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Mental health legislation codifies and consolidates fundamental principles, values, goals, objectives and mental health policy. Such legislation is essential to guarantee that the dignity of patients is preserved and that their fundamental rights are protected. This article considers legislation in south Asia, specifically the Mental Health Act in India, and argues that the act has shortcomings that serve as a barrier to mental health services. The authors argue for a modern mental health law that gives priority to protecting the rights of people with mental disorder, promotes development of community‐based care and improves access.

This study aims to examine the impact of fiscal deficit, exchange rate and trade openness on current account deficit (CAD). The study tried to empirically investigate the ‘twin deficits hypothesis’ and ‘compensation hypothesis’ in the Indian context.

Autoregressive distributed lagARDL) bound test approach was used by taking annual time series data from 1978 to 2021. The estimates confirm a significant long-run and short-run relationship between dependent variables, i.e. CAD and independent variables such as the fiscal deficit, exchange rate and trade openness.

The results show that positive shocks of all explanatory variables significantly affect the CAD. CAD and fiscal deficit are significantly associated, as the coefficient of fiscal deficit is positive and significant. The study also found that exchange rate and trade openness significantly affect the CAD. The coefficients of exchange rate and trade openness are positive and significant. The findings show that an increase in CADs results from liberal trade policies that help domestic industries grow their trade and expansionary fiscal policy, leading to a higher fiscal deficit. The negative and significant error correction term suggests that short-run disequilibrium converges to long-run equilibrium at a speed of 19.2%. The findings validate the ‘twin deficits hypothesis’ and ‘compensation hypothesis’ in the Indian context.

It can be inferred from the study that liberal policy to promote economic growth and trade openness should be designed and promoted judiciously. An excessive liberalised approach may impact other macroeconomic variables such as current account balances. Integrating the domestic market with global markets poses a big challenge for countries like India that aspire to penetrate global markets. Furthermore, the Indian policy makers should rigorously work and promote the policies such as Fiscal Responsibility and Budget Management (FRBM) as reduction in fiscal deficits, trade imbalances will also be reduced.

This study contributes to the existing literature on ‘twin deficit’ and trade openness by giving new evidence on the trilemma between designing sustainable fiscal policy by spending wisely without imperilling the country's global presence and CAD.

This work can be used as a building block in other settings such as GPU, Map-Reduce, Spark or any other. Also, DDPML can be deployed on other distributed systems such as P2P networks, clusters, clouds computing or other technologies.

In the age of Big Data, all companies want to benefit from large amounts of data. These data can help them understand their internal and external environment and anticipate associated phenomena, as the data turn into knowledge that can be used for prediction later. Thus, this knowledge becomes a great asset in companies' hands. This is precisely the objective of data mining. But with the production of a large amount of data and knowledge at a faster pace, the authors are now talking about Big Data mining. For this reason, the authors’ proposed works mainly aim at solving the problem of volume, veracity, validity and velocity when classifying Big Data using distributed and parallel processing techniques. So, the problem that the authors are raising in this work is how the authors can make machine learning algorithms work in a distributed and parallel way at the same time without losing the accuracy of classification results. To solve this problem, the authors propose a system called Dynamic Distributed and Parallel Machine Learning (DDPML) algorithms. To build it, the authors divided their work into two parts. In the first, the authors propose a distributed architecture that is controlled by Map-Reduce algorithm which in turn depends on random sampling technique. So, the distributed architecture that the authors designed is specially directed to handle big data processing that operates in a coherent and efficient manner with the sampling strategy proposed in this work. This architecture also helps the authors to actually verify the classification results obtained using the representative learning base (RLB). In the second part, the authors have extracted the representative learning base by sampling at two levels using the stratified random sampling method. This sampling method is also applied to extract the shared learning base (SLB) and the partial learning base for the first level (PLBL1) and the partial learning base for the second level (PLBL2). The experimental results show the efficiency of our solution that the authors provided without significant loss of the classification results. Thus, in practical terms, the system DDPML is generally dedicated to big data mining processing, and works effectively in distributed systems with a simple structure, such as client-server networks.

The authors got very satisfactory classification results.

DDPML system is specially designed to smoothly handle big data mining classification.