Analyzing risks of occupational injuries in basic industries

Our research aim was to analyze occupational injuries in basic industries in Irkutsk region. Materials and methods. Occupational injuries (OI) in basic industries were analyzed using data from statistical reports issued in 2010–2019. To analyze OI in dynamics, we calculated relative values of OI and applied linear regression and Shewhart charts. Normalized intensity indicators method was used to reveal different probability of injuries in various industries as well as to predict OI risks. Results. Analysis of OI in dynamics indicates that there is a stable descending trend in a number of injuries. However, in spite of this apparent descending trend, OI values are stably by 1.3–3.0 times higher in some industries than on average in the region. The highest frequency coefficient (FC) for occupational injuries was detected in wood processing where it was equal to 5.35 [2.90–7.71] per 1,000 workers; the indicator varied within 1.00–2.93 per 1,000 workers in other industries. Shewhart chart for FC indicates that systems of occupational health and safety management are not efficient enough in all the analyzed industries since FC exceeds the upper limit in some years. We established that severity of occupational injuries tended to grow in wood processing (Cs = +3.23; 5.33 %), metallurgy (Cs = +0.94; 1.26 %), land transport (Cs = +2.42; 4.39 %), and aircraft production (Cs = +0.59; 1.68 %). The greatest number of fatal OI was detected in mining, construction, and agriculture as a share of fatal OI in the overall structure of occupational injuries amounted to 22.0 %, 19.2 %, and 11.7 % in these brunches accordingly. A probability that an injury becomes fatal is also the highest in them, 11.7, 9.0, and 6.0 accordingly. “Wood processing and production of wood articles”, “Aircraft production”, and “Construction” are among industries where risks of occupational injuries are the most probable.

A Comparative Analysis of Control Charts for Monitoring Process Mean

Control charts serve as an effective tool for controlling and monitoring process quality in industries of production and service. The Shewhart chart is the first control chart that was used to detect large mean shifts in a process. Since then, to increase the Shewhart chart’s sensitivity, synthetic type control charts, such as synthetic control chart, side sensitive group runs (SSGR) control chart, have been proposed. SSGR chart ismore efficient compared to the Shewhart chart and synthetic chart,primarily due to the side sensitive feature in SSGR chart. Meanwhile, exponentially weighted moving average (EWMA) chart isoften used to detect small process changes. In practice, the evaluation of a control chart’s performance is vital. Nevertheless, the cost of implementing a control chart is an important factor that influences the choice of a control chart. The cost of repairs, sampling, nonconforming products from a failure in detecting out-of-control status, and investigating false alarms, can be significantly high. Therefore, the aim of this paper is to compare the implementation cost of synthetic, SSGR and EWMA charts, so that quality practitioners can identify the most cost-effective chart to implement. Here, the cost function was adopted to compute the implementation cost of the control chart. According to the findings, quality practitioners are recommended to adopt the SSGR chart,since it is more economical compared to the synthetic chart. However, the cost to implement anEWMA chart is higher than the synthetic and SSGR charts. In light of this, this study allows for quality practitioners to have a better idea on the selection of the control chart to implement, with respect to its cost.

Monitoring patient flow in a hospital emergency department: ARMA-based nonparametric GLRT scheme

Overcrowding in emergency departments (EDs) is a primary concern for hospital administration. They aim to efficiently manage patient demands and reducing stress in the ED. Detection of abnormal ED demands (patient flows) in hospital systems aids ED managers to obtain appropriate decisions by optimally allocating the available resources following patient attendance. This paper presents a monitoring strategy that provides an early alert in an ED when an abnormally high patient influx occurs. Anomaly detection using this strategy involves the amalgamation of autoregressive-moving-average (ARMA) time series models with the generalized likelihood ratio (GLR) chart. A nonparametric procedure based on kernel density estimation is employed to determine the detection threshold of the ARMA-GLR chart. The developed ARMA-based GLR has been validated through practical data from the ED at Lille Hospital, France. Then, the ARMA-based GLR method’s performance was compared to that of other commonly used charts, including a Shewhart chart and an exponentially weighted moving average chart; it proved more accurate.

Statistical Process Control Vibrasi Bearing untuk Identifikasi Degradasi

Statistical Process Control (SPC) is usually applied to the production process of goods, with the aim of detecting the quality of a production item that is within or beyond the specified specifications. In this study, SPC was applied to the bearing vibration signal to detect the first observable defect on a machine that functions as part of a prognostic tool for maintenance decision making. The detection of damage and prognostic are two important aspects in machine maintenance based on current conditions or better known as Condition (data) Based Maintenance (CBM). This paper discusses the shewhart average level chart and adaptive shewhart average level chart to detect the first observable defect. The shewhart chart is built with two assumptions, i.e. that the data must vary randomly around an established mean and follows a normal distribution. However, the adaptive Shewhart chart there is no need for normal assumption. The exploration of our data shows that the assumption of normality is not fulfilled, so that the Shewhart average level chart is not implemented. The adaptive Shewhart chart shows that the warning line for bearing 1 amounted to 5.547 and 3.631, for bearing 2 amounted to 5.491 and 3.635, for bearing 3 amounted to 5.762 and 3, 573, for bearing 4 of 5.604 and 33.615. The action line for bearing 1 is 6.026 and 3.152, for bearing 2 is 5.955 and 3.171, for bearing 3 is 6.309 and 3.026, for bearing 4 is 6.101 and 3.118. The first observable defect was t = 81 for bearing 1, t = 146 for bearing 2, t = 40 for bearing 3 and t = 61 for bearing 4. The adaptive Shewart chart can be used as a toll to estimate the initiation of transition state from normal to degenerate.

Understanding variation in reported covid-19 deaths with a novel Shewhart chart application

Objective Motivated by the coronavirus disease 2019 (covid-19) pandemic, we developed a novel Shewhart chart to visualize and learn from variation in reported deaths in an epidemic. Context Without a method to understand if a day-to-day variation in outcomes may be attributed to meaningful signals of change—rather than variability we would expect—care providers, improvement leaders, policy-makers, and the public will struggle to recognize if epidemic conditions are improving. Methods We developed a novel hybrid C-chart and I-chart to detect within a geographic area the start and end of exponential growth in reported deaths. Reported deaths were the unit of analysis owing to erratic reporting of cases from variability in local testing strategies. We used simulation and case studies to assess chart performance and define technical parameters. This approach also applies to other critical measures related to a pandemic when high-quality data are available. Conclusions The hybrid chart detected the start of exponential growth and identified early signals that the growth phase was ending. During a pandemic, timely reliable signals that an epidemic is waxing or waning may have mortal implications. This novel chart offers a practical tool, accessible to system leaders and frontline teams, to visualize and learn from daily reported deaths during an epidemic. Without Shewhart charts and, more broadly, a theory of variation in our epidemiological arsenal, we lack a scientific method for a real-time assessment of local conditions. Shewhart charts should become a standard method for learning from data in the context of a pandemic or epidemic.

Ultrasound-Assisted Syntheses of Chalcones: Experimental Design and Optimization

Chalcones are a group of compounds that belong to the flavonoid family and have a wide variety of uses, including a high therapeutic potential for multiple diseases, such as, anticancer, antifungal or antibacterial agents. As is well known, chalcones are commonly synthesized by Claisen-Schmidt condensation, aldol condensation involving the appropriate aldehydes and ketones, in presence of acid or base as catalyst followed by dehydration reactions. However, under conventional conditions it is carried out with prolonged reaction times and requires expensive catalysts. For this reason, alternative source of energy, microwave or ultrasound, are employed. On the other hand, in all chemical processes a considerable amount of variables (instrumental parameters, reagents, temperatures, times, etc.) take part so a large number of experiments must be carried out in order to define the optimal conditions. In addition, the experimental design technique -important tool- allows the optimization of conditions leading to better yields in shorter times. Here and in line with previous research, we explore the synthesis, assisted by ultrasound, of (E)-1,3-diphenyl-2-propen-1-one like a model reaction. Taguchi Design was the mathematical method employed to determine the best working condition. In conclusion, the desired product is obtained quantitatively, without undesired by-products, and in short reaction times. Additionally, the reaction was used, as an alternative method, to monitor the ultrasound equipment using the control chart methodology (Shewhart chart), which allowed us to study how a process changes over time.