Ground movements due to the construction of cut-and-cover structures and slurry shield tunnel of the Cairo Metro

This paper presents an evaluation of the settlement prediction techniques used to estimate the surface settlements associated with the construction of the Greater Cairo Metro Line 2. The construction of the Cairo Metro involved the construction of cut-and-cover underground stations and bored tunneling. A typical underground station was executed using top-down construction technique. The twenty two meters excavation was carried inside a watertight box with 50-m-deep diaphragm walls to form the sides and a 7-m thick grouted plug at the bottom. Tunneling was performed using a slurry shield tunnel boring machine, TBM, having an internal diameter of 9.48 m. This analysis is the first step in view of enhancing the procedures of settlement prediction and appraising potential damages to overlying structures and utilities for the future construction of the twin road tunnels in the historical urban environment of Al Azhar area and Khan El Khalily market in Cairo.     

Cat and Mouse Optimizer with Artificial Intelligence Enabled Biomedical Data Classification

Biomedical data classification has become a hot research topic in recent years, thanks to the latest technological advancements made in healthcare. Biomedical data is usually examined by physicians for decision making process in patient treatment. Since manual diagnosis is a tedious and time consuming task, numerous automated models, using Artificial Intelligence (AI) techniques, have been presented so far. With this motivation, the current research work presents a novel Biomedical Data Classification using Cat and Mouse Based Optimizer with AI (BDC-CMBOAI) technique. The aim of the proposed BDC-CMBOAI technique is to determine the occurrence of diseases using biomedical data. Besides, the proposed BDC-CMBOAI technique involves the design of Cat and Mouse Optimizer-based Feature Selection (CMBO-FS) technique to derive a useful subset of features. In addition, Ridge Regression (RR) model is also utilized as a classifier to identify the existence of disease. The novelty of the current work is its designing of CMBO-FS model for data classification. Moreover, CMBO-FS technique is used to get rid of unwanted features and boosts the classification accuracy. The results of the experimental analysis accomplished by BDC-CMBOAI technique on benchmark medical dataset established the supremacy of the proposed technique under different evaluation measures.     

Artificial Fish Swarm Optimization with Deep Learning Enabled Opinion Mining Approach

Sentiment analysis or opinion mining (OM) concepts become familiar due to advances in networking technologies and social media. Recently, massive amount of text has been generated over Internet daily which makes the pattern recognition and decision making process difficult. Since OM find useful in business sectors to improve the quality of the product as well as services, machine learning (ML) and deep learning (DL) models can be considered into account. Besides, the hyperparameters involved in the DL models necessitate proper adjustment process to boost the classification process. Therefore, in this paper, a new Artificial Fish Swarm Optimization with Bidirectional Long Short Term Memory (AFSO-BLSTM) model has been developed for OM process. The major intention of the AFSO-BLSTM model is to effectively mine the opinions present in the textual data. In addition, the AFSO-BLSTM model undergoes pre-processing and TF-IFD based feature extraction process. Besides, BLSTM model is employed for the effectual detection and classification of opinions. Finally, the AFSO algorithm is utilized for effective hyperparameter adjustment process of the BLSTM model, shows the novelty of the work. A complete simulation study of the AFSO-BLSTM model is validated using benchmark dataset and the obtained experimental values revealed the high potential of the AFSO-BLSTM model on mining opinions.     

Automated Arabic Text Classification Using Hyperparameter Tuned Hybrid Deep Learning Model

The text classification process has been extensively investigated in various languages, especially English. Text classification models are vital in several Natural Language Processing (NLP) applications. The Arabic language has a lot of significance. For instance, it is the fourth mostly-used language on the internet and the sixth official language of the United Nations. However, there are few studies on the text classification process in Arabic. A few text classification studies have been published earlier in the Arabic language. In general, researchers face two challenges in the Arabic text classification process: low accuracy and high dimensionality of the features. In this study, an Automated Arabic Text Classification using Hyperparameter Tuned Hybrid Deep Learning (AATC-HTHDL) model is proposed. The major goal of the proposed AATC-HTHDL method is to identify different class labels for the Arabic text. The first step in the proposed model is to pre-process the input data to transform it into a useful format. The Term Frequency-Inverse Document Frequency (TF-IDF) model is applied to extract the feature vectors. Next, the Convolutional Neural Network with Recurrent Neural Network (CRNN) model is utilized to classify the Arabic text. In the final stage, the Crow Search Algorithm (CSA) is applied to fine-tune the CRNN model’s hyperparameters, showing the work’s novelty. The proposed AATC-HTHDL model was experimentally validated under different parameters and the outcomes established the supremacy of the proposed AATC-HTHDL model over other approaches.     

Ni supported on La2O3+ZrO2 for dry reforming of methane: The impact of surface adsorbed oxygen species

This research investigates the effect of doping Ni/La₂O₃+ZrO₂ with nitrates of Ca, Cr, Ga, and Gd on the conversion of the feed and H₂/CO molar ratio. The development of these catalysts is intended to tackle inefficiency of dry reforming of CH₄ which stems from deactivation and sintering of active metal. All promoted catalysts perform better than the un-promoted catalyst. Cr promoted sample gives the best performance with CH₄ and CO₂ conversion averaging 83 and 88% respectively. Also, it maintains good stability. The relative ease of reducing the catalyst plus its porous nature are responsible for its performance. Ca promoted sample has the same area as that of the support indicating the movement of Ni–Ca out of the pores of the support during calcination. According to thermogravimetric analysis, the un-promoted catalyst records the highest amount of carbon while the Ca promoted sample has about 18%.     

Exploitation of vehicle's kinetic energy in power management of tow -wheel drive electric vehicles based on ANFIS DTC-SVM comparative study

The concept of the hybrid power system in electric vehicles means that there are many sources in this electric vehicle. The electric vehicle of two-wheel drives motors doesn't exploit the two front wheel; this kind of electric vehicle prompted us to propose using the front wheels in electric vehicle energy management, which creates another energy source. The hybrid vehicle can associate more than one source to each other to secure a long time working. The two rear wheels are generally controlled by classical controllers as the DTC-SVM controller that is one of many methods to control a motor's speed. It Based on three classical controllers. We want to replace the PI speed controller with an intelligent controller and show the possibility of integrating it in this kind of control. In this paper, we exploit the electric vehicle's Kinetic energy in energy management by combining the permanent magnet synchronous generator in the vehicle's front wheels, and integrating the ANFIS controller with back motors. The generator's power represents about 19% of the total electric vehicle power. The ANFIS management strategy gave the best résults 96.6 as efficiency and the smallest consumption of Air/fuel compared with the others methods about 55.75–199 (Ipm).     

Cardiac Protective Effect of Kirenol against Doxorubicin-Induced Cardiac Hypertrophy in H9c2 Cells through Nrf2 Signaling via PI3K/AKT Pathways

Kirenol (KRL) is a biologically active substance extracted from Herba Siegesbeckiae. This natural type of diterpenoid has been widely adopted for its important anti-inflammatory and anti-rheumatic properties. Despite several studies claiming the benefits of KRL, its cardiac effects have not yet been clarified. Cardiotoxicity remains a key concern associated with the long-term administration of doxorubicin (DOX). The generation of reactive oxygen species (ROS) causes oxidative stress, significantly contributing to DOX-induced cardiac damage. The purpose of the current study is to investigate the cardio-protective effects of KRL against apoptosis in H9c2 cells induced by DOX. The analysis of cellular apoptosis was performed using the terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) staining assay and measuring the modulation in the expression levels of proteins involved in apoptosis and Nrf2 signaling, the oxidative stress markers. Furthermore, Western blotting was used to determine cell survival. KRL treatment, with Nrf2 upregulation and activation, accompanied by activation of PI3K/AKT, could prevent the administration of DOX to induce cardiac oxidative stress, remodeling, and other effects. Additionally, the diterpenoid enhanced the activation of Bcl2 and Bcl-xL, while suppressing apoptosis marker proteins. As a result, KRL is considered a potential agent against hypertrophy resulting from cardiac deterioration. The study results show that KRL not only activates the IGF-IR-dependent p-PI3K/p-AKT and Nrf2 signaling pathway, but also suppresses caspase-dependent apoptosis.     

Pakistani traffic-sign recognition using transfer learning

Multimedia Tools and Applications - Initially, the traffic-sign recognition was done using the conventional image processing techniques which are sluggish and can cause fatal delays in real-world...     

Metaheuristic Resource Allocation Strategy for Cluster Based 6G Industrial Applications

The emergence of Beyond 5G (B5G) and 6G networks translated personal and industrial operations highly effective, reliable, and gainful by speeding up the growth of next generation Internet of Things (IoT). Industrial equipment in 6G encompasses a huge number of wireless sensors, responsible for collecting massive quantities of data. At the same time, 6G network can take real-world intelligent decisions and implement automated equipment operations. But the inclusion of different technologies into the system increased its energy consumption for which appropriate measures need to be taken. This has become mandatory for optimal resource allocation in 6G-enabled industrial applications. In this scenario, the current research paper introduces a new metaheuristic resource allocation strategy for cluster-based 6G industrial applications, named MRAS-CBIA technique. MRAS-CBIA technique aims at accomplishing energy efficiency and optimal resource allocation in 6G-enabled industrial applications. The proposed MRAS-CBIR technique involves three major processes. Firstly, Weighted Clustering Technique (WCT) is employed to elect the optimal Cluster Heads (CHs) or coordinating agents with the help of three parameters namely, residual energy, distance, and node degree. Secondly, Decision Tree-based Location Prediction (DTLP) mechanism is applied to determine the exact location of Management Agent (MA). Finally, Fuzzy C-means with Tunicate Swarm Algorithm (FCM-TSA) is used for optimal resource allocation in 6G industrial applications. The performance of the proposed MRAS-CBIA technique was validated and the results were examined under different dimensions. The resultant experimental values highlighted the superior performance of MRAS-CBIR technique over existing state-of-the-art methods.