Harmony search-based hidden Markov model optimization for online classification of single trial eegs during motor imagery tasks

This paper presents an improved method based on single trial EEG data for the online classification of motor imagery tasks for brain-computer interface (BCI) applications. The ultimate goal of this research is the development of a novel classification method that can be used to control an interactive robot agent platform via a BCI system. The proposed classification process is an adaptive learning method based on an optimization process of the hidden Markov model (HMM), which is, in turn, based on meta-heuristic algorithms. We utilize an optimized strategy for the HMM in the training phase of time-series EEG data during motor imagery-related mental tasks. However, this process raises important issues of model interpretation and complexity control. With these issues in mind, we explore the possibility of using a harmony search algorithm that is flexible and thus allows the elimination of tedious parameter assignment efforts to optimize the HMM parameter configuration. In this paper, we illustrate a sequential data analysis simulation, and we evaluate the optimized HMM. The performance results of the proposed BCI experiment show that the optimized HMM classifier is more capable of classifying EEG datasets than ordinary HMM during motor imagery tasks.     

A fast identification algorithm with outliers under Box-Cox transformation-based annealing robust radial basis function networks

In this article, a Box-Cox transformation-based annealing robust radial basis function networks (ARRBFNs) is proposed for an identification algorithm with outliers. Firstly, a fixed Box-Cox transformation-based ARRBFN model with support vector regression (SVR) is derived to determine the initial structure. Secondly, the results of the SVR are used as the initial structure in the fixed Box-Cox transformation-based ARRBFNs for the identification algorithm with outliers. At the same time, an annealing robust learning algorithm (ARLA) is used as the learning algorithm for the fixed Box-Cox transformation-based ARRBFNs, and applied to adjust the parameters and weights. Hence, the fixed Box-Cox transformation-based ARRBFNs with an ARLA have a fast convergence speed for an identification algorithm with outliers. Finally, the proposed algorithm and its efficacy are demonstrated with an illustrative example in comparison with Box-Cox transformation-based radial basis function networks.     

Study on the electrical conductivity and morphology of porous polypyrrole layers prepared electrochemically in the presence of pyridinium chlorochromate

The electrical conductivity and morphology of thick (up to 3 mm) porous polypyrrole (PPy) layers produced electrochemically from pyrrole in acetonitrile (ACN) solutions have been studied. The electrical conductivity of pressed porous layers ranges from 1 to 10 Scm^–1, which is about one order of magnitude less than that in films which were prepared under similar conditions but without PnClCr. Analysis of the temperature dependence of conductivity has confirmed the major role of hopping in relation to tunnelling in charge transport inside the PPy layers even at lower temperatures. Scanning electron microscopy (SEM) showed a globular structure, which is different from the usual cauliflower-like structure of PPy films prepared without any oxidizing agent. Globular particles of about 1–3 m diameter have been found under a thin smooth crust on the electrode side of the sample. Globular particles form linked chain-like or larger round formations poorly filling the space. Closely packed fibrils of about 20 nm diameter and over 100 nm in length were found inside the aggregates.     

What is the best method of surgical training?: A report of America's leading senior surgeons

OBJECTIVES: To characterize the career choices and developments made by leading senior surgeons in this country and to examine hypothetically whether application of a short tracking program would have hindered their career decisions. DESIGN: A survey pertaining to each surgeon's career, decisions, and opinions concerning surgical training. SETTING AND PARTICIPANTS: Senior surgeons of regional and national surgical societies. MAIN OUTCOME MEASURE: Survey responses. RESULTS: A total of 352 surveys (41.4%) were received. Respondents answered that the most common reasons for choosing a specialty were role models or mentors (56%), research (51%), and available patient population (23%). The 2 most common stages in a career at which the respondents became interested in a specialty, or an area of expertise, were at the junior residency level (when the specialty was chosen) and at the assistant professor level (when a more specific topic within the specialty was chosen). The most common stage at which the group believed they acquired their expertise was also at the assistant professor level. Seventy-one percent of respondents believed broad training was superior to a short tracking system, although none had participated in shortened surgical training. CONCLUSIONS: Most leading senior surgeons in this country still believe that broad surgical training is superior and should be maintained. Because career specialties in this surveyed group were generally chosen in early residency, a hypothetical application of the short tracking system would have still allowed for these important decisions to be made. Also, it seems likely that specialty and career development would not have been hindered because "expertization" mostly occurred after training was completed. Regardless of training method, a role model or mentor seems most important in career choices and developments.     

Infective endocarditis complicated with rapidly progressive glomerulonephritis: a case report

Despite a general national decline in criminal activities in the 1990s, juvenile criminal offenses continue to increase, (including violent, property, and delinquency acts). In addition increased numbers of children are being held in juvenile jails. It is all but impossible for pediatric health providers to think that "their patients" and "their practices" are immune from the epidemic of crime that affects and is caused by "just kids."     

CHL1 encodes a component of the low-affinity nitrate uptake system in Arabidopsis and shows cell type-specific expression in roots

The Arabidopsis CHL1 (AtNRT1) gene confers sensitivity to the herbicide chlorate and encodes a nitrate-regulated nitrate transporter. However, how CHL1 participates in nitrate uptake in plants is not yet clear. In this study, we examined the in vivo function of CHL1 with in vivo uptake measurements and in situ hybridization experiments. Under most conditions tested, the amount of nitrate uptake by a chl1 deletion mutant was found to be significantly less than that of the wild type. This uptake deficiency was reversed when a CHL1 cDNA clone driven by the cauliflower mosaic virus 35S promoter was expressed in transgenic chl1 plants. Furthermore, tissue-specific expression patterns showed that near the root tip, CHL1 mRNA is found primarily in the epidermis, but further from the root tip, the mRNA is found in the cortex or endodermis. These results are consistent with the involvement of CHL1 in nitrate uptake at different stages of root cell development. A functional analysis in Xenopus oocytes indicated that CHL1 is a low-affinity nitrate transporter with a K(m) value of approximately 8.5 mM for nitrate. This finding is consistent with the chlorate resistance phenotype of chl1 mutants. However, these results do not fit the current model of a single, constitutive component for the low-affinity uptake system. To reconcile this discrepancy and the complex uptake behavior observed, we propose a "two-gene" model for the low-affinity nitrate uptake system of Arabidopsis.     

Device reliability under electrical stress and photo response of oxide TFTs

Abstract— The stability of oxide TFTs has been the main focus of this research and is probably the most crucial requirement for the successful application to flat‐panel displays. Although the high Fermi level of oxide semiconductors makes TFTs basically stable under electrical stress, the device reliability under diverse variations of electrical stress is affected by materials such as active semiconductors and gate insulators, processes for the formation of back/front channels and passivation layers, and device configurations among other things. How these factors affect the device reliability have been investigated and a review of the stability is presented. In addition, several categories of the light instability of oxide TFTs is presented and the origin is discussed.     

Adaptive product tracking in RFID-enabled large-scale supply chain

The Radio Frequency Identification (RFID) technology is gradually being adopted and deployed for product flow management in the supply chain. In order to track RFID-tagged products efficiently in the RFID-enabled, large-scale supply chain, this paper first presents the design of a product tracking system that can collaborate with the EPC Network, a suite of network services for RFID data management in the supply chain. Next, we explain a product monitoring procedure that is performed by comparing the actual path of a product with its planned path. Finally, we develop an adaptive product search algorithm based on a reinforcement learning technique to efficiently locate a product deviated from its planned path. Experiment results are provided to show the performance of the search algorithm.