Search Results (536)

Climate change (CC) is a growing threat to water security in agricultural regions, particularly in semi-arid areas. This study evaluates the impact of CC on reference evapotranspiration (ET₀) in Irrigation District 001 Pabellón de Arteaga, Aguascalientes (DR 001), with the aim of strengthening its sustainable management. We used historical data (2002–2023) and future projections (2026–2100) from 22 CMIP6 global climate models, previously corrected for bias under the scenarios SSP2-4.5 and SSP5-8.5. The evaluation of the correction methods showed that PTF-scale performed best in correcting precipitation, solar radiation, relative humidity, and wind speed, although the latter showed a low correlation. The maximum, mean, and minimum temperatures showed a better fit with the RQUANT and QUANT methods. The ACCESS-ESM1-5 model displayed the best performance in six of the nine corrected variables; therefore, it was the most suitable model to estimate ET₀. The uncertainty analysis showed that the FAO-56 method, although characterized by a higher current error, is more robust for future projections. A progressive increase in ET₀ is projected under both CC scenarios, ranging from 13.0 to 15.8% (SSP2-4.5), and between 12.5 and 20.4% (SSP5-8.5). The results highlight the urgent need to implement water adaptation strategies in DR 001 and make informed decisions to achieve resilient water management in the face of CC. Full article

To meet the stringent reliability requirements of rotor blades in turbomachines, greater effort should be devoted to improving both aerodynamic and structural performance in blade design. This paper introduces an aero-structural multi-disciplinary design optimization (MDO) method for compressor rotor blades using a discrete adjoint method and an equivalent stress model (ESM). The principles of the ESM are firstly introduced, and its accuracy in calculating equivalent stress is validated through comparison with a commercial program. Both the aerodynamic performance and the maximum equivalent stress (MES) are selected as optimization objectives. To modify the blade profile, the steepest descent optimization method is utilized, in which the necessary sensitivities of the cost function to the design parameters are calculated by solving the adjoint equations. Finally, the aero-structural MDO of a transonic compressor rotor, NASA Rotor 67, is conducted, and the Pareto solutions are obtained. The optimization results demonstrate that the adiabatic efficiency and the MES are competitive in improving multi-disciplinary performance. For most of the Pareto solutions, the MES can be considerably reduced with increased adiabatic efficiency. Full article

Background/Objectives: Our understanding of mood instability as a clinically important feature of many psychiatric conditions has been increasing over the last decade, but there remains a lack of clarity around the optimal ways to calculate mood instability in real time. We conducted a systematic review in order to describe the statistical methods used in studies investigating mood instability that collected mood data using ESM (Experience Sampling Methodology). Results: From a total of 229 papers, we found 15 discrete statistical methods were used a total of 319 times. In 76 (33%) studies, more than one statistical method was used, and 39 (17%) studies employed distinct statistical methods for particular aspects of affect dynamics. Conclusions: Based on our findings, we recommend standardisation of statistical methods to strengthen future research on mood instability and ultimately support better clinical outcomes for individuals with mood difficulties. Full article

Fluctuations in precipitation usually affect the ecological environment and human socioeconomics through events such as floods and droughts, resulting in substantial economic losses. The high-resolution models in the Coupled Model Intercomparison Project Phase 6 (CMIP6) are vital for simulating precipitation patterns in China; however, significant uncertainties still exist. This study utilized the quantile mapping (QM) method to correct biases in nine high-resolution Earth System Models (ESMs) and then comprehensively evaluated their precipitation simulation capabilities over the Chinese mainland from 1985 to 2014. Based on the selected models, the Bayesian Model Averaging (BMA) method was used to integrate them to obtain the spatial–temporal variation in precipitation over the Chinese mainland. The results showed that the simulation performance of nine models for precipitation from 1985 to 2014 was significantly improved after the bias correction. Six out of the nine high-resolution ESMs were selected to generate the BMA ensemble model. For the BMA model, the precipitation trend and the locations of significant points were more closely aligned with the observational data in the summer than in other seasons. It overestimated precipitation in the spring and winter, while it underestimated it in the summer and autumn. Additionally, both the BMA model and the worst multi-model ensemble (WMME) model exhibited a negative mean bias in the summer, while they displayed a positive mean bias in the winter. And the BMA model outperformed the WMME model in terms of mean bias and bias range in both the summer and winter. Moreover, high-resolution models delivered precipitation simulations that more closely aligned with observational data compared to low-resolution models. Full article

The climatic variability of near-surface air temperature (NSAT) over the Caspian region (35–60° N; 40–65° E) was analyzed in this study. The analysis was based on a comparison of data from various sources: weather stations, NOAA OISSTv2 satellite-based data, atmospheric reanalyses ECMWF ERA5, NASA MERRA-2, and NCEP/NCAR, and the outputs from 33 Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). CMIP6 models results from the historical and Shared Socioeconomic Pathways (SSPs) experiments were utilized. Over the period 1940–2023, NSAT exhibited variable changes across the Caspian region. Weather stations in the northwestern part of the region indicated NSAT increases of 0.9 ± 0.2 °C for 1985–2023. In the central-western part of the Caspian region, the increase in average NSAT between 1940–1969 and 1994–2023 was 1.4 °C with a spatial standard deviation of 0.3 °C. In the southern part of the Caspian region, the increase in average NSAT between 1986–2004 and 2005–2023 was 0.8 ± 0.1 °C. Importantly, all 33 CMIP6 models, as well as the ERA5 reanalysis, captured an average NSAT increase of approximately 1.3 ± 0.5 °C for the whole Caspian region between 1940–1969 and 1994–2023. From the ERA5 data, the increase in NSAT was more pronounced in the north (~1.6 °C) than in the central Caspian region, with the most significant warming observed in the mountainous regions of Iran (up to 3.0 °C). Under various CMIP6 SSPs scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5), projections indicate an increase in average NSAT across the study region. Comparing the periods 1994–2023 and 2070–2099, the projected NSAT increases are 1.7 ± 0.7 °C, 2.8 ± 0.8 °C, 4.0 ± 0.9 °C, and 5.2 ± 1.2 °C, respectively. For the earlier period of 2024–2053 relative to 1994–2023, the projected NSAT increases are 1.2 ± 0.4 °C, 1.3 ± 0.4 °C, 1.4 ± 0.4 °C, and 1.7 ± 0.5 °C. Notably, the projected increase in NSAT is slower over the Caspian Sea compared to the surrounding land areas. Full article

Eggshell membrane (ESM) is composed of approximately 90% protein. Our previous studies in healthy adults demonstrated that two months of daily ESM intake improved respiratory function, zigzag walking speed, and skin elasticity. The present study aims to address the knowledge gap regarding the in vivo effects of ESM in the context of inflammatory bowel disease (IBD). Proteomic analysis was performed on powdered ESM used as a dietary supplement. To investigate its pharmacokinetics in mice, tritium (³H)-labeled ESM was prepared using the ⁶Li(n,α)³H nuclear reaction. The therapeutic potential of ESM was further examined in a 2.0% dextran sulfate sodium (DSS)-induced murine model of IBD. In addition, fecal samples from both mice and healthy human subjects were analyzed using a modified terminal restriction fragment length polymorphism (T-RFLP) method. Lysozyme C (LYZ) was the most abundant protein (47%), followed by lysyl oxidase (12%) in ESM used in this study. ³H-ESM was mixed with MediGel, and orally administered to mice. Radioactivity levels were measured in blood, organs (duodenum, small intestine, large intestine, liver, kidney, lung, skin), and rectal feces at 0.5, 2, 5, 24, 48, and 72 h post-administration. Radioactivity in feces indicated excretion of undigested components, while systemic distribution suggested potential whole-body effects of ESM. Oral ESM and LYZ significantly alleviated body weight loss, diarrhea, and hematochezia in a DSS-induced murine model of IBD, leading to a significantly lower disease activity index on day 3 and showing a similar trend on day 5. Gut microbiota analysis showed increased Bacteroidales in the DSS group, while the ESM + DSS group maintained levels similar to the control. In humans, a double-blind, randomized controlled trial was conducted to evaluate the effects of ESM on gut microbiota in healthy adults. Participants received either ESM or placebo for 8 weeks. revealed a significant increase in alpha diversity at weeks 1 and 8 in the ESM group (p < 0.05), with between-group differences evident from week 1 (p < 0.01). ESM intake reduced Bacteroides and significantly increased Bifidobacterium and Lactobacillales at weeks 4 and 8. These findings suggest ESM supplementation promotes beneficial modulation of gut microbiota. These findings suggest that ESM, through its major protein components such as LYZ, may serve as a promising dietary intervention for maintaining intestinal health and mitigating inflammation in the context of IBD. Full article

Although the impact of spatial resolution on river flow simulation has been examined in several studies, unresolved uncertainties remain regarding parameter sensitivity and the applicability of different routing models. This study investigated the resolution dependency of the total runoff integrating pathway (TRIP) river routing model while focusing on East Asia. With the increasing spatial resolution of Earth system models (ESMs), understanding the effects of resolution changes on river discharge characteristics is essential for conducting accurate hydrological simulations. In this study, we conducted sensitivity experiments using the TRIP model at resolutions of 0.5°, 1°, and 0.125° while considering idealized and real-case scenarios. The results indicate significant improvements in the representation of river networks and discharge dynamics at higher resolutions, highlighting the need for parameter adjustments, particularly with respect to flow velocity and meandering factors. Parameters were optimized based on matching the travel time of runoff from precipitation sources to river mouths. The optimized parameters yielded consistent river storage and discharge results across different resolutions, enhancing the reliability of high-resolution hydrological modeling. Our study highlights the importance of resolution-aware modeling in improving the simulations of hydrological processes in different climate systems. Notably, our study can serve as a foundation for future interdisciplinary studies on climate modeling, river discharge and flow simulations, and hydrogeology. Full article

Methane is a short-lived climate forcer (SLCF) that has a pivotal influence on the Earth’s climate. This work focuses on mean methane concentrations and their year-to-year variability for the period 2003–2014 between four CMIP6 (Coupled Model Intercomparison Project Phase 6) Earth System Model simulations and CAMS (Copernicus Atmosphere Monitoring Service) reanalysis fields. The selected CMIP6 models are CNRM-ESM2-1, GFDL-ESM4.1, UKESM1, and EC-Earth3-AerChem, while monthly averaged fields from the CAMS global greenhouse gas reanalysis (EGG4) were employed. It is shown that the EC-Earth3-AerChem model closely aligns with CAMS methane concentration pattern, whereas other models display notable differences. Full article

The Honeycomb Distribution Network is a new distribution network architecture that utilizes the Smart Power-Exchange Station (SPES) to enable power interconnection and mutual assistance among multiple microgrids/distribution units, thereby supporting high-proportion integration of distributed renewable energy and promoting a sustainable energy transition. To promote the continuous and reliable operation of the Honeycomb Distribution Network, this paper proposes a Hierarchical Switching Control Strategy to address the issues of DC bus voltage (U_dc) fluctuation in the SPES of the Honeycomb Distribution Network, as well as the state of charge (SOC) and charging/discharging power limitation of the energy storage module (ESM). The strategy consists of the system decision-making layer and the converter control layer. The system decision-making layer selects the main converter through the importance degree of each distribution unit and determines the control strategy of each converter through the operation state of the ESM’s SOC. The converter control layer restricts the ESM’s input/output active power—this ensures the ESM’s SOC and input/output active power stay within the power boundary. Additionally, it combines the Flexible Virtual Inertia Adaptive (FVIA) control method to suppress U_dc fluctuations and improve the response speed of the ESM converter’s input/output active power. A simulation model built in MATLAB/Simulink is used to verify the proposed control strategy, and the results demonstrate that the strategy can not only effectively reduce U_dc deviation and make the ESM’s input/output power reach the stable value faster, but also effectively avoid the ESM entering the unstable operation area. Full article

This study investigates the impact of climate change on the Mudurnu Stream Basin in northwest Türkiye by analyzing climate parameters in the Mediterranean region. Historical data from EC-Earth2, HadGEM2-ES, and MPI-ESM-MR GCMs from the CMIP5 Euro-CORDEX archive were assessed, and future precipitation and temperature data were derived using five statistical bias correction methods for the selected EC-Earth2 model under RCP4.5 and RCP8.5 scenarios. The SWAT model was employed to simulate future runoff amounts for the Mudurnu Stream Basin. The findings reveal notable changes in precipitation and temperature. The annual and seasonal variations of total precipitation and average, maximum, and minimum temperatures for the RCP4.5 and RCP8.5 scenarios in the Sakarya and Mudurnu regions were analyzed and determined. The projections for future river flow indicate a significant increase in precipitation during the rainy seasons. The Mudurnu Stream mainstem will experience an increase in flow of between 70 and 140% under RCP4.5 and between 80 and 160% under RCP8.5. In the Dinsiz Stream tributary, a 32–55% increase is observed for the spring and summer months. In this context, the rainfall and runoff projections required for the estimation of potential drought and flood risks in the near and distant future were calculated. Full article

Biological-sequence representation methods are pivotal for advancing machine learning in computational biology, transforming nucleotide and protein sequences into formats that enhance predictive modeling and downstream task performance. This review categorizes these methods into three developmental stages: computational-based, word embedding-based, and large language model (LLM)-based, detailing their principles, applications, and limitations. Computational-based methods, such as k-mer counting and position-specific scoring matrices (PSSM), extract statistical and evolutionary patterns to support tasks like motif discovery and protein–protein interaction prediction. Word embedding-based approaches, including Word2Vec and GloVe, capture contextual relationships, enabling robust sequence classification and regulatory element identification. Advanced LLM-based methods, leveraging Transformer architectures like ESM3 and RNAErnie, model long-range dependencies for RNA structure prediction and cross-modal analysis, achieving superior accuracy. However, challenges persist, including computational complexity, sensitivity to data quality, and limited interpretability of high-dimensional embeddings. Future directions prioritize integrating multimodal data (e.g., sequences, structures, and functional annotations), employing sparse attention mechanisms to enhance efficiency, and leveraging explainable AI to bridge embeddings with biological insights. These advancements promise transformative applications in drug discovery, disease prediction, and genomics, empowering computational biology with robust, interpretable tools. Full article

Urban flooding and the management of stormwater present significant challenges that necessitate innovative and sustainable solutions. This research examines the effectiveness of green stormwater infrastructure (GSI) for resilient storm sewer systems using the Storm Water Management Model (SWMM), based on customized local climate scenarios. Daily climate data downscaled by four CMIP6 models—CESM2, GFDL-CM4, GFDL-ESM4, and NorESM2-MM—was used. The daily data was disaggregated into 15 min temporal resolution using the HyetosMinute R-package. Two GSI types—bio-retention and rain gardens—were evaluated with a maximum coverage of 30%. The analysis focuses on two future climate scenarios, SSP2-4.5 and SSP5-8.5, predicted under the Shared Socioeconomic Pathways (SSPs) framework. The performance of the stormwater network was assessed for mid-century (2041–2060) and late century (2081–2100), both before and after integration of GSI. Three performance metrics were applied: node flooding volume, number of nodes flooded, and pipe surcharging duration. The simulation results showed an average reduction in flooding volumes ranging between 86 and 98% over the area after integration of GSI. Similarly, reductions ranging between 78 and 89% and between 75 and 90% were observed in pipe surcharging duration and number of nodes vulnerable to flooding, respectively, following GSI. These findings underscore the potential of GSI in fostering sustainable urban water management and enhancement of sustainable development goals (SDGs). Full article

Global warming has made water resources more uneven in space and time, making water management harder. This study used the HBV-XGBoost model to see how climate change affects runoff in the Xiangxi River Basin. The HBV model simulated water processes, and XGBoost improved predictions by handling complex relationships. This study used the SDSM to create climate data for 2025–2100 and looked at runoff trends under different emission scenarios. The HBV-XGBoost model performed better than the HBV model in simulating runoff. Future predictions showed big differences in runoff trends under various SSP scenarios in the 2040s and 2080s. For example, under SSP585, the ACCESS-CM2 model projected a May runoff increase from 1527.52 m³/s to 2344.42 m³/s by the 2080s, and ACCESS-ESM1-5 projected an increase from 1462.11 m³/s to 2889.58 m³/s. All GCMs predicted a large rise in annual runoff under SSP585 by the 2080s, with FGOALS-g3 showing the highest growth rate of 76.54%. The model accurately simulated runoff changes and provided useful insights for adapting water management to climate change. However, this study has limitations, including uncertainties in machine learning models, potential input data biases, and varying applicability under different conditions. Future work should explore more climate models and downscaling methods to improve accuracy and consider local policies to better address climate impacts on water resources. Full article

Methane is a potent greenhouse gas (GHG) emitted from several anthropogenic sources, most notably enteric fermentation in domestic ruminants, primarily during exhalation. To date, however, it is unclear whether the excretion route of methane exhaled by ruminants occurs through the mouth or nostrils and what the pattern of excretion is; this is important in designing equipment and methods to measure the methane emitted by ruminants. Thus, the objective of this experiment was to quantify the exhaled methane excreted by dairy cows via the nostrils and mouth while resting, grazing, and ruminating, as well as the pattern of concentration during and between belching. Four tests were conducted in this study with four dairy cows each. In the first test, resting cows, carrying an electronic spirometry mask (ESM) assembled with a methane sensor in its outlet, were used to measure the concentration of methane in the air expelled through the nostrils and to model the methane concentration between belches; in the second test, a mouth mask was used to measure methane excreted through the mouth; in the third test, an ESM with a methane sensor assembled over the mouth was used to measure methane excreted through the mouth; finally, in the fourth test, a methane sensor was manually placed at 2.0–3.0 cm in front of the mouth of ruminating cows to measure the methane concentration. In the first test, a pattern of methane concentration between belches was identified, as well as a pattern of the methane concentration and volume of air exhaled during belching. Methane excretion through the mouth was not detected in any of the tests; hence, it is concluded that dairy cows, while resting, ruminating, and grazing, emit enteric methane only through the nostrils under normal respiration conditions. This is important in the respiratory physiology of ruminants and in designing equipment and methodology to measure cranial methane excretion. Full article

Climate warming is anticipated to significantly alter the distribution and composition of plant species in the Arctic, thereby cascading through food webs and affecting both associated fauna and entire ecosystems. To elucidate the trend in plant distribution in response to climate change, we employed the MaxEnt model to project the future ranges of 25 representative Arctic and Circumpolar plant species (including grasses and shrubs). Species distribution data, in conjunction with bioclimatic variables derived from climate projections of three selected General Circulation Models (GCMs), ESM2, IPSL, and MPIE, were utilized to fit the MaxEnt models. Subsequently, we predicted the potential distributions of these species under three Shared Socioeconomic Pathways (SSPs)—SSP126, SSP245, and SSP585—across a timeline spanning 2010, 2050, 2100, 2200, 2250, and 2300 AD. Range shift indices were applied to quantify changes in plant distribution and range sizes. Our results show that the ranges of nearly all species are projected to diminish progressively over time, with a more pronounced rate of reduction under higher emission scenarios. The species are generally expected to shift northward, with the distances of these shifts positively correlated with both the time intervals from the current state and the intensity of thermal forcing associated with the SSPs. Arctic species (A_Spps) are anticipated to face higher extinction risks compared to Boreal–Arctic species (B_Spps). Additional indices, such as range gain, loss, and overlap, consistently corroborate these patterns. Notably, the peak range shift speeds differ markedly between SSP245 and SSP585, with the latter extending beyond 2100 AD. In conclusion, under all SSPs, A_Spps are generally expected to experience more significant range shifts than B_Spps. In the SSP585 scenario all species are projected to face substantial range reductions, with Arctic species being more severely affected and consequently facing the highest extinction risks. These findings provide valuable insights for developing conservation recommendations for polar plant species and have significant ecological and socioeconomic implications. Full article