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This study thoroughly compares multiple single bidirectional and multiport converters (MPCs), highlighting the significant role of MPCs in multi-input and multi-output (MIMO) systems. MPCs offer a more efficient and cost-effective solution than multiple single converters, especially in applications involving photovoltaic (PV), electric vehicles (EVs) with storage systems, and power grids. This research emphasizes the importance of multi-input converters (MICs) in integrating diverse voltage sources. It notes the rising popularity of multi-output DC-DC converters in portable electronics, owing to their reduced component count, lower costs, and compact design. This paper emphasizes comparisons based on diverse aspects and applications, shedding light on recent developments in basic bidirectional converters. Additionally, it delves into the advancements in MPC topologies, focusing on efficiency, reliability, and modularity improvements. These advancements are crucial for harnessing cost reduction, simplicity, and compactness. Furthermore, this paper introduces an innovative multiport DC-DC converter tailored for integrating and managing renewable sources. This new converter design enhances PV system and battery storage performance by reducing power conversion steps, using fewer components, and improving voltage-boosting capabilities. Its unique bidirectional buck-boost structure allows for versatile connections between sources and loads with varying voltage and power requirements. The performance of this novel converter is evaluated through MATLAB/Simulink simulations under different scenarios. Experimental studies further validate its effectiveness, marking a significant contribution to power conversion and management in integrating renewable sources such as DC microgrids.

Wireless sensor networks (WSNs) play a pivotal role in diverse applications such as environmental monitoring, industrial automation, healthcare, and smart cities. The motivation behind the development of WSNs stems from their impact in providing real-time data on various environmental parameters. The challenge for WSNs is to achieve strong security and efficient energy saving together. Traditional methods sought to find solutions either through security or energy. In response, this study proposed a secure and energy-efficient framework for enhancing security measures in WSNs while minimizing the impact on energy resources by using the Enhanced Consumed Energy Leach (ECP-LEACH) protocol and the Enhanced Random Forest Classifier for Low Execution Time (ERF-LET) algorithm for attack detection named Security-Enhanced Energy Conservation with ERF-LET (S-2EC-ERF). The integration of the detection algorithm at the node level played a pivotal role in fortifying the security posture of individual nodes by detecting and mitigating potential security threats. Leveraging a comprehensive dataset obtained from NS3 simulations, the ERF-LET algorithm demonstrated its proficiency in differentiating between normal and attack packets, thereby laying a strong foundation for subsequent evaluations, where it achieved an accuracy of 98.193%. The proposed methodology was further validated through real-time simulations conducted on the NS3. The results demonstrated the superiority of the proposed S-2EC-ERF in terms of the packet delivery ratio (PDR), average throughput, end-to-end delay, and mean energy consumption compared to the Security-Enhanced Energy Conservation with Logistic Regression (S-2EC-LR), Security-Enhanced Energy Conservation with Decision Tree (S-2EC-DT), and Security-Enhanced Energy Conservation with AdaBoost (S-2EC-Ada) algorithms.

In recent years, the combination of wireless body sensor networks (WBSNs) and the Internet ofc Medical Things (IoMT) marked a transformative era in healthcare technology. This combination allowed for the smooth communication between medical devices that enabled the real-time monitoring of patient’s vital signs and health parameters. However, the increased connectivity also introduced security challenges, particularly as they related to the presence of attack nodes. This paper proposed a unique solution, an enhanced random forest classifier with a K-means clustering (ERF-KMC) algorithm, in response to these challenges. The proposed ERF-KMC algorithm combined the accuracy of the enhanced random forest classifier for achieving the best execution time (ERF-ABE) with the clustering capabilities of K-means. This model played a dual role. Initially, the security in IoMT networks was enhanced through the detection of attack messages using ERF-ABE, followed by the classification of attack types, specifically distinguishing between man-in-the-middle (MITM) and distributed denial of service (DDoS) using K-means. This approach facilitated the precise categorization of attacks, enabling the ERF-KMC algorithm to employ appropriate methods for blocking these attack messages effectively. Subsequently, this approach contributed to the improvement of network performance metrics that significantly deteriorated during the attack, including the packet loss rate (PLR), end-to-end delay (E2ED), and throughput. This was achieved through the detection of attack nodes and the subsequent prevention of their entry into the IoMT networks, thereby mitigating potential disruptions and enhancing the overall network efficiency. This study conducted simulations using the Python programming language to assess the performance of the ERF-KMC algorithm in the realm of IoMT, specifically focusing on network performance metrics. In comparison with other algorithms, the ERF-KMC algorithm demonstrated superior efficacy, showcasing its heightened capability in terms of optimizing IoMT network performance as compared to other common algorithms in network security, such as AdaBoost, CatBoost, and random forest. The importance of the ERF-KMC algorithm lies in its security for IoMT networks, as it provides a high-security approach for identifying and preventing MITM and DDoS attacks. Furthermore, improving the network performance metrics to ensure transmitted medical data are accurate and efficient is vital for real-time patient monitoring. This study takes the next step towards enhancing the reliability and security of IoMT systems and advancing the future of connected healthcare technologies.

Solar desalination is a sustainable approach to producing fresh water from saline water. Researchers have tried modifications to solar desalination systems to enhance the distillate output. This survey aims to recognize the characteristics of research work for investigating solar desalination systems. The essential terms to be focused on are computational fluid dynamics (CFD) and solar radiation consideration for the investigation. The data for this bibliometric study was taken from Scopus, with 1932 publications. The characteristics of the research work were analyzed by identifying the research publications, research subject areas, journals, most contributed countries, and data from the authors. The research trend was investigated utilizing yearly research growth, geographical contributions, source quality of publications, and participation of various institutions. VOSviewer software was used for network analysis of essential keywords used in relevant research works and to understand collaborations between the authors and co-authors. About 76% of the total publications belong to the document type articles. It illuminates research tendencies related to the topic under consideration. Results show that desalination research work concerned with CFD or solar radiation was mainly investigated in Engineering and Environmental Science, with their share of more than 50% of the total publications. Further study of relevant research works was assessed using network analysis that helped to link different keywords and authors’ collaborations. This survey helps to spot the increasing research trends and the necessity of further research.

In this study, we report on an active and stable CoMgAl layered double hydrotalcite (LDH) catalyst for phenol degradation by heterogeneous activation of peroxymonosulfate (PMS). The CoMgAl-LDH catalyst was synthesized by hydrothermal method. The PMS/CoMgAl-LDH system overcomes the drawbacks of traditional Fenton processes. Various effects, e.g., scavengers, chloride ion, catalyst dosage, PMS concentration, temperature, and pH, were also inspected to evaluate the system. The results indicated that the PMS/CoMgAl-LDH system had extremely high efficiency for phenol degradation; 0.1 mM phenol could be completely degraded by 0.3 g/L catalyst and 3 mM PMS within 60 min at 30 °C. The CoMgAl-LDH catalyst appeared to possess outstanding reusability and stability. After four rounds of recycling, nearly 100% of the phenol was removed within 80 min by the PMS/CoMgAl-LDH system, with only 0.05 mg/L Co2+ leaching. A sulfate radical was the main oxidation species in the PMS/Co-LDH system. The degradation rate of phenol was influenced by temperature, and the activation energy was 65.19 kJ/mol. These advantages proved the PMS/CoMgAl-LDH system is an effective strategy for the treatment of organic contaminants.

Titanium has been one of the traditional metals used in the medical industry since 1940. This work modeled and simulated a hip-joint replacement implant using Creo 5.0 and DEFORM 3D (v11.0), respectively. Four titanium-based billets were modeled; out of four billets, three billets were coated with a specified thickness, and one was uncoated. Among the three coated billets, one billet was coated with a 500-micron and two billets coated with a 1000-micron thickness. At the end of the simulation, the coating materials formed patches on the surface of the forged parts. The coating material Ti-6Al-4V (high O2) produced excellent mechanical properties in contrast to the CP-Ti material, which displayed low mechanical properties and did not match the core property. Hence, it was suggested to provide a bulk coating of Ti-6Al-4V (high O2) on the billet to improve the physio-mechanical properties and biocompatibility. Four points were selected on the surface of the forged parts at different locations for identifying the property variations concerning forging time. Results found that coating thickness required more on the side surface of the billet material than on the upper and lower surfaces to enhance its properties.

In this study, a metasurface-based reflectarray is designed for X-band applications. The unit cells are equipped with an H-shaped slotted patch for additional resonance and phase range. Linear phase variation by altering the length of the patch is realized with a range exceeding 480∘. The reflectarray is designed and fabricated on a thin and high-quality Rogers 5880 substrate. The Finite Element Boundary Integral (FEBI) method is used to simulate a 23×23 element reflectarray and then fabricated to achieve the measured results using an anechoic chamber. The peak gain of the proposed reflectarray is 25.5 dBi recorded with an aperture efficiency of 63.7% at a center frequency of 10 GHz. The cross-polarization and side-lobe levels in the entire band are less than -33 dB and -21 dB, respectively. Moreover, the proposed reflectarray antenna achieves a 20% 1-dB gain bandwidth.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that comprises amyloid-beta protein (Aβ) as a main component of neuritic plaques. Its deposition is considered a trigger for AD pathogenesis, progression, and the clinical symptoms of cognitive impairment. Some distinct pathological features of AD include phosphorylation of tau protein, oxidative stress, and mitochondrial dysfunction. These pathological consequences tend to produce reactive oxygen species (ROS), resulting in the dysregulation of various signaling pathways of neuroinflammation and neurodegeneration. The relationship between the Aβ cascade and oxidative stress in AD pathogenesis is like a "chicken and egg" story, with the etiology of the disease regarding these two factors remaining a question of "which comes first." However, in this review, we have tried our best to clarify the interconnection between these two mechanisms and to show the precise cause-and-effect relationship. Based on the above hallmarks of AD, several therapeutic strategies using natural antioxidants, monoclonal antibodies, and vaccines are employed as anti-Aβ therapy to decrease ROS, Aβ burden, chronic neuroinflammation, and synaptic failure. These natural antioxidants and immunotherapeutics have demonstrated significant neuroprotective effects and symptomatic relief in various in vitro and in vivo models, as well as in clinical trials for AD. However, none of them have received final approval to enter the drug market for mitigating AD. In this review, we extensively elaborate on the pitfalls, assurances, and important crosstalk between oxidative stress and Aβ concerning current anti-Aβ therapy. Additionally, we discuss future strategies for the development of more Aβ-targeted approaches and the optimization of AD treatment and mitigation.