The flow of an electroconductive incompressible ternary hybrid nanofluid with heat conduction in a boundary layer including metallic nanoparticles (NPs) over an extended cylindrical with magnetic induction effects is reported in this research. The ternary hybrid nanofluid has been synthesized with the dispersion of titanium dioxide, cobalt ferrite, and magnesium oxide NPs in the base fluid water. For a range of economical and biological applications, a computational model is designed to augment the mass and energy conveyance rate and promote the performance and efficiency of thermal energy propagation. The model has been written as a system of partial differential equations. Which are simplified to the system of ordinary differential equations through similarity replacements. The computing approach parametric continuation method is used to further process the resultant first order differential equations. The results are validated with the bvp4c package for accuracy and validity. The outcomes are displayed and analyzed through Figures and Tables. It has been observed that the inverse Prandtl magnetic number and a larger magnetic constant reduce the fluid flow and elevate the energy profile. The variation of ternary hybrid NPs significantly boosts the thermophysical features of the base fluid.

The rapid development to accommodate population growth has a detrimental effect on water quality, which is deteriorating. Consequently, water quality prediction has emerged as a topic of great interest during the past decade. Existing water quality prediction approaches lack the desired accuracy. Moreover, the available datasets have missing values, which reduces the performance efficiency of classifiers. This study presents an automatic water quality prediction method that resolves the issue of missing values from the data and obtains a higher water quality prediction accuracy. This study proposes a nine-layer multilayer perceptron (MLP) which is used with a K-nearest neighbor (KNN) imputer to deal with the problem of missing values. Experiments are performed, and performance is compared with seven machine learning algorithms. Performance is further analyzed regarding two scenarios: deleting missing values and the use of a KNN imputer to deal with missing values. Results suggest that the proposed nine-layer MLP model can achieve an accuracy of 0.99 for water quality prediction with the KNN imputer. K-fold cross-validation further corroborates this performance.

Increased population necessitates an expansion of infrastructure and urbanization, resulting in growth in the construction industry. A rise in population also results in an increased plastic waste, globally. Recycling plastic waste is a global concern. Utilization of plastic waste in concrete can be an optimal solution from recycling perspective in construction industry. As environmental issues continue to grow, the development of predictive machine learning models is critical. Thus, this study aims to create modelling tools for estimating the compressive and tensile strengths of plastic concrete. For predicting the strength of concrete produced with plastic waste, this research integrates machine learning algorithms (individual and ensemble techniques), including bagging and adaptive boosting by including weak learners. For predicting the mechanical properties, 80 cylinders for compressive strength and 80 cylinders for split tensile strength were casted and tested with varying percentages of irradiated plastic waste, either as of cement or fine aggregate replacement. In addition, a thorough and reliable database, including 320 compressive strength tests and 320 split tensile strength tests, was generated from existing literature. Individual, bagging and adaptive boosting models of decision tree, multilayer perceptron neural network, and support vector machines were developed and compared with modified learner model of random forest. The results implied that individual model response was enriched by utilizing bagging and boosting learners. A random forest with a modified learner algorithm provided the robust performance of the models with coefficient correlation of 0.932 for compressive strength and 0.86 for split tensile strength with the least errors. Sensitivity analyses showed that tensile strength models were least sensitive to water and coarse aggregates, while cement, silica fume, coarse aggregate, and age have a substantial effect on compressive strength models. To minimize overfitting errors and corroborate the generalized modelling result, a cross-validation K-Fold technique was used. Machine learning algorithms are used to predict mechanical properties of plastic concrete to promote sustainability in construction industry.

In efforts to improve plant productivity and enhance defense mechanisms against biotic and abiotic stresses, endophytic bacteria have been used as an alternative to chemical fertilizers and pesticides. In the current study, 25 endophytic microbes recovered from plant organs of Triticum aestivum L. (wheat) were assessed for biotic (phyto-fungal pathogens) and abiotic (salinity, drought, and heavy metal) stress tolerance. Among the recovered isolates, BPR-9 tolerated maximum salinity (18% NaCl), drought (15% PEG-6000), and heavy metals (µg mL⁻¹): Cd (1200), Cr (1000), Cu (1000), Pb (800), and Hg (30). Based on phenotypic and biochemical characteristics, as well as 16S rDNA gene sequencing, endophytic isolate BPR-9 was recognized as Priestia aryabhattai (accession no. OM743254.1). This isolate was revealed as a powerful multi-stress-tolerant crop growth promoter after extensive in-vitro testing for plant growth-promoting attributes, nutrient (phosphate, P; potassium, K; and zinc, Zn) solubilization efficiency, extracellular enzyme (protease, cellulase, amylase, lipase, and pectinase) synthesis, and potential for antagonistic activity against important fungal pathogens viz. Alternaria solani, Rhizoctonia solani, Fusarium oxysporum, and Ustilaginoidea virens. At elevated salt levels, increases were noted in indole-3-acetic acid; siderophores; P, K, and Zn-solubilization; ACC deaminase; and ammonia synthesized by Priestia aryabhattai. Additionally, under in-vitro plant bioassays, wheat seedlings inoculated with P. aryabhattai experienced superior growth compared to non-inoculated seedlings in high salinity (0–15% NaCl) environment. Under NaCl stress, germination rate, plant length, vigor indices, and leaf pigments of wheat seedlings significantly increased following P. aryabhattai inoculation. Furthermore, at 2%-NaCl, B. aryabhattai greatly and significantly (p ≤ 0.05) decreased relative leaf water content, membrane damage, and electrolyte leakage compared with the non-inoculated control. Catalase, superoxide dismutase, and peroxidase activity increased by 29, 32, and 21%, respectively, in wheat seedlings exposed to 2% NaCl and inoculated with the bacteria. The present findings demonstrate that endophytic P. aryabhattai strains might be used in the future as a multi-stress reducer and crop growth promoter in agronomically important crops including cereals.

Global food security is a critical challenge to fulfill the demands of an exponentially growing population. To date, growers rely on chemicals; the broad-spectrum application of synthetic molecules leads to environmental contamination, resistance development, residual toxicity, pest resurgence, and a detrimental effect on human health and cattle. Crop production needs to be improved considering environmental and human health concerns to ensure food security. Furthermore, economically important crops are prone to attack by insect pests, causing considerable yield losses. Microbes are an eco-friendly, versatile alternative, and a potential candidate for combatting destructive pests below the economic injury level and improving the plant's health and productivity. Several microbial pathogens, including parasites, predators, parasitoids, pollinators, and many beneficial microorganisms, possess toxic properties against target organisms but do not cause harm to the non-target organisms. Entomopathogens (ENMs) have great potential for pest suppression due to their remarkable properties. Bacteria are host-specific, but fungi have a broader host range and can be significantly affected by both soil-dwelling and terrestrial insect pests. Virulent pathogens cause mortality in target insect pests known as ENMs and can penetrate through natural openings, ingestions, and integuments to cause a possible effect on target insect pests. The objective of using ENMs is to sustain productivity, improve environmental health, reduce pesticides, and conserve natural resources. Moreover, research is ongoing to discover other possible aspects, especially exploring potential ENMs. Therefore, there is a need for identification, isolation, and bioformulation to overcome the existing issues. This study is mainly focused on the status of bio-formulations, pathogenicity, their mode of action, and the potential application of different types of microbial formulations for sustainable pest management.

Geopolymers may be the best alternative to ordinary Portland cement because they are manufactured using waste materials enriched in aluminosilicate. Research on geopolymer composites is accelerating. However, considerable work, expense, and time are needed to cast, cure, and test specimens. The application of computational methods to the stated objective is critical for speedy and cost-effective research. In this study, supervised machine learning approaches were employed to predict the compressive strength of geopolymer composites. One individual machine learning approach, decision tree, and two ensembled machine learning approaches, AdaBoost and random forest, were used. The coefficient correlation (R²), statistical tests, and k-fold analysis were used to determine the validity and comparison of all models. It was discovered that ensembled machine learning techniques outperformed individual machine learning techniques in forecasting the compressive strength of geopolymer composites. However, the outcomes of the individual machine learning model were also within the acceptable limit. R² values of 0.90, 0.90, and 0.83 were obtained for AdaBoost, random forest, and decision models, respectively. The models’ decreased error values, such as mean absolute error, mean absolute percentage error, and root-mean-square errors, further confirmed the ensembled machine learning techniques’ increased precision. Machine learning approaches will aid the building industry by providing quick and cost-effective methods for evaluating material properties.