scholarly journals The New Safety Trends: The Challenges through Industry 4.0

2022 ◽  
Vol 18 ◽  
pp. 255-267
Author(s):  
Di Nardo Mario ◽  
Borowsky Piotr ◽  
Maryam Gallab ◽  
Murino Teresa ◽  
Yu Haoxuan

Industrial engineering achieved rapid growth in providing safety measurements in all industries, following different safety policies to prevent faults in sectors. Industrial safety is an essential feature to give an accident-free environment. The safety policies and measurements encourage the industrial people to work in different perilous conditions. Industries prepare their safety policy and safety manual to identify various faults and risks. It is necessary to create awareness in industrial working members, and industries maintain special departments for safety. The safety guidelines prevent occupational injuries and accidents. The safety rules and regulations reduce the waste of human and other resources in industries. The study evaluates safety models used in industry to identify issues involved in the selection, implementation, and evaluation. This research provides insight into the overall process for industrial safety and, most essential, overviews on the methodology. Predicting industrial faults and risks emphasized the industrial engineering process and used machine learning algorithms for classifications. Many issues and challenges discussed industrial safety and provided novel innovation ideas for researchers.

2019 ◽  
Vol 8 (4) ◽  
pp. 3836-3840

Understanding occupational incidents is one of the important measures in workplace safety strategy. Analyzing the trends of the occupational incident data helps to identify the potential pain points and helps to reduce the loss. Optimizing the Machine Learning algorithms is a relatively new trend to fit the prediction model and algorithms in the right place to support human beneficial factors. The aim of this research is to build a prediction model to identify the occupational incidents in chemical and gas industries. This paper describes the architecture and approach of building and implementing the prediction model to predict the cause of the incident which can be used as a key index for achieving industrial safety in specific to chemical and gas industries. The implementation of the scoring algorithm coupled with prediction model should bring unbiased data to obtain logical conclusion. The prediction model has been trained against FACTS (Failure and Accidents Technical information system) is an incidents database which have 25,700 chemical industrial incidents with accident descriptions for the years span from 2004 to 2014. Inspection data and sensor logs should be fed on top of the trained dataset to verify and validate the implementation. The outcome of the implementation provides insight towards the understanding of the patterns, classifications, and also contributes to an enhanced understanding of quantitative and qualitative analytics. Cutting edge cloud-based technology opens up the gate to process the continuous in-streaming data, process it and output the desired result in real-time. The primary technology stack used in this architecture is Apache Kafka, Apache Spark Streaming, KSQL, Data frames, and AWS Lambda functions. Lambda functions are used to implement the scoring algorithm and prediction algorithm to write out the results back to AWS S3 buckets. Proof of concept implementation of the prediction model helps the industries to see through the incidents and will layout the base platform for the various safety-related implementations which always benefits the workplace's reputation, growth, and have less attrition in human resources.


Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1557 ◽  
Author(s):  
Ilaria Conforti ◽  
Ilaria Mileti ◽  
Zaccaria Del Prete ◽  
Eduardo Palermo

Ergonomics evaluation through measurements of biomechanical parameters in real time has a great potential in reducing non-fatal occupational injuries, such as work-related musculoskeletal disorders. Assuming a correct posture guarantees the avoidance of high stress on the back and on the lower extremities, while an incorrect posture increases spinal stress. Here, we propose a solution for the recognition of postural patterns through wearable sensors and machine-learning algorithms fed with kinematic data. Twenty-six healthy subjects equipped with eight wireless inertial measurement units (IMUs) performed manual material handling tasks, such as lifting and releasing small loads, with two postural patterns: correctly and incorrectly. Measurements of kinematic parameters, such as the range of motion of lower limb and lumbosacral joints, along with the displacement of the trunk with respect to the pelvis, were estimated from IMU measurements through a biomechanical model. Statistical differences were found for all kinematic parameters between the correct and the incorrect postures (p < 0.01). Moreover, with the weight increase of load in the lifting task, changes in hip and trunk kinematics were observed (p < 0.01). To automatically identify the two postures, a supervised machine-learning algorithm, a support vector machine, was trained, and an accuracy of 99.4% (specificity of 100%) was reached by using the measurements of all kinematic parameters as features. Meanwhile, an accuracy of 76.9% (specificity of 76.9%) was reached by using the measurements of kinematic parameters related to the trunk body segment.


2019 ◽  
Vol 59 (2) ◽  
pp. 182-191 ◽  
Author(s):  
Pavel V. Yemelin ◽  
Sergey S. Kudryavtsev ◽  
Natalya K. Yemelina

The purpose of the article is to present an analytical system that allows users to proces data necessary for an industrial risk analysis and management, to monitor the level of industrial safety in a given site, and to fulfil essential tasks within the field of occupational safety. This system’s implementation will make the industrial safety management at industrial sites more effective. Multifactorial, probabilistic, determined models of accidents’ hazard and severity indexes are integrated into the computing core of the Information and Analytical System. Then, statistical methods determine the risk assessment of occupational injuries and diseases. The <em>Information and Analytical System for Hazard Level Assessment and Forecasting Risk of Emergencies in the Republic of Kazakhstan</em> allows users to work efficiently with large volumes of information and form a united analytical electronic report about the state of industrial safety. The main objective of the monitoring system is to conduct a comprehensive analysis and assessment of the state of accidents, traumas and occupational sickness rates at industrial sites, the results being classified by the degree of hazard and insalubrity of manufacture. The introduction of the computer monitoring system in the specialized services of the Emergency Management Committee and the Ministry of Investment and Development of the Republic of Kazakhstan, and at industrial enterprises throughout the country, will allow users to analyse the state of the industrial and occupational safety constantly and objectively; as a consequence, the implementation will go a long way towards comprehensively approaching the task of increasing safety levels at industrial sites.


2017 ◽  
Vol 114 (45) ◽  
pp. 11850-11855 ◽  
Author(s):  
Jean W. Fredy ◽  
Alejandro Méndez-Ardoy ◽  
Supaporn Kwangmettatam ◽  
Davide Bochicchio ◽  
Benjamin Matt ◽  
...  

Chemists have created molecular machines and switches with specific mechanical responses that were typically demonstrated in solution, where mechanically relevant motion is dissipated in the Brownian storm. The next challenge consists of designing specific mechanisms through which the action of individual molecules is transmitted to a supramolecular architecture, with a sense of directionality. Cellular microtubules are capable of meeting such a challenge. While their capacity to generate pushing forces by ratcheting growth is well known, conversely these versatile machines can also pull microscopic objects apart through a burst of their rigid tubular structure. One essential feature of this disassembling mechanism is the accumulation of strain in the tubules, which develops when tubulin dimers change shape, triggered by a hydrolysis event. We envision a strategy toward supramolecular machines generating directional pulling forces by harnessing the mechanically purposeful motion of molecular switches in supramolecular tubules. Here, we report on wholly synthetic, water-soluble, and chiral tubules that incorporate photoswitchable building blocks in their supramolecular architecture. Under illumination, these tubules display a nonlinear operation mode, by which light is transformed into units of strain by the shape changes of individual switches, until a threshold is reached and the tubules unleash the strain energy. The operation of this wholly synthetic and stripped-down system compares to the conformational wave by which cellular microtubules disassemble. Additionally, atomistic simulations provide molecular insight into how strain accumulates to induce destabilization. Our findings pave the way toward supramolecular machines that would photogenerate pulling forces, at the nanoscale and beyond.


10.12737/720 ◽  
2013 ◽  
Vol 2 (4) ◽  
pp. 43-48
Author(s):  
Девисилов ◽  
Vladimir Devisilov ◽  
Старостин ◽  
I. Starostin

The first introduction training with third-year students of &#34;Ecology and Industrial Safety&#34; Chair (Bauman Moscow State Technical University) is described in this paper. This training includes the acquaintance with various profile enterprises, dangerous and harmful factors of production and measures for their elimination. This group of enterprises includes the specialized productions ensuring the technosphere safety (sewage and waste treatment, atmosphere emissions) as well. The main questions of this training’s organization and carrying out are considered as follows: insight into the program, carrying out excursions on the enterprise, preparation and protection of reports. Knowledge and experience, received during the training, are used further at a choice of subject related to term projects and graduation works and their performance.


2021 ◽  
Vol 37 (3) ◽  
pp. 21-27
Author(s):  
T. Tairova ◽  
N. Romanenko ◽  
O. Slypachuk

Development of scientifically based measures for the prevention of occupational injuries due to alcohol intoxication of workers are based on modeling of the labor protection system. In order to develop effective preventive measures to prevent accidents at work, the mathematical model of the labor protection system (OS) was built, which takes into account many indicators that assess violations of labor and industrial discipline related to alcohol consumption. The study was based on actual statistics on occupational injuries. The application of the method of mathematical modeling on the basis of injury indicators is justified, as the proposed approach allows to ensure the targeting of preventive measures, the complexity and alternative solutions to problems, the objectivity of management decisions. The proposed scientific approaches to the development of preventive measures for labor protection allow to increase the level of industrial safety, optimize the size of penalties for violations of labor and industrial discipline, regulate relations arising in the course of work related to alcohol consumption in the workplace. Limitations / implications of research. The developed scientific approaches to the prevention of occupational injuries due to alcohol intoxication of workers are universal, they can be applied to different sectors of the economy. Practical consequences. The obtained theoretical conclusions, based on statistical data on occupational injuries, are brought to the level of specific proposals suitable for practical use in the planning of preventive measures for labor protection at enterprises. The presented scientific approaches to solving management problems in labor protection are based on a component method of assessing the occurrence of traumatic events due to violations of labor and production discipline by both employees and employers. For a comprehensive analysis of industrial risks, the main and concomitant causes that led to accidents due to alcohol intoxication of workers were taken into account. This allowed to provide a systematic approach to the assessment of production conditions and behavioral reactions of staff.


2019 ◽  
Author(s):  
Thomas M. Kaiser ◽  
Pieter B. Burger

Machine learning continues to make strident advances in the prediction of desired properties concerning drug development. Problematically, the efficacy of machine learning in these arenas is reliant upon highly accurate and abundant data. These two limitations, high accuracy and abundance, are often taken together; however, insight into the dataset accuracy limitation of contemporary machine learning algorithms may yield insight into whether non-bench experimental sources of data may be used to generate useful machine learning models where there is a paucity of experimental data. We took highly accurate data across six kinase types, one GPCR, one polymerase, a human protease, and HIV protease, and intentionally introduced error at varying population proportions in the datasets for each target. With the generated error in the data, we explored how the retrospective accuracy of a Naïve Bayes Network, a Random Forest Model, and a Probabilistic Neural Network model decayed as a function of error. Additionally, we explored the ability of a training dataset with an error profile resembling that produced by the Free Energy Perturbation method (FEP+) to generate machine learning models with useful retrospective capabilities. The categorical error tolerance was quite high for a Naïve Bayes Network algorithm averaging 39% error in the training set required to lose predictivity on the test set. Additionally, a Random Forest tolerated a significant degree of categorical error introduced into the training set with an average error of 29% required to lose predictivity. However, we found the Probabilistic Neural Network algorithm did not tolerate as much categorical error requiring an average of 20% error to lose predictivity. Finally, we found that a Naïve Bayes Network and a Random Forest could both use datasets with an error profile resembling that of FEP+. This work demonstrates that computational methods of known error distribution like FEP+ may be useful in generating machine learning models not based on extensive and expensive in vitro-generated datasets.


Author(s):  
JAMES H. CROSS II ◽  
R.STEPHEN DANNELLY

A technique for reverse engineering graphical user interfaces (GUIs) produced with Xtoolkit source code is presented. Two independent graphical representations are automatically generated to assist GUI programmers in the development, testing, maintenance, and reengineering of X-based GUI source code. This capability to generate both structural and behavioral views has the potential to provide major improvements in the comprehensibility of X source code. Whereas generating widget instance trees to describe the structure of an X interface is common, the automatic generation of dialogue state diagrams to describe the behavior of an X interface is unique to our technique. The intent of this paper is to provide insight into the functional details of our automated reverse engineering process for the benefit of other reverse engineering researchers and programming tool developers.


2020 ◽  
Vol 25 (2) ◽  
pp. 85-90 ◽  
Author(s):  
Paul Stretton

The Lilypond is a new conceptual model to describe patient safety performance. It radically diverges from established patient safety models to develop the reality of complexity within the healthcare systems as well as incorporating Safety II principles. There are two viewpoints of the Lilypond that provide insight into patient safety performance. From above, we are able to observe the organisational outcomes. This supersedes the widely used Safety Triangle and provides a more accurate conceptual model for understanding what outcomes are generated within healthcare. From a cross-sectional view, we are able to gain insights into how these outcomes come to manifest. This includes recognition of the complexity of our workplace, the impact of micro-interactions, effective leadership behaviours as well as patterns of behaviour that all provide learning. This replaces the simple, linear approach of The Swiss Cheese Model when analysing outcome causation. By applying the principles of Safety II and replacing outdated models for understanding patient safety performance, a more accurate, beneficial and respectful understanding of safety outcomes is possible.


2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Atsushi Makino

Relevant to the self-propagating high-temperature synthesis (SHS) process, an analytical study has been conducted to investigate the effects of electric field on the combustion behavior because the electric field is indispensable for systems with weak exothermic reactions to sustain flame propagation. In the present study, use has been made of the heterogeneous theory which can satisfactorily account for the premixed mode of the bulk flame propagation supported by the nonpremixed mode of particle consumption. It has been confirmed that, even for the SHS flame propagation under electric field, being well recognized to be facilitated, there exists a limit of flammability, due to heat loss, as is the case for the usual SHS flame propagation. Since the heat loss is closely related to the representative sizes of particles and compacted specimen, this identification provides useful insight into manipulating the SHS flame propagation under electric field, by presenting appropriate combinations of those sizes. A fair degree of agreement has been demonstrated through conducting an experimental comparison, as far as the trend and the approximate magnitude are concerned, suggesting that an essential feature has been captured by the present study.


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