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Unveiling the Potential of Bacillus paramycoides, a Halotolerant Endophytic Bacterium with Heavy Metal Tolerance and Plant Growth Promotion Properties
The use of heavy metal resistant plant growth promoting endophytes is an effective method for improving crop yield and cleaning up contaminated sites. In our study, we have isolated thirteen bacterial endophytes from the shoots of Alternanthera philoxeroides, an aquatic plant from Bellandur lake, Bangalore, India. Among the isolates, Bacillus paramycoides showed significant plant growth promotion properties including an extortionate amount of indole acetic acid (IAA) production (144.69 1.01 g/mL) along with other plant growth promoting attributes like ammonia production, nitrogen fixation, phosphate, potassium solubilization, 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase and siderophore production. The isolate also demonstrated the ability to resist pathogen attacks by producing extracellular enzymes, which could have potential industrial uses. Furthermore, it displayed resistance to multiple heavy metals like chromium (Cr), copper (Cu), lead (Pb), zinc (Zn) and cadmium (Cd) as well as the ability to tolerate high salt concentrations (up to 7% NaCl). These characteristics make it an ideal candidate for promoting plant growth in stressful environments and as an effective bioremediation agent. 2024 World Researchers Associations. All rights reserved. -
Unveiling the potential of large language models: Redefining learning in the age of generative AI
Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) are transforming industries by fostering innovation, automating tasks, and enhancing creativity. By enabling personalized user interactions, sophisticated content creation, and advanced data analytics, they are revolutionizing industries such as healthcare, education, and customer service. As these technologies evolve, they can fundamentally change communication and decision-making processes and incorporate AI into everyday life. The objective of this book chapter is to examine the architecture and components, features, functionality, domain-specific applications, recent advances, and future developments of LLMs. Ongoing research aims to reduce biases, increase energy efficiency, and facilitate interpretation. As LLMs continue to evolve, they have the potential to transform many industries, including education, customer service, content creation, and more. As a result, they will be essential for the development of future AI-powered applications. 2024, IGI Global. All rights reserved. -
Unveiling the potential: iodide-infused nickel-enhanced MXene composite for high-performance sodium ion hybrid capacitors
2D-MXenes have gained much popularity for energy storage applications such as hybrid capacitors, and they have shown very competitive performance, especially as electrode materials for sodium ion hybrid capacitors. However, they suffer from various problems, such as morphology distortion and fast capacity fading, which results in the poor performance of the battery. As a result, researchers have focused more on MXene-based composite materials to address these issues. In this work, we report a sodium iodide and nickel-decorated MXene-based composite (Ti2C/Ni/NaI) material as an electrode for a sodium ion hybrid capacitor. Ti2C MXene and Ni were able to provide physical and mechanical strength, and iodine was able to produce redox activity. The composite had a rough surface with readily aggregated 2D-MXene sheets and was uniformly covered with Ni, Na, and I atoms. Several vibrational bands and peaks associated with Ti, Ni, Na, C and O in the Raman while XPS spectra confirmed the effective incorporation of dopants into the MXene sheets and successful synthesis of the Ti2C/Ni/NaI composite. The fabricated hybrid capacitor exhibited good capacity retention of 59% after 10,000 cycles at a current density of 0.5 mA g?1; thus, the Ti2C/Ni/NaI composite can be a promising electrode material for sodium-based hybrid capacitors. 2025 The Author(s). Published by IOP Publishing Ltd. -
Unveiling the potential: iodide-infused nickel-enhanced MXene composite for high-performance sodium ion hybrid capacitors
2D-MXenes have gained much popularity for energy storage applications such as hybrid capacitors, and they have shown very competitive performance, especially as electrode materials for sodium ion hybrid capacitors. However, they suffer from various problems, such as morphology distortion and fast capacity fading, which results in the poor performance of the battery. As a result, researchers have focused more on MXene-based composite materials to address these issues. In this work, we report a sodium iodide and nickel-decorated MXene-based composite (Ti2C/Ni/NaI) material as an electrode for a sodium ion hybrid capacitor. Ti2C MXene and Ni were able to provide physical and mechanical strength, and iodine was able to produce redox activity. The composite had a rough surface with readily aggregated 2D-MXene sheets and was uniformly covered with Ni, Na, and I atoms. Several vibrational bands and peaks associated with Ti, Ni, Na, C and O in the Raman while XPS spectra confirmed the effective incorporation of dopants into the MXene sheets and successful synthesis of the Ti2C/Ni/NaI composite. The fabricated hybrid capacitor exhibited good capacity retention of 59% after 10,000 cycles at a current density of 0.5 mA g?1; thus, the Ti2C/Ni/NaI composite can be a promising electrode material for sodium-based hybrid capacitors. 2025 The Author(s). Published by IOP Publishing Ltd. -
Unveiling the Quassia indica derived synthesis of Co3O4/ZnO nanohybrids for efficient dye degradation and cytotoxicity assessment
While there are exciting possibilities in nanotechnology, creating environmentally safe nanoparticles with a variety of uses in photocatalysis and biomedicine continues to be a significant issue. This work addresses the gap by introducing Quassia indica leaf extract as a bio reductant and stabilizer in the green synthesis of cobalt oxide-zinc oxide nanoparticles (QI: Co3O4/ZnO NP). The synthesized nanoparticles were characterized using various techniques, including UVvisible spectroscopy, X-ray diffraction (XRD), dynamic light scattering (DLS), high resolution transmission electron microscopy (HR-TEM), selected area electron diffraction (SAED), Fourier transform infrared spectroscopy (FTIR), field emission scanning electron microscopy (FE-SEM), and energy dispersive X-ray spectroscopy (EDX). The existence of hexagonal zinc oxide and cubic cobalt oxide phases in the synthesized nanoparticles was verified by XRD analysis. The elemental composition was confirmed by EDX, which showed that oxygen, zinc, and cobalt were present. The average hydrodynamic diameter of 244.5 d. nm was found via DLS measurements, indicating well dispersed nanoparticles. Under UV light irradiation, photocatalytic activity of QI: Co3O4/ZnO NP was assessed for the degradation of textile dyes (Reactive Blue-222, Reactive Blue-220, Reactive Red-120, and Reactive Yellow-145). Phytotoxicity tests were conducted to examine the possible environmental impact of the deteriorated dye solution, revealing promising results in mitigating the detrimental impact of industrial dyes. QI: Co3O4/ZnO NP was also assessed for cytotoxicity studies in DLA and EAC cells which showed a concentration-dependent cytotoxic effect. The research outcomes emphasize the significant potential of these nanoparticles in diverse arena by offering a sustainable and efficacious resolution to the present-day problems. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025. -
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 -
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 -
Unveiling the Redox Characteristics of Rutin Trihydrate-Canvas-Based Sensor for Hydrazine Sensing in Water Samples
The inclusion of redox mediators into electrocatalytic systems facilitates rapid electron shuttling kinetics and boosts the overall catalytic performance of the electrode. This approach overcomes the sluggish reaction dynamics associated with direct electron transfer, which may be impeded by restricted analyte access to the electrodes active sites. In contrast to conventional synthetic redox mediators, naturally sourced phytomolecule rutin trihydrate (RT), extracted from apple juice, offers potential ecological advantages. This bands with green chemistry principles and sustainability in electroanalytical approaches. The current work presents an eco-friendly and direct electrochemical approach to fabricate a redox-active RT-immobilized MWCNT-infused PEDOT hybrid material-modified glassy carbon electrode (GCE/MWCNT + PEDOT@RT). The developed electrode showcased a sharp and stable redox signal at E0 = 0.63 V vs Ag/AgCl with no surface-fouling characteristics. The efficacious functionalization of RT onto MWCNT + PEDOT was corroborated by a remarkable increase in the surface characteristics, enhanced electrochemical current responses, and low charge transfer resistance. The GCE/MWCNT + PEDOT@RT exhibited highly selective and sensitive sensing responses toward the toxic and potentially carcinogenic hydrazine (HZ) via cyclic voltammetry and differential pulse voltammetry techniques, yielding a low detection limit (DL) of 1.02 ?M and a sensitivity of 0.032 ?A ?M-1 in a linear dynamic range between 0 and 1350 ?M. In addition, the method was highly efficient for HZ detection in real samples of tanker, tap, and wastewater samples, producing a good recovery of ?98%. 2025 American Chemical Society. -
Unveiling the Redox Characteristics of Rutin Trihydrate-Canvas-Based Sensor for Hydrazine Sensing in Water Samples
The inclusion of redox mediators into electrocatalytic systems facilitates rapid electron shuttling kinetics and boosts the overall catalytic performance of the electrode. This approach overcomes the sluggish reaction dynamics associated with direct electron transfer, which may be impeded by restricted analyte access to the electrodes active sites. In contrast to conventional synthetic redox mediators, naturally sourced phytomolecule rutin trihydrate (RT), extracted from apple juice, offers potential ecological advantages. This bands with green chemistry principles and sustainability in electroanalytical approaches. The current work presents an eco-friendly and direct electrochemical approach to fabricate a redox-active RT-immobilized MWCNT-infused PEDOT hybrid material-modified glassy carbon electrode (GCE/MWCNT + PEDOT@RT). The developed electrode showcased a sharp and stable redox signal at E0 = 0.63 V vs Ag/AgCl with no surface-fouling characteristics. The efficacious functionalization of RT onto MWCNT + PEDOT was corroborated by a remarkable increase in the surface characteristics, enhanced electrochemical current responses, and low charge transfer resistance. The GCE/MWCNT + PEDOT@RT exhibited highly selective and sensitive sensing responses toward the toxic and potentially carcinogenic hydrazine (HZ) via cyclic voltammetry and differential pulse voltammetry techniques, yielding a low detection limit (DL) of 1.02 ?M and a sensitivity of 0.032 ?A ?M-1 in a linear dynamic range between 0 and 1350 ?M. In addition, the method was highly efficient for HZ detection in real samples of tanker, tap, and wastewater samples, producing a good recovery of ?98%. 2025 American Chemical Society. -
Unveiling the response of food inflationto the economic policy uncertainty, energy price shocks andcarbon emission
Purpose This research paper examines the impact of economic policy uncertainty, energy price shocks and carbon emissions on food inflation from a global perspective, for the period of 20012023. Design/methodology/approach To calibrate the economic policy uncertainty, carbon emissions and energy price shock, we apply the economic uncertainty index of Baker etal. (2016), carbon dioxide in a million tonnes and the energy price index. Finally, to accomplish the relevant objectives, we exert the panel autoregressive distributed lag (ARDL) and panel Granger non-causality model. Findings We can summarise the key empirical insights from this pragmatic examination as follows: Initially, the panel ARDL outcome suggests that in the long-run, economic policy uncertainty and energy inflation positively influence food inflation. The result further reveals that a surge in economic policy uncertainty and energy inflation would lead to an increase in food prices in the long run in these panel countries. Secondly, the relevant outcome demonstrates that, in the long run, carbon emissions do not have a significant impact on food prices across the panel nation. Finally, the causality analysis concludes that there is unidirectional causality from energy inflation, carbon emissions and economic policy uncertainty to food inflation. Originality/value This investigation aims to add three aspects to the theme of food inflation. First of all, we endeavour to capture the presence of the underlying impact of economic policy uncertainty, energy price shock and carbon emissions on food prices. Second, current research extends the literature by employing panel data econometric analysis in the above context. Furthermore, our research is novel in that we consider carbon emissions to reveal their impact on food prices, whereas none of the previous analyses ever contemplated the impact of carbon emissions on food prices. Finally, by extending this analysis to a heterogeneous economic outlook that includes both advanced and emerging economies globally, it provides policymakers with a clear understanding of an effective strategy for managing food inflation and achieving sustainability. 2025 Emerald Publishing Limited -
Unveiling the Role of Psychological Pain within Informal Institutions in Addressing Intimate Partner Violence
This study redefines the exploration of Intimate Partner Violence (IPV) by emphasizing psychological pain as the pivotal element of trauma, shifting away from focusing solely on aftermath experiences. Psychological pain has been considered as a core area for this research through the lens of biopsychosocial model and unbearable psychache. These theoretical approaches examine psychological pain as the foundational factor in subsequent victim experiences and reactions involved in intimate partner violences (IPV). Utilizing an in-depth case study method, it rigorously analyzes a victim's narrative within the IPV realm, detailing the intricate connection between psychological pain and resulting trauma. The participant of this study is visually impaired and the perceived pain and its intensity in the context of disability have also been analyzed. This pain significantly influences victimization and exacerbates physical suffering. IPV, trauma, and visual impairment intersect, creating complex challenges for individuals and communities. The paper discusses pain and IPV in the context of informal institutions and their complementary or challenging roles. By emphasizing psychological pain as the core of trauma dynamics, this research redefines the understanding of pain involved in IPV. The insights gained can contribute to the crucial implications for interventions among survivors in the realm of intimate partner violence. 2024 by authors, all rights reserved. -
Unveiling the root causes of diabetes using explainable AI
Diabetes is a non-communicable wide spread disease across the world. To investigate the risky factors that are associated with diabetes, and to start early and customized treatment, researchers are fascinated to explore existing machine learning or deep learning models and to develop more reliable algorithms. The advancement in technology and the increase in world population is an enriching source to prompt and explore the factors that decide a person to be diabetic. Several algorithms and approaches are in place to address these factors but are lacking in emphasizing with more interpretable features which convinces patent to trust the medicine, treatment, and have meaningful conversation with the physicians and artificially intelligent systems. To encourage the participation of people with diabetes for customized treatment and considering societal needs, this chapter explores the possibility of Explainable Artificial Intelligence (XAI) in diabetes detection and figuring out the significant features that dominate diabetes. 2025 Elsevier Inc. All rights reserved. -
Unveiling the supercapacitive behavior of electrospun Cr2CTx/carbon nanofiber membrane
A novel electrospinning-based strategy was employed to fabricate Cr2CTx/carbon nanofibers using Cr2CTx MXene and polyvinyl alcohol (PVA) as precursors. This approach enables the formation of porous, conductive composite MXene layers dispersed in carbon nanofibers. The resulting material exhibited notable supercapacitive performance, delivering 338.8 F g?1 capacitance, 67.7 Wh kg?1 energy, and 1998 W kg?1 power density. This journal is The Royal Society of Chemistry, 2025 -
Unveiling the synergistic effect of amorphous CoW-phospho-borides for overall alkaline water electrolysis
Amorphous transition-metal-phospho-borides (TMPBs) are emerging as a new class of hybrid bifunctional catalysts for water-splitting. The present work reports the discovery of CoWPB as a new promising material that adds to the smaller family of TMPBs. The optimized compositions, namely Co4WPB5 and Co2WPB1 could achieve 10 mA/cm2 at just 72 mV and 262 mV of overpotentials for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), respectively, in 1 M KOH. Furthermore, the catalyst showed good performance in a 2-electrode assembly (1.59 V for 10 mA/cm2) with considerable stability (70 h stability, 10,000 operating cycles). Detailed morphological and electrochemical characterizations unveiled insights into the role of all elements in catalyst's improved performance. The presence of W was found to be crucial in improving the electronic conductivity and charge redistribution, making CoWPB suitable for both HER and OER. In computational simulation analysis, two configurations with different atomic environments, namely, CoWPBH and CoWPBO were found to have the lowest calculated overpotentials for HER and OER, respectively. It was found that the surface P-sites in CoWPBH were HER-active while the Co-sites in CoWPBO were OER-active sites. The study presents new knowledge about active sites in such multi-component catalysts that will foster more advancement in the area of water electrolysis. 2024 Hydrogen Energy Publications LLC -
Unveiling the Temporal Properties of MAXI J1820+070 through AstroSat Observations
We present here the results of the first broadband simultaneous spectral and temporal studies of the newly detected black hole binary MAXI J1820+070 as seen by Soft X-ray Telescope and Large Area X-ray Proportional Counter (LAXPC) on board AstroSat. The observed combined spectra in the energy range 0.7-80 keV were well modeled using disk blackbody emission, thermal Comptonization, and a reflection component. The spectral analysis revealed that the source was in its hard spectral state (? = 1.61) with a cool disk (kT in = 0.22 keV). We report the energy dependent time-lag and root mean squared (rms) variability at different frequencies in the energy range 3-80 keV using LAXPC data. We also modeled the flux variability using a single-zone stochastic propagation model to quantify the observed energy dependence of time lag and fractional rms variability, and then compared the results with that of Cygnus X-1. Additionally, we confirm the detection of a quasi-periodic oscillation with the centroid frequency at 47.7 mHz. 2020. The American Astronomical Society. All rights reserved.. -
Unveiling the therapeutic potential of azopyridine derivatives for trypsin inhibition: a DFT and In-Vitro approach
Heterocyclic azo derivatives have emerged as promising scaffolds for drug development. This study focused on the synthesis, computational analysis, and biological evaluation of a series of azopyridine derivatives (1a, 1d, 1 g, 1 h, 1 m, 1p, and 1s) as potential trypsin inhibitors. Density Functional Theory calculations indicated that derivative 1 h exhibited the lowest HOMO-LUMO energy gap 3.167 eV and was characterised as a soft molecule, suggesting strong binding capabilities. Molecular docking studies confirmed that 1 h binds favourably to the active site of trypsin with a glide score of ?6.581 kcal/mol and binding energy of ?29.95 kcal/mol. Along with docking studies, the stability of the trypsin-1 h complex was further analyzed using molecular dynamic simulations at 200 ns. The results showed that the ligand molecule 1 h bound strongly at the active site of trypsin. In-vitro enzyme assays determined the IC50 value of the molecule as 100 M, demonstrating enhanced potency. These results indicate that AzPy derivatives, particularly 1 h, hold considerable promise as therapeutic agents for inflammatory disorders and cancer, paving the way for further exploration in drug development and targeted therapies. Further research is warranted to explore 1hs efficacy, safety, and structure-activity relationships. Highlights: DFT studies were used to classify molecules based on their softness and hardness. Molecular docking, simulation, and in-vitro studies have identified potential anti-trypsin activity of candidate molecules. Experimental and computational calculations were in close agreement. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Unveiling the therapeutic potential of azopyridine derivatives for trypsin inhibition: a DFT and In-Vitro approach
Heterocyclic azo derivatives have emerged as promising scaffolds for drug development. This study focused on the synthesis, computational analysis, and biological evaluation of a series of azopyridine derivatives (1a, 1d, 1 g, 1 h, 1 m, 1p, and 1s) as potential trypsin inhibitors. Density Functional Theory calculations indicated that derivative 1 h exhibited the lowest HOMO-LUMO energy gap 3.167 eV and was characterised as a soft molecule, suggesting strong binding capabilities. Molecular docking studies confirmed that 1 h binds favourably to the active site of trypsin with a glide score of ?6.581 kcal/mol and binding energy of ?29.95 kcal/mol. Along with docking studies, the stability of the trypsin-1 h complex was further analyzed using molecular dynamic simulations at 200 ns. The results showed that the ligand molecule 1 h bound strongly at the active site of trypsin. In-vitro enzyme assays determined the IC50 value of the molecule as 100 M, demonstrating enhanced potency. These results indicate that AzPy derivatives, particularly 1 h, hold considerable promise as therapeutic agents for inflammatory disorders and cancer, paving the way for further exploration in drug development and targeted therapies. Further research is warranted to explore 1hs efficacy, safety, and structure-activity relationships. Highlights: DFT studies were used to classify molecules based on their softness and hardness. Molecular docking, simulation, and in-vitro studies have identified potential anti-trypsin activity of candidate molecules. Experimental and computational calculations were in close agreement. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Unveiling the transformative role of chatbots: An insight from industry
In the 21st century, technological advancements have left an indelible mark on various industries. This study seeks to gauge the effectiveness of Chatbots with a specific lens on four pivotal sectors: Banking, Healthcare, Education and Information Technology. Acting as virtual conversation agents, Chatbots simulate human-like interactions. Through the lens of the Dialogue Manager theory, this research aims to pinpoint crucial attributes spanning distinct dimensions. These include the realm of the attributes, namely customer experience (user-friendliness, attentiveness, clarity, awareness and security), company experience (passion, timeliness, customer engagement, tracking customer), communication experience (observer, respectfulness, emotions, good listener, preciseness) and technology experience (updated technology, industry applied, quick response, problem-solving, high backup). This study investigates user perceptions, facilitating a more profound comprehension of Chatbot efficacy. The outcomes benefit the aforementioned industries, aiding them in refining their automation processes for heightened user-friendliness. Moreover, the potential for extending this research with empirical analysis opens avenues for further exploration. 2025 Shimmy Francis and Sangeetha Rangasamy. All rights reserved. -
Unveiling thePower ofBayesian Optimization: Methods, Insights, andApplications
Bayesian optimization (BO) has emerged as a popular approach for optimizing expensive black-box functions, which are common in modern machine learning, scientific research, and industrial design. This paper provides a comprehensive review of the recent advances in Bayesian optimization techniques, addressing new methodological developments such as multi-fidelity optimization, transfer learning, and neural network surrogates. Additionally, we explore the increasing role of BO in complex, high-dimensional, and multi-objective optimization problems, as well as its application in various fields like hyperparameter tuning, reinforcement learning, and neural architecture search. The goal of this review is to offer both theoretical insights and practical guidelines to researchers and practitioners working in areas where BO is a suitable tool. Finally, we discuss key challenges and propose directions for future research in the rapidly evolving field of Bayesian optimization. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. -
Unveiling virtual interactive marketplaces: Shopping motivations in the Metaverse through the lens of uses and gratifications theory
The emergence of Metaverse has transformed the consumer shopping experience. This novel e-commerce platform offers a fresh approach to shopping, with Generation Z primarily exploring this innovative technology. Our research examines shopping within the Metaverse by developing a model based on the Uses and Gratifications Theory and Metaverse-related factors. A total of 1220 Gen Z consumers were surveyed, and data was collected using a structured questionnaire. Further, analysis of collected data was done using PLS-SEM. The results reveal that information seeking, perceived enjoyment, escapism, social interaction, sense of immersion, and personalization influence the shopping intention in the Metaverse, and perceived risk negatively influences the shopping intention of consumers. Further, shopping intention influences the potential use of Metaverse for shopping, and this relationship is moderated by technological innovativeness. This investigation into the adoption of the Metaverse for retail purposes augments the current Metaverse research and enhances the uses and gratifications theory within the Metaverse domain. Metaverse e-commerce professionals, including managers and developers, can acquire valuable perspectives on consumer shopping tendencies in the Metaverse from this study. 2025 The Author(s)