AN ECONOMIC ANALYSIS OF THE DEMAND FOR CARE IN A FREE-STANDING EMERGENCY CENTER

Free-standing emergency centers (FECs) represent a new approach to the delivery of health care which are competing for patients with more conventional forms of ambulatory care in many parts of the U.S. Currently, little is known about these centers and their patient populations. The purpose of this study, therefore, was to describe the patients who visited two commonly-owned FECs, and determine the reasons for their visits. An economic model of the demand for FEC care was developed to test its ability to predict the economic and sociodemographic factors of use. Demand analysis of other forms of ambulatory services, such as a regular source of care (RSOC), was also conducted to examine the issues of substitution and complementarity.^ A systematic random sample was chosen from all private patients who used the clinics between July 1 and December 31, 1981. Data were obtained by means of a telephone interview and from clinic records. Five hundred fifty-one patients participated in the study.^ The typical FEC patient was a 26 year old white male with a minimum of a high school education, and a family income exceeding $25,000 a year. He had lived in the area for at least twenty years, and was a professional or a clerical worker. The patients made an average of 1.26 visits to the FECs in 1981. The majority of the visits involved a medical complaint; injuries and preventive care were the next most common reasons for visits.^ The analytic results revealed that time played a relatively important role in the demand for FEC care. As waiting time at the patients' regular source of care increased, the demand for FEC care increased, indicating that the clinic serves as a substitute for the patients' usual means of care. Age and education were inversely related to the demand for FEC care, while those with a RSOC frequented the clinics less than those lacking such a source.^ The patients used the familiar forms of ambulatory care, such as a private physician or an emergency room in a more typical fashion. These visits were directly related to the age and education of the patients, existence of a regular source of care, and disability days, which is a measure of health status. ^

Field analysis of solar PV-based collective systems for rural electrification.

This article analyses the long-term performance of collective off-grid photovoltaic (PV) systems in rural areas. The use of collective PV systems for the electrification of small medium-size villages in developing countries has increased in the recent years. They are basically set up as stand-alone installations (diesel hybrid or pure PV) with no connection with other electrical grids. Their particular conditions (isolated) and usual installation places (far from commercial/industrial centers) require an autonomous and reliable technology. Different but related factors affect their performance and the energy supply; some of them are strictly technical but others depend on external issues like the solar energy resource and users’ energy and power consumption. The work presented is based on field operation of twelve collective PV installations supplying the electricity to off-grid villages located in the province of Jujuy, Argentina. Five of them have PV generators as unique power source while other seven include the support of diesel groups. Load demand evolution, energy productivity and fuel consumption are analyzed. Besides, energy generation strategies (PV/diesel) are also discussed.

Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones, by means of the common factors following a multiplicative seasonal VARIMA(p, d, q) × (P, D, Q)s model. Additionally, a bootstrap procedure that does not need a backward representation of the model is proposed to be able to make inference for all the parameters in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing enhanced coverage of forecasting intervals. A challenging application is provided. The new proposed model and a bootstrap scheme are applied to an innovative subject in electricity markets: the computation of long-term point forecasts and prediction intervals of electricity prices. Several appendices with technical details, an illustrative example, and an additional table are available online as Supplementary Materials.

Olfactory fMRI Connectivity Analysis Based on Granger Causality with Application in Anosmia Assessment

In this work, we describe hubs organization within the olfactory network with Functional Magnetic Resonance Imaging (fMRI). Granger causality analyses were applied in the supposed regions of interest (ROIs) involved in olfactory tasks, as described in [1]. We aim to get deeper knowledge about the hierarchy of the regions within the olfactory network and to describe which of these regions, in terms of strength of the connectivity, impair in different types of anosmia.

Analysis of the Iberian electricity forward market hedging efficiency

An assessment of the hedging performance in the Iberian Forward Electricity Market is performed. Aggregated data from the Portuguese and Spanish clearing houses for energy derivatives are considered. The hedging performance is measured through the ratio of the final open interest of a month derivatives contract divided by its accumulated cleared volume. The base load futures in the Iberian energy derivatives exchange show the lowest ratios due to good liquidity. The peak futures show bigger ratios as their reduced liquidity is produced by auctions fixed by Portuguese regulation. The base load swaps settled in the clearing house located in Spain show initially large values due to low registered volumes, as this clearing house is mainly used for short maturity (daily and weekly swaps). This hedging ratio can be a powerful oversight tool for energy regulators when accessing to all the derivatives transactions as envisaged by European regulation.

Analysis of advanced European nuclear fuel cycle scenarios including transmutation and economic estimates

Four European fuel cycle scenarios involving transmutation options (in coherence with PATEROS and CPESFR EU projects) have been addressed from a point of view of resources utilization and economic estimates. Scenarios include: (i) the current fleet using Light Water Reactor (LWR) technology and open fuel cycle, (ii) full replacement of the initial fleet with Fast Reactors (FR) burning U?Pu MOX fuel, (iii) closed fuel cycle with Minor Actinide (MA) transmutation in a fraction of the FR fleet, and (iv) closed fuel cycle with MA transmutation in dedicated Accelerator Driven Systems (ADS). All scenarios consider an intermediate period of GEN-III+ LWR deployment and they extend for 200 years, looking for long term equilibrium mass flow achievement. The simulations were made using the TR_EVOL code, capable to assess the management of the nuclear mass streams in the scenario as well as economics for the estimation of the levelized cost of electricity (LCOE) and other costs. Results reveal that all scenarios are feasible according to nuclear resources demand (natural and depleted U, and Pu). Additionally, we have found as expected that the FR scenario reduces considerably the Pu inventory in repositories compared to the reference scenario. The elimination of the LWR MA legacy requires a maximum of 55% fraction (i.e., a peak value of 44 FR units) of the FR fleet dedicated to transmutation (MA in MOX fuel, homogeneous transmutation) or an average of 28 units of ADS plants (i.e., a peak value of 51 ADS units). Regarding the economic analysis, the main usefulness of the provided economic results is for relative comparison of scenarios and breakdown of LCOE contributors rather than provision of absolute values, as technological readiness levels are low for most of the advanced fuel cycle stages. The obtained estimations show an increase of LCOE ? averaged over the whole period ? with respect to the reference open cycle scenario of 20% for Pu management scenario and around 35% for both transmutation scenarios. The main contribution to LCOE is the capital costs of new facilities, quantified between 60% and 69% depending on the scenario. An uncertainty analysis is provided around assumed low and high values of processes and technologies.

Explaining light vehicle demand evolution in interurban toll roads: a dynamic panel data analysis in Spain

Tolls have increasingly become a common mechanism to fund road projects in recent decades. Therefore, improving knowledge of demand behavior constitutes a key aspect for stakeholders dealing with the management of toll roads. However, the literature concerning demand elasticity estimates for interurban toll roads is still limited due to their relatively scarce number in the international context. Furthermore, existing research has left some aspects to be investigated, among others, the choice of GDP as the most common socioeconomic variable to explain traffic growth over time. This paper intends to determine the variables that better explain the evolution of light vehicle demand in toll roads throughout the years. To that end, we establish a dynamic panel data methodology aimed at identifying the key socioeconomic variables explaining changes in light vehicle demand over time. The results show that, despite some usefulness, GDP does not constitute the most appropriate explanatory variable, while other parameters such as employment or GDP per capita lead to more stable and consistent results. The methodology is applied to Spanish toll roads for the 1990?2011 period, which constitutes a very interesting case on variations in toll road use, as road demand has experienced a significant decrease since the beginning of the economic crisis in 2008.

Environmental analysis of a Concentrated Solar Power (CSP) plant hybridised with different fossil and renewable fuels

The environmental performance of a 50 MW parabolic trough Concentrated Solar Power (CSP) plant hybridised with different fuels was determined using a Life Cycle Assessment methodology. Six different scenarios were investigated, half of which involved hybridisation with fossil fuels (natural gas, coal and fuel oil), and the other three involved hybridisation with renewable fuels (wheat straw, wood pellets and biogas). Each scenario was compared to a solar-only operation. Nine different environmental categories as well as the Cumulative Energy Demand and the Energy Payback Time (EPT) were evaluated using Simapro software for 1 MWh of electricity produced. The results indicate a worse environmental performance for a CSP plant producing 12% of the electricity from fuel than in a solar-only operation for every indicator, except for the eutrophication and toxicity categories, whose results for the natural gas scenario are slightly better. In the climate change category, the results ranged between 26.9 and 187 kg CO2 eq/MWh, where a solar-only operation had the best results and coal hybridisation had the worst. Considering a weighted single score indicator, the environmental impact of the renewable fuels scenarios is approximately half of those considered in fossil fuels, with the straw scenario showing the best results, and the coal scenario the worstones. EPT for solar-only mode is 1.44 years, while hybridisation scenarios EPT vary in a range of 1.72 -1.83 years for straw and pellets respectively. The fuels with more embodied energy are biomethane and wood pellets.

Data analysis using circular causality in networks

Complex systems in causal relationships are known to be circular rather than linear; this means that a particular result is not produced by a single cause, but rather that both positive and negative feedback processes are involved. However, although interpreting systemic interrelationships requires a language formed by circles, this has only been developed at the diagram level, and not from an axiomatic point of view. The first difficulty encountered when analysing any complex system is that usually the only data available relate to the various variables, so the first objective was to transform these data into cause-and-effect relationships. Once this initial step was taken, our discrete chaos theory could be applied by finding the causal circles that will form part of the system attractor and allow their behavior to be interpreted. As an application of the technique presented, we analyzed the system associated with the transcription factors of inflammatory diseases.

A Sociocybernetics Data Analysis Using Causality in Tourism Networks

The aim of this paper is to propose a mathematical model to determine invariant sets, set covering, orbits and, in particular, attractors in the set of tourism variables. Analysis was carried out based on a pre-designed algorithm and applying our interpretation of chaos theory developed in the context of General Systems Theory. This article sets out the causal relationships associated with tourist flows in order to enable the formulation of appropriate strategies. Our results can be applied to numerous cases. For example, in the analysis of tourist flows, these findings can be used to determine whether the behaviour of certain groups affects that of other groups and to analyse tourist behaviour in terms of the most relevant variables. Unlike statistical analyses that merely provide information on current data, our method uses orbit analysis to forecast, if attractors are found, the behaviour of tourist variables in the immediate future.

Causal Maps and Indirect Influences Analysis in the Diagnosis of Second-Home Tourism Impacts

This paper proposes a method for diagnosing the impacts of second-home tourism and illustrates it for a Mediterranean Spanish destination. This method proposes the application of network analysis software to the analysis of causal maps in order to create a causal network model based on stakeholder-identified impacts. The main innovation is the analysis of indirect relations in causal maps for the identification of the most influential nodes in the model. The results show that the most influential nodes are of a political nature, which contradicts previous diagnoses identifying technical planning as the ultimate cause of problems.

La Région Méditerranéenne en 2020 et son Rôle dans le Réseau Energétjgue Européen, Vol. I: Analyse de la demande d'energie dans les PSEM (Horizons 2000-2010-2020)= The Mediterranean region in 2020 and its Role in the European Energy Network, Vol. I: Analysis of the energy demand in the SEMC (Horizons 2000-2010-2020).

None.

Analysis of the application of thermogalvanic cells to the conversion of low grade heat to electricity /

"Work Performed Under Contract No. AC02-77CH00178."

An analysis of the automobile market: modeling of the long-run determinants of the demand for automobiles. Volume III: appendices to the Wharton E.F.A. Automobile Demand Model. Final report.

Transportation Department, Washington, D.C.