO-based ensemble provides a highly accurate and reliable solution for multi-fault detection in PV arrays. Its performance surpasses existing approaches, making it a strong candidate for practical implementation in real PV monitoring systems.