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We have done geometry optimization of two conformers of Lantadene series C and D. The fundamental vibrational frequencies with intensity have been done by using B3LYP/6-311G (d, p) method. The complete vibrational assignments of wavenumbers are made on the basis of potential energy distribution (PED). The nonbonding interactions in Lantadenes C and D are calculated by using the same level theory at the bond critical point (BCP). The calculated topological parameters at BCP are utilized to determine the nature and strength of interactions. We have also computed HOMO–LUMO gap and plotted frontier orbital HOMO–LUMO surfaces, molecular electrostatic potential surfaces to explain the reactive nature of Lantadenes C and D. The electronic transition spectra UV–Vis spectra of Lantadenes C and D are calculated by using TDDFT theory. The values of hyperpolarizability show a probable use of these compounds in electro-optical applications. The natural bonding orbital theory is utilized to calculate transition of electron from donor to acceptor which is very useful to describe nonbonding as well as bonding interactions. The calculated value of Log P and Log S for Lantadene C Lantadene D established its pharmaceutical behaviors. The biological activity of Lantadenes C and D is also calculated by using PASS online server for Pa > 7 0 %. The docking of Lantadene C Lantadene D is also performed by using Auto dock with predicted drug by Swiss dock online server. [ABSTRACT FROM AUTHOR]

In order to preserve and manage the plant genetic resources, it is crucial to investigate the genetic diversity among plant accessions. The goal of the study was to undertake a morpho-physio-biochemical analysis of the 15 tomato cultivars. The study also attempted to screen tomato varieties for biotic and abiotic stress tolerance using molecular markers and to assess the molecular genetic diversity of chosen tomato varieties using RAPD primers. The selected tomato varieties were examined for a range of morphological traits including germination rate, plant height, fruit form, fruit weight, number of trusses per plant, and number of blooms per plant. Out of 15 tomato varieties, five were classified as determinate types, two as semideterminate types and eight as indeterminate kinds based on growth characteristics. With twelve varieties, round fruit form dominated over oval-round and flat fruit in terms of shape. Pusa Sadabahar produced fruit with a high average weight. The plant with the highest yield was identified as EC 608438. Ten RAPD markers were employed to analyse the genetic diversity of tomato cultivars, generating 136 total alleles. Of these, 128 alleles displayed polymorphism whereas 8 alleles showed monomorphic, resulting in 93.04% polymorphism. In order to find the resistance alleles for several abiotic and biotic stress resistance genes, including salts, fusarium wilt, nematodes and tomato yellow mosaic virus, tomato cultivars were also molecularly screened. The existence of resistance alleles from multiple resistance genes has been identified in these fifteen tomato genotypes through analysis. [ABSTRACT FROM AUTHOR]

Background: Electrocautery for surgical incision is rarely used for fear of unhealthy healing and poor scar cosmesis. There is paucity of literature on the use of diathermy for incisions in elective surgeries in the randomised setting. The present study was designed to address this lacunae in literature. Material and Methods: This was a hospital-based prospective randomised control study conducted among 100 patients who underwent elective inguinal hernia surgery in a tertiary care center in Tamil Nadu, India from January 2019 to June 2020. The patients were randomised into two groups based on computer generated random numbers table. Post operative wound was evaluated on post operative day 2 and 5 using the Southamptom wound grade and scar cosmesis was assessed using the Manchester scar score at 6 months postoperatively. Results: On statistical analysis, for incision time, there was no significant difference between the two groups, whereas on evaluating the blood loss between the two groups there was statistically significant difference. There was no statistically significant difference between the two groups on day 2 and day 5 of the wound complication and 6 months postoperatively there was no significant difference between the two groups in terms of scar cosmesis. Conclusion: Diathermy incisions are comparable to a scalpel incision in terms of incision time but diathermy results in significantly less blood loss. Postoperative wound healing and scar characteristics were found to be similar for both groups with no statistically significant difference. [ABSTRACT FROM AUTHOR]

Demand response (DR) modifies the pattern from a straightforward to an interactive one, allowing for the investigation of consumer participation in the power market and progress. The most important technique for evaluating the status of DR program implementation among those discussed in the earlier literature is consumer baseline load (CBL). Without using the DR program, CBL depicts the consumer consumption pattern that might emerge. There hasn't been much study done on CBL computation for residential consumers, and most of it ignores the influence of weather on these estimates. This study describes a novel method for computing CBL for residential consumers in the context of a smart grid. The findings show how weather affects CBL estimations and consumer desire to upgrade home insulation. The proposed method is examined in four test cases. The day-wise load data for the first test case has been collected from the NO1 region of Nord Pool and the remaining three test case data have been collected from the Connecticut and New Hampshire region of New England ISO. [ABSTRACT FROM AUTHOR]

Tuberose (Polianthes tuberosa L.) is an important flower and grown in diverse climatic conditions for commercial purpose. Tuberose gained popularity in recent past due to its beauty, look, and many hues and aesthetic values. Despite of its beauty, tuberose flowers have a short shelf life, causing them to dry up early and decay within few days. Many research groups' attempts to improve the shelf life and flower quality of tuberose cut flowers by manipulating different chemicals and their concentration and combinations. Majority of these approaches include the use of non-harmful substances mainly salicylic acid, nanoparticles, and sucrose have been tried. These substances are known to extend the life and increase the quality of cut flowers. Many studies in the past showed that these substances separately or in combination have beneficial effect to extend the vase life and to improve floral longevity of tuberose cut flowers. Through this review, we have made an effort to discuss various strategies which may help to improve the vase life of tuberose cut flowers and their long-term maintenance in a distinctive and eco-friendly manner. [ABSTRACT FROM AUTHOR]

Demand side management initiatives have gained attention recently because of the development of the smart grid and consumer-focused regulations. The demand side management programme has numerous goals. One of the main goals is to control energy demand by altering customer demand. This can be done in several ways, including financial discounts and behaviour changes brought about by providing knowledge to support the grid's stressed conditions. In this study, demand side management techniques for future smart grids are presented, including load shifting and strategic conservation. There are many controlled devices on the grid. The load shifting and day before strategic conservation approaches mentioned in this study are derived analytically for the minimization problem. For resolving this minimization issue, the RUNge Kutta optimizer (RUN) was developed. On a test smart grid with two service zones, one with residential consumers and the other with commercial consumers, simulations are performed. By contrasting the outcomes with the slime mould algorithm (SMA), Sine Cosine Algorithm (SCA), moth–flame optimization (MFO), and whale optimization algorithm (WOA), RUN demonstrates its effectiveness. The simulation findings demonstrate that the suggested demand side management solutions produce significant cost savings while lowering the smart grid's peak load demand. [ABSTRACT FROM AUTHOR]

Demand response (DR) program empowers the dynamic prices to actively optimize the consumption. This optimized consumption plays a vital role in resolving the complex operation and reliability issues in the electricity market. The human behavior aspect of consumers explained by several models that have been reported in the literature. These models depend on the classical utility factor. The effect of price on the consumer's decision in the field of energy efficiency and reduction of consumption based on behavioral characteristics are two important aspects of DR programs. In absence of such characteristics, results become non‐viable. In this paper, the footprint of two time‐based DR programs is explored on the peak reduction namely; Real‐Time Pricing (RTP) and Peak Time Rebate (PTR). Artificial Neural Network (ANN) based topologies for two DR programs are proposed. The proposed topologies employ variation in demand and price, subsequently for simulating an online DR simulator. Demand before and after the RTP and PTR was calculated and compared with four ANN‐based DR topologies namely; Radial Basis Function Neural Network‐Demand Response (RBFN‐DR), Feedforward Backprop‐Demand Response (FFBP‐DR), Layer Recurrent‐Demand Response (LR‐DR), and Generalized Regression‐Demand Response (GR‐DR). The proposed models are tested on three test cases. The first case tested on hourly data of New England ISO of Connecticut on August 18, 2014, the second case tested on hourly data of the same system on August 18, 2020, and the third case tested on hourly residential data of test smart grid. By assessing the results from all three test cases, depicted that RBFN‐DR proved its efficacy by giving better results for both price‐based programs namely; RTP and PTR. [ABSTRACT FROM AUTHOR]

In deregulated environment, stability issues have become dominant. Reliability of the power is essential for successful operation of the power system. Often high and dynamic loading conditions present new challenges in terms of decision of the control strategies to the system operator at energy management centre. For the achievement of voltage stability, identification of weak buses is very important. Line stability indices are important predictors of the weak buses in the over loaded system. Identification of the weak buses is the first step of control strategy. This paper presents an effective methodology based on Artificial Neural Network (ANN) to predict the Fast Voltage Stability Index (FVSI). Comparative analysis of different topologies of ANN is carried out based on the capability of the prediction of FVSI. Results are validated through offline Newton Raphson (NR) simulation method. The proposed methodology is tested over IEEE-14 and IEEE-30 test bus System. [ABSTRACT FROM AUTHOR]

Dynamic operating conditions along with contingencies often present formidable challenges to the power engineers. Decisions pertaining to the control strategies taken by the system operators at energy management centre are based on the information about the system’s behavior. The application of ANN as a tool for voltage stability assessment is empirical because of its ability to do parallel data processing with high accuracy, fast response, and capability to model dynamic, nonlinear, and noisy data. This paper presents an effective methodology based on Radial Basis Function Neural Network (RBFN) to predict Global Voltage Stability Margin (GVSM), for any unseen loading condition of the system. GVSM is used to assess the overall voltage stability status of the power system. A comparative analysis of different topologies of ANN, namely, Feedforward Backprop (FFBP), Cascade Forward Backprop (CFB), Generalized Regression (GR), Layer Recurrent (LR), Nonlinear Autoregressive Exogenous (NARX), ELMAN Backprop, and Feedforward Distributed Time Delay Network (FFDTDN), is carried out on the basis of capability of the prediction of GVSM. The efficacy of RBFN is better than other networks, which is validated by taking the predictions of GVSM at different levels of Additive White Gaussian Noise (AWGN) in input features. The results obtained from ANNs are validated through the offline Newton Raphson (N-R) method. The proposed methodology is tested over IEEE 14-bus, IEEE 30-bus, and IEEE 118-bus test systems. [ABSTRACT FROM AUTHOR]