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At the proposed electron-proton collider LHeC electroweak interactions can be uniquely studied in a largely unexplored kinematic region of spacelike momentum transfer. We simulate inclusive neutral- and charged-current deep-inelastic lepton proton scattering cross section data at center-of-mass energies of 1.2 and 1.3 TeV, and estimate the uncertainties of Standard Model parameters as well as of parameters describing physics beyond the Standard Model. A precision at sub-percent level is expected for the measurement of the weak neutral-current couplings of the light-quarks to the Z boson, g A / V u / d , improving their present precision by more than an order of magnitude. The weak mixing angle can be determined with a precision of about Δ sin 2 θ W = ± 0.00015 , and its scale dependence can be studied in the range between about 25 and 700 GeV. An indirect determination of the W -boson mass in the on-shell scheme is possible with an experimental uncertainty down to Δ m W = ± 6 MeV . We discuss how measurements in deep-inelastic scattering compare with those in the timelike domain, and which aspects are unique, for instance electroweak parameters in charged-current interactions. We conclude that the LHeC will determine electroweak physics parameters, in the spacelike region, with unprecedented precision leading to thorough tests of the Standard Model and possibly beyond.

Nuclear parton distributions are far from being known today because of an infant experimental base. Based on design studies of the LHeC and using new simulations, of the inclusive neutral and charged current cross section measurements and of the strange, charm and beauty densities in nuclei, it is demonstrated how that energy frontier electron-ion collider would unfold the complete set of nuclear PDFs in a hugely extended kinematic range of deep inelastic scattering, extending in Bjorken x down to values near to 10−6 in the perturbative domain. Together with a very precise and complete set of proton PDFs, the LHeC nPDFs will thoroughly change the theoretical understanding of parton dynamics and structure inside hadrons.

Background Checkpoint-inhibitor immunotherapies have had a profound effect in the treatment of cancer by inhibiting down-regulation of T-cell response to malignancy. The cardiotoxic potential of these agents was first described in murine models and, more recently, in numerous clinical case reports of pericarditis, myocarditis, pericardial effusion, cardiomyopathy, and new arrhythmias. The objective of our study was to determine the frequency of and associated risk factors for cardiotoxic events in patients treated with immune checkpoint inhibitors. Methods Medical records of patients who underwent immunotherapy with durvalumab, ipilimumab, nivolumab, and pembrolizumab at Wake Forest Baptist Health were reviewed. We collected retrospective data regarding sex, cancer type, age, and cardiovascular disease risk factors and medications. We aimed to identify new diagnoses of heart failure, atrial fibrillation, ventricular fibrillation/tachycardia, myocarditis, and pericarditis after therapy onset. To assess the relationship between CVD risk factors and the number of cardiac events, a multivariate model was applied using generalized linear regression. Incidence rate ratios were calculated for every covariate along with the adjusted P -value. We applied a multivariate model using logistic regression to assess the relationship between CVD risk factors and mortality. Odds ratios were calculated for every covariate along with the adjusted P -value. Adjusted P -values were calculated using multivariable regression adjusting for other covariates. Results Review of 538 medical records revealed the following events: 3 ventricular fibrillation/tachycardia, 12 pericarditis, 11 atrial fibrillation with rapid ventricular rate, 0 myocarditis, 8 heart failure. Significant risk factors included female gender, African American race, and tobacco use with IRR 3.34 (95% CI 1.421, 7.849; P  = 0.006), IRR 3.39 (95% CI 1.141, 10.055; P  = 0.028), and IRR 4.21 (95% CI 1.289, 13.763; P  = 0.017) respectively. Conclusions Our study revealed 34 significant events, most frequent being pericarditis (2.2%) and atrial fibrillation (2.0%) with strongest risk factors being female gender, African American race, and tobacco use. Patients who meet this demographic, particularly those with planned pembrolizumab treatment, may benefit from early referral to a cardio-oncologist. Further investigation is warranted on the relationship between CTLA-4 and PD-L1 expression and cardiac adverse events with ICIs, particularly for these subpopulations.

Turbulence plays an important role in fluids and plasmas, but its detection at the level of individual particles remains challenging. In this work, we introduce a long short-term memory (LSTM) neural network for the classification of turbulence in complex plasmas based on particle-resolved trajectory data. The model is trained on a large synthetic dataset of particle trajectories generated from multiple turbulent and non-turbulent flow fields, including homogeneous isotropic turbulence, channel-flow turbulence and structured non-turbulent reference fields. From basic trajectory features such as velocity, acceleration, jerk, angle change and curvature, the network learns to identify motion patterns characteristic of turbulence. The trained model achieves a classification accuracy of 99.35%, with a precision of 99.89% and a recall of 98.83% on a strictly separated test dataset, demonstrating its ability to distinguish turbulent from non-turbulent trajectories across previously unseen flow configurations. The approach is further evaluated using molecular dynamics simulations of obstacle-driven complex plasma flows. In these simulations, trajectories classified as turbulent are predominantly associated with elevated turbulent kinetic energy and vorticity, even though these quantities are not provided as explicit input features. The absence of a sharp threshold in either quantity indicates that the classification is not based on a trivial energy criterion but instead exploits additional temporal and geometric information encoded in the particle motion. Overall, this study demonstrates that an LSTM-based analysis can be used to classify turbulent and non-turbulent particle trajectories in complex plasmas using particle-bound kinematic features alone and that the resulting classifier generalizes across diverse simulated flow regimes.

Detecting micron-sized particles is an essential task for the analysis of complex plasmas because a large part of the analysis is based on the initially detected positions of the particles. Accordingly, high accuracy in particle detection is desirable. Previous studies have shown that machine learning algorithms have made great progress and outperformed classical approaches. This work presents an approach for tracking micron-sized particles in a dense cloud of particles in a dusty plasma at Plasmakristall-Experiment 4 using a U-Net. The U-net is a convolutional network architecture for the fast and precise segmentation of images that was developed at the Computer Science Department of the University of Freiburg. The U-Net architecture, with its intricate design and skip connections, has been a powerhouse in achieving precise object delineation. However, as experiments are to be conducted in resource-constrained environments, such as parabolic flights, preferably with real-time applications, there is growing interest in exploring less complex U-net architectures that balance efficiency and effectiveness. We compare the full-size neural network, three optimized neural networks, the well-known StarDist and trackpy, in terms of accuracy in artificial data analysis. Finally, we determine which of the compact U-net architectures provides the best balance between efficiency and effectiveness. We also apply the full-size neural network and the the most effective compact network to the data of the PK-4 experiment. The experimental data were generated under laboratory conditions.

Particle tracking velocimetry (PTV) forms the basis for many fluid dynamic experiments, in which individual particles are tracked across multiple successive images. However, when the experimental setup involves high-speed, high-density particles that are indistinguishable and follow complex or unknown flow fields, matching particles between images becomes significantly more challenging. Reliable PTV algorithms are crucial in such scenarios. Previous work has demonstrated that the Self-Organizing Map (SOM) machine learning approach offers superior outcomes on complex-plasma data compared with traditional methods, though its performance is sensitive to hyperparameter calibration, which requires optimization for specific flow scenarios. In this article, we describe how the dependence of the various hyperparameters on different flow scenarios was studied and the optimal settings for diverse flow conditions were identified. Based on these results, automatic hyperparameter calibration was implemented in the PTV framework. Furthermore, the SOM’s performance was directly compared with that of the preceding conventional PTV method, Trackpy, for complex plasmas using synthetic data. Finally, as a new approach to identifying incorrectly matched particle traces, a Long Short-Term Memory (LSTM) neural network was developed to sort out all inaccuracies to further improve the outcome. Combined with automatic hyperparameter calibration, outlier detection and additional computational speed optimization, this work delivers a robust, versatile and efficient framework for PTV analysis.

Objective To determine if rigid adherence (where medically appropriate) to an algorithm/checklist-based patient care pathway can reduce the duration of hospitalization and complication rates in patients undergoing head and neck reconstruction with free tissue transfer. Methods Study design was a retrospective case-control study of patients undergoing major head and neck cancer resections and reconstruction at a tertiary referral centre. The intervention was rigid adherence to a pre-existing care pathway including flow algorithms and multidisciplinary checklists incorporated into patient charting and care orders. 157 patients were enrolled prospectively and were compared to 99 patients in a historical cohort. Patient charts were reviewed and information related to the patient, procedure, and post-operative course was extracted. The two groups were compared for number of major and minor complications (using the Clavien-Dindo system) and length of stay in hospital. Results Comparing pre- and post-intervention groups, no significant difference was identified in duration of hospital stay (21.5 days vs. 20.5 days, p  = 0.750), the rate of major complications was significantly higher in the pre-intervention cohort (25.3 % vs. 14.0 %, p  = 0.031), the rate of minor complications was not significantly higher (34.3 % vs 30.8 %, p  = 0.610). Conclusion Rigid adherence to our patient care pathway, and improved charting techniques including flow algorithms and multidisciplinary checklists has improved patient care by showing a significant reduction in the rate of major complications.

An increasing political contestation of the existing private-property order can currently be observed. With serious demands for expropriation, even outside marginalized splinter groups, a concept is returning that has largely disappeared from the focus of political theory. In this context, the article aims at a decidedly political-scientific examination of the legal institution of expropriation by locating the concept in the architecture of the democratic rule of law. Shifting between the poles of the constitutional guarantee of private property on the one hand and a potentiality of democratic contestation of the property order on the other, it is made clear that the concept of expropriation highlights the aporias of the democratic rule of law. The thesis is presented by means of a theoretical-historical contouring using the example of the intensive discussions on expropriation in the Weimar Republic and, in particular, with a more in-depth examination of the positions of Carl Schmitt and Otto Kirchheimer, which are subsequently figured as antipodes. While Schmitt seeks to make plausible a far-reaching rejection of expropriation potentials with a narrow concept of the rule of law, Kirchheimer focuses on an extensive interpretation of democratic power of disposition over the private-property order. The Weimar crisis years are examined as a kind of \"laboratory\" in order to profit from the extraordinary degree of crisis-induced searching and to work out a political science theoretical language for contemporary discussions of expropriation.

Eine zunehmende politische Infragestellung der bestehenden Privateigentumsordnung ist gegenwärtig zu konstatieren. Mit ernstzunehmenden Enteignungsforderungen auch außerhalb marginalisierter Splittergruppen kehrt ein Begriff zurück, der weitestgehend aus dem Blickfeld der Politischen Theorie verschwand. Der vorliegende Beitrag zielt in diesem Zusammenhang auf eine dezidiert politikwissenschaftliche Untersuchung des Rechtsinstituts der Enteignung, indem der Begriff in der Architektur des demokratischen Rechtsstaates verortet wird. Changierend zwischen den Polen der rechtsstaatlichen Garantie des Privateigentums einerseits und einer Potenzialität demokratischer Infragestellungen der Eigentumsordnung andererseits wird deutlich gemacht, dass der Enteignungsbegriff auf herausgehobene Weise die Aporien des demokratischen Rechtsstaates sichtbar macht. Die These wird durch eine theoriegeschichtliche Konturierung am Beispiel der intensiven Enteignungsdiskussionen in der Weimarer Republik und insbesondere mit tiefergehender Untersuchung der im Weiteren als Antipoden figurierten Positionen Carl Schmitts und Otto Kirchheimers dargelegt. Während Schmitt mit einem engen Rechtsstaatsbegriff eine weitgehende Zurückweisung von Enteignungspotenzialitäten zu plausibilisieren sucht, stellt Kirchheimer auf eine extensive Interpretation demokratischer Verfügungsgewalt über die Privateigentumsordnung ab. Die Weimarer Krisenjahre werden als eine Art „Laboratorium“ befragt, um vom außergewöhnlichen Maß kriseninduzierter Suchbewegungen zu profitieren und eine politikwissenschaftliche Theoriesprache für gegenwärtige Enteignungsdiskussionen in Ansätzen herauszuarbeiten.

The load-adaptive behavior of the muscles in the human musculoskeletal system offers great potential for minimizing resource and energy requirements in many technical systems, especially in drive technology and robotics. However, the lack of knowledge about suitable technical linear actuators that can reproduce the load-adaptive behavior of biological muscles in technology is a major reason for the lack of successful implementation of this biological principle. In this paper, therefore, the different types of linear actuators are investigated. The focus is particularly on artificial muscles and rope pulls. The study is based on literature, on the one hand, and on two physical demonstrators in the form of articulated robots, on the other hand. The studies show that ropes are currently the best way to imitate the load-adaptive behavior of the biological model in technology. This is especially illustrated in the context of this paper by the discussion of different advantages and disadvantages of the technical linear actuators, where ropes, among other things, have a good mechanical and control behavior, which is very advantageous for use in an adaptive system. Finally, the next steps for future research are outlined to conclude how ropes can be used as linear actuators to transfer load-adaptive lightweight design into technical applications.