Dynamic Modeling of Multi-Elastic Body Systems using Kane’s Method and Congruency Transformations

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Impacts of using a social robot to teach music to children with low-functioning autism

This article endeavors to present the impact of conducting robot-assisted music-based intervention sessions for children with low-functioning (LF) autism. To this end, a drum/xylophone playing robot is used to teach basic concepts of how to play the instruments to four participants with LF autism during nine educational sessions. The main findings of this study are compared to similar studies conducted with children with high-functioning autism. Our main findings indicated that the stereotyped behaviors of all the subjects decreased during the course of the program with an approximate large Cohen’s d effect size. Moreover, the children showed some improvement in imitation, joint attention, and social skills from the Pre-Test to Post-Test. In addition, regarding music education, we indicated that while the children could not pass a test on the music notes or reading music phrases items because of their cognitive deficits, they showed acceptable improvements (with a large Cohen’s d effect size) in the Stambak Rhythm Reproduction Test, which means that some rhythm learning occurred for the LF participants. In addition, we indicated that parenting stress levels decreased during the program. This study presents some potential possibilities of performing robot-assisted interventions for children with LF autism

Drosophila Muller F Elements Maintain a Distinct Set of Genomic Properties Over 40 Million Years of Evolution

The Muller F element (4.2 Mb, ~80 protein-coding genes) is an unusual autosome of Drosophila melanogaster; it is mostly heterochromatic with a low recombination rate. To investigate how these properties impact the evolution of repeats and genes, we manually improved the sequence and annotated the genes on the D. erecta, D. mojavensis, and D. grimshawi F elements and euchromatic domains from the Muller D element. We find that F elements have greater transposon density (25–50%) than euchromatic reference regions (3–11%). Among the F elements, D. grimshawi has the lowest transposon density (particularly DINE-1: 2% vs. 11–27%). F element genes have larger coding spans, more coding exons, larger introns, and lower codon bias. Comparison of the Effective Number of Codons with the Codon Adaptation Index shows that, in contrast to the other species, codon bias in D. grimshawi F element genes can be attributed primarily to selection instead of mutational biases, suggesting that density and types of transposons affect the degree of local heterochromatin formation. F element genes have lower estimated DNA melting temperatures than D element genes, potentially facilitating transcription through heterochromatin. Most F element genes (~90%) have remained on that element, but the F element has smaller syntenic blocks than genome averages (3.4–3.6 vs. 8.4–8.8 genes per block), indicating greater rates of inversion despite lower rates of recombination. Overall, the F element has maintained characteristics that are distinct from other autosomes in the Drosophila lineage, illuminating the constraints imposed by a heterochromatic milieu

A new continuous model for flexural vibration analysis of a cracked beam

In this paper a new continuous model for vibration analysis of a beam with an open edge crack is presented. A quasi-linear displacement filed is suggested for the beam and the strain and stress fields are calculated. The equation of motion of the beam is calculated using the Hamilton principle. The calculated equation of motion is solved with a modified weighted residual method and the natural frequencies and mode shapes are obtained. The results are compared with those obtained by finite element method and an excellent agreement has been observed. The presented model is a simple and accurate method for analysis of the cracked beam behavior near or far from the crack tip

Recognizing Acceptance Level in Facial Expressions by Neural Network

Today, robots play an important role in the daily lives of people with disabilities and even ordinary people, so that in almost all areas of treatment and assistance, education and rehabilitation, games and entertainment, we see the presence of all kinds of social robots. Because working in the above areas requires a strong spirit and performing a specific action with constant quality many times, robots can take the place of humans well and do their job without fatigue and boredom and with a constant quality. However, one of the disadvantages that may exist in human-robot interactions is the lack of emotional mutual understanding, which means that usually the robot has no emotional understanding of human moods and sometimes this is the reason why the quality of interactions decreases. Perhaps, the perception of people's satisfaction can be considered as a major parameter in our interactions between humans and robots, meaning that creating a proper interaction always increases the level of satisfaction in humans and on the other hand, people express dissatisfaction. They can express their unwillingness to continue an interaction. Hence, this paper attemts to use a canonical neural network model to find people's level of acceptance when facing a predetermined scenario. Unlike numerous and valuable studies that use deep neural network to diagnose facial expressions and the raw image of the person as the input of the network, in this paper, the histogram vector of directional slopes of face as a characteristic vector describing the level of acceptance and a small neural network model is used as classifier. The obtained model, in addition to the high power of satisfaction, has the ability to generalize and recognize unlearnt negative emotions. Small size and low processing cost are two very important elements in the efficiency of separate systems, which are considered as two basic constraints in the model. Sometimes, other parameters are ignored to achieve these two important ones

IMECE2002-DSC-34358 DYNAMICAL MODELING AND ANALYSIS OF THE HUMAN JUMPING PROCESS

ABSTRACT A dynamical model was developed in order to study the jumping process in human, and the effect of factors like joint speeds and hand motion. An experiment was designed and setup to compare the theoretical model with the experimental observations. Time histories of vertical force, mass center velocity and driving torques were obtained too. Using dynamical equations, the effect of joint speeds on the maximum values of these quantities is discussed. It is shown that reducing the joint speeds of a body can lead to an unsuccessful jump in which the body does not enter the flight phase. An increase in speed reduces the take-off time (the time necessary for the body to leave the ground) and increases the body's linear velocity at take-off, as well as, the maximum value of driving torques. Effect of hand motion is also investigated through suppressing motion of the Shoulder and Elbow. It is observed that hand motion has an improving effect on the body's linear velocity. Although speed of joints did not show to have a great influence on most torques, those at the Shoulder and Elbow were observed to be more sensitive to it