Abstract
Distributed and heterogeneous systems (DHS), running over interconnected mobile and cloud-based platforms, are used in a growing number of domains for provisioning end-to-end services to users. Testing DHS is particularly important and challenging, with little support being provided by current tools. In order to assess the current state of the practice regarding the testing of DHS and identify opportunities and priorities for research and innovation initiatives, we conducted an exploratory survey that was responded by 147 software testing professionals that attended industry-oriented software testing conferences. The survey allowed us to assess the relevance of DHS in software testing practice, the most important features to be tested in DHS, the current status of test automation and tool sourcing for testing DHS, and the most desired features in test automation solutions for DHS. Some follow up interviews allowed us to further investigate drivers and barriers for DHS test automation. We expect that the results presented in the paper are of interest to researchers, tool vendors and service providers in this field.

Abstract
The use of rigid robot manipulators with good performance in industrial applications demands a proper robust and optimized control technique. Several works have proven the efficient use of metaheuristics optimization algorithms to work with complex problems in the robotic area. In this work, it is proposed the use of Grey Wolf Optimizer (GWO) with chaotic basis to optimize the parameters of a robust Higher Order Sliding Modes (HOSM) controller for the position control in joint space of a rigid robot manipulator. A total of seven test cases were considered varying the chosen chaotic map, face to the original GWO and the general repeatability of such algorithm is improved using chaotic versions. Also, two cost functions were tested within the HOSM optimization. Simulation results suggest that both algorithm and cost function formulations influence the chaotic map choice. In fact, the chattering problem, presented by HOSM controllers, is reduced when the cost function attempts to minimize the total variation of the control signal.

Abstract
In this work we develop a theoretical model to describe the propagation of an optical pulse in a 4-level atomic system. We investigate the existence of dissipative soliton solutions and analyze the stability of these solitary waves, comparing the analytical results with computational simulations based on the effective (1+1)-dimensional model derived from the Maxwell-Bloch equation under the slowly-varying envelope approximation.

Abstract
The use of big data analytics for forecasting business trends is gaining momentum among professionals. At the same time, supply chain risk management is important for practitioners to consider because it outlines ways through which firms can allay internal and external threats. Predicting and addressing the risks that social issues cause in the supply chain is of paramount importance to the sustainable enterprise. The aim of this research is to explore the application of big data analytics in mitigating supply chain social risk and to demonstrate how such mitigation can help in achieving environmental, economic, and social sustainability. The method involves an expert panel and survey identifying and validating social issues in the supply chain. A case study was used to illustrate the application of big data analytics in identifying and mitigating social issues in the supply chain. Our results show that companies can predict various social problems including workforce safety, fuel consumptions monitoring, workforce health, security, physical condition of vehicles, unethical behavior, theft, speeding and traffic violations through big data analytics, thereby demonstrating how information management actions can mitigate social risks. This paper contributes to the literature by integrating big data analytics with sustainability to explain how to mitigate supply chain risk.

Abstract
Given the difficulty of invasive methods to assess muscle action during natural human movement, surface electromyography (sEMG) has been increasingly used to capture muscle activity in relation to kinesiological analysis of specific tasks. Isolated isometric, concentric and eccentric forms of muscle action have been receiving the most attention for research purposes. Nevertheless natural muscle action frequently involves the use of a preceding eccentric muscle action as a form of potentiation of immediate muscle concentric action, in what is designated as muscle stretch-shortening cycle (SSC). The most frequently applied protocols for the evaluation of SSC on vertical jumps are by virtue of their reproducibility and control of experimental conditions, squat jump (SJ) without countermovement (CM), countermovement jump (CMJ) with long CM and drop jump (DJ) with short CM. The methods used to extract information and relationship of the captured signals also present a high diversity, with the question about the consistency of the methods and obtained results. The objective of this study is to evaluate the consistency of the analysis and results by applying different EMGs signal analysis techniques related to strategic muscle groups of the lower limbs at different countermovement evaluated in vertical jumps. Raw sEMG signals of 5 lower limb muscles of 6 subjects during SJ, CMJ and DJ were rectified, filtered and obtained their envelope, and then correlated (CR) for detection of synergistic, agonist and antagonist activity, applied principal component analysis (PCA) for the detection of uncorrelated components explaining maximum variability and normalized cross-correlation (CCRN) for detection of maximum correlations and time lag. CR of EMG envelopes presented higher coactivities (CoA) in DJ relative to SJ and these CoA superior to CMJ with greater synergy in DJ relative to SJ and CMJ that present several loop cycles corresponding to time lag of activity. CCRN of the EMG envelopes presented also higher CoA in DJ when compared to SJ and both higher CoA to CMJ. PCA allowed to detect a principal component (PC) explaining 92.2% of the variability of EMG in DJ, 90.6% in SJ and 78.7% in CMJ, the second PC responsible for the explanation of 4.9% variability in DJ, 6.7% in SJ and 15.3% in CMJ.

Abstract
Introduction Crohn's disease (CD) is a chronic inflammatory bowel disease known to carry a high risk of disabling and many times requiring surgical interventions. This article describes a decision-tree based approach that defines the CD patients' risk or undergoing disabling events, surgical interventions and reoperations, based on clinical and demographic variables. Materials and methods This multicentric study involved 1547 CD patients retrospectively enrolled and divided into two cohorts: a derivation one (80%) and a validation one (20%). Decision trees were built upon applying the CHAIRT algorithm for the selection of variables. Results Three-level decision trees were built for the risk of disabling and reoperation, whereas the risk of surgery was described in a two-level one. A receiver operating characteristic (ROC) analysis was performed, and the area under the curves (AUC) Was higher than 70% for all outcomes. The defined risk cut-off values show usefulness for the assessed outcomes: risk levels above 75% for disabling had an odds test positivity of 4.06 [3.50-4.71], whereas risk levels below 34% and 19% excluded surgery and reoperation with an odds test negativity of 0.15 [0.09-0.25] and 0.50 [0.24-1.01], respectively. Overall, patients with B2 or B3 phenotype had a higher proportion of disabling disease and surgery, while patients with later introduction of pharmacological therapeutic (1 months after initial surgery) had a higher proportion of reoperation. Conclusions The decision-tree based approach used in this study, with demographic and clinical variables, has shown to be a valid and useful approach to depict such risks of disabling, surgery and reoperation.