Investigation of Water Hammer Effect Through Pipeline System

This paper we study the condition where the water hammer effect is occurs in pipe line. Water hammer can cause the pipe to break if the pressure is high enough. The experiment will be set-up to investigate the water hammer effect in order to avoid the water hammer effect happen. The prevention of water hammer effect will be propose and prove the prevention method is successfully reduce the water hammer effect. The prevention method using is installing the bypass pipe with non-return valve. The experiment is done by capture the vibration signal by using data acquisition device and accelerometer. The pressure signal is capture after a sudden shutoff for the valve. The signal is than analyze and convert to wave speed. The project is differentiating and compares the water hammer phenomenon with different pipe material, pipe length, inlet diameter of pipe, and pressure in pipeline. From the experiment, result shown that the lower strength material pipe, smaller inlet diameter pipe, and longer pipe will deal with lager water hammer effect. Besides, the prevention method by installing by pass pipe with non-return valve of water hammer effect is proved successfully reduce the water hammer phenomenon by 33.33% of pressure

Force Characterization of a Rotary Motion Electrostatic Actuator based on Finite Element Method (FEM) Analysis

Two types of rotary motion electrostatic actuators were designed and analyzed using Finite Element Method (FEM) analysis. This paper discussed the comparisons and detailed thrust force analysis of the two actuators. Both designs have similar specifications; i.e the number of rotor’s teeth to stator’s teeth ratio, radius and thickness of rotor, and gap between stator and rotor. Two structures were designed & evaluated; (a) Side-Driven Electrostatic Actuator and (b) Bottom-Driven Electrostatic Actuator. The paper focuses on comparing & analyzing the generated electrostatic thrust force for both designs when the electrostatic actuator’s parameters are varied. Ansys Maxwell 3D software is used to design and analyze the generated thrust force of the two rotary motion electrostatic actuators. The FEM analyses have been carried out by (i) varying the actuator size; (ii), varying the actuator thickness and (iii) varying the actuator teeth ratio. The FEM analysis shows that the Bottom-Drive Electrostatic Actuator exhibit greater thrust force, 4931.80N compared to the Side-Drive Electrostatic Actuator, 240.96N; when the actuator’s radius is 700m, thickness is 50m, gap between the stator and rotor is 2m and the teeth ratio is 16:12

Development of Low Wind Speed Anemometer

Anemometer is a measuring device used to measure the wind speed of an area. Before design or installing a wind turbine, it is important to determine the average wind speed of that particular area throughout the year. But it is illogically to purchase anemometer to measure the wind velocity for a year period. The purpose of this project is to design and fabricate a small scale of anemometer which will able to give the wind velocity with an acceptable range of uncertainty. The fabrication of the anemometer is developed using design methodology and simulation to obtain the optimized design. The designed anemometer has the mean absolute percentage error (MAPE) of 3.23 % when compared with Dwyer series 471 thermo-anemometer

Parkinson’s Disease-related Circulating microRNA Biomarkers - a Validation Study

Parkinson’s disease (PD) is the second most common neurodegenerative disease. One of the major challenges in studying this progressive neurological disorder is to identify and develop biomarkers for early detection. Recently, several blood-based microRNA (miRNA) biomarkers for PD have been reported. However, follow-up studies with new, independent cohorts have been rare. Previously, we identified a panel of four circulating miRNA biomarkers for PD (miR-1826, miR-450b-3p, miR-505, and miR-626) with biomarker performance of 91% sensitivity and 100% specificity. However, the expression of miR-450b-3p could not be detected in a new, independent validation set. In our current study, we improved the detection power by including a non-biased pre-amplification step in quantitative real-time PCR (qRT-PCR) and reevaluated the biomarker performance. We found the panel of four PD-related miRNAs achieved the predictive power of 83% sensitivity and 75% specificity in our validation set. This is the first biomarker validation study of PD which showed reproducibility and robustness of plasma-based circulating miRNAs as molecular biomarkers and qRT-PCR as potential diagnostic assay

Parkinson\u27s Disease-related Circulating microRNA Biomarkers - a Validation Study

Parkinson’s disease (PD) is the second most common neurodegenerative disease. One of the major challenges in studying this progressive neurological disorder is to identify and develop biomarkers for early detection. Recently, several blood-based microRNA (miRNA) biomarkers for PD have been reported. However, follow-up studies with new, independent cohorts have been rare. Previously, we identified a panel of four circulating miRNA biomarkers for PD (miR-1826, miR-450b-3p, miR-505, and miR-626) with biomarker performance of 91% sensitivity and 100% specificity. However, the expression of miR-450b-3p could not be detected in a new, independent validation set. In our current study, we improved the detection power by including a non-biased pre-amplification step in quantitative real-time PCR (qRT-PCR) and reevaluated the biomarker performance. We found the panel of four PD-related miRNAs achieved the predictive power of 83% sensitivity and 75% specificity in our validation set. This is the first biomarker validation study of PD which showed reproducibility and robustness of plasma-based circulating miRNAs as molecular biomarkers and qRT-PCR as potential diagnostic assay

Confident Predictability: Identifying reliable gene expression patterns for individualized tumor classification using a local minimax kernel algorithm

<p>Abstract</p> <p>Background</p> <p>Molecular classification of tumors can be achieved by global gene expression profiling. Most machine learning classification algorithms furnish global error rates for the entire population. A few algorithms provide an estimate of probability of malignancy for each queried patient but the degree of accuracy of these estimates is unknown. On the other hand <it>local minimax learning </it>provides such probability estimates with best finite sample bounds on expected mean squared error on an individual basis for each queried patient. This allows a significant percentage of the patients to be identified as <it>confidently predictable</it>, a condition that ensures that the machine learning algorithm possesses an error rate below the tolerable level when applied to the confidently predictable patients.</p> <p>Results</p> <p>We devise a new learning method that implements: (i) feature selection using the k-TSP algorithm and (ii) classifier construction by local minimax kernel learning. We test our method on three publicly available gene expression datasets and achieve significantly lower error rate for a substantial identifiable subset of patients. Our final classifiers are simple to interpret and they can make prediction on an individual basis with an individualized confidence level.</p> <p>Conclusions</p> <p>Patients that were predicted confidently by the classifiers as cancer can receive immediate and appropriate treatment whilst patients that were predicted confidently as healthy will be spared from unnecessary treatment. We believe that our method can be a useful tool to translate the gene expression signatures into clinical practice for personalized medicine.</p

PAGER 2.0: an update to the pathway, annotated-list and gene-signature electronic repository for Human Network Biology

Integrative Gene-set, Network and Pathway Analysis (GNPA) is a powerful data analysis approach developed to help interpret high-throughput omics data. In PAGER 1.0, we demonstrated that researchers can gain unbiased and reproducible biological insights with the introduction of PAGs (Pathways, Annotated-lists and Gene-signatures) as the basic data representation elements. In PAGER 2.0, we improve the utility of integrative GNPA by significantly expanding the coverage of PAGs and PAG-to-PAG relationships in the database, defining a new metric to quantify PAG data qualities, and developing new software features to simplify online integrative GNPA. Specifically, we included 84 282 PAGs spanning 24 different data sources that cover human diseases, published gene-expression signatures, drug-gene, miRNA-gene interactions, pathways and tissue-specific gene expressions. We introduced a new normalized Cohesion Coefficient (nCoCo) score to assess the biological relevance of genes inside a PAG, and RP-score to rank genes and assign gene-specific weights inside a PAG. The companion web interface contains numerous features to help users query and navigate the database content. The database content can be freely downloaded and is compatible with third-party Gene Set Enrichment Analysis tools. We expect PAGER 2.0 to become a major resource in integrative GNPA. PAGER 2.0 is available at http://discovery.informatics.uab.edu/PAGER/

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science