Artificial Intelligence Based Data Offloading Technique for Secure MEC Systems

Mobile edge computing (MEC) provides effective cloud services and functionality at the edge device, to improve the quality of service (QoS) of end users by offloading the high computation tasks. Currently, the introduction of deep learning (DL) and hardware technologies paves a method in detecting the current traffic status, data offloading, and cyberattacks in MEC. This study introduces an artificial intelligence with metaheuristic based data offloading technique for Secure MEC (AIMDO-SMEC) systems. The proposed AIMDO-SMEC technique incorporates an effective traffic prediction module using Siamese Neural Networks (SNN) to determine the traffic status in the MEC system. Also, an adaptive sampling cross entropy (ASCE) technique is utilized for data offloading in MEC systems. Moreover, the modified salp swarm algorithm (MSSA) with extreme gradient boosting (XGBoost) technique was implemented to identification and classification of cyberattack that exist in the MEC systems. For examining the enhanced outcomes of the AIMDO-SMEC technique, a comprehensive experimental analysis is carried out and the results demonstrated the enhanced outcomes of the AIMDO-SMEC technique with the minimal completion time of tasks (CTT) of 0.680.     

Comparative assessment of response surface methodology quadratic models and artificial neural network method for dry reforming of natural gas

The performance of 0.5% wt Rh/γ-AL₂O₃catalyst for the dry reforming of natural gas using carbon dioxide has been considered. In the present work a comparative study has been performed using Radial Basis Function Neural Network (RBFNN) and Response Surface Methodology (RSM) quadratic models to investigate their predictive ability for the effect of two different operating parameters, naSSSmely the hourly space velocity and the reaction temperature on the conversion of the different components comprising commercial natural gas. The predictive capabilities of the two methodologies were compared employing statistical error functions. The results indicated the superiority of RBF in the prediction capability; for example, the F-ratio for the CH₄ reactant is 86 and 1088185 employing RSM and ANN methods, respectively. Also for the various components involved in the reaction system R² ranges from 0.74 to 0.96 in case of RSM while it is 1.0 for all components employing ANN. This is due to ANN ability to approximate the non-linearity between the input and output variables.     

Iron removal from aqueous solutions by amphistegina filter

A semi-pilot filtration unit had been assembled to determine the potential and the effectiveness of the single-media filtration of amphistegina tests and granular activated carbon (GAC) filters in the removal of iron from aqueous solutions in a comparative study. The results revealed that the maximum adsorption capacities of Fe(II) by GAC and amphistegina filters at 313 K and higher flow rate (60 l/min) were 4.19 and 1.34 mg/g, while at lower flow rate (20 l/min) were 3.68 and 3.64 mg/g, respectively. Also, Freundlich and Langmuir adsorption isotherms were used to verify the adsorption performance and thermodynamic parameters were discussed.     

Automated Machine Learning Enabled Cybersecurity Threat Detection in Internet of Things Environment

Recently, Internet of Things (IoT) devices produces massive quantity of data from distinct sources that get transmitted over public networks. Cybersecurity becomes a challenging issue in the IoT environment where the existence of cyber threats needs to be resolved. The development of automated tools for cyber threat detection and classification using machine learning (ML) and artificial intelligence (AI) tools become essential to accomplish security in the IoT environment. It is needed to minimize security issues related to IoT gadgets effectively. Therefore, this article introduces a new Mayfly optimization (MFO) with regularized extreme learning machine (RELM) model, named MFO-RELM for Cybersecurity Threat Detection and classification in IoT environment. The presented MFO-RELM technique accomplishes the effectual identification of cybersecurity threats that exist in the IoT environment. For accomplishing this, the MFO-RELM model pre-processes the actual IoT data into a meaningful format. In addition, the RELM model receives the pre-processed data and carries out the classification process. In order to boost the performance of the RELM model, the MFO algorithm has been employed to it. The performance validation of the MFO-RELM model is tested using standard datasets and the results highlighted the better outcomes of the MFO-RELM model under distinct aspects.     

Modification of titanium oxide layer by calcium and phosphorus

The aim of the study was to investigate the possibility of calcium and phosphorus ion implantation into an oxide film applied onto titanium during anodic passivation. The corrosion resistance of modified titanium in Tyrode's physiological solution has been identified. Anodic oxidation was carried out in two solutions. The first contained 20 g dm⁻³ NaH₂PO₂ in 4.3 M H₃PO₄ (K1), whereas the other, 20 g dm⁻³ Ca(H₂PO₂)₂ in 4.3 M H₃PO₄ (K2). Voltage of 100 and 150 V was applied. It has been found out that it is possible to incorporate Ca and P into the emerging passive layer. The application of the voltage of 150 V makes it very porous. It has been also demonstrated that titanium so modified presents higher resistance to corrosion in the investigated environment than titanium not modified in Tyrode's solution.     

Quantum Artificial Intelligence Based Node Localization Technique for Wireless Networks

Artificial intelligence (AI) techniques have received significant attention among research communities in the field of networking, image processing, natural language processing, robotics, etc. At the same time, a major problem in wireless sensor networks (WSN) is node localization, which aims to identify the exact position of the sensor nodes (SN) using the known position of several anchor nodes. WSN comprises a massive number of SNs and records the position of the nodes, which becomes a tedious process. Besides, the SNs might be subjected to node mobility and the position alters with time. So, a precise node localization (NL) manner is required for determining the location of the SNs. In this view, this paper presents a new quantum bird migration optimizer-based NL (QBMA-NL) technique for WSN. The goal of the QBMA-NL approach is for determining the position of unknown nodes in the network by the use of anchor nodes. The QBMA-NL technique is mainly based on the mating behavior of bird species at the time of mating season. In addition, an objective function is derived based on the received signal strength indicator (RSSI) and Euclidean distance from the known to unknown SNs. For demonstrating the improved performance of the QBMA-NL technique, a wide range of simulations take place and the results reported the supreme performance over the recent NL techniques.     

Big Data 2.0 Processing Systems: Taxonomy and Open Challenges

Data is key resource in the modern world. Big data has become a popular term which is used to describe the exponential growth and availability of data. In practice, the growing demand for large-scale data processing and data analysis applications spurred the development of novel solutions from both the industry and academia. For a decade, the MapReduce framework, and its open source realization, Hadoop, has emerged as a highly successful framework that has created a lot of momentum in both the research and industrial communities such that it has become the defacto standard of big data processing platforms. However, in recent years, academia and industry have started to recognize the limitations of the Hadoop framework in several application domains and big data processing scenarios such as large scale processing of structured data, graph data and streaming data. Thus, we have witnessed an unprecedented interest to tackle these challenges with new solutions which constituted a new wave of mostly domain-specific, optimized big data processing platforms. In this article, we refer to this new wave of systems as Big Data 2.0 processing systems. To better understand the latest ongoing developments in the world of big data processing systems, we provide a taxonomy and detailed analysis of the state-of-the-art in this domain. In addition, we identify a set of the current open research challenges and discuss some promising directions for future research.     

Adult Exposure to Di-N-Butyl Phthalate (DBP) Induces Persistent Effects on Testicular Cell Markers and Testosterone Biosynthesis in Mice

Studies indicate that phthalates are endocrine disruptors affecting reproductive health. One of the most commonly used phthalates, di-n-butyl phthalate (DBP), has been linked with adverse reproductive health outcomes in men, but the mechanisms behind these effects are still poorly understood. Here, adult male mice were orally exposed to DBP (10 or 100 mg/kg/day) for five weeks, and the testis and adrenal glands were collected one week after the last dose, to examine more persistent effects. Quantification of testosterone, androstenedione, progesterone and corticosterone concentrations by liquid chromatography-mass spectrometry showed that testicular testosterone was significantly decreased in both DBP treatment groups, whereas the other steroids were not significantly altered. Western blot analysis of testis revealed that DBP exposure increased the levels of the steroidogenic enzymes CYP11A1, HSD3β2, and CYP17A1, the oxidative stress marker nitrotyrosine, and the luteinizing hormone receptor (LHR). The analysis further demonstrated increased levels of the germ cell marker DAZL, the Sertoli cell markers vimentin and SOX9, and the Leydig cell marker SULT1E1. Overall, the present work provides more mechanistic understanding of how adult DBP exposure can induce effects on the male reproductive system by affecting several key cells and proteins important for testosterone biosynthesis and spermatogenesis, and for the first time shows that these effects persist at least one week after the last dose. It also demonstrates impairment of testosterone biosynthesis at a lower dose than previously reported.