Cost uncertainty with multiple variable inputs

2003 ◽  
Vol 31 (3) ◽  
pp. 290-290
Author(s):  
Moawia Alghalith
Keyword(s):  
Cost Uncertainty ◽  
Multiple Variable

scholarly journals “P3”: an adaptive modeling tool for post-COVID-19 restart of surgical services

JAMIA Open ◽  
2021 ◽  
Vol 4 (2) ◽  
Author(s):  
Divya Joshi ◽  
Ali Jalali ◽  
Todd Whipple ◽  
Mohamed Rehman ◽  
Luis M Ahumada
Keyword(s):  
Data Warehouse ◽  
Predictive Analytics ◽  
Surgical Patient ◽  
Protective Equipment ◽  
Operating Room Personnel ◽  
Surgical Services ◽  
Multiple Variable ◽  
Multiple Variables ◽  
Using Data ◽  
User Friendly

Abstract Objective To develop a predictive analytics tool that would help evaluate different scenarios and multiple variables for clearance of surgical patient backlog during the COVID-19 pandemic. Materials and Methods Using data from 27 866 cases (May 1 2018–May 1 2020) stored in the Johns Hopkins All Children’s data warehouse and inputs from 30 operations-based variables, we built mathematical models for (1) time to clear the case backlog (2), utilization of personal protective equipment (PPE), and (3) assessment of overtime needs. Results The tool enabled us to predict desired variables, including number of days to clear the patient backlog, PPE needed, staff/overtime needed, and cost for different backlog reduction scenarios. Conclusions Predictive analytics, machine learning, and multiple variable inputs coupled with nimble scenario-creation and a user-friendly visualization helped us to determine the most effective deployment of operating room personnel. Operating rooms worldwide can use this tool to overcome patient backlog safely.

scholarly journals Selling to strategic customers with cost uncertainty

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Guodaohou Song ◽  
Xiaofang Wang
Keyword(s):  
Production Cost ◽  
Learning Effect ◽  
Optimal Pricing ◽  
Pricing Strategies ◽  
Strategic Customers ◽  
Cost Uncertainty ◽  
Intertemporal Pricing ◽  
Negative Comments ◽  
Second Period

AbstractProduction cost can be influenced by previous sales in an uncertain way. In reality, production cost may decrease in the number of initial buyers due to the learning effect, or increase in the number of initial buyers due to the quality-improving pressure from negative comments of unhappy users. Taking this uncertainty into account, this paper studies the optimal intertemporal pricing strategies of a firm when selling to strategic customers in two periods where production cost in the second period randomly changes with the number of buyers in the first period. Our results suggest how firms should adjust their optimal pricing strategies under different market circumstances.

scholarly journals Improved parallel magnetic resonance imaging reconstruction with multiple variable density sampling

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jinhua Sheng ◽  
Yuchen Shi ◽  
Qiao Zhang
Keyword(s):  
Magnetic Resonance Imaging ◽  
Variable Density ◽  
Reduction Factor ◽  
Resonance Imaging ◽  
Parallel Magnetic Resonance Imaging ◽  
Multiple Variable ◽  
Space Data ◽  
Sampling Pattern ◽  
Undersampled Data ◽  
Reduction Factors

AbstractGeneralized auto-calibrating partially parallel acquisitions (GRAPPA) and other parallel Magnetic Resonance Imaging (pMRI) methods restore the unacquired data in k-space by linearly calculating the undersampled data around the missing points. In order to obtain the weight of the linear calculation, a small number of auto-calibration signal (ACS) lines need to be sampled at the center of the k-space. Therefore, the sampling pattern used in this type of method is to full sample data in the middle area and undersample in the outer k-space with nominal reduction factors. In this paper, we propose a novel reconstruction method with a multiple variable density sampling (MVDS) that is different from traditional sampling patterns. Our method can significantly improve the image quality using multiple reduction factors with fewer ACS lines. Specifically, the traditional sampling pattern only uses a single reduction factor to uniformly undersample data in the region outside the ACS, but we use multiple reduction factors. When sampling the k-space data, we keep the ACS lines unchanged, use a smaller reduction factor for undersampling data near the ACS lines and a larger reduction factor for the outermost part of k-space. The error is lower after reconstruction of this region by undersampled data with a smaller reduction factor. The experimental results show that with the same amount of data sampled, using NL-GRAPPA to reconstruct the k-space data sampled by our method can result in lower noise and fewer artifacts than traditional methods. In particular, our method is extremely effective when the number of ACS lines is small.

Multiple variable effects in the customisation of fused deposition modelling 3D-printed medicines: A design of experiments (DoE) approach

2021 ◽  
Vol 597 ◽  
pp. 120331
Author(s):  
Juliana dos Santos ◽  
Monique Deon ◽  
Guilherme Silveira da Silva ◽  
Ruy Carlos Ruver Beck
Keyword(s):  
Design Of Experiments ◽  
Fused Deposition Modelling ◽  
Fused Deposition ◽  
Multiple Variable ◽  
3D Printed

scholarly journals A longitudinal study of suicide and suicide attempt in northwest of Iran: incidence, predictors, and socioeconomic status and the role of sociocultural status

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ali Fakhari ◽  
Mostafa Farahbakhsh ◽  
Elham Davtalab Esmaeili ◽  
Hosein Azizi
Keyword(s):  
Socioeconomic Status ◽  
Longitudinal Study ◽  
Religious Commitment ◽  
Incidence Rates ◽  
Demographic Characteristics ◽  
Protective Effects ◽  
Wide Range ◽  
Multiple Variable ◽  
History Of

Abstract Background A detailed community-level understanding of socioeconomic status (SES) and sociocultural status (SCS) of suicides and suicide attempters (SAs) in a prospective design could have significant implications for policymakers at the local prevention and treatment levels. The effect of SCS and SES on SAs is poorly understood and investigated in Iran. The present study aimed to investigate the incidence, trend, and role of SES and SCS on suicide and SAs. Methods A longitudinal study was conducted based on the registry for SAs in Malekan County, Iran, from 2015 to 2018. Demographic characteristics, SES, SCS, incidence rates, and predictors of suicidal behaviors were measured via structured instruments. Simple and multiple logistic regressions were used to estimate crude and adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Results A total of 853 SAs (32 suicides and 821 attempts) were identified during the study. Trend analysis revealed that the suicide rate significantly decreased from 2014 (10.28) to 2018 (1.75) per 100,000. In the final multiple variable models, age (26–40), male sex, unemployment, antisocial activities, history of SA, hanging method, and season (spring) increased the suicide risk while religious commitment had protective effects on suicide. Conclusions Our findings indicated that demographic characteristics, low SES, and SCS are associated with suicide. In this county, trend of suicide and SA were decreased from 2014 to 2018. This study findings highlight the need to consider a wide range of contextual variables, socio-demographic, SES, and SCS in suicide prevention strategies. Improving inter-sectoral collaborations and policymakers’ attitudes are imperative for SA reduction.

Control of Volumetric Properties of Hot-Mix Asphalt by Field Management

1996 ◽  
Vol 1543 (1) ◽  
pp. 125-131
Author(s):  
Prithvi S. Kandhal ◽  
Kee Y. Foo ◽  
John A. D'Angelo
Keyword(s):  
Hot Mix Asphalt ◽  
Linear Regression Analysis ◽  
Volumetric Properties ◽  
Production Test ◽  
Independent Variables ◽  
Dependent Variables ◽  
Preceding Work ◽  
Multiple Variable ◽  
Asphalt Content ◽  
Variable Analysis

Significant differences in the volumetric properties of laboratory-designed and plant-produced hot-mix asphalt (HMA) generally exist as demonstrated by FHWA Demonstration Project No. 74. The volumetric properties include voids in the mineral aggregate (VMA) and voids in the total mix (VTM). Guidelines for HMA contractors are needed to reconcile these differences and maintain control of volumetric properties during HMA production. The HMA mix design and field production test data (such as asphalt content, gradation, and volumetric properties) from 24 FHWA demonstration projects were entered into a data base and statistically analyzed. The objective was to identify and, if possible, quantify the independent variables (such as asphalt content and the percentages of material passing the No. 200 and other sieves) that significantly affect dependent variables VMA and VTM. The statistical analysis methods consisted of correlation analysis, stepwise multiple-variable analysis, and linear-regression analysis. On the basis of preceding work, guidelines have been developed for HMA contractors to reconcile the differences between the volumetric properties of the job mix formula and the produced HMA mix.

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