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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. -
Beyond the Balance: Ethics, Justice, and Governance in the Environment-Society Nexus
This chapter discusses the dynamic relationship existing between the environment and human societies, conceptually defined as the Environment-Society Nexus. It explores how different environmental conditions come to shape social structures, policies, and cultural narratives while documenting the ways in which human activities and institutions shape environmental outcomes. Drawing on a multidisciplinary approach, this study synthesizes key findings emanating from ecology, sociology, political science, and economics to put forward a holistic understanding of this interrelationship. It does so by pointing out critical themes, such as resource governance, environmental justice, and sustainable development, through an explanation of how decisions developed by society pertaining to land use, resource extraction, and economic development contribute to or detract from ecological resilience and biodiversity. The book chapter also looks into the changes that result from environmental changes, such as climate change, pollution, and habitat degradation, with regard to social inequalities and how resilience occurs among communities. It then gives case studies and theoretical models to show how often marginalized communities bear the brunt of environmental degradation when, as a matter of fact, they are those who contribute the least to its causes. It supports the argument for equitable policies. This chapter concludes by saying that an integrative perspective on the solution of contemporary environmental problems defines the need for frameworks of ecological sustainability combined with social equity for the long-term resilience and well-being of human and natural systems. 2026 John Wiley & Sons Ltd. All rights reserved. -
The Establishment of Quantum Networks
The establishment of quantum networks marks an important revolution in quantum information science. This chapter delivers an exploration of the basics, building blocks, architecture, challenges, and future directions of quantum networks. The chapter starts with an introduction to quantum networks, the importance of quantum networks, and potential applications. Quantum networks influence the principles of quantum mechanics, including quantum entanglement and superposition, to facilitate secure communication, quantum computing, and quantum key distribution. The objective and scope of the chapter are defined, providing the first step for a comprehensive analysis. Essential concepts in quantum information science are conferred in the second section. Readers are familiarized to quantum mechanics and its significance to quantum networks. The quantum entanglement and superposition are explained, as they form the basis for several quantum message protocols. Quantum computing and quantum key distribution are the integral components of quantum networks and are also explored. The building blocks of quantum networks are covered in the next section. The Quantum hardware including quantum bits, quantum gates, and quantum processors is also discussed. The purpose of quantum communication channels, such as quantum optical fibers, quantum links, and satellite-based quantum communication, is inspected. These components are essential for transmitting as well as processing quantum information reliably and efficiently. The architecture of quantum networks is detailed in the subsequent section. Various network topologies, ranging from point-to-point connections to quantum indigenous area networks, quantum metropolitan area networks, and quantum wide area networks, are explored in this section. The functions of quantum network nodes and the use of quantum repeaters, quantum switches, and quantum memories are explained. Furthermore, quantum network protocols, including quantum teleportation, routing and switching, error correction, and key distribution, are discussed in detail. The establishment of quantum networks presents a number of challenges, which are addressed in the fifth section. Quantum noise, essential obstacles to quantum communication, are studied. Approaches for quantum error correction are explored. The considerations for network scalability, security, synchronization, management, and monitoring are also discussed. Solutions to these challenges are essential for the effective deployment of quantum networks. The current state of quantum network progress is presented in the sixth section. Investigational quantum networks and real-world implementations are also discussed. Case studies and success stories highlight the practical applications and potential impact of quantum networks. Future directions and emerging trends in quantum networks are outlined in the seventh section. Exploration includes the interoperability of quantum networks, the expansion of a global quantum internet, the development of quantum networks worldwide, quantum cloud computing, quantum sensor networks, and the incorporation of quantum machine learning into network operations. These improvements have the potential to reshape industries and scientific fields, providing a concrete path for transformative technologies. In conclusion, this chapter delivers an outline of the establishment of quantum networks. It covers the fundamentals, building blocks, architecture, challenges, and future directions of quantum networks. By investigating into these topics, this chapter aims to encourage researchers and engineers to explore the enormous opportunities presented by quantum networks and contribute to their improvement and realization. 2025 Scrivener Publishing LLC.. -
Exploring Quantum Computing in Weather Forecasting: Leveraging Optimization Algorithms for Long-Term Accuracy
Weather forecasting holds immense importance for businesses and society, necessitating accurate long-term predictions. Conventional computing faces challenges in achieving this precision due to the complexity of weather data processing. This study explores the potential of quantum computing and optimization techniques to enhance long-term weather forecasts. It delves into the foundational principles of quantum computing and the capabilities of optimization algorithms, highlighting their aptitude for addressing weather forecastings intricate optimization problems. Quantum computing, with its unique features of parallelism and advanced optimization, offers exciting opportunities for improving the precision of longrange weather forecasts. Examining the complexities of weather data analysis, the study emphasizes challenges arising from enormous datasets. It explores the potential of quantum optimization algorithms to extract insights and enhance meteorological data analysis. Additionally, it investigates quantum optimization methods for specific challenges in weather forecasting. This section compares quantum algorithms with traditional methodologies, evaluating their impact on forecast accuracy. The study concludes by showcasing the transformative role of quantum computing in advancing weather prediction capabilities, laying a robust foundation for future research and advancements in quantum computing technologies, and driving the evolution of more accurate long-term weather forecasts. 2025 Scrivener Publishing LLC. -
Liquid Crystal as a Potential Biosensing Material
Liquid crystal (LC) biosensors are based on the mechanism of ordering transformations of LC molecules. Because of the anisotropic nature of LC molecules, LC has an extraordinary response to external stimuli and has highly responsive optical properties. The elastic force between LC molecules helps them for their orderly arrangement, which changes in response to a multitude of external stimuli such as temperature, adding biomolecules, and applying electric and magnetic fields. LC biosensors can be classified into LC-solid, LC-aqueous, and LC-droplet interface biosensors based on the LC-interaction surfaces. LC biosensors can detect target molecules such as nucleic acid, proteins, amino acids, and glucose. Aptamer-based LC biosensors have also been developed because of their high sensitivity. The advantage of LC in making a label-free biosensor lies in the fact that it requires minimal instrumentation, which heavily reduces the expense. There, it is one of the most promising types of biosensing techniques that are being developed. 2025 Scrivener Publishing LLC. -
2D Pnictogens for Biosensing and Imaging Applications
2D pnictogens are considered the most promising nano agents among the family of 2D materials. The pnictogens contain phosphorus, arsenene, antimonene, and bismuthine. It has inherent tunable midrange band gaps and unparalleled mobility. It is popular due to its efficient photothermal behavior, loading capability the drug, high charge carrier mobility, in-plane anisotropic properties and less toxicity. The pnictogens nanosheet is obtained by using the Shear exfoliation method. The exfoliation is feasible when there is the existence of weak van der Waals forces between individual layers and during covalent interactions between layers, these elements will adopt the rhombohedral structure. Raman spectroscopy acts as the molecular fingerprints in identifying the number of layers and temperature dependence peak shift of pnictogen substances. The biosensor device can be made by using a layer-by-layer method for each pnictogen, cross-linking agent, and enzyme. Pnictogens can be employed in the application of photothermal therapy (PTT) to overcome the hinder which is because of the lack of efficient cell perception and cell toxicity. It can be used in biomedical applications to make the candidates absorb the ultraviolet and infrared lights as preferable. It can also be used in Biosensors, diagnosis, therapy and in anti-bacterial, anti-inflammation, anti-tumor, neurodegenerative treatment, and tissue repair. Pnictogen has many advantages in the application of biosensing and imaging techniques, but the main disadvantage of pnictogens are low thermal and chemical stability and degradation in ambient conditions. Therefore, this chapter focuses on different characteristics and various applications of pnictogen. 2025 Scrivener Publishing LLC. -
Silicene and Germanene for Biosensing and Imaging Application
The discovery of two-dimensional materials creates an immense effect in the field of biomedicine especially in sensing applications. Silicene and germanene are group 14 elements, analogs of graphene. Several experiments and theoretical information have proven the excellent properties of silicene and germanene as semiconductors, capacitors, photocatalysts, and sensors. Computational and other studies show the ability of these two-dimensional materials in biosensing applications. These graphene analogs have fascinating characteristics there are no band gaps in the semiconductors which leads to a lack of stability and is liable to oxidation. Since there is an essentiality of band gap for biosensing applications which can be corrected by making some modifications in these analogs using certain metal substrates. In this chapter, we will summarize silicene and germanene elements, their various properties and their applications in biosensing and bioimaging. This suggests silicene and germaneneas promising nanoelements for the evolution of new electronic equipment for biosensing and other uses in future. 2025 Scrivener Publishing LLC. -
Designer Crops for Biotic Stress Tolerance: Technologies and Applications
The interplay of population growth and climate change makes the creation of specialized crops in a context teeming with both biotic and abiotic hazards exceptionally challenging. Despite the optimism surrounding integrated pest and disease control based on host plant tolerance, the ongoing appearance of novel races and biotypes of major plant diseases and parasites poses a significant and growing threat to global agricultural output. Crop plants sustain severe harm from biotic stressors like diseases and pests. Managing these pressures has an influence on ecosystems and the environment in addition to raising production costs. Growers, exporters, and the health of both people and animals are concerned about the growing usage of chemical agents to combat biotic stressors. To address this, the most efficient approach is to employ resistant cultivars, as they offer cost-effectiveness, health benefits, and environmental friendliness. For enhancing global food security, the utilization of genome-editing tools requires close collaboration and dialogue among scientists, policymakers, regulators, and journalists. This concerted endeavor aims to simultaneously tackle public apprehensions about genetically modified crops. This chapter offers a comprehensive examination of the impact of biotic stress on crop plants and underscores the significance of developing crops with the ability to withstand such stresses. Additionally, it delves into diverse technologies created for crop enhancement and explores the economic significance of currently available engineered crops in the market. 2026 by John Wiley & Sons, Inc. -
Multilevel Inverter-Fed Closed Loop Control and Analysis of Induction Motor Drive
Multilevel inverters have discovered more extensive extent of utilization in moderate and also in high-power adjustable-speed drives. This chapter introduces a vector control scheme of induction motor drive which includes pulse width modulations for reducing harmonics and total harmonic distortion (THD). For better control of induction motor, indirect vector control has been applied which offers advantages such as removal of flux sensor, more dynamic responses, and minimum torque pulses is applied. The inverter named neutral point clamped inverter is applied for observing dynamic control of the motor drive along with efficiency. The main attention of this chapter is to improve the performance of indirect vector controller. The THD analysis proves the better operation of induction motor as compared to conventional voltage source inverter-fed induction motor drive. By the help of MATLAB simulation, the dynamic performance as well as steady-state of multilevel inverter-based drive are analyzed. 2024 Scrivener Publishing LLC. -
The Adverse Impact of Yellow Disease of Leaves in Different Plant Species
Yellow Leaf Disease (YLD), or chlorosis, reduces crop health and productivity, affecting plants like citrus, wheat, and bananas. This study reviews the causes of YLD, including bacterial, viral, and fungal infections, along with poor nutrition and environmental stress. It highlights the importance of early detection through novel methods like molecular diagnostics and remote sensing. The review also stresses the need to understand the interaction between disease, nutrition, and environment for effective management. Breeding YLD-resistant crops is proposed as a potential solution. This work serves as a foundation for future research to mitigate YLD's impact on agriculture. Grenze Scientific Society, 2025. -
Empowering Agriculture using New Approach of Machine Learning Techniques to Detect Early Plant Diseases
Plant health plays a critical role in ensuring global food security and sustaining agricultural productivity, as it directly influences crop yields and economic stability. Reducing losses and enhancing farm management techniques depend on early plant disease detection. This research suggests a new hybrid framework that combines deep learning (DL) and machine learning (ML) to improve disease detection's precision and effectiveness. The ML component effectively processes structured data, providing clear and reliable recommendations, while the DL model focuses on extracting detailed features from high resolution plant images through advanced image processing. By combining these complementary techniques, the framework achieves high precision, scalability, and real-time disease monitoring capabilities. This innovation supports farmers and agricultural experts in making timely, informed decisions, reducing crop losses and advancing sustainable farming practices. Ultimately, better precision agriculture is made possible by the integration of these cutting edge technology, which supports sustainable agricultural development and global food security. Grenze Scientific Society, 2025. -
Leveraging Deep Learning for Early Detection of Alzheimer's Disease from MRI Scans
Alzheimer's disease (AD) remains shrouded in mystery, with its early detection posing a significant challenge. This research paper delves into the cutting-edge realm of deep learning, exploring its potential to explore the brain's secrets and revolutionize AD diagnostics using Magnetic Resonance Imaging (MRI) data. Upon comprehensively reviewing the performance of six state-of-the-art models and studying their strengths and limitations on MRI data, this paper proposes a novel deep-learning architecture based on the InceptionV3 model for Alzheimer's Disease prediction using MRI data. The proposed architecture leverages convolutional neural networks (CNNs) to extract subtle brain structure and function patterns, potentially identifying early AD signatures before noticeable cognitive decline. The proposed model is validated on a large-scale MRI dataset that comprises four stages of dementia, demonstrating more insights. Inception V3 base model yielded 82% accuracy, measured using the metric Area Under the Curve (AUC), on the dataset, and an improved AUC of 87% was achieved by performing data augmentation to remove the class imbalance in the dataset. The proposed deep learning model built on top of Inception V3 exhibited an improved performance with an AUC of 88% underlining the potential of deep learning models in early AD detection. The paper's findings will contribute to the ongoing effort to revolutionize AD diagnosis and accelerate the development of personalized treatment strategies. Grenze Scientific Society, 2025. -
Analysing the Effectiveness of Solana Blockchain Platform and PoH Consensus Algorithm in Providing a Solution for Blockchain Scalability Problem
Solana started its journey in April 2018 and is now a public blockchain - based platform which aspires better scalability than other existing blockchains while providing security and decentralization. It backs the development of decentralized applications and smart contracts (DApps). The goal of the study is to confirm several of its characteristics, like its transaction throughput, or the pace in which legitimate transactions are committed to a Solana network block over the course of a one-second period (TPS). A secondary dataset that was gathered over the course of 60 days and made available on GitHub was utilized. Our data analysis findings demonstrate that the transaction throughput on an average is about 3006 TPS at a much lower transaction fees than the fees users pay for many other blockchains that facilitate the same operations, such as use of smart contracts and the development of DApps. The document explains the workings of the Solana blockchain, which, in the words of its creators, claims to address the scalability issue without compromising security and decentralization. Grenze Scientific Society, 2025.