Efficient Innovation (France)

The mussel Mytilus galloprovincialis is a bivalve mollusc of high commercial interest, which frequently breeds in coastal areas with high levels of ship traffic noise. Studies on the effect of this stressor on mussels are scarce, which is why we set out to design a system to study the effect of ship noise on mussel behaviour (valve opening-closure rhythms). The proposed experimental set-up uses a high frequency non-invasive valvometry system to detect shell closure reactions related to instantaneous increases in sound level. The main findings are that mussels reacted to ship noise levels above 114 dB re 1μPa (63Hz-4kHz), that most reactions occurred within 13 s after a sudden increase in level, and that there does not appear to be a clear relationship between closure depth and noise level. Although mussels may not be sensitive to sound pressure but only to the associated particle movement, the results of this work contribute to highlighting the sensitivity of this species to noise, and the need to carry out experiments with a larger number of specimens and in conditions that allow the acoustic field to be reproduced more realistically.

CO2 was fixed by synthesizing a cyclic carbonate, which was used to prepare non-isocyanate polyurethane that can be reprocessed.

Surface carbon coating and bulk boron doping enabling high-rate and long durable Si anode.

This article examines the performance of time-series momentum strategies using daily returns for 78 futures markets across four major asset classes between January 1985 and December 2017. The authors find that the 252-day, 63-day, and 21-day momentum strategies perform similarly to the previously documented 12-month, 3-month, and 1-month momentum strategies, respectively. The performance is stronger with volatility-based position sizing, robust to implementation considerations such as a one-day gap between signal generation and execution, and persistent across asset classes and subperiods. The authors introduce a shorter duration momentum strategy with a weekly rebalancing frequency, which cannot be replicated using monthly returns. The authors find that the short-term strategy is a strong diversifier to the longer-term strategies, but the benefit may be reduced, or even completely offset, if the quality of trade execution is poor. The authors also find that the positive contribution of short-term momentum is driven by its superior diversifying characteristics rather than by the rebalancing frequency effect. TOPICS:Factor-based models, performance measurement, portfolio construction, style investing Key Findings • The authors examine the performance of time-series momentum using daily rather than monthly returns with standard lookback periods of 1, 3, and 12 months and a rebalancing period of one month. • They introduce a shorter duration momentum strategy with weekly rebalancing frequency. • The authors show that the short-term momentum strategy is a strong diversifier to the longer-term strategies but that the benefit is heavily dependent on the quality of trade execution.

In order to increase the toughness of epoxy resin and make full use of biological resources, a tung oil-based hyperbranched polymer (TOHBP) was synthesized.

The last few years have come with a sudden rise in ransomware attack incidents, causing significant financial losses to individuals, institutions and businesses. In reaction to these attacks, ransomware detection has become an important topic for research in recent years. Currently, there are two broad categories of ransomware detection techniques: signature-based and behaviour-based analyses. On the one hand, signature-based detection, which mainly relies on a static analysis, can easily be evaded by code-obfuscation and encryption techniques. On the other hand, current behaviour-based models, which rely mainly on a dynamic analysis, face difficulties in accurately differentiating between user-triggered encryption from ransomware-triggered encryption. In the current paper, we present an upgraded behavioural ransomware detection model that reinforces the existing feature space with a new set of features based on grouped registry key operations, introducing a monitoring model based on combined file entropy and file signature. We analyze the new feature model by exploring and comparing three different linear machine learning techniques: SVM, logistic regression and random forest. The proposed approach helps achieve improved detection accuracy and provides the ability to detect novel ransomware. Furthermore, the proposed approach helps differentiate user-triggered encryption from ransomware-triggered encryption, allowing saving as many files as possible during an attack. To conduct our study, we use a new public ransomware detection dataset collected in our lab, which consists of 666 ransomware and 103 benign binaries. Our experimental results show that our proposed approach achieves relatively high accuracy in detecting both previously seen and novel ransomware samples.

Coming to the Netherlands to pursue doctoral research was not what I planned on when I was working in Palo Alto, California.Little did I know that the city of Enschede is one among the six sister cities to Palo Alto!The Twente region led by university of Twente remains a cauldron of entrepreneurial activity.I am forever in debt to Prof.Aard Groen