Explore the vast range of titles available.

Background Indigenous people are the most vulnerable and marginalised parts of society. Health services available to the tribal people are in developing face when compared to non-indigenous people. The situation with palliative care services exhibits a similar discrepancy. This study aims to explore the challenges and needs of palliative care within the tribal communities of Kerala. Methods The study followed a case study method conducted among the Paniya tribal community in Kambhatti, Maani, and Ozhakodi settlements of the Wayanad district, Kerala. The researchers used semi-structured interviews and observation as data collection methods. The data were collected from tribal people, ASHA workers, and promoters. Tribal people older than 18 years are considered for this study. A six-step model of reflexive thematic analysis was used for analysis. Open coding, followed by axial coding, was used to analyse the data collected from the participants. The codes were combined to form themes. Results A total of 12 participants were interviewed for the study, among them 8 participants were tribal people, 2 participants were promoters and 2 participants were ASHA workers. The majority of the tribal individuals live in joint families inside their settlement. Most of the participants were women, compared to men. Most of the women are employed through the Mahatma Gandhi National Rural Employment Guarantee Act. Nine major themes related to the challenges of palliative care were derived after the analysis are, lack of awareness, accessibility, financial issues, cultural beliefs and practice, fear and communication barriers, insufficient health care workforce, palliative care services, and health-related issues. Conclusions This study highlights the significance of palliative care for tribal populations. There is an increased need for palliative care, accompanied by challenges with receiving it in a culturally acceptable and sensitive manner. Developing a hybrid healthcare approach that integrates both traditional and modern medications exclusively for tribes is essential. The tribal population requires support in accessing comprehensive palliative care services from various professionals. It is crucial to establish healthcare programs aimed at addressing the entire healthcare needs of the tribal populations.

People of Meenatchipatti a village in Madurai district of Tamilnadu adhere to 'Thokkam' treatment for generations. This practice is also found in many other villages in the same district. The psychological relief function that this treatment is believed to perform among the patients suffering from certain ailments, has created interest for undertaking a study. This paper deals with the ethnomedical treatment particularly the 'Thokkam' treatment and the mode of its operation in the chosen research setting. [PUBLICATION ABSTRACT]

Existing grand-potential based multicomponent phase-field model is extended to handle systems with interstitial sublattice. This is achieved by treating the concentration of alloying elements in site-fraction. Correspondingly, the chemical species are distinguished based on their lattice positions, and their mode of diffusion, interstitial or substitutional, is appropriately realised. An approach to incorporate quantitative driving-force, through parabolic approximation of CALPHAD data, is introduced. By modelling austenite decomposition in ternary Fe–C–Mn, albeit in a representative microstructure, the ability of the current formalism to handle phases with interstitial components, and to distinguish interstitial diffusion from substitutional in grand-potential framework is elucidated. Furthermore, phase transformation under paraequilibrium is modelled to demonstrate the limitation of adopting mole-fraction based formulation to treat multicomponent systems.

Purpose: This study examines the impact of the covid-19 pandemic on the Indian stock market from December 2019 to May 2022. The major objective of the study is to evaluate the reactions of major stock indices in response to the volatility index during the three waves of covid-19 and to project future movements for investments in the equity market. Design/methodology/approach: The study compares the responses of major sectors like Automobiles, Banking, Consumer durables, Financial Services, and overall nifty movements concerning to volatility index. For this purpose, the study first applied the Augmented Dickey-Fuller test to check stationarity conditions. The study also used the pairwise Granger Causality and Regression model for empirical analysis. Findings: The major findings of the study is that almost all the major sectoral stocks resumed to the pre-covid level with a steady state and volatility was not observed in the first wave of covid 19. During the second and third waves, the growth responses from the indices movements are moderate but volatility is observed, particularly high volatility was observed in the third wave due to uncertainties in the economy. Finally, the study concluded by stating that a higher level of the negative effect of covid-19 was observed in the Indian stock market during the first wave of covid-19. Later, the negative effect turns out to be positive in terms of returns, but a higher level of volatility persists. Research limitations/implications: The study also observed that the Indian stock market is not only responsive to the performance of the company, it is also responsive to several other factors like India’s border disputes with China and Pakistan, geopolitical unrest, and the rise in petroleum costs brought on by inflationary pressures. Originality/value: Weak forecasting is observed in the Indian equity market as a result of expectations of global instability. KCI Citation Count: 0

Purpose: This study examines the impact of the covid-19 pandemic on the Indian stock market from December 2019 to May 2022. The major objective of the study is to evaluate the reactions of major stock indices in response to the volatility index during the three waves of covid-19 and to project future movements for investments in the equity market. Design/methodology/approach: The study compares the responses of major sectors like Automobiles, Banking, Consumer durables, Financial Services, and overall nifty movements concerning to volatility index. For this purpose, the study first applied the Augmented Dickey-Fuller test to check stationarity conditions. The study also used the pairwise Granger Causality and Regression model for empirical analysis. Findings: The major findings of the study is that almost all the major sectoral stocks resumed to the pre-covid level with a steady state and volatility was not observed in the first wave of covid 19. During the second and third waves, the growth responses from the indices movements are moderate but volatility is observed, particularly high volatility was observed in the third wave due to uncertainties in the economy. Finally, the study concluded by stating that a higher level of the negative effect of covid-19 was observed in the Indian stock market during the first wave of covid-19. Later, the negative effect turns out to be positive in terms of returns, but a higher level of volatility persists. Research limitations/implications: The study also observed that the Indian stock market is not only responsive to the performance of the company, it is also responsive to several other factors like India’s border disputes with China and Pakistan, geopolitical unrest, and the rise in petroleum costs brought on by inflationary pressures. Originality/value: Weak forecasting is observed in the Indian equity market as a result of expectations of global instability.

Outcomes of coarsening experiments performed in solid–liquid mixture of various Pb–Sn systems, under microgravity, are investigated to explicate the influence of volume fraction on coarsening kinetics. A computer vision technique capable of efficiently ascertaining the number of Sn-rich solid precipitate is developed. After sufficient training and validation, this object detection model is employed to micrographs of Pb–Sn systems captured at different stages of coarsening. The change in the number density of the precipitate with time is monitored for different Pb–Sn systems of varying phase fractions, ranging from 5 to 80% precipitates, and the corresponding kinetics coefficient is determined using regression. Examining the kinetic coefficients in relation to phase fractions unravels that the volume of Sn-rich solid has negligible effect on the steady-state coarsening rate of the Pb–Sn solid–liquid mixture under microgravity. The indefinite and marginal effect of precipitate volume-fraction on kinetic coefficient apparently substantiates the trans-interface diffusion governed coarsening in Pb–Sn system, as opposed to generally held dominance of matrix diffusion.

Biotransformation of organic wastes into value-added products is gaining interest owing to waste management issues, exhaustion of fossil fuels and the demand for biodegradable plastics. Lactic acid is widely used for polymers, foods, beverages, medicines, cosmetics and clothing. However, the major obstacle in large-scale fermentation of lactic acid is achieving enhanced yield, productivity and optical purity with cheap resources. Therefore, we review methods and recovery techniques for production of microbial lactic acid using cheap fermentative substrates. New strategies allow to alleviate limitations associated with substrate inhibition, product inhibition, undesirable by-products, sensitivity to toxic compounds, inefficient utilization of mixed sugars and overuse of neutralizing agents. Efficient utilization of mixed sugars can be achieved with simultaneous saccharification and fermentation using mixed cultures, isolating carbon catabolic repression-negative strains and altering the metabolic pathway. Lactic acid productivity can be improved by co-culture, maintaining high cell density and periodically removing end-products accumulated in the fermentation medium. Inhibition by toxic compounds can be eliminated by using engineered feedstock which releases less inhibitors, by using inhibitor-tolerant microbes and by development of genetically engineered strains. Fed-batch fermentation was found to be better than other operation modes due to less substrate inhibition.

Realizing increasingly complex artificial intelligence (AI) functionalities directly on edge devices calls for unprecedented energy efficiency of edge hardware. Compute-in-memory (CIM) based on resistive random-access memory (RRAM) 1 promises to meet such demand by storing AI model weights in dense, analogue and non-volatile RRAM devices, and by performing AI computation directly within RRAM, thus eliminating power-hungry data movement between separate compute and memory 2 – 5 . Although recent studies have demonstrated in-memory matrix-vector multiplication on fully integrated RRAM-CIM hardware 6 – 17 , it remains a goal for a RRAM-CIM chip to simultaneously deliver high energy efficiency, versatility to support diverse models and software-comparable accuracy. Although efficiency, versatility and accuracy are all indispensable for broad adoption of the technology, the inter-related trade-offs among them cannot be addressed by isolated improvements on any single abstraction level of the design. Here, by co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, we present NeuRRAM—a RRAM-based CIM chip that simultaneously delivers versatility in reconfiguring CIM cores for diverse model architectures, energy efficiency that is two-times better than previous state-of-the-art RRAM-CIM chips across various computational bit-precisions, and inference accuracy comparable to software models quantized to four-bit weights across various AI tasks, including accuracy of 99.0 percent on MNIST 18 and 85.7 percent on CIFAR-10 19 image classification, 84.7-percent accuracy on Google speech command recognition 20 , and a 70-percent reduction in image-reconstruction error on a Bayesian image-recovery task. A compute-in-memory neural-network inference accelerator based on resistive random-access memory simultaneously improves energy efficiency, flexibility and accuracy compared with existing hardware by co-optimizing across all hierarchies of the design.

Purpose: The major objective of the study is to evaluate the applicability and efficacy of the monetary method by utilizing India's money supply and balance of payments account. Design/methodology/approach: Annual data f rom 1991 to 2024 is used in the research, particularly since LERMS was implemented in 1991. For stationary settings, this study used the Philips-Perron and Augmented Dickey-Fuller tests. The Autoregressive Distributed Lag-Error Correction Model (ARDL-ECM) was used in the study to analyze the short- and long-term relationships. Findings: The ARDL model test findings show a long-term relationship between BOP and independent variables such as capital outflows, money supply, and exchange rates. There was no short-term causation found using the ARDL-ECM model. Additionally, since LERMS, the analysis found no automatic restoration procedure in India's balance of payments. Therefore, this analysis concluded that the monetary method was less useful in resolving imbalances in India's balance of payments. Additionally, the study noted that in the setting of India's BOP, the function of monetary policy is ineffective. Research limitations/implications: For a developing country like India, this might not be a problem in the current environment. However, based on the Mundell-Flemings assignment rule, the study highly recommended Friedman's monetary rule for the long-term effectiveness of monetary policy in order to achieve external equilibrium. Originality/value: Testing the effectiveness of monetary policy in achieving external sector by comparing short-run effects with long-run effects is considered to be the uniqueness of this study.