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The present review deals with the serological and molecular techniques available for detection of fungal pathogens in plants, followed by management practices using bioinoculants which can induce resistance in the hosts. Specific detection of pathogens in the plant tissues even before the appearance of symptoms are done either through protein based immunodiagnostic techniques or DNA/RNA based molecular techniques. Immunotechniques depend on specific interaction between antigen of the pathogen or infected tissues and antibodies prepared against the specific antigen. Positive reactions are detected through either qualitative or quantitative techniques. Among the immunodiagnostic techniques, the most widely and commonly used is the enzyme linked immunosorbent assay (ELISA), Dot blot, Western blot and Immunofluorescence. The molecular techniques are predominantly PCR based and include different variations with increasing sensitivity such as nested PCR, multiplex PCR, reverse transcriptase PCR, real time PCR, loop-mediated isothermal amplification (LAMP) and nano based PCR. Thus, by use of one or more sophisticated techniques it is possible to detect pathogens rapidly and specifically. Pathogen detection is generally followed by management techniques, and in the current scenario ecologically safe bioinoculants are used for induction of immunity in the plants. There are several bioinoculants based on beneficial microorganisms such as plant growth promoting bacteria, biocontrol agents and arbuscular mycorrhizal fungi. Application of these microorganisms, singly or as a consortium, has been reported to induce immunity in plants. Induced systemic resistance (ISR) operates through signal molecules and pathways involving jasmonate and ethylene, and in very few cases, salicylate. Upregulation of these pathways leading to enhanced accumulation of antimicrobial compounds (phytoalexin) help in warding off the pathogen during subsequent attack.

Sodium hydrosulphite is used commercially as the reducing agent for vat dyes in the dyeing of cotton. Large amounts of sodium sulphate, sulphur oxyanion and toxic sulphite are produced during the dyeing due to the dissociation of sodium hydrosulphite leading to severe air and water pollution. This research focuses on the use of alkaline iron (II) salt as the reducing agent for vat dyeing on cotton fabrics through a complete replace¬ment of hydrosulphite. The 34 Box-Behnken design was used to achieve optimum parameters and statistically analyse the performance of the new reducing system. The results showed that the alkaline iron (II) salt system was relatively effective in developing a comparable dyebath reduction potential, surface colour strength of cotton and colourfastness, if compared to the hydrosulphite-based reducing system. The dyebath stability in the presence and absence of the dye also showed superior results compared to that of the hydrosulphite system. Hence, it can be said that a complete substitution of sodium hydrosulphite with alkaline iron (II) salt is possible.

Application of sodium hydrosulfite along with NaOH successfully reduces and solubilizes all vat dyes at specific temperature. However, being a highly toxic chemical, use of hydrosulfite has raised tremendous ecological concern. In this study, attempts were made to dye cotton with alkaline lipase along with iron (II) salt to study if unstable and highly toxic sodium hydrosulfite could be replaced with it. A 43 Box-Behnken design was run for statistical analysis to obtain optimum parameters for dyeing of cotton using alkaline lipase. The results showed that alkaline lipase along with iron (II) salt was Quite effective and capable of developing comparable dyebath potential, dye receptivity on cotton with comparable dye strength and color fastness as obtained in sodium hydrosulfite based reducing system.

The COVID 19 global pandemic has engulfed humanity with a huge impact on health systems across the world. Many patients develop myocardial injury which can lead to significant cardiovascular complications including HF. This will require aggressive management strategies which are evolving. Guideline directed drug therapy including ACEI/ARB/ARNI is to be continued in patients with pre-existing HF. Long-term cardiovascular effects of COVID-19 are yet to be ascertained. Protection of health care personnel from contracting the disease should be given high priority.

In the post Moore’s Law era, neuromorphic computing emerged as a promising solution to overcome the von Neumann bottleneck with a goal to create computing architectures that operate more like the human brain. Many neural processors have been designed in the push toward this goal, but developing a neural core architecture that is flexible enough to take advantage of both digital and analog implementations without a system-wide revision has proven to be a challenge. This paper proposes a new neural core (nCore) design that achieves this multi-context flexibility with use of a two-stage pipelined structure. The pipelined structure enables hardware sharing such that each nCore contains the functionality of multiple "virtual neurons" that save in area utilization and power consumption relative to previous neural core designs. The proposed nCore design has been implemented on a FPGA as well as an ASIC in a 65 nm CMOS process. Results are presented which show that the proposed nCore outperforms an existing digital neural core design containing a single neuron per core, DANNA2, in terms of both area and power per neuron. The improvements are made more evident with an increasing the number of functioning neurons per core.

In this present study, gene expression programing (GEP) has been used for training a model for the subgrid scale (SGS) scalar dissipation rate (SDR) for a large range of filter widths, using a database of statistically planar turbulent premixed flames, featuring different turbulence intensities and heat release parameters. GEP is based on the idea to iteratively improve a population of model candidates using the survival-of-the-fittest concept. The resulting model is a mathematical expression that can be easily implemented, shared with the community, and analyzed for physical consistency, as illustrated in this work. Efficient evaluation of the cost function and a smart choice of basis functions have been found to be essential for a successful optimization process. The GEP based model has been found to outperform an existing algebraic model from the literature. However, the optimization process was found to be quite intricate, and the SGS SDR closure turned out to be difficult. Some of these problems have been explained using the model-agnostic interpretation method, which requires the existence of a trained artificial neural network (ANN). ANNs are known for their ability to represent complex functional relationships and serve as an additional benchmark solution for the GEP based model.

The periodic growth in megacities has incremented the power consumption in the name of modernized techniques and hustle-free lifestyles. In the state of Tamil Nadu, the voltage and power regulation has created a drastic decrements in the efficiency for the tail end consumers. Thus, resulting in shortage of voltage and vehemently opposing the performance factors of home appliances, micro-industries and minute gadgets used on a day-to-day basis. In this paper, a solution is provided to magnificently ameliorate by catering a high voltage distribution system, simulating its results for serving, good to the Tamil Nadu Electricity Board having fed by the Electrical Transient Analysis Program Software. Therefore, in reference to the contingent PSO algorithm enabling to convert Low Voltage Distribution System to High Voltage Distribution Systems, sufficiently acquiring a redemption for utmost performance usage of the transformer. The relational composition of the supply in the LVDS and HDVS magnitudes demonstrate the necessary output demands. The line losses vividly broadcast the allocated transformer and the HDVS holds good in showcasing power loss in advanced tail and voltage prescriptions.

Enzymatically-generated hydrogen peroxide (H2O2) using glucose oxidase (GOX) is a sustainable alternative for cotton textile bleaching. GOX catalyzes the oxidation of glucose to gluconic acid with generation of H2O2 as a side product for bleaching. This work involves the study of cotton fabric bleaching with enzymatically-generated H2O2. Enzyme and glucose concentrations significantly affect peroxide generation as well as bleaching and were optimized using statistical experimental design in reference to maximum whiteness. Furthermore, the sustainability of this bleaching system was premeditated by GOX immobilization. GOX was covalently immobilized onto a natural and inexpensive support (chitosan) based on statistical experimental design results. The immobilization improved whitening results as well as allowing repeated application of enzyme for H2O2 generation, which was successively implemented for cotton textile bleaching.

The effects of the definition of the reaction progress variable and equivalence ratio on the validity of Damköhler's hypotheses for turbulent premixed flames belonging to the thin reaction zone regime have been studied using multi-step chemistry direct numerical simulations of statistically planar [Formula: see text]–air premixed flames with equivalence ratios of 0.8 and 1.0. Although [Formula: see text]–air premixed flames with equivalence ratios of 0.8 and 1.0 have effective Lewis numbers close to unity, local differential diffusion effects can play a non-negligible role in determining the turbulent burning velocity and flame surface area in all cases. However, the augmentations of burning rate and flame surface area under turbulence do not occur in equal proportion, but their ratio remains of the order of unity. This conclusion holds irrespective of the definition of the reaction progress variable for the cases considered here. Damköhler's second hypothesis, which relates the ratio of turbulent burning velocity and the unstretched laminar burning velocity to the ratio of turbulent diffusivity and molecular diffusivity, has been found not to hold in the sense of equality, but it is valid in an order of magnitude sense for all choices of reaction progress variable definition. The findings of the current analysis indicate that Damköhler's first and second hypotheses should only be interpreted in an order of magnitude sense in the thin reaction zone regime even when the effective Lewis number remains close to unity.

Emission levels play a crucial role for the thermal power companies to limit their flue gas in the competitive market. Realistic power bidding optimizations demand the responsibility of power regulators for searching of the optimal power bidding from economic emission aspect. Different discrete bidding situations have been analyzed in this attempt when a number of thermal power generating utilities demand bidding price at different percentages of their respective marginal price. These utilities compete among themselves not only for gaining maximum profit, but also they are made bound here to offer energy price along with flue gas emission level as minimum as possible. Pareto optimization has been searched by establishing Nash equilibrium of Game Theory considering minimum total objective of fuel cost and flue gas emission from the consumers’ aspect along with maximum profit gain for the power bidders. These multi-objective optimization results show that at economic and environmentally constrained situations, power bidders can earn more profit not only at optimally higher bidding price, but also at optimally lower bidding price along with selling higher quantity of electricity. Those bidders who bid at optimally lower marginal price along with selling lower quantity of electricity to satisfy minimum total objective are found suffering loss. Overall study shows that cost functions and emission functions as well as marginal prices of respective units play important role in different environmentally constrained bidding situations. An Indian Utility of 62 buses has been used for study, and program code is written in MATLAB R2013b platform.

The unprecedented and rapidly spreading Coronavirus Disease-19 (COVID-19) pandemic has challenged public health care systems globally. Based on worldwide experience, India has initiated a nationwide lockdown to prevent the exponential surge of cases. During COVID-19, management of cardiovascular emergencies like acute Myocardial Infarction (MI) may be compromised. Cardiological Society of India (CSI) has ventured in this moment of crisis to evolve a consensus document for care of acute MI. However, this care should be individualized, based on local expertise and governmental advisories.