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A new method of environmental control that does not depend on motor functions is eagerly awaited to support independent living for people with severe quadriplegia. In this study, we conducted an exploratory case study of brain state discrimination in a quadriplegic subject to develop a brain-computer interface controlled by a mental task execution. We measured near-infrared spectroscopy (NIRS) signals in a patient with a cervical spinal cord injury while performing mental tasks. A block design with a task and a rest separated by 30 seconds was used to measure brain function. The utilized mental tasks were mental arithmetic and Japanese word chains. Seventeen trials of the NIRS signal were acquired for each task, and 52 samples with 24-dimensional features per trial data were extracted. Random forest was used as the classifier, and the number of correct responses in the binary discrimination of the brain states were calculated by cross-validation. The exact binomial test was used for the statistical analysis, and a two-tailed test with a significance level of 5% was performed. The results showed that the number of correct responses was 15 out of 17 (p = 0.002) for the mental arithmetic task and 14 out of 17 (p = 0.013) for the Japanese word chains task, for an overall accuracy of 85%. These results indicate that this method can discriminate the brain state of a patient with quadriplegia from the NIRS signal. By applying these findings to a brain-computer interface, it will be possible to provide a new means of environmental control for individuals with quadriplegia. [ABSTRACT FROM AUTHOR]

Various studies on autonomous robots that perform physical tasks in living environments are being conducted at present to solve the social problems of an aging society with a low birth rate. However, because it remains difficult for all robots to work autonomously in complicated environments, remote control robots may be useful for practical applications. As the purpose of remote control robots is to make direct connections between the operator and user, they may reduce affinity between the robot and the user. In this study, we define autonomous remote control robots with the aspects of both autonomy and remote operation explicitly. Affinity is compared among autonomous remote control robots, remote control robots, and autonomous robots, through controlled experiments on interactions in fetch-and-carry tasks using a domestic mobile manipulator, Human Support Robot, on a test field that is compliant with the World Robot Summit 2020 Rulebook. The results demonstrate that affinity exhibits the following order: autonomous robots, autonomous remote control robots, and remote control robots. Considering that autonomous robots remain under development, autonomous remote control robots may be beneficial for expanding the abilities of physical tasks and social acceptance. [ABSTRACT FROM AUTHOR]

In this study, we investigated the effect of the dynamic changes in brain activation during neurofeedback training in the classification of the different brain states associated with the target tasks. We hypothesized that ongoing activation patterns could change during neurofeedback session due to learning effects and, in the process, could affect the performance of brain state classifiers trained using data obtained prior to the session. Using a motor imagery paradigm, we then examined the application of an incremental training approach where classifiers were continuously updated in order to account for these activation changes. Our results confirmed our hypothesis that neurofeedback training could be associated with dynamic changes in brain activation characterized by an initially more widespread brain activation followed by a more focused and localized activation pattern. By continuously updating the trained classifiers after each feedback run, significant improvement in accurately classifying the different brain states associated with the target motor imagery tasks was achieved. These findings suggest the importance of taking into account brain activation changes during neurofeedback in order to provide more reliable and accurate feedback information to the participants, which is critical for an effective neurofeedback application. [ABSTRACT FROM AUTHOR]

Motor imagery (MI), a covert cognitive process where an action is mentally simulated but not actually performed, could be used as an effective neurorehabilitation tool for motor function improvement or recovery. Recent approaches employing brain--computer/brain--machine interfaces to provide online feedback of the MI during rehabilitation training have promising rehabilitation outcomes. In this study, we examined whether participants could volitionally recall MI-related brain activation patterns when guided using neurofeedback (NF) during training. The participants' performance was compared to that without NF. We hypothesized that participants would be able to consistently generate the relevant activation pattern associated with the MI task during training with NF compared to that without NF. To assess activation consistency, we used the performance of classifiers trained to discriminate MI-related brain activation patterns. Our results showed significantly higher predictive values of MI-related activation patterns during training with NF. Additionally, this improvement in the classification performance tends to be associated with the activation of middle temporal gyrus/inferior occipital gyrus, a region associated with visual motion processing, suggesting the importance of performance monitoring during MI task training. Taken together, these findings suggest that the efficacy of MI training, in terms of generating consistent brain activation patterns relevant to the task, can be enhanced by using NF as a mechanism to enable participants to volitionally recall task-related brain activation patterns. [ABSTRACT FROM AUTHOR]

The first two examples of a tetrasilatetrathia[8]circulene were synthesized in two steps from a tetraiodotetrathienylene by using a fourfold intramolecular dehydrogenative silylation of C−H bonds as the key step. A single-crystal X-ray diffraction analysis revealed that tetrasilatetrathia[8]circulene contains a perfectly planar [8]radialene skeleton. The excited-state dynamics of tetrasilatetrathia[8]circulene were evaluated by steady-state and time-resolved spectroscopic measurements, revealing an efficient intersystem crossing in the excited state. [ABSTRACT FROM AUTHOR]

SUMMARY When people listen to other's speech for the first time, they always attribute personality traits to the speaker subconsciously. We consider that if robots can predict personality traits of users from their speech, the communication in human-machine interaction will improve significantly. This paper proposes an approach for the automatic estimation of the traits, in which the listeners attribute to unanimous speakers. And the discrimination experiments based on hidden Markov model and canonical discrimination analysis show that, it is possible to predict with high accuracy (more than 75%), whether a speaker is perceived to be in the higher or lower part of the 'Extraversion', 'Openness', and 'conscientiousness' by using nonverbal information. [ABSTRACT FROM AUTHOR]

We have succeeded in the diversity-oriented synthesis of tetrathia[8]circulenes by sequential C–H borylation and annulation from cyclic tetrathiophene, and time-resolved microwave conductivity studies have proved that the intrinsic hole mobilities of tetrathia[8]circulenes are dependent on the chain length of the alkyl substituents at the peripheral positions. [ABSTRACT FROM AUTHOR]

The mechanisms of the [2 + 2 + 2] cycloaddition reaction of three ethyne molecules were studied by ab initio molecular orbital and density functional methods. The transition states range from that of the concerted mechanism with D3h symmetry to that of the stepwise mechanism with C2 symmetry. The transition state structure and the activation energy depend on the basis set and computational method employed in the analysis. The activation energy barrier was determined to be in the range of 36-44 kcal/mol. The activation energy determined by various methods corresponds to the interaction energy, which is related to the electron correlation energy. The best estimation of the activation energy barrier is 41.6 kcal/mol, achieved from the relation between the interaction energy and the activation energy. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

With the development of computer networks, it has become easy to have huge databases. Accordingly, it is becoming difficult for users to extract knowledge from such databases. In this paper we focus on data mining, especially classification. In real-world data mining, the missing value problem occurs in cases such as speech containing noise, facial occlusion, and the like. When a test sample has missing values, a classification system cannot handle them. In previous studies, various imputation methods have been developed, with the objective of solving the missing value problem with numerous explanatory variables, even if some explanatory variables were ineffective for imputation. It has been said that the use of many variables degrades learning efficiency, and thus we believe that imputation methods should be developed considering the relations among explanatory variables. It is also effective to consider the relations between the test sample and each of the training samples. Therefore, we have proposed an imputation method using a Bayesian network with weighted learning. Experiments have confirmed that the proposed method imputes missed values with approximate values, and the classification system successfully classified test samples in which missed values were imputed by the proposed method, with better success than some conventional imputation methods. [ABSTRACT FROM AUTHOR]

An artificial ecosystem model was developed to study complex behavior of real organisms. The subject of the research is multigenerational migration of the Monarch butterfly in North America, and we approached the problem from an evolutionary simulation. The ecosystem consists of artificial agents and five areas. The model is based on real organisms and the areas where they live simulated with a genetic algorithm. We then designed a model of agents that have genetic factors. These genetic factors determine the behavioral strategies, physical features, and transformational strategies of the agent. Each area is modeled on areas where we can see the migration of the Monarch, and the environmental factors of each area change periodically. Because of the genetic factor of the agent and the change of the environmental factors, the agent adapts to the environment and evolves gradually by using a genetic algorithm. Results of the evolutionary simulation show that multigenerational migration behavior of the agent emerges as its genetic factors adapt to periodic changes of the environmental factors in their evolution. The migration process of agents and their genetic factors are discussed, and the migration of the agent and that of the Monarch butterfly are compared. [ABSTRACT FROM AUTHOR]

We used the method of moments (MoM), which is a numerical electromagnetic field computation method, to study the surge characteristics of twisted pair single-phase electric power lines. The results show that wires stranded with less than several turns per meter have almost the same surge characteristics as parallel lines, but the surge impedance decreases in tens of % when the number of strands increases. To verify the simulation results, we measured the surge characteristics of a model line of twisted pair experimentally, and there is good accordance between them. It is possible to simulate the surge in twisted pair covered with polymer dielectric insulators by MoM. Moreover, we studied the surge characteristics of twisted pair which is inserted into a metal tube. According to the results, the effect of the strand is smaller than that in free space without the metal pipe. There are two propagation modes in common mode: fast surge and slow surge. Few influences of the number of strands on the surge velocity occur in the fast propagation surge, but the increase of the surge impedance and the slowdown of the propagation speed are caused by twining in the slow propagation mode. © 2009 Wiley Periodicals, Inc. Electr Eng Jpn, 167(3): 1–9, 2009; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20737 [ABSTRACT FROM AUTHOR]

This paper proposes a method for dynamic load balancing which works efficiently in the workstation cluster environment. A parallel abductive reasoning system based on the proposed method is also proposed. In the workstation cluster environment, parallel processing is performed using workstations with various processing abilities and loads. Thus, dynamic load balancing must consider the performance and loading conditions of the workstations. This paper proposes a method of estimating the processing ability of the workstation during operation, and also a dynamic load balancing algorithm to balance the estimated conditions. Using the proposed method, a parallel abductive reasoning system which can derive the most preferable explanation for a given observation is implemented. The results of experiments on the system are also reported. © 2006 Wiley Periodicals, Inc. Syst Comp Jpn, 37(3): 80–89, 2006; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10190 [ABSTRACT FROM AUTHOR]

This paper presents the fundamental surge phenomenon when a step voltage is applied to a horizontal line above the ground which has branch feeders. We study line current and voltage at joint with the method of moments that calculates electromagnetic field in frequency domain. It is widely noted that the current reflection factor changes to a third at the junction of the same three lines in conventional transmission line theory based on TEM wave, and the stepwise current reflected appears overlapping to the incident current. On the other hand, the reflected current from the joint shows the inductive characteristic or the capacitive one depending on the angle between lines, which varies gradually and not stepwise in our numerical analysis considering TM wave. The time characteristic of the voltage at joint depends on the angles also, and the peak voltage slightly exceeds the voltage by the transmission line theory. These phenomena are caused by the electromagnetic coupling between the main line and branch lines. In addition to the computer simulations, we measured the currents in the reduced-scale model lines and the results show the validity of our analysis regarding changes of the reflected current depending on the angles. © 2003 Wiley Periodicals, Inc. Electr Eng Jpn, 144(2): 1–8, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.10176 [ABSTRACT FROM AUTHOR]

AbstractBackground and Objectives: In geriatrics and dementia care, early diagnosis is crucial. We developed a dementia screening model using drawing features from clock drawing tests (CDT) and investigated the features contributing to the discrimination of dementia and its screening performance. Methods: This study included 129 older adults attending a dementia outpatient clinic. We obtained information on the diagnosis of dementia and CDT data from medical records and quantified 12 types of drawing features according to the Freedman scoring system. Based on the dementia diagnosis information, participants were assigned to two groups: 58 in the dementia diagnosis group and 71 in the non-diagnosis group. Using Boruta, an iterative feature selection algorithm, and a support vector machine, a machine learning method, we analyzed the drawing features contributing to dementia discrimination and evaluated discrimination performance. Results: Five types of drawing features were selected as contributors to discrimination, including “numbers in the correct position,” “minute target number indicated,” and “hand in correct proportion.” These features exhibited a discriminating sensitivity of 0.74 ± 0.16 and specificity of 0.74 ± 0.18 for detecting dementia. Conclusion: This study demonstrated a method for identifying individuals likely to be diagnosed with dementia among patients attending a dementia outpatient clinic using drawing features. The knowledge of drawing features contributing to dementia differentiation may assist healthcare practitioners in clinical reasoning and provide novel insights for clinical practice. In the future, we plan to develop a primary screening for dementia based on machine learning using CDT. [ABSTRACT FROM AUTHOR]