bluuebunny/crossref_metadata_2025 huggingface dataset:
Mathematical Model for Chemical Reactions in Electrolytes Applied to Cytochrome c Oxidase: An Electro-Osmotic Approach
Authors: Xu Shixin , Eisenberg Robert , Song Zilong , Huang Huaxiong
Published: December 2023

This study introduces a mathematical model for electrolytic chemical reactions, employing an energy variation approach grounded in classical thermodynamics. Our model combines electrostatics and chemical reactions within well-defined energetic and dissipative functionals. Extending the energy variation method to open systems consisting of charge, mass, and energy inputs, this model explores energy transformation from one form to another. Electronic devices and biological channels and transporters are open systems. By applying this generalized approach, we investigate the conversion of an electrical current to a proton flow by cytochrome c oxidase, a vital mitochondrial enzyme contributing to ATP production, the ‘energetic currency of life’. This model shows how the enzyme’s structure directs currents and mass flows governed by energetic and dissipative functionals. The interplay between electron and proton flows, guided by Kirchhoff’s current law within the mitochondrial membrane and the mitochondria itself, determines the function of the systems, where electron flows are converted into proton flows and gradients. This important biological system serves as a practical example of the use of energy variation methods to deal with electrochemical reactions in open systems. We combine chemical reactions and Kirchhoff’s law in a model that is much simpler to implement than a full accounting of all the charges in a chemical system.
Zero-Inflated Text Data Analysis using Generative Adversarial Networks and Statistical Modeling
Authors: Jun Sunghae
Published: December 2023

In big data analysis, various zero-inflated problems are occurring. In particular, the problem of inflated zeros has a great influence on text big data analysis. In general, the preprocessed data from text documents are a matrix consisting of the documents and terms for row and column, respectively. Each element of this matrix is an occurred frequency of term in a document. Most elements of the matrix are zeros, because the number of columns is much larger than the rows. This problem is a cause of decreasing model performance in text data analysis. To overcome this problem, we propose a method of zero-inflated text data analysis using generative adversarial networks (GAN) and statistical modeling. In this paper, we solve the zero-inflated problem using synthetic data generated from the original data with zero inflation. The main finding of our study is how to change zero values to the very small numeric values with random noise through the GAN. The generator and discriminator of the GAN learned the zero-inflated text data together and built a model that generates synthetic data that can replace the zero-inflated data. We conducted experiments and showed the results, using real and simulation data sets to verify the improved performance of our proposed method. In our experiments, we used five quantitative measures, prediction sum of squares, R-squared, log-likelihood, Akaike information criterion and Bayesian information criterion to evaluate the model’s performance between original and synthetic data sets. We found that all performances of our proposed method are better than the traditional methods.
Synthesis and Structural Characterization of Layered Ni+1/+2 Oxides Obtained by Topotactic Oxygen Release on Nd2−xSrxNiO4−δ Single Crystals
Authors: Hareesh Chavana , Ceretti Monica , Papet Philippe , Bosak Alexeï , Meven Martin , Paulus Werner
Published: December 2023

Layered nickelate oxides containing Ni1+/Ni2+ are isoelectronic to Cu2+/Cu3+ compounds and of present interest with respect to recent findings of superconductivity in a series of different compositions. It is thereby questionable why superconductivity is still rare to find in nickelates, compared to the much larger amount of superconducting cuprates. Anisotropic dz2 vs. dx2−y2 orbital occupation as well as interface-induced superconductivity are two of the main advanced arguments. We are here interested in investigating the feasibility of synthesizing layered nickelate-type oxides, where the Ni1+/Ni2+ ratio can be tuned by oxygen and/or cation doping. Our strategy is to synthesize Sr-doped n = 1 Ruddlesden–Popper type Nd2−xSrxNiO4+δ single crystals, which are then reduced by H2 gas, forming Nd2−xSrxNiO4−δ via a topotactic oxygen release at moderate temperatures. We report here on structural studies carried out on single crystals by laboratory and synchrotron diffraction using pixel detectors. We evidence the general possibility to obtain reduced single crystals despite their increased orthorhombicity. This must be regarded as a milestone to obtain single crystalline nickelate oxides, which further on contain charge-ordering of Ni1+/Ni2+, opening the access towards anisotropic properties.
The Effect of the Distance between Ultrasonic Horn and Torch on the Microstructure of Ultrasonic-Assisted Gas Tungsten Arc Welded Inconel 690 Alloy Joint
Authors: Xia Yunhao , Cai Xiaoyu , Dong Bolun , Lin Sanbao
Published: December 2023

The study focuses on investigating the relationship between the ultrasonic effect and microstructure of ultrasonic-assisted gas tungsten arc welding (UA-GTAW) Inconel690 alloy joints. The influence of ultrasonic vibrations on Inconel690 plates was examined, while also clarifying the distribution pattern of the ultrasonic effect across the plate. Furthermore, actual welding experiments were performed by varying the distance between the ultrasonic horn and the welding torch. The results revealed that there were changes in both grain growth direction within the weld zone and refinement effects achieved under different distances. The optimal refinement of primary and secondary dendrite arm spacing was observed at distances of 60 mm and 180 mm between the welding torch and ultrasonic horn. The hardness of weld zone reached 235HV1 when the distance between ultrasonic horn and welding torch is 180 mm.
A Least Squares Fitting Method for Uncertain Parameter Estimation in Solidification Model
Authors: Wang Yuhan , Xie Zhi
Published: December 2023

This study proposes an automated method for estimating the uncertain parameters of the solidification model in response to the inefficient and time-consuming problem of manually estimating multiple uncertain parameters of the solidification model. The method establishes an uncertain parameter estimation model based on the relationship between the simulated images equiaxed crystal ratio and the uncertain parameters of the solidification model, fits the parameters of the model by the least squares method, and finally estimates the uncertain parameters in the solidification model using the parameters of the fitted model. In comparison with the traditional method of calculating uncertain parameters manually through empirical formulas, this method reduces the difficulty of tuning parameters and solves the problem of tuning multiple parameters simultaneously in the nonlinear solidification model. The experimental results show that the proposed method can accurately estimate the uncertain parameters of the solidification model, improve the efficiency and accuracy of the solidification model estimation parameters, and play a guiding role in simulating the solidification process of continuously casting billet to control the solidification structure.
End-to-End Continuous Small-Scale Drug Substance Manufacturing: From a Continuous In Situ Nucleator to Free-Flowing Crystalline Particles
Authors: Kufner Anne Cathrine , Meier Marc , Wohlgemuth Kerstin
Published: December 2023

In the evolving landscape of pharmaceutical manufacturing, a comprehensive continuous production process is being crafted for the small-scale production of active pharmaceutical ingredients. This study focuses on continuous crystallization with separate nucleation and crystal growth units, as well as continuous downstream processing, encompassing filtration, washing, and drying until the formation of free-flowing particles. We introduce a novel continuous nucleator designed based on solubility data and produced via 3D printing, enabling the fast and precise small-scale manufacturing of a nucleator meeting the requirements for nucleation and further growth processes. The nucleator is evaluated with regard to its suitability for continuous long-term operation across various coupled crystallizers. As a practical application example, it is connected to a slug flow crystallizer to enable high-quality continuous crystallization. Additionally, the full integration of downstream processes using a continuous vacuum screw filter to achieve free-flowing product particles is realized. Even under non-optimized process conditions, with the help of the in situ generation of nuclei, free-flowing product particles are successfully obtained. This is particularly useful during drug development when no material is available for seed addition and to quickly obtain products for further characterization.
Genome Sequence of the Plant-Growth-Promoting Endophyte Curtobacterium flaccumfaciens Strain W004
Authors: Chebotar Vladimir K. , Gancheva Maria S. , Chizhevskaya Elena P. , Baganova Maria E. , Keleinikova Oksana V. , Husainov Kharon A. , Pishchik Veronika N.
Published: December 2023

We report the whole-genome sequences of the endophyte Curtobacterium flaccumfaciens strain W004 isolated from the seeds of winter wheat, cv. Bezostaya 100. The genome was obtained using Oxford Nanopore MinION sequencing. The bacterium has a circular chromosome consisting of 3.63 kbp with a G+C% content of 70.89%. We found that Curtobacterium flaccumfaciens strain W004 could promote the growth of spring wheat plants, resulting in an increase in grain yield of 54.3%. Sequencing the genome of this new strain can provide insights into its potential role in plant–microbe interactions.
From Histopathology to High-Resolution Ultrasound Imaging of Skin Scars
Authors: Ricci Vincenzo , Cocco Giulio , Donati Danilo , Farì Giacomo , Chang Ke-Vin , Özçakar Levent
Published: December 2023

Nowadays, modern ultrasound machines and high-frequency transducers allow us to accurately assess the superficial soft tissues of the human body. In this sense, sonographic evaluation of the skin and related pathologies is progressively growing in the pertinent literature. To the best of our knowledge, a standardized sonographic protocol focused on the assessment of pathological skin scars is still lacking. As such, the main purpose of the present study was to propose a technical guide to sonographically assess skin scars in the daily practice of clinicians—starting from knowledge on their histopathological features. In order to standardize the ultrasound examination, a superficial-to-deep, layer-by-layer approach has been proposed to optimize its reproducibility and to promote a common language among the different healthcare providers.
Machine Learning Models for Prediction of Sex Based on Lumbar Vertebral Morphometry
Authors: Diac Madalina Maria , Toma Gina Madalina , Damian Simona Irina , Fotache Marin , Romanov Nicolae , Tabian Daniel , Sechel Gabriela , Scripcaru Andrei , Hancianu Monica , Iliescu Diana Bulgaru
Published: December 2023

Background: Identifying skeletal remains has been and will remain a challenge for forensic experts and forensic anthropologists, especially in disasters with multiple victims or skeletal remains in an advanced stage of decomposition. This study examined the performance of two machine learning (ML) algorithms in predicting the person’s sex based only on the morphometry of L1–L5 lumbar vertebrae collected recently from Romanian individuals. The purpose of the present study was to assess whether by using the machine learning (ML) techniques one can obtain a reliable prediction of sex in forensic identification based only on the parameters obtained from the metric analysis of the lumbar spine. Method: This paper built and tuned predictive models with two of the most popular techniques for classification, RF (random forest) and XGB (xgboost). Both series of models used cross-validation and a grid search to find the best combination of hyper-parameters. The best models were selected based on the ROC_AUC (area under curve) metric. Results: The L1–L5 lumbar vertebrae exhibit sexual dimorphism and can be used as predictors in sex prediction. Out of the eight significant predictors for sex, six were found to be particularly important for the RF model, while only three were determined to be important by the XGB model. Conclusions: Even if the data set was small (149 observations), both RF and XGB techniques reliably predicted a person’s sex based only on the L1–L5 measurements. This can prove valuable, especially when only skeletal remains are available. With minor adjustments, the presented ML setup can be transformed into an interactive web service, freely accessible to forensic anthropologists, in which, after entering the L1–L5 measurements of a body/cadaver, they can predict the person’s sex.
Addressing Challenges in Diagnosis, Differential Diagnosis, and Treatment of Pemphigus: A Case Series
Authors: Pranadwista Zulfa Fidi , Rahayuningtyas Etis Duhita , Sufiawati Irna
Published: December 2023

Pemphigus is a rare autoimmune disease characterized by skin blisters and erosions, with or without mucosal involvement. The clinical presentation of pemphigus can resemble other bullous diseases, leading to challenges in diagnosis. This report aims to address the challenges in diagnosing and treating oral pemphigus. Three patients, ranging in age from 26 to 55 years, complained of a sore throat and mouth canker sores. Extra-oral examination revealed dry lips in case 1, while serosanguinolenta crust on the lip that bled easily was found in case 2. Intra-oral examinations in all cases showed multiple painful, sloughing-covered, erosive lesions on the entire oral mucosa. The histopathological examination of case 1 revealed pemphigus foliaceous, whereas cases 2 and 3 showed pemphigus vulgaris. Secondary syphilis-like pemphigus was given as a differential diagnosis in case 2 due to the histopathological changes not being specific. The patients were instructed to maintain oral hygiene and treated with corticosteroid, analgesic, antifungal, and anti-inflammation mouthwash, as well as vitamins and minerals. All cases showed improvement in oral lesions within 14 days to a month. In conclusion, pemphigus may mimic other bullous diseases, making diagnosis challenging. A comprehensive clinical and laboratory assessment is necessary to provide accurate diagnosis and treatment.
Recent Advances in Imaging Macular Atrophy for Late-Stage Age-Related Macular Degeneration
Authors: Cheng Anny M. S. , Chalam Kakarla V. , Brar Vikram S. , Yang David T. Y. , Bhatt Jineel , Banoub Raphael G. , Gupta Shailesh K.
Published: December 2023

Age-related macular degeneration (AMD) is a leading cause of blindness worldwide. In late-stage AMD, geographic atrophy (GA) of dry AMD or choroidal neovascularization (CNV) of neovascular AMD eventually results in macular atrophy (MA), leading to significant visual loss. Despite the development of innovative therapies, there are currently no established effective treatments for MA. As a result, early detection of MA is critical in identifying later central macular involvement throughout time. Accurate and early diagnosis is achieved through a combination of clinical examination and imaging techniques. Our review of the literature depicts advances in retinal imaging to identify biomarkers of progression and risk factors for late AMD. Imaging methods like fundus photography; dye-based angiography; fundus autofluorescence (FAF); near-infrared reflectance (NIR); optical coherence tomography (OCT); and optical coherence tomography angiography (OCTA) can be used to detect and monitor the progression of retinal atrophy. These evolving diverse imaging modalities optimize detection of pathologic anatomy and measurement of visual function; they may also contribute to the understanding of underlying mechanistic pathways, particularly the underlying MA changes in late AMD.
Commonality Evaluation and Prediction Study of Light and Small Multi-Rotor UAVs
Authors: Zhang Yongjie , Zeng Yongqi , Cao Kang
Published: December 2023

Light small-sized, multi-rotor UAVs, with their notable advantages of portability, intelligence, and low cost, occupy a significant share in the civilian UAV market. To further reduce the full lifecycle cost of products, shorten development cycles, and increase market share, some manufacturers of these UAVs have adopted a series development strategy based on the concept of commonality in design. However, there is currently a lack of effective methods to quantify the commonality in UAV designs, which is key to guiding commonality design. In view of this, our study innovatively proposes a new UAV commonality evaluation model based on the basic composition of light small-sized multi-rotor UAVs and the theory of design structure matrices. Through cross-evaluations of four models, the model has been confirmed to comprehensively quantify the degree of commonality between models. To achieve commonality prediction in the early stages of multi-rotor UAV design, we constructed a commonality prediction dataset centered around the commonality evaluation model using data from typical light small-sized multi-rotor UAV models. After training this dataset with convolutional neural networks, we successfully developed an effective predictive model for the commonality of new light small-sized multi-rotor UAV models and verified the feasibility and effectiveness of this method through a case application in UAV design. The commonality evaluation and prediction models established in this study not only provide strong decision-making support for the series design and commonality design of UAV products but also offer new perspectives and tools for strategic development in this field.
Fixed-Time Extended Observer-Based Adaptive Sliding Mode Control for a Quadrotor UAV under Severe Turbulent Wind
Authors: Miranda-Moya Armando , Castañeda Herman , Wang Hesheng
Published: December 2023

This paper presents a fixed-time extended state observer-based adaptive sliding mode controller evaluated in a quadrotor unmanned aerial vehicle subject to severe turbulent wind while executing a desired trajectory. Since both the state and model of the system are assumed to be partially known, the observer, whose convergence is independent from the initial states of the system, estimates the full state, model uncertainties, and the effects of turbulent wind in fixed time. Such information is then compensated via feedback control conducted by a class of adaptive sliding mode controller, which is robust to perturbations and reduces the chattering effect by non-overestimating its adaptive gain. Furthermore, the stability of the closed-loop system is analyzed by means of the Lyapunov theory. Finally, simulation results validate the feasibility and advantages of the proposed strategy, where the observer enhances performance. For further demonstration, a comparison with an existent approach is provided.
A Communication Anti-Jamming Scheme Assisted by RIS with Angular Response
Authors: Wang Jinpeng , Jiang Wenyu , Huang Kaizhi , Sun Xiaoli
Published: December 2023

By optimizing the reconfigurable intelligent surface (RIS) reflection coefficients, the channel capacity of legitimate users can be increased, thereby enhancing the anti-jamming performance of communication systems. However, existing studies on RIS-assisted anti-jamming assume that there is no coupling between the RIS reflection coefficients and the incident angle of electromagnetic (EM) waves, which is quite unreasonable. Therefore, we consider the effect of the incident angle of EM waves on the reflection coefficients of the RIS and propose a communication anti-jamming scheme assisted by an RIS with angular response. Specifically, a problem is formulated to optimize the RIS reflection coefficients so that the legitimate signal is amplified, but the jamming signal is attenuated, thus enhancing the legitimate channel capacity. However, the coupling of the EM incident angle and the RIS reflection coefficients causes the problem to be non-convex. To tackle this problem, we equivalently transform the RIS reflection coefficients optimization problem into a quadratically constrained quadratic programming (QCQP) problem using the complex Taylor expansion and the multidimensional complex quadratic transform (MCQT) and solve it utilizing the alternating direction method of the multipliers (ADMM) algorithm. The simulation results reveal that, compared to other schemes supported by RIS without angular response, the proposed scheme is able to achieve a significant improvement in the anti-jamming performance.
On the Relationship between Feature Selection Metrics and Accuracy
Authors: Epstein Elise , Nallapareddy Naren , Ray Soumya
Published: December 2023

Feature selection metrics are commonly used in the machine learning pipeline to rank and select features before creating a predictive model. While many different metrics have been proposed for feature selection, final models are often evaluated by accuracy. In this paper, we consider the relationship between common feature selection metrics and accuracy. In particular, we focus on misorderings: cases where a feature selection metric may rank features differently than accuracy would. We analytically investigate the frequency of misordering for a variety of feature selection metrics as a function of parameters that represent how a feature partitions the data. Our analysis reveals that different metrics have systematic differences in how likely they are to misorder features which can happen over a wide range of partition parameters. We then perform an empirical evaluation with different feature selection metrics on several real-world datasets to measure misordering. Our empirical results generally match our analytical results, illustrating that misordering features happens in practice and can provide some insight into the performance of feature selection metrics.
Using Problem-Based Learning and Gamification as a Catalyst for Student Engagement in Data-Driven Engineering Education: A Report
Authors: Čubela Dino , Rossner Alexander , Neis Pascal
Published: December 2023

This report explores the integration of problem-based learning, gamification, and data-driven approaches in engineering education. With a focus on the course “GeoGovernment 1”, this framework aimed to engage students actively and foster self-directed learning. By tackling real-world issues like automated teller machine (ATM) burglaries in Rhineland-Palatinate (Germany), students gained experience in data analyses and geoinformatics technology. This approach not only motivated students but also enhanced their prospects in science, technology, engineering, and mathematics (STEM) fields, equipping them with skills necessary for their future careers. The course structure emphasized student-centered learning, with educators playing facilitative roles to provide guidance. In summary, the combination of problem-based learning, gamification, and data-driven approaches offers a promising solution to address the challenges faced by STEM education, providing an engaging and effective learning experience for students, and ultimately preparing them for the demands of the ever-evolving professional landscape.
Assessing EFL Students’ Performance and Self-Efficacy Using a Game-Based Learning Approach
Authors: Meccawy Maram , Alzahrani Asma , Mattar Zinah , Almohammadi Rawan , Alzahrani Samah , Aljizani Ghadeer , Meccawy Zilal
Published: December 2023

Educational technology plays a major role in today’s learning as it offers significant advantages in delivering learning content, classroom communications, and assessing learners. Assessment is a cornerstone in modern and formal education and is particularly necessary for tracking progress, measuring knowledge or achievement, and planning future educational steps. This study investigates the effects of using an online game-based learning (GBL) approach in students’ assessment in terms of their performance and self-efficacy in English as a foreign language (EFL) learning. A quasi-experiment was conducted on intermediate school-level students, who were divided into two groups, namely control and experimental. Each group underwent an English language test conducted through a Google Form and a role-playing game developed to mimic the functionality of an assessment tool. The results indicate that the performance of the experiment group was neither affected positively nor negatively using the GBL assessment tool. On the other hand, the results also show that using such a tool has positively affected students’ self-efficacy levels.
Prevention of Work Absence Due to Back Pain: A Network Meta-Analysis
Authors: Ta Kim-Ngan Thi , Bai Chyi-Huey , Chuang Kai-Jen
Published: December 2023

This paper reviewed the most effective strategies for preventing work absence due to back pain (BP) and BP episodes (the number of people reporting back pain). We searched randomized controlled trials (RCTs) of prevention strategies for BP from previous meta-analyses, PubMed, CENTRAL, and Embase and conducted a network meta-analysis. Thirteen RCTs (2033 participants) were included. Low- to high-quality evidence showed that exercise combined with ergonomics, education, back belts, and education combined with ergonomics did not prevent sickness absenteeism or BP episodes. There was moderate-quality evidence that exercise, especially resistance exercise, was the best prevention strategy to reduce the number of people reporting absenteeism due to BP (risk ratio [RR] = 0.10; 95% CI: 0.01 to 0.69). Moderate-quality evidence suggested that resistance and stretching exercises combined with education was the best prevention strategy to reduce pain (RR = 0.80; 95% CI: 0.67 to 0.96) and the number of absenteeism days for BP (standardized mean difference [SMD] = −0.39; 95% CI: −0.77 to −0.02). In conclusion, exercise, especially resistance and stretching exercises, and exercise combined with education were ranked as the best interventions to prevent sickness absenteeism and BP episodes.
Multi-Vehicle Navigation Using Cooperative Localization
Authors: Oliveros Juan Carlos , Ashrafiuon Hashem
Published: December 2023

This paper assesses the effectiveness of cooperative localization for improving the performance of closed-loop control systems for networks for autonomous multi-vehicle navigation. Nonlinear dynamic models of two- and three-dimensional vehicles are presented along with their linearized forms. A nonlinear control algorithm is then presented based on the dynamic model. Relative position measurement equations and their linearized forms are introduced. The state and measurement equations are then employed for the propagation and update steps of an EKF-based cooperative localization algorithm. Initially, a series of experiments with networks of quadcopters and mobile robots are presented to validate the performance of cooperative localization for state estimation with the continuous or intermittent presence of absolute measurements or their complete absence. Finally, the performance of the control algorithm is evaluated with and without cooperative localization to demonstrate its effectiveness for improving performance.
Enhancing Privacy Preservation in Vehicular Trust Management Systems through Blockchain Technology
Authors: Jin Nian , Meng Kun , Ding Jie , Sun Lijun , Wu Haiqin , Chen Xiao
Published: December 2023

The advent of the Internet of Vehicles (IoV) has led to a significant surge in data generation from vehicles, prompting the innovative utilization of data transactions within the IoV environment. However, due to the inherent trustless nature of data transactions in IoV, concerns have arisen regarding the lack of trust between involved parties and the potential compromise of user privacy. To address these issues, blockchain technology emerges as a suitable decentralized distributed storage and security management solution, offering transparency and security in data transactions. In this study, we leverage blockchain to integrate with the IoV, devising a robust trust management framework, and devising a privacy protection scheme to safeguard user privacy concerns. Additionally, we employ the performance evaluation process algebra (PEPA) method for system modeling and performance analysis to assess the efficacy of our proposed solution. Empirical findings demonstrate that our approach effectively enhances the performance of data transactions within the IoV while ensuring that the privacy of users remains intact.
Using the Displaced Phase Center Azimuth Multiple Beams Technique with Spaceborne Synthetic Aperture Radar Systems for Multichannel Reconstruction of Accelerated Moving Targets
Authors: Xu Wei , Chen Yu , Huang Pingping , Tan Weixian , Qi Yaolong
Published: December 2023

The displaced phase center multiple azimuth beams (DPCMAB) technique can help spaceborne synthetic aperture radar (SAR) systems obtain the high-resolution wide-swath (HRWS) imaging capacity, and azimuth multichannel reconstruction is usually required due to azimuth non-uniform sampling. Compared with stationary and moving targets, the range history and azimuth signal model of the moving target with an acceleration are obviously different. The azimuth multichannel signal model of an accelerated moving target is established, and the relationship between acceleration and Doppler parameters is analyzed. Furthermore, the impact of the acceleration on azimuth multichannel reconstruction and imaging results is simulated and analyzed. According to the azimuth multichannel signal model, an azimuth multichannel reconstruction approach for accelerated moving targets is proposed. The key point of the proposed reconstruction approach is the modified azimuth multichannel matrix, which is related not only to azimuth and slant velocities but also accelerations. The target’s velocities and accelerations are obtained using multiple Doppler parameter estimations. Compared with the conventional method of processing the raw data of accelerated moving targets, this proposed method could distinctly suppress image defocusing and pairs of false targets. Simulation results on point targets validate the proposed azimuth multichannel reconstruction approach.
Few-Shot Classification Based on the Edge-Weight Single-Step Memory-Constraint Network
Authors: Shi Jing , Zhu Hong , Bi Yuandong , Wu Zhong , Liu Yuanyuan , Du Sen
Published: December 2023

Few-shot classification algorithms have gradually emerged in recent years, and many breakthroughs have been made in the research of migration networks, metric spaces, and data enhancement. However, the few-shot classification algorithm based on Graph Neural Network is still being explored. In this paper, an edge-weight single-step memory-constraint network is proposed based on mining hidden features and optimizing the attention mechanism. According to the hidden distribution characteristics of edge-weight data, a new graph structure is designed, where node features are fused and updated to realize feature enrichment and full utilization of limited sample data. In addition, based on the convolution block attention mechanism, different integration methods of channel attention and spatial attention are proposed to help the model extract more meaningful features from samples through feature attention. The ablation experiments and comparative analysis of each training mode are carried out on standard datasets. The experimental results obtained prove the rationality and innovation of the proposed method.
Multi-Agent Reinforcement Learning for Highway Platooning
Authors: Kolat Máté , Bécsi Tamás
Published: December 2023

The advent of autonomous vehicles has opened new horizons for transportation efficiency and safety. Platooning, a strategy where vehicles travel closely together in a synchronized manner, holds promise for reducing traffic congestion, lowering fuel consumption, and enhancing overall road safety. This article explores the application of Multi-Agent Reinforcement Learning (MARL) combined with Proximal Policy Optimization (PPO) to optimize autonomous vehicle platooning. We delve into the world of MARL, which empowers vehicles to communicate and collaborate, enabling real-time decision making in complex traffic scenarios. PPO, a cutting-edge reinforcement learning algorithm, ensures stable and efficient training for platooning agents. The synergy between MARL and PPO enables the development of intelligent platooning strategies that adapt dynamically to changing traffic conditions, minimize inter-vehicle gaps, and maximize road capacity. In addition to these insights, this article introduces a cooperative approach to Multi-Agent Reinforcement Learning (MARL), leveraging Proximal Policy Optimization (PPO) to further optimize autonomous vehicle platooning. This cooperative framework enhances the adaptability and efficiency of platooning strategies, marking a significant advancement in the pursuit of intelligent and responsive autonomous vehicle systems.
Intelligent Meta-Heuristic-Based Optimization of Traffic Light Timing Using Artificial Intelligence Techniques
Authors: Khasawneh Mohammed A. , Awasthi Anjali
Published: December 2023

This research examines worldwide concerns over traffic congestion, encompassing aspects such as security, parking, pollution, and congestion. It specifically emphasizes the importance of implementing appropriate traffic light timing as a means to mitigate these issues. The research utilized a dataset from Montreal and partitioned the simulated area into various zones in order to determine congestion levels for each individual zone. A range of prediction algorithms has been employed, such as Long Short-Term Memory (LSTM), Decision Tree (DT), Recurrent Neural Network (RNN), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressive Integrated Moving Average (SARIMA), to predict congestion levels at each traffic light. This information was used in a mathematical formulation to minimize the average waiting time for vehicles inside the road network. Many meta-heuristics were analyzed and compared, with the introduction of an Enhanced Bat Algorithm (EBAT) suggested for addressing the traffic signal optimization problem. Three distinct scenarios are described: fixed (with a constant green timing of 40 s), dynamic (where the timing changes in real-time based on the current level of congestion), and adaptive (which involves predicting congestion ahead of time). The scenarios are studied with low and high congestion scenarios in the road network. The Enhanced Bat Algorithm (EBAT) is introduced as a solution to optimize traffic signal timing. It enhances the original Bat algorithm by incorporating adaptive parameter tuning and guided exploration techniques that are informed by predicted congestion levels. The EBAT algorithm provides a more effective treatment for congestion problems by decreasing travel time, enhancing vehicle throughput, and minimizing pollutant emissions.
Injectivity Assessment of Radial-Lateral Wells for CO2 Storage in Marine Gas Hydrate Reservoirs
Authors: Guo Boyun , Zhang Peng
Published: December 2023

The carbon dioxide (CO2) leak from conventional underground carbon storage reservoirs is an increasing concern. It is highly desirable to inject CO2 into low-temperature reservoirs so that CO2 can be locked inside the reservoir in a solid state as CO2 hydrates. Marine gas hydrate reservoirs and surrounding water aquifers are attractive candidates for this purpose. However, the nature of the low permeability of these marine sediments hinders the injection of CO2 on a commercial scale due to the low injectivity of wells with conventional completions. This study investigates the injection of CO2 into low-permeability marine reservoirs through a new type of well, namely a radial-lateral well (RLW). A mathematical model was developed in this study to predict the CO2 injectivity of the RLW. The model comparison shows that the use of RLW to replace vertical wells can improve CO2 injectivity by over 30 times, and the use of RLW to replace frac-packed wells can increase CO2 injectivity by over 10 times. A case study and sensitivity analysis were performed with field data from the South China Sea. The result of the analysis reveals that the injectivity of the RLW is nearly proportional to reservoir permeability, lateral wellbore length, and the number of laterals. The CO2 injection rate is predicted to be 19 tons/day to 250 tons/day, which is 3 to 15 times higher than the injectivity of frac-packed wells. It is feasible to inject CO2 into the low-permeability, low-temperature marine reservoirs at commercial flow rates. This work provides an analytical tool to predict the CO2 injectivity of RLW in low-temperature marine reservoirs for leak-free CO2 storage.
Prospects of an Acid Gas Re-Injection Process into a Mature Reservoir
Authors: Kanakaki Eirini Maria , Samnioti Anna , Koffa Evangelia , Dimitrellou Irene , Obetzanov Ivan , Tsiantis Yannis , Kiomourtzi Paschalia , Gaganis Vassilis , Stamataki Sofia
Published: December 2023

This study provides insights into the experience gained from the investigation of the dynamic behavior of a mature sour hydrocarbon reservoir modeling under an acid gas re-injection process production strategy. The primary objective was to analyze and evaluate the production behavior of proposed injection zones by assessing various injection scenarios and obtaining oil production over time. To achieve that, a workflow was developed to prioritize potential injection areas, select the optimal wells, determine the optimal operational parameters and optimize a pilot application design based on expected performance. Within this framework, the study encompasses diverse acid gas injection schemes on a pilot scale approach, including acid gas combined with waterflooding. The outcome of this analysis will eventually lead to the identification of the most promising and highest-performing injection scheme, elucidating the optimal range of operating parameters. This optimal combination forms the basis for the economic analysis of the venture and the subsequent detailed design of a full-scale application, where real-world implementation will validate the projected results.
Calculation of Losses in a Motor Fed by a Conventional Inverter and a Battery Distributed Inverter
Authors: Jardot Rémi , Krebs Guillaume , Lahlou Anas , Roy Francis , Marchand Claude
Published: December 2023

In the past decade, car manufacturers have started electrifying the traction chain of their vehicles. Although these vehicles attract more and more drivers, most of them have a limited range and are prohibitively expensive. Manufacturers must therefore offer high-performance conversion chains (particularly in terms of efficiency) while controlling costs. The power converter is a particularly crucial element of the conversion chains: it supplies the traction motor, and its structure and the way it is controlled can greatly influence the overall efficiency of the drive train. This paper studies two conversion structures that can be used as vehicle power converters, which are modeled and associated with an electric machine. The first is a classical three-phase inverter, and the second is a breakthrough architecture called IBIS (Intelligent Battery Integrated System). This battery integrates the conversion function directly into the battery, which reduces material costs. Two loss phenomena are also studied and modeled (with the help of finite element methods): iron losses in the electrical machine (magnetic losses in the ferromagnetic material used) and copper losses in the conductors (AC and DC losses in the conductors). The impact of the architecture is evaluated on a set of operating points from a road cycle standardized by the WLTP procedure.
Analysis of Reactive Power in Electrical Networks Supplying Nonlinear Fast-Varying Loads
Authors: Sayenko Yuriy , Pawelek Ryszard , Baranenko Tetiana
Published: December 2023

This study concerns problems related to the assessment of reactive power in power networks with nonlinear fast-varying loads, such as electric arc furnaces, rolling mill drives, etc. The operation of this type of load is characterized by the introduction of interharmonic currents (including higher harmonics) into the power supply network and a relatively low power factor. Rapid changes in the RMS value of the current also cause voltage fluctuations and the related phenomenon of light-flickering. Therefore, there is a need to evaluate the power selection of compensating devices, taking into account the random nature of load changes and the distortion of current and voltage waveforms, in particular, interharmonic components, the impact of which has not been fully investigated so far. To analyze the random nature of load changes, autocorrelation functions were used, which allowed for the estimation of the expected values of the arc furnace current distortion coefficient (based on the recorded waveforms). In order to determine the parameters of reactive power compensating devices, and in particular capacitor banks, an autocorrelation function in the exponential-cosine-sine form was used, which meets the conditions of differentiation. This study contains comparative results of the reactive power of capacitor banks determined using different methods. The criterion for selecting capacitor bank parameters was the minimization of energy losses in the power supply network. The calculations presented in this study show that by taking into account higher harmonics and interharmonics in the voltage and currents of fast-varying loads, the installed power of the capacitor bank can be reduced by approximately 7%, and energy losses in the power grid can be reduced by 3–5%.
Degradation Behavior of Glue-On Three-Dimensional Printed Plastic Horseshoes in Equine Stables
Authors: Nakagawa Yuki , Yoshida Kano , Kaneko Daisaku , Ikeda Shin-ichi
Published: December 2023

Hoof and leg problems in racehorses can cause serious injuries and decrease their value. Although therapeutic shoeing using special horseshoes can increase the effectiveness of veterinary care, it is labor-intensive and burdensome for farriers. A three-dimensional (3D) printed horseshoe fabricated by additive manufacturing has high design flexibility for use in special horseshoes. However, the mechanical properties of 3D printed plastics for use as horseshoes remain unclear. In this study, a proposed 3D printed plastic was subjected to degradation tests under the simulated equine growth environment, and changes in strength during the period of use were investigated. It was found that the strength of polylactic acid and polycarbonate, which are commonly used for 3D printing, was not significantly affected by the environment.
Effects of a New Plant Growth Regulator on Callus Induction from Immature Embryo Explants of Korean Pine (Pinus koraiensis)
Authors: Nie Shuai , Wang Yue , Yan Yong , Liu Shanshan , Guo Wenhui , Yang Ling , Shen Hailong
Published: December 2023

Somatic embryogenesis is currently the most promising technique for the large-scale production and breeding of conifer species. Nonetheless, the low explant induction rate in this process hampers the development of improved materials. In this study, three immature Korean pine (Pinus koraiensis) embryos capable of induction were used as experimental materials. Various concentrations of brassinolide (BL), melatonin (Mel), salicylic acid (SA), glutathione (GSH), and ascorbic acid (AsA) were added to induce embryogenic callus tissue. The results show that BL had the most significant positive effect on promoting induction and increasing explant survival. Mel was also found to slightly increase the induction and survival rates of explants. When 1.00 mg·L−1 BL was added to the explants derived from stock tree I, which had strong self-induction ability, for 30 days, the callus induction rate rose to 400% compared to the control group. Following the addition of 25 mg·L−1 Mel to stock tree I for 30 days, the callus induction rate further increased to 450% of the control group. The effect of GSH on callus induction was insignificant. The addition of 0.10 g·L−1 to stock tree I for 30 days resulted in only a 150% higher induction rate compared to the control group. When 1.00 mg·L−1 BL was applied to explants with weak self-induction ability (stock tree III) for 15 days, the callus induction rate increased to 600% of that of the control group. The callus induction rate further increased to 800% of the control group after 50 mg·L−1 BL were added to stock tree III for 15 days. This study presents a method to improve the induction of embryogenic callus tissue in Korean pine.
Wood Vinegar from Slow Pyrolysis of Eucalyptus Wood: Assessment of Removing Contaminants by Sequential Vacuum Distillation
Authors: Pimenta Alexandre Santos , Gama Gil Sander Próspero , Feijó Francisco Marlon Carneiro , Braga Renata Martins , de Azevedo Tatiane Kelly Barbosa , de Melo Rafael Rodolfo , de Oliveira Miranda Neyton , de Andrade Gabriel Siqueira
Published: December 2023

Eucalyptus wood vinegar (WV) is a versatile natural product employed in several areas such as for agriculture, zootechnics, and veterinary applications. However, the product may contain contaminants such as polycyclic aromatic hydrocarbons, methanol, metals, and so on in its raw form. Thus, refining is required to remove them. Only after reaching a high-purity grade can WV be applied as a basis for pharmaceuticals, antimicrobials, and additives for animal husbandry. This work aimed to assess the effect of refining on the contaminant content of a WV produced on an industrial scale and traded as agricultural input in the Brazilian market. WV was refined by sequential vacuum distillations and had its contaminant contents assessed before and after refining. The following chemical classes were investigated: metals and other elements, insecticides, fungicides, and herbicides (carbamates, organochlorines, organophosphates, pyrethroids, and others), mycotoxins, polychlorinated dioxins and furans, polycyclic aromatic hydrocarbons, and volatile organic compounds, encompassing more than 100 chemical species. Also, the WV physical–chemical properties before and after refining were determined. Gas chromatography and mass spectrometry (GC/MS) were employed to attain the WV’s chemical profile. The main chemical components, such as phenolic compounds, furans, ketones, pyrans, etc., were annotated in the WV chemical profile. No traces of insecticides, fungicides, herbicides, heavy metals, mycotoxins, polychlorinated dioxins, or polychlorinated furans were found in raw WV or refined versions. However, several other undes/irable chemicals were determined in the raw product, such as PAHs and volatile organic compounds (acetaldehyde, acetone, ethylene glycol, and methanol). Nevertheless, all of them were effectively removed from the raw WV after the first or second sequential refining steps, resulting in a pure product. Therefore, only one refining step can be applied to raw WV to yield a high-quality product for pharmaceutical and animal husbandry applications.