Employee Turnover In The Small Business: Practical Insights From Urban Child Care Centers

This paper investigates the claims of alarming turnover rates in the childcare work force (as claimed by the Center for the Childcare workforce) in the context of organizational literature.  Data collected from 70 childcare centers of New York City partially support the traditional claim that behavioral variables (such as job satisfaction and commitment) explain variations in turnover rate.  Additionally, it claims that negative work outcomes (or work itself could be cause of voluntary turnover) in this instance, the behavior problem, discipline problem, funding uncertainty and the like may be included in any deterministic model of employee turnover.  Finally, this study did not find overwhelming evidence supporting the alarming rate of turnover in the childcare centers

Organizational Profile, HR Practices And The Perceived Quality And Performance Of Small Businesses: Empirical Highlights From The Urban Child Care Centers

This paper examines the relationships among the organizational profile, the human resource practices, and the quality and profitability of private childcare centers of a large metropolis.  Organizational profile variables included: size (measured in terms of total FTE employees), age (measured in terms of number of years in service), HR practices included: empowerment and enrichment strategies, benefits, grievances, affirmative action, promotion, supervision, participation in decision-making, and the like. Quality is a variable reflecting the perception of service relative to the population of similar centers.  It also includes the application of quality concepts such as benchmarking, and continuous improvement. The organizational performance includes such variables as perceived relative profitability, annual revenues, and behavioral measures of employees (i.e., motivation, morale, commitment, trust, turnover, and job satisfaction).  Data was collected as a pilot project in 2001 through a mail questionnaire as well as through personal interviews of the owners/directors of 70 (out of 200 selected) private childcare centers of New York City. The findings of correlation and the regression analyses support a number of illustrative hypotheses pertaining to the association among the selected variables.  The paper interpreted the finding in the light of existing organizational theories and concludes with a list of highlights (imperatives) for the owner/directors of urban child care centers

Role of intraoperative arthrogram in decision making of closed versus medial open reduction of developmental hip dysplasia

Background: Hip arthrography in developmental dysplasia of the hip (DDH) has the advantages of viewing the shape and size of the cartilaginous part of both the femoral head and acetabulum, besides the soft tissue obstacles. The aim of the work was to assess the role of arthrogram in evaluation of closed reduction of developmental dysplasia of the hip.Methods: This prospective study included 30 patients with 36 involved hips who presented with DDH. This study included 7 males (23.3%) and 23 females (76.7%). There were 6 patients with bilateral hip involvement (20.0%) and 24 patients with unilateral involvement (80.0%). In this study we used 3 ml Urografin 76% diluted in 7 ml saline and 3 to 5 ml of diluted Urografin was injected into the hip joint.Results: Out of the 36 hips, closed reduction was successful in 33 hips (91.7%) and failed in 3 (8.3%). After the interpretation of the arthrogram we considered the reduction of 7 (21.2%) reduced hips nonconcentric because of the soft tissue obstacles. In the 7 hips with nonconcentric reduction besides the 3 hips with failed closed reduction, we proceeded to open reduction of the hip through medial approach. At the end of follow up, all hips (100%) showed concentric reduction without re-dislocation in any case.Conclusions: Hip arthrogram is a very reliable method in diagnosing hip concentricity and the presence of soft tissue obstacles in management of DDH by closed reduction. Arthrogram helps the operator to decide open reduction in such cases achieving excellent results in all cases

Determination of caffeine in roasted and irradiated coffee beans with gamma rays by high performance liquid chromatography

The present study was carried out to investigate a simple, quick and organic solvent saving procedure for the high performance liquid chromatography determination of caffeine in two different coffee beans (Indonesian and Brazilian) which roasted at two different temperatures (150 & 220 ºC) and irradiated at 3, 6, and 9 kGy doses by gamma rays. A linear calibration curve was generated with caffeine concentration ranging from 0.005 to 0.25 mg/g with correlation coefficient (R2= 0.9995, n=4) and relative standard deviation ? 2.1 %. The developed procedure provided a 7.3 x 10-9 mg/g and 2.2 x 10-8 mg/g limit of detection and limit of quantification, respectively. The developed method was repeatable and could be applied to determine trace amounts of caffeine in popular irradiated coffee beans with three different irradiation doses. Moreover, irradiation treatments at doses up to 9 kGy showed no significant effect on the caffeine content. Keywords: Caffeine determination; High performance liquid chromatography;  Coffee bean; Roasting; Gamma rays; Statistical analysi

Developing a Thermally Stable Ester-Based Drilling Fluid for Offshore Drilling Operations by Using Aluminum Oxide Nanorods

Funding: This work was supported by an Institutional Links grant, ID 352343681, under the Newton–Mosharafa Fund partnership. The grant is funded by the UK Department for Business, Energy and Industrial Strategy, and Science, Technology & Innovation Funding Authority (STDF) and delivered by the British Council. For further information, please visit www.newtonfund.ac.uk. Science, Technology & Innovation Funding Authority (STDF) support was under the grant No. (30894). Acknowledgments: The authors would like to acknowledge the School of Engineering at the University of Aberdeen for providing the required facilities to complete this research. In addition, the authors would like to thank the support from MI-SWACO in providing commercial emulsifiers and consumables used in this project.Peer reviewedPublisher PD

Inferring epidemic dynamics using Gaussian process emulation of agent-based simulations

Computational models help decision makers understand epidemic dynamics to optimize public health interventions. Agent-based simulation of disease spread in synthetic populations allows us to compare and contrast different effects across identical populations or to investigate the effect of interventions keeping every other factor constant between ``digital twins''. FRED (A Framework for Reconstructing Epidemiological Dynamics) is an agent-based modeling system with a geo-spatial perspective using a synthetic population that is constructed based on the U.S. census data. In this paper, we show how Gaussian process regression can be used on FRED-synthesized data to infer the differing spatial dispersion of the epidemic dynamics for two disease conditions that start from the same initial conditions and spread among identical populations. Our results showcase the utility of agent-based simulation frameworks such as FRED for inferring differences between conditions where controlling for all confounding factors for such comparisons is next to impossible without synthetic data.Comment: To be presented in Winter Simulation Conference 2023, repository link: https://github.com/abdulrahmanfci/gpr-ab

Estimating Treatment Effects Using Costly Simulation Samples from a Population-Scale Model of Opioid Use Disorder

Large-scale models require substantial computational resources for analysis and studying treatment conditions. Specifically, estimating treatment effects using simulations may require a lot of infeasible resources to allocate at every treatment condition. Therefore, it is essential to develop efficient methods to allocate computational resources for estimating treatment effects. Agent-based simulation allows us to generate highly realistic simulation samples. FRED (A Framework for Reconstructing Epidemiological Dynamics) is an agent-based modeling system with a geospatial perspective using a synthetic population constructed based on the U.S. census data. Given its synthetic population, FRED simulations present a baseline for comparable results from different treatment conditions and treatment conditions. In this paper, we show three other methods for estimating treatment effects. In the first method, we resort to brute-force allocation, where all treatment conditions have an equal number of samples with a relatively large number of simulation runs. In the second method, we try to reduce the number of simulation runs by customizing individual samples required for each treatment effect based on the width of confidence intervals around the mean estimates. In the third method, we use a regression model, which allows us to learn across the treatment conditions such that simulation samples allocated for a treatment condition will help better estimate treatment effects in other conditions. We show that the regression-based methods result in a comparable estimate of treatment effects with less computational resources. The reduced variability and faster convergence of model-based estimates come at the cost of increased bias, and the bias-variance trade-off can be controlled by adjusting the number of model parameters (e.g., including higher-order interaction terms in the regression model).Comment: To be presented in IEEE International Conference on Biomedical and Health Informatics 2023, repository link: https://github.com/abdulrahmanfci/intervention-estimatio