bluuebunny/crossref_metadata_2025_split huggingface dataset:
Online Motion Planning for All-Wheel Drive Autonomous Race Cars
Authors: Huisman Mischa , Lefeber Erjen 
Published: October 2024

AbstractThe advent of autonomous racing events, such as Formula Student Driverless Cup, requires online motion planning algorithms that push the vehicle to its limits while ensuring vehicle stability and preventing road departure. A popular method to find the optimal control input to drive at the limits of the car is Nonlinear Model Predictive Control (NMPC). However, when NMPC is used, often a trade-off has to be made between performance, accuracy, and computational complexity. In this manuscript, the principle of cascading different vehicle models is used to construct the prediction horizon. Initially, a two-track model optimizes steering and motor input, utilizing torque vectoring benefits. The horizon is then extended with a single-track model, and a lower fidelity point mass model, effectively reducing computational complexity. Furthermore, by adopting a curvilinear reference frame, a transformation towards the spatial domain is obtained, which allows us to use time as an optimization variable. A simulation study is performed for varying prediction horizon lengths which show the advantages of the cascaded vehicle model, achieving an 86% reduction in computation time with comparable lap times.
Vertical Dynamics Control Using Active Tires and Preview
Authors: der Sande Tom van , Kronnie Nick te 
Published: October 2024

AbstractIn this paper, the effects of adding a tire actuator to a vehicle with active suspension and preview control are studied. For the controller, the LQR method is used. The dual actuator system with preview control is compared to other conventional systems to determine the performance gain. The results show a gain of 182.3% in comfort compared to the passive suspension. The dual actuator system with preview control outperforms systems with fewer actuators and is less affected by the trade-off between handling and comfort.
Optimal Path Generator Embedded Model Predictive Control for Automated Vehicles
Authors: Sago Takashi , Arai Yoshihide , Ueyama Yuki , Harada Masanori 
Published: October 2024

AbstractThis paper investigates real-time optimal control for an automated vehicle. The model predictive control that generates the optimal trajectories has found wide applications in recent years due to increased computational performance. Numerical simulations of the full-vehicle model investigate the applicability of a path generator embedded model predictive control for the vehicle in the general shape road. The path generator is constructed by deep learning using the multiple open-loop optimal control problem solutions as the training dataset. Results demonstrate that the sequentially calculating optimal control command has the potential for real-time optimal control in the presence of the obstacle.
Charging Stations for Electric Vehicles in SUMO Simulation Environment and Their Impact on the Traffic Flow
Authors: Spachtholz Eva , Glomsda Marvin , Kranefeld Ingmar , Kracht Frédéric Etienne , Schramm Dieter 
Published: October 2024

AbstractPlanning of charging stations is becoming increasingly prevalent. Consequently, it is of paramount importance to assess their impact on traffic flow and, subsequently, their optimal positioning. To identify these optimal positions, microscopic traffic flow simulations represent an efficacious tool. Different positions can be trialed until an optimal solution is identified. The open-source microscopic traffic flow simulation tool SUMO would be an optimal choice for this purpose, but does not yet offer the possibility of efficiently implementing charging stations in its traffic networks. As part of a current research project, a novel method for implementing charging stations at their real location within the SUMO environment is presented. Exemplary locations are selected in the German city of Essen.
Tuning Time Delays to Improve the Performance of a Steering Controller
Authors: Li Jialin , Takacs Denes , Lu Jianwei , Voros Illes , Stepan Gabor 
Published: October 2024

AbstractTime delays and lags in control loops can cause instability and pose significant challenges to engineers. This study investigates a steering controller using the dynamic bicycle model, where the steering system dynamics are approximated by a steering lag. A higher-level controller calculates the desired steering angle based on the vehicle’s lateral position and yaw angle by considering various time delays related to these states. Stability charts are plotted for delay combinations, and the most stable gain setups for the feedback controller are determined. The results indicate that an appropriate increase in one of the time delays expands the stable domain of control gains for the vehicle system, and it enhances the performance of the vehicle controller.
Subjective-Objective Assessment of Different Torque Vectoring Control Strategies
Authors: Asperti Michele , Vignati Michele , Sabbioni Edoardo 
Published: October 2024

AbstractWith the increasing demand for enhanced vehicle performance and handling dynamics, torque vectoring has become a key technology in contemporary automotive engineering. Over the years, various control algorithms have been developed and their performance assessed using objective metrics that measure a vehicle's ability to follow a reference quantity for lateral dynamics. However, this evaluation approach is limited as it does not consider feedback from actual drivers. This paper addresses this critical gap by incorporating drivers’ subjective evaluations through Driver-in-the-Loop simulations. The study conducts a comprehensive analysis of both objective metrics and subjective driver assessments, investigating the impact of different torque vectoring control strategies on vehicle dynamics. By correlating quantitative metrics with human perception, this integrated approach enhances the understanding of the effectiveness of torque vectoring control strategies and their real-world implications for driver satisfaction.
Retaining Cornering Performance and Reducing Energy Consumption with Torque Vectoring and Suspensions Tuning
Authors: Asperti Michele , Vignati Michele , Sabbioni Edoardo 
Published: October 2024

AbstractWith the automotive industry's shift towards sustainability and energy efficiency, optimizing vehicle handling dynamics has become secondary. Additionally, there is a growing trend towards comfort-oriented design over handling performance. However, advancements such as integrating multiple independently controlled electric motors enable torque vectoring, offering a promising solution for reconciling these conflicting objectives. This paper proposes a novel approach to jointly improve vehicle handling and energy efficiency. Advanced simulation techniques are used to explore various suspension configurations to balance cornering performance and energy consumption. A torque vectoring controller is then designed in combination with meticulously tuned suspensions. This innovative approach, which considers active control design alongside suspension setup, achieves superior performance. Desired vehicle cornering capabilities are attained while ensuring significant efficiency in straight-line driving, which constitutes most road driving.
Design and Implementation of a Slip Control for Electric Formula Student Vehicle Using Sliding Mode Control
Authors: Alföldi Ádám , Fényes Dániel , Gáspár Péter 
Published: October 2024

AbstractThis paper presents a slip control design method for a four wheel driven electric race car with low hardware requirements. In addition, to achieve robustness against the changing frictional conditions, a discrete-time Luenberger tractive force observer is designed. The tuning is carried out using the high-precision vehicle dynamics simulation software CarMaker. The performance of the controller is demonstrated in real-world tests. An extensive comparison is given to show the advantage of the proposed method over a previously designed PID controller.
Towards field theory of multiple D0-branes. Hamiltonian mechanics and quantization of simplest 3D prototype of multiple D0-brane system
Authors: Bandos Igor , Sarraga Unai D. M. 
Published: October 2024

Abstract
Recently we have constructed a completely supersymmetric nonlinear action possessing the properties expected from multiple D0-brane system. Its quantization should result in an interesting supersymmetric field theory in the (super)space with additional matrix coordinates which can provide an important insights in the study of String Theory. As a first stage toward this aim, in this paper we construct the Hamiltonian mechanics and perform covariant quantization of the simplest three dimensional counterpart of the ten dimensional multiple D0-brane model. We obtain a supersymmetric system of equations in a (super)spacetime enlarged by bosonic and fermionic matrix coordinates which appears as a result of such quantization and discuss some of its properties.
Lattice study on finite density QC2D towards zero temperature
Authors: Iida Kei , Itou Etsuko , Murakami Kotaro , Suenaga Daiki 
Published: October 2024

Abstract
We investigate the phase structure and the equation of state (EoS) for dense two-color QCD (QC2D) at low temperature (T = 40 MeV, 324 lattice) for the purpose of extending our previous works [1, 2] at T = 80 MeV (164 lattice). Indeed, a rich phase structure below the pseudo-critical temperature Tc as a function of quark chemical potential μ has been revealed, but finite volume effects in a high-density regime sometimes cause a wrong understanding. Therefore, it is important to investigate the temperature dependence down to zero temperature with large-volume simulations. By performing 324 simulations, we obtain essentially similar results to the previous ones, but we are now allowed to get a fine understanding of the phase structure via the temperature dependence. Most importantly, we find that the hadronic-matter phase, which is composed of thermally excited hadrons, shrinks with decreasing temperature and that the diquark condensate scales as ⟨qq⟩ ∝ μ2 in the BCS phase, a property missing at T = 80 MeV. From careful analyses, furthermore, we confirm a tentative conclusion that the topological susceptibility is independent of μ. We also show the temperature dependence of the pressure, internal energy, and sound velocity as a function of μ. The pressure increases around the hadronic-superfluid phase transition more rapidly at the lower temperature, while the temperature dependence of the sound velocity is invisible. Breaking of the conformal bound is also confirmed thanks to the smaller statistical error.
Enhancing stock price prediction using GANs and transformer-based attention mechanisms
Authors: Li Siyi , Xu Sijie 
Published: October 2024

Abstract
Stock price prediction plays an important role in financial decision-making, enabling investors and analysts to make informed choices regarding trading and investment strategies. Traditional statistical methods have been utilized for the prediction of stock price, but it is often difficult for them to capture complex patterns, adapt to changing market conditions, handle large datasets, and automatically extract relevant features. Recent advancements in machine learning and deep learning offer promising solutions to address these challenges. In this paper, we propose a new approach to enhance the stock price prediction by leveraging generative adversarial networks (GANs) and transformer-based attention mechanisms. GANs are utilized to generate synthetic stock price data, and incorporating market sentiment and volatility. Attention mechanisms will selectively concentrate on the important features and patterns in the data, which may do good to the identification of key market indicators which will impact stock prices. By integrating market social media news which can tell about the sentiment and volatility, our model aims to improve the accuracy and robustness of stock price forecasts. We also address the limitations of GANs and attention mechanisms separately used in stock price prediction, such as unrealistic data generation and overfitting, by employing regularization techniques and incorporating additional data sources. Experimental evaluations using real-world stock market data will be conducted to compare the performance of our proposed models with conventional approaches. The findings of this research have implications for investors, financial analysts, and other stakeholders who are engaged in the stock market ecosystem, providing valuable insights for the investment strategies.
Choice of alcohol over a natural reward: an experimental study in light and heavy social drinkers
Authors: Karlsson Hanna , Mcntyre Sarah , Gustavson Sarah , Andersson David , Szczot Ilona , Heilig Markus , Perini Irene 
Published: October 2024

Abstract

Rationale & Objectives
A core symptom of alcohol use disorder (AUD) is a progressively increased choice of alcohol over alternative rewards despite negative consequences. Here, we investigated choice between personalized alcohol vs. natural rewards in a laboratory setting, and compared this behavior between non-treatment-seeking heavy drinkers and light social drinkers.

Methods
30 light social drinkers (15 men drinking < 15 drinks/week and 15 women drinking < 10 drinks/week) and 30 heavy, non-treatment-seeking drinkers (drinking more than these levels; 15 women). In the Concurrent Choice Alcohol Food (CCAF) task, participants chose between individually tailored images of alcohol and snack rewards and collected points towards the respective reward. To assess cost sensitivity, points associated to the images varied so that they favored alcohol or snack, or were equal, creating three relative point levels.

Results
Choice preference for alcohol was strongly correlated with Alcohol Use Disorder Identification Test (AUDIT) scores, supporting the external validity of the choice procedure. Compared to light drinkers, heavy drinkers showed increased choice preference for alcohol, as indicated by a between-group difference in points of subjective equality, a metric that quantifies the relative point level at which alcohol and snacks were equally likely to be chosen. In both groups, choice preference strongly depended on the relative point level of alcohol compared to snacks, suggesting that responding for alcohol in heavy drinkers was sensitive to costs.

Conclusions
Our results replicate previous findings of a relationship between self-reported alcohol use and choice preference for alcohol. We also found that choice behavior was strongly dependent on relative cost of alcohol in both groups, although price sensitivity was lower in heavy compared to light drinkers. An increased choice preference for alcohol in heavy drinkers suggests that they attribute a higher relative reinforcing value to alcohol compared to natural rewards.
A snapshot of the Physcomitrella N-terminome reveals N-terminal methylation of organellar proteins
Authors: Hoernstein Sebastian N. W. , Schlosser Andreas , Fiedler Kathrin , van Gessel Nico , Igloi Gabor L. , Lang Daniel , Reski Ralf 
Published: October 2024

Abstract
Key message
Analysis of the N-terminome of Physcomitrella reveals N-terminal monomethylation of nuclear-encoded, mitochondria-localized proteins.

Abstract
Post- or co-translational N-terminal modifications of proteins influence their half-life as well as mediating protein sorting to organelles via cleavable N-terminal sequences that are recognized by the respective translocation machinery. Here, we provide an overview on the current modification state of the N-termini of over 4500 proteins from the model moss Physcomitrella (Physcomitrium patens) using a compilation of 24 N-terminomics datasets. Our data reveal distinct proteoforms and modification states and confirm predicted targeting peptide cleavage sites of 1,144 proteins localized to plastids and the thylakoid lumen, to mitochondria, and to the secretory pathway. In addition, we uncover extended N-terminal methylation of mitochondrial proteins. Moreover, we identified PpNTM1 (P. patens alpha N-terminal protein methyltransferase 1) as a candidate for protein methylation in plastids, mitochondria, and the cytosol. These data can now be used to optimize computational targeting predictors, for customized protein fusions and their targeted localization in biotechnology, and offer novel insights into potential dual targeting of proteins.

Graphical abstract
A radiographic artificial intelligence tool to identify candidates suitable for partial knee arthroplasty
Authors: York Thomas J. , Szyszka Bartosz , Brivio Angela , Musbahi Omar , Barrett David , Cobb Justin P. , Jones Gareth G. 
Published: October 2024

Abstract
Introduction
Knee osteoarthritis is a prevalent condition frequently necessitating knee replacement surgery, with demand projected to rise substantially. Partial knee arthroplasty (PKA) offers advantages over total knee arthroplasty (TKA), yet its utilisation remains low despite guidance recommending consideration alongside TKA in shared decision making. Radiographic decision aids exist but are underutilised due to clinician time constraints.

Materials and methods
This research develops a novel radiographic artificial intelligence (AI) tool using a dataset of knee radiographs and a panel of expert orthopaedic surgeons’ assessments. Six AI models were trained to identify PKA candidacy.

Results
1241 labelled four-view radiograph series were included. Models achieved statistically significant accuracies above random assignment, with EfficientNet-ES demonstrating the highest performance (AUC 95%, F1 score 83% and accuracy 80%).

Conclusions
The AI decision tool shows promise in identifying PKA candidates, potentially addressing underutilisation of this procedure. Its integration into clinical practice could enhance shared decision making and improve patient outcomes. Further validation and implementation studies are warranted to assess real-world utility and impact.
Resistance- and endurance-trained young men display comparable carotid artery strain parameters that are superior to untrained men
Authors: Hornby-Foster Ian , Richards Cory T. , Drane Aimee L. , Lodge Freya M. , Stembridge Michael , Lord Rachel N. , Davey Hannah , Yousef Zaheer , Pugh Christopher J. A. 
Published: October 2024

Abstract

Purpose
Central arterial stiffness, a predictor of cardiovascular risk, attenuates with endurance-exercise in ageing populations. However, in young individuals, this effect is inconsistent and emerging evidence suggests resistance-exercise may increase arterial stiffness. Two-dimensional (2D)-Strain imaging of the common carotid artery (CCA) is more sensitive at detecting endurance-training induced alterations in CCA stiffness than conventional methods, but has not been used to examine CCA stiffness in young resistance-trained individuals. Therefore, we compared CCA 2D-Strain parameters at rest, during acute exercise and recovery between resistance-trained, endurance-trained, and untrained young men.

Methods
Short-axis CCA ultrasound images were obtained from 12 endurance-trained [27yrs (95%CI; 24–29)], 14 resistance-trained [24yrs (23–26)] and 12 untrained [23yrs (22–24] men at rest, during isometric handgrip (IHG) exercise and recovery. 2D-Strain analysis quantified CCA peak circumferential strain (PCS) and systolic (S-SR) and diastolic (D-SR) strain rates. Conventional stiffness indices included aortic pulse-wave velocity, CCA β-stiffness (β
1) and Petersons elastic modulus (E
p).

Results
Resting conventional stiffness indices were not different between groups (P > 0.05). Resting PCS and S-SR were comparable between resistance- [11.6% (10.6–12.5) and 1.46 s−1 (1.37–1.55), respectively] and endurance-trained [11.4% (10.7–12.2) and 1.5 s−1 (1.38–1.62)] men and superior to untrained men [9.5% (9.19–9.9); P < 0.004 and 1.24 s−1 (1.17 – 1.31); P < 0.018)]. Both trained groups displayed comparable reductions in PCS and S-SR during IHG, which returned to resting values during recovery (P < 0.001), whereas these parameters remained unchanged in untrained men. D-SR decreased during IHG in all groups (P < 0.001), but to a lesser extent in endurance-trained men (P < 0.023), whereas β
1 and E
p increased to a similar magnitude in all groups and returned to resting values during recovery (P < 0.001).

Conclusion
Resistance- and endurance-trained men display comparable CCA 2D-Strain parameters that are superior to untrained men, which contends previous reports that resistance-training increases CCA stiffness.
Idiosyncratic gesture use in a mother-infant dyad in chimpanzees (Pan troglodytes schweinfurthii) in the wild
Authors: van Boekholt Bas , Clark Isabelle , Lahiff Nicole J. , Lee Kevin C. , Slocombe Katie E. , Wilke Claudia , Pika Simone 
Published: October 2024

Abstract
One promising method to tackle the question, “In which modality did language evolve?” is by studying the ontogenetic trajectory of signals in human’s closest living relatives, including chimpanzees (Pan troglodytes). Concerning gestures, current debates centre on four different hypotheses: “phylogenetic ritualization”, “social transmission through imitation”, “ontogenetic ritualization”, and “social negotiation”. These differ in their predictions regarding idiosyncratic gestures, making such occurrences a crucial area of investigation. Here, we describe a novel and potential idiosyncratic behaviour — ‘hand-on-eye’ — which was initially observed in one mother-infant dyad in a community of chimpanzees living in the wild. We systematically investigated the form, sequential organisation, intentionality, usage, function, and distribution of the behaviour over a five-year period. The results showed that ‘hand-on-eye’ was nearly exclusively deployed in a single mother-infant dyad, was accompanied by hallmarks of intentionality, and served to initiate or resume joint dorsal travel. Although the behaviour was observed once in each of three other mother-infant dyads, these lacked the same frequency and hallmarks of intentionality. ‘Hand-on-eye’ thus qualifies as an idiosyncratic gesture. The proposed developmental pathway gives support to both the “ontogenetic ritualization” and “social negotiation” hypotheses. It also stresses the crucial need for longitudinal approaches to tackle developmental processes that are triggered by unique circumstances and unfold over relatively long time windows.
German Parents and Educators of Two to Four-Year-Old Children as Informants for the Strengths and Difficulties Questionnaire (SDQ)
Authors: Dubiel Simone , Cohen Franziska , Anders Yvonne 
Published: October 2024

AbstractScreeners are used in early intervention and early childhood education and care programs to identify children’s potential need for further evaluation and diagnostics. The Strengths and Difficulties Questionnaire (SDQ) is a brief behavioral screening instrument that can be completed by both parents and educators to assess the social and emotional traits of children. However, multiple informants’ reports vary. In this study, the extent to which parents’ (n = 241) and educators’ (n = 157) differ and agree in their assessments of children aged 3.5 years on average, was examined. T-tests were used to examine differences between informants and correlations within a multitrait-multimethod matrix (MTMM) in their agreement. Results showed moderate to high levels of rater agreement ranging from r = .35 and r = .53 on the five subscales of the SDQ. We found that hyperactivity, peer relationship problems, and prosocial behavior vary due to meaningful reasons, e.g., the home vs. pre-school setting, and the informant’s relationship towards the child. Hyperactivity seems to be relatively consistent across settings. Methodological variations might explain differences in emotional symptoms and conduct problems. Considering ratings from multiple informants outlines a more comprehensive view of children’s behavior and should be preferred over single-informant research designs.