Mining Time-delayed Gene Regulation Patterns from Gene Expression Data

Discovered gene regulation networks are very helpful to predict unknown gene functions. The activating and deactivating relations between genes and genes are mined from microarray gene expression data. There are evidences showing that multiple time units delay exist in a gene regulation process. Association rule mining technique is very suitable for finding regulation relations among genes. However, current association rule mining techniques cannot handle temporally ordered transactions. We propose a modified association rule mining technique for efficiently discovering time-delayed regulation relationships among genes.By analyzing gene expression data, we can discover gene relations. Thus, we use modified association rule to mine gene regulation patterns. Our proposed method, BC3, is designed to mine time-delayed gene regulation patterns with length 3 from time series gene expression data. However, the front two items are regulators, and the last item is their affecting target. First we use Apriori to find frequent 2-itemset in order to figure backward to BL1. The Apriori mined the frequent 2-itemset in the same time point, so we make the L2 split to length one for having relation in the same time point. Then we combine BL1 with L1 to a new ordered-set BC2 with time-delayed relations. After pruning BC2 with the threshold, BL2 is derived. The results are worked out by BL2 joining itself to BC3, and sifting BL3 from BC3. We use yeast gene expression data to evaluate our method and analyze the results to show our work is efficient

Constructing Employability Indicators for Enhancing the Effectiveness of Engineering Education for the Solar Industry

The aim of this research is to establish a set of employability indicators that capture the competency requirements and performance expectations that solar energy enterprises have of their employees. In the qualitative component of the study, 12 administrators and 32 engineers in the industry were interviewed, and meetings with focus groups were conducted to formulate a questionnaire for a survey of Taiwanese solar energy companies for the confirmation and prioritisation of the employability indicators. On the basis of the results of the quantitative component, an interpretational model relating competence, job performance, working attitude, and employability for solar corporation recruitment and training purposes as well as for school curricular development was developed. The interpretation model formulated effectively interprets the relationship between solar enterprises’ expectations and students’ employability. The research contributes a framework for the selection and cultivation of talent, as well as providing a basis for fundamental development of the solar engineering curriculum

The Impact of Information Asymmetry and Client Credit on Lending Performance - Taiwan’s Evidence

[[abstract]]Using panel data from Taiwan, this paper examines the impact of information asymmetry and client credit on lending performance. Banks without huge losses from bad debts and from credit card lending are categorized as high lending performance banks, whereas banks with heavy losses from bad debts and from credit card lending are categorized as low lending performance banks. Firms are also divided into micro and small businesses (MSBs) and medium and large businesses (MLBs). Logit regression is used to identify the determinants of lending performance, including the levels of information asymmetry and client credit records. The empirical results show that: (1) MLBs with good information transparency tend to establish relationships with banks that are characterized by huge losses from bad debts and from credit card lending. (2) Small foreign firms, as well as MLBs with high profitability, cash and R&D expenditure ratios prefer having relationships with banks with good lending performance and low credit risk. (3) MLBs and MSBs with poor credit records prefer having relationships with banks that have good lending performance and low credit risk.[[booktype]]紙

Traffic Simulation Model for Urban Networks: CTM-URBAN

Congestion on urban transportation networks around the world is frequently encountered and its economic and environmental footprint cannot be ignored. One of the solutions used to alleviate this problem is deployment of Intelligent Transportation Systems (ITS). The effectiveness of ITS solutions to manage traffic demand more efficiently relies heavily on accurate travel time prediction, which is a difficult task to achieve using currently available simulation methods. This study proposes an urban network simulation model named CTM-URBAN, a modified version of the Cell Transmission Method (CTM) which was originally developed to simulate highway traffic. CTM-URBAN is a simple and versatile simulation framework designed to simulate more realistically traffic flows in an urban network with various traffic control devices. CTM-URBAN allows building, calibrating, and maintaining a large simulation network with a minimum of effort. A case study is presented to demonstrate that CTM-URBAN is able to predict travel time through signal-controlled intersections more accurately than the original CTM based on comparison with results from a microscopic simulator

Noninvasive prediction of Blood Lactate through a machine learning-based approach.

We hypothesized that blood lactate concentration([Lac]blood) is a function of cardiopulmonary variables, exercise intensity and some anthropometric elements during aerobic exercise. This investigation aimed to establish a mathematical model to estimate [Lac]blood noninvasively during constant work rate (CWR) exercise of various intensities. 31 healthy participants were recruited and each underwent 4 cardiopulmonary exercise tests: one incremental and three CWR tests (low: 35% of peak work rate for 15 min, moderate: 60% 10 min and high: 90% 4 min). At the end of each CWR test, venous blood was sampled to determine [Lac]blood. 31 trios of CWR tests were employed to construct the mathematical model, which utilized exponential regression combined with Taylor expansion. Good fitting was achieved when the conditions of low and moderate intensity were put in one model; high-intensity in another. Standard deviation of fitting error in the former condition is 0.52; in the latter is 1.82 mmol/liter. Weighting analysis demonstrated that, besides heart rate, respiratory variables are required in the estimation of [Lac]blood in the model of low/moderate intensity. In conclusion, by measuring noninvasive cardio-respiratory parameters, [Lac]blood during CWR exercise can be determined with good accuracy. This should have application in endurance training and future exercise industry

The Multi-shadow Analysis of LED Secondary Optics

AbstractDue to these advantages, small size, low heat radiation, long life time, and high luminous efficiency, the light-emitting diodes (LEDs) have been used widely to the varied lighting in recent years. However, the LEDs have the higher intensity of light in central region and the scattering in the surrounding during lighting, so it is necessary to modify the LED projection by the secondary optical lens. The extra secondary optical lens can enhance the light collection efficiency of LED, but it will readily induce the multi-shadow phenomenon during lighting, which has a significant impact on the human vision. In this study, the LED illumination module with/without secondary optical lens, total internal reflected (TIR) lens or reflection mirror cup, can be simulated by the Apilux(R) optical software. The result indicates that the approach can identify the level of multi-shadow images according to the deviations in light intensity, and will be new performance criteria of LEDs for users

Updated constraints on Georgi-Machacek model, and its electroweak phase transition and associated gravitational waves

With theoretical constraints such as perturbative unitarity and vacuum stability conditions and updated experimental data of Higgs measurements and direct searches for exotic scalars at the LHC, we perform an updated scan of the allowed parameter space of the Georgi-Machacek (GM) model. With the refined global fit, we examine the allowed parameter space for inducing strong first-order electroweak phase transitions (EWPTs) and find only the one-step phase transition is phenomenologically viable. Based upon the result, we study the associated gravitational wave (GW) signals and find most of which can be detected by several proposed experiments. We also make predictions on processes that may serve as promising probes to the GM model in the near future at the LHC, including the di-Higgs productions and several exotic scalar production channels.Comment: 42 pages, 11 figures, 9 table

On Initial Trust Building for eCommerce: Revisiting from the Perspective of Signal Theory and Trust Transference

Trust building for consumers has been a main stream of research in e-commerce. However, little research pays attention to how consumers treat the revealed information about warranty, privacy statement, assurance, and related statements. Although this information is provided in real-world settings, their effectiveness has not been fully understood. This study attempts to look into this issue by employing signal theory and perspective of trust transference. Empirical results gathered from lab experiment show that warranty perception, rather than the assurance itself, is the critical antecedent of initial trust building. Once consumers discredit the revealed information in a web site, the signals will fail to induce consumers’ trust. Information from a trusted third party may be an efficient way to build consumer trust. However, it should be noted that information from trusted third party will not be effective if consumers fail to notice them, or misunderstand their meanings. Hence, e-tailers should devote to build initial trust by applying assurance and quality signals from independent institutions