Search Results (35,129)

Rapid sand filtration is a critical step in the water treatment process, as its effectiveness directly impacts the supply of safe drinking water. However, optimising filtration processes in water treatment plants (WTPs) presents a significant challenge due to the varying operational parameters and conditions. This study applies explainable machine learning to enhance insights into predicting direct filtration operations at the Ålesund WTP in Norway. Three baseline models (Multiple Linear Regression, Support Vector Regression, and K-Nearest Neighbour (KNN)) and three ensemble models (Random Forest (RF), Extra Trees (ET), and XGBoost) were optimised using the GridSearchCV algorithm and implemented on seven filter units to predict their filtered water turbidity. The results indicate that ML models can reliably predict filtered water turbidity in WTPs, with Extra Trees models achieving the highest predictive performance (R² = 0.92). ET, RF, and KNN ranked as the three top-performing models using Alternative Technique for Order of Preference by Similarity to Ideal Solution (A-TOPSIS) ranking for the suite of algorithms used. The feature importance analysis ranked the filter runtime, flow rate, and bed level. SHAP interpretation of the best model provided actionable insights, revealing how operational adjustments during the ripening stage can help mitigate filter breakthroughs. These findings offer valuable guidance for plant operators and highlight the benefits of explainable machine learning in water quality management. Full article

Highway culverts are essential components of transportation infrastructure, designed to convey water beneath highways and protect embankments from flooding. However, extreme flood events often impose hydraulic loads, overtopping, and debris accumulation that can trigger erosion, scour, blockage, and in severe cases, catastrophic washout. This paper presents a comprehensive review of highway culvert behavior under flooding conditions, integrating insights from hydraulics, geotechnical engineering, and structural performance. The review is organized around four themes: (1) types of flooding and their interactions with culverts; (2) hydraulic performance during flood events; (3) common failure modes, including scour, debris blockage, and structural instability; and (4) mitigation strategies to enhance resilience. Advances in hydraulic modeling, including 1D, 2D, 3D, and CFD approaches, are summarized, with attention to their accuracy, applicability limits, and validation needs. Representative experimental, numerical, and empirical studies are grouped by common properties to highlight key findings and constraints. Finally, emerging research opportunities are discussed, including the need for quantitative relationships between culvert geometry and flood intensity, methods to assess structural capacity loss during flooding, and the integration of artificial intelligence and computer vision for rapid post-flood inspection. This synthesis establishes a foundation for more robust evaluation, design, and maintenance strategies, supporting the long-term resilience of highway culverts in an era of increasingly frequent and severe floods. Full article

The electrical arcs generated by high-speed dynamic separation between pantograph and catenary systems pose a significant threat to the operational safety of high-speed railways. Environmental factors, particularly relative humidity and airflow, critically influence arc characteristics. This study establishes a two-dimensional pantograph–catenary arc model based on magnetohydrodynamic theory, validated through a self-developed experimental platform. Research findings demonstrate that as relative humidity increases from 25% to 100%, the core arc temperature decreases from 10,500 K to 9000 K due to enhanced heat dissipation in humid air and electron capture by water molecules; the peak arc voltage rises from 37.25 V to 48.17 V resulting from accelerated deionization processes under high humidity conditions; the average arc energy in polar regions increases from 2.5 × 10⁻⁴ J/m³ to 3.5 × 10⁻⁴ J/m³, exhibiting a saddle-shaped distribution; and the maximum arc pressure declines from 5.3 Pa to 3.7 Pa. Under airflow conditions of 10–30 m/s, synergistic effects between airflow and humidity further modify arc behavior. The most pronounced temperature fluctuations and most frequent arc root migration occur at 100% humidity with 30 m/s airflow, while the shortest travel distance and longest persistence are observed at 25% humidity with 10 m/s airflow, as airflow accelerates heat dissipation and promotes arc root alternation. Experimental measurements of arc radiation intensity and temperature distribution show excellent agreement with simulation results, verifying the model’s reliability. This study quantitatively elucidates the influence patterns of humidity and airflow on arc characteristics, providing a theoretical foundation for enhancing pantograph–catenary system reliability. Full article

This study examines the effects of the extreme drought and heatwaves that occurred in Romania during the summer of 2024 on the microbiological and mycotoxicological quality of wheat (Triticum aestivum) stored until April 2025, as well as on the quality of wholemeal flour and bread derived from it. Comparative analyses were conducted against the contamination in wheat harvested in 2024. The hot and dry conditions significantly influenced the microbial and mycotoxicological contamination of both freshly harvested and stored wheat, as well as the derived flour and bread, due to their notably reduced moisture content and water activity. Although levels of total fungi, Fusarium-damaged kernels, and mycotoxins deoxynivalenol, aflatoxin B1, and ochratoxin A remained well below regulatory thresholds, higher contamination was observed in Transylvania and Moldavia Moldavia—particularly in the Curvature Carpathians, likely due to their cooler and wetter microclimates. The observed quality changes were strongly associated with alterations in physico-chemical, rheological, and colorimetric parameters, posing potential economic challenges for the milling and baking industries. The study recommends implementing integrated regional strategies to enhance wheat resilience, optimize production systems, and improve contamination control in response to increasing climate stress across Southeastern Europe. Full article

Groundwater plays a critical role in maintaining streamflow during low-flow periods. However, accurately quantifying groundwater flow still remains a modeling challenge. Prolonged low-flow or drought conditions necessitate long-term simulations, further increasing the complexity of achieving reliable results. To address these issues, a novel modeling framework (HYD module in LoFloDes) that integrates a one-dimensional (1D) river module with two-dimensional (2D) groundwater module via bidirectional coupling, enabling robust and accurate simulations of both groundwater and river dynamics throughout their interactions, especially over extended periods, was developed. The HYD module was applied to the Rur River, calibrated using gridded groundwater data, groundwater and river gauge data from 2002 to 2005 and validated from 1991 to 2020. During validation periods, the simulated river and groundwater levels generally reproduced observed trends, although suboptimal performance at certain gauges is attributed to unmodeled local anthropogenic influences. Comparative simulations demonstrated that the incorporation of groundwater–river interactions markedly enhanced model performance, especially at the downstream Stah gauge, where the coefficient of determination (R²) increased from 0.83 without interaction to 0.9 with interaction. Consistent with spatio-temporal patterns of this interaction, simulated groundwater contributions increased from upstream to downstream and were elevated during low-flow months. These findings underscore the important role of groundwater contributions in local river dynamics along the Rur River reach. The successful application of the HYD module demonstrates its capacity for long-term simulations of coupled groundwater–surface water systems and underscores its potential as a valuable tool for integrated river and groundwater resources management. Full article

Snow is an essential meteorological variable and an indicator of the changing climate. Its variations, particularly in snow depth and snow water equivalent, result mainly from changes in winter precipitation and air temperature. Recently, these conditions have been thoroughly investigated worldwide, revealing a general prevailing decline in precipitation and increasing tendencies in air temperatures. However, no systematic or up-to-date studies for Bulgaria exist. The main goal of the current project is to fill this national knowledge gap in the snow conditions in our mountains. For that purpose, we used 31 stations with altitudes ranging from 527 to 2925 m a.s.l. for the period between 1961 and 2020, covering two significant reference climatic periods. We extracted data about snow cover maximums, mean air temperatures, and precipitation amounts for the whole winter season in mountainous regions from October to April; however, we mainly present the results for the three winter months: December, January, and February. Most of the stations do not demonstrate any significant trends for snow depth maximums, except for the three lower stations in central west Bulgaria, which show significant increases. On the opposite end of the scale, two of the highest stations demonstrated notable decreases. The time series for the precipitation amounts are also predominantly indefinite. Significant decreasing trends can be found at the highest three alpine stations. The change in the mean seasonal air temperature is predominantly positive—17 of the stations show positive trends, and for 12, the increases are significant. The altitude of the strongest seasonal temperature rise lies between 1000 and 1700 m. Finally, due to the obvious nonlinearity of some of the time series, we decided to check for change points and a nonlinear approach to fit the data. This analysis demonstrates general changes in the investigated characteristics from the beginning of the 1970s to the middle of the 1980s. Full article

Drought is a leading constraint on plant productivity and will intensify with climate change. Plant acclimation emerges from a multilayered regulatory system that integrates signaling, transcriptional reprogramming, RNA-based control, and chromatin dynamics. Within this hierarchy, non-coding RNAs (ncRNAs) provide a unifying regulatory layer; microRNAs (miRNAs) modulate abscisic acid and auxin circuits, oxidative stress defenses, and root architecture. This balances growth with survival under water-deficient conditions. Small interfering RNAs (siRNAs) include 24-nucleotide heterochromatic populations that operate through RNA-directed DNA methylation, which positions ncRNA control at the transcription–chromatin interface. Long non-coding RNAs (lncRNAs) act in cis and trans, interact with small RNA pathways, and can serve as chromatin-associated scaffolds. Circular RNAs (circRNAs) are increasingly being detected as responsive to drought. Functional studies in Arabidopsis and maize (e.g., ath-circ032768 and circMED16) underscore their regulatory potential. This review consolidates ncRNA biogenesis and function, catalogs drought-responsive modules across model and crop species, especially cereals, and outlines methodological priorities, such as long-read support for isoforms and back-splice junctions, stringent validation, and integrative multiomics. The evidence suggests that ncRNAs are tractable entry points for enhancing drought resilience while managing growth–stress trade-offs. Full article

Streamflow forecasting in snowmelt-dominated basins is essential for water resource planning, flood mitigation, and ecological sustainability. This study presents a comparative evaluation of statistical, machine learning (Random Forest), and deep learning models (Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Spatio-Temporal Graph Neural Network (STGNN)) using 30 years of data from 20 monitoring stations across the Upper Colorado River Basin (UCRB). We assess the impact of integrating meteorological variables—particularly, the Snow Water Equivalent (SWE)—and spatial dependencies on predictive performance. Among all models, the Spatio-Temporal Graph Neural Network (STGNN) achieved the highest accuracy, with a Nash–Sutcliffe Efficiency (NSE) of 0.84 and Kling–Gupta Efficiency (KGE) of 0.84 in the multivariate setting at the critical downstream node, Lees Ferry. Compared to the univariate setup, SWE-enhanced predictions reduced Root Mean Square Error (RMSE) by 12.8%. Seasonal and spatial analyses showed the greatest improvements at high-elevation and mid-network stations, where snowmelt dynamics dominate runoff. These findings demonstrate that spatio-temporal learning frameworks, especially STGNNs, provide a scalable and physically consistent approach to streamflow forecasting under variable climatic conditions. Full article

This study evaluates the feasibility of employing rainwater as an alternative feedstock for hydrogen production via electrolysis. While conventional systems typically rely on high-purity water—such as deionized or distilled variants—these can be cost-prohibitive and environmentally intensive. Rainwater, being naturally available and minimally treated, presents a potential sustainable alternative. In this work, a series of comparative experiments was conducted using a proton exchange membrane electrolyzer system operating with both deionized water and rainwater collected from different Austrian locations. The chemical composition of rainwater samples was assessed through inductively coupled plasma, ion chromatography and visual rapid tests to identify impurities and ionic profiles. The electrolyzer’s performance was evaluated under equivalent operating conditions. Results indicate that rainwater, in some cases, yielded comparable or marginally superior efficiency compared to deionized water, attributed to its inherent ionic content. The study also examines the operational risks linked to trace contaminants and explores possible strategies for their mitigation. Full article

Polysiloxanes are unique polymers used in medicine and materials science and are ideal for various modifications. Classic functionalization methods involve a covalent approach, but finer tuning of the properties of the final polymers can also be achieved through sub-sequent noncovalent modifications. This study introduces a fundamentally new approach to polysiloxane functionalization by incorporating cooperative noncovalent interaction centers: selenium-based chalcogen bonding donors and polyfluoroaromatic π-hole acceptors into a single polymer platform. We developed an efficient nucleophilic substitution strategy using poly((3-chloropropyl)methylsiloxane) as a platform for introducing Se-containing groups with polyfluoroaromatic substituents. Three synthetic approaches were evaluated; only direct modification of Cl-PMS-2 proved successful, avoiding catalyst poisoning and crosslinking issues. The optimized methodology utilizes mild conditions and achieved high substitution degrees (74–98%) with isolated yields of 60–79%. Comprehensive characterization using ¹H, ¹³C, ¹⁹F, ⁷⁷Se, and ²⁹Si NMR, TGA, and contact angle measurements revealed significantly enhanced properties. Modified polysiloxanes demonstrated improved thermal stability (up to 37 °C higher decomposition temperatures, 50–60 °C shifts in DTG maxima) and increased hydrophobicity (water contact angles from 69° to 102°). These systems potentially enable chalcogen bonding and arene–perfluoroarene interactions, providing foundations for materials with applications in biomedicine, electronics, and protective coatings. This dual-functionality approach opens pathways toward adaptive materials whose properties can be tuned through supramolecular modification while maintaining the inherent advantages of polysiloxane platforms—flexibility, biocompatibility, and chemical inertness. Full article

The present study investigated the heat extraction behavior of the horizontal well closed-loop geothermal systems under multi-factor influences. Particularly, the numerical model was established based on the geological condition of the geothermal field in Xiong’an New Area, and the XGBoost-SHAP (eXtreme Gradient Boosting and SHapley Additive exPlanations) algorithm was employed for multivariable analysis. The results indicated that the produced water temperature and thermal power of a 3000 m-long horizontal well were 2.61 and 4.23 times higher than those of the vertical well, respectively, demonstrating tantalizing heat extraction potential. The horizontal section length (SHAP values of 8.13 and 165.18) was the primary factor influencing production temperature and thermal power, followed by the injection rate (SHAP values of 1.96 and 64.35), while injection temperature (SHAP values of 1.27 and 21.42), geothermal gradient (SHAP values of 0.95 and 19.97), and rock heat conductivity (SHAP values of 0.334 and 17.054) had relatively limited effects. The optimal horizontal section length was 2375 m. Under this condition, the produced water temperature can be maintained higher than 40 °C, thereby meeting the heating demand. These findings provide important insights and guidance for the application of horizontal wells in hydrothermal reservoirs. Full article

Multi-principal element nitrides have great application potential in protective coatings. However, the investigation of the oxidation and corrosion resistance of multi-principal element nitride coatings is still insufficient. The synthesis and high-temperature performance of AlCrNbSiTiN multi-principal element nitride coatings fabricated through optimized arc ion plating (AIP) were explored. Leveraging the high ionization efficiency and ion kinetic energy characteristic of AIP, coatings with significantly fewer internal defects were obtained. These coatings demonstrate superior mechanical properties, including a maximum hardness of 36.5 GPa and critical crack propagation resistance (CPR) values approaching 2000 N². Optimal coatings exhibited exceptional water vapor corrosion resistance (5.15 at% O after 200 h). The coatings prepared at −150 V had the optimal corrosion resistance, with the coating resistance and corrosion current density being 1.68 × 10⁴ Ω·cm² and 0.79 μA·cm⁻², respectively. AlCrNbSiTiN coatings produced under these optimized AIP conditions exhibit remarkably high-temperature oxidation, highlighting their potential for use in demanding engineering applications. Full article

A novel Ag(I) coordination polymer, [Ag₂(bipy)(btca)]_n, (SCP 1) was synthesized using 4,4′-bipyridyl (bipy) and 1,2,4,5-benzene-tetracarboxylic acid (H₄BTC). Characterization by FT-IR, ¹H/¹³C NMR, and single-crystal X-ray diffraction confirmed its 3D network structure. The structure of SCP 1 consists of two chains arranged in …ABAB… fashion. Chain A is one-dimensional, containing [Ag(4,4′-bipy)]_n chain, while chain B is free, containing uncoordinated 1,2,4,5-benzene tetracarboxylate and water molecules. The stacking and argentophilic interactions extend the chain A of [Ag(4,4′-bipy)]_n into a two-dimensional layer. In contrast, chain B of uncoordinated 1,2,4,5-benzene tetracarboxylate and water molecules form a 1-D chain through extensive hydrogen bonds between water molecules and BTC ions and between water molecules themselves. Chains A and B are connected through extensive hydrogen bonds, generating a three-dimensional network structure. This Silver I supramolecular coordination polymer (SCP 1) demonstrated high catalytic activity as a recyclable heterogeneous catalyst for the synthesis of 5-substituted 1H-tetrazoles via [3+2] cycloaddition of NaN₃ and terminal nitriles under solvent-free conditions in a Q-tube pressure reactor (yields: 94–99%). A mechanistic proposal involving cooperative Lewis acidic Ag(I) sites and Brønsted acidic -COOH groups facilitates the cycloaddition and protonation steps. SCP 1 catalyst exhibits reusability up to 4 cycles without significant loss of activity. The structural stability of the SCP 1 catalyst was assessed based on PXRD and FTIR analyses of the catalyst after usage, confirming its integrity during the recycling process. Full article

Obesity, characterized by the accumulation of excess adipocytes, is a significant risk factor for type 2 diabetes and non-alcoholic fatty liver disease. Medicinal plants, including Hibiscus sabdariffa, have been traditionally employed to prevent or treat conditions such as obesity and inflammation due to their safety profile and minimal side effects during long-term use. However, the anti-obesity potential of Hibiscus syriacus, a taxonomically distinct species within the same genus, remains unexplored. In this study, we screened 181 varieties of H. syriacus buds for anti-obesity effects and identified the water extract of the ‘Pyeonghwa’ bud (HPWE) as a potent inhibitor of adipogenesis. Using 3T3-L1 murine pre-adipocyte cells, we demonstrated that HPWE significantly reduced lipid accumulation without inducing cytotoxicity. Mechanistically, HPWE downregulated the expression of key adipogenic signaling proteins and transcription factors, including peroxisome proliferator-activated receptor gamma (PPARγ) and CCAAT/enhancer-binding protein alpha (C/EBPα), which serve as molecular markers of adipogenesis. Additionally, in vivo experiments employing a high-fat-diet-induced obesity mouse model using C57BL/6 species confirmed the anti-obesity effects of HPWE. Collectively, these findings suggest that HPWE represents a promising candidate for the prevention of obesity. Full article

The cold-regulated (Cor413) gene family encodes plant-specific, multispanning transmembrane proteins that localize to the plasma and thylakoid membranes; these genes are regulated by environmental stimuli. In this study, the Cor413-1 gene, isolated from the drought and saline-tolerant wild species Saccharum spontaneum, was engineered into the elite sugarcane cultivar Co 86032 to produce a commercially superior cultivar with improved abiotic stress tolerance. Expression analysis of the Cor413-1 gene transgenic lines under drought and salinity stress exhibited distinct gene expression patterns. During stress conditions, transgenic events, such as Cor413-9 and Cor413-3, showed notable resilience to salt stress and had a high relative expression of the Cor413-1 gene and other stress-related genes. The evaluation of physiological parameters showed that under stress conditions, transgenic events experienced milder wilting and less cell membrane injury than the non-transgenic control. Transgenic lines also demonstrated elevated relative water content and better photosynthetic efficiency, with events like Cor413-10 and Cor413-12 showing exceptional performance. Biochemical analyses indicated elevated proline content, higher activity of enzymatic antioxidants such as sodium dismutase (SOD), catalase (CAT), and Ascorbate peroxidase (APX), and a low level of malondialdehyde MDA production in the transgenic lines. Thus, demonstrating the potential of the Cor413-1 gene for developing multiple stress-tolerant cultivars. Full article