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Objective- To evaluate and compare the level of TSH in premenopausal women (reproductive age group) and post-menopausal women. Material And Methods- The study was carried out on 100 premenopausal and 100 post-menopausal women attending Out Patient Departments at RIMS, Ranchi, during the period of January 2018- October 2019. Study Design: - Observational Study. Statistics- Statistical analysis was done using SPSS software. The data were represented by counts, percentage and mean ± standard deviation. Statistical analysis of TSH was done by t-test to compare these parameters in premenopausal and post-menopausal women. A p-value of

Protein secondary structure is the local conformation assigned to protein sequences with the help of its three-dimensional structure. Assigning the local conformation to protein sequences requires much computational work. There exists a vast literature on the protein secondary structure prediction approaches (more than 20 techniques), but to date, none of the existing techniques is entirely accurate. Thus, there is an excellent room for developing new models of protein secondary structure prediction to address the issues of prediction accuracy. In the present study, ensemble techniques such as AdaBoost- and Bagging-based deep learning models are proposed to predict the protein secondary structure. The data from standard datasets, namely CB513, RS126, PTOP742, PSA472, and MANESH, have been used for training and testing purposes. These standard datasets possess less than 25% redundancy. The model is evaluated using performance measures: Q8 and Q3 cross-validation accuracy, class precision, class recall, kappa factor, and testing on a dataset that is not used for training purposes, i.e., blind test. The ensembling technique used along with variability in datasets can remove the bias of each dataset by balancing it and making the features more distinguishable, leading to the improvement in accuracy as compared to the conventional and existing techniques. The proposed model shows an average improvement of ~ 2% and ~ 3% accuracy over the existing methods in a blind test for Q8 and Q3 accuracy.

Due to environmental issues and smart grid development, distributed energy resources, energy storage systems, and demand response (DR) are gaining attention to reduce the pollution and fossil fuel usage. This paper presents a customer’s preferences based innovative home appliances scheduling framework considering numerous constraints and DR for scheduling household appliances incorporating local energy grid and energy storage systems, including electrical and thermal energy storage. First, the models of household appliances and air conditioning load are built as the shiftable and non-schedulable loads and a flexible thermal load, respectively. Second, an enhanced normalized normal constraint (ENNC) strategy based on game theory (GT) is presented for a novel home appliance scheduling (HAS) framework with the objectives to optimize consumption cost, end-users comfort, and peak to average ratio. Then, the fuzzy compromising (FCP) method is proposed to optimize overall energy cost and gaseous emissions for the novel HAS framework with a residential local energy grid. In addition to this, conditional value at risk (CVaR) has also been incorporated in the objective function to resolve the sudden absence of distributed energy resources and power failures. Finally, case studies on data from Dallas, Texas, USA are performed, and the simulation results show that the proposed strategy is computationally inexpensive and outperforms other approaches in terms of electricity cost, gaseous emissions, and customer’s comfort. The proposed approach gives a significantly lower cost of 104.30 cents and gaseous emissions of about 18.753 kg for an entire day of novel HAS with DR adoption. Thus, it can provide help for DR accomplishment and precise prediction of electricity consumption.

Nowadays, the most notable uncertainty for an electricity utility lies in the electrical demand and generation in power systems. Demand response (DR) accomplishment due to the home appliances energy management has acquired considerable attention for the reliable and cost-optimized power grid. The optimum schedule of home appliances is a challenging task due to uncertain electricity prices and consumption patterns. Given this background, an innovative home appliance scheduling (IHAS) framework is proposed based on the fusion of the grey wolf and crow search optimization (GWCSO) algorithm. Using the proposed technique, the cost of electricity reduction, users-comfort maximization, and peak to average ratio reduction is analyzed for home appliances in the presence of real-time price signals (RTPS). The proposed optimization algorithm is also employed for Air Conditioners (ACs) scheduling and end-users comfort maximization in its usage due to the high percentage of ACs load. Simulation results indicate that the proposed GWCSO approach is robust, computationally efficient, and outperforms conventional ones in terms of electricity cost, peak to average ratio, and it also demonstrate that there is a trade-off between users’ comfort considering appliances waiting time and electricity cost. Thus, it can provide guidance for precise electricity consumption predictions and different DR actions.

Typhoid is one of the infectious diseases of human. Outbreaks of typhoid fever caused by S. typhi and S. paratyphi A are still a serious health problem worldwide. There are number of test available now days from molecular to immunological and biochemical to microbiological. In our study we have tried to establish the relevance of widal test in the diagnosis of typhoid fever. Both the tube and slide agglutination widal test were used to investigate the incidence of ty- phoid fever in 80 test individuals comprising 60 typhoid fever patients and 20 apparently healthy individuals comprised of adult and children. The test results were compared with those of standard blood culture tests. We tested different individu- als and were categorized according to their age above 10 years (20), and between 5- 10 years (20), and children under 5 years (20). Results showed that the accuracy of the widal tube and slide agglutination tests as used in the study in terms of sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were 71%, 62%, 91% and 31%, respectively. It is concluded that widal test will remains relevant as a diagnosis tool for typhoid fever, which is more con- venient, cheaper and faster than the other molecular and serological test.