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Role of nanomaterials in the development of nanobiosensors for infectious diseases
Transmissible illnesses brought on by viruses, bacteria, fungi, and parasites are referred to as infectious diseases. These can escalate into undesirable pandemic circumstances that disrupt both regular life functions and the world's population. These in turn have an effect on the current global economy, lead to joblessness, induce stress on the body, mind, and emotions, and put human survival in jeopardy. Consequently, in order to avert worldwide life impairment, prompt discovery, treatment, isolation, and control of the spread of pandemic infectious diseases within the town of origin are essential. As of right now, the World Health Organization (WHO) lists 12 infectious diseases that can be fatal: COVID-19, severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), human immunodeficiency virus (HIV), human papilloma virus (HPV), influenza, hepatitis, herpes simplex virus (HSV), Zika virus, chikungunya, dengue, and rota virus. Biosensors are becoming more and more potent instruments for diagnosing infectious diseases. Analytical tools that may transform biochemical data into detectable signals such as optical, electrical, magnetic, or thermal signals are referred to as biosensors. The growing need for highly selective, low-concentration sensing of a wide variety of chemicals has spurred the creation of sophisticated instruments known as nanobiosensors, which combine biological components, advanced materials, and nanoscale materials. The design, principle, underlying reasoning, receptor, and molecular features of sensor systems with a focus on the recent COVID-19 pandemic are all covered in this chapter. For critical comparison, electrochemical biosensor systems which included a variety of sophisticated nanostructures like semiconductors, metal organic frameworks (MOFs), MXenes, polymeric nanocomposites, metal and metal oxide nanoparticles, and combinations of biomolecules reported recently were specifically divided into distinct sub-sections. This chapter focuses on the difficulties that exist today in converting lab research into practical device applications, as well as the potential for the future and the commercialization of electrochemical diagnostic devices for the detection of corona viruses. It is anticipated that the background information and overall advancements presented in this study will be instructive for sensor researchers and will make it easier to design and fabricate electrochemical sensors for viruses that pose a threat to human life, with a wider range of applications for any desired pathogen. 2025 Scrivener Publishing LLC. All rights reserved. -
Biocompatibility and toxicity of nanomaterials in the designing of tools for the diagnosis of infectious diseases
Nanomaterials have revolutionized the landscape of infectious disease diagnostics by offering unparalleled advantages in terms of sensitivity, specificity, and rapidity. However, their integration into diagnostic tools necessitates a profound understanding of their biocompatibility and toxicity profiles to ensure both diagnostic accuracy and patient life. The successful translation of these nanomaterials into practical diagnostic tools hinges on a thorough understanding of their biocompatibility and toxicity. Biocompatibility, a fundamental characteristic of nanomaterials refers to their ability to coexist with biological systems without causing harm or triggering immune responses. This chapter unfolds the pivotal role of biocompatibility assessment, examining the compatibility of nanomaterials with biological systems. In vitro and in vivo evaluation methods, and adherence to regulatory standards, are emphasized as essential components of biocompatibility analysis. Simultaneously, the exploration of nanotoxicity and its hazardous effect highlights the significance of establishing safe exposure limits. Toxicity is a pressing concern when dealing with nanomaterials. The chapter explores the factors that contribute to nanomaterials toxicity, including size-dependent effects, surface modifications, and the route of exposure. It also delves into the mechanisms by which nanomaterials can exert toxicity, such as reactive oxygen species, reaction with surface expanded group, and penetration into the cell. To mitigate the potential risk associated with nanomaterials, the chapter discusses strategies for improving biocompatibility. Finally, it gives a glance into the various tools prepared highlighting the successful integration of biocompatible nanotoxicity of nanomaterials into infectious disease diagnostics. Ultimately, this chapter emphasizes the need for comprehensive biocompatibility and toxicity evaluation as integral components of designing effective and safe diagnostic tools thereby contributing to the advancement of healthcare through cutting-edge nanotechnology. 2025 Scrivener Publishing LLC. All rights reserved. -
Intrusion Detection Through Deep Learning: Emerging Trends and Challenges
The chapter begins with an introduction that sets the stage for a comprehensive journey into the world of deep learning. The chapter then delves into the critical components of deep learning, including neural network architectures, convolutional neural networks (CNNs), recurrent and recursive networks, and the application of deep learning. Moreover, it explains intrusion detection, its classification, and its methodology. By the end of the chapter, readers will have gained a solid understanding of the fundamental principles and tools necessary to delve deeper into the application of deep learning in intrusion detection, and challenges inherent in it. 2026 John Wiley & Sons, Inc. Published 2026 by John Wiley & Sons, Inc. -
Bivariate Cointegrated Model with Gamma Innovations
The nature of time-bound data is its non-stationarity, that is, the constant presence of factors such as trend, seasonality, or both. Adopting mechanisms such as the method of differencing or ordinary least squares results in a loss of information or overestimation or underestimation of the parameters, respectively. A cointegration study reflects the notion of a long-run equilibrium, which is a concept of sensitivity in macroecometrics. Thus, cointegration can be defined as the onset of a longterm equilibrium between two or more time series that evolve under the influence of time, with the potential advantage of establishing a dynamic relationship using standard methods. Thus, this study explores the theoretical approach of estimating an error correction model for a cointegrated bivariate VAR (2) model with gamma innovation. To obtain the parameter estimates of the proposed model, we employ the conditional maximum likelihood estimation, implemented through the NewtonRaphson algorithm, because of the gamma distributions non-closed form nature. A theoretical study is strengthened by artificial simulations that support mathematical derivations. 2025 Scrivener Publishing LLC. -
Fake News Detection in Healthcare Using Machine Learning
The internet has revolutionary power in todays society, acting as an unmatched catalyst for technical innovation, worldwide connectedness, and information dissemination. It has transformed communication and made knowledge more accessible to all, and given people, companies, and society the tools they need to prosper in the connected digital world. However, this power is responsible for navigating issues, such as the proliferation of fake news and safeguarding information integrity. As peoples health comes first, false information about it might have disastrous consequences. Even for the most knowledgeable professionals in the field, identifying false information about health can be difficult because of the variety of factors that must be considered. New advances in machine learning have enabled automatic classification of bogus news. For the detection of fake news correctly, we must train the automation in such a way that it captures the bogus correctly, and for that the data we input is of at most importance and, in fact, the most important as well. To enhance the models capacity to discern between authentic and fake news, this study investigates the extraction of structural and semantic information from text using a combination of named entity recognition and syntactic parsing. Utilizing these characteristics, we trained a variety of machine learning algorithms, assessed their effectiveness, and found that the Random Forest classifier outperformed the others in classification. 2025 Scrivener Publishing LLC. -
Accelerated Reliability Sampling Plan Based on Transformed Lindley Distribution
This study presents the development of Accelerated Reliability Sampling Plans (ARSPs) in the form of a Lindley distribution, considering the risks to both the producer and consumer. Sampling plan tables with varying values for both the risks WERE formulated. By leveraging a known Acceleration Factor (AF) as a foundation, ARSPs were systematically evaluated for their sensitivity to AF fluctuations, ensuring robustness under diverse testing scenarios. An example was used to illustrate the practical application of the formulated sampling plan tables. The proposed ARSPs facilitate efficient product reliability assessment under accelerated conditions, potentially reducing testing time and cost while achieving the desired reliability targets. 2025 Scrivener Publishing LLC. -
Leveraging ResNeXt50 and LSTM for Enhanced Plant Disease Detection: A Hybrid Model Proposal
This chapter explores the implementations of deep learning algorithms along with remote sensing technologies for precise identification and categorization of plant diseases, focusing on enhancing accuracy and efficiency in agricultural practices. This research study intends to succeed in building a hybrid model for the classification and forecasting of diseased plants with high accuracy. Plant disease detection and classification is a critical field of study within agricultural science and technology. It involves identifying and categorizing diseases affecting plants to ensure timely and effective management practices. Early and accurate identification of plant diseases is crucial to minimize crop loss, maintain food security, and reduce the use of pesticides, which can have adverse environmental and health effects. In any country, both the yield and the quality of agricultural products are essential for the success of agriculture. Plant disease (i.e. abnormal growth or functionality) detection is tough work, which has prompted numerous investigators to apply image processing, machine learning (ML), computer vision, and big data analytics, etc., techniques, which make the challenging assignment easier. The proposed approach integrates the deep convolutional neural network ResNeXt50 with long short-term memory (LSTM) networks to tackle the dual tasks of plant leaf disease classification and segmentation. The ResNeXt50 backbone extracts intricate spatial features from plant leaf images, while the LSTM component models the temporal dynamics of disease progression. This hybrid model exploits the hierarchical feature representation of ResNeXt50 and the sequential learning capabilities of LSTM to enhance accuracy and contextual understanding of plant leaf diseases. The model's training accuracy was enhanced to a maximum of 99.74% and a validation accuracy of 95.44%, scoring 94% in F1, 96% in recall, and 96% in accuracy. Comparative analysis reveals that the ResNeXt50 + LSTM model outperforms other classifiers, including Inception V3, AlexNet, ResNet50, and VGG16, addressing overfitting and vanishing gradient issues. The model demonstrates superior performance in handling imbalanced data and excels in plant disease prediction, validated through various benchmarks and datasets. This study confirms the hybrid model's robustness and potential for practical application in plant pathology. 2025 by The Institute of Electrical and Electronics Engineers, Inc. -
Deep Learning in Waste Management and Recycling in Digital Smart City
For waste management and recycling in smart cities, the fast growth of urban populations and the subsequent rise in garbage creation have posed considerable issues. For cities to be sustainable and ecologically friendly, good waste management and the promotion of recycling practises are crucial. Deep learning techniques have become a potent tool for solving complicated issues and streamlining numerous procedures in a variety of fields in recent years. In the framework of smart cities, this chapter proposes improved Deep learning model with IOT Architecture for recycling and garbage management. 2025 Scrivener Publishing LLC. -
Exploring the Blockchain-Enabled Metaverse: A Comparative Study of Leading Platforms
The integration of metaverse and secure-based blockchain is transforming several domains, including the area of virtual employment fairs. This chapter comprehensively examined technologies and covers the areas and platform that is both immersive and secure for job searchers and recruiters. It provides a novel case study of a virtual job fair, focusing on its system architecture with metaverse and blockchain. The Decentraland platform is focused and comprises essential elements for metaverse environment and blockchain network. This will help through analyzing as well as interactions between attendees, recruiters, and system administrators the operational process, with an improved security, transparency, and user engagement. The study recognizes promising advancements, yet it accentuates important obstacles and unsolved issues, such as expansion, data protection, and portability. These concerns must be addressed in order to fully exploit the promise of the metaverse and blockchain in revolutionizing virtual interactions. 2025 Scrivener Publishing LLC. -
Amalgamation of IoT, Blockchain, Artificial Intelligence for Metaverse
The metaverse is a set of technologies that uses a computer to create a virtual world of reality and human connections. Some of the most significant enablers of this evolution are the Internet of Things (IoT), blockchain, and artificial intelligence (AI). These technologies not only provide a better experience but also introduce a new way of how security, efficiency, and inter-activeness should be done within the Metaverse. This chapter discusses the intersection between IoT, blockchain, and AI and its relevance in the framework of Metaverse. It discusses how IoT devices generate environments, the blockchain maintains high levels of security and provides digital ownership, and AI facilitates interactions. The rise of these technologies guarantees that the use of the virtual worlds will be consistent and also enhance the user experience, yet the blend of these technologies brings a number of difficulties like interoperability problems, data privacy problems, and also the concern of combining such a lot of various systems. The Metaverse has been explored in these challenges to achieve its full potential. The objective of this chapter is to paint a picture of how IoT, blockchain, and AI should be utilized to improve the Metaverse. This chapter presents an analysis of technical and ethical issues, offers potential solutions to the current problems, and outlines the possible directions of further development. Our study thus points out that the application of these technologies together offers an enormous opportunity to propel the development of the Metaverse in its quest to deliver virtual spaces that are secure, intelligent, and interactive. The final part of the chapter outlines the long-term effect on the society as well as the future prospects for development and the potential ethical challenges in this popping field of study. 2025 Scrivener Publishing LLC. -
Future Trends of Roadmap to Metaverse Technology
The term metaverse is used to describe the interconnected network of technologies such as the Internet of Things (IoT), blockchain, artificial intelligence (AI), and other fields of technology, such as the medical field. Similar to how the Internet of Things and the Metaverse are digital twins, the latter makes extensive use of the former in its simulated office. In the blockchain-based Metaverse, this data serves as a means of tracing the provenance of various pieces of information. Such information is becoming useful in the Metaverse, which is used to train AI. With the help of AI and blockchain technology, Metaverse creates a digital virtual world where people may securely and freely participate in social and economic activities that go beyond the bounds of the actual world. In this article, we will discuss the technology used by the metaverse and the possibilities that exist for the metaverse in the healthcare arena. 2025 Scrivener Publishing LLC. -
Demystifying the Metaverse Era: The Enabling Technologies and Industry Use Cases
Metaverse can be called a 3D shared virtual space that is hyper realistic, immer-sive, instinctive, and interactive. Through metaverse, people try to visualize life in the manner that do not exist in the real world. The potential and promising digital technologies turn out to be a huge enabler of the metaverse dream. This chapter is to delineate the various versatile metaverse applications, implementation technol-ogies, and use cases (individual as well as industrial). 2025 Scrivener Publishing LLC. -
File fragment classification: A comprehensive survey of research advances
A crucial task in digital forensics is file fragment classification, which involves classifying file fragments into their respective types based on their content. It is integral to digital forensics and data recovery, where investigators reconstruct and analyze fragmented files to gather evidence in criminal cases, data breaches, or other cybercrimes. This comprehensive survey paper offers insights into the different methodologies used for file fragment classification, including but not restricted to specialized approaches, hierarchical classification, and neural networks. The paper also highlights the challenges in file fragment classification, such as the need for format standardization, limited training data, scalability, and noise and ambiguity. A research gap analysis of the existing literature was conducted, and it was identified that further research could be done to explore the effectiveness of different approaches for file fragment classification, including transfer learning, ensemble methods, and so on. 2025 Scrivener Publishing LLC. All rights reserved. -
Navigating network security: A study on contemporary anomaly detection technologies
In an era that has been overtaken by digital connectivity, the fact that networks are the center of modern commerce and the operation of life is more than ever evident. And it stands to reason that ensuring proper management of these networks is cardinal especially during the explosion of network attacks and cybersecurity incidents. Security of networks is a high priority, and system intrusion detection is one of the most important tools used for this purpose. The paper discusses various approaches that operate from the classical clustering techniques to the cutting-edge methods integrating blockchain, autoencoders, and graph neural networks. The advised technique supports a holistic and formative model that integrates the contributions of the already accumulated literature. The approach involves combining techniques from various domains such as cloud security, machine learning, and intrusion detection systems. Research is summed up by pinpointing the key emphasis taken to deal with the dynamic nature of network security through cross-functional thinking, offering the prospect of the future in the face of dynamic cyber threats. 2025 Scrivener Publishing LLC. All rights reserved. -
A Comparative Analysis of AlexNet and ResNet for Pneumonia Detection
Pneumonia damages the lungs and results in swelling and fluid build-up in the air sacs, a major problem that can be assessed using AI techniques. Rapid detection is very important for timely treatment and effective medical care. Chest X-ray imaging stands out as a forefront diagnostic modality for pneumonia, owing to its non-invasive characteristics, cost-effectiveness, and ubiquitous accessibility across many medical facilities. A large volume of data, which can be used to generate medical intelligence, is being collected daily. In recent years, CNNs have exhibited exceptional efficacy in diverse medical image-processing endeavors; X-ray images of the chest play a pivotal role, particularly in diagnosing pneumonia. In CNN architectures, AlexNet and ResNet emerge as the frequently utilized and effective models for various medical problems and diagnoses. As a result, our research involves a comparative analysis of how well AlexNet and ResNet perform in identifying pneumonia. According to our experimental results, ResNet outperforms AlexNet regarding effective classification parameters. 2025 Scrivener Publishing LLC. -
Transformative Trends in AI for Environmental Monitoring: Challenges, Applications
The integration of artificial intelligence (AI) and machine learning (ML) is reshaping environmental monitoring, responding to the escalating complexities of issues like climate change and pollution. This article presents a comprehensive overview of current trends, challenges, and applications in AI-driven environmental monitoring. While technologies like remote sensing and Internet of Things (IoT) have improved data resolution, the sheer volume necessitates AI for efficient processing. The review emphasizes the role of AI in real-time monitoring, providing timely insights critical for addressing natural disasters and pollution. Exploring various environmental monitoring verticals-air and water quality, climate change modeling, biodiversity, and disaster prediction-the article highlights AIs versatility in addressing diverse concerns. Challenges such as data quality, bias, interpretability, and privacy are examined, underlining ethical considerations in biased models impacting marginalized communities. This chapter discusses common environmental modeling methodologies, ranging from empirical to geospatial modeling, elucidating their advantages and challenges. 2025 Scrivener Publishing LLC. -
Population-Environment Nexus: Interactions, Impacts, and Sustainability
Research regarding the delicate balance between population dynamics and environmental sustainability underlines ecological preservation and human growth. This essay examines the linkage between population and the environment: the global, historical, and contemporary perspectives, and policy frameworks for linking population, the environment, and policymaking. The research methodology implemented is doctoral, focusing on theories given by different demographers, for example, Malthusian warnings on population increasing more rapidly than the availability of resources are, on the contrary, contrasted by Boserup's optimism based on innovation being sustainable and similar theories. The rapid urbanization, resource consumption, and population growth are examined against the backdrop of pressing global issues like biodiversity loss, deforestation, climate change, and water scarcity. Case studies from high-income and low-income countries show how the environmental loads differ across socioeconomic spectrums. The research emphasizes on measures like family planning, sustainable development goals, and environmental governance in addressing these problems. Innovative strategies such as clean energy, sustainable agriculture, and circular economies are recognized as being essential in reducing environmental degradation and promoting inclusive growth. Intergenerational justice and resource equity are critical ethical considerations that should be considered with utmost care when developing sustainable policies. This paper draws on cross-disciplinary viewpoints and international cooperation to provide a meaningful contribution to ensuring a sustainable future for the next generation in a delicate balance between ecological conservation and population growth. 2026 John Wiley & Sons Ltd. All rights reserved. -
Social Inclusion and Sustainability
Sustainability and social inclusion are closely intertwined concepts, both centered on ensuring equal access to opportunities, resources, and participation in decision-makingwhile also committing to the long-term protection of the environment. The essay describes how social inclusion catalyzes sustainable development; more specifically, it illuminates why the inclusion of marginalized groups in economic and environmental policy is so crucial. Including marginalized groups in sustainable development processes catalyzes a variety of views and promotes social justice, thereby maximizing the effectiveness of efforts toward sustainability. Access to resources, employment opportunities, and education go hand in hand in building a just and fair society. Sustainable job opportunities are tied to environmental goals and narrow the economic gap. Equal access to education also allows everybody, regardless of their background, to join in and benefit from the environmentally sound practices. To minimize the inequalities associated with environmental degradation and climatic change impacts on vulnerable populations, environmental justice is key to social inclusion, as every citizen should be represented through buildings that cause minimum damage to the environment. There should be an investment in inclusive urban development, sustainable housing, and infrastructure for human settlement. This paper emphasizes that social inclusion and sustainability goals harmoniously balance each other in the pursuit of an equitable, resilient, and ecologically responsible future. 2026 John Wiley & Sons Ltd. All rights reserved. -
Social-Ecological System Framework Network
The Social-Ecological Systems Framework Network (SESFN) provides a holistic approach to understanding the complicated relationships between ecological and social systems. By integrating network analysis, SESFN unveils the dynamic interconnections and interdependencies that shape these systems, offering critical insights into governance, resilience, and adaptive capacity. This framework is a powerful tool for addressing contemporary challenges such as biodiversity conservation, resource management, and climate change. Through interdisciplinary collaboration, SESFN facilitates stakeholder engagement, combining traditional knowledge with scientific research to foster sustainable practices. The application of SESFN has established its effectiveness in promoting adaptive management and improving both ecosystem health and human well-being. As global environmental challenges deepen, SESFN emerges as a pivotal and essential framework for crafting innovative solutions to achieve sustainability and resilience across diverse social-ecological contexts. 2026 John Wiley & Sons Ltd. All rights reserved. -
Sustaining Tomorrow: Strategies for Long-Term Environmental Governance
Long-term environmental governance (EG) is crucial for addressing global environmental challenges such as climate change, biodiversity loss, and resource depletion. This chapter explores the principles, challenges, and emerging trends in EG, emphasizing the necessity of sustainability-focused policies that transcend short-term interests. It discusses key governance frameworks, including the Paris Agreement and the Convention on Biological Diversity (CBD), highlighting their successes and limitations. The study underscores the role of corporate responsibility, technological innovations, and collaborative governance in promoting sustainable environmental management. It also identifies political, economic, and global disparities as significant barriers to effective governance. This chapter concludes with policy recommendations advocating adaptive governance structures, enhanced international cooperation, and localized sustainability initiatives. By integrating scientific advancements, stakeholder engagement, and long-term policy planning, EG can ensure a balanced approach to development and ecological preservation, securing a sustainable future for coming generations. 2026 John Wiley & Sons Ltd. All rights reserved.