Propagation of adhesives in joints during capillary adhesive bonding of microcomponents

 Capillary adhesive bonding is used successfully to integrate microsystems. To ensure high reliability and quality of the interconnection technique, it is imperative that the propagation of adhesives in the joints be controlled. Two adhesives frequently used in capillary adhesive bonding were examined: a one-component, UV-curing methacrylate adhesive (Dymax 191-M), and a two-component epoxy resin bonding adhesive curing at room temperature (Epo-tek 302-3M). The propagation of these adhesives in joints with different gap heights of 2–20 μm between two PMMA surfaces and between one PMMA and one PI surface was measured and compared with the theoretical adhesive propagation in accordance with the Hagen–Poiseuille equation for a gap flow, with the capillary pressure taken into account. Once the dynamic viscosity, the wetting angle and the surface tension of the adhesive have been determined as a function of the measuring time, the measured propagation of the Epo-tek 302-3M and Dymax 191-M adhesives can be described in good agreement with the theory for all the gap heights under study. Received: 30 March 1999 / Accepted: 12 April 1999     

Predicting adequacy of vancomycin regimens: A learning-based classification approach to improving clinical decision making

Clinicians' drug regimen decision making is critical, particularly when involving high-alert medications. In this study, we use decision-tree induction C4.5 and a backpropagation neural network to construct decision support systems for predicting the regimen adequacy of vancomycin, a glycopeptide antimicrobial antibiotic effective for Gram-positive bacterial infections. We comparatively evaluate the respective systems using a total of 987 clinical vancomycin cases collected from a major tertiary medical center in southern Taiwan. We supplement each system using Bagging and then examine the predictive power of the extended system. Overall, our evaluation results show the overall accuracy of the decision support system based on C4.5 or the neural network to be significantly higher than that of the benchmark one-compartment pharmacokinetic model. Use of Bagging can considerably improve the effectiveness of each system across different performance measures, particularly for cases of decision classes in which the base systems (i.e., without Bagging) perform modestly. Taken together, our evaluation results seem to favor the use of Bagging to enhance the performance of decision support systems constructed using decision-tree induction C4.5.     

基于电容传感器的围护结构含水率层析方法

针对围护结构含水率测试深度的要求,研究了同面电容传感器探头的结构尺寸优化设计方法,通过仿真和实验研制了不同探测深度灵敏度的测试探头,将围护结构沿深度方向离敞化为若干层,利用实验数据拟合得到各层对于不同探头的输出与含水率的函数关系.通过传感器多结构组合测试,从耦台项中分离出各层的含水率.实验结果表明:该传感器组合测试方法可以达到含水率层析的目的,测试精度与使用的传感器数量有关.     

Effect of microstructure on blood cell clogging in blood separators based on capillary action

In this study, the blood cell clogging phenomenon occurring in blood separators based on capillary action is carefully investigated and how to minimize the reduction in plasma separation speed caused by clogging is discussed. Four different blood separators are fabricated on optically transparent glass substrates to clearly observe the blood plasma separation and the blood cell clogging in the microfluidic devices. Each separation experiment is captured by a high-speed video camera. The captured images are analyzed using a theoretical model proposed in this study to quantify the effect of the microstructure on the degree of blood cell clogging. Finally, design guidelines for the microstructure of the micro blood separator are discussed based on the analysis. D. Kim and J. Y. Yun contributed equally to this work.     

Predicting the RVE size of micro-particle composite for electrical purpose using particles dispersion developed

Representative volume element (RVE) is a technique to determine and predict the mechanical and physical effective properties of any random homogeneous or heterogeneous composite material models. An optimal size of RVE model became a major objective in order to be statistically equivalent to the real composite material. In this paper, possible approaches to solving the RVE size problem is by developing a particles dispersion model in implementing microstructure of silver (Ag) micro-particle composite for electrical purpose. In this technique, a micro scale of scanning electronic microscope condition was observed for verification on Ag morphology in an epoxy matrix. The morphological analysis is used to guide the particles dispersion model that can be considered to repeat the whole macro-scale of the composite. In the RVE size study, the various composite model sizes were analyzed on the electrical conductivity for convergence study. The model size was determined by a ratio between width of RVE model and the particle size which given in δ = W/D. The numbers of RVE size (δ) were set in a size of 3, 4, 5, 6, 7, 8 and 9. The efficiency of size that represents the precision of the estimation of the electrical purpose has been successfully determined by a particle dispersion model developed at model size of δ = 7. The predicted conductivity at model size of δ = 7 shows a good agreement with experimental data with the results presented at 5.06 × 10^?1 and 2.69 × 10^?1 S/cm for modeling and experiments, respectively.     

Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data

Predicting student failure at school has become a difficult challenge due to both the high number of factors that can affect the low performance of students and the imbalanced nature of these types of datasets. In this paper, a genetic programming algorithm and different data mining approaches are proposed for solving these problems using real data about 670 high school students from Zacatecas, Mexico. Firstly, we select the best attributes in order to resolve the problem of high dimensionality. Then, rebalancing of data and cost sensitive classification have been applied in order to resolve the problem of classifying imbalanced data. We also propose to use a genetic programming model versus different white box techniques in order to obtain both more comprehensible and accuracy classification rules. The outcomes of each approach are shown and compared in order to select the best to improve classification accuracy, specifically with regard to which students might fail.     

Linguistic fuzzy model identification based on PSO with different length of particles

To generate the structure and parameters of fuzzy rule base automatically, a particle swarm optimization algorithm with different length of particles (DLPPSO) is proposed in the paper. The main finding of the proposed approach is that the structure and parameters of a fuzzy rule base can be generated automatically by the proposed PSO. In this method, the best fitness (f_gbest) and the number (N_gbest) of active rules of the best particle in current generation, the best fitness (f_pbesti) which ith particle has achieved so far and the number (N_pbesti) of active rules of it when the best position emerged are utilized to determine the active rules of ith particle in each generation. To increase the diversity of structure, mutation operator is used to change the number of active rules for particles. Compared with some other PSOs with different length of particles, the algorithm has good adaptive performance. To indicate the effectiveness of the give algorithm, a nonlinear function and two time series are used in the simulation experiments. Simulation results demonstrate that the proposed method can approximate the nonlinear function and forecast the time series efficiently.     

不同移动模型下无线多播性能预测模型

为研究稳健回归预测模型在多播路由协议下的预测效果,分别在随机路点移动模型、高斯马尔科夫移动模型和参考点组移动模型下对多播路由协议总开销进行仿真和预测。仿真结果表明,不同移动模型下的总开销预测模型预测值曲线与观测值曲线基本吻合,预测模型所做预测的值与观测值的平均相对误差在3种模型中分别为8.56％、9.27％、4.03％,具有较准确的预测能力。     

Predicting flow strength of austenitic steels with an IPANN model using different training strategies

Model construction and training strategies of an IPANN were developed to improve the prediction accuracy of the hot strength of a series of austenitic steels with different carbon content deformed under a wide range of conditions. The prediction accuracy is largely dependent on the training schemes and model structure because the flow strength varies with deformation conditions and chemical compositions in a very complex way. The scheme for selecting training data of every independent input was optimised, so that a generalised model could be achieved with less training data. With the strategies introduced in this work, the effect of the carbon content and deformation was accurately presented in both the work hardening and dynamic recrystallisation regimes.     

Predicting different conceptualizations of system use: Acceptance in hedonic volitional context (Facebook)

This research examines the relationship between the predictors of use and the different conceptualizations of system use in a hedonic volitional setting (Facebook). Using the unified theory of acceptance and use of technology (UTAUT) model, an investigation into the three aspects of system use: the user, system and task were carried out. Results from a cross-sectional survey of 449 students show that behavioral intention has a significant influence on all aspects and dimensions of system use including cognitive absorption and deep structure use. Performance expectancy, effort expectancy and social influence are significantly related to system use. From the component model, performance expectancy is only significant with deep structure use. Hedonic performance expectancy is found to be significantly related to cognitive absorption. Results also demonstrate that predictors of usage have a significant relationship with the user aspect of system use. The variance explained in usage conceptualized as the user/task aspects is much higher than that of the system/task aspects or one-dimensional measures. Overall, conceptualizing system use using the user/task aspects offers greater explanatory power in Facebook use.     

Performance of AERMOD at different time scales

As high-density monitoring networks observing pollutant concentrations are costly to establish and maintain, researchers often employ various models to estimate concentrations of air pollutants. The AMS/EPA Regulatory Model (AERMOD) is a fairly recent and promising model for estimating concentrations of air pollutants, but the effectiveness of this model at different time scales remains to be verified. This paper evaluates the performance of AERMOD in estimating sulfur dioxide (SO₂) concentrations in Dallas and Ellis counties in Texas. Results suggest that SO₂ concentrations simulated by AERMOD at the 8 h, daily, monthly, and annual intervals match their respective observed concentrations much better compared with the simulated 1 and 3 h SO₂ concentrations. In addition, AERMOD performs better in simulating SO₂ concentrations when combined point and mobile emission sources are used as model inputs rather than using point or mobile emission sources alone. Results also suggest that, at the monthly scale, AERMOD performs much better in simulating the high end of the spectrum of SO₂ concentrations in the study area compared to results at the 1, 3, 8 h, and daily scales. These results not only help us better understand the performance of AERMOD but also provide useful information to researchers who are interested in applying AERMOD in various applications, such as the utilization of AERMOD in chronic exposure assessment in epidemiological studies where long-term (i.e., monthly and/or annual) air pollution concentration estimations are often used.     

Broadening of neutral analyte band in electroosmotic flow through slit channel with different zeta potentials of the walls

The study is concerned with addressing hydrodynamic dispersion of an electroneutral non-adsorbed solute being transported by electroosmotic flow through a slit channel formed by walls with different zeta potentials. The analysis is conducted in terms of the plate height which, using the Van Deemter equation, can be expressed through the cross-sectional mean flow velocity, the solute molecular diffusion coefficient and a length scale parameter having meaning of the minimum achievable plate height and depending on the velocity distribution within the channel cross-section. The minimum plate height is determined by substituting distribution of electroosmotic velocity into the preliminary derived integral expression that is valid for any given velocity distribution within a slit channel cross-section. The electroosmotic velocity distribution within the slit channel cross-section is obtained by solving one-dimensional version of the Stokes equation accounting for electric force exerted on the local equilibrium electric space charge. The major obtained result is an analytical expression which represents the minimum plate height normalized by half of channel width as a function of two dimensionless parameters, namely, half of channel width normalized by the Debye length, and the ratio of the wall zeta potentials. The obtained result reveals a substantial increase in the minimum plate height compared with the case of equal wall zeta potentials. Different limiting cases of the obtained relationships are analyzed and possible applications are discussed.     

Label-free continuous lateral magneto-dielectrophoretic microseparators for highly efficient enrichment of circulating nucleated cells from peripheral blood

We have developed a continuous lateral magneto-dielectrophoretic (MAP-DEP) microseparator for the highly efficient enrichment of circulating nucleated cells from peripheral blood, based on native magnetic and dielectric properties of blood cells. Lateral magnetophoretic (MAP) force is achieved using a high-gradient magnetic field, caused by a ferromagnetic wire array inlaid on the bottom glass substrate. Lateral dielectrophoretic (DEP) force is achieved using a planar interdigitated electrode array, patterned on the top glass substrate. Red blood cells in peripheral blood are primarily driven by the lateral MAP force, while white blood cells are primarily forced by the lateral DEP force, the direction of which is opposite to that of the lateral MAP force. These lateral MAP and DEP forces are produced evenly on the whole area of the microchannel, thereby achieving highly efficient enrichment. The experimental results showed that the lateral MAP-DEP microseparator can continuously enrich circulating nucleated cells by 20,000-fold from peripheral blood simply by using an external magnetic flux of 0.3 T and a 2-MHz sinusoidal voltage of 4 Vp-p. Additionally, by using the intrinsic magnetic and dielectric properties of blood cells, we eliminated the need for laborious sample preparation procedures before and after enrichment, and also reduced the cost.     

Predicting stock index increments by neural networks: The role of trading volume under different horizons

Recent studies show that there is a significant bidirectional nonlinear causality between stock return and trading volume. In this research, we reinforce this statement and the results presented in some earlier literatures and further investigate whether trading volume can significantly improve the prediction performance of neural networks under short-, medium-and long-term forecasting horizons. An application of component-based neural networks is used in forecasting one-step ahead stock index increments. The models are also augmented by the addition of different combinations of indices’ and component stocks’ trading volumes as inputs to form more general ex-ante forecasting models. Neural networks are trained with the data of stock returns and volumes from NASDAQ, DJIA and STI indices. Results indicate that augmented neural network models with trading volumes lead to improvements, at different extents, in forecasting performance under different terms of forecasting horizon. Empirical results indicate that trading volumes lead to modest improvements on the performance of stock index increments prediction under medium-and long-term horizons.     

Investigation of different neural models for blood cell type identification

The analysis of blood cells in microscope images can provide useful information concerning the health of patients; however, manual classification of blood cells is time-consuming and susceptible to error due to the different morphological features of the cells. Therefore, a fast and automated method for identifying the different blood cells is required. In this paper, we investigate the use of different neural network models for the purpose of cell identification. The neural models are based on the back propagation learning algorithm and differ in design according to the way data features are extracted from the cell microscopic images. Three different topologies of neural networks are investigated, and a comparison between these models is drawn. Experimental results suggest that the proposed method performs well in identifying blood cell types regardless of their irregular shapes, sizes, and orientation.     

Correct execution of transactions at different isolation levels

Many transaction processing applications execute at isolation levels lower than SERIALIZABLE in order to increase throughput and reduce response time. However, the resulting schedules might not be serializable and, hence, not necessarily correct. The semantics of a particular application determines whether that application will run correctly at a lower level and, in practice, it appears that many applications do. The decision to choose an isolation level at which to run an application and the analysis of the correctness of the resulting execution is usually done informally. We develop a formal technique to analyze and reason about the correctness of the execution of an application at isolation levels other than SERIALIZABLE. We use a new notion of correctness, semantic correctness, a criterion weaker than serializability, to investigate correctness. In particular, for each isolation level, we prove a condition under which the execution of transactions at that level will be semantically correct. In addition to the ANSI/ISO isolation levels of READ UNCOMMITTED, READ COMMITTED, and REPEATABLE READ, we also prove a condition for correct execution at the READ-COMMITTED with first-committer-wins and at SNAPSHOT isolation. We assume that different transactions in the same application can be executing at different levels, but that each transaction is executing at least at READ UNCOMMITTED.     

Age-related identification of emotions at different image sizes

OBJECTIVES: The goal of this study was to determine whether older adults are placed at a unique disadvantage in identifying emotions at small image sizes. BACKGROUND: Technologies relevant to older adults often display small images, though no studies have examined whether these systems are usable for this population. Given the importance of successful emotional processing, we draw on research showing age-related difficulties in processing negative emotions to examine older adults' performance on images with reduced sizes. METHODS: Sixty participants, 20 in each age group, were shown the names of emotions, followed by a facial expression, and were asked to indicate as rapidly as possible if they matched. RESULTS: For response time we found an interaction of age and window size, showing that older adults, unlike younger adults, are slower with small sizes than with large sizes. For accuracy, we found an interaction of age and emotion, indicating that older adults are less accurate in the perception of fearful, sad, and surprised stimuli. CONCLUSIONS: Only older adults are disadvantaged by smaller images. Also, interactions of age and emotion reflect older adults' difficulty processing negative emotions. APPLICATIONS: Our results have implications for the design of videoconferencing technology and mobile systems and extend previous research on aging and emotion.