A new hybrid approach to human error probability quantification-applications in maritime operations

Human Reliability Analysis (HRA) has always been an essential research issue in safety critical systems. Cognitive Reliability Error Analysis Method (CREAM), as a well-known second generation HRA method is capable of conducting both retrospective and prospective analysis, thus being widely used in many sectors. However, the needs of addressing the use of a deterministic approach to configure common performance conditions (CPCs) and the assignment of the same importance to all the CPCs in a traditional CREAM method reveal a significant research gap to be fulfilled. This paper describes a modified CREAM methodology based on an Evidential Reasoning (ER) approach and a Decision Making Trial and Evaluation Laboratory (DEMATEL) technique for making human error probability quantification in CREAM rational. An illustrative case study associated with maritime operations is presented. The proposed method is validated by sensitivity analysis and the quantitative analysis result is verified through comparing the real data collected from Shanghai coastal waters. Its main contribution lies in that it for the first time addresses the data incompleteness in HEP, given that the previous relevant studies mainly focus on the fuzziness in data. The findings will provide useful insights for quantitative assessment of seafarers' errors to reduce maritime risks due to human errors

Record linkage research and informed consent: who consents?

BACKGROUND: Linking computerized health insurance records with routinely collected survey data is becoming increasingly popular in health services research. However, if consent is not universal, the requirement of written informed consent may introduce a number of research biases. The participants of a national health survey in Taiwan were asked to have their questionnaire results linked to their national health insurance records. This study compares those who consented with those who refused. METHODS: A national representative sample (n = 14,611 adults) of the general adult population aged 20 years or older who participated in the Taiwan National Health Interview Survey (NHIS) and who provided complete survey information were used in this study. At the end of the survey, the respondents were asked if they would give permission to access their National Health Insurance records. Information given by the interviewees in the survey was used to analyze who was more likely to consent to linkage and who wasn't. RESULTS: Of the 14,611 NHIS participants, 12,911 (88%) gave consent, and 1,700 (12%) denied consent. The elderly, the illiterate, those with a lower income, and the suburban area residents were significantly more likely to deny consent. The aborigines were significantly less likely to refuse. No discrepancy in gender and self-reported health was found between individuals who consented and those who refused. CONCLUSION: This study is the first population-based study in assessing the consent pattern in a general Asian population. Consistent with people in Western societies, in Taiwan, a typical Asian society, a high percentage of adults gave consent for their health insurance records and questionnaire results to be linked. Consenters differed significantly from non-consenters in important aspects such as age, ethnicity, and educational background. Consequently, having a high consent rate (88%) may not fully eliminate the possibility of selection bias. Researchers should take this source of bias into consideration in their study design and investigate any potential impact of this source of bias on their results

Adaptive Subspace Sampling for Class Imbalance Processing-Some clarifications, algorithm, and further investigation including applications to Brain Computer Interface

© 2020 IEEE. Kohonen's Adaptive Subspace Self-Organizing Map (ASSOM) learns several subspaces of the data where each subspace represents some invariant characteristics of the data. To deal with the imbalance classification problem, earlier we have proposed a method for oversampling the minority class using Kohonen's ASSOM. This investigation extends that study, clarifies some issues related to our earlier work, provides the algorithm for generation of the oversamples, applies the method on several benchmark data sets, and makes an application to a Brain Computer Interface (BCI) problem. First we compare the performance of our method using some benchmark data sets with several state-of-The-Art methods. Finally, we apply the ASSOM-based technique to analyze a BCI based application using electroencephalogram (EEG) datasets. Our results demonstrate the effectiveness of the ASSOM-based method in dealing with imbalance classification problem

Controlling Cherenkov angles with resonance transition radiation

Cherenkov radiation provides a valuable way to identify high energy particles in a wide momentum range, through the relation between the particle velocity and the Cherenkov angle. However, since the Cherenkov angle depends only on material's permittivity, the material unavoidably sets a fundamental limit to the momentum coverage and sensitivity of Cherenkov detectors. For example, Ring Imaging Cherenkov detectors must employ materials transparent to the frequency of interest as well as possessing permittivities close to unity to identify particles in the multi GeV range, and thus are often limited to large gas chambers. It would be extremely important albeit challenging to lift this fundamental limit and control Cherenkov angles as preferred. Here we propose a new mechanism that uses constructive interference of resonance transition radiation from photonic crystals to generate both forward and backward Cherenkov radiation. This mechanism can control Cherenkov angles in a flexible way with high sensitivity to any desired range of velocities. Photonic crystals thus overcome the severe material limit for Cherenkov detectors, enabling the use of transparent materials with arbitrary values of permittivity, and provide a promising option suited for identification of particles at high energy with enhanced sensitivity.Comment: There are 16 pages and 4 figures for the manuscript. Supplementary information with 18 pages and 5 figures, appended at the end of the file with the manuscript. Source files in Word format converted to PDF. Submitted to Nature Physic

First Assessment of NOx Sources at a Regional Background Site in North China Using Isotopic Analysis Linked with Modeling

important role in the formation of atmospheric particles. Thus, NOx emission reduction is critical for improving air quality, especially in severely air-polluted regions (e.g., North China). In this study, the source of NOx was investigated by the isotopic composition (delta N-15) of particulate nitrate (p-NO3-) at Beihuangcheng Island (BH), a regional background site in North China. It was found that the delta N-15-NO3- (n = 120) values varied between -1.7 parts per thousand and +24.0 parts per thousand and the delta O-18-NO3- values ranged from 49.4 parts per thousand to 103.9 parts per thousand. On the basis of the Bayesian mixing model, 27.78 +/- 8.89%, 36.53 +/- 6.66%, 22.01 +/- 6.92%, and 13.68 +/- 3.16% of annual NOx could be attributed to biomass burning, coal combustion, mobile sources, and biogenic soil emissions, respectively. Seasonally, the four sources were similar in spring and fall. Biogenic soil emissions were augmented in summer in association with the hot and rainy weather. Coal combustion increased significantly in winter with other sources showing an obvious decline. This study confirmed that isotope-modeling by delta N-15-NO3- is a promising tool for partitioning NOx sources and provides guidance to policymakers with regard to options for NOx reduction in North China

The Ubiquitin Ligase Ubr2, a Recognition E3 Component of the N-End Rule Pathway, Stabilizes Tex19.1 during Spermatogenesis

Ubiquitin E3 ligases target their substrates for ubiquitination, leading to proteasome-mediated degradation or altered biochemical properties. The ubiquitin ligase Ubr2, a recognition E3 component of the N-end rule proteolytic pathway, recognizes proteins with N-terminal destabilizing residues and plays an important role in spermatogenesis. Tex19.1 (also known as Tex19) has been previously identified as a germ cell-specific protein in mouse testis. Here we report that Tex19.1 forms a stable protein complex with Ubr2 in mouse testes. The binding of Tex19.1 to Ubr2 is independent of the second position cysteine of Tex19.1, a putative target for arginylation by the N-end rule pathway R-transferase. The Tex19.1-null mouse mutant phenocopies the Ubr2-deficient mutant in three aspects: heterogeneity of spermatogenic defects, meiotic chromosomal asynapsis, and embryonic lethality preferentially affecting females. In Ubr2-deficient germ cells, Tex19.1 is transcribed, but Tex19.1 protein is absent. Our results suggest that the binding of Ubr2 to Tex19.1 metabolically stabilizes Tex19.1 during spermatogenesis, revealing a new function for Ubr2 outside the conventional N-end rule pathway

Efficacy of seasonal influenza vaccination in children in Hong Kong: a randomized controlled trial

Oral Presentation: abstract no. VII-1BACKGROUND: Seasonal influenza vaccination is most effective in preventing influenza infection and disease in healthy school-age children when circulating strains are similar to those included in the vaccine. The efficacy of seasonal influenza vaccination against 2009 pandemic influenza A(H1N1) remains unclear…postprintThe 14th International Symposium on Respiratory Viral Infections, Istanbul, Turkey, 23-26 March 2012

Classifying and scoring of molecules with the NGN: new datasets, significance tests, and generalization

<p>Abstract</p> <p/> <p>This paper demonstrates how a Neural Grammar Network learns to classify and score molecules for a variety of tasks in chemistry and toxicology. In addition to a more detailed analysis on datasets previously studied, we introduce three new datasets (BBB, FXa, and toxicology) to show the generality of the approach. A new experimental methodology is developed and applied to both the new datasets as well as previously studied datasets. This methodology is rigorous and statistically grounded, and ultimately culminates in a Wilcoxon significance test that proves the effectiveness of the system. We further include a complete generalization of the specific technique to arbitrary grammars and datasets using a mathematical abstraction that allows researchers in different domains to apply the method to their own work.</p> <p>Background</p> <p>Our work can be viewed as an alternative to existing methods to solve the quantitative structure-activity relationship (QSAR) problem. To this end, we review a number approaches both from a methodological and also a performance perspective. In addition to these approaches, we also examined a number of chemical properties that can be used by generic classifier systems, such as feed-forward artificial neural networks. In studying these approaches, we identified a set of interesting benchmark problem sets to which many of the above approaches had been applied. These included: ACE, AChE, AR, BBB, BZR, Cox2, DHFR, ER, FXa, GPB, Therm, and Thr. Finally, we developed our own benchmark set by collecting data on toxicology.</p> <p>Results</p> <p>Our results show that our system performs better than, or comparatively to, the existing methods over a broad range of problem types. Our method does not require the expert knowledge that is necessary to apply the other methods to novel problems.</p> <p>Conclusions</p> <p>We conclude that our success is due to the ability of our system to: 1) encode molecules losslessly before presentation to the learning system, and 2) leverage the design of molecular description languages to facilitate the identification of relevant structural attributes of the molecules over different problem domains.</p

Long-Term Mortality of Patients with Septic Ocular or Central Nervous System Complications from Pyogenic Liver Abscess: A Population-Based Study

Background: Taiwan is endemic for pyogenic liver abscess (PLA). Septic ocular or central nervous system (CNS) complications derived from PLA can result in catastrophic disability. We investigated the epidemiology and long-term prognosis of PLA patients with septic ocular or CNS complications over an 8-year period. Methodology/Principal Findings: We extracted 21,307 patients with newly diagnosed PLA from a nationwide health registry in Taiwan between 2000 and 2007. The frequency of and risk factors for PLA with septic ocular or CNS complications were determined. The 2-year survival of these patients was compared between those with and without septic ocular or CNS complications. Septic ocular or CNS complications accounted for 2.1 % of all PLA patients. Age and the Charlson comorbidity index were significantly lower in PLA patients with ocular or CNS complications than those without. Diabetes and age,65 years were independent predictors of septic ocular or CNS complications. The 2-year mortality of patients with septic ocular or CNS complications was similar to those without complications (24.8 % vs. 27.5%, p = 0.502). However, among patients,65 years old and a Charlson index #1, the 2-year mortality was significantly higher in those with than without complications (18.6 % vs. 11.8%, p = 0.001). Conclusions/Significance: Physicians should recognize that catastrophic disability due to ocular or neurologica