bluuebunny/crossref_metadata_2025 huggingface dataset:
Dynamic Envelope Optimization of Articulated Vehicles Based on Multi-Axle Steering Control Strategies
Authors: Sun Zhaocong , Yang Shizhi , Meng Joshua H. , Zhang Chi , Cui Zhousen , Wang Heqian , Wang Wenjun
Published: January 2025

Steer-by-wire technology, critical for autonomous driving, enables full-wheel steering in articulated vehicles, significantly enhancing maneuverability in complex driving environments. This study investigates dynamic envelope optimization for articulated multi-body vehicles by integrating coordinated multi-axle steering control strategies with higher-order Bezier curve designs. Unlike traditional approaches that primarily focus on single-axle steering, this research emphasizes the advantages of multi-axle steering control, which significantly reduces the dynamic envelope and enhances maneuverability. To address the challenges posed by constrained road environments, a comparative analysis of Septimic Bezier curves under various control point configurations was conducted, demonstrating their effectiveness in achieving smoother curvature transitions and steering comfort. The results highlight the pivotal role of reducing curvature peaks and increasing curvature continuity in optimizing vehicle performance. Furthermore, advanced steering control strategies, such as Articulation Angle Reference (AAR) and Dual Ackermann Steering (DAS), were shown to outperform conventional methods by ensuring precise trajectory control and improved stability. This study provides actionable insights for enhancing vehicle handling and safety in complex driving scenarios, offering a framework for future road design and multi-axle steering system development.
Research on Traversal Path Planning and Collaborative Scheduling for Corn Harvesting and Transportation in Hilly Areas Based on Dijkstra’s Algorithm and Improved Harris Hawk Optimization
Authors: Liu Huanyu , Luo Jiahao , Zhang Lihan , Yu Hao , Liu Xiangnan , Wang Shuang
Published: January 2025

This study addresses the challenges of long traversal paths, low efficiency, high fuel consumption, and costs in the collaborative harvesting of corn by harvesters and grain transport vehicles in hilly areas. A path-planning and collaborative scheduling method is proposed, combining Dijkstra’s algorithm with the Improved Harris Hawk Optimization (IHHO) algorithm. A field model based on Digital Elevation Model (DEM) data is created for full coverage path planning, reducing traversal path length. A field transfer road network is established, and Dijkstra’s algorithm is used to calculate distances between fields. A multi-objective collaborative scheduling model is then developed to minimize fuel consumption, scheduling costs, and time. The IHHO algorithm enhances search performance by introducing quantum initialization to improve the initial population, integrating the slime mold algorithm for better exploration, and applying an average differential mutation strategy and nonlinear energy factor updates to strengthen both global and local search. Non-dominated sorting and crowding distance techniques are incorporated to enhance solution diversity and quality. The results show that compared to traditional HHO and HHO algorithms, the IHHO algorithm reduces average scheduling costs by 4.2% and 14.5%, scheduling time by 4.5% and 8.1%, and fuel consumption by 3.5% and 3.2%, respectively. This approach effectively reduces transfer path costs, saves energy, and improves operational efficiency, providing valuable insights for path planning and collaborative scheduling in multi-field harvesting and transportation in hilly areas.
Hyper-seq Technology and Genome-Wide Selection Breeding of Soybeans
Authors: Wang Qingyu , He Miaohua , Zhou Yonggang , Xu Rui , Liang Tiyun , Pei Shuangkang , Chen Jianyuan , Yang Lin , Xia Yu , Luo Xuan , Li Haiyan , Xia Zhiqiang , Zou Meiling
Published: January 2025

Soybeans (Glycine max (L.) Merr.) are a multifunctional crop that contributes significantly to global food security, economic development, and agricultural sustainability. Genomic selection (GS) is widely used in plant breeding, which can effectively reduce breeding costs and shorten the breeding cycle compared to traditional breeding methods. In this study, Hyper-seq technology was used to gather data on 104,728 single nucleotide polymorphism (SNP) sites from 420 natural populations of soybean that were chosen as experimental materials. Furthermore, three years’ worth of phenotypic data on the population’s main stem node count were gathered for this investigation. Comparative analysis was used to assess the validity and accuracy of a number of GS models, including Ridge Regression Best Linear Unbiased Prediction (RRBLUP), Genomic Best Linear Unbiased Prediction (GBLUP), and various Bayesian techniques (Bayesian_A, Bayesian_B, Bayesian_C, Bayesian_RR, Bayesian_LOOS, and Bayesian_RKHS). Each model’s performance was compared using fivefold cross-validation. The research findings indicate that the data obtained by Hyper-seq technology is particularly useful for breeding experiments, including genome-wide selection. The most accurate of them is Bayesian_A, whereas the one with the quickest computational efficiency is GBLUP. Using Hyper-seq technology requires integrating at least 15,000 SNPs to guarantee the model’s stability. It is also important to note that, even if 153 Hyper-seq datasets are 50% less expensive than 153 Whole Genome Sequencing datasets, the difference in prediction accuracy between the two datasets is less than 4%. This discovery further validates the reliability and efficacy of Hyper-seq technology within the domain of genome-wide selection breeding.
Co-Supplementation of Diet with Saccharomyces cerevisiae and Thymol: Effects on Growth Performance, Antioxidant and Immunological Responses of Rainbow Trout, Oncorhynchus mykiss
Authors: Yousefi Morteza , Adineh Hossein , Taheri Mirghaed Ali , Hoseini Seyyed Morteza
Published: January 2025

This study assessed the effects of thymol and yeast Saccharomyces cerevisiae on the growth performance, antioxidant capacity and immunological responses and gut immunological transcripts in rainbow trout, Oncorhynchus mykiss. Two hundred and seventy fish (6.62 ± 0.18 g) were distributed in 18 aquaria (15 fish per aquarium) as a 2 × 3 factorial design. The fish were fed on diets containing 0 and 1 × 108 cfu/g of yeast and 0, 250 and 500 mg/kg of thymol for 60 days. The results showed that dietary yeast supplementation led to significantly improved fish weight gain and feed efficiency (p < 0.05). Thymol and yeast supplementation affected the hepatic antioxidant status, leading to lower lipid peroxidation and higher glutathione reserves (p < 0.05). Thymol and yeast supplementation led to significantly lowered plasma aspartate aminotransferase levels (p < 0.05). Dietary yeast supplementation led to significantly elevated plasma and mucus lysozyme, plasma alternative complement, immunoglobulin and mucus alkaline phosphatase levels. In contrast, thymol supplementation led to significantly increased plasma lysozyme and mucus alkaline phosphatase levels (p < 0.05), whereas it had no significant effects on other immunological parameters. The mucus bactericidal activities were improved by dietary yeast but both supplements enhanced the plasma bactericidal activity (p < 0.05). Thymol and yeast supplementation significantly up-regulated the expression of tumor necrosis factor-alpha, interleukin-1 beta, transforming growth factor-beta, beta-defensin and heat shock protein-70 in the hindgut (p < 0.05). In conclusion, dietary yeast supplementation was particularly effective in enhancing fish growth performance and non-specific immunity, while thymol supplementation primarily led to improved antioxidant capacity. Notably, a dietary dose of 250 mg/kg of thymol alone failed to affect the gut transcription as much as 500 mg/kg; however, the co-supplementation of 250 mg/kg of thymol and yeast led to comparable results.
Lysosome-Mitochondrial Crosstalk in Cellular Stress and Disease
Authors: Kiraly Szilvia , Stanley Jack , Eden Emily R.
Published: January 2025

The perception of lysosomes and mitochondria as entirely separate and independent entities that degrade material and produce ATP, respectively, has been challenged in recent years as not only more complex roles for both organelles, but also an unanticipated level of interdependence are being uncovered. Coupled lysosome and mitochondrial function and dysfunction involve complex crosstalk between the two organelles which goes beyond mitochondrial quality control and lysosome-mediated clearance of damaged mitochondria through mitophagy. Our understanding of crosstalk between these two essential metabolic organelles has been transformed by major advances in the field of membrane contact sites biology. We now know that membrane contact sites between lysosomes and mitochondria play central roles in inter-organelle communication. This importance of mitochondria–lysosome contacts (MLCs) in cellular homeostasis, evinced by the growing number of diseases that have been associated with their dysregulation, is starting to be appreciated. How MLCs are regulated and how their coordination with other pathways of lysosome–mitochondria crosstalk is achieved are the subjects of ongoing scrutiny, but this review explores the current understanding of the complex crosstalk governing the function of the two organelles and its impact on cellular stress and disease.
Learning Analytics and Educational Data Mining in Augmented Reality, Virtual Reality, and the Metaverse: A Systematic Literature Review, Content Analysis, and Bibliometric Analysis
Authors: Lampropoulos Georgios , Evangelidis Georgios
Published: January 2025

This study aims to examine the combination of educational data mining and learning analytics with virtual reality, augmented reality, mixed reality, and the metaverse, its role in education, and its impact on teaching and learning. Therefore, a systematic literature review, a bibliometric and scientific mapping analysis, and a content analysis are carried out based on 70 relevant documents identified from six databases, namely, ACM, ERIC, IEEE, ScienceDirect, Scopus, and Web of Science (WoS) following the PRISMA framework. The documents were separated into the following three categories, (i) Theoretical and Review studies, (ii) Proposal and Showcase studies, and (iii) Experimental and Case studies and were examined from different dimensions through an in-depth content analysis using both quantitative and qualitative approaches. The documents were further analyzed using scientometric tools, such as Bibliometrix and VOSviewer and topic modeling through Latent Dirichlet Allocation (LDA). The most prominent topics, areas, and themes were revealed and the outcomes regarding the influence of this combination on learning and teaching were summarized. Based on the results, this combination can effectively enrich education, positively affect learning and teaching, offer deep and meaningful learning, and support both students and teachers. Additionally, it can support different educational approaches and strategies, various learning styles, and special education and be utilized in both formal and informal learning environments. The real-time identification, tracking, monitoring, analysis, and visualization of multimodal learning data of students’ behavior, emotions, cognitive and affective states and the overall learning and teaching processes emerged as a significant benefit that contributes greatly to the realization of adaptive and personalized learning. Finally, it was revealed that the combination of extended reality technologies with learning analytics and educational data mining can support collaborative learning and social learning, improve students’ self-efficacy and self-regulated learning, and increase students’ learning gains, academic achievements, knowledge retention, motivation, and engagement.
Optimal Scheduling of Microgrids Based on an Improved Dung Beetle Optimization Algorithm
Authors: Yue Yuntao , Ren Haoran , Liu Dong , Zhang Lenian
Published: January 2025

More distributed energy resources are being integrated into microgrid systems, making scheduling more complex and challenging. In order to achieve the utilization of renewable energy and peak load shifting on a microgrid system, an optimal scheduling model is established. Firstly, a microgrid operation model including a photovoltaic array, wind turbine, micro gas turbine, diesel generator, energy storage, and grid connection is constructed, considering the demand response and the uncertainty of wind and solar power. The modeling demand response is determined via a price–demand elasticity matrix, whereas the uncertainty of wind and solar power is established using Monte Carlo sampling and a K-means clustering algorithm. Secondly, a multi-objective function that includes operational and environmental treatment costs is constructed. To optimize the objective function, an Improved Dung Beetle Optimization algorithm (IDBO) is proposed. A tent mapping, non-dominated sorting, and reverse elite learning strategy is proposed to improve the Dung Beetle Optimization algorithm (DBO); therefore, the IDBO is developed. Finally, the proposed model and algorithm are validated through some simulation experiments. A benchmark function test proves that IDBO has a fast convergence speed and high accuracy. The microgrid system scheduled by IDBO has the lowest total cost, and its ability to achieve peak load shifting and improve the utilization of renewable energy is proved through tests involving different scenarios. The results show that compared with traditional optimal scheduling models and algorithms, this approach is more reliable and cost-effective.
Integrating Machine Learning and Material Feeding Systems for Competitive Advantage in Manufacturing
Authors: Çağlayan Müge Sinem , Aksoy Aslı
Published: January 2025

In contemporary business environments, manufacturing companies must continuously enhance their performance to ensure competitiveness. Material feeding systems are of pivotal importance in the optimization of productivity, with attendant improvements in quality, reduction of costs, and minimization of delivery times. This study investigates the selection of material feeding methods, including Kanban, line-storage, call-out, and kitting systems, within a manufacturing company. The research employs six machine learning (ML) algorithms—logistic regression (LR), decision trees (DT), random forest (RF), support vector machines (SVM), K-nearest neighbors (K-NN), and artificial neural networks (ANN)—to develop a multi-class classification model for material feeding system selection. Utilizing a dataset comprising 2221 materials and an 8-fold cross-validation technique, the ANN model exhibits superior performance across all evaluation metrics. Shapley values analysis is employed to elucidate the influence of pivotal input parameters within the selection process for material feeding systems. This research provides a comprehensive framework for material feeding system selection, integrating advanced ML models with practical manufacturing insights. This study makes a significant contribution to the field by enhancing decision-making processes, optimizing resource utilization, and establishing the foundation for future studies on adaptive and scalable material feeding strategies in dynamic industrial environments.
Analysis of Sparse Trajectory Features Based on Mobile Device Location for User Group Classification Using Gaussian Mixture Model
Authors: Kakimoto Yohei , Omae Yuto , Takahashi Hirotaka
Published: January 2025

Location data collected from mobile devices via global positioning system often lack semantic information and can form sparse trajectories in space and time. This study investigates whether user age groups can be accurately classified solely from such sparse spatial–temporal trajectories. We propose a feature extraction method based on a Gaussian mixture model (GMM), which assigns representative points (RPs) by clustering the location data and aggregating user trajectories into these RPs. We then construct three machine learning (ML) models—support vector classifier (SVC), random forest (RF), and deep neural network (DNN)—using the GMM-based features and compare their performance with that of the improved DNN (IDNN), which is an existing feature extraction approach. In our experiments, we introduced a missing value ratio θth to quantify trajectory sparsity and analyzed the effect of trajectory sparsity on the classification accuracy and generalizability performance of the ML models. The results indicate that GMM-based features outperform IDNN-based features in both classification accuracy and generalization performance. Notably, the RF model achieved the highest accuracy, whereas the SVC model displayed stable generalizability. As the missing value ratio θth increases, the IDNN becomes more susceptible to overfitting, whereas the GMM-based approach preserves accuracy and robustness. These findings suggest that sparse trajectories can still offer meaningful classification performance with appropriate feature design and model selection even without semantic information. This approach holds promise for domains where large-scale, sparse trajectory data are common, including urban planning, marketing analysis, and public policy.
Analyzing Cutting Temperature in Hard-Turning Technique with Standard Inserts Through Both Simulation and Experimental Investigations
Authors: Minh Duc Pham , Hieu Giang Le , Nguyen Van Thuc
Published: January 2025

The cutting temperature in hard turning is extremely high, which reduces tool life, lowers machined-surface quality, and affects dimensional control. However, hard turning differs greatly from conventional turning in that the cutting process mainly happens at the tool-nose radius due to the extremely shallow depth of the cut. This paper provides a comprehensive and systematic analysis of this issue based on an evaluation of tool geometry in hard turning via finite element analysis (FEA) simulations and experiments. The effect of tool angles on cutting temperature in hard turning is analyzed. The impacts of cutting-edge angle, rake angle, inclination angle, and average local rake angle on the cutting temperature are investigated via central composite design (CCD). The simulated results and the empirically measured cutting temperature exhibit comparable patterns, with a minor 2% difference. Increasing the cutting-edge angle, negative rake angle and negative inclination angle enhances the local negative rake angles of the cutting-edge elements at the tool-nose radius involved in the cutting process. Notably, the most important component influencing cutting temperature in hard turning is the inclination angle, as opposed to normal turning, where the rake angle dominates the heat generation. Following this is the cutting-edge angle and the rake angle, which each contribute 40.75%, 32.39%, and 7.03%. These findings could enhance the application of the hard-turning technique by improving tool life and surface quality by focusing on optimizing the inclination angle.
Exposure of Xenogeneic Biomaterial to the Oral Environment and Its Impact on Tissue Healing of Immediate Dental Implants: A Case–Control Study
Authors: Carvalho Valessa F. , Garcez-Filho João , Okamoto Roberta , Frigério Paula B. , Santos Priscila L. , Novaes Junior Arthur B. , Messora Michel R. , Taba Jr Mario
Published: January 2025

This study evaluated the clinical and tomographic outcomes of socket healing. Immediate implants were placed in the molar area, and the gap was filled with either deproteinized bovine bone mineral (B) or collagen matrix (BM), n = 14/group. Scores of epithelization healing, immunoassay for VEGF, IL-1β, and FGF from wound exudate, keratinized mucosa variation (ΔKM), and bone levels were evaluated. The B group had slower tissue maturation than BM (p < 0.05), but gingival epithelialization was similar (p > 0.05). At the restorative phase, the B group exhibited greater ΔKM at prosthesis installation—1 to 2 months of postoperative (increase of 0.29 mm) compared to the BM group (reduction of −1.5 mm) (p < 0.05). Inflammatory tissue responses as well as vertical and horizontal bone remodeling were similar (p > 0.05). Crestal bone remodeling was limited to less than 0.8 mm for both groups. Taken together, the B and BM groups behaved similarly and promoted stable conditions for biomaterial incorporation in the socket healing after immediate implant placement in molar areas.
Functionality of the Search and Rescue Transponder (SART) in Maritime Search and Rescue Actions
Authors: Malyszko Marzena , Wielgosz Miroslaw , Rzepka Brunon
Published: January 2025

In this article, the authors present contemporary problems in search and rescue operations at sea. The research focuses on the detection of the SART (Search and Rescue Transponder) device. This device is used to call for help and assist the rescuing vessel in tracking. Issues with their functionality may reduce the likelihood of finding a survivor. The authors designed an experiment to assess the effectiveness of using the device. The research conducted is a real-world experiment that involved a ship radar, a liferaft, a SART device, and a radar reflector. The experiment consisted of multiple trials to detect, locate, and track the device, as well as to assess the radar image features. Four scenarios were developed, considering different distances and radar settings. Performance evaluation indicators were also developed. The results are presented both graphically and numerically. A brief discussion of the obtained results and concise conclusions are provided. Along with the research findings, recommendations for the use of SART and radar on ships are also presented, as well as recommendations for improving training. The results are applicable to improving the effectiveness of SAR operations.
A Novel Method Combining Radial Projection with Simultaneous Multislice Imaging for Measuring Cerebrovascular Pulse Wave Velocity
Authors: Shim Jeong-Min , Kang Chang-Ki , Son Young-Don
Published: January 2025

Magnetic resonance imaging (MRI) using a simultaneous multislice technique can measure dynamic vascular elasticity over time. However, conventional k-space undersampling can cause signal interference, owing to vertical projection between blood vessels within the same hemisphere. Here, we proposed a radial projection method that can reduce signal interference between the blood vessels and aimed to verify the theoretical and practical effects of this method. A dataset from the internal and common carotid arteries (ICA and CCA) was used for both projection methods. Pulse wave velocity (PWV) was calculated using the ICA and CCA time series, and the methods were compared using the mean absolute error of PWV. The feasibility of the radial projection method in an actual MRI environment was also evaluated. PWVs of the radial projection method were statistically indistinguishable from the ground truth. And the radial projection method was less sensitive to background noise levels and showed similar results to the ground truth. This method could effectively avoid signal interference between vessels and was feasible for use in real MRI environments, maintaining high temporal resolution even with fewer sampling timepoints. Therefore, it can contribute to the early diagnosis and treatment of cerebrovascular diseases through accurate and dynamic PWV measurements.
MAIL: Micro-Accelerator-in-the-Loop Framework for MCU Integrated Accelerator Peripheral Fast Prototyping
Authors: Kwon Jisu , Park Daejin
Published: January 2025

The resource-constrained MCU-based platform is unable to use high-performance accelerators such as GPUs or servers due to insufficient resources for ML applications. We define a Micro-Accelerator (MA) that can accelerate ML operations by being connected to the on-chip bus peripheral of the MCU core. ML applications using general-purpose accelerators have a well-equipped SDK environment, making design and verification flow straightforward. In contrast, MA must be connected to the MCU core and on-chip bus interface within the chip. However, evaluating the interaction between the MCU core and an MA is challenging, as it requires the MA to connect with the core and the on-chip bus interface during target software execution. The cost of fabricating physical MA hardware is enormous, compounded by licensing issues with commercial cores. We propose a MA-in-the-loop (MAIL) framework that integrates a custom-designed MA into an emulation platform. This platform enables virtual execution by loading software onto the MCU, allowing observation of hardware-software interactions during ML execution. The proposed framework in this paper is a mixture of software that can emulate the environment in which general ML applications run on the MCU and RTL simulations to profile the acceleration on the MA. To evaluate the flow of ML software execution and performance changes according to the various architectures of MA in the framework, the MA can be reconfigured at runtime to explore the design space. To benchmark our proposed framework, we compared TinyML application profiles to the pure software execution. Experimental results show that the MA-accelerated framework performs comparably to actual MCUs, validating the efficacy of the proposed approach.
Grasp Pattern Recognition Using Surface Electromyography Signals and Bayesian-Optimized Support Vector Machines for Low-Cost Hand Prostheses
Authors: Grattarola Alessandro , Mora Marta C. , Cerdá-Boluda Joaquín , Ortiz José V. García
Published: January 2025

Every year, thousands of people undergo amputations due to trauma or medical conditions. The loss of an upper limb, in particular, has profound physical and psychological consequences for patients. One potential solution is the use of externally powered prostheses equipped with motorized artificial hands. However, these commercially available prosthetic hands are prohibitively expensive for most users. In recent years, advancements in 3D printing and sensor technologies have enabled the design and production of low-cost, externally powered prostheses. This paper presents a pattern-recognition-based human–prosthesis interface that utilizes surface electromyography (sEMG) signals, captured by an affordable device, the Myo armband. A Support Vector Machine (SVM) algorithm, optimized using Bayesian techniques, is trained to classify the user’s intended grasp from among nine common grasping postures essential for daily life activities and functional prosthetic performance. The proposal is viable for real-time implementations on low-cost platforms with 85% accuracy in grasping posture recognition.
Hydraulic Resistance Device for Force, Power, and Force–Velocity–Power Profile Assessment During Resisted Sprints with Different Overloads
Authors: Sašek Matic , Cvjetičanin Oskar , Šarabon Nejc
Published: January 2025

This study aimed to evaluate the ability of a hydraulic resistance device (HRD) to assess the force and power output and force–velocity–power profile of short sprints, while examining the effects of hydraulic overload on these outcomes. Twenty-eight amateur athletes performed 20 m sprints under minimal (MiL), moderate (MoL), and high (HiL) overloads. Sprint velocity was measured with the HRD, while resistance force (Fr) was assessed from the pressure via the HRD and from the reaction force via the force plate (FP). Using velocity and Fr during the sprints, maximal velocity (vmax), average horizontal force (Favg), average power (Pavg), and FvP profile variables (F0, v0, and Pmax) were calculated. A two-way ANOVA analysed the effects of overload and calculation method. In addition, a correlation between the HRD and FP measurements was evaluated. For all variables, very high to excellent correlation between the HRD and FP was observed (r ≥ 0.96). However, the Favg, Pavg, F0, and Pmax calculated by the HRD were lower than the FP across all overloads (η2 ≥ 0.51; p < 0.001). Regardless of the method used, Favg, Pavg, and F0 were highest at HiL (η2 ≥ 0.38; p < 0.001), and v0 was highest at MiL (η2 = 0.35; p < 0.001), whereas overload had no significant effect on Pmax (η2 = 0.01; p = 0.770). The HRD is a feasible means for monitoring force and power output during hydraulic resisted sprints but should not be directly compared to other resistance devices. HiL produced the highest Favg, Pavg, and F0 and may be optimal for increasing power output and improving acceleration performance.
A Reinforcement Learning-Based Solution for the Capacitated Electric Vehicle Routing Problem from the Last-Mile Delivery Perspective
Authors: Aslan Yıldız Özge , Sarıçiçek İnci , Yazıcı Ahmet
Published: January 2025

The growth of the urban population and the increase in e-commerce activities have resulted in challenges for last-mile delivery. On the other hand, electric vehicles (EVs) have been introduced to last-mile delivery as an alternative to fossil fuel vehicles. Electric vehicles (EVs) not only play a pivotal role in reducing greenhouse gas emissions and air pollution but also contribute significantly to the development of more energy-efficient and environmentally sustainable urban transportation systems. Within these dynamics, the Electric Vehicle Routing Problem (EVRP) has begun to replace the Vehicle Routing Problem (VRP) in last-mile delivery. While classic vehicle routing ignores fueling, both the location of charging stations and charging time should be included in the Electric Vehicle Routing Problem due to the long recharging time. This study addresses the Capacitated EVRP (CEVRP) with a novel Q-learning algorithm. Q-learning is a model-free reinforcement learning algorithm designed to maximize an agent’s cumulative reward over time by selecting optimal actions. Additionally, a new dataset is also published for the EVRP considering field constraints. For the design of the dataset, real geographical positions have been used, located in the province of Eskisehir, Türkiye. It also includes environmental information, such as streets, intersections, and traffic density, unlike classical EVRP datasets. Optimal solutions are obtained for each instance of the EVRP by using the mathematical model. The results of the proposed Q-learning algorithm are compared with the optimal solutions of the presented dataset. Test results show that the proposed algorithm provides remarkable advantages in obtaining routes in a shorter time for EVs.
Fate of the Mandible in Class III Patients Subjected to Bimaxillary Surgery with a New 3D Planning Reference
Authors: Hernández-Alfaro Federico , de la Fuente-Vázquez Carlos , Valls-Ontañón Adaia , Haas-Junior Orion-Luiz , Giralt-Hernando Maria , Masià-Gridilla Jorge
Published: January 2025

Class III patients have classically been managed through monomaxillary techniques, mainly involving mandibular setback movements. More recently, according to the upper incisor to soft tissue plane (UI-STP) or Barcelona Line (BL) planning protocol, bimaxillary procedures for upper maxilla advancement and reduced mandibular setback have been recommended in order to secure better aesthetic outcomes and avoid upper airway constriction. The present study describes the jaw movements in the sagittal plane performed in class III patients subjected to bimaxillary surgery following the BL protocol. A retrospective evaluation was performed on 124 class III patients subjected to bimaxillary surgery. All subjects underwent upper maxilla advancement. A total of 112 patients received mandible advancement movement (90.3%), nine received mandibular setback (7.25%), and the mandible underwent no movement along the sagittal dimension in the three remaining patients (2.4%). Mandibular advancement was significantly the most frequent treatment option. The presented results suggest that when the BL planning protocol is used as an aesthetic and functional reference, class III occlusion appears mostly related to maxillary sagittal hypoplasia instead of mandible hyperplasia, so bimaxillary advancement surgery should be considered as one of the first-choice procedures for the treatment of these patients.
The Development of a Novel Aluminosilicate Catalyst Fabricated via a 3D Printing Mold for Biodiesel Production at Room Temperature
Authors: Díaz-González Selene , Rodríguez Karina Elvira , Díaz Laura
Published: January 2025

Biodiesel production has gained attention as a sustainable alternative to fossil fuels, but challenges related to catalyst recovery and energy consumption remain. In this study, a novel lithium-impregnated aluminosilicate catalyst (LiSA) was developed using a 3D-printed mold, providing precise control over its structure to optimize performance. The structured catalyst featured a cylindrical shape with multiple circular channels, enhancing fluid dynamics and reactant interaction in a fixed-bed reactor. Catalyst characterization by SEM, TGA, XRD, and ICP-MS confirmed high thermal stability and uniform pore distribution. Jatropha curcas oil was used as feedstock, with diethyl ether (DEE) acting as a cosolvent to improve methanol solubility and enable transesterification at room temperature. The process achieved a high fatty acid methyl ester (FAME) yield, averaging 97.1% over 508 min of continuous operation, demonstrating the catalyst’s stability and sustained activity. By reducing mass transfer limitations and energy demands, this approach highlights the potential of 3D-printed catalysts to advance sustainable biodiesel production, offering a scalable and efficient pathway for green energy technologies.
Carbon Emission Reduction in Traffic Control: A Signal Timing Optimization Method Based on Rainbow DQN
Authors: Lv Juan , Wang Zhaowei , Ma Jianxiao
Published: January 2025

To improve intersection traffic flow and reduce vehicle energy consumption and emissions at intersections, a signal optimization method based on deep reinforcement learning (DRL) is proposed. The algorithm uses Rainbow DQN as the core framework, incorporating vehicle position, speed, and acceleration information into the state space. The reward function simultaneously considers two objectives: reducing vehicle waiting times and minimizing carbon emissions, with the vehicle queue length as a weighted factor. Additionally, an ACmix module, which integrates self-attention mechanisms and convolutional layers, is introduced to enhance the model’s feature extraction and information representation capabilities, improving computational efficiency. The model is tested using an actual intersection as the study object, with a signal intersection simulation built in SUMO. The proposed approach is compared with traditional Webster signal timing, actuated signal timing, and control strategies based on DQN and D3QN models. The results show that the proposed strategy, through real-time signal timing adjustments, reduces the average vehicle waiting time by approximately 27.58% and the average CO2 emissions by about 7.34% compared with the actuated signal timing method. A comparison with DQN and D3QN models further demonstrates the superiority of the proposed model, achieving a 15% reduction in average waiting time and a 6.5% reduction in CO2 emissions. The model’s applicability is validated under various scenarios, including different proportions of electric vehicles and traffic volumes. This study aims to provide a flexible signal control strategy to enhance intersection vehicle flow and reduce carbon emissions. It offers a reference for the development of green, intelligent transportation systems and holds practical significance for promoting urban carbon reduction efforts.
Korpelevich Method for Solving Bilevel Variational Inequalities on Riemannian Manifolds
Authors: Liao Jiagen , Wan Zhongping
Published: January 2025

The bilevel variational inequality on Riemannian manifolds refers to a mathematical problem involving the interaction between two levels of optimization, where one level is constrained by the other level. In this context, we present a variant of Korpelevich’s method specifically designed for solving bilevel variational inequalities on Riemannian manifolds with nonnegative sectional curvature and pseudomonotone vector fields. This variant aims to find a solution that satisfies certain conditions. Through our proposed algorithm, we are able to generate iteration sequences that converge to a solution, given mild conditions. Finally, we provide an example to demonstrate the effectiveness of our algorithm.
Intracerebral Hemorrhage-Associated Iron Release Leads to Pericyte-Dependent Cerebral Capillary Function Disruption
Authors: Balk Stefanie , Panier Franziska , Brandner Sebastian , Coras Roland , Blümcke Ingmar , Ekici Arif B. , Sembill Jochen A. , Schwab Stefan , Huttner Hagen B. , Sprügel Maximilian I.
Published: January 2025

Intracerebral hemorrhage leads to immediate brain injury due to local mechanical damage, on which current treatment approaches are focused, but it also induces secondary brain injury. The purpose of this study is to characterize blood components, degradation products and their effects in secondary brain injury. Immunocyto- and immunohistochemistry, Fluorescence-Activated Cell Sorting, WST-1 assays and RNA sequencing were applied using human cell cultures and human ex vivo brain tissue slices. Brain tissue was immediately collected, cooled and sliced during neurosurgical operations to perform experiments on living tissue slices of the human brain. Among the blood degradation products, free iron (Fe2+ and Fe3+), but not hemoglobin, induced detrimental effects on pericyte function and survival (78.5% vs. 94.3%; p-value < 0.001). RNA sequencing revealed ferroptosis as the underlining cellular mechanism, mediated via GPX-4 (log2 fold change > 1.0, p-value < 1.08 × 10−30) in pathway analysis and eventually resulting in oxidative cell death. Pericytes located at cerebral capillary branching sites were specifically affected by ferroptosis, leading to capillary disruption and vasoconstriction, which were partially prevented by ferrostatin-1. Free iron induces the pericyte-dependent disruption of cerebral capillary function and represents a therapeutic target to attenuate secondary injury after intracerebral hemorrhage.
Hybrid Neural Network Models to Estimate Vital Signs from Facial Videos
Authors: Zheng Yufeng
Published: January 2025

Introduction: Remote health monitoring plays a crucial role in telehealth services and the effective management of patients, which can be enhanced by vital sign prediction from facial videos. Facial videos are easily captured through various imaging devices like phone cameras, webcams, or surveillance systems. Methods: This study introduces a hybrid deep learning model aimed at estimating heart rate (HR), blood oxygen saturation level (SpO2), and blood pressure (BP) from facial videos. The hybrid model integrates convolutional neural network (CNN), convolutional long short-term memory (convLSTM), and video vision transformer (ViViT) architectures to ensure comprehensive analysis. Given the temporal variability of HR and BP, emphasis is placed on temporal resolution during feature extraction. The CNN processes video frames one by one while convLSTM and ViViT handle sequences of frames. These high-resolution temporal features are fused to predict HR, BP, and SpO2, capturing their dynamic variations effectively. Results: The dataset encompasses 891 subjects of diverse races and ages, and preprocessing includes facial detection and data normalization. Experimental results demonstrate high accuracies in predicting HR, SpO2, and BP using the proposed hybrid models. Discussion: Facial images can be easily captured using smartphones, which offers an economical and convenient solution for vital sign monitoring, particularly beneficial for elderly individuals or during outbreaks of contagious diseases like COVID-19. The proposed models were only validated on one dataset. However, the dataset (size, representation, diversity, balance, and processing) plays an important role in any data-driven models including ours. Conclusions: Through experiments, we observed the hybrid model’s efficacy in predicting vital signs such as HR, SpO2, SBP, and DBP, along with demographic variables like sex and age. There is potential for extending the hybrid model to estimate additional vital signs such as body temperature and respiration rate.
Development and Optimization of a Cost-Effective Electrochemical Immunosensor for Rapid COVID-19 Diagnosis
Authors: Lima Thaís Machado , Leal Daiane Martins , Ferreira Zirlane Coelho , Souza Fernando de Jesus , de Oliveira Danilo Bretas , Rocha-Vieira Etel , Martins Helen Rodrigues , Pereira Arnaldo César , Ferreira Lucas Franco
Published: January 2025

The coronavirus disease (COVID-19) pandemic has created an urgent need for rapid, accurate, and cost-effective diagnostic tools. In this study, an economical electrochemical immunosensor for the rapid diagnosis of COVID-19 was developed and optimized based on charge transfer resistance (Rct) values obtained by electrochemical impedance spectroscopy (EIS) from the interaction between antibodies (anti-SARS-CoV-2) immobilized as a bioreceptor and the virus (SARS-CoV-2). The sensor uses modified pencil graphite electrodes (PGE) coated with poly(4-hydroxybenzoic acid), anti-SARS-CoV-2, and silver nanoparticles. The immobilization of anti-SARS-CoV-2 antibodies was optimized at a concentration of 1:250 for 30 min, followed by blocking the surface with 0.01% bovine serum albumin for 10 min. The optimal conditions for virus detection in clinical samples were a 1:10 dilution with a response time of 20 min. The immunosensor responded linearly in the range of 0.2–2.5 × 106 particles/μL. From the relationship between the obtained signal and the concentration of the analyzed sample, the limit of detection (LOD) and limit of quantification (LOQ) obtained were 1.21 × 106 and 4.04 × 106 particles/μL, respectively. The device did not cross-react with other viruses, including Influenza A and B, HIV, and Vaccinia virus. The relative standard deviation (RSD) of the six immunosensors prepared using the shared-pool sample was 3.87. Decreases of 22.3% and 12.4% were observed in the response values of the ten immunosensors stored at 25 °C and 4.0 °C, respectively. The sensor provides timely and accurate results with high sensitivity and specificity, offering a cost-effective alternative to the existing diagnostic methods.
Approach–Avoidance Bias in Virtual and Real-World Simulations: Insights from a Systematic Review of Experimental Setups
Authors: Grasso-Cladera Aitana , Madrid-Carvajal John , Walter Sven , König Peter
Published: January 2025

Background: Approach and avoidance bias (AAB) describes automatic behavioral tendencies to react toward environmental stimuli regarding their emotional valence. Traditional setups have provided evidence but often lack ecological validity. The study of the AAB in naturalistic contexts has recently increased, revealing significant methodological challenges. This systematic review evaluates the use of virtual reality (VR) and real-world setups to study the AAB, summarizing methodological innovations and challenges. Methods: We systematically reviewed peer-reviewed articles employing VR and real-world setups to investigate the AAB. We analyzed experimental designs, stimuli, response metrics, and technical aspects to assess their alignment with research objectives and identify limitations. Results: This review included 14 studies revealing diverse methodologies, stimulus types, and novel behavioral responses, highlighting significant variability in design strategies and methodological coherence. Several studies used traditional reaction time measures yet varied in their application of VR technology and participant interaction paradigms. Some studies showed discrepancies between simulated and natural bodily actions, while others showcased more integrated approaches that preserved their integrity. Only a minority of studies included control conditions or acquired (neuro)physiological data. Conclusions: VR offers a potential ecological setup for studying the AAB, enabling dynamic and immersive interactions. Our results underscore the importance of establishing a coherent framework for investigating the AAB tendencies using VR. Addressing the foundational challenges of developing baseline principles that guide VR-based designs to study the AAB within naturalistic contexts is essential for advancing the AAB research and application. This will ultimately contribute to more reliable and reproducible experimental paradigms and develop effective interventions that help individuals recognize and change their biases, fostering more balanced behaviors.
Mechanical Properties of Reinforcement Cage Underreamed Anchor Bolts and Their Application in Soft Rock Slope Stabilization
Authors: Luo Xi , Chen Ruxin , Ji Yifan , Li Peilong , Ma Ziqiang , Jiang Xiuming
Published: January 2025

The utilization of reinforcement cage underreamed anchor bolts is prevalent in the reinforcement of foundation pit engineering, but there are few studies on the reinforcement of soft rock slopes and the influence of its parameters on slope stability. This study combines laboratory tests to analyze the mechanical properties of reinforced and non-reinforced bolts with finite element analysis to model the anchorage support system in soft rock slopes. Key parameters affecting the stability of the slope, such as bolt diameter, expansion section diameter, and anchorage depth, were considered. The findings indicate that the inclusion of a reinforcement cage leads to a more rational distribution of mechanical properties, promoting even axial force distribution to the grouting medium. An increase in bolt diameter enhances slope stability, while the expansion section diameter has minimal impact when a strong bond exists between the grouting body and the rock mass. However, in the absence of such bonding, increasing the expansion section diameter significantly improves slope stability. Deeper anchorage also correlates with higher stability, though the rate of increase in safety factor slows as the anchorage depth approaches the critical slip plane. In conjunction with field application, the research outcomes can exert a certain directive impact on practical engineering and can be used as a reference for the design method of bolt support for soft rock slope
Design and Operation of a Novel Cross Fin in Hot-Water Production System for Buildings
Authors: Cui Qiang , Ning Tao , Huang Chuanqing , Wu Chunyan , Su Junwei
Published: January 2025

The importance of phase change heat storage (PCHS) in solar thermal applications is limited by the low thermal conductivity of phase change materials (PCMs) and the uneven temperature distribution during heat transfer. This study proposes to use composite fins for heat exchange in the PCHS module and integrate them into a hot-water production system (HWPS) for building heating. The effectiveness of the novel fin structure is assessed through thorough numerical simulations and experimental validation. An examination of melting fractions, temperature distribution, and flow characteristics of the molten PCMs across various fin structures indicates that increasing the lengths and quantities of the cross fins can alleviate the challenge of incomplete melting at the end of the charging process. Notably, expanding the surface area of the cross fins results in a 7.37-fold increase in the average thermal storage rate and a 781.25% enhancement in the average temperature response compared to the original design. These findings show that the new composite fin design greatly improves the heat storage performance of an HWPS, which is of great significance for building energy conservation.