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A comprehensive review on the need for integrated strategies and process modifications for per- and polyfluoroalkyl substances (PFAS) removal: Current insights and future prospects
Alarming concern over the persistence and toxicity of per- and polyfluoroalkyl substances (PFAS) in the environment has created an imperative need for designing and redesigning strategies for their detection and remediation. Conventional PFAS removal technologies that uses physical, chemical, or biological methods. Increase in the diversity and quantity of PFAS entering the environment has necessitated the need for developing more advanced and integrated strategies for their removal. Despite of the advances reported in this domain, there exist a huge research gap that need to be mentored to tackle the problems associated with mitigation of combined toxicity of wide variety of PFAS in the environment. The possibility of PFAS to combine with other emerging contaminants poses an additional threat to the existing treatment methods thereby stressing the need for a continuous monitoring and updating the treatment processes. This review work aims at understanding the structure, entry, and fate of different types of PFAS in to the environment. Further an in-depth discussion regarding the different levels of toxicity associated with PFAS is elaborated in the review. The process description of recent PFAS remediation techniques along with their significance, limitations and possibility of integration is discussed in detail. Further a detailed outlook on the advantages and limitations of PFAS removal methods and an insight into the recently developed PFAS removal methods is outlined in this review. 2024 The Authors -
A comprehensive review on tissue culture studies and secondary metabolite production in Bacopa monnieri L. Pennell: a nootropic plant
Bacopa monnieri L. Pennell, commonly known as Brahmi, is an important medicinal plant that belongs to the family Plantaginaceae. Brahmi is rich in innumerable bioactive secondary metabolites, especially bacosides that can be employed to reduce many health issues. This plant is used as a neuro-tonic and treatment for mental health, depression, and cognitive performance. Brahmi is also known for its antioxidant, anti-inflammatory, and anti-hepatotoxic activities. There is a huge demand for its raw materials, particularly for the extraction of bioactive molecules. The conventional mode of propagation could not meet the required commercial demand. To overcome this, biotechnological approaches, such as plant tissue culture techniques have been established for the production of important secondary metabolites through various culture techniques, such as callus and cell suspension cultures and organ cultures, to allow for rapid propagation and conservation of medicinally important plants with increased production of bioactive compounds. It has been found that a bioreactor-based technology can also enhance the multiplication rate of cell and organ cultures for commercial propagation of medicinally important bioactive molecules. The present review focuses on the propagation and production of bacoside A by cell and organ cultures of Bacopa monnieri, a nootropic plant. The review also focuses on the biosynthesis of bacoside A, different elicitation strategies, and the over-expression of genes for the production of bacoside-A. It also identifies research gaps that need to be addressed in future studies for the sustainable production of bioactive molecules from B. monnieri. 2022 Informa UK Limited, trading as Taylor & Francis Group. -
A Comprehensive Review on Using Electronic Waste as a Construction Material
The study explores the use of electronic waste (E-waste) as a sustainable alternative in a variety of construction applications, addressing the rising global issue of E-waste management. E-waste, consisting of both metallic and non-metallic components, contains valuable and hazardous materials, which can lead to environmental damage, if improperly managed. The study demonstrates different methods for incorporating E-waste into bituminous mixes, high-strength concrete, and other composite materials. Research shows that, using E-waste improves the strength as well as durability properties of concrete. It acts as a practical alternative for disposing of E-waste and promotes the use of sustainable building methods. Furthermore, the potential of employing E-waste in the construction of flexible pavements is reviewed, which demonstrates positive results in improving the mechanical characteristics of asphalt mixtures. The study also highlights how E-waste can help with sustainable building methods by cutting down on landfill waste, preserving natural resources, and lowering carbon footprints. The results show that the use of E-waste not only offers a more suitable choice than traditional materials but also helps to lessen pollution from solid waste, which is consistent with worldwide efforts to improve environmental sustainability. Overall, this study shows that E-waste is a useful material for the construction, providing a creative waste management strategy that is in line with worldwide sustainability objectives. The results indicate that E-waste can be an important source of eco-friendly building materials, reducing environmental pollution and encouraging responsible resource utilization, if it is treated and integrated properly. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Comprehensive Review onCrop Disease Prediction Based onMachine Learning andDeep Learning Techniques
Leaf diseases cause direct crop losses in agriculture, and farmers cannot detect the disease early. If the diseases are not detected early and correctly, the farmer must undergo huge losses. It may lead to the wrong pesticide or over pesticide, directly affect crop productivity and economy, and indirectly affect human health. Sensitive crops have various leaf diseases, and early prediction of these diseases remains challenging. This paper reviews several machine learning (ML) and deep learning (DL) methods used for different crop disease segmentation and classification. In the last few years, computer vision and DL techniques have made tremendous progress in object detection and image classification. The study summaries the available research on different diseases on various crops based on machine learning (ML) and deep learning (DL) techniques. It also discusses the data sets used for research and the accuracy and performance of existing methods. It does mean that the methods and available data sets presented in this paper are not projected to replace published solutions for crop disease identification, perhaps to enhance them by finding the possible gaps. Seventy-five articles are analysed and reviewed to find essential issues that involve additional study for future research in this domain to promote continuous progress for data sets, methods, and techniques. It mainly focuses on image segmentation and classification techniques used to solve agricultural problems. Finally, this paper provides future research scope and challenges, limitations, and research gaps. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Comprehensive Study for Application of Blockchain Technologies for the Decentralized Grid Utilization Possibilities
This thorough research explores the world of blockchain technology and its significant effects on the use of decentralized grids. Decentralized networks have shown promise in addressing the rising need for renewable energy and effective resource management. Blockchain, a technology based on distributed ledgers, presents creative approaches to improve grid oversight, enable through peer-to- energy trade, and guarantee openness and protection in the management of the grid. This paper investigates the advantages and disadvantages of using blockchain in decentralized grids. From trading in renewable energy to grid optimization and demand response, we examine a variety of use cases and applications. We offer insight into the practical viability and scalability of blockchain-based solutions through a thorough analysis of real-world deployments and case reports. We also address the legislative and technological challenges to be solved before blockchain technology can realize its full potential in decentralized grid setups. Our research strongly emphasizes that regulatory architectures, seamless integration, and standardization all contribute to supporting the harmonious adaptation of blockchain technology worldwide. This study provides lawmakers, industry players, and investigators in the efforts to build a sustainable and effective energy future with an informative tool: it will shed light on the potential and constraints presented by blockchain technology in a decentralized environment of grid usage. 2025 IEEE. -
A Comprehensive Study of Blockchain Technology Based Decentralised Ledger Implementations
Information management and decentralized, secure transactions are made possible by the groundbreaking idea of blockchain technology. Blockchain has attracted considerable attention from a wide range of businesses as a result of the rising popularity of cryptocurrencies like Bitcoin and Ethereum. The primary goal of this paper is to present a thorough examination of decentralized ledger systems based on blockchain technology. It examines at the basic ideas, underpinnings, and real-world uses of blockchain in many industries. The ability to scale, effectiveness, privacy, security, seamless integration, governing scenarios, and consumption of energy are just a few of the technical factors that are looked at in relation to the deployment of blockchain technology. In this research paper we also pinpoint forthcoming developments and future prospects for the blockchain industry like Solutions for scalability at the subsequent layer, protocols for seamless integration, integration with the Internet of Things (IoT), apps for decentralized finance (DeFi), and digital currencies that are issued by central banks (CBDCs) are a few examples. 2023 IEEE. -
A Comprehensive Study on Computer-Aided Cataract Detection, Classification, and Management Using Artificial Intelligence
The day-to-day popularity of computer-aided detection is increasing medical field. Cataract is a main cause of blindness in the entire world. Compared with the other eye diseases, computer-aided development in the area of cataract is remaining underexplored. Several researches are done for automated detection of cataract. Many study groups have proposed many computer-aided systems for detecting cataract, classifying the different type, identification of stages, and calculation of lens power selection prior to cataract surgery. With the advancement in the artificial intelligence and machine learning, future cataract-related research work can undergo very useful achievements in the coming days. The paper studies various recent researches done related to cataract detection, classification, and grading using various artificial intelligence techniques. Various comparisons are done based on the methodology used, type of dataset, and the accuracy of various methodologies. Based on the comparative study, research gap is identified, and a new method is proposed which can overcome the disadvantages and gaps of the studied work. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Comprehensive Study On Detection Of Emotions Using Human Body Movements: Machine Learning Approach
Identifying emotions from human beings is the most challenging area in artificial intelligence. There are different modules used to identify emotions like speech, face, EEG, Physiological Signals, and body movement. However, emotional recognition from body movement is the need of time. The review focuses on identifying various emotions with the help of the full-body movement model and the parts-based model. The aim of the survey is to identify the recent work done by the researchers with the help of full-body movements and body parts-based models. Recently, little research has been done on the identification of emotions using body movements, but most of the time it has succeeded to some extent. Identifying various human emotions using body movements is a really very challenging task. This research work discovers that the various popular machine learning algorithms like Support Vector Machines, Neural Networks, and convolutional neural networks are majorly used to identify basic emotions. 2023 American Institute of Physics Inc.. All rights reserved. -
A Comprehensive Study on E-learning Environments for Deaf or Hard of Hearing Learners
Quality education is the fundamental right of every individual regardless of the disabilities they have. For the Deaf or Hard of Hearing (d/DHH) people, e-learning is the most promising way to access the educational materials referred to as digital learning objects (LO) at any time and space which increase their autonomous learning skills. This form of instruction delivery was widely accepted during the outbreak of Covid-19. Hence a background study has been conducted to investigate the challenges in teaching the d/DHH learners during the pandemic. This research work aims at providing a personalized e-learning environment to the d/DHH student community belonging to St. Clare Oral Higher Secondary School for The Deaf, situated in Kerala. To build personalized systems, the primary step is to review the existing e-learning solutions available in the literature and the adaptation techniques implemented by them to offer personalization in line with the components of traditional adaptive e-learning systems. The study carried out in this paper illuminates the need of personalized e-learning platforms that adapt the basic needs, abilities and disabilities of deaf learners which will find the 'best learning solutions' in the form of learning objects. 2023 IEEE. -
A Comprehensive Study on Electric Vehicle Charging Infrastructure
Issues of global warming and hike in the fuel price have taken electric vehicles (EVs) to be popular among the ordinary people. But the main drawbacks are related to the vehicle price and the scarcity of charging infrastructure. In this paper, a review of various charging infrastructures of electric vehicles that are existing and emerging are discussed. The paper also gives an overview of the charging standards for EVs. The Electrochemical Society -
A Comprehensive Study on Parametric Optimization of Plasma-Sprayed Cr2C3 Coatings on Al6061 Alloy
Plasma spray, a widely employed thermal spray method, is known for enhancing coatings with heightened microhardness, density, and bonding strength. In this study, Taguchis approach was applied to optimize processing parameters for plasma spray-coated surfaces, aiming to reduce porosity, increase hardness, and fortify the connection between Cr2C3 coatings. The design of experiments method facilitated the optimization of process parameters, utilizing signal-to-noise ratios and ANOVA analysis to assess the significance of each processing parameter and identify optimal parameter combinations. Powdered feed rate and stand-off distance emerged as the two most critical processing variables influencing permeability and hardness, contingent on signal-to-noise ratios. S/N ratio analysis was employed to determine the optimal processing parameters for permeability, hardness, and bonding strength. For porosity, the optimal stand-off distance, powdered feed rate, and current density were identified as 60rpm, 50g/min, and 460ampsmm/s, respectively. Exemplary process conditions for hardness included a powdered feed rate of 60g/min, a stand-off distance of 80rpm, and a current density of 480 amps. Lastly, for strength properties, the ideal process variables were a stand-off distance of 80rpm, a current density of 480amps, and a powdered feed rate of 60g/min. Despite small differences between projected R2 and modified R2 values in statistical data on permeability, hardness, and bonding strength, the proximity to the one emphasizing the fit of the linear regression used for analysis was evident. Fracture results from the binding strength test postulate mixed adhesion-cohesion type failures in the Cr2C3 coatings. The Institution of Engineers (India) 2024. -
A Comprehensive Study on Parametric Optimization of Plasma-Sprayed Cr2C3 Coatings on Al6061 Alloy
Plasma spray, a widely employed thermal spray method, is known for enhancing coatings with heightened microhardness, density, and bonding strength. In this study, Taguchis approach was applied to optimize processing parameters for plasma spray-coated surfaces, aiming to reduce porosity, increase hardness, and fortify the connection between Cr2C3 coatings. The design of experiments method facilitated the optimization of process parameters, utilizing signal-to-noise ratios and ANOVA analysis to assess the significance of each processing parameter and identify optimal parameter combinations. Powdered feed rate and stand-off distance emerged as the two most critical processing variables influencing permeability and hardness, contingent on signal-to-noise ratios. S/N ratio analysis was employed to determine the optimal processing parameters for permeability, hardness, and bonding strength. For porosity, the optimal stand-off distance, powdered feed rate, and current density were identified as 60rpm, 50g/min, and 460ampsmm/s, respectively. Exemplary process conditions for hardness included a powdered feed rate of 60g/min, a stand-off distance of 80rpm, and a current density of 480 amps. Lastly, for strength properties, the ideal process variables were a stand-off distance of 80rpm, a current density of 480amps, and a powdered feed rate of 60g/min. Despite small differences between projected R2 and modified R2 values in statistical data on permeability, hardness, and bonding strength, the proximity to the one emphasizing the fit of the linear regression used for analysis was evident. Fracture results from the binding strength test postulate mixed adhesion-cohesion type failures in the Cr2C3 coatings. The Institution of Engineers (India) 2024. -
A comprehensive study on the assessment of chemically modified Azolla pinnata as a potential cadmium sequestering agent
The major environmental issue raised throughout the world is the egression of toxic pollutants in water bodies. Hence, employment of novel technological interventions such as bioremediation and phytoremediation for mitigating the toxic effects caused by the pollutants has gained attention. The aquatic macrophyte, Azolla pinnata is utilized as a biofiltering agent in the present study for the chelation of metal toxicants from the artificial wastewater system. The nutritive value of A. pinnata was determined to be 268.99Kcal/100g energy and the mineral profiling showed the highest amount of calcium (54.7ppm), iron (14.04ppm) and manganese (7.96 ppm). The quantitative screening of total phenolic and total flavonoid contents showed a maximum of 402.334.29 mg/g GAE and 105.253.81 mg/g QE respectively and the sample exhibited strong antioxidant activity in quenching the DPPH radicals with an IC50 value of 88.27?g/ml. Similarly, the highest bioactivity was observed in methanolic and chloroform extract of A. pinnata biomass showing the zone of growth inhibition against E. coli (17mm) and S. aureus (18mm). The results recorded from the SEM-EDX, GCMS, FTIR and XRD confirmed the adsorptive properties of biomass. The chemically modified and unmodified Azolla exposed to cadmium metal solution showed the maximum adsorption of about 0.470.001 and 0.480.003 ppm in 60mins using the unmodified biomass with dosage of 0.75 and 1.0g respectively. Moreover, the results recorded from the instrumental characterization for the adsorptive properties of Azolla biomass proved that cadmium chelation is due to the modifications caused in porosity, surface structure and the addition of functional groups in the treated biomass surface. 2023 The Ceramic Society of Japan. -
A Comprehensive Study Using Convolutional Neural Networks as a Method for Multi-class Skin Cancer Image Classification
Skin disorders occur more frequently than other kinds of diseases. Skin diseases can be attributed to a number of aspects, like fungi, bacteria, viruses, allergies, and so on. The rapid advancement of healthcare centered around lasers and photonics has rendered it feasible to diagnose skin disorders in a more accurate and timely manner. However, the cost of such a diagnostic remains extremely limited and prohibitively expensive. As a result, the use of image processing methods is beneficial in the initial phases of designing a computerized dermatology screening system. The retrieval of characteristics is an extremely important step in classifying skin disorders. The use of computer vision may play a crucial role in the diagnosis of a variety of skin conditions using a variety of approaches. The strategy we have proposed is straightforward and quick and requires no expensive technology besides a computer and a camera. When applied to the inputs of a colored picture, the method is successful. After that, resize a portion of the image to retrieve attributes with a pretrained convolutional neural network. The attribute was then classified using the multi-class XGBoost program. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Comprehensive Survey of Methods for Identifying Counterfeit Banknotes Using Image Processing and Machine Learning
The world economy is threatened by counterfeit currencies. Counterfeit currencies are often difficult, time-consuming and ineffective to identify manually. Automated methods based on image processing techniques and machine learning algorithms are helpful in detecting counterfeit notes. This survey paper reviews the current strategies on fake banknote detection using image processing techniques and machine learning algorithms. We discuss various stages of the detection process, including image acquisition, preprocessing, feature extraction and classification. Furthermore, we analyze the limitations and comparative performance of different algorithms and approaches mentioned in the literature. The survey aims to provide insights into the various methodologies, challenges and future directions in the field of fake banknote detection, facilitating the development of more robust and effective counterfeit detection systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
A Comprehensive Survey on Decoder Design using Quantum-dot Cellular Automata
QCA offer a compelling alternative to CMOS technology, providing benefits such as low power consumption, high speed, high density, and the ability to surpass the nanoscale limitations of CMOS. QCA is increasingly being adopted in VLSI designs as a solution for reducing power consumption and thermal dissipation. This paper analyzes the area, cell count, and latency of different 2:4 decoders to determine the most efficient design based on these factors. Decoders play a critical role in Quantum-dot Cellular Automata(QCA) by enabling efficient data routing, memory addressing, and logic control while minimizing power consumption and reducing interconnect complexity. The study employs a specialized logic gate known as the Toffoli gate, which is renowned for its capability in reversible computing, allowing information processing without data loss. Future advancements in 2:4 decoders using QCA should prioritize optimizing clocking schemes, improving fault tolerance, and developing scalable architectures to address fabrication challenges and enhance reliability in practical applications. The circuits are simulated using QCA Designer software. 2025 IEEE. -
A Comprehensive Survey on Deep Learning Techniques for Digital Video Forensics
With the help of advancements in connected technologies, social media and networking have made a wide open platform to share information via audio, video, text, etc. Due to the invention of smartphones, video contents are being manipulated day-by-day. Videos contain sensitive or personal information which are forged for one's own self pleasures or threatening for money. Video falsification identification plays a most prominent role in case of digital forensics. This paper aims to provide a comprehensive survey on various problems in video falsification, deep learning models utilised for detecting the forgery. This survey provides a deep understanding of various algorithms implemented by various authors and their advantages, limitations thereby providing an insight for future researchers. 2024 World Scientific Publishing Co. -
A comprehensive survey on features and methods for speech emotion detection
Human computer interaction will be natural and effective when the interfaces are sensitive to human emotion or stress. Previous studies were mainly focused on facial emotion recognition but speech emotion detection is gaining importance due its wide range of applications. Speech emotion recognition still remains a challenging task in the field of affective computing as no defined standards exist for emotion classification. Speech signal carries large information related to the emotions conveyed by a person. Speech recognition system fails miserably if robust techniques are not implemented to address the variations in speech due to emotion. Emotion detection from speech has two main steps. They are feature extraction and classification. The goal of this paper is to give an overview on the types of corpus, features and classification techniques that are associated with speech emotion recognition. 2015 IEEE. -
A comprehensive survey on machine learning techniques to mobilize multi-camera network for smart surveillance
Deploying a web of CCTV cameras for surveillance has become an integral part of any smart citys security procedure. This, however, has led to a steady increase in the number of cameras being deployed. These cameras generate a large amount of data, which needs to be further analyzed. Our next step is to achieve a network of cameras spread across a city that does not require any human assistance to detect, recognize and track a person. This paper incorporates various algorithmic techniques used in order to make surveillance systems and their use cases so as to enable less human intervention dependent as much as possible. Even though many of these methods do carry out the task graciously, there are still quite a few obstructions such as computational resources required for model building, training time for the models, and many more issues that hinder the process and hence, constrain the possibility of easy implementation. In this paper, we also intend to shift the paradigm by providing evidence toward the use of technologies like Fog computing and edge computing coupled with the surveillance technology trends, which can help to achieve the goal in a sustainable manner with lesser overheads. 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. -
A comprehensive survey on machine learning techniques to mobilize multi-camera network for smart surveillance
Deploying a web of CCTV cameras for surveillance has become an integral part of any smart citys security procedure. This, however, has led to a steady increase in the number of cameras being deployed. These cameras generate a large amount of data, which needs to be further analyzed. Our next step is to achieve a network of cameras spread across a city that does not require any human assistance to detect, recognize and track a person. This paper incorporates various algorithmic techniques used in order to make surveillance systems and their use cases so as to enable less human intervention dependent as much as possible. Even though many of these methods do carry out the task graciously, there are still quite a few obstructions such as computational resources required for model building, training time for the models, and many more issues that hinder the process and hence, constrain the possibility of easy implementation. In this paper, we also intend to shift the paradigm by providing evidence toward the use of technologies like Fog computing and edge computing coupled with the surveillance technology trends, which can help to achieve the goal in a sustainable manner with lesser overheads. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023.