GLA University

In agriculture science, automation increases the quality, economic growth and productivity of the country. The export market and quality evaluation are affected by assorting of fruits and vegetables. The crucial sensory characteristic of fruits and vegetables is appearance that impacts their market value, the consumer’s preference and choice. Although, the sorting and grading can be done by human but it is inconsistent, time consuming, variable, subjective, onerous, expensive and easily influenced by surrounding. Hence, an astute fruit grading system is needed. In recent years, various algorithms for sorting and grading are done by various researchers using computer vision. This paper presents a detailed overview of various methods i.e. preprocessing, segmentation, feature extraction, classification which addressed fruits and vegetables quality based on color, texture, size, shape and defects. In this paper, a critical comparison of different algorithm proposed by researchers for quality inspection of fruits and vegetables has been carried out.

Artificial Intelligence (AI)-based ChatGPT developed by OpenAI is now widely accepted in several fields, including education. Students can learn about ideas and theories by using this technology while generating content with it. ChatGPT is built on State of the Art (SOA), like Deep Learning (DL), Natural Language Processing (NLP), and Machine Learning (ML), an extrapolation of a class of ML-NLP models known as Large Language Model (LLMs). It may be used to automate test and assignment grading, giving instructors more time to concentrate on instruction. This technology can be utilised to customise learning for kids, enabling them to focus more intently on the subject matter and critical thinking ChatGPT is an excellent tool for language lessons since it can translate text from one language to another. It may provide lists of vocabulary terms and meanings, assisting students in developing their language proficiency with resources. Personalised learning opportunities are one of ChatGPT’s significant applications in the classroom. This might include creating educational resources and content tailored to a student’s unique interests, skills, and learning goals. This paper discusses the need for ChatGPT and the significant features of ChatGPT in the education system. Further, it identifies and discusses the significant applications of ChatGPT in education. Using ChatGPT, educators may design lessons and instructional materials specific to each student’s requirements and skills based on current trends. Students may work at their speed and concentrate on the areas where they need the most support, resulting in a more effective and efficient learning environment. Both instructors and students may profit significantly from using ChatGPT in the classroom. Instructors may save time on numerous duties by using this technology. In future, ChatGPT will become a powerful tool for enhancing students’ and teachers’ experience.

The size- and shape-dependent band gap energy of semiconductor compound nanomaterials (SCNs) is formulated. The model theory is based on the cohesive energy of the nanocrystals compared to the bulk crystals. We have considered CdSe, CdTe, ZnS, ZnSe and ZnTe semiconductors compounds for the study of size- and shape-dependent band gap energy. It is found that the band gap energy of SCN depends upon the particle size and shape. The model predicts that the band gap energy increases as particle size of the semiconductor nanomaterials decreases. The results obtained are compared with the available experimental data, which support the validity of the model reported.

Financial service providers find blockchain technology useful to enhance authenticity, security, and risk management. Several institutions are adopting blockchain in trade and finance systems to build smart contracts between participants, improve efficiency and transparency, and open up newer revenue opportunities. Blockchain’s unique recording capabilities make the existing clearing and settlement process redundant. Banks and other financial entities are adopting blockchain-enabled IDs to identify people. Better results come from organisations’ capacity to foresee emerging trends in financial blockchain applications and develop blockchain functionality. The transfer of asset ownership and addressing the maintenance of a precise financial ledger. Measurement, communication, and analysis of financial information are three significant areas to be focussed on by accounting professionals. Blockchain clarifies asset ownership and the existence of obligations for accountants, and it has the potential to improve productivity. This paper identifies and studies relevant articles related to blockchain for finance. This paper focuses on Blockchain technology and its importance for financial services. Further takes up various tools, strategies, and featured services in Blockchain-based financial services. Finally, the paper identifies and evaluates the significant applications of Blockchain technology in financial services. Credit reports significantly impact the financial lives of customers. Recent data breaches demonstrate the superior security of blockchain-based credit reporting over conventional server-based reporting. Blockchain-based systems enable the faster, more cost-effective, and more customised issuance of digital securities. With its adoption, the market for investors can be expanded, costs for issuers can be reduced, and counterparty risk can be reduced due to the ability to customise digital financial instruments to the demands of investors. It uses mutualised standards, protocols, and shared procedures to give network users a single common source of truth. Participants in the business network can now more easily collaborate, manage data, and agree with this technology’s application.

Industry 4.0 involves innovations with upcoming digital technologies, and blockchain is one of them. Blockchain can be incorporated to improve security, privacy, and data transparency both for small and large enterprises. Industry 4.0 is a synthesis of the new production methods that allow manufacturers to achieve their target more rapidly. Research has been conducted on various Industry 4.0 technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big data, and Blockchain, and how they could create significant interruptions in recent years. These technologies provide various possibilities in the world of manufacturing and supply chain. Blockchain is a technology that has gained much recognition and can enhance the manufacturing and supply chain environment. Various fields now have fascinating insights into the advantages of blockchain. Several research articles on “Blockchain” and “Industry 4.0” from Google Scholar, Scopus, and other relevant sources are identified and reviewed for this study. This paper discusses the major potential of Blockchain in Industry 4.0. Various drivers, enablers, and associated capabilities of Blockchain technology for Industry 4.0 are discussed for insights. Different Industry 4.0 spheres/sub-domains for Blockchain technology realisation are also discussed. Finally, we have identified and studied fourteen significant applications of Blockchain in Industry 4.0. It is a range of new developments and hope for immense opportunities that are changing Industry 4.0. This technology would work to achieve amplified outcomes and work individually to enhance the process.

Paratuberculosis, a chronic disease affecting ruminant livestock, is caused by Mycobacterium avium subsp. paratuberculosis (MAP). It has direct and indirect economic costs, impacts animal welfare and arouses public health concerns. In a survey of 48 countries we found paratuberculosis to be very common in livestock. In about half the countries more than 20% of herds and flocks were infected with MAP. Most countries had large ruminant populations (millions), several types of farmed ruminants, multiple husbandry systems and tens of thousands of individual farms, creating challenges for disease control. In addition, numerous species of free-living wildlife were infected. Paratuberculosis was notifiable in most countries, but formal control programs were present in only 22 countries. Generally, these were the more highly developed countries with advanced veterinary services. Of the countries without a formal control program for paratuberculosis, 76% were in South and Central America, Asia and Africa while 20% were in Europe. Control programs were justified most commonly on animal health grounds, but protecting market access and public health were other factors. Prevalence reduction was the major objective in most countries, but Norway and Sweden aimed to eradicate the disease, so surveillance and response were their major objectives. Government funding was involved in about two thirds of countries, but operations tended to be funded by farmers and their organizations and not by government alone. The majority of countries (60%) had voluntary control programs. Generally, programs were supported by incentives for joining, financial compensation and/or penalties for non-participation. Performance indicators, structure, leadership, practices and tools used in control programs are also presented. Securing funding for long-term control activities was a widespread problem. Control programs were reported to be successful in 16 (73%) of the 22 countries. Recommendations are made for future control programs, including a primary goal of establishing an international code for paratuberculosis, leading to universal acknowledgment of the principles and methods of control in relation to endemic and transboundary disease. An holistic approach across all ruminant livestock industries and long-term commitment is required for control of paratuberculosis.

Social media is a popular medium for the dissemination of real-time news all over the world. Easy and quick information proliferation is one of the reasons for its popularity. An extensive number of users with different age groups, gender, and societal beliefs are engaged in social media websites. Despite these favorable aspects, a significant disadvantage comes in the form of fake news, as people usually read and share information without caring about its genuineness. Therefore, it is imperative to research methods for the authentication of news. To address this issue, this article proposes a two-phase benchmark model named WELFake based on word embedding (WE) over linguistic features for fake news detection using machine learning classification. The first phase preprocesses the data set and validates the veracity of news content by using linguistic features. The second phase merges the linguistic feature sets with WE and applies voting classification. To validate its approach, this article also carefully designs a novel WELFake data set with approximately 72 000 articles, which incorporates different data sets to generate an unbiased classification output. Experimental results show that the WELFake model categorizes the news in real and fake with a 96.73% which improves the overall accuracy by 1.31% compared to bidirectional encoder representations from transformer (BERT) and 4.25% compared to convolutional neural network (CNN) models. Our frequency-based and focused analyzing writing patterns model outperforms predictive-based related works implemented using the Word2vec WE method by up to 1.73%.

Carbon dots (CDs) are a new category of crystalline, quasi-spherical fluorescence, "zero-dimensional" carbon nanomaterials with a spatial size between 1 nm to 10 nm and have gained widespread attention in recent years. Green CDs are carbon dots synthesised from renewable biomass such as agro-waste, plants or medicinal plants and other organic biomaterials. Plant-mediated synthesis of CDs is a green chemistry approach that connects nanotechnology with the green synthesis of CDs. Notably, CDs made with green technology are economical and far superior to those manufactured with physicochemical methods due to their exclusive benefits, such as being affordable, having high stability, having a simple protocol, and being safer and eco-benign. Green CDs can be synthesized by using ultrasonic strategy, chemical oxidation, carbonization, solvothermal and hydrothermal processes, and microwave irradiation using various plant-based organic resources. CDs made by green technology have diverse applications in biomedical fields such as bioimaging, biosensing and nanomedicine, which are ascribed to their unique properties, including excellent luminescence effect, strong stability and good biocompatibility. This review mainly focuses on green CDs synthesis, characterization techniques, beneficial properties of plant resource-based green CDs and their biomedical applications. This review article also looks at the research gaps and future research directions for the continuous deepening of the exploration of green CDs.

Harmine, a beta-carboline alkaloid, is widely distributed in the plants, marine creatures, insects, mammalians as well as in human tissues and body fluids. Harmine was originally isolated from seeds of Peganum harmal in 1847 having a core indole structure and a pyridine ring. Harmine has various types of pharmacological activities such as antimicrobial, antifungal, antitumor, cytotoxic, antiplasmodial, antioxidaant, antimutagenic, antigenotoxic and hallucinogenic properties. It acts on gamma-aminobutyric acid type A and monoamine oxidase A or B receptor, enhances insulin sensitivity and also produces vasorelaxant effect. Harmine prevents bone loss by suppressing osteoclastogenesis. The current review gives an overview on pharmacological activity and analytical techniques of harmine, which may be useful for researcheres to explore the hidden potential of harmine and and will also help in developing new drugs for the treatment of various diseases.

The rise of drug resistance in cancer cells presents a formidable challenge in modern oncology, necessitating the exploration of innovative therapeutic strategies. This review investigates the latest advancements in overcoming drug resistance mechanisms employed by cancer cells, focusing on emerging therapeutic modalities. The intricate molecular insights into drug resistance, including genetic mutations, efflux pumps, altered signaling pathways, and microenvironmental influences, are discussed. Furthermore, the promising avenues offered by targeted therapies, combination treatments, immunotherapies, and precision medicine approaches are highlighted. Specifically, the synergistic effects of combining traditional cytotoxic agents with molecularly targeted inhibitors to circumvent resistance pathways are examined. Additionally, the evolving landscape of immunotherapeutic interventions, including immune checkpoint inhibitors and adoptive cell therapies, is explored in terms of bolstering anti-tumor immune responses and overcoming immune evasion mechanisms. Moreover, the significance of biomarker-driven strategies for predicting and monitoring treatment responses is underscored, thereby optimizing therapeutic outcomes. For insights into the future direction of cancer treatment paradigms, the current review focused on prevailing drug resistance challenges and improving patient outcomes, through an integrative analysis of these emerging therapeutic strategies.

Single-atom introduced carbon nanomaterials show favorable oxygen-reduction reaction (ORR) and oxygen-evolution reaction (OER) performance for renewable energy applications. Nevertheless, the electronic-structure regulation by decorating heterogeneous single-metal-atoms and the engineering of a single-atom active-sites’ microenvironment need to be optimized simultaneously, which is challenging. Herein, we develop an atomic-interfacial-regulation approach to fabricate dual single Fe/Co atoms synchronized with both nitrogen/sulfur atoms on defective/graphitic/porous carbon nanosheets (Fe,Co/DSA-NSC). The unsymmetrically organized N and S coordinated Fe/Co bridged atomic-sites [Fe-(N2S)/Co-(N2S) moiety] are established to prompt charge-transfer, lowering the energy barrier of oxygenated reaction-intermediates and leading to boost the reaction-kinetics. As estimated, the Fe,Co/DSA-NSC exhibits an improved ORR/OER activity with higher half-wave potential and lower overpotential (E1/2 = 879 mV and η10 = 210 mV, respectively) and also good cycling stability toward zinc-air batteries. This discovery hence provides a widespread scheme for the synergistic-principles of dual-single-atom catalysts and controlled regulation of an active-sites’ microenvironment toward energy applications.

Diseases in fruit cause devastating problem in economic losses and production in agricultural industry worldwide. In this paper, a solution for the detection and classification of apple fruit diseases is proposed and experimentally validated. The image processing based proposed approach is composed of the following main steps, in the first step K-Means clustering technique is used for the image segmentation, in the second step some state of the art features are extracted from the segmented image, and finally images are classified into one of the classes by using a Multi-class Support Vector Machine. Our experimental results express that the proposed solution can significantly support accurate detection and automatic classification of apple fruit diseases. The classification accuracy for the proposed solution is achieved up to 93%.

INTRODUCTION: Image segmentation in medical physics plays a vital role in image analysis to identify the affected tumour. The process of subdividing an image into its constituent parts that are homogeneous in feature is called Image segmentation, and this process concedes to extract some useful information. Numerous image segmentation techniques have been developed, and these techniques conquer different restrictions on conventional medical segmentation techniques. This paper presents a review of medical image segmentation techniques and statistical mechanics based on the novel method named as Lattice Boltzmann method (LBM). The beauty of LBM is to augment the computational speed in the process of medical image segmentation with an accuracy and specificity of more than 95% compared to traditional methods. As there is not much information on LBM in medical physics, it is intended to present a review of the research progress of LBM.OBJECTIVE: As there is no review paper on the research progress of the LB method, this paper presents a review with an objective to give some thought regarding the different segmentation for medical image and novel LB method to advance interest for future investigation and exploration in medical image segmentation.METHODS: This paper in attendance a short review of medical image segmentation techniques based on Thresholding, Region-based, Clustering, Edge detection, Model-based and the novel method Lattice Boltzmann method (LBM).CONCLUSION: In this paper, we outlined various segmentation techniques applied to medical images, emphasize that none of these problem areas has been acceptably settled, and all of the algorithms depicted are available for broad improvement. Since LBM has the benefits of speed and adaptability of modelling to guarantee excellent image processing quality with a reasonable amount of computer resources, we predict that this method will become a new research hotspot in image processing.

The rechargeable aqueous zinc–iodine (Zn–I2) battery has emerged as a promising electrochemical energy storage technology. However, poor cycling stability caused by the dissolution of iodine species into the electrolyte limited its practical application. Herein, we report a nitrogen-doped porous carbon (NPC) material in gram scales. Performed as an iodine host in the Zn–I2 battery, the NPC shows a high specific capacity (345.3 mAh g–1 at 0.2 C), superior rate capability (53.2% capacity retention at 10 C), and remarkable cycling stability (10 000 cycles at 10 C with a capacity retention of 80.9%). More importantly, DFT computations reveal that the graphitic-N (N-Q) exhibits the strongest adsorption of iodine; however, pyridinic-N (N-6) shows the weakest adsorption of iodine. Moreover, the N-6/N-Q ratio is an essential parameter that significantly determined the electrochemical performance of Zn–I2 batteries. Therefore, the improved long-term cycling stability and rate capability of the as-designed Zn–I2 battery are attributable to the decrease of the N-6/N-Q ratio. This work is of great significance for devolving highly reversible Zn–I2 batteries.

A colloidal mixture of nanometer-sized (<100 nm) metallic and non-metallic particles in conventional cutting fluid is called nanofluid. Nanofluids are considered to be potential heat transfer fluids because of their superior thermal and tribological properties. Therefore, nano-enhanced cutting fluids have recently attracted the attention of researchers. This paper presents a summary of some important published research works on the application of nanofluid in different machining processes: milling, drilling, grinding, and turning. Further, this review article not only discusses the influence of different types of nanofluids on machining performance in various machining processes but also unfolds other factors affecting machining performance. These other factors include nanoparticle size, its concentration in base fluid, lubrication mode (minimum quantity lubrication and flood), fluid spraying nozzle orientation, spray distance, and air pressure. From literature review, it has been found that in nanofluid machining, higher nanoparticle concentration yields better surface finish and more lubrication due to direct effect (rolling/sliding/filming) and surface enhancement effect (mending and polishing) of nanoparticles compared to dry machining and conventional cutting fluid machining. Furthermore, nanofluid also reduces the cutting force, power consumption, tool wear, nodal temperature, and friction coefficient. Authors have also identified the research gaps for further research.

This paper provides a synergistic review of the current scenario and future prospects of aluminum metal matrix composites (AMMCs). AMMCs are advanced materials that have garnered significant attention in recent years due to their unique characteristics, including high strength, low density, and excellent wear resistance. This review covers various aspects of AMMCs, including processing techniques, mechanical and physical properties, microstructural features, and applications. The review begins with an overview of different processing techniques for AMMCs, including stir casting, powder metallurgy, and infiltration methods. It then discusses the mechanical and physical properties of AMMCs, such as tensile strength, hardness, and thermal conductivity, and how these properties are influenced by various factors such as reinforcement type, size, and volume fraction. Furthermore, the microstructural features of AMMCs, including the distribution of reinforcements, are discussed in detail. The review also highlights the potential applications of AMMCs in various industries such as aerospace, automotive, and electronics. Finally, the review concludes by discussing the current scenario and future prospects of AMMCs. It highlights the challenges that need to be addressed to enhance the performance of AMMCs and the potential areas of research that could lead to the development of advanced AMMCs with superior properties. Overall, this review provides a comprehensive overview of the current status and future prospects of AMMCs and highlights the importance of continued research and development in this field.

Carbon nanotubes (CNTs), are safe, biocompatible, bioactive, and biodegradable materials, and have sparked a lot of attention due to their unique characteristics in a variety of applications, including medical and dye industries, paper manufacturing and water purification. CNTs also have a strong film-forming potential, permitting them to be widely employed in constructing sensors and biosensors. This review concentrates on the application of CNT-based nanocomposites in the production of electrochemical sensors and biosensors. It emphasizes the synthesis and optimization of CNT-based sensors for a range of applications and outlines the benefits of using CNTs for biomolecule immobilization. In addition, the use of molecularly imprinted polymer (MIP)-CNTs in the production of electrochemical sensors is also discussed. The challenges faced by the current CNTs-based sensors, along with some the future perspectives and their future opportunities, are also briefly explained in this paper.

The development in the use of polyaniline (PANI) in advanced studies makes us draw attention to the presented research and combine it into one study like this one. The unique composition of PANI qualifies it for use in electrochemical applications in addition to many other applications whose use depends on its mechanical properties. Based on this, it is necessary to limit the reactions that produce PANI and the cheapest cost, and then limit the current uses in the formation of nanocomposites with metals, their oxides, and/or carbon nanocomposites in order to determine what is missing from them and work on it again to expand its chemistry. The development in the use of PANI in advanced studies makes us draw attention to the research presented on PANI and combine it into one study. One of the very important things that made PANI possess a very huge research revolution are preparation in a variety of ways, easy and inexpensive, from which a daily product can be obtained with very high purity, as well as its distinctive properties that made it the focus of researchers in various scientific departments. The unique structure of PANI, which is easy to prepare in its pure form or with various chemical compounds including metals, metal oxides, and carbon nanomaterials (such as carbon nanotubes, graphene, graphene oxide, and reduced graphene oxide), qualifies it for use in electrochemical applications. The various studies reviewed showed that PANI gave good results in the applications of super capacitors. In some of the studies mentioned later, it gave a specific capacitance of 503 F/g, cycle stability 85% at 10,000 cycles, energy density 8.88 kW/kg, and power density 96 W h/kg. It was also noted that these values improved significantly when using PANI with its nanocomposites. Because of its good electrical conductivity and the possibility of preparing it with a high surface area with nanostructures in the form of nanowires, nanofibers, and nanotubes, PANI was used as a gas sensor. We have noticed, through the studies conducted in this field, that the properties of PANI as a basic material in gas sensors are greatly improved when it is prepared in the form of PANI nanocomposites, as explained in detail later. From this review, we tried with great effort to shed light on this attractive polymer in terms of its different preparation methods, its distinctive properties, its nanocomposites, and the type of polymerization used for each nanocomposites, as well as its applications in its pure form or with its nanocomposites in the supercapacitor and gas sensor applications.

Hyperspectral imaging has emerged as an effective powerful tool in plentiful military, environmental, and civil applications over the last three decades. The modern remote sensing approaches are adequate for covering huge earth surfaces with phenomenal temporal, spectral, and spatial resolutions. These features make HSI more effective in various applications of remote sensing depending upon the physical estimation of identical material identification and manifold composite surfaces having accomplished spectral resolutions. Recently, HSI has attained immense significance in the research on safety and quality assessment of food, medical analysis, and agriculture applications. This review focuses on HSI fundamentals and its applications like safety and quality assessment of food, medical analysis, agriculture, water resources, plant stress identification, weed & crop discrimination, and flood management. Various investigators have promising solutions for automatic systems depending upon HSI. Future research may use this review as a baseline and future advancement analysis.

Purpose The purpose of this study is to investigate the influence of environmental concern, perceived value, personal norms and willingness to pay on generation Z’s purchase intention for recycled clothing. Design/methodology/approach The data were collected from five Indian universities. A total of 497 usable responses were analyzed. Confirmatory factor analysis was used for examining the validity and reliability of the scales. Further, the structural equation modeling was used to assess the relationship among the constructs. Findings Findings suggested that willingness to pay, environmental concern, perceived value and personal norms influence generation Z’s purchase intention for recycled clothing. Willingness to pay, environmental concern and perceived value were major predictors of purchase intention for recycled clothing. Practical implications This study holds much importance to the marketers of recycled clothing as it provides useful insights for formulating the appropriate promotional strategies. The study also contributes to the consumer behavior literature by addressing the existing research gap. Originality/value Most of the studies existing in this area have focused on the manufacturing side only except few which explored the consumption side of recycled clothing. Hence, the current study is an attempt to fill this research gap.