bluuebunny/crossref_metadata_2025 huggingface dataset:
Inhibitory Potential of Silychrystin on ERR‐γ, Keap‐1, and NF‐κB Proteins in Acute Pancreatitis Explored Through Molecular Docking and Molecular Dynamics Simulation
Authors: Das Tridip J. , Ullah Tanzim Nishad , Tiwari Shaurya , Gowala Upasa , Deka Satyendra , Bhattacharya Kunal , Tag Hui , Hui Pallabi K.
Published: October 2024

Acute pancreatitis (AP) presents a substantial burden in India, characterized by evolving disease patterns and life‐threatening complications. Globally, the increasing incidence of AP underscores the urgent need for effective therapeutic interventions. Key proteins such as ERR‐γ, Keap‐1, and NF‐κB play pivotal roles in the pathophysiology of AP, contributing to dysregulated molecular pathways and cellular interactions. Targeting these proteins offers a promising avenue to modulate these dysregulated pathways and improve AP outcomes. Smilax perfoliata, widely recognized for its diverse medicinal properties, presents a promising opportunity for the exploration of novel antipancreatitis agents, although data on its efficacy remains limited. In this study, we employed molecular docking simulations to evaluate the binding interactions between 23 identified phytocompounds from Smilax perfoliata and the target proteins ERR‐γ, Keap‐1, and NF‐κB. Among the compounds tested, silychrystin exhibited the highest binding affinity with Keap‐1 (−11.2 kcal/mol), followed by ERR‐γ (−10.2 kcal/mol) and NF‐κB (−8.5 kcal/mol). Additionally, a 200‐ns molecular dynamics simulation using GROMACS (GROningen MAchine for Chemical Simulations) confirmed the stability of the silychrystin‐target protein complex, revealing consistent interaction stability and minimal structural fluctuations. These findings suggest that silychrystin could serve as a promising therapeutic candidate for AP, warranting further research to evaluate its efficacy in preclinical and clinical settings.
POE‐Mediated Tunable Quantum Yield of Carbon Dots−Derived From Agapanthus Africanus (L.) Hoffmann Leaves
Authors: Nunes Paulo J. , Pereira Rui F. P. , Nunes S. C. , Correia Sandra F. H. , Fu Lianshe , Ferreira Rute A. S. , Fernandes Mariana , Bermudez Verónica de Zea
Published: October 2024

The green synthesis of carbon dots (CDs) from natural sources is a challenging goal. Herein CDs are produced from Agapanthus africanus (L.) Hoffmann leaves by carbonization at 200/300 °C for 2/3 h. Samples are named CZ‐X‐Y, where Z, X, and Y represent carbonization, temperature, and time, respectively. CZ‐200‐3, CZ‐300‐2, and CZ‐300‐3 CDs have average sizes of 3.7 ± 0.7, 5.3 ± 1.2, and 5.1 ± 1.6 nm, respectively. Their surface, devoid of chlorophyll, contains ─OH, ─C═O, and ─C(═O)OH groups and sylvite. Isolated CZ‐300‐3 emits at 400 nm (excited at 260 nm) and exhibits an emission quantum yield (QY) value of 2 ± 1%. Embedding in the d‐U(600)/d‐(900) di‐ureasil matrices resulted in transparent films with emission intensity maxima at 420/450 nm (360 nm), and QY values of 7 ± 1/16 ± 2% (400 nm). The enhancement of the QY value of the bare CDs agrees with an efficient passivation provided by the hybrid host. The hydrophilic CZ‐300‐3 CDs also exerted a marked surface modifying role, changing the surface roughness and the wettability of the hybrid films.
Rapid Assessment of Biomarkers on Single Extracellular Vesicles Using “Catch and Display” on Ultrathin Nanoporous Silicon Nitride Membranes
Authors: Walker Samuel N. , Lucas Kilean , Dewey Marley J. , Badylak Stephen F. , Hussey George S. , Flax Jonathan , McGrath James L.
Published: October 2024

Extracellular vesicles (EVs) are particles released from cells that facilitate intercellular communication and have tremendous diagnostic and therapeutic potential. Bulk assays lack the sensitivity to detect rare EV subsets relevant to disease, and while single EV analysis techniques remedy this, they are often undermined by complicated detection schemes and prohibitive instrumentation. To address these issues, a microfluidic technique for EV characterization called “catch and display for liquid biopsy (CAD‐LB)” is proposed. In this method, minimally processed samples are pipette‐injected and fluorescently labeled EVs are captured in the nanopores of an ultrathin membrane.  This enables the rapid assessment of EV number and biomarker colocalization by light microscopy. Here, nanoparticles are used to define the accuracy and dynamic range for counting and colocalization. The same assessments are then made for purified EVs and for unpurified EVs in plasma. Biomarker detection is validated using CD9 and Western blot analysis to confirm that CAD‐LB accurately reports relative protein expression levels. Using unprocessed conditioned media, CAD‐LB captures the known increase in EV‐associated ICAM‐1 following endothelial cell cytokine stimulation. Finally, to demonstrate CAD‐LB's clinical potential, EV biomarkers indicative of immunotherapy responsiveness are successfully detected in the plasma of bladder cancer patients treated with immune checkpoint blockade.
Onion‐Like Carbon Nanozyme: Controlling Peroxidase‐Like Activity by Carbon Hybridization Patterns for Antibacterial Therapy
Authors: Shi Yuxi , Zheng Xiangyun , Zhao Qi , Feng Yuchen , Zhang Hanxin , Gao Guanyue , Wang Hao , Zhi Jinfang
Published: October 2024

Since the inception of the concept of nanozymes, there has been a growing interest in the rational design and controllable synthesis of nanozymes with adjustable activities. In this study, onion‐liked carbon (OLC) with remarkable peroxidase‐like (POD) activity are developed through delicately controlling the sp2/sp3 configuration. The investigation reveals that enzymatic activity of OLC increases first and then decreases with the increased graphitic degree, with the highest activity observed at a moderate sp2/sp3 ratio of 17.17%. A series of experiments and theoretical calculations are conducted to elucidate the catalytic mechanism, and the structure‐dependent activity is attributed to a synergistic effect of surface adsorption and electron transfer processes. The POD activity enables the OLC to catalyze the decomposition of H2O2, producing reactive oxygen species for eradicating Gram‐positive and Gram‐negative bacteria. Additionally, toxicity tests based on nematode and mouse models confirmed the excellent biocompatibility of OLC. Furthermore, the OLC exhibited antibacterial ability and promoted bacterial‐infected wound healing in a mouse model. This work not only gives a deeper understanding of the structure‐activity relationship and catalytic mechanism of carbon‐based nanozymes, but also unveils a novel avenue for antibacterial therapy and wound healing applications.
Reversibly Compressible Silanated Cellulose Nanofibril Aerogel for Triboelectric Taekwondo Scoring Sensors
Authors: Rajabi‐Abhari Araz , Yoo Hyunjoon , Kim Ji‐Seok , Yoon Hong Yeon , Park Jeong Young , Yan Ning , Tabassian Rassoul , Oh Il‐Kwon
Published: October 2024

The integration of bio‐based materials into triboelectric nanogenerators (TENGs) for energy harvesting from human body motions has sparked considerable research attention. Here, a silanated cellulose nanofibril (SCNF) aerogel is reported for structurally reliable TENGs and reversely compressible Taekwondo scoring sensors under repeated impacts. The preparation of the aerogel involves silanizing cellulose nanofibers (CNFs) with vinyltrimethoxysilane (VTMS), following by freeze–drying and post‐heating treatment. The SCNF aerogel with crosslinked physico‐chemical bonding and highly porous network is found to exhibit superior mechanical strength and reversible compressibility as well as enhanced water repellency and electron‐donating ability. The TENG having a tribo‐positive SCNF layer exhibits exceptional triboelectric performances, generating a voltage of 270 V, current of 11 µA, and power density of 401.1 mW m−2 under an applied force of 8 N at a frequency of 5 Hz. With its inherent merits in material composition, structural configuration, and device sensitivity, the SCNF TENG demonstrates the capability to seamlessly integrate into a Taekwondo protection gear, serving as an efficient self‐powered sensor for monitoring hitting scores. This study highlights the significant potential of a facilely fabricated SCNF aerogel for the development of high‐performance, bio‐friendly, and cost‐effective Bio‐TENGs, enabling their application as self‐powered wearable devices and sports engineering sensors.
Enhance the Performance of CZTSSe Solar Cells Through Inhibiting the Cu2+, Tu, and (─COOH) Association Reaction
Authors: Wang Lei , SiQin Letu , Wang Yiming , Li Shuyu , Xin Wenjing , Guo Jingyuan , Liu Ruijian , Luan Hongmei , Zhu Chengjun
Published: October 2024

The Sol‐gel precursor solution reaction mechanism has a significant impact on the Cu2ZnSn(S, Se)4 (CZTSSe) solar cells. It is discovered that in the Cu2ZnSnS4 (CZTS) precursor solution (CZTS‐PS) in the preparation, there is an association reaction among Cu2+, thiourea (Tu), and carboxyl (‐COOH), which is an important reason for the undesirable CZTSSe solar cells. The strong association reaction generates excessive Cu2+ ions, forming the CuxSe secondary phase on the surface of the CZTSSe absorber. The secondary phase causes a short circuit and deterioration of gadget performance. Following a 6‐h aging period for the CZTS‐PS, the average photoelectric conversion efficiency (PCE) of the device is enhanced to 8.02%, and there is also an improvement in device uniformity, as evidenced by a decrease in the standard deviation to less than 1. To inhibit the association reaction and eliminate the aging time phenomenon, a strategy is developed using hydrochloric acid to regulate the CZTS‐PS environment. This strategy shifts the REDOX reaction in Cu2++Sn2+ toward the formation of Cu1++Sn4+, leading to a decrease in the defect concentrations of VSn(−/0) and CuSn(−/0), which increases the carrier concentration and reduces the impact of band tailing. The average power conversion efficiency (PCE) of the devices improved from 7.45% to 9.26%, the PCE of the best‐performing CZTSSe solar cells increased from 9.25% to 9.83%, and the consistency among the devices is further enhanced, as indicated by a reduction in the standard deviation from 0.98 to 0.44. Ultimately, the device performance of the Cu2++Sn2+‐DMF system improved by 11.01% (without the MgF2 layer) after optimization. This study serves as a reference for regulating the environment of the CZTS‐PS to further enhance the CZTSSe devices' performance, and the photoelectric conversion efficiency is improved by ≈30%.
Revealing the Role of Ruthenium on the Performance of P2‐Type Na0.67Mn1‐xRuxO2 Cathodes for Na‐Ion Full‐Cells
Authors: Altin Emine , Moeez Iqra , Kwon Eunji , Bhatti Ali Hussain Umar , Yu Seungho , Chung Kyung Yoon , Arshad Muhammad , Harfouche Messaoud , Buldu Murat , Altundag Sebahat , Bulut Fatih , Sahinbay Sevda , Altin Serdar , Ates Mehmet Nurullah
Published: October 2024

Herein, P2‐type layered manganese and ruthenium oxide is synthesized as an outstanding intercalation cathode material for high‐energy density Na‐ion batteries (NIBs). P2‐type sodium deficient transition metal oxide structure, Na0.67Mn1‐xRuxO2 cathodes where x varied between 0.05 and 0.5 are fabricated. The partially substituted main phase where x = 0.4 exhibits the best electrochemical performance with a discharge capacity of ≈170 mAh g−1. The in situ X‐ray Absorption Spectroscopy (XAS) and time‐resolved X‐ray Diffraction (TR‐XRD) measurements are performed to elucidate the neighborhood of the local structure and lattice parameters during cycling. X‐ray photoelectron spectroscopy (XPS) revealed the oxygen‐rich structure when Ru is introduced. Density of States (DOS) calculations revealed the Fermi‐Level bandgap increases when Ru is doped, which enhances the electronic conductivity of the cathode. Furthermore, magnetization calculations revealed the presence of stronger Ru─O bonds and the stabilizing effect of Ru‐doping on MnO6 octahedra. The results of Time‐of‐flight secondary‐ion mass spectroscopy (TOF‐SIMS) revealed that the Ru‐doped sample has more sodium and oxygenated‐based species on the surface, while the inner layers mainly contain Ru–O and Mn–O species. The full cell study demonstrated the outstanding capacity retention where the cell maintained 70% of its initial capacity at 1 C‐rate after 500 cycles.
Experimental and Numerical Investigation of Slip Effect on Nanofiber Filter Performance at Low Pressures
Authors: Pan Zhengyuan , Ou Qisheng , Romay Francisco J. , Chen Weiqi , Liang Yun , Pui David Y. H.
Published: October 2024

Nanofiber filters are widely used in air filtration applications due to their superior performance over microfiber filters. Velocity slip around nanofibers has been identified as a key factor contributing to their high figure of merit, yet its impact on filter performance, especially particle collection efficiency, remains unclear due to the difficulty in isolating the slip effect as the sole variable. This study combines experimental and simulation methods to investigate the slip effect by adjusting the air molecule mean free path, rather than varying fiber size as done in previous studies. Filter media with mean fiber sizes ranging from 16.2 to 0.084 µm are utilized. An image‐based regression method is developed to address the challenge of determining the solidity of thin nanofiber layers. The results show that the slip effect is enhanced as the testing pressure decreases, reducing pressure drop by less than 15% for microfiber filters and over 50% for nanofiber filters ≈100 nm. The enhanced slip effect at low pressures (i.e., relatively low pressure compared to the ambient environment) significantly improves filtration efficiency, especially for particles larger than 100 nm. It also proposes semi‐empirical equations for predicting filter performance in slip and transition flow regimes.
Effect of BaSO4 on Phase Transformation and Microstructure of CaOFe2O3TiO2 System During Sintering
Authors: Ju Jian‐Tao , Yang Xi‐Ming , Xiang Xin‐Ru , Guo Wen‐Ke , Xing Xiang‐Dong
Published: October 2024

The Fe2O3CaOTiO2BaSO4 system is established through miniature sintering experiments to reveal the mechanism of BaSO4 and vanadium‐titanium magnetite (VTM) with the help of X‐ray diffraction, scanning electron microscope and energy dispersive spectroscopy, and thermogravimetry. The results show that in the absence of BaSO4, the phase of the sinter consists of CaFe2O4, TiO2, Fe2O3, CaTiO3, and CaFe2O5. When the content of BaSO4 is 1% and 2%, CaTiO3 decreases and the number of needle‐like CF increases. Some Ba2+ solidly dissolve into CF and CaTiO3 to form trace BaFe12O19 and BaTi2O5. When the content of BaSO4 increases to 4%, the CaSO4 phase appears, the formation of C2F is accelerated, and the content of CF and CaTiO3 continues to decline. The needle‐like calcium ferrate gradually transforms into columnar and lamellar. As the BaSO4 content continues to increase to 6% and 8%, although the trend of each phase is similar to that at 4%, it is almost entirely composed of columnar calcium ferrite, barium ferrite, and incomplete tetragonal and rhombic Fe2O3. The results of the study provide a theoretical basis for the utilization of VTM and barium‐containing iron ores in practical production.
Optimizing the Steel Ladle Thermal Management: Toward a Sustainable and Cost‐Effective Ladle Fleet Logistics
Authors: Ruela Victor , van Beurden Paul , Luchini Bruno , Hofmann Rene , Birkelbach Felix
Published: October 2024

Significant effort has been made to understand the recommended operational practices required for safe and energy‐efficient steel ladle logistics. However, the discussion of how thermal management affects sustainability and costs is still limited. Hence, this article proposes a methodology to explicitly minimize direct and indirect emissions involved in the ladle dispatching problem. For that, linear regression models correlating the refractory wear with the thermal state of the ladle and process conditions are integrated into the optimization model. Next, mixed‐integer linear programming solvers can be employed to estimate the Pareto front efficiently. Finally, the costs can be estimated by applying the refractory value‐in‐use model. As an example, the proposed method is applied to the Tata Steel, IJmuiden, use case. The results show limited opportunity to reduce refractory consumption and indirect emissions by increasing the heating applied. Hence, evaluating additional sustainability indicators beyond CO2 emissions and replacing natural gas with cleaner fuels becomes essential in this context. The Value‐in‐use model reveals that up to 8% in refractory costs can be saved, but most benefits come from reducing steel temperature losses. Steelmakers can leverage this method to adapt their processes more effectively to achieve more sustainable and cost‐effective operations.
A powerful molecular marker to detect mutations at sorghum LOW GERMINATION STIMULANT 1
Authors: Adeyanju Adedayo O. , Rich Patrick J. , Ejeta Gebisa
Published: October 2024

The parasitic weed Striga (Striga hermonthica) limits productivity of sorghum (Sorghum bicolor) and other cereals in sub‐Saharan Africa and elsewhere. Improved host plant genetics is an effective control method but verified loci contributing to Striga resistance are limited. LOW GERMINATION STIMULANT 1 remains the only known sorghum locus affecting resistance to Striga. Functional loss (lgs1) alleles at this locus result in low Striga germination stimulant activity. We developed a robust polymerase chain reaction (PCR)‐based LGS1 marker that detects all known natural lgs1 alleles. We have successfully used this marker to improve Striga resistance in our sorghum breeding program. To check its utility among diverse sets of germplasm, we genotyped 406 lines of the sorghum association panel (SAP) with the marker and phenotyped them for Striga germination stimulant activity. The SAP contains 23 lines (6%) with lgs1 mutations that involve a complete loss of this gene. Three previously described deletion alleles (lgs1‐1, lgs1‐2, and lgs1‐3) ranging from 28.5 to 34 kbp are present among SAP members with a new one, lgs1‐6, missing nearly 50 kbp relative to the reference genome. All 23 members of the SAP carrying lgs1 alleles had low Striga germination stimulant activity. The smaller previously described intragenic deletion mutations lgs1‐4 and lgs1‐5 are not present in the SAP. The LGS1 marker is useful for both detecting sources of lgs1 and introgressing Striga resistance into new genetic backgrounds.
A comparison of home range estimates using the time local convex hull (T‐LoCoH) and minimum convex polygon (MCP) methods for African savannah elephants in a semi‐arid protected area
Authors: Mukomberanwa Nobert T. , Taru Phillip , Utete Beaven , Ngorima Patmore
Published: October 2024

Knowledge of home ranges (HRs) helps conservationists understand movement patterns and can aid management including avoidance of human‐wildlife conflicts. This study examined the African savannah elephant seasonal HRs and space use using telemetry data in Mana Pools National Park, Zimbabwe. The objectives were to (i) compare the HR sizes and (ii) construct utilization distribution of African savannah elephants using the minimum convex polygon (MCP) method and the time‐local convex hull (T‐LoCoH). The results revealed that the dry, transitional, and wet season HR sizes estimated by the MCP method were significantly larger than those of the T‐LoCoH method. Significant differences were observed between core T‐LoCoH home‐range distributions for the wet, transition, and dry seasons. T‐LoCoH more accurately represented the HR size and nuances of repeated movements and internal spaces than the MCP method. The findings show larger‐scale movements in the transition season, which would enhance the potential for human–elephant conflicts.
Aspects of inclined magnetohydrodynamics and heat transfer in a non‐Newtonian tri‐hybrid bio‐nanofluid flow past a wedge‐shaped artery utilizing artificial neural network scheme
Authors: Shah Syed Zahir Hussain , Ayub Assad , Bhatti Saira , Khan Umair , Ishak Anuar , Sherif El‐Sayed M. , Pop Ioan
Published: October 2024

The incorporation of three distinct nanoparticles in blood within the context of cubic autocatalysis holds significant potential for enhancing biomedical applications, particularly in targeted drug delivery and therapeutic interventions. The increased reaction rate improves the efficiency of catalytic processes within the bloodstream. This research investigates the thermal transport characteristics of a trihybrid Carreau nanofluid (blood) containing copper (Cu), titanium dioxide (TiO2), and aluminum oxide (Al2O3), nanoparticles in the context of a wedge‐shaped artery under the influence of autocatalytic cubic autocatalysis. Effects of thermal radiation, and heat generation are used for heat transport analysis, heterogeneous‐homogeneous chemical process included for blood concentration, and an inclined magnetic field is imposed for securitization of blood velocity. Also, the generated PDEs from the physical model are handled through similarity transformations and converted into ODEs. Bvp4c, a numerical technique is used to get the solution and then Levenberg‐Marquardt neural network (LM‐NN), a multilayer neural network scheme is used to train and predict the solution for each parameter. In addition, the numerical values of volumetric friction of coefficients enhance the thermal conductivity, and the heat transport rate is increased. The magnetic parameter, radiation and chemical processes enhance the rate of heat transport while the Weissenberg number reduces the velocity profile.
Human-Centered Collaborative Decision-Making and Steering Control with Reinforcement Learning
Authors: Yan Liang , Wu Xiaodong , Lu Hangyu
Published: October 2024

This paper presents a novel human-centered collaborative driving scheme using model-free reinforcement learning (RL) approach. The human-machine cooperation is achieved in both decision-making and steering control levels to improve driving safety while leaving space for human freedom as much as possible. A Markov decision process is firstly derived from the collaborative driving problem, then a RL agent is developed and trained to cooperatively control the vehicle steering under the guidance of a heuristic reward function. Twin delayed deep deterministic policy gradient (TD3) is conducted to attain the optimal control policy. In addition, two extended algorithms with distinct agent action definitions and training patterns are also devised. The effectiveness of the RL-based copilot system is finally validated in an obstacle avoidance scenario by simulation experiments. Driving performance and training efficiency of different RL agents are measured and compared to demonstrate the superiority of the proposed method.
Interactive and Robust Prevention of Lane Departure
Authors: Edamoto Syouma , Kimura Shuuji , Tamura Tsutomu , Gao Richard , Fuchs Robert
Published: October 2024

Lane Keeping Assistance (LKA) is one of the most common Advanced Driver Assistance System (ADAS) functions on the market, yet it is still not well accepted by drivers. Although LKA can reduce the occurrence of traffic accidents by correcting the vehicle heading in the event of an unintentional lane departure, poor usability often results in manual deactivation of the function. We provide considerations on how to specify LKA functions in general and propose admittance control as a solution to improve the state-of-the-art from the widely used concept of torque overlay, which is limited by a trade-off between automated and manual driving modes. Using the proposed LKA function, unintentional lane departure is prevented while maintaining comfortable reaction torque which allows the driver to easily steer the vehicle.
Energy Efficiency Oriented Robust Model Predictive Stability Control for Autonomous Electric Vehicles
Authors: Tian Ziang , Yu Huilong , Xi Junqiang
Published: October 2024

The four-wheel independent steering and drive autonomous vehicle is a typical over-actuated system. The complexity of controlling it is increasing with the number of actuators. Since the model-based approach can solve the constrained multiple output problem, it is mostly utilized in the existing works. However, they usually investigate a single objective optimization, while employing simplified prediction models to relieve computational burdens. In this case, the robustness of the controller will inevitably suffer from model mismatch, which makes it hard to fulfill the various demands of autonomous driving. This work proposes a multi-objective control framework, which optimizes stability and energy efficiency simultaneously. Furthermore, robust model predictive control is introduced to address the model mismatch. Compared with the state-of-the-art, the effectiveness of the proposed approach has been validated by hardware-in-the-loop tests. Under the double lane change Maneuver, the longitudinal speed is maintained 1.7% higher. The vehicle stability is enhanced, while the motor energy loss and tire slip energy are reduced by 23.3% and 8.3%, respectively.
Mapping and Localization Method for Autonomous Vehicles on Roads Using Environmental Magnetic Field
Authors: Ishii Kyoya , Shimono Keisuke , Suda Yoshihiro , Ando Takayuki , Mukumoto Hirotaka , Urakawa Kazuo
Published: October 2024

Vehicle localization is one of the key technical factors for autonomous vehicles. It requires high accuracy, precision, and robustness towards various road conditions. Popular localization methods include global navigation satellite system (GNSS) and visual methods, but their accuracy can degrade in some conditions. This work proposes to use the environmental magnetic field (EMF) for localization to complement the shortcomings of existing methods. EMF is a combination of the Earth’s geomagnetic field and magnetic field induced by man-made objects. It has local fluctuations that can be paired with coordinate positions and is time-invariant within a practical timescale. Past works considering the localization of road vehicles had few problems when applying them to the localization of autonomous vehicles. This work overcomes the problems in the existing method by creating a two-dimensional magnetic field map using Gaussian Process regression, using magnetic markers to enhance EMF fluctuations, and utilizing the Monte Carlo localization algorithm. The proposed method was validated through actual vehicle tests, and its robustness towards other vehicles was examined.
Robust Inverse Vehicle Map Regression Based on Laplace Distribution
Authors: Penet Maxime , Le Gall Gaetan
Published: October 2024

This paper deals with the identification of the relationship between vehicle acceleration and driver available actuators. The vehicle is modeled based on how a driving task is performed. The model is constructed using neural networks whose weights are identified using data collected through non tailored driving sessions. To take into account disturbances, the model follows a Laplace distribution. This leads to a more robust estimate of the vehicle knowledge and the confidence we have in it. The approach is illustrated on a prototype vehicle equipped with a petrol engine, plus a device to actuate the pedals.
MLIO: Multiple LiDARs and Inertial Odometry
Authors: Dahal Pragyan , Arrigoni Stefano , Bijelic Mario , Braghin Francesco
Published: October 2024

With the decreasing cost of LiDAR sensors, sensor setups with multiple LiDARs are becoming available. In such advanced setups with multiple LiDARs the sensor temporal asynchronicity and spatial miscalibration are critical factors for vehicle localization increasing measurement uncertainty. Hence, simple merging of synchronized point clouds as done in some literature can lead to sub-optimal results. To tackle this problem we propose MLIO, a factor graph-based odometry computation algorithm that fuses multiple LiDARs with an inertial measurement unit (IMU) and provides an accurate solution mitigating the effect of temporal asynchronisity and spatial miscalibration.

The proposed algorithm is validated using a custom dataset. We compare the proposed algorithm with the state-of-the-art LiDAR-only odometry algorithms, such as KISS-ICP, and LiDAR-IMU fusion LIO-SAM and demonstrate its superiority. We were able to achieve up to 40% and 16% increment in positional and orientation accuracy compared to KISS-ICP and 25% increment in positional accuracy compared to LIO-SAM.
Powerslide Control with Deep Reinforcement Learning
Authors: Jaumann Florian , Schuster Tobias , Unterreiner Michael , Gräber Torben , Edelmann Johannes , Plöchl Manfred
Published: October 2024

Controlling a vehicle’s powerslide motion in the presence of a human driver is a challenging control task, but one that may have a significant impact on vehicle safety, for example, during rapid evasive manoeuvres. Reinforcement Learning, a data-driven optimal control strategy, has gained increasing attention in recent years, demonstrating its effectiveness in successfully controlling various nonlinear systems. In this work, a novel powerslide controller is designed for an all-wheel drive battery electric vehicle with individually driven front and rear axles and a human driver in closed-loop using Reinforcement Learning. The performance of the proposed controller is analysed, and its robustness to steering disturbances and changes in road friction is demonstrated.
Simulating Effects of Suspension Damper Degradation on Common Sensor Signals for Diagnosis Models in the Context of Condition-Based Maintenance
Authors: Ott Lorenz , Gräber Torben , Unterreiner Michael , Edelmann Johannes , Plöchl Manfred
Published: October 2024

Degraded suspension dampers strongly influence vehicle safety and ride comfort, but often occur after several years of operation. Related workshop checks are usually not degradation-adaptive, so they can be significantly delayed to the need for maintenance. To make the maintenance adaptive to degradations, onboard diagnosis methods can be used, which rely on the degradation status extracted from sensor signals.To support the development of sensitive yet robust diagnosis models, a model that can simulate and explain the effects of damper degradation in common sensor signals is proposed. This paper focuses on low-frequency effects in signals of the wheel speed sensors, which are ultra low cost and always available in modern vehicles. As a result, the model shows a good qualitative match to real-world test drives, specifically in the frequency domain. Therefore, various real-world measurements were conducted, in particular, test bench measurements of degraded dampers and vehicle on-road tests.
Gradient Correction for Asynchronous Stochastic Gradient Descent in Reinforcement Learning
Authors: Gao Jiaxin , Lyu Yao , Wang Wenxuan , Yin Yuming , Ma Fei , Li Shengbo Eben
Published: October 2024

Distributed stochastic gradient descent techniques have gained significant attention in recent years as a prevalent approach for reinforcement learning. Current distributed learning predominantly employs synchronous or asynchronous training strategies. While the asynchronous scheme avoids idle computing resources present in synchronous methods, it grapples with the stale gradient issue. This paper introduces a novel gradient correction algorithm aimed at alleviating the stale gradient problem. By leveraging second-order information within the worker node and incorporating current parameters from both the worker and server nodes, the gradient correction algorithm yields a refined gradient closer to the desired value. Initially, we outline the challenges associated with asynchronous update schemes and derive a gradient correction algorithm employing local second-order approximations. Subsequently, we propose an asynchronous training scheme incorporating gradient correction within the generalized policy iteration framework. Lastly, in the context of trajectory tracking tasks, we compare the impact of employing gradient correction versus its absence in an asynchronous update scheme. Simulation results underscore the superiority of our proposed training scheme, demonstrating notably faster convergence and higher policy performance compared to the existing asynchronous update methods.
Stability Issues in Adaptive Cruise Control Systems and Traffic Implication
Authors: Donà Riccardo , Mattas Konstantinos , Albano Giovanni , Váss Sandor , Ciuffo Biagio
Published: October 2024

Adaptive Cruise Control (ACC) systems under short headway configurations have been found to have a potentially detrimental impact on the transport network due to the string instability effect. Such phenomenon results in traffic perturbations amplification downstream causing increasing fuel consumption and posing safety threats. However, recent findings summarized in this paper show how even the simpler platoon stability might not be attained with current ACC-equipped vehicles raising additional concerns regarding their unregulated operation. In fact, as part of a recent campaign involving state-of-the-art assisted vehicles, an ACC displayed a low-frequency oscillatory behavior around the equilibrium speed. This work, by leveraging a mixed simulation/empirical approach, uncovers the harmful influences of such behavior. Ultimately, we found that despite the poor stability phenomenon might not be impactful for one vehicle, the overall repercussions on the transportation network can be dramatically detrimental raising the need for a regulatory framework for lower-level automation.
The Influence of Transient Tire Force Transmission on Sideslip Angle Estimation
Authors: Puščul Dženana , Schabauer Martin , Lex Cornelia
Published: October 2024

Knowledge of the sideslip angle is significant in development, testing and validation of advanced driver assistance systems and vehicle control systems, to enhance vehicle performance and stability. This article investigates the influence of having additional information about transient tire behavior during estimation of the sideslip angle. Since transient behavior depends on the dynamic excitation, the effect during different driving maneuvers (excitations) such as steady-state cornering, slalom and general handling as well as on different road surfaces is investigated. The results show that considering transient force transmission even by a pragmatic approach leads to a significant improvement.
Risk-Predictive Path Planning in Urban Autonomous Driving: A Geometric Approach to VRU Crossing
Authors: Fujinami Yohei , Raksincharoensak Pongsathorn
Published: October 2024

This research introduces a method for autonomous driving systems to safely overtake bicycles in urban environments. It identifies the risk of sudden crossing when overtaking a bicycle and proposes a solution to minimize this risk. The method classifies the situation into three conditions related to the drivable space of the road and speed of the bicycle, and it determines the target speed and positions. The proposed algorithm uses onboard sensor information and assumptions of the bicycle’s virtual sudden-crossing motion, enabling real-time calculations in a practical environment. Simulations demonstrate the method’s effectiveness, showing it can generate a natural overtaking speed and lateral gap based on road width and bicycle speed. The proposed method is compatible with waypoint-based path generation methods we have proposed in previous research, making it a promising solution for future autonomous driving systems. Future research will discuss the method’s implementation in automated vehicles, contributing to safer and more efficient autonomous driving systems.
A Geometric Electric Motor Model for Optimal Vehicle Family Design
Authors: Clemente Maurizio , Borsboom Olaf , Salazar Mauro , Hofman Theo
Published: October 2024

This paper presents a design optimization framework that jointly optimizes battery size with the geometric dimensions of the electric motor for a family of battery electric vehicles, with global optimality guarantees. As opposed to conventional models, we devise a quasi-static model of the motor internal losses as a function of both its geometry and operating points, using a convex surrogate modeling approach. Specifically, we implement a low-level motor scaling, capturing the impact on performance and losses of changing the motor geometry in axial and radial directions. Hence, we leverage the framework to solve a concurrent optimization problem and identify the optimal module sizing for a family of electric vehicles. Finally, we test our framework on a benchmark problem where we jointly design motor and battery for three different types of vehicles (a city car, a compact car, and a cross over), whereby the prediction efficiency is in line with the high-fidelity modeling software.
Model-Free Automated Reversing of Articulated Heavy Goods Vehicles
Authors: Rahman Shammi , Gordon Timothy , Henderson Leon , Gao Yangyan , Coleman Sonya , Kerr Dermot
Published: October 2024

This paper presents a technique for automated reversing control of articulated vehicles. Reversing articulated Heavy Goods Vehicles (HGVs) can be a challenging and time consuming task for a human driver, sometimes requiring multiple forward and backward motions to reduce errors. Here, the aim is to automate the task to provide high levels of precision using Artificial Flow Guidance (AFG). AFG uses simple geometry to define a spatially distributed motion reference, requiring only short-range error corrections and possessing global convergence properties. AFG has previously been applied to rigid and articulated vehicles in forward motion, with demonstrable benefits in terms of tracking precision and robustness. Here results focus on the tractor-semitrailer, but the AFG approach is equally applicable to the reversing of longer combination vehicles.
Yaw Stability Control of Vehicles Using a Slip Polytope Validated with Real Tests
Authors: Erdinc Umur , Jonasson Mats , Kati Maliheh Sadeghi , Laine Leo , Jacobson Bengt , Fredriksson Jonas
Published: October 2024

Articulated heavy vehicles (AHVs) face yaw instabilities, especially under extensive propulsion or regenerative braking force on the driven axles, risking their directional stability and potentially leading to jackknifing. Hence, safe operating envelopes (SOEs) are essential for allocating propulsion and braking forces among different units. This study proposes a novel approach to ensure yaw stability by reducing longitudinal slip limits of the electric motors (EMs) based on side-slip, enhancing stability and acceleration performance. Validation through simulations and real vehicle tests shows promising results.
Motion Control of a 6 $$\times $$ 4 Heavy Vehicle: Autonomous Collision Avoidance Using Integrated Chassis Control
Authors: Asiabar Aria Noori , Gordon Timothy , Gao Yangyan , Henderson Leon , Laine Leo
Published: October 2024

This paper considers the coordinated chassis control of a 6 $$\times $$
×
4 HGV tractor unit using a multivariable nonlinear controller for a transient handling manoeuvre under friction-limited conditions. The controller’s performance is evaluated through simulation. It receives Centre of Gravity (CG) longitudinal and lateral acceleration targets, corresponding to curvature and longitudinal acceleration requests, and aims for the CG to track the target accelerations. It employs the Modified Hamiltonian Algorithm (MHA) to generate steering and braking commands for the tractor. A combined-slip Magic Formula tyre model used within the algorithm allows for simultaneous stability control and path tracking, even in scenarios where the vehicle is operating at the limits of tyre adhesion. The manoeuvre is an autonomous obstacle avoidance on a packed snow. Results show the advantages and possible limitations of tracking acceleration targets for integrated chassis control.
Investigating Characteristics and Opportunities for Rear-Wheel Steering
Authors: Lind Jonsson Oskar , Balachandran Arvind , Zhou Jian , Olofsson Björn , Nielsen Lars
Published: October 2024

The potential of additional steering possibilities (like rear-wheel or all-wheel steering) is analyzed for critical situations to investigate possible safety improvements. For this purpose, a dynamic optimization problem is formulated to find the best possible maneuver. The optimization criterion is to maximize the entry speed into a constant radius $$90^\circ $$

90
∘

-curve. The optimization problem is solved for different steering topologies, and the results quantify the increase in maximum entry speed, highlighting the potential for safety improvements. Further, the optimal steering strategies are determined, and they show interesting characteristics like initial diagonal driving or, in other cases, a transition from initial out-of-phase steering to in-phase steering.
Development and Experimental Assessment of a Control Logic for Hydroplaning Prevention
Authors: Montini Edoardo , Salierno Marco , Frigerio Stefano , Melzi Stefano
Published: October 2024

Hydroplaning plays a crucial role in road safety. The water layer wedged between the tires and the road reduces the capability of the vehicle to respond to the driver’s inputs. Factors like vehicle speed, water layer thickness, tread pattern, and tire wear affect hydroplaning onset. Based on the technical literature and experimental tests, the research developed a tire model to simulate hydroplaning effects. Scaling factors were added to the MFTyre model to reproduce the changes in cornering stiffness, relaxation length, friction coefficient, and motion resistance. Then, a control logic to counteract hydroplaning was designed and implemented on a 14-dof vehicle model. As last, the effectiveness of the control logic was assessed through a series of indoor tests on a dynamic driving simulator.
Effects of External Tire Heating on Rolling Resistance Energy Consumption
Authors: Askerdal Mikael , Bruzelius Fredrik , Fredriksson Jonas
Published: October 2024

It is known that the rolling resistance decreases with increasing tire temperature. If the tires could be heated to a high temperature, the rolling resistance’s energy loss could be reduced. The question arises whether the reduced rolling resistance energy consumption overcomes the energy required to heat the tire.

This paper investigates the effects of external heating and improved tire insulation theoretically. The results indicate that external tire heating can be beneficial only if the heat used is waste heat, generated from a heat pump or similar with a coefficient of performance greater than one or taken from the grid.
Safety Filter for Lane-Keeping Control
Authors: Jiang Chenhuan , Gan Hanyu , Vörös Illés , Takács Dénes , Orosz Gábor
Published: October 2024

A safe lane-keeping controller is designed using a control barrier function (CBF) which ensures that if the vehicle starts between the lane boundaries then it does not leave the lane. The safety filter is applied on the top of a nominal path-following controller of the kinematic single-track model in order to modify the control input when the vehicle gets close to the boundary of the safe set in state space. Numerical simulations and phase portraits are used to demonstrate the performance of the proposed safety-critical controller.
Neural Network Tire Force Modeling for Automated Drifting
Authors: Broadbent Nicholas Drake , Weber Trey , Mori Daiki , Gerdes J. Christian
Published: October 2024

Automated drifting presents a challenge problem for vehicle control, requiring models and control algorithms that can precisely handle nonlinear, coupled tire forces at the friction limits. We present a neural network architecture for predicting front tire lateral force as a drop-in replacement for physics-based approaches. With a full-scale automated vehicle purpose-built for the drifting application, we deploy these models in a nonlinear model predictive controller tuned for tracking a reference drifting trajectory, for direct comparisons of model performance. The neural network tire model exhibits significantly improved path tracking performance over the brush tire model in cases where front-axle braking force is applied, suggesting the neural network’s ability to express previously unmodeled, latent dynamics in the drifting condition.
Surrogate Modeling of Suspensions with High Stiffness Element for Real-Time Analysis Using Machine Learning
Authors: Hiraga Tojuro , Shiiba Taichi
Published: October 2024

The aim of this study is to generate a surrogate model of a suspension system with high stiffness elements for real-time analysis using machine learning. A Long Short-Term Memory networks was used as a machine learning method to generate surrogate models for three-degree-of-freedom quarter car model with a bush element. To evaluate the performance of the machine learning models, the simulation results and computation time were compared with the 3DOF model. As a result, it was confirmed that the response of the body acceleration was predicted with good accuracy by predicting the bush deformation and calculating the body acceleration based on the prediction results, instead of predicting the body acceleration directly. It was also confirmed that the machine learning model can accurately predict the body acceleration in less calculation time than the original model.
Bifurcation Analysis of a Nonlinear Vehicle Model on Banked Road
Authors: Zerbato Luca , Galvagno Enrico , Velardocchia Mauro
Published: October 2024

Towards the transition to automated driving, lateral stability of the vehicle represents a key requirement to guarantee the safety of passengers and vulnerable road users, especially during emergency operating conditions where nonlinearities arise. The present paper aims at investigating the effect of road banking angle on vehicle plane motion stability. To perform this analysis, a pure lateral nonlinear double track model is numerically derived for an oversteering vehicle. Lateral load transfer and its distribution among the axles are included for exploiting the tyre saturation region. The stability analysis is conducted by searching for the vehicle steady-state conditions and deriving the linearised equations around the equilibrium points. Moreover, the phase-plane plot is adopted to draw the states trajectories and to identify potential unstable regions. Finally, the bifurcation analysis as function of the road banking angle is investigated to highlight possible change of the phase portrait topology. The results show that a saddle-node bifurcation may occur when the vehicle is negotiating a certain level of bank road angle, affecting the vehicle yaw stability region.
Speed Profile Generation for a Dual Motor Equipped Electrified Series Hybrid Tracked Vehicle Through Dynamic Programming Based Energy Optimization Procedure
Authors: Gocer Ismail , Baslamisli S. Caglar
Published: October 2024

Electrification is a widely explored area in many fields and is becoming a widely researched topic in tracked vehicles. Achieving efficient operation for such vehicles is crucial similar to traditional vehicles. This paper introduces a speed profile generation procedure for a dual track – series hybrid tracked vehicle with an emphasis on fuel economy and trip time optimization. A Dynamic Programming method is proposed in spatial domain, considering the properties of predefined road geometries. The study includes examination of speed and torque estimation of the sprockets, particularly during steering maneuvers. Unlike wheeled vehicles, tracked vehicles have to overcome the turning resistance moment of skid steered tracks. Taking these dynamics into account, the study investigates efficient operation, presenting over a predefined road track including straight road, inclined road and curve sections, discussing the tradeoff between fuel economy and trip time optimality. As the result of problem solution, speed profiles are generated, and operation trends of the powertrain components are discussed.