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Air quality index improvement through machine learning and quantum computing: a framework for advancing air quality prediction using quantum-inspired metaheuristics on climate change to achieve positive health
Climate change significantly exacerbates air quality deterioration, intensifying health risks and environmental instability. Air pollution poses significant challenges to public health and environmental sustainability. Accurate prediction of the Air Quality Index (AQI) is crucial for timely interventions and policy-making. As urbanization and industrial activities intensify, there is an urgent need for accurate and real-time air quality monitoring systems. Advanced machine learning (ML) techniques have shown promise in air quality forecasting and classification. Recently, quantum-inspired computational paradigms have emerged as innovative tools to overcome the limitations of traditional models, particularly in areas like feature selection, optimization, and spatial-temporal pattern recognition. This study presents a comprehensive analysis of various machine learning and deep learning models for AQI prediction, utilizing pollutant concentration data. It also explores quantum computing-inspired approaches. We explore the efficacy of different algorithms, datasets, and preprocessing techniques. This paper critically reviews high-impact research that explores the intersection of climate-induced changes and air quality prediction using ML. It identifies trends, gaps, and emerging methodologies. We conduct a comparative analysis of datasets, prediction models, and performance metrics. The paper focuses on three case studies. The first case study focuses on the Indian aspect using an Indian dataset and the global aspects with different global datasets, and the second case study uses quantum-inspired approaches. We further evaluate the performance of 10 state-of-the-art ML models, offering a roadmap for future research and deployment. Effective air quality forecasting is vital in urban planning decisions. This also plays an essential role need in environmental management and the protection of public health. This issue directly deals with Sustainable Development Goal (SDG) 3 and SDG 13. SDG 3 is related to positive health and SDG 13 is related to climate action. Conventional predictive models in ML face challenges due to multiple reasons. Effective feature selection is one such challenge as well as effective hyperparameter tuning. These challenges limit the effectiveness of artificial intelligence models. In the proposed framework, searching is enhanced using quantum jump- and quantum mechanics-related principles. This approach leads to the development of a quantum-inspired particle swarm optimization called QPSO. QPSO is able to provide more promising results by bridging the gaps of traditional optimization techniques. Model convergence is accelerated by using quantum-inspired feature selection techniques. 2026 Elsevier Inc. All rights reserved. -
Multiobjective portfolio optimization using multilevel quantum inspired optimization algorithms: a comparative study
The study of portfolio optimization has been a significant focus for computer science and finance researchers, with frequent publication of innovative methods. Numerous works have illustrated that conventional approaches like quadratic programming struggle with nonlinear constraints. This chapter compares ant colony optimization and particle swarm intelligence optimization within classical and quantum inspired frameworks, utilizing qubits and qutrits. This study analyzes benchmark datasets from the NASDAQ, Dow Jones, and BSE spanning over a decade. A pioneering effort has been made to develop a multiobjective portfolio optimization technique through a multilevel quantum inspired optimization algorithm. The experimental results demonstrate that the quantum inspired metaheuristic technique that utilizes qutrits slightly outperforms classical and qubit based quantum inspired methods. 2026 Elsevier Inc. All rights reserved. -
Ethical considerations in nanomedicine research involving women
This chapter elaborates social impact and perspectives of advanced technologies as well as critically discuss the ethical issues of womens participation in nano-medicine research. It starts by addressing the issue that women have been under sampled in clinical research but it is moral to take their samples farther into research most notably with certain special health conditions and unique reactions to drug treatments that are likely to be unique to women. One of the most emphasized concepts is informed consent, for the purpose of precisions and clarity in explaining its purpose and usefulness, as well as the probable consequences in the form of some narrowly speculative treatments with particular relevance to certain probabilities remaining beyond the range of perception on the female body after some or other nano-scale procedures. The chapter also talks about the ethical implications in the present strategies to increase the efficacy and safety for women only therapies like nanomedicine; the author urges for stringent preclinical and clinical guidelines which should have examined the following gender variations. It also examines the regulation with guidance and suggests contingency structures that are employed in the management of the ethical use of nanomedicine in women health care. Therefore this chapter tries to develop ethics together with science, incorporate these met ethical considerations into methods and guidelines in research, to contribute to enhancement of beneficial impact of the nanomedicine for womens rights and well-being. 2026 Elsevier Inc. All rights reserved.. -
Water purification membranes: state of the art, fundamentals, challenges, and opportunities
The rising global demand for clean and safe water has intensified the necessity for effective and sustainable purification technologies. This chapter thoroughly overviews membrane-based water purification systems, highlighting their basic principles, material types, operational mechanisms, and evolving roles in addressing water scarcity. It begins with the historical progression of membrane technology, discussing the various membrane types alongside essential separation processes including ultrafiltration, nanofiltration, reverse osmosis, and membrane distillation. The text also covers recent innovations in nanocomposite membranes, cutting-edge material design, and membrane module configurations, focusing on enhancing performance and energy efficiency. Special focus is placed on membrane fouling, its causes, effects, and strategies for mitigation, backed by computational modeling and machine learning insights. The chapter begins by exploring emerging trends, such as the development of fit-for-purpose membranes, their integration into zero liquid discharge systems, and scalable fabrication methods. Together, this information highlights the transformative capacity of membrane technology in tackling global water issues. 2026 Elsevier Inc. All rights reserved.. -
Detection and management of sucralose pollution: innovations in sustainable monitoring, mitigation, and energy utilization
Sucralose, a widely utilized artificial sweetener, has emerged as a potential environmental contaminant, persisting in aquatic environments and causing ecological risks. The use of suitable methods and sustainable materials can be applied for the detection of sucralose in wastewater, sludge, and other waste matrices Using sustainable materials and novel techniques for discerning sucralose. Leveraging waste-derived components, particularly in electrochemical sensing systems with competent nanomaterials can be a promising avenue for sucralose detection. Moreover, integrating waste-derived carbon materials like activated carbon or carbon black as electrodes enhances sensor sensitivity by augmenting organic molecule adsorption capacities, thus amplifying detection capabilities. Furthermore, waste-derived substrates such as cellulose fibers or agricultural residues can be engineered to serve as substrates or immobilization matrices in enzyme-based sensors for sucralose detection. While the electrochemical systems current output may not directly serve energy applications, its potential utilization for energy generation or storage, through technologies like electrochemical capacitors or batteries, underscores the broader sustainability objectives by repurposing energy from the detection process for additional beneficial applications. The amalgamation of sustainable methodologies in sucralose pollution detection and management not only contributes to environmental preservation but also aligns with the broader trajectory toward sustainable resource utilization and conservation. 2026 Elsevier Inc. All rights reserved. -
Electrochemical synthesis of nanoparticles
Nanomaterials possess diverse applications across environmental, medical, and energy sectors, owing to their unique properties dictated by structure, size, composition, and morphology. These characteristics of nanoparticles are pivotal in materials science. Their distinctive properties made an interest in specific applications, prompting the exploration of synthesis methods tailored for various purposes, particularly in electrochemistry. This chapter examines a nanoparticle synthesis technique capable of producing nanoparticles suitable for diverse applications, emphasizing electrochemical synthesis. This method offers efficient fabrication of nanoparticles at low temperatures with high purity, presenting an environmentally friendly preparation approach. Electrochemical synthesis, particularly through electrodeposition, involves the controlled passage of an electric current between the electrodes immersed in an electrolyte solution. This method yields stable nanoparticles with robust electrical contact, poised for utilization in batteries, fuel cells, supercapacitors, catalysis, optoelectronics, and beyond. The chapter elucidates the electrochemical synthesis process, underscoring its potential for advancing nanoparticle-based technologies across multiple disciplines. 2025 Elsevier Inc. All rights reserved. -
Graphene-based nanocomposites for energy conversion and storage
Graphene, consisting of a single layer of carbon atoms organized in a hexagonal pattern, exhibits exceptional electrical, mechanical, and thermal characteristics. The combined effects arising from the synergy of graphene with other materials assume a pivotal role in enhancing the overall performance of energy devices. The initial section of this chapter focuses on the utilization of graphene composites in energy conversion technologies, spanning solar cells, water-splitting devices, and fuel cells. Subsequently, this chapter explores the application of graphene composites in energy storage systems such as lithium-ion batteries and supercapacitors. Graphene provides a significant surface area, facilitates rapid electron transport, and offers mechanical stability, all positively impacting the energy storage capacity and cyclic stability of these devices. Furthermore, the chapter discusses the current research trends, challenges, and future prospects concerning graphene composites for energy conversion and storage applications. 2025 Elsevier Inc. All rights reserved. -
Polyurethane nanocomposites for food packaging applications
Progress in food packaging technology improves modern food trade by simplifying transportation and sales, while offering enhanced protection. Traditional food packaging uses petroleum-based and nonbiodegradable packaging materials, leading to severe environmental and health-related issues. Polymer nanocomposites can reduce traditional plastic consumption and achieve a high recycling efficiency while maintaining the desired barrier and mechanical properties. Polyurethane, a polymer with good mechanical properties, is characterized by low pollution, easy modification, and environmental protection. Innovations in its structure and functionality through the incorporation of nanoscale fillers have led to enhanced barrier properties, thermal stability, and biodegradability. Polyurethane nanocomposites (PUNCs) exhibit multifunctional properties owing to the synergistic effect of polymers and nanofillers, and are used to increase the shelf life of food products, reducing food waste and foodborne illness, thereby contributing to sustainable development goals. This chapter begins by elucidating the fundamental properties of PU and the significance of various nanocomposites in tailoring these properties to fit packaging requirements. This chapter also describes the role of PUNCs in extending the shelf life of food, providing ultraviolet light protection, barrier, antimicrobial, and antioxidant properties, and enhancing the mechanical strength of packaging materials. The recent advancements in food packaging applications of PUNCs and the challenges and future perspectives related to large-scale production, consumer acceptability, recyclability, and potential health implication aspects are discussed in this chapter. 2026 Elsevier Ltd. All rights reserved. -
Polyurethane nanocomposites for supercapacitor applications
Polymer nanocomposites have received a lot of interest recently in materials research as they display a variety of distinct properties compared to those of their counterpart polymer micro-composites, whose matrices include the same inorganic components. The flexible features of polyurethane (PU) nanocomposites, which may be easily adjusted to fulfill the specific needs in energy storage, have led to the rapid development of these materials in recent years. Numerous types of functional nanofiller integration have led to the advancement of PU-based nanocomposites. Details on PU nanocomposites' synthesis, characteristics, and uses in supercapacitors are covered in this chapter. There have been several approaches explored for the synthesis of various PU nanocomposites, including electrospinning, dip-coating, spray coating, and one-step carbonization. Recent advancements in the use of PU nanocomposites as supercapacitors, along with their challenges and possibilities in the future, are also discussed herein. This chapter also reviews recent developments in smart supercapacitors, including their various properties such as long-lasting cycling stability, excellent specific capacitance, high energy density, and good capacitance retention. Functions of supercapacitors include self-healing, shape memory, shape editing, and photodetection, along with specific emphasis on their recyclability and recoverability. 2026 Elsevier Ltd. All rights reserved. -
Polyurethane nanocomposites for electromagnetic interference shielding applications
Polyurethane (PU) is composed of polyisocyanate and polyol units joined through urethane linkages. The isocyanate and polyol units constitute different domains in PU, which are, in turn, responsible for its properties such as softness, flexibility, and hardness. Shielding of electromagnetic (EM) radiation is generally attained by reflecting EM waves from the surface, absorbing the signals, or by multiple internal reflections. Pristine PU is not an efficient electromagnetic interference (EMI) shielding material because of its nonconducting nature. But the EMI shielding can be improved by incorporating conducting polymers into the PU-based nanocomposite. Significant factors that influence the shielding effect of PU nanocomposites are the thickness and conductivity of the film. Features that predominantly influence the EMI shielding performance of nanocomposites are identified as the nanofillers used, the dispersion state, and the interaction between the filler and the polymer. This book chapter attempts to explain the EMI shielding of PU nanocomposites, synthesis of PU-based EMI shielding materials, different nanofillers used along with PU nanocomposites, and their efficiency in EMI shielding. 2026 Elsevier Ltd. All rights reserved. -
Physical aging of polymers and polymer nanocomposites
This chapter offers an illustration of physical aging in polymers and their nanocomposites. Physical aging, explained as time-dependent property changes taking place at fixed temperature and less stress without external forces or influences, is crucial for recognizing long-term performance and strength of polymer materials. The text discovers how physical aging establishes differently in amorphous polymers and semicrystalline polymers along with their nanocomposite, with specific consideration to polymer blends. Molecular mechanisms focusing on physical aging are addressed, indicating how polymers in nonequilibrium states undertake structural relaxations to equilibrium when stored below their glass transition temperature (Tg). The chapter scrutinizes factors affecting aging rates, including quench depth (?TA=Tage ? Tg), nanofillers, and the effect of molecular mobility. Additionally, it evaluates theoretical models and molecular simulation methodologies increasingly assisted to overcome limits of conventional experimental methods in determining intrinsic aging mechanisms. The work completes by classifying future research directions essential for evolving understanding of polymer aging behavior, with suggestions for refining service life and storage constancy of polymer-based materials and equipment. 2026 Elsevier Ltd. All rights reserved. -
Physical aging of biopolymers and their nanocomposites
With growing environmental concerns, particularly around the widespread use of conventional plastics, there is an urgent need to develop sustainable alternatives like bioplastics and biocomposites. These eco-friendly materials offer the potential to significantly reduce the carbon footprint while enhancing product performance and durability in various applications. This chapter aims to expand scientific understanding of biocomposites, focusing on their behavior under different aging conditions. A comprehensive analysis is provided on aging processes, aging mechanisms, and strategies to improve the longevity and performance of biocomposites. Special emphasis is placed on future research directions and the adoption of innovative aging techniques to optimize the performance of biopolymers. This review explores both the advantages and challenges of using biocomposites as replacements for traditional petroleum-based plastics, with a particular focus on their degradation behavior over time. The insights presented here are essential for driving further research and development in bio-based and biodegradable polymers, highlighting their potential for both academic inquiry and industrial application. By addressing key aspects of biocomposite aging, this chapter aims to guide researchers in overcoming existing challenges and advancing the field toward a more sustainable future. 2026 Elsevier Ltd. All rights reserved. -
Surface-modified carbon nanomaterials
Surface-modified or engineered nanostructures have become an essential aspect of surface modification in various domains. They inherit promising properties that can be tailored based on specific requirements enabled by design and fabrication essentials. Among them, carbon-based materials are potential candidates for various applications in drug delivery, energy storage, environmental profiling, and disease diagnosis. The biomedical applications of carbon-based nanomaterials have recently boomed for disease-specific prevention, diagnosis, treatment, and recovery. They can be extended for precise, specific, and sophisticated approaches to yield long-lasting and favourable outcomes. In this book chapter, we will be discussing about the interesting properties of carbon, such as good mechanical strength, high electrical conductivity, and desirable morphological features. In continuation, the history of carbon surfaces and nanomaterials will be discussed to provide a background knowledge of the element. We will also discuss the relevant reports on emerging carbon-based nanomaterials, their attributes, and applications in distinct arenas involving electrochemical-based approaches. The protocols for surface modification will be summarised in the later section of the chapter. Finally, their downsides will be compared. These nanostructured carbon surfaces confer the advantages of small size, enabling advantages over bulk phase materials. Finally, the toxicity of surface-modified carbon materials has been studied in depth before the summation of the chapter. 2026 Elsevier Inc. All rights reserved. -
Journey toward Internet of Bio-Nano Things: evolution, trends, and future challenges
The Internet of Bio-Nano Things (IoBNT) is a pioneering vision of embedding biological components into the existing network of interconnected nanodevices to create sophisticated webs of nano-scale biological and artificial components. This framework extends the conventional Internet of Things architecture to include biological components to facilitate new relationships between artificial nano-scale devices and biological systems. IoBNT enables unprecedented functionalities in healthcare, environmental monitoring, agriculture, and food protection through multiple communication channels, including molecular communication, THz-band electromagnetic nanocommunication, acoustic nanocommunication, and FRET-based nanocommunication. Nonetheless, there are still many issues to be solved in the areas of nanonetwork implementation, bio-cyber interface development, big data analytics, security, energy harvesting, and biocompatibility. Some of the potential countermeasures to security risks include device hardening, cryptographic mechanisms, external device delegation, intrusion detection, and hardware-based solutions. Although IoBNT presents a great prospect for change across various sectors, there is a need for more research to resolve technical, biological, and security issues so that it can be adopted in real life. 2026 Elsevier Inc. All rights reserved.. -
Big data management and smart drug delivery system based on Internet of Bio-Nano Things
Integrating big data analytics and Internet of Bio-Nano Things (IoBNT) presents disruptive prospects in healthcare, especially in advancing intelligent medication delivery systems. This chapter examines the complex dynamics of big data management, emphasizing the collection, processing, and analysis of extensive biomedical data produced by IoBNT-enabled devices. IoBNT, an innovative network of bio-nanosensors and actuators, enables accurate and real-time monitoring of physiological states, hence advancing personalized and targeted drug delivery systems. Key components like data integration, predictive analytics, machine learning algorithms, and the way contemporary communication protocols enable smooth data flow between bio-nanodevices and healthcare infrastructure. The chapter also explores the challenges associated with data security, privacy, next scalability within the frameworks of IoBNT designs. Using big data analytics, IoBNT-based innovative drug delivery systems can improve therapeutic outcomes by optimizing dosage, timing, and administration paths. This chapter underscores the potential of the Internet of Things and big data to transform healthcare in the future through a more agile, responsive, and efficient method of disease management. The discussion is supported by analyzing emerging trends and concrete case studies, illustrating how these advancements can lead to significant improvements. 2026 Elsevier Inc. All rights reserved.. -
Challenges and opportunities in multimodal learning research
The trend of multimodal learning, which involves processing and interpreting data through multiple modes such as text, images, and audio, is one aspect that highlights a great frontier in the artificial intelligence (AI) and machine learning (ML) domains. This project explores the technical, practical, and ethical considerations of research studies on multimodality. It starts with preliminary ethical considerations that should drive progress in AI and ML technologies, which include ideas of transparency, accountability, equality, and privacy. Analysis of this paper holds prime importance for moral concerns, as it discusses the issues of bias in AI algorithms and gives strategies that may reduce the level of bias in multimodal patterns. This technology part of the research focuses on technical challenges concerning accountability and transparency in multimodal machine decision-making methodologies. Privacy concerns regarding extensive use of AI and ML have been brought forward, along with the strategy for defensive personal statistics. At each step, an opportunity for innovation and development will be sought and mapped through the complex ethical landscapes of multimodal knowledge research. Through these considerations, this observation attempts to provide a close analysis in which future recommendations and discussions in the realm of AI and multimodal learning are addressed. 2026 Elsevier Inc. All rights reserved. -
Multimodal data analytics for climate and water resources management
The incorporation of multimodal data analytics into climate and water resource management has become a groundbreaking strategy for tackling intricate environmental issues. This chapter examines the importance of integrating various data sourcesincluding satellite imagery, weather sensors, textual reports, and social media feedsto develop a comprehensive perspective on climate and water systems. It addresses key challenges such as data heterogeneity, computational demands, and potential biases while showcasing the significant benefits of multimodal data in enhancing predictive modeling and decision-making. The discussion extends to advanced methodologies for data acquisition, integration, and feature extraction, with a focus on machine learning and deep learning techniques. Additionally, real-world applications in climate prediction, drought and flood forecasting, and water quality assessment are explored. The chapter also considers ethical concerns and future advancements in multimodal analytics, emphasizing the importance of responsible data utilization and innovative research to strengthen climate adaptation and water resource management efforts. 2026 Elsevier Inc. All rights reserved. -
Surveillance and security: integrating video, audio, and sensor data
This period of rapid technological development offers enormous prospects for enhancement in security and surveillance systems through integration of sensor, audio, and video data. Conventional surveillance systems, that rely primarily on video feeds, have been undergoing the process of incorporating audio and sensor data to provide monitoring solutions that are more comprehensive and accurate. In addition to this the research provides an overview of the possibility of strengthening security in a number of settings. It also deals with the strategy involved in the integration of multimodal data as well as challenges associated with this process. This expansion is crucial for the deployment of these integrated systems. Moreover, in the application of machine learning (ML) and artificial intelligence (AI) technologies in making sense of such vast volumes of data as output by the respective systems, the detection of threats with higher precision and speed has improved. Complex sensor, audio, and video pattern analysis are theoretically possible using models of AI and ML. This study culminates in the presentation of a list of prospective future research subjects. It is meant to integrate technology innovation with social aspects such as privacy and security systems. Among them are the development of lightweight algorithms, ethical frameworks, and integration solutions that are efficient. 2026 Elsevier Inc. All rights reserved. -
Multimodal data analytics for social media and user behavior
The introduction of social media has prompted an explosion of diverse data types, such as textual content, pix, videos, and audio. Traditional unimodal analysis techniques do not effectively depict the difficult interactions between exceptional fact sorts and consumer sports. Multimodal data analytics addresses this difficulty through fusing one-of-a-kind modalities to unlock deeper insights, improving the accuracy and scope of social media analysis. This bankruptcy looks into the significance of multimodal facts in understanding consumer behavior, sentiment analysis, content material engagement assessment, and trend prediction. The bankruptcy starts off with the exploration of various sources of statistics in social media analytics, together with textual content posts, visual content, and consumer interactions. It then explores preprocessing and function extraction strategies utilized to prepare raw multimodal data for the usage of gadget gaining knowledge of. In-intensity methodologies, inclusive of natural language processing for text evaluation, computer vision for photo and video interpretation, and speech recognition for audio processing, are expounded in extraordinary detail. Integration of these modalities via fusion techniquesearly fusion, past due fusion, and hybrid modelsis also explored. 2026 Elsevier Inc. All rights reserved. -
Multimodal data generation and synthesis
Multimodal data generation and synthesis have become new promising directions in artificial intelligence research, making possible the combination and transformation of the different data modalities: text, images, audio, and video. In this chapter a look will be made about the principles, methodologies, applications, and challenges linked with multimodal data, bringing attention to the current trends and needs regarding multimodal systems and systems approaches to tackle complex real-world challenges across the medical and health care, autonomous systems, entertainment, and extended reality (XR) fields. The chapter introduces multimodal data and discusses how the approach differs from unimodal methods, considering the merits of working with multiple data forms. Multimodal systems present richer and more comprehensive representations that lead to better decision-making and provide a better interaction with users. The complexity due to alignment, synchronization, and representation of diverse modes is inherently difficult. This section further discusses state-of-the-art techniques in multimodal synthesis, especially focusing on generative approaches like generative adversarial networks (GANs), variational autoencoders (VAEs), and diffusion models. These methods are shown to facilitate cross-modal transformations, such as text-to-image or audio-to-video synthesis, driving innovation in artificial intelligence and beyond. Applications of multimodal data synthesis are discussed in detail, underscoring its transformative impact. In health care, for instance, synthesizing medical images paired with textual annotations enhances diagnostic accuracy and medical training. Autonomous vehicles benefit from the integration of LiDAR, visual, and auditory data, enabling robust decision-making in real-time environments. Similarly, in entertainment and XR, multimodal synthesis is redefining content creation, making immersive experiences more personalized and dynamic. The chapter also delves into novel applications such as multimodal translation, exemplified by systems that translate sign language into spoken text, fostering inclusivity and accessibility. Despite its potential, multimodal synthesis faces critical challenges, including bias in data and models, privacy concerns, and the ethical implications of creating hyperrealistic synthetic data, such as deepfakes. All these raise pressing concerns, and addressing these requires robust privacy-preserving techniques, bias-mitigation strategies, and stringent ethical guidelines. 2026 Elsevier Inc. All rights reserved.