scholarly journals Extraction Force Prediction for Male Entrapment Victims with Different Body Types Submerged below the Grain Surface

2019 ◽  
Vol 25 (2) ◽  
pp. 77-90
Author(s):  
Charles V. Schwab ◽  
Lauren E. Schwab ◽  
Pamela J. Schwab
Keyword(s):  
Prediction Model ◽  
Adult Male ◽  
Sensitivity Analyses ◽  
Male Body ◽  
Confined Space ◽  
Common Input ◽  
Model Calculations ◽  
Input Variables ◽  
Body Shapes ◽  
Grain Surface

Abstract. One contributor to agriculture’s high death rate is confined space fatalities caused by entrapment in grain. Over 1,000 grain-related fatalities have been documented by researchers in 43 states, and states with the largest grain storage capacities have been shown to experience a proportionally larger number of suffocation fatalities. Several researchers have measured extraction forces in specific conditions, but a reference standard is needed for estimating the extraction forces for grain suffocation victims in common conditions. A prediction model for estimating extraction forces was developed using the principle of boundary shear, an approximation of human surface area, and a commonly accepted equation for lateral granular pressure. This research reintroduces the prediction model for extraction forces and explores several sensitivity analyses of the input variables. It also updates the anthropometric data used in the model calculations and produces extraction force estimates for adult male victims with different body shapes submerged below the grain surface. Results from the prediction model are presented graphically for common input variables, various entrapment depths, and adult male body shapes. Keywords: Farm safety, Grain suffocation, Prediction model, Rescue, Safety.

scholarly journals Estimates of Victim Surface Area and Associated Extraction Force for Partial Entrapment in Grain

2021 ◽  
Vol 27 (1) ◽  
pp. 53-68
Author(s):  
Charles V. Schwab ◽  
Lauren E. Schwab ◽  
Pamela J. Schwab
Keyword(s):  
Body Mass Index ◽  
Prediction Model ◽  
Surface Area ◽  
Medial Malleolus ◽  
List Type ◽  
Male Body ◽  
Male Victim ◽  
Farm Safety ◽  
Grain Surface ◽  
Partial Extraction

HighlightsEight selected anthropometric landmarks were useful for estimating victim surface area and entrapment depth.Surface area estimates for a partially entrapped male victim ranged from 0.0716 to 2.7296 m2.Partial extraction force estimates for a male victim ranged from 0.29 to 3,693 N.Partial extraction force estimates were 18% greater on average when including the arm surface area than when not including the arms.Abstract. A prediction model for estimating extraction forces on entrapped victims was enhanced and modernized in 2018 from the original 1985 model. The prediction model was divided into two conditions based on the victim’s relative position to the grain surface. The first condition was when the victim is completely below the grain surface. The second condition was when the victim’s shoulders are above the grain surface; this condition is the focus of this research. A variable in the prediction model that changes with the depth of entrapment is the surface area of the victim. A sample of 60 male models was used to approximate the human surface area at optimal discrete positions selected based on visually identifiable anthropometric landmarks. The surface area estimates for those 60 partially entrapped male models ranged from 0.0716 to 2.7296 m2. Extraction forces for twelve partially entrapped male body types with various combinations of stature and body mass index were calculated. The extraction forces were calculated for conditions when the victim’s arms were raised (above the grain) and lowered (in the grain). Results from the prediction model showed that surface area contributed less to the partial extraction force for short underweight bodies than for tall extremely obese bodies. At the lower landmarks, i.e., medial malleolus (MM) and knee crease (KN), surface area did not contribute noticeably to the partial extraction force. The contribution of surface area was not noticeable until the victim was buried up to the crotch (landmark CR). Keywords: Farm safety, Grain entrapment, Prediction model, Rescue, Safety.

A Primer on the Assessment of Economic Impacts of Leisure Events

2020 ◽  
Vol 24 (1) ◽  
pp. 47-56
Author(s):  
Ove Oklevik ◽  
Grzegorz Kwiatkowski ◽  
Mona Kristin Nytun ◽  
Helene Maristuen
Keyword(s):  
Impact Assessment ◽  
Economic Impact ◽  
Study Design ◽  
Impact Analysis ◽  
Sensitivity Analyses ◽  
Primary Data ◽  
Music Festival ◽  
Economic Impact Analysis ◽  
Economic Impact Assessment ◽  
Input Variables

The quality of any economic impact assessment largely depends on the adequacy of the input variables and chosen assumptions. This article presents a direct economic impact assessment of a music festival hosted in Norway and sensitivity analyses of two study design assumptions: estimated number of attendees and chosen definition (size) of the affected area. Empirically, the article draws on a state-of-the-art framework of an economic impact analysis and uses primary data from 471 event attendees. The results show that, first, an economic impact analysis is a complex task that requires high precision in assessing different monetary flows entering and leaving the host region, and second, the study design assumptions exert a tremendous influence on the final estimation. Accordingly, the study offers a fertile agenda for local destination marketing organizers and event managers on how to conduct reliable economic impact assessments and explains which elements of such analyses are particularly important for final estimations.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

scholarly journals Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy

2021 ◽  
Vol 20 ◽  
pp. 153303382110246
Author(s):  
Jihwan Park ◽  
Mi Jung Rho ◽  
Hyong Woo Moon ◽  
Jaewon Kim ◽  
Chanjung Lee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Random Forest ◽  
Radical Prostatectomy ◽  
Prediction Model ◽  
Biochemical Recurrence ◽  
Clinical Decision ◽  
Outcome Variable ◽  
Tertiary Hospitals ◽  
Input Variables

Objectives: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. Patients and Methods: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. Results: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. Conclusion: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.

Prediction Modeling of PM-10 in Chiangmai City Moat by Using Artificial Neural Networks

2015 ◽  
Vol 781 ◽  
pp. 628-631 ◽  
Author(s):  
Rati Wongsathan ◽  
Issaravuth Seedadan ◽  
Metawat Kavilkrue
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Chiang Mai ◽  
Adverse Health Effects ◽  
High Pollution ◽  
Artificial Neural ◽  
Input Variables ◽  
Pm 10 ◽  
Respiratory Conditions

A mathematical prediction model has been developed in order to detect particles with a diameter of 10 micrometers or less (PM-10) that are responsible for adverse health effects because of their ability to cause serious respiratory conditions in areas of high pollution such as Chiang Mai City moat area. The prediction model is based on 3 types of Artificial Neural Networks (ANNs), including Multi-layer perceptron (MLP-NN), Radial basis function (RBF-NN), and hybrid of RBF and Genetic algorithm (RBF-NN-GA). The model uses 8 input variables to predict PM-10, consisting of 4 air pollution substances ( CO, O3, NO2 and SO2) and 4 meteorological variables related PM-10 (wind speed, temperature, atmospheric pressure and relative humidity). These 3 types of ANN have proved efficient instrument in predicting the PM-10. However, the performance of RBF-NN was superior in comparison with MLP-NN and RBF-NN-GA respectively.

scholarly journals Modeling the Costs of Hospital-Acquired Infections in New Zealand

2003 ◽  
Vol 24 (3) ◽  
pp. 214-223 ◽  
Author(s):  
Nicholas Graves ◽  
Tanya M. Nicholls ◽  
Arthur J. Morris
Keyword(s):  
New Zealand ◽  
Economic Cost ◽  
Cost Effective ◽  
Sensitivity Analyses ◽  
Primary Data ◽  
Health Board ◽  
District Health ◽  
Hospital Acquired Infections ◽  
Input Variables ◽  
Hospital Acquired

AbstractObjective:To model the economic costs of hospital-acquired infections (HAIs) in New Zealand, by type of HAI.Design:Monte Carlo simulation model.Setting:Auckland District Health Board Hospitals (DHBH), the largest publicly funded hospital group in New Zealand supplying secondary and tertiary services. Costs are also estimated for predicted HAIs in admissions to all hospitals in New Zealand.Patients:All adults admitted to general medical and general surgical services.Method:Data on the number of cases of HAI were combined with data on the estimated prolongation of hospital stay due to HAI to produce an estimate of the number of bed days attributable to HAI. A cost per bed day value was applied to provide an estimate of the economic cost. Costs were estimated for predicted infections of the urinary tract, surgical wounds, the lower and upper respiratory tracts, the bloodstream, and other sites, and for cases of multiple sites of infection. Sensitivity analyses were undertaken for input variables.Results:The estimated costs of predicted HAIs in medical and surgical admissions to Auckland DHBH were $10.12 (US $4.56) million and $8.64 (US $3.90) million, respectively. They were $51.35 (US $23.16) million and $85.26 (US $38.47) million, respectively, for medical and surgical admissions to all hospitals in New Zealand.Conclusions:The method used produces results that are less precise than those of a specifically designed study using primary data collection, but has been applied at a lower cost. The estimated cost of HAIs is substantial, but only a proportion of infections can be avoided. Further work is required to identify the most cost-effective strategies for the prevention of HAI.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

scholarly journals Sensitivity analysis of point and parametric pedotransfer functions for estimating water retention of soils in Algeria

SOIL ◽  
2016 ◽  
Vol 2 (4) ◽  
pp. 647-657 ◽  
Author(s):  
Sami Touil ◽  
Aurore Degre ◽  
Mohamed Nacer Chabaca
Keyword(s):  
Water Retention ◽  
Sensitivity Analyses ◽  
Pedotransfer Functions ◽  
Sand Fraction ◽  
Significant Difference ◽  
Point Pressure ◽  
Input Variables ◽  
Clay Percentage ◽  
Different Sources ◽  
Linear Regressions

Abstract. Improving the accuracy of pedotransfer functions (PTFs) requires studying how prediction uncertainty can be apportioned to different sources of uncertainty in inputs. In this study, the question addressed was as follows: which variable input is the main or best complementary predictor of water retention, and at which water potential? Two approaches were adopted to generate PTFs: multiple linear regressions (MLRs) for point PTFs and multiple nonlinear regressions (MNLRs) for parametric PTFs. Reliability tests showed that point PTFs provided better estimates than parametric PTFs (root mean square error, RMSE: 0.0414 and 0.0444 cm3 cm−3, and 0.0613 and 0.0605 cm3 cm−3 at −33 and −1500 kPa, respectively). The local parametric PTFs provided better estimates than Rosetta PTFs at −33 kPa. No significant difference in accuracy, however, was found between the parametric PTFs and Rosetta H2 at −1500 kPa with RMSE values of 0.0605 cm3 cm−3 and 0.0636 cm3 cm−3, respectively. The results of global sensitivity analyses (GSAs) showed that the mathematical formalism of PTFs and their input variables reacted differently in terms of point pressure and texture. The point and parametric PTFs were sensitive mainly to the sand fraction in the fine- and medium-textural classes. The use of clay percentage (C %) and bulk density (BD) as inputs in the medium-textural class improved the estimation of PTFs at −33 kPa.

scholarly journals Practical Risk Assessment of Ground Vibrations Resulting from Blasting, Using Gene Expression Programming and Monte Carlo Simulation Techniques

2020 ◽  
Vol 10 (2) ◽  
pp. 472 ◽  
Author(s):  
Amir Mahdiyar ◽  
Danial Jahed Armaghani ◽  
Mohammadreza Koopialipoor ◽  
Ahmadreza Hedayat ◽  
Arham Abdullah ◽  
...  
Keyword(s):  
Gene Expression ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Gene Expression Programming ◽  
Sensitivity Analyses ◽  
Accurate Estimation ◽  
Blasting Operation ◽  
Simulation Techniques ◽  
Input Variables ◽  
The Impact

Peak particle velocity (PPV) is a critical parameter for the evaluation of the impact of blasting operations on nearby structures and buildings. Accurate estimation of the amount of PPV resulting from a blasting operation and its comparison with the allowable ranges is an integral part of blasting design. In this study, four quarry sites in Malaysia were considered, and the PPV was simulated using gene expression programming (GEP) and Monte Carlo simulation techniques. Data from 149 blasting operations were gathered, and as a result of this study, a PPV predictive model was developed using GEP to be used in the simulation. In order to ensure that all of the combinations of input variables were considered, 10,000 iterations were performed, considering the correlations among the input variables. The simulation results demonstrate that the minimum and maximum PPV amounts were 1.13 mm/s and 34.58 mm/s, respectively. Two types of sensitivity analyses were performed to determine the sensitivity of the PPV results based on the effective variables. In addition, this study proposes a method specific to the four case studies, and presents an approach which could be readily applied to similar applications with different conditions.

scholarly journals Advanced approaches to hot-mix asphalt dynamic modulus prediction

2008 ◽  
Vol 35 (7) ◽  
pp. 699-707 ◽  
Author(s):  
Halil Ceylan ◽  
Kasthurirangan Gopalakrishnan ◽  
Sunghwan Kim
Keyword(s):  
Prediction Model ◽  
Dynamic Modulus ◽  
Prediction Models ◽  
Hot Mix Asphalt ◽  
Logical Structure ◽  
Ann Model ◽  
Model Predictions ◽  
Input Variables ◽  
Artificial Neural Network Ann ◽  
National Cooperative

The dynamic modulus (|E*|) is one of the primary hot-mix asphalt (HMA) material property inputs at all three hierarchical levels in the new Mechanistic–empirical pavement design guide (MEPDG). The existing |E*| prediction models were developed mainly from regression analysis of an |E*| database obtained from laboratory testing over many years and, in general, lack the necessary accuracy for making reliable predictions. This paper describes the development of a simplified HMA |E*| prediction model employing artificial neural network (ANN) methodology. The intelligent |E*| prediction models were developed using the latest comprehensive |E*| database that is available to researchers (from National Cooperative Highway Research Program Report 547) containing 7400 data points from 346 HMA mixtures. The ANN model predictions were compared with the Hirsch |E*| prediction model, which has a logical structure and a relatively simple prediction model in terms of the number of input parameters needed with respect to the existing |E*| models. The ANN-based |E*| predictions showed significantly higher accuracy compared with the Hirsch model predictions. The sensitivity of input variables to the ANN model predictions were also examined and discussed.

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