The Demand for Volunteer Labor: A Study of Hospital Volunteers

The authors challenge the assumption that organizations are willing to use all the volunteer labor available to them. Rather, they are influenced by the costs incurred of utilizing volunteer labor. This article provides a modest first look at the demand for volunteers by nonprofit institutions. Specifically, the article presents an economic analysis of the demand of volunteer labor by hospitals in the Toronto area and examines some of the factors that may determine the hospitals’ willingness to use volunteer labor. Using data generated from 28 hospitals in Toronto, which use a total of more than 2 million volunteer hr per year, the authors show that the quantity of volunteer hours demanded is a decreasing function of their costs. Other factors such as productivity, output, and labor market institutions also influence the demand for volunteers

Valuing Volunteers: An Economic Evaluation of the Net Benefits of Hospital Volunteers

The use of volunteers in hospitals has been an age-old practice. This nonmarket community involvement is a distinctive aspect of North American life. Hospitals may be attracted to increase the use of volunteers, both to provide increased quality of care and to contain costs. Hospitals rely on the use of professional administrators to use the donated time of volunteers efficiently. This study examines the benefits and costs of volunteer programs and derives an estimate of the net value of volunteer programs that accrue to the hospitals and volunteers. In particular, the costs and benefits to hospitals are detailed. Using 31 hospitals in and around Toronto and surveying hospital volunteer administrators, hospital clinical staff members, and volunteers themselves, a striking pay-off for hospitals was found: an average of $6.84 in value from volunteers for every dollar spent—a return on investment of 684%. Civic and community participation is indeed valuable

A 3 year retrospective study on gestational trophoblastic disease in a government obstetrical tertiary care centre

Background: The aim of this study is to assess the post diagnostic outcome of Gestational Trophoblastic Disease, a heterogeneous group of disorders, in a government obstetrical tertiary care centre.Methods: The study was conducted in the Institute of Obstetrics & Gynecology, Madras Medical College as a retrospective study. A total of 75 cases were studied over a 3 year period from January 2012 to December 2015. The parameters which were studied included age group, antecedent pregnancy, beta hCG values, histopathological types and Treatment profile.Results: Of the 75 cases, 55 cases (73%) were in the 21-39 age group. The spectrum of disorders that were studied included 69 cases of complete mole, 2 cases of partial mole, 1 case of twin pregnancy with single live foetus and partial mole, 1 case of triplet pregnancy with two live foetuses and partial mole, 1 case of epithelioid trophoblastic tumour and 1 case of choriocarcinoma. Of the 75 cases, 16 cases underwent chemotherapy. No mortality was observed during the study period.Conclusions: Close monitoring and follow up with beta hCG values is of utmost importance in the management of Gestational trophoblastic disease. In cases of gestational trophoblastic neoplasia (GTN), WHO/FIGO scoring should be done and managed with chemotherapy according to the risk assessment

Determination of Regional Scale Evapotranspiration of Texas from NOAA - AVHRR Satellite

Evapotranspiration (ET) is defined as the combined loss of water by evaporation from soil and transpiration from plants. Depending on the geographic location, 60-80% of total annual precipitation is lost in the form of evapotranspiration. Since ET accounts for a major portion of water lost to the atmosphere, accurate estimation is essential for the success of hydrologic modeling studies. ET is estimated using climatic data like net radiation, air temperature, wind velocity, vapor pressure deficit and relative humidity obtained from the nearest weather stations. However, interpolating ET using data obtained from a point data source to derive regional ET could introduce errors of large magnitude. During the last two decades, GIS and Remote Sensing have evolved as an indispensable tool for monitoring natural resources. Due to the availability of spatially distributed data from satellites, and adopting GIS principles, accurate determination of ET is possible. The present study aims at deriving spatially distributed ET using NOAA-AVHRR satellite data

Data Reconciliation in Reaction Systems using the Concept of Extents

Abstract of the conference paper Concentrations measured during the course of a chemical reaction are corrupted with noise, which reduces the quality of information. Since these measurements are used for identifying kinetic models, the noise impairs the ability to identify accurate models. The noise in concentration measurements can be reduced using data reconciliation, exploiting for example the material balances derived from stoichiometry as constraints. However, additional constraints can be obtained via the transformation of concentrations into extents and invariants, which leads to more efficient identification of kinetic models for multiple reaction systems. This paper uses the transformation to extents and invariants and formulates the data reconciliation problem accordingly. This formulation has the advantage that non-negativity and monotonicity constraints can be imposed on selected extents. A simulated example is used to demonstrate that reconciled measurements lead to the identification of more accurate kinetic models. Extended abstract Reliable kinetic models of chemical reaction systems should include information on all rate processes of significance in the system. Apart from chemical reactions, such models should also describe the mass exchanged with the environment via the inlet and outlet streams and the mass transferred between phases. Model identification and the estimation of rate parameters is carried out using measurements that are obtained during the course of the reaction [1]. Model identification often leads to the combinatorial complexity of identifying simultaneously all rate processes [1]. Alternatively, it can be carried out incrementally by transforming the concentrations to extents and identifying each extent separately [2]. Since measurements are inevitably corrupted by random measurement errors, the identification of kinetic models and estimation of rate parameters are affected by error propagation [3]. Data reconciliation is a technique that uses constraints to obtain more accurate estimates of variables by reducing the effect of measurement errors [4]. Data reconciliation can be formulated as an optimization problem constrained by the law of conservation of mass [5, 6] and positivity of reconciled concentrations. Consequently, model identification can be performed with reconciled concentrations. This paper presents a reformulation of the original reconciliation problem directly in terms of extents. This allows using additional constraints such as the monotonicity of extents. Such a reformulation improves the accuracy of the reconciled extents and hence of concentrations, and leads to better model discrimination and parameter estimation. The advantages derived from the use of reconciled extents are illustrated using a simulated example. References: [1] Bardow et al., Chem. Eng. Sci., 2004, 59, 2673 - 2684 [2] Bhatt et al., AIChE J., 2010, 56, 2873 - 2886 [3] Billeter et al., Chem. Intell. Lab. Syst., 2008, 93, 120 - 131 [4] S. Narasimhan and C. Jordache, Data Reconciliation and Gross Error Detection, Elsevier, 1999 [5] Reklaitis et al., Chem. Eng. Sci., 1975, 30, 243 - 247 [6] Srinivasan et al., IFAC Workshop on Thermodynamic Foundations of Mathematical Systems Theory, Lyon, 2013.<br

On the use of shape constraints for state estimation in reaction systems

State estimation techniques are used for improving the quality of measured signals and for reconstructing unmeasured quantities. In chemical reaction systems, nonlinear estimators are often used to improve the quality of estimated concentrations. These nonlinear estimators, which include the extended Kalman filter, the receding-horizon nonlinear Kalman filter and the moving-horizon estimator, use a state-space representation in terms of concentrations. An alternative to the representation of chemical reaction systems in terms of concentrations consists in representing these systems in terms of extents. This paper formulates the state estimation problem in terms of extents, which allows imposing additional shape constraints on the sign, monotonicity and concavity/convexity properties of extents. The addition of shape constraints often leads to significantly improved state estimates. A simulated example illustrates the formulation of the state estimation problem in terms of concentrations and extents, and the use of shape constraints