Using computer simulation in operating room management: impacts of information quality on process performance

High quality information has a significant impact on improving operation performance and patient satisfaction, as well as resolving patient disputes. Based on the analysis of the perioperative process, information quality is considered as an important contributory factor in improving patient throughput. In this paper, we propose a conceptual framework to use computer simulations in modeling information flow of hospital process for operating room management (ORM). Additionally, we conduct simulation studies in different levels of the information quality for ORM. The results of our studies provide evidence that information quality can drive process performance in several phases of the ORM

In-Pavement Fiber Bragg Grating Sensor for Vehicle Counting

Traffic volume studies are conducted to determine the number, movements, and classifications of roadway vehicles at a given location and period. Typically, there are two methods for conducting traffic volume studies: manual and automatic counting. When manual counting is used, a person records the traffic volume on the site or alternatively from video recordings and this estimate can have a large margin of error. Automatic counting is based on measurement technologies, including pneumatic road tubes, inductive loops, infrared, microwave Doppler/radar, passive acoustic, video image detection, and Bluetooth devices. However, they are costly to install and have various limitations, such as high maintenance cost, availability of power source, and dependence on surrounding environment. Currently, weigh-in-motion (WIM) technology has become popular for automatic vehicle counting. In this paper, a three-dimensional glass fiber-reinforced polymer packaged fiber Bragg grating sensor (3-D GFRP-FBG) is introduced for in-pavement vehicle counting. The 3D GFRP-FBG sensor was installed on I-94 freeway, at MnROAD facility, Minnesota. When a vehicle passes over the road, the pavement produces strain signals that are picked up by wavelength changes. These strain peaks can be tracked to achieve vehicle counting. The sensors were laid out 9 feet from the road centerline with 16 feet distance between them to detect all the vehicles travelling on the right side of the road. The feasibility tests show the ability of the sensors to detect vehicles from small cars to semi tractor-trailer. For a 250-second period, the sensor detected 23 vehicles, with a total of 69 axles

A Fuzzy-Logic Approach to Dynamic Bayesian Severity Level Classification of Driver Distraction Using Image Recognition

open access articleDetecting and classifying driver distractions is crucial in the prevention of road accidents. These distractions impact both driver behavior and vehicle dynamics. Knowing the degree of driver distraction can aid in accident prevention techniques, including transitioning of control to a level 4 semi- autonomous vehicle, when a high distraction severity level is reached. Thus, enhancement of Advanced Driving Assistance Systems (ADAS) is a critical component in the safety of vehicle drivers and other road users. In this paper, a new methodology is introduced, using an expert knowledge rule system to predict the severity of distraction in a contiguous set of video frames using the Naturalistic Driving American University of Cairo (AUC) Distraction Dataset. A multi-class distraction system comprises the face orientation, drivers’ activities, hands and previous driver distraction, a severity classification model is developed as a discrete dynamic Bayesian (DDB). Furthermore, a Mamdani-based fuzzy system was implemented to detect multi- class of distractions into a severity level of safe, careless or dangerous driving. Thus, if a high level of severity is reached the semi-autonomous vehicle will take control. The result further shows that some instances of driver’s distraction may quickly transition from a careless to dangerous driving in a multi-class distraction context

Reduce toxic emissions of As, Cr, and Cu phases during woody biomass gasification: A thermodynamic equilibrium study

All of the selected papers will be published in Proceedings in E-Format and are eligible for free publication in the Journal of Environmental ScienceGasification of blended waste wood samples resulting from different activities and operations would be beneficial for reducing toxic emissions of metal(loid) elements while producing energy. This paper deals with willow wood (40%) and demolition waste wood (60%) gasification specifically focusing on the phase transformation temperature and speciation formation of As, Cr, and Cu which are regularly present in woody biomass. The gasification of mixed fuel was modelled under atmospheric pressure as typical reaction zones; partial combustion reaction (PCR) and boudouard reaction (BR). The PCR performed at temperature range of 0-1800 (°C) and both equivalence and steam/air ratios were 0.28 and 1:2, respectively. On the other hand, the BR model was operated from 0 to 1300 (°C) along with typical CO2 to biomass ratio of 1:3. The samples were selected from ETI-UK database (83 willow wood) and ECN PHYLLIS2 database (9 demolition waste wood). Further, @Risk analysis simulation package was exploited to estimate the best composition data of each element in these samples. Refinement of the obtained results by PCR reveals that the phase transformation temperature of both As and Cr increased about 150 (°C) and 100 (°C), respectively, comparing to those obtained by gasification of willow wood. On the other hand, solid –gas phase transition of Cr was decreased about 100(°C) comparing to that when only demolition wood was gasified. In regards to BR, the phase transformation temperature of As, Cr, and Cu was similar (-1100(°C)) for all gasified woods. However, only concentration shifts were observed in gaseous phase of these elements. Eventually, the results from this study could be useful to reduce emissions and to disposal contamination waste wood via gasification process

Deep learning assessment of breast terminal duct lobular unit involution: Towards automated prediction of breast cancer risk

Terminal duct lobular unit (TDLU) involution is the regression of milk-producing structures in the breast. Women with less TDLU involution are more likely to develop breast cancer. A major bottleneck in studying TDLU involution in large cohort studies is the need for labor-intensive manual assessment of TDLUs. We developed a computational pathology solution to automatically capture TDLU involution measures. Whole slide images (WSIs) of benign breast biopsies were obtained from the Nurses\u27 Health Study. A set of 92 WSIs was annotated for acini, TDLUs and adipose tissue to train deep convolutional neural network (CNN) models for detection of acini, and segmentation of TDLUs and adipose tissue. These networks were integrated into a single computational method to capture TDLU involution measures including number of TDLUs per tissue area, median TDLU span and median number of acini per TDLU. We validated our method on 40 additional WSIs by comparing with manually acquired measures. Our CNN models detected acini with an F1 score of 0.73±0.07, and segmented TDLUs and adipose tissue with Dice scores of 0.84±0.13 and 0.87±0.04, respectively. The inter-observer ICC scores for manual assessments on 40 WSIs of number of TDLUs per tissue area, median TDLU span, and median acini count per TDLU were 0.71, 0.81 and 0.73, respectively. Intra-observer reliability was evaluated on 10/40 WSIs with ICC scores of \u3e0.8. Inter-observer ICC scores between automated results and the mean of the two observers were: 0.80 for number of TDLUs per tissue area, 0.57 for median TDLU span, and 0.80 for median acini count per TDLU. TDLU involution measures evaluated by manual and automated assessment were inversely associated with age and menopausal status. We developed a computational pathology method to measure TDLU involution. This technology eliminates the labor-intensiveness and subjectivity of manual TDLU assessment, and can be applied to future breast cancer risk studies

Possible interactions and interferences of copper, chromium, and arsenic during the gasification of contaminated waste wood

A considerable proportion (about 64%) of biomass energy is produced from woody biomass (wood and its wastes). However, waste wood (WW) is very often contaminated with metal(loid) elements at concentrations leading to toxicity emissions and damages to facilities during thermal conversion. Therefore, procedures for preventing and/or alleviating the negative impacts of these elements require further development, particularly by providing informative and supportive information regarding the phase transformations of the metal(loid)s during thermal conversion processes. Although it is well known that phase transformation depends on different factors such as elements’ vaporization characteristics, operational conditions, and process configuration; however, the influences of reaction atmosphere composition in terms of interactions and interferences are rarely addressed. In response, since Cu, Cr, and As (CCA-elements) are the most regulated elements in woody biomass, this paper aims to explore the possible interactions and interferences among CCA-elements themselves and with Ca, Na, S, Cl, Fe, and Ni from reaction atmosphere composition perspectives during the gasification of contaminated WW. To do so, thermodynamic equilibrium calculations were performed for Boudouard reaction (BR) and partial combustion reaction (PCR) with temperature ranges of 0–1300 °C and 0–1800 °C, respectively, and both reactions were simulated under pressure conditions of 1, 20, and 40 atm. Refinement of the occurred interactions and interferences reveals that Ni-As interactions generate dominant species As2Ni5 and As8Ni11, which increase the solid–gaseous transformation temperature of As. Moreover, the interactions between Ca and Cr predominantly form C3Cr7; whereas the absence of Ca leads to Cr2Na2O4 causing instability in the Cr phase transformatio

Interactions and interferences of Cu, Cr and As during contaminated waste wood gasification: A thermodynamic equilibrium study

Waste wood (WW) is one of the major sources of renewable energy. However, it often contaminated with metal(loid) elements at concentrations leading to toxicity emissions and damages to facilities during thermal conversion. Thence, procedures for preventing and/or reducing the negative impacts of these elements require further understanding, specifically their phase transformations during thermal conversion processes. Although it is well known that phase transformation depends on different factors such as vaporization characteristics of elements, operational conditions and process configuration, influences of atmosphere composition of the reaction are rarely investigated. Based on thermodynamic equilibrium principles, this study investigates the behaviors of most regulated elements (Cu, Cr and As) in contaminated WW in relation to the presence/absence of Ca, Na, S, Cl, Fe and Ni during gasification. Thermodynamic calculations were performed across gasification temperature range of 0-1800°C, under the atmospheric pressure. Refinement of possible interactions and interferences reveals that Ni-As interactions generate dominant species As 2 Ni 5 and As 8 Ni 11 , which increase the solid-gaseous transformation temperature of As. Furthermore, interactions between Ca and Cr predominantly forms C 3 Cr 7 ; whereas absence of Ca leads to form CnNa 2 O 4 which causes instability in Cr phase formation. The findings of this study indicate that the evaluation of speciation due to interactions and interferences can provide quantitative and qualitative assessments of the metal(loid) behavior in gasification

Coping Mechanism among Parents of Children with Autism Spectrum Disorder: A Review

AbstractThis review presented the current literature on coping mechanisms among parents of children with autism spectrum disorder (ASD), focusing on types of coping mechanisms among parents and different coping mechanisms between mothers and fathers of children with ASD.A search of published literature in English was conducted using Google Scholar, PsycINFO, Medline, Scopus, CINAHL, EBSCO, Springer, Ovid, PubMed, and Cochrane Library up to February 2020. Overall, 18 articles were relevant to the review. The review included thirteen studies for types of coping mechanisms among parents of children with ASD and five studies for different coping mechanisms between mothers and fathers. Coping mechanisms demonstrated by parents when caring for their child include problem-focused and emotion-focused coping. A comparison between fathers and mothers in our review showed that mothers used emotion-focused coping more than fathers, while fathers used problem-focused coping more frequently than mothers.The review provides an exciting opportunity to advance our knowledge on types of coping mechanisms and gender difference in using coping mechanisms among parents of children with ASD. The review also sheds new light on developing supportive interventions by healthcare providers to improve coping mechanisms among parents of children with ASD

Soil cone index in relation to soil texture, moisture content, and bulk density for no-tillage and conventional tillage

Soil cone index (CI) is a widely used soil mechanical property to assess soil strength in tillage research. In this study, literature data relating CI to tillage practices are compiled into two datasets, one for no-tillage and the other for conventional tillage. Each dataset is analyzed to examine how CI varies with soil depth, textural parameters, bulk density, and moisture content. The results showed that for both no-tillage and conventional tillage, values of CI decrease with the increase in clay fraction, and increase with the increase in sand and silt fractions of soil. Similarly, higher bulk density and greater soil depth result in higher CI value, while higher moisture content reduces CI.  Based on the literature data, regression equations were obtained to estimate CI under no-tillage and conventional tillage systems. In those regression equations, values of CI were linear functions of the other soil variables such as soil textural parameters and moisture content. Those regression equations were validated with field data collected from different sites in Manitoba, Canada. Over half of the results from the regression equations had good agreement with the field measurements, indicated by their relative errors of 20% or lower; however, greater discrepancies were noticed in some cases.  Keywords: Tillage, soil, cone index, bulk density, moisture content, soil texture, regressio