Assessing the vulnerability of changing coasts,Hainan Island,China

Knowledge of coastline changes and vulnerability is of great importance to local government departments that are responsible for the management and development of coastal zones.To study the nature of change and vulnerability along the coasts of the Hainan Island,we collected a large number of sediment samples through the last few years,and reconstructed the changes of the coastline by combining the data of sediment grain-size analysis and the nautical charts/TM RS imaginary.Contrary to being almost free from erosion（as expected from the findings that the coastlines are in a relatively stable state）,four major cities in Hainan（i.e.,Haikou,Wenchang,Sanya and Changjiang） turned out to be suffered from a moderate coastal vulnerability primarily because of the large populations that impose considerable pressure on the coastlines.Thus,the assessment methodology utilized in this study,including both anthropogenic and natural factors,serves as a useful tool to obtain a comprehensive understanding of coastline vulnerability for local government,in terms of coastal management and adaptation.

Factors Affecting Vulnerability of Ready-Made Garment Factory Buildings in Bangladesh: An Assessment Under Vertical and Earthquake Loads

The importance of workplace safety in the ready-made garment(RMG) industry in Bangladesh came to the forefront after a series of disastrous events in recent years. In order to reduce the loss of lives and to ensure sustainable development, an in-depth understanding of the determining factors governing structural vulnerability in the RMG industry is needed. This research explores the key factors influencing the vulnerability of factory buildings under both vertical and earthquake loads. For this purpose,an ordered probit model was applied to 3746 RMG factory buildings to determine the key factors that influenced their vertical load vulnerability. A smaller subset of the original sample, 478 buildings, was examined by the same modeling method in greater detail to assess the key factors that influenced their earthquake load vulnerability. This research reveals that column capacity, structural system,and construction materials are the most influential factors for both types of vulnerabilities. Among other factors, soil liquefaction and irregular internal frame affect earthquake load vulnerability significantly. These findings are expected to enable factory owners and designers to better weigh the appropriate vulnerability factors in order to make informed decision that increase workplace safety. Theresearch findings will also help the designated authorities to conduct successful inspections of factory buildings and take actions that reduce vulnerability to both vertical and earthquake loads.

Relationship between serum vasoactive factors and plaque morphology in patients with non-ST-segment elevated acute coronary syndrome

Background Vasoactive factors have been reported to correlate with vulnerable plaque and acute coronary syndrome （ACS）. This study aimed to investigate the relationship between vasoactive factors and plaque morphology in patients suffering from non-ST-segment elevated ACS. Methods From April 2007 to April 2009, 124 consecutive patients suffering from non-ST-segment elevated ACS who had received coronary angiography （CAG） and intravascular ultrasound （IVUS） in the People＇s Liberation Army General Hospital and Beijing Anzhen Hospital were enrolled in this study. Three serum vasoactive factors, plasma soluble vascular endothelial growth factor receptor-1 （sFIt-1）, placental growth factor （PLGF） and interleukin-18 （IL-18）, were measured by enzyme-linked-immunosorbent serologic assay of the patients. The levels of vasoactive factors were compared between vulnerable plaque group and stable plaque group, and between unstable angina pectoris （UAP） group and non-ST-segment elevation acute myocardial infarction （NSTE-AMI） group. The relationship between the plaque morphology and levels of vasoactive factors was analyzed. Results The levels of vasoactive factors were similar between the UAP group （69 patients） and NSTE-AMI group （55 patients）. The levels of sFIt-1 and PLGF in the vulnerable plaque group were significantly higher than those in the stable plaque group. The level of IL-18 was correlated positively with plaque morphology. Multivariate Logistic regression analysis showed that the level of PLGF was an independent risk factor for vulnerable plaque （OR=2.115, 95%Cl 1.415-5.758, P=0.018）. Using the ROC curve, PLGF was a significant factor for the diagnosis of vulnerable plaque （the diagnostic point was 26.3 ng/L, the proportion of square area under the ROC curve was 0.799, 95%C/0.758-0.839, P 〈0.001 ; the sensitivity of PLGF under the ROC curve was 86%, and the specificity 63%）. Conclusion Both IL-18 and PLGF are biomarkers for vulnerable plaques and helpful to predict vulnerable plaque.

Accurate and Simplified Prediction of AVF for Delay and Energy Efficient Cache Design

With continuous technology scaling, on-chip structures are becoming more and more susceptible to soft errors. Architectural vulnerability factor （AVF） has been introduced to quantify the architectural vulnerability of on-chip structures to soft errors. Recent studies have found that designing soft error protection techniques with the awareness of AVF is greatly helpful to achieve a tradeoff between performance and reliability for several structures （i.e., issue queue, reorder buffer）. Cache is one of the most susceptible components to soft errors and is commonly protected with error correcting codes （ECC）. However, protecting caches closer to the processor （i.e., L1 data cache （LID）） using ECC could result in high overhead. Protecting caches without accurate knowledge of the vulnerability characteristics may lead to over-protection. Therefore, designing AVF-aware ECC is attractive for designers to balance among performance, power and reliability for cache, especially at early design stage. In this paper, we improve the methodology of cache AVF computation and develop a new AVF estimation framework, soft error reliability analysis based on SimpleScalar. Then we characterize dynamic vulnerability behavior of LID and detect the correlations between L1D AVF and various performance metrics. We propose to employ Bayesian additive regression trees to accurately model the variation of L1D AVF and to quantitatively explain the important effects of several key performance metrics on L1D AVF. Then, we employ bump hunting technique to reduce the complexity of L1D AVF prediction and extract some simple selecting rules based on several key performance metrics, thus enabling a simplified and fast estimation of L1D AVF. Based on the simplified and fast estimation of L1D AVF, intervals of high L1D AVF can be identified online, enabling us to develop the AVF-aware ECC technique to reduce the overhead of ECC. Experimental results show that compared with traditional ECC technique which provides complete ECC protection throughout the entire lifetime of a program, AVF-aware ECC technique reduces the L1D access latency by 35% and saves power consumption by 14% for SPEC2K benchmarks averagely.