In this paper, we describe how to rank employees for risk of turnover by using data obtained from a happiness self-reporting app. Two data sources are used: daily happiness and social interactions. The data spans 2.5 years and 4,356 employees of 34 companies based in Barcelona. For each employee, we build features at three levels: individual, company level and social interaction graph level. We develop various turnover risk models and we compare how different features affect performance prediction. The results show that the top three features that explain turnover risk are: ratio of likes received (likeability), posting frequency (engagement), and relative happiness (employee happiness normalized by company mean). Surprisingly, a priori expected explanatory features such as mean happiness level and the ratio of likes (positivity), were not significant. [email protected] = 80% out of a test set with 116 churns, sample size N=2k.

Previous research has traditionally analyzed emoji sentiment from the point of view of the reader of the content not the author. Here, we analyze emoji sentiment from the point of view of the author and present an emoji sentiment benchmark that was built from an employee happiness dataset where emoji happen to be annotated with daily happiness of the author of the comment. The data spans over 3 years, and 4k employees of 56 companies based in Barcelona. We compare sentiment of writers to readers. Results indicate that, there is an 82% agreement in how emoji sentiment is perceived by readers and writers. The disagreement concentrates in negative emoji, where the authors report to feel 26% worse than perceived by readers. Emoji use was not found to be correlated with author moodiness. Authors that use emoji are happier than authors that never use emoji.

In this research, omnidirectional crawler unit with the torus configuration is proposed. It is composed of two curved crawler belts whose inner and outer trajectories have the same length. The prototype is designed and developed. The basic motion of the prototype is confirmed experimentally.

BACKGROUND: Predicting protein complexes from protein-protein interaction data is becoming a fundamental problem in computational biology. The identification and characterization of protein complexes implicated are crucial to the understanding of the molecular events under normal and abnormal physiological conditions. On the other hand, large datasets of experimentally detected protein-protein interactions were determined using High-throughput experimental techniques. However, experimental data is usually liable to contain a large number of spurious interactions. Therefore, it is essential to validate these interactions before exploiting them to predict protein complexes. RESULTS: In this paper, we propose a novel graph mining algorithm (PEWCC) to identify such protein complexes. Firstly, the algorithm assesses the reliability of the interaction data, then predicts protein complexes based on the concept of weighted clustering coefficient. To demonstrate the effectiveness of the proposed method, the performance of PEWCC was compared to several methods. PEWCC was able to detect more matched complexes than any of the state-of-the-art methods with higher quality scores. CONCLUSIONS: The higher accuracy achieved by PEWCC in detecting protein complexes is a valid argument in favor of the proposed method. The datasets and programs are freely available at http://faculty.uaeu.ac.ae/nzaki/Research.htm.

Detecting protein complexes from protein-protein interaction (PPI) network is becoming a difficult challenge in computational biology. Observations show that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. This paper introduces a novel method for detecting protein-complexes from PPI by using a protein ranking algorithm (ProRank) and incorporating an evolutionary relationships between proteins in the network. The method successfully predicted 57 out of 81 benchmarked protein complexes created from the Munich Information Center for Protein Sequence (MIPS). The level of the accuracy achieved using ProRank in comparison to other recent methods for detecting protein complexes is a strong argument in favor of our proposed method. Datasets, programs and results are available at http://faculty.uaeu.ac.ae/nzaki/ProRank.htm.

In this videopaper, we introduce a climbing system inspired by gecko foot that uses magnets instead of Van der Waals interaction to achieve controlled adhesion.

We introduce a distributed compliant device inspired in gecko foot. It consists of a holder and small independent adhesion units on a flexible support. Unlike gecko, that uses Van der Waals force to achieve adhesion, the proposed design can be based on other adhesion phenomena, (for example magnetic or electrostatic force). We evaluate an implementation based on magnets. Key features are: limited surface roughness compliance, efficiency and cost-performance.

In this paper, mechanical design of a novel spherical wheel shape for a omni-directional mobile robot is presented. The wheel is used in a omnidirectional mobile robot realizing high step-climbing capability with its hemispherical wheel. Conventional Omniwheels can realize omnidirectional motion, however they have a poor step overcoming ability due to the sub-wheel small size. The proposed desing solves this drawback by means of a 4 wheeled design. “Omni-Ball” is formed by two passive rotational hemispherical wheels and one active rotational axis. An actual prototype model has been developed to illustrate the concept and to perform preliminary motion experiments, through which the basic performance of the Omnidirectional vechicle with this proposed Omni-Ball mechanism was confirmed. An prototype has been developed to illustrate the concept. Motion experiments, with a test vehicle are also presented.

We introduce a climbing system, inspired by gecko foot that uses magnets in place of Van der Waals interaction. This is possible because both forces, except for a scale factor, have similar force-curves. Taking advantage of this fact, we manufactured a hand-size "magnetic" version of a gecko-foot. This allowed us to conduct naked-eye experiments. As a result we identify four shape function relations that might partially explain the design of gecko feet. Additionally, we report experimental data, introduce a quantitative concept to compare climbing systems and show a prototype of magnetic hair