Energy efficient optimal deployment of industrial wireless mesh networks using transient trigonometric Harris Hawks optimizer.

Wireless mesh networks (WMNs) play a vital role in modern communication systems, and optimizing the placement of wireless mesh routers is crucial for achieving efficient network performance in terms of coverage and connectivity. However, network congestion caused by overlapping routers poses challenges in WMN optimization. To address these issues, researchers have explored metaheuristic algorithms to strike a balance between coverage and connectivity in WMNs. This study introduces a novel hybrid optimization algorithm, namely Transient Trigonometric Harris Hawks Optimizer (TTHHO), specifically designed to tackle the optimization problems in WMNs. The primary objective of TTHHO is to find an optimal placement of routers that maximizes network coverage and ensures full connectivity among mesh routers. Notably, TTHHO's unique advantage lies in its efficient utilization of residual energy, strategically placing the sink node in areas with higher energy levels. The effectiveness of TTHHO is demonstrated through a comprehensive comparison with seven well-known algorithms, including Harris Hawks optimization (HHO), Sine Cosine Algorithm (SCA), Gray Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Moth Flame Optimization (MFO), Equilibrium Optimizer (EO), and Transient Search Optimizer (TSO). The proposed algorithm is rigorously validated using 33 benchmark functions, and statistical analyses and simulation results confirm its superiority over other algorithms in terms of network connectivity, coverage, congestion reduction, and convergence. The simulation outcomes demonstrate the effectiveness and efficacy of the proposed TTHHO algorithm in optimizing WMNs, making it a promising approach for enhancing the performance of wireless communication systems.

Health Control of Tree Trunk Utilizing Microwave Imaging and Reverse Problem Algorithms.

The voids in their trunk significantly affect tropical trees' health. Both the wood and timber industries may face substantial financial losses because of the lack of an effective technique to inspect the defected trees through deep zonal monitoring. Microwave imaging offers the advantages of mobility, processing time, compactness, and resolution over alternative imaging methods. An ultra-wide band (UWB) imaging system consisting of UWB antennas and a reverse problem algorithm is proposed. Several conditions, such as the size of trunk samples (16-30 cm), number of targets, size of voids, heterogeneity of media, and number of layers, are considered in experimental studies. Based on these studies, cylindrical wooden models with 100 and 140 mm diameters, one void at the center, and three voids in different locations were 3D printed. After proving the system's ability through simulation and measurements on 3D models, a rubber-wood trunk with a length of 75 cm was cut into smaller pieces. The images created utilizing the measured data showed that the system could detect voids in the rubber trunk. Furthermore, the system indicated a high percentage of reliability and repeatability.

Inhibitory effects of mixed flavonoid supplements on unraveled DSS-induced ulcerative colitis and arthritis.

Introduction: The mixed flavonoid supplement (MFS) [Trimethoxy Flavones (TMF) + epigallocatechin-3-gallate (EGCG)] can be used to suppress inflammatory ulcers as an ethical medicine in Ayurveda. The inflammation of the rectum and anal regions is mostly attributed to nuclear factor kappa beta (NF-κB) signaling. NF-κB stimulates the expression of matrix metalloproteinase (MMP9), inflammatory cytokines tumor necrosis factor (TNF-α), and interleukin-1ß (IL-1ß). Although much research targeted the NF-κB and MMP9 signaling pathways, a subsequent investigation of target mediators in the inflammatory ulcer healing and NF-κB pathway has not been done. Methods: The docking studies of compounds TMF and EGCG were performed by applying PyRx and available software to understand ligand binding properties with the target proteins. The synergistic ulcer healing and anti-arthritic effects of MFS were elucidated using dextran sulfate sodium (DSS)-induced colon ulcer in Swiss albino rats. The colon mucosal injury was analyzed by colon ulcer index (CUI) and anorectic tissue microscopy. The IL-1ß, tumor necrosis factor (TNF-α), and the pERK, MMP9, and NF-κB expressions in the colon tissue were determined by ELISA and Western blotting. RT-PCR determined the mRNA expression for inflammatory marker enzymes. Results: The docking studies revealed that EGCG and TMF had a good binding affinity with MMP9 (i.e., -6.8 and -6.0 Kcal/mol) and NF-kB (-9.4 and 8.3 kcal/mol). The high dose MFS better suppressed ulcerative colitis (UC) and associated arthritis with marked low-density pERK, MMP9, and NF-κB proteins. The CUI score and inflammatory mediator levels were suppressed with endogenous antioxidant levels in MFS treated rats. Conclusion: The MFS effectively unraveled anorectic tissue inflammation and associated arthritis by suppressing NF-κB-mediated MMP9 and cytokines.

Modeling of high voltage induction motor cooling system using linear regression mathematical models.

Achieving reliable power efficiency from a high voltage induction motor (HVIM) is a great challenge, as the rigorous control strategy is susceptible to unexpected failure. External cooling is commonly used in an HVIM cooling system, and it is a vital part of the motor that is responsible for keeping the motor at the proper operating temperature. A malfunctioning cooling system component can cause motor overheating, which can destroy the motor and cause the entire plant to shut down. As a result, creating a dynamic model of the motor cooling system for quality performance, failure diagnosis, and prediction is critical. However, the external motor cooling system design in HVIM is limited and separately done in the past. With this issue in mind, this paper proposes a combined modeling approach to the HVIM cooling system which consists of integrating the electrical, thermal, and cooler model using the mathematical model for thermal performance improvement. Firstly, the development of an electrical model using an established mathematical model. Subsequently, the development of a thermal model using combined mathematical and linear regression models to produce motor temperature. Then, a modified cooler model is developed to provide cold air temperature for cooling monitoring. All validated models are integrated into a single model called the HVIM cooling system as the actual setup of the HVIM. Ultimately, the core of this modeling approach is integrating all models to accurately represent the actual signals of the motor cooler temperature. Then, the actual signals are used to validate the whole structure of the model using Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) analysis. The results demonstrate the high accuracy of the HVIM cooling system representation with less than 1% error tolerance based on the industrial plant experts. Thus, it will be helpful for future utilization in quality maintenance, fault identification and prediction study.

Deterioration of Electrical Load Forecasting Models in a Smart Grid Environment.

Smart Grid (S.G.) is a digitally enabled power grid with an automatic capability to control electricity and information between utility and consumer. S.G. data streams are heterogenous and possess a dynamic environment, whereas the existing machine learning methods are static and stand obsolete in such environments. Since these models cannot handle variations posed by S.G. and utilities with different generation modalities (D.G.M.), a model with adaptive features must comply with the requirements and fulfill the demand for new data, features, and modality. In this study, we considered two open sources and one real-world dataset and observed the behavior of ARIMA, ANN, and LSTM concerning changes in input parameters. It was found that no model observed the change in input parameters until it was manually introduced. It was observed that considered models experienced performance degradation and deterioration from 5 to 15% in terms of accuracy relating to parameter change. Therefore, to improve the model accuracy and adapt the parametric variations, which are dynamic in nature and evident in S.G. and D.G.M. environments. The study has proposed a novel adaptive framework to overcome the existing limitations in electrical load forecasting models.

High Gain Triple-Band Metamaterial-Based Antipodal Vivaldi MIMO Antenna for 5G Communications.

This paper presents a miniaturized dual-polarized Multiple Input Multiple Output (MIMO) antenna with high isolation. The antenna meets the constraints of sub-6 GHz 5G and the smartphones' X-band communications. A vertically polarized modified antipodal Vivaldi antenna and a horizontally polarized spiral antenna are designed and integrated, and then their performance is investigated. Three frequency bands of 3.8 GHz, 5.2 GHz, and 8.0 GHz are considered, and the proposed dual-polarized antenna is studied. High isolation of greater than 20 dB is obtained after integration of metamaterial elements, and without applying any other decoupling methods. The proposed triple-band metamaterial-based antenna has 1.6 GHz bandwidth (BW) (2.9 GHz-4.5 GHz), 13.5 dBi gain, and 98% radiation efficiency at 3.8 GHz. At 5.2 GHz it provides 1.2 GHz BW, 9.5 dBi gain, and 96% radiation efficiency. At 8.0 GHz it has 1 GHz BW, 6.75 dBi gain, and 92% radiation efficiency. Four antenna elements (with eight ports) were laid out orthogonally at the four corners of a mobile printed circuit board (PCB) to be utilized as a MIMO antenna for 5G communications. The performance of the MIMO antenna is examined and reported.