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Adsorption and storage of hydrogen- A computational model approach
Due to the imperative global energy transition crisis, hydrogen storage and adsorption technologies are becoming popular with the growing hydrogen economy. Recently, complex hydrides have been one of the most reliable materials for storing and transporting hydrogen gas to various fuel cells to generate clean energy with zero carbon emissions. With the ever-increasing carbon emissions, it is necessary to substitute the current energy sources with green hydrogen-based efficient energy-integrated systems. Herein, we propose an input-output model that comprehends complex hydrides such as lithium and magnesium alanates, amides and borohydrides to predict, estimate, and directly analyse hydrogen storage and adsorption. A critical and thorough comparative analysis of the respective complex hydrides for hydrogen adsorption and storage is discussed, elucidating the storage applications in water bodies. Several industrial scale-up processes, economic analysis, and plant design of hydrogen storage and adsorption approaches are suggested through volumetric and gravimetric calculations. 2024 Elsevier Inc. -
Evaluation of an interprofessional collaborative practice training module for the management of children with autism spectrum disorder
Background: Protocols instituted for behavioral treatment and skills training programs for the management of autism spectrum disorder (ASD) suffer from lack of collaborative approaches. The tenets of interprofessional collaborative practice (IPCP) focus on preparing a panel of health care professionals (HCPs) from different professions who can work together to enable the common goal of ensuring that children with ASD can participate in society. This study was designed to pilot this approach through an IPCP training module on ASD for care providers from multiple professions. Methods: An interventional study with pre-post analysis began with formation of the interprofessional (IP) team, who developed an IPCP module, addressing the knowledge and skills needed for the collaborative management of neurodevelopmental issues of children with ASD. This module was delivered through an online training workshop using various teaching learning methods to the participants from seven different health professions after obtaining informed consent. Perceptions of interprofessional collaboration and competencies of IPCP were assessed using standard IP tools and reflective summaries and analyzed through a mixed-methods approach. Results: A total of 42 HCPs from seven professions, including speech and hearing, occupational therapy, clinical psychology, physiotherapy, pediatrics, nursing, and pedodontics, participated in the study. Pre-post analysis of PINCOM-Q and Dow-IPEC data and thematic analysis revealed a significant difference in the perceptions of interprofessional collaboration and competencies levels of IPCP. Conclusion: This study suggests that use of IPCP principles in the training of professionals working with ASD is a promising and feasible option to develop more competent health professionals. The training enhanced the abilities of professionals to work in field of ASD as conveyed by the participants. They also expressed confidence in the knowledge of IP core competencies after the completion of the module. 2022 -
Valorisation of coffee husk as replacement of sand in alkali-activated bricks
The coffee industry is known to generate voluminous amount of waste during its production process. Different types of waste such as coffee hush ash and spent coffee ground, to name a few, have been extensively researched as a substitute in the construction industry. However, the utilization of coffee husk as a substitute for construction materials has seen limited exploration. In particular, there are no studies which investigate the utilization of waste coffee husk (WCH) in alkali-activated bricks. Therefore, in this research WCH was employed as a substitute to sand in alkali-activated bricks. Alkali-activated bricks were synthesized with ground granulated blast furnace slag (GGBFS), fly ash (FA), sand, and sodium silicate solution (SS). Sand was replaced with WCH at replacement rates of 0 %, 5 %, 10 %, 15 %, 20 %, and 30 % by volume. The developed bricks were evaluated for strength, density, water absorption, porosity, and efflorescence. Additionally, structural and morphological characteristics of bricks were assessed by Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Thermogravimetric analysis (TGA), and Scanning electron microscopy (SEM) analysis. The results indicate that bricks with WCH improve the compressive strength with a maximum value of 15.7 MPa, and reduce the density with a minimum value of 1509 kg/m3 for composites with 30 % WCH, respectively. The water absorption and porosity of bricks increased with incorporation of WCH due to porous structure of WCH. The physico-chemical analysis of the bricks shows effective geopolymerization in the composite system with WCH, and further the bricks with 30 % WCH depict thermal stability with insignificant weight loss at 575 ?. Finally, the composites with 30 % WCH classify as good quality bricks as per IS 1077: 1992 specifications, and this will improve practical feasibility of such materials in the construction industry. 2024 The Authors -
Discrimination Experiences of Old Settlers in Sikkim: A Qualitative Exploration
Race-based stigma and discrimination have been extensively studied from the perspective of the northeastern community due to their minority status in most states of India. Discrimination experiences of the mainland Indians in the northeastern states, where they are a minority, are little discussed. The Rajya Sabha (upper house of the parliament) Committee of Petitions in 2014 acknowledged that the old settlers were treated as second-class citizens in Sikkim. In the present study, we explored the existence and manifestation of discrimination experiences of old settlers who settled in Sikkim before 1975 and perceive themselves to be stigmatized. This study focused on Sikkim because the state merged with India in 1975 and has had less time integrating with migrants or mainlanders than other northeastern states. We conducted nine semi-structured interviews with seven male and two female participants from the Marwari, Bihari, and Punjabi mainland communities. Using thematic analysis, we developed 1 global theme, 2 organizing themes, and 24 basic themes. The analysis showed the existence of discrimination and racism against old settlers and their manifestations at institutional and interpersonal levels. The findings are important from a policymaking perspective as they provide evidence to the conclusion reached by the Rajya Sabha Committee on Petitions and provide valued suggestions for reports on race-based discrimination in India. The Author(s) under exclusive licence to National Academy of Psychology (NAOP) India 2023. -
Knowledge, Attitude, and Stigma Among Adolescents: Effect of Mental Health Awareness and Destigmatisation (MHAD) Program
Background: Stigma against mental health problems is a common issue for adolescents aged 1418 years. However, comprehensive programs that simultaneously address awareness and stigma reduction tailored to the specific needs of this age group are lacking. Method: This study investigated the effectiveness of the Mental Health Awareness and Destigmatisation Program (MHAD) in reducing stigma and improving knowledge and attitudes towards peers with mental health problems. A quasi-experimental pre-post design was employed among adolescents aged 1418 years from an educational institution in Bangalore. After excluding those with high baseline mental health symptoms (PSC-17 > 20), a preassessment was conducted on adolescents' knowledge, attitude, and stigma (n = 52) using the Mental Health Knowledge Schedule, Self-structured Case Vignettes, and Peer Mental Health Stigmatization Scale. After completing the 6-week program, three participants were excluded from the post-assessment, as their attendance was less than 50%. A total of 49 (mean age = 16 years) adolescents were included in the post-assessment. Results: The paired sample t-test revealed significant improvements in all stigma scores. The Wilcoxon signed-rank test indicated a significant improvement in Recognition of Mental Illness scores. Conclusion: Findings showed that MHAD, an education-based program, was effective in reducing adolescents' stigma towards peers with mental health problems and improving their overall recognition of mental health symptoms. Research across larger adolescent populations is essential to enhance these interventions' long-term impact and sustainability. By closely monitoring and expanding research efforts, we can gain deeper insights into how these programs foster self-awareness, a crucial factor in recognizing mental health needs, challenging stigma, and promoting help-seeking behaviors among adolescents. 2024 Wiley Periodicals LLC. -
Rejuvenating human resource accounting research: a review using bibliometric analysis
The current study attempts to map the intellectual structure of Human Resource Accounting to understand the research gaps and future trajectories. The study employs systematic literature review technique to extract relevant literature, bibliometric analysis to map the intellectual structure of research in human resource accounting, to identify underlying research themes and content analysis to identify avenues for future research. Based on 2438publications, author keyword co-occurrences extracted four themes namely, Human Resource Management, Intellectual Capital, Human Capital, and Voluntary Disclosure. The study also summarizes significant findings of papers under each cluster through content analysis identifying areas for future research. The study provides a birds eye view of the intellectual structure of academic research efforts in the field of human resource accounting. The study is one of the first attempt to comprehensively review the academic literature from Scopus database employing systematic literature review, bibliometric methods, and content analysis in the field of human resource accounting. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023. -
Balancing cerebrovascular disease data with integrated ensemble learning and SVM-SMOTE
The paper addresses the challenge of imbalanced classification in the context of cerebrovascular diseases, including stroke, transient ischemic attack (TIA), and vascular dementia. The imbalanced nature of cerebrovascular disease datasets poses significant challenges to conventional machine learning algorithms, making precise diagnosis and effective management difficult. The aim of the paper is to propose a novel approach, the INTEL_SS algorithm, which combines ensemble learning techniques with Support Vector Machine-Synthetic Minority Over-sampling Technique (SVM-SMOTE) to effectively handle the imbalanced nature of cerebrovascular disease datasets. The goal is to improve the accuracy of diagnosis and management of cerebrovascular diseases through advanced machine learning techniques. The proposed methodology involves several key steps, including preprocessing, SVM-SMOTE, and ensemble learning. Preprocessing techniques are used to improve the quality of the dataset, SVM-SMOTE is employed to address class imbalance, and ensemble learning methods such as bagging, boosting, and stacking are utilized to improve overall classification performance. The experimental results demonstrate that the INTEL_SS algorithm outperforms existing methods in terms of accuracy, precision, recall, F1-score, and AUC-ROC. Performance metrics are used to assess the effectiveness of the proposed approach, and the results consistently show the superiority of INTEL_SS compared to state-of-the-art imbalanced classification algorithms. The paper concludes that the INTEL_SS algorithm has the potential to enhance the diagnosis and management of cerebrovascular diseases, offering new opportunities to apply machine learning techniques to improve healthcare outcomes. The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2024. -
Empowering BRICS economies: The crucial role of green finance, information and communication technologyand innovation in sustainable development
This study delves into the crucial role of green finance, information and communication technology (ICT), technological innovation, and renewable energy in the Brazil, Russia, India, and China (BRICS) countries from 2000 to 2021. The findings highlight the importance of green finance in reducing the ecological footprint and promoting eco-friendly initiatives, sustainable practices, environmental technology innovation, and heightened environmental awareness. This means 1% increase in green related finance has reduced ecological footprint by 0.72% in BRICS economies. Additionally, technological innovation and the consumption of renewable energy play a significant role in enhancing environmental sustainability. Conversely, the study reveals that ICT has a considerable impact on the ecological footprint, but the interaction effect with green finance helps to mitigate its negative effects and improve the environmental quality. Meanwhile, non-renewable energy, gross domestic product (GDP) per capita, and urbanization have an adverse effect on the environment. To strengthen green finance in BRICS countries, governments can establish comprehensive policy frameworks that prioritize sustainability and create a conducive climate for incentivizing investment in environmentally friendly endeavors. 2024 ERP Environment and John Wiley & Sons Ltd. -
Immobilized proline-based electro-organocatalyst for the synthesis of bis-?-diketone via Knoevenagel condensation reaction
In the quest for more sustainable chemical processes, we devised a technique using electro-organocatalysis to synthesize bis-?-diketone compounds via Knoevenagel condensation of benzaldehyde and dimedone. Our approach involves a modified electrode fabricated via anchoring L-proline onto a carbon fiber paper electrode supported by poly-3,4-diaminobenzoic acid (PDABA), which enhances efficiency in addition to the simple catalyst separation from the reaction mixture in heterogeneous catalysis. The electrochemical and surface topographical studies for the fabricated electrode were carried out, revealing high efficiency in comparison to the bare carbon fiber paper electrode. This electrochemical reaction operates under mild conditions utilizing lithium perchlorate and acetonitrile, yielding high amounts of the desired product. This study showcases a promising pathway for producing valuable organic compounds in an environmentally friendly manner, marking a significant stride forward in sustainable synthesis practices. 2024 Elsevier Ltd -
AttGRU-HMSI: enhancing heart disease diagnosis using hybrid deep learning approach
Heart disease is a major global cause of mortality and a major public health problem for a large number of individuals. A major issue raised by regular clinical data analysis is the recognition of cardiovascular illnesses, including heart attacks and coronary artery disease, even though early identification of heart disease can save many lives. Accurate forecasting and decision assistance may be achieved in an effective manner with machine learning (ML). Big Data, or the vast amounts of data generated by the health sector, may assist models used to make diagnostic choices by revealing hidden information or intricate patterns. This paper uses a hybrid deep learning algorithm to describe a large data analysis and visualization approach for heart disease detection. The proposed approach is intended for use with big data systems, such as Apache Hadoop. An extensive medical data collection is first subjected to an improved k-means clustering (IKC) method to remove outliers, and the remaining class distribution is then balanced using the synthetic minority over-sampling technique (SMOTE). The next step is to forecast the disease using a bio-inspired hybrid mutation-based swarm intelligence (HMSI) with an attention-based gated recurrent unit network (AttGRU) model after recursive feature elimination (RFE) has determined which features are most important. In our implementation, we compare four machine learning algorithms: SAE + ANN (sparse autoencoder + artificial neural network), LR (logistic regression), KNN (K-nearest neighbour), and nae Bayes. The experiment results indicate that a 95.42% accuracy rate for the hybrid model's suggested heart disease prediction is attained, which effectively outperforms and overcomes the prescribed research gap in mentioned related work. The Author(s) 2024. -
Terahertz-based optoelectronic properties of ZnS quantum dot-polymer composites: For device applications
Terahertz (THz) technology integration with nanomaterials is receiving excellent attention for next-generation applications, including enhanced imaging and communication. The excellent optical properties in THz domain can lead to preparation of low-cost CMOS camera which can convert THz radiation into optical signal in very efficient manner. In the present study, we have studied the properties of Zinc Sulfide quantum dots (ZnS QDs) embedded with Polyvinyl Alcohol (PVA) composites films using THz Signal at room temperature. The optical characterizations such as refractive index, absorption coefficients and dielectric constants of these samples were measured in the 0.12.0 THz range. Additionally, optical impedance, surface roughness, and reflection coefficient in TE and TM mode between 0.1 and 2.0 THz range were determined for these samples based on surface roughness-based reflection and scattering properties. The surface roughness factor was used to measure the optical impedance of the ZnS QDs based polymer films. The measured values of the absorption coefficient at 266 nm are compared with THz radiation, and the refractive indices of these samples range from 1.75 to 2.0. Finally, these samples were subjected to UV light excitation (?exe = 266 nm) of 0.15 ns duration and 400 nm for the fluorescence and corresponding life time measurements. We observed two numbers of fluorescence lines in nanosecond based excited domain whereas 400 nm excitation-based fluorescence life time lies between 13.811.39 ns range along with shift in fluorescence lines between 538.7 to 560.7 nm, respectively. 2024 -
Mindfully fashioned: Sustaining style through product value retention
In the ever-evolving landscape of fashion industry, the pursuit of sustainability and mindful consumption has emerged as an imperative. This study presents an innovative and integrated framework that amalgamates the Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) approach. This comprehensive framework explores the dynamic interplay between Sustainable Mindfulness (SM), Product Value Retention (PVR), Brand Loyalty (BL) and Circular Practices (CP) in the fashion domain. The study underscores Theory of Planned Behavior (TPB) as the theoretical foundation, shedding light on how attitudes, subjective norms, and perceived behavioral control shape SM and, subsequently, PVR. This study explores into the multidimensional aspects of sustainable fashion, enabling a holistic understanding of the nuanced relationships between PVR strategies, and sustainable consumption attitudes. The study sets the stage for a more harmonious coexistence between style and sustainability, charting a course for the fashion industry that values mindfulness, longevity, and environmental responsibility. The findings offer actionable insights for fashion managers, emphasizing strategic approaches to enhance sustainability, consumer awareness, and circular initiatives in the industry. This study not only advances theoretical discourse but also offers actionable insights for fashion brands and policymakers aiming to foster SM among diverse consumer segments. It sets the stage for a more harmonious coexistence between style and sustainability, charting a course for the fashion industry that values mindfulness, longevity, and environmental responsibility. 2024 Elsevier Ltd -
Electrochemical sensing of vitamin B6 using platinum nanoparticles decorated poly(2-aminothiazole)
Vitamin B6 (Vit B6), also known as pyridoxine, is pivotal in fundamental physiological and metabolic processes within the body. Insufficient levels of this essential nutrient may contribute to various health complications. We introduce an electrochemical sensor designed to determine Vit B6 levels precisely. This sensor is constructed through a two-step process: first, by modifying a bare carbon fiber paper electrode (CFP) with poly(2-aminothiazole) (PAT), and second, by electrodepositing platinum nanoparticles onto the modified electrode surface, giving the final working electrode- Pt/PAT/CFP. Electrochemical impedance spectroscopy (EIS) and Cyclic voltammetry (CV) were utilized to examine the electrochemical characteristics of the developed sensor. The characterization of the sensor was done through a range of analytical techniques, including X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and optical profilometric studies. Furthermore, we optimized the sensor's performance by assessing the impact of pH, scan rates, and analyte concentrations. The sensor showed a wide linear dynamic range of 5.0 nM80 M and a low detection limit of 0.054 M. We have successfully quantified Vit B6 levels in tablet formulations and dried palm date fruits. The outcomes of this study hold the promise of substantial progress in Vit B6 quantification, with far-reaching implications across pharmaceuticals, healthcare, and nutritional science. 2024 Elsevier B.V. -
Training in Cultural Competence for Mental Health Care: A Mixed-Methods Study of Students, Faculty, and Practitioners from India and USA
Although the need to train clinicians to provide effective mental health care to individuals from diverse backgrounds has been recognized worldwide, a bulk of what we know about training in cultural competence (CC) is based on research conducted in the United States. Research on CC in mental health training from different world populations is needed due to the context-dependent nature of CC. Focusing on India and USA, two diverse countries that provide complementary contexts to examine CC, we explored graduate students, practicing clinicians, and faculty members perspectives regarding CCtraining they received/provided and future training needs using mixed-methods. The data were collected using focus groups (n = 25 groups total: 15 in India, 11 in USA), and a survey (n = 800: 450 in India, 350 in USA). Our data highlight the salient social identities in these countries, and the corresponding constituents of CC training. Participants in India described a practical emphasis to their CC training (e.g., learning about CC through life experiences and clinical practice experiences) more so than through coursework, whereas participants in USA described varying levels of courseworkrelated toCC along with practice. Participants in both countries considered enormity of CC as a challenge, while those in the US also identified CC training limited to a white, straight, male perspective, hesitancy in engaging with diversity topics, and limited time and competence of the faculty. Strengths of CC training in India and USA are mutually informative in generating recommendations for enhancing the training in both countries. The Author(s) 2024. -
One-pot hydrothermal synthesis of 3D garland BiOI, spherical ZnO, and CNFs onto Ni foam: Supercapacitor performance with enhanced electrochemical properties
This study reported one-pot hydrothermal synthesis of 3D garland BiOI, spherical ZnO, and carbon nanofibers (CNFs) onto Ni foam substrate with improved supercapacitor performance and enhanced electrochemical properties. The synthesized nanocomposites exhibited high specific capacitance (SC) of 1073 g?1 at a current density of 1 A/g and excellent cycling stability with 88.6% retention of original capacity after 5000 cycles in 2M KOH aqueous solution. The findings highlight the potential of 3D materials for use as electrode materials in advanced supercapacitor applications due to their high energy storage capabilities. 2024 Elsevier Ltd -
Comprehensive evaluation and performance analysis of machine learning in heart disease prediction
Heart disease is a leading cause of mortality on a global scale. Accurately predicting cardiovascular disease poses a significant challenge within clinical data analysis. The present study introduces a prediction model that utilizes various combinations of information and employs multiple established classification approaches. The proposed technique combines the genetic algorithm (GA) and the recursive feature elimination method (RFEM) to select relevant features, thus enhancing the models robustness. Techniques like the under sampling clustering oversampling method (USCOM) address the issue of data imbalance, thereby improving the models predictive capabilities. The classification challenge employs a multilayer deep convolutional neural network (MLDCNN), trained using the adaptive elephant herd optimization method (AEHOM). The proposed machine learning-based heart disease prediction method (ML-HDPM) demonstrates outstanding performance across various crucial evaluation parameters, as indicated by its comprehensive assessment. During the training process, the ML-HDPM model exhibits a high level of performance, achieving an accuracy rate of 95.5% and a precision rate of 94.8%. The systems sensitivity (recall) performs with a high accuracy rate of 96.2%, while the F-score highlights its well-balanced performance, measuring 91.5%. It is worth noting that the specificity of ML-HDPM is recorded at a remarkable 89.7%. The findings underscore the potential of ML-HDPM to transform the prediction of heart disease and aid healthcare practitioners in providing precise diagnoses, exerting a substantial influence on patient care outcomes. The Author(s) 2024. -
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. -
Lignin nanoparticles from Ayurvedic industry spent materials: Applications in Pickering emulsions for curcumin and vitamin D3 encapsulation
Lignin nanoparticles (LNP), extracted from spent materials of Dashamoola Arishta (Ayurvedic formulation), shared a molecular weight of 14.42 kDa with commercial lignin. Processed into LNPs (496.43 0.54 nm) via planetary ball milling, they demonstrated stability at pH 8.0 with a zeta potential of ?32 0.27 mV. Operating as Pickering particles, LNP encapsulated curcumin and vitamin D3 in sunflower oil, forming LnE + Cu + vD3 nanoemulsions (particle size: 347.40 0.71 nm, zeta potential: ?42.27 0.72 mV) with high encapsulation efficiencies (curcumin: 87.95 0.21%, vitamin D3: 72.66 0.11%). The LnE + Cu + vD3 emulsion exhibited stability without phase separation over 90 days at room (27 2 C) and refrigeration (4 1 C) temperatures. Remarkably, LnE + Cu + vD3 exhibited reduced toxicity, causing 29.32% and 34.99% cell death in L6 and RAW264.7 cells respectively, at the highest concentration (50 ?g/mL). This underscores the potential valorization of Ayurvedic industry spent materials for diverse industrial applications. 2024 Elsevier Ltd -
APPLICATION OF ARTIFICIAL INTELLIGENCE IN PHARMACEUTICAL INDUSTRY
Artificial Technology is the blockbuster technology today. Pharmaceutical industry is no exception to the technology onslaught. Pharmaceutical industry adapting to the Artificial Intelligence (AI) to improve the overall performance of the industry processes, through improved efficiency in the operations and reduced lead time in the drug discovery. This is done through AIs ability of scanning huge data to speed up the drug discovery stage by identifying prospective drug candidates through technology like Structure-Based Virtual Screening (SBVS) and Fragment-Based Drug Discovery (FBDD). A nascent drug approach called as drug repurposing is very prospective through AI, and AI makes it possible to integrate nanotechnology, targeted drug development and personalised treatment based on genetic and proteomic data. AI has huge applications in the very important drug development stage of clinical trials. Selection of suitable participants, predicting drug responses will have huge cost reduction with the AI technology. In addition to drug trials, AI is transforming the pharmaceutical marketing process. Personalised communication, predictive sales forecasting, automated content generation and sentiment analysis are some of the possible as of now. These applications make the companies offer tailor made marketing strategies specific to physicians and patients and monitor the brand reputation and bring efficiency in the supply chain. Albeit the potential benefits, adoption of AI fully in the pharmaceutical industry has its own challenges. In the areas of data privacy, regulatory compliance and ethics related to drug testing, AI could face serious challenges. As the technology evolves, AI will have its impact on the pharmaceutical industry offering huge growth opportunities. India could emerge as a potential superpower in the pharmaceutical industry if AI is properly harnessed for industry growth. India can be the pharmacy for the entire world in the coming days if industry finds a way to utilize AI properly. 2024, Indian Pharmaceutical Association. All rights reserved. -
Simultaneous X-Ray and Optical Polarization Observations of the Blazar Mrk 421
We present near-simultaneous X-ray and optical polarization measurements in the high synchrotron peaked (HSP) blazar Mrk 421. The X-ray polarimetric observations were carried out using Imaging X-ray Polarimetry Explorer (IXPE) on 2023 December 6. During IXPE observations, we also carried out optical polarimetric observations using 104 cm Sampurnanand telescope at Nainital and multiband optical imaging observations using 2 m Himalayan Chandra Telescope at Hanle. From model-independent analysis of IXPE data, we detected X-ray polarization with degree of polarization (?X) of 8.5% 0.5% and an electric vector position angle (?X) of 10.6 1.7 in the 2?8 keV band. From optical polarimetry on 2023 December 6, in B, V, and R bands, we found values of ?B = 4.27% 0.32%, ?V = 3.57% 0.31%, and ?R = 3.13% 0.25%. The value of ?B is greater than that observed at longer optical wavelengths, with the degree of polarization suggesting an energy-dependent trend, gradually decreasing from higher to lower energies. This is consistent with that seen in other HSP blazars and favors a stratified emission region encompassing a shock front. The emission happening in the vicinity of the shock front will be more polarized due to the ordered magnetic field resulting from shock compression. The X-ray emission, involving high-energy electrons, originates closer to the shock front than the optical emission. The difference in the spatial extension could plausibly account for the observed variation in polarization between X-ray and optical wavelengths. This hypothesis is further supported by the broadband spectral energy distribution modeling of the X-ray and optical data. 2024. The Author(s). Published by the American Astronomical Society.