The Influence of Task Characteristics on the Intermanual Asymmetry of Motor Overflow

The two studies reported here were designed to test the proposition that greater motor overflow occurs when movements are performed by the non-dominant hand. Unlike previous studies using normal adults, the task in these studies did not require force production. In the first study, a group of 19 right-handed participants performed unweighted finger lifting. That the frequency of motor overflow occurrence was the same regardless of which hand performed the task, did not support findings from other studies where tasks involving force production resulted in more overflow when performed by the non-dominant hand. To investigate further the influence of task characteristics on motor overflow occurrence, in the second study participants were required to remember and reproduce a prescribed sequence of four finger lifts. Left- and right-handed participants ( N =30) performed both single and sequenced finger lifting. The relative frequency of motor overflow (unintended lifts of fingers of the passive hand) was compared between hand preference groups, active hand and task type (single/sequenced). Contrary to the expectation that motor overflow would be greater for the sequenced finger lifting task, overflow was exhibited with a significantly greater frequency on single finger lifting. This finding indicates that task characteristics influence the pattern of overflow occurrence in normal adults. The task used in this study did not involve force production and did not result in an intermanual asymmetry of motor overflow. This contrasts with findings from other studies requiring adults to exert forces where greater overflow occurred when the non-dominant hand was active. However, this study confirms previous findings which show that left-handers produce greater overflow compared to right-handers regardless of the task being performed and the hand performing the task.

Do individual perceptions of group cohesion reflect shared beliefs? An empirical analysis

This study's primary purpose was to examine the degree to which individual perceptions of cohesiveness reflect shared beliefs in sport teams. The secondary purposes were to examine how the type of cohesion, the task interactive nature of the group, and the absolute level of cohesion relate to the index of agreement. Teams (n = 192 containing 2,107 athletes) were tested on the Group Environment Questionnaire. Index of agreement values were greater for the group integration (GI) manifestations of cohesiveness (GI-task, rwg(j) = .721; GI-social,rwg(j) = .694) than for the individual attractions to the group (ATG) manifestations (ATG-task, rwg(j) = .621; ATG-social, rwg(j) = .563). No differences were found for interactive versus coactive/independent sport teams. A positive relationship was observed between the absolute level of cohesiveness and the index of agreement. Results were discussed in terms of their implication for the aggregation of individual perceptions of cohesion to represent the group construct.

Type-aware task placement in geo-distributed data centers with low OPEX using data center resizing

With the rising demands on cloud services, the electricity consumption has been increasing drastically as the main operational expenditure (OPEX) to data center providers. The geographical heterogeneity of electricity prices motivates us to study the type-aware task placement problem over geo-distributed data centers. With the consideration of the diversity of user requests and server clusters in modern data centers, we formulate an optimization problem that minimizes OPEX while guaranteeing the quality-of-service, i.e., the expected response time of tasks. Furthermore, an efficient solution is designed for this formulated problem. The experimental results show that our proposal achieves much higher cost-efficiency than the greedy algorithm and much approaches the optimal results. © 2014 IEEE.

The association between investigative interviewers' knowledge of question type and adherence to best-practice interviewing

It is well established that not all investigative interviewers adhere to ‘best-practice’ interview guidelines (i.e., the use of open-ended questions) when interviewing child witnesses about abuse. However, little research has examined the sub skills associated with open question usage. In this article, we examined the association between investigative interviewers' ability to identify various types of questions and adherence to open-ended questions in a standardized mock interview. Study 1, incorporating 27 trainee police interviewers, revealed positive associations between open-ended question usage and two tasks; a recognition task where trainees used a structured protocol to guide their response and a recall task where they generated examples of open-ended questions from memory. In Study 2, incorporating a more heterogeneous sample of 40 professionals and a different training format and range of tests, positive relationships between interviewers' identification of questions and adherence to best-practice interviewing was consistently revealed. A measure of interviewer knowledge about what constitutes best-practice investigative (as opposed to knowledge of question types) showed no association with interviewer performance. The implications of these findings for interviewer training programs are discussed.

Movement scaling in children with ADHD-combined type

Objective
The aim of the study was to investigate motor performance in children with ADHD using a size-scaling handwriting task.

Method
In all, 14 male children with ADHD and 14 typically developing (TD) children (age 7-15) wrote 10-mm and 40-mm cursive letter “l.”

Results
Children with ADHD were unable to maintain their writing accurately at 40 mm, falling short by several millimeters; this was not evident in the TD children. Children with ADHD also had slightly faster and more fluent writing than TD children.

Conclusion
It was concluded that children with ADHD have difficulties scaling handwriting movement in the larger 40-mm condition that may reflect poor planning and modulation of movement, despite having faster and more fluent movements.

Is there a link between motor performance variability and social-communicative impairment in children with ADHD-CT : a kinematic study using an upper limb Fitts’ aiming task

 Objective: This study investigated the relationship between motor performance and social-communicative impairment in children with ADHD-combined type (ADHD-CT). Method: An upper limb Fitts’ aiming task was used as a measure of motor performance and the Social Responsiveness Scale as a measure of social-communicative/autistic impairment in the following groups: ADHD-CT (n = 11) and typically developing (TD) controls (n = 10). Results: Children with ADHD-CT displayed greater variability in their movements, reflected in increased error variance over repeated aiming trials compared with TD controls. Motor performance variability was associated with social-communicative deficits in the ADHD-CT but not in the TD group. Conclusion: Social-communicative impairments further complicate the clinical picture of ADHD-CT; therefore, further research in this area is warranted to ascertain whether a particular pattern of motor disturbance in children with ADHD-CT may be clinically useful in identifying and assessing children with a more complex ADHD presentation. © 2012 SAGE Publications.

EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems

The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.

The role of Transaction Cost Economics in information technology outsourcing research : a meta-analysis of the choice of contract type

Two recent reviews report that the empirical findings in information technology outsourcing (ITO) research are frequently inconsistent with the prevailing dominant analytical framework of transaction cost economics (TCE). While employing similar methodologies, the two reviews propose different strategies to resolve the inconsistencies. One is to improve the methodological rigor, specifically, the operationalization of TCE constructs. The other is to abandon TCE in favor of a new analytical framework. This paper presents a meta-analysis of the empirical findings on the choice of contract type as a function of task uncertainty. The results support both strategies. Refining the operationalization of TCE constructs, specifically of task uncertainty, would have improved the reliability of findings on TCE-based relationships between task uncertainty and the choice of contract type. However, independent of such methodological improvements, TCE is of limited relevance in recent ITO research for predicting the choice of contract type. Generalizing these findings, we conclude that ITO research requires a new analytical framework to further develop the theory of ITO and to provide sound guidance to the ITO industry.

Modified AHP for gene selection and cancer classification using type-2 fuzzy logic

This paper proposes a modification to the analytic hierarchy process (AHP) to select the most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) for cancer classification. Unlike the conventional AHP, the modified AHP allows us to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test, and signal-to-noise ratio. The IT2FLS is introduced for the classification task due to its great ability for handling nonlinear, noisy, and outlier data, which are common problems in cancer microarray gene expression profiles. An unsupervised learning strategy using the fuzzy c-means clustering is employed to initialize parameters of the IT2FLS. Other classifiers such as multilayer perceptron network, support vector machine, and fuzzy ARTMAP are also implemented for comparisons. Experiments are carried out on three well-known microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, and prostate. Rather than the traditional cross validation, leave-one-out cross-validation strategy is applied for the experiments. Results demonstrate the performance dominance of the IT2FLS against the competing classifiers. More noticeably, the modified AHP improves the classification performance not only of the IT2FLS but of all other classifiers as well. Accordingly, the proposed combination between the modified AHP and IT2FLS is a powerful tool for cancer classification and can be implemented as a real clinical decision support system that is useful for medical practitioners.

Multi-task transfer learning for in hospital-death prediction for ICU patients

Multi-Task Transfer Learning (MTTL) is an efficient approach for learning from inter-related tasks with small sample size and imbalanced class distribution. Since the intensive care unit (ICU) data set (publicly available in Physionet) has subjects from four different ICU types, we hypothesizethat there is an underlying relatedness amongst various ICU types. Therefore, this study aims to explore MTTL model for in-hospital mortality prediction of ICU patients. We used singletask learning (STL) approach on the augmented data as well as individual ICU data and compared the performance with the proposed MTTL model. As a performance measurement metrics, we used sensitivity (Sens), positive predictivity (+Pred), and Score. MTTL with class balancing showed the best performance with score of 0.78, 0.73, o.52 and 0.63 for ICU type 1(Coronary care unit), 2 (Cardiac surgery unit), 3 (Medical ICU) and 4 (Surgical ICU) respectively. In contrast the maximum score obtained using STL approach was 0.40 for ICU type 1 & 2. These results indicates that the performance of in-hospital mortality can be improved using ICU type information and by balancing the ’non-survivor’ class. The findings of the study may be useful for quantifying the quality of ICU care, managing ICU resources and selecting appropriate interventions.