Observation and characterisation of trapped electron modes in Wendelstein 7-X

In the past, quasi coherent (QC) modes were reported for nearly all tokamaks. The general definition describes modes as QC when the magnitude squared coherence is in the range of 0.3 to 0.6. QC modes are observed in the plasma core as well as in the plasma edge and can have quite different physical origins. The one in the core are observed in plasmas with low collisionality, where the electron temperature exceeds the ion temperature in the plasma core. This is the case for electron cyclotron heating in general. The origin of these modes are electrons trapped within a magnetic mirror, as reported in the past from various fusion devices. The so-called trapped-electron modes (TEMs) belong to drift wave instabilities and can be destabilized by electron-temperature gradients in the plasma core. From the diagnostic point of view, QC modes appear as fluctuations in electron density and temperature. Therefore, the microwave reflectometer is very well suited to monitor these modes. This paper describes experiments, conducted at the Wendelstein 7-X stellarator (W7-X), which aim at detecting QC modes at low wave numbers. A poloidal correlation reflectometer installed at W7-X, is able to measure low wave numbers (). For medium line-averaged densities () the plasma core is accessible for this diagnostic. For different magnetic configurations and plasma parameters, broad QC structures are observed in the coherence spectra. From the analysis of the rotation and the poloidal structure, these QC modes show the properties of electron-temperature-gradient driven TEMs. A linear relation between the mode velocity and the rotation frequency is found. The relation is uniform and confirms the nature of QC-mode observation as TEM in tokamaks, too

Synthesis of activated cyclic carbonate monomers for NIPU synthesis

Non-isocyanates polyurethanes (NIPUs) are an emerging class of polymers developed to replace conventional polyurethanes synthesized from toxic isocyanates. NIPUs are produced via polyaddition reaction of cyclic carbonate and amines, thus reducing the exposure to hazardous chemicals. Despite the safety advantages, NIPU technology faces several challenges, particularly slow reactivity of cyclic carbonate monomers and insufficient polymer properties, which limit its ability to replace conventional PUs. This thesis developed in the framework of the NIPU-EJD project (funded by the European Union; H2020 Grant agreement 955700) is focused on the synthesis of cyclic carbonates monomers with a focus on enhancing their performance under mild conditions. Therefore, additional emphasis was placed over the synthesis of novel unsaturated cyclic carbonates (i.e. exo- and endo-vinylene carbonates), which demonstrated superior reactivity compared to saturated carbonates. Several synthetic strategies were tested to access monomers from bio- and petro-based sources and to implement different reactive groups. Moreover, saturated carbonates activated by urethane-linkages were synthesized and their adhesive properties tested. All the monomers were tested in conversion studies of model aminolysis reaction to evaluate the reactivity of the novel carbonates and, when possible, used for thermoplastic formulations

Predicting 3D ground reaction forces across various movement tasks: a convolutional neural network study comparing different inertial measurement unit configurations

Ground reaction forces (GRFs) are crucial for understanding movement biomechanics and for assessing the load on the musculoskeletal system. While inertial measurement units (IMUs) are increasingly used for gait analysis in natural environments, they cannot directly capture GRFs. Machine learning can be applied to predict 3D-GRFs based on IMU data. However, previous studies mainly focused on vertical GRF (vGRF) and isolated movement tasks. This study aimed to systematically evaluate the prediction accuracy of convolutional neural networks (CNNs) for 3D-GRFs using IMUs from single and multiple sensor configurations across various movement tasks. 20 healthy participants performed six movement tasks including walking, stair ascent, stair descent, running, a running step turn and a running spin turn at self-selected speeds. CNNs were trained to predict 3D-GRFs on IMU time series data for different configurations (lower body [7 IMUs], single leg [4 IMUs], femur-tibia [2 IMUs], tibia [1 IMU] and pelvis [1 IMU]). Prediction accuracies were assessed based on leave-one-subject-out cross validations using Pearson correlation (r) and relative root mean squared error (relRMSE). Across all tasks, CNNs predicted vGRF most accurately (r = 0.98, relRMSE ≤ 7.44 %), followed by anterior-posterior GRF (r ≥ 0.92, relRMSE ≤ 14.24 %), with medial–lateral GRF being the least accurate (r ≥ 0.74, relRMSE ≤ 29.46 %). CNNs predicted vGRF consistently across tasks, with similar accuracy for multi IMU (average r = 0.98, average relRMSE: 6.06 %) and single IMU configurations (average r = 0.98, average relRMSE: 6.88 %), supporting single IMU configurations for vGRF in practical applications. During cutting maneuvers, the lower body configuration reduces the relRMSE for mlGRF (5.23–12.46 %) and apGRF (1.53–3.16 %) compared to single IMU configurations. However, for mlGRF and apGRF during cutting tasks, lower body configuration improve accuracy, highlighting a trade-off between simplicity and performance