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Isolation and characterization of plant growth promoting bacteria (PGPB) from the rhizosphere of Spinacea oleracea L.
As the years pass by, there is an increase in abiotic stress conditions around the environment that directly or indirectly affect agriculture around the world. Therefore, there is a dire need to increase the sustainability of plants. Plant Growth Promoting Bacteria (PGPB) play an important role in maintaining the physiology and growth of plants under various stress conditions. This study looks into the isolation and characterization of different PGPB from Spinacia oleracea L. and their tolerance against salinity and commonly used commercial pesticides against the Spinacia family. The techniques used are isolation by serial dilution, 16sRna sequencing, characterization of different PGPB assays for confirmation such as ammonia production, catalase test, phosphate solubilisation, potassium solubilization, siderophore production, indole-3-acetic acid production, biofilm formation assay, halotolerance and tolerance study using Minimal Inhibitory Concentration (MIC). PGPB were isolated and characterized from Spinacia oleracea L., which was under an abiotic stress environment. Isolates were Bacillus clarus, Bacillus licheniformis, Paenibacillus alvei SJ6 and Paenibacillus alvei SJ8, having quantities as high as 78.10.004 mgL-1 phosphate solubilization, 43.8 mgL?1 of indole-3-acetic acid production, 14.5660.011 psu of siderophore production and 0.62 0.027 mol mL?1 of ammonia production. All isolates also had considerable amounts of halotolerance up to 10%, whereas Bacillus licheniformis had 12.5% halotolerance. The bacterial isolates had considerable tolerance against commonly used commercial pesticides against green leafy vegetables such as chlorpyriphos + cypermethrin combination and fungicides such as mancozeb. Therefore, this study looks into the isolation of potential plant growth promoting bacteria that have considerable amount of halotolerance and pesticide tolerance. 2025 World Researchers Associations. All rights reserved. -
Study of cognitive adaptiveness of isolated Plant Growth Promoting Bacteria in nutritionally stress condition
The biological processes behind bacterial memory in different species are still under terra incognita. Additionally, the ability of learning through association in prokaryotes is still unknown. Cross-fertilization between the study of multicellular creatures' cognitive capacities and that of bacteria is possible. Therefore, Plant Growth Promoting Bacteria (PGPB) can be used to analyze this cognitive adaptation of bacteria under stress because PGPB is crucial to the maintenance of plant physiology and growth under a variety of stress scenarios. This study focuses on analyzing preliminary evidence of cognitive adaptability in PGPB under nutritional stress conditions. The isolated PGPB were treated with nutritional deprivation in both periodical and non-periodical manners and their performance was compared with the control group. The characteristics of PGPB, such as ammonia production, siderophore production, phosphate solubilization and indole-3-acetic acid, as well as anti-oxidant activities such as DPPH activity, hydroxyl radical scavenging activity and hydrogen peroxide scavenging activities, were analysed and compared to periodically and non-periodically stressed PGPB with control. In the isolated PGPB post-nutrition deprivation treatment, it was evident that the periodically stressed performed better than the non-periodically stress-exposed PGPB compared to the control wherein the isolates produced as high as 2.5510 mol mL-1 ammonia, 23.0406 mgL?1 indole-3-acetic acid, 69.16 0.71 psu siderophore and 123.5780.429mgL-1 phosphate solubilised. Out of the four isolated PGPB, the two novel strains, Paenibacillus alvei SJ6 and Paenibacillus alvei SJ8, have shown to possess the supreme ability to adapt to periodic nutritional stress compared to the other isolates in our study. 2025 World Researchers Associations. All rights reserved. -
The Adoption of AR and VR Emergency Room Procedures
[No abstract available] -
Synthetic Data Augmentation for Robust Solar Flare Classification: A Comparative Analysis of Conditional DCGAN, VAE, and Diffusion Models
Solar flares are extremely dangerous to the ground and space-based resources. Solar flares have to be classified properly and in good time to offer protection to assets in both the environments. Deep Learning-based flares have been divided into 3 classes (C, M and X). The main problems with this kind of classification are that high impact M- or X-class solar flares are extremely rare, and cannot be well sampled, thus leading to a very unbalanced sample. This paper exemplifies a comparative analysis of three models of Conditional Generative Models (cDCGAN models, cVAE models and cDDPM) to produce realistic images of solar flares considering each of the low frequency and high-impact solar flare types. The research question will be how such models can be evaluated in terms of their capacity to create realistic, class-specific images (magnetograms, and EUV) and time-series data which could be used to create class-balanced training samples. The initial experiments make use of the cDCGAN, cVAE, cDDPM architecture and considering the generation of class-conditional solar flare images portray high levels of stability (convergence was stated in less than 600 epochs in the case of the cDCGAN, cVAE models and cDDPM) and the generation of images which could be considered as practically indistinguishable to real life images. The results indicate that cDDPM may be a possible solution to a high-fidelity production of solar features. We measured the efficiency of these models in quantitative terms (popular metrics, like the Frhet Inception Distance, Structural Similarity Index) in a manner that we could determine the best manner of training model based solar flare classification systems using realistic data. This research is aligned with Sustainability Development Goals 9- Industry, Innovation and Infrastructure with focus on verticals 9.1 and 9.5. 2026 IEEE. -
Seasonal study on the Aquatic and Terrestrial Habitat of Edayar region, Ernakulam, Kerala, India
This study examines the plant diversity and physicochemical characteristics of both aquatic and terrestrial ecosystems in the industrialized region of Edayar, Kadungalloor, Ernakulam, Kerala, India. The research is conducted seasonally, encompassing the four seasons of Kerala: southwest monsoon, northeast monsoon, winter season and summer season. Edayar is home to approximately 400 industries. The main objective of this study is to assess the plant diversity with a specific focus on herb and macrophyte diversity, in the Edayar region, along with analyzing the physicochemical properties of soil and water. Random sampling using quadrat techniques is employed to collect data on species diversity. Diversity indices, such as the Simpson Index and Shannon-Wiener index are utilized to analyze the recorded species diversity. Scoparia dulcis L. among herb species and Eichhornia crassipes (Mart.) Solms among macrophytes were found dominating in all the seasons. The results for the physico-chemical analysis of water and soil were found approaching the threshold of standard limits.The findings provide valuable insights into plant diversity and ecological dynamics of the Edayar region, which have been significantly impacted by industrial activities. The outcomes serve as a basis for the development and implementation of effective conservation and management strategies to mitigate potential ecological risks associated with industrial activities in the region. 2024 World Researchers Associations. All rights reserved. -
Accumulation of heavy metals (Cr, Cu, As, Cd, Pb, Zn, Fe, Ni, Co) in the water, soil and plants collected from Edayar Region, Ernakulam, Kerala, India
The accumulation of heavy metals in the environment is a significant concern due to their potential toxicity and persistence. This study investigates the levels of heavy metal contamination in the water, soil and plants of the Edayar region in Ernakulam, Kerala, India. The region has experienced industrialization and urbanization, leading to concerns about heavy metal pollution. The study aims to assess the concentrations of chromium (Cr), copper (Cu), arsenic (As), cadmium (Cd), lead (Pb), zinc (Zn), iron (Fe), nickel (Ni) and cobalt (Co) in water, soil, aquatic and terrestrial plants. Samples were collected from various locations within the Edayar region, and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) was conducted to quantify heavy metal concentrations. The findings of this study will contribute to the assessment of heavy metal pollution in the Edayar region. Plants with a high diversity index were taken for analysis from both aquatic and terrestrial habitats. Scoparia dulcis L. seems to specialize in metal accumulation, possibly for protective purposes. Synedrella nodiflora Gaertn demonstrates adaptability to metal-rich environments through robust metal uptake and tolerance mechanisms. Alternanthera philoxeroides (Mart.) Griseb, on the other hand, appears to have developed mechanisms to manage heavy metal exposure. The results indicate significant levels of heavy metal contamination across all samples, with the highest concentrations detected in soil, followed by water and plants. Chromium and lead levels in soil exceeded the permissible limits set by international standards, posing potential risks to human health and the ecosystem. The accumulation patterns in plants varied, with higher bioaccumulation factors observed for zinc and copper, suggesting their preferential uptake. This study highlights the urgent need for remediation strategies and continuous monitoring to mitigate the impact of heavy metal pollution in the Edayar region. The results will help in understanding the environmental impact of human activities. Copyright: The Author(s). -
Unraveling the Interplay Between Biodiversity and Heavy Metal Content in Elookkaras Aquatic and Terrestrial Ecosystems
Background and Objective: There exists a notable correlation between biodiversity and the concentration of heavy metals, particularly concerning their role in bioremediation efforts. This study was about the heavy metal content in the aquatic and terrestrial ecosystem of Eloorkkara, located in the Kadungalloor Grama Panchayat of Kerala, India. Materials and Methods: Sampling was systematically carried out across all four seasons in order to capture the fluctuations in seasonal disturbances. Eight samples each of groundwater, river water, aquatic soil and terrestrial soil were randomly collected from the study area. Additionally, three dominant plant species from both aquatic and terrestrial habitats were carefully selected for analysis. Utilizing Inductively Coupled Plasma Mass Spectrometry (ICP-MS), the samples underwent thorough analysis to measure the levels of Cr, Cu, As, Cd, Pb, Zn, Fe, Ni and Co concentrations. Results: Indicate significant differences in heavy metal concentrations across various plant species and throughout seasonal changes, emphasizing the complex processes involved in metal accumulation. Terrestrial ecosystems exhibited higher species richness compared to aquatic ecosystems. Areas with high biodiversity tended to have lower metal concentration suggesting a potential mitigating effect of diverse ecosystems and areas with poor diversity had higher heavy metal concentration suggesting the vulnerability of degraded ecosystems. Conclusion: The research highlights the crucial role of biodiversity in influencing the absorption and dispersion of heavy metals within ecosystems. These findings carry significant implications for environmental management and conservation efforts aimed at curbing heavy metal pollution and safeguarding biodiversity in Elookkara and analogous environments. 2024 Chandni Asha Syamlal and D. Sayantan. -
Application of Regression Analysis of Student Failure Rate
The education sector has been rapidly growing and is currently facing several challenges. One such challenge is identifying students who are at risk of failing, as this can help educators provide targeted interventions to improve student performance. Machine learning models have been developed to predict the probability of student failure based on various student performance metrics to address this issue. In this paper, we present a regression-based model that predicts the probability of student failure using student performance metrics such as attendance, previous academic performance, and demographic information. The model was trained on a dataset of students and achieved high accuracy in predicting the probability of student failure. While the model performs well in predicting the probability of student failure, there is always room for improvement. Possible enhancements to the model include feature engineering, ensemble learning, hyperparameter tuning, deep learning, and interpretability. These enhancements can improve the models accuracy, stability, and transparency, leading to better predictions and targeted interventions for at-risk students. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Soft Computing Approach for Student Dropouts in Education System
The education system has increased the number of dropouts in the coming years, decreasing the number of educated people. Education system refers to a group of institutions like ministries of education, local education bodies, teacher training institutes, universities, colleges, schools, and more whose primary purpose is to provide education to all the people, especially young people and children in educational settings. The research aims to improve the student dropout rate in the education system by focusing on students performance and feedback. The students dropout rate can be calculated based on complexity, credits, attendance, and different parameters. This study involves the extensive study that inculcates student dropout with their performance and other parameters with soft computing approaches. There are various soft computing approaches used in the education system. The approaches and techniques used are sequential pattern mining, sentimental analysis, text mining, outlier decision, correlation mining, density estimation, etc. The approaches and techniques will be beneficial to calculating and decreasing the rate of dropout of students in the education system. The research will make a unique contribution to improved education by calculating the dropout rate of students. In particular, we argue that the dropout rate is increasing, so soft computing techniques can be the solution to improvise/reduce the dropout rate. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Investigation of Diesel Engine Performance, Emissions and Combustion Characteristics Utilizing Emulsified Biodiesel at Varied CRs
In this experimental investigation, a varied CR (CR) diesel engine is fuelled with palmyra biodiesel B20 (20% palmyra methyl ester + 80% diesel) and emulsified palmyra biodiesel (85% B20 + 10% water + 5% surfactant), with span 80 and tween 80 (hlb of 6.43) used as surfactants. The study aims to evaluate the performance, emissions and combustion characteristics of the engine at varying CR of 17, 17.5 and 18 with the standard CR set at 17.5. Results show that increasing the CR leads to an improvement in Brake Thermal Efficiency (BTE), with a 3.89% higher BTE observed at a CR of 18 compared to 17. Additionally, higher CRs result in significant reductions in emissions, including hydrocarbons by 25.49%, carbon monoxide by 28.35% and smoke by 11.82% compared to running on neat diesel. These findings highlight the potential of emulsified palmyra biodiesel at higher CRs to improve the engine efficiency and reduce emissions, emphasizing its viability as a sustainable alternative fuel. 2025. Carbon Magics Ltd. -
Artificial Intelligence in Banking Security-Technical Innovations and Challenges
The accelerating adoption of artificial intelligence (AI) technologies in the banking sector has introduced transformative possibilities for enhancing security frameworks against increasingly sophisticated cyber threats. This research investigates the technical innovations driven by AI, such as machine learning algorithms, biometric authentication systems, and natural language processing, and their impact on improving fraud detection, cybersecurity monitoring, and compliance automation. The paper identifies how AI systems, through real-time analysis of large-scale transaction data, can locate abnormal behavioral patterns and respond proactively to potential threats, significantly reducing human error and response time. A detailed analysis of the current literature reveals a significant research gap in integrating explainable AI, secure data governance frameworks, and scalable models suited for diverse banking environments. The outcome of this research highlights the need for a balanced approach that fosters technological innovation while addressing regulatory compliance, ethical concerns, and operational constraints, paving the way for a secure and intelligent banking infrastructure. 2025 IEEE. -
Social support and help-seeking worldwide
Social support has long been associated with positive physical, behavioral, and mental health outcomes. However, contextual factors such as subjective social status and an individuals cultural values, heavily influence social support behaviors (e.g., perceive available social support, accept support, seek support, provide support). We sought to determine the current state of social support behaviors and the association between these behaviors, cultural values, and subjective social support across regions of the world. Data from 6,366 participants were collected by collaborators from over 50 worldwide sites (67.4% or n = 4292, assigned female at birth; average age of 30.76). Our results show that individuals cultural values and subjective social status varied across world regions and were differentially associated with social support behaviors. For example, individuals with higher subjective social status were more likely to indicate more perceived and received social support and help-seeking behaviors; they also indicated more provision of social support to others than individuals with lower subjective social status. Further, horizontal, and vertical collectivism were related to higher help-seeking behavior, perceived support, received support, and provision of support, whereas horizontal individualism was associated with less perceived support and less help-seeking and vertical individualism was associated with less perceived and received support, but more help-seeking behavior. However, these effects were not consistently moderated by region. These findings highlight and advance the understanding of how cross-cultural complexities and contextual distinctions influence an individual's perception, processing, and practice of social support embedded in the changing social landscape. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Developing a steady state wear equation for AA7050 hybrid composites/steel interface at elevated temperature
In this research work, an attempt was made to reinforce AA7050/5Gr composites with multi-walled carbon nanotube (MWCNT) of varying weight percentages processed through stir casting route. SEM with EDS mapping revealed that the particles were uniformly distributed over the composites. Hardness reduces with increasing MWCNT weight percentage owing to the inverse hall petch effect and increment in void content. A third-body abrasion, which happens when the CNT in the aluminium matrix material detaches from the surface and erodes material from the composites pin as well as the counter face, causes the wear resistance to rise with the addition of CNT particles. A mechanically mixed layer, which avoids direct metal-to-metal contact and thus increases wear resistance, was created at the abraded surface and at high temperature, where the reduction of wear rate was due to the development of oxide protective layer. A steady-state wear equation for the contacting surface at high temperature (R = 1/Y ?(ln W/2X)) for AA7050 hybrid compositessteel interface was developed. The enhancement in wear resistance was directly proportional to the proportion of ferrous content present on the surface, which was confirmed on the elemental analysis. Pock marks, micropits, craters and cracks were the features observed on the worn surface morphology, whereas delamination and plasticisation were the observed modes of wear mechanism. 2023 Informa UK Limited, trading as Taylor & Francis Group. -
Small finance banks and financial inclusion in India /
Research Review Journals, Vol.4, Issue 3, pp.1586-1588, ISSN No: 2455-3085. -
Financial access indicators of financial inclusion: A comparative analysis of SAARC countries /
International Journal Intelligent Enterprise, Vol.7, Issue 1/2/3, pp.28-36 -
Emotional intelligence and work life balance of women IT professional in Bangalore /
Adarsh Journal of Management Research, Vol.7, Issue 2, pp.241-253, ISSN No: 0974-7028. -
Role of knowledge management strategies on employees performance in selected information technology companies In Bangalore /
International Journal of Management And Social Sciences, Vol.8, Issue 2.1, pp.54-58, ISSN No: 2349-9761. -
Dual purpose behavior of Ni-PTC MOF for high performance supercapacitor and water splitting applications
Metal-organic frameworks (MOFs) have elicited significant interest as next-generation materials for storing and converting energy, owing to their structural versatility and tunable physicochemical properties. In the present work, a nickel-based MOF, referred to as Ni-PTC, was synthesized via a straightforward method and explored for its dual functionality as a supercapacitor electrode and an electrocatalyst for overall water splitting. Structural and morphological analyses confirmed the materials high surface area, hierarchical porosity, and excellent crystallinity. As a supercapacitor electrode, Ni-PTC delivered a high specific capacitance of 953.86 F g?1 at 1 A g?1 and demonstrated superior cycling durability, retaining 92 % of its initial capacitance after 5000 cycles. Its electrocatalytic performance was assessed for both hydrogen (HER) and oxygen evolution reactions (OER), exhibiting overpotentials of 241 mV and 400 mV, respectively, at a current density of 10 mA cm?2. The catalyst also showed excellent operational stability, underscoring its potential in energy-related applications. 2026 Elsevier B.V. -
International multi conference on computing, communication, electrial & nanotechnology /
Materials Today Proceedings, Vol.11, Issue 3, pp.889-115, ISSN No:2214-7853. -
Impact of the professional development programme on mentorship among Salesian college faculty
The National Education Policy (NEP, 2020) in India emphasises faculty professional development in mentorship as a key strategy for strengthening higher education. This study examines the impact of a Professional Development Programme on Mentorship (PDP-M) in enhancing self-efficacy and outcome expectancy among 37 faculty members at Salesian College. The intervention consisted of a 15-hour module, comprising 6 h of structured core sessions and 9 h of supplementary activities, adapted from the TPDP-M framework. A quasi-experimental, single-group prepost design was employed with in-service faculty participants. Quantitative measures of self-efficacy and outcome expectancy were collected before and after the intervention. Statistical analysis revealed statistically significant improvements in the total mentorship score, self-efficacy, and outcome expectancy. This study demonstrates that short, intensive, context-sensitive professional development programmes can effectively strengthen faculty mentoring capacity, offering a scalable model for higher education reform. 2026 Informa UK Limited, trading as Taylor & Francis Group.