电源管理半导体上半年出现短缺销售不断上升,供应商疲于满足需求

据iSuppli公司,2010年上半年电源管理半导体销售收入强劲上升,出乎供应商意料,可能导致近期价格上涨. 2010年第一季度,电源管理半导体销售收入达到69亿美元,比2009年第四季度的67亿美元增长2.9%.该市场在2009年第三季度曾陷入停滞,第四季度开始扩张,第一季度数据显示该市场仍在扩张.     

Scaling potential and MOSFET integration of thermally stable Gd silicate dielectrics

We investigate the potential of gadolinium silicate (GdSiO) as a thermally stable high-k gate dielectric in a gate first integration scheme. There silicon diffuses into gadolinium oxide (Gd₂O₃) from a silicon oxide (SiO₂) interlayer specifically prepared for this purpose. We report on the scaling potential based on detailed material analysis. Gate leakage current densities and EOT values are compatible with an ITRS requirement for low stand by power (LSTP). The applicability of this GdSiO process is demonstrated by fully functional silicon on insulator (SOI) metal oxide semiconductor field effect transistors (MOSFETs).     

The occurrence of Escherichia coli O157 in/on faeces,carcasses and fresh meats from cattle

The aim of this study was to investigate whether Escherichia coli O157 is present in/on raw beef in Serbia. Correlated faecal and carcasses samples from 115 slaughtered cattle plus 26 uncorrelated carcass samples were examined. E. coli O157 detection and identification was performed using selective enrichment and immunomagnetic separation followed by selective media-plating and biochemical tests.     

Reliable method for driving events recognition

Motor vehicles greatly influence human life but are also a major cause of death and road congestion, which is an obstacle to future economic development. We believe that by learning driving patterns, useful navigation support can be provided for drivers. In this paper, we present a simple and reliable method for the recognition of driving events using hidden Markov models (HMMs), popular stochastic tools for studying time series data. A data acquisition system was used to collect longitudinal and lateral acceleration and speed data from a real vehicle in a normal driving environment. Data were filtered, normalized, segmented, and quantified to obtain the symbolic representation necessary for use with discrete HMMs. Observation sequences for training and evaluation were manually selected and classified as events of a particular type. An appropriate model size was selected, and the model was trained for each type of driving events. Observation sequences from the training set were evaluated by multiple models, and the highest probability decides what kind of driving event this sequence represents. The recognition results showed that HMMs could recognize driving events very accurately and reliably.     

Waves on thin films of pseudoplastic liquids

Wavy flow of a thin pseudoplastic fluid film down a smooth vertical plate has been analytically studied. A boundary layer equation of motion was solved to obtain expressions which permit the prediction of characteristic parameters values of pseudoplastic liquids in wave motion. The presented analysis gives better agreement between predicted and measured values of wavelength and critical regime for wave inception than the existing theories. Also a good agreement was obtained between predicted and measured values of the mean film thickness.     

Preparation of smallest microparticles of poly-D,L-lactide by modified precipitation method: influence of the process parameters

Biodegradable microspheres such as those made of poly-D,L-lactide (PDLLA) are widely investigated delivery systems for drugs or antigens. The aim of this study was to examine experimental conditions in order to produce PDLLA microspheres with the best properties for controlled and sustained drug delivery by the modified precipitation method. For this purpose, the following parameters were varied: co-solvent (methanol or ethanol), the concentration of stabilizer polyvinyl alcohol (PVA), chloroform-to-water ratio and the speed and time of homogenization. Scanning electron microscopy (SEM) and stereological analysis were used to characterize the particles. The average size and morphology of the microspheres varied substantially with preparation conditions from 8.44-1.25 microm. Results showed that the smallest particles were obtained with methanol, 1% PVA and with 10 min of homogenization at 21,000 rpm.     

Comparison procedure of predicting the time to default in behavioural scoring

The paper deals with the problem of predicting the time to default in credit behavioural scoring. This area opens a possibility of including a dynamic component in behavioural scoring modelling which enables making decisions related to limit, collection and recovery strategies, retention and attrition, as well as providing an insight into the profitability, pricing or term structure of the loan. In this paper, we compare survival analysis and neural networks in terms of modelling and results. The neural network architecture is designed such that its output is comparable to the survival analysis output. Six neural network models were created, one for each period of default. A radial basis neural network algorithm was used to test all six models. The survival model used a Cox modelling procedure. Further, different performance measures of all models were discussed since even in highly accurate scoring models, misclassification patterns appear. A systematic comparison ‘3 + 2 + 2’ procedure is suggested to find the most effective model for a bank. Additionally, the survival analysis model is compared to neural network models according to the relative importance of different variables in predicting the time to default. Although different models can have very similar performance measures they may consist of different variables. The dataset used for the research was collected from a Croatian bank and credit customers were observed during a 12-month period. The paper emphasizes the importance of conducting a detailed comparison procedure while selecting the best model that satisfies the users’ interest.     

Bio-Inspired Photoelectric Artificial Synapse based on Two-Dimensional Ti3C2Tx MXenes Floating Gate

The highly parallel artificial neural systems based on transistor-like devices have recently attracted widespread attention due to their high-efficiency computing potential and the ability to mimic biological neurobehavior. For the past decades, plenty of breakthroughs related to synaptic transistors have been investigated and reported. In this work, a kind of photoelectronic transistor that successfully mimics the behaviors of biological synapses has been proposed and systematically analyzed. For the individual device, MXenes and the self-assembled titanium dioxide on the nanosheet surface serve as floating gate and tunneling layers, respectively. As the unit electronics of the neural network, the typical synaptic behaviors and the reliable memory stability of the synaptic transistors have been demonstrated through the voltage test. Furthermore, for the first time, the UV-responsive synaptic properties of the MXenes floating gated transistor and its applications, including conditional reflex and supervised learning, have been measured and realized. These photoelectric synapse characteristics illustrate the great potential of the device in bio-imitation vision applications. Finally, through the simulation based on an artificial neural network algorithm, the device successfully realizes the recognition application of handwritten digital images. Thus, this article provides a highly feasible solution for applying artificial synaptic devices to hardware neuromorphic networks.