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Option or necessity: Role of environmental education as transformative change agent
There is a consensus around the importance of environmental education in mitigating the ill effects of environmental problems and preserving the natural environment and promoting green behaviours. The present paper studies the role of environmental education based on transformative learning theory. It intends to present and test a model proposal using sequential mediation analysis of several constructs as the Environmental Education Support (EES) and Volunteer Attitude (VA). A quantitative study was carried out by using data obtained through online questionnaires from several Indian and Brazilian Higher Education Institutions. A multivariate statistical method was employed to analyse the data by using partial least squares structural equation modelling. The results demonstrated that environmental education positively influences students environmental concern, willingness to be environmentally friendly, and volunteer attitude. As a novelty, it reports that environmental education beliefs, concern for the environment and willingness to be environmentally friendly sequentially mediate the relationship between environmental education support and volunteering attitude. 2023 Elsevier Ltd -
Social Identity of Kodavas Understanding Evolution and Transitions
The Kodavas of Kodagu district in Karnataka have a distinct social structure and follow a set of unique social codes and values peculiar to the community. Various influences have resulted in shifting social identities, which maybe a potential indicator of an identity crisis within the group. The present study follows a Constructivist Grounded Theory approach to inquiry, to arrive at an analytical schema of the process of social identity formation of the Kodavas. The analysis of data collected from forty-one middle and older adults, highlight the core traditional attributes of the Kodava identity, factors contributing to identity transition and its reflection in contemporary times. 2023 Tata Institute of Social Sciences. All rights reserved. -
One-Pot Synthesis of Silver Nanoparticles Derived from Aqueous Leaf Extract of Ageratum conyzoides and Their Biological Efficacy
The main objective of the present research work is to assess the biological properties of the aqueous plant extract (ACAE) synthesised silver nanoparticles from the herbal plant Ageratum conyzoides, and their biological applications. The silver nanoparticle syntheses from Ageratum conyzoides (Ac-AgNPs) were optimised with different parameters, such as pH (2, 4, 6, 8 and 10) and varied silver nitrate concentration (1 mM and 5 mM). Based on the UVvis spectroscopy analysis of the synthesised silver nanoparticles, the concentration of 5 mM with the pH at 8 was recorded as the peak reduction at 400 nm; and these conditions were optimized were used for further studies. The results of the FE-SEM analysis recorded the size ranges (~3090 nm), and irregular spherical and triangular shapes of the AC-AgNPs were captured. The characterization reports of the HR-TEM investigation of AC-AgNPs were also in line with the FE-SEM studies. The antibacterial efficacies of AC-AgNPs have revealed the maximum zone of inhibition against S. typhi to be within 20 mm. The in vitro antiplasmodial activity of AC-AgNPs is shown to have an effective antiplasmodial property (IC50:17.65 ?g/mL), whereas AgNO3 has shown a minimum level of IC50: value 68.03 ?g/mL, and the Ac-AE showed >100 ?g/mL at 24 h of parasitaemia suppression. The ?-amylase inhibitory properties of AC-AgNPs have revealed a maximum inhibition similar to the control Acarbose (IC50: 10.87 ?g/mL). The antioxidant activity of the AC-AgNPs have revealed a better property (87.86% 0.56, 85.95% 1.02 and 90.11 0.29%) when compared with the Ac-AE and standard in all the three different tests, such as DPPH, FRAP and H2O2 scavenging assay, respectively. The current research work might be a baseline for the future drug expansion process in the area of nano-drug design, and its applications also has a lot of economic viability and is a safer method in synthesising or producing silver nanoparticles. 2023 by the authors. -
A phenomenological exploration of Indian women's body image within intersecting identities in a globalizing nation
The goal of the study was to examine Indian women's body image experiences utilizing an intersectional framework. Using phenomenological method, the study attempted to explore how experiences of gender oppression intersect with salient social identities to produce experiences of body dissatisfaction in Indian women. Thirty-Five Indian women in the age group 1840 years participated in semi-structured interviews. Overall, women experienced and discussed their bodies in terms of physical features they liked and disliked. Three themes emerged that comprised body image experiences of Indian women- (a) Beautiful, thin and fair- three social imperatives for women, (b) Internalization and (c) Body image management. Each of these impacted women negatively and contributed to greater body monitoring, increased indulgence in unhealthy behaviours and heightened body dissatisfaction. Women also discussed coping techniques for managing such experiences. Researchers and practitioners are encouraged to take into account culturally constructed beauty norms and unique socio-cultural factors for Indian women that determine body image. Findings are interpreted in the context of evolving socio-cultural norms that have recolonised Indian women's embodiment in a globalizing nation. 2023 Elsevier Ltd -
Smart city initiatives and disaster resilience of cities through spatial planning in Pune city, India
Cities are attracting populations at alarming rate. Cities provide the need of populations in every way from livelihoods to livability. In doing so it is exhausting its resources resulting in increasing threats of risk. An initiative like Smart City Mission is aiming to enhance the capacities of the cities to increase livability and quality of life for its population and decrease threats of risk. This study examines the impact of smart city initiatives on resilience to earthquakes and floods through a spatial planning perspective for the city of Pune in State of Maharashtra through series of structured interviews with key stakeholders. The findings suggest that smart city initiative is still in its primary stage and requires assimilation with the development strategy to contribute to the resilience of the city. The study further proposes the need to integrate the smart city initiative with all the current and future developmental projects. 2023, World Research Association. All rights reserved. -
Spontaneous hydrogen production using gadolinium telluride
Developing materials for controlled hydrogen production through water splitting is one of the most promising ways to meet current energy demand. Here, we demonstrate spontaneous and green production of hydrogen at high evolution rate using gadolinium telluride (GdTe) under ambient conditions. The spent materials can be reused after melting, which regain the original activity of the pristine sample. The phase formation and reusability are supported by the thermodynamics calculations. The theoretical calculation reveals ultralow activation energy for hydrogen production using GdTe caused by charge transfer from Te to Gd. Production of highly pure and instantaneous hydrogen by GdTe could accelerate green and sustainable energy conversion technologies. 2023 -
Intensity of hospital waste generation and disposal in the selected hospitals in Kerala, India: an analysis based on hospital ownership
Management of hospital wastes has been considered as an integral part of hospital hygiene and infection control, which in turn depends on the intensity of waste generation and disposal. This study analyses the ownership-wise intensities of hospital waste generated, treated and disposed in the selected hospitals in the state of Kerala, India. These intensities are examined using secondary data collected from four districts of Kerala for the period from 2010 to 2014. The intensity of hospital waste generation is measured on the basis of per bed per kilogram per day and also per patient per kilogram per day basis. The study shows that private hospitals are producing significantly higher amount of waste than government and co-operative hospitals. However, private hospitals are found to be more efficient compared to government hospitals in treating and disposing the hospital waste. It is also found that the co-operative hospitals are well-organized in treating and disposing the liquid waste compared with other hospitals in Kerala. 2023, The Author(s), under exclusive licence to Springer Nature Japan KK, part of Springer Nature. -
Biomass derived carbon quantum dots embedded PEDOT/CFP electrode for the electrochemical detection of phloroglucinol
Carbon nanocomposites have garnered a lot of attention among various nanomaterials due to their distinct characteristics, such as large surface area, biocompatibility, and concise synthetic routes. They are also a viable contender for electrochemical applications, notably sensing, due to their intriguing electrochemical features, which include large electroactive surface area, outstanding electrical conductivity, electrocatalytic activity, and high porosity and adsorption capability. Herein, an electrochemical sensor for phloroglucinol (PL) was designed using a CFP electrode modified with biomass-derived carbon quantum dots (S-CQD) doped on conducting organic polymer poly(3,4-ethylene dioxythiophene) (PEDOT) via electrodeposition method. The obtained nanocomposite (S-CQD+PEDOT) on the CFP electrode possesses a high surface area. The higher electrocatalytic activity of S-CQD and significant conductivity of PEDOT- modified electrode enhance the electrocatalytic activity for the phloroglucinol oxidation. The oxidation peak current of PL shows a higher response on the finally modified electrode than the other electrodes. The developed electrochemical sensor for the selective and sensitive detection of PL showed a good linear range of 36 -360 nM and a detection limit of 11 nM. The modified electrodes were characterized using Transmission electron spectroscopy (TEM), Fourier Transform infrared spectroscopy (FT-IR), and X-ray photon spectroscopy (XPS). Finally, the developed method was successfully used to detect Phloroglucinol from industrial effluents with RSD (0.841.02%) and (98.5101.2%) of recovery. 2023 -
Experimental analysis and RSM-based optimization of friction stir welding joints made of the alloys AA6101 and C11000
In the present study, the evaluation of FSW input parameters on output response ultimate tensile strength (UTS) of the friction stir welded AA6101-C11000 joint is in agreement. The response surface methodology (RSM) was adapted for generating the mathematical regression equation to predict the UTS and to develop the FSW parameters to attain the highest UTS of the FSW joints. The central composite design (CCD) method from RSM with five levels and three factors, i.e., tool rotational speed, feed rate, and tool offset used to conduct and minimize the number of tests. During FSW, base sheet cu (hard metal) was stationed on the advancing side (+1 mm, +1.68 mm tool offset) and the base sheet Al (soft metal) on the retreating side (?1 mm, ?1.68 mm tool offset). The radiography studies were accomplished to inspect the internal flaws of the FSW joints (Al-Cu).The XRD and SEM investigation of the ruptured specimens during the tensile test to evaluate the IMCs phase anatomy and fracture analysis. The maximum UTS value measured during the experimental work was 142.69 MPa at 1000 rpm, 40 mm min?1, and ?1.68 mm tool offset. The highest joint efficiency obtained was 82% compared with the AA6101 UTS value. RSM adapted for this work was 92% accurate and satisfactory. 2023 The Author(s). Published by IOP Publishing Ltd. -
Automated Brain Imaging Diagnosis and Classification Model using Rat Swarm Optimization with Deep Learning based Capsule Network
Earlier identification of brain tumor (BT) is essential to increase the survival rate of the patients. The commonly used imaging technique for BT diagnosis is magnetic resonance imaging (MRI). Automated BT classification model is required for assisting the radiologists to save time and enhance efficiency. The classification of BT is difficult owing to the non-uniform shapes of tumors and location of tumors in the brain. Therefore, deep learning (DL) models can be employed for the effective identification, prediction, and diagnosis of diseases. In this view, this paper presents an automated BT diagnosis using rat swarm optimization (RSO) with deep learning based capsule network (DLCN) model, named RSO-DLCN model. The presented RSO-DLCN model involves bilateral filtering (BF) based preprocessing to enhance the quality of the MRI. Besides, non-iterative grabcut based segmentation (NIGCS) technique is applied to detect the affected tumor regions. In addition, DLCN model based feature extractor with RSO algorithm based parameter optimization processes takes place. Finally, extreme learning machine with stacked autoencoder (ELM-SA) based classifier is employed for the effective classification of BT. For validating the BT diagnostic performance of the presented RSO-DLCN model, an extensive set of simulations were carried out and the results are inspected under diverse dimensions. The simulation outcome demonstrated the promising results of the RSO-DLCN model on BT diagnosis with the sensitivity of 98.4%, specificity of 99%, and accuracy of 98.7%. 2023 World Scientific Publishing Company. -
AI-based wavelet and stacked deep learning architecture for detecting coronavirus (COVID-19) from chest X-ray images
A novel coronavirus (COVID-19), belonging to a family of severe acute respiratory syndrome coronavirus 2 (SARs-CoV-2), was identified in Wuhan city, Hubei, China, in November 2019. The disease had already infected more than 681.529665 million people as of March 13, 2023. Hence, early detection and diagnosis of COVID-19 are essential. For this purpose, radiologists use medical images such as X-ray and computed tomography (CT) images for the diagnosis of COVID-19. It is very difficult for researchers to help radiologists to do automatic diagnoses by using traditional image processing methods. Therefore, a novel artificial intelligence (AI)-based deep learning model to detect COVID-19 from chest X-ray images is proposed. The proposed work uses a wavelet and stacked deep learning architecture (ResNet50, VGG19, Xception, and DarkNet19) named WavStaCovNet-19 to detect COVID-19 from chest X-ray images automatically. The proposed work has been tested on two publicly available datasets and achieved an accuracy of 94.24% and 96.10% on 4 classes and 3 classes, respectively. From the experimental results, we believe that the proposed work can surely be useful in the healthcare domain to detect COVID-19 with less time and cost, and with higher accuracy. 2023 Elsevier Ltd -
Modeling Environmentally Conscious Purchase Behavior: Examining the Role of Ethical Obligation and Green Self-Identity
Due to environmental degradation, using environment-friendly products has become necessary to reduce carbon emissions. However, the consumption of such products is still below expectations because these products are usually costlier than their traditional counterparts. The current study aims to investigate consumer behavior towards environment-friendly products using Ajzens theory of planned behavior as a theoretical model. The study seeks to examine the role of the key determinates of environmentally conscious purchase behavior, such as ethical obligation and green self-identity. A total of 386 responses were collected from consumers living in a few major cities of northern India using purposive sampling. The data were analyzed using structural equation modeling in Amos 22.0. The results demonstrated that attitudes towards environment-friendly products perceived behavioral control and green self-identity as the major determinants of green purchase intentions. In addition, attitude was reported to mediate the effect of ethical obligation on green purchase intentions and green self-identity was found to moderate the effect of attitude on green purchase intentions. Additionally, green self-identity was also reported to moderate the relationship between ethical obligation and attitude. The study adds value to the existing literature by signifying the role of green self-identity and ethical obligation in stimulating consumers green purchase intentions. The findings of the study are also meaningful for marketers and policymakers. 2023 by the authors. -
Classification of a New-Born Infant's Jaundice Symptoms Using a Binary Spring Search Algorithm with Machine Learning
A yellowing of the skin and eyes, called jaundice, is the consequence of an abnormally high bilirubin concentration in the blood. All across the world, both newborns and adults are afflicted by this illness. Jaundice is common in new-borns because their undeveloped livers have an imbalanced metabolic rate. Kernicterus is caused by a delay in detecting jaundice in a newborn, which can lead to other complications. The degree to which a newborn is affected by jaundice depends in large part on the mitotic count. Nonetheless, a promising tool is early diagnosis using AI-based applications. It is straightforward to implement, does not require any special skills, and comes at a minimal cost. The demand for AI in healthcare has led to the realisation that it may have practical applications in the medical industry. Using a deep learning algorithm, we created a method to categorise jaundice cases. In this study, we suggest using the binary spring search procedure (BSSA) to identify features and the XGBoost classifier to grade histopathology images automatically for mitotic activity. This investigation employs real-time and benchmark datasets, in addition to targeted methods, for identifying jaundice in infants. Evidence suggests that feature quality can have a negative effect on classification accuracy. Furthermore, a bottleneck in classification performance may emerge from compressing the classification approach for unique key attributes. Therefore, it is necessary to discover relevant features to use in classifier training. This can be achieved by integrating a feature selection strategy with a classification classical. Important findings from this study included the use of image processing methods in predicting neonatal hyperbilirubinemia. Image processing involves converting photos from analogue to digital form in order to edit them. Medical image processing aims to acquire data that can be used in the detection, diagnosis, monitoring, and treatment of disease. Newburn jaundice detection accuracy can be verified using image datasets. As opposed to more traditional methods, it produces more precise, timely, and cost-effective outcomes. Common performance metrics such as accuracy, sensitivity, and specificity were also predictive. 2023 Lavoisier. All rights reserved. -
Dynamics of chaotic waterwheel model with the asymmetric flow within the frame of Caputo fractional operator
The chaotic waterwheel model is a mechanical model that exhibits chaos and is also a practical system that justifies the Lorenz system. The chaotic waterwheel model (or Malkus waterwheel model) is modified with the addition of asymmetric water inflow to the system. The hereditary property of the modified chaotic waterwheel model is analyzed to determine the system's stability and identify the parameter that contributes to the stability We also examine the factor that leads to the bifurcation. We determine the well-posed nature of the modified system. The modified chaotic waterwheel model is defined with the Caputo fractional operator. The existence and uniqueness, boundedness, stability, Lyapunov stability, and numerical simulation are studied for the modified fractional waterwheel model. The bifurcation parameter and Lyapunov exponent are examined to study the chaotic nature of the system with respect to the fractional order. The nature of the system is captured with the help of the efficient numerical approach AdamsBashforthMoulton Method. The numerical approach demonstrates that the chaotic nature of the modified chaotic waterwheel is changed into unstable nature, which could further reduce to the stable case with suitable values of the parameter. This analysis is justified with the help of Lyapunov exponent. We consider irrational order (?,e) in the present work to illustrate the reliability of fractional order. 2023 Elsevier Ltd -
Subspace graph topological space of graphs
A graph topology defined on a graph G is a collection T of subgraphs of G which satisfies the properties such as K0, G ? T and T is closed under arbitrary union and finite intersection. Let (X, T ) be a topological space and Y ? X then, TY = {U ? Y: U ? T } is a topological space called a subspace topology or relative topology defined by T on Y. In this P1 we discusses the subspace or the relative graph topology defined by the graph topology T on a subgraph H of G. We also study the properties of subspace graph topologies, open graphs, d-closed graphs and nbd-closed graphs of subspace graph topologies. 2023, Proyecciones. All Rights Reserved. -
The impact of slip mechanisms on the flow of hybrid nanofluid past a wedge subjected to thermal and solutal stratification
This investigation aims to inspect the flow and thermal characteristics of hybrid nanoparticles under the effect of thermophoresis and Brownian motion. The hybrid nanofluid is formed by dispersing the silver nanoparticles into the base fluid composed of tungsten oxide and water. The resulting hybrid nanofluid is assumed to flow over a moving wedge. The wedge is a geometry that can be commonly seen in many manufacturing industries, moulding industries, etc., where friction creates more heat and cooling becomes a necessary process. This study currently focuses on such areas of the industries. In this regard, the flow expressions in the form of Partial Differential Equations (PDEs) are obtained by incorporating the modified Buongiorno's model and using boundary layer approximations. The modified Buongiorno model helps us analyze the impact of volume fraction along with the slip mechanisms. Suitable transformations are used to achieve the nondimensional form of governing equations, and further, it transforms the PDE to Ordinary Differential Equation (ODE). The RKF-45 is used to solve the obtained ODE and the boundary conditions. Furthermore, graphic analysis of the solutions for fluid velocity, energy distributions and dimensionless concentration is provided. It was noted that the behavior of the Nusselt and Sherwood numbers was determined by analyzing numerous parameters. The conclusions show that they decrease with greater values of the stratification factors. Additionally, with higher values of the wedge parameter, the magnitude of the velocity field and the thermal boundary layer diminish. 2023 World Scientific Publishing Company. -
Visual encoding of nudge influencers and exploring their effect on sustainable consumption among children
With the growing number of nuclear families that have a higher disposable income, and a willingness to spend for disparate reasons possibly on the only child in the family, children are unquestionably emerging as a critical market segment that marketers would do well to target. However, while marketing to children is necessary, given the current focus on sustainability, encouraging responsible consumption seems to be a prerequisite. Making children environmentally literate would thereby, significantly help in the ongoing efforts to save our planet from environmental degradation. Based on this backdrop, this study investigates the significance of encouraging children to consume 'sustainably'. Drawing upon Richard H Thaler and Cass R Sunstein's Nudge theory, along with the United Nation's Sustainable Development Goals (UNSDG -12), we employ a novel methodology to visually encode information gleaned from the extant literature. Specifically, we discuss the significance of developing sustainable habits in children and analyze the 'nudges' that motivate children to adopt sustainable habits. Additionally, we specify different nudge elements derived from the extant literature and plot them in a RADAR chart. We observe that 'simplified process' and 'ease of access' nudging have the greatest effect when delivered in school. This study has academic, managerial, and societal implications. The findings of the study would help managers to focus on the nudges in their campaigns. Research scholars and academicians could understand the significance of using the 'RADAR' chart methodology and can expand their studies in various other domains. The present study also helps to understand the extant literature and plan for future research in the domain of sustainable consumption. The findings of the study would help schools and parents understand the effective nudges that result in creating responsible consumers that would largely benefit society. 2023 The Authors -
Structural health monitoring using AI and ML based multimodal sensors data
Climatic changes, sudden or gradual, influence the structural health of buildings and bridges due to variations in temperature and humidity. Risk and disaster management plays a vital role in the decision-making process for safeguarding structures. Data analytics from sensors systems in smart structures aid in taking appropriate action in securing buildings during natural calamities. The correlation between climate and structural measuring responses can be further improved using artificial intelligence (AI)- machine learning (ML) algorithms to monitor and predict structural health and take any precautionary steps before the event of a casualty. Linear regression is an efficient tool for analyzing structural health. The proposed work's objective is to monitor and predict the structural health and inform the concerned authorities in the event of a failure in advance, using AI-ML approaches. We have analyzed various sensor data sets to predict the health of a structure based on the crack developed. From the data obtained for experimentation, mean width of the crack is observed as 2.38 cm and mean length of the crack is 63.36 cm. 2023 The Authors -
Next generation employability andcareer sustainability inthehospitality industry 5.0
Purpose: With an industry 5.0 revolution taking place in the hospitality industry, a shift from manual to cognitive labor is anticipated, characterized by greater sustainability, resilience and a human-centric approach. In this regard, hospitality educators' ability and willingness to teach novel topics such as automation at work, upskilling of employees, man-machine interaction and service robots have become more important than ever. This study aims to interpret the perspectives of hospitality educators about bridging the gap in the employability skills of (next-gen) hospitality graduates and the concerns relating to career sustainability in times of transition. Design/methodology/approach: A case study method was used given the novelty of the topic in a developing country like India. A qualitative survey with open-ended questions, is employed to understand the viewpoints of Indian hospitality educators, including those with more than 15years of teaching experience. In-depth interviews were conducted with 23 hospitality educators to reach the theoretical saturation point. MAXQDA software was used to analyze the qualitative data collected in the study. Findings: The findings reveal the challenges and motivations of hospitality educators in adapting to frequently changing business environments. In doing so, it sheds light on the methods employed to create a generation of hospitality graduates aligned with the changing dynamics of the industry. Originality/value: The paper presents the viewpoints of hospitality educators in India in relation to a futuristic approach to next-gen employability and career sustainability. Whilst numerous studies have focused on the role of robots and artificial intelligence in replacing the human component of the service environment, the concept of people working alongside advanced technologies is fairly new and needs to be fully explored. 2023, Emerald Publishing Limited. -
RayleighBard Convection in a Bi-viscous Bingham Fluid with Weak Vertical Harmonic Oscillations: Linear and Non-linear Analyses
Linear and weakly non-linear stability analyses of RayleighBard convection in a bi-viscous Bingham fluid layer are performed in the presence of vertical harmonic vibrations. In the linear analysis, expression is obtained for the correction Rayleigh-number arising due to the vibrations. The non-linear-analysis based on the GinzburgLandau equation is used to compute the Nusselt-number in terms of the correction Rayleigh-number. The mean-Nusselt-number is then obtained as a function of the scaled-Rayleigh-number, the frequency and the amplitude of modulation, the Prandtl number, and the bi-viscous Bingham fluid parameter. The non-autonomous amplitude-equation is numerically solved using the RungeKuttaFehlberg45 method. It is found that the influence of increasing the amplitude of modulation is to result in a delayed-onset situation and thereby to an enhanced-heat-transport situation. For small and moderate frequencies, the influence of increasing the frequency of oscillations is to decrease the critical Rayleigh-number. However, the mean-Nusselt-number decreases with increase in the frequency of oscillations only in the case of small frequencies. An increase in the value of the bi-viscous Bingham fluid parameter leads to advanced-onset and thereby to an enhanced-heat-transport situation. At very large frequencies, the effect of modulation on onset and heat-transport ceases. 2023, The Author(s), under exclusive licence to Springer Nature India Private Limited.