Energy Saving Potential of a Temperature Test Chamber by implementing a Heat-Pump

Energy savings become more and more important – also for the rather small industry sector of environmental simulation. So far the technical focus was mainly to realize challenging test cycles which are often gives as temperature profiles. The energy efficiency of the test devices are not specified by any standard. Nevertheless, especially test cycles with intermittent cooling and heating over a wide temperature range need a substantial electrical energy input. As state-of-the-art technology for cooling a vapor compression cycle and for heating an electrical heater is used. In this work a standard temperature test chamber with a specified temperature range from -75 °C to 180 °C using a cascade cooling system and an electric heater is investigated. As a first step the baseline chamber was investigated experimentally and the energy consumption quantified. In the next step the refrigeration cycle was modified with a heat pump capability as well as further cycle modification that indicated energy saving potential in preliminary studies. A comparison to the baseline chamber is carried and reveals substantial energy saving potential

Guilt-Specific Processing in the Prefrontal Cortex

Guilt is a central moral emotion due to its inherent link to norm violations, thereby affecting both individuals and society. Furthermore, the nature and specificity of guilt is still debated in psychology and philosophy, particularly with regard to the differential involvement of self-referential representations in guilt relative to shame. Here, using functional magnetic resonance imaging (fMRI) in healthy volunteers, we identified specific brain regions associated with guilt by comparison with the 2 most closely related emotions, shame and sadness. To induce high emotional intensity, we used an autobiographical memory paradigm where participants relived during fMRI scanning situations from their own past that were associated with strong feelings of guilt, shame, or sadness. Compared with the control emotions, guilt episodes specifically recruited a region of right orbitofrontal cortex, which was also highly correlated with individual propensity to experience guilt (Trait Guilt). Guilt-specific activity was also observed in the paracingulate dorsomedial prefrontal cortex, a critical "Theory of Mind” region, which overlapped with brain areas of self-referential processing identified in an independent task. These results provide new insights on the unique nature of guilt as a "self-conscious” moral emotion and the neural bases of antisocial disorders characterized by impaired guilt processin

The Relationship between Trait Empathy and Memory Formation for Social vs. Non-Social Information

Background: To navigate successfully through their complex social environment, humans need both empathic and mnemonic skills. Little is known on how these two types of psychological abilities relate to each other in humans. Although initial clinical findings suggest a positive association, systematic investigations in healthy subject samples have not yet been performed. Differentiating cognitive and affective aspects of empathy, we assumed that cognitive empathy would be positively associated with general memory performance, while affective empathy, due to enhanced other-related emotional reactions, would be related to a relative memory advantage for information of social as compared to non-social relevance. Methods: We investigated in young healthy participants the relationship between dispositional cognitive and affective empathy, as measured by Davis’ Interpersonal Reactivity Index (Journal of Personality and Social Psychology, 44, 113–126, 1983), and memory formation for stimuli (numbers presented in a lottery choice task) that could be encoded in either a social (other-related) or a non-social (self-related) way within the task. Results: Cognitive empathy, specifically perspective taking, correlated with overall memory performance (regardless of encoding condition), while affective empathy, specifically empathic personal distress, predicted differential memory for socially vs. non-socially encoded information. Conclusion: Both cognitive and affective empathy are associated with memory formation, but in different ways, depending on the social nature of the memory content. These results open new and so far widely neglected avenues of psychological research on the relationship between social and cognitive skills.<br

When Does Oxytocin Affect Human Memory Encoding? The Role of Social Context and Individual Attachment Style

The neuropeptide oxytocin plays an essential role in regulating social behavior and has been implicated in a variety of human cognitive processes in the social domain, including memory processes. The present study investigates the influence of oxytocin on human memory encoding, taking into account social context and personality, which have previously been neglected as moderators for how oxytocin affects memory encoding. To examine the role of social context of encoding, we employed an established experimental paradigm in which participants perform a word-categorization task in either a joint (social) or individual (non-social) setting. To investigate the role of socially relevant personality factors, participants’ adult attachment style (AAS) was assessed. Previous research has identified attachment style as a potent moderator of oxytocin effects in the social-cognitive domain, but here we investigated for the first time its role in memory encoding. Participants were invited in pairs and received either placebo or oxytocin intranasally. Forty-five minutes later, they were instructed to react to different word categories within a list of successively presented words. This task was performed individually in the non-social condition and simultaneously with the partner in the social condition. After a 24-h delay, memory for all words was tested individually in a surprise recognition memory test. Oxytocin effects on memory accuracy depended on participants’ AAS. Specifically, oxytocin positively affected memory for participants who scored low on attachment dependence (who find dependence on others uncomfortable), but negatively affected memory for high scorers (who are comfortable depending on others). Oxytocin effects were not moderated by social vs. non-social context at encoding, and we discuss reasons for this outcome. Regardless of encoding condition or personality, oxytocin led to more liberal responding in the recognition memory test, which was also reflected in significantly higher false alarm rates (FARs) and a trend towards higher hit rates (HRs) compared to placebo. Overall, our results are consistent with an interactionist view on oxytocin effects on human cognitive functioning. Future research should further examine how oxytocin affects response biases via previous encoding and the ways in which biological dispositions linked to attachment style affect the process of memory encoding

Probing Convolutional Neural Networks for Event Reconstruction in {\gamma}-Ray Astronomy with Cherenkov Telescopes

A dramatic progress in the field of computer vision has been made in recent years by applying deep learning techniques. State-of-the-art performance in image recognition is thereby reached with Convolutional Neural Networks (CNNs). CNNs are a powerful class of artificial neural networks, characterized by requiring fewer connections and free parameters than traditional neural networks and exploiting spatial symmetries in the input data. Moreover, CNNs have the ability to automatically extract general characteristic features from data sets and create abstract data representations which can perform very robust predictions. This suggests that experiments using Cherenkov telescopes could harness these powerful machine learning algorithms to improve the analysis of particle-induced air-showers, where the properties of primary shower particles are reconstructed from shower images recorded by the telescopes. In this work, we present initial results of a CNN-based analysis for background rejection and shower reconstruction, utilizing simulation data from the H.E.S.S. experiment. We concentrate on supervised training methods and outline the influence of image sampling on the performance of the CNN-model predictions.Comment: 8 pages, 4 figures, Proceedings of the 35th International Cosmic Ray Conference (ICRC 2017), Busan, Kore

A sensorized modular training platform to reduce vascular damage in endovascular surgery

Purpose Endovascular interventions require intense practice to develop sufficient dexterity in catheter handling within the human body. Therefore, we present a modular training platform, featuring 3D-printed vessel phantoms with patient-specific anatomy and integrated piezoresistive impact force sensing of instrument interaction at clinically relevant locations for feedback-based skill training to detect and reduce damage to the delicate vascular wall. Methods The platform was fabricated and then evaluated in a user study by medical (n=10) and non-medical (n=10) users. The users had to navigate a set of guidewire and catheter through a parkour of 3 modules including an aneurismatic abdominal aorta, while impact force and completion time were recorded. Eventually, a questionnaire was conducted. Results The platform allowed to perform more than 100 runs in which it proved capable to distinguish between users of different experience levels. Medical experts in the fields of vascular and visceral surgery had a strong performance assessment on the platform. It could be shown, that medical students could improve runtime and impact over 5 runs. The platform was well received and rated as promising for medical education despite the experience of higher friction compared to real human vessels. Conclusion We investigated an authentic patient-specific training platform with integrated sensor-based feedback functionality for individual skill training in endovascular surgery. The presented method for phantom manufacturing is easily applicable to arbitrary patient-individual imaging data. Further work shall address the implementation of smaller vessel branches, as well as real-time feedback and camera imaging for further improved training experience

LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes

The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions. We propose LapSeg3D, a novel DNN-based approach for the voxel-wise annotation of point clouds representing surgical scenes. As the manual annotation of training data is highly time consuming, we introduce a semi-autonomous clustering-based pipeline for the annotation of the gallbladder, which is used to generate segmented labels for the DNN. When evaluated against manually annotated data, LapSeg3D achieves an F1 score of 0.94 for gallbladder segmentation on various datasets of ex-vivo porcine livers. We show LapSeg3D to generalize accurately across different gallbladders and datasets recorded with different RGB-D camera systems

LapSeg3D: Weakly Supervised Semantic Segmentation of Point Clouds Representing Laparoscopic Scenes

The semantic segmentation of surgical scenes is a prerequisite for task automation in robot assisted interventions. We propose LapSeg3D, a novel DNN-based approach for the voxel-wise annotation of point clouds representing surgical scenes. As the manual annotation of training data is highly time consuming, we introduce a semi-autonomous clustering-based pipeline for the annotation of the gallbladder, which is used to generate segmented labels for the DNN. When evaluated against manually annotated data, LapSeg3D achieves an F1 score of 0.94 for gallbladder segmentation on various datasets of ex-vivo porcine livers. We show LapSeg3D to generalize accurately across different gallbladders and datasets recorded with different RGB-D camera systems.Comment: 6 pages, 5 figures, accepted at the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), Kyoto, Japa

Augmented Reality-based Robot Control for Laparoscopic Surgery

Minimally invasive surgery is the standard formany abdominal interventions, with an increasing use of tele-manipulated robots. As collaborative robots enter the field ofmedical interventions, their intuitive control needs to be ad-dressed. Augmented reality can thereby support a surgeonby representing the surgical scene in a natural way. In thiswork, an augmented reality based robot control for laparo-scopic cholecystectomy is presented. A user can interact withthe virtual scene to clip the cystic duct and artery as well asto manipulate the deformable gallbladder. An evaluation wasperformed based on the SurgTLX and system usability scale