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The swelling pressure of bentonite and bentonite mixtures is critical in designing barrier systems for deep geological radioactive waste repositories. Accurately predicting the maximum swelling pressure is essential for ensuring these systems' long-term stability and sealing characteristics. In this study, we developed a constrained machine learning model based on the extreme gradient boosting (XGBoost) algorithm tuned with grey wolf optimization (GWO) to determine the maximum swelling pressure of bentonite and bentonite mixtures. A dataset containing 305 experimental data points was compiled, including relevant soil properties such as montmorillonite content, liquid limit, plastic limit, plasticity index, initial water content, and soil dry density. The GWO-XGBoost model, incorporating a penalty term in the loss function, achieved an R 2 value of 0.9832 and an RMSE of 0.5248 MPa in the testing phase, outperforming feed-forward and cascade-forward neural network models. The feature importance analysis revealed that dry density and montmorillonite content were the most influential factors in predicting maximum swelling pressure. While the developed model demonstrates high accuracy and reliability, it may have limitations in capturing extreme values due to the complex nature of bentonite swelling behavior. The proposed approach provides a valuable tool for predicting the maximum swelling pressure of bentonite-based materials under various conditions, supporting the design and analysis of effective barrier systems in geotechnical engineering applications.

\"This comprehensive reference text discusses the fundamental concepts of artificial intelligence and its applications in a single volume. Artificial Intelligence: Fundamentals and Applications presents a detailed discussion of basic aspects and ethics in the field of artificial intelligence and its applications in areas, including electronic devices and systems, consumer electronics, automobile engineering, manufacturing, robotics and automation, agriculture, banking, and predictive analysis. Aimed at senior undergraduate and graduate students in the field of electrical engineering, electronics engineering, manufacturing engineering, pharmacy, and healthcare, this text: Discusses advances in artificial intelligence and its applications. Presents the predictive analysis and data analysis using artificial intelligence. Covers the algorithms and pseudo-codes for different domains. Discusses the latest development of artificial intelligence in the field of practical speech recognition, machine translation, autonomous vehicles, and household robotics. Covers the applications of artificial intelligence in fields, including pharmacy and healthcare, electronic devices and systems, manufacturing, consumer electronics, and robotics.\"--Back cover.

Over the last fifty years, arboviral infections have made an unparalleled contribution to worldwide disability and morbidity. Globalization, population growth, and unplanned urbanization are the main causes. Dengue is regarded as the most significant arboviral illness among them due to its prior dominance in growth. The dengue virus is mostly transmitted to humans by Aedes mosquitoes. The human body infected with dengue virus (DenV) will experience certain adverse impacts. To keep the disease under control, some of the preventative measures implemented by different countries need to be updated. Manual diagnosis is typically employed, and the accuracy of the diagnosis is assessed based on the experience of the healthcare professionals. Because there are so many patients during an outbreak, incompetence also happens. Remote monitoring and massive data storage are required. Though cloud computing is one of the solutions, it has a significant latency, despite its potential for remote monitoring and storage. Also, the diagnosis should be made as quickly as possible. The aforementioned issue has been resolved with fog computing, which significantly lowers latency and facilitates remote diagnosis. This study especially focuses on incorporating machine learning and deep learning techniques in the fog computing environment to leverage the overall diagnostic efficiency of dengue by promoting remote diagnosis and speedy treatment. A dual-level dengue diagnosis framework has been proposed in this study. Level-1 diagnosis is based on the symptoms of the patients, which are sent from the edge layer to the fog. Level-1 diagnosis is done in the fog to manage the storage and computation issues. An optimized and normalized lightweight MLP has been proposed along with preprocessing and feature reduction techniques in this study for the Level-1 Diagnosis in the fog computing environment. Pearson Correlation coefficient has been calculated between independent and target features to aid in feature reduction. Techniques like K-fold cross-validation, batch normalization, and grid search optimization have been used for increasing the efficiency. A variety of metrics have been computed to assess the effectiveness of the model. Since the suggested model is a “black box,” explainable artificial intelligence (XAI) tools such as SHAP and LIME have been used to help explain its predictions. An exceptional accuracy of 92% is attained with the small dataset using the proposed model. The fog layer sends the list of probable cases to the edge layer. Also, a precision of 100% and an F1 score of 90% have been attained using the proposed model. The list of probable cases is sent from the fog layer to the edge layer, where Level-2 Diagnosis is carried out. Level-2 diagnosis is based on the serological test report of the suspected patients of the Level-1 diagnosis. Level-2 diagnosis is done at the edge using the rule-based inference method. This study incorporates dual-level diagnosis, which is not seen in recent studies. The majority of investigations end at Level 1. However, this study minimizes incorrect treatment and fatality rates by using dual-level diagnosis and assisting in confirmation of the disease.

In this paper, we employ bibliometric analysis to empirically analyse the research on social entrepreneurship published between 1996 and 2017. By employing methods of citation analysis, document co-citation analysis, and social network analysis, we analyse 1296 papers containing 74,237 cited references and uncover the structure, or intellectual base, of research on social entrepreneurship. We identify nine distinct clusters of social entrepreneurship research that depict the intellectual structure of the field. The results provide an overall perspective of the social entrepreneurship field, identifying its influential works and analysing scholarly communication between these works. The results further aid in clarifying the overall centrality features of the social entrepreneurship research network. We also examine the integration of ethics into social entrepreneurship literature. We conclude with a discussion on the structure and evolution of the social entrepreneurship field.

\"The key objectives of the book are to explore the strengths of blockchain adaptation in manufacturing industries and logistics management, presenting different use cases of and future research trends\"-- Provided by publisher.

Multiple-input multiple-output (MIMO) technology has emerged as a highly promising solution for wireless communication, offering an opportunity to overcome the limitations of traffic capacity in high-speed broadband wireless network access. By utilizing multiple antennas at both the transmitting and receiving ends, the MIMO system enhances the efficiency and performance of wireless communication systems. This manuscript specifies a comprehensive review of MIMO antenna design approaches for fifth generation (5G) and beyond. With an introductory glimpse of cellular generation and the frequency spectrum for 5G, profound key enabling technologies for 5G mobile communication are presented. A detailed analysis of MIMO performance parameters in terms of envelope correlation coefficient (ECC), total active reflection coefficient (TARC), mean effective gain (MEG), and isolation is presented along with the advantages of MIMO technology over conventional SISO systems. MIMO is characterized and the performance is compared based on wideband/ultra-wideband, multiband/reconfigurable, circular polarized wideband/circular polarized ultra-wideband/circular polarized multiband, and reconfigurable categories. The design approaches of MIMO antennas for various 5G bands are discussed. It is subsequently enriched with the detailed studies of wideband (WB)/ultra-wideband (UWB), multiband, and circular polarized MIMO antennas with different design techniques. A good MIMO antenna system should be well decoupled among different ports to enhance its performance, and hence isolation among different ports is a crucial factor in designing high-performance MIMO antennas. A summary of design approaches with improved isolation is presented. The manuscript summarizes the various MIMO antenna design aspects for NR FR-1 (new radio frequency range) and NR FR-2, which will benefit researchers in the field of 5G and forthcoming cellular generations.

\"It was the first major conflict of the 20th century, a war that devastated the whole of Europe and expanded across the entire globe, decimating a generation. From the assassination of the Archduke Franz Ferdinand in Sarajevo to the armistice of 1918, World War One: 1914-1918 provides a complete overview of the war that shaped the modern world from the viewpoint of the servicemen who fought in it, creating a unique graphic novel history of one of the most destructive conflicts of all time.\"--Back cover.

Nowadays, in a world full of uncertainties and the threat of digital and cyber-attacks, blockchain technology is one of the major critical developments playing a vital role in the creative professional world. Along with energy, finance, governance, etc., the healthcare sector is one of the most prominent areas where blockchain technology is being used. We all are aware that data constitute our wealth and our currency; vulnerability and security become even more significant and a vital point of concern for healthcare. Recent cyberattacks have raised the questions of planning, requirement, and implementation to develop more cyber-secure models. This paper is based on a blockchain that classifies network participants into clusters and preserves a single copy of the blockchain for every cluster. The paper introduces a novel blockchain mechanism for secure healthcare sector data management, which reduces the communicational and computational overhead costs compared to the existing bitcoin network and the lightweight blockchain architecture. The paper also discusses how the proposed design can be utilized to address the recognized threats. The experimental results show that, as the number of nodes rises, the suggested architecture speeds up ledger updates by 63% and reduces network traffic by 10 times.