Digital Innovation Risk Management Model of Discrete Manufacturing Enterprise Based on Big Data Analysis

At present, most risk management work mainly relies on manpower, and manpower relies on the professional knowledge of relevant skilled workers to discover hidden safety risks in production activities. This article combines relevant big data theories and 4V characteristics to analyze and investigate safety production and big data, study data structure, data source and data type. Using 5W1H scientific big data and applications, this analysis method analyzes the theoretical basis, applications and beneficiaries of big data related to safety production, some of which are application links and important theoretical issues. Secondly, it studies the security risk management model based on big data, proposes a risk management model based on big data, the technical basis of big data and the idea of a three-dimensional model, and applies the systematic space method, which is reflected in three aspects of risk management. In the end, a risk identification model based on big data, a risk assessment classification model, and a risk early warning and pre-control model are defined.

A Computational Framework for Identifying Age Risks in Drug-Adverse Event Pairs

The identification of associations between drugs and adverse drug events (ADEs) is crucial for drug safety surveillance. An increasing number of studies have revealed that children and seniors are susceptible to ADEs at the population level. However, the comprehensive explorations of age risks in drug-ADE pairs are still limited. The FDA Adverse Event Reporting System (FAERS) provides individual case reports, which can be used for quantifying different age risks. In this study, we developed a statistical computational framework to detect age group of patients who are susceptible to some ADEs after taking specific drugs. We adopted different Chi-squared tests and conducted disproportionality analysis to detect drug-ADE pairs with age differences. We analyzed 4,580,113 drug-ADE pairs in FAERS (2004 to 2018Q3) and identified 2,523 pairs with the highest age risk. Furthermore, we conducted a case study on statin-induced ADE in children and youth. The code and results are available at https://github.com/Zhizhen-Zhao/Age-Risk-Identification

Genetically Based Atrial Fibrillation: Current Considerations for Diagnosis and Management

Atrial fibrillation (AF) is the most common atrial arrhythmia and is subcategorized into numerous clinical phenotypes. Given its heterogeneity, investigations into the genetic mechanisms underlying AF have been pursued in recent decades, with predominant analyses focusing on early onset or lone AF. Linkage analyses, genome wide association studies (GWAS), and single gene analyses have led to the identification of rare and common genetic variants associated with AF risk. Significant overlap with genetic variants implicated in dilated cardiomyopathy syndromes, including truncating variants of the sarcomere protein titin, have been identified through these analyses, in addition to other genes associated with cardiac structure and function. Despite this, widespread utilization of genetic testing in AF remains hindered by the unclear impact of genetic risk identification on clinical outcomes and the high prevalence of variants of unknown significance (VUS). However, genetic testing is a reasonable option for patients with early onset AF and in those with significant family history of arrhythmia. While many knowledge gaps remain, emerging data support genotyping to inform selection of AF therapeutics. In this review we highlight the current understanding of the complex genetic basis of AF and explore the overlap of AF with inherited cardiomyopathy syndromes. We propose a set of criteria for clinical genetic testing in AF patients and outline future steps for the integration of genetics into AF care.

The utilization of project risk monitoring and control practices and their relationship with project success in construction projects

Risk monitoring and control is often poorly implemented in construction projects because of a failure to monitor and manage identified risks. Construction companies experience significant losses due to project managers' lack of project risk monitoring and control in construction projects. Most studies have concentrated on risk identification, risk assessment, and risk analysis processes while neglecting crucial risk management processes of risk control, risk monitoring, and risk response. The lack of research on these three crucial processes highlights a gap in the literature concerning how these processes can increase the delivery of successful projects. The purpose of this study was to examine whether the utilization of project risk monitoring and control practices was related to project success in construction projects in the United States. An electronic survey instrument was used to collect data from a sample of 50 construction project managers in the Dallas-Fort Worth area in the state of Texas, in the United States. Spearman rho correlation analysis was used to examine the relationship between project risk monitoring and control practices and project success. The results of this study indicated that all project risk monitoring and control practices, including risk reassessment, risk audits, contingency reserves analysis, and risk status meetings, were significantly and positively related to project success in construction projects. One of the recommendations presented in this study was that future research should conduct the same study in developing countries to see if the study’s findings remain the same and generalizable. The study concluded that construction organizations should regularly consider the importance and usage of project risk monitoring and control practices and apply them to improve the success rate of a project.

Developing the model for cross-sectoral cooperation for promoting health and wellbeing

In the rapidly progressing world where different sectors become more interconnected, cross-sectoral cooperation in health promotion lacks a specific set of instruments, navigating partners through the cooperation process in project implementation. Cross-sectoral cooperation is an everyday practice in business and has become an integral part of promoting health and wellbeing comprehensively and sustainably. In this paper, we propose a developed Model for cross-sectoral cooperation, which has been designed within the Interreg Baltic Sea Region project “Urban Labs for Better Health for All in the Baltic Sea Region” (Healthy Boost), aiming to boost cross-sectoral cooperation for health and wellbeing in cities and municipalities. The Model is developed based on literature research and self-assessment of cross-sectoral cooperation for health promotion in Healthy Boost partner cities and municipalities in Latvia, Poland, Russia, Finland, Estonia, Lithuania, and Sweden. Composed of five major domains (risk identification, leadership, coordination, communication, and motivation) and four stages of cooperation (mapping, planning, implementation, and assessment), it provides a checklist of helpful questions for identifying solutions effectively and systematically. The Model can be used both as a navigational tool and as an “emergency” tool to manage cross-sectoral cooperation challenges successfully.