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Enhancement of nitrogen assimilation and photosynthetic efficiency by novel iron pulsing technique in Oryza sativa L. var Pankaj
Rice is a major food crop. Due to urbanization and climate change, rice production is declining, posing a threat to the increasing food demand. For this, a modified technique of priming is used to enhance plant vigor. In the present study an endogenous rice cultivar was treated with two different iron salts for 72 h and grown for 14 days in nutrient solution. This increased the iron content of the samples which further escalated the photosynthetic efficiency and carbon assimilation in the treated plants. Photosynthesis being correlated to nitrogen assimilation, nitrogen assimilation intermediates and protein content were also elevated in treated plants. Plants showed no symptoms of stress as evident from low malondialdehyde content and increased antioxidant enzymes activity. From this study it can be inferred that, treatment with iron during germination, helps to trigger growth by facilitating photosynthesis and nitrogen assimilation. 2019 Elsevier Masson SAS -
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. -
Unveiling the realm of AI governance in outer space and its importance in national space policy
This article explores the notable legal concerns that may arise from the growing utilisation of artificial intelligence and machine learning in outer space. Whether it is conducting space exploration, clearing orbital debris, or extracting resources from specific areas in space, these activities are becoming more popular. Therefore, it is necessary to establish a regulatory framework to ensure consistency and objective standards. In order for national space legislation to effectively address the challenges presented by activities involving robots with different levels of autonomy and numerous objectives, it is essential to appraise the nature of these challenges. The article aims to investigate the relationship between the Montreal Declaration for a Responsible Development of Artificial Intelligence, 2017, and outer space laws and principles. It also examines the legal status of autonomous space objects, such as planetary rovers that are currently in operation or will be in the near future. Ultimately, the article highlights the importance of national space policy in addressing the appropriate regulation of artificial intelligence in outer space. In conclusion, this article has also discussed the potential effectiveness of utilising artificial intelligence-based methodologies and strategies to enhance current space policy. 2024 IAA -
Balancing commercialization and sustainability in outer space: Addressing new challenges
One notable aspect of NewSpace is the growing influence of private non-state actors. Nation-states have also implemented policies that facilitate the involvement of private non-state players in space activities. The lack of progress in international space law since the Moon Agreement of 1979 highlights the need to reconsider and update the existing legal system. Therefore, it is necessary to create a different framework based on the concept of commercial reality. The article emphasises the impact of NewSpace on the technological and commercial aspects of the outer space industry. An analysis is conducted on the crucial requirement for regulatory reform in the field of outer space law and policy, which arises from these advancements in the future. The persistent obstacle in addressing the commercial development of NewSpace is the lack of clarity surrounding property rights in outer space, particularly concerning the ownership and utilisation of space resources. This article examines the issue presented by the national space laws, which have conflicting and inconsistent views on property rights in outer space. The article also aims to investigate the practicality of establishing an international framework for space cooperation. The importance of the New Age institutional mechanism for international collaboration and the agenda for the proposed mechanism are emphasized. The proposed institutional framework aims to achieve a balance between commercialization and profit-driven market pressures, considering their potential negative effects on the sustainable usage of outer space. In conclusion, the article discusses India's appropriate course of action in response to the emerging problems in the field of space exploration. 2025 IAA -
Antibacterial performance of chitosan-modified magnesium fluoride nanoparticles: Synthesis and characterization
The rise of multidrug-resistant bacterial infections threatens human health by reducing the effectiveness of conventional antibiotics. This growing challenge highlights the urgent need for advanced nano-based antibacterial materials capable of overcoming resistance and providing broad-spectrum protection. In this study, magnesium fluoride (MgF2) NPs and Chitosan modified MgF2 (MgF2-Cs) were synthesized via a facile wet-chemical route and characterized to evaluate their structural, surface, and antibacterial properties. XRD confirmed the formation of tetragonal MgF2 with crystallite sizes of 29nm for MgF2 and 22nm for MgF?Cs, the reduction attributed to Cs-induced surface modification. FTIR, PL, and XPS analyses verified successful Cs incorporation through the presence of OH, NH?, CN, and OC=O functional groups and the preservation of the MgF2 lattice. DLS further supported increased hydrodynamic size upon polymer coating. PL analysis showed enhanced blue-green emission around 497nm in MgF2Cs, suggesting increased defect density and corresponding ROS-generation ability. Antibacterial activity against Gram-positive: S. aureus, S. pneumoniae and Gram-negative: K. pneumoniae, S. dysenteriae bacteria demonstrated significantly improved inhibition for MgF2Cs, with zone diameters of 1521mm, surpassing MgF2 (1216mm) and Cs (1115mm. The MIC and MBC values for MgF?-Cs against K. pneumoniae were determined to be 0.6mgmL?1 and 0.9mgmL?1, respectively. The enhanced antibacterial performance is attributed to synergistic effects of defect-mediated ROS production and Csbacteria electrostatic interactions. 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies. -
Forecasting Bitcoin Price During Covid-19 Pandemic Using Prophet and ARIMA: An Empirical Research
Bitcoin and other cryptocurrencies are the alternative and speculative digital financial assets in today's growing fintech economy. Blockchain technology is essential for ensuring ownership of bitcoin, a decentralized technology. These coins display high volatility and bubble-like behavior. The widespread acceptance of cryptocurrencies poses new challenges to the corporate community and the general public. Currency market traders and fintech researchers have classified cryptocurrencies as speculative bubbles. The study has identified the bitcoin bubble and its breaks during the COVID-19 pandemic. From 1st April 2018 to 31st March 2021, we used high-frequency data to calculate the daily closing price of bitcoin. The prophet model and Arima forecasting methods have both been taken. We also examined the explosive bubble and found structural cracks in the bitcoin using the ADF, RADF, and SADF tests. It found five multiple breaks detected from 2018 to 2021 in bitcoin prices. ARIMA(1,1,0) fitted the best model for price prediction. The ARIMA and Facebook Prophet model is applied in the forecasting, and found that the Prophet model is best in forecasting prices. 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Analysing Crypto Trends: Unveiling Ethereum and Bitcoin Price Forecasts Through Analytics-Driven Weighted Moving Averages
This research meticulously analyses the performance dynamics of two paramount cryptocurrencies, Bitcoin and Ethereum, over 2,682 observations. Preliminary findings indicate a near alignment in the mean returns of both assets, with Ethereum marginally outperforming Bitcoin. Interestingly, Ethereums superior returns are accompanied by heightened volatility, underlined by its more significant standard deviation. Both cryptocurrencies manifest negative skewness, hinting at a proclivity for negative returns, with Bitcoin showing a sharper skew. Their pronounced kurtosis values attest to the potential for extreme price swings. Regarding forecasting efficacy, the Weighted Moving Average (WMA) method emerges as superior for both assets, yielding the most accurate predictions. At the same time, the Exponential Moving Average (EMA) demonstrates the highest forecast errors. Further, the Relative Strength Index (RSI) evaluation suggests Ethereum may be oversold, alluding to potential investment opportunities. In contrast, Bitcoin, with its mid-range RSI, resides in a neutral zone devoid of clear market signals. The findings shed light on the nuanced performance and forecasting landscape of these leading cryptocurrencies, offering pivotal insights for potential investors. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
An impact of AI and client acquisition strategies in real capital ventures
In the contemporary business environment, marked by rapid changes, client acquisition stands out as a pivotal factor for companies aiming at sustained growth, particularly in sectors such as finance and real estate. The ability to attract and retain clients is not only a measure of a company"s current success but also a fundamental driver for its future viability. This study focuses on Real Capital Ventures LLP, a company operating at the intersection of finance and real estate, aiming to unravel the intricacies of its client acquisition strategies. The overarching goal is to conduct an exhaustive examination of the current approaches employed by the firm and provide nuanced recommendations for refinement. By doing so, the study aspires to contribute to the enhancement of the effectiveness of Real Capital Ventures LLP"s client acquisition, ensuring its continued success in a fiercely competitive market. 2024 by IGI Global. All rights reserved. -
The Shift towards a Green Economy: Addressing the Sustainability Challenges of Technological Transformation
The Green Economy is a progressive framework for development and growth that promotes economic progress along with environmental stability, as well as prioritizing people's needs in their social life. It is also crucial operational tool to apply the de-growth notion, to promote development and clean technologies. The Chapter stresses the interest of sustainable technology development and calls for formal decision-makers and society actors to be aware about bottlenecks in this field. Five interconnected challenges structure discussions: uncertainty about green capitalism and subjects responsible state interventions to shape optimal mixes (the mix problem); and distributional issues. The chapter suggests that the `inter-connected' dimension of sustainable technological development brings into question traditional approaches to a relationship between private enterprise and successful innovation. It also highlights the importance of understanding and utilizing effective policy tools in varying institutional context, while raising a research agenda for further investigation. 2026, IGI Global Scientific Publishing. All rights reserved. -
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. -
New distribution records and molecular characterization of six commercially important groupers (Teleostei: Perciformes: Epinephelidae) from the Lakshadweep Islands, India
This study documents the first recorded occurrence of six commercially important grouper fish species (Teleostei: Perciformes: Epinephelidae) from the Lakshadweep Islands, India. The species documented in this study are the Snubnose grouper Epinephelus macrospilos (Bleeker, 1855), Brownspotted grouper Epinephelus chlorostigma (Valenciennes, 1828), Dotted grouper Epinephelus epistictus (Temminck and Schlegel, 1842), Slender grouper Anyperodon leucogrammicus (Valenciennes, 1828), White-edged lyretail Variola albimarginata Baissac, 1953 and Oblique-banded grouper Epinephelus radiatus (Day, 1868). Both morphological examination and molecular analysis were carried out to characterize and confirm the identity of these species. This investigation represents the first documentation of these six grouper species from the Lakshadweep Islands. The results enhance current understanding of the marine biodiversity of the region and provide valuable information for the management and sustainability of its commercially important fisheries. Copyright 2026 Magnolia Press -
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. -
KnowSOntoWSR: Web Service Recommendation System Using Semantically Driven QoS Ontology-Based Knowledge-Centred Paradigm
Web services have significantly expanded and become a key enabling technology for online data, application and resource sharing. Designing new methods for efficient and reliable web service recommendation has been of tremendous importance with the growing usage and prominence of web services. It would be ideal for a system to suggest online services that are in line with consumers preferences without requesting specific query information from them. Quality of Service (QoS) is vital for characterising non-functional aspects of Web services as they become more prevalent and widely used on the World Wide Web. The KnowSOntoWSR framework, which is built on a knowledge-driven and semantically inclined model that adheres to QoS ontology, is proposed in this research. AWS and WebSphere are employed as knowledge tags, and the powerful machine learning classifier XGBoost is applied. The features and recommendations are computed using the Twitter semantic similarity. The proposed framework outperforms the baseline models estimates with an accuracy of 95.94% and average F-measure of 95.93%. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Navigating the Metaverse: TCCM Approach for Comprehensive Review of Avatar Marketing Strategies
Despite the growing importance of avatars in reshaping consumer interactions, there remains a discernible gap in the literature necessitating a comprehensive synthesis of existing knowledge. In response, this study conducts a systematic-literature-review employing the Theory-Context-Characteristics-Methodology (TCCM) framework. Study aims to delineate the current state of the field by addressing key research foci and conducting a meticulous TCCM analysis. Drawing from the TCCM-framework, our investigation encompasses an exploration of (1) dominant theories guiding research endeavours, (2) contexts within which avatar marketing is situated, (3) key characteristics characterizing the studies, (4) diverse study methodologies employed, and (5) the overall trajectory of research in this domain. Study unfolds a panoramic view of avatar marketing in the metaverse, synthesizing existing knowledge, and illuminating the key dimensions that shape this dynamic field. Insights gained from systematic-literature-review contribute to a deeper understanding of the theories, contexts, characteristics, and methodologies that have underpinned research endeavours thus far. 2013 The Korean Society of Management Information Systems. All rights reserved. -
Perceptions of Safety and Social Interaction in Urban Neighborhoods
This study aims to understand the patterns of safety and social relations in residential areas of cities with special reference to Delhi. Qualitative data from the interviews and focus group discussions are combined with quantitative data collected from the survey questionnaire. This paper aims to establish the factors that affect residents perception of safety such as urban design features, engagement activities, and social capital. The study emphasizes the importance of lit streets, accessible greens, and pedestrian infrastructure in improving safety perceptions and social inclusion. Neighborhood watch and other cultural activities surface as vital in the promotion of trust and responsibility among people in the community. This paper also reveals how social networks and community resilience influence the dynamics of neighborhoods. Suggested strategies for the improvement of urban planning are the incorporation of the residents opinions into the design process, financing of public areas, and backing of projects that enhance social connections. By so doing, cities can foster an environment within which people feel secure, valued, and able to play an active part in the development of the citys fabric. 2025, Green Publication. All rights reserved. -
Regulatory Challenges and Compliance in Decentralized Finance (DeFi): Comparative Study Between India and USA
Decentralized Finance (DeFi) is an emerging force transforming the global financial landscape by leveraging blockchain technology to eradicate the middlemen and assist peer- to- peer financial transactions. However, a decentralized and pseudonymous nature brings a big challenge in its regulation and compliance, especially in Know Your Customer (KYC) and Anti- Money Laundering (AML) regulations, market misconduct and adaptation, new cryptocurrency innovations, and safety and security issues. This paper comparatively analyses the two regulatory frameworks, compliance mechanisms, technical adaptations, and measures of cybersecurity regulating DeFi in India and the United States. By examining the salient regulatory challenges and compliance strategies in both jurisdictions, this study aims to provide insights that help foster a balanced regulatory environment that promotes innovation without undermining financial stability or consumer protection. 2025 by IGI Global Scientific Publishing. All rights reserved. -
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.