874 resultados para value for money and performance
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The goal of mangrove restoration projects should be to improve community structure and ecosystem function of degraded coastal landscapes. This requires the ability to forecast how mangrove structure and function will respond to prescribed changes in site conditions including hydrology, topography, and geophysical energies. There are global, regional, and local factors that can explain gradients of regulators (e.g., salinity, sulfides), resources (nutrients, light, water), and hydroperiod (frequency, duration of flooding) that collectively account for stressors that result in diverse patterns of mangrove properties across a variety of environmental settings. Simulation models of hydrology, nutrient biogeochemistry, and vegetation dynamics have been developed to forecast patterns in mangroves in the Florida Coastal Everglades. These models provide insight to mangrove response to specific restoration alternatives, testing causal mechanisms of system degradation. We propose that these models can also assist in selecting performance measures for monitoring programs that evaluate project effectiveness. This selection process in turn improves model development and calibration for forecasting mangrove response to restoration alternatives. Hydrologic performance measures include soil regulators, particularly soil salinity, surface topography of mangrove landscape, and hydroperiod, including both the frequency and duration of flooding. Estuarine performance measures should include salinity of the bay, tidal amplitude, and conditions of fresh water discharge (included in the salinity value). The most important performance measures from the mangrove biogeochemistry model should include soil resources (bulk density, total nitrogen, and phosphorus) and soil accretion. Mangrove ecology performance measures should include forest dimension analysis (transects and/or plots), sapling recruitment, leaf area index, and faunal relationships. Estuarine ecology performance measures should include the habitat function of mangroves, which can be evaluated with growth rate of key species, habitat suitability analysis, isotope abundance of indicator species, and bird census. The list of performance measures can be modified according to the model output that is used to define the scientific goals during the restoration planning process that reflect specific goals of the project.
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The purpose of this study was threefold: first, to investigate variables associated with learning, and performance as measured by the National Council Licensure Examination for Registered Nurses (NCLEX-RN). The second purpose was to validate the predictive value of the Assessment Technologies Institute (ATI) achievement exit exam, and lastly, to provide a model that could be used to predict performance on the NCLEX-RN, with implications for admission and curriculum development. The study was based on school learning theory, which implies that acquisition in school learning is a function of aptitude (pre-admission measures), opportunity to learn, and quality of instruction (program measures). Data utilized were from 298 graduates of an associate degree nursing program in the Southeastern United States. Of the 298 graduates, 142 were Hispanic, 87 were Black, non-Hispanic, 54 White, non-Hispanic, and 15 reported as Others. The graduates took the NCLEX-RN for the first time during the years 2003–2005. This study was a predictive, correlational design that relied upon retrospective data. Point biserial correlations, and chi-square analyses were used to investigate relationships between 19 selected predictor variables and the dichotomous criterion variable, NCLEX-RN. The correlation and chi square findings indicated that men did better on the NCLEX-RN than women; Blacks had the highest failure rates, followed by Hispanics; older students were more likely to pass the exam than younger students; and students who passed the exam started and completed the nursing program with a higher grade point average, than those who failed the exam. Using logistic regression, five statistical models that used variables associated with learning and student performance on the NCLEX-RN were tested with a model adapted from Bloom's (1976) and Carroll's (1963) school learning theories. The derived model included: NCLEX-RNsuccess = f (Nurse Entrance Test and advanced medical-surgical nursing course grade achieved). The model demonstrates that student performance on the NCLEX-RN can be predicted by one pre-admission measure, and a program measure. The Assessment Technologies Institute achievement exit exam (an outcome measure) had no predictive value for student performance on the NCLEX-RN. The model developed accurately predicted 94% of the student's successful performance on the NCLEX-RN.
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Using multiple regression analysis, lodging managers’ annual mean salaries in 143 Metropolitan Statistical Areas (MSA) within the U.S. were analyzed to identify what relationships existed with variables related to general MSA characteristics, along with the lodging industry’s size and performance. By examining the relationship between these variables, the authors predict the long-term possibility of predicting lodging industry managers’ salaries. These predictions may have an impact on financial performance of an individual lodging property or organization. Through this paper, this concept was applied and explored within U.S. MSAs. These findings may have value for a variety of stakeholders, including human resources practitioners, the hospitality education community, and individuals considering lodging management careers.
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The article examines developments in the marketisation and privatisation of the English National Health Service, primarily since 1997. It explores the use of competition and contracting out in ancillary services and the levering into public services of private finance for capital developments through the Private Finance Initiative. A substantial part of the article examines the repeated restructuring of the health service as a market in clinical services, initially as an internal market but subsequently as a market increasing opened up to private sector involvement. Some of the implications of market processes for NHS staff and for increased privatisation are discussed. The article examines one episode of popular resistance to these developments, namely the movement of opposition to the 2011 health and social care legislative proposals. The article concludes with a discussion of the implications of these system reforms for the founding principles of the NHS and the sustainability of the service.
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Purpose: The purpose of this paper is to examine whether the ownership of public firms is related to accounting and market performance, comparing family and non-family listed firms. Design/methodology/approach: We use regression analysis, considering a sample of Portuguese family and non-family firms for the period between 1999 and 2010. Findings: Overall, the results show that family firms are older, are more indebted and have higher debt costs than non-family firms. However, they present lower levels of risk. The evidence suggests that family firms outperform non-family firms when we consider a market performance measure. The market performance of family-controlled firms is more sensitive to the crisis periods and age, compared to their counterparts. The empirical findings suggest that under economic adversity, the performance is especially compromised by the firms’ age. Research limitations/implications: A limitation of this study is the small size of the sample, which derives from the small size of the Portuguese stock market, the Euronext Lisbon. Originality/value: This paper offers some insights on the ownership of public firms and firm performance by investigating a small European economy. The study also contributes to the stream of firm performance, considering new independent variables as determinants of firm performance, such as operational risk. Finally, the study examines the interaction between ownership and performance under both steady and adverse economic conditions, giving the opportunity to analyze whether firm performance differs according to market conditions.
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Thesis (Ph.D.)--University of Washington, 2016-07
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PURPOSE We aimed to evaluate the added value of diffusion-weighted imaging (DWI) to standard magnetic resonance imaging (MRI) for detecting post-treatment cervical cancer recurrence. The detection accuracy of T2-weighted (T2W) images was compared with that of T2W MRI combined with either dynamic contrast-enhanced (DCE) MRI or DWI. METHODS Thirty-eight women with clinically suspected uterine cervical cancer recurrence more than six months after treatment completion were examined with 1.5 Tesla MRI including T2W, DCE, and DWI sequences. Disease was confirmed histologically and correlated with MRI findings. The diagnostic performance of T2W imaging and its combination with either DCE or DWI were analyzed. Sensitivity, positive predictive value, and accuracy were calculated. RESULTS Thirty-six women had histologically proven recurrence. The accuracy for recurrence detection was 80% with T2W/DCE MRI and 92.1% with T2W/DWI. The addition of DCE sequences did not significantly improve the diagnostic ability of T2W imaging, and this sequence combination misclassified two patients as falsely positive and seven as falsely negative. The T2W/DWI combination revealed a positive predictive value of 100% and only three false negatives. CONCLUSION The addition of DWI to T2W sequences considerably improved the diagnostic ability of MRI. Our results support the inclusion of DWI in the initial MRI protocol for the detection of cervical cancer recurrence, leaving DCE sequences as an option for uncertain cases.
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In order to address the increasing stakeholder requirements for environmentally sustainable products and processes, firms often need the participation of their supply chain partners. Green supply chain management has emerged as a set of managerial practices that integrate environmental issues into supply chain management. If implemented successfully, green supply chain management can be a way to achieve competitive advantage while enhancing the environmental sustainability of the firm. The overall purpose of this dissertation is to contribute to the discussion on green supply chain management practices from the perspective of their drivers and performance implications. The theoretical background arises from the literature on competitive strategy, firm performance and green supply chain management. The research questions are addressed by analysing firm-level data from manufacturing, trading and logistics firms operating in Finland. The empirical data comes from two consecutive Finland State of Logistics surveys in 2012 and 2014, combined with financial reporting data from external databases. The data is analysed with multiple statistical methods. First, the thesis contributes to the discussion of the drivers of GSCM practices. To enhance the understanding of the relationship between competitive strategy and GSCM practices, a conceptual tool to describe generic competitive strategy approaches was developed. The findings suggest that firms pursuing marketing differentiation are more likely to be able to compete by having only small environmental effects and by adopting a more advanced form of external green supply chain management, such as a combination of strong environmental collaboration and the increased environmental monitoring of suppliers. Furthermore, customer requirements for environmental sustainability are found to be an important driver in the implementation of internal GSCM practices. Firms can respond to this customer pressure by passing environmental requirements on to their suppliers, either through environmental collaboration or environmental monitoring. Second, this thesis adds value to the existing literature on the effects of green supply chain management practices on firm performance. The thesis provides support for the idea that there is a positive relationship between GSCM practices and firm performance and enhances the understanding of how different types of GSCM practices are related to 1) financial, 2) operational and 3) environmental performance in manufacturing and logistics. The empirical results suggest that while internal GSCM practices have the strongest effect on environmentalperformance, environmental collaboration with customers seems to be the most effective way to improve financial performance. In terms of operational performance, the findings were more mixed, suggesting that the operational performance of firms is more likely to be affected by firm characteristics than by the choices they make regarding their environmental collaboration. This thesis is also one of the first attempts to empirically analyse the relationship between GSCM practices and performance among logistics service providers. The findings also have managerial relevance. Management, especially in manufacturing and logistics industries, may benefit by gaining knowledge about which types of GSCM practice could provide the largest benefits in terms of different performance dimensions. This thesis also has implications for policy-makers and regulators regarding how to promote environmentally friendly activities among 1) manufacturing; 2) trading; and 3) logistics firms.
Barriers to capturing the value of advanced services and digitisation in the road transport industry
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Servitization and digitization together provide significant opportunities to raise the performance and profitability of the road transport industry. To date these opportunities are only sparsely captured and the potential economic, social and environmental value is forgone. We set out to investigate the barriers to capturing the value of servitization and digitization in the road transport industry.
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Nowadays, the spreading of the air pollution crisis enhanced by greenhouse gases emission is leading to the worsening of global warming. Recently, several metropolitan cities introduced Zero-Emissions Zones where the use of the Internal Combustion Engine is forbidden to reduce localized pollutants emissions. This is particularly problematic for Plug-in Hybrid Electric Vehicles, which usually work in depleting mode. In order to address these issues, the present thesis presents a viable solution by exploiting vehicular connectivity to retrieve navigation data of the urban event along a selected route. The battery energy needed, in the form of a minimum State of Charge (SoC), is calculated by a Speed Profile Prediction algorithm and a Backward Vehicle Model. That value is then fed to both a Rule-Based Strategy, developed specifically for this application, and an Adaptive Equivalent Consumption Minimization Strategy (A-ECMS). The effectiveness of this approach has been tested with a Connected Hardware-in-the-Loop (C-HiL) on a driving cycle measured on-road, stimulating the predictions with multiple re-routings. However, even if hybrid electric vehicles have been recognized as a valid solution in response to increasingly tight regulations, the reduced engine load and the repeated engine starts and stops may reduce substantially the temperature of the exhaust after-treatment system (EATS), leading to relevant issues related to pollutant emission control. In this context, electrically heated catalysts (EHCs) represent a promising solution to ensure high pollutant conversion efficiency without affecting engine efficiency and performance. This work aims at studying the advantages provided by the introduction of a predictive EHC control function for a light-duty Diesel plug-in hybrid electric vehicle (PHEV) equipped with a Euro 7-oriented EATS. Based on the knowledge of future driving scenarios provided by vehicular connectivity, engine first start can be predicted and therefore an EATS pre-heating phase can be planned.
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This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.
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Artioli, GG, Gualano, B, Franchini, E, Batista, RN, Polacow, VO, and Lancha, AH Jr. Physiological, performance, and nutritional profile of the Brazilian Olympic Wushu (kung-fu) team. J Strength Cond Res 23(1): 20-25, 2009-The purpose of the present study was to determine physiological, nutritional, and performance profiles of elite Olympic Wushu (kung-fu) athletes. Ten men and four women elite athletes took part in the study. They completed the following tests: body composition, nutritional assessment, upper-body Wingate Test, vertical jump, lumbar isometric strength, and flexibility. Blood lactate was determined at rest and after the Wingate Test. Blood lactate was also determined during a training session (combat and Taolu training). We found low body fat (men: 9.5 +/- 6.3%; women: 18.0 +/- 4.8%), high flexibility (sit-and-reach-men: 45.5 +/- 6.1 cm; women: 44.0 +/- 6.3 cm), high leg power (vertical jump-men: 37.7 +/- 8.4 cm; women: 32.3 +/- 1.1 cm), high lumbar isometric strength (men: 159 6 13 cm; women: 94 6 6 cm), moderate arm mean and peak power (Wingate Test-men: 4.1 +/- 0.4 and 5.8 +/- 0.5 W.kg(-1), respectively; women: 2.5 +/- 0.3 and 3.4 +/- 0.3 W.kg(-1), respectively), and elevated blood lactate after the Wingate Test (men: 10.8 +/- 2.0 mmol.L(-1); women: 10.2 +/- 2.0 mmol.L(-1)) and during training (combat: 12.0 +/- 1.8 mmol.L(-1); Taolu: 7.7 +/- 3.3 mmol.L(-1)). Men athletes consume a high-fat, low-carbohydrate diet, whereas women consume a moderate, high-carbohydrate diet. Energy consumption was markedly variable. In conclusion, Olympic Wushu seems to be a highly anaerobic-dependent combat sport. Low body fat, high flexibility, leg anaerobic power, isometric strength, and moderately high arm anaerobic power seem to be important for successful competitive performance.
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The purpose of this study was to evaluate oxidative stress, antioxidant biomarkers, and performance during a multiday 210-km endurance race. Nine endurance athlete horses participated in this study. Samples were always taken at the same times of day, before the beginning of the race and after every day of competition. Analytic measurements included glutathione reductase (GR) and catalase activity, thiobarbituric acid-reactive substances (TBARs), and reactive carbonylated derivatives. Competition intensity was low, with an average speed of 12.56 +/- 0.9 km/h. Four horses were unable to finish the race because of metabolic problems or fatigue. GR activity increased progressively (P < .001) throughout the competition, and TBARs showed a significant rise compared with baseline values (P < .01) but remained at the same levels throughout the 3 days of competition. Catalase and reactive carbonylated derivatives did not show any significant alterations in any time period. The best performance was obtained from horses who demonstrated higher GR capacity and/or lower TBAR concentration. In conclusion, redox. status seems to modulate horses` performance in endurance races, but further Studies are needed to better determine the adequate oxidant/antioxidant ratio to acquire optimal performance.
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1, Studies of evolutionary temperature adaptation of muscle and locomotor performance in fish are reviewed with a focus on the Antarctic fauna living at subzero temperatures. 2. Only limited data are available to compare the sustained and burst swimming kinematics and performance of Antarctic, temperate and tropical species. Available data indicate that low temperatures limit maximum swimming performance and this is especially evident in fish larvae. 3, In a recent study, muscle performance in the Antarctic rock cod Notothenia coriiceps at 0 degrees C was found to be sufficient to produce maximum velocities during burst swimming that were similar to those seen in the sculpin Myoxocephalus scorpius at 10 degrees C, indicating temperature compensation of muscle and locomotor performance in the Antarctic fish. However, at 15 degrees C, sculpin produce maximum swimming velocities greater than N, coriiceps at 0 degrees C, 4, It is recommended that strict hypothesis-driven investigations using ecologically relevant measures of performance are undertaken to study temperature adaptation in Antarctic fish, Recent detailed phylogenetic analyses of the Antarctic fish fauna and their temperate relatives will allow a stronger experimental approach by helping to separate what is due to adaptation to the cold and what is due to phylogeny alone.
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Objective: Existing evidence suggests that family interventions can be effective in reducing relapse rates in schizophrenia and related conditions. Despite this, such interventions are not routinely delivered in Australian mental health services. The objective of the current study is to investigate the incremental cost-effectiveness ratios (ICERs) of introducing three types of family interventions, namely: behavioural family management (BFM); behavioural intervention for families (BIF); and multiple family groups (MFG) into current mental health services in Australia. Method: The ICER of each of the family interventions is assessed from a health sector perspective, including the government, persons with schizophrenia and their families/carers using a standardized methodology. A two-stage approach is taken to the assessment of benefit. The first stage involves a quantitative analysis based on disability-adjusted life years (DALYs) averted. The second stage involves application of 'second filter' criteria (including equity, strength of evidence, feasibility and acceptability to stakeholders) to results. The robustness of results is tested using multivariate probabilistic sensitivity analysis. Results: The most cost-effective intervention, in order of magnitude, is BIF (A$8000 per DALY averted), followed by MFG (A$21 000 per DALY averted) and lastly BFM (A$28 000 per DALY averted). The inclusion of time costs makes BFM more cost-effective than MFG. Variation of discount rate has no effect on conclusions. Conclusions: All three interventions are considered 'value-for-money' within an Australian context. This conclusion needs to be tempered against the methodological challenge of converting clinical outcomes into a generic economic outcome measure (DALY). Issues surrounding the feasibility of routinely implementing such interventions need to be addressed.