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Advancements in EEG and EMG Signals for Motor Imagery Classification and Artifact Removal: A Comprehensive Review and Analysis
An essential noninvasive method for assessing brain electrical activity and gaining important knowledge about how the brain functions is electroencephalography (EEG). Understanding the brain's reactions to particular sensory, cognitive, or motor events requires understanding event-related potentials (ERPs), which are derived from EEG. By displaying variations in frequency content across time, time- frequency analysis improves ERP interpretation. Each of the five EEG frequency bands - delta, theta, alpha, beta, and gamma - has a unique clinical significance and is linked to different physiological and cognitive processes. In order to improve motor control and rehabilitation, this work focuses on the development of NeuroMotor Fusion approaches, which integrate EEG and Electromyography (EMG) signals for motor imagery classification. It looks at new developments in the classification of motor imagery and investigates cutting edge methods such as VR motor priming and brain-computer interfaces (BCIs). The study also discusses the difficulties in removing artifacts from EEG and EMG signals, using hybrid techniques to reduce ocular and muscular artifacts. The study produced a 96.2% accuracy rate in motor function enhancement using the ShallowFBCSPNet model architecture and the MOABBDataset "BNCI2014-001". These findings show that NeuroMotor Fusion has a great deal of promise for use in neurological disease support, individualized motor skill training, and rehabilitation. 2025 IEEE. -
Advancements in Electronic Healthcare: A Bibliometric Analysis
Electronic healthcare has changed the traditional form of medical treatment. The integrated approach of interconnected devices had enhanced the process of record keeping and dissemination, benefitting Doctors, patients, and other stakeholders. This study aims to highlight the research carried out in the field of electronic healthcare from the year 2011 to 2020. Metadata of 821 publications from Scopus database was extracted and analyzed. VOS viewer was used to generate the network diagrams and link strengths. It was found that Harvard Medical School and European Commission were the top publication affiliation and funder, respectively. United Stated dominated with the maximum number of publications till 2017 but was surpassed by publications from India from 2018 onwards. Publications inclined toward Internet of things, network security, retrospective study, and authentication toward the end of this decade indicating the shift in trend for the future. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Advancements in Hand Gesture Technology for Enhancing Accessibility in Disability Assistance
Hand gesture recognition technology has become a crucial innovation in assistive technology, providing enhanced accessibility and independence for individuals with disabilities. In order to enhance communication, mobility, and rehabilitation support, this paper investigates the development and integration of AI-powered gesture recognition systems with wearable devices, augmented reality (AR), and the Internet of Things (IoT). The adaptability and ease of use of the current solutions, such as sign language interpretation and smart prosthetics, are severely limited. We propose a novel framework that combines cloud-based data storage, haptic feedback mechanisms, and real-time AI processing to create a highly responsive and personalized user experience to fill in these gaps. The research focuses on accuracy, responsiveness, and ease of use during its comprehensive analysis of prototype testing data and user feedback. The system is able to continually improve the accuracy of gesture recognition and adapt to the requirements of each user by making use of deep learning algorithms. The study also emphasizes the possibility of incorporating brain-computer interfaces (BCIs) for improved control and responsiveness. By providing individualized therapeutic exercises and real-time feedback, our findings suggest that incorporating gesture-controlled interfaces into rehabilitation programs can significantly benefit stroke patients and individuals with motor impairments. Gesture-based smart home control is also made possible by IoT connectivity, which makes life easier for people with limited mobility. An assessment of the system's impact, obstacles to widespread adoption, and potential future directions for improving AI models and making them more affordable are presented at the study's conclusion. The goal of this study is to help close the digital divide for people with disabilities and contribute to the ongoing development of accessible technology. 2025 IEEE. -
Advancements in Medical Imaging: Detecting Kidney Stones in CT Scans using a ELM-I AdaBoost-RT Model
Kidney stones have been more common in recent years, leading many to believe that the condition is common. The condition's strong relationship with other terrible diseases makes it a major threat to public health. The development of instruments and procedures that facilitate the diagnosis and treatment of this ailment has the potential to enhance the effectiveness and efficiency of health care. Preprocessing, feature extraction, level set segmentation, and model training are the four steps that make up this approach. Part of the preprocessing includes eliminating the skeletal skeleton and soft-organs. Level set segmentation is commonly used for object tracking, motion segmentation, and image segmentation. An extremely effective feature extraction method called Gray level co-occurrence matrix (GLCM) is suggested for extracting the necessary characteristics from the segmented image. That ELM-I-AdaBoost-RT was used all during training. This cutting-edge technique achieves an average accuracy of 95.83%, surpassing both ELM and AdaBoost. 2024 IEEE. -
Advancements in optical steganography for secure medical data transmission in telehealth systems
Secure medical data transfer technologies have advanced as a result of the brisk growth of telehealth services. This study provides a thorough review of the most up-to-date research on using optical steganography to conceal medical records from prying eyes. Data concealing capacity has been increased without sacrificing picture quality using new techniques that make it difficult for unauthorised parties to access hidden information. Using adaptive steganography methods, medical data may be encoded in images in a way that makes it impossible to detect or extract by prying eyes. By concealing information over many picture layers, multi-layer steganography adds an extra degree of protection from prying eyes. The development of steganographic techniques has been spurred on by the use of machine learning and artificial intelligence to enhance steganalysis and the use of quantum characteristics to offer an extra layer of security in quantum steganography. Combining this with cryptographic safeguards like encryption provides an additional layer of security. In order to successfully safeguard sensitive medical data during transmission, standardisation and compliance in optical steganography are becoming more important as telehealth systems become more widespread. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Advancements in Psychotherapy and Treatment: The Use of AI Interventions for Psychopathologies
This chapter explores the potential of Artificial Intelligence in mental health, with a special focus on psychopathologies. The researchers expand on AIs role in assisting clinicians diagnosis, symptom tracking, predictive modelling, and therapy for psychiatric and neurodevelopmental disorders like Depression, Anxiety, Post-Traumatic Stress Disorder (PTSD), Substance Use Disorder, Autism Spectrum Disorder (ASD), and ADHD. Crucial interventions include wearables for anxiety, virtual reality exposure for PTSD, and robotic social companions for children with ASD. Despite these advancements, the use of AI chatbots as a replacement for therapy has been a subject of debate, largely due to issues around safety. The study highlights potential limitations, including risks in user interactions, limited therapeutic support, algorithmic biases, accessibility issues, and ethical concerns; advocating for Human in the Loop models where AI and clinicians work together, and calling for ethically designed AI systems that augment access without compromising on empathy, nuance and relational depth. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Advancements in Solar-Powered UAV Design Leveraging Machine Learning: A Comprehensive Review
Unmanned Aerial Vehicles (UAVs), commonly known as drones, have seen significant innovations in recent years. Among these innovations, the integration of solar power and machine learning has opened up new horizons for enhancing UAV capabilities. This review article provides a comprehensive overview of the state-of-the-art in solarpowered UAV design and its synergy with machine learning techniques. We delve into the various aspects of solar-powered UAVs, from their design principles and energy harvesting technologies to their applications across different domains, all while emphasizing the pivotal role that machine learning plays in optimizing their performance and expanding their functionality. By examining recent advancements and challenges, this review aims to shed light on the future prospects of this transformative technology. The Authors, published by EDP Sciences, 2024. -
Advancements in Sustainable Techniques for Dried Meat Production: an Updated Review
Dried meat is one of the ethnic and aesthetic food products popular among global civilizations and communities. The background of the production is associated with several methods practiced conventionally in the olden days. This review focused on investigating the advantages, challenges, research gaps, and technological intervention in dried meat production in the modern era. Moreover, it presented a gestalt of cutting-edge thermal and non-thermal food processing technologies and their effectiveness in extending shelf life. It delved into the specific characteristics of dried meat, including biochemical, sensory, and microbiological properties and processing techniques, and addressed the contamination sources. The pros and cons of various drying methods like hot-air drying, vacuum pulsed electric field, microwave-assisted techniques, and non-thermal drying processes are comprehended. The impact on meat's structural properties, nutritional value, shelf-life, quality control, and food safety are thoroughly presented. Moreover, the review explored the biochemical dynamics of the drying process and underscored the health risks associated with mycotoxin contamination in dried meat products. Furthermore, the study also presented the avenues of AI-based platforms and non-destructive technology for validating the quality of dried meat products. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks
Wireless Sensor Networks (WSNs) are used in various healthcare and military surveillance applications. As more sensitive data is transmitted across the network, achieving security becomes critical. Ensuring security is also challenging because most sensors are deployed in remote areas, making them vulnerable to many security attacks. Sybil attacks are one of the most destructive attacks. Security against Sybil attackers can be attained by implementing effective detection techniques to distinguish attackers from genuine nodes. This paper reviews existing machine learning-based approaches for detecting Sybil attacks, and their performance is compared based on different parameters. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Advancements in thiol-yne click chemistry: Recent trends and applications in polymer synthesis and functionalization
The 2022 Nobel Prize in chemistry brought the world's attention to click chemistry, a field as fascinating as its name, characterized by attractive features like high yield, stereospecificity, and broad scope. Since its inception, click chemistry has been synonymous with copper-catalyzed azide-alkyne reactions, owing to their remarkable advantages. However, as the field advanced, there has been a proactive search to develop metal-free alternatives for more biocompatible applications. This led to the extensive adoption of thiol-ene click reactions in the past decade. Yet, due to the growing requirement for polymers with complex architecture and functionality, in recent years, thiol-yne click reactions have come to the forefront with additional advantages over its ene counterparts, allowing the addition of two thiols to an alkyne. This review provides a concise overview of some of the significant developments in polymer synthesis and functionalization utilizing thiol-yne click chemistry. The focus is primarily on radical-mediated thiol-yne reactions, considering the substantial body of work in this area. Moreover, the review provides adequate discussion on other mechanistic pathways like nucleophilic and metal-catalyzed thiol-yne reactions that have gained traction recently. 2024 Elsevier Ltd -
Advances in Carbon-Element Bond Construction under Chan-Lam Cross-Coupling Conditions: A Second Decade
Copper-mediated carbon-heteroatom bond-forming reactions involving a wide range of substrates have been in the spotlight for many organic chemists. This review highlights developments between 2010 and 2019 in both stoichiometric and catalytic copper-mediated reactions, and also examples of nickel-mediated reactions, under modified Chan-Lam cross-coupling conditions using various nucleophiles; examples include chemo- and regioselective N-arylations or O-arylations. The utilization of various nucleophiles as coupling partners together with reaction optimization (including the choice of copper source, ligands, base, and other additives), limitations, scope, and mechanisms are examined; these have benefitted the development of efficient and milder methods. The synthesis of medicinally valuable or pharmaceutically important nitrogen heterocycles, including isotope-labeled compounds, is also included. Chan-Lam coupling reaction can now form twelve different C-element bonds, making it one of the most diverse and mild reactions known in organic chemistry. 1 Introduction 2 Construction of C-N and C-O Bonds 2.1 C-N Bond Formation 2.1.1 Original Discovery via Stoichiometric Copper-Mediated C-N Bond Formation 2.1.2 Copper-Catalyzed C-N Bond Formation 2.1.3 Coupling with Azides, Sulfoximines, and Sulfonediimines as Nitrogen Nucleophiles 2.1.4 Coupling with N, N -Dialkylhydroxylamines 2.1.5 Enolate Coupling with sp 3-Carbon Nucleophiles 2.1.6 Nickel-Catalyzed Chan-Lam Coupling 2.1.7 Coupling with Amino Acids 2.1.8 Coupling with Alkylboron Reagents 2.1.9 Coupling with Electron-Deficient Heteroarylamines 2.1.10 Selective C-N Bond Formation for the Synthesis of Heterocycle-Containing Compounds 2.1.11 Using Sulfonato-imino Copper(II) Complexes 2.2 C-O Bond Formation 2.2.1 Coupling with (Hetero)arylboron Reagents 2.2.2 Coupling with Alkyl- and Alkenylboron Reagents 3 C-Element (Element = S, P, C, F, Cl, Br, I, Se, Te, At) Bond Forma tion under Modified Chan-Lam Conditions 4 Conclusions. 2021 Georg Thieme Verlag. All rights reserved. -
Advances in Crime Identification: A Machine Learning Perspective
Crime profoundly impacts individuals, communities, and families. Technological advancements have provided perpetrators with new opportunities for criminal activities. The primary objective of the police department is to resolve crimes, ensuring justice for the victims. Additionally, preventing such incidents is crucial for creating a safer world. The landscape of criminal justice has undergone a significant shift with the integration of machine learning techniques, unlocking unparalleled potential for accuracy and efficiency. This study thoroughly examines the concept of various applications of machine learning in crime detection, prediction, and prevention. We examine the evolution of these technologies, from early developments to state- of-the-art methodologies, conducting a thorough analysis of their strengths, limitations, and ethical considerations. Moreover, the paper sheds light on crimes discussed in academic circles, serving as a repository for scholars and researchers. This facilitates informed discussions and guides future research endeavours. 2024 IEEE. -
Advances in detecting non-steroidal anti-inflammatory drugs (NSAIDs) using molecular receptors and nanostructured assemblies
The detection and quantification of non-steroidal anti-inflammatory drugs (NSAIDs) are crucial due to their widespread use and potential impact on human health and the environment. This review provides a comprehensive survey of the recent advancements in sensing technologies for NSAIDs, focusing on molecular receptors and nanostructured assemblies. Molecular receptors based on different fluorescent molecules such as anthracene, naphthalimide, squaraine, quinoline, BINOL, etc. offer high selectivity and sensitivity for NSAID detection. In parallel, nanostructured assemblies including CdSe/ZnS, Cd/S quantum dots (QDs), carbon dot-containing imprinted polymers, Ag and Au nanoparticles (NPs), hydrogel-embedded chemosensors, etc. were utilized for NSAID detection. This review highlights the different binding pathways with the change of various photophysical properties combining molecular recognition elements with nanomaterials to develop innovative sensors that achieve rapid, sensitive, and selective detection of NSAIDs. The review also discusses current challenges and future prospects in the field and based on reported designed receptors and nanostructured assemblies. To the best of our knowledge, no reviews have been reported on this topic so far. Thus, this review will fruitfully guide researchers to design various new molecular receptors and nanostructured materials to detect NSAIDs. 2024 RSC. -
Advances in Food Packaging with Nanotechnology-Enhanced Biomaterials
The growing concern about food safety and the need for environmental sustainability has triggered the shift from traditional plastic packaging to eco-friendly packaging materials derived from biological resources, enhanced with nanotechnology. Different challenges, such as low mechanical strength, thermal instability, barrier properties and microbial susceptibility, are being observed in traditional packaging. In this chapter, we discuss nanomaterials that are integrated with biopolymers for food packaging to address these issues and increase their physicochemical properties, functionalities, as well as practical applications. Biopolymers such as polylactic acid (PLA), chitosan, gelatin, cellulose and starch-based films, are increasingly being used and further reinforced with organic nanoparticles (nanocellulose, nano starch, carbon dots, and protein nanoparticles) and inorganic nanoparticles (silver, zinc oxide, titanium dioxide etc.) are addressed in the chapter. This chapter classifies and analyses nanomaterials according to their origin and function, demonstrating the enhancement of barrier properties, antimicrobial activity, UV shielding, and thermal resistance in nano-biocomposites. Special attention is paid to their application in smart packaging systems, including active systems that release antimicrobials or antioxidants and intelligent systems containing nano sensors that check for freshness and contamination. Examples of enhanced shelf life and quality preservation are discussed in fruits, vegetables, dairy, meat and bakery goods. The chapter also examines important safety, nanoparticle migration, toxicity regulatory issues, and the environmental impact, highlighting the emerging need for globally unified rules. To sum up, the worlds targets for sustainable food systems, reducing waste, consumer health protection and safeguarding public health are enabled by nano-biomaterials, making it the most suitable innovative solution to the challenges of packaging. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. -
Advances in sensor technologies for detecting soil pollution
The present chapter elucidates progressions in the surveillance of soil pollution, with a specific emphasis on integrated systems and sensor technologies. Future trends (e.g., enhanced selectivity, regulatory adoption), deployment platforms (field-deployable, wireless networks), and sensor types (electrochemical, optical, and biosensors) are discussed. Increasing sensitivity and specificity, facilitating on-site, real-time analysis, and integrating sensing with remediation strategies are priorities. The discourse highlights the revolutionary capacity that soil pollution sensors possess to propel environmental monitoring and management forward. Collaboration among stakeholders is critical for successfully implementing sensorbased approaches and driving innovation. 2024, IGI Global. All rights reserved. -
Advances in suicide prevention: Critical overview of the gaps in suicide risk assessments, multimodal strategies, medicolegal risks, and the emerging evidence
The CDC reports that the United States has the highest suicide rates in over 80 years. Numerous public policies aimed at reducing the rising suicide rates, such as Aetna's partnership with the American Foundation for Suicide Prevention (AFSP) and the zero-suicide initiative, continue to challenge these attempts. It, therefore, remains imperative to explore the shortcomings of these efforts that hamper their efficiency in reducing suicide rates. Advancements in research over time have sparked scientific skepticism, encouraging re-evaluation of established concepts. The current paper tests prevalent assumptions and arguments to uncover a scientifically informed approach to addressing rising suicide rates in clinical settings. The Author(s), 2024. -
Advances in Surface-Enhanced Raman Spectroscopy for the Detection of Synthetic Dyes in the Food Matrices
Food adulteration is a global concern that is a significant public health risk. Such issues demand sophisticated analytical devices with high-speed sensitivity toward detecting adulterants. The surface enhanced Raman spectroscopic (SERS) technique is an advanced tool that gains wide appreciation due to its very high sensitivity and selectivity. Being an excellent ultra-trace contaminant-detecting tool even within complex food matrices, this resource becomes fundamental in food safety protection. SERS is found to detect trace concentrations of synthetic dyes. Recent advancements have been made on new substrates that enhance signal stability among noble metals, hybrid nanostructures, and metal-organic frameworks (MOFs). These substrates are further functionalized with specific chemical moieties to improve the selectivity and reduce the matrix interference. SERS, combined with advanced computing tools like machine learning and chemometric algorithms, has completely changed the data analysis scenario; nowadays, high-throughput simultaneous detection and accurate adulterant quantification are possible. With rapid outputs and less sample preparation, portable SERS devices revealed promise in on-site food safety monitoring. Subsequent developments that address several fundamental concerns regarding cost, reusability, and scalability ultimately make SERS devices more widely adaptable. This chapter covers the emerging aspects of SERS substrates, enhancement strategies, and computational improvements concerning synthetic dye detection. It speaks of the potential of SERS as a flexible tool for ensuring public health and food safety worldwide while discussing the challenges and opportunities in developing inexpensive, reusable substrates. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Advances in text steganography theory and research: A critical review and gaps
There is an immense advancement in science and technology, and computing systems with the highest degree of security are the present hot topic; however, the domination of hackers and espionage in terms of disclosing the sensitive information are steadily increasing. This chapter presents a theoretical view and critical examination of the few text steganography methods in the contemporary world. It tells the direction in which research has developed over the past few years. Cryptography, the encipherment to a certain extent, protects the data by making it unreadable but not safe. Improvisation of the same can be done using another layer of protection that is steganography in which the secret embedded inside the cover text will not be revealed. 2021, IGI Global. -
Advances in the use of ceramic catalysts in fine chemical synthesis
Ceramics are versatile materials that have been put to many different uses. Catalysis is one such area where they have been used, both as catalyst and as a robust support material for catalysts. Properties like porosity and thermal and mechanical stability make ceramics attractive in these applications. Oxidation, esterification, hydrogenation, reduction, condensation reaction, and FriedelCrafts reaction are important reactions, which have uses spanning a wide range of applications, most notably in energy and environment. This chapter gives the recent advancements in ceramic materials used in the synthetic applications of the abovementioned reactions. The type and class of the ceramic material used and its role have been mentioned for these reactions. 2023 Elsevier Ltd. All rights reserved. -
Advances in Type II Diabetes Prediction: A Comprehensive Review of Machine Learning Techniques
Type II diabetes mellitus, on the other hand has been regarded as one of the growing concerns globally and thus clearly raises the need for making accurate forecasts of diabetes. The risk for Type II diabetes can be predicted using Ma-chine Learning as well as any other form to make the predictions much more enhanced than the traditional methods. This paper aims to give a broad overview of literature that has so far been available on the ML algorithms used in the management of Type II diabetes including such supervised algorithms as logistic regression, alphabet regression, random forest, support vector regression along with other methods such as, ensemble learning, deep learning, and hybrid. Analysis of the main aspects for the performance model such as parameter selection, the way to face and cope with imbalance parameters, interpretability and generalizability across different populations, another aspect that was regarded is the possibility of using real-time data collected with wearable devices and applying tissue and other biomarkers for better prediction. Finally, the key obstacles and future directions towards developing ML algorithms and models explainable and clinically relevant have been introduced to help researchers and practitioners toward effective, personalized, and scalable interventions. 2025 IEEE.