Pattern of disinfectants use and their adverse effects on the consumers after COVID-19 outbreak

Background and objective: The aim of this study was to investigate the pattern of disinfectants use within outbreak of COVID-19 and estimate their adverse effects on the consumer�s health. Methods: In this descriptive-analytical study, 1090 participants were entered into the study without age and gender limitations. A researcher-made electronic checklist containing 43 questions was applied to collect data. The checklist included three sections: demographic characteristics (8 questions), disinfection of body and non-living surfaces (21 questions), and adverse effects of disinfectants on health (15 questions). Results: 87 of participants used the incorrect proportions of water and alcohol to make this disinfectant available at home. The percentage of people with wrong proportion of sodium hypochlorite was 74.2. Approximately 42 of participants experienced at least one disorder on their hands, feet, eyes, respiratory or gastrointestinal systems after sequential uses of disinfectants. The most common disorders among the participants were found to be skin dryness (76.3), obsession (42.2), skin itching (41.2), coughing (41.1), and eyes irritation (39.5). The mean frequency of hand washing and hand disinfecting were 15.28 and 10.74 times per a day, respectively, and the clean-up in case of surfaces was 2.99 times a day. The frequency of hand washing and disinfecting in women group (16.4 and 11.2 times a day) were higher than in men (14.0 and 10.3 times a day) group. In addition, these self-care actions in married people (15.6 and 11.0 times a day) were higher compared to those in single people (14.0 and 10.6 times a day). Conclusion: Being unaware of participants with instruction for preparation and use disinfectants may harm their health. Therefore, it is suggested that the authorities provide the necessary training program for public through official media. © 2020, Springer Nature Switzerland AG

A Stochastic Simulation Framework for Truck and Shovel Selection and Sizing in Open Pit Mines

Material handling in open pit mining accounts for about 50% of production costs. The selection and deployment of efficient, safe, and economic loading and haulage systems is thus critical to the production process. The problems of truck and shovel selection and sizing include determination of the optimal number and capacities of haulage and loading units, as well as their allocation and operational strategies. Critical survey and analysis of the literature has shown that deterministic, stochastic, and experimental approaches to these problems result in considerably different outputs. This paper presents a comprehensive simulation framework for the problem of truck and shovel selection and sizing based on the random processes underlying the network-continuous-discrete event nature of the mining operation. The framework builds on previous research in this field and attempts to address limitations of available methodologies in the form of a comprehensive algorithm. To test the validity of the framework a large open pit mine was evaluated. The stochastic processes governing the uncertainties underlying the material loading and haulage input variables were defined and built into the stochastic model. Discrete event simulation was used to simulate the stochastic model. The proposed model resulted in several modifications to the case study

Work breakdown structure (WBS) development for underground construction

A work breakdown structure (WBS) can prove to be pivotal to successful project management planning. There are few published studies about the methodologies or tools to develop the appropriate WBS for a project, and those that are available are limited to the specific areas of construction such as apartment-building construction and boiler manufacturing. This research has an emphasis on developing a methodology with higher generalizability, which has the capability to be customized to complex underground projects. To address this issue, a new methodology that employs hierarchical neural networks to develop the WBS of complex underground projects is presented. This methodology has been applied to several tunnel case studies and it has been shown that for a real project, the model is able to generate the WBS and its activities that are comparable to those generated by a project planner. Consequently, it is concluded that these modeling methods have the capacity to significantly improve the WBSs for complex underground projects and improve key project tasks, such as workload planning, cost estimating and scheduling

Measuring the Effectiveness of Mining Shovels

Electric and hydraulic shovels are the dominant loading machinery in surface mining operations. Despite their critical role in production and their high capital and operating costs, no reliable and comprehensive quantitative performance metric is available. In this paper, a stochastic shovel effectiveness (SSE) measure is proposed for the purpose of quantifying the performance effectiveness of these shovels. The SSE is based on the widely used method of overall equipment effectiveness (OEE) in the manufacturing industry. The OEE measures the performance effectiveness of equipment by multiplying its mechanical availability, utilization and production quality. In manufacturing processes, quality rate is the ratio of the total number of products minus the number of defective products - equivalent to the number of acceptable products - to the total number of products. The SSE similarly uses the mechanical-availability and utilization terms, but instead of quality rate it uses a new parameter named bucket rate. The variability or randomness of the input data, that is, availability, utilization and bucket rate, are further incorporated into the SSE, and a final stochastic SSE distribution is derived in the form of a probability density function. One hydraulic and one electric shovel in a surface mining operation were selected to test the validity of the proposed method. The SSE scores for the two shovels, operating continuously for one year, were derived and compared. As with the OEE, the three-parameter SSE method yielded more representative results for overall performance measurement than a single-parameter approach. Using Monte Carlo simulation, a three-parameter Weibull and a normal distribution were derived for quantifying the overall effectiveness of hydraulic and electric shovels, respectively. As a decision aid, the proposed methodology promises to render a more informative tool than traditional metrics for mine equipment maintenance and management

The effects of the viscosity and density on the natural frequency of the cylindrical nanoshells conveying viscous fluid

In the present paper, the mechanical interaction between a cylindrical nanoshell and the surrounding fluid is investigated with considering small scale effects based on nonlocal elasticity theory. The Navier–Stokes equations are applied to model the external force between viscous fluid and nanoshell. The mechanical analysis of the cylindrical nanoshell which is immersed by several viscous fluids is studied to investigate the influences of the viscosity and density of the fluids on vibration characteristics of the cylindrical nanoshell. First-order shear deformation theory is applied to consider the shear effects. The governing differential equations of motion are obtained based on Hamilton’s principle for simply support boundary conditions. The effects of several parameters including nonlocal parameter, viscosity of fluids and geometrical properties are investigated on the natural frequency of the cylindrical nanoshells. Numerical results reveal that the viscosity and density of the fluids have considerable influences on the natural frequency of the cylindrical nanoshells

Preparation, characterization and Cr(VI) adsorption evaluation of NaOH-activated carbon produced from Date Press Cake; an agro-industrial waste

Date Press Cake (DPC) is an inevitable by-product of date processing industries and may pose environmental problems if not managed properly. In this study, DPC was converted into activated carbon using solid NaOH under various activation conditions. The prepared activated carbon showed high specific surface area (2025.9 m(2) g(-1)) and microporous texture (86.01). It was successfully applied for the adsorption of Cr(VI) from aqueous solutions with maximum monolayer adsorption capacities as high as 282.8 mg g(-1) (pH = 2) and 198.0 mg g(-1) (pH = 5). The kinetic and isotherm experimental data of Cr(VI) adsorption onto the activated carbon were best described by Elovich and Redlich-Peterson models, respectively. It was found that the Cr(VI) adsorption onto the DPC-derived activated carbon was predominantly a chemisorption process with limited desorption rates (below 50). Overall, Date Press Cake could be considered as an abundant and renewable agroindustrial precursor for the production of high quality activated carbon

Enhanced K-Nearest Neighbors Method Application in Case of Draglines Reliability Analysis

Dragline’s availability plays a major role in sustaining economicfeasibility and operation of opencast coal mine. Thus, its reliability is essentialfor the production availability of mine. The dragline’s reliability and maintenanceoptimization are key issues, which should seriously be considered. Draglines’unexpected failures and consequently unavailability result in delayed productionsand increased maintenance and operating costs. The applications ofmethodologies which can predict the failure mode of dragline based on thehistorical dataset of failure are not only useful to reduce the maintenance andoperating costs but also increase the availability and the production rate of miningmachineries. In this research a historical failure dataset of a dragline has beenutilized in order to analyze and conduct predictive maintenance. Authors havealready utilized the K-Nearest Neighbors (KNN) algorithm in order to predict thefailure mode; however, there was a chance of getting into local optimum byutilization of the mentioned methodology. In this case, combination of GeneticAlgorithm and K-Nearest Neighbor algorithm (i.e. called enhanced K-NearestNeighbors) was applied for the failure dataset, so the probability of localoptimum has been decreased by application of Genetic Algorithm. In previousstudies, the Artificial Neural Network methods and conventional method of KNearestNeighbor has been applied to the same dataset, yet the result fromenhanced K-Nearest Neighbor reveals better regression analysis.ISBN för värdpublikation: 978-3-319-99219-8, 978-3-319-99220-4</p