Bentham Science Publishers

Data Science and Interdisciplinary Research: Recent Trends and Applications is a compelling edited volume that offers a comprehensive exploration of the latest advancements in data science and interdisciplinary research. Through a collection of 10 insightful chapters, this book showcases diverse models of machine learning, communications, signal processing, and data analysis, illustrating their relevance in various fields.

Key Themes:

Advanced Rainfall Prediction: Presents a machine learning model designed to tackle the challenging task of predicting rainfall across multiple countries, showcasing its potential to enhance weather forecasting.

Efficient Cloud Data Clustering: Explains a novel computational approach for clustering large-scale cloud data, addressing the scalability of cloud computing and data analysis.

Secure In-Vehicle Communication: Explores the critical topic of secure communication in in-vehicle networks, emphasizing message authentication and data integrity.

Smart Irrigation 4.0: Details a decision model designed for smart irrigation, integrating agricultural sensor data reliability analysis to optimize water usage in precision agriculture.

Smart Electricity Monitoring: Highlights machine learning-based smart electricity monitoring and fault detection systems, contributing to the development of smart cities.

Enhanced Learning Environments: Investigates the effectiveness of mobile learning in higher education, shedding light on the role of technology in shaping modern learning environments.

Coastal Socio-Economy Study: Presents a case study on the socio-economic conditions of coastal fishing communities, offering insights into the livelihoods and challenges they face.

Signal Noise Removal: Shows filtering techniques for removing noise from ECG signals, enhancing the accuracy of medical data analysis and diagnosis.

Deep Learning in Biomedical Research: Explores deep learning techniques for biomedical research, particularly in the realm of gene identification using Next Generation Sequencing (NGS) data.

Medical Diagnosis through Machine Learning: Concludes with a chapter on breast cancer detection using machine learning concepts, demonstrating the potential of AI-driven diagnostics.

This volume bridges the gap between data science and interdisciplinary research, making it a valuable resource for researchers, academics, and professionals seeking to leverage cutting-edge technologies for transformative applications.

Advances in Data Science Driven Technologies (Volume 5): Data-Informed Leadership in Higher Education is an executive playbook for institutional excellence. It demonstrates how purposeful faculty development, human-centred design, and data-informed leadership transform technology investments into improved learning outcomes, equity, and graduate success. Spanning thirteen chapters, the book moves from people to platforms—building the educator’s toolkit, embedding analytics-driven quality assurance, and integrating immersive technologies such as virtual reality. Case studies, policy-aligned frameworks, and implementation playbooks equip readers with a roadmap to redesign curricula, elevate student experience, and lead sustainable digital transformation.

Key Features

Upskills faculty and leaders with evidence-based frameworks.

Guides faculty developers in the redesigning of courses and programmes with structured approaches.

Provides toolkits for data-driven decision-making and student-success analytics.

Translates strategy into classroom practice through reviews and case studies.

Enables readers to implement and scale innovations with practical checklists and plans.

This book highlights the applications of deep learning algorithms in implementing big data and IoT enabled smart solutions to treat and care for terminally ill patients. It presents 5 concise chapters showing how these technologies can empower the conventional doctor patient relationship in a more dynamic, transparent, and personalized manner. The key topics covered in this book include: - The Role of Deep Learning in Healthcare Industry: Limitations - Generative Adversarial Networks for Deep Learning in Healthcare - The Role of Blockchain in the Healthcare Sector - Brain Tumor Detection Based on Different Deep Neural Networks Key features include a thorough, research-based overview of technologies that can assist deep learning models in the healthcare sector, including architecture and industrial scope. The book also presents a robust image processing model for brain tumor screening. Through this book, the editors have attempted to combine numerous compelling views, guidelines and frameworks. Healthcare industry professionals will understand how Deep Learning can improve health care service delivery.

"Degenerative aortic valve disease is the most prominent cardiac valve disease in Western societies. This volume describes some of the more important issues and problems for this condition: its progressive character and the underlying mechanisms of this progression diagnostic difficulties 1) ascertainment of valvular origin of symptoms in elderly; 2) the challenge of the low output - low gradient syndrome; 3) moderate aortic valve calcification during CABG; 4) prediction of the rate of progression (who will need surgery on short term and who not). the burden on the left ventricle and its consequences (danger of postponement of surgery) the effect and the modalities (access, types of valves) of surgical treatment on survival (and QoL) the mode of registering postoperative complications determining predictors for valve related, non-valve related cardiac and non-cardiac postoperative complications. The e-book is a unique presentation, specific to degenerative aortic valve disease and its treatment including information about ways to deal with the progressive character of the disease (autophagy as a mode of cell death). Cardiologists still avoid or delay referring patients to the surgeon for the sake of age, left ventricular function or co-morbidity. Therefore, the e-book benefits readers by addressing the above issue and providing critical information for changing referral policy, which would ultimately enhance postoperative survival of patients suffering from heart valve disease."

Demystifying Emerging Trends in Green Technology explores the transformative intersection of computational intelligence, disruptive technologies, and green innovations. This volume offers insights into diverse fields such as blockchain, IoT, artificial intelligence, machine learning, and sustainable development. Each chapter presents cutting-edge research and practical solutions addressing environmental sustainability, energy efficiency, and eco-friendly technologies.

With contributions from leading researchers, this book discusses advancements like blockchain-based security, green marketing, smart waste management, sustainable agriculture, and innovative healthcare solutions. It emphasizes the role of interdisciplinary approaches in driving a greener and smarter future.

Key Features:

- Integration of AI, IoT, and blockchain in sustainable systems

- Applications in healthcare, agriculture, energy, and environmental science

- Practical and innovative solutions for real-world challenges

- Insights into future trends in green technology and disruptive innovation

In Density Matrix Theories in Quantum Physics, the author explores new possibilities for the main quantities in quantum physics – the statistical operator and the density matrix. The starting point in this exploration is the Lindblad equation for the statistical operator, where the main element of influence on a system by its environment is the dissipative operator. Bondarev has developed the theory of the harmonic oscillator, in which he finds the density matrix and proves the Heisenberg relation. Bondarev has written the dissipative diffusion and attenuation operators and proven the equivalence of the Wigner and Fokker–Planck equations using them. He further develops theories of the light-emitting diode and ball lightning. Bondarev also derives equations for the density matrix of a single particle and a system of identical particles. These equations have a remarkable property: when the density matrix has a diagonal shape they turn into a quantum kinetic equation for probability. Additional chapters in the book present new theories of experimentally discovered phenomena, such as the step kinetics of bimolecular reactions in solids, superconductivity, superfluidity, the energy spectrum of an arbitrary atom, lasers, spasers, and graphene.

Density Matrix Theories in Quantum Physics is an informative reference for theoretical physicists interested in new theories on the subject of complex physical phenomena, quantum theory and density matrices.

Dermatological treatments have changed continuously throughout recent decades. Old drugs are recognized as being effective in new indications and new methods of dosage or application have been reported. Novel drugs have also changed the therapeutic approach in numerous diseases, improving patient health and quality of life. Conversely, new adverse reactions, drug interactions and unfamiliar risks have also increased. This e-book organizes information about different treatments available for treating dermatological conditions, focusing on a practical approach and systematizing drug interactions, adverse events, and patterns of intake or application.

This e-book is comprised of 3 sections, based on the way the treatment is applied: topical treatments, systemic treatments and physical therapies. Topical therapy is probably the most important mode to treat diseases of skin, releasing the active drug directly on the affected area. Both local and global effects can be achieved, as well as different adverse reactions are sometimes obtained. Systemic treatments are important in dermatology in conditions affecting a huge surface or when a potent means of therapy is needed. Physical therapies are highly specific in nature with respect to skin diseases, using different light sources combined with several substances to obtain the desired effect on epidermal structures. Every section contains several chapters dedicated to the different groups of drugs. Specific emphasis is given to the practical usage of each drug with comprehensive tables facilitating the information.

Symbolic analyzers have the potential to offer knowledge to sophomores as well as practitioners of analog circuit design. Actually, they are an essential complement to numerical simulators, since they provide insight into circuit behavior which numerical analyzers do not provide.

Symbolic analysis of electronic circuits addresses the generation of symbolic expressions for the parameters that describe the performance of linear and nonlinear circuits in three domains: DC, AC and time; some or all the circuit parameters can be kept as symbols.

Due to the fact that these expressions remain valid during the change of component values (as long as models remain valid), designers can have both, an insight into the behavior of the network and use the expressions to optimize the circuit’s performances.

This e-book presents, details and exemplifies famous symbolic analysis techniques. Industrial R&D topics, recent developments and future trends in the field of symbolic analysis are also highlighted. This makes the e-book a good resource for circuit analysis. Thus, it is intended for students and researchers as well as for industry designers.

Research into stem cells started in the 1960s with experiments on spleen cultures. Evans and Kaufman made a breakthrough in mouse embryo culturing and embryonic stem cell extraction in 1981, followed by the work of Thomson in 1998 on the technique for extracting human embryonic stem cells. Since then, stem cell research has rapidly expanded as a therapeutic avenue for different diseases in humans. This book explains the basic developmental biology of stem cells including the development of stem cells during the implantation stage in utero to the regulation of stem cell division. Medical applications of stem cells in the therapy of diseases such as cancer, neurodegenerative diseases, and bone diseases are also explained in subsequent chapters. The book also explains the effect of parasitic cells on stem cell growth. Concepts in the book are explained in a simple clear manner, making this book an informative reference for non-experts, students and professionals in the field of biology and medicine.

Diabetes and Breast Cancer: An Analysis of Signaling Pathways an in-depth monograph that explores the cross-talk between diabetes and breast cancer. The edited volume sheds light on the complex relationship between the two diseases by explaining the critical regulatory signaling in their progression.

Topics covered in the book include the genetic similarities between type 2 diabetes and breast cancer, signaling pathways for breast cancer cell invasion and migration (including fat cadherin, Wnt signaling and IGF signaling).and research on associated conditions such as neuropathy, inflammation, hypoxia and obesity. A summary of natural

Scholars, academicians, and professionals in medicine and pharmaceutical research who want to gain a better understanding of the complex interplay between diabetes and breast cancer will learn about the underlying biochemical mechanisms, the pharmacological targets, and the pharmacogenomics and precision medicine approaches for the simultaneous management of both diseases

Key Features:

- Gives a comprehensive overview of the cross-talk between diabetes and breast cancer

- Covers information on multiple signaling pathways and associated conditions

- Provides insights contributed by biomedical experts with detailed references