22 research outputs found
Analysis of Negligence in field of Neonatology in Medical Commissions Office in Tehran, Iran
Get PDFBackground: Despite progression of medical’s science, patient’s complaints are increased. Analysis of risk factors cause to decrease complaints.Methods: This research is a retrospective study of medical malpractice cases in neonates’ from 2012 to2014 in Medical Commissions Office in Tehran province.Results: Among 171 cases were studied, 53 cases were in neonatal period. 30.1% was announced medical malpractice. Ages of most infants were 0-10 days (88.6%), 66% of babies were male, 69.8% had full-term gestation, 62.2% APGAR (Appearance, Pulse, Grimace, Activity, Respiration) score of neonates was above 7 and 73.5% of cases were the first child of the family. Most babies had cerebral palsy and asphyxia (20.7%), then respiratory distress syndrome (11.3%), furthermore shoulders dystocia and brachial plexus damage (7.5%). Ages of most plaintiffs (parents of neonates) were 30-39 years. Most of them were male, and their levels of education were high school and lower. The most cause of parent’s claim was medical mistake.Conclusion: Levels of parents’ education may relationship with complaints and increasing their awareness can effect to decrease these
Business Case for a Micro-Combined Heat and Power Fuel Cell System in Commercial Applications
Full text linkCombined heat and power fuel cell systems (CHP-FCSs) provide consistent electrical power and hot water with greater efficiency and lower emissions than alternative sources. These systems can be used either as baseload, grid-connected, or as off-the-grid power sources. This report presents a business case for CHP-FCSs in the range of 5 to 50 kWe. Systems in this power range are considered micro-CHP-FCS. For this particular business case, commercial applications rather than residential or industrial are targeted. To understand the benefits of implementing a micro-CHP-FCS, the characteristics that determine their competitive advantage must first be identified. Locations with high electricity prices and low natural gas prices are ideal locations for micro-CHP-FCSs. Fortunately, these high spark spread locations are generally in the northeastern area of the United States and California where government incentives are already in place to offset the current high cost of the micro-CHP-FCSs. As a result of the inherently high efficiency of a fuel cell and their ability to use the waste heat that is generated as a CHP, they have higher efficiency. This results in lower fuel costs than comparable alternative small-scale power systems (e.g., microturbines and reciprocating engines). A variety of markets should consider micro-CHP-FCSs including those that require both heat and baseload electricity throughout the year. In addition, the reliable power of micro-CHP-FCSs could be beneficial to markets where electrical outages are especially frequent or costly. Greenhouse gas emission levels from micro-CHP-FCSs are 69 percent lower, and the human health costs are 99.9 percent lower, than those attributed to conventional coal-fired power plants. As a result, FCSs can allow a company to advertise as environmentally conscious and provide a bottom-line sales advantage. As a new technology in the early stages of adoption, micro-CHP-FCSs are currently more expensive than alternative technologies. As the technology gains a foothold in its target markets and demand increases, the costs will decline in response to improved manufacturing efficiencies, similar to trends seen with other technologies. Transparency Market Research forecasts suggest that the CHP-FCS market will grow at a compound annual growth rate of greater than 27 percent over the next 5 years. These production level increases, coupled with the expected low price of natural gas, indicate the economic payback period will move to less than 5 years over the course of the next 5 years. To better understand the benefits of micro-CHP-FCSs, The U.S. Department of Energy worked with ClearEdge Power to install fifteen 5-kWe fuel cells in the commercial markets of California and Oregon. Pacific Northwest National Laboratory is evaluating these systems in terms of economics, operations, and their environmental impact in real-world applications. As expected, the economic analysis has indicated that the high capital cost of the micro-CHP-FCSs results in a longer payback period than typically is acceptable for all but early-adopter market segments. However, a payback period of less than 3 years may be expected as increased production brings system cost down, and CHP incentives are maintained or improved
Deep learning for estimating energy savings of early-stage facade design decisions
No full textThe selection of high-performance building facade systems is essential to promote building energy efficiency. However, this selection is highly dependent on early-stage design decisions, which are extremely challenging considering numerous design parameters with early-stage uncertainties. This paper aims to evaluate the applicability of deep learning networks in estimating the energy savings of different facade alternatives in the early-stage design of buildings. The energy performance of two competing façade systems (i.e., Ultra-High-Performance Fiber-Reinforced-Concrete and conventional panels) was estimated for different scenarios through building energy simulations using EnergyPlus™. Three deep learning networks were trained using the collected data from the simulation of fourteen buildings in fourteen different locations to estimate the heating, cooling, and total site energy savings. The accuracy of trained deep networks was compared with the accuracy of three common data-driven prediction models including, Gradient Boosting Machines, Random Forest, and Generalized Linear Regression. The results showed that the deep learning network trained to predict building total site energy savings had the highest accuracy among other models with a mean absolute error of 1.59 and a root mean square error of 3.48, followed by Gradient Boosting Machines, Random Forest, and last Generalized Linear Regression. Similarly, deep networks trained to predict building cooling and heating energy savings had the lowest mean average error of 0.20 and 1.17, respectively, compared to other predictive models. It is expected the decision support system developed based on this methodology helps architects and designers to quantify the energy savings of different facade systems in early stages of design decisions
Comparative study of vision tracking methods for tracking of construction site resources
No full textVision tracking has significant potential for tracking resources on large scale, congested construction sites, where a small number of cameras strategically placed around the site could replace hundreds of tracking tags. The correlation of vision tracking 2D positions from multiple views can provide the 3D position. However, there are many 2D vision trackers available in the literature, and little information is available on which one is most effective for construction applications. In this paper, a comparative study of various vision tracker categories is carried out, to identify which one is most effective in tracking construction resources. Testing parameters for evaluating categories of trackers are identified, and benefits and limitations of each category are presented. The most promising trackers are tested using a database of construction operations videos. The results indicate the effectiveness of each tracker in relation to each parameter of the test, and the most suitable tracker needed to research effective 3D vision trackers of construction resources
