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Real-time human action prediction using pose estimation with attention-based LSTM network
Human action prediction in a live-streaming videos is a popular task in computer vision and pattern recognition. This attempts to identify activities in an image or video performed by a human. Artificial intelligence(AI)-based technologies are now required for the security and human behaviour analysis. Intricate motion patterns are involved in these actions. For the visual representation of video frames, conventional action identification approaches mostly rely on pre-trained weights of various AI architectures. This paper proposes a deep neural network called Attention-based long short-term memory (LSTM) network for skeletal based activity prediction from a video. The proposed model has been evaluated on the BerkeleyMHAD dataset having 11 action classes. Our experimental results are compared against the performance of the LSTM and Attention-based LSTM network for 6 action classes such as Jumping, Clapping, Stand-up, Sit-down, Waving one hand (Right) and Waving two hands. Also, the proposed method has been tested in a real-time environment unaffected by the pose, camera facing, and apparel. The proposed system has attained an accuracy of 95.94% on BerkeleyMHAD dataset. Hence, the proposed method is useful in an intelligent vision computing system for automatically identifying human activity in unpremeditated behaviour. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. -
Corporate Default Prediction Model: Evidence from the Indian Industrial Sector
The unprecedented pandemic COVID-19 has impacted businesses across the globe. A significant jump in the credit default risk is expected. Credit default is an indicator of financial distress experienced by the business. Credit default often leads to bankruptcy filing against the defaulting company. In India, the Insolvency and Bankruptcy Code (IBC) is the law that governs insolvency and bankruptcy. As reported by the Insolvency and Bankruptcy Board of India (IBBI), the number of companies filing for bankruptcy under IBC is on a rise, and the industrial sector has witnessed the maximum number of bankruptcy filings. The present article attempts to develop a credit default prediction model for the Indian industrial sector based on a sample of 164 companies comprising an equal number of defaulting and nondefaulting companies. A total of 120 companies are used as training samples and 44 companies as the testing samples. Binary logistic regression analysis is employed to develop the model. The diagnostic ability of the model is tested using receiver operating characteristic curve, area under the curve and annual accuracy. According to the study, return on assets, current ratio, debt to total assets ratio, sales to working capital ratio and cash flow to total assets ratio is statistically significant in predicting default. The findings of the study have significant implications in lending and investment decisions. 2021 MDI. -
Secure approach to sharing digitized medical data in a cloud environment
Without proper security mechanisms, medical records stored electronically can be accessed more easily than physical files. Patient health information is scattered throughout the hospital environment, including laboratories, pharmacies, and daily medical status reports. The electronic format of medical reports ensures that all information is available in a single place. However, it is difficult to store and manage large amounts of data. Dedicated servers and a data center are needed to store and manage patient data. However, self-managed data centers are expensive for hospitals. Storing data in a cloud is a cheaper alternative. The advantage of storing data in a cloud is that it can be retrieved anywhere and anytime using any device connected to the Internet. Therefore, doctors can easily access the medical history of a patient and diagnose diseases according to the context. It also helps prescribe the correct medicine to a patient in an appropriate way. The systematic storage of medical records could help reduce medical errors in hospitals. The challenge is to store medical records on a third-party cloud server while addressing privacy and security concerns. These servers are often semi-trusted. Thus, sensitive medical information must be protected. Open access to records and modifications performed on the information in those records may even cause patient fatalities. Patient-centric health-record security is a major concern. End-to-end file encryption before outsourcing data to a third-party cloud server ensures security. This paper presents a method that is a combination of the advanced encryption standard and the elliptical curve Diffie-Hellman method designed to increase the efficiency of medical record security for users. Comparisons of existing and proposed techniques are presented at the end of the article, with a focus on the analyzing the security approaches between the elliptic curve and secret-sharing methods. This study aims to provide a high level of security for patient health records. 2023 Xi'an Jiaotong University -
Two dimensional fuzzy context-free languages and tiling patterns
Fuzzy context-free languages are powerful compared to fuzzy regular languages as they are generated by fuzzy context-free grammars and fuzzy pushdown automata, which follow an enhanced computational mechanism. A two dimensional language (picture language) is a collection of two dimensional words, which are a rectangular array of symbols made up of finite alphabets. Two dimensional automata can recognize two dimensional languages that could not be recognized by one dimensional automata. In this paper, we introduce two dimensional fuzzy context-free languages generated by the two dimensional fuzzy context-free grammars and accepted by the two dimensional fuzzy pushdown automata in order to deal with the vagueness that arises in two dimensional context-free languages. We can construct a two dimensional fuzzy context free grammar from the given two dimensional fuzzy pushdown automata and vice versa. In addition, we prove that two dimensional fuzzy context-free languages are closed under union, column concatenation, column star, homomorphism, inverse homomorphism, reflection about right-most vertical, reflection about base, conjugation and half-turn and also show that two dimensional fuzzy context-free languages are not closed under matrix homomorphism, quarter-turn and transpose. Further, we have given the applications and the uses of closure properties in the formation of tiling patterns. 2024 Elsevier B.V. -
Does environmental reporting ofbanks affect their financial performance? Evidence from India
Purpose: The present study aims to investigate the effect of environmental reporting on the financial performance of banks in India. Design/methodology/approach: The study is based on the secondary data. The sample includes the banks listed in the NSE Nifty Bank Index from 20162017 to 20202021. The environmental reporting data was obtained through the content analysis technique. The financial data was collected from the CMIE Prowess database. Panel regression analysis was used to analyse the data. Findings: The findings indicate a negative significant influence of environmental reporting on the ROA and ROE of banks. On the other hand, environmental reporting does not significantly influence the EPS of banking institutions. Originality/value: To the best of the authors knowledge, this study is the first to contribute to the scarce literature on the influence of environmental reporting on financial performance, pertinently in the context of a developing nation's banking sector. 2023, Emerald Publishing Limited. -
Enhancement of tensile strength and elastic modulus using bio-waste based carbon nanospheres doped polymer nanocomposites
The Carbon Nano Spheres (CNS) derived from areca nuts were synthesized from pyrolysis process and were used as fillers for fabrication of polymer nano composite materials. The filler materials are loaded in 0.05%, 0.1% and 0.5% loading percentages. The optimum sample was subjected to heat treatment. The tensile strength, elastic modulus and % of elongation were investigated for all samples. The Scanning Electron Microscope (SEM) images revealed the morphological features of optimum samples and hence the uniform dispersion of CNS in polymer matrix. The 0.1% samples showed 10% improvement in Ultimate Tensile Strength (UTS) and 24% improvement in Elastic modulus compared to bare epoxy material. When 0.1% samplewas subjected to heat treatment under 200C the UTS improved by 23%. Hence, CNS reinforced composite materials exhibited unique properties like high strength, less weight and low cost making them suitable for various structural applications such as aerospace, automotive, construction, and electronics industries. The Polymer Society, Taipei 2024. -
Imidazopyridine Hydrazine Conjugates as Potent Anti-TB Agents with their Docking, SAR, and DFT Studies
Novel imidazopyridines hydrazine conjugates were designed and synthesized for their anti-tubercular (anti-TB) activity. A cytotoxicity assay was conducted with Vero cells to determine the safety profile of the most effective compounds. It was found that compound (IA3) (MIC=0.78 ?M) and (IA8) (MIC=1.12 ?M) were nearly 3.7 and 2.5 times more active than pyrazinamide. Based on Density functional theory (DFT), these molecules exhibited better charge transfer between molecular orbital's, which made them suitable for biological applications. Molecular docking on Mycobacterium tuberculosis InhA bound to NITD-916 (PDB: 4R9S) revealed that compounds possessed greater binding affinity towards proteins. In addition, the most active anti-TB compounds (IA3) and (IA8) exhibited high levels of interaction with the target protein and exceptional safety profile, suggesting they may prove to be effective leads for new drugs. 2024 Wiley-VCH GmbH. -
Unpacking the burden of hypertension and diabetes in Karnataka: implications for policy and practice based on NFHS-5 findings
Objective: To investigate the prevalence, risk factors, and healthcare-seeking patterns of hypertension and diabetes in Karnataka, India, and to offer knowledge that might guide public health initiatives intended to lessen the burden of these illnesses. Methods: In order to examine the prevalence, risk factors, and healthcare-seeking behaviour of hypertension and diabetes in Karnataka, India, a cross-sectional study is carried out using the information gathered from 26,574 households on 30,455 women and 4516 men (who were in their reproductive period) from the National Family Health Survey (201920). The information was summarised using descriptive statistics, which included frequencies and percentages. The association between different risk variables and the likelihood of getting diabetes and hypertension was examined using the chi-squared test and a logistic regression model. Data were analysed using STATA software version 16. Results: The study found that age, gender, education level, religion, and BMI are all significantly associated with hypertension and diabetes (p < 0.001). Tobacco use and alcohol consumption were not significantly associated with hypertension, but tobacco use was significantly associated with diabetes (p < 0.001). However, alcohol consumption was not found to be significantly associated with diabetes whereas the older age groups, males, underweight, overweight and obese, and tobacco use were all associated with increased odds of diabetes. On the other hand, females, secondary education or higher, and alcohol consumption were associated with decreased odds of diabetes. Conclusion: In conclusion, the study found a high prevalence of hypertension and diabetes in Karnataka, India, and identified several risk factors associated with these diseases. The study also highlighted the need for improved healthcare-seeking behaviour among people with hypertension and diabetes. The findings can inform public health interventions aimed at reducing the burden of these diseases in Karnataka and similar settings. The Author(s), under exclusive licence to Research Society for Study of Diabetes in India 2023. -
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. -
Analysis of zoochemical from Meretrix casta (Mollusca: Bivalvia) extracts, collected from Rameswaram, Tamil Nadu, India and their pharmaceutical activities
The marine ecosystem's diverse animal species offer a unique opportunity to discover marine-derived natural products. While numerous invertebrates have been studied, research on Indian marine invertebrates, especially Meretrix casta, remains limited. This study explores the zoochemical composition of ethyl acetate and methanolic extracts from Meretrix casta off Rameswaram, Tamil Nadu, India, and evaluates their bioactive potential, focusing on antioxidant properties, glucose uptake in yeast cells, and alpha-amylase activity. The results reveal the presence of alkaloids, flavonoids, polyphenols, sterols, terpenoids, and cardiac glycosides in both extracts, highlighting their bioactive potential. Although their antioxidant capacity is slightly lower than ascorbic acid, the extracts demonstrated significant alpha-amylase inhibition, suggesting their potential in blood sugar regulation and diabetes management. These findings underscore the therapeutic potential of M. casta in developing anti-diabetic compounds, warranting further pharmacological exploration. Authors. -
Corrigendum to Computational simulation of surface tension and gravitation-induced convective flow of a nanoliquid with cross-diffusion: An optimization procedure [applied mathematics and computation 425 (2022) 127108]
This corrigendum addresses both the physical configuration and certain typographical errors in [1] to improve clarity. These corrections do not impact the originality, results, or mathematical validity of [1]. 2024 -
HER2 siRNA Facilitated Gene Silencing Coupled with Doxorubicin Delivery: A Dual Responsive Nanoplatform Abrogates Breast Cancer
The present study investigated the concurrent delivery of antineoplastic drug, doxorubicin, and HER2 siRNA through a targeted theranostic metallic gold nanoparticle designed using polysaccharide, PSP001. The as-synthesized HsiRNA@PGD NPs were characterized in terms of structural, functional, physicochemical, and biological properties. HsiRNA@PGD NPs exposed adequate hydrodynamic size, considerable ? potential, and excellent drug/siRNA loading and encapsulation efficiency. Meticulous exploration of the biocompatible dual-targeted nanoconjugate exhibited an appealing biocompatibility and pH-sensitive cargo release kinetics, indicating its safety for use in clinics. HsiRNA@PGD NPs deciphered competent cancer cell internalization, enhanced cytotoxicity mediated via the induction of apoptosis, and excellent downregulation of the overexpressing target HER2 gene. Further in vivo explorations in the SKBR3 xenograft breast tumor model revealed the appealing tumor reduction properties, selective accumulation in the tumor site followed by significant suppression of the HER2 gene which contributed to the exclusive abrogation of breast tumor mass by the HsiRNA@PGD NPs. Compared to free drugs or the monotherapy constructs, the dual delivery approach produced a synergistic suppression of breast tumors both in vitro and in vivo. Hence the drawings from these findings implicate that the as-synthesized HsiRNA@PGD NPs could offer a promising platform for chemo-RNAi combinational breast cancer therapy. 2024 American Chemical Society. -
Stock market prediction employing ensemble methods: the Nifty50 index
Accurately forecasting stock fluctuations can yield high investment returns while minimizing risk. However, market volatility makes these projections unlikely. As a result, stock market data analysis is significant for research. Analysts and researchers have developed various stock price prediction systems to help investors make informed judgments. Extensive studies show that machine learning can anticipate markets by examining stock data. This article proposed and evaluated different ensemble learning techniques such as max voting, bagging, boosting, and stacking to forecast the Nifty50 index efficiently. In addition, an embedded feature selection is performed to choose an optimal set of fundamental indicators as input to the model, and extensive hyperparameter tuning is applied using grid search to each base regressor to enhance performance. Our findings suggest the bagging and stacking ensemble models with random forest (RF) feature selection offer lower error rates. The bagging and stacking regressor model 2 outperformed all other models with the lowest root mean square error (RMSE) of 0.0084 and 0.0085, respectively, showing a better fit of ensemble regressors. Finally, the findings show that machine learning algorithms can help fundamental analyses make stock investment decisions. 2024, Institute of Advanced Engineering and Science. All rights reserved. -
Space taxonomy: Need for a progressive tax regime
Konstantin Tsiolkovsky famously stated that while the Earth serves as the birthplace of humanity, it is not a place where mankind can indefinitely remain. Perhaps during that period, the prospect of exploring the mysteries of outer space appeared to be an unattainable aspiration. However, in the present day, there are no longer any limitations, not even the sky, since human ingenuity has facilitated access to outer space for humanity. This access is not just for the purposes of research and exploration but also for economic endeavours. Until now, the commercial utilisation of outer space has advanced at a very sluggish rate. However, firms including SpaceX, Orion Span, Virgin Galactic, and Blue Origin have achieved significant advancements in the growth of the space industry. The revenue generated by various space-related endeavours has experienced a significant 73% increase over the last ten years. The global space economy, estimated to be valued at USD one trillion in the coming years, is primarily driven by commercial activities. This presents a formidable challenge to the existing national and international taxation systems. Similar to the open seas, space is also considered res communis omnium, meaning it belongs to the entire community, and presents comparable taxing challenges with potentially uncertain solutions. The three fundamental elements of every taxation regulation, such as the Organisation for Economic Co-operation and Development or the United Nations Model Double Taxation Convention, are the taxpayer's place of residence, the origin of their income, and the methods by which they generate their money. The current tax system does not have the necessary concepts and provisions to adapt to the rapid advancements in commercial space technology. This paper examines the legal issues surrounding commercial activities conducted in space, including the nature and handling of the income generated in various legal systems. It also addresses concerns such as tax avoidance and excessive taxation, emphasising the necessity for a globally coordinated approach to effectively tax commercial activities in space. 2024 -
Cocos nucifera L.-derived porous carbon nanospheres/ZnO composites for energy harvesting and antibacterial applications
Carbon nanomaterials (CNMs) have been the subject of extensive research for their potential applications in various fields, including photovoltaics and medicine. In recent years, researchers have focused their attention on CNMs as their high electrical conductivity, low cost, and large surface area are promising in replacing traditional platinum-based counter electrodes in dye-sensitized solar cells (DSSC). In addition to their electrical properties, CNMs have also displayed antibacterial activity, making them an attractive option for medical applications. The combination of CNMs with metal oxides to form composite materials represents a promising approach with significant potential in various fields, including energy and biology. Here, we introduce porous carbon nanospheres (PCNS) derived from Cocos nucifera L. and its ZnO composite (PCNS/ZnO) as an alternative material, which opens up new research insights for platinum-free counter electrodes. Bifacial DSSCs produced using PCNS-based counter electrodes achieved power conversion efficiencies (PCE) of 3.98% and 2.02% for front and rear illumination, respectively. However, with PCNS/ZnO composite-based counter electrodes, the efficiency of the device increased significantly, producing approximately 5.18% and 4.26% for front and rear illumination, respectively. Moreover, these CNMs have shown potential as antibacterial agents. Compared to PCNS, PCNS/ZnO composites exhibited slightly superior antibacterial activity against tested bacterial strains, including gram-positive Bacillus cereus (B. cereus) and Staphylococcus aureus (S. aureus), and gram-negative Vibrio harveyi (V. harveyi) and Escherichia coli (E. coli) with MIC values of 125, 250, 125, and 62.5g/ml, respectively. It is plausible that the outcomes observed were influenced by the synergistic effects of the composite material. Graphical abstract: (Figure presented.) The Author(s), under exclusive licence to Korean Carbon Society 2024. -
Assessment of the strength of grignard reagent for the synthesis of secondary and tertiary alcohols of terpenes using metal plate flow reactor
The secondary and tertiary alcohols of terpenes were synthesized from aldehydes, and ketones using allyl magnesium chloride by the continuous metal flow reactor method. The flow process was conducted using metal plate reactors of 10 ml capacity in the presence of solvent mixtures, instead of large amounts of pure solvent like tetrahydrofuran. The products were isolated, and confirmed using gas chromatography and Nuclear magnetic resonance techniques respectively. Subsequently, the optimization studies were conducted to obtain mild, and economical reaction conditions, by varying the amount of allyl magnesium chloride, temperature, pressure, retention time, and flow rates. A comparison between batch processes and flow processes proved the advantages of the flow process in terms of reproducibility and product yield without the requirement of excess reagents compared to the batch process. The product yield was found to be excellent (6097 %) and reproducible at (150) gram scale through flow process. The scope of the reaction was studied by synthesizing terpene alcohols using different carbonyl compounds at optimized reaction conditions, which resulted in high product yield. This research addresses a crucial gap in terpene alcohol synthesis, offering a scalable and environmentally friendly approach with broad applicability. 2024 -
Building a resilient future: collaborative sustainability regulation
The challenge of sustainability lies in achieving a balance between satisfying present needs and protecting resources for future generations with an emphasis on its three pillars - environmental, social and governance. This study explored sustainable development encompassing environmental, social and governance aspects along with sustainability reporting through various sustainability frameworks. A systematic review of literature for the period 2010-23 on major worldwide sustainability frameworks was conducted, by offering insights into enhancing reporting mechanisms for a sustainable future. Secondary data related to sustainability reports were obtained from the Sustainability Accounting Standards Board and International Integrated Reporting Council, which helped in examining sector and year variations across countries. The results reflected that mandatory sustainability disclosures help to meet the United Nations Sustainable Development Goals and global sustainability frameworks help to set standards, disseminate information and promote transparency. Collaboration of investment, company action and sustainability organizations can lead to a sustainable global economy. The adoption of sustainability reporting can help organizations by fostering a proper understanding of sustainability practices, improving transparency and identifying potential business opportunities in sectors with lower sustainability. The paper provided insights into sustainability reporting published across various countries in both advanced as well as emerging and developing economies. The analysis showed which sectors and time periods have had the most sustainability reports and which areas needed to be targeted for action to advance sustainable development. 2024 The Author(s). Published by Oxford University Press on behalf of National Institute of Clean-and-Low-Carbon Energy. -
A comprehensive investigation of ethyl 2-(3-methoxybenzyl) acrylate substituted pyrazolone analogue: Synthesis, computational and biological studies
In this study, we successfully synthesized ethyl 2-(3-methoxybenzyl) acrylate-substituted pyrazolones derivative (EMH) through the reaction of Baylis-Hillman acetate with pyrazolones. We conducted comprehensive screenings to evaluate its invitro antifungal, antibacterial, and antioxidant properties. The molecule demonstrated notable in vitro antifungal and antibacterial activities attributed to the presence of anisole, enhancing absorption rates through increased lipid solubility and improving pharmacological effects. Structure-activity relationship (SAR) studies supported these findings. Additionally, insilico studies delved into the molecular interactions of the synthesized molecule with DNA Gyrase, Lanosterol 14 alpha demethylase, and KEAP1-NRF2 proteins, revealing strong binding interactions at specific sites. Furthermore, we employed ab-initio techniques to theoretically estimate the photophysical properties of the compounds. Ground state optimization, dipole moment, and HOMO-LUMO energy levels were calculated using the DFT-B3LYP-6-31G(d) basis set. The theoretical HOMO-LUMO values indicated high electronegativity and electrophilicity index. NBO analysis confirmed the presence of intermolecular ONH hydrogen bonds resulting from the interaction of the lone pair of oxygen with the anti-bonding orbital. Overall, our results suggest that anisole-substituted pyrazolones derivatives exhibit promising applications in both photophysical and biological domains. 2024 -
Development and Psychometric Validation of Teachers Receptivity to Change Scale
In this article, we report the development and psychometric validation of the Teachers Receptivity to Change Scale (TRCS). The sample included secondary school teachers of Kerala, India. In India, the teachers receptivity to change becomes important in the context of the newly drafted National Education Policy, (2020) which places teachers at the center of the reforms. The present study proceeded through five phases namely item analysis, exploratory factor analysis, confirmatory factor analysis, validation of the scale, and testretest reliability. The development of the tool started with the generation of a pool of items followed by item analysis. The exploratory factor analysis extracted four factors and the confirmatory factor analysis confirmed the four-factors namely individual, organizational, educational, and bridging factors. The structural equation modelling established the four-correlated factor construct of teachers receptivity to change and an additive model indexing teachers receptivity to change as the sum of the four factors. Both the model fit indices indicated an excellent fit. The validity of the TRCS established by correlating the teachers receptivity to change and its factors with multidimensional work motivation scale and engaged teachers scale indicated a moderate correlation. The final 28 item TRCS showed adequate internal consistency (Cronbachs alpha = 0.897) and discriminant validity. The test re-test reliability analysis (Cronbachs alpha = 0.884) confirmed the temporal stability of the scale. The findings recommend a psychometric reliable and valid scale for assessing teachers receptivity to change with implications for teachers, researchers, and policy makers. De La Salle University 2023. -
Deep learning based model for computing percentage of fake in user reviews using topic modelling techniques
Sentiment analysis plays a vital role in real time environment for knowing the history of a product or any other specific entity. Due to large number of users in the www, chances are there that many fake users may upload the fake reviews to damage the business for the sake of money. Identifying the fake reviews or percentage of fake content in the review is yet a challenging task. In this paper, an attempt has been made to find the percentage of fake in the review data. Two methodologies are combined to address this issue. Concept of spelling checking, topic modelling and deep learning for context extraction is extensively used to build the effective model. Proposed technique is exhaustively checked for efficiency with many trails of experiments. Also, the training and testing samples were shuffled for experimentation. The results of the models show its goodness. The details of the results can be found at experiments section. 2024 The Author(s)