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Lead-free ceramics are promising candidates for replacing lead-based piezoelectric materials such as lead-zirconate-titanate (PZT) if they can compete in dielectric and ferroelectric characteristics. In this work, for lead-free piezoelectric ceramic, 0.74(Bi0.5Na0.5TiO3)-0.26(SrTiO3) (BNT-ST26) and niobium-substituted (Nb-BNT–ST26) ceramics were synthesized by solid-state reactions. The evolution of niobium substitution to the perovskite phase structure of BNT-ST26 ceramics was confirmed by X-ray diffraction (XRD) analysis and Raman spectra. Electromechanical properties of Nb-BNT-ST26 ceramics initially increased with the addition of niobium up to 0.5% and decreased with a further increase in Nb content. Temperature-dependent dielectric curves showed that the depolarization temperature (Td) decreased below room temperature because of Nb substitution. The composition with 0.5% Nb yielded a maximum bipolar strain (Smax) of 0.265% and normalized strain of d33* ~ 576 pm/V under an electric field of 4.6 kV/mm at room temperature. At this critical concentration of 0.5% Nb, maximum saturation polarization of 26 μC/cm2 was achieved. The dielectric constant with temperature peaks became more diffused and the depolarization temperature decreased with the increasing Nb content. The study concludes that Nb-doped BNT-ST26 is an excellent material for high-temperature, stable, frequency-dependent, lead-free piezoelectric devices.

ABSTRACT Kashmir grey langur (Semnopithecus ajax) (the langur) belongs to family Cercopithecidae and order Primates. Understanding food habits of wild mammals is of great importance to ecology and wildlife management. Present study was designed to determine the diet composition of Kashmir grey langur in Machiara National Park, Azad Jammu and Kashmir, Pakistan during summer 2015 and winter 2015-16 from fecal material using microhistological technique. Sixty fecal samples were collected from the study area during summer 2015 and winter 2015-16, i.e. thirty samples during each season. These samples were analyzed for the determination of food composition using microhistological technique. A total of 23 plant species were observed during summer and 15 plant species during winter season. During both the seasons Indian or Himalayan Chestnut Aesculus indica was found as the dominant plant species in the diet having relative importance value (RIV) 8.36 and 10.92 in summer and winter, respectively. Diet breadth of all the plant species was also calculated using Levin's measure of niche breadth (B). Grand viburnum Vibernum foetens showed the greatest value of diet breadth (23.52) during summer season, while during winter season wild Himalayan pear Pyrus pashia showed the greatest value of diet breadth (16.02). Future management of the National Park would require protection of core habitat of the langur in MNP. Wildlife managers should focus on conservation and increasing the number of preferred forage species of Kashmir grey langur i.e. Asculus indica, Cedrus deodara, Vibernum foetens, Pyrus pashia and Eleagnus orientalis in MNP.

The astonishing development of broad genomics and proteomics tools have catalyzed a new era in both therapeutic interventions and nutrition in prostate cancer. The terms pharmacogenomics and nutrigenomics have been derived out of their genetic forbears as large-scale genomics technologies have been established in the last decade. It is unquestionable that rationale of both disciplines is to individualize or personalize medicine and food and nutrition, and eventually health, by tailoring the drug or the food to the individual genotype. The purpose of this review is to significantly inspect results from current research concerning the mechanisms of action of phytonutrients and potential effects on prostate cancer. Substantial emerging data supports the synergistic adiministration of nutraceuticals with TRAIL in prostate cancer progression to circumvent TRAIL refractoriness. Nonetheless, developing novel scientific methods for discovery, validation, characterization and standardization of these multicomponent phyto-therapeutics is vital to their recognition into mainstream medicine. The key to interpret a personalized response is a greater comprehension of nutrigenomics, proteomics and metabolomics.

In economies, cleaner technology, increased demand for renewable energy, and more efficient use of natural resources contribute to meeting environmental sustainability targets. The Chinese economy is no exception in its attempts to conserve economic and natural resources via collaborative efforts to embrace cleaner technology, green energy sources, and resource conservation management to preserve resources for future generations. This research examines the influence of cleaner technologies, green energy sources, and natural resource management on reducing greenhouse gas emissions using quarterly data for the Chinese economy from 2000Q1 to 2020Q4. The findings demonstrate that increasing demand for green energy reduces greenhouse gas emissions, hence substantiating the premise of ‘green is clean’ energy development. Additionally, optimum resource usage enhances environmental quality, corroborating the ‘resource cleaner blessing’ hypothesis. The positive link between inward foreign direct investment and greenhouse gas emissions substantiates the ‘pollution haven’ concept, according to which inward foreign direct investment uses unsustainable technology in manufacturing processes, hence degrading air quality indicators. Inadequate access to clean cooking technology and increased population density has a detrimental effect on the country’s environmental sustainability agenda, which must be corrected via sustainable regulations. The causality estimates show the feedback relationship between renewable energy demand (and economic growth) and cleaner technology, between economic growth and green energy (and inbound foreign direct investment), and between population density and economic growth (and green energy). The Impulse Response function estimates suggested that economic growth and population density would likely increase GHG emissions. In contrast, cleaner technology, green energy demand, natural resource management, and inbound foreign direct investment would likely decrease greenhouse gas emissions for the next ten-year time period. The sustainability of the environment and natural resources in China is bolstered by developing cleaner technologies, a greater reliance on renewable energy sources, and better management of natural resources.

In this study, a new computing model by developing the strength of feed-forward neural networks with Levenberg-Marquardt Method (NN-BLMM) based backpropagation is used to find the solution of nonlinear system obtained from the governing equations of unsteady squeezing flow of Heat and Mass transfer behaviour between parallel plates. The governing partial differential equations (PDEs) for unsteady squeezing flow of Heat and Mass transfer of viscous fluid are converting into ordinary differential equations (ODEs) with the help of a similarity transformation. A dataset for the proposed NN-BLMM is generated for different scenarios of the proposed model by variation of various embedding parameters squeeze Sq, Prandtl number Pr, Eckert number Ec, Schmidt number Sc and chemical-reaction-parameter γ . Physical interpretation to various embedding parameters is assigned through graphs for squeeze Sq, Prandtl Pr, Eckert Ec, Schmidt Sc and chemical-reaction-parameter γ . The processing of NN-BLMM training (T.R), Testing (T.S) and validation (V.L) is employed for various scenarios to compare the solutions with the reference results. For the fluidic system convergence analysis based on mean square error (MSE), error histogram (E.H) and regression (R.G) plots is considered for the proposed computing infrastructures performance in term of NN-BLMM. The results based on proposed and reference results match in term of convergence up to 10-02 to 10-08 proves the validity of NN-BLMS. The Optimal Homotopy Asymptotic Method (OHAM) is also used for comparison and to validate the results of NN-BLMM.

In this research work, an effective Levenberg–Marquardt algorithm-based artificial neural network (LMA-BANN) model is presented to find an accurate series solution for micropolar flow in a porous channel with mass injection (MPFPCMI). The LMA is one of the fastest backpropagation methods used for solving least-squares of nonlinear problems. We create a dataset to train, test, and validate the LMA-BANN model regarding the solution obtained by optimal homotopy asymptotic (OHA) method. The proposed model is evaluated by conducting experiments on a dataset acquired from the OHA method. The experimental results are obtained by using mean square error (MSE) and absolute error (AE) metric functions. The learning process of the adjustable parameters is conducted with efficacy of the LMA-BANN model. The performance of the developed LMA-BANN for the modelled problem is confirmed by achieving the best promise numerical results of performance in the range of E-05 to E-08 and also assessed by error histogram plot (EHP) and regression plot (RP) measures.

Solid supported catalysts have several synthetic applications. Herein, finely ground eggshells were used as a solid support for the preparation of transition metal (Ni, Zn, Cu, Sn and Co) based catalysts to synthesize 2,4-dinitrophenylhydrazone (3) and dihydropyrimidinones (7 and 8) . The effect of catalyst load, time and temperature on product yield was studied. Box Behnken Model was employed, and three predictors named catalyst amount (A), reaction time (B), and reaction temperature (C) were used to find the correlation of the predictors with the yield. Second order polynomial equation was used to estimate the effects of these factors. According to the statistical model, about 12% increase in yield was observed as a result of one-unit increase in reaction time while all other terms were kept constant. The values of S (18.1616) and R 2 (71.2%) indicate that the statistical model gave an adequate fit to data. Quadratic model for the response surface was used for the analysis of variance (ANOVA) results, the larger F-values, and smaller p-values indicated that the predictors are in good agreement. The linear model terms of predictors were found to be significantly effective for yield ( P  < 0.05). The response surface and contour plots were also in agreement with the predicted model.

Chickpea (Cicer arietinum L.) is one of the main pulse crops of Pakistan. The yield of chickpea is affected by a variety of biotic and abiotic factors. Due to their environmentally friendly nature, different nanoparticles are being synthesized and applied to economically important crops. In the present study, Trichoderma harzianum has been used as a stabilizing and reducing agent for the mycosynthesis of zinc oxide nanoparticles (ZnO NPs). Before their application to control Fusarium wilt of chickpea, synthesized ZnO NPs were characterized. X-ray diffraction (XRD) analysis revealed the average size (13 nm) of ZnO NPs. Scanning electron microscopy (SEM) indicated their spherical structure, and energy dispersive X-ray analysis (EDX) confirmed the oxide formation of ZnO NPs. Transmission electron microscopy (TEM) described the size and shape of nanoparticles, and Fourier transform infrared (FTIR) spectroscopy displayed the presence of reducing and stabilizing chemical compounds (alcohol, carboxylic acid, amines, and alkyl halide). Successfully characterized ZnO NPs exhibited significant mycelial growth inhibition of Fusarium oxysporum, in vitro. In a greenhouse pot experiment, the priming of chickpea seeds with ZnO NPs significantly increased the antioxidant activity of germinated plants and they displayed 90% less disease incidence than the control. Seed priming with ZnO NPs helped plants to accumulate higher quantities of sugars, phenol, total proteins, and superoxide dismutase (SOD) to create resistance against wilt pathogen. These nanofungicides were produced in powder form and they can easily be transferred and used in the field to control Fusarium wilt of chickpea.

In this article, an effective computing approach is presented by exploiting the power of Levenberg-Marquardt scheme (LMS) in a backpropagation learning task of artificial neural network (ANN). It is proposed for solving the magnetohydrodynamics (MHD) fractional flow of boundary layer over a porous stretching sheet (MHDFF BLPSS) problem. A dataset obtained by the fractional optimal homotopy asymptotic (FOHA) method is created as a simulated data simple for training (TR), validation (VD), and testing (TS) the proposed approach. The experiments are conducted by computing the results of mean-square-error (MSE), regression analysis (RA), absolute error (AE), and histogram error (HE) measures on the created dataset of FOHA solution. During the learning task, the parameters of trained model are adjusted by the efficacy of ANN backpropagation with the LMS (ANN-BLMS) approach. The ANN-BLMS performance of the modeled problem is verified by attaining the best convergence and attractive numerical results of evaluation measures. The experimental results show that the approach is effective for finding a solution of MHDFF BLPSS problem.

Soil salinity is one of the main yield-limiting factors in various crops. Under different environmental stresses, many rhizobacteria have demonstrated encouraging role in enhancing plant growth and tolerate stress conditions. In this study, three potential 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase- and exopolysaccharides (EPS)-secreting bacterial strains including Bacillus megaterium, B. tequilensis, and Pseudomonas putida have been assessed for their growth-promoting characteristics. These bacterial strains positively affected the physiology, biochemistry, and antioxidant enzymatic activities of wheat plant, under salinity stress. Results of this study depicted that the inoculation of PGPR positively invigorates growth attributes like relative water content and photosynthetic pigments of wheat seedling under saline conditions. Moreover, plants inoculated with PGPR also showed decreased concentration of malondialdehyde (MDA) and hydrogen peroxide (H2O2). Inoculation of PGPR reduced electrolytic leakage and enhanced enzymatic activity for the scavenging of reactive oxygen species (ROS). These PGPR also increased the production of proline and total soluble sugar. Expression analysis of selected genes by qPCR revealed higher expression of Salt Overly Sensitive (SOS1 and SOS4) genes and predicted their potential role in stress tolerance. These genes can be further overexpressed in wheat plant to tolerate salinity stress. On the basis of these findings, it can be concluded that the priming of seeds with aforementioned PGPR can decrease the adverse effects of salinity on wheat plant.