Search Results (146)

Background: Higher intake of sugar-sweetened beverages (SSBs) increases the risk of childhood obesity, while the effects of non-caloric beverages (NCBs) and dairy beverages remain inconsistent. This study investigated changes in beverage intake following a 10-week lifestyle camp and explored associations between changes in beverage intake and anthropometric measures. Methods: Children from two camp sites and aged 7–14 years (n:190) with overweight/obesity were included and pooled for the present study. Beverage intake was assessed by a Food Frequency Questionnaire at baseline, at 10 weeks, and at a one- and three-year follow-up. Anthropometry was assessed at the same timepoints. Results: Compared to baseline, participants had lower odds of reporting a high intake of SSBs (OR: 0.14, 95%-CI: 0.07; 0.28), NCBs (OR: 0.19, 95%-CI: 0.11; 0.34) and chocolate milk (OR: 0.18, 95%-CI: 0.09; 0.36) at 10 weeks, relative to a low intake, and were more likely to report a high intake of water. One and three years after the camp, the changes attenuated, reaching baseline levels for water, SSBs, and NCBs at the three-year follow-up. Across time, only weak to moderate correlations were observed between changes in beverage intake and BMI-SDS, body fat (%), and skeletal muscle mass (Spearman’s rho = +/− 0.19–0.31). Conclusions: The lifestyle camp intervention effectively improved beverage intake among children with overweight/obesity; however, the changes were not sustained, emphasizing the need for long-term support to maintain the health benefits. Without a control group, it is not possible to determine whether these changes reflect natural variations in beverage intake. Full article

Offshore wind turbine support structures are in harsh and unsteady marine environments, and their dynamic characteristics could change gradually after long-term service. To better understand the status and improve remaining life estimation, it is essential to conduct in situ measurement and update the numerical models of these support structures. In this paper, the modal properties of a 5.5 MW offshore wind turbine were first identified by a widely used operational modal analysis technique, frequency-domain decomposition, given the acceleration data obtained from eight sensors located at four different heights on the tower. Then, a finite element model was created in MATLAB R2020a and a set of model parameters including scour depth, foundation stiffness, hydrodynamic added mass and damping coefficients was updated in a Bayesian inference frame. It is found that the posterior distributions of most parameters significantly differ from their prior distributions, except for the hydrodynamic added mass coefficient. The predicted natural frequencies and damping ratios with the updated parameters are close to those values identified with errors less than 2%. But relatively large differences are found when comparing some of the predicted and identified mode shape coefficients. Specifically, it is found that different combinations of the scour depth and foundation stiffness coefficient can reach very similar modal property predictions, meaning that model updating results are not unique. This research demonstrates that the Bayesian inference framework is effective in constructing a more accurate model, even when confronting the inherent challenge of non-unique parameter identifiability, as encountered with scour depth and foundation stiffness. Full article

Fire is a key driver of ecosystem dynamics under global change, and understanding its complex relationship with the climate system is crucial for regional wildfire risk management and the development of ecological adaptation strategies. The western United States is a critical region for studying fire–climate interactions due to its pronounced environmental gradients, diverse fire regimes, and high vulnerability to climate change, which together provide a robust natural laboratory for examining spatial variability in fire responses. Based on tree-ring fire-scar records systematically collected from five major ecoregions in the western United States via the International Tree-Ring Data Bank (ITRDB), this study reconstructed fire history sequences spanning 430–454 years. By integrating methods such as correlation analysis, random forest regression, superposed epoch analysis, and effect size assessment, we systematically revealed the spatial differentiation patterns of fire frequency and fire spatial extent across different ecoregions, quantified the relative contributions of key climatic drivers, and identified climatic anomaly characteristics during extreme fire years. The results indicate that: (1) there are significant differences in fire frequency between different ecological areas; (2) summer drought conditions (PDSI) are the most consistent and strongest driver of fire across all ecoregions, and ENSO (NINO3) also shows a widespread negative correlation; (3) random forest models indicate that the Sierra Nevada and Madrean Archipelago ecoregions are the most sensitive to multiple climatic factors, while fire in regions such as the Northern Rockies may be more regulated by non-climatic processes; (4) extreme fire years across all ecoregions are associated with significant negative PDSI anomalies with prominent effect sizes, confirming that severe drought is the dominant cross-regional precondition for extreme fire events. This study emphasizes the region-specific nature of fire–climate relationships and provides a scientific basis for developing differentiated, ecoregion-specific fire prediction models and prevention strategies. The methodological framework and findings offer valuable insights for fire regime studies in other global forest ecosystems facing similar climate challenges. Full article

This article presents a novel procedure for detecting, locating, and quantifying damage caused by a loose bolted joint in a modular plate structure. The primary aims of this work are to locate the loose joint with a minimum number of measurement points and to quantify the damage based on acquired modal data. The proposed method is based on the direct correlation of patterns of modal property changes using simulated and measured data. These patterns combine the relative shifts in natural frequencies and the norms of relative changes in mode shapes, derived from a pre-computed database of finite element method (FEM) simulations for various potential damage scenarios. The experimental validation demonstrates that the procedure can effectively and accurately locate the position of a loose joint using only five accelerometers. A foundational study on damage quantification is presented through a sensitivity analysis using FEM model data on a single connection plate of the test structure. The results demonstrate the nonlinear relationship between the damage state and natural frequency change, based on the mode shape, mode number, and the location of the damage. Full article

Structural health monitoring (SHM) of composite wind turbine blades (WTBs) is crucial for improving power efficiency, reducing maintenance costs, and ensuring long-term structural reliability. Traditional frequency-based damage detection, often derived from simplified isotropic beam principles, can be challenged by the anisotropy, heterogeneity, and geometric complexity of composite WTBs. Moreover, as global indicators, natural frequencies are sensitive to environmental variations but are also limited in localizing damage. To overcome these challenges, this research introduces a combined approach of relative natural frequency change (RNFC) and Bayesian probability, referred to as the B-RNFC method. The framework includes four stages: (i) analyzing the correlation between natural frequencies and damage conditions (location and severity) in composite cantilever beams and WTBs; (ii) developing normalized RNFC curves from various damage sizes to establish a spatial damage reference dataset, which is then used for the next steps; (iii) integrating the resulting frequency-related data with Bayesian probability to identify damage locations and map them onto the structures; and (iv) evaluating the performance of the B-RNFC in multiple-damage localization. Simulation results demonstrate the effective damage localization range of the B-RNFC method. For a simple cantilever beam, this range is 20–80% of the distance from the fixed end. When applied to the composite WTB, this effective range corresponds to 40–80% of the blade length from the root. In addition, the proposed method can localize the dual damages when the damages are symmetrically located or when one damage is at the mid-span. Full article

Surface water loss, regulated by natural factors such as surface properties and atmospheric conditions, is a complex process across multiple spatiotemporal scales. This study compared the statistical characteristics of drydown time scale (τ) derived from multi-frequency microwave brightness temperatures (TB, including L-band and C-band), SMAP (Soil Moisture Active Passive) soil moisture (SM) products, and in situ observation data. It mainly conducted a sensitivity analysis of τ to depth, climate type, vegetation coverage, and soil texture, and compared the sensitivity differences between signals of different frequencies. The statistical results of τ showed a pattern varying with sensing depth: C-band TB (0~3 cm) < L-band TB (0~5 cm) < in situ observation (4~8 cm), i.e., the shallower the depth, the faster the drying. τ was sensitive to Normalized Difference Vegetation Index (NDVI) when NDVI < 0.7 and climate types, but relatively insensitive to soil texture. The global median τ retrieved from TB aligned with the spatial pattern of climate classifications; drier climates and sparser vegetation coverage led to faster drying, and L-band TB was more sensitive to these factors than C-band TB. The attenuation magnitude of L-band TB was smaller than that of C-band TB, but the degree of change in its attenuation effect was greater than that of C-band TB, particularly regarding variations in NDVI and climate types. Furthermore, given the similar sensing depths of SMAP SM and L-band TB, their τ statistical characteristics were compared and found to differ, indicating that depth is not the sole reason SMAP SM dries faster than in situ observations. Full article

Climate change has intensified in recent years, with one of its notable consequences being an increased frequency of extreme temperature events—manifesting as both excessively hot and cold days driven by temperature anomalies. In this study, we examine how daily temperature anomalies affect the market valuation of climate-aligned firms in the United States, relative to broader market trends. Using economic valuations of 33 publicly traded U.S. firms associated with renewable energy, electric vehicles, hydrogen fuel, and other sustainability-focused sectors from 2010 to 2024, we assess the effect of temperature anomalies aggregated at the national level, weighted by population, real gross state product, and gross domestic product. Our findings reveal that temperature anomalies—whether unusually warm or cold—are associated with a same-day increase in the financial performance of environmentally friendly firms, followed by a reversal the next day. This short-lived effect is driven primarily by days when temperatures deviate from historical norms but remain within the usual comfort range. When anomalies are large enough to create extreme conditions—pushing already hot days hotter or cold days colder—the pattern reverses: returns decline on the day of the anomaly and rebound the following day. These results are robust to controls for macroeconomic conditions, including the 10-year Treasury–Federal Funds Rate spread, 3-month Treasury–Federal Funds Rate spread, and crude oil prices. Together, the findings highlight the transitory nature of climate-related investor responses and show that market reactions depend on whether temperature shocks merely depart from historical norms or push conditions into genuinely extreme territory. Full article

Climate change is elevating temperatures, shifting weather patterns, and increasing frequency and severity of extreme weather events. Despite the urgency with which solutions are needed, relatively few studies comprehensively investigate the effectiveness of alternative flood risk management options under different climate conditions. Specifically, we are interested in a comparison of the effectiveness of resistance, nature-based, and managed retreat strategies. Using an integrated 1D-2D PCSWMM model, this paper presents a comprehensive investigation into the effectiveness of alternative adaptation strategies in reducing flood risks in Eastwick, a community of Philadelphia, PA, subject to fluvial, pluvial, and coastal flood hazards. While addressing the urgent public need to develop local solutions to this community’s flood problems, the research also presents transferable insights into the limitations and opportunities of different flood risk reduction strategies, manifested here by a levee, watershed-scale green stormwater infrastructure (GSI) program, and a land swap. The effectiveness of these options is compared, respectively, under compound climate change conditions, with the spatiotemporal patterns of precipitation and Delaware river tidal conditions based on Tropical Storm Isaias (2020). The hypothesis was that the GSI and managed retreat approaches would be superior to the levee, due to their intrinsic ability to address the compound climate hazards faced by this community. Indeed, the findings illustrate significant differences in the predicted flood extents, depths, and duration of flooding of the various options under both current and future climate scenarios. However, the ideal remedy to flooding in Eastwick is more likely to require an integrated approach, based on more work to evaluate cost-effectiveness, stakeholder preferences, and various logistical factors. The paper concludes with a call for integrating multiple strategies into multifunctional flood risk management. Full article

Background: This study aimed to examine changes in heart rate variability (HRV) across three match-related time points (pre-match, during the match, and post-match) and to explore whether these physiological responses differed between winners and losers in competitive padel. Methods: Twelve matches were analyzed, involving 11 high-level Finnish padel players ranked within the national top 24. HRV was recorded before, during, and immediately after each match, with each measurement lasting a minimum of five min. Time-domain (e.g., SDNN, RMSSD, pNN50), frequency-domain (e.g., LF, HF), and non-linear (e.g., SD1, SD2) HRV metrics were extracted for analysis. All matches took place in Tampere, Finland, under controlled conditions. Results: Results revealed significant intra-match fluctuations in HRV across all domains. Moreover, losing players exhibited consistently higher relative heart rate during the match, suggesting greater physiological strain. Conclusions: This study contributes novel evidence on the dynamic nature of autonomic responses in padel and supports the integration of HRV monitoring in performance and recovery management protocols for high-level athletes. Full article

The Mapleton Falls National Park transmission line corridor in Queensland, Australia, has received a number of vegetation management treatments over the last decade to maintain and protect the infrastructure and to ensure continuous electricity supply. Recent treatments have included ‘mega-mulching’ (mechanical mastication of vegetation to a mulch layer) in 2020 and targeted herbicide treatment of woody vegetation, with the aim of reducing vegetation height by encouraging a native herbaceous groundcover beneath the transmission lines. We measured vegetation structure (cover and height) and composition (species presence in 15 × 2 m plots), at 12 transects, 90 m in length on the transmission line corridor, to determine if management goals were being achieved and to determine how the vegetation and fire hazard (based on the overall fuel hazard assessment method) varied among the treated corridor, the forest edge environment, and the natural forest. The results showed that vegetation structure and composition in the treated zones had been modified to a state where herbaceous plant species were dominant; there was a significantly (p < 0.05) higher native grass cover and cover of herbs, sedges, and ferns in the treated zones, and a lower cover of trees and tall woody plants (>1 m in height) in these areas. For example, mean native grass cover and the cover of herbs and sedges in the treated areas was 10.2 and 2.8 times higher, respectively, than in the natural forest. The changes in the vegetation structure (particularly removal of tall woody vegetation) resulted in a lower overall fuel hazard in the treated zones, relative to the edge zones and natural forest. The overall fuel hazard was classified as ‘high’ in 83% of the transects in the treated areas, but it was classified as ‘extreme’ in 75% of the transects in the adjacent forest zone. Importantly, there were few introduced species recorded. The results suggest that fuel management has been successful in reducing wildfire risk in the transmission corridor. Temporal monitoring is recommended to determine the frequency of ongoing fuel management. Full article

Wood, as a natural material that stores carbon, is gaining increasing attention and has potential for use in interior architectural applications. Given the long indoor stay time characteristic of modern society, it is important to scientifically understand the effects of indoor wood application on the occupants. In this study, three residential buildings with an identical area and structure were constructed with different degrees of wood coverage (0%, 45%, 90%) using Larix kaempferi. Subsequently, indoor air quality (IAQ) evaluations and relative humidity measurements were conducted to assess the physical and chemical changes in each environment. The IAQ in wooden and non-wooden environments met the recommended IAQ standards established in South Korea. The results of the 8-month observation showed that, the higher the wood coverage ratio, the more the indoor humidity fluctuations were alleviated, and, in the case of the 90% wood coverage ratio condition, the humidity was maintained 5.2% lower in the summer and 10.9% higher in the winter compared to the 0% condition. To further assess the physiological responses induced by the wooden environment, the heart rate variability (HRV) was measured and compared for 26 participants exposed to each environment for two hours. In environments with a 0% and 90% degree of wood coverage, no statistically significant differences were found in the participants’ HRV indicators. But, in the group exposed to the 45% wooden environment, the results showed an increase in HRV indicators, natural logarithm of high frequency power (lnHF): 4.87 → 5.40 (p < 0.05), and standard deviation of normal-to-normal intervals (SDNN): 30.57 → 38.48 (p < 0.05), which are known indicators of parasympathetic nervous system activation. Full article

The accurate mapping of crop planting patterns is vital for sustainable agriculture and food security, particularly in regions with complex cropping systems and limited cloud-free observations. This research focuses on the Jianghan Plain in southern China, where diverse planting structures and persistent cloud cover make consistent monitoring challenging. We integrated multi-temporal Sentinel-2 and Landsat-8 imagery from 2017 to 2021 on the Google Earth Engine platform and applied a sample migration strategy to construct multi-year training data. A random forest classifier was used to identify nine major planting patterns at a 10 m resolution. The classification achieved an average overall accuracy of 88.3%, with annual Kappa coefficients ranging from 0.81 to 0.88. A spatial analysis revealed that single rice was the dominant pattern, covering more than 60% of the area. Temporal variations in cropping patterns were categorized into four frequency levels (0, 1, 2, and 3 changes), with more dynamic transitions concentrated in the central-western and northern subregions. A multiscale geographically weighted regression (MGWR) model revealed that economic and production-related factors had strong positive associations with crop planting patterns, while natural factors showed relatively weaker explanatory power. This research presents a scalable method for mapping fine-resolution crop patterns in complex agroecosystems, providing quantitative support for regional land-use optimization and the development of agricultural policies. Full article

Carbon dioxide (CO₂) is a predominant greenhouse gas significantly contributing to atmospheric heat retention, primarily driven by anthropogenic activities intensifying the greenhouse effect. This study aims to evaluate the diversity of plant species, above-ground biomass (AGB), and carbon stock within a dry dipterocarp forest, which is a vital local natural resource. This study presents a comprehensive evaluation of plant species diversity, AGB, and carbon stock capacity within a dry dipterocarp forest at the Nature Study Center, Mahasarakham University, located in the Kham Riang Subdistrict of Kantharawichai District, Maha Sarakham Province, spanning an area of 20.80 hectares. Ten sample plots, each measuring 40 × 40 m, were established and distributed across the study area. The diameter at breast height (DBH) and the height of the trees were meticulously recorded for all trees within these plots. Advanced statistical techniques were employed to calculate the relative dominance (RD), relative frequency (RF), and Importance Value Index (IVI), alongside a comprehensive assessment of plant species diversity. The AGB was assessed using precise allometric equations, with a focus on analyzing carbon storage within woody biomass. The findings revealed the presence of 52 tree species across 26 families within the forest. The total AGB was measured at 144.510 tons, with carbon stock reaching 67.920 tCO₂. These results offer critical insights into enhancing land management strategies to optimize carbon stock, thereby playing a vital role in mitigating greenhouse gas emissions, a significant factor in climate change dynamics. Full article

Understanding a catchment’s geomorphological and erosion processes is essential for sustainable land management and soil conservation. This study investigates the Xynias drained lake catchment in Central Greece using a twofold geospatial modelling approach that combines morphometric analysis with the Revised Universal Soil Loss Equation (RUSLE) to evaluate the area’s landscape evolution, surface drainage features, and soil erosion processes. The catchment exhibits a sixth-order drainage network with a dendritic and imperfect pattern, shaped by historical lacustrine conditions and the carbonate formations. The basin has an elongated shape with steep slopes, high total relief, and a mean hypsometric integral value of 26.3%, indicating the area is at an advanced stage of geomorphic maturity. The drainage density and frequency are medium to high, reflecting the influence of the catchment’s relatively flat terrain and carbonate formations. RUSLE simulations also revealed mean annual soil loss to be 1.16 t ha⁻¹ y⁻¹ from 2002 to 2022, along with increased erosion susceptibility in hilly and mountainous areas dominated by natural vegetation. In comparison to these areas, agricultural regions displayed less erosion risk. These findings demonstrate the effectiveness of combining GIS with remote sensing for detecting erosion-prone areas, informing conservation initiatives. Along with the previously stated results, more substantial conservation efforts and active land management are required to meet the Sustainable Development Goals (SDGs) while considering the monitored land use changes and climate parameters for future catchment management. Full article

The vibration of the belt drive system in fresh wolfberry sorting machines significantly impacts the sorting efficiency of wolfberries. To analyze the vibration changes induced by the belt drive, a simulation model was developed using multi-body dynamics software, Recur Dyn. The lateral vibration characteristics of the grading device’s belt were examined under varying initial tensions, speeds, and deflection angles. Response surface methodology (RSM) was employed to determine the relative influence of these factors on the belt’s vibration characteristics. The analysis indicated the order of influence, from greatest to least, as initial tension, deflection angle, and speed. Aiming to minimize the vibration amplitude at the belt’s midpoint, the optimal parameter combination was determined. The operating conditions yielding the minimum amplitude were found to be an initial tension of 520 N/mm, a drive speed of 60 rpm, and a belt deflection angle of 5°. Concurrently, a transverse vibration modal analysis was conducted to study the system’s natural frequencies and corresponding mode shapes, aiding in the identification of potential resonance issues. Finally, under optimal operating conditions, guided by the results of the belt simulation test, a 10 mm fillet was introduced at the edge of the pulley, effectively mitigating wear and vibration. Specifically, when the effective length of the transmission mechanism is set to 2200 mm and the total length of the fixed device is configured as 1600 mm, the amplitude attenuation rate achieves its peak value. This study demonstrates that the integration of theoretical analysis with simulation techniques provides a robust approach for optimizing the structural design of the grading device. Full article