bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring Inner Speech Recognition via Cross-Perception Approach in EEG and fMRI
Authors: Qin Jiahao , Zong Lu , Liu Feng
Published: September 2024

Multimodal brain signal analysis has shown great potential in decoding complex cognitive processes, particularly in the challenging task of inner speech recognition. This paper introduces an innovative I nner Speech Recognition via Cross-Perception (ISRCP) approach that significantly enhances accuracy by fusing electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. Our approach comprises three core components: (1) multigranularity encoders that separately process EEG time series, EEG Markov Transition Fields, and fMRI spatial data; (2) a cross-perception expert structure that learns both modality-specific and shared representations; and (3) an attention-based adaptive fusion strategy that dynamically adjusts the contributions of different modalities based on task relevance. Extensive experiments on the Bimodal Dataset on Inner Speech demonstrate that our model outperforms existing methods across accuracy and F1 score.
POSS and PAG Dual-Containing Chemically Amplified Photoresists by RAFT Polymerization for Enhanced Thermal Performance and Acid Diffusion Inhibition
Authors: Yu Haimeng , Liu Shaoshuai , Fu Haiyan , Cui Zepeng , Zhang Liangshun , Tian Jia
Published: September 2024

A random copolymer (PTBM), utilized as deep ultra-violet (DUV) photoresist, was prepared by reversible addition-fragmentation chain transfer (RAFT) polymerization with tert-butyl methacrylate (tBMA), methyl methacrylate (MMA), triphenylsulfonium p-styrenesulfonate (TPS-SS), and functional poly (sesquicarbonylsiloxanes) (POSS-MA) as the monomer components, and 4-cyano-4-[(dodecylsulfanylthiocarbonyl) sulfanyl]pentanoic acid (CDSPA) as the RAFT reagent. Fourier transform infrared spectroscopy (FT-IR) and proton nuclear magnetic resonance (1H NMR) proved successful synthesis. Ultraviolet absorption spectroscopy (UV) analysis verified the transparency of the polymer in the DUV band. RAFT polymerization kinetics showed that the polymerization rate conformed to the first-order kinetic relationship, and the polymerization process exhibited a typical controlled free radical polymerization behavior. Thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and static thermo-mechanical analysis (TMA) showed that the incorporation of POSS groups improved the thermal properties of the copolymer. According to scanning electron microscopy (SEM) images, the copolymerization of photoacid monomers (TPS-SS) resulted in photoresist copolymers exhibiting good resistance to acid diffusion and low roughness.
Method for Remaining Useful Life Prediction of Turbofan Engines Combining Adam Optimization-Based Self-Attention Mechanism with Temporal Convolutional Networks
Authors: Wang Hairui , Li Dongjun , Li Ya , Zhu Guifu , Lin Rongxiang
Published: September 2024

Conducting the remaining useful life (RUL) prediction for an aircraft engines is of significant importance in enhancing aircraft operation safety and formulating reasonable maintenance plans. Addressing the issue of low prediction model accuracy due to traditional neural networks’ inability to fully extract key features, this paper proposes an engine RUL prediction model based on the adaptive moment estimation (Adam) optimized self-attention mechanism–temporal convolutional network (SAM-TCN) neural network. Firstly, the raw data monitored by sensors are normalized, and RUL labels are set. A sliding window is utilized for overlapping sampling of the data, capturing more temporal features while eliminating data dimensionality. Secondly, the SAM-TCN neural network prediction model is constructed. The temporal convolutional network (TCN) neural network is used to capture the temporal dependency between data, solving the mapping relationship of engine degradation characteristics. A self-attention mechanism (SAM) is employed to adaptively assign different weight contributions to different input features. In the experiments, the root mean square error (RMSE) values on four datasets are 11.50, 16.45, 11.62, and 15.47 respectively. These values indicate further reduction in errors compared to methods reported in other literature. Finally, the SAM-TCN prediction model is optimized using the Adam optimizer to improve the training effectiveness and convergence speed of the model. Experimental results demonstrate that the proposed method can effectively learn feature data, with prediction accuracy superior to other models.
Study on the Ensuring of Reliability and Repeatability of Research in the Area of Marine Ecology through Calibration of Underwater Acoustics Devices
Authors: Listewnik Karol Jakub , Mindykowski Janusz
Published: September 2024

This paper emphasizes the crucial needs and reasons to ensure the reliability and repeatability of underwater acoustic measurements. As an exemplification of the aforementioned acoustic measurements challenges, the development of metrological infrastructure in the area of underwater acoustics in GUM (Central Office of Measures, Poland) is presented. An analysis of presented solutions was carried out mainly in the low frequency range, in comparison with other solutions recently developed worldwide. Moreover, factors influencing the sensitivity of hydroacoustic measuring devices are discussed. The summary of this discussion outlines the further works aimed at ensuring the reliability and repeatability of underwater acoustic measurements. The conclusions present the current state of the calibration infrastructure of underwater acoustic devices, with particular emphasis on marine ecology and fisheries sciences.
Carbon Emission Analysis of RC Core Wall-Steel Frame Structures
Authors: Gao Jiangjun , Shen Zhengliang , Shao Zerui , Pan Xinyu , Tang Deshuang , Zhao Kun , Chen Yao , Lv Hengzhu
Published: September 2024

The development of super high-rise building projects has become crucial for mitigating land shortages in rapidly growing urban areas. Super high-rise steel structures, particularly RC core wall-steel frame systems, have become the preferred choice due to their superior performance, high prefabrication level, and construction efficiency. Despite their benefits, super high-rise buildings face challenges related to higher energy consumption and carbon emissions. Consequently, it is important to analyze the carbon emissions of these buildings throughout their lifecycle and propose low-carbon construction strategies. A carbon emission analysis focused on super high-rise buildings with RC core wall-steel frame structures is conducted in this study. A carbon emission analysis model is constructed based on BIM-enabled LCA through a real-world case study. The emission factor method is combined with the BIM model to calculate carbon emission. Furthermore, carbon emissions across various construction strategies are compared, with a particular focus on the manufacturing processes of the main materials. The results indicate that incorporating admixtures in concrete, along with adopting the electric arc furnace (EAF) method and utilizing recycled scrap steel in steel manufacturing, significantly reduces the carbon emissions of the buildings. Lastly, effective low-carbon approaches for these buildings are proposed.
Integrated Platform for Determining Solderability Parameters: Module for Measuring the Surface Tension of Liquid Solders
Authors: Bąkała Marcin , Błaszczyk Piotr
Published: September 2024

This article describes a module and method for measuring the surface tension of liquid solders implemented on a measuring device as part of an integrated platform for automatic measurement of brazebility parameters at high temperatures. A concept for constructing a test stand is presented, with a description of the individual functional blocks. The developed stand allows for testing of the solder’s surface tension. The surface tension is one of the parameters that describe the thermodynamics of interfacial reactions and the structure of newly created joints. Determining the physicochemical interactions between liquid and solid substances is crucial for various industrial processes in fields such as metallurgy, electronics, and aviation, mainly where soldering and brazing technologies are employed. A series of bubble experiments in solder for a ceramic capillary is carried out to verify the proposed method using the developed system. One of these experiments is described in this article.
Accuracy, Time, and Comfort of Different Intraoral Scanners: An In Vivo Comparison Study
Authors: Lione Roberta , De Razza Francesca Chiara , Gazzani Francesca , Lugli Letizia , Cozza Paola , Pavoni Chiara
Published: September 2024

Background: The purpose of this study was to compare the accuracy of three different intraoral scanners (IOSs) and to evaluate the patients’ experience. Methods: Thirty subjects were scanned with three different IOSs (TRIOS Color®, iTero Element 5D®, and iTero Lumina®): a sample of 90 maxillary casts was collected. The inclusion criteria were permanent dentition, absence of defects during the dental impression, and complete record of the palatal vault. After the scanning procedure, patients were asked to answer a questionnaire (10 answers, scale from 1 to 10) to compare their experiences with iTero Element and iTero Lumina in the scanning procedure. Results: Differences were recorded in the upper central incisor region (f-ratio of 4.186 for Mesiodistal 1.1 and f-ratio of 4.222 for Mesiodistal 2.1, p < 0.05), while intercanine width Upper 13–23 showed the smallest f-ratio (0.226) when compared to the other results, followed by Mesiodistal 1.4 (0.433). Patients reported more comfort, less pain, decreased duration, and a better technology visualization of the impression with iTero Lumina. No significant differences concerning dryness of the mouth and gag reflex were found. Conclusions: The examined IOSs offer comparable accuracy in capturing dental arch dimensions. The interviewed patients expressed an overall preference for digital impressions performed with iTero Lumina, linked to increased comfort, painless practice, and a better technology visualization of the impression.
Research on a Chassis Stability Control Method for High-Ground-Clearance Self-Propelled Electric Sprayers
Authors: Zhou Lingxi , Hu Chenwei , Chen Yuxiang , Guo Peijie , Liu Jinyi , Chen Yu , Cao Jiayu
Published: September 2024

In response to the complex working conditions and poor driving stability of high-clearance self-propelled sprayers, a nonlinear model of the chassis power system was established based on the independently controllable torque of each wheel of the developed electric sprayer. A layered-architecture chassis drive control strategy was formulated, and a stability control framework comprising an instability judgment module, an upper controller, and a lower controller was constructed based on the analysis of the impact of the centroid slip angle, the yaw rate, and the wheel slip rate on driving stability. An ideal reference model was established based on the seven-degree-of-freedom model of the sprayer, and the current state of the sprayer body was determined using the instability judgment module. A drive anti-slip controller and a yaw moment controller based on fuzzy PID theory and sliding mode control theory were designed. Additionally, an optimal torque distribution algorithm was developed based on tire characteristics to rationally allocate drive torque to each wheel, ensuring the stability of the sprayer during operation. Simulation tests were conducted using MATLAB/Simulink to evaluate the sprayer under four different driving conditions during transport and field operations. The test results showed that the “SMC + optimal distribution” control method in the chassis stability control strategy reduced the maximum deviations of the yaw rate and centroid slip angle by an average of 89.5% and 13.6%, respectively, compared to no control. The wheel slip rate during straight driving was well maintained at around 15%, enhancing the driving stability of the sprayer.
A Comparison of Two Fluorescent Stains for the Evaluation of Bioceramic Sealer Penetration into the Tubular Dentine Using Confocal Microscopy
Authors: Delgado Amaia , Castagnola Raffaella , Grande Nicola Maria , Loroño Gaizka , Conde Antonio , Estevez Roberto , Marigo Luca , Pérez-Alfayate Ruth
Published: September 2024

This study aimed to compare the percentage of bioceramic sealer penetration and maximum penetration sealer depending on the fluorescent staining method when evaluated with confocal laser scanning microscopy (CLSM). In total, 42 single-rooted premolars were instrumented. Two groups of 20 teeth each were filled using the single cone technique. TotalFill BC Sealer® mixed with Rhodamine B was used for the first group and TotalFill BC Sealer® mixed with Fluo-3 for the second group. Two teeth made up the control group. All teeth were sectioned at 3 and 6 mm from the apex and were analyzed with CLSM. The canal perimeter where the tubular penetration happened and the maximum penetration of each sample were calculated. There were no significant differences between the two groups in either the apical or medial sections. In terms of maximum penetration, Rhodamine B had greater tubular penetration in the apical sections, and it had similar results to Fluo-3 in the medial sections. TotalFill BC Sealer® has a higher tubular penetration in the medial slices than in the apical slices regardless of the fluorescent stain used. Rhodamine B has higher results than Fluo-3 in terms of the percentage of sealer penetration and maximum penetration.
Influence of Accumulation of Humidity under Wound Dressings and Effects on Transepidermal Water Loss (TEWL) and Skin Hydration
Authors: Rauscher Marc , Rauscher Andreas , Hu Linda Y. , Schlitt Hans J. , Krauß Sabrina , Illg Claudius , Reis Wolfertstetter Patricia , Hofmann Aybike , Knorr Christian , Denzinger Markus
Published: September 2024

The moisture content of the human skin, but also the loss of water through the skin, the transepidermal water loss (TEWL), plays a significant role in the skin’s health. Various medical indications require the use of a wound dressing. However, how the skin environment changes under a wound dressing has not yet been sufficiently investigated. Skin moisture and TEWL values were measured in 20 healthy volunteers before and after the application of a total of 23 different wound dressings distributed over the back. Significant changes in the parameters from day 1 to day 2 were tested. Wound dressings change the underlying skin environment. Occlusive dressings significantly increase skin hydration and TEWL. The findings could contribute to quantitative analysis and monitoring of topical-wound therapy in the future.
Decoding Success: Predictive Analysis of UEFA Euro 2024 to Uncover Key Factors Influencing Soccer Match Outcomes
Authors: Stafylidis Andreas , Mandroukas Athanasios , Michailidis Yiannis , Metaxas Thomas I.
Published: September 2024

This study presents the analysis of the UEFA Euro 2024 in Germany, focusing on the impact of the first goal on match outcomes, goal distribution between halves and quarters, and the relationship between offensive, defensive, and goalkeeping metrics and match outcomes. Moreover, a regression model is developed to identify the key factors that significantly contribute to teams’ success. The analysis of the 36 group stage matches revealed that scoring the first goal significantly increased the likelihood of a positive match outcome. There were no significant differences between goals scored in the first and second halves or per 15 min of the game. Kruskal–Wallis tests highlighted that winning teams had more assists, attempts on target and runs into the penalty area. Defensive metrics showed that winning teams recovered more balls, while goalkeeping metrics revealed that winning teams had more clean sheets. The logistic regression model identified “Attempts on Target” and “Passes into Attacking Third” as significant positive predictors of winning, while “Attempts on Target Outside Area” and “Crosses Attempted” were negative predictors. These findings offer valuable insights for coaching staff to develop strategies focusing on key performance indicators that enhance the likelihood of winning.
Prediction of Total Soluble Solids Content Using Tomato Characteristics: Comparison Artificial Neural Network vs. Multiple Linear Regression
Authors: Kabaş Aylin , Ercan Uğur , Kabas Onder , Moiceanu Georgiana
Published: September 2024

Tomatoes are among the world’s most significant vegetables, both in terms of production and consumption. Harvesting takes place in tomato production when the important quality attribute of total soluble solids content reaches its maximum possible level. Tomato total soluble solids content (TSS) is among the most crucial attribute parameters for assessing tomato quality and for tomato commercialization. Determination of total soluble solids content by conventional measurement methods is both destructive and time-consuming. Therefore, the tomato processing industry needs a rapid identification method to measure total soluble solids content (TSS). In this study, we aimed to estimate how much soluble solids there are in beef tomato fruit by Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) methods. The models were assessed using the Coefficient of Determination (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) metrics. The training data set results of the MLR model established to estimate the amount of brix in tomato fruit, calculated as MAE: 0.2349, RMSE: 0.3048, R2: 0.8441, and MAPE: 5.5368, while, according to the ANN model, MAE: 0.0250, RMSE: 0.031, R2: 0.9982 and MAPE: 0.5814. According to the metric outcomes, the ANN-based model performed better in both the training and testing parts.
Time-Hopping Concentration-Position Shift Keying for Multiple Access in Concentration-Encoded Molecular Communication
Authors: Kim Su-Jin , Singh Pankaj , Jung Sung-Yoon
Published: September 2024

Molecular communication (MC) represents a paradigm shift in communication technologies, extending beyond traditional electromagnetic methods by incorporating advances in nanotechnology, biotechnology, and communication theory. This innovative approach holds promise for groundbreaking applications in diverse fields such as medicine, military operations, and environmental monitoring. MC employs molecules to carry and transmit data. In concentration-encoded molecular communication (CEMC), information is represented through the concentration levels of the transmitted molecules. In this study, we introduce a novel method named time-hopping concentration-position shift keying (TH-CPSK), designed to facilitate multiple access within MC networks. The TH-CPSK method encodes information based on the position of the transmitted molecular concentration, significantly enhancing the accuracy of data transmission by reducing interference in MC channels. Numerical simulations reveal that an increase in the number of users adversely affects communication performance. Furthermore, our findings indicate that augmenting the number of hops improves communication performance at transmitter-receiver distances of 1 cm and 2 cm. Conversely, at a distance of 3 cm, performance degradation is observed, attributed to the increased complexity. Therefore, it is important to carefully select the number of hops considering the molecular channel’s characteristics. Overall, TH-CPSK can enhance the efficiency and reliability of CEMC systems, offering a significant step forward in the realization of MC’s potential applications.
Research on the Migration and Settlement Laws of Backflow Proppants after Fracturing Tight Sandstone
Authors: Cheng Hanlie , Qin Qiang
Published: September 2024

This article studies the migration and settlement laws of backflow proppants after fracturing tight sandstone. This paper proposes a fitting method based on a multi-task learning network to address the issue of interference from multiple physical parameters during the transport and settlement processes of proppants. This method can effectively handle multi-dimensional interference factors and fit the mapping logic of multiple engineering parameters to transport patterns through the continuous correction of multi-layer networks. We first introduce the characteristics of tight sandstone reservoirs and their important value in mining, as well as the status of current research on the migration and settlement laws of proppants at home and abroad. Based on this, we then deeply analyze the sedimentation rate model of proppants in tight sandstone backflow and the equilibrium height of proppants under multiple factors of interference while considering the distribution characteristics of proppants. In order to more accurately simulate the transport and settlement laws of proppants, this paper introduces a multi-task learning network. This network can comprehensively consider multi-dimensional parameters, learn the inherent laws of data through training, and achieve accurate fitting of the transport and settlement laws of proppants. This study trained and tested the model using actual production data, and the results showed that the proposed model can fit the input–output relationship well, thus effectively supporting the study of proppant transport and settlement laws.
YOLOv8n–CBAM–EfficientNetV2 Model for Aircraft Wake Recognition
Authors: Ma Yuzhao , Tang Xu , Shi Yaxin , Chan Pak-Wai
Published: September 2024

In the study of aircraft wake target detection, as the wake evolves and develops, the detection area of the LiDAR often shows the presence of two distinct vortices, one on each side. Sometimes, only a single wake vortex may be present. This can lead to a reduction in the accuracy of wake detection and an increased likelihood of missed detections, which may have a significant impact on the flight safety. Hence, we propose an algorithm based on the YOLOv8n–CBAM–EfficientNetV2 model for wake detection. The algorithm incorporates the lightweight network of EfficientNetV2 and the Convolutional Block Attention Module (CBAM) based on the YOLOv8n model, which achieves the lightweight improvement in the YOLOv8n algorithm and the improvement in detection accuracy. First, this study classifies the wake vortices in the wake greyscale images obtained at Hong Kong International Airport, based on the Range–Height Indicator (RHI) scanning characteristics of the LiDAR and the symmetry of the wake vortex pairs. The classification is used to detect left and right vortices for more accurate wake detection in wind field images, which thereby improves the precision rate of target detection. Subsequently, experiments are conducted using a YOLOv8n–CBAM–EfficientNetV2 model for aircraft wake detection. Finally, the performance of the YOLOv8n–CBAM–EfficientNetV2 model is analysed. The results show that the algorithm proposed in this study can achieve a 96.35% precision rate, 93.58% recall rate, 95.06% F1-score, and 250 frames/s. The results show that the method proposed in this study can be effectively applied in aircraft wake detection.
Approaching Breakthrough: Resource-Efficient Micropollutant Removal with MBR-GAC Configuration
Authors: Baresel Christian , Salem Marion , Roberts Ross , Malovanyy Andriy , Lemström Heidi , Esfahani Bahare
Published: September 2024

The removal of micropollutants from municipal wastewater is crucial to mitigate negative environmental impacts on aquatic ecosystems. However, existing advanced treatment techniques often require extensive fossil resources to achieve the targeted removal of a broad range of micropollutants. This study presents the combination of Membrane Bioreactors (MBRs) and subsequent Granular Activated Carbon (GAC) filters as a resource-efficient solution. Based on long-term pilot studies at a municipal WWTP in Stockholm, Sweden, this investigation explores the MBR-GAC configuration as a sustainable alternative for quaternary treatment at WWTPs. Results from over three years demonstrate a high removal efficiency of over 80% for targeted pharmaceuticals and other organic micropollutants, such as per- and polyfluoroalkyl substances (PFASs), from the WWTP inlet to the outlet. The synergy between MBR and GAC technologies provides this high removal efficiency with considerably lower resource consumption and cost compared to traditional GAC installations. No breakthrough of micropollutants has been observed to date indicating even better resource efficiency than presented in this paper.
A Radish Root Ferment Filtrate for Cosmetic Preservation: A Study of Efficacy of Kopraphinol
Authors: Juliano Claudia Clelia Assunta , Magrini Giovanni Antonio
Published: September 2024

Preserving cosmetic products from microbial contamination is crucial to ensuring their safety, efficacy, and shelf life. A number of synthetic antimicrobial preservatives are available, but, since the global market demand for natural ingredients is increasing, cosmetic manufacturers are considering replacing conventional preservatives with natural alternatives. In this context, the objective of this investigation was to characterize the antimicrobial activity of the natural preservative, intended for cosmetic purposes, Kopraphinol (INCI name: Lactobacillus/Radish Root Ferment Extract Filtrate). It was tested against a panel of selected bacteria and mycetes by using conventional microbiological techniques (determination of MIC, time killing assay), and a challenge test was used to verify its potential preservative in an O/W hydrophilic cream (Cetomacrogol cream base). Kopraphinol has shown an interesting antimicrobial effectiveness, with M.I.C.s ranging from 0.78% to 6.25% for bacteria and from 1.56% to 5% for mycetes. Moreover, it fulfilled challenge test criterion A and has proven effective against microbial contamination, leading to a 3 log reduction of inoculum after 7 days for bacteria and a 2 log reduction at 14 days for fungi. The results obtained show that Kopraphinol can be considered a promising and effective candidate for the antimicrobial preservation of cosmetics and could successfully complement or even replace conventional preservatives.
Rapid and Cost-Effective Fabrication and Performance Evaluation of Force-Sensing Resistor Sensors
Authors: Jung Jinwoo , Lee Kihak , Kim Bonghwan
Published: September 2024

In this study, we developed a cost-effective and rapid method for fabricating force-sensing resistor (FSR) sensors as an alternative to commercial force sensors. Our aim was to achieve performance characteristics comparable to existing commercial products while significantly reducing costs and fabrication time. We analyzed the material composition of two widely used commercial force sensors: Interlink FSR-402 and Flexiforce A201-1. Based on this analysis, we selected 4B and 9B pencils, which contain high concentrations of graphite, and silicone sealant to replicate these material properties. The fabrication process involved creating piezoresistive sheets by shading A4 copy paper with 4B and 9B pencils to form a uniform layer of graphite. Additionally, we prepared a mixture of 9B pencil lead powder and silicone sealant, ensuring a consistent application on the paper substrate. Measurement results indicated that the force sensor fabricated using a mixture of 9B pencil powder and silicone sealant exhibited electrical and mechanical characteristics closely resembling those of commercial sensors. Load tests revealed that the hand-made sensors provided a proportional voltage output in response to increasing and decreasing loads, similar to commercial FSR sensors. These results suggest that our fabrication method can produce reliable and accurate FSR sensors suitable for various applications, including wearable technology, robotics, and force-sensing interfaces. Overall, this study demonstrates the potential for creating cost-effective and high-performance FSR sensors using readily available materials and simple fabrication techniques.
Digital Surface Model Generation from Satellite Images Based on Double-Penalty Bundle Adjustment Optimization
Authors: Li Henan , Yin Junping , Jiao Liguo
Published: September 2024

Digital Surface Model (DSM) generation from high-resolution optical satellite images is an important topic of research in the remote sensing field. In optical satellite imaging systems, the attitude information of the cameras recorded by satellite sensors is often biased, which leads to errors in the Rational Polynomial Camera (RPC) model of satellite imaging. These errors in the RPC model can mislead the DSM generation. To solve the above problems, we propose an automatic DSM generation method from satellite images based on the Double-Penalty bundle adjustment (DPBA) optimization algorithm. In the proposed method, two penalty functions representing the camera’s attitude and the spatial 3D points, respectively, are added to the reprojection error model of the traditional bundle adjustment optimization algorithm. Instead of acting on images directly, the penalty functions are used to adjust the reprojection error model and improve the RPC parameters. We evaluate the performance of the proposed method using high-resolution satellite image pairs and multi-date satellite images. Through some experiments, we compare the accuracy and completeness of the DSM generated by the proposed method, the Satellite Stereo Pipeline (S2P) method, and the traditional bundle adjustment (BA) method. Compared to the S2P method, the experiment results of the satellite image pair indicate that the proposed method can significantly improve the accuracy and the completeness of the generated DSM by about 1–5 m and 20%–60% in most cases. Compared to the traditional BA method, the proposed method improves the accuracy and completeness of the generated DSM by about 0.01–0.05 m and 1%–3% in most cases. The experiment results can be a testament to the feasibility and effectiveness of the proposed method.
Classification of Logging Data Using Machine Learning Algorithms
Authors: Mukhamediev Ravil , Kuchin Yan , Yunicheva Nadiya , Kalpeyeva Zhuldyz , Muhamedijeva Elena , Gopejenko Viktors , Rystygulov Panabek
Published: September 2024

A log data analysis plays an important role in the uranium mining process. Automating this analysis using machine learning methods improves the results and reduces the influence of the human factor. In particular, the identification of reservoir oxidation zones (ROZs) using machine learning allows a more accurate determination of ore reserves, and correct lithological classification allows the optimization of the mining process. However, training and tuning machine learning models requires labeled datasets, which are hardly available for uranium deposits. In addition, in problems of interpreting logging data using machine learning, data preprocessing is of great importance, in other words, a transformation of the original dataset that allows improving the classification or prediction result. This paper describes a uranium well log (UWL) dataset generated with the employment of floating data windows and designed to solve the problems of identifying ROZ and lithological classification (LC) on sandstone-type uranium deposits. Comparative results of the ways of solving these problems using classical machine learning methods and ensembles of machine learning algorithms are presented. It has been shown that an increase in the size of the floating data window can improve the quality of ROZ classification by 7–9% and LC by 6–12%. As a result, the best-quality indicators for solving these problems were obtained, f1_score_macro = 0.744 (ROZ) and accuracy = 0.694 (LC), using the light gradient boosting machine and extreme gradient boosting, respectively.
Analysis of Longitudinal Braking Stability of Lightweight Liquid Storage Mini-Track Vehicles
Authors: Zhang Cuihong , Cao Xuepeng , Xu Lijia , Wang Yan , He Yutian , Liu Xiaohui
Published: September 2024

In this study, a theoretical model was established for longitudinal braking stability (LBS) of a lightweight liquid storage mini-track vehicle to investigate its behaviors. The influence of the tank liquid filling ratio (TLR), initial velocity (IV), and initial braking deceleration (IBD) on the LBS of the tracked vehicle was studied via FLUENT simulation. The simulation results showed that when the IV and the IBD were constant, the TLR significantly affected the braking efficiency. Conversely, when the TLR was 0.7, the braking efficiency decreased by 32%, resulting in the worst LBS. The braking efficiency at different IVs decreased by ~30% when using a constant IBD and TLR, and the braking stability exhibited little difference. Further, the braking efficiency decreased noticeably with increasing IBD; meanwhile, the braking longitudinal stability worsened. When the IBD was increased to 5 m⋅s−2, the braking efficiency decreased by 48.4%. The road experiment showed that when the tracked vehicle was accelerated and braked under limit conditions, the stability requirements were met. Finally, by combining the simulation and road experiment, the driving parameters of the tracked vehicle that could satisfy the LBS were obtained.
Research on Ride Comfort Control of Air Suspension Based on Genetic Algorithm Optimized Fuzzy PID
Authors: Zhang Shaobo , Li Mei , Li Jinsong , Xu Jie , Wang Zelong , Liu Shuaihang
Published: September 2024

The air suspension system’s superior variable stiffness, low vibration frequency, and resistance to road impacts significantly elevate both the comfort of vehicle occupants and the overall ride quality. By effectively controlling the air suspension system, its superior characteristics can be fully exploited to enhance the overall performance of vehicles. However, the parameter tuning process of the fuzzy PID controller for air suspension involves subjectivity and blindness, which affects the performance of the suspension system. To overcome these shortcomings, a control strategy combining genetic algorithms with fuzzy PID control is proposed. This strategy involves a genetic algorithm-optimized fuzzy PID air suspension control approach specifically targeting the fuzzy PID controller for air suspension. A 1/4 two-degree-of-freedom air suspension fuzzy PID controller is designed in MATLAB 2019a, utilizing genetic algorithms to optimize the PID parameter tuning process. The ride comfort of the fuzzy PID air suspension after tuning is then investigated. In the study of ride comfort on Class B road surfaces, the simulation and experimental results were consistent. Using a genetic algorithm to optimize a fuzzy PID-controlled air suspension resulted in reductions of the root mean square values for vertical body acceleration, suspension deflection, and wheel dynamic load by 30%, 26%, and 9%, respectively, compared to passive suspension. These reductions are further improvements over the corresponding indices controlled by the fuzzy PID alone, which decreased by 23%, 18%, and 6%, respectively. Thus, the control effect of the genetic algorithm-optimized fuzzy PID is superior to that of the fuzzy PID control. This demonstrates that the fuzzy PID control of air suspension optimized by genetic algorithms can further improve the comfort of vehicle occupants and the ride comfort of driving, providing a reference for active control of air suspension systems.
The Effects of Activated Carbon Toothpastes on Orthodontic Elastomeric Chains—An In Vitro Study
Authors: Mariano Pereira Pedro , Bugiaghis Iman , Do Carmo Mariana Isidro , Proença Luis
Published: September 2024

Objectives: Using toothpaste with activated carbon might increase the decay of orthodontic elastomeric chains’ (ECs) tensile strength, thereby compromising orthodontic treatment. Therefore, this study aimed to evaluate the influence of activated charcoal toothpaste on orthodontic ECs. Materials and Methods: A total sample of 180 EC segments from 3M Unitek®, Ormco® and Ortho Classic® brands were equally divided into 12 groups, each comprising 15 specimens. These pieces were kept in artificial saliva at 37 °C and brushed twice daily for 28 days, with three distinct types of toothpaste: Colgate® Total, Colgate® Max White, and Dr Organic® Extra Whitening Charcoal Toothpaste. The latter two toothpastes contain activated charcoal. Tensile strength, resistance to rupture and colour variation were evaluated at time zero and day 28. Descriptive statistics, one-way ANOVA and Tukey HSD tests were performed at p ≤ 0.05. Results: Toothpaste with and without activated carbon significantly reduced the tensile strength and resistance to rupture of the ECs, and altered EC colour (p < 0.0001). There was inconsistency in the effect of the activated carbon on EC characteristics, most probably due to the different compositions of the ECs and percentages of whitening agents in the toothpastes. Conclusions: The material composition of ECs contributes to their tensile strength decay, resistance to rupture and colour change over time. The variable percentage of activated carbon in a toothpaste likely underlies the different effects observed, depending on the EC brand. Clinical Relevance: It might be reasonable to advise patients wearing ECs to avoid using toothpaste with activated carbon until further evidence becomes available.
Effect of CO2 Nanobubble Water on the Fracture Properties of Cemented Backfill Materials under Different Aggregate Fractal Dimensions
Authors: Cao Xiaoxiao , Hamanaka Akihiro , Shimada Hideki , Sasaoka Takashi
Published: September 2024

In order to cope with climate change and achieve the goal of carbon neutrality, the use of carbonization technology to enhance the performance of cement-based materials and achieve the purpose of carbon sequestration has become a very promising research direction. This paper considers the use of CO2NBW as mixing water for cement-based materials, aiming to improve the carbonization efficiency of materials to achieve the goal of carbon neutrality. This time, the effect of CO2NBW on cementitious filling materials under different aggregate fractal dimensions was studied through uniaxial compression tests and acoustic emission technology. The effect of CO2NBW on the mechanical properties and crack evolution of the material was discussed. The results showed that CO2 nanobubbles significantly improved the strength of cemented filling materials under different fractal dimensions, and the uniaxial compressive strength was most significantly improved by 23.04% when the fractal dimension was 2.7824. In addition, the characteristics of acoustic emission ring counts and energy parameters indicate that CO2 nanobubbles help improve the overall pore structure of the sample, affecting the macroscopic strength. However, the addition of CO2 nanobubbles reduces the limit energy storage ratio of elastic strain energy, which indicates that excessive CO2 concentration may affect the hydration reaction of the cementing material.
Analysing Urban Traffic Patterns with Neural Networks and COVID-19 Response Data
Authors: Svabova Lucia , Culik Kristian , Hrudkay Karol , Durica Marek
Published: September 2024

Accurate traffic prediction is crucial for urban planning, especially in rapidly growing cities. Traditional models often struggle to account for sudden traffic pattern changes, such as those caused by the COVID-19 pandemic. Neural networks offer a powerful solution, capturing complex, non-linear relationships in traffic data for more precise prediction. This study aims to create a neural network model for predicting vehicle numbers at main intersections in the city. The model is created using real data from the sensors placed across the city of Zilina, Slovakia. By integrating pandemic-related variables, the model assesses the COVID-19 impact on traffic flow. The model was developed using neural networks, following the data-mining methodology CRISP-DM. Before the modelling, the data underwent thorough preparation, emphasising correcting sensor errors caused by communication failures. The model demonstrated high prediction accuracy, with correlations between predicted and actual values ranging from 0.70 to 0.95 for individual sensors and vehicle types. The results highlighted a significant pandemic impact on urban mobility. The model’s adaptability allows for easy retraining for different conditions or cities, making it a robust, adaptable tool for future urban planning and traffic management. It offers valuable insights into pandemic-induced traffic changes and can enhance post-pandemic urban mobility analysis.
Optimizing Glaucoma Diagnosis with Deep Learning-Based Segmentation and Classification of Retinal Images
Authors: Alkhaldi Nora A. , Alabdulathim Ruqayyah E.
Published: September 2024

Glaucoma, a leading cause of permanent blindness worldwide, necessitates early detection to prevent vision loss, a task that is challenging and time-consuming when performed manually. This study proposes an automatic glaucoma detection method on enhanced retinal images using deep learning. The system analyzes retinal images, generating masks for the optic disc and optic cup, and providing a classification for glaucoma diagnosis. We employ a U-Net architecture with a pretrained residual neural network (ResNet34) for segmentation and an EfficientNetB0 for classification. The proposed framework is tested on publicly available datasets, including ORIGA, REFUGE, RIM-ONE DL, and HRF. Our work evaluated the U-Net model with five pretrained backbones (ResNet34, ResNet50, VGG19, DenseNet121, and EfficientNetB0) and examined preprocessing effects. We optimized model training with limited data using transfer learning and data augmentation techniques. The segmentation model achieves a mean intersection over union (mIoU) value of 0.98. The classification model shows remarkable performance with 99.9% training and 100% testing accuracy on ORIGA, 99.9% training and 99% testing accuracy on RIM-ONE DL, and 98% training and 100% testing accuracy on HRF. The proposed model outperforms related works and demonstrates potential for accurate glaucoma classification and detection tasks.
Optimal Deployment of Container Weighing Equipment: Models and Properties
Authors: Yang Zhaojing , Xu Min , Tian Xuecheng , Jin Yong , Wang Shuaian
Published: September 2024

Container weighing is crucial to the safety of the shipping system and has garnered significant attention in the maritime industry. This research develops a container weighing optimization model and validates several propositions derived from this model. Then, a case study is conducted on ports along the Yangtze River, and the sensitivity analysis of the model is provided. We report the following findings. First, the model can be solved efficiently for large-scale optimization problems. Second, as the number of weighing machines increases, the container weighing mode changes—from selectively weighing containers at their origin ports, then weighing containers at their transshipment ports or destination ports, to all of the containers weighed at their origin ports. Third, in order to improve the safety benefits of weighing containers, port authorities can increase the weighing capacity of weighing machines. The research provides theoretical guidance for shipping system managers to design container weighing plans that enhance maritime safety.
Free Ammonia Strategy for Nitrite-Oxidizing Bacteria (NOB) Suppression in Mainstream Nitritation Start-Up
Authors: Jeong Soyeon , Park Seongjae , Kim Hojun , Yoon Seongwon , Park Sewon , Kim Doheung , Kim Jeongmi , Kim Yeonju , Yu Jaecheul , Lee Taeho
Published: September 2024

The partial nitritation (PN)–anammox (PN/A) process offers a sustainable alternative to nitrogen management in wastewater treatment, addressing the high costs and increasing the low eco-friendliness associated with traditional nitrification/denitrification processes. Stable partial nitritation (PN) is critical for effective PN/A operation, and this study specifically focused on the need to suppress nitrite-oxidizing bacteria (NOB) to facilitate the enrichment of ammonia-oxidizing bacteria (AOB). Utilizing two sequencing batch reactors (SBRs), PN1 and PN2 with different free ammonia (FA) concentrations, this study aimed to evaluate the NOB suppression strategy while enriching AOB. The PN2 reactor, which operated with a higher initial FA concentration (50 mg/L), successfully maintained high nitritation activity, with 96.1% ammonium removal efficiency (ARE) and 95.1% nitrite accumulation efficiency (NAE) at reduced influent NH4+-N concentrations (50 mg NH4+-N/L, FA 10 mg/L). In contrast, PN1 showed inadequate NOB suppression due to lower FA concentrations (10 mg/L). These results suggest that initiating the nitritation process with higher FA concentrations can effectively suppress NOB, enhancing the stability and efficiency of PN/A processes in mainstream applications.
Color Analysis of Brocade from the 4th to 8th Centuries Driven by Image-Based Matching Network Modeling
Authors: Feng Hui , Sheng Xibin , Zhang Lingling , Liu Yuwan , Gu Bingfei
Published: September 2024

To achieve the color matching rules for the textiles discovered during Silk Road excavations between the 4th and 8th centuries, this research proposed an image-based matching network modeling method. The Silk Road facilitated trade and cultural exchange between the East and West, and the textiles found along the way depict the development of fabrics in a color scheme with great cultural significance. A total of 165 images with brocade patterns were collected from a book with a detailed description of the Western influences on textiles along the Silk Road. Two different clustering methods, including the K-means clustering method and octree quantization approach, were used to extract the primary and secondary colors. By combining the HSV color space with the PCCS color system, the color distribution was analyzed to discover the features of representative color patterns. The co-occurrence relationship of the auxiliary colors was explored using the Apriori algorithm, and a total of eight association rules were established. The results showed that the K-means clustering algorithm can show a better effect of color classification to obtain three primary colors and nine secondary colors. The matching mechanism with a visualized network model was also proposed, which showed that reddish-yellow tones are the main colors in the brocade patterns, and the light and soft tones separately account for 27% and 20%. Beige and brown are the most common colorways, with a confidence level of 47%. One style of brocade pattern was used to demonstrate different appearances within various color networks, which could be applied to 3D virtual fitting. This image-based matching network modeling approach makes the color matching schemes visible, and can assist fashion design with fabric features influenced by historical and cultural development.