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CRISPR plants against heavy metal and metalloid stresses: Methods and applications
Metal and metalloid stresses present substantial obstacles for plants, exerting an impact on agricultural productivity as well as environmental well-being. The assimilation and accumulation of heavy metals, for example, cadmium and lead, along with metalloids such as arsenic, has the potential to affect the growth and development of plants detrimentally. To address these stresses, innovative biotechnological approaches involving genetic engineering, clustered regularly interspaced short palindromic repeat (CRISPR) technology, phytoremediation, nanobiotechnology, and panomics are required to augment plant-metal tolerance. The dawn of CRISPR technology has revolutionized the field of plant biotechnology with its unparalleled precision in enhancing a plants capability to tolerate heavy metal and metalloid stresses. This present review aims to discuss the various applications and techniques of CRISPR technology in the enhancement of metal stress-tolerant capability in plants. It places special attention on the technologys critical role in abbreviating the detrimental effects of metal stress on plant growth and productivity. CRISPR technology enables researchers to engage in precise gene editing, thereby allowing for the deliberate targeting of specific genes associated with metal transport, detoxification, and stress responses in plants. By manipulating these key genes, CRISPR facilitates the creation of plant varieties that exhibit enhanced resilience to the challenges posed by heavy metal and metalloid stresses. In addition, this approach contributes to the advancement of agricultural sustainability and environmental stewardship by enhancing a plants ability to resist and overcome heavy metals contamination. Moreover, in the face of a changing climate landscape influenced by metal pollution challenges, the precise gene editing capabilities of CRISPR can be harnessed to engineer plants that possess heightened resilience to metal stress, thereby playing a vital role in ensuring food security and promoting sustainable agriculture. The review also underscores the pivotal role of CRISPR technology in shaping the future of research on plant stress tolerance and highlights its immense potential in addressing the evolving challenges associated with metal stress in plant systems. 2025 Elsevier Inc. All rights are reserved. -
Electrospinning of polyetheretherketone-based homopolymers and block copolymers
Electrospinning involves the fabrication of ultrafine fibers, typically ranging in diameter from nanometers to micrometers. This process entails applying a high voltage to a polymer solution or melt, resulting in the production of fibers that can be collected on a designated surface. Polyetheretherketone (PEEK) is a semicrystalline linear polycyclic aromatic polymer with high thermal stability. It is a high-performance thermoplastic known for its mechanical strength, thermal stability, and chemical resistance. The rigid radiolucency, stable physicochemical properties, and biocompatibility of electrospun PEEK homopolymer fibers make them suitable reinforcements in composite materials, medical sutures, removable prosthetics, vertebral surgery, orthopedics, and scaffolds for tissue engineering. PEEK homopolymers offer a wide range of advantages; however, they have a high melting temperature, high viscosity in the molten state, and a low glass transition temperature. Blending PEEK with other polymers and the formation of block copolymers introduces an additional set of functionalities by combining the properties of PEEK and other polymers. PEEK block copolymers can be electrospun with tailored properties and diverse morphologies, resulting in enhanced processability and compatibility for broad applications, including medical implants, filtration membranes, and reinforcing materials. This chapter discusses the principles and parameters of electrospinning, the factors responsible for the electrospinning of PEEK-based homopolymers and block copolymers, issues such as solubility, spinnability, and related costs, and possible solutions for overcoming these issues. Various applications of electrospun PEEK homopolymers and block copolymers are also discussed in this chapter. 2026 Elsevier Inc. All rights reserved. -
Unveiling the root causes of diabetes using explainable AI
Diabetes is a non-communicable wide spread disease across the world. To investigate the risky factors that are associated with diabetes, and to start early and customized treatment, researchers are fascinated to explore existing machine learning or deep learning models and to develop more reliable algorithms. The advancement in technology and the increase in world population is an enriching source to prompt and explore the factors that decide a person to be diabetic. Several algorithms and approaches are in place to address these factors but are lacking in emphasizing with more interpretable features which convinces patent to trust the medicine, treatment, and have meaningful conversation with the physicians and artificially intelligent systems. To encourage the participation of people with diabetes for customized treatment and considering societal needs, this chapter explores the possibility of Explainable Artificial Intelligence (XAI) in diabetes detection and figuring out the significant features that dominate diabetes. 2025 Elsevier Inc. All rights reserved. -
Explainable artificial intelligence in epilepsy management: Unveiling the model interpretability
The field of epileptic seizure classification has witnessed significant advancements in the use of electroencephalogram (EEG) data for accurate and timely diagnoses. This study introduces a comprehensive framework for EEG-based seizure classification, encompassing data preprocessing and the application of machine learning techniques, specifically the supervised learning classifier known as Extreme Gradient Boosting (Xgboost). Machine learning methods have shown promising accuracy in binary classification tasks, particularly in distinguishing between seizure and healthy EEG signals. However, the need for a robust explanation of these results and decision-making processes is imperative for technical verification and clinical validation, especially for potential clinical applications. Explainable Artificial Intelligence (XAI) emerges as a critical component in addressing this need. In this chapter, we propose and discuss a binary classification model that leverages Xgboost to classify EEG signals as either Seizure or normal, a crucial aspect in epilepsy diagnosis. XAI techniques such as LIME (Local Interpretable Model-agnostic Explanations) and SHAP (Shapley Additive Explanations) are incorporated to elucidate the model's predictions. LIME offers localized interpretability by creating surrogate models for individual predictions, revealing the essential EEG features influencing each classification decision. Conversely, SHAP provides a global perspective on feature importance, shedding light on the collective impact of EEG features on classification outcomes. The synergy between LIME and SHAP enhances our understanding of the model's predictions and the intricate nuances within EEG data. This research highlights the transformative potential of LIME and SHAP in EEG-based seizure classification. The integration of XAI techniques not only enhances the transparency and interpretability of the model but also empowers clinicians and researchers to make more informed decisions, ultimately improving patient care and outcomes in epilepsy management. By bridging the gap between complex EEG data and actionable insights, this study marks a significant paradigm shift in the application of XAI techniques in medical diagnostics. It paves the way for a new era in epilepsy diagnosis and management, where advanced machine learning models guided by LIME and SHAP play a crucial role in revolutionizing healthcare practices. 2025 Elsevier Inc. All rights reserved. -
Tribological aspects of Al and Mg composites
It is well known that the technical function of a large number of engineering components/parts depends on motion. However, the term motion here is not as simple as it sounds, because it comes with consequences in the form of friction and wear. Along with lubrication, the science that deals with friction and wear is known as tribology. Therefore, it is necessary to pay more attention to tribology and acquire knowledge on the tribological behavior of materials, as the tribological characteristics such as friction and wear have been causing poor efficiency in engineering structures, huge economic losses, and environmental impacts. One way of addressing these issues lies in the development of lightweight materials based on metals such as aluminum and magnesium. Although one cannot employ these metals in their pure form, but modification in their microstructure and properties can certainly address the needs required for tribological applications. Keeping this in mind, this chapter covers the properties of aluminum and magnesium metals, basic aspects of tribology and most importantly, the work carried out on the friction and wear behavior of aluminum- and magnesium-based composites. The importance of this chapter lies in promoting better knowledge of the tribological behavior of aluminum and magnesium composites, especially from a various wear parameters point of view. The influence of material composition and wear parameters on tribological behavior is covered with a follow-up section on numerical and optimization methods employed for predicting tribological characteristics. 2026 Elsevier Inc. All rights reserved.. -
Raman spectroscopy: an introduction, instrumentation, and its applications in polymer composites and nanocomposites
Raman spectroscopy, a nondestructive technique based on molecular vibrations, offers insights into molecular structures and interactions through the inelastic scattering of monochromatic light. This method facilitates the identification of chemicals by using unique molecular fingerprints. The ability of this technique to analyze samples in situ, in any form, is very advantageous. Over the years, improvements have been made in the sensitivity and resolution of Raman spectroscopy in instrumentation and data analysis, which has broadened its range of applications in chemistry, biology, and materials research. Raman spectroscopy has become a vital tool for applied research and basic sciences. In the area of composites and nanocomposites, it offers tremendous possibilities, such as material identification, phase separation, and defect analysis, reinforcement agent characterization, stress and strain analysis, crystallinity and orientation, and micromechanical deformation. 2026 Elsevier Ltd. All rights reserved. -
Blockchain Technology: Proof of Work vs Proof of Stake Consensus Mechanism
The purpose of blockchain technology is to record transactions after they have been confirmed. Blockchain?s ability to guarantee decentralized, transparent transactions with solid security has increased the appeal of new virtual currencies. The consensus mechanism, one of the key components of a blockchain network?s operation, plays a crucial role in a decentralized system. The debate surrounding an adequate consensus method has attracted significant attention, particularly in comparing Proof of Work (PoW) and Proof of Stake (PoS). This chapter comprehensively examines the Proof of Work (PoW) and Proof of Stake (PoS) algorithms and compares other consensus algorithms utilized in contemporary blockchain systems. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Business Resilience
Business resilience has emerged as one of the key elements for ensuring the survival and success of the organization in the 21st century which has been marked by socio-economic crisis, notable disruptions, and tumultuous market shifts. The term Business resilience is defined as the capacity of an organization to navigate through adversity, adapt to changing conditions, and emerge stronger from setbacks. Financial, operational, organizational, digital, and supply chain resilience are among the measures that resilient organizations invest on. Companies that are financially strong put careful money management first and protect shareholders wealth. Organizational resilience promotes flexibility and ongoing learning, whereas operational resilience guarantees the continuation of vital business operations. While supply chain resilience concentrates on minimizing disruptions, digital resilience entails strong cybersecurity measures. Effective leadership is essential for managing uncertainty, and a flexible strategy for enhancing resilience guarantees continuous adjustment. In the end, corporate resilience is about overcoming hardship and integrating resilience as a strategic requirement for sustained success in a changing business climate. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Movie-Induced Tour Guiding: Concepts and Future Implications in South Asian Perspective
Movies have an extensive impact on tourism and its promotion. Movie-induced tourism has been a worldwide phenomenon for the last couple of decades, but this phenomenon is confined to the marketing and promotion of tourism destinations. Here, a new approach has been introduced for co-creating a quality destination experience through traditional tour guiding. Considering the increasing emphasis on tourists experience, satisfaction, and destination imagery over the decades, this concept of movie-induced tour guiding will produce a synergistic value in the overall process of the outdoor leisure tour packages. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Digital Content Marketing
Digital Content Marketing (DCM) is the strategic creation and distribution of relevant brand-related content on digital platforms, aiming to foster favorable brand engagement, trust, and relationships with current or potential customers. Despite its widespread use in practice, academic research on DCM is limited, creating a significant knowledge gap. This study addresses this gap by conducting an exploratory research synthesis of DCM literature from 2008 to 2024. The primary objective is to provide a comprehensive overview of pertinent studies and create a conceptual integration that elucidates DCM?s impact on marketing practices. Utilizing a targeted search on the Web of Science Database, 15 published papers were identified and analyzed based on specified search criteria. The findings contribute to a more holistic understanding of DCM?s role as a prevalent trend in contemporary marketing practices. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Sustainability Reporting
Today, with the increased awareness among the various stakeholders, the success and growth of companies are not gauged by their financial performance but by the impact of their business operation on the environment and society. Companies are under immense pressure from different stakeholders to undertake sustainability practices and publish sustainability reports. Due to this, sustainability reporting has transformed from a voluntary exercise to a strategic imperative for companies. This chapter aims to explain the concept of sustainability reporting (SR), various drivers, and its benefits for the various stakeholders. It also provides a brief overview of the evolution of the notion of SR and the discourse of voluntary versus mandatory approach to SR. Further, this chapter discusses prominent global SR guidelines, frameworks, and challenges in the adoption of sustainability reporting practices. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Ethical Sourcing
In an era where sustainable development is a celebrated concept, the notion of ethical sourcing has been explored comprehensively as companies are making a conscious and sustainable effort to gauge their supply chains and examine the sources of procurement of their material and services. This chapter delves deeper into the importance of ethical sourcing, focusing on its benefits and fundamental needs. It also talks about why ethical sourcing is an essential concept in corporate realms, drawing a distinction between sustainable and ethical sourcing. Today, ethical sourcing can be an important tool in the hands of corporations to draw a competitive edge and there would be serious repercussions of indulging in unethical practices. Further, it discusses key principles that act as a beacon of light for sourcing the material ethically, the three levels of ethical sourcing, and how technology can be leveraged to ensure the same. It is crucial to have partnerships and collaborations for sustainable procurement along with robust mechanisms to measure the impact of ethical sourcing. 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Emergency response to natural disaster victim identification: Blockchain to the rescue
Rapid, coordinated action by various stakeholders is required to respond effectively to a natural disaster. Suffice it; such efficiency has been missing in many previous rescue attempts. Is blockchain capable of making this happen? The Disaster Victim Identification (DVI) process is a sophisticated operation in which post-mortem (PM) identifying data, including fingerprints, DNA, and dental records, are acquired and matched with antemortem (AM) data from the missing people list. Although there are solutions to human identification, they must provide the tools required to achieve human identification promptly. Blockchain technology is one of the technologies that has gained much attention recently and is undergoing heavy media operations. It creates trustworthy, secure, and comprehensive ecosystems by disseminating siloed AM and PM data across systems, preventing breaches, redundancies, inconsistencies, and errors. Using real-world scenarios, the authors present several good use cases in this chapter to gain a holistic understanding of the challenges and how blockchain technology addresses such challenges and facilitates multi-jurisdictional data information sharing in conjunction with the upcoming distribution of patients electronic medical and dental records. 2025 Elsevier Inc. All rights reserved. -
Optimization procedure for multilayer heat transfer in nanoliquid with Joule heating using response surface methodology
In this chapter, magnetohydrodynamic flow (MHD) and heat transfer in a multilayer vertical channel are studied with one phase containing pure water and the other phase containing oil-based Cu nanofluid. The effects of viscous dissipation and Joule heating are included in the energy equation. The modeled equations are coupled and nonlinear; they are solved using the regular perturbation method (RPM) and the differential transform method (DTM). The analysis examines the impact of the Hartmann number, thermal Grashof number, nanoparticle volume fraction (NVF), and Brinkman number on the Nusselt number, velocity, and temperature distributions. Furthermore, an optimization of the Nusselt number is performed for three different levels of the Hartmann number (5?M?6), the Brinkman number (0.1?Br?0.3), and the NVF (1%???3%) using the Response Surface Methodology (RSM). The Hartmann number and NVF were found to suppress flow, while the thermal Grashof number and the Brinkman number increase the flow field. Sensitivity computations reveal that the Nusselt number on the left wall is more sensitive to the Hartmann number and the NVF, while the Nusselt number of the right wall is more sensitive to the Brinkman number and NVF. 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Simulation and multiscale modeling of carbon nanomaterials
Carbon nanomaterials have become more and more significant for simulation and multiscale modeling due to their distinctive features and prospective uses in a variety of disciplines. We give a thorough computational analysis of the electrical, mechanical, and thermal characteristics of carbon nanotubes, graphene, and fullerenes in this chapter. Our simulations combine classical and quantum mechanical techniques, such as density functional theory and molecular dynamics. We are able to bridge the gap between atomistic simulations and macroscopic behavior thanks to our multiscale modeling technique, which offers important insights into the behavior of carbon nanomaterials at various length and time scales. For the creation and advancement of novel nanomaterials for diverse applications, our findings offer a basic knowledge of the characteristics of carbon nanomaterials. 2025 Elsevier Inc. All rights reserved. -
Synthesis of carbon nanomaterials from vegetables
This chapter looks into new horizons of sustainable nanotechnology developed through innovative carbocentrism that focuses on the development of carbon based nanomaterials from different categories of vegetables. This chapter is centered on the green synthesis of vegetable-derived sweet potato, garlic, lemon, and radish into carbon dots (CDs), graphene sheets, and carbon quantum dots through hydrothermal and aqueous extraction methods. To surpass traditional methods of nanomaterial synthesis, researchers are developing vegetable-derived nanomaterials that possess unique properties such as fluorescence and ranging surface functionalities. Such practices are recommended for reducing environmentally hazardous substances while upholding important eco-friendly principles and sustainable accountable nanotechnology. These methodologies address the misuse of dangerous substances and provides effective eco friendly approaches which emphasizenew direction towards sustainable nanotechnology. The versatility of these vegetable-derived carbon nanomaterials is evident in their applications, spanning from biomedical fields, such as drug delivery and bioimaging to environmental monitoring, particularly in the selective detection of metal ions. The advancements of medical technology are much needed in society today that is being more particular about green approaches and innovations. This willthe help low toxic and biocompatible nanomaterials live up to their full potential for eco-friendly biomedical technologies. This chapter serves as a comprehensive exploration of the synthesis, applications, and broader implications of carbon nanomaterials from vegetables, providing valuable insights into the evolving landscape of green nanotechnology. 2025 Elsevier Inc. All rights reserved. -
Characterization and properties of plant extract-derived nanostructured carbon materials
The remarkable features of carbon nanomaterials, which include graphene, fullerenes, carbon nanotubes (CNTs), and carbon nanofibers, have elevated them to the forefront of material science study. These nanoparticles are very desirable for a variety of applications, such as electronics, energy storage, composites, and biomedical devices, due to their exceptional mechanical, electrical, thermal, and chemical properties. This chapter offers a thorough rundown of the essential characteristics and cutting-edge methods for characterizing carbon nanomaterials. We examine the inherent qualities of several carbon nanostructures, emphasizing their surface chemistry, electrical and thermal conductivity, and mechanical strength. Every one of these characteristics is essential for figuring out whether carbon nanoparticles are appropriate for a given application. The chapter also covers cutting-edge characterization methods that are crucial for assessing the caliber and characteristics of carbon nanomaterials. AFM, X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM), scanning electron microscopy (SEM), and other spectroscopic techniques are covered in detail. These methods offer vital information on the shape, composition, purity, density of defects, and electrical characteristics of carbon nanomaterials. 2025 Elsevier Inc. All rights reserved. -
Synthesis methods of nanostructured carbon materials
One of the most crucial factors in the synthesis of carbon nanomaterials is the choice of synthesis method. Since the synthesis process is heavily reliant on the particle size and molecular structure, it has a significant impact on the final properties of the nanoparticles. The top-down and bottom-up approaches are the two primary approaches. In contrast to the top-down method, which breaks down larger carbon sources like graphite or bulk carbon materials into nanoscale structures, the bottom-up method uses a variety of chemical reactions, including dehydration, carbonization, polymerization, and other irreversible chemical reactions, to create nanoparticles. In this chapter, the various top-down and bottom-up synthesis routes are covered, along with their effects on the physico-chemical characteristics of carbon nanomaterials. 2025 Elsevier Inc. All rights reserved. -
Advanced Machine Learning Model for Optimizing Pricing Strategies for Logistic Firms
Cost optimization in logistics is a very crucial aspect for businesses to remain profitable and competitive by identifying and eliminating unnecessary costs. Most of the researchers concentrated primarily on demand modeling, vehicle routing challenges, and warehouse cost optimization, hence the existing models underperform. This study introduces a novel prediction model that optimizes costs by considering critical factors such as labor charges, material costs, transportation expenses, task types, and branch location. The current model is worked on a primary dataset of 2468 rows and 28 columns which was obtained from an established relocation company in India with all the confidentiality followed. To improve model performance, the required features were adjusted by rigorous feature engineering and data pretreatment techniques such as box-cox scaling, Winsorization, robust scaling, and one-hot encoding. Three ensemble learning techniques were tested: AdaBoost, XGBoost, and gradient boosting. The gradient boosting model correctly captured the complicated nonlinear connections between cost components and income, enabling for cost optimization decisions across a wide range of operational conditions. The proposed model has shown excellent results with the values achieving an MSE of 15% which demonstrates the effectiveness in cost optimization. However, the presence of residuals and potential outliers suggests that more model refinement and process improvements are required. The studys findings offer a data-driven framework for logistics and relocation companies to reduce costs, boost profitability, and gain a competitive advantage in the marketplace. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Using Document Similarity Algorithms for Suicidal Detection in Social Media: A Case Study of User Tweets
Suicidal detection and treatment from the clinical and public health perspective are reactive. For an action whose consequences are irreversible, a reactive approach to the problem cannot be the answer. A proactive approach is needed to solve and detect suicidal intent. Social media has become the television and diary of millennials and Gen z alike; hence, it is imperative to create techniques and approaches to study their actions in this particular space. This research involved creating document similarity algorithms from Corpora mined from the Twitter Developer API. Making the data unique to this platform, a methodology design involving validating data at various spectrum and selecting an appropriate threshold to classify the similarity levels were created as well as a lexicon unique to the Twitter Dataset. With an accuracy score of 84%, the Jaccard document similarity algorithm was able to spot suicidal intent from users tweets, and with an accuracy of 93%, it was also able to spot non-suicidal intent. The Jaccard model seemed to be the most durable and computationally efficient for the problem and was chosen as the algorithm for detecting suicidal tendencies in users tweets. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.