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Bacillus velezensis-synthesized silver nanoparticles and its efficacy in controlling the Aedes aegypti
Abstract: Dengue fever and dengue haemorrhagic fever are diseases that do not have any potential medications. The severity of these diseases is fatal and thus poses a severe threat to mankind. Aedes aegypti is the vector that carries and spreads the dengue virus. Therefore, controlling the development and population of mosquitoes is crucial. Many insecticides and other strategies of control have not become successful in their purpose. Therefore, establishing potential compounds that are environmentally safe and productive in inhibiting the growth of mosquitoes is still to be acquired. Bacillus velezensis (MW219533) was utilized in the synthesis of silver nanoparticles with silver nitrate as the metal ion source. The silver nanoparticles were characterized and confirmed using UVvisible spectrometry that indicated a peak at 421 nm. Further analytical measurements such as X-ray diffraction, Fourier transform infrared spectroscopy, scanning electron microscopy and energy dispersive X-ray analysis confirmed the presence of crystalline, cylindrical-shaped silver nanoparticles of size 5659 nm. The LC50 was found to be 581.39, 616.37, 760.93, 801.94 and 867.66 g l?1 when tested against the five developmental stages of Aedes aegypti, such as first instar, second instar, third instar, fourth instar stages of larvae and pupae, respectively. The predatory efficacy of Poecilia reticulata was calculated with exposure to silver nanoparticles. Our study aims on developing an environmentally safe and economical approach to reduce the development of mosquitoes in the environment. The work signifies the biological method towards controlling the larvae and pupae stages of A. aegypti as well as to mark its safety at the aquatic level of the life cycle that leaves no traces of pollution on the environment. Graphical abstract: [Figure not available: see fulltext.] 2023, Indian Academy of Sciences. -
Improving Groundwater Forecasting Accuracy with a Hybrid ARIMA-XGBoost Approach.
In addressing the critical challenge of accurate groundwater level prediction, this study explores the comparative performance of various machine learning models. We implement a novel hybrid model combining ARIMA and Extreme Gradient Boosting (XGB) for the prediction of groundwater levels, and compare it against traditional models including ARIMA, XGBoost, LightGBM, Random Forest, and Decision Trees. Traditional approaches often rely on single models; however, our research seeks to delve into the intricacies of hybrid model architectures. Combining the strengths of ARIMA and XGB, we aim to build a highly accurate and efficient groundwater level prediction system. Comprehensive evaluations were conducted using metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE), The future scope of machine learning in water resource management includes integrating such models with real-time monitoring systems and expanding their applications to diverse environmental conditions and regions. 2024 IEEE. -
A Hybrid Evolutionary Deep Learning Model Integrating Multi-Modal Data for Optimizing Ovarian Cancer Diagnosis
This research intends to enhance ovarian cancer detection by the combination of state-of-the-art machine learning algorithms with extensive multi-modal datasets. The Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), and VGG16 models were thoroughly assessed, displaying remarkable precision, recall, F1 scores, and overall accuracy. Notably, VGG16 emerged as a strong performance with a precision of 0.97, recall of 0.96, F1 score of 0.97, and accuracy reaching 98.65%. The addition of confusion matrices enables a thorough insight on each model's classification performance. Leveraging multiple datasets, spanning CT and MRI scans with demographic and biographical facts, promotes the holistic knowledge of ovarian cancer features. While the suggested Hybrid Evolutionary Deep Learning Model was not deployed in this work, the results underscore the potential for its development in future research. These discoveries signify a huge leap forward in early detection capabilities and individualized treatment techniques for ovarian cancer patients. As technology and medicine combine, this study tracks a road for breakthrough diagnostic approaches, empowering clinicians and encouraging favourable results in the continuing struggle against ovarian cancer. 2024 IEEE. -
Analysis of Market Behavior Using Popular Digital Design Technical Indicators and Neural Network
Forecasting the future price movements and the market trend with combinations of technical indicators and machine learning techniques has been a broad area of study and it is important to identify those models which produce results with accuracy. Technical analysis of stock movements considers the price and volume of stocks for prediction. Technical indicators such as Relative Strength Index (RSI), Stochastic Oscillator, Bollinger bands, and Moving Averages are used to find out the buy and sell signals along with the chart patterns which determine the price movements and trend of the market. In this article, the various technical indicator signals are considered as inputs and they are trained and tested through machine learning techniques to develop a model that predicts the movements accurately. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Perovskites: Emergence of highly efficient third-generation solar cells
For decades, human beings have been trying to plug into the sun to satisfy our energy requirements. Solar energy harvesting technology is, at present, in its third generation. Among the emerging photovoltaics, perovskite solar cells, which are fast advancing, have great future scope as solar energy harvesters. Rapid technological growth within the decade makes it the most potent among third-generation photovoltaics. Since its introduction in 2009, photoconversion efficiencies (PCE) of perovskite solar cells has hiked from 3.9% to 25.8% by 2021. Despite the swift increase in PCE, perovskite photovoltaics have to cross many hurdles to reach the stage of commercialization. Issues like low stability and lead toxicity are matters of great concern. The choice of material in each layer and the interfacial engineering to create matching between surfaces play a significant role in enhancing device performance. This review focuses on the materials and functions of four different layers of perovskite solar cells: light-absorbing, electron transport, hole transport, and counter electrodes. A brief discussion of perovskite-silicon tandem and 2D/3D multidimensional solar cells is also included in the review. The emergence of environment-friendly, economically feasible, and efficient solar cell materials turns out to be milestones in the path toward the commercialization of perovskite solar energy harvesters. Highlights: This review discusses the emergence of perovskite solar cells, which are of great importance in the rapidly growing photovoltaic technology. An overview of materials, structure, and working of different perovskite solar cell layers- active layer, hole transport layer, electron transport layer, and counter electrode, is given in the review. The evolution of different solar cell materials is discussed, and their performance is compared qualitatively and quantitatively. 2022 John Wiley & Sons Ltd. -
Extrinsic pseudocapacitance: Tapering the borderline between pseudocapacitive and battery type electrode materials for energy storage applications
Extrinsic pseudocapacitance, which can also be referred to as induced pseudocapacitance, is, at present, one of the most widely explored fields in energy storage. Extrinsic pseudocapacitive mechanism can be imparted to an otherwise diffusion-controlled faradaic energy storage material by external methods like size engineering, compositional modification, doping, anion intercalation, and morphological modifications. As a significant mechanism that plays a borderline role between battery-type and pseudocapacitive nature of energy storage, extrinsic pseudocapacitance tends to narrow down the boundary between these conventionally diverse systems, which in turn would contribute a lot to the development of hybrid energy storage technologies. For effective utilization in upcoming energy storage technologies, a critical analysis on the effect of this mechanism on reported devices shall turn into a valid account. This review gives a detailed insight into extrinsic pseudocapacitance, its significance, and recently reported materials, methods, and devices. The future outlook and challenges in transforming extrinsic pseudocapacitive mechanisms into a promising strategy for next-generation energy storage devices are also discussed. 2023 Elsevier Ltd -
Nitrogen-Oxygen Co-Functionalized Waste Cassava Peel-Derived Carbon Dots for White Led
White light emitting diodes (WLEDs)are most sought after, with the broad spectra ranging from cool to warm white light being skillfully utilized to create various modes of lighting effects. The fabrication of WLEDs is generally sophisticated, involving either multiple components emitting in different regions or single-component phosphors with complex elemental compositions. In the present work, WLEDs utilizing solvent-tuned carbon dots derived from waste cassava peel are reported through a facile one-step microwave-assisted solvothermal method. The carbon dots show evident UV absorption and correspondingly emit broad visible light spectra when dispersed in a dimethylformamide (DMF)-polyvinyl alcohol (PVA)blend, making themselves suitable white lightemitting down conversion materials. The successful transformation of a 400nm UV LED into a WLED with a general colour rendering index (CRI) of 83 and colour correlation temperature (CCT) of 4426 K gives a promising future outlook toward developing eco and economic-friendly WLEDs. 2025 Wiley-VCH GmbH. -
Analysis of attention deficit hyperactivity disorder using various classifiers
Attention Deficit Hyperactivity Disorder (ADHD) is a neurobehavioral childhood impairment that wipes away the beauty of the individual from a very young age. Data mining classification techniques which are becoming a very important field in every sector play a vital role in the analysis and identification of these disorders. The objective of this paper is to analyze and evaluate ADHD by applying different classifiers like Nae Bayes, Bayes Net, Sequential Minimal Optimization, J48 decision tree, Random Forest, and Logistic Model Tree. The dataset employed in this paper is the first publicly obtainable dataset ADHD-200 and the instances of the dataset are classified into low, moderate, and high ADHD. The analysis of the performance metrics and therefore the results show that the Random Forest classifier offers the highest accuracy on ADHD dataset compared to alternative classifiers. With the current need to provide proper evaluation and management of this hyperactive disorder, this research would create awareness about the influence of ADHD and can help ensure the proper and timely treatment of the affected ones. Springer Nature Singapore Pte Ltd 2021. -
Synergy Unleashed: Smart Governance, Sustainable Tourism, and the Bioeconomy
This study investigates the transformational potential of smart Governance in the tourism sector to enhance the operational effectiveness, transparency, and efficacy of governmental actions. This research synthesises the body of knowledge regarding the use of technology and data-driven methods in Governance using a literature review methodology. A conceptual framework is suggested to highlight the complex effects of smart Governance on many stakeholders in the travel industry. The study uses a multidimensional paradigm that includes agile leadership, stakeholder alliances, network management, and adaptive Governance. It explains how these complementary components construct a revolutionary ecology that encourages creativity, adaptability, and inclusive growth. Organisations can acquire insights into visitor behaviours, preferences, and traffic patterns by utilising data analytics and digital platforms, which can improve resource allocation, infrastructure construction, and policy formation. Applications that use real-time data enable dynamic crowd control, traffic optimisation, and safety improvements. The report also highlights how local communities may be involved in smart Governance to promote inclusive decision-making. This framework helps promote deeper study into the actual application and outcomes of smart Governance, which has the potential to change the travel sector. This multidisciplinary approach fosters resilience, innovation, and responsible, inclusive development. This study promotes real-world applications that fully utilise this synergy to further the interconnected objectives of sustainable tourism, bioeconomic growth, and efficient Governance. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Quarantined effects and strategies of college students COVID-19
Purpose: The world is battling with one of the biggest health crisis caused by novel COVID-19. This paper aims to understand the effect of quarantine on the psychological health of college students and the coping strategies adopted by them. Design/methodology/approach: The study adopted the interview method and focused on two crucial open-ended questions: how quarantine has impacted and what are the strategies adopted to overcome the same. The response was recorded through email and phone from a sample of 30 students. Findings: Most of the students stated that they are going through issues like anxiety, depression, infection fear, ambiguity due to this pandemic and the lockdown related to it. However, they engage themselves with various activities that help them to combat this situation. Practical implications: Education institutions can focus on conducting online fest and other events to engage students more productively. They can also focus on developing a wellness application to support these students. They can provide solutions and tips to balance mental health and wellness during these times. Originality/value: Everyone knows about COVID-19 and the measures taken related to it, but not much about the impact of it on mental health. This paper discusses the negative impact of quarantine on students and coping strategies adopted by them. The strategies mentioned in the study can guide quarantined people, student community, parents, counsellors and academic facilitators to handle the situation in a better way. 2020, Emerald Publishing Limited. -
Health informatics and its contribution to health sectors
In most developed countries, healthcare sectors take more than 10% of the GDP, and it is one of the most significant and most rapidly growing sectors globally. With such growth of the healthcare department, data management becomes challenging; a robust platform helps to address these challenges. Health Informatics (HI) is an upcoming development, an interdisciplinary field in healthcare sectors; it combines the Internet of Things (IoT) and Artificial Intelligence (AI) in the healthcare software, which helps boost the overall operational efficiency of the healthcare departments. These AI algorithms integrated into IoT devices help acquire, store, retrieve, and use health and medical-related data. Patient data are enormous in healthcare sectors, and it is required for various purposes by hospital administrators, insurance agents, doctors, nurses, and other health departments. Accessing and managing these datasets often becomes challenging; HI is one of those innovations that has helped address these challenges to a large extent. The chapter discusses informatics, related definitions, HI, and its relation with other disciplines. The chapter also provides an educational overview of the evolution of HI, different HI technologies, benefits and challenges of HI to its various stakeholders. It ends with some thoughts on HI's future growth. The Institution of Engineering and Technology 2023. All rights reserved. -
Social Characteristics and Its Relationship with Intent to Stay-with Reference to Financial Sectors
One of the challenging tasks of the HR management of the organization is to design the job in such a way that facilitates a good work culture/atmosphere for the employees to ensure their stay in the organization. The present study analyzed the role of social characteristics of the job with their intention to leave among the employees working in the finance sector. Primary data were collected from 250 employees working at all levels of management in the finance and banking sector in Indias southwest region through the Convenience sampling method. Morgeson and Humphrey (2006) developed the work design questionnaire which was adopted and used for data collection. Hierarchical multiple regression used for applied for data analysis. The results show that social characteristics cannot predict intent to stay. Also, age and gender do not have a significant role as mediating factors to social characteristics and intent to stay. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Psychological capital as an antecedent of employee engagement and its relationship with intention to stay
Employee engagement is an evolving concept in human resources (HR). Most organizations strive to attain employee engagement because of the various organization-related outcomes. It is important for employees to feel engaged emotionally, socially, and intellectually with the work and organization. Various antecedents affect employee engagement and, in turn, result in an organization-related positive outcome. This chapter discusses in-depth PsyCap as an antecedent of employee engagement and how it relates to intent to stay regarding employees working in travel organizations in India and aims to build relevant theoretical frameworks based on the findings. The chapter also discusses some strategies organizations can implement to achieve employee engagement based on the findings. 2022, IGI Global. -
Cognitive technology for the Indian higher education: A Language teaching and Learning application
Past decade witnessed a technological boom in the world. Regardless of the age every person in the world owns a mobile device which can be connected to internet. The technologies and applications for these mobile devices are one of the inevitable part people's day to day lives. The past decade also evidenced the development of Artificial Intelligence, Machine Learning (ML), Natural Language Processing (NLP), Image Processing (IP), Speech Recognition (SR) and Big DataAnalytics (BDA), etc. which lead to the development of Intelligent applications for the fields like business, health care, weather, media, etc. The field which uses the technology in a slow pace is education system. This paper is majorly focused on the Indian higher education system and the technologies used in their teaching and learning. One of the major drawbacks of Indian higher education system is the traditional teacher centric teaching and learning process. The usage of technology in their education system limited to chock and board to power point presentation. Some of the elite Universities in India uses Massive Online Open Courses (MOOC) but majority of the education institution still follows the old method of teaching and learning. This paper profiles cognitive technology based applications which can be used for the betterment of current system. The proposed model in this paper is for the language course learning. The application is centered on ML and NLP. Copyright 2019 American Scientific Publishers All rights reserved. -
CMSFE: Cross-Model SSL Feature Extraction for Enhanced Remote Sensing Data Representation
Automatic Labeling of Remote Sensing Data fastens analysis in various applications such as environmental monitoring, urban planning, and disaster management. Supervised machine learning approaches rely on labeled datasets created through time-consuming processes. Creation of labeled datasets requires higher resources and such datasets are harder to obtain in most of the domains, and especially in Remote Sensing. This study proposes Cross-Model Self-Supervised Feature Extraction (CMSFE), a novel approach that enhances representation learning in unlabeled remote sensing datasets by integrating features from multiple pre-trained models and refining them through self-supervised learning (SSL). The extracted features are integrated to form a comprehensive and robust feature set that aids in separating different cluster of imagery. Experimental results with EuroSAT dataset demonstrate the quality of feature extraction in separating various classes without any manual intervention or labeling. Dimensionality Reduction and Manifold Learning is applied for visual interpretation of extracted feature space. These features can be further reused for analysis or modeling, highlighting the potential of SSL-based feature extraction methods in remote sensing to enhance representation learning and reduce dependency on labeled data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Marital Stress and Domestic Violence during the COVID- 19 Pandemic
Marital stress and domestic violence is prevalent in every society around the world. It has become a major concern during the Covid-19 pandemic. Governments have resorted to lockdown measures in order to contain the pandemic. The pandemic has made the weaker and more vulnerable people in a household more exposed to abusive partners. Social isolation and home confinement have detrimental effects on ones mental and physical well-being. Women have been shown to be at a very high risk from violence during The Covid19 pandemic. The research paper aims to understand the factors which compel women to stay in abusive and stressful marriages and the ways in which they can be empowered to lead their life with dignity and self-respect. The cultural contexts of most societies force women to stay in abusive marriages as the woman is often portrayed as the symbol of unity in families. Understanding the cultural bindings of women trapped in abusive households during the COVID-19 pandemic is a very crucial aspect as this can help in understanding the fear and apprehensions of women trapped in destructive marriages. This can be a key factor which can make it easier for support groups while providing counselling and other kinds of support to women trapped in abusive marriages. The paper also discusses the impact of abusive relationships on children and how it negatively shapes their personality and their emotional well- being. 2021 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. -
A Deep Learning Method for Autism Spectrum Disorder
The present study uses deep learning methods to detect autism spectrum disorder (ASD) in patients from global multi-site database Autism Brain Imaging Data Exchange (ABIDE) based on brain activity patterns. ASD is a neurological condition marked by repetitive behaviours and social difficulties. A deep learning-based approach using transfer learning for automatic detection of ASD is proposed in this study, which uses characteristics retrieved from the intracranial brain volume and corpus callosum from the ABIDE data set. T1-weighted MRI scans provide information on the intracranial brain volume and corpus callosum. ASD is detected using VGG-16 based on transfer learning. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Design optimisation and fabrication of amino acid based molecularly imprinted sensor for the selective determination of food additive tartrazine
In this work, we developed a new molecularly imprinted polymer detector for tartrazine's rapid and selective detection. Electropolymerisation using L-Methionine resulted in the polymer immobilised on the carbon fibre paper electrode's surface. MIP film was formed by electropolymerisation in the presence of the template tartrazine. The polymer frame comprises cavities after template removal, which can specifically bind to the analyte molecule. Without pre-treatment, the developed sensor MIPMet/CFP detects tartrazine in beverage samples precisely and rapidly. The sensor has a linear response in the concentration range of 0.6 nM- 160 nM, high sensitivity (601964 AM-1cm?2), and a low detection limit of 27 pM under optimum conditions. MIPMet/CFP sensor displayed the ability to distinguish target analyte from interferants selectively. The performance of the MIPMet/CFP sensor in assessing tartrazine in different saffron powder and packed juice samples suggests that it could be used to detect tartrazine fast and effectively. 2022 Elsevier Ltd -
VNPR system using artificial neural network
Vehicle number plate recognition (VNPR) is a technique used to extract the license plate from a sequence of images. The extracted information in the database can be used in the applications like electronic payment systems such as toll payment, parking lots etc. An effective VNPR can be implemented based on the quality of the acquired images. It is used for real time application and it has to recognize the number plates of all types under different environmental conditions. Different algorithms has been used which depends on the features present in the images. It should be generalised to extract different types of license plate from the images. In this paper we propose a new method which is robust enough to recognize the characters from the number plates with help of artificial neural network. This algorithm is practical for the front view and rear view of orientation of the vehicle. 2016 IEEE. -
Single-monomer dual templated MIP based electrochemical sensor for tartrazine and brilliant blue FCF
In this study, a dual-templated molecularly imprinted polymer-based electrochemical sensor was developed for the simultaneous analysis of two food additive dyes, brilliant blue FCF and tartrazine. Using a 3-aminophenyl boronic acid (3-APBA) monomer and the dual templates of brilliant blue FCF (BB) and tartrazine (TZ), the molecularly imprinted polymer (MIP) layer was electropolymerized on the carbon fibre paper (CFP) electrode. By using BB and TZ as template molecules along the electro-polymerization of 3-APBA, then removing both template molecules, the MIP film was generated on the surface of the CFP electrode. Due to the high surface area provided by modification, several complementary binding sites for template molecules are formed on the surface of the MIP sensor during this process of sensor fabrication. On the MIP/CFP electrode, the electrochemical behavior of BB and TZ was assessed. The monomer/template ratio, pH values, and influencing parameters like the electro-polymerization scanning cycles were all optimized. This sensor was applied to detect brilliant blue FCF and tartrazine in beverage and food samples using MIPAPBA/CFP electrode. 2023