Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

In the Smart Grid (SG) residential environment, consumers change their power consumption routine according to the price and incentives announced by the utility, which causes the prices to deviate from the initial pattern. Thereby, electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability. Due to the massive amount of data, big data analytics for forecasting becomes a hot topic in the SG domain. In this paper, the changing and non-linearity of consumer consumption pattern complex data is taken as input. To minimize the computational cost and complexity of the data, the average of the feature engineering approaches includes: Recursive Feature Eliminator (RFE), Extreme Gradient Boosting (XGboost), Random Forest (RF), and are upgraded to extract the most relevant and significant features. To this end, we have proposed the DensetNet-121 network and Support Vector Machine (SVM) ensemble with Aquila Optimizer (AO) to ensure adaptability and handle the complexity of data in the classification. Further, the AO method helps to tune the parameters of DensNet (121 layers) and SVM, which achieves less training loss, computational time, minimized overfitting problems and more training/test accuracy. Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes. Our proposed method has achieved a minimal value of the Mean Average Percentage Error (MAPE) rate i.e., 8% by DenseNet-AO and 6% by SVM-AO and the maximum accurateness rate of 92% and 95%, respectively.     

Mathematical Modelling of Rotavirus Disease Through Efficient Methods

The design of evolutionary approaches has a vital role in the recent development of scientific literature. To tackle highly nonlinear complex problems, nonlinear ordinary differential equations, partial differential equations, stochastic differential equations, and many more may called computational algorithms. The rotavirus causes may include severe diarrhea, vomiting, and fever leading to rapid dehydration. By the report of the World Health Organization (WHO), approximately 600,000 children die worldwide each year, 80 percent of whom live in developing countries. Two million children are hospitalized each year. In Asia, up to 45 percent of the children hospitalized for diarrhea may be infected with rotavirus. The rotavirus model is categorized into five-compartment like susceptible (S), breastfeeding (M), vaccinated (V), infected (I), and recovered children (R). Positivity, boundedness, equilibria, reproduction number, and stability results are part of the qualitative analysis of the model. After that, the design of the evolutionary approaches on the model predicts the efficiency, visualization, long-term behavior of the disease, and best results of the rotavirus disease. In the end, evolutionary computations are an appropriate tool for double-checking the qualitative examination of the model.     

Intelligent Cloud Based Load Balancing System Empowered with Fuzzy Logic

Cloud computing is seeking attention as a new computing paradigm to handle operations more efficiently and cost-effectively. Cloud computing uses dynamic resource provisioning and de-provisioning in a virtualized environment. The load on the cloud data centers is growing day by day due to the rapid growth in cloud computing demand. Elasticity in cloud computing is one of the fundamental properties, and elastic load balancing automatically distributes incoming load to multiple virtual machines. This work is aimed to introduce efficient resource provisioning and de-provisioning for better load balancing. In this article, a model is proposed in which the fuzzy logic approach is used for load balancing to avoid underload and overload of resources. A Simulator in Matlab is used to test the effectiveness and correctness of the proposed model. The simulation results have shown that our proposed intelligent cloud-based load balancing system empowered with fuzzy logic is better than previously published approaches.     

Optimality of Solution with Numerical Investigation for Coronavirus Epidemic Model

The novel coronavirus disease, coined as COVID-19, is a murderous and infectious disease initiated from Wuhan, China. This killer disease has taken a large number of lives around the world and its dynamics could not be controlled so far. In this article, the spatio-temporal compartmental epidemic model of the novel disease with advection and diffusion process is projected and analyzed. To counteract these types of diseases or restrict their spread, mankind depends upon mathematical modeling and medicine to reduce, alleviate, and anticipate the behavior of disease dynamics. The existence and uniqueness of the solution for the proposed system are investigated. Also, the solution to the considered system is made possible in a well-known functions space. For this purpose, a Banach space of function is chosen and the solutions are optimized in the closed and convex subset of the space. The essential explicit estimates for the solutions are investigated for the associated auxiliary data. The numerical solution and its analysis are the crux of this study. Moreover, the consistency, stability, and positivity are the indispensable and core properties of the compartmental models that a numerical design must possess. To this end, a nonstandard finite difference numerical scheme is developed to find the numerical solutions which preserve the structural properties of the continuous system. The M-matrix theory is applied to prove the positivity of the design. The results for the consistency and stability of the design are also presented in this study. The plausibility of the projected scheme is indicated by an appropriate example. Computer simulations are also exhibited to conclude the results.     

Enhancing breast pectoral muscle segmentation performance by using skip connections in fully convolutional network

The precise detection and segmentation of pectoral muscle areas in mediolateral oblique (MLO) views is an essential step in the development of a computer-aided diagnosis system to access breast malignant lesions or parenchyma. The goal of this article is to develop a robust and fully automatic algorithm for pectoral muscle segmentation from mammography images. This paper presents an image enhancement approach that improves the quality of mammogram scans and a convolutional neural network-based fully convolutional network architecture enhanced with residual connections for automatic segmentation of the pectoral muscle from the MLO views of a digital mammogram. For this purpose, the model is tested and trained on three different mammogram datasets named MIAS, INBREAST, and DDSM. The ground truth labels of the pectoral muscle were identified under the supervision of experienced radiologists. For training and testing, 10-fold cross-validation was used. The proposed model was compared with baseline U-Net-based architecture. Finally, we used a postprocessing step to find the actual boundary of the pectoral muscle. Our presented architecture generated a mean Intersection over Union (IoU) of 97%, dice similarity coefficient (DSC) of 96% and 98% accuracy on testing data. The proposed architecture for pectoral muscle segmentation from the MLO views of mammogram images with high accuracy and dice score can be quickly merged with the breast tumor segmentation problem.     

A new variable control chart under failure‐censored reliability tests for Weibull distribution

In this paper, we propose a new variable control chart under type II or failure‐censored reliability tests by assuming that the lifetime of a part follows the Weibull distribution with fixed and stable shape parameter. The purpose is to monitor the mean and the variance of a Weibull process. In fact, the mean and the variance are related to the scale parameter. The necessary measures are given to calculate the average run length (ARL) for in‐control and shifted processes. The tables of ARLs are presented for various shift constants and specified parameters. A simulation study is given to show the performance of the proposed control chart. The efficiency of the proposed control chart is compared with a control chart based on the conditional expected value under type II censoring. An example is also given for the illustration purpose.     

Dynamical Behavior and Sensitivity Analysis of a Delayed Coronavirus Epidemic Model

Mathematical delay modelling has a significant role in the different disciplines such as behavioural, social, physical, biological engineering, and bio-mathematical sciences. The present work describes mathematical formulation for the transmission mechanism of a novel coronavirus (COVID-19). Due to the unavailability of vaccines for the coronavirus worldwide, delay factors such as social distance, quarantine, travel restrictions, extended holidays, hospitalization, and isolation have contributed to controlling the coronavirus epidemic. We have analysed the reproduction number and its sensitivity to parameters. If,     

Changes in biochemical,hemodynamic, and dialysis adherence parameters in hemodialysis patients during Ramadan

This paper aimed to study the effect of Ramadan fasting on biochemical and clinical parameters and compliance for dialysis. A prospective multicenter observational cross‐sectional study comparing fasting with a non‐fasting stable adult hemodialysis patients for demographic and biochemical parameters, compliance with dialysis, inter‐dialytic weight gain, pre‐ and post‐blood pressure, and frequency of intradialytic hypotensive episodes was carried out. Six hundred thirty‐five patients, of whom 64.1% fasted, were studied. The fasters were younger (53.3 ± 16.2 vs. 58.4 ± 16.1 years; P = 0.001) but had similar duration on dialysis (P = 0.35). More fasters worked (22.0% vs. 14.6%; P = 0.001) and missed dialysis sessions during Ramadan. No differences were noted between groups in sex, diabetic status, or dialysis shift or day. There were no differences in the pre‐ and post‐dialysis blood pressure; serum potassium, albumin or weight gain; diabetic status; sex; and dialysis shift time or days. However, serum phosphorous was significantly higher in the fasting group (2.78 ± 1.8 vs. 2.45 ± 1.6 mmol/L; P = 0.045). There were no intragroup differences in any of the parameters studied when comparing the findings during Ramadan with those in the month before Ramadan. Fasters were significantly younger and more likely to be working, to miss dialysis sessions, and to have higher serum phosphorous levels. No other differences were observed.     

Half-Metallic Ferromagnetism in New Half-Heusler Compounds: an Ab Initio Study of CrTiX (X = Si,Ge, Sn,Pb)

In this work, half-metallic (HM) properties of new Half-Heusler (HH) ferromagnetic compounds CrTiX (X = Si, Ge, Sn, Pb) are studied by means of first principle band structure calculation within the framework of density functional theory (DFT). From the spin-polarized calculations using full-potential linearized augmented plane-wave (FP-LAPW)method, we found that all of these compounds are stable in ferromagnetic MgAgAs-type crystal structure. The latticeparameters of CrTiX compounds increase with increasing atomic radius of X atom and ranges from 5.76 to 6.38 Å. The calculated electronic structure of these compounds in MgAgAs-type structure shows that they are HM materials with an integer magnetic moment of 4 μ _B. Densities of states, electronic band structure, and origin of ferromagnetism have been discussed, and robust HM nature of these compounds is analyzed which makes them fascinating compounds for spintronic devices.     

Biochar applications and modern techniques for characterization

Biochar simply is the material produced when biomass undergoes any chemical processes under the conditions of pyrolysis. The variety of biomasses, including wood waste, agricultural crop leftover, organic waste, animal manure, and forestry residues, have been considered as raw material to produce biochar. Biochar is widely used for generation of heat and power and an addition to soils, in which it serves as a fertilizer and carbon sequestration agent. Also in the form of being activated, it finds significant role for various adsorption applications. The most beneficial use of a given char depends on its physical and chemical characteristics, even though the relationship of char properties to these applications is not well defined. Various widely used modern analytical techniques, which are applicable and crucial for biochar characterization, have been reviewed in the present work, such as solid state nuclear magnetic resonance spectroscopy, Fourier transform infrared spectroscopy, scanning electron microscopy, transmission electron microscopy, X-rays photoelectron spectroscopy, X-rays diffraction, thermogravimetric analysis, near edge X-rays absorption fine structure spectroscopy, and gas chromatography-mass spectroscopy. Utilization of these modern techniques provides the quantitative as well as qualitative information, i.e., determining the sizes, shapes, and physicochemical characteristics of biochar, which is reliable to track changes in the carbon arrangement over reaction time and temperature, and will be useful for efficient production of biochar and application. It provides the useful information for the researchers in this area and is beneficial not only for the effective biochar production, but also for potential utilization/application, and not only for environment but also for agriculture.