Structural and Passivative Behaviors of Cu(In) Thin Film

This investigation prepares a low-resistivity and self-passivated Cu(In) thin film. The dissociation behaviors of dilute Cu-alloy thin films, containing 1.5–5at.%In, were prepared on glass substrates by a cosputter deposition, and were subsequently annealed in the temperature range of 200–600 °C for 10–30 min. Thus, self-passivated Cu thin films in the form In₂O₃/Cu/SiO₂ were obtained by annealing Cu(In) alloy films at an elevated temperature. Structural analysis indicated that only strong copper diffraction peaks were detected from the as-deposited film, and an In₂O₃ phase was formed on the surface of the film by annealing the film at an elevated temperature under oxygen ambient. The formation of In₂O₃/Cu/SiO₂ improved the resistivity, adhesion to SiO₂, and passivative capability of the studied film. A dramatic reduction in the resistivity of the film occurred at 500 °C, and was considered to be associated with preferential indium segregation during annealing, yielding a low resistivity below 2.92 μΩcm. The results of this study can be potentially exploited in the application of thin-film transistor–liquid crystal display gate electrodes and copper metallization in integrated circuits.     

大尺寸TFT-LCD电视的彩色和图像增强

最近几年,薄膜晶体管液晶显示器(TFT-LCD)技术取得了快速而显著的进步,其性能超过了PDP、CRT以及投影显示。毫无疑问,TFT-LCD已经成为高清电视应用的最佳实现方案。本文将介绍进一步增强大尺寸LCD电视的彩色和图像性能的技术。     

Detection of OFDM Signals in Fast-Varying Channels With Low-Density Pilot Symbols

In fast-varying channels, an orthogonal frequency-division multiplexing system needs to insert denser pilot symbols among transmitted symbols in tracking the variation of a channel. However, using denser pilot symbols reduces transmission throughput. In this paper, we propose a pseudopilot algorithm for data detection in fast-varying channels without increasing the pilot density. Our algorithm is based on a regressional model-based least-squares-fitting approach. Within a block of received symbols, we select some data symbols and regard them as pseudopilot symbols. The receiver considers all the possible patterns of the pseudopilots and associates each of them with a data sequence and a corresponding metric. The associated data sequence, whose metric is minimum, is selected as the detected data sequence. Our algorithm is not based on a decision-directed or decision-feedback architecture because the pseudopilots do not come from any detected symbols. The proposed algorithm needs to search all the possible patterns of the pseudopilots, and the complexity may increase with the number of pseudopilots and constellation size. To reduce the number of search, we further propose two modified approaches. The simulation results show that the performance of the proposed algorithms could approach a bit-error probability lower bound that is obtained by letting the receiver know the true values of the pseudopilots. Compared with the linear interpolation method, the proposed algorithm shows obvious improvement in fast-varying channels. The proposed modified approaches could also effectively reduce the number of search while maintaining the performance. We also give the complexity analysis of the proposed algorithm and an approach to determine the degree of the regression polynomial.     

Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models

This study presented a new diagnosis system for myocardial infarction classification by converting multi-lead ECG data into a density model for increasing accuracy and flexibility of diseases detection. In contrast to the traditional approaches, a hybrid system with HMMs and GMMs was employed for data classification. A hybrid approach using multi-leads, i.e., lead-V1, V2, V3 and V4 for myocardial infarction were developed and HMMs were used not only to find the ECG segmentations but also to calculate the log-likelihood value which was treated as statistical feature data of each heartbeat's ECG complex. The 4-dimension feature vector extracted by HMMs was clustered by GMMs with different numbers of distribution (disease and normal data). SVMs classifier was also examined for comparison with our system in experimental result. There were total 1129 samples of heartbeats from clinical data, including 582 data with myocardial infarction and 547 normal data. The sensitivity of this diagnosis system achieved 85.71%, specificity achieved 79.82% and accuracy achieved 82.50% statistically.     

Introduction of affinity set and its application in data-mining example of delayed diagnosis

At least 44,000 people die in hospitals each year as a result of medical errors, and these deaths are becoming the eighth-leading cause of death in the United States. Thus, medical providers have the responsibility to pay attention for reducing avoidable medical errors and improve patient safety as best as they can. It requires the rapid evaluation and prioritisation of life threatening injuries in the primary survey followed by a detailed secondary survey in the emergency room. However, time is always valuable and limited such that some important vital signs may be delayed and ignored. This research explores delayed diagnosis problem and uses the affinity set by Topology concept to classify/focus on key attributes causing delayed diagnosis (missed injury) in order to reduce error risk. Results interestingly indicate that when a patient can breathe normally, but his (or her) blood-pressure or pulse is abnormal, a high probability of delayed diagnosis exists. This affinity work also compares the performance with the model of rough set (Rosetta), neural network, support vector machine and logistic regression. And our affinity model shows its advantage by prediction accuracy and explanation power.     

Preparation and characterization of methoxy‐poly(ethylene glycol) side chain grafted onto chitosan as a wound dressing film

Chitosan has received extensive attention as a biomedical material; however, the poor solubility of chitosan is the major limiting factor in its utilization. In this study, chitosan‐based biomaterials with improved aqueous solubility were synthesized. Two molecular weights (750 Da and 2000 Da) of methoxypoly(ethylene glycol) (mPEG) were grafted onto chitosan (mPEG‐g‐chitosan) to form a ～100‐μm‐thick plastic film as a wound dressing. The chemical structures of the mPEG‐g‐chitosan copolymers were confirmed using Fourier transform infrared spectroscopy (FTIR), and the thermal properties were characterized using thermogravimetry analysis (TGA). Their microstructures were observed using scanning electron microscopy (SEM). The other properties were analyzed via the swelling ratio, tensile strength, elongation, and water vapor transmission rate (WVTR). Biocompatibility evaluations through biodegradability, cytotoxicity, and antimicrobial effect studies were also performed. The obtained mPEG‐g‐chitosan copolymers were soluble in slightly acidic aqueous solutions (pH～6.5) at a concentration of 10 wt %. The optimal mPEG‐g‐chitosan hydrogels had swelling ratios greater than 100% and WVTRs greater than 2000 g/m²/day. Their performance against Staphylococcus aureus will be subjected to further improvements with respect to medical applications. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42340.     

Predictive maintenance of abnormal wind turbine events by using machine learning based on condition monitoring for anomaly detection

Journal of Mechanical Science and Technology - The predictive maintenance of wind turbines has become a critical issue with the rapid development of wind power generation. The early detection of...     

Maximum entropy probability distributions for flood frequency analysis

It is customary to assume a frequency distribution in flood frequency analysis. The parameters of the distribution are estimated by using observed or transformed data. The fitted distribution is then used to estimate the magnitudes of floods of different frequencies.

The maximum entropy (ME) probability distribution is defined as the 'minimally prejudiced probability distribution which maximizes the entropy subject to constraints supplied by the given information'. In spite of many attractive features of the ME distribution, it has not been used in its general form in practice. The main reason for not using the ME distribution in its general form is that the parameter estimation problem associated with the ME distribution is not easy. Recently this problem has been solved and an algorithm has been developed to estimate the parameters of the ME distribution.

The objective of the research reported in the present paper is to fit ME distributions to flood data. The ME distributions are compared with other well known distributions. The computational aspects and selection of orders of distributions are also discussed. The ME distribution is shown to be versatile and fits a variety of flood data very well.     

Unusual exfoliation of layered silicate clays by non-aqueous amine diffusion mechanism

The organic ionic exchange intercalation of smectite clays is conventionally performed in water swelling conditions. Here we report a different mechanism for modifying the smectite clays in alcohol solvents. The modification of sodium montmorillonite with poly(oxyethylene-oxypropylene)-amines (POA-amine) were compared for the differences between in water and in alcohol. In water, the intercalation of hydrophobic poly(oxypropylene)-rich amines in the silicate galleries expands the interlayer spacing up to 10 nm and even to exfoliation (featureless in X-ray diffraction pattern) with over 5 amine equivalents, where as hydrophilic poly(oxyethylene)-rich amines could only intercalate in a low spacing of 1.8 nm. On the other hand, all of the POA-amines in isopropanol afford the exfoliation with over, 3 amine equivalents. The mechanism of this unexpected exfoliation is explained by the thermodynamic formations of an imaginary membrane surrounding the clay units, followed by amine diffusion from the solvent into the clay galleries.