bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on the Small Target Recognition Method of Automobile Tire Marking Points Based on Improved YOLOv5s
Authors: Guo Zhongfeng , Yang Junlin , Sun Jiahui , Zhao Wenzeng
Published: July 2023

At present, the identification of tire marking points relies primarily on manual inspection, which is not only time-consuming and labor-intensive but also prone to false detections, significantly impacting enterprise efficiency. To achieve accurate recognition of tire marking points, this study proposes a small target feature recognition method for automotive tire marking points. In image pre-processing, MSRCR (Multi-Scale Retinex with Color Restoration) is invoked to enhance image features, which can be adapted to different environmental detection tasks. The YOLOv5s network is improved by adding a parameter-free simAM (Similarity Attention Mechanism) attention mechanism to improve the detection efficiency; adding a small target prediction head in the network to improve the minimum recognition size of the network; and changing the loss function to improve the network recognition performance. MAP, precision, and recall are important parameters. The comparison experiment with the traditional YOLOv5s network shows that the mAP of the improved YOLOv5s network and the original network is 0.86 and 0.955, respectively, and the mAP is increased by 9.5%. The precision is 0.87 and 0.96, an improvement of 9%, and the recall rate is 0.84 and 0.89, an improvement of 4%; the improved YOLOv5s model has a higher confidence level for small target recognition and is more suitable for application in practical detection tasks.
Multi-Objective Parameter Optimization of Submersible Well Pumps Based on RBF Neural Network and Particle Swarm Optimization
Authors: Liu Zhi-Min , Gao Xiao-Guang , Pan Yue , Jiang Bei
Published: July 2023

In order to improve the hydraulic performance of a submersible well pump, steady and transient simulations were carried out based on ANSYS CFX software. The head and efficiency of the submersible well pump under standard operating conditions were taken as the optimization objectives, and the impeller outlet placement angle, outlet width, and vane wrap angle were selected as the optimization variables using the Plackett-Burman experimental design method. The RBF neural network training samples were constructed using the uniform experimental design method to build a hydraulic performance prediction model for the submersible well pump, and a multi-objective particle swarm optimization was used to solve the model and obtain the Pareto optimal solution set. Using the head and efficiency of the initial model as the boundary, the Pareto optimal solution and the corresponding structural parameters are sought. After the optimization, the head of the individual with the better head is increased by about 2.65 m, and the efficiency of the individual with the better efficiency is increased by about 2.3 percentage points compared with that of the initial model. The pressure gradient in the impeller flow channel is more obvious, the work capacity is significantly improved, the vortex area of the spatial guide vane is smaller, the flow line is more regular, and the pressure pulsation amplitude at the inlet and outlet of the impeller and the spatial guide vane is significantly reduced.
Research Progress of Dynamic Measurement Technology of Atom Gravimeter
Authors: Huang Chunfu , Li An , Qin Fangjun
Published: July 2023

After more than 30 years of development, the measurement performance of atom gravimeters in the laboratory has reached a high level. More and more compact, small, portable instruments begin to appear, and field measurements have been conducted gradually. At present, the field measurements of atom gravimeters are mostly static or “stop-and-go” quasi-dynamic experiments, and the research on dynamic measurement is still in its infancy. High-precision absolute gravity dynamic surveying in the field has shown attractive prospects in many aspects, and many researchers have carried out research on it. This paper first reviews the main research work of the atom gravimeter, especially its dynamic measurement technology. Then it introduces the reported principle, scheme, and equipment of atom gravimeter dynamic measurement. The generation mechanism and suppression methods of the main error sources of dynamic measurement, such as vibration noise, accelerometer drift, and carrier dynamic effect, are analyzed. Finally, the application prospects of atom gravimeter dynamic measurement technology in gravity field mapping, navigation, and underwater target detection are discussed.
Research on Lightweight Model for Rapid Identification of Chunky Food Based on Machine Vision
Authors: Guo Zhongfeng , Yang Junlin , Liu Siyi
Published: July 2023

To meet the demands of the food industry for automatic sorting of block-shaped foods using DELTA robots, a machine vision detection method capable of quickly identifying such foods needs to be studied. This paper proposes a lightweight model that incorporates the CBAM attention mechanism into the YOLOv5 model, replaces ordinary convolution with ghost convolution, and replaces the position loss function with SIoU loss. The resulting YOLOv5-GCS model achieves a mAP increase from 95.4% to 97.4%, and a reduction in parameter volume from 7.0 M to 6.2 M, compared to the YOLOv5 model. Furthermore, the first 17 layers of the MobileNetv3-large network are replaced with the CSPDarkNet53 network in YOLOv5-GCS, resulting in the YOLOv5-MGCS lightweight model, with a high FPS of 83, which is capable of fast identification of block-shaped foods.
Specific-Input LIME Explanations for Tabular Data Based on Deep Learning Models
Authors: An Junkang , Zhang Yiwan , Joe Inwhee
Published: July 2023

Deep learning researchers believe that as deep learning models evolve, they can perform well on many tasks. However, the complex parameters of deep learning models make it difficult for users to understand how deep learning models make predictions. In this paper, we propose the specific-input local interpretable model-agnostic explanations (LIME) model, a novel interpretable artificial intelligence (XAI) method that interprets deep learning models of tabular data. The specific-input process uses feature importance and partial dependency plots (PDPs) to select the “what” and “how”. In our experiments, we first obtain a basic interpretation of the data by simulating user behaviour. Second, we use our approach to understand “which” features deep learning models focus on and how these features affect the model’s predictions. From the experimental results, we find that this approach improves the stability of LIME interpretations, compensates for the problem of LIME only focusing on local interpretations, and achieves a balance between global and local interpretations.
Drosophila Infestations of California Strawberries and Identification of Drosophila suzukii Using a TaqMan Assay
Authors: Ganjisaffar Fatemeh , Abrieux Antoine , Gress Brian E. , Chiu Joanna C. , Zalom Frank G.
Published: July 2023

In contrast to most other Drosophila species that infest processing (overripe) strawberries, the spotted-wing drosophila (SWD) can also infest firm and ripe fruit intended for the fresh market. However, fresh fruit infestations of this invasive species did not become an economic problem for California growers until a decade after SWD’s first detection in California strawberries in 2008. This outbreak corresponds to the development of reported insecticide resistance in SWD populations from strawberry and other berry crops following years of incidental exposure of insecticide applications against other key pests. The objective of this study was to determine the current levels of Drosophila infestation in fresh market and processing strawberries which would inform the choice of insecticides to use for control. We sampled fresh market and processing strawberries from 17 fields over a two-year period in the three major strawberry production areas of California and determined the numbers of emerged SWD and non-SWD Drosophila adult flies. In addition, since holding fruit for adult emergence to determine species composition is impractical for making rapid control decisions and could be inaccurate due to potential interspecific competitions among larvae in the fruit, we developed a TaqMan assay that facilitates larval identification.
A Survey on Bug Deduplication and Triage Methods from Multiple Points of View
Authors: Qian Cheng , Zhang Ming , Nie Yuanping , Lu Shuaibing , Cao Huayang
Published: July 2023

To address the issue of insufficient testing caused by the continuous reduction of software development cycles, many organizations maintain bug repositories and bug tracking systems to ensure real-time updates of bugs. However, each day, a large number of bugs is discovered and sent to the repository, which imposes a heavy workload on bug fixers. Therefore, effective bug deduplication and triage are of great significance in software development. This paper provides a comprehensive investigation and survey of the recent developments in bug deduplication and triage. The study begins by outlining the roadmap of the existing literature, including the research trends, mathematical models, methods, and commonly used datasets in recent years. Subsequently, the paper summarizes the general process of the methods from two perspectives—runtime information-based and bug report-based perspectives—and provides a detailed overview of the methodologies employed in relevant works. Finally, this paper presents a detailed comparison of the experimental results of various works in terms of usage methods, datasets, accuracy, recall rate, and F1 score. Drawing on key findings, such as the need to improve the accuracy of runtime information collection and refine the description information in bug reports, we propose several potential future research directions in the field, such as stack trace enrichment and the combination of new NLP models.
Smooth Interpolation Design with Consideration of Corner Tolerance Constraints for Robotics
Authors: Li Hung-Ming , Tsai Meng-Shiun , Zhang Ting-Hua , Cheng Chih-Chun
Published: July 2023

This paper presents a novel method for interpolation design that ensures the continuity of a velocity profile and satisfies a specified corner tolerance constraint. The method uses an S-shaped profile to generate trajectories for each line segment in the task space. The velocity profiles of each segment are overlapped to control the smoothness of the corners and reduce the cycle time. This study defined an overlapping time parameter that is associated with the corner tolerance and the cycle time. Moreover, a corner tolerance constraint equation was derived that can allow for a given tolerance to be satisfied. This constraint equation enables the use of the proposed velocity profile overlap (VPO) method to specify corner tolerances for each corner of the trajectory. The proposed method was compared against the conventional acceleration/deceleration after interpolation (ADAI) method. The results demonstrate that the proposed VPO method can achieve higher accuracy and lower cycle time than the ADAI method.
Investigation on Intelligent Early Warning of Rock Burst Disasters Using the PCA-PSO-ELM Model
Authors: Yuan Haiping , Ji Shuaijie , Liu Gaoliang , Xiong Lijun , Li Hengzhe , Cao Zhanhua , Xia Zijin
Published: July 2023

In order to conduct an intelligent early warning assessment of stope rock burst disasters in mining areas, and effectively prevent and control them, the principal component analysis (PCA) method was embraced to perform dimensionality reduction and feature information extraction from 10 main factors that affect the occurrence of rock bursts. On this basis, six principal component elements of the influencing factors of rock bursts have been obtained as the input vectors for an extreme learning machine (ELM). In the meantime, the parameter optimization ability of the PSO algorithm was adopted, the input weight values of the ELM and the threshold values of the hidden layer were optimized, and the functions of the three models were completely combined. Therefore, an early warning model of rock bursts based on the PCA-PSO-ELM combined algorithm was creatively proposed and the risk rank of rock bursts in the Yanshitai Coal Mine was predicted and evaluated. Consequently, the research results indicated that the prediction accuracy of the PCA-PSO-ELM model improved the prediction performance and generalization ability and reached a 100% contrast with the three models, namely the BP neural network, the radial basis function, and the extreme learning machine, which presented an updated method for the early warning investigation of rock burst disasters and had favorable engineering significance.
The Impact of Organic, Inorganic Fertilizers, and Biochar on Phytochemicals Content of Three Brassicaceae Vegetables
Authors: Antonious George
Published: July 2023

The need for soil fumigants of natural origin such as glucosinolates (GSLs) has increased due to the general prevention of manmade soil fumigants. GSLs and other phytochemicals (vitamin C and phenols) present in Brassica vegetables such as turnips, arugula, and mustard have antioxidant properties, and hence have important health attributes. The study examined how different soil amendments (chicken manure CM, vermicompost Vermi, horse manure HM, sewage sludge SS, elemental inorganic fertilizer Inorg, organic fertilizer Org, and biochar) impact the concentrations of glucosinolates (GSLs), vitamin C, phenols, and reducing sugars in three varieties of turnips (Purple Top White Globe PTWG, Scarlet Queen Red SQR, and Tokyo Cross TC), arugula, and mustard greens grown under field conditions. The results showed that mustard greens contained higher concentrations of GSLs (974 µg g−1 fresh shoots) than arugula (651 µg g−1 fresh shoots), and the TC variety of turnip had the highest concentrations of GSLs, vitamin C, and sugars. Additionally, amending the soil with SS, CM, and HM significantly increased the vitamin C content in mustard shoots by 82%, 90%, and 31%, respectively, and the total phenols by 77%, 70%, and 36%, respectively, compared to the control treatment. The increased inorganic fertilizers cost, and availability of large amounts of animal manure made animal manure application to cropland an attractive disposal option.
Control of Quarter-Car Active Suspension System Based on Optimized Fuzzy Linear Quadratic Regulator Control Method
Authors: Abut Tayfun , Salkim Enver
Published: July 2023

Vehicle suspension systems, which affect driving performance and passenger comfort, are actively researched with the development of technology and the insufficient quality of passive suspension systems. This paper establishes the suspension model of a quarter of the car and active control is realized. The suspension model was created using the Lagrange–Euler method. LQR, fuzzy logic control (FLC), and fuzzy-LQR control algorithms were developed and applied to the suspension system for active control. The purpose of these controllers is to improve car handling and passenger comfort. Undesirable vibrations occur in passive suspension systems. These vibrations should be reduced using the proposed control methods and a robust system should be developed. To enhance the performance of the fuzzy logic control (FLC) and fuzzy-LQR control methods, the optimal values of the coefficients of the points where the feet of the member functions touch are calculated using the particle swarm optimization (PSO) algorithm. Then, the designed controllers were simulated in the computer environment. The success of the control performance of the applied methods concerning the passive suspension system was compared in percentages. The results are presented and evaluated graphically and numerically. Using the integral time-weighted absolute error (ITAE) criterion, the methods were compared with each other and with the studies in the literature. As a result, it was found that the proposed control method (fuzzy-LQR) is about 84.2% more successful in body motion, 90% in car acceleration, 84.5% in suspension deflection, and 86.7% in tire deflection compared to the studies in the literature. All these results show that the car’s ride comfort has been significantly improved.
Simulation and Experimental Studies of Optimization of σ-Value for Block Matching and 3D Filtering Algorithm in Magnetic Resonance Images
Authors: Park Minji , Kang Seong-Hyeon , Kim Kyuseok , Lee Youngjin ,   for the Alzheimer’s Disease Neuroimaging Initiative
Published: July 2023

In this study, we optimized the σ-values of a block matching and 3D filtering (BM3D) algorithm to reduce noise in magnetic resonance images. Brain T2-weighted images (T2WIs) were obtained using the BrainWeb simulation program and Rician noise with intensities of 0.05, 0.10, and 0.15. The BM3D algorithm was applied to the optimized BM3D algorithm and compared with conventional noise reduction algorithms using Gaussian, median, and Wiener filters. The clinical feasibility was assessed using real brain T2WIs from the Alzheimer’s Disease Neuroimaging Initiative. Quantitative evaluation was performed using the contrast-to-noise ratio, coefficient of variation, structural similarity index measurement, and root mean square error. The simulation results showed optimal image characteristics and similarity at a σ-value of 0.12, demonstrating superior noise reduction performance. The optimized BM3D algorithm showed the greatest improvement in the clinical study. In conclusion, applying the optimized BM3D algorithm with a σ-value of 0.12 achieved efficient noise reduction.
Effects of External Cross-Frames on the Ultimate Behavior of a Twin Steel Trapezoidal Box-Girder Bridge
Authors: Kim Janghwan , Kwon Byeong Wan , Kim Seungwon
Published: July 2023

In the construction of a twin-steel box-girder bridge, external cross-frames are temporarily used to increase torsional resistance. These members are regarded as secondary and are removed after a composite action in the bridge is achieved. Recently, attention has been paid to the possibility that external cross-frames can be a redundant source in bridges with girder fractures. In this study, the effects of external cross-frames on a damaged bridge were investigated using finite-element bridge models. Therefore, full-scale bridge models of a twin-steel trapezoidal box-girder bridge were constructed using K-type external cross-frames. Various failure modes, including the buckling and yielding of the cross-frames, were incorporated into the bridge models to account for realistic behaviors in the damaged bridge. The analysis results showed that the external cross-frames increased the load-carrying capacity of the bridge with one-girder fracture damage by transversely redistributing the live loads.
An Efficient and Robust Topology Optimization Method for Thermoelastically Damped Microresonators
Authors: Fu Yu , Li Li , Hu Yujin
Published: July 2023

The challenges of computational cost and robustness are critical obstacles in topology optimization methods, particularly for the iterative process of optimizing large-scale multiphysical structures. This study proposes an efficient and robust topology optimization method for minimizing the thermoelastic damping of large-scale microresonators. An evolutionary structural optimization method is adopted to passively determine the search direction of optimizing large-scale thermoelastic structures. To efficiently reduce the computational cost of the iterative process of an optimizing process, a model reduction method is developed based on the projection-based model reduction method whose reduced basis is generated within the Neumann series subspace. However, the projection-based model reduction method may be unstable when topology modifications are made during an iteration optimization process. To ensure robustness, a modal validation technique is first implemented in the iterative process to stabilize the iteration and narrow down the search domain, and a posterior evaluation of the Neumann series expansion is then developed to retain the convergence of the projection-based model reduction method. Furthermore, the efficiency and accuracy of the proposed topology optimization method are validated through numerical examples. Two large-scale numerical models are also used to demonstrate the advantage of the proposed method. It is found that large-scale thermoelastic structures with a phase-lag heat conduction law can be designed passively, precisely, and efficiently by using the proposed topology optimization method.
A Vehicle–Bridge Interaction Element: Implementation in ABAQUS and Verification
Authors: Dong Yufeng , Zhang Wenyang , Shamsabadi Anoosh , Shi Li , Taciroglu Ertugrul
Published: July 2023

Vibration analysis of bridges induced by train loads is a crucial aspect of railway design, particularly considering the complexity of vehicle components such as bogie-suspension systems. Consequently, railway engineers have endeavored to improve the computational efficiency and applicability of train models using the finite-element method. This paper introduces a toolbox implemented in ABAQUS through a user-defined element (UEL) subroutine, which incorporates the vehicle–bridge interaction (VBI) element theory. This toolbox effectively handles diverse vehicle–bridge interaction systems. In the proposed theory, the wheel-track contact force is derived based on the bridge response, eliminating the need for an iterative process and significantly reducing computational workload compared to classical physics-based analysis. The presented approach is validated through a moving sprung mass model and a moving rigid bar model. Furthermore, a case study is conducted on a three-dimensional finite-element model of a high-speed railway bridge in China, based on a design sketch, to showcase the capabilities of the developed scheme. The study demonstrates the practical application of the proposed methodology in analyzing vehicle–bridge structures with high complexity.
Experimental Investigation of Precast Rocking Walls Incorporating Tension-Compression and Shear Steel Energy Dissipaters
Authors: Liu Jiabin , Peng Zhanhui , Guan Dongzhi , Lin Yu
Published: July 2023

To fully utilize the potential of earthquake-resistance capacity in rocking wall systems and improve repairability after earthquakes, a precast rocking wall structure is developed with the installation of multiple steel energy dissipaters, i.e., tension–compression and shear steel energy dissipaters. Quasistatic tests were carried out on three specimens to evaluate the seismic performance of the proposed system. A simulation investigation based on OpenSees was conducted to study the effects of the initial stress of strands and the main design parameters. The results indicated that the steel energy dissipaters suffer visible plastic deformation and exhibit excellent energy dissipation capacity. The proposed rocking wall structure presents excellent seismic behavior, satisfactory self-centering capacity, and repairability of the recovering function after an earthquake by replacing only the damaged energy dissipaters. Furthermore, the proposed system provides a new way to achieve the hierarchical energy dissipation mechanism of a structure.
An Explanation of the LSTM Model Used for DDoS Attacks Classification
Authors: Bashaiwth Abdulmuneem , Binsalleeh Hamad , AsSadhan Basil
Published: July 2023

With the rise of DDoS attacks, several machine learning-based attack detection models have been used to mitigate malicious behavioral attacks. Understanding how machine learning models work is not trivial. This is particularly true for complex and nonlinear models, such as deep learning models that have high accuracy. The struggle to explain these models creates a tension between accuracy and explanation. Recently, different methods have been used to explain deep learning models and address ambiguity issues. In this paper, we utilize the LSTM model to classify DDoS attacks. We then investigate the explanation of LSTM using LIME, SHAP, Anchor, and LORE methods. Predictions of 17 DDoS attacks are explained by these methods, where common explanations are obtained for each class. We also use the output of the explanation methods to extract intrinsic features needed to differentiate DDoS attacks. Our results demonstrate 51 intrinsic features to classify attacks. We finally compare the explanation methods and evaluate them using descriptive accuracy (DA) and descriptive sparsity (DS) metrics. The comparison and evaluation show that the explanation methods can explain the classification of DDoS attacks by capturing either the dominant contribution of input features in the prediction of the classifier or a set of features with high relevance.
A Study on Particulate Matter from an Area with High Traffic Intensity
Authors: Mustață Dan-Marius , Ionel Ioana , Popa Rareș-Mihăiță , Dughir Ciprian , Bisorca Daniel
Published: July 2023

This research focuses on analyzing concentrations of particulate matter (PM) next to a highly congested road section, with 39,900 as the maximum number of vehicles per 24 h, in the vicinity of Timisoara, Romania. The concentrations are measured in multiple episodes using two different measurement methods, gravimetric and dynamic light scattering, showing a dispersion range of the PM concentrations. The presence of metal particles in the samples are analyzed using an electron microscope. Additionally, the impact on human health is described by evaluating the results for inhalable-, thoracic-, and alveolar-sized particles.
The Construction Methods and Control Mechanisms for Subway Station Undercrossing an Existing Tunnel at Zero Distance
Authors: Zhou Zihan , Zhou Xiaohan , Li Longping , Liu Xinrong , Wang Linfeng , Wang Ziqiang
Published: July 2023

The existing tunnel will inevitably be disturbed when the new subway station is constructed cross under it at zero distance. Three innovative construction schemes were proposed, their effects on deformation control were compared by numerical simulations, and the mechanism of the influence of different tunnel support and excavation methods on the deformation control in them was further explored in order to seek a better scheme in this paper. The results show that the settlement of the existing tunnel in Scheme I (L-type beam + full-face excavation) is the greatest, and that the one in Scheme III (temporary steel column + double-pilot tunnel) is the smallest, but the settlement during excavation changes significantly, along with the large deformation on the side walls of the undercrossing area. Scheme III is not recommended because it is difficult to control and the construction effect is mediocre. Due to the existence of a large free face, the settlement of both sides of the existing tunnel is larger when the L-type beam supports the tunnel, and the bottom of the middle wall bears large compressive stress. The settlement of the existing tunnel excavated by the double-pilot tunneling method is less than that of the full-face tunneling method under the same tunneling support method, and the increase in settlement during the tunneling with the L-type beams is greater than that with the pier columns. In comprehensive consideration, Scheme II (pier column + double-pilot tunnel method) or combined pier column + double-pilot tunnel can control the deformation better.
Design of a Miniaturized Electron Cyclotron Resonance Ion Source for High-Voltage Proton Accelerator
Authors: Yu Hua-Fei , He Zi-Feng , Zhao Ming-Hua , Wan Wei-Shi , Liu Huan-Ling , Wu Yue , Lv Wen-Zhuang , Zhou Da-Yong , Lu Huan-Ting
Published: July 2023

The Electron Cyclotron Resonance (ECR) ion source fulfills high-current, high-efficiency, and compactness requirements for high-voltage proton accelerators. It is a cathode-free source that uses microwaves to heat a magnetically confined plasma, so there is no cathode loss resulting in a short service life. We finished the design for a miniaturization ECR ion source system, including a microwave system and source body. The traditional microwave system’s scale, which is approximately 1 m, has been reduced to 0.234 m, and the transmission efficiency is greater than 90%. The influence of cavity size and magnetic field distribution on gas ionization is analyzed under the condition that the outer size of the permanent magnet ring is limited, and the optimal scheme of cavity size and saddle-shaped magnetic field distribution is obtained. This design meets the requirement of fitting the ion source system into the restricted space in the high-voltage accelerator’s head.
Application of Iterative Virtual Events Internal Multiple Suppression Technique: A Case of Southwest Depression Area of Tarim, China
Authors: Xiao Mingtu , Xie Junfa , Wang Weihong , Liu Wenqing , Sun Jiaqing , Jin Baozhong , Zhang Tao , Zhao Yuhe , Wang Yihui
Published: July 2023

The seismic records in the Cambrian southwest depression of the Tarim Basin exhibit discrepancies when compared to the actual geological setting, which is caused by the presence of multiples. Despite the application of the Radon transform, multiple interferences persist beneath the Cambrian salt in the pre-stack data, with significant variations in energy and frequency across the horizontal direction. In addition, other multiple suppression methods are also difficult to handle this problem. To address this issue, we have developed an iterative virtual event internal multiple suppression method for post-stack data. This novel algorithm extends the traditional virtual event internal multiple suppression approach, eliminating the need for data regularization and avoiding the problem of the traditional virtual events method requiring sequential extraction of primaries from relevant layers, which greatly improves computational efficiency and simplifies the implementation steps of the traditional method. Numerical experiments demonstrate the efficacy of our method in suppressing internal multiples in both synthetic and field data while preserving primary signals. When applied to real seismic data profiles, the iterative method yields structural characteristics that align more closely with sedimentary laws and reduces disparities in energy and frequency of multiples along the horizontal axis. Consequently, our method provides a robust foundation for subsequent hydrocarbon source rock prediction.
A Comparison of Monocular Visual SLAM and Visual Odometry Methods Applied to 3D Reconstruction
Authors: Herrera-Granda Erick P. , Torres-Cantero Juan C. , Rosales Andrés , Peluffo-Ordóñez Diego H.
Published: July 2023

Pure monocular 3D reconstruction is a complex problem that has attracted the research community’s interest due to the affordability and availability of RGB sensors. SLAM, VO, and SFM are disciplines formulated to solve the 3D reconstruction problem and estimate the camera’s ego-motion; so, many methods have been proposed. However, most of these methods have not been evaluated on large datasets and under various motion patterns, have not been tested under the same metrics, and most of them have not been evaluated following a taxonomy, making their comparison and selection difficult. In this research, we performed a comparison of ten publicly available SLAM and VO methods following a taxonomy, including one method for each category of the primary taxonomy, three machine-learning-based methods, and two updates of the best methods to identify the advantages and limitations of each category of the taxonomy and test whether the addition of machine learning or updates on those methods improved them significantly. Thus, we evaluated each algorithm using the TUM-Mono dataset and benchmark, and we performed an inferential statistical analysis to identify the significant differences through its metrics. The results determined that the sparse-direct methods significantly outperformed the rest of the taxonomy, and fusing them with machine learning techniques significantly enhanced the geometric-based methods’ performance from different perspectives.
Improved Prediction of Aquatic Beetle Diversity in a Stagnant Pool by a One-Dimensional Convolutional Neural Network Using Variational Autoencoder Generative Adversarial Network-Generated Data
Authors: Hu Miao , Jiang Shujiao , Jia Fenglong , Yang Xiaomei , Li Zhiqiang
Published: July 2023

Building a reasonable model for predicting biodiversity using limited data is challenging. Expanding limited experimental data using a variational autoencoder generative adversarial network (VAEGAN) to improve biodiversity predictions for a region is a new strategy. Aquatic beetle diversity in a large >30-year-old artificial pool that had not had human interference in Nanshe Village (Dapeng Peninsula, Shenzhen City, Guangdong Province, China) was investigated. Eight ecological factors were considered. These were water temperature, salinity, pH, water depth, proportional area of aquatic plants, proportional area of submerged plants, water area, and water level. Field sampling was performed for 1 or 2 days in the middle or late part of each month for a year. A type D net was swept 10 times in the same direction in each ~1 m × ~1 m sample square, generating 132 datasets (experimental data). In total, 39 aquatic beetle species were collected, 19 of which were assigned to Hydrophilidae, 16 to Dytiscidae, 3 to Noteridae, and 1 to Gyrinidae. A one-dimensional convolutional neural network (1-D CNN) was used to assess and predict the grade of the number of individuals and the number of aquatic beetle species. The Bayesian-optimized 1-D CNN established using 112 experimental datasets as the training set and the other 20 datasets as validation and testing sets gave a 74.0% prediction accuracy for the grade of the number of individuals and a 70.0% prediction accuracy for the number of species. The impact of insufficient sample data on the model was assessed using a VAEGAN to expand the training set from 112 to 512 samples, and then the Bayesian-optimized 1-D CNN-based VAEGAN prediction model was re-established. This improved prediction accuracy for the grade of the number of individuals to 86.0% and for the number of species to 85.0%. The grade of the number of individuals’ prediction accuracy was 88.0% and the number of species’ prediction accuracy was 85.0% when the random effects of only obtaining a single individual of a species were excluded. The results indicated that the accuracy of the 1-D CNN in predicting the aquatic beetle species number and abundance from relevant environmental factors can be improved using a VAEGAN to expand the experimental data.
Optimization for Asphalt Pavement Maintenance Plans at Network Level: Integrating Maintenance Funds, Pavement Performance, Road Users, and Environment
Authors: Guan Xinfang , Zhang Hongchao , Du Xiaobo , Zhang Xiyu , Sun Mutian , Bi Yufeng
Published: July 2023

As an infrastructure project, the cost and benefits of road maintenance should be measured through social costs. In order to fully consider user interests and make road maintenance decisions more reasonable, two types of user costs that may affect road maintenance benefits have been quantified through analysis of existing literature. At the same time, environmental issues have gradually become the focus of various industries, and in optimizing road maintenance decisions, the impact of environmental issues on decision making should also be considered. This article first analyzes and quantifies the user costs that affect the effectiveness of road maintenance. Secondly, based on the concept of sustainable development of roads, the optimization of road maintenance decisions is divided into two steps: the first step is to determine the minimum maintenance budget funds, and the second step is to determine the optimal plan. The specific optimization method is to use the 0-1 mathematical programming method to establish a network-level pavement maintenance decision optimization model based on a quantitative model. The most reasonable maintenance optimization plan is determined from four aspects: maintenance funds, maintenance performance improvement value, user benefit improvement, and reducing environmental impact. Finally, a provincial road network for a case study is selected. The applicability of the new model is verified through a case study. This study can help decision makers deal with asphalt pavement maintenance arrangements at the network level with four decision objectives: maintenance funds, pavement performance, road users, and environmental impacts.
Modern Technologies for Waste Management: A Review
Authors: Czekała Wojciech , Drozdowski Jędrzej , Łabiak Piotr
Published: July 2023

Facing the problem of increasing waste, scientists, foundations, and companies around the globe resulted in ideas and invented technologies to slow down the process. Sources of waste range from industrial waste (e.g., construction and demolition materials, hazardous wastes, ashes) to municipal solid waste (e.g., food wastes, paper, cardboard, plastics, textiles). Modern solutions do not focus only on technological aspects of waste management but also on sociological ones. Thanks to the Internet and social media platforms, scientists can influence ecological consciousness and awareness on a much larger scale. This research was conducted using the search keywords related to modern technologies for waste management. This paper presents a spectrum of selected modern solutions that changed in recent years and how they have impacted waste management. It also discusses challenges and future directions of waste management in the context of the circular economy. The use of modern solutions in waste management allows to achieve selected goals of sustainable development.
Study on Migration Monitoring Technology of Chromium-Contaminated Site Based on Dual-Frequency Induced Polarization Method
Authors: Liu Zhimin , Wei Kaiyao , Pan Yue
Published: July 2023

In order to reveal the migration process of chromium-contaminated sites, COMSOL5.6 software was used to build the initial model of the chromium pollution source and analyze the migration change characteristics over time based on the rule of groundwater movement and Convection–Dispersion equation. COMSOL provides fully coupled multiphysics modeling for most engineering fields. The results show that: the permeable layer with a high permeability coefficient during the migration of chromium pollutants has a strong adsorption effect on the heavily polluted part, and it will enhance the lateral diffusion of the polluted area within a short period. Based on the migration model, the intermediate gradient and the symmetrical quadrupole sounding model are built. The variation law of apparent amplitude frequency and apparent resistivity under flat and undulating terrain is analyzed based on the dual frequency IP (Induced Polarization) method. The results show that: The intermediate gradient detection is better than the symmetrical quadrupole sounding in the correspondence between the peak value of apparent amplitude frequency and the valley value of apparent resistivity. The arrangement of the bathymetric measurement point in the center of the projected edge of the pollution field can be a better way to monitor the pollution. Monitoring with the intermediate gradient detection device and the symmetrical quadrupole sounding device creates “multiple peaks” in the curve as it passes through the valley. Arranging the power supply electrodes on the mountain frontiers on both sides of the raised peaks and synthesizes the apparent amplitude frequency and apparent resistivity curves of the pollution field, which can effectively determine the specific orientation of the heavily polluted area of the pollution field. This research makes theoretical additions to the migration characteristics of the Cr pollution field. It provides technical guidance for the real-time monitoring of the pollution field, which is of positive significance for promoting ecological and environmental protection.
The Antibacterial Effect of the Films Coated with the Layers Based on Uncaria tomentosa and Formitopsis betulina Extracts and ZnO Nanoparticles and Their Influence on the Secondary Shelf-Life of Sliced Cooked Ham
Authors: Ordon Magdalena , Burdajewicz Weronika , Sternal Józef , Okręglicki Marcin , Mizielińska Małgorzata
Published: July 2023

Microbial surface contamination of the cooked ham sliced by shop assistants has an influence on its potential secondary shelf-life. Active packaging may be the solution. The first goal of the work was to obtain coatings based on Formitopsis betulina and Uncaria tomentosa extracts (separately), with the addition of nanoparticles of ZnO (as a synergetic agent to increase plant extracts’ antimicrobial effectiveness), that could be active against selected bacterial strains. Another aim of the work was to determine the influence of obtained active layers on microbial purity and texture of the samples. The results of the study demonstrated that the layers containing F. betulina or U. tomentosa extracts with the addition of ZnO nanoparticles inhibited Staphylococcus aureus, Bacillus atrophaeus, and Escherichia coli growth completely and decreased the number of Pseudomonas syringae cells. Analyzing microbial purity of sliced cooked ham portions after storage, it was observed that the active packaging based on U. tomentosa extracts had a greater influence on the total count than the coatings containing F. betulina extracts and that both active packaging materials affected the microbial purity of the cooked ham. Analyzing the textural parameter changes after 48 h of storage, a decrease in the gumminess, chewiness and cohesiveness values was noted, but only for the ham portions stored in bags covered with the coating based on U. tomentosa. The effectiveness of the active packaging on textural parameters after 96 h of storage was not noticed.
Tacita Dean’s Affective Intermediality: Precarious Visions in-between the Visual Arts, Cinema, and the Gallery Film
Authors: Pethő Ágnes
Published: July 2023

Tacita Dean’s art relies on the perception of liminalities, of moving in-between, of one medium unfolding into another through dispersed, “molecular” sensations, either subverting or augmenting impressions of art forms perceived on the level of larger, structural wholes. Arguing against the wide-angle perspective employed by media studies approaches and for a close-up analysis of an “affective intermediality” in Tacita Dean’s art, the author looks at the landmark exhibitions at the National Gallery, the National Portrait Gallery, and the Royal Academy in London organised in 2018. The article singles out some of the individual works in the context of the exhibition as a work of art, and focuses on questions like the cross-media phenomenon of the “cinematic”, the affective performativity of the various dispositifs employed in her installations of celluloid films, the affordances of Dean’s signature aperture-gate masking technique, as well as the relation between narrative cinema experienced in a theatrical space and film as the medium of a visual artist. The essay concludes with a brief analysis of her gallery film, Antigone (2018), unravelling an allegorical journey through cosmic time and atmospheric landscapes, viewed as an ode to the “blind vision” of photochemical film and as a synthesis of key features of her intermediality conceived as a strategy for the re-sensitization of mediums by approaching one art from the point of view of another.
A Study on Greenhouse Gas (PFCs) Reduction in Plasma Scrubbers to Realize Carbon Neutrality of Semiconductors and Displays
Authors: Lee Bong Jae , Hwang Yujin , Jo Dong Ki , Jeong Jongmoon
Published: July 2023

Perfluorinated compounds (PFCs) are used for manufacturing purposes in the semiconductor and display industries, resulting in an increased need for emission reduction due to the significant global warming potential of the associated greenhouse gases. The decomposition characteristics of etch-type and water film (WF)-type plasma-wet scrubbers were investigated. The PFCs used in the study were CF4, SF6, NF3, CHF3, C2F6, C3F8, and C4F8, and the destruction removal efficiency (DRE) and by-product gas generation rate were confirmed based on the changes in the parameters (total flow rate and power) of the plasma-wet scrubber. When the total flow rate reached 100 L/min and the measured maximum power (11 kW), the reduction efficiency of CF4 in the etch type was 95.60% and the DRE of other PFCs was 99.99%. Moreover, for the WF type, the DRE of CF4 was 90.06%, that of SF6 was 96.44%, and that of other PFCs was 99.99%. When the total flow rate reached 300 L/min and 11 kW, the DRE of SF6 in the etch type was 99%, and the DRE of NF3, CHF3, C2F6, C3F8, and C4F8 was 95.57%, 87.06%, 70.74%, 81.45%, and 95.59%, respectively. In addition, in the WF type, the DRE of SF6 was 94.39%, and the DRE of NF3, CHF3, C2F6, C3F8, and C4F8 was 99.80%, 95.34%, 85.38%, 88.49%, and 98.22%, respectively. The decomposition efficiency was high for the etch type for gases with small flow rates or no by-product gas generation. The by-product gas generation rate was significantly lower for the WF type.
Sichuan Rainfall Prediction Using an Analog Ensemble
Authors: Lai Pengyou , Yang Jingtao , Liu Lexi , Zhang Yu , Sun Zhaoxuan , Huang Zhefan , Shao Duanzhou , He Linbin
Published: July 2023

This study aimed to address the significant bias in 0–44-day precipitation forecasts under numerical weather conditions. To achieve this, we utilized observational data obtained from 156 surface stations in the Sichuan region and reanalysis grid data from the National Centers for Environmental Prediction Climate Forecast System Model version 2. Statistical analysis of the spatiotemporal characteristics of precipitation in Sichuan was conducted, followed by a correction experiment based on the Analog Ensemble algorithm for 0–44-day precipitation forecasts for different seasons in the Sichuan region. The results show that, in terms of spatial distribution, the precipitation amounts and precipitation days in Sichuan Province gradually decreased from east to west. Temporally, the highest number of precipitation days occurred in autumn, while the maximum precipitation amount was observed in summer. The Analog Ensemble algorithm effectively reduced the error in the model forecast results for different seasons in the Sichuan region. However, the correction effectiveness varied seasonally, primarily because of the differing performance of the AnEn method in relation to precipitation events of various magnitudes. Notably, the correction effect was the poorest for heavy-rain forecasts. In addition, the degree of improvement of the Analog Ensemble algorithm varied for different initial forecast times and forecast lead times. As the forecast lead time increased, the correction effect gradually weakened.