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Green space and mental well-being research in India: An urgent need for intervention
In recent years, numerous studies have highlighted the positive impact of green spaces on mental health and overall well-being. However, a closer examination reveals a skewed green space research contribution, with developed countries taking the lead. Despite substantial burden of mental health issues, there is a noticeable dearth of green space research within India's academic landscape. In the current paper, we address this gap through a brief review that positions the scope of green space psychology (GSP) in India. We conducted the literature review using a machine learning tool called Crawling Scholar. Our review comprised 325 studies, focusing on five key parameters: the year of publication, geographical context, research design, psychological variables examined, and study population. Our findings indicate a significant body of global research on GSP, while the contribution from Indian scholarship remains negligible. Based on this discrepancy, we propose that incorporating GSP as an intervention and preventive measure could play a crucial role in addressing India's mental health challenges. By integrating traditional practices with the emerging field of GSP, we can harness the potential of green spaces to promote mental well-being. Our findings further underscore the importance of expanding research on GSP within the Indian context and emphasize the need for further investigations into its efficiency. By shedding light on the current status of GSP research in India, we aim to raise awareness among researchers, policymakers, and mental health professionals, fostering a collaborative effort to leverage the benefits of green spaces for the betterment of mental health infrastructure in India. 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies. -
Study of the Balmer Decrements for Galactic Classical Be Stars Using the Himalayan Chandra Telescope of India
In a recent study, Banerjee et al. (2021) produced an atlas of all major emission lines found in a large sample of 115 Galactic field Be stars using the 2-m Himalayan Chandra Telescope (HCT) facility located at Ladakh, India. This paper presents our further exploration of these stars to estimate the electron density in their discs. Our study using Balmer decrement values indicate that their discs are generally optically thick in nature with electron density (ne) in their circumstellar envelopes (CEs) being in excess of 1013 cm-3 for around 65% of the stars. For another 19% stars, the average ne in their discs probably range between 1012 cm-3 and 1013 cm-3. We noticed that the nature of the H? and H? line profiles might not influence the observed Balmer decrement values (i.e. D34 and D54) of the sample of stars. Interestingly, we also found that around 50% of the Be stars displaying D34 greater than 2.7 are of earlier spectral types, i.e. within B0B3. 2024 Societe Royale des Sciences de Liege. All rights reserved. -
Study of transient nature of classical Be stars using multi-epoch optical spectroscopy
Variability is a commonly observed property of classical Be stars (CBe) stars. In extreme cases, complete disappearance of the H? emission line occurs, indicating a disc-less state in CBe stars. The disc-loss and reappearing phases can be identified by studying the H? line profiles of CBe stars on a regular basis. In this paper, we present the study of a set of selected nine bright CBe stars, in the wavelength range of 62006700 to better understand their disc transient nature through continuous monitoring of their H? line profile variations for five consecutive years (20152019). Based on our observations, we suggest that four of the program stars (HD 4180, HD 142926, HD 164447 and HD 171780) are possibly undergoing disc-loss episodes, whereas one other star (HD 23302) might be passing through disc formation phase. The remaining four stars (HD 237056, HD 33357, HD 38708 and HD 60855) have shown signs of hosting a stable disc in recent epochs. Through visual inspection of the overall variation observed in the H? EW for these stars, we classified them into groups of growing, stable and dissipating discs, respectively. Moreover, our comparative analysis using the BeSS database points out that the star HD 60855 has passed through a disc-less episode in 2008, with its disc formation happening probably over a timescale of only two months, between January and March 2008. 2022, Indian Academy of Sciences. -
Optical spectroscopy of Galactic field classical Be stars
In this study, we analyse the emission lines of different species present in 118 Galactic field classical Be stars in the wavelength range of 3800-9000 We re-estimated the extinction parameter (AV) for our sample stars using the newly available data from Gaia DR2 and suggest that it is important to consider AV while measuring the Balmer decrement (i.e. D34 and D54) values in classical Be stars. Subsequently, we estimated the Balmer decrement values for 105 program stars and found that ?20 per cent of them show D34 ? 2.7, implying that their circumstellar disc are generally optically thick in nature. One program star, HD 60855 shows H? in absorption - indicative of disc-less phase. From our analysis, we found that in classical Be stars, H? emission equivalent width values are mostly lower than 40 which agrees with that present in literature. Moreover, we noticed that a threshold value of ?10of H? emission equivalent width is necessary for FeII emission to become visible. We also observed that emission line equivalent widths of H?, P14, FeII 5169, and OI 8446for our program stars tend to be more intense in earlier spectral types, peaking mostly near B1-B2. Furthermore, we explored various formation regions of Ca II emission lines around the circumstellar disc of classical Be stars. We suggest the possibility that Ca II triplet emission can originate either in the circumbinary disc or from the cooler outer regions of the disc, which might not be isothermal in nature. 2021 Oxford University Press. All rights reserved. -
Affiliate Marketing and the Symbiotic Relationship in the Pharma Industry
The objective of the study is to understand the dynamic relationship between customers and the healthcare industry giants in the Indian context. The purpose revolves around how the consumer is benefitting and at the same time, how the indirect third-party affiliates also earn marginal profits along with serving the customers. The study is backed by both primary and secondary data, which were collected from 173 individuals from various fields through a questionnaire. The convenience sampling method was used to select the respondents, and the Technology Acceptance Model (TAM) was used to propose the model for the study. There exists a parallel symbiotic relationship between consumers, pharmaceutical companies, and affiliates. The application of this research can be put to use for the startups, which want to explore and excel in this industry along with the future researchers who want to forecast and study the progress of the pharma companies in the long run. The empirical evidence of this paper highlights a unique relationship between affiliates, the pharma sector, and customers, which drives customer buying behavior and a combination that has not been explored yet. The study provides a unique understanding of how feedback from customers in third-party applications can benefit and produce huge profit margins down the line. 2025 Apple Academic Press, Inc. -
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 -
Fine-tuning Language Models for Predicting the Impact of Events Associated to Financial News Articles
Investors and other stakeholders like consumers and employees, increasingly consider ESG factors when making decisions about investments or engaging with companies. Taking into account the importance of ESG today, FinNLP-KDF introduced the ML-ESG-3 shared task, which seeks to determine the duration of the impact of financial news articles in four languages - English, French, Korean, and Japanese. This paper describes our team, LIPIs approach towards solving the above-mentioned task. Our final systems consist of translation, paraphrasing and fine-tuning language models like BERT, Fin-BERT and RoBERTa for classification. We ranked first in the impact duration prediction subtask for French language. 2024 ELRA Language Resource Association. -
Bacha Posh: Gender Construct in Afghan Culture Examined through the Lens of Children in Literature
With the fall of the Taliban in 2001 and their return in 2021, Afghanistan has undergone drastic socio-political changes. In many families, children are introduced to the practice of Bacha Posh (dressing up like a boy), an Afghan cultural custom where girls are dressed up as boys until they are married off. Despite children being central to this practice, it has not been studied through their eyes. This article examines the custom of Bacha Posh through the childrens perspective and situates it within the current socio-political scenario of the country. A textual and cultural analysis of three literary works is carried out through a study of their child characters to examine how Afghan culture creates its own gender construct. Two are significant works of childrens literature that revolve around real-life stories of Bacha Posh Nadia Hashimis One Half from the East (2016) and Deborah Ellis The Breadwinner (2000). The third work is The Underground Girls of Kabul (2014) by Jenny Nordberg, a seminal work in the study of Bacha Posh in which Nordberg focuses on the practice of Bacha Posh and presents the voice of children. This article then goes on to study the impact of the restrictive nature of the Taliban regime on girls and its influence on the cultural custom of Bacha Posh. It demonstrates how this practice creates an unstable gender construct among children, as evidenced by the gender dysphoria that some girls experience. It thus demonstrates the impact of culture on gender through filling in the gaps between culture, literature and politics. 2023, The International Academic Forum (IAFOR). All rights reserved. -
Computationally Efficient Machine Learning Methodology for Indian Nobel Laureate Classification
A computationally efficient methodology for Indian Nobel Laureate classification is proposed in this study, emphasizing the optimization of image categorization through supervised learning techniques. Leveraging advancements in Convolutional Neural Networks (CNNs), the research aims to enhance the efficiency and precision of image classification tasks. The study utilizes Logistic Regression for dataset analysis, initially employing browser extensions for mass downloading categorized image data. Haar cascade classifiers are then used for data wrangling, focusing on facial, nose, and mouth recognition. Following this, feature engineering through wavelet transformation reduces image dimensionality, preparing the dataset for the chosen ML model, Logistic Regression. The primary focus is to simplify technology for improved image categorization. Support Vector Machines (SVM), Random Forest, and Logistic Regression are examined, with Logistic Regression emerging as the most effective model, achieving an accuracy rate of 87.5%. A thorough evaluation using Confusion Matrices reveals Logistic Regression's superior performance in classifying images of Indian Nobel laureates. A strategic up-sampling approach is implemented to address dataset inconsistencies, ensuring balanced representation across classes. The Haar wavelet transform is then applied for feature extraction, optimizing the dataset for ML models. The dataset is split into training and testing sets (80-20), and the three models are trained and evaluated for accuracy. Logistic Regression proves to be the best performer, offering insights into prominent leaders' identification. The research offers a detailed pipeline for data preprocessing, feature engineering, and model assessment, culminating in a robust image categorization system. Logistic Regression emerges as a reliable method for biographical picture identification, demonstrating superior accuracy over SVM and Random Forest. This research underscores the importance of efficient and accurate image classification methodologies for practical applications in real-world scenarios, particularly in recognizing influential leaders. 2024 IEEE. -
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. -
Is carbon neutrality a reality for India?
India, the third-largest carbon dioxide emitter in the world, aims to achieve zero emissions by 2070. India is committed to its Panchamrit and has launched various initiatives such as green bonds, carbon credits, carbon market, investing in green hydrogen, etc. However, given the present scenario with respect to the dependency on coal-based power generation and lack of green financing, the present article assesses the different solutions and their practicality in achieving carbon neutrality. (2024), (Indian Academy of Sciences). All rights reserved. -
The Shame of Ageing During Fourth Industrial Revolution: A Thematic Analysis of Indian Adults
The Fourth Industrial Revolution (4IR), a term popularised by Klaus Schwab in 2016, connected the physical-biological and the digital world. This is an era of artificial intelligence and computational technologies suited to satiate the needs of the human race. The emphasis is also on a digital identity we have developed alongside our physical and psychological entities. Millennials and Gen Z have a cognizant grip on their digital identity and are known to use the fruits of 4IR in their everyday livelihood. However, with the advent of Industry 4.0, the generation of Baby Boomers and Gen X have had to undergo much re-learning and accommodate the newer ways of integrating digitalization in their lives. The process has brought about occupational threats and shaming related to failure to upgradation and flexibility. This article explores the influences of these social experiences on the identity and self-concept of the quinquagenarians and the sexagenarians. The article follows a qualitative method where using a thematic approach, the emerging themes from the in-depth interviews will be analyzed in detail to form a theoretical framework for shaming among the Indian Baby Boomers and Gen X. The variables in focus are adjustment, coping styles, resilience, the purpose of life, and Self-Image. The study explores the themes of Indian adults, which emerge from interviewing 46 participants, who have been associated with full-time employment and are between 77 and 59 years of age, representing the Baby Boomers, and those between 43 and 58 years of age, representing Gen X. The analysis adopts a psychoanalytic approach, where the data is interpreted using an Eriksonian lens. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Suicide and Youth: Positive Psychology Perspective
Suicidality and self-harm among adolescents and young adults need immediate attention and support. This is often seen as a cry for help where a feeling of entrapment experienced by individuals pushes them to see self-harm as a coping mechanism. Lack of social support and co-morbid emotional disorders influences suicidal ideation into planning and action. A general feeling of helplessness that gets triggered often leads to anguish. These emotions get internalized and directed inwards leading to self-directed anger which facilitates a suicidal act. Early detection and identification can prevent the loss of lives and help individuals to learn effective ways of coping. Gatekeeper Training for caregivers, teachers, and their peers will help in detecting the early signs of suicide risk. Intervention based on positive psychology will help not only in crisis management but also as preventive and maintenance therapy in holistic mental health. The concepts of hope, forgiveness, self-compassion, gratitude, and resilience can be incorporated into the intervention programs to build a better therapeutic system for youths dealing with suicidality. Practicing effective coping mechanisms every day and making it a ritual of ones daily life is the need of the hour. Integrating adaptive ways of emotional expression and learning matured means to deal with painful emotions and trauma is what needs to be incorporated into the intervention plan. The chapter focuses on these aspects and aims to make a connection between assessing and easy identification of youths in distress related to suicidality and providing a holistic intervention to help them cope with the situation. The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023. -
Framework for Sustainable Energy Management using Smart Grid Panels Integrated with Machine Learning and IOT based Approach.
Maintaining a consistent supply of power is essential for the well-being of the economy, the public, and one's own health. The generation of energy, as well as its distribution, monitoring, and management, are all undergoing fundamental changes as a result of the implementation of a smart grid (SG), which is authorised to include communication technology and sensors into power systems. There are a lot of problems that need to be fixed before the interoperability of the smart grid can be determined. The integration of renewable energy sources and smart grid technology market size and energy management is a sustainable solution to the problem of energy demand management. The importance work quickly toward the development of an efficient Energy Management Model (EMM) that integrates smart grids and renewable energy sources. When it comes to the modelling of complex and non-linear data, machine learning (ML), Internet of Things (IoT) approaches often perform better than statistical models. So, utilizing a machine learning approach for the EMM is a good option since it simplifies the EMM by generating a single trained model to anticipate its performance characteristics across all conditions. This may be accomplished via the use of an EMM created using an ML method. It was recommended that a certain flexibility sample be used as a control mechanism for incursion into the smart grid. The outcomes of the experiment indicate that the demand-side management (DSM) device is more resistant to infiltration and is enough to lower the energy usage of the smart grid. 2024, Ismail Saritas. All rights reserved. -
Rice Yield Forecasting in West Bengal Using Hybrid Model
Agriculture in India is the primary source of revenue, yet farmers still face challenges. The primary goal of agricultural development is to produce a high crop yield. The Datasets collected for the study of real-world time series include a blend of linear and nonlinear patterns. A mixture of linear and non - linear models, rather than a single linear or non - linear model, gives a more precise forecasting models for time series data. The ARIMA and ANN prediction models are combined in this paper to create a Hybrid model. This model is used to predict rice yield for all 18 West Bengal districts during the Kharif season, based on 20years of information(20002019) collected from various sources such as India Meteorological Department, Area, and production Statistics, DAV from NASA, etc. The hybrid model aims to enhance efficiency indicators such as MSE, MAE, and MAPE, demonstrating excellent performance for rice yield prediction in all the districts of West Bengal. In the future, it can be applied to other crops that can support farmers in their farming. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Multiple Approaches in Retail Analytics to Augment Revenues
Knowledge is power. The retail sector has been revolutionized around the clock by the plentiful product knowledge available to customers. Today, customers can use the knowledge available online at any time to study, compare and purchase products from anywhere. Retail companies can stay ahead of shopper trends by using retail information analytics to discover and analyze online and in-store shopper patterns. A product recommender will suggest products from a wide selection that would otherwise be very difficult to locate for the customer. The algorithm would recommend various products, increase the sales of items that would otherwise be difficult to sell. Market basket analysis is a common use scenario for the search for frequent patterns, which involves analyzing the transactional data of a retail store to decide which items are bought together. To do so data from online resource has been taken, which is analyzed and several conclusions were made. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Fraud detection in the era of AI: Harnessing technology for a safer digital economy
Fraudulent activities have increased along with the new prospects of the digital economy's quick growth for both consumers and enterprises. Conventional techniques of fraud detection are insufficient to keep up with these ever-evolving fraudulent strategies. In this sense, machine learning (ML) and artificial intelligence (AI) have become potent instruments to prevent and detect fraud and guarantee the safety of online transactions. This study examines the function of AI and ML and shows how these technologies can spot irregularities and intricate patterns that would be challenging to find with conventional methods. The study includes various methods of AI-based fraud detection and analyses important ethical issues related to these practices. Furthermore, the study looks at developing technology and trends that will probably influence fraud detection in the future. In conclusion, the revolutionary potential of AI and ML in building a safer digital economy is analysed. 2024, IGI Global. All rights reserved. -
From bean to brain: Coffee, gray matter, and neuroprotection in neurological disorders spectrum
Coffee is a popular drink enjoyed around the world, and scientists are very interested in studying how it affects the human brain. This chapter looks at lots of different studies to understand how drinking coffee might change the brain and help protect it from neurodegenerative disorders especially like schizophrenia. With the help of available literature a link between the coffee mechanism and neurodegenerative disorders is established in this chapter. Researchers have found that drinking coffee can change the size of certain parts of the brain that control things like thinking and mood. Scientists also study how coffee's ingredients, especially caffeine, can change how the brain works. They think these changes could help protect the brain from diseases. This chapter focuses on how coffee might affect people with schizophrenia as hallucination is caused during and after excess consumption of caffeine. There's still a lot we don't know, but researchers are learning more by studying how different people's brains respond to coffee over time. Overall, this chapter shows that studying coffee and the brain could lead to new ways to help people with brain disorders. This study also draws ideas for future research and ways to help people stay healthy. 2024 Elsevier B.V. -
An Innovative Method for Fuel Consumption and Maintenance Cost of Heavy-Duty Vehicles based on SR-GRU-CNN Algorithm
A heavy-duty vehicle's fuel usage, and thus its carbon dioxide emissions, are significantly impacted by the driver's behavior. The average fuel economy of a car varies by about 28% between drivers. Fuel efficiency can be improved by driver education, monitoring, and feedback. Fuel efficiency-based incentives are one form of feedback that can be provided. The largest challenge for transportation companies implementing such incentive programs is how to accurately evaluate drivers' fuel consumption. The processes of preprocessing, feature extraction, and model training are all utilized in the suggested method. Principal component analysis (PCA) is widely utilized in data science's preprocessing stage. GMM is used for feature extraction. Afterwards, SR-GRU-CNN is used to train the models based on the selected features. When compared to the two most popular alternatives, CNN and SR-GRU, the proposed methodexcels. 2023 IEEE. -
Cluster analysis for european neonatal jaundice
The objective of this paper is to propose and analyze clustering techniques for neonatal jaundice which will help in grouping the babies of similar symptoms. A variety of methods have been introduced in the literature for neonatal jaundice classification and feature selection. As far as we know, clustering techniques are not used for neonatal jaundice data set. This paper studies and proposes clustering techniques such as K-Means, Genetic K-Means and Bat K-Means for jaundice disease. To find the number of clusters elbow method is used. The clusters are validated using RMSE, SI and HI. The experimental results carried out in this paper shows bat k-means clustering performs better than K-means and genetic K-means. 2018, Springer International Publishing AG.