bluuebunny/crossref_metadata_2025 huggingface dataset:
Examining Thresholding and Factors Impacting Snow Cover Detection Using Nighttime Images
Authors: Stopic Renato , Dias Eduardo
Published: February 2023

Nighttime remote sensing data from the Visible Infrared Imaging Radiometer suite day/night band (VIIRS DNB) enable snow cover detection from full moonlight reflection. Using nighttime data is particularly relevant in areas with limited daytime hours due to high latitudes. Previous studies demonstrated the potential of using thresholding methods in detecting snow, but more research studies are needed to understand the factors that influence their accuracy. This study explored seven thresholding algorithms in four case study areas with different characteristics and compared the classified snow results to the MODIS MOD10A1 snow cover product. The results found that Li thresholding delivers higher accuracies for most case studies, with an overall accuracy between 65% and 81%, while mean thresholding performed best in mountainous regions (70%) but struggled in other areas. Most false negatives are caused by forests, especially closed and evergreen forests. The analysis of NDVI data matches these findings, with the NDVI of false negatives being significantly higher than true positives. False positives appear to be primarily located in or around built-up areas. This study provides insights into where nighttime VIIRS DNB data can be used to increase snow cover data temporal and spatial coverage.
Analysis of Spatial-Temporal Differentiation and Influencing Factors of Ecosystem Services in Resource-Based Cities in Semiarid Regions
Authors: Zhu Shichao , Zhao Yanling , Huang Jinlou , Wang Shaoqing
Published: February 2023

The spatial-temporal differentiation characteristics and driving mechanisms of ecosystem services are of great significance for optimizing the pattern of land spatial protection and realizing regional sustainable development. Existing studies seldom consider the segmental influence mechanism of various influencing factors on different levels of ecosystem service value (ESV). Therefore, this paper analyzes the temporal and spatial differentiation evolution characteristics of ESV in semiarid regions through an improved ESV evaluation model. The spatial panel quantile regression (SPQR) model was introduced to explore the relationship between various types of influencing factors and ESV in different intervals. The results showed the following: (1) The changes in ESV in Baotou City from 2000 to 2018 tended to be stable, but the spatial differentiation of ESV intensified. The aggregation feature of the low-ESV region is significant and gradually expanding. (2) Precipitation was the dominant factor increasing the ESV in each interval, and temperature had a significant negative impact on the low-ESV area. (3) Higher land use integrity accelerates the decline of ESV in the surrounding areas of built-up areas. The high-ESV area was more sensitive to the intensity of human activity. The direction of human activities should be effectively controlled, and the structure of comprehensive land use should be optimized to enhance the service function of regional ecosystems. This research provides new thinking for the ecological restoration zoning of regional territorial spatial planning and the sustainable development of resource-based cities.
Reconstruction of Sentinel Images for Suspended Particulate Matter Monitoring in Arid Regions
Authors: Duan Pan , Zhang Fei , Jim Chi-Yung , Tan Mou Leong , Cai Yunfei , Shi Jingchao , Liu Changjiang , Wang Weiwei , Wang Zheng
Published: February 2023

Missing data is a common issue in remote sensing. Data reconstruction through multiple satellite data sources has become one of the most powerful ways to solve this issue. Continuous monitoring of suspended particulate matter (SPM) in arid lakes is vital for water quality solutions. Therefore, this research aimed to develop and evaluate the performance of two image reconstruction strategies, spatio-temporal fusion reflectance image inversion SPM and SPM spatio-temporal fusion, based on the measured SPM concentration data with Sentinel-2 and Sentinel-3. The results show that (1) ESTARFM (Enhanced Spatio-temporal Adaptive Reflection Fusion Model) performed better than FSDAF (Flexible Spatio-temporal Data Fusion) in the fusion image generation, particularly the red band, followed by the blue, green, and NIR (near-infrared) bands. (2) A single-band linear and non-linear regression model was constructed based on Sentinel-2 and Sentinel-3. Analysis of the accuracy and stability of the model led us to the conclusion that the red band model performs well, is fast to model, and has a wide range of applications (Sentinel-2, Sentinel-3, and fused high-accuracy images). (3) By comparing the two data reconstruction strategies of spatio-temporal fused image inversion SPM and spatio-temporal fused SPM concentration map, we found that the fused SPM concentration map is more effective and more stable when applied to multiple fused images. The findings can provide an important scientific reference value for further expanding the inversion research of other water quality parameters in the future and provide a theoretical basis as well as technical support for the scientific management of Ebinur Lake’s ecology and environment.
Spatial Downscaling of GRACE Data Based on XGBoost Model for Improved Understanding of Hydrological Droughts in the Indus Basin Irrigation System (IBIS)
Authors: Ali Shoaib , Khorrami Behnam , Jehanzaib Muhammad , Tariq Aqil , Ajmal Muhammad , Arshad Arfan , Shafeeque Muhammad , Dilawar Adil , Basit Iqra , Zhang Liangliang , Sadri Samira , Niaz Muhammad Ahmad , Jamil Ahsan , Khan Shahid Nawaz
Published: February 2023

Climate change may cause severe hydrological droughts, leading to water shortages which will require to be assessed using high-resolution data. Gravity Recovery and Climate Experiment (GRACE) satellite Terrestrial Water Storage (TWSA) estimates offer a promising solution to monitor hydrological drought, but its coarse resolution (1°) limits its applications to small regions of the Indus Basin Irrigation System (IBIS). Here we employed machine learning models such as Extreme Gradient Boosting (XGBoost) and Artificial Neural Network (ANN) to downscale GRACE TWSA from 1° to 0.25°. The findings revealed that the XGBoost model outperformed the ANN model with Nash Sutcliff Efficiency (NSE) (0.99), Pearson correlation (R) (0.99), Root Mean Square Error (RMSE) (5.22 mm), and Mean Absolute Error (MAE) (2.75 mm) between the predicted and GRACE-derived TWSA. Further, Water Storage Deficit Index (WSDI) and WSD (Water Storage Deficit) were used to determine the severity and episodes of droughts, respectively. The results of WSDI exhibited a strong agreement when compared with the Standardized Precipitation Evapotranspiration Index (SPEI) at different time scales (1-, 3-, and 6-months) and self-calibrated Palmer Drought Severity Index (sc-PDSI). Moreover, the IBIS had experienced increasing drought episodes, e.g., eight drought episodes were detected within the years 2010 and 2016 with WSDI of −1.20 and −1.28 and total WSD of −496.99 mm and −734.01 mm, respectively. The Partial Least Square Regression (PLSR) model between WSDI and climatic variables indicated that potential evaporation had the largest influence on drought after precipitation. The findings of this study will be helpful for drought-related decision-making in IBIS.
Boost Correlation Features with 3D-MiIoU-Based Camera-LiDAR Fusion for MODT in Autonomous Driving
Authors: Zhang Kunpeng , Liu Yanheng , Mei Fang , Jin Jingyi , Wang Yiming
Published: February 2023

Three-dimensional (3D) object tracking is critical in 3D computer vision. It has applications in autonomous driving, robotics, and human–computer interaction. However, methods for using multimodal information among objects to increase multi-object detection and tracking (MOT) accuracy remain a critical focus of research. Therefore, we present a multimodal MOT framework for autonomous driving boost correlation multi-object detection and tracking (BcMODT) in this research study to provide more trustworthy features and correlation scores for real-time detection tracking using both camera and LiDAR measurement data. Specifically, we propose an end-to-end deep neural network using 2D and 3D data for joint object detection and association. A new 3D mixed IoU (3D-MiIoU) computational module is also developed to acquire more precise geometric affinity by increasing the aspect ratio and length-to-height ratio between linked frames. Meanwhile, a boost correlation feature (BcF) module is proposed for the affinity calculation of the appearance of similar objects, which comprises an appearance affinity calculation module for similar objects in adjacent frames that are calculated directly using the feature distance and feature direction’s similarity. The KITTI tracking benchmark shows that our method outperforms other methods with respect to tracking accuracy.
Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils
Authors: Broeg Tom , Blaschek Michael , Seitz Steffen , Taghizadeh-Mehrjardi Ruhollah , Zepp Simone , Scholten Thomas
Published: February 2023

Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one requirement to design and execute effective climate and food policies. In digital soil mapping (DSM), machine learning algorithms are used to predict soil properties from covariates derived from traditional soil mapping, digital elevation models, land use, and Earth observation (EO). However, such DSM models are trained for a specific dataset and region and have so far only allowed limited general statements to be made that would enable the models to be transferred to different regions. In this study, we test the transferability of SOC models for cropland soils using five different covariate groups: multispectral soil reflectance composites (satellite), soil legacy data (soil), digital elevation model derivatives (terrain), climate parameters (climate), and combined models (combined). The transferability was analyzed using data from two federal states in southern Germany: Bavaria and Baden-Wuerttemberg. First, baseline models were trained for each state with combined models performing best in both cases (R2 = 0.68/0.48). Next, the models were transferred and tested with soil samples from the other state whose data were not used during model calibration. Only satellite and combined models were transferable, but accuracy declined in both cases. In the final step, models were trained with samples from both states (mixed-data models) and applied to each state separately. This process significantly improved the accuracies of satellite, terrain, and combined models, while it showed no effect on climate models and decreased the models based on soil covariates. The experiment underlines the importance of EO for the transfer and extrapolation of DSM models.
Biases’ Characteristics Assessment of the HY-2B Scanning Microwave Radiometer (SMR)’s Observations
Authors: Li Zeting , Han Wei , Xu Haiming , Xie Hejun , Zou Juhong
Published: February 2023

The second Chinese ocean dynamic environment satellite Haiyang-2B (HY-2B), carrying a scanning microwave radiometer (SMR) to provide information on the ocean and atmosphere, was successfully launched on 25 October 2018. Before the data assimilation, it is necessary to characterize and evaluate the biases of the HY-2B SMR observations. This study is the first to conduct a systematic assessment of the SMR radiance data based on observation minus background simulation (O-B). Three types of numerical weather prediction (NWP) datasets, including ECMWF Reanalysis v5 (ERA5), the analysis fields from the NCEP Global Forecast System (NCEP-GFS), and the analysis fields from the Global Regional Assimilation and Prediction System-Global Forecast System (GRAPES-GFS), were used as input information for RTTOV v12.3 to simulate the SMR’s observed brightness temperature (TB) under clear-sky conditions. Study results showed that the O-B biases and IQR of the SMR for most channels were within −2.5–0.4 K and smaller than 4 K, respectively. The SMR observations were generally consistent with the RTTOV simulations, even based on the different NWP fields. These results indicate a good prospect for the assimilated application of HY-2B SMR radiance data. However, due to the impact of RFI, the SMR’s descending data for two 10.7 GHz channels showed some significant positive biases larger than 50 K over the seas of the European region. In addition, it seems that the bias characteristics of the SMR’s ascending data were obviously different from those of the descending data. It was also found that the variation trend of scan-position-dependent bias was generally stable for the SMR’s ascending data but fluctuates significantly for the descending data, with a maximum amplitude greater than 0.7 K for some channels.
Breath Analysis for Early Detection of Rising Ketone Bodies in Postpartum Dairy Cows Classified as at Risk of Ketosis
Authors: van Erp-van der Kooij Elaine , Derix Janiek , van Gorp Stijn , Timmermans Amy , Krijnen Charles , Fodor István , Dingboom Liesbeth
Published: February 2023

Ketosis is a metabolic disorder associated with high production and low energy intake in dairy cows. Fat reserves are mobilized, and gluconeogenesis occurs. Traditionally, rapid tests for blood, milk or urine are used to detect increased ketone body levels in case of ketosis. Breath analysis is relatively new and relevant for the development of automatic sensor systems for early warning. This study aims to determine whether and when a postpartum rise in ketone bodies occurs in breath with elevated blood β-hydroxybutyrate (BHB) in cows at risk of ketosis. Postpartum breath, blood, urine and milk samples were taken daily until day 7, with one prepartum sample excluding milk, and ketone body concentrations were determined. Concentrations of blood BHB were 0.4–2.6 mmol/L (lab) and 0.3–3.1 mmol/L (rapid test), breath acetone was 2.3–20.0 ppm, urine acetoacetate 0–8 mmol/L and milk BHB 0–500 µmol/L. A rise in blood BHB was related to that in urine acetoacetate and milk BHB and followed by a rise in breath acetone. However, breath acetone levels of ketotic and non-ketotic cows were similar. We conclude that rising ketone bodies can be detected in blood, urine, milk and breath, but to use breath analysis as an alternative to rapid tests, longitudinal sampling is needed.
Two-Level Attention Module Based on Spurious-3D Residual Networks for Human Action Recognition
Authors: Chen Bo , Meng Fangzhou , Tang Hongying , Tong Guanjun
Published: February 2023

In recent years, deep learning techniques have excelled in video action recognition. However, currently commonly used video action recognition models minimize the importance of different video frames and spatial regions within some specific frames when performing action recognition, which makes it difficult for the models to adequately extract spatiotemporal features from the video data. In this paper, an action recognition method based on improved residual convolutional neural networks (CNNs) for video frames and spatial attention modules is proposed to address this problem. The network can guide what and where to emphasize or suppress with essentially little computational cost using the video frame attention module and the spatial attention module. It also employs a two-level attention module to emphasize feature information along the temporal and spatial dimensions, respectively, highlighting the more important frames in the overall video sequence and the more important spatial regions in some specific frames. Specifically, we create the video frame and spatial attention map by successively adding the video frame attention module and the spatial attention module to aggregate the spatial and temporal dimensions of the intermediate feature maps of the CNNs to obtain different feature descriptors, thus directing the network to focus more on important video frames and more contributing spatial regions. The experimental results further show that the network performs well on the UCF-101 and HMDB-51 datasets.
A Modular In-Vehicle C-ITS Architecture for Sensor Data Collection, Vehicular Communications and Cloud Connectivity
Authors: Rocha David , Teixeira Gil , Vieira Emanuel , Almeida João , Ferreira Joaquim
Published: February 2023

The growth of the automobile industry in recent decades and the overuse of personal vehicles have amplified problems directly related to road safety, such as the increase in traffic congestion and number of accidents, as well as the degradation of the quality of roads. At the same time, and with the contribution of climate change effects, dangerous weather events have become more common on road infrastructure. In this context, Cooperative Intelligent Transport Systems (C-ITS) and Internet of Things (IoT) solutions emerge to overcome the limitations of human and local sensory systems, through the collection and distribution of relevant data to Connected and Automated Vehicles (CAVs). In this paper, an intra- and inter-vehicle sensory data collection system is presented, starting with the acquisition of relevant data present on the Controller Area Network (CAN) bus, collected through the vehicle’s On-Board-Diagnostics II (OBD-II) port, as well as on an on-board smartphone device and possibly other additional sensors. Short-range communication technologies, such as Bluetooth Low Energy (BLE), Wi-Fi, and ITS-G5, are employed in conjunction with long-range cellular networks for data dissemination and remote cloud monitoring. The results of the experimental tests allow the analysis of the road environment, as well as the notification in near real-time of adverse road conditions to drivers. The developed data collection system reveals itself as a potentially valuable tool for improving road safety and to iterate on the current Road Weather Models (RWMs).
Ultrasensitive Optical Fiber Sensors Working at Dispersion Turning Point: Review
Authors: Xu Shengyao , Kang Peng , Hu Zhijie , Chang Weijie , Huang Feng
Published: February 2023

Optical fiber sensors working at the dispersion turning point (DTP) have served as promising candidates for various sensing applications due to their ultrahigh sensitivity. In this review, recently developed ultrasensitive fiber sensors at the DTP, including fiber couplers, fiber gratings, and interferometers, are comprehensively analyzed. These three schemes are outlined in terms of operation principles, device structures, and sensing applications. We focus on sensitivity enhancement and optical transducers, we evaluate each sensing scheme based on the DTP principle, and we discuss relevant challenges, aiming to provide some clues for future research.
Table Tennis Track Detection Based on Temporal Feature Multiplexing Network
Authors: Li Wenjie , Liu Xiangpeng , An Kang , Qin Chengjin , Cheng Yuhua
Published: February 2023

Recording the trajectory of table tennis balls in real-time enables the analysis of the opponent’s attacking characteristics and weaknesses. The current analysis of the ball paths mainly relied on human viewing, which lacked certain theoretical data support. In order to solve the problem of the lack of objective data analysis in the research of table tennis competition, a target detection algorithm-based table tennis trajectory extraction network was proposed to record the trajectory of the table tennis movement in video. The network improved the feature reuse rate in order to achieve a lightweight network and enhance the detection accuracy. The core of the network was the “feature store & return” module, which could store the output of the current network layer and pass the features to the input of the network layer at the next moment to achieve efficient reuse of the features. In this module, the Transformer model was used to secondarily process the features, build the global association information, and enhance the feature richness of the feature map. According to the designed experiments, the detection accuracy of the network was 96.8% for table tennis and 89.1% for target localization. Moreover, the parameter size of the model was only 7.68 MB, and the detection frame rate could reach 634.19 FPS using the hardware for the tests. In summary, the network designed in this paper has the characteristics of both lightweight and high precision in table tennis detection, and the performance of the proposed model significantly outperforms that of the existing models.
Vehicle Detection and Recognition Approach in Multi-Scale Traffic Monitoring System via Graph-Based Data Optimization
Authors: Wieczorek Grzegorz , Tahir Sheikh Badar ud din , Akhter Israr , Kurek Jaroslaw
Published: February 2023

Over the past few years, significant investments in smart traffic monitoring systems have been made. The most important step in machine learning is detecting and recognizing objects relative to vehicles. Due to variations in vision and different lighting conditions, the recognition and tracking of vehicles under varying extreme conditions has become one of the most challenging tasks. To deal with this, our proposed system presents an adaptive method for robustly recognizing several existing automobiles in dense traffic settings. Additionally, this research presents a broad framework for effective on-road vehicle recognition and detection. Furthermore, the proposed system focuses on challenges typically noticed in analyzing traffic scenes captured by in-vehicle cameras, such as consistent extraction of features. First, we performed frame conversion, background subtraction, and object shape optimization as preprocessing steps. Next, two important features (energy and deep optical flow) were extracted. The incorporation of energy and dense optical flow features in distance-adaptive window areas and subsequent processing over the fused features resulted in a greater capacity for discrimination. Next, a graph-mining-based approach was applied to select optimal features. Finally, the artificial neural network was adopted for detection and classification. The experimental results show significant performance in two benchmark datasets, including the LISA and KITTI 7 databases. The LISA dataset achieved a mean recognition rate of 93.75% on the LDB1 and LDB2 databases, whereas KITTI attained 82.85% accuracy on separate training of ANN.
Fake News Detection Model on Social Media by Leveraging Sentiment Analysis of News Content and Emotion Analysis of Users’ Comments
Authors: Hamed Suhaib Kh. , Ab Aziz Mohd Juzaiddin , Yaakub Mohd Ridzwan
Published: February 2023

Nowadays, social media has become the main source of news around the world. The spread of fake news on social networks has become a serious global issue, damaging many aspects, such as political, economic, and social aspects, and negatively affecting the lives of citizens. Fake news often carries negative sentiments, and the public’s response to it carries the emotions of surprise, fear, and disgust. In this article, we extracted features based on sentiment analysis of news articles and emotion analysis of users’ comments regarding this news. These features were fed, along with the content feature of the news, to the proposed bidirectional long short-term memory model to detect fake news. We used the standard Fakeddit dataset that contains news titles and comments posted regarding them to train and test the proposed model. The suggested model, using extracted features, provided a high detection accuracy of 96.77% of the Area under the ROC Curve measure, which is higher than what other state-of-the-art studies offer. The results prove that the features extracted based on sentiment analysis of news, which represents the publisher’s stance, and emotion analysis of comments, which represent the crowd’s stance, contribute to raising the efficiency of the detection model.
An Integrated Interferometric Fiber Optic Sensor Using a 638 nm Semiconductor Laser for Air-Water Surface Velocity Measurements
Authors: Song Ran , Zhang Xinyu , Jiang Lili , Zhang Zhijun , Qiao Zhigang , Hao Xianglong , Su Juan , Lu Chenxu , Yang Guangbing , Xiong Xuejun , Gao Liyuan , Wu Chi
Published: February 2023

An integrated interferometric fiber optic velocimetry sensor has been proposed and demonstrated at the central wavelength of 638 nm. The sensor is based on the principle of two laser-beams’ interference. The light signal scattered from the particles or vapor is demodulated to measure the water surface velocity and water vapor velocity. Three velocity measurement experiments are carried out to measure the velocity, and the experimental data shows that the velocity increases linearly in the range of 4 mm·s−1 to 100 mm·s−1, with a slope of linear fitting curve of 0.99777 and the R-Square of 1.00000. The velocity calculated from frequency shift fits well with the reference velocity. The maximum average relative error in the three velocity measurements is less than 2.5%. In addition, the maximum speed of 4.398 m·s−1 is confirmed in the rotating disk calibration experiment, which expands the sensor’s velocity measurement range. To solve the problem that it is difficult to directly measure the velocity of small-scale water surface flow velocity, especially from the aspect of the low velocity of air-water surface, the interferometric fiber optic sensor can be applied to the measurement of water surface velocity and wind velocity on the water surface.
Support for the Vulnerability Management Process Using Conversion CVSS Base Score 2.0 to 3.x
Authors: Nowak Maciej Roman , Walkowski Michał , Sujecki Sławomir
Published: February 2023

COVID-19 forced a number of changes in many areas of life, which resulted in an increase in human activity in cyberspace. Furthermore, the number of cyberattacks has increased. In such circumstances, detection, accurate prioritisation, and timely removal of critical vulnerabilities is of key importance for ensuring the security of various organisations. One of the most-commonly used vulnerability assessment standards is the Common Vulnerability Scoring System (CVSS), which allows for assessing the degree of vulnerability criticality on a scale from 0 to 10. Unfortunately, not all detected vulnerabilities have defined CVSS base scores, or if they do, they are not always expressed using the latest standard (CVSS 3.x). In this work, we propose using machine learning algorithms to convert the CVSS vector from Version 2.0 to 3.x. We discuss in detail the individual steps of the conversion procedure, starting from data acquisition using vulnerability databases and Natural Language Processing (NLP) algorithms, to the vector mapping process based on the optimisation of ML algorithm parameters, and finally, the application of machine learning to calculate the CVSS 3.x vector components. The calculated example results showed the effectiveness of the proposed method for the conversion of the CVSS 2.0 vector to the CVSS 3.x standard.
Generated Image Editing Method Based on Global-Local Jacobi Disentanglement for Machine Learning
Authors: Zhang Jianlong , Yu Xincheng , Wang Bin , Chen Chen
Published: February 2023

Accurate semantic editing of the generated images is extremely important for machine learning and sample enhancement of big data. Aiming at the problem of semantic entanglement in generated image latent space of the StyleGAN2 network, we proposed a generated image editing method based on global-local Jacobi disentanglement. In terms of global disentanglement, we extract the weight matrix of the style layer in the pre-trained StyleGAN2 network; obtain the semantic attribute direction vector by using the weight matrix eigen decomposition method; finally, utilize this direction vector as the initialization vector for the Jacobi orthogonal regularization search algorithm. Our method improves the speed of the Jacobi orthogonal regularization search algorithm with the proportion of effective semantic attribute editing directions. In terms of local disentanglement, we design a local contrast regularized loss function to relax the semantic association local area and non-local area and utilize the Jacobi orthogonal regularization search algorithm to obtain a more accurate semantic attribute editing direction based on the local area prior MASK. The experimental results show that the proposed method achieves SOTA in semantic attribute disentangled metrics and can discover more accurate editing directions compared with the mainstream unsupervised generated image editing methods.
Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects
Authors: Mahmud Md Sultan , Zahid Azlan , Das Anup Kumar
Published: February 2023

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production.
Design of Piezoelectric Acoustic Transducers for Underwater Applications
Authors: Pyun Joo Young , Kim Young Hun , Park Kwan Kyu
Published: February 2023

Interest in underwater transducers has persisted since the mid-1900s. Underwater transducers are designed in various shapes using various materials depending on the purpose of use, such as to achieve high power, improve broadband, and enhance beam steering. Therefore, in this study, an analysis is conducted according to the structural shape of the transducer, exterior material, and active material. By classifying transducers by structure, the transducer design trends and possible design issues can be identified. Researchers have constantly attempted new methods to improve the performance of transducers. In addition, a methodology to overcome this problem is presented. Finally, this review covers old and new research, and will serve as a reference for designers of underwater transducer.
Point Cloud Registration via Heuristic Reward Reinforcement Learning
Authors: Chen Bingren
Published: February 2023

This paper proposes a heuristic reward reinforcement learning framework for point cloud registration. As an essential step of many 3D computer vision tasks such as object recognition and 3D reconstruction, point cloud registration has been well studied in the existing literature. This paper contributes to the literature by addressing the limitations of embedding and reward functions in existing methods. An improved state-embedding module and a stochastic reward function are proposed. While the embedding module enriches the captured characteristics of states, the newly designed reward function follows a time-dependent searching strategy, which allows aggressive attempts at the beginning and tends to be conservative in the end. We assess our method based on two public datasets (ModelNet40 and ScanObjectNN) and real-world data. The results confirm the strength of the new method in reducing errors in object rotation and translation, leading to more precise point cloud registration.
Analysis of Human Factors in Typical Accident Tests of Certain Type Flight Simulator
Authors: Xing Guanghua , Sun Yingjun , He Fajiang , Wei Pengcheng , Wu Shicheng , Ren Haojie , Chen Zhixiong
Published: February 2023

With the improvement of modern aviation equipment manufacturing technology, there are relatively few failures due to the unreliability of the aircraft. However, human factors which resulted in air crashes and unsafe events are raised. In this paper, for many typical accident scenarios of a particular plane, the flight simulation verifications of the pilot’s workload and behavior are carried out on the certain transport category airplane, namely the six-degrees-of-freedom full-motion flight simulator. The subjective and physiological evaluation methods combine to analyze the human factors of pilots in the sudden typical accident scene during a flight mission. The study uses eye trackers, professional heart rate monitors, cameras, and other equipment to collect the pilot’s physiological information during the flight mission, and allows the pilots to fill in the subjective evaluation scale, establishing a subjective and objective evaluation index system. Thus, the human factors of pilots in typical fault situations are analyzed. The analysis shows the combined personal and accurate evaluation method, with the test equipment and environment proposed by this paper being feasible for the human factor evaluation in the accident or incident of transport category airplanes. It will benefit aviation stakeholders in determining the proper action to decrease the workload to an acceptable level.
Visualization Analysis of Global Self-Regulated Learning Status, Hotspots, and Future Trends Based on Knowledge Graph
Authors: Huang Linchao , Zhou Jincheng , Wang Dan , Wang Fang , Liu Jiu , Shi Dingpu , Chen Xiaohong , Yang Duo , Pan Qingna
Published: February 2023

Self-regulated learning (SRL) has been an important topic in the field of global educational psychology research since the last century, and its emergence is related to researchers’ reflections on several educational reforms. To better study the research history and developmental trend of SRL, in this work, the Web of Science core collection database was used as a sample source, “self-regulated learning” was searched as the theme, and 1218 SSCI documents were collected from 30 September 1986, to 2022. We used CiteSpace software to visualize and analyze the number of publications, countries, institutions, researchers, keywords, highly cited literature, authors’ co-citations, keyword clustering, and timeline in the field of self-regulated learning research, and to draw related maps. It was found that the articles related to self-regulated learning were first published in the American Journal of Educational Research in 1986, and that self-regulated learning-related research has received increasing attention in recent decades, wherein research on self-regulated learning is roughly divided into three periods: the budding period from 1986 to 2002, the flat development period from 2003 to 2009, and the rapid development period from 2010 to 2022. The number of papers published in the United States, China, Australia, and Germany is relatively high, and the number of papers published in Spain is low compared with that in the United States. During this period, the University of North Carolina in the United States and McGill University in Canada were the institutions with the most publications; Azevedo Roger and Lajoie Susanne P were the most-published scholars in the field of self-regulated learning research; the journal publication with the highest impact factor was Computers Education; and the primary research interests in self-regulated learning mainly focused on Performance, Strategy, Students, Achievement, Motivation, and Metacognition. Furthermore, the most-cited study related to SRL research was Formative assessment and self-regulated learning: a model and seven principles of good feedback practice.
Development of Composite Acoustic Panels of Waste Tyre Textile Fibres and Paper Sludge
Authors: Ružickij Robert , Kizinievič Olga , Grubliauskas Raimondas , Astrauskas Tomas
Published: February 2023

Since society is moving towards sustainable development, interest in secondary use of waste has recently become significant. This paper investigates a process to develop an acoustic material, using two types of waste. Composite acoustic panels were developed using waste tyre textile fibres (WTTF) and paper sludge (PS), and polyvinyl acetate (PVA) were used as a binder. Non-acoustic (bulk density, airflow resistivity) and acoustic (sound absorption coefficient, sound transmission loss) parameters were studied. Composite acoustic panels with different proportions of WTTF/PS/PVA (sixteen samples) were subjected to testing for the sound absorption coefficient according to ISO 10534-2 and sound transmission loss according to ASTM E2611. The density of all samples varied between 155.2 and 709.9 kg/m3, the thickness between 14.4 and 20.5 mm, and the airflow resistivity between 29.5 and 101.5 kPa∙s/m2. The results reveal that the proportion of various waste materials in mixtures can improve the acoustic performance of panels. The combination that gives the highest αavg. with a value of 0.50 was experimentally found to be 70% WTTF mixed with 15% PVA and 15% H2O. The average sound absorption coefficient with a value of 0.46 was also found to be 25% WTTF mixed with 25% PS and 25% PVA and 25% H2O. In sound transmission loss, the most effective was 50% PS and the 50% PVA composite, the TLeq was 28.3 dB, while the composites together with 30% WTTF, 20% PS and 25% PVA, and 25% H2O showed 18.9 dB loss. The results obtained using WTTF and/or PS wastes are attractive and show great and promising development potential.
OFDI, Industrial Structure Upgrading and Green Development—Spatial Effect Based on China’s Evidence
Authors: Xu Shan , Zhou Yuan
Published: February 2023

Green economic development is a worldwide concern. This paper not only contributes to the advancement of studies pertaining to green development but also offers policy recommendations for China to achieve a green and low-carbon economic transformation from the perspective of outward foreign direct investment (OFDI) and industrial structure. A mechanism for the effects of OFDI and industrial structure upgrading on green total factor productivity (GTFP) is proposed in this study. Based on measurement and analysis of the evolution characteristics of GTFP and industrial structure level of 30 provinces (municipalities and autonomous regions) in China from 2004 to 2019, the spatial Durbin model is applied to test their spatial effects. The findings demonstrate that (1) the overall trend of China’s GTFP and industrial structure level is upward, with obvious regional non-equilibrium and spatial dependence; (2) both OFDI and industrial structure upgrading can promote green development independently, with the spatial spillover effect of Industrial structure advancement being more evident; (3) the synergistic effect between OFDI and industrial structure advancement is greater than that between OFDI and industrial structure rationalization, and the spatial spillover effect on regions with comparable economic development is greater than that of surrounding regions; (4) in view of the different levels of openness between regions, the independent and synergistic effects in coastal regions and non-coastal regions are heterogeneous. Therefore, China should optimize OFDI, promote the efficiency of resource allocation, maximize the technology spillover, and strengthen interregional cooperation in order to transform towards a green economy.
Multi-Hazard Risk Assessment and Landslide Susceptibility Mapping: A Case Study from Bensekrane in Algeria
Authors: Benzenine Faïla , Allal Mohamed Amine , Abdelbaki Chérifa , Kumar Navneet , Goosen Mattheus , Gathenya John Mwangi
Published: February 2023

Landslides and their disastrous consequences on the environment and human life have emphasized the need for a better understanding of the dangers associated with slope movement. The objective of this research was to assess and utilize mapping methods for predicting the hazards of landslides and thus to limit the damage of these phenomena more effectively. In the current investigation, multi-hazard mapping was employed in evaluating the risk of slope movements for the municipality of Bensekrane in Tlemcen in Algeria. There has been no hazard assessment made for the study area although it has factors responsible for triggering landslides. The standard Farès method (arithmetic and probabilistic) was employed, and the results were compared with those obtained from the modified Farès technique (arithmetic and probabilistic), which was developed based on a synthesis or combination of previous approaches. In the modified Farès technique, dynamic factors were also included, such as seismic activity, vegetation cover and groundwater level, and, thus, it was considered more reliable. However, the choice of method depended mainly on the availability of data from the study area. The maps obtained showed that the study area is susceptible to slope movements and will be employed for land use planning. The maps obtained by the arithmetic modified Farès method were different from those obtained by the arithmetic Farès method. The former presented a large part of the surface (88%) with an average hazard, unlike the latter, which presented the largest surface (66%) and a low hazard. The maps generated by the probabilistic modified Farès method showed a surface with a high hazard, unlike that obtained by the probabilistic Farès method, where a high hazard did not exist. These differences between the maps were due to the addition of dynamic factors. It is better to choose the modified Farès method, which takes into account all the factors that exist in reality. In this study, enhanced spatial, natural hazard maps were created using the modified Farès method to better aid decision makers and builders in making correct choices for increased safety and town planning. It is crucial to be able to utilize reliable maps based on multi-hazard risk assessment for land development purposes to lessen the possibility of destructive landslides. The modified Farès method can be applied to any other comparable areas around the world.
The Effects of Principals’ Instructional Leadership on Primary School Students’ Academic Achievement in China: Evidence from Serial Multiple Mediating Analysis
Authors: Li Jiazhe , Chan Philip Wing Keung , Hu Yongmei
Published: February 2023

It is essential to reorient education in order to improve student learning outcomes and offer instruction in sustainable development. This reorientation should particularly focus on updating the educational concepts and behaviors of principals and teachers. It is a common practice to see principals’ instructional leadership (PIL) as a crucial aspect that may facilitate the development of teaching and learning in a sustainable manner. This article identified the influence path of principals’ instructional leadership on students’ academic achievement (SAA) through teachers’ teaching practice. In 2016 and 2017, survey data were gathered from 60,419 students and parents, 2232 teachers, and 506 principals from all 231 primary schools in the capital city of a central province in China. This paper uses a structural equation model to examine serial, multiple, mediating effects based on a value-added model to identify effective teachers’ professional development (ETPD). The research findings show that ETPD in Chinese primary schools includes teacher cooperation and communication but not professional guidance, innovation, and individual teaching reflection. PIL does not have a significant direct positive effect on SAA, but teaching strategies (TS), rather than ETPD, can mediate between PIL and SAA. Finally, PIL influences TS through ETPD and ultimately influences SAA. This paper argues that for providing sustainable and high-quality education, not all TPDs are effective for Chinese primary school teachers. Principals can promote SAA by encouraging teacher cooperation and communication to improve teaching strategies. Chinese principals should shift their attention more from administrative matters to the sustainable development of teaching and learning.
Quality of Life Prediction in Driving Scenes on Thailand Roads Using Information Extraction from Deep Convolutional Neural Networks
Authors: Thitisiriwech Kitsaphon , Panboonyuen Teerapong , Kantavat Pittipol , Kijsirikul Boonserm , Iwahori Yuji , Fukui Shinji , Hayashi Yoshitsugu
Published: February 2023

In the modern era, urban design and sustainable development are vital topics for megacities, as they are important for the wellbeing of its residents. One of the effective key performance indices (KPIs) measuring the city plan’s efficiency in quantity and quality factors is Quality of Life (QOL), an index that policymakers can use as a critical KPI to measure the quality of urbanscape design. In the traditional approach, the researchers conduct the questionnaire survey and then analyze the gathered data to acquire the QOL index. The conventional process is costly and time-consuming, but the result of the evaluation area is limited. Moreover, it is difficult to embed in an application or system; we proposed artificial intelligence (AI) approaches to solve the limitation of the traditional method in Bangkok as a case study. There are two steps for our proposed method. First, in the knowledge extraction step, we apply deep convolutional neural networks (DCNNs), including semantic segmentation and object detection, to extract helpful information images. Second, we use a linear regression model for inferring the QOL score. We conducted various state-of-the-art (SOTA) models and public datasets to evaluate the performance of our method. The experiment results show that our novel approach is practical and can be considered for use as an alternative QOL acquisition method. We also gain some understanding of drivers’ insights from the experiment result.
A Study of Microfiber Phytoremediation in Vertical Hydroponics
Authors: dos Santos Naiara , Clyde-Smith Dominic , Qi Ying , Gao Fan , Busquets Rosa , Campos Luiza C.
Published: February 2023

Microfibers (MFs) are one of the most prevalent microplastic (MP) sub-groups found in the aquatic environment released from many sources, including household laundry. MPs pose risks to the growth rate of terrestrial/aquatic biota and through biomagnification. Although MFs can be ingested by humans, their toxic effects and potential impact on public health are not yet clearly understood. Moreover, the removal of MPs, including MFs, during wastewater treatment is a challenge, since treatment plants are not designed to collect them. Therefore, this work aims to study the potential of the in situ phytoremediation of microfibers from a domestic washing machine effluent by growing barley in a vertical hydroponic system. The temporal variation in barley growth, water quality parameters, length distribution of MFs, and their removal were evaluated over 4 weeks. We investigated the MFs’ interaction with two systems: without barley (System NP) (used as a control) and with barley (System P). The results show the barley growth is negatively affected at the end of 4 weeks, mainly by the accumulation of phosphate and the presence of fungi. However, the level of dissolved oxygen in System P is satisfactory and the presence of MFs decreases considerably (mainly for MFs > 600 µm) from different interactions with the barley roots. These interactions were corroborated by microscopy images. The total removal of MFs through the hydroponic system was 52% in week 2, decreasing to 42%. This is the first time that the removal of MFs has been evaluated using vertical hydroponics, which demonstrates that this phytoremediation system can be used at the household level. It also shows that vertical hydroponics, as an experimental methodology, for the analyses of MFs’ impacts on plant health has merit. It is expected that this study will contribute to new investigations of MF removal by green technologies.