Abstract
Climatic data collected between 1963 and 2010 in the Portuguese Dão wine growing region were analysed to evaluate the relationship between climatic indices, seasonal weather and the berry composition of the red cultivar 'Touriga Nacional'. The trends over time for the classical temperature-based indices (growing season temperature, growing degree days, biologically effective degree days, Huglin index and cool night index) were significantly positive and can be mostly attributed to the effects of climate change. The dryness index showed a negative trend although not significant. These indices were able to explain 9 and 45% of the variability in total soluble solids and titratable acidity, respectively, using a multiple stepwise regression analysis. The proportion of explained variability was much improved, to 52% for total soluble solids and 65% for titratable acidity, when new climatic indices were used. The new indices resulted from the generalisation of the classical indices based upon chronological time specification as well as taking into consideration the phenological time instead. Our data shows that the classical climatic indices were not able to sufficiently explain the berry composition, and that new climatic indices should be used for a better understanding of the climate drivers of berry quality.

Abstract
In the vehicle routing problem with heterogeneous fleet and variable costs (HFVRP), the group of routes to be developed to satisfy the demand of the customer must be determined, considering the minimization of the total costs of the travelled distance. Heuristic algorithms based on local searches use simple movements (neighborhoods) to generate feasible solutions to problems related to route design. In this article, we conduct a comparative analysis of granular neighborhoods in a Tabu Search for the HFVRP, in terms of the quality of the obtained solution. The computational experiments, performed on instances of benchmarking for the HFVRP, showed the efficiency and effectiveness of implementing some neighborhoods in metaheuristic algorithms of path, such as the Tabu Search.

Abstract
AbstractTranscriptional dysregulation is a key feature of cancer. Transcription factors (TFs) are the main link between signalling pathways and the transcriptional regulatory machinery of the cell, positioning them as key oncogenic inductors and therefore potential targets of therapeutic intervention. We implemented a computational pipeline to infer TF regulatory activities from basal gene expression and applied it to publicly available and newly generated RNA-seq data from a collection of 1,010 cancer cell lines and 9,250 primary tumors. We show that the predicted TF activities recapitulate known mechanisms of transcriptional dysregulation in cancer and dissect mutant-specific effects in driver genes. Importantly, we show the potential for predicted TF activities to be used as markers of sensitivity to the inhibition of their upstream regulators. Furthermore, combining these inferred activities with existing pharmacogenomic markers significantly improves the stratification of sensitive and resistant cell lines for several compounds. Our approach provides a framework to link driver genomic alterations with transcriptional dysregulation that helps to predict drug sensitivity in cancer and to dissect its mechanistic determinants.

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.