Implications of Statistical Spread to Experimental Analysis in a Novel Miniature Kolsky Bar

Kolsky Bar systems are subjected to inherent system error as all measurement devices are. This is especially true in that as the bar diameter decreases, the system becomes more sensitive to errors such as friction and misalignment. In this work we present a technique for identifying and quantifying the error of a Kolsky system. We also present a method of generating statistically significant bounds for Kolsky systems so that anomalous or improperly executed experiments can be quantitatively identified. This method does not rely on the intuition of the experimentalist to identify an anomalous experiment. After presenting our method for error identification, a series of tests are performed on 2024Aluminum alloy samples. A method is then presented where the system error, as well as some error contributed by a variance in sample dimension, are removed from the calculated error related to the stress on the samples. The result shows the effective variance of the sample response is quite high in the elastic loading period, but reduces when plasticity dominates. This is attributed to the presence of high frequency content in the travelling elastic waves which cannot be accurately measured currently, but is effectively damped out when plastic deformation dominates.

Inclusive Human Intention Prediction with Wearable Sensors: Machine Learning Techniques for the Reaching Task Use Case

Human intentions prediction is gaining importance with the increase in human–robot interaction challenges in several contexts, such as industrial and clinical. This paper compares Linear Discriminant Analysis (LDA) and Random Forest (RF) performance in predicting the intention of moving towards a target during reaching movements on ten subjects wearing four electromagnetic sensors. LDA and RF prediction accuracy is compared to observation-sample dimension and noise presence, training and prediction time. Both algorithms achieved good accuracy, which improves as the sample dimension increases, although LDA presents better results for the current dataset.

Economic Empowerment of Women in Socio-Cultural Perspectives in Federally Administrative Tribal Area (Pakistan): A Case Study of Kurram Agency

The study investigates the indicators that effect women’s economic empowerment. The data collected through questionnaires, with 350 sample dimension. Pragmatic results expose that enrolment of female in education, decision making power, decision concerning household asset and economic opportunities have positive effected on WEE and significant. Participation in economy & educational retrieve hold positive and insignificant effect on WEE due to limited access to educationalists, masculinity basis, male dominancy etc. Problems of socio-cultural, poverty have negative and insignificant effect on WEE because of cultural restrictions; female workers are bounded to teaching field & functioning in their own lands. Finally, the recommendations of the study are that free access should be given to women for education regardless of ethnicity, sexual partialities and may be allowed to work along their male equivalents in order to raise the level of economic empowerment of women. The same will subsequently enhance their potential for elimination of poverty, illiteracy and self-reliance in the society.

Multi-View Information-Theoretic Co-Clustering for Co-Occurrence Data

Multi-view clustering has received much attention recently. Most of the existing multi-view clustering methods only focus on one-sided clustering. As the co-occurring data elements involve the counts of sample-feature co-occurrences, it is more efficient to conduct two-sided clustering along the samples and features simultaneously. To take advantage of two-sided clustering for the co-occurrences in the scene of multi-view clustering, a two-sided multi-view clustering method is proposed, i.e., multi-view information-theoretic co-clustering (MV-ITCC). The proposed method realizes two-sided clustering for co-occurring multi-view data under the formulation of information theory. More specifically, it exploits the agreement and disagreement among views by sharing a common clustering results along the sample dimension and keeping the clustering results of each view specific along the feature dimension. In addition, the mechanism of maximum entropy is also adopted to control the importance of different views, which can give a right balance in leveraging the agreement and disagreement. Extensive experiments are conducted on text and image multiview datasets. The results clearly demonstrate the superiority of the proposed method.

Thermal transport of small systems

This article focuses on the phonon transport or thermal transport of small systems, including quasi-one-dimensional systems such as carbon nanotubes. The Fourier law well describes the thermal transport phenomena in normal bulk materials. However, it is no longer valid when the sample dimension reduces down to below the mean-free path of phonons. In such a small system, the phonons propagate coherently without interference with other phonons. The article first considers the Boltzmann–Peierls formula of diffusive phonon transport before discussing coherent phonon transport, with emphasis on the Landauer formulation of phonon transport, ballistic phonon transport and quantized thermal conductance, numerical calculation of the phonon-transmission function, and length dependence of the thermal conductance.