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Abstract The open nature of Wireless Sensor Networks (WSNs) renders them an easy target to malicious code propagation, posing a significant and persistent threat to their security. Various mathematical models have been studied in recent literature for understanding the dynamics and control of the propagation of malicious codes in WSNs. However, due to the inherent randomness and uncertainty present in WSNs, stochastic modeling approach is essential for a comprehensive understanding of the propagation of malicious codes in WSNs. In this paper, we formulate a general stochastic compartmental model for analyzing the dynamics of malicious code distribution in WSNs and suggest its possible control. We incorporate the stochasticity in the classical deterministic model for the inherent unpredictability in code propagation, which results in a more appropriate representation of the dynamics. A basic theoretical analysis including the stability results of the model with randomness is carried out. Moreover, a higher-order spectral collocation technique is applied for the numerical solution of the proposed stochastic model. The accuracy and numerical stability of the model is presented. Finally, a comprehensive simulation is depicted to verify theoretical results and depict the impact of parameters on the model’s dynamic behavior. This study incorporates stochasticity in a deterministic model of malicious codes spread in WSNs with the implementation of spectral numerical scheme which helps to capture these networks’ inherent uncertainties and complex nature.

Abstract In this study, we suggested an innovative approach by introducing an Adaptive Exponential Weighted Moving Average (AEWMA) control chart utilizing Variable Sample Size (VSS) under Bayesian methodology. The proposed methodology utilized an integer linear function to dynamically adjust sample sizes according to the AEWMA statistic. Another appealing feature of our adaptive framework is the integration of the smoothing constant of an EWMA chart, which enhances monitoring responsiveness. We reveal the superiority of our recommended control chart by extensive simulations to existing Bayesian EWMA and Bayesian AEWMA control charts using Fixed sample size (FSS). The offered Bayesian VAEWMA control chart is more sensitive to detection improvement, a decrease in the false alarm rate, and overall more effective than the existing methods. These findings provide additional justification for the basic notion that process control statistical tools needed to be dynamic, as the manufacturing process itself was dynamic. The results suggest the importance of introducing adaptive SPC methods in dynamic manufacturing environments. A real data application is performed to evaluate the validity and optimal performance of our recommended chart.

Abstract In recent years, there has been a growing interest in incorporating fractional calculus into stochastic delay systems due to its ability to model complex phenomena with uncertainties and memory effects. The fractional stochastic delay differential equations are conventional in modeling such complex dynamical systems around various applied fields. The present study addresses a novel spectral approach to demonstrate the stability behavior and numerical solution of the systems characterized by stochasticity along with fractional derivatives and time delay. By bridging the gap between fractional calculus, stochastic processes, and spectral analysis, this work contributes to the field of fractional dynamics and enriches the toolbox of analytical tools available for investigating the stability of systems with delays and uncertainties. To illustrate the practical implications and validate the theoretical findings of our approach, some numerical simulations are presented.

This article discusses the development and analysis of a broadband filter capable of suppressing specific frequencies of wireless applications at 4.7 GHz for WLAN, 8.3 GHz, and 11.5 GHz (for satellite communication) by combining a T-shaped stub loaded resonator with a central square ring to achieves high performance and compact size within the bandwidth of interest. The filter achieves low signal loss of 0.4 dB across the entire passband of 14.9 GHz bandwidth, good reflection suppression exceeding 10 dB, and independently adjustable multi stopbands. It utilizes a simple planar topology for a small footprint of 0.52 λg × 0.32 λg, where λg represents the wavelength at 4.7 GHz. The proposed filter evaluated using classical method an even-odd-mode analysis, and a 3D software tool HFSS-15 is used for simulations. The physical prototype is built on a Rogers RO-4350 substrate using LPKF S63 ProtoLaser machine. To confirm the accuracy and performance of the prototype, measurements were conducted using a ZNB20 vector network analyser. The simulations and measurement results confirm its effectiveness, paving the way for applications in various fields such as radar systems, medical imaging, and wireless communication.

Abstract The fractional stochastic delay differential equation (FSDDE) is a powerful mathematical tool for modeling complex systems that exhibit both fractional order dynamics and stochasticity with time delays. The purpose of this study is to explore the stability analysis of a system of FSDDEs. Our study emphasizes the interaction between fractional calculus, stochasticity, and time delays in understanding the stability of such systems. Analyzing the moments of the system’s solutions, we investigate stochasticity’s influence on FSDDS. The article provides practical insight into solving FSDDS efficiently using various numerical techniques. Additionally, this research focuses both on asymptotic as well as Lyapunov stability of FSDDS. The local stability conditions are clearly presented and also the effects of a fractional orders with delay on the stability properties are examine. Through a comprehensive test of a stability criteria, practical examples and numerical simulations we demonstrate the complexity and challenges concern with the analyzing FSDDEs.

Hepatitis B is one of the global health issues caused by the hepatitis B virus (HBV), producing 1.1 million deaths yearly. The acute and chronic phases of HBV are significant because worldwide, approximately 250 million people are infected by chronic hepatitis B. The chronic stage is a long-term, persistent infection that can cause liver damage and increase the risk of liver cancer. In the case of multiple phases of infection, a generalized saturated incidence rate model is more reasonable than a simply saturated incidence because it captures the complex dynamics of the different infection phases. In contrast, a simple saturated incidence rate model assumes a fixed shape for the incidence rate curve, which may not accurately reflect the dynamics of multiple infection phases. Considering HBV and its various phases, we constructed a model to present the dynamics and control strategies using the generalized saturated incidence. First, we proved that the model is well-posed. We then found the reproduction quantity and model equilibria to discuss the time dynamics of the model and investigate the conditions for stabilities. We also examined a control mechanism by introducing various controls to the model with the aim to increase the population of those recovered and minimize the infected people. We performed numerical experiments to check the biological significance and control implementation.

The impact of heat generation and activation energy on the Darcy-Forchheimer flow of magnetized fluid with nanoparticles in the condition of blood flowing arteries is being investigated by researchers from all over the world in an effort to understand the heat transmission component for the treatment of numerous diseases. The assessment of heat transmission has been made with the enclosure of influence like the viscous dissipation, heat source, Joules heating, and thermal radiation. Utilized is a tetra hybrid nanofluid made up of silver (Ag), gold (Au), titanium oxide (TiO2), coper (Cu), and Blood as the base fluid. The main justification for the use of gold, copper, and silver nanoparticles as drug delivery nanomaterials is their potential for drug transport and imaging. The purpose of the presented model is to assess the performance of the well-known tetra hybrid nanofluid models developed by Yamada-Ota and Xue. The main PDE are transformed into ODE via similarity renovations, and these ODE are then numerically solved using the MATLAB built-in bvp4c scheme. The arteries' internal temperature rises as a result of thermal radiation and viscous dissipation. The velocity and thermal distributions show an increasing behavior with greater variations in the flow parameter. The Yamada-Ota model outperforms the Xue tetra hybrid nanofluid model in terms of heat transmission effectiveness.

Task Scheduling is a crucial challenge in cloud computing as diversified tasks come rapidly onto cloud console dynamically from heterogeneous resources which consists of different task lengths, processing capacities. Generating schedules for these type of tasks is a challenge for Cloud Service Provider(CSP). Therefore, to generate task schedules in cloud paradigm effectively by considering type of task arising to cloud console and match it with respective Virtual Machine (VM), a task scheduler is formulated by using Deep Deterministic Policy Gradient (DDPG) algorithm which is used as methodology to design scheduler. This scheduler works in three stages. In the initial stage, tasks are classified based on length and processing capacity to identify them whether they are High Performance Computing (HPC) tasks or High Throughput Computing (HTC) tasks. After classification, in the second stage, resources are to be tracked which matches the corresponding nature of tasks. Finally, in the third stage, according to the VM priorities calculated based on electricity unit cost and tasks are mapped according to the priorities to the corresponding VMs. Simulations are conducted using Cloudsim with fabricated workload distributions and realtime worklogs. Finally, our proposed Hybrid workload Deep Deterministic Policy Gradient Task scheduler(HDDPGTS) evaluated over DQN, A2C algorithms. From results, it proved that our proposed HDDPGTS significantly improved makespan, Energy consumption, scheduling overhead, scalability over baseline approaches.

Abstract Background The standard surgical treatment of localized prostate cancer (PCa) has been rapidly changed along the last two decades from open to laparoscopic and finally robot-assisted techniques. Herein, we compare the three procedures for radical prostatectomy (RP), namely radical retropubic (RRP), laparoscopic (LRP), and robot-assisted laparoscopic (RALRP) regarding the perioperative clinical outcome and complication rate in four academic institutions. Methods A total of 394 patients underwent RP between January 2016 and December 2018 in four academic institutions; their records were reviewed. We recorded the patient age, BMI, PSA level, Gleason score and TNM stage, type of surgery, the pathological data from the surgical specimen, the perioperative complications, unplanned reoperating, and readmission rates within 3 months postoperatively. Statistical significance was set at (P

This paper aims to introduce a robust framework for forecasting demand, including data preprocessing, data transformation and standardization, feature selection, cross-validation, and regression ensemble framework. Bagging (random forest regression (RFR)), boosting (gradient boosting regression (GBR) and extreme gradient boosting regression (XGBR)), and stacking (STACK) are employed as ensemble models. Different machine learning (ML) approaches, including support vector regression (SVR), extreme learning machine (ELM), and multilayer perceptron neural network (MLP), are adopted as reference models. In order to maximize the determination coefficient (R2) value and reduce the root mean square error (RMSE), hyperparameters are set using the grid search method. Using a steel industry dataset, all tests are carried out under identical experimental conditions. In this context, STACK1 (ELM + GBR + XGBR-SVR) and STACK2 (ELM + GBR + XGBR-LASSO) models provided better performance than other models. The highest accuracies of R2 of 0.97 and 0.97 are obtained using STACK1 and STACK2, respectively. Moreover, the rank according to performances is STACK1, STACK2, XGBR, GBR, RFR, MLP, ELM, and SVR. As it improves the performance of models and reduces the risk of decision-making, the ensemble method can be used to forecast the demand in a steel industry one month ahead.

Online social networks (ONSs) such as Twitter have grown to be very useful tools for the dissemination of information. However, they have also become a fertile ground for the spread of false information, particularly regarding the ongoing coronavirus disease 2019 (COVID-19) pandemic. Best described as an infodemic, there is a great need, now more than ever, for scientific fact-checking and misinformation detection regarding the dangers posed by these tools with regards to COVID-19. In this article, we analyze the credibility of information shared on Twitter pertaining the COVID-19 pandemic. For our analysis, we propose an ensemble-learning-based framework for verifying the credibility of a vast number of tweets. In particular, we carry out analyses of a large dataset of tweets conveying information regarding COVID-19. In our approach, we classify the information into two categories: credible or non-credible. Our classifications of tweet credibility are based on various features, including tweet- and user-level features. We conduct multiple experiments on the collected and labeled dataset. The results obtained with the proposed framework reveal high accuracy in detecting credible and non-credible tweets containing COVID-19 information.

Abstract Mesoporous α–Fe2O3 has been synthesized via a simple sol-gel procedure in the presence of Pluronic (F-127) triblock copolymer as structure directing agent. Silver (Ag) nanoparticles were deposited onto α–Fe2O3 matrix by the photochemical reduction approach. Morphological analysis revealed the formation of Ag nanoparticles with small sizes