Why Plant-Level Productivity Studies are Often Misleading, and an Alternative Approach to Interference

Applied economists often wish to measure the effects of managerial decisions or policy changes on plant-level productivity patterns. But plant-level data on physical quantities of output, capital, and intermediate inputs are usually unavailable. Therefore, when constructing productivity measures, most analysts proxy these variables with real sales revenues, depreciated capital spending, and real input expenditures. The first part of this paper argues that the resultant productivity indices have little to do with technical efficiency, product quality, or contributions to social welfare. Nonetheless, they are likely to be correlated with policy shocks and managerial decisions in misleading ways. The second part of the paper develops an alternative approach to inference. Using Steven Berry's (1994, RAND Journal) representation of equilibrium in a differentiated product market, we show how to impute each plant's unobserved marginal costs and product quality from its observed revenues and costs, and how to use this mapping to calculate plant-specific welfare-based performance measures. (Bayesian estimation techniques are required because the vector of unknown parameters is under-identified.) The final part of the paper demonstrates our methodology using panel data on Colombian pulp and paper plants.

Response of hydrological cycle to tiny random sea surface temperature disturbances

Thirteenth Conference on Hydrology, American Meteorological Society, J45-J4

Evaluation of the Princeton Ocean Model Using South China Sea Monsoon Experiment (SCSMEX) Data

The Princeton Ocean Model (POM) has been implemented in the South China Sea for hindcast of circulation and thermohaline structure. A two-step technique is used to initialize POM with temperature, salinity, and velocity for 1 April 1998 and integrate it from 1 April 1998 with synoptic surface forcing for 3 months with and without data assimilation. Hydrographic and current data acquired from the South China Sea Monsoon Experiment (SCSMEX) from April through June 1998 are used to verify, and to assimilate into, POM. The mean SCSMEX data (Apr–Jun 1998) are about 0.58°C warmer than the mean climatological data above the 50-m depth, and slightly cooler than the mean climatological data below the 50-m depth, and are fresher than the climatological data at all depths and with the maximum bias (0.2–0.25 ppt) at 75-m depth. POM without data assimilation has the capability to predict the circulation pattern and the temperature field reasonably well, but has no capability to predict the salinity field. The model errors have Gaussian-type distri bution for temperature hindcast, and non-Gaussian distribution for salinity hindcast with six to eight times more frequencies of occurrence on the negative side than on the positive side. Data assimilation enhances the model capability for ocean hindcast, if even only conductivity–temperature–depth (CTD) data are assimilated. When the model is reinitialized using the assimilated data at the end of a month (30 Apr; 31 May 1998) and the model is run for a month without data assimilation (hindcast capability test), the model errors for both temperature and salinity hindcast are greatly reduced, and they have Gaussian-type distributions for both temperature and salinity hindcast. Hence, POM gains capability in salinity hindcast when CTD data are assimilated.Office of Naval ResearchNaval Oceanographic OfficeNaval Postgraduate Schoo

Contrast-free detection of myocardial fibrosis in hypertrophic cardiomyopathy patients with diffusion-weighted cardiovascular magnetic resonance.

BackgroundsPrevious studies have shown that diffusion-weighted cardiovascular magnetic resonance (DW-CMR) is highly sensitive to replacement fibrosis of chronic myocardial infarction. Despite this sensitivity to myocardial infarction, DW-CMR has not been established as a method to detect diffuse myocardial fibrosis. We propose the application of a recently developed DW-CMR technique to detect diffuse myocardial fibrosis in hypertrophic cardiomyopathy (HCM) patients and compare its performance with established CMR techniques.MethodsHCM patients (N = 23) were recruited and scanned with the following protocol: standard morphological localizers, DW-CMR, extracellular volume (ECV) CMR, and late gadolinium enhanced (LGE) imaging for reference. Apparent diffusion coefficient (ADC) and ECV maps were segmented into 6 American Heart Association (AHA) segments. Positive regions for myocardial fibrosis were defined as: ADC > 2.0 μm(2)/ms and ECV > 30%. Fibrotic and non-fibrotic mean ADC and ECV values were compared as well as ADC-derived and ECV-derived fibrosis burden. In addition, fibrosis regional detection was compared between ADC and ECV calculating sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) using ECV as the gold-standard reference.ResultsADC (2.4 ± 0.2 μm(2)/ms) of fibrotic regions (ADC > 2.0 μm(2)/ms) was significantly (p < 0.01) higher than ADC (1.5 ± 0.2 μm(2)/ms) of non-fibrotic regions. Similarly, ECV (35 ± 4%) of fibrotic regions (ECV > 30%) was significantly (p < 0.01) higher than ECV (26 ± 2%) of non-fibrotic regions. In fibrotic regions defined by ECV, ADC (2.2 ± 0.3 μm(2)/ms) was again significantly (p < 0.05) higher than ADC (1.6 ± 0.3 μm(2)/ms) of non-fibrotic regions. In fibrotic regions defined by ADC criterion, ECV (34 ± 5%) was significantly (p < 0.01) higher than ECV (28 ± 3%) in non-fibrotic regions. ADC-derived and ECV-derived fibrosis burdens were in substantial agreement (intra-class correlation = 0.83). Regional detection between ADC and ECV of diffuse fibrosis yielded substantial agreement (κ = 0.66) with high sensitivity, specificity, PPV, NPV, and accuracy (0.80, 0.85, 0.81, 0.85, and 0.83, respectively).ConclusionDW-CMR is sensitive to diffuse myocardial fibrosis and is capable of characterizing the extent of fibrosis in HCM patients

Uncertainty of the South China Sea prediction using NSCAT and NCEP winds during tropical storm Ernie 1996

Journal of Geophysical Research, American Geophysical Unio

The role of imaging in 2019 novel coronavirus pneumonia (COVID-19).

Almost the entire world, not only China, is currently experiencing the outbreak of a novel coronavirus that causes respiratory disease, severe pneumonia, and even death. The outbreak began in Wuhan, China, in December of 2019 and is currently still ongoing. This novel coronavirus is highly contagious and has resulted in a continuously increasing number of infections and deaths that have already surpassed the SARS-CoV outbreak that occurred in China between 2002 and 2003. It is now officially a pandemic, announced by WHO on the 11th of March. Currently, the 2019 novel coronavirus (SARS-CoV-2) can be identified by virus isolation or viral nucleic acid detection; however, false negatives associated with the nucleic acid detection provide a clinical challenge and thus make the imaging examination crucial. Imaging exams have been a main clinical diagnostic criteria for the 2019 novel coronavirus disease (COVID-19) in China. Imaging features of multiple patchy areas of ground glass opacity and consolidation predominately in the periphery of the lungs are characteristic manifestations on chest CT and extremely helpful in the early detection and diagnosis of this disease, which aids prompt diagnosis and the eventual control of this emerging global health emergency. Key Points • In December 2019, China, an outbreak of pneumonia caused by a novel, highly contagious coronavirus raised grave concerns and posed a huge threat to global public health. • Among the infected patients, characteristic findings on CT imaging include multiple, patchy, ground-glass opacity, crazy-paving pattern, and consolidation shadows, mainly distributed in the peripheral and subpleural areas of both lungs, which are very helpful for the frontline clinicians. • Imaging examination has become the indispensable means not only in the early detection and diagnosis but also in monitoring the clinical course, evaluating the disease severity, and may be presented as an important warning signal preceding the negative RT-PCR test results

Diagnostic Accuracy of Three-Dimensional Whole-Heart Magnetic Resonance Angiography to Detect Coronary Artery Disease with Invasive Coronary Angiography as a Reference: A Meta-Analysis

Objective: We aimed to evaluate the diagnostic performance of three-dimensional whole-heart magnetic resonance coronary angiography (MRCA) in detecting coronary artery disease (CAD) with invasive coronary angiography as the reference standard. Methods: We searched PubMed and Embase for studies evaluating the diagnostic performance of three-dimensional whole-heart MRCA for the diagnosis of CAD with invasive coronary angiography as the reference standard. The bivariate mixed-effects regression model was applied to synthesize available data. The clinical utility of whole-heart MRCA was calculated by the posttest probability based on Bayes’s theorem. Results: Eighteen studies were included, of which 16 provided data at the artery level. Patient-based analysis revealed a pooled sensitivity of 0.90 (95% confidence interval [CI] 0.87–0.93) and specificity of 0.79 (95% CI 0.73–0.84), while the pooled estimates were 0.86 (95% CI 0.82–0.89) and 0.89 (95% CI 0.84–0.92), respectively, at the artery level. The areas under the summary receiver operating characteristic curve were 0.93 (95% CI 0.90–0.95) and 0.92 (95% CI 0.90–0.94) at the patient and artery levels, respectively. With a pretest probability of 50%, the patients’ posttest probabilities of CAD were 81% for positive results and 11% for negative results. Conclusions: Whole-heart MRCA can be an alternative noninvasive method for diagnosis and assessment of CAD

Estimation of biogenic VOC emissions and their corresponding impact on ozone and secondary organic aerosol formation in China

Biogenic volatile organic compounds (BVOC) play an important role in global environmental chemistry and climate. In the present work, biogenic emissions from China in 2017 were estimated based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN). The effects of BVOC emissions on ozone and secondary organic aerosol (SOA) formation were investigated using the WRF-CMAQ modeling system. Three parallel scenarios were developed to assess the impact of BVOC emissions on China's ozone and SOA formation in July 2017. Biogenic emissions were estimated at 23.54 Tg/yr, with a peak in the summer and decreasing from southern to northern China. The high BVOC emissions across eastern and southwestern China increased the surface ozone levels, particularly in the BTH (Beijing-Tianjin-Hebei), SCB (Sichuan Basin), YRD (Yangtze River Delta) and central PRD (Pearl River Delta) regions, with increases of up to 47 μg m−3 due to the sensitivity of VOC-limited urban areas. In summer, most SOA concentrations formed over China are from biogenic sources (national average of 70%). And SOA concentrations in YRD and SCB regions are generally higher than other regions. Excluding anthropogenic emissions while keeping biogenic emissions unchanged results that SOA concentrations reduce by 60% over China, which indicates that anthropogenic emissions can interact with biogenic emissions then facilitate biogenic SOA formation. It is suggested that controlling anthropogenic emissions would result in reduction of both anthropogenic and biogenic SOA.Peer reviewe