Essential oil nanoemulsions have received much attention due to their biological activities. Thus, a thyme essential oil nanoemulsion (Th-nanoemulsion) was prepared using a safe and eco-friendly method. DLS and TEM were used to characterize the prepared Th-nanoemulsion. Our findings showed that the nanoemulsion was spherical and ranged in size from 20 to 55.2 nm. The micro-broth dilution experiment was used to evaluate the in vitro antibacterial activity of a Th-emulsion and the Th-nanoemulsion. The MIC50 values of the thymol nanoemulsion were 62.5 mg/mL against Escherichia coli and Klebsiella oxytoca, 250 mg/mL against Bacillus cereus, and 125 mg/mL against Staphylococcus aureus. Meanwhile, it emerged that the MIC50 values of thymol against four strains were not detected. Moreover, the Th-nanoemulsion exhibited promising antifungal activity toward A. brasiliensis and A. fumigatus, where inhibition zones and MIC50 were 20.5 ± 1.32 and 26.4 ± 1.34 mm, and 12.5 and 6.25 mg/mL, respectively. On the other hand, the Th-nanoemulsion displayed weak antifungal activity toward C. albicans where the inhibition zone was 12.0 ± 0.90 and MIC was 50 mg/mL. Also, the Th-emulsion exhibited antifungal activity, but lower than that of the Th-nanoemulsion, toward all the tested fungal strains, where MIC was in the range of 12.5–50 mg/mL. The in vitro anticancer effects of Taxol, Th-emulsion, and Th-nanoemulsion were evaluated using the standard MTT method against breast cancer (MCF-7) and hepatocellular carcinoma (HepG2). Additionally, the concentration of VEGFR-2 was measured, and the activities of caspase-8 (casp-8) and caspase-9 (casp-9) were evaluated. The cytotoxic effect was the most potent against the MCF-7 breast cancer cell line after the Th-nanoemulsion treatment (20.1 ± 0.85 µg/mL), and was 125.1 ± 5.29 µg/mL after the Th-emulsion treatment. The lowest half-maximal inhibitory concentration (IC50) value, 20.1 ± 0.85 µg/mL, was achieved when the MCF-7 cell line was treated with the Th-nanoemulsion. In addition, Th-nanoemulsion treatments on MCF-7 cells led to the highest elevations in casp-8 and casp-9 activities (0.66 ± 0.042 ng/mL and 17.8 ± 0.39 pg/mL, respectively) compared to those with Th-emulsion treatments. In comparison to that with the Th-emulsion (0.982 0.017 ng/mL), the VEGFR-2 concentration was lower with the Th-nanoemulsion treatment (0.672 ± 0.019ng/mL). In conclusion, the Th-nanoemulsion was successfully prepared and appeared in nanoform with a spherical shape according to DLS and TEM, and also exhibited antibacterial, antifungal, as well as anticancer activities.

One of the most hazardous diseases that influences human health globally is microbial infection. Therefore, bimetallic nanoparticles have received much attention for controlling microbial infections in the current decade. In the present study, bimetallic selenium–silver nanoparticles (Se-Ag NPs) were effectively biosynthesized using watermelon rind WR extract through the green technique for the first time. UV-visible spectroscopy, transmission electron microscopy (TEM), and energy dispersive X-ray spectroscopy (EDX) methods were used to characterize the produced NPs. The results indicated that the bimetallic Se-Ag NPs had synergistic antimicrobial activity at low concentrations, which helped to reduce the toxicity of Ag NPs after the bimetallic Se-Ag NPs preparation and increase their great potential. Se-Ag NPs with sizes ranging from 18.3 nm to 49.6 nm were detected by TEM. Se-Ag NP surfaces were uniformly visible in the SEM picture. The cytotoxicity of bimetallic Se-Ag NPs was assessed against the Wi38 normal cell line to check their safety, where the IC₅₀ was 168.42 µg/mL. The results showed that bimetallic Se-Ag NPs had antibacterial action against Candida albicans, Escherichia coli, Pseudomonas aeruginosa, Klebsiella oxytoca, Bacillus subtilis, and Staphylococcus aureus with a minimum inhibitory concentration (MIC) of 12.5 to 50 µg/mL. Additionally, bimetallic Se-Ag NPs had promising anticancer activity toward the MCF7 cancerous cell line, where the IC₅₀ was 21.6 µg/mL. In conclusion, bimetallic Se-Ag NPs were biosynthesized for the first time using WR extract, which had strong antibacterial, antifungal and anticancer properties.

This study highlights the impact of formal agricultural practices and their adverse effect on the deterioration of underground water quality, with special emphasis on toxic elements, including pesticides, herbicides, fungicides, plasticizer accumulation and heavy-metal contamination. A comprehensive study was conducted at various recently developed societies of Sadiqabad that were formerly used for agricultural purposes. Ten various societies were selected, and three samples from each society were collected from different regions of these areas. Data regarding the physicochemical properties, metal contamination and accumulation of pesticide residues were determined using standard protocols. The results revealed that almost all the physicochemical properties of water samples selected from these sites were close to the WHO’s recommended limits. The range for physicochemical properties was pH (6.4–7.7), electrical conductivity (168–766 µ S cm⁻¹), turbidity (6–17 NTU), total hardness (218–1030 mg L⁻¹), chloride contents (130–870 mg L⁻¹) and phosphate contents (2.55–5.11 mg L⁻¹). Among heavy metals, lead and arsenic concentrations in all sampling sites were found to be above the recommended limits. The decreasing pattern in terms of water-quality deterioration with respect to physicochemical properties was FFT > USM > CRH > UCS > CHS > MAH > FFC > CGA > GIH > AGS. Overall, 95 different kinds of toxic elements, including pesticides, herbicides, plasticizer, etc., were detected in the groundwater samples. The toxic compounds in the groundwater were categorized into pesticides, herbicides, plasticizer, plant growth regulators, fungicides, acaricides and insecticides. Most of these parameters showed peak values at the Fatima Fertilizer Company area and Chief Residencia Housing Society. Pesticide contamination showed that water-filtration plants have a big positive impact on the drinking quality of water. Proper monitoring of the pesticides must be performed, as the majority of the pesticides showed low priority. The monitoring method of the pesticides needs to be updated so that the occurrence of recently authorized pesticides is demonstrated.

In mechanized agricultural activities, fuel is particularly important for tillage operations. In this study, the impact of seven distinct parameters on fuel usage per unit of draft power was examined. The parameters are tractor power, soil texture index, plowing speed, plowing depth, width of implement, and both initial soil moisture content and soil bulk density. This study investigated the construction of an artificial neural network (ANN) model for tractor-specific fuel consumption predictions for two tillage implements: chisel and moldboard plows. The ANN model was created based on the collection of related data from previous research studies, and the validation was performed using actual field experiments in clay soil using a chisel plow. The developed ANN model (9-22-1) was confirmed by graphical assessment; additionally, the root-mean-square error (RMSE) was computed. Based on the RMSE, the results demonstrated a good agreement for specific fuel consumption per draft power between the observed and predicted values, with corresponding RMSE values of 0.08 L/kWh and 0.075 L/kWh for the training and testing datasets, respectively. The novelty of the work presented in this paper is that, for the first time, a farm machinery manager can optimize tractor fuel consumption per draft power by carefully controlling certain parameters, such as initial soil moisture content, tractor power, plowing speed, implement width, and depth of plowing. The results show that the input parameters make a significant contribution to the output over the used data with different percentages. Accordingly, the contribution analysis showed that the implement width had a high impact on tractor-specific fuel consumption for both plows at 30.13%; additionally, the chisel and moldboard plows contributed 4.19% and 4.25% in predicting tractor fuel consumption per draft power. This study concluded that practical useful advice for agricultural production can be achieved through optimizing fuel consumption rate by selecting the proper levels of affecting parameters to reduce fuel costs. Moreover, an ANN model could be used to develop future tractor fuel-planning schemes for tillage operations.

In arid regions, drastic seasonal variations in the climatic parameters are common; thus, a high potential of geothermal effects for heating/cooling applications is expected. However, such applications are very limited in these regions due to the lack of information about underground temperature profiles of the surface and shallow zones. Therefore, this study aims to (i) measure the underground temperature profile for one year to determine the optimum depth for burying EAHE pipes; (ii) examine the possibility of water vapour condensation occurring in the buried EAHE pipes, if the air let into the pipes was humid; and (iii) quantify the maximum cooling/heating capacity, if an EAHE was implemented. The results show that a 3-m depth is optimal to bury EAHE pipes, where the ground temperature is 32 °C in the summer and 29 °C in the winter. These temperatures would provide a maximum cooling/heating capacity of 1000/890 MJ day⁻¹ for each 1 m³ of humid air exhausted from a greenhouse. If the EAHE were to operate in a closed loop with a greenhouse, the condensation of water vapour in the EAHE pipes would be impossible during the cooling process. The results of this study are useful for designers using geothermal effects for indoor space cooling and heating in arid regions.

An area of growing interest in wheat-breeding programs for abiotic stresses is the accurate and expeditious phenotyping of large genotype collections using nondestructive hyperspectral sensing tools. The main goal of this study was to use data from canopy spectral signatures (CSS) in the full-spectrum range (400–2500 nm) to estimate and predict the plant biomass dry weight at booting (BDW-BT) and anthesis (BDW-AN) growth stages, and biological yield (BY) of 64 spring wheat germplasms exposed to 150 mM NaCl using 13 spectral reflectance indices (SRIs, consisting of seven vegetation-related SRIs and six water-related SRIs) and partial least squares regression (PLSR). SRI and PLSR performance in estimating plant traits was evaluated during two years at BT, AN, and early milk grain (EMG) growth stages. Results showed significant genotypic differences between the three traits and SRIs, with highly significant two-way and three-way interactions between genotypes, years, and growth stages for all SRIs. Genotypic differences in CSS and the relationships between the three traits and a single wavelength over the full-spectrum range depended on the growth stage. Water-related SRIs were more strongly correlated with the three traits compared with vegetation-related SRIs at the BT stage; the opposite was found at the EMG stage. Both types of SRIs exhibited comparable associations with the three traits at the AN stage. Principal component analysis indicated that it is possible to assess plant biomass variations at an early stage (BT) through published and modified SRIs. SRIs coupled with PLSR models at the BT stage exhibited good prediction capacity of BDW-BT (57%), BDW-AN (82%), and BY (55%). Overall, results demonstrated that the integration of SRIs and multivariate models may present a feasible tool for plant breeders to increase the efficiency of the evaluation process and to improve the genetics for salt tolerance in wheat-breeding programs.