Surface modification of polyetherurethaneureas and their antithrombogenicity

Polyetherurethaneurea (PU) films were treated by oxygen plasma discharge followed by acrylic acid (AA) grafting. The carboxyl groups of the AA-grafted PU (PU-AA) surface were coupled with bovine serum albumin and heparin via water soluble carbodiimide. Surface characterization of the modified PUs was carried out by attenuated total reflection Fourier transform infrared (ATR-FTIR) spectroscopy and electron spectroscopy for chemical analysis (ESCA). The amount of immobilized albumin and heparin on the PU surface was 1.8 and 1.5 g/cm², respectively, as determined by the dye interaction method. Interactions between the surface-modified PUs and blood components such as plasma proteins and platelets were investigated to evaluate the blood compatibility of the samples. Plasma recalcification time (PRT) and activated partial thromboplastin time (APTT) of the albumin-immobilized PU (PU-Al) were almost the same as those of PU, while platelets were less adhered on the PU-Al than on PU. On the other hand, PRT and APTT of the PU-He were significantly longer than those of the PU, PU-AA, and PU-Al. Moreover, adhesion of platelets was effectively suppressed on the PU-He, leading to good in vitro blood compatibility.     

Microstructure and fracture of SiC-particulate-reinforced cast A356 aluminum alloy composites

A microstructural analysis of local microfracture of cast A356 Al-SiC _p composites fabricated by permanent mold re-casting and squeeze-casting methods was made. Notch fracture toughness tests were conducted on these composites to identify critical fracture parameters using a stress-modified critical-strain criterion. The composite microstructure shows continuous networks of densely populated SiC and eutectic Si particles along the intercellular regions. Squeeze casting produces a more homogeneous structure and larger spacing of brittle particles and increases the tensile ductility and fracture toughness, while strength levels are almost identical to the re-casting case. The calculated values of the microstructurally characteristic distancel* for the re-cast and squeeze-cast composites are about 40 μm, which is comparable to the average sizes of the intercellular network. However, the reference critical strain for squeeze casting is larger than that for re-casting, showing a trend to higher ductility and fracture toughness.     

SSPQL: Stochastic shortest path-based Q-learning

Reinforcement learning (RL) has been widely used as a mechanism for autonomous robots to learn state-action pairs by interacting with their environment. However, most RL methods usually suffer from slow convergence when deriving an optimum policy in practical applications. To solve this problem, a stochastic shortest path-based Q-learning (SSPQL) is proposed, combining a stochastic shortest path-finding method with Q-learning, a well-known model-free RL method. The rationale is, if a robot has an internal state-transition model which is incrementally learnt, then the robot can infer the local optimum policy by using a stochastic shortest path-finding method. By increasing state-action pair values comprising of these local optimum policies, a robot can then reach a goal quickly and as a result, this process can enhance convergence speed. To demonstrate the validity of this proposed learning approach, several experimental results are presented in this paper.     

Simulation framework for small scale engagement

Developing future weapons systems has become increasingly complicated and costly. The armed forces of major nations use modeling and simulation techniques for new weapons systems from the conceptual stage to design, production, deployment and training stages to shorten the development cycle and guarantee their effectiveness. Failure in the development cycle carries too much loss in time and money. Therefore, computer-based modeling and simulation techniques are applied from the conceptual stage to gauge the efficacy of new weapons systems. The objective of this study is to develop a modeling and simulation methodology for small scale engagement using the DEVS formalism. The entities required for modeling and simulation are divided into three categories: combat, logical, and environmental entities. Combat entities represent the military hardware or combatants; logical entities represent the judgment and decision entities for the interaction between various entities; and environmental entities emulate the constituents of real combat environment. The combat entities are further modeled into Shell and Core Parts to maximize their reusability under various combat scenarios. The proposed framework is verified using a one-on-one combat engagement simulation (written in C++) between two submarines.     

Theoretical and experimental characterization of wettability of various nanolens arrayed polymer surfaces replicated with nanodimpled aluminum mold insert

Various types of polymer surfaces with a nanolens array, which has an entrant shape with a low aspect ratio, were fabricated and the wettability of the fabricated surfaces was evaluated in both theoretical and experimental ways. The nanolens array was replicated on three different polymer surfaces of polydimethylsiloxane (PDMS), cyclic olefin copolymer (COC), and polymethylmethacrylate (PMMA) by means of replica molding and hot embossing with a nanodimpled aluminum mold that was manufactured by a chemical oxidation process. From the theoretical and experimental evaluations of the wettability it was found that the measured contact angles were very similar with the theoretically estimated ones and also the hydrophilicity and hydrophobicity of the hydrophilic PMMA and hydrophobic PDMS, respectively, surfaces were reinforced by the nanolens array within the Wenzel wetting state.     

Optimization of optical lens-controlled scanning electron microscopic resolution using generalized regression neural network and genetic algorithm

A scanning electron microscope (SEM) is a sophisticated equipment employed for fine imaging of a variety of surfaces. In this study, prediction models of SEM were constructed by using a generalized regression neural network (GRNN) and genetic algorithm (GA). The SEM components examined include condenser lens 1 and 2 and objective lens (coarse and fine) referred to as CL1, CL2, OL-Coarse, and OL-Fine. For a systematic modeling of SEM resolution (R), a face-centered Box–Wilson experiment was conducted. Two sets of data were collected with or without the adjustment of magnification. Root-mean-squared prediction error of optimized GRNN models are GA 0.481 and 1.96×10^-12 for non-adjusted and adjusted data, respectively. The optimized models demonstrated a much improved prediction over statistical regression models. The optimized models were used to optimize parameters particularly under best tuned SEM environment. For the variations in CL2 and OL-Coarse, the highest R could be achieved at all conditions except a larger CL2 either at smaller or larger OL-Coarse. For the variations in CL1 and CL2, the highest R was obtained at all conditions but larger CL2 and smaller CL1.     

Filtering Data Streams for Entity-Based Continuous Queries

The idea of allowing query users to relax their correctness requirements in order to improve performance of a data stream management system (e.g., location-based services and sensor networks) has been recently studied. By exploiting the maximum error (or tolerance) allowed in query answers, algorithms for reducing the use of system resources have been developed. In most of these works, however, query tolerance is expressed as a numerical value, which may be difficult to specify. We observe that in many situations, users may not be concerned with the actual value of an answer, but rather which object satisfies a query (e.g., "who is my nearest neighbor?”). In particular, an entity-based query returns only the names of objects that satisfy the query. For these queries, it is possible to specify a tolerance that is "nonvalue-based.” In this paper, we study fraction-based tolerance, a type of nonvalue-based tolerance, where a user specifies the maximum fractions of a query answer that can be false positives and false negatives. We develop fraction-based tolerance for two major classes of entity-based queries: 1) nonrank-based query (e.g., range queries) and 2) rank-based query (e.g., k-nearest-neighbor queries). These definitions provide users with an alternative to specify the maximum tolerance allowed in their answers. We further investigate how these definitions can be exploited in a distributed stream environment. We design adaptive filter algorithms that allow updates be dropped conditionally at the data stream sources without affecting the overall query correctness. Extensive experimental results show that our protocols reduce the use of network and energy resources significantly.     

Organic wavelength‐converting‐film‐based hybrid planar white light‐emitting diodes

Abstract— In this study, organic wavelength‐converting films (WCFs) applied to InGaN blue LED‐based hybrid planar WLED has been fabricated. The organic dye layer in the WCF was formed between the upper and bottom polymer sheets by using a simple roll‐laminating technique. Subsequently, the hybrid planar WLEDs have been fabricated based upon these films. The luminous efficiency of green WCF‐based hybrid planar WLEDs with a single blue LED chip was 34.6 lm/W and that of red‐WCF‐assisted green WCF‐based hybrid planar WLEDs was 27.3 lm/W under 20 mA. The use of WCF to fabricate hybrid planar WLEDs showed better stability than that of directly coating organic color‐convergence materials (CCMs) on the LED chips. It only decreased to about 10% of the initial wavelength‐converting intensity after 1 hour of continual operation at 20 mA.     

Frictional properties of VA‐mode alignment‐film surfaces studied by frictional force microscopy

Abstract— The surface‐friction characteristics of the post‐bake temperature were compared to that of the rubbing condition on VA‐mode alignment films (AFs) using frictional force microscopy (FFM). The surface roughness and the surface frictional force increased when the temperature was elevated. However, the frictional asymmetry could not be detected by the frictional loop due to the instability of the side‐chain molecular arrangement. For the inter‐influences of the total friction and surface roughness, the density of the side chain was changed and it was intentionally rubbed for evaluation. The results showed that the total friction was higher when the rubbing was stronger and side‐chain density lower, and the surface roughness also increased in the same manner. This can be explained by the potential interaction due to the curved structure of the side‐chain surface that establishes the probe that is scanned and the top surface layer. In order to confirm the influence of the pre‐tilt angle on the tilt of the side chain, a test cell was produced under the same conditions and evaluated. The results showed that the pre‐tilt‐angle decreased according to the difference in density of the side chain and strength of rubbing after injecting liquid‐crystal (LC).     

An adaptive peer-to-peer live streaming system with incentives for resilience

Recently, there have been a lot of research efforts on peer-to-peer (P2P) live streaming services. P2P systems can be easily deployed since a participating peer’s resources (i.e., upload link bandwidth) can be exploited to distribute contents. However, how to adapt to leaving peers and how to encourage peers to contribute resources voluntarily are still challenging issues. In this paper, we propose Climber, an adaptive P2P live streaming system with incentives for resilience. Climber is based on the hybrid structure of a tree and a mesh, so as to achieve self-improvement and adaptation to users’ dynamic joining and leaving. Moreover, Climber substantiates an incentive mechanism that provides better resilience for peers with more upload bandwidth allocated. Simulation results reveal that Climber significantly reduces the topology maintenance cost compared to SplitStream and NICE-PRM. Also, simulation and analytical results verify that Climber can bound the level of disruption by dynamically adapting to the user churning rate.