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Abstract The California bearing ratio (CBR) is one of the basic subgrade strength characterization properties in road pavement design for evaluating the bearing capacity of pavement subgrade materials. In this research, a new model based on the Gaussian process regression (GPR) computing technique was trained and developed to predict CBR value of hydrated lime-activated rice husk ash (HARHA) treated soil. An experimental database containing 121 data points have been used. The dataset contains input parameters namely HARHA—a hybrid geometrical binder, liquid limit, plastic limit, plastic index, optimum moisture content, activity and maximum dry density while the output parameter for the model is CBR. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), Relative Root Mean Square Error (RRMSE), and performance indicator (ρ). The obtained results through GPR model yield higher accuracy as compare to recently establish artificial neural network (ANN) and gene expression programming (GEP) models in the literature. The analysis of the R2 together with MAE, RMSE, RRMSE, and ρ values for the CBR demonstrates that the GPR achieved a better prediction performance in training phase with (R2 = 0.9999, MAE = 0.0920, RMSE = 0.13907, RRMSE = 0.0078 and ρ = 0.00391) succeeded by the ANN model with (R2 = 0.9998, MAE = 0.0962, RMSE = 4.98, RRMSE = 0.20, and ρ = 0.100) and GEP model with (R2 = 0.9972, MAE = 0.5, RMSE = 4.94, RRMSE = 0.202, and ρ = 0.101). Furthermore, the sensitivity analysis result shows that HARHA was the key parameter affecting the CBR.

The spinel ferrites have been manufacturing long time because of their variety of applications in technological and industrial field. In the following study, the samarium doped spinel ferrites were under examination. The prepared compositions were denoted as; SrSmxFe2–xO4, x = 0, 0.025, 0.05, 0.075, 0.10. Sol–gel procedure was employed to make these samples. All the samples were investigated through the X-ray diffraction technique. The phase of prepared product was detected for the first three samples. However, the orthorhombic phase, that is, SmFeO3, was traced when samarium (Sm) composition reached 0.075 and above. The smaller width of the M–H loop confirmed the formation of soft ferrites. Saturation and remanence values were diminished with the substitution of samarium ions. But the anisotropy constant as well as coercivity values was enhanced with the samarium contents. Above mentioned features of spinel ferrites showed their possible usage in high-density recording applications.

Presently, biodiesel offers a sustainable and renewable substitute for exhausting fossil fuels such as Petro-diesel. Large scale production of biodiesel could lead to positive outcomes in terms of environmental quality by reducing greenhouse gasses emission and Societal-Economic development. Hence, this research work focused on biodiesel synthesis from novel and non-edible seed oil of Monotheca buxifolia using green nano-particles of calcium oxide synthesized with aqueous leaves extract of Boerhavia procumbens. Advanced techniques like X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Energy diffraction X-Ray (EDX) were used to characterize calcium oxide nano-particles. High yield of biodiesel (95%) was obtained at optimum reaction conditions such as methanol to oil molar ratio of 9:1, catalyst loading 0.83 (wt.%), reaction time 180 min and temperature 85 °C. Gas Chromatography/Mass spectroscopy (GC/MS) analysis of biodiesel revealed four distinct peaks of methyl esters. Results of Fourier-transform infrared spectroscopy (FTIR) and Nuclear magnetic resonance (NMR) confirmed the formation of methyl esters in biodiesel sample. Fuel properties of biodiesel such as density (0.821 kg/m3), viscosity (5.35 mm2/s), cloud point (-8 ◦C), pour point (-9 ◦C) and flash point (95 ◦C) were found equivalent to international ASTM D-6571, EN 14,214 and China GB/T 20828–2007 standards. It was finally concluded that Monotheca buxifolia is a highly potential, eco-friendly, and cheaper biomass feedstock for biodiesel production.

The structural, electrical, and magneto-elastic properties of lanthanide base nitride (Ln = Dy-Lu) anti-perovskites were investigated using density functional theory (DFT). The reported structural outcomes are consistent with the experiment and decrease from Dy to Lu due to the decrease ofatomic radii of Ln atoms. According to the electronic band profile, the metallic characteristics of these compounds are due to the crossing over of Ln-f states at the Fermi level and are also supported by electrical resistivity. The resistivity of these compounds at room temperature demonstrates that they are good conductors. Their mechanical stability, anisotropic, load-bearing, and malleable nature are demonstrated by their elastic properties. Due to their metallic and load-bearing nature, in addition to their ductility, these materials are suitable as active biomaterials, especially when significant acting loads are anticipated, such as those experienced by such heavily loaded implants as hip and knee endo-prostheses, plates, screws, nails, dental implants, etc. In thesecases, appropriate bending fatigue strength is required in structural materials for skeletal reconstruction. Magnetic properties show that all compounds are G-type anti-ferromagnetic, with the Neel temperatures ranging from 24 to 48 K, except Lu3Nin, which is non-magnetic. Due to their anti-ferromagnetic structure, magnetic probes cannot read data contained in anti-ferromagnetic moments, therefore, data will be unchanged by disrupted magnetic field. As a result, these compounds can be the best candidates for magnetic cloaking devices.

The efficiency of photovoltaics (PVs) is related to cover material properties and light management in upper layers of the device. This article investigates new polyimide (PI) covers for PVs that enable light trapping through their induced surface texture. The latter is attained via a novel strategy that involves multi-directional rubbing followed by plasma exposure. Atomic force microscopy (AFM) is utilized to clarify the outcome of the proposed light-trapping approach. Since a deep clarification of either random or periodic surface morphology is responsible for the desired light capturing in solar cells, the elaborated texturing procedure generates a balance among both discussed aspects. Multidirectional surface abrasion with sand paper on pre-defined directions of the PI films reveals some relevant modifications regarding both surface morphology and the resulted degree of anisotropy. The illuminance experiments are performed to examine if the created surface texture is suitable for proper light propagation through the studied PI covers. The adhesion among the upper layers of the PV, namely the PI and transparent electrode, is evaluated. The correlation between the results of these analyses helps to identify not only adequate polymer shielding materials, but also to understand the chemical structure response to new design routes for light-trapping, which might significantly contribute to an enhanced conversion efficiency of the PV devices.

The increase in population growth and demand is rapidly depleting natural resources. Irrigation plays a vital role in the productivity and growth of agriculture, consuming no less than 75% of fresh water utilization globally. Irrigation, being the largest consumer of water across the globe, needs refinements in its process, and because it is implemented by individuals (farmers), the use of water for irrigation is not effective. To enhance irrigation management, farmers need to keep track of information such as soil type, climatic conditions, available water resources, soil pH, soil nutrients, and soil moisture to make decisions that resolve or prevent agricultural complexity. Irrigation, a data-driven technology, requires the integration of emerging technologies and modern methodologies to provide solutions to the complex problems faced by agriculture. The paper is an overview of IoT-enabled modern technologies through which irrigation management can be elevated. This paper presents the evolution of irrigation and IoT, factors to be considered for effective irrigation, the need for effective irrigation optimization, and how dynamic irrigation optimization would help reduce water use. The paper also discusses the different IoT architecture and deployment models, sensors, and controllers used in the agriculture field, available cloud platforms for IoT, prominent tools or software used for irrigation scheduling and water need prediction, and machine learning and neural network models for irrigation. Convergence of the tools, technologies and approaches helps in the development of better irrigation management applications. Access to real-time data, such as weather, plant and soil data, must be enhanced for the development of effective irrigation management applications.

The present study was conducted to test the efficiency and applicability of the EROSION-3D (Jürgen Schmidt, Berlin, Germany) model in the lower Shivaliks of Northwest India. Two similar micro-watersheds in terms of soil characteristics and land use were selected for calibration and validation of the model. The model was calibrated using the data from seven rainstorms of micro-watershed A and validated using data from 11 rainstorms in micro-watershed B. After calibration, the overall statistical parameters, namely RMSE (0.44 mm), percent error (4.92), correlation coefficient (0.94) and model efficiency (88%), clearly indicate that the model simulated runoff quite well in conditions prevalent in Shivaliks. For sediment yield, the overall statistical parameters, namely RMSE (0.365 Mg ha−1), percent error (12.71), correlation coefficient (0.97) and model efficiency (88.32%), clearly indicate that the model simulated sediment yield also quite well. During the validation process, for runoff, RMSE (1.72 mm), percent error (19.19), correlation coefficient (0.94), and model efficiency (81.25%) and, for sediment yield, RMSE (0.19 Mg ha−1), percent error (17.16), correlation coefficient (0.97), and model efficiency (74.48%) clearly demonstrate the reasonable accuracy of the EROSION-3D model to predict runoff and soil loss from micro-watersheds located in Shivaliks of Northwest India. Therefore, the EROSION-3D may be used as decision support system for soil conservation management decisions in the lower Shivaliks of India.

Precise quantification of evaporation has a vital role in effective crop modelling, irrigation scheduling, and agricultural water management. In recent years, the data-driven models using meta-heuristics algorithms have attracted the attention of researchers worldwide. In this investigation, we have examined the performance of models employing four meta-heuristic algorithms, namely, support vector machine (SVM), random tree (RT), reduced error pruning tree (REPTree), and random subspace (RSS) for simulating daily pan evaporation (EPd) at two different locations in north India representing semi-arid climate (New Delhi) and sub-humid climate (Ludhiana). The most suitable combinations of meteorological input variables as covariates to estimate EPd were ascertained through the subset regression technique followed by sensitivity analyses. The statistical indicators such as root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), Willmott index (WI), and correlation coefficient (r) followed by graphical interpretations, were utilized for model evaluation. The SVM algorithm successfully performed in reconstructing the EPd time series with acceptable statistical criteria (i.e., NSE = 0.937, 0.795; WI = 0.984, 0.943; r = 0.968, 0.902; MAE = 0.055, 0.993 mm/day; and RMSE = 0.092, 1.317 mm/day) compared with the other applied algorithms during the testing phase at the New Delhi and Ludhiana stations, respectively. This study also demonstrated and discussed the potential of meta-heuristic algorithms for producing reasonable estimates of daily evaporation using minimal meteorological input variables with applicability of the best candidate model vetted in two diverse agro-climatic settings.

This research aims to assess the efficiency of the synthesized corncob as a cost-effective and eco-friendly adsorbent for the removal of heavy metals. Therefore, to carry out the intended research project, initially, the corncob was doped with nanoparticles to increase its efficiency or adsorption capacity. The prepared adsorbent was evaluated for the adsorption of cadmium (Cd) and chromium (Cr) from aqueous media with the batch experiment method. Factors that affect the adsorption process are pH, initial concentration, contact time and adsorbent dose. The analysis of Cd and Cr was performed by using atomic absorption spectrometry (AAS), while the characterization of the adsorbent was performed using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). The results showed that there is a significant difference before and after corncob activation and doping with CeO2 nanoparticles. The maximum removal for both Cd and Cr was at a basic pH with a contact time of 60 min at 120 rpm, which is 95% for Cd and 88% for Cr, respectively. To analyze the experimental data, a pseudo-first-order kinetic model, pseudo-second-order kinetic model, and intra-particle diffusion model were used. The kinetic adsorption studies confirmed that the experimental data were best fitted with the pseudo-second-order kinetic model (R2 = 0.989) and intra-particle diffusion model (R2 = 0.979). This work demonstrates that the cerium oxide/corncob nanocomposite is an inexpensive and environmentally friendly adsorbent for the removal of Cd and Cr from wastewater.

Riverbank filtration (RBF) is considered as a natural treatment process. During this process, a group of chemical, physical, and biological processes occur when water moves through the soil along the bank of the River Nile, which can act as a conventional treatment process. RBF is one of the most effective solutions that the Egyptian government and responsible parties should embrace. Egypt has started to use the RBF technique widely in many sites through the path of the River Nile. This study provides a detailed analysis of the RBF technique; it represents the outlet quality of the water in a study performed on the River Nile. The effect of RBF on water quality can be measured using the software designed for this study. The study’s main aim is to improve the water quality of the River Nile by removing heavy metals from the water by using an effective and fast method of treatment, which is riverbank filtration. The results of the research’s experimental study show the average percentage of metal removal for iron, cobalt, lead, zinc, and copper are 74.04%, 74.44%, 70.72%, 75.1%, and 70.8%, respectively. These results have proved that RBF acts as a substantial barrier versus heavy metals.

The reduction of chemical oxygen demand (COD) from palm oil mill effluent (POME) is very significant to ensure aquatic protection and the environment. Continuous adsorption of COD in a fixed bed column can be an effective treatment process for its reduction prior to discharge. Adsorption capacity of bone derived biocomposite synthesized from fresh cow bones, zeolite, and coconut shells for the reduction in the organic pollutant parameter was investigated in this study in a fixed bed column. The effect of influent flow rate (1.4, 2.0, and 2.6 mL/min) was determined at an influent pH 7. The optimum bed capacity on the fabricated composite of surface area of 251.9669 m2/g was obtained at 1.4 mL/min at breakthrough time of 5.15 h influent POME concentration. The experimental data were fitted to Thomas, Adams–Bohart, and Yoon–Nelson models fixed bed adsorption models. It was revealed that the results fitted well to the Adams Bohart model with a correlation coefficient of R2 > 0.96 at different influent concentration. Adsorption rate constant was observed to increase at lower flow rate influent concentration, resulting in longer empty bed contact time (EBCT) for the mass transfer zone of the column to reach the outlet of the effluent concentration. In general, the overall kinetics of adsorption indicated that the reduction in COD from POME using a bone-biocomposite was effective at the initial stage of adsorption. The pore diffusion model better described the breakthrough characteristics for COD reduction with high correlation coefficient. Shorter breakthrough time compared to EBCT before regeneration indicated that the bone composite was suitable and effective for the reduction in COD from POME using fixed bed column adsorption.

The worldwide spread and increasing prevalence of carbapenem-resistant Enterobacteriaceae (CRE) is of utmost concern and a problem for public health. This resistance is mainly conferred by carbapenemase production. Such strains are a potential source of outbreaks in healthcare settings and are associated with high rates of morbidity and mortality. In this study, we aimed to determine the dominance of NDM-producing Enterobacteriaceae at a teaching hospital in Karachi. A total of 238 Enterobacteriaceae isolates were collected from patients admitted to Jinnah Postgraduate Medical Centre (Unit 4) in Karachi, Pakistan, a tertiary care hospital. Phenotypic and genotypic methods were used for detection of metallo-β-lactamase. Out of 238 isolates, 52 (21.8%) were CRE and 50 isolates were carbapenemase producers, as determined by the CARBA NP test; two isolates were found negative for carbapenemase production by CARB NP and PCR. Four carbapenemase-producing isolates phenotypically appeared negative for metallo-β-lactamase (MBL). Of the 52 CRE isolates, 46 (88.46%) were blaNDM positive. Most of the NDM producers were Klebsiella pneumoniae, followed by Enterobacter cloacae and Escherichia coli. In all the NDM-positive isolates, the blaNDM gene was found on plasmid. These isolates were found negative for the VIM and IPM MBLs. All the CRE and carbapenem-sensitive isolates were sensitive to colistin. It is concluded that the NDM is the main resistance mechanism against carbapenems and is dominant in this region.

Recently, groundwater resources in Egypt have become one of the important sources to meet human needs and activities, especially in coastal areas such as the western area of Port Said, where seawater desalination cannot be used due to the problem of oil spill and the reliance upon groundwater resources. Thus, the purpose of the study is the sustainable management of the groundwater resources in the coastal aquifer entailing groundwater abstraction. In this regard, the Visual MODFLOW and SEAWAT codes were used to simulate groundwater flow and seawater intrusion in the study area for 50 years (from 2018 to 2068) to predict the drawdown, as well as the salinity distribution due to the pumping of the wells on the groundwater coastal aquifer based on field investigation data and numerical modelling. Different well scenarios were used, such as the change in well abstraction rate, the different numbers of abstraction wells, the spacing between the abstraction wells and the change in screen depth in abstraction. The recommended scenarios were selected after comparing the predicted drawdown and salinity results for each scenario to minimize the seawater intrusion and preserve these resources from degradation.

Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS) is classically used in U-Pb dating to measure U and Pb isotopic concentrations. Recently, it has become frequently used in fission-track (FT) chronometry too. As an advantage, the U-Pb and FT double dating will enable efficiently determining the crystallization ages and the thermo-tectonic history concurrently as samples volume, analytical time, efforts, and cost will be greatly reduced. To demonstrate the validity of this approach, a Younger granite (Ediacaran age) sample from North Eastern Desert (NED), Egypt was analyzed for U-Pb and FT double dating. The integration of multiple geochronologic data yielded a zircon U-Pb crystallization age of 599 ± 30 Ma, after emplacement, the rock cooled /uplifted rapidly to depths of 9–14 km as response to the post-Pan African Orogeny erosional event as indicated by apatite U-Pb age of 474 ± 9 Ma. Afterwards, the area experienced a slow cooling/exhumation for a short period, most-likely as response to denudation effect. During the Devonian, the area was rapidly exhumed to reach depths of 1.5–3 km as response to the Hercynian tectonic event, as indicated by a zircon FT age of 347 ± 16 Ma. Then the studied sample has experienced a relatively long period of thermal stability between the Carboniferous and the Eocene. During the Oligocene-Miocene, the Gulf of Suez opening event affected the area by crustal uplift to its current elevation. This integration of Orogenic and thermo-tectonic information reveals the validity, efficiency, and importance of double dating of U-Pb and FT techniques using LA-ICP-MS methodology.

The study involves the fabrication of an aluminum liquid membrane sensor based on the association of aluminum ions with the cited reagent 2,9-dimethyl-4,11-diphenyl -1,5,8,12-tetraaza cyclote tradeca-1,4,8,11-tetraene [DDTCT]. The characteristics slope (58 mV), rapid and linear response for aluminum ion was displayed by the proposed sensor within the concentration range 2.5 × 10−7–1.5 × 10−1 M, the detection limit (1.6 × 10−7) M, the selectivity behavior toward some metal cations, the response time 10 s), lifetime (150 days), the effect of pH on the suggested electrode potential and the requisite analytical validations were examined. The suitable pH range was (5.0–8.0), in this range the proposed electrode response is independent of pH. The suggested electrode was applied to detect the aluminum ions concentration in food products, real samples and standard alloys. The resulting data by the suggested electrode were statistically analyzed, and compared with the previously reported aluminum ion-selective electrodes in the literature.