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Wheat’s nutritional value is critical for human nutrition and food security. However, more attention is needed, particularly regarding the content and concentration of iron (Fe) and zinc (Zn), especially in the context of climate change (CC) impacts. To address this, various controlled field experiments were conducted, involving the cultivation of three wheat cultivars over three growing seasons at multiple locations with different soil and climate conditions under varying Fe and Zn treatments. The yield and yield attributes, including nutritional values such as nitrogen (N), Fe and Zn, from these experiments were integrated with national yield statistics from other locations to train and test different machine learning (ML) algorithms. Automated ML leveraging a large number of models, outperformed traditional ML models, enabling the training and testing of numerous models, and achieving robust predictions of grain yield (GY) ( R 2 > 0.78), N ( R 2 > 0.75), Fe ( R 2 > 0.71) and Zn ( R 2 > 0.71) through a stacked ensemble of all models. The ensemble model predicted GY, N, Fe, and Zn at spatial explicit in the mid-century (2020–2050) using three Global Circulation Models (GCMs): GFDL-ESM4, HadGEM3-GC31-MM, and MRI-ESM2-0 under two shared socioeconomic pathways (SSPs) specifically SSP2-45 and SSP5-85, from the downscaled NEX-GDDP-CMIP6. Averaged across different GCMs and SSPs, CC is projected to increase wheat yield by 4.5%, and protein concentration by 0.8% with high variability. However, it is expected to decrease Fe concentration by 5.5%, and Zn concentration by 4.5% in the mid-century (2020–2050) relative to the historical period (1980–2010). Positive impacts of CC on wheat yield encountered by negative impacts on nutritional concentrations, further exacerbating challenges related to food security and nutrition.

The effect of microwave pre-treatment on the magnetic properties of tennantite and enargite was investigated. Magnetic susceptibility, XRD, and XPS characterization of tennantite and enargite before and after treatment were conducted to explore the changes in their magnetic properties. Moreover, magnetic separation of chalcopyrite binary mixtures with enargite and tennantite was performed. The results showed insignificant effects on the magnetic susceptibility of the two minerals after microwave pre-treatment. Magnetic separation results showed arsenic rejection by 84.2%, and 76.3% in the case of enargite and tennantite binary mixtures with chalcopyrite; respectively.

By integrating various irrigation and agriculture management techniques, it is possible to considerably improve water productivity. In order to examine the impact of irrigation scheduling (1.0 and 1.2 pan evaporation coefficient), planting method (ridge and raised bed), and potassium silicate foliar application (0 ppm and 100 ppm) on maize ( Zea mays L) growth, yield, and water-related factors, a two-season field experiment was conducted in a hot-dry climate region of southern Egypt during 2017 and 2018. The results showed that the seasonal irrigation requirement and consumptive use were higher at 1.2 than the 1.0 pan evaporation coefficient, irrespective of the planting methods. Raised bed planting method saved about 19% of applied water (two seasons average) compared to the ridge planting method. Plants treated with potassium silicates attained higher yields compared to the control, irrespective of the irrigation level and planting method. Moreover, irrigation at 1.2 pan evaporation resulted in the lowest daily ETc values, i.e., 3.15, 6.0, 6.7, and 2.8 mm for plant growth stages, i.e., ini, dev, mid and late. This resulted in the lowest Kc values (0.47, 0.91, 1.16, and 0.61) at different plant growth stages (ini, dev, mid and late). Based on the study findings, it is recommended to use a deficit irrigation of 0.15% based on accumulated pan evaporation values of 1.2, coupled with raised bed planting method and the application of 100 ppm potassium silicates, for optimal maize water productivity and net return.

Incidence of enteric viruses in sewage, the efficacy of wastewater treatment plants to remove these viruses, and health effects from their release into the surface water are very important environmental issues in the microbiology field. One of the most pathogenic enteric viruses is adenovirus which can cause a serious disease such as gastroenteritis with low grade fever and mild dehydration in humans. In this study we performed qualitative polymerase chain reaction (PCR) analysis of HAdV on 60 stool samples from children with acute gastroenteritis admitted to Abu-Rish hospital and 96 environmental samples (32 raw sewage, 32 treated sewage, 32 sewage sludge) collected from Zenin wastewater treatment plant (WWTP). HAdV were detected in 17 (28.3%) of stool, 27 (84.4%) of raw sewage, 16 (50%) of treated sewage and 25 (78%) of sludge samples. The viral concentrations were in the range of 2.02 × 106–7.23 × 106, 8.7 × 105–4.3 × 106, 1.22 × 104–3.7 × 106 and 1.48 × 106–1.77 × 107 GC/mL in stool, raw sewage, treated sewage, and sludge, respectively. HAdV was detected throughout the whole year of sample collection. Moreover, our results suggested that males were more susceptible to adenovirus infections than females. The results indicate that the high incidence of HAdV in the treated sewage may cause adverse health effects. This article has been made Open Access thanks to the generous support of a global network of libraries as part of the Knowledge Unlatched Select initiative.

Wastewater plays a major role in water pollution causing transmission of several viral pathogens, including Aichi virus (AiV) and human bocavirus (HBoV), associated with gastrointestinal illness in humans. In this study, we investigated the presence of AiV and HBoV in aquatic, sludge, sediment matrices collected from Abu-Rawash wastewater treatment plant (WWTP), El-Rahawy drain, Rosetta branch of the River Nile in Egypt by conventional polymerase chain reaction (PCR). AiV RNA was detected in 16.6% (2/12), 8.3% (1/12), 8.3% (1/12), 22% (16/72), 12.5% (3/24), 4% (1/24), and 0/24 (0%) of untreated raw sewage, treated sewage, sewage sludge, drainage water, drain sediment, river water, and river sediment, respectively. On the other hand, HBoV DNA was detected in 41.6% (5/12), 25% (3/12), 16.6% (2/12), 48.6% (35/72), 29% (7/24), 3/24 (12.5%), 4% (1/24) of untreated raw sewage, treated raw sewage, sewage sludge, drainage water, drain sediment, river water, and river sediment, respectively. This study provides data on the presence of these viruses in various types of water samples that are valuable to environmental risk assessment. In addition, the current study demonstrates the importance of environmental monitoring as an additional tool to investigate the epidemiology of AiV and HBoV circulating in a given community.

Soil degradation constitutes a critical challenge, particularly in arid and semi-arid regions, where its impacts are most severe. To mitigate these impacts, comprehensive strategies must be developed to rehabilitate and restore soil functionality. The interplay of physical, chemical, and biological characteristics jointly influences the evolution of soil processes and the restoration of its functions. This study aims to evaluate the soil quality, land degradation, and ecological risks using an integrated approach that combines GIS and remote sensing with machine learning (ML) models, specifically artificial neural network (ANN), random forest (RF), and decision tree (DT) methodologies. Utilizing Landsat ETM + images alongside a Digital Elevation Model (DEM), a geomorphological map was generated, revealing that the studied area consists of two distinct landscapes: floodplain and lacustrine plain. The results indicated that primary forms of soil degradation in the examined region included salinization, alkalization, compaction, and waterlogging. Remarkably, 90.61% of the study area was classified as high quality, whereas 9.39% was categorized as moderate quality. Furthermore, measurements of soil pollution showed considerable variation in the concentrations of trace elements throughout the area. The geo-accumulation index (I geo ) revealed significant variations of heavy metals between different soil sample sites. Specifically, the samples exhibited pollution levels (I geo < 0) for As, Cd, and Se. In contrast, the levels of Cu, Pb, Zn, and U indicated a very high degree of pollution (I geo < 3). Furthermore, assessments of contamination degree (CD), potential ecological risk (PER), and pollution load index (PLI) demonstrated that all tested soil samples were found to be highly contaminated by the analyzed elements. The ANOVA results also indicated that there were no significant differences in model performance. Nevertheless, even slight enhancements in the accuracy of Soil Quality Index (SQI) predictions could result in substantial economic benefits and facilitate more effective resource allocation. The ANN model displayed even better accuracy for CD prediction, with R 2 values of 0.98 and 0.95 during calibration (Cal.) and validation (Val.), respectively. The DT model demonstrated exceptional performance in predicting PLI, attaining R² values of 0.99 and 0.97 during Cal. and Val., respectively. In particular, the DT model showed strong predictive accuracy for PER, with R² values of 0.97 and 0.95 during Cal. and Val., respectively. This study presents an innovative perspective on enhancing the integration of various techniques for a more comprehensive understanding of soil quality. Highlighting feature selection strategies, it aims to improve both model accuracy and interpretability.

In this paper, we present a newly modified machine learning model that employs a long short-term memory (LSTM) neural network model with the golden jackal optimization (GJO) algorithm to predict the tribological performance of Cu–Al2O3 nanocomposites. The modified model was applied to predict the wear rates and coefficient of friction of Cu–Al2O3 nanocomposites that were developed in this study. Electroless coating of Al2O3 nanoparticles with Ag was performed to improve the wettability followed by ball milling and compaction to consolidate the composites. The microstructural, mechanical, and wear properties of the produced composites with different Al2O3 content were characterized. The wear rates and coefficient of friction were evaluated using sliding wear tests at different loads and speeds. From a materials point of view, the manufactured composites with 10% Al2O3 content showed huge enhancement in hardness and wear rates compared to pure copper, reaching 170% and 65%, respectively. The improvement of the properties was due to the excellent mechanical properties of Al2O3, grain refinement, and dislocation movement impedance. The developed model using the LSTM-GJO algorithm showed excellent predictability of the wear rate and coefficient of friction for all the considered composites.

One of the main drawbacks of utilizing mass concrete is the high amount of heat produced during the hydration of cementitious materials. Low-heat high-performance concrete (LHHPC) is a special type of concrete with low Portland cement content and low heat of hydration. The main aim of this research is to experimentally explore the potential use of blast furnace cement (CEM III) and fly ash (FA) in LHHPC. CEM III is a type of cement with low heat of hydration. FA was used at various dosages, namely 10%, 20%, 30%, and 40%, as a partial replacement of CEM III for producing more sustainable LHHPC. The mechanical and micro-structural characteristics of the LHHPC mixes were investigated. In addition, the concrete thermal conductivity and heat of hydration were predicted and compared using ANSYS finite element software. The experimental results showed that 40% FA as a CEM III partial replacement decreased the heat of hydration in LHHPC by 38.7%. In addition, the produced LHHPC showed low thermal conductivity, which indicates a decrease in early-age cracks. The produced LHHPC showed a constant compressive strength of 90 days compared with the corresponding 28-day compressive strength. The experimental results were supported by scanning electron microscope (SEM) analysis and the numerical analysis for the LHHPC. The 3D finite element model provided accurate predictions for temperature distribution. The results of this research indicated that FA and CEM III can successfully produce LHHPC with adequate strength and low heat of hydration.

Malnutrition increases bleeding risk by reducing thrombogenicity, impairing platelet aggregation, prolonging bleeding time, and promoting systemic inflammation, which affects vascular permeability and angiogenesis. The Prognostic Nutritional Index (PNI), calculated from serum albumin and lymphocyte count, reflects both nutritional and inflammatory status. This study aimed to assess PNI's association with bleeding risk in acute coronary syndrome (ACS) patients on dual antiplatelet therapy (DAPT). This prospective, single-center observational cohort study enrolled 1843 patients presenting with acute coronary syndrome (ACS) who underwent percutaneous coronary intervention (PCI). ROC analysis determined 42.7 as the optimal PNI cut-off value for risk stratification. Participants were stratified into distinct groups based on Prognostic Nutritional Index (PNI) cut-off values, a composite marker derived from serum albumin levels and peripheral lymphocyte counts, reflecting both nutritional and inflammatory status. Patients were prospectively followed for 12 months post-discharge to assess the occurrence of actionable bleeding events, with the aim of evaluating the association between PNI and post-PCI bleeding risk. The study cohort had a mean age of 66.4, with 65.16% male. After PCI, 98.04% were on DAPT. Patients were divided into Group I (PNI ≥ 42.7, n = 1290) and Group II (PNI < 42.7, n = 553). During follow-up, 5.58% of patients experienced actionable bleeding, with 3.5% in Group I and 10.3% in Group II (p < 0.0001). Multivariable Cox regression analysis revealed that PNI < 42.7 was a significant independent predictor of bleeding (HR: 1.7; 95% CI: 1.1-2.5; p < 0.003). Baseline PNI is an independent predictor of post-discharge bleeding in ACS patients on DAPT after PCI, suggesting it could be a valuable tool for risk stratification of bleeding in these patients.

Background Differentiation between the grades of brain gliomas is a crucial step in the management of patients. The gold standard technique for grading is biopsy but MR imaging may play a more substantial role as a non-invasive method by using promising molecular sequences. Our purpose was to assess the added value of the relative amide proton transfer signal [rAPT] to advanced multiparametric MRI protocol. Methods We enrolled a pathologically confirmed 102 patients with low-grade glioma [n = 38] and high-grade glioma [n = 64] who underwent advanced multiparametric MRI protocol on the same scanner. The protocol included anatomic, diffusion, MRS, and perfusion sequences. The newly added sequence was Amide proton transfer. The rAPT values of all lesions were investigated by two neuroradiologists to assess the inter-rater agreement of using interclass correlation coefficient [ICC]. HGGs demonstrated significantly higher mean values of relative cerebral blood volume (rCBV), choline to creatine ratio (Cho/cr), and rAPT with lower Apparent diffusion coefficient (ADC) values compared to LGGs. ROC analyses revealed medium to high diagnostic performance with an AUC of 0.941 for rAPT, 0.907 for mean ADC, and 0.906 for rCBV. Discriminant function analysis of two models, the first one included mean ADC, rCBV, and Cho/Cr, while in the second Model, we added rAPT to them. Model two demonstrated higher accuracy and a significant difference in the AUC after adding the rAPT. The inter-rater agreement was reasonable (ICC 0.61). Conclusions rAPT adds significant value to multiparametric MRI for distinguishing LGG from HGG.