Development Of Risk Assessment System Research Articles

Wearable Sensors and Artificial Intelligence for Physical Ergonomics: A Systematic Review of Literature.

Physical ergonomics has established itself as a valid strategy for monitoring potential disorders related, for example, to working activities. Recently, in the field of physical ergonomics, several studies have also shown potential for improvement in experimental methods of ergonomic analysis, through the combined use of artificial intelligence, and wearable sensors. In this regard, this review intends to provide a first account of the investigations carried out using these combined methods, considering the period up to 2021. The method that combines the information obtained on the worker through physical sensors (IMU, accelerometer, gyroscope, etc.) or biopotential sensors (EMG, EEG, EKG/ECG), with the analysis through artificial intelligence systems (machine learning or deep learning), offers interesting perspectives from both diagnostic, prognostic, and preventive points of view. In particular, the signals, obtained from wearable sensors for the recognition and categorization of the postural and biomechanical load of the worker, can be processed to formulate interesting algorithms for applications in the preventive field (especially with respect to musculoskeletal disorders), and with high statistical power. For Ergonomics, but also for Occupational Medicine, these applications improve the knowledge of the limits of the human organism, helping in the definition of sustainability thresholds, and in the ergonomic design of environments, tools, and work organization. The growth prospects for this research area are the refinement of the procedures for the detection and processing of signals; the expansion of the study to assisted working methods (assistive robots, exoskeletons), and to categories of workers suffering from pathologies or disabilities; as well as the development of risk assessment systems that exceed those currently used in ergonomics in precision and agility.

Application of Modern Clinical Risk Scores in the Global Assessment of Risks Related to the Diagnosis and Treatment of Acute Coronary Syndromes in Everyday Medical Practice.

This article presents an overview of contemporary risk assessment systems used in patients with myocardial infarction. The full range of risk scales, both recommended by the European Society of Cardiology and others published in recent years, is presented. Scales for assessing the risk of ischemia/death as well as for assessing the risk of bleeding are presented. A separate section is devoted to systems assessing the integrated risk associated with both ischemia and bleeding. In the first part of the work, each of the risk scales is described in detail, including the clinical trials/registers on the basis of which they were created, the statistical methods used to develop them, as well as the specification of their individual parameters. The next chapter presents the practical application of a given scale in the patient risk assessment process, the timing of its application on the timeline of myocardial infarction, as well as a critical assessment of its potential advantages and limitations. The last part of the work is devoted to the presentation of potential directions for the development of risk assessment systems in the future.

A Medical Decision Support System to Assess Risk Factors for Gastric Cancer Based on Fuzzy Cognitive Map.

Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.

Risk Assessment Using Event Tree Analysis for Sustainable Concrete Structures with Construction Management in Vietnam

After the collapse of Can Tho Bridge, a severe accident with 54 people dead and 80 injured, the risk assessment of sustainable concrete structures for Vietnam construction industry becomes an extremely important subject. In spite of many accidents in construction, there is still no research in the risk management of construction industry in Vietnam. Based on the situation, “Development of Risk assessment system for Sustainable Concrete Structures with Construction Management in Vietnam” has been conducted. The first survey was conducted with the cooperation of experienced person who has responsibility in construction–related companies in Vietnam. In order to collect actual data concerning the cause, the type and the probability of various incidents, the repair cost after the failure occurrence and the proactive cost with purpose of incident prevention to prolong the life of concrete structures and reduce the life cycle cost in Vietnam. As a result of this investigation, Event Tree Analysis (ETA) is used t...

Effects of Technological Processes on the Tenacity and Inactivation of Norovirus Genogroup II in Experimentally Contaminated Foods

Contaminated food is a significant vehicle for human norovirus transmission. The present study determined the effect of physicochemical treatments on the tenacity of infective human norovirus genogroup II in selected foods. Artificially contaminated produce was subjected to a number of processes used by the food industry for preservation and by the consumer for storage and preparation. Virus recovery was carried out by using ultrafiltration and was monitored by using bacteriophage MS2 as an internal process control. Norovirus was quantified by using monoplex one-step TaqMan real-time reverse transcription (RT)-PCR and an external standard curve based on recombinant RNA standards. An RNase pretreatment step was used to avoid false-positive PCR results caused by accessible RNA, which allowed detection of intact virus particles. Significant reductions in titers were obtained with heat treatments usually applied by consumers for food preparation (baking, cooking, roasting). Generally, processes used for preservation and storage, such as cooling, freezing, acidification (>or=pH 4.5), and moderate heat treatments (pasteurization), appear to be insufficient to inactivate norovirus within a food matrix or on the surface of food. Besides data for persistence in processed food, comparable data for individual matrix-specific protective effects, recovery rates, and inhibitory effects on the PCRs were obtained in this study. The established procedure might be used for other noncultivable enteric RNA viruses that are connected to food-borne diseases. The data obtained in this study may also help optimize the process for inactivation of norovirus in food by adjusting food processing technologies and may promote the development of risk assessment systems in order to improve consumer protection.

Analytical Evaluation of Slope Failure Location Using ASTER Image Data

In this research, the applicability of the satellite multispectral data to the predictions of slope failures has been attempted by using the data characterized by its broadness, synchronism, and periodicity, and by analyzing the causalities of various elements considered to be involved in the slope failures through the use of the qualification theory II. Moreover, the risk assessment chart for slope failure will be made based on the result of it, which ultimately will make it possible to specify the slopes with failure risks on the basis of the development of risk assessment system for slope failure.

Identifying factors related to the severity of mammalian browsing damage in eucalypt plantations

Several mammalian herbivores damage young eucalypts growing in commercial Tasmanian plantations. Damage severity varies between plantations and damage control programs could be improved if the factors influencing this spatial variation were identified. In this study, measures of damage, herbivore abundance, plantation characteristics and the characteristics of forest adjacent to plantation areas were collected at 32 Eucalyptus nitens plantations during the first year of growth. Two exploratory analyses were conducted on two separate suites of site descriptors. The first, which modelled damage as a function of vegetation cover and herbivore scat counts, explained 71% of the between-site variation in damage severity. Damage was positively related to scat deposition rate for the brushtail possum and grass cover, and negatively related to fern (mainly bracken) cover. The second analysis modelled damage as a function of plantation characteristics that could be measured before planting and which may, therefore, be useful to forestry managers as damage predictors. This model explained 47% of between-site variation. Damage was negatively related to area to perimeter ratio and the degree of canopy closure in forest adjacent to plantations. Damage was positively related to the proportion of the perimeter that adjoined forest. Our results suggest that the importance of the brushtail possum, as a contributor to variation in damage between plantations, needs to be reviewed and that several measures of plantation characteristics have potential for use in future development of risk assessment systems.