Browse Articles

Hydrolyzed MOF based Mo@h-ZIF-8/ZnS composite for enhanced photodegradation of dye pollutants using a combined experimental and RSM approach

Transient electromagnetic imaging of saltwater intrusion at the shrinking Dead Sea

Abstract The Dead Sea (DS) area faces critical environmental challenges, including saltwater intrusion (SWI), widespread sinkhole formation, and topographic changes, largely driven by declining DS water levels. These hazards adversely affect the region’s stability, hydrosystems, and agricultural facilities. In particular, the Ghor Al-Haditha (GAH) region in southern DS has been severely affected by these challenges. This study focuses on imaging saltwater intrusion pathways and their relationship with structural and hydrological features in the GAH region using the transient electromagnetic (TEM) method. A total of 195 TEM soundings of single-turn loop were conducted, spatially covering an area of 4 × 3 km² with a focus along three key stream channel profiles. The data are interpreted using 1D Occam and Marquardt-Levenberg inversion methods. Results are presented as spatial resistivity models at various depths, complemented by interpreted cross-sections for detailed analysis. The derived subsurface resistivity models reveal a saltwater interface with resistivity values less than 1.0 Ωm, detected at 100 m depth and following subsurface stream channels in the area. The main SWI extends 1.75 km inland in the shallow aquifer, most clearly along a well-defined channel in the central part of the study area and serving as a proxy for illustrating the significance of known and hidden hydrogeological pathways in this region, where higher intrusion rates are observed. Additionally, minor anomalies near fault and concealed fault zones may suggest localized upwelling linked to deeper saltwater migration. At the scale of the geophysical survey, the SWI predominantly encompasses the sinkhole belt, while spatially, it appears to be constrained by two bounding stream systems to the north and south. The mid-region resistivity model highlights a stratified subsurface structure comprising freshwater, brackish, and brine zones, emphasizing the model’s value in understanding aquifer vulnerability and guiding water management strategies in the GAH area.

Integrating visceral protein ratios and mid-arm circumference predicts survival and malnutrition in gastric cancer

The regulation of miR-155 strand selection by CELF2, FUBP1 and KSRP proteins

Modelling of acid brown 14 and acid yellow 36 dyes adsorption from water by self-nitrogen-doped activated carbon

Abstract Acid Brown 14 (AB14) and Acid Yellow 36 (AY36) are synthetic azo dyes extensively utilized in numerous industries, resulting in detrimental environmental consequences. This study aims to manufacture self-nitrogen-doped porous activated carbon (AC7-800) and investigate its effectiveness in removing the AB14 and AY36 dyes from water solutions. The AC7-800 was created by combining fish waste (with a protein composition of 60% as a nitrogen source), which served as a self-nitrogen dopant. An equal mass ratio (1:1:1) of sawdust, fish waste, and zinc chloride underwent a hydrothermal treatment at 180 °C for 5 h. Subsequently, the material underwent pyrolysis for 1 h in a continuous flow of nitrogen gas at 800 °C to produce AC7-800. The AC7-800 adsorbent was successfully tested and approved to eliminate colours from water in batch trials. The AC7-800 samples were analyzed using BET, SEM, EDX, XRD, FTIR, TGA, and DTA techniques. The results demonstrated the practical synthesis of AC7-800 with a nitrogen mass percentage concentration of 13.73%. The specific surface area, mean pore diameter and monolayer volume were measured to be 437.51 m2 g− 1, 2.01 nm, and 100.52 cm3 g− 1, respectively. The objective is to examine the elimination of AB14 and AY36 dyes from a water-based solution using various factors such as initial dye concentration, solution pH, AC7-800 dosage, and contact time. The efficacy of AC7-800 in removing AB14 and AY36 dyes was found to be dependent on the pH level. The highest elimination efficiency of 63.29% and 85.86% was achieved at pH 1.5 for AB14 and AY36 dyes, respectively. Additionally, the maximum adsorption capacity (Q m ) for AB14 and AY36 dyes was determined to be 107.5 and 263.2 mg g− 1, respectively. The equilibrium data demonstrated a good association with the Langmuir model (LIM) for both dyes, although the best-fit kinetic model was the pseudo-second-order model (PSOM). Electrostatic interactions between the dye molecules and the charged spots on the AC7-800 surface cause both dyes to adsorb. The prepared AC7-800 can be considered a highly effective, accessible, and environmentally acceptable adsorbent for the adsorption of AB14 and AY36 dyes from simulated water. AB14 and AY36 dyes adsorption to AC7-800 was predicted by the response-surface methodology (RSM) and artificial neural networks (ANN) models. The ANN model was more effective in predicting AB14 and AY36 dyes adsorption than the D-optimal RSM, and it was highly applicable in the sorption process.

ReactorNet based on machine learning framework to identify control rod position for real time monitoring in PWRs

Abstract This paper presents a novel approach, ReactorNet, a machine learning framework leveraging thermal neutron flux imaging to enable real-time monitoring of pressurized water reactors (PWRs). By integrating EfficientNetB0 with a hybrid classification-regression architecture, the model accurately identifies control rod positions and operational parameters through thermal neutron flux patterns detected by ex-core sensors. Principal Component Analysis (PCA) and Clustering Analysis decode radial flux variations linked to rod movements, while simulations of a 2772-MW(th) PWR using TRITON FORTRAN validate the framework. This framework outperforms Vision Transformers and ResNet50, achieving superior multi-class accuracy (97.5%) and reduced the mean absolute error (MAE) of regression. Test-Time Augmentation and cross-validation mitigate data limitations, ensuring robustness. This work bridges AI and nuclear engineering, demonstrating EfficientNetB0’s potential for precise, real-time reactor monitoring, enhancing operational safety and efficiency.

Research on sentiment index and real estate demand forecasting based on BERT-BiLSTM and ADL-MIDAS models

Investigating industrial by-product for soil conditioning addressing environmental risk and waste reduction alternatives

Non-invasive evaluation of advanced glycation end products in hair as early markers of diabetes and aging

Integration of multi-omics quantitative trait loci evidence reveals novel susceptibility genes for Alzheimer’s disease

Identification of foam cell like M2 macrophages, AEBP1 biomarkers, and resveratrol as potential therapeutic in MASLD using Ecotyper and WGCNA

Research on the impact of artificial intelligence applications on agricultural green development

Knowledge graph convolutional networks with user preferences for course recommendation

Exploring weighting schemes for the discovery of informative generalized between pathway models to uncover pathways in genetic interaction networks

Efficient extracellular expression and rapid screening of human-like recombinant gelatin in Komagataella phaffii

Randomized clinical trial for pop titrated versus the slow coagulation cyclophotocoagulation in treating dark irises neovascular glaucoma

Monitoring and investigation to control the brain network disease under immunotherapy by using fractional operator

Comparing soil microbial diversity in smallholder plantain backyard gardens and main farms in Western and Central Africa

Fire susceptibility assessment in the Carpathians using an interpretable framework

Abstract Climate change endangers the Carpathian region by increasing the risk of fires. In response, our study provides a harmonised dataset with twenty-seven variables and develops an interpretable machine learning-based framework for assessing fire susceptibility across all seven countries of the region. We applied a two-stage process: first, using various feature selection techniques to refine predictors before the modeling phase, and second, utilising the SHAP framework to interpret model predictions. Between these steps, advanced machine learning models were optimised and trained in the H2O environment, demonstrating high predictive accuracy. Our findings revealed eight fire susceptibility clusters. The resulting dataset, susceptibility maps, and detailed interpretative insights serve as a valuable resource for local communities and policy-makers in the region.

CT changes in a randomized trial comparing early therapies in an outpatient population at high risk of severe COVID19 disease