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The COVID-19 pandemic has demonstrated the urgency of better chronic disease management and the importance of making it an integral part of the recovery agenda in Europe. This paper aims to explore the shift towards digital and integrated care systems in Europe.

In this viewpoint paper the Expert Group for Integrated Care and Digital Health Europe (EGIDE) group argues that an orchestrated shift towards integrated care holds the solution to the chronic disease pandemic.

The development of integrated care cannot happen without shifting towards a digitalised healthcare system via large-scale initiatives like the European Health Data Space (EHDS) and the involvement of all stakeholders.

The EGIDE group has identified some foundational principles, which can guide the way to realise the full potential of the EHDS for integrated care and can support the involved stakeholders’ thinking.

This study aims to assess the effect of water variation on bioethanol production from cassava peels (CP) using Saccharomyces cerevisiae yeast as the ethanologenic agent.

The milled CP was divided into three treatment groups in a small-scale flask experiment where each 20 g CP was subjected to two-stage hydrolysis. Different amount of water was added to the fermentation process of CP. The fermented samples were collected every 24 h for various analyses.

The results of the fermentation revealed that the highest ethanol productivity and fermentation efficiency was obtained at 17.38 ± 0.30% and 0.139 ± 0.003 gL⁻¹ h⁻¹. The study affirmed that ethanol production was increased for the addition of water up to 35% for the CP hydrolysate process.

The finding of this study demonstrates that S. cerevisiae is the key player in industrial ethanol production among a variety of yeasts that produce ethanol through sugar fermentation. In order to design truly sustainable processes, it should be expanded to include a thorough analysis and the gradual scaling-up of this process to an industrial level.

This paper is an original research work dealing with bioethanol production from CP using S. cerevisiae microbe.

1. Hydrolysis of cassava peels using 13.1 M H₂SO₄ at 100 ^oC for 110 min gave high Glucose productivity

2. Highest ethanol production was obtained at 72 h of fermentation using Saccharomyces cerevisiae

3. Optimal bioethanol concentration and yield were obtained at a hydration level of 35% agitation

4. Highest ethanol productivity and fermentation efficiency were 17.3%, 0.139 g.L⁻¹.h⁻¹

Hydrolysis of cassava peels using 13.1 M H₂SO₄ at 100 ^oC for 110 min gave high Glucose productivity

Highest ethanol production was obtained at 72 h of fermentation using Saccharomyces cerevisiae

Optimal bioethanol concentration and yield were obtained at a hydration level of 35% agitation

Highest ethanol productivity and fermentation efficiency were 17.3%, 0.139 g.L⁻¹.h⁻¹

This study specifically seeks to investigate the strategic implementation of machine learning (ML) algorithms and techniques in healthcare institutions to enhance innovation management in healthcare settings.

The papers from 2011 to 2021 were considered following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. First, relevant keywords were identified, and screening was performed. Bibliometric analysis was performed. One hundred twenty-three relevant documents that passed the eligibility criteria were finalized.

Overall, the annual scientific production section results reveal that ML in the healthcare sector is growing significantly. Performing bibliometric analysis has helped find unexplored areas; understand the trend of scientific publication; and categorize topics based on emerging, trending and essential. The paper discovers the influential authors, sources, countries and ML and healthcare management keywords.

The study helps understand various applications of ML in healthcare institutions, such as the use of Internet of Things in healthcare, the prediction of disease, finding the seriousness of a case, natural language processing, speech and language-based classification, etc. This analysis would help future researchers and developers target the healthcare sector areas that are likely to grow in the coming future.

The study highlights the potential for ML to enhance medical support within healthcare institutions. It suggests that regression algorithms are particularly promising for this purpose. Hospital management can leverage time series ML algorithms to estimate the number of incoming patients, thus increasing hospital availability and optimizing resource allocation. ML has been instrumental in the development of these systems. By embracing telemedicine and remote monitoring, healthcare management can facilitate the creation of online patient surveillance and monitoring systems, allowing for early medical intervention and ultimately improving the efficiency and effectiveness of medical services.

By offering a comprehensive panorama of ML's integration within healthcare institutions, this study underscores the pivotal role of innovation management in healthcare. The findings contribute to a holistic understanding of ML's applications in healthcare and emphasize their potential to transform and optimize healthcare delivery.

This research aims to determine to what degree registered and nonregistered Rohingyas differ in their difficulties and coping strategies.

Kutupalong registered and one nonregistered camp (Camp 2E) were selected as the study area, and a mixed-methods approach was followed to collect the data. Six in-depth interviews and two focus group discussions (FGDs) were conducted first, and then the questionnaire survey was conducted on 315 Rohingyas, comprising 116 registered and 199 non-registered refugees.

The results indicate a substantial difference in the difficulties and coping techniques of registered and nonregistered refugees in food, residence, health and security. Except for the health and security issue, the registered Rohingyas (RRs) have a relatively better life than the nonregistered Rohingyas (NRRs). The main problem registered refugees undergo is economic, followed by health service, food, residence, social and security issue. For nonregistered refugees, economic and social issues receive maximum attention, while security is their last concern. The coping strategies show that all strategies against difficulties significantly differ between registered and nonregistered Rohingyas.

Based on their registration status, this research may assist humanitarian workers and policymakers in better understanding of Rohingya refugees' livelihood strategies and challenges in Bangladesh. The findings may also help practitioners and policymakers build new programs and services to assist complex and difficult refugee groups in improving their livelihoods and access to essential amenities.

Previous research shows little attention to the variations between registered and unregistered refugees. However, almost no studies have compared the challenges and coping methods of registered and unregistered Rohingya refugees in Bangladesh and other regions. This research was meant to define and offer an in-depth analysis of the Rohingya refugees' livelihood strategies in the Kutupalong registered and nonregistered camp in Bangladesh to fill the knowledge gap.