The assessment and prediction of water quality are important aspects of water resource management. Therefore, the groundwater (GW) quality of the Nubian Sandstone Aquifer (NSSA) in El Kharga Oasis was evaluated using indexing approaches, such as the drinking water quality index (DWQI) and health index (HI), supported with multivariate analysis, artificial neural network (ANN) models, and geographic information system (GIS) techniques. For this, physical and chemical parameters were measured for 140 GW wells, which indicated Ca–Mg–SO₄, mixed Ca–Mg–Cl–SO₄, Na–Cl, Ca–Mg–HCO₃, and mixed Na–Ca–HCO₃ water facies under the influence of silicate weathering, rock–water interactions, and ion exchange processes. The GW in El Kharga Oasis had high levels of heavy metals, particularly iron (Fe) and manganese (Mn), with average concentrations above the limits recommended by the World Health Organization (WHO) for drinking water. The DWQI categorized most of the samples as not suitable for drinking (poor to very poor class), while some samples fell in the good water class. The results of the HI indicated a potential health risk due to the ingestion of water, with the risk being higher for children in only one location. However, for both children and adults, there was a low risk of dermal and ingestion exposure to the water in all locations. The contaminants could be from natural sources, such as minerals leaching from rocks and soil, or from human activities. Based on the results of ANN modeling, ANN-SC-13 was the most accurate prediction model, since it demonstrated the strongest correlation between the best characteristics and the DWQI. For example, this model’s thirteen characteristics were extremely important for predicting DWQI. The R² value for the training, cross-validation (CV), and test data was 0.99. The ANN-SC-2 model was the best in measuring HI ingestion in adults. The R² value for the training, CV, and test data was 1.00 for all models. The ANN-SC-2 model was the most accurate at detecting HI dermal in adults (R² = 0.99, 0.99, and 0.99 for the training, CV, and test data sets, respectively). Finally, the integration of physicochemical parameters, water quality indices (WQIs), and ANN models can help us to understand the quality of GW and its controlling factors, and to implement the necessary measures that prevent outbreaks of various water-borne diseases that are detrimental to human health.

Standard methods are limited for monitoring and managing water quality indicators (WQIs) in real-time and on a large scale. Consequently, there is an urgent need to use reliable, practical, swift, and cost-effective monitoring tools that can be easily deployed and assist decision makers in assessing key indicators relevant to surface water quality in a comprehensive manner. Surface water samples were collected and evaluated for water quality at 16 distinct sites across the Qaroun Lake in 2018 and 2019. Different WQIs, including total dissolved solids (TDS), transparency, total suspended solids (TSS), chlorophyll-a (Chl-a), and total phosphorus (TP), were tested for aquatic utilization. An integrated approach comprising WQIs, geospatial techniques, hyperspectral reflectance indices (SRIs) (commonly used SRIs, two-band and three-band SRIs (Spectral index calculated from water spectral reflectance of two or three wavelengths)), and partial least square regression (PLSR) models were used to assess the water quality of Qaroun Lake. According to the findings, the water quality attributes are polluted to varying degrees. The majority of commonly used SRIs presented moderately relationship with four WQIs (transparency, TSS, Chl-a, and TP) (R² = 0.45 to 0.64), while the majority of newly two-band SRIs (NSRIs-2b) indicated moderate to strong relationships with WQIs (R² = 0.51 to 0.74), and the majority of newly three band SRIs (NSRIs-3b) presented strong relationships with WQIs (R² = 0.67 to 0.81). Broadly, the highest coefficients of determination were noticed with the NSRIs-3b followed by the NSRIs-2b and then the commonly used SRIs. For example, the NSRIs-3b (NDSI_648,712,696) had stronger relationships with transparency, TSS, and Chl-a with R² = 0.77, 0.66, and 0.81, respectively, than other SRIs. In addition, the NSRIs-3b (NDSI_620,610,622) showed the highest R² of 0.73 with TSS. The NSRIs-3b coupling with PLSR predicted the WQIs with satisfactory accuracy in the calibration (reach up R² = 0.85) and validation (reach up R² = 0.81) datasets. The overall findings of this research study showed that deriving an optimized NSRIs-3b from spectrum region and combining it with PLSR model could be a practical tool for managing water quality of the Qaroun Lake by accurately, timely, and non-destructively monitoring the WQIs.

Fresh tomato fruits (TFs) contain a high moisture content of 90–94%, which makes storage and transportation over long distances difficult. Lately, numerous investigators have employed diverse solar dryers (SDs) in conjunction with stationary solar collectors (SCs) to dry tomatoes; however, the effectiveness of this technique is limited due to the sun’s constant motion throughout the day. Consequently, the current study set out to create an SD that is outfitted with an autonomous sun tracking system and an internet of things (IoT)-based photovoltaic system connected to an SC to continually track the sun and increase the quantity of energy absorbed. Furthermore, we investigated some operating parameters that impact the SD’s performance, taking into account three tomato slice thicknesses (STs) (4.0, 6.0, and 8.0 mm) and three air velocities (1.0, 1.5, and 2.0 m/s). The obtained data demonstrated a notable rise in the efficiency of the SD integrated with the automatic SC tracker throughout the course of the day when compared to the fixed SC, where the latter’s efficiency improved by 21.6%, indicating a strong degree of agreement. The results demonstrated a notable 20–25% reduction in drying time and a 4.9 °C increase in air temperature within the SC integrated with an automatic solar collector tracker (ASCT) at 2:00 p.m., as compared to the SC integrated with a fixed SC. The results of this study also demonstrated that there were no appreciable variations in the air speeds used to dry the tomatoes; however, the thickness of the tomato slices (TSs) had a significant impact; using 4 mm thick tomato slices resulted in a 50% reduction in drying time. Furthermore, the highest efficiency of the PV system was discovered to be 17.45%. Although the two solar dryers have very similar payback times, there are more dried tomatoes available in the markets.

This research intends to offer a scientific foundation for environmental monitoring and early warning which will aid in the environmental protection management of Qaroun Lake. Qaroun Lake is increasingly influenced by untreated wastewater discharge from many anthropogenic activities, making it vulnerable to pollution. For that, six environmental pollution indices, namely contamination factor (Cf), enrichment factor (EF), geo-accumulation index (Igeo), degree of contamination (Dc), pollution load index (PLI), and potential ecological risk index (RI), were utilized to assess the bottom sediment and to determine the different geo-environmental variables affecting the lake system. Cluster analysis (CA), and principal component analysis (PCA) were used to explore the potential pollution sources of heavy metal. Moreover, the efficiency of partial least-square regression (PLSR) and multiple linear regression (MLR) were tested to assess the Dc, PLI, and RI depending on the selected elements. The sediment samples were carefully collected from 16 locations of Qaroun Lake in two investigated years in 2018 and 2019. Total concentrations of Al, As, Ba, Cd, Co, Cr, Cu, Fe, Ga, Hf, Li, Mg, Mn, Mo, Ni, P, Pb, Sb, Se, Zn, and Zr were quantified using inductively coupled plasma mass spectra (ICP-MS). According to the Cf, EF, and I_geo results, As, Cd, Ga, Hf, P, Sb, Se, and Zr demonstrated significant enrichment in sediment and were derived from anthropogenic sources. According to Dc results, all collected samples were categorized under a very high degree of contamination. Further, the results of RI showed that the lake is at very high ecological risk. Meanwhile, the PLI data indicated 59% of lake was polluted and 41% had PLI < 1. The PLSR and MLR models based on studied elements presented the highest efficiency as alternative approaches to assess the Dc, PLI, and RI of sediments. For examples, the validation (Val.) models presented the best performance of these indices, with R²val = 0.948–0.989 and with model accuracy ACCv = 0.984–0.999 for PLSR, and with R²val = 0.760–0.979 and with ACCv = 0.867–0.984 for MLR. Both models for Dc, PLI, and RI showed that there was no clear overfitting or underfitting between measuring, calibrating, and validating datasets. Finally, the combinations of Cf, EF, I_geo, PLI, Dc, RI, CA, PCA, PLSR, and MLR approaches represent valuable and applicable methods for assessing the risk of potentially harmful elemental contamination in the sediment of Qaroun Lake.

β-glucuronidase is a lysosomal glycosidase enzyme which catalyzes the extracellular matrix of cancer and normal cells and the glycosaminoglycans of the cell membrane, which is important for cancer cell proliferation, invasion, and metastasis. Liver cancer, colon carcinoma, and neoplasm bladder are triggered by the increase of the level of β-glucuronidase activity. The most valuable structures are indole and oxadiazole which has gain immense attention because of its pharmacological behavior and display many biological properties. Twenty-two (1–22) analogs of indole based oxadiazole were synthesized and screened for their inhibitory potential against β-glucuronidase. Majority of the compounds showed potent inhibitory potential with IC₅₀ values ranging between 0.9 ± 0.01 to 46.4 ± 0.9 µM, under positive control of standard drug d-saccharic acid 1,4 lactone (IC₅₀ = 48.1 ± 1.2 µM). Structural activity relationship (SAR) has been established for all synthesized compounds. To shed light on molecular interactions between the synthesized compounds and β-glucuronidase, 1, 4, and 6 compounds were docked into the active binding site of β-glucuronidase. The obtained results showed that this binding is thermodynamically favorable and β-glucuronidase inhibition of the selected compounds increases with the number of hydrogen bonding established in selected compound-β-glucuronidase complexes.