Shear Lag Effect on Bolted C-shaped Cold-formed Steel Tension Members

This study is concentrated on the investigation of the shear lag effect on cold-formed steel tension members. C-shaped sections with different dimensions tested by using bolted connections were discussed in this study. The comparisons were made between the test results and predictions computed based on several specifications. In order to study the stress distribution at the various locations of the cross section of specimen, the finite-element software ANSYS was also utilized in this research. Based on the experimental results, it was found that the tension strengths of test specimens predicted by the AISC-Code (1999), which takes account of the shear lag effect, provide good agreement with the test values. The predictions according to AISI-Code (1996) and AS/NZS 4600 Code (1996) seem to be overestimated as comparing to the test results. It is also noted that there is quite a discrepancy between the test results and the values predicted by British Standard (1998)

Overview of Some Intelligent Control Structures and Dedicated Algorithms

Automatic control refers to the use of a control device to make the controlled object automatically run or keep the state unchanged without the participation of people. The guiding ideology of intelligent control is based on people’s way of thinking and ability to solve problems, in order to solve the current methods that require human intelligence. We already know that the complexity of the controlled object includes model uncertainty, high nonlinearity, distributed sensors/actuators, dynamic mutations, multiple time scales, complex information patterns, big data process, and strict characteristic indicators, etc. In addition, the complexity of the environment manifests itself in uncertainty and uncertainty of change. Based on this, various researches continue to suggest that the main methods of intelligent control can include expert control, fuzzy control, neural network control, hierarchical intelligent control, anthropomorphic intelligent control, integrated intelligent control, combined intelligent control, chaos control, wavelet theory, etc. However, it is difficult to want all the intelligent control methods in a chapter, so this chapter focuses on intelligent control based on fuzzy logic, intelligent control based on neural network, expert control and human-like intelligent control, and hierarchical intelligent control and learning control, and provide relevant and useful programming for readers to practice

Tetra-μ-benzoato-bis­[(6-methyl­quino­line)­copper(II)]

In the title compound, [Cu2(C7H5O2)4(C10H9N)2], the paddle-wheel-type dinuclear complex is constructed by four bridging benzoate groups and two terminal 6-methyl­quinoline ligands. The asymmetric unit contains one-half of the whole mol­ecule, and there is an inversion center at the mid-point of the Cu⋯Cu bond. The octa­hedral coordination of each Cu atom, with four O atoms in the equatorial plane, is completed by the N atom of the 6-methyl­quinoline mol­ecule [Cu—N = 2.212 (2) Å] and by another Cu atom [Cu⋯Cu = 2.6939 (13) Å]. The Cu atom lies 0.234 Å out of the plane of the four O atoms. The molecular packing is stabilized by one intramolecular C—H⋯O as well as C—H⋯π and π–π interactions

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

<p>Abstract</p> <p>Background</p> <p>Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.</p> <p>Results</p> <p>We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes.</p> <p>Conclusion</p> <p>The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. SpecificDB provides comprehensive information and a user-friendly interface.</p

Genome-wide identification of specific oligonucleotides using artificial neural network and computational genomic analysis

<p>Abstract</p> <p>Background</p> <p>Genome-wide identification of specific oligonucleotides (oligos) is a computationally-intensive task and is a requirement for designing microarray probes, primers, and siRNAs. An artificial neural network (ANN) is a machine learning technique that can effectively process complex and high noise data. Here, ANNs are applied to process the unique subsequence distribution for prediction of specific oligos.</p> <p>Results</p> <p>We present a novel and efficient algorithm, named the integration of ANN and BLAST (IAB) algorithm, to identify specific oligos. We establish the unique marker database for human and rat gene index databases using the hash table algorithm. We then create the input vectors, via the unique marker database, to train and test the ANN. The trained ANN predicted the specific oligos with high efficiency, and these oligos were subsequently verified by BLAST. To improve the prediction performance, the ANN over-fitting issue was avoided by early stopping with the best observed error and a k-fold validation was also applied. The performance of the IAB algorithm was about 5.2, 7.1, and 6.7 times faster than the BLAST search without ANN for experimental results of 70-mer, 50-mer, and 25-mer specific oligos, respectively. In addition, the results of polymerase chain reactions showed that the primers predicted by the IAB algorithm could specifically amplify the corresponding genes. The IAB algorithm has been integrated into a previously published comprehensive web server to support microarray analysis and genome-wide iterative enrichment analysis, through which users can identify a group of desired genes and then discover the specific oligos of these genes.</p> <p>Conclusion</p> <p>The IAB algorithm has been developed to construct SpecificDB, a web server that provides a specific and valid oligo database of the probe, siRNA, and primer design for the human genome. We also demonstrate the ability of the IAB algorithm to predict specific oligos through polymerase chain reaction experiments. SpecificDB provides comprehensive information and a user-friendly interface.</p

Linking Student Performance in Massachusetts Elementary Schools with the “Greenness” of School Surroundings Using Remote Sensing

Various studies have reported the physical and mental health benefits from exposure to “green” neighborhoods, such as proximity to neighborhoods with trees and vegetation. However, no studies have explicitly assessed the association between exposure to “green” surroundings and cognitive function in terms of student academic performance. This study investigated the association between the “greenness” of the area surrounding a Massachusetts public elementary school and the academic achievement of the school’s student body based on standardized tests with an ecological setting. Researchers used the composite school-based performance scores generated by the Massachusetts Comprehensive Assessment System (MCAS) to measure the percentage of 3rd-grade students (the first year of standardized testing for 8–9 years-old children in public school), who scored “Above Proficient” (AP) in English and Mathematics tests (Note: Individual student scores are not publically available). The MCAS results are comparable year to year thanks to an equating process. Researchers included test results from 2006 through 2012 in 905 public schools and adjusted for differences between schools in the final analysis according to race, gender, English as a second language (proxy for ethnicity and language facility), parent income, student-teacher ratio, and school attendance. Surrounding greenness of each school was measured using satellite images converted into the Normalized Difference Vegetation Index (NDVI) in March, July and October of each year according to a 250-meter, 500-meter, 1,000-meter, and 2000-meter circular buffer around each school. Spatial Generalized Linear Mixed Models (GLMMs) estimated the impacts of surrounding greenness on school-based performance. Overall the study results supported a relationship between the “greenness” of the school area and the school-wide academic performance. Interestingly, the results showed a consistently positive significant association between the greenness of the school in the Spring (when most Massachusetts students take the MCAS tests) and school-wide performance on both English and Math tests, even after adjustment for socio-economic factors and urban residency

Clinical meaning of age-related expression of fecal cytokeratin 19 in colorectal malignancy

<p>Abstract</p> <p>Background</p> <p>Colorectal cancer (CRC) is one of the leading causes of malignant death worldwide. Because young age of onset is often considered a poor prognostic factor for CRC, it is important to identify the poor outcomes of CRC in a younger population and to consider an aggressive approach by implementing early treatment. Our aim was to specifically quantify the fecal cytokeratin 19 (CK19) transcript from CRC patients and investigate its correlation with clinical stage, tumor malignancy, and age.</p> <p>Methods</p> <p>The quantitation of fecal CK19 transcript was determined by a quantitative real-time reverse transcription polymerase chain in 129 CRC patients (45 younger than 60 years at diagnosis) and 85 healthy controls. The levels of CK19 protein were examined both in colonic cell lines and tissues.</p> <p>Results</p> <p>The analysis of 45 younger CRC patients (age ≤ 60 years) revealed that patients at the M1 stage had significantly higher expression levels of fecal CK19 mRNA when compared with healthy controls (<it>p </it>< 0.001) and patients at the M0 stage (<it>p </it>= 0.004). Additionally, the degree of consistency between the mean level of fecal CK19 mRNA and the distant metastatic rate in each age interval was up to 89% (<it>p </it>= 0.042).</p> <p>Conclusion</p> <p>These results indicate that high levels of fecal CK19 mRNA represent a potential marker for colorectal malignancy and for aggressive treatment of younger CRC patients.</p