Similarity Principle and its Acoustical Verification

This study finds a similarity principle the waves emanated from the same source are similar to each other as long as two wave receivers are close enough to each other the closer to each other the wave receivers are the more similar to each other the received waves are We define the similarity mathematically and verify the similarity principle by acoustical experiment

The Power of One: Effects of CEO Duality on Compensation Committee Quality and CEO Compensation

This paper contributes to the corporate governance literature by focusing on how Chief Executive Officer (“CEO”) duality and compensation committee quality are related to CEO compensation in the period since passage of the Sarbanes Oxley Act (“SOX”). Unlike research prior to SOX that focused chiefly on committee members’ independence, we measure compensation committee quality in two ways. We consider the average number of board directorships held by compensation committee members as well as the proportion of committee members with prior or current CEO duality experience. We introduce the latter variable as a new measure of quality as it has not been utilized in research conducted prior to or since the passage of SOX. Using a sample of 100 2007 Fortune 500 firms, we find that CEO duality does not have a significant effect on CEO compensation. However, we document a positive relationship between average number of directorships and CEO compensation and also find evidence that CEO duality moderates the relationship between our measures of compensation committee quality and CEO compensation

An Evidential Fractal Analytic Hierarchy Process Target Recognition Method

Target recognition in uncertain environments is a hot issue, especially in extremely uncertain situation where both the target attribution and the sensor report are not clearly represented. To address this issue, a model which combines fractal theory, Dempster-Shafer evidence theory and analytic hierarchy process (AHP) to classify objects with incomplete information is proposed. The basic probability assignment (BPA), or belief function, can be modelled by conductivity function. The weight of each BPA is determined by AHP. Finally, the collected data are discounted with the weights. The feasibility and validness of proposed model is verified by an evidential classifier case in which sensory data are incomplete and collected from multiple level of granularity. The proposed fusion algorithm takes the advantage of not only efficient modelling of uncertain information, but also efficient combination of uncertain information

Resolving lipid mediators maresin 1 and resolvin D2 prevent atheroprogression in mice

RATIONALE: Atheroprogression is a consequence of non-resolved inflammation and currently a comprehensive overview of the mechanisms preventing resolution is missing. However, in acute inflammation, resolution is known to be orchestrated by a switch from inflammatory to resolving lipid mediators. Therefore we hypothesized that lesional lipid mediator imbalance favors atheroprogression. OBJECTIVE: To understand the lipid mediator balance during atheroprogression and to establish an interventional strategy based on delivery of resolving lipid mediators. METHODS AND RESULTS: Aortic lipid mediator profiling of aortas from Apoe(-/-) mice fed a high fat diet for four weeks, eight weeks, or four months revealed an expansion of inflammatory lipid mediators, Leukotriene B4 (LTB4) and Prostaglandin E2 (PGE2), and a concomitant decrease of resolving lipid mediators, Resolvin D2 (RvD2) and Maresin 1 (MaR1), during advanced atherosclerosis. Functionally, aortic LTB4 and PGE2 levels correlated with traits of plaque instability while RvD2 and MaR1 levels correlated with signs of plaque stability. In a therapeutic context, repetitive RvD2 and MaR1 delivery prevented atheroprogression as characterized by halted expansion of the necrotic core and accumulation of macrophages along with increased fibrous cap thickness and smooth muscle cell numbers. Mechanistically, RvD2 and MaR1 induced a shift in macrophage profile towards a reparative phenotype which secondarily stimulated collagen synthesis in smooth muscle cells. CONCLUSIONS: We present evidence for the imbalance between inflammatory and resolving lipid mediators during atheroprogression. Delivery of RvD2 and MaR1 successfully prevented atheroprogression suggesting that resolving lipid mediators potentially represent an innovative strategy to resolve arterial inflammation

Examining the effectiveness of the new Basel III banking standards : experience from the South African Customs Union (SACU) banks

This dissertation explored the efficacy of the new Basel III banking standards in SACU, grounded on the conjecture that they are not reflective of economies of SACU, but are merely an intensification of Basel II, rather than a substantial break with it. Firstly, loans and assets were tested for causality, since Basel III believes growth in these variables led to securitization. The leverage ratio has been introduced in Basel III as an anti-cyclical buffer. The OLS technique was employed to test for its significance in determining growth in bank assets. SACU feels the impact of debt, with credit is marginally treated in Basel III and is not introspective of the realities of its economies. ANOVA tests using debt, credit and GDP were done to determine a better method of addressing cyclicality. The leverage ratio was insignificant in Namibia, with debt and credit having momentous impacts on GDP in SACU.EconomicsM. Com. (Economics

Suppression of Tumor Necrosis Factor Production by Alcohol in Lipopolysaccharide-Stimulated Culture

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66388/1/j.1530-0277.1994.tb00917.x.pd

Lobster in a Changing Gulf of Maine: Investigating the Temporal Impact on Molting and the Fishing Fleet

We investigated the phenological and fisheries dynamics surrounding the spring molt of American lobster (Homarus americanus, Milne Edwards 1847) in the Gulf of Maine. We created a time series from Maine Department of Marine Resources Lobster Sea Sampling data using logistic models to estimate the timing and duration of the spring molt for eastern, central, and western regions of the Maine coast. These estimates revealed substantial inter-annual variability in the timing of the spring molt for all regions and that 2012 was indeed anomalously early relative to other years. Each region experienced significantly different molt timing for any given year, indicating that there are spatially-distinct molting phenologies along the Maine coastline. Generalized Linear Models were constructed using the molting time series and hindcasted bottom temperatures from the Northeast Coastal Ocean Forecasting System using the Finite Volume Composite Ocean Model to analyze how nearshore and offshore bottom ocean temperatures might shape molting trends and differences. This analysis revealed that the influence of nearshore temperatures was significant in the eastern region only and the relationship between nearshore temperatures and the timing of the spring molt weakened from east to west. Logistic models were also applied to Maine Department of Marine Resources Landings Program data to estimate and evaluate multiple landings-based proxies for the timing of the spring molt via the fishing fleet’s ability to synchronize with the lobster molting phenology. Newshell landings, as a percent of the annual weekly maximum, were identified as the best proxy, given relative difference from the annual in-situ estimates of spring molt timing and lower standard error values. The fleet’s ability to synchronize with variable spring molting phenology was assessed using a correlation analysis. This analysis revealed that both eastern and western fleets followed the same temporal patterns as the lobster molt timing in their region and the western fleet showed a poorer, more variable ability to absolutely synchronize their timing when compared to the eastern fleet. Maine lobstermen were interviewed to investigate how they achieve an optimal synchrony, revealing the utilization of several environmental and non-environmental variables. General temperature, lunar and tidal phases, and Penobscot River discharge were fishermen-nominated variables tested using correlation analysis. These analyses showed that fishermen methodology and its association with spring molt timing were spatially variable. General temperatures displayed the same weakening association with spring molt from east to west; tidal phase was significant in the east only; and river discharge was significantly associated in the eastern and central regions. River discharge association with molting was also temporally variable, showing strongest significant positive relationships during April. We discuss these investigations into the temporal and spatial dynamics of the spring lobster molt along the Maine coast and the fishery’s response to inter-annual variation, creating a baseline of information about the spring molt for Maine. We also discuss the degree to which the fleet is able to approximate and adapt to inter-annual variation in this phenology and some of the methods they have been using to accomplish this synchrony

Performance analysis of various machine learning algorithms for CO2 leak prediction and characterization in geo-sequestration injection wells

The effective detection and prevention of CO2 leakage in active injection wells are paramount for safe carbon capture and storage (CCS) initiatives. This study assesses five fundamental machine learning algorithms, namely, Support Vector Regression (SVR), K-Nearest Neighbor Regression (KNNR), Decision Tree Regression (DTR), Random Forest Regression (RFR), and Artificial Neural Network (ANN), for use in developing a robust data-driven model to predict potential CO2 leakage incidents in injection wells. Leveraging wellhead and bottom-hole pressure and temperature data, the models aim to simultaneously predict the location and size of leaks. A representative dataset simulating various leak scenarios in a saline aquifer reservoir was utilized. The findings reveal crucial insights into the relationships between the variables considered and leakage characteristics. With its positive linear correlation with depth of leak, wellhead pressure could be a pivotal indicator of leak location, while the negative linear relationship with well bottom-hole pressure demonstrated the strongest association with leak size. Among the predictive models examined, the highest prediction accuracy was achieved by the KNNR model for both leak localization and sizing. This model displayed exceptional sensitivity to leak size, and was able to identify leak magnitudes representing as little as 0.0158% of the total main flow with relatively high levels of accuracy. Nonetheless, the study underscored that accurate leak sizing posed a greater challenge for the models compared to leak localization. Overall, the findings obtained can provide valuable insights into the development of efficient data-driven well-bore leak detection systems.<br/