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The purpose of this paper is to amalgamate recommendations from contemporary national reports on healthcare provision and needs of people with intellectual disabilities. The paper combines these with recommendations for undergraduate curricula of medical students from the GMC and Royal College of Psychiatrists (RCPsych).

National reports from Mencap, Department of Health, Disability rights commission, NHS Executive, the GMC and RCPsych were searched for relevant recommendations to undergraduate medical education in ID psychiatry. Recommendations were collated and grouped to form a single list for use when planning future curricula or auditing existing teaching.

In total, 13 relevant recommendations were identified in nine groups. These included that people with intellectual disabilities and their carers should be involved in the teaching. That teaching should include the ethical, moral and legal obligations and should include the mental capacity act and equality act. Institutional and individual discrimination should be highlighted as a problem and diagnostic overshadowing should be covered.

This paper has collated in a single document the combined recommendations of multiple reports. These are relevant to medical undergraduate teaching and may also be of use to other undergraduate healthcare courses. They are a useful template for others wishing to examine or benchmark the content of their medical undergraduate ID psychiatry teaching against an amalgamated list.

This study examines chronic illness, disability and social inequality within an exposure-vulnerabilities theoretical framework.

Using the National Survey of Drug Use and Health (NSDUH), a preeminent source of national behavioral health estimates of chronic medical illness, stress and disability, for selected sample years 2005–2014, we construct and analyze two foundational hypotheses underlying the exposure-vulnerabilities model: (1) greater exposure to stressors (i.e., chronic medical illness) among racial/ethnic minority populations yields higher levels of serious psychological distress, which in turn increases the likelihood of medical disability; (2) greater vulnerability among minority populations to stressors such as chronic medical illness exacerbates the impact of these conditions on mental health as well as the impact of mental health on medical disability.

Results of our analyses provided mixed support for the vulnerability (moderator) hypothesis, but not for the exposure (mediation) hypothesis. In the exposure models, while Blacks were more likely than Whites to have a long-term disability, the pathway to disability through chronic illness and serious psychological distress did not emerge. Rather, Whites were more likely than Blacks and Latinx to have a chronic illness and to have experienced severe psychological distress (both of which themselves were related to disability). In the vulnerability models, both Blacks and Latinx with chronic medical illness were more likely than Whites to experience serious psychological distress, although Whites with serious psychological distress were more likely than these groups to have a long-term disability.

Several possibilities for understanding the failure to uncover an exposure dynamic in the model turn on the potential intersectional effects of age and gender, as well as several other covariates that seem to confound the linkages in the model (e.g., issues of stigma, social support, education).

This study (1) extends the racial/ethnic disparities in exposure-vulnerability framework by including factors measuring chronic medical illness and disability which: (2) explicitly test exposure and vulnerability hypotheses in minority populations; (3) develop and test the causal linkages in the hypothesized processes, based on innovations in general structural equation models, and lastly; (4) use national population estimates of these conditions which are rarely, if ever, investigated in this kind of causal framework.

The increasing prevalence of a wide range of infectious diseases, as well as the underwhelming results of vaccination rates that may be traced back to problems with vaccine procurement and distribution, have brought to the fore the importance of vaccine supply chain (VSC) management in recent years. VSC is the cornerstone of effective vaccination; hence, it is crucial to enhance its performance, particularly in low- and middle-income countries where immunization rates are not satisfactory.

In this paper, the authors focus on VSC performance improvement of India by proposing supply contracts under demand uncertainty. The authors propose three contracts – wholesale price (WSP), cost sharing (CS) and incentive mechanism (IM) for the government-operated immunization program of India.

The authors' findings indicate that IM is capable of coordinating the supply chain, whereas the other two contracts are inefficient for the government. To validate the model, it is applied to a real-world scenario of coronavirus disease 2019 (COVID-19) in India, and the findings show that an IM contract improves the overall efficiency of the system by 23.72%.

Previous studies focused mainly on the influenza VSC industry within developed nations. Nonetheless, there exists a dearth of literature pertaining to the examination of supply contracts and their feasibility for immunization programs that are administered by the government and aimed at optimizing societal benefits. The authors' findings can be beneficial to the immunization program of India to optimize their VSC cost.

One of the most important components of healthcare is the timely delivery of pharmaceutical products, such as life-saving medicines. However, disruptions like COVID-19 bring new challenges and risks to the pharmaceutical supply chain (PSC) and healthcare organizations that impact their operational performance. This study focuses on mitigating risks in India's generic medicine supply chain (GMSC) as a result of various disruptions, which can assist policymakers develop appropriate plans and strategies to build resilience in the Jan Aushadhi Scheme (JAS) of micro, small and medium enterprises (MSMEs) in order to improve their overall performance.

Risk-causing vulnerabilities and resilience capabilities are identified from the literature review and expert's opinions. Following that, the vulnerabilities are classified into cause-and-effect vulnerabilities, and supply chain resilient capabilities (SCRCs) are measured using a hybrid fuzzy DEMATEL and best worst method (FDEMATEL-BMW) framework.

The outcome of the study reveals that transportation breakdown, loss of human resources and loss of suppliers are the potential risk-causing vulnerabilities that lead to vulnerabilities like shortages of medicines, loss of in-hand stock qualities and loss of sales/revenue. In addition, the analysis suggests that the sustainability of an organization with maximum weightage is the critical factor for building resilience in GMSC followed by flexibility, agility and visibility.

The integration of resilience into Jan Aushadhi GMSC can help in managing disruptions efficiently and effectively to mitigate risk and optimize MSMEs overall performance.

To the best of the authors’ knowledge, this work will be the first of its kind to model resilience in GMSC of MSMEs using a hybrid framework.

Industry 4.0 is regarded as a modern industrial phase wherein convergence between horizontal and vertical production systems, along with their interactions, would enable industries to obtain excellent industrial efficiency. This research thus explores the barriers relevant to Industry 4.0 and their cause and effect relations using the decision-making trial and evaluation laboratory (DEMATEL) technique, and this will assist in the implementation of Industry 4.0 in manufacturing industries.

A comprehensive literature review is carried out to outline the barriers for the Industry 4.0 implementation. The investigative techniques used in this research were conducted utilizing a questionnaire as an instrument for collecting data. The data collected were analysed using the method DEMATEL.

The findings indicate that key barriers to implementing Industry 4.0 include their cause and effect relationships. Reported “implementation costs” have been a critical barrier for Industry 4.0 implementation. Prioritization of barriers and their cause and effect relationships was achieved so that decision-makers could concentrate on overcoming such barriers for Industry 4.0 implementation.

The research is intended to assist executives in recognizing the barriers to Industry 4.0 implementation in their manufacturing industries. Furthermore, the strong functional and empirical importance of emerging and digital manufacturing technologies ensures that understanding the necessary barriers to implementation is important.

This is one of the first few empirical studies in the field of Indian manufacturing industries that uses the DEMATEL method framework as a foundation for recognizing cause-effect interactions among the barriers that affect Industry 4.0.

In the Indian system of medicine – Ayurveda, Musa paradisiaca has been mentioned as a remedy for various diseases and ailments. Based on the folkloric use, the purpose of this paper is to verify and compare the hypoglycemic potential of unripe, ripe and overripe fruit extract of Musa paradisiaca.

Hypoglycemic activity of fruit extracts has been evaluated using various in vitro methods, namely, determination of glucose adsorption capacity, glucose uptake in yeast cells, amylolysis kinetics and glucose diffusion.

The extracts of unripe, ripe and overripe fruits of Musa paradisiaca adsorbed glucose, and the adsorption of glucose increased remarkably with an increase in glucose concentration. In the amylolysis kinetic experimental model, the rate of glucose diffusion was found to increase with time, and all the extracts of unripe, ripe and overripe fruits of Musa paradisiaca demonstrated significant inhibitory effects on the movement of glucose into external solution across the dialysis membrane as compared to the control. The extracts under study also promoted glucose uptake by the yeast cells in all the five glucose concentrations used in the study.

Here, the authors have verified and compared the hypoglycemic potential of Musa paradisiaca, its unripe fruit extract was found to show a better activity than ripe and overripe fruit extracts.

Banana, being an all season readily available fruit, is widely consumed due to its ready availability and low cost. It acts as a complete food for even low socio-economic classes of society, owing to its rich nutritional values. Even in a processed and unprocessed manner, it is an important constituent of diet. The research suggests that instead of consuming ripe and overripe fruit, the unripe fruit will help in management of diabetes.

The purpose of this paper is to stir the deliberation on understanding grassroots innovation (GI) phenomenon through the lived experience approach and attempt to address the existing void in current literature.

This paper outlines a human science research approach for studying the subjective reality embedded in the GI phenomenon. Such an approach provides a better and more bottom-up understanding of the underlying individual and interpersonal dynamics shaping the GI.

This study provides a richer understanding of the underlying individual and interpersonal dynamics shaping the GI phenomena. This may serve as an aid for future research on scaling, managing GI and developing entrepreneurial capabilities of the grassroots innovators (GIrs). The study also confirms that no single unilateral theory can fully explain the lived experiences of the GIrs at the ideation, opportunity recognition, prototyping and scaling stage of GI. Rather, it is quintessential to have an integrated holistic perspective for understanding GI. This study also highlights the importance of hermeneutic phenomenology in pro-poor innovation research and practice in the near future.

This paper’s main limitation is whether the findings can be generalized in a wider context. The authors acknowledge this limitation. However, the purpose of this study is not to generalize the findings but rather provide a contextual understanding of what constitutes the lived experiences of GI. The authors recommend that a future study covering greater number of GIrs across India be undertaken to gain a better appreciation of the bigger picture.

Systematic approaches for tapping into GI are conspicuously non-existent and hence a contextual understanding through the proposed holistic lens will assist in thriving of the GI phenomena in South Asian countries such as India.

To avoid the high cost of poor quality (COPQ), there is a constant need for minimizing the formation of defects during manufacturing through defect detection and process parameters optimization. This research aims to develop, design and test a smart system that detects defects, categorizes them and uses this knowledge to enhance the quality of subsequent parts.

The proposed system integrates data collected from the deep learning module with the machine learning module to develop and improve two regression models. One determines if set process parameters would yield a defective product while the second model optimizes them. The deep learning model utilizes final product images to categorize the part as defective or not and determines the type of defect based on image analysis. The developed framework of the system was applied to the forging process to determine its feasibility during actual manufacturing.

Results reveal that implementation of such a smart process would lead to significant contributions in enhancing manufacturing processes through higher production rates of acceptable products and lower scrap rates or rework. The role of machine learning is evident due to numerous benefits which include improving the accuracy of the regression model prediction. This artificial intelligent system enhances itself by learning which process parameters could lead to a defective product and uses this knowledge to adjust the process parameters accordingly overriding any manual setting.

The proposed system was applied only to the forging process but could be extended to other manufacturing processes.

This paper studies how an artificial intelligent (AI) system can be developed and used to enhance the yield of good products.