A review on performance of waste materials in self compacting concrete (scc)

Self-compacting concrete (SCC) was first developed in late 80’s in Japan. SCC is well known for its self-consolidation and able to occupy spaces in the formwork without any vibration and become new interesting topic in Construction and Building Materials Research. There were various SCC researches that have been carried out in Turkey, Malaysia, Thailand, Iran, United Kingdom, Algeria, and India.The aim of this review is to summaries the alternative material used in the mix design from 2009 to 2015 through available literature. It hascommon materials such as Limestone Powder (LP), Fly Ash (FA), Silica Fume and Granulated Blast Furnace Slag (GBFS). While there are many alternative or recycled material can be used in producing SCC. This review only focus on waste material fromMarble Powder (MP), Dolomite Powder (DP), Crump Rubber (CR), Recycled Aggregate (RA) and Rise Husk Ash (RHA).Each type of materialshassimilarity effect in fresh and hardened state of SCC. Therefore, this paper will provide significant and useful information to those new to SCC and fellow researchers for future studies on SCC

Training Process Reduction Based On Potential Weights Linear Analysis To Accelarate Back Propagation Network

Learning is the important property of Back Propagation Network (BPN) and finding the suitable weights and thresholds during training in order to improve training time as well as achieve high accuracy. Currently, data pre-processing such as dimension reduction input values and pre-training are the contributing factors in developing efficient techniques for reducing training time with high accuracy and initialization of the weights is the important issue which is random and creates paradox, and leads to low accuracy with high training time. One good data preprocessing technique for accelerating BPN classification is dimension reduction technique but it has problem of missing data. In this paper, we study current pre-training techniques and new preprocessing technique called Potential Weight Linear Analysis (PWLA) which combines normalization, dimension reduction input values and pre-training. In PWLA, the first data preprocessing is performed for generating normalized input values and then applying them by pre-training technique in order to obtain the potential weights. After these phases, dimension of input values matrix will be reduced by using real potential weights. For experiment results XOR problem and three datasets, which are SPECT Heart, SPECTF Heart and Liver disorders (BUPA) will be evaluated. Our results, however, will show that the new technique of PWLA will change BPN to new Supervised Multi Layer Feed Forward Neural Network (SMFFNN) model with high accuracy in one epoch without training cycle. Also PWLA will be able to have power of non linear supervised and unsupervised dimension reduction property for applying by other supervised multi layer feed forward neural network model in future work.Comment: 11 pages IEEE format, International Journal of Computer Science and Information Security, IJCSIS 2009, ISSN 1947 5500, Impact factor 0.42

A review on performance of waste materials in self compacting concrete (scc)

Self-compacting concrete (SCC) was first developed in late 80’s in Japan. SCC is well known for its self-consolidation and able to occupy spaces in the formwork without any vibration and become new interesting topic in Construction and Building Materials Research. There were various SCC researches that have been carried out in Turkey, Malaysia, Thailand, Iran, United Kingdom, Algeria, and India.The aim of this review is to summaries the alternative material used in the mix design from 2009 to 2015 through available literature. It hascommon materials such as Limestone Powder (LP), Fly Ash (FA), Silica Fume and Granulated Blast Furnace Slag (GBFS). While there are many alternative or recycled material can be used in producing SCC. This review only focus on waste material fromMarble Powder (MP), Dolomite Powder (DP), Crump Rubber (CR), Recycled Aggregate (RA) and Rise Husk Ash (RHA).Each type of materialshassimilarity effect in fresh and hardened state of SCC. Therefore, this paper will provide significant and useful information to those new to SCC and fellow researchers for future studies on SCC

Frequent Lexicographic Algorithm for Mining Association Rules

The recent progress in computer storage technology have enable many organisations to collect and store a huge amount of data which is lead to growing demand for new techniques that can intelligently transform massive data into useful information and knowledge. The concept of data mining has brought the attention of business community in finding techniques that can extract nontrivial, implicit, previously unknown and potentially useful information from databases. Association rule mining is one of the data mining techniques which discovers strong association or correlation relationships among data. The primary concept of association rule algorithms consist of two phase procedure. In the first phase, all frequent patterns are found and the second phase uses these frequent patterns in order to generate all strong rules. The common precision measures used to complete these phases are support and confidence. Having been investigated intensively during the past few years, it has been shown that the first phase involves a major computational task. Although the second phase seems to be more straightforward, it can be costly because the size of the generated rules are normally large and in contrast only a small fraction of these rules are typically useful and important. As response to these challenges, this study is devoted towards finding faster methods for searching frequent patterns and discovery of association rules in concise form. An algorithm called Flex (Frequent lexicographic patterns) has been proposed in obtaining a good performance of searching li-equent patterns. The algorithm involved the construction of the nodes of a lexicographic tree that represent frequent patterns. Depth first strategy and vertical counting strategy are used in mining frequent patterns and computing the support of the patterns respectively. The mined frequent patterns are then used in generating association rules. Three models were applied in this task which consist of traditional model, constraint model and representative model which produce three kinds of rules respectively; all association rules, association rules with 1-consequence and representative rules. As an additional utility in the representative model, this study proposed a set-theoretical intersection to assist users in finding duplicated rules. Four datasets from UCI machine learning repositories and domain theories except the pumsb dataset were experimented. The Flex algorithm and the other two existing algorithms Apriori and DIC under the same specification are tested toward these datasets and their extraction times for mining frequent patterns were recorded and compared. The experimental results showed that the proposed algorithm outperformed both existing algorithms especially for the case of long patterns. It also gave promising results in the case of short patterns. Two of the datasets were then chosen for further experiment on the scalability of the algorithms by increasing their size of transactions up to six times. The scale-up experiment showed that the proposed algorithm is more scalable than the other existing algorithms. The implementation of an adopted theory of representative model proved that this model is more concise than the other two models. It is shown by number of rules generated from the chosen models. Besides a small set of rules obtained, the representative model also having the lossless information and soundness properties meaning that it covers all interesting association rules and forbid derivation of weak rules. It is theoretically proven that the proposed set-theoretical intersection is able to assist users in knowing the duplication rules exist in representative model

Pengklonan dan Pengekspresan Gen Xilanase Termostabil daripada Bacllus Coagulans St-6 ke dalam Escherichia Coli HB101

Bacillus coagulans ST-6 merupakan bakteria termofilik yang berupaya mengekspreskan aktiviti xilanase termostabil. Gen xilanase tersebut telah beIjaya diklonkan dengan menyelitkan serpihan DNA daripada genom B. coagu/ans ST-6 yang telah dipotong oleh enzim Bam HI ke dalam tapak Bam HI yang terdapat di dalam vektor pBR322. Seterusnya plasmid rekombinan tersebut ditransformasikan ke dalam Eshcerichia coli HB101 dan koloni yang mengandungi gen xilanase dipilih dengan menggunakan agar media bercampur substrat RBB-xilan. Daripada 3,270 transforman yang terpilih, hanya dua koloni didapati mengekspreskan aktiviti xilanase iaitu dengan kewujudan zon cerah di sekelilingnya. Penyaringan seterusnya mendapati hanya satu koloni sahaja yang stabil untuk kajian selanjutnya. Plasmid rekombinan tersebut dinamakan sebagai pBNX. Pemotongan plasmid pBNX dengan enzim Bam HI mendapati DNA selitan tersebut bersaiz 2.56 kb. Kajian selanjutnya menunjukkan ia mengandungi tapak pemotongan bagi enzim Hind 111, Sal 1 dan &0 RV tetapi tiada tapak bagi enzim Eco R1, Pst 1, Kpn 1 dan Sac 1. Keputusan daripada penghibridan dengan prob DNA selitan melalui kaedah pemblotan Southern telah mengesahkan DNA selitan bagi plasmid rekombinan (pBNX) tersebut berasal daripada B. coagu/ans ST-6. Enzim xilanase kasar yang dihasilkan oleh klon tersebut menunjukkan ciri-ciri yang sarna dengan enzim xilanase kasar daripada B. coagu/ans ST-6, di mana suhu dan pH optimumnya adalah SOoC dan 7.2. Enzim xilanase daripada E. coli (PBNX) dan B. coagulans ST-6 juga stabil pada suhu 60˚C. Pengsubklonan DNA selitan dalam plasmid pBNX ke dalam pUC19 dan pUC18 juga telah dilakukan dan kedua-duanya mengekspreskan aktiviti xilanase. Plasmid kedua-duanya dikenali sebagai pBNXl dan pBNX2. Tindakbalas dengan enzim pembatas Sal 1 menunjukkan cara kemasukan DNA selitan dalam kedua-dua subklon mempunyai orientasi yang sama

Relationship Between Mothers' Parenting Styles, Muslim Adolescents' Personality, Self-Esteem and Academic Achievement

Previous research studies of parenting styles and self-esteem have been explored extensively in the context of the Western perspective. In Islamic perspective, mothers are considered as the first educators for their children. Thus, there is a need to investigate the impact of mothers’ parenting styles and Muslim adolescents’ personality in the Muslim orientation. Previous research studies of student academic achievement has been linked to self-esteem but nevertheless did not relate to parenting styles and personality among adolescent Muslims. Thus, this present study was designed to fill in the literature gaps that exist in this area. This study employed ex-post facto design. Generally, the purpose of this study was to propose a model of mothers’ parenting styles and adolescents’ factors. Specifically the model was estimated to measure the relationships between mothers’ parenting styles (authoritarian, authoritative and permissive), Muslim adolescents’ personality, self-esteem and academic achievement. The instruments adaptation in this study was designed quantitatively and distributed to a sample size of 360 students’ ages 15 years old (Form Three) from Islamic religious schools under the Selangor State Islamic Religious Department. The selections of schools were based on multistage cluster sampling. Using Structural Equation Modeling analysis, the study has to re-specify the hypothesized model due to any insignificance of relationships between authoritarian and permissive mothers’ parenting styles towards Muslim adolescents’ personality. The findings have revealed that authoritative mothers’ parenting style influenced the Muslim adolescents’ personality significantly. In addition, Muslim adolescents’ personality gave an impact to self-esteem. As a result, self-esteem will promote academic achievement among the Muslim adolescent students in the selected Islamic religious schools. The results have given implications to parents, counselors, teachers and policy makers. In addition, these findings will hopefully help to contribute to an extension of the literature reviews and methodology

A study on accuracy of predefined screening criteria for selective ordering of chest x-ray in routine medical examination among students enrolling into higher learning institution attending Hospital Universiti Sains Malaysia

INTRODUCTION: The practice of doing chest x-ray in routine medical examination (RME) is still prevalent in Malaysia although many studies argue the benefit of routine chest x-ray in asymptomatic individuals. There is no standardized RME form used by various institutions in Malaysia. There are also no clear guidelines on who should have a chest x-ray and who should not. Therefore, there is a need to develop a set of screening criteria for selective ordering of chest x-ray in RME to reduce health care cost and to avoid unnecessary radiation risk. OBJECTIVES: The objectives of the study are: 1. To develop an accurate set of screening criteria from literature review. 2. To determine the sensitivity, specificity, positive predictive value and negative predictive value of the predefined screening criteria. The set of screening criteria is intended to be used as a screening tool for selective ordering of chest x-ray in RME 3. To determine the prevalence of abnormal chest x-ray in routine medical examination. 4. To determine the sensitivity, specificity, positive predictive value and negative predictive value of chest x-ray interpretation made by medical officers. The agreement between medical officers and radiologist is also determined. METHODOLOGY: A total of 408 students who came to Hospital Universiti Sains Malaysia between 1st June 2004 and 31st December 2004 participated in the study. They were screened by the predefined screening criteria developed by the researcher. The decision on chest x-ray requirement was determined based on the screening criteria. All the chest x-rays were reported both by medical officers and an appointed radiologist. RESULTS: The results from this study showed that the predefined screening criteria developed by the researcher has a sensitivity, specificity, positive predictive value and negative predictive value of 26.1 %, 66.8%, 4.5% and 93.8% respectively. The prevalence of abnormal chest x-ray is 5.64% (95% C.l: 0.03-0.08). The sensitivity, specificity, positive predictive value and negative predictive value of chest x-ray interpretation by medical officers are 17.4%, 98.2%, 36.4% and 95.2% respectively The agreement on chest x-ray interpretation between medical officer and radiologist was poor (kappa=0.206). CONCLUSIONS: From this study, it can be concluded that the prevalence of abnormal chest x-ray in RME is low. The set of screening criteria developed by the researcher is not accurate to be used as a screening tool for detecting abnormal chest x-ray in RME. However, the high negative predictive value means that the probability if a student is not indicated for chest x-ray to have a normal result is high. There is considerable discrepancy between medical officers' chest x-ray interpretation and that of trained radiologist. Chest xray findings did not influence the decision of fitness for enrolment. Further research needs to be done to improve the accuracy of the screening criteria

Protein expression of Late Elongated Hypocotyl (LHY) homolog genes of teak in Escherichia coli

Expression of an isolated gene in a system that directly translates it into a protein is an important step to study the protein encoded by the gene. The isolated gene can be expressed in vivo by a heterologous system. In this study, a bacteria system was used to translate the Tectona grandis Late Elongated Hypocotyl (Tg-LHY) gene, which was isolated from flowering tissues of teak (Tectona grandis). The gene was cloned into the pET 14b vector (Novagen) and transformed into BL 21(DE3)/pLysS and Rosetta 2 expression host cells (Novagen). Rosetta 2 host cell has been found to be a good candidate to express the Tg-LHY protein from plant origin, as it recognizes the codon that was found in plant but rarely used in bacteria. The expressed protein was about an expected size, which was 90 kD. Western blot analysis using antibody against His-tag, which was fused to the Tg-LHY protein, proved that the expressed protein was Tg-LHY protein

Position score weighting technique for mining web content outliers.

The existing mining web content outlier methods used stemming algorithm to preprocess the web documents and leave the domain dictionary in their root words. The stemming algorithm was usually used to reduce derived words to their stem, base or root form. The stemming algorithm sometimes does not leave a real word after removing the stem and it caused a problem to match words in the full word profile with the domain dictionary. Therefore this study uses stemmed domain dictionary and applies it with Term Frequency with Position Score (TF.PS) weighting technique which is derived from TF.IDF weighting technique from Information Retrieval (IR) in dissimilarity measure phase to see the efficiency of these technique for determining the outliers in the web content. The dataset is from The 20 Newsgroups Dataset. The result for stemmed domain dictionary with TF.PS weighting technique achieves up to 98.19% of accuracy and 90% of F1-Measure which is higher than previous techniques

Analysis of expressed sequence tags derived from inflorescence shoot of ,i>Tectona grandis (teak)

Teak Inflorescence Shoot Stage 4 (TIS4) shoots bearing the floral meristems were used to construct a cDNA librariy with insert size range of 1500 - 5000 bp. The titer of the library was 7.5 x 105 pfu/ml(primary) and 4.5 x 109 pfu/ml (amplified). EST generation and analysis were performed using the cDNA library where a total of 1384 plaques were randomly picked and their inserts PCR-amplified using T3and T7 universal primers. Only 1125 plaques generated single amplified fragments, each which were purified and sequenced using the SK universal primer. The generated raw 5’ ESTs were filtered and clustered. A total of 674 nonredundants (69 consensus sequences and 605 singletons) were generated and their identities searched through BLASTX. Of the 674 nonredundants, 107 of them (15.9%) showed no hits or no identity. All the 567 nonredundants identified through BLASTX were categorized into theirfunctional categories and were further analysed using InterProScan to detect their protein signatures and to assign their GO numbers. From all the sequences analysed, only 186 (32.8%) sequences were given the GO numbers and grouped into the three GO main categories namely biological process, cellular component and molecular function. Several important ESTs were highlighted based on their functional categories. There were five sequences found to be related to flowering and light induction