Dynamics of hard-sphere suspension using Dynamic Light Scattering and X-Ray Photon Correlation Spectroscopy: dynamics and scaling of the Intermediate Scattering Function

Intermediate Scattering Functions (ISF's) are measured for colloidal hard sphere systems using both Dynamic Light Scattering (DLS) and X-ray Photon Correlation Spectroscopy (XPCS). We compare the techniques, and discuss the advantages and disadvantages of each. Both techniques agree in the overlapping range of scattering vectors. We investigate the scaling behaviour found by Segre and Pusey [1] but challenged by Lurio et al. [2]. We observe a scaling behaviour over several decades in time but not in the long time regime. Moreover, we do not observe long time diffusive regimes at scattering vectors away from the peak of the structure factor and so question the existence of a long time diffusion coefficients at these scattering vectors.Comment: 21 pages, 11 figure

Driver Attention Assessment Using Physiological Measures from EEG, ECG, and EDA Signals †

In this paper, we consider the evaluation of the mental attention state of individuals driving in a simulated environment. We tested a pool of subjects while driving on a highway and trying to overcome various obstacles placed along the course in both manual and autonomous driving scenarios. Most systems described in the literature use cameras to evaluate features such as blink rate and gaze direction. In this study, we instead analyse the subjects' Electrodermal activity (EDA) Skin Potential Response (SPR), their Electrocardiogram (ECG), and their Electroencephalogram (EEG). From these signals we extract a number of physiological measures, including eye blink rate and beta frequency band power from EEG, heart rate from ECG, and SPR features, then investigate their capability to assess the mental state and engagement level of the test subjects. In particular, and as confirmed by statistical tests, the signals reveal that in the manual scenario the subjects experienced a more challenged mental state and paid higher attention to driving tasks compared to the autonomous scenario. A different experiment in which subjects drove in three different setups, i.e., a manual driving scenario and two autonomous driving scenarios characterized by different vehicle settings, confirmed that manual driving is more mentally demanding than autonomous driving. Therefore, we can conclude that the proposed approach is an appropriate way to monitor driver attention

Stress evaluation in simulated autonomous and manual driving through the analysis of skin potential response and electrocardiogram signals

The evaluation of car drivers\u2019 stress condition is gaining interest as research on Autonomous Driving Systems (ADS) progresses. The analysis of the stress response can be used to assess the acceptability of ADS and to compare the driving styles of different autonomous drive algorithms. In this contribution, we present a system based on the analysis of the Electrodermal Activity Skin Potential Response (SPR) signal, aimed to reveal the driver\u2019s stress induced by different driving situations. We reduce motion artifacts by processing two SPR signals, recorded from the hands of the subjects, and outputting a single clean SPR signal. Statistical features of signal blocks are sent to a Supervised Learning Algorithm, which classifies between stress and normal driving (non-stress) conditions. We present the results obtained from an experiment using a professional driving simulator, where a group of people is asked to undergo manual and autonomous driving on a highway, facing some unexpected events meant to generate stress. The results of our experiment show that the subjects generally appear more stressed during manual driving, indicating that the autonomous drive can possibly be well received by the public. During autonomous driving, however, significant peaks of the SPR signal are evident during unexpected events. By examining the electrocardiogram signal, the average heart rate is generally higher in the manual case compared to the autonomous case. This further supports our previous findings, even if it may be due, in part, to the physical activity involved in manual driving

Supervised learning techniques for stress detection in car drivers

6noIn this paper we propose the application of supervised learning techniques to recognize stress situations in drivers by analyzing their Skin Potential Response (SPR) and the Electrocardiogram (ECG). A sensing device is used to acquire the SPR from both hands of the drivers, and the ECG from their chest. We also consider a motion artifact removal algorithm that allows the generation of a single cleaned SPR signal, starting from the two SPR signals, which could be characterized by artifacts due to vibrations or movements of the hands on the wheel. From both the cleaned SPR and the ECG signals we compute some statistical features that are used as input to six Machine Learning Algorithms for stress or non-stress episodes classification. The SPR and ECG signals are also used as input to Deep Learning Algorithms, thus allowing us to compare the performance of the different classifiers. The experiments have been carried out in a firm specialized in developing professional car driving simulators. In particular, a dynamic driving simulator has been used, with subjects driving along a straight road affected by some unanticipated stress-evoking events, located at different positions. We obtain an accuracy of 88.13% in stress recognition using a Long Short-Term Memory (LSTM) network.openopenZontone P.; Affanni A.; Bernardini R.; Del Linz L.; Piras A.; Rinaldo R.Zontone, P.; Affanni, A.; Bernardini, R.; Del Linz, L.; Piras, A.; Rinaldo, R

Comparative assessment of drivers' stress induced by autonomous and manual driving with heart rate variability parameters and machine learning analysis of electrodermal activity

12openopenZontone, P; Affanni, A; Bernardini, R; Brisinda, D; Del Linz, L; Formaggia, F; Minen, D; Minen, M; Savorgnan, C; Piras, A; Rinaldo, R; Fenici, RZontone, P; Affanni, A; Bernardini, R; Brisinda, D; Del Linz, L; Formaggia, F; Minen, D; Minen, M; Savorgnan, C; Piras, A; Rinaldo, R; Fenici,

Relationship between programmed cell death and the cell cycle in the tobacco BY-2 cell line

Ethylene is an established plant growth regulator linked with programmed cell death (PCD). To investigate the relationship between the cell cycle and PCD, ethylene was used to see if it induced mortality in a cell cycle specific manner. Tobacco BY-2 cultures synchronized with aphidicolin were treated with ethylene. Cell cycle progression and mortality, measured at hourly intervals, showed distinct peaks of mortality at the G2/M boundary and S-phase. In conjunction with this, DNA fragmentation increased at G2/M. Furthermore, ethylene caused a significant reduction in cell size of the cycling population. Simultaneous addition of silver nitrate with ethylene ameliorated ethylene-induced G2/M mortality, although a toxic effect of silver alone was evident. Due to the toxicity of silver, 1-MCP, an alternative chemical for blocking ethylene receptors was used. 1-MCP neither affected the BY-2 cell cycle nor mortality levels. In addition, 1-MCP ameliorated ethylene-induced G2/M mortality. To balance the chemical approaches to blocking ethylene receptors, tobacco BY-2 cells were transformed with Atetrl that encodes a dominant insensitive form of the Arabidopsis ETR1 ethylene receptor. Atetrl expression caused a massive perturbation to the tobacco BY-2 cell cycle, especially in S-phase, and resulted in high levels of mortality throughout the cell cycle. Ethylene treatment caused a doubling of G2 duration but did not affect temporal distribution of mortality. However, ethylene treatment generated a peak of mortality in S-phase. These results suggest that ethylene induces PCD at G2/M through the known ethylene signaling pathway. Furthermore, it confirms that 1-MCP and Atetrl result in ethylene insensitivity. To examine the G2/M transition, Spcdc25, a positive regulator of G2/M in fission yeast was transformed into the tobacco BY-2 cell line. This resulted in premature entry into mitosis, a shortened cell cycle, and reduced cell size. This was similar to Spcdc25 over-expression in fission yeast and suggests the presence of a CDC25-like phosphatase in plants.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Exploring Physiological Signal Responses to Traffic-Related Stress in Simulated Driving †

In this paper, we propose a relatively noninvasive system that can automatically assess the impact of traffic conditions on drivers. We analyze the physiological signals recorded from a set of individuals while driving in a simulated urban scenario in two different traffic scenarios, i.e., with traffic and without traffic. The experiments were carried out in a laboratory located at the University of Udine, employing a driving simulator equipped with a moving platform. We acquired two Skin Potential Response (SPR) signals from the hands of the drivers, and an electrocardiogram (ECG) signal from their chest. In the proposed scheme, the SPR signals are then processed through a Motion Artifact (MA) removal algorithm such that possible motion artifacts arising during the drive are reduced. An analysis considering the scalogram of the single cleaned SPR signal is presented. This signal, along with the ECG, is then fed to various Machine Learning (ML) algorithms. More specifically, some statistical features are extracted from each signal segment which, after being analyzed through a binary ML model, are labeled as corresponding to a stressful situation or not. Our results confirm the applicability of the proposed approach to identify stress in the two scenarios. This is also in accordance with our findings considering the SPR signal scalograms

Skin potential response for stress recognition in simulated urban driving

In this paper, we address the problem of possible stress conditions arising in car drivers, thus affecting their driving performance. We apply various Machine Learning (ML) algorithms to analyse the stress of subjects while driving in an urban area in two different situations: one with cars, pedestrians and traffic along the course, and the other characterized by the complete absence of any of these possible stress-inducing factors. To evaluate the presence of a stress condition we use two Skin Potential Response (SPR) signals, recorded from each hand of the test subjects, and process them through a Motion Artifact (MA) removal algorithm which reduces the artifacts that might be introduced by the hand movements. We then compute some statistical features starting from the cleaned SPR signal. A binary classification ML algorithm is then fed with these features, giving as an output a label that indicates if a time interval belongs to a stress condition or not. Tests are carried out in a laboratory at the University of Udine, where a car driving simulator with a motorized motion platform has been prearranged. We show that the use of one single SPR signal, along with the application of ML algorithms, enables the detection of possible stress conditions while the subjects are driving, in the traffic and no traffic situations. As expected, we observe that the test individuals are less stressed in the situation without traffic, confirming the effectiveness of the proposed slightly invasive system for detection of stress in drivers