Resolving vibrational from electronic coherences in two-dimensional electronic spectroscopy: The role of the laser spectrum

The observation of coherent quantum effects in photosynthetic light-harvesting complexes prompted the question whether quantum coherence could be exploited to improve the efficiency in new energy materials. The detailed characterization of coherent effects relies on sensitive methods such as two-dimensional electronic spectroscopy (2D-ES). However, the interpretation of the results produced by 2D-ES is challenging due to the many possible couplings present in complex molecular structures. In this work, we demonstrate how the laser spectral profile can induce electronic coherence-like signals in monomeric chromophores, potentially leading to data misinterpretation. We argue that the laser spectrum acts as a filter for certain coherence pathways and thus propose a general method to differentiate vibrational from electronic coherences

Investigation of the ultrafast photodynamics of a triazene compound

Triazenverbindungen spielen eine wichtige Rolle in der organischen Chemie und bei der Bildung neuer Heterozyklen, zudem werden sie als Anti-Krebs-Mittel eingesetzt. Die Triazenverbindung Berenil wird vor allem als Medikament zur Behandlung der Schlafkrankheit bei Tieren verwendet. Die Photodynamiken von Berenil wurden mit Hilfe der Methode der Fluoreszenz-Aufkonversion und der Methode der UV/Vis-Transienten Absorption untersucht. Durch die Untersuchungen konnte gezeigt werden, dass die Dynamiken im angeregten Zustand extrem schnell sind, auch wenn die Viskosität des Lösungsmittels erhöht oder Berenil an Biomoleküle gebunden wird. Diese schnellen Dynamiken können durch die sogenannte Fahrrad-Pedal-Bewegung erklärt werden, die zu einer Isomerisierung führt. Die thermodynamische Rückrelaxation läuft im Gegensatz dazu auf einer deutlich längeren Zeitskala ab

Manual and semi-automatic determination of elbow angle-independent parameters for a model of the biceps brachii distal tendon based on ultrasonic imaging

Mechtenberg M, Grimmelsmann N, Meyer HG, Schneider A. Manual and semi-automatic determination of elbow angle-independent parameters for a model of the biceps brachii distal tendon based on ultrasonic imaging. PLoS ONE . 2022;17(10): e0275128.Tendons consist of passive soft tissue with non linear material properties. They play a key role in force transmission from muscle to skeletal structure. The properties of tendons have been extensively examined in vitro. In this work, a non linear model of the distal biceps brachii tendon was parameterized based on measurements of myotendinous junction displacements in vivo at different load forces and elbow angles. The myotendinous junction displacement was extracted from ultrasound B-mode images within an experimental setup which also allowed for the retrieval of the exerted load forces as well as the elbow joint angles. To quantify the myotendinous junction movement based on visual features from ultrasound images, a manual and an automatic method were developed. The performance of both methods was compared. By means of exemplary data from three subjects, reliable fits of the tendon model were achieved. Further, different aspects of the non linear tendon model generated in this way could be reconciled with individual experiments from literature

Three-Dimensional (3D) Printing of Polymer-Metal Hybrid Materials by Fused Deposition Modeling

Fused deposition modeling (FDM) is a three-dimensional (3D) printing technology that is usually performed with polymers that are molten in a printer nozzle and placed line by line on the printing bed or the previous layer, respectively. Nowadays, hybrid materials combining polymers with functional materials are also commercially available. Especially combinations of polymers with metal particles result in printed objects with interesting optical and mechanical properties. The mechanical properties of objects printed with two of these metal-polymer blends were compared to common poly (lactide acid) (PLA) printed objects. Tensile tests and bending tests show that hybrid materials mostly containing bronze have significantly reduced mechanical properties. Tensile strengths of the 3D-printed objects were unexpectedly nearly identical with those of the original filaments, indicating sufficient quality of the printing process. Our investigations show that while FDM printing allows for producing objects with mechanical properties similar to the original materials, metal-polymer blends cannot be used for the rapid manufacturing of objects necessitating mechanical strength

Evaluation of sEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network

Limb movement prediction based on surface electromyography (sEMG) for the control of wearable robots, such as active orthoses and exoskeletons, is a promising approach since it provides an intuitive control interface for the user. Further, sEMG signals contain early information about the onset and course of limb movements for feedback control. Recent studies have proposed machine learning-based modeling approaches for limb movement prediction using sEMG signals, which do not necessarily require domain knowledge of the underlying physiological system and its parameters. However, there is limited information on which features of the measured sEMG signals provide the best prediction accuracy of machine learning models trained with these data. In this work, the accuracy of elbow joint movement prediction based on sEMG data using a simple feedforward neural network after training with different single- and multi-feature sets and data segmentation parameters was compared. It was shown that certain combinations of time-domain and frequency-domain features, as well as segmentation parameters of sEMG data, improve the prediction accuracy of the neural network as compared to the use of a standard feature set from the literature

Evaluation of sEMG Signal Features and Segmentation Parameters for Limb Movement Prediction Using a Feedforward Neural Network

Limb movement prediction based on surface electromyography (sEMG) for the control of wearable robots, such as active orthoses and exoskeletons, is a promising approach since it provides an intuitive control interface for the user. Further, sEMG signals contain early information about the onset and course of limb movements for feedback control. Recent studies have proposed machine learning-based modeling approaches for limb movement prediction using sEMG signals, which do not necessarily require domain knowledge of the underlying physiological system and its parameters. However, there is limited information on which features of the measured sEMG signals provide the best prediction accuracy of machine learning models trained with these data. In this work, the accuracy of elbow joint movement prediction based on sEMG data using a simple feedforward neural network after training with different single- and multi-feature sets and data segmentation parameters was compared. It was shown that certain combinations of time-domain and frequency-domain features, as well as segmentation parameters of sEMG data, improve the prediction accuracy of the neural network as compared to the use of a standard feature set from the literature

Block diagram for each computational method of feature extraction is shown.

The computed features are from left to right: the mytendinous junction, bone line and bone reference point. The mytendinous junction is tracked with a neural network based on the MobileNetV2 algorithm. The bone line is tracked via Hough-Line-Transformation. The bone reference point is tracked by a method based on the Lucas-Kanade algorithm.</p