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Reward Based Garbage Monitoring and Collection System Using Sensors
Most of the time in our surroundings we come across the overfilled garbage bins near the lakes. When the bins are full, people just throw the waste here and there, which eventually goes into the lakes and pollutes the water bodies. This is because of improper dumping of garbage that is practiced in our society. With the increase in population, this problem is taking really bad shape. The prime need is to maintain a clean and healthy environment with proper disposal of waste. This paper presents a small effort to reduce this garbage problem. An Android app has been created which keeps on checking whether the dustbin is full. Also, the people will be rewarded for throwing waste into the dustbins. A QR code has been attached to the dustbin which will be scanned for rewarding the people. The dustbins use an IR sensor that detects the receiver of waste in bins. Major part of this proposed system includes the proper working of mobile application and proximity sensors. Arduino is used to maintain the proper connection with sensors and application and that is done by Bluetooth sensor. The main objective of this proposed system is to lure people to put waste into the dustbin along with the contribution towards smart city vision. This paper also gives a brief overview of the technologies and work done so far in this field. 2024 River Publishers. -
Heat and mass transfer analysis of Casson-based hybrid nanofluid flow in the presence of an aligned magnetic field: An application toward mechanical engineering
This examination explores the flow of a hybridized nanofluid (HyNf) containing silica and tin oxide nanoparticles mixed with engine oil (EO/SnO2-SiO2). The flowing occurs via a permeable material constrained by a semi-infinite flat plate. The study takes into account various factors such as convective heat and mass transference, chemical reactions, the Dufour effect, the Lorentz force, thermal radiative fluxing, and radiative absorbing. The research involves converting the managing formulas of the flowing model into a dimensionless form and applying the regular perturbation procedure to find solutions for the rate of fluid flow, temperature, and species diffusion. The surface frictional factor, Nusselt quantity, and Sherwood quantity reflect the shearing stress, rates of heat transference, and rates of mass transport at the plate, respectively. An analysis is conducted on the impact of several factors, including the suction variable, magnetic variable, radiation-absorbing factor, Casson parameter, and Dufour number, on the flow and related quantities. This analysis is based on an examination of graphs and tables. The findings suggest that the heat transference rate in the Casson hybridized nanofluid is better than that in the mono nanofluid. It is exposed that the temperature reduces at the plate having improved frequency of oscillation and also fluid velocity declines for improving values of aligned magnetized field (Formula presented.), but it shows the reverse phenomenon with Gr1 and Gc1. IMechE 2024. -
Fractional Approach for Belousov-Zhabotinsky Reactions Model with Unified Technique
The Belousov-Zhabotinsky reaction model represents chemical oscillators that exhibit periodic vibrations as a result of complex physic-chemical phenomena. The non-linear behaviour exhibited by Belousov-Zhabotinsky model is the cause of Turing patterns, birth of spiral waves, rise of limit cycle attractors, and deterministic chaos in many chemical reaction processes. Due to these noteworthy characteristics, in this paper, we have analyzed mathematical Belousov-Zhabotinsky model by a novel numerical approach q-Homotopy analysis transformation method. To interpret new observations, we have incorporated Caputo fractional derivative in the model. The numerical result are presented graphically and concerning the absolute error of solutions. With the help of the homotopy parameter curve, we have projected the convergence region with reference to diverse values of fractional derivative. This work establishes that the projected numerical algorithm is a well-organized tool to analyze the multifaceted coupled partial differential equation representing Belousov-Zhabotinsky type reactions. 2024 NSP Natural Sciences Publishing Cor. -
Feminism in Practice: Learning from the Barefoot Solar Mamas
The Barefoot College (India) is an NGO working in the fields of education, skills development, health, drinking water, and solar power mainly to train older, rural women who are determined to challenge restrictive gender roles in their respective communities. Since its inception, the NGO has trained over 2,000 rural women as solar engineers across 93 countries worldwide and has brought electricity to over 18,000 homes. Barefoot trainers employ non-normative methods of sharing knowledge such as color coding, sign language, and practical experience. This paper conducts a critical assessment of the Barefoot College Solar Electrification Programme to explore how it empowers illiterate and semi-literate women from remote rural areas around the world to become solar engineers (or Solar Mamas). It utilizes qualitative research methods to analyze this women's empowerment project as a landmark practical application of decolonial feminist theory. The paper contends that the Barefoot approach both challenges and conforms to the Women in Development and Gender and Development approaches of the past. The research is grounded methodologically in feminist praxis and also borrows from the conceptual frameworks of Feminist Political Ecology and Women and the Politics of Place. Stories and personal experiences from Solar Mamas have been highlighted to understand the real world impact of the program. The main findings indicate that the Barefoot College's innovative approach to empower marginalized communities and educate older women is achieved through decentralizing control and demystifying technology. (2024), (Bridgewater State College). All Rights Reserved. -
Optimal ordering and discounting policy for a segmented market with price and freshness dependent demand for mixed quality product
Owing to various factors, fresh produce purchased by the retailer is initially of mixed quality. A random proportion of the lot would generally have lost some freshness before being received in stock, while the remaining items would still be fresh. This calls for some discount initially for the former, and later, when the latter product is not so fresh. For demand declining with increase in selling price and decrease in freshness, this paper deals with optimal ordering and discounting policy when the lot received is of mixed quality and the market has two segments differentiated by the initial product quality sold simultaneously at widely different prices. Sufficient conditions for existence and uniqueness of optimal cycle length and the optimal discount are obtained. Sensitivity analysis reveals that increase in freshness time and proportion of initially fresh items in the lot result in increased profit rate. Copyright 2024 Inderscience Enterprises Ltd. -
Automated and Interpretable Fake News Detection With Explainable Artificial Intelligence
Fake news is a piece of misleading or forged information that affects society, business, governments, etc., hence is an imperative issue. The solution presented here to detect fake news involves purely using rigorous machine learning approaches in implementing a hybrid of simple yet accurate fake text detection models and fake image detection models to detect fake news. The solution considers the text and images of any news article, extracted using web scraping, where the text segment of a news article is analyzed using an ensemble model of the Nae Bayes, Random Forest, and Decision Tree classifier, which showed improved results than the individual models. The image segment of a news article is analyzed using only a Convolution Neural Network, which showed optimal accuracy similar to the text model. To better train the text models, data preprocessing and aggregation methods were used to combine various fake-real news datasets to have ample amounts of data. Similarly, the CASIA dataset was used to train the image model, over which Error Level Analysis was performed to detect fake images. model results are represented as confusion matrices and are measured using various performance metrics. Also, to explain predictions from the hybrid model, Explainable Artificial Intelligence is used. 2024 Taylor & Francis Group, LLC. -
A comprehensive molecular docking-based study to identify potential drug-candidates against the novel and emerging severe fever with thrombocytopenia syndrome virus (SFTSV) by targeting the nucleoprotein
Severe fever with thrombocytopenia syndrome (SFTS) is a newly emerging haemorrhagic fever that is caused by an RNA virus called Severe fever with Thrombocytopenia Syndrome virus (SFTSV). The disease has spread globally with a case fatality rate of 30%. The nucleoprotein (N) of the virus has a pivotal role in replication and transcription of RNA inside the host. Considering that no specific treatment regime is suggested for the disease, N protein may be regarded as the potential candidate drug target. In the present study, in silico molecular docking was performed with 130 compounds (60 natural compounds and 70 repurposed synthetic drugs) against the N protein. Based on the binding affinity (kcal mol?1), we selected Cryptoleurine (?10.323kcalmol?1) and Ivermectin (?10.327kcalmol?1) as the top-ranked ligands from the natural compounds and repurposed synthetic drugs groups respectively, and pharmacophore analysis of these compounds along with other high performing ligands revealed that two aromatic and one acceptor groups could strongly interact with the target protein. Finally, molecular dynamic simulations of Cryptoleurine and Ivermectin showed stable interactions with the N protein of SFTSV. To conclude, Cryptoleurine and Ivermectin can be considered as a potential therapeutic agent against the infectious SFTS virus. Graphical abstract: (Figure presented.) The Author(s) under exclusive licence to Archana Sharma Foundation of Calcutta 2024. -
Propensity Score Matching and a Difference in Difference Approach to Assess ESGs Influence on Indian Acquirer Performance
This research involves an in-depth analysis of the intricate relationship between Environmental, Social, and Governance scores and the financial and operational performance of Indian acquirers. The research methodology employed herein entails a meticulously crafted design, incorporating a blend of the Propensity Score Matching and Difference-in-Differences model. This strategic amalgamation serves to rigorously assess the impact of ESG factors on the performance outcomes of Indian acquirers involved in M&As. The empirical findings of this study reveal a robust and statistically significant correlation between M&A endeavours and ESG considerations. Notably, the research discerns that M&A activities tend to exert an adverse influence on ESG performance metrics within the Indian corporate landscape. This nuanced insight underscores the multifaceted interplay between strategic corporate actions and the broader sustainability and governance landscape, thereby offering valuable implications for scholars and practitioners in finance and corporate strategy. 2024, University of Wollongong. All rights reserved. -
Photophysical and antitubercular studies on newly synthesised structurally architectured sulphonamide
This study presents the synthesis and characterisation of four mono-azo sulphonamide derivatives through diazo-coupling electrophilic substitution reactions. The structural analysis of the synthesised molecules was conducted utilising FT-IR, 1H-NMR and HR-MS techniques. Absorption and fluorescence maxima of the synthesised molecules were determined across solvents of varying polarity to explore Solvatochromic behaviour. Density functional theory was employed to elucidate electronic and optical properties, including the computation of HOMOLUMO energies using Gaussian 09W software, with comparisons to experimental data. Molecular electrostatic potential 3D plots identified electrophilic and nucleophilic sites. Solvent interactions were evaluated using KamletAbboud Taft and Catalan parameters. Further, global chemical reactivity descriptors were estimated to ascertain chemical reactivity of the molecules. Additionally, the effectiveness of the colourant anti-tubercular activity was evaluated using in vitro and molecular docking techniques. The biological activity results reveal that methyl-pyridone and barbituric acid coupled with sulphamethizole (SMP and SMB) displayed excellent anti-tubercular activity compared with the standard Gentamycin. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Prioritizing evaluation criteria of IoT-driven warehousing startups: asilver lining to the unorganized sector in food supply chain
Purpose: This research is designed to meet two research objectives: firstly, to weigh up the criteria of Internet of Things (IoT) adoption in warehousing startups; secondly, to rank warehousing startups on the basis of benefits they derive from IoT adoption catering to an unorganized sector in the food supply chain. Design/methodology/approach: A blend of analytic hierarchy process (AHP) and complex proportional assessment (COPRAS) methods of multi-criteria decision-making techniques were applied. AHP determined the weights of various criteria using pairwise comparison, and COPRAS technique ranked the 10 warehousing startups on account of performance indicators. The study has been conducted at the warehousing startups of Bangalore, a hub of food warehousing startups. Findings: The critical findings of the study revealed that these food warehouse startups attain improved productivity in terms of enhancing efficiency when implemented with IoT adoption. When evaluated using both AHP and COPRAS techniques, the combined results show WH5 as the best performing and WH10 as the least performing warehouse startups. Practical implications: Warehouses that are embarking on their business opportunity in food storage can strategize to leverage the benefits of IoT in terms of food safety and security, capacity planning, layout design, space utilization and resilience. Originality/value: Despite the numerous research works on food supply chain, the research on IoT in warehousing startups is limited. The rankings for the 10 food warehousing startups integrated with IoT using AHP-COPRAS approaches are the novelty of this work. 2024, Emerald Publishing Limited. -
Endurance and Evolution: Exploring Levels of Resilience Among Indian Breast Cancer Survivors
Resilience for Indian women with breast cancer involves maintaining positivity and adaptability amid the complex challenges affecting their physical, emotional, and social well-being. However, research focused on resilience amongst this population in Indian settings is limited. Therefore, the aim of the study is to explore the experience of levels, patterns, and processes of resilience in Indian women living with breast cancer. A qualitative phenomenological approach was used to study resilience. Thirty-three participants from two hospitals underwent semistructured interviews, including survivors, women in cancer therapy, and family members. Data collected via audio recordings were analyzed using reflective thematic analysis techniques. The finding describes four themes of experience of resilience in women living with breast cancer. (a) Cancer diagnosis is a life-changing experience. Breast cancer diagnosis and therapy cause existential crisis, psychological distress, and social stigma. (b) Restoring healthy perception in an adverse event. Navigating challenges and achieving a balance between internal and external factors. (c) Types of supportthe pathway to resilience. Enhanced their resilience through internal support including attributes, past experiences, sociodemographic factors, and brain fitness. External support includes family, friends, religious or spiritual advisors, medical care, role models, other cancer survivors, and comfortable environments. (d) Learning and growing from the experience. Gained a better perspective on life, ultimately resulting in a new normal and finding meaning in the experience. Data show breast cancer survivors experience dynamic resilience, highlighting the need for culturally tailored interventions and supportive avenues within cancer care by healthcare providers and policymakers. The Author(s) 2024. -
DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI
A brain tumour is an abnormal mass of tissue. Brain tumours vary in size, from tiny to large. Moreover, they display variations in location, shape, and size, which add complexity to their detection. The accurate delineation of tumour regions poses a challenge due to their irregular boundaries. In this research, these issues are overcome by introducing the DTDO-ZFNet for detection of brain tumour. The input Magnetic Resonance Imaging (MRI) image is fed to the pre-processing stage. Tumour areas are segmented by utilizing SegNet in which the factors of SegNet are biased using DTDO. The image augmentation is carried out using eminent techniques, such as geometric transformation and colour space transformation. Here, features such as GIST descriptor, PCA-NGIST, statistical feature and Haralick features, SLBT feature, and CNN features are extricated. Finally, the categorization of the tumour is accomplished based on ZFNet, which is trained by utilizing DTDO. The devised DTDO is a consolidation of DTBO and CDDO. The comparison of proposed DTDO-ZFNet with the existing methods, which results in highest accuracy of 0.944, a positive predictive value (PPV) of 0.936, a true positive rate (TPR) of 0.939, a negative predictive value (NPV) of 0.937, and a minimal false-negative rate (FNR) of 0.061%. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
INVESTING IN WOMEN, INVESTING IN THE PLANET: QUANTIFYING THE IMPACT OF WOMEN'S EMPOWERMENT ON ENVIRONMENTAL SUSTAINABILITY; [INVESTIR NAS MULHERES, INVESTIR NO PLANETA: QUANTIFICAR O IMPACTO DO EMPODERAMENTO DAS MULHERES NA SUSTENTABILIDADE AMBIENTAL]; [INVERTIR EN LAS MUJERES, INVERTIR EN EL PLANETA: CUANTIFICAR EL IMPACTO DEL EMPODERAMIENTO DE LAS MUJERES EN LA SOSTENIBILIDAD AMBIENTAL]
Objective: This study finds out the correlation between the indicators of womens empowerment, including variables like gender parity index in tertiary education, female labour force participation and seats held by women in national parliament, and a variable of environmental sustainability such as CO2 emissions (metric tons per capita). The aim is to analyse existing datasets to know the impact of independent variables on dependent variable. Method: The study uses multiple linear regression to evaluate the effects of independent variables indicators of women's empowerment on the dependent variable, CO2 emissions, using secondary data from the World Bank covering the years 1990 to 2022. The Breusch-Pagan and Breusch-Godfrey LM tests are used to look at heteroskedasticity and autocorrelation, respectively, and VIF is used to find multicollinearity. Results and Conclusion: The study concludes that there is a statistically significant relationship between lower CO2 emissions and increases in the percentage of female seats in the national parliament (-3.73) and higher female labour force participation (-6.04). The gender parity index (GPI) in tertiary education, which is -0.2997, does not, however, appear to have a statistically significant impact on CO2 emissions. Implications: This research can serve as a cause for redesigning gender-responsive environmental initiatives and promoting a more sustainable and equitable future. Originality/Value: This study contributes empirical knowledge to the body of literature by showing the potential contribution of women's empowerment in addressing environmental issues and emphasising the significance of taking gender into account in environmental policy and decision-making processes. 2024 ANPAD - Associacao Nacional de Pos-Graduacao e Pesquisa em Administracao. All rights reserved. -
Development and validation of a multi-dimensional scale to measure the factors influencing fintech firms capacity to impact digital financial inclusion
The purpose of the present study is to develop a multi-item scale to measure the factors that affect fintech firms capacity to impact digital financial inclusion. Fintech, or financial service delivery supported by advanced technology, has tremendously changed the financial services landscape. It has a potential to improve digital financial inclusion and help the poor. Digital financial inclusion is important since it ensures cost-saving digital mechanisms to provide financial services to the financially excluded and underserved populations. Following an inductive method, a qualitative study was undertaken among managerial staff in fintech firms. The scale development process involved the collection of primary data for pre-testing the questionnaire. The study identified four factors that affect a fintech firms capability of impacting digital financial inclusion: resources and capabilities, business models, networks and partnerships, and market and environment. Digital financial inclusion scale is composed of digital skills, access, and quality of access. The final scale consisted of sixty-four items. Though financial inclusion is usually measured from a demand-side perspective, this study provides a supply-side measure for digital financial inclusion. Thus, it can help in identifying and understanding the factors that may hamper fintech firms capability to attain desirable outcomes with respect to digital financial inclusion. 2024 Conscientia Beam. All Rights Reserved. -
An efficient ZnO and Ag/ZnO honeycomb nanosheets for catalytic green one-pot synthesis of coumarins through Knoevenagel condensation and antibacterial activity
This study pioneers the synthesis of porous Ag/ZnO nanosheets, focusing on their role as a catalyst in Knoevenagel condensation. Notably, these nanosheets display exceptional catalytic efficacy and captivating antibacterial properties. The research delves into the Ag/ZnO catalyst's recyclability and proposes a potential reaction mechanism, marking the first comprehensive exploration of Knoevenagel condensation on porous Ag/ZnO nanosheets. Key findings underscore the successful synthesis of coumarin derivatives using various o-hydroxy benzaldehyde and 1,3-dicarbonyl compounds, with nano-Ag/ZnO serving as a catalyst via a monomode microwave-assisted approach. X-ray diffraction (XRD), Field Emission Scanning Electron Microscopy (FE-SEM), Transmission Electron Microscopy (TEM) and UV-Vis spectroscopy were used in conjunction with other physicochemical methods to characterize the synthesized catalytic samples. The method boasts advantages such as high product yields, brief reaction durations, and the ability to reuse the catalyst for multiple cycles. The Ag/ZnO nanosheets, functioning as an acid catalyst, activate carbonyl groups and facilitate their interaction with methylene-containing active molecules. In addition, antibacterial activity assessments demonstrate the superior effectiveness of Ag/ZnO nanocomposites compared to ZnO nanosheets against Staphylococcus aureus germs. This multifaceted study not only advances catalytic synthesis but also unveils promising biological applications of porous Ag/ZnO nanosheets. 2024 Walter de Gruyter GmbH, Berlin/Boston 2024. -
Deep Dive Into Diabetic Retinopathy Identification: A Deep Learning Approach with Blood Vessel Segmentation and Lesion Detection
In the landscape of diabetes-related ocular complications, diabetic retinopathy stands as a formidable challenge, reigning as the leading cause of vision impairment worldwide. Despite extensive research, the quest for effective treatments remains an ongoing pursuit. This study explores the burgeoning domain of AI-driven approaches in ocular research, particularly focusing on diabetic retinopathy detection. It delves into various diagnostic methodologies, encompassing the detection of microaneurysms, identification of hemorrhages, and segmentation of blood vessels, primarily utilizing retinal fundus photographs. Our findings juxtapose conventional machine learning techniques against deep neural networks, showcasing the remarkable efficacy of Convolutional neural network (CNN) and Random Forest (RF) in segmenting blood vessels and the robustness of deep learning in lesion identification. As we navigate the quest for clearer vision, artificial intelligence takes center stage, promising a transformative leap forward in the realm of vision care. 2024 River Publishers. -
Elevating pyrrole derivative synthesis: a three-component revolution
Pyrrole is an essential chemical with considerable relevance as a pharmaceutical framework for many biologically necessary medications. The growing demand for biologically active compounds calls for a simple one-pot method for generating novel pyrrole derivatives. Nots surprisingly, several multicomponent reactions (MCRs) aim to synthesize pyrrole derivatives. However, this review presents the three-component synthesis of pyrrole derivatives, highlighting the significance of multicomponent reaction in synthesizing eclectic multi-functionalised pyrrole covering the selected literature on the three-component synthesis of substituted pyrrole from 2016 to late 2023. Furthermore, this article classifies the reactions based on the starting material with functional groups involved in the pyrrole ring formation. Graphical Abstract: (Figure presented.) The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Enhanced Postoperative Brain MRI Segmentation with Automated Skull Removal and Resection Cavity Analysis
Brain tumors present a significant medical challenge, often necessitating surgical intervention for treatment. In the context of postoperative brain MRI, the primary focus is on the resection cavity, the void that remains in the brain following tumor removal surgery. Precise segmentation of this resection cavity is crucial for a comprehensive assessment of surgical efficacy, aiding healthcare professionals in evaluating the success of tumor removal. Automatically segmenting surgical cavities in post-operative brain MRI images is a complex task due to challenges such as image artifacts, tissue reorganization, and variations in appearance. Existing state-of-the-art techniques, mainly based on Convolutional Neural Networks (CNNs), particularly U-Net models, encounter difficulties when handling these complexities. The intricate nature of these images, coupled with limited annotated data, highlights the need for advanced automated segmentation models to accurately assess resection cavities and improve patient care. In this context, this study introduces a two-stage architecture for resection cavity segmentation, featuring two innovative models. The first is an automatic skull removal model that separates brain tissue from the skull image before input into the cavity segmentation model. The second is an automated postoperative resection cavity segmentation model customized for resected brain areas. The proposed resection cavity segmentation model is an enhanced U-Net model with a pre-trained VGG16 backbone. Trained on publicly available post-operative datasets, it undergoes preprocessing by the proposed skull removal model to enhance precision and accuracy. This segmentation model achieves a Dice coefficient value of 0.96, surpassing state-of-the-art techniques like ResUNet, Attention U-Net, U-Net++, and U-Net. (2024) Sobha Xavier P., Sathish P. K. and Raju G. -
Contemporary Indian Way of Settling Down: Emerging Adults Perspective
Settling down in India historically entailed a culturally constructed notion for individuals, focusing on marriage. An exploration of the modern Indian idea of Settling down was explored in light of the driving forces of globalization and increased migration. The current study explored the concept of Settling down among emerging adults aged between 18 and 29 years who had migrated within the borders of India for education or employment purposes. To this end, semi-structured interviews were conducted. The reflexive thematic analysis method was employed for analysing the data. Emerging themes unveiled that despite marriage being endorsed by a few of the participants, co-habiting relationships were convenient and burden-free. Employment, financial independence, and professional stability emerged as the primary markers of Settling down among migrant emerging adults. It was also recognized that migration had a critical impact on peoples decisions about settling down.. 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. -
Empowering Adolescent Emergent Readers in Government Schools: An Exploration of Multimodal Texts as Pathways to Comprehension
This exploratory study, which was part of a larger investigation into multimodality, looked at the comprehension levels of 62 Grade 8 students from two government schools who were identified as emerging readers out of a group of 118 students. Through observations and interactions with teachers and students, the potential for multimodal texts to enhance comprehension was highlighted. The study specifically compared the effectiveness of a digital comic (Text A) and an audio-visual text (Text B) in enabling comprehension among these emergent readers. Participants were instructed to narrate the content and share their interpretations of these texts, with their responses recorded and analyzed. Feedback revealed a marked preference for Text B among 45 of the 62 emergent readers assessed. Employing theoretical frameworks related to comprehension, language production, multimodality, and task structure, this research concentrated on the subset of 45 students who favored Text B. The findings underscore the importance of aligning instructional materials with students preferred learning modalities, suggesting that such alignment enhances comprehension. The study proposes a refined approach to literacy education policy, advocating for the inclusion of diverse modalities to better meet the varied learning needs of students. 2024 Association of Literacy Educators and Researchers.