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Iron-pulsing, a novel seed invigoration technique to enhance crop yield in rice: A journey from lab to field aiming towards sustainable agriculture
Bulk fertilizer application is one of the easiest means of improving yield of crops however it comes with several environmental impediments and consumer health menace. In the wake of this situation, sustainable agricultural practices stand as pertinent agronomic tool to increase yield and ensure sufficient food supply from farm to fork. In the present study, efficacy of iron-pulsing in improving the rice yield has been elucidated. This technique involves seed treatment with different concentrations (2.5, 5 and 10 mM) of iron salts (FeCl3 and FeSO4) during germination. FeCl3 or FeSO4 was used to treat the sets and depending on the concentration of the salts, the sets were named as C2.5, C5, C10 and S2.5, S5, S10 (where C and S stands for FeCl3 and FeSO4 respectively and the numbers succeeding them denotes the concentration of salt in mM). Our investigation identified 72 h of treatment as ideal duration for iron-pulsing. At this time point, the seedling emergence attributes and activities of ?-amylase and protease increased. The relative water uptake of the seeds also increased through upregulation of aquaporin expression. The treatment efficiently maintained the ROS balance with the aid of antioxidant enzymes and increased the iron content within the treated seeds. After transplantation in field, photosynthetic rate and chlorophyll content enhanced in the treated plants. Finally, the post-harvest agro-morphological traits (represented through panicle morphology, 1000 seed weight, harvest index) and yield showed significant improvement with treatment. Sets C5 and S5 showed optimum efficiency in terms of yield improvement. To our best knowledge, this study is the first report deciphering the efficacy of iron-pulsing as a safe, cost effective and promising technique to escalate the yield of rice crops without incurring an environmental cost. Thus, iron-pulsing is expected to serve as a potential tool to address global food security in years to come. 2021 Elsevier B.V. -
Iron pulsing, a cost effective and affordable seed invigoration technique for iron bio-fortification and nutritional enrichment of rice grains
Rice being a major staple food for millions of people, it has been one of the major targets for bio-fortification and iron bio-fortification in rice has been in prime focus to address global micronutrient malnutrition. Commonly practiced methods for obtaining Fe biofortified rice includes soil amendments and foliar spray with iron salts, breeding and development of transgenic rice varieties with Fe-enriched grain are associated with impediments like high cost, labor intensiveness, sub-optimal outcome and approval for commercialization respectively. Iron pulsing technique has reportedly enhanced the carbon and nitrogen assimilation in rice seedlings, which has been translated in yield. Based on the previous findings, in the present study, we have undermined the efficacy of iron pulsing, in improving the iron content and nutritional status of rice kernel obtained from pulsed plants. The present study documents that kernel of seeds obtained from iron pulsed plants have a higher amounts of iron, carbohydrate, protein, lipid, vitamins, nutrient and anti-oxidants than that of non-treated ones. The iron localization studies revealed that iron was mostly present in the endosperm and embryo. Besides, the ferritin expression levels also validated the fact that, the treated grains have accumulated more iron. Thus, iron-pulsing can serve as a novel and propitious sustainable agricultural innovation for iron bio-fortification and improvisation of the overall nutritional value of the rice grains that is affordable, user and consumer friendly in years to come. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Multi-Model Traffic Forecasting in Smart Cities using Graph Neural Networks and Transformer-based Multi-Source Visual Fusion for Intelligent Transportation Management
In the intelligent transportation management of smart cities, traffic forecasting is crucial. The optimization of traffic flow, reduction of congestion, and improvement of theoverall transportation systemefficiency all depend on accurate traffic pattern projections. In order to overcome the difficulties causedby the complexity and diversity of urban traffic dynamics, this research suggests a unique method for multi-modal traffic forecasting combining Graph Neural Networks (GNNs) and Transformer-based multi-source visual fusion. GNNs are employed in this method to capture the spatial connections betweenvarious road segments and to properly reflect the basic structure of the road network. The model's ability to effectively analyse traffic dynamics and relationships between nearby locations is enhanced by graphsrepresenting the road layout, which also increases theoutcome of traffic predictions. Recursive Feature Elimination (RFE) is employed to improve the model's feature selection process and choose the most pertinent features for traffic prediction, producing forecasts that are more effective and precise. Utilizing real-time data, the performance of the suggested strategywasassessed, enabling it to adjust to shifting traffic patterns and deliver precise projections for intelligent transportation management. The empirical outcomes show exceptional results ofperformance metrics for the proposed approach, achieving anamazing accuracy of 99%. The resultsshow that the suggested techniques findings have the ability to anticipate traffic and exhibit a superior level of reliability whichsupports efficient transportation management in smart cities. The Author(s), under exclusive licence to Intelligent Transportation Systems Japan 2024. -
Camera-based tri-lingual script identification at word level using a combination of SFTA and LBP features
This paper exhibit the identification of scripts at word level from the camera-based multi-script document images. The Camera-based document images suffer from noise while capturing documents and scripts are challenging to identify when noise is present. The scripts like Tamil, Punjabi, English, Oriya, Telugu, Gujarathi, Malayalam, Kannada, Hindi, Bengali, and Urdu combinations considered. The experiment conducted on a large dataset consisting of 77,000-word images and each script has 7000-word images word images. The texture features are combined to get the highest recognition accuracy. The recognition rate is 77.94% and 82.39% from SFTA features and 89.82% and 93.94% from LBP features, by using KNN and SVM classifiers, for combined feature vector KNN has given 94.45%, and SVM has given 93.88% recognition accuracy. 2019 SERSC. -
DFT studies on D?A substituted bis-1,3,4-oxadiazole for nonlinear optical application
In the present work, we have synthesized novel D?A substituted bis-1,3,4-oxadiazoles derivatives and studied nonlinear optical properties using density functional theory (DFT). The FT-IR and 1H NMR data confirmed the structure of the molecule. The HOMOLUMO, energy band gap, molecular electrostatic potential map, and global chemical reactivity descriptors were estimated using the DFT and TD-DFT with B3LYP, CAM-B3LYP and WB97XD using 6-31G (d) levels basis set and results show all synthesized molecules have excellent chemical hardness, chemical potential, excellent chemical strength, and excellent chemical stability. The static and dynamic linear polarizability, first hyperpolarizability and second hyperpolarizability components were estimated using time-dependent density functional theory. The first-order hyperpolarizability ? (2x; x, x) computed at a wavelength of 1064nm was found to be 55 times greater than the urea molecule. The dynamic molecular second-order hyperpolarizabilities ? (?3x;x,x,x) suggested good nonlinear properties for the designed molecule. The Author(s), under exclusive licence to The Optical Society of India 2024. -
Emotional Inhibition and Personality as Predictors of Anxiety and Depression in Young Adults
Purpose: Anxiety and depression have been major contributors to the global burden of disease, and the impact has been exacerbated following the COVID-19 pandemic. Therefore, the aim of this study was to understand the association between emotional suppression and the introverted-extraverted dimension of personality in young people and anxiety and depression. Method: Participants were 152 Indian females between the age group of 18-25 years who provided basic demographic details and completed three questionnaires via a google form. Findings: Results described a significant negative correlation of anxiety r (152) = .500, p <0.01and depression r(152)=.471, p <0.01 with emotional inhibition. There was also a significant positive correlation of anxiety r (152) = .288,p < 0.01 and depression r(152)= .288, p <0.01 with personality. While Emotional inhibition emerged as a significant negative predictor of anxiety (R2= .250) as well as of depression (R2=.222), personality (R2=.243) emerged as a significant predictor of depression. Conclusion/Value: Contrary to popular belief, the results of this study suggest that anxiety and depression are inversely related to emotional inhibition. It restores the complexity of emotions and the need to investigate their role in various pathologies. These findings provide an initial basis for further investigation into the role of emotional expression and suppression in the Indian population. 2024 RJ4All. -
A comprehensive survey on machine learning techniques to mobilize multi-camera network for smart surveillance
Deploying a web of CCTV cameras for surveillance has become an integral part of any smart citys security procedure. This, however, has led to a steady increase in the number of cameras being deployed. These cameras generate a large amount of data, which needs to be further analyzed. Our next step is to achieve a network of cameras spread across a city that does not require any human assistance to detect, recognize and track a person. This paper incorporates various algorithmic techniques used in order to make surveillance systems and their use cases so as to enable less human intervention dependent as much as possible. Even though many of these methods do carry out the task graciously, there are still quite a few obstructions such as computational resources required for model building, training time for the models, and many more issues that hinder the process and hence, constrain the possibility of easy implementation. In this paper, we also intend to shift the paradigm by providing evidence toward the use of technologies like Fog computing and edge computing coupled with the surveillance technology trends, which can help to achieve the goal in a sustainable manner with lesser overheads. 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. -
Anti-vibrio effects of the precious Tibetan pill, Rinchen Drangjor Rilnag Chenmo (RDRC)
Tibetan precious pills are an integral part of TTM (Traditional Tibetan Medicine). Among them, Rinchen Drangjor Rilnag Chenmo (RDRC) has been named King of Precious Pills due to its efficacy in treating a multitude of human disorders. RDRC has a complex formulation with about 140 ingredients, mostly from medicinal plants and a few precious stones and metals. Not many studies have been done on the experimental validation of antimicrobial properties of this important pill. The current study investigated the antimicrobial activity of the extracts of RDRC. Both aqueous and chloroform extracts were evaluated for their antibacterial potential against a total of seven different bacterial species, which are pathogenic, including three species of Vibrio, viz. V. vulnificus, V. parahaemolyticus and V. harveyi using the well-diffusion method and also by assessing MIC and MBC values. Its antifungal potential was also studied against two fungal strains Aspergillus Niger and Talaromyces islandicus. It was found that the chloroform extract of RDRC exerted a positive antibacterial effect on all the Vibrio species tested, and the least MIC of 3.33 mg/ml was observed for V. parahaemolyticus. This is the first study of its kind on the anti-Vibrio effect of the Tibetan precious pill, Rinchen Drangjor Rilnag Chenmo. Dhargyal et al (2021). This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided the original author and source are credited (https://creativecommons.org/licenses/by/4.0/). -
Body image issues and self-concept dilemmas in adolescents living with thalassemia
Thalassemia, a genetic blood disorder, involves an inability to produce sufficient hemoglobin and comprises two types: alpha thalassemia and beta thalassemia. Beta thalassemias immediate treatment measures include frequent blood transmissions, stem cell and bone marrow transplants; all capable of altering an individuals idea of body image, self-concept, growth, and socialization, resulting in several emotional, psychological, and behavioral concerns. This study aimed at comprehending the dilemmas of body image and self-concept encountered by adolescents with thalassemia, particularly the resulting influence on physical development and socialization. Using the phenomenological interpretivism approach of qualitative research, data was collected using purposive-convenient sampling from 11 adolescents, both boys and girls ranging from ages 12 to 18, living with thalassemia and undergoing treatment. The research highlights adolescent concerns with body image, specifically with complexion, facial features, being either underweight or overweight, all amalgamating into a self-concept dilemma. Moreover, results point to the significant influence of experiences with family, peers, educational institutions, and hospital staff. Therapeutic attention, through regular screening and counselling, should be provided to adolescent thalassemia patients to address the psychological aspects of the chronic illness. 2021 Informa UK Limited, trading as Taylor & Francis Group. -
Skin lesion classification using decision trees and random forest algorithms
Any superficial skin growth that does not resemble the surrounding area is referred to as skin lesion. It can occur in the form of mole, bump, cyst, rash or other changes that can be classified either as primary or secondary lesion. While primary skin lesions correspond to those changes in color or texture, secondary lesions occur as a primary lesion progression. Skin lesion image segmentation and classification at the early stages can help the patients recover through proper medication and treatment. Many algorithms for segmentation and classification are available in the literature but they all fail to extract lesion boundaries perfectly and classify them with more accuracy. To improve the reliability of the skin image segmentation and classification, we propose to use decision trees and random forest algorithms in this works and compare them with different data sets. The proposed method can generate high-resolution feature maps that can help to preserve the spatial details of the image. While tested against the ISIC 2017 and HAM10000 dataset, we found that the proposed method is more accurate as compared to the existing algorithms in this domain and is also very robust to artifacts or hair fibers present in the skin images. 2020, Springer-Verlag GmbH Germany, part of Springer Nature. -
Handwritten tibetan character recognition using hidden markov model
The Tibetan language which is one of the four oldest and most original languages of Asia is elemental to Tibetan identity, culture and religion and it convey very specific social and cultural behaviors, and ways of thinking. The annihilation of the Tibetan language will have tremendous consequences for the Tibetan culture and hence it is important to preserve it. Tibetan language is mainly used in Tibet, Bhutan, and also in parts of Nepal and India. Tibetan script is devised based on the Devanagari model and Sanskrit based grammars. In this paper, a method for Tibetan handwritten character recognition based on density and distance feature detection is presents. To get a better classification result, images are converted into binary and noise removal is done by using Otzsos method. Features are extracted by normalizing the image based on distance and density of the pixel in the image. Finally, Hidden Markov Model is used for character classification. BEIESP. -
Machine Learning and Deep Learning Analysis of Vehicle Carbon Footprint
Clearly climate change is one of the most significant hazards to mankind nowadays. And daily the situation has become worse. No other way characterises climate change except through changes in the patterns of temperature and weather. Human activity generates the primary greenhouse gas emissions. Among these activities are burning coal, oil, natural gas, as well as other fuels; agricultural techniques, industrial operations, deforestation, burning coal, oil. Mostly resulting from human activities, the average temperature of the planet has significantly increased by almost 1.1 degrees Celsius since the late 1800s. One theory holds that internal combustion engines affect roughly thirteen percent. The objective of this work is to do an analysis of a complicated dataset involving fuel consumption in urban and highway environments as well as mixed combinations since the relevance of these variables in modelling attempts dictates. Reduced CO2 emissions emissions and environmental impact follow from reduced fuel use. The project used numerous machine learning and deep learning approaches to comprehend data analysis. Moreover, this work investigates the dataset to acquire knowledge and concurrently solves problems such overfitting and outliers. Control of complexity is achieved using several methods like VIF, PCA, and Cross-Validation. Models combining CNN and RNN performed really well with an accuracy of 0.99. The R-squared metrics are utilized in order to do the evaluation of the model. Apart from linear regression, support vector machines, Elastic Net with a rewardable accuracy, random forest was applied. It has rather good 0.98 accuracy. We can therefore state that our model analyzed the data properly and generated accurate output since the results we obtained during the assessment phase exactly the same ones we obtained during the training stage. Mass data cleansing is required as well as further study to increase machine learning model accuracy and performance. 2024 The authors. -
Testing the Diversifying Asset Hypothesis between Clean Energy Stock Indices and Oil Price
In theory, geopolitical risk and political uncertainty can directly affect energy markets. Fluctuations lead to the cost of clean energy sources as they compete with traditional energy. The purpose of this study is to analyse financial integration and test the diversifying asset hypothesis between clean energy indices, specifically the Clean Energy Fuels (CLNE), Nasdaq Clean Edge Green Energy (CELS), S&P Global Clean Energy (SPGTCLEN), TISDALE Clean Energy (TCEC.CN), Wilderhill (ECO) and West Texas Intermediate (WTI) stock indices, over the period from 1 January 2018 to 23 November 2023. Analysing the results reveals a scenario where most of the clean energy indices show cointegration with each other, indicating long-term relationships that reflect common trends in the clean energy sector. However, the relative independence of the WTI suggests that Oil still acts as an important and potentially diversifying external factor for investors focused on sustainable energy. Structural breaks in 2021 and 2022 in several indices point to significant events that have altered market dynamics, possibly including changes in environmental policies, technological innovations and the impacts of the COVID-19 pandemic. The cointegration evidence and structural breaks provide valuable information for building investment portfolios. Investors can consider the WTI to diversify portfolios dominated by clean energy assets, taking advantage of Oils relative independence. On the other hand, the high correlation between clean energy indices suggests that, within this sector, diversification options are more limited, requiring careful analysis of the specific characteristics of each index and the macroeconomic forces affecting them. 2024, Econjournals. All rights reserved. -
Exploring the Relationship between Clean Energy Indices and Oil Prices: a Ten-Day Window approach
This paper aims to assess the comovements between clean energy indices, namely the Clean Energy Fuels (CLNE), Nasdaq Clean Edge Green Energy (CELS), S&P Global Clean Energy (SPGTCLEN), TISDALE Clean Energy (TCEC.CN), Wilderhill (ECO), West Texas Intermediate (WTI) stock indices, over the period from 1 January 2018 to 23 November 2023. We used 10-day windows to analyse the duration and nature of the shocks. Granger causality tests revealed that 20 of the 30 possible pairs showed significant movements, with the WTI influencing all the clean energy indices, highlighting its global importance. CELS also showed a robust influence on all pairs, while SPGTCLEN had a significant but less far-reaching influence. The CLNE and ECO indices showed limited influences, suggesting the potential for diversification, the TCEC.CN proved to be independent and a determining factor for portfolio diversification. The Impulse Response Functions (IRF) confirmed significant movements between CELS, SPGTCLEN and WTI, reflecting the market's response to policies and adjustments in expectations. Fluctuations in oil prices substantially affect clean energy indices, highlighting the interconnectedness and volatility of these markets. In conclusion, these results indicate that despite the growth of clean energy, the sector is still influenced by fluctuations in the fossil fuel market. 2024, Creative Publishing House. All rights reserved. -
Delving into the Exchange-Traded Funds (ETFs) Market: Understanding Market Efficiency
Exchange-traded funds (ETFs) are the most popular products in the financial sector today. There is extensive literature on the multifractal analysis of some stock markets, but not about the multifractal behaviour of the ETF market. This study examines the efficiency of stock index ETFs worldwide from an Efficient Market Hypothesis (EMH) perspective, using the ETFs: Ishares Msci World ETF (URTH), Ishares Russell 1000 ETF (IWB), SPDR S&P 500 ETF TRUST (SPY), Ishares Global Clean En. ETF (ICLN), Ishares USD Green Bond ETF (BGRN), from 1 January 2021 to 24 May 2024. It analyses a pre-conflict and a geopolitical conflict to uncover distinct patterns of behaviour reflecting significant changes in market conditions. Before the conflict, the Ishares MSCI World, Ishares Russell 1000, SPDR S&P 500 and Ishares USD Green Bond ETFs showed signs of anti-persistence in returns, indicating a lack of strong relationship or predictability between short-term price movements. The Ishares Global Clean Energy ETF did not reject the random walk hypothesis, suggesting that returns follow a pattern closer to random, where market prices already efficiently reflect all available information. During the conflict, there was a transition in the ETFs' behaviour patterns, as evidenced by the increases in slope values for Ishares MSCI World, Ishares Russell 1000, SPDR S&P 500, Ishares Global Clean Energy and Ishares USD Green Bond. Thus, the possible transition from anti-persistence to long-term memories in ETF returns during the conflict. For portfolio managers, these findings highlight the need to continually adapt investment strategies to manage risks better and take advantage of opportunities in a dynamic and complex investment environment. 2024, Creative Publishing House. All rights reserved. -
Analyzing online food delivery industries using pythagorean fuzzy relation and composition
Food and beverages constitute a significant portion of the family expenditure, which motivates the food delivery companies in striving hard to meet the customer needs through their dynamic food delivery apps. The online food ordering system is one of the most profitable marketing strategies for restaurant businesses. The face of the restaurant industry has shifted from the traditional dine-in culture to takeaways, online ordering, and home deliveries. Digital technology and social media have a significant role in ensuring the efficiency and popularity of a food delivery app. The four essential factors for a food delivery company to satisfy the needs of the consumers in day to day life are choice of restaurants, speed of delivery, payment option and quality of service. The objective of this study is to discern and analyse these four essential factors adopted by the leading four food delivery companies and evaluate the perceptions of the consumers. The best online food delivering company is identified using Pythagorean Fuzzy Relation (PFR)and composition. The analysis concludes that Zomato food application is the best in consumers perception.The outcome of the survey is made more efficient by adopting a mathematical approach. Copyright IJHTS. -
Impact of COVID-19 on Delivery of Quality Hospitality Education in India
The Covid-19 pandemic caused many industries globally to undergo radical changes in their operational systems, disrupting the service delivery processes. The education industry is no exception to this phenomenon. India's higher educational institutions witnessed the immense challenge of taking the teaching process online with limited means and infrastructural support. This study aimed to assess the impact of the pandemic on the delivery of education online in India with particular reference to hospitality courses. A survey of 250 students and interview of 10 faculty members from 5 universities offering hospitality course across India showed that the online learning system is far from satisfactory and effective. Moreover, teachers need to undergo training sessions in order to improve their online teaching skills and create newer methods of imparting skills and evaluating students' performance. IJHTS -
A study of entrepreneurial choices and challenges encountered by young graduates
India, one of the most populous countries is growing phenomenally, though the challenges of unemployment is compounding. An unique method of overcoming this issue is through motivation of college students in becoming entrepreneurs, which will not only create employment but will also reduce the pressure of gaining employment on the students. However, flexible government policies in favor of entrepreneurs will facilitate the economic development of the country. In this study, a quantitative method is used to collect the data on entrepreneurship and the changing preferences of college students. A survey (N= 209) among college students of Bengaluru, India is conducted to identify the impact of entrepreneurship on work life choices of young graduates, evaluate the emergence of entrepreneurs in influencing decisions and analyzing the differing choices of males and females in terms of entrepreneurial selections. Analysis of the collected data indicates that Indian Government policy, unskilled labor, entrepreneurial education, family background and caste are factors affecting the entrepreneurial growth rate in Bangalore. Entrepreneurship education in Bangalore is still in the early stages, thus, depriving the college students from acquiring gainful practical knowledge. The structure of a conventional learning system and lack of social experiences also affects the learning process. 2019, International Journal of Scientific and Technology Research. All rights reserved. -
Women chefs in Indian hospitality industry: Challenges and strategies /
International Multidisciplinary Research Journal, Vol.4, Issue 7, pp.117-132, ISSN No: 2231-5063. -
Reflections on the issues and determinants associated with women's career progression in hospitality industry at Bengaluru /
Social Sciences International Research Journal, Vol.2, Special Issue, ISSN: 2935-0544.