Explore the vast range of titles available.

Zinc or copper deficiency and salinity are known soil problems and often occur simultaneously in agriculture soils. Plants undergo various changes in physiological and biochemical processes to respond to high salt in the growing medium. There is lack of information on the relation of exogenous application of Zn and Cu with important salinity tolerance mechanisms in plants. Therefore, the present study was conducted to determine the effect of foliar Zn and Cu on two maize cultivars (salt-tolerant cv. Yousafwala Hybrid and salt-sensitive cv. Hybrid 1898). Salinity caused a significant reduction in water and turgor potentials, stomatal conductance, and transpiration and photosynthetic rate, while increase in glycine betaine, proline, total soluble sugars, and total free amino acids was evident in plants under saline regimes. Furthermore, there was significant decline in P, N, Ca, K, Mn, Fe, Zn, and Cu and increase in Na and Cl contents in plants fed with NaCl salinity. Nitrate reductase activity was lower in salt-stressed plants. However, foliar application of Zn and Cu circumvented salinity effect on water relations, photosynthesis, and nutrition and this was attributed to the better antioxidant system and enhanced accumulation of glycine betaine, proline, total free amino acids, and sugars. The results of the present study suggested that Zn application was superior to Cu for mediating plant defense responses under salinity.

In this article, the ratio-dependent prey-predator system perturbed with time noise is numerically investigated. It relates to the population densities of the prey and predator in an ecological system. The initial prey-predator models only depend on the time and a couple of the differential equations. We are considering a model where the prey-predator interaction is influenced by both space and time and the need for a coupled nonlinear partial differential equation with the effect of the random behavior of the environment. The existence of the solutions is guaranteed by using Schauder’s fixed point theorem. The computation of the underlying model is carried out by two schemes. The proposed stochastic forward Euler scheme is conditionally stable and consistent with the system of the equations. The proposed stochastic non-standard finite difference scheme is unconditionally stable and consistent with the system of the equations. The graphical behavior of a test problem for different values of the parameters is shown which depicts the efficacy of the schemes. Our numerical results will help the researchers to consider the effect of the noise on the prey-predator model.

This study was carried out to investigate the insecticidal properties of Beauveria bassiana , Metarhizium anisopliae and Heterorhabditis bacteriophora for their virulence against different larval instars of Rhynchophorus ferrugineus (Olivier). Both fungi were either applied alone or in combination, with H. bacteriophora simultaneously or 1 and 2 weeks after fungal application; EPN were also applied alone. Moreover, assessment of host development, diet consumption, frass production and weight gain were observed at sub-lethal dose rates. In combined treatments, additive and synergistic interactions were observed. Synergism was observed more frequently in H. bacteriophora  +  B. bassiana combinations than in H. bacteriophora  +  M. anisopliae combinations, and was higher in early instars than old instars. In 2 nd and 4 th instars, synergy was noted in H. bacteriophora  +  B. bassiana combinations at 0, 7 and 14 d intervals and in 6 th instar synergy was observed only in H. bacteriophora  +  B. bassiana combinations (at 0 and 7 d intervals). A decrease in pupation, adult emergence and egg hatching was enhanced in the combined treatments. Furthermore, reduced weights and variation in duration of insect developmental stages were observed among entomopathogens and enhanced in H. bacteriophora  +  B. bassiana combinations. Larvae treated with sub-lethal concentrations exhibited reductions in food consumption, growth and frass production and weight gain.

The disease dynamics affect the human life. When one person is affected with a disease and if it is not treated well, it can weaken the immune system of the body. Human Immunodeficiency Virus (HIV) is a virus that attacks the immune system, of the body which is the defense line against diseases. If it is not treated well then HIV progresses to its advanced stages and it is known as Acquired Immunodeficiency Syndrome (AIDS). HIV is typically a disease that can transferred from one person to another in several ways such as through blood, breastfeeding, sharing needles or syringes, and many others. So, the need of the hour is to consider such important disease dynamics and that will help mankind to save them from such severe disease. For the said purpose the reaction-diffusion HIV CD4 + T cell model with drug therapy under the stochastic environment is considered. The underlying model is numerically investigated with two time-efficient schemes and the effects of various parameters used in the model are analyzed and explained in a real-life scenario. Additionally, the obtained results will help the decision-makers to avoid such diseases. The random version of the HIV model is numerically investigated under the influence of time noise in It o ^ sense. The proposed stochastic backward Euler (SBE) scheme and proposed stochastic Implicit finite difference (SIFD) scheme are developed for the computational study of the underlying model. The consistency of the schemes is proven in the mean square sense and the given system of equations is compatible with both schemes. The stability analysis proves that both schemes and schemes are unconditionally stable. The given system of equations has two equilibria, one is disease-free equilibrium (DFE) and the other is endemic equilibrium. The simulations are drawn for the different values of the parameters. The proposed SBE scheme showed the convergent behavior towards the equilibria for the given values of the parameters but also showed negative behavior that is not biological. The proposed SIFD scheme showed better results as compared with the stochastic SBE scheme. This scheme has convergent and positive behavior towards the equilibria points for the given values of the parameters. The effect of various parameters is also analyzed. Simulations are drawn to evaluate the efficacy of the schemes.

In this paper, the concept of blind algorithm with automatic gain control (AGC) is introduced in adaptive antenna system for signal optimization with an aim to estimate the desired response in adaptive fashion. Blind algorithm with AGC is a hybrid two-stage adaptive filtering algorithm; sequentially combining constant modulus algorithm (CMA) and Bessel least mean square (BLMS) algorithm. Blind Bessel beamformer with AGC does not require external reference signal to update its weight vectors and step size for convergence but updates itself from own reference signal obtained from the output of CMA. Similarly, step size is obtained from the correlation matrix which is the product of the signals induced in array elements of antenna. BLMS is the modified version of LMS algorithm; based on the non-uniform step size exploiting the asymptotic decay property of Bessel function of the first kind. The output of CMA provides input and reference signals for BLMS that makes it blind. The contributions of this paper include the development of novel blind theory concept and presentation of an AGC method in order to make the Bessel beamformer blind which can update itself electronically through the correlation matrix depending on the signal array vector with the aim to make the signal power constant.

Salt stress is becoming a major issue for the world’s environment and agriculture economy. Different iron [Fe] sources can give an environmentally friendly alternative for salt-affected soil remediation. In this study the effects of Iron sulfate on Luffa cylindrica (Sponge gourd) cultivated in normal and saline water irrigated soil were examined. When FeSO 4 (0.01, 0.025, 0.05, 0.1 ppm) were applied to salt affected soil, the length, fresh and dry biomass of sponge gourd plant roots and shoots inclined by an average of 33, 28, 11, 21, 18 and 22%, respectively. In plants irrigated with saline water, leaf count was raised successively (23–115%) with increasing concentration of FeSO 4 (0.025-0.1 ppm) compared to stress only plants. The use of FeSO 4 boosted sponge gourd growth characteristics in both normal and salt-affected soils compared to respective controls. The application of Iron sulfate under salt stress boosted photosynthetic indices such as chlorophyll a (22%), chlorophyll b (34%), carotenoids (16%), and total chlorophyll levels (22%). Iron sulfate application also exhibited incline in primary (total free amino acids, 50%; total soluble proteins, 46%) and secondary (total phenolics, 9%; flavonoid content, 51%) metabolites in salt-affected soils. Oxidative enzymatic activities such as catalase (CAT), peroxidase (POD), polyphenol oxidase (PPO) and DPPH scavenging activity (36%) were also increased by foliar spray of FeSO 4 in control and salt stressed L. cylindrica plants. FeSO 4 had a considerable impact on the growth and development of Luffa cylindrica in normal and salt-affected soils. It is concluded that FeSO 4 application can effectively remediate salt affected soil and improve the production of crop plants.

This study performed in-situ microwave pyrolysis of plastic waste into hydrogen, liquid fuel and carbon nanotubes in the presence of Zeolite Socony Mobil ZSM-5 catalyst. In the presented microwave pyrolysis of plastics, activated carbon was used as a heat susceptor. The microwave power of 1 kW was employed to decompose high-density polyethylene (HDPE) and polypropylene (PP) wastes at moderate temperatures of 400–450 °C. The effect of plastic composition, catalyst loading and plastic type on liquid, gas and solid carbon products was quantified. This in-situ CMP reaction resulted in heavy hydrocarbons, hydrogen gas and carbon nanotubes as a solid residue. A relatively better hydrogen yield of 129.6 mmol/g as a green fuel was possible in this process. FTIR and gas chromatography analysis revealed that liquid product consisted of C 13+ fraction hydrocarbons, such as alkanes, alkanes, and aromatics. TEM micrographs showed tubular-like structural morphology of the solid residue, which was identified as carbon nanotubes (CNTs) during X-ray diffraction analysis. The outer diameter of CNTs ranged from 30 to 93 nm from HDPE, 25–93 nm from PP and 30–54 nm for HDPE-PP mixure. The presented CMP process took just 2–4 min to completely pyrolyze the plastic feedstock into valuable products, leaving no polymeric residue.

This study investigated the production of Cu2+-doped CoFe2O4 nanoparticles (CFO NPs) using a facile sol−gel technique. The impact of Cu2+ doping on the lattice parameters, morphology, optical properties, and electrical properties of CFO NPs was investigated for applications in electrical devices. The XRD analysis revealed the formation of spinel-phased crystalline structures of the specimens with no impurity phases. The average grain size, lattice constant, cell volume, and porosity were measured in the range of 4.55–7.07 nm, 8.1770–8.1097 Å, 546.7414–533.3525 Å3, and 8.77–6.93%, respectively. The SEM analysis revealed a change in morphology of the specimens with a rise in Cu2+ content. The particles started gaining a defined shape and size with a rise in Cu2+ doping. The Cu0.12Co0.88Fe2O4 NPs revealed clear grain boundaries with the least agglomeration. The energy band gap declined from 3.98 eV to 3.21 eV with a shift in Cu2+ concentration from 0.4 to 0.12. The electrical studies showed that doping a trace amount of Cu2+ improved the electrical properties of the CFO NPs without producing any structural distortions. The conductivity of the Cu2+-doped CFO NPs increased from 6.66 × 10−10 to 5.26 × 10−6 ℧ cm−1 with a rise in Cu2+ concentration. The improved structural and electrical characteristics of the prepared Cu2+-doped CFO NPs made them a suitable candidate for electrical devices, diodes, and sensor technology applications.

Titanium dioxide nanoparticles (TiO 2 -NPs) were prepared ultrasonically and simultaneously coated over cotton fabric activated with atmospheric pressure dielectric barrier (DBD) discharge. Titanium isopropoxide and extract of Aloe vera plant were used as a precursor and reducing agent, respectively, for the biosynthesis of TiO 2 -NPs. Cotton fabric was pre-treated with DBD plasma for different plasma exposure times. The effect of DBD plasma treatment on wettability and stability of TiO 2 -NPs on cotton fabric were studied. The obtained samples were investigated using Fourier transform infrared spectroscopy, UV–vis spectroscopy, scanning electron microscopy and X-ray diffraction to check the surface functionality, crystallite size and surface morphology. TiO 2 -NPs showed both spherical and triangular surface morphologies with dominating anatase phase and average crystallite size of 11.27 nm. The optimum condition for wettability was defined by using a water drop test method. Self-cleaning property of raw cotton, plasma-treated and plasma-TiO 2 -coated cotton fabric was evaluated by degrading methylene blue solution under daylight irradiation. The plasma-TiO 2 -coated fabric showed high self-cleaning efficiency due to firm bonding of nanoparticles on plasma-functionalized surface. The tensile strength of the fabric decreased slightly on plasma functionalization due to etching and pitting effects but increased after TiO 2 coating of fabric.

Coxiella burnetii  (Cb) is a hardy, stealth bacterial pathogen lethal for humans and animals. Its tremendous resistance to the environment, ease of propagation, and incredibly low infectious dosage make it an attractive organism for biowarfare. Current research on the classification of Coxiella and features influencing its presence in the soil is generally confined to statistical techniques. Machine learning other than traditional approaches can help us better predict epidemiological modeling for this soil-based pathogen of public significance. We developed a two-phase feature-ranking technique for the pathogen on a new soil feature dataset. The feature ranking applies methods such as ReliefF (RLF), OneR (ONR), and correlation (CR) for the first phase and a combination of techniques utilizing weighted scores to determine the final soil attribute ranks in the second phase. Different classification methods such as Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), Logistic Regression (LR), and Multi-Layer Perceptron (MLP) have been utilized for the classification of soil attribute dataset for Coxiella positive and negative soils. The feature-ranking methods established that potassium, chromium, cadmium, nitrogen, organic matter, and soluble salts are the most significant attributes. At the same time, manganese, clay, phosphorous, copper, and lead are the least contributing soil features for the prevalence of the bacteria. However, potassium is the most influential feature, and manganese is the least significant soil feature. The attribute ranking using RLF generates the most promising results among the ranking methods by generating an accuracy of 80.85% for MLP, 79.79% for LR, and 79.8% for LDA. Overall, SVM and MLP are the best-performing classifiers, where SVM yields an accuracy of 82.98% and 81.91% for attribute ranking by CR and RLF; and MLP generates an accuracy of 76.60% for ONR. Thus, machine models can help us better understand the environment, assisting in the prevalence of bacteria and decreasing the chances of false classification. Subsequently, this can assist in controlling epidemics and alleviating the devastating effect on the socio-economics of society.