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There is a growing interest in the medical use of psychedelic substances, as preliminary studies using them for psychiatric disorders have shown positive results. In particular, one of these substances is N, N-dimethyltryptamine (DMT), an agonist serotonergic psychedelic that can induce profound alterations in the state of consciousness. In this work, we use an exploratory tool to reveal DMT-induced changes in brain activity using EEG data and provide new insights into the mechanisms of action of this psychedelic substance. We used a two-class classification based on (A) the connectivity matrix or (B) complex network measures derived from it as input to a support vector machine. We found that both approaches could detect changes in the brain's automatic activity, with case (B) showing the highest AUC (89%), indicating that complex network measurements best capture the brain changes that occur due to DMT use. In the second step, we ranked the features that contributed the most to this result. For case (A), we found that differences in the high alpha, low beta, and delta frequency bands were most important in distinguishing between the state before and after DMT inhalation, which is consistent with the results described in the literature. Further, the connection between the temporal (TP8) and central cortex (C3) and between the precentral gyrus (FC5) and the lateral occipital cortex (P8) contributed most to the classification result. The connection between regions TP8 and C3 has been found in the literature associated with finger movements that might have occurred during DMT consumption. However, the connection between cortical areas FC5 and P8 has not been found in the literature and is presumably related to the volunteers' emotional, visual, sensory, perceptual, and mystical experiences during DMT consumption. For case (B), closeness centrality was the most crucial complex network measure. Furthermore, we discovered larger communities and longer average path lengths when DMT was used and the converse when not, showing that the balance between functional segregation and integration had been disrupted. These findings support the idea that cortical brain activity becomes more entropic under psychedelics. Overall, a robust computational workflow has been developed here with interpretability of how DMT (or other psychedelics) modify brain networks and insights into their mechanism of action. Finally, the same methodology applied here may help interpret EEG time series from patients who consumed other psychedelic drugs.
Heart disease, also known as cardiovascular disease, encompasses a variety of heart conditions that can result in sudden death for many people. Examples include high blood pressure, ischaemia, irregular heartbeats and pericardial effusion. Electrocardiogram (ECG) signal analysis is frequently used to diagnose heart diseases, providing crucial information on how the heart functions. To analyse ECG signals, quantile graphs (QGs) is a method that maps a time series into a network based on the time-series fluctuation proprieties. Here, we demonstrate that the QG methodology can differentiate younger and older patients. Furthermore, we construct networks from the QG method and use machine-learning algorithms to perform the automatic diagnosis, obtaining high accuracy. Indeed, we verify that this method can automatically detect changes in the ECG of elderly and young subjects, with the highest classification performance for the adjacency matrix with a mean area under the receiver operating characteristic curve close to one. The findings reported here confirm the QG method’s utility in deciphering intricate, nonlinear signals like those produced by patient ECGs. Furthermore, we find a more significant, more connected and lower distribution of information networks associated with the networks from ECG data of the elderly compared with younger subjects. Finally, this methodology can be applied to other ECG data related to other diseases, such as ischaemia.
Progress in mechanobiology allowed us to better understand the important role of mechanical forces in the regulation of biological processes. Space research in the field of life sciences clearly showed that gravity plays a crucial role in biological processes. The space environment offers the unique opportunity to carry out experiments without gravity, helping us not only to understand the effects of gravitational alterations on biological systems but also the mechanisms underlying mechanoperception and cell/tissue response to mechanical and gravitational stresses. Despite the progress made so far, for future space exploration programs it is necessary to increase our knowledge on the mechanotransduction processes as well as on the molecular mechanisms underlying microgravity-induced cell and tissue alterations. This white paper reports the suggestions and recommendations of the SciSpacE Science Community for the elaboration of the section of the European Space Agency roadmap “Biology in Space and Analogue Environments” focusing on “How are cells and tissues influenced by gravity and what are the gravity perception mechanisms?” The knowledge gaps that prevent the Science Community from fully answering this question and the activities proposed to fill them are discussed.
This contribution quantifies the birefringence within injection-molded cyclic olefin copolymer plates and discusses its impact on the mechanical properties of the plates. It also focuses on the impact of birefringence on integrated waveguides and Bragg gratings and provides fabrication guidelines for such structures. The anisotropy in all three dimensions of the workpiece is examined by means of polarimetry and a prism coupler. It is found that the birefringence is inhomogenously distributed within the workpieces, whereas the maximum birefringence not only varies locally, but also depends on the observation direction. Overall, a maximum birefringence of 10 × 10−4 is found at the plate’s surface near the injection gate. The anisotropy then reduces exponentially towards the center of the workpiece and saturates at 1.8 × 10−4, in a depth of 0.4 mm. Thus, the birefringence strongly affects near-surface photonic structures. It is found that, depending on their orientation and the local birefringence of the substrate, waveguides and Bragg gratings fabricated with comparable parameters behave completely differently in terms of polarization-dependent optical attenuation, cross-sectional intensity distribution and Bragg reflection signal. For example, the support of the TM mode can vary between total loss and an optical attenuation of 0.9 dB × cm−1. In consequence, this study underlines the importance of quantifying the birefringent state of an injection-molded cyclic olefin copolymer workpiece if it is supposed to serve as a substrate for integrated photonic structures. The study furthermore demonstrates that birefringence effects can be omitted by burying the photonic structures deeper into the volume of the thermoplastic.
In this contribution we deal with the problem of producing “reasonable” data, when considering recorded energy consumption data, which are at certain sections incomplete and/or erroneous. This task is important, when energy providers employ prediction models for expected energy consumption, which are based on past recorded consumption data, which then of course should be reliable and valid. In a related contribution Yilmaz (2022), GAN-based methods for producing such “artificial data” have been investigated. In this contribution, we describe an alternative and complementary method based on signal inpainting, which has been successfully applied to audio processing Lieb and Stark (2018). After giving a short overview of the theory of proximity-based convex optimization, we describe and adapt an iterative inpainting scheme to our problem. The usefulness of this approach is demonstrated by analyzing real-world-data provided by a German energy supplier.
Predicting Autonomous Driving Behavior through Human Factor Considerations in Safety-Critical Events
(2024)
This paper investigates the ability of autonomous driving systems to predict outcomes by
considering human factors like gender, age, and driving experience, particularly in the context of
safety-critical events. The primary objective is to equip autonomous vehicles with the capacity to
make plausible deductions, handle conflicting data, and adjust their responses in real-time during
safety-critical situations. A foundational dataset, which encompasses various driving scenarios
such as lane changes, merging, and navigating complex intersections, is employed to enable vehicles
to exhibit appropriate behavior and make sound decisions in critical safety events. The deep
learning model incorporates personalized cognitive agents for each driver, considering their distinct
preferences, characteristics, and requirements. This personalized approach aims to enhance the
safety and efficiency of autonomous driving, contributing to the ongoing development of intelligent
transportation systems. The efforts made contribute to advancements in safety, efficiency, and overall
performance within autonomous driving systems. To describe the causal relationship between external
factors like weather conditions and human factors, and safety-critical driver behaviors, various
data mining techniques can be applied. One commonly used method is regression analysis. Additionally,
correlation analysis is employed to reveal relationships between different factors, helping to
identify the strength and direction of their impact on safety-critical driver behavior.
Keywords: car following; decision making; driving behavior; naturalistic driving studies; safety-critical
events; cognitive vehicles
1. Introduction
Despite the increasing prevalence of vehicle automation, the persistently high number
of car crashes remains a concern. Safety-critical events in human-driven scenarios have
become more intricate and partially uncontrollable due to unforeseen circumstances. Investigating
human driving behavior is imperative to establish traffic baselines for mixed
traffic, encompassing traditional, automated, and autonomous vehicles (AVs). Various
factors, such as weather conditions affecting visibility in longitudinal car-following (CF)
behavior [1,2], influence human driving behavior [3].
Car-following behavior, illustrating how a following vehicle responds to the lead
vehicle in the same lane, is a crucial aspect. Existing car-following models often make
assumptions about homogeneous drivers, neglecting significant heterogeneity in driving
experience, gender, character, emotions, and sociological, psychological, and physiological
traits. Failing to account for this heterogeneity hampers a comprehensive understanding of
car-following behavior, limiting model accuracy and applicability. In the development of
more realistic car-following models for mixed traffic, acknowledging the diversity among
drivers is crucial. By including individual variations such as risk-taking tendencies, reaction
times, decision-making processes, and driving styles, the modeling of real-world
driving complexities can be improved. Simplifying drivers into a few categories overlooks
the richness and variety of their characteristics, prompting the need for a more comprehensive
approach to capture nuances within different driver profiles. To address these
Smart Cities
In recent years, the demand for sustainable and degradable materials and electronic devices has increased significantly.
Among a range of biodegradable polymers, poly(lactic acid) (PLA) is a good alternative to conventional petrol-based polymers because of its attractive mechanical properties and its easy processability. Recently, PLA has also been described as a promising dielectric material with piezoelectric and electret properties. We expect that PLA—after further optimization— will play an important role as a material for environmentally friendly sensors in the future, where first applications such as air filters and pressure sensors have already been proposed. However, degradation under normal ambient conditions is very slow, and an accelerated and controllable degradation process is highly desirable for any type of PLA-based sensors.
Enzymatic hydrolysis with embedded enzymes has been proposed as an approach to accelerate and control degradation. In this work, we investigate the properties of PLA in terms of dielectric and mechanical properties with a focus on its ability to store charges after the enzyme proteinase K (Trit. album) has been incorporated. Results reveal that proteinase K has a positive effect on the charge stability of solvent-cast PLA electrets after complete evaporation of the solvent. Furthermore, we observed a concentration-dependent acceleration of mass loss in a Tris-HCl buffer. A fast degradation within only one day occurred at a concentration of 6 wt% proteinase K.
Modellierung des Antriebsstrangs einer Textilmaschine zum Zweck der modellbasierten Steuerung
(2024)
Zur Produktion von gewirkten Textilien werden mehrere Nadeln auf eine Legebarre gesetzt, die durch Servoantriebe positioniert werden. Aufgrund der erzwungenen Bewegung kann der Antriebsstrang bei hohen Drehzahlen zur Schwingung angeregt werden. Die richtige Wahl der Steuerkurven ist daher eine sehr wichtige und anspruchsvolle Aufgabe, die durch ein Antriebsstrangmodell unterstützt werden soll. Dafür wird am Beispiel eines Teststands ein Modell des Antriebsstranges in MATLAB/Simulink® entwickelt. Für das Antriebsstrangmodell müssen eine geeignete Modellordnung und Modellparameter gewählt werden. Die unbekannten Modellparameter werden durch eine Parameteridentifikation ermittelt. Mit einer Validierung wird ein geeignetes Anregungsspektrum für die Bestimmung der Modellordnung und die Parameteridentifikation ermittelt.
Integrating artificial intelligence (AI) into decision-making processes is key to improving organizational performance. However, trust in AI-based decision support systems (DSSs), similar to other information systems, is important for successful integration. A disruptive phenomenon, “algorithm aversion”, can impede AI trust and, thus, acceptance. Although AI recommendations outperform human recommendations in different decision-making fields, individuals underweight recommendations from AI-based DSSs compared to human decision-makers due to a lack of AI trust. We conducted a lab experiment to investigate the role of AI recommendations in workplace-related tasks, first focusing on the mediating effect of AI trust and the negative impact of algorithm aversion on decision-making performance and the moderating effect of technical competence. Second, we analyzed the ability of gamification to reduce this phenomenon. We provide evidence regarding how to enhance decision-making performance when AI recommendations are deployed and identify countermeasures against algorithm aversion to facilitate the adoption of AI-based DSSs.