Search results

This study aims to analyze a conspicuous corpus of literature related to the field of technology-based intensive care research and to develop an architecture model of the future smart intensive care unit (ICU).

Papers related to the topics of electronic health record (EHR), big data, data flow and clinical decision support in ICUs were investigated. These concepts have been analyzed in combination with secondary use of data, prediction models, data standardization and interoperability challenges. Based on the findings, an architecture model evaluated using MIMIC III is proposed.

Research identified issues regarding implementation of systems, data sources, interoperability, management of big data and free text produced in ICUs and lack of accuracy of prediction models. ICU should be treated as part of a greater system, able to intercommunicate with other entities.

The research examines the current needs of ICUs in interoperability and data management. As environment changes dynamically, continuous assessment and evaluation of the model with other ICU databases is required.

The proposed model improves ICUs interoperability in national health system, ICU staff intercommunication, remote access and decision support. Its modular approach ensures that ICUs can have their own particularities and specialisms while ICU functions provide ongoing expertise and training to upgrade its staff.

Purpose: ESG indices serve as vital tools for investors to measure a company’s sustainability performance, reflecting its economic, environmental, and social standings. However, integrating ESG faces numerous constraints, particularly in emerging economies. This study aims to identify the key challenges of ESG integration in emerging economies.

Methodology: Systematic search and reporting framework following a five-stage iterative process, encompassing: (1) formulating the research question, (2) identifying pertinent studies, (3) selecting studies, (4) organising data, and (5) compiling, summarising, and presenting the findings.

Findings: Several important factors including the lack of quality and availability of non-financial data, underdeveloped regulatory frameworks, technological constraints, difficulties in supply chain integration, cultural and social barriers, financial constraints, and a general lack of awareness and understanding of ESG issues. An unbalanced approach to ESG compliance, with companies often focusing primarily on societal issues while neglecting environmental aspects. Despite these challenges, the research contributes to the discussion on the significant benefits of ESG integration, including improved risk management, access to new markets and capital, and enhanced reputation.

Implication: Overcoming these challenges requires concerted efforts from governments, businesses, and international organisations to develop supportive policies, develop inclusive capacity-building systems, and raise awareness of ESG issues.

Limitation: The scope of available literature and the inherent biases within selected studies.

Future research: Future studies could analyse deeper into case studies, and comparative research to better understand how ESG integration operates in emerging economies and to evaluate how effective the strategies implemented are in tackling the challenges that have been identified.

This paper aims to present an integrated system designed for quality control and inspection in additive manufacturing (AM) technologies.

The study undertakes a comprehensive examination of the process in three distinct stages. First, the quality of the feedstock material is inspected during the preprocessing step. Subsequently, the main research topic of the study is directed toward the 3D printing process itself with real-time monitoring procedures using computer vision methods. Finally, an evaluation of the 3D printed parts is conducted, using measuring methods and mechanical experiments.

The main results of this technical paper are the development and presentation of an integrated solution for quality control and inspection in AM processes.

The proposed solution entails the development of a promising tool for the optimization of the quality in 3D prints based on machine learning algorithms.