bluuebunny/crossref_metadata_2025 huggingface dataset:
A Test Method for Finding Early Dynamic Fracture of Rock: Using DIC and YOLOv5
Authors: Zhang Qinghe , Zhang Bing , Chen Chen , Li Ling , Wang Xiaorui , Jiang Bowen , Zheng Tianle
Published: August 2022

Intelligent monitoring and early warning of rock mass failure is vital. To realize the early intelligent identification of dynamic fractures in the failure process of complex fractured rocks, 3D printing of the fracture network model was used to produce rock-like specimens containing 20 random joints. An algorithm for the early intelligent identification of dynamic fractures was proposed based on the YOLOv5 deep learning network model and DIC cloud. The results demonstrate an important relationship between the overall strength of the specimen with complex fractures and dynamic fracture propagation, and the overall specimen strength can be judged semi-quantitatively by counting dynamic fracture propagation. Before the initiation of each primary fracture, a strain concentration area appears, which indicates new fracture initiation. The dynamic evolution of primary fractures can be divided into four types: primary fractures, stress concentration areas, new fractures, and cross fractures. The cross fractures have the greatest impact on the overall strength of the specimen. The overall identification accuracy of the four types of fractures identified by the algorithm reached 88%, which shows that the method is fast, accurate, and effective for fracture identification and location, and classification of complex fractured rock masses.
Systematic Literature Review on Visual Analytics of Predictive Maintenance in the Manufacturing Industry
Authors: Cheng Xiang , Chaw Jun Kit , Goh Kam Meng , Ting Tin Tin , Sahrani Shafrida , Ahmad Mohammad Nazir , Abdul Kadir Rabiah , Ang Mei Choo
Published: August 2022

The widespread adoption of cyber-physical systems and other cutting-edge digital technology in manufacturing industry production facilities may motivate stakeholders to embrace the idea of Industry 4.0. Some industrial companies already have different sensors installed on their machines; however, without proper analysis, the data collected is not useful. This systematic review’s main goal is to synthesize the existing evidence on the application of predictive maintenance (PdM) with visual aids and to identify the key knowledge gaps in areas including utilities, power generation, industry, and energy consumption. After a thorough search and evaluation for relevancy, 37 documents were identified. Moreover, we identified the visual analytics of PdM, including anomaly detection, planning/scheduling, exploratory data analysis (EDA), and explainable artificial intelligence (XAI). The findings revealed that anomaly detection was a major domain in PdM-related works. We conclude that most of the literature lacks depth in terms of an overall framework that combines data-driven and knowledge-driven techniques of PdM in the manufacturing industry. Some works that utilized both techniques indicated promising results, but there is insufficient research on involving maintenance personnel’s feedback in the latter stage of PdM architecture. Thus, there are still pertinent issues that need to be investigated, and limitations that need to be overcome before PdM is deployed with minimal human involvement.
Deep Learning-Based Subtask Segmentation of Timed Up-and-Go Test Using RGB-D Cameras
Authors: Choi Yoonjeong , Bae Yoosung , Cha Baekdong , Ryu Jeha
Published: August 2022

The timed up-and-go (TUG) test is an efficient way to evaluate an individual’s basic functional mobility, such as standing up, walking, turning around, and sitting back. The total completion time of the TUG test is a metric indicating an individual’s overall mobility. Moreover, the fine-grained consumption time of the individual subtasks in the TUG test may provide important clinical information, such as elapsed time and speed of each TUG subtask, which may not only assist professionals in clinical interventions but also distinguish the functional recovery of patients. To perform more accurate, efficient, robust, and objective tests, this paper proposes a novel deep learning-based subtask segmentation of the TUG test using a dilated temporal convolutional network with a single RGB-D camera. Evaluation with three different subject groups (healthy young, healthy adult, stroke patients) showed that the proposed method demonstrated better generality and achieved a significantly higher and more robust performance (healthy young = 95.458%, healthy adult = 94.525%, stroke = 93.578%) than the existing rule-based and artificial neural network-based subtask segmentation methods. Additionally, the results indicated that the input from the pelvis alone achieved the best accuracy among many other single inputs or combinations of inputs, which allows a real-time inference (approximately 15 Hz) in edge devices, such as smartphones.
Research on the Circumstances of a Car–Cyclist Collision, Based on the Trajectory of the Cyclist’s Movement after the Collision
Authors: Sokolovskij Edgar , Juodka Edvinas
Published: August 2022

This article examines a simulated collision between a car and a cyclist, assessing the trajectory of the cyclist’s movement after the impact, namely the throwing distances and angles of the cyclist and bicycle. Information about the car and cyclist models used for the study is provided. Special software PC CRASH 8.1 for the analysis and reconstruction of traffic accidents was used to simulate a car–cyclist collision. Simulations of car–cyclist collisions were carried out, with different speeds for the car and the cyclist, and locations at the time of the impact. The movement of a bicycle after a crash tends to be irregular and is dependent on various parameters that are usually not possible to evaluate. Therefore, the parameters of the movement of the bicycle after the collision (the throwing angle and the distance) usually do not allow determination of the speed of the car before the accident. The movement of the cyclist after impact was more informative for determining the speed of the car before the accident. For example, when there was an angle of 30°, 60°, or 90° between the longitudinal axes of the car and the cyclist, there was a clear dependence between the speed of the car and the cyclist’s throwing distance, and usually also between the speed of the car and the cyclist’s throwing angle. Thus, in such cases, it is possible to determine approximately the initial speed of the car before the collision, based on the trajectory of the cyclist’s movement after the impact, namely his throwing distance and angle. In cases of real traffic accidents, with knowledge of the location of the car–cyclist collision and the position of the cyclist after the traffic accident, the speed of the car before the accident can be determined according to the abovementioned dependencies. Thus, the proposed methodology could be used in the reconstruction and examination of traffic accidents.
Augmentation Method for High Intra-Class Variation Data in Apple Detection
Authors: Li Huibin , Guo Wei , Lu Guowen , Shi Yun
Published: August 2022

Deep learning is widely used in modern orchard production for various inspection missions, which helps improve the efficiency of orchard operations. In the mission of visual detection during fruit picking, most current lightweight detection models are not yet effective enough to detect multi-type occlusion targets, severely affecting automated fruit-picking efficiency. This study addresses this problem by proposing the pioneering design of a multi-type occlusion apple dataset and an augmentation method of data balance. We divided apple occlusion into eight types and used the proposed method to balance the number of annotation boxes for multi-type occlusion apple targets. Finally, a validation experiment was carried out using five popular lightweight object detection models: yolox-s, yolov5-s, yolov4-s, yolov3-tiny, and efficidentdet-d0. The results show that, using the proposed augmentation method, the average detection precision of the five popular lightweight object detection models improved significantly. Specifically, the precision increased from 0.894 to 0.974, recall increased from 0.845 to 0.972, and mAP0.5 increased from 0.982 to 0.919 for yolox-s. This implies that the proposed augmentation method shows great potential for different fruit detection missions in future orchard applications.
Interactive Application of Data Glove Based on Emotion Recognition and Judgment System
Authors: Lin Wenqian , Li Chao , Zhang Yunjian
Published: August 2022

In this paper, the interactive application of data gloves based on emotion recognition and judgment system is investigated. A system of emotion recognition and judgment is established based on the set of optimal features of physiological signals, and then a data glove with multi-channel data transmission based on the recognition of hand posture and emotion is constructed. Finally, the system of virtual hand control and a manipulator driven by emotion is built. Five subjects were selected for the test of the above systems. The test results show that the virtual hand and manipulator can be simultaneously controlled by the data glove. In the case that the subjects do not make any hand gesture change, the system can directly control the gesture of the virtual hand by reading the physiological signal of the subject, at which point the gesture control and emotion control can be carried out at the same time. In the test of the manipulator driven by emotion, only the results driven by two emotional trends achieve the desired purpose.
Object-Cooperated Ternary Tree Partitioning Decision Method for Versatile Video Coding
Authors: Lee Sujin , Park Sang-hyo , Jun Dongsan
Published: August 2022

In this paper, we propose an object-cooperated decision method for efficient ternary tree (TT) partitioning that reduces the encoding complexity of versatile video coding (VVC). In most previous studies, the VVC complexity was reduced using decision schemes based on the encoding context, which do not apply object detecion models. We assume that high-level objects are important for deciding whether complex TT partitioning is required because they can provide hints on the characteristics of a video. Herein, we apply an object detection model that discovers and extracts the high-level object features—the number and ratio of objects from frames in a video sequence. Using the extracted features, we propose machine learning (ML)-based classifiers for each TT-split direction to efficiently reduce the encoding complexity of VVC and decide whether the TT-split process can be skipped in the vertical or horizontal direction. The TT-split decision of classifiers is formulated as a binary classification problem. Experimental results show that the proposed method more effectively decreases the encoding complexity of VVC than a state-of-the-art model based on ML.
Multiple Enhanced Sparse Representation via IACMDSR Model for Bearing Compound Fault Diagnosis
Authors: Zhang Long , Zhao Lijuan , Wang Chaobing , Xiao Qian , Liu Haoyang , Zhang Hao , Hu Yanqing
Published: August 2022

For the sake of addressing the issue of extracting multiple features embedded in a noise-heavy vibration signal for bearing compound fault diagnosis, a novel model based on improved adaptive chirp mode decomposition (IACMD) and sparse representation, namely IACMDSR, is developed in this paper. Firstly, the IACMD is employed to simultaneously separate the distinct fault types and extract multiple resonance frequencies induced by them. Next, an adaptive bilateral wavelet hyper-dictionary that digs deeper into the periodicity and waveform characteristics exhibited by the real fault impulse response is constructed to identify and reconstruct each type of fault-induced feature with the help of the orthogonal matching pursuit (OMP) algorithm. Finally, the fault characteristic frequency can be detected via an envelope demodulation analysis of the reconstructed signal. A simulation and two sets of experimental results confirm that the developed IACMDSR model is a powerful and versatile tool and consistently outperforms the leading MCKDSR and MCKDMWF models. Furthermore, the developed model has satisfactory capability in practical applications because the IACMD has no requirement for the input number of the signal components and the adaptive bilateral wavelet is powerfully matched to the real fault-induced impulse response.
Implementation of a Fuzzy Inference System to Enhance the Measurement Range of Multilayer Interferometric Sensors
Authors: Vargas-Rodriguez Everardo , Guzman-Chavez Ana Dinora , Guzman-Cabrera Rafael , Florez-Fuentes Anderson Smith
Published: August 2022

This work presents a novel methodology to implement a fuzzy inference system (FIS) to overcome the measurement ambiguity that is typically observed in interferometric sensors. This ambiguity occurs when the measurand is determined by tracing the wavelength position of a peak or dip of a spectral fringe. Consequently, the sensor measurement range is typically limited to the equivalent of 1 free spectral range (FSR). Here, it is demonstrated that by using the proposed methodology, the measurement range of this type of sensor can be widened several times by overcoming the ambiguity over some FSR periods. Furthermore, in order to support the viability of the methodology, it was applied to a couple of temperature interferometric sensors. Finally, experimental results demonstrated that it was possible to quintuple the measurement range of one of the tested sensors with a mean absolute error of MAE = 0.0045 °C, while for the second sensor, the measurement range was doubled with an MAE = 0.0073 °C.
Explainability and Transparency of Classifiers for Air-Handling Unit Faults Using Explainable Artificial Intelligence (XAI)
Authors: Meas Molika , Machlev Ram , Kose Ahmet , Tepljakov Aleksei , Loo Lauri , Levron Yoash , Petlenkov Eduard , Belikov Juri
Published: August 2022

In recent years, explainable artificial intelligence (XAI) techniques have been developed to improve the explainability, trust and transparency of machine learning models. This work presents a method that explains the outputs of an air-handling unit (AHU) faults classifier using a modified XAI technique, such that non-AI expert end-users who require justification for the diagnosis output can easily understand the reasoning behind the decision. The method operates as follows: First, an XGBoost algorithm is used to detect and classify potential faults in the heating and cooling coil valves, sensors, and the heat recovery of an air-handling unit. Second, an XAI-based SHAP technique is used to provide explanations, with a focus on the end-users, who are HVAC engineers. Then, relevant features are chosen based on user-selected feature sets and features with high attribution scores. Finally, a sliding window system is used to visualize the short history of these relevant features and provide explanations for the diagnosed faults in the observed time period. This study aimed to provide information not only about what occurs at the time of fault appearance, but also about how the fault occurred. Finally, the resulting explanations are evaluated by seven HVAC expert engineers. The proposed approach is validated using real data collected from a shopping mall.
Development and Assessment of a Movement Disorder Simulator Based on Inertial Data
Authors: Carissimo Chiara , Cerro Gianni , Ferrigno Luigi , Golluccio Giacomo , Marino Alessandro
Published: August 2022

The detection analysis of neurodegenerative diseases by means of low-cost sensors and suitable classification algorithms is a key part of the widely spreading telemedicine techniques. The choice of suitable sensors and the tuning of analysis algorithms require a large amount of data, which could be derived from a large experimental measurement campaign involving voluntary patients. This process requires a prior approval phase for the processing and the use of sensitive data in order to respect patient privacy and ethical aspects. To obtain clearance from an ethics committee, it is necessary to submit a protocol describing tests and wait for approval, which can take place after a typical period of six months. An alternative consists of structuring, implementing, validating, and adopting a software simulator at most for the initial stage of the research. To this end, the paper proposes the development, validation, and usage of a software simulator able to generate movement disorders-related data, for both healthy and pathological conditions, based on raw inertial measurement data, and give tri-axial acceleration and angular velocity as output. To present a possible operating scenario of the developed software, this work focuses on a specific case study, i.e., the Parkinson’s disease-related tremor, one of the main disorders of the homonym pathology. The full framework is reported, from raw data availability to pathological data generation, along with a common machine learning method implementation to evaluate data suitability to be distinguished and classified. Due to the development of a flexible and easy-to-use simulator, the paper also analyses and discusses the data quality, described with typical measurement features, as a metric to allow accurate classification under a low-performance sensing device. The simulator’s validation results show a correlation coefficient greater than 0.94 for angular velocity and 0.93 regarding acceleration data. Classification performance on Parkinson’s disease tremor was greater than 98% in the best test conditions.
Object-Based Change Detection Algorithm with a Spatial AI Stereo Camera
Authors: Göncz Levente , Majdik András
Published: August 2022

This paper presents a real-time object-based 3D change detection method that is built around the concept of semantic object maps. The algorithm is able to maintain an object-oriented metric-semantic map of the environment and can detect object-level changes between consecutive patrol routes. The proposed 3D change detection method exploits the capabilities of the novel ZED 2 stereo camera, which integrates stereo vision and artificial intelligence (AI) to enable the development of spatial AI applications. To design the change detection algorithm and set its parameters, an extensive evaluation of the ZED 2 camera was carried out with respect to depth accuracy and consistency, visual tracking and relocalization accuracy and object detection performance. The outcomes of these findings are reported in the paper. Moreover, the utility of the proposed object-based 3D change detection is shown in real-world indoor and outdoor experiments.
Quantification of Serum Exosome Biomarkers Using 3D Nanoporous Gold and Spectrophotometry
Authors: Al Mannai Amera , Al-Ansari Tareq , Saoud Khaled M.
Published: August 2022

Tumor-derived exosomes may provide biomarkers for cancer treatment. Using sputtering technology, an affinity-based device to capture exosomes was developed using nanoporous substrate (NPG)-coated silicon microscopy. Immunology-based techniques detect and purify exosomes using gold coating with a specific antigen. Inverted fluorescent microscopy was used to detect target exosomes quantitatively utilizing fluorescent nanospheres as the label. We quantified the expression of CD63 surface protein markers on exosomes from conditioned culture media of breast cancer cells. The exosomes that targeted specific proteins with controls were statistically analyzed and compared to those that targeted non-specific proteins. Results from SEM showed that the exosomes were circular, between 30 and 150 nanometers in size. The porous gold substrates captured more exosomes than the nonporous substrates. Nitric acid treatments at different times resulted in a variety of pore sizes. Despite the increase in the size of the pores, the number of exosomes found in the porous gold substrate treated for 10 min nearly doubled compared to the one treated for 5 min. In this work, a fluorescence biosensor was developed to detect breast cancer exosomes using nanoporous gold substrates (NPG). Assay and model exosomes of specific breast cancer cells showed that exosomes exhibit diagnostic surface protein markers, reflecting the protein profile of their parent cells. Furthermore, the specific binding between the exosome surface antibodies and the targets identified the CD63 biomarkers on the exosome, suggesting these markers’ diagnostic potential. This study can accelerate exosome research in determining tumor-related exosomes and develop novel cancer diagnostic methods.
Progressive Early Image Recognition for Wireless Vision Sensor Networks
Authors: AlHarami AlKhzami , Abubakar Abubakar , Zhang Bo , Bermak Amine
Published: August 2022

A wireless vision sensor network (WVSN) is built by using multiple image sensors connected wirelessly to a central server node performing video analysis, ultimately automating different tasks such as video surveillance. In such applications, a large deployment of sensors in the same way as Internet-of-Things (IoT) devices is required, leading to extreme requirements in terms of sensor cost, communication bandwidth and power consumption. To achieve the best possible trade-off, we propose in this paper a new concept that attempts to achieve image compression and early image recognition leading to lower bandwidth and smart image processing integrated at the sensing node. A WVSN implementation is proposed to save power consumption and bandwidth utilization by processing only part of the acquired image at the sensor node. A convolutional neural network is deployed at the central server node for the purpose of progressive image recognition. The proposed implementation is capable of achieving an average recognition accuracy of 88% with an average confidence probability of 83% for five subimages, while minimizing the overall power consumption at the sensor node as well as the bandwidth utilization between the sensor node and the central server node by 43% and 86%, respectively, compared to the traditional sensor node.
Hydration Assessment Using the Bio-Impedance Analysis Method
Authors: AlDisi Reem , Bader Qamar , Bermak Amine
Published: August 2022

Body hydration is considered one of the most important physiological parameters to measure and one of the most challenging. Current methods to assess hydration are invasive and require costly clinical settings. The bio-impedance analysis offers a noninvasive and inexpensive tool to assess hydration, and it can be designed to be used in wearable health devices. The use of wearable electronics in healthcare applications has received increased attention over the last decade. New, emerging medical devices feature continuous patient monitoring and data collection to provide suitable treatment and preventive actions. In this paper, a model of human skin is developed and simulated to be used as a guide to designing a dehydration monitoring system based on a bio-impedance analysis technique. The study investigates the effect of applying different frequencies on the dielectric parameters of the skin and the resulting measured impedance. Two different interdigitated electrode designs are presented, and a comparison of the measurements is presented. The rectangular IDE is printed and tested on subjects to validate the bio-impedance method and study the interpretation of its results. The proposed design offers a classification criterion that can be used to assess dehydration without the need for a complex mathematical model. Further clinical testing and data are needed to refine and finalize the criteria.
Antidepressant Effect of Neuropeptide Y in Models of Acute and Chronic Stress
Authors: Andriushchenko Nika , Nebogina Kira , Zorkina Yana , Abramova Olga , Zubkov Eugene , Ochneva Aleksandra , Ushakova Valeria , Pavlov Konstantin , Gurina Olga , Chekhonin Vladimir , Morozova Anna
Published: August 2022

The search for potential effective antidepressants with minimal side effects is necessary. Peptides are possible applicants for this role. We investigated the antidepressant effect of neuropeptide Y (NY), alone and in combination with clomipramine, in models of acute and chronic stress induced by ultrasound of variable frequencies. Rats were divided into the following groups: the control group, stress group, and stress groups with intranasal administration of NY (100 μg/kg) or clomipramine (7.5 mg/kg), or their combination. Rat behavior was evaluated using a sucrose preference test and forced swimming test in an acute stress model, and a sucrose preference test, forced swimming test, social interaction test, open field test, and Morris water maze test in a chronic stress model. The results of our experiment demonstrated a protective effect of intranasal NY in a model of acute stress, which was comparable to the antidepressant effect of clomipramine. When the same dose was chronically administered, NY also demonstrated an antidepressant action, although expressed in a lesser degree than clomipramine. The combination of NY and clomipramine was much less effective in the chronic stress paradigm compared to the separated drug administration, but was just as effective in the acute stress paradigm. Until now, there was no convincing evidence for the efficacy of the chronic administration of neuropeptide Y; we demonstrated its effectiveness in the animal model of depressive-like behavior. However, our hypothesis that neuropeptide Y can enhance the effect of a classical antidepressant was not confirmed.
Characterization and Sustainability Potential of Recycling 3D-Printed Nylon Composite Wastes
Authors: Al-Mazrouei Noura , Al-Marzouqi Ali H. , Ahmed Waleed
Published: August 2022

The revolution of 3D-printing technology has caused an additional source of plastic waste, especially the new generation of composite filaments that are linked with the commercial fused deposition modeling process, adding pressure to find a sustainable solution to tackle the emerging waste problem. This study aims to investigate the mechanical and thermal properties of a blended recycled composite material produced by mixing two different 3D-printed reinforced composite wastes, carbon fiber CF/nylon, and glass fiber GF/nylon filaments that were mixed at different percentages using a hot extrusion procedure, tested by a tensile testing machine, and processed with five different weight ratios to study the impact of blend ratios on the material characteristics of the recycled composites and to find the optimum weight ratios with the most preferred properties. The results revealed that the maximum tensile strength of the GF/nylon composite was achieved with 60 wt%. The highest elastic modulus value was recorded at 60 wt% GF/nylon. Moreover, it was noted that at 80 wt% of GF/nylon, the ductility is at the peak value among the composites.
Using a Sensitivity Analysis and Spatial Clustering to Determine Vulnerability to Potentially Toxic Elements in a Semiarid City in Northwest Mexico
Authors: Vizuete-Jaramillo Efrain , Meza-Figueroa Diana , Reyes-Castro Pablo A. , Robles-Morua Agustin
Published: August 2022

The Getis-Ord Gi* statistic clustering technique was used to create a hot spot exposure map using 14 potentially toxic elements (PTEs) found in urban dust samples in a semiarid city in northwest Mexico. The dust distribution and deposition in this city are influenced by the seasonal wind and rain from the North American Monsoon. The spatial clustering patterns of hot spots were used in combination with a sensitivity analysis to determine which variables most influenced the PTE hot spot exposure base map. The hot spots areas (%) were used as indicators of environmental vulnerability, and a final integrated map was selected to represent the highest vulnerability of PTEs with a 99% level of confidence. The results of the sensitivity analysis indicated that the flood zones and pervious and impervious zones were the most sensitive variables due to their weight in the spatial distribution. The hot spot areas were reduced by 60.4% by not considering these variables. The hot spot analysis resulted in an effective tool that allowed the combination of different spatial layers with specific characteristics to determine areas that present greater vulnerability to the distribution of PTEs, with impacts on public and environmental health.
Environmental Assessment of an Innovative High-Performance Experimental Agriculture Field
Authors: Frota de Albuquerque Landi Fabiana , Fabiani Claudia , Pisello Anna Laura , Petrozzi Alessandro , Milone Daniele , Cotana Franco
Published: August 2022

To increase food availability, optimizing production systems and reducing burdens related to human activities is essential in a scenario of population growth and limited natural resources. In this context, the life cycle methodology can represent a valuable asset for assessing the environmental performance of agricultural products and services. This study sought to investigate and characterize potential impacts of an experimental tomato field at the University of Perugia and evaluate if the production increment obtained using high-reflective mulching compensated for the emissions caused by this extra component. The first-year crop campaign was the baseline reference to measure the system’s efficiency. A CML baseline method applied demonstrated that the covered field (F1) was associated with more than 23 kg CO2 eq emissions (25% attributed to the mulch) in comparison to about 18 kg CO2 eq of the non-covered sector (F2). In addition, electronic components and drainage systems were linked with most toxicity indicators. However, the F1 field’s higher productivity compensated for the mulch impact, resulting in 9% lower CO2 equivalent emissions per kg yield in the first year and 18% lower each year for 30 years. The results encourage application of this approach in urban contexts with several benefits.
Comparative Analysis of the Optimized KNN, SVM, and Ensemble DT Models Using Bayesian Optimization for Predicting Pedestrian Fatalities: An Advance towards Realizing the Sustainable Safety of Pedestrians
Authors: Yang Lei , Aghaabbasi Mahdi , Ali Mujahid , Jan Amin , Bouallegue Belgacem , Javed Muhammad Faisal , Salem Nermin M.
Published: August 2022

Over the past three decades, more than 8000 pedestrians have been killed in Australia due to vehicular crashes. There is a general assumption that pedestrians are often the most vulnerable to crashes. Sustainable transportation goals are at odds with the high risk of pedestrian fatalities and injuries in car crashes. It is imperative that the reasons for pedestrian injuries be identified if we are to improve the safety of this group of road users who are particularly susceptible. These results were obtained mostly through the use of well-established statistical approaches. A lack of flexibility in managing outliers, incomplete, or inconsistent data, as well as rigid pre-assumptions, have been criticized in these models. This study employed three well-known machine learning models to predict road-crash-related pedestrian fatalities (RCPF). These models included support vector machines (SVM), ensemble decision trees (EDT), and k-nearest neighbors (KNN). These models were hybridized with a Bayesian optimization (BO) algorithm to find the optimum values of their hyperparameters, which are extremely important to accurately predict the RCPF. The findings of this study show that all the three models’ performance was improved using the BO. The KNN model had the highest improvement in accuracy (+11%) after the BO was applied to it. However, the ultimate accuracy of the SVM and EDT models was higher than that of the KNN model. This study establishes the framework for employing optimized machine learning techniques to reduce pedestrian fatalities in traffic accidents.
Impact of Teenage EFL Learners’ Psychological Needs on Learning Engagement and Behavioral Intention in Synchronous Online English Courses
Authors: Zhou Sijing , Zhu Huiling , Zhou Yu
Published: August 2022

There is a relatively small body of literature that is concerned with the extent to which students are actively engaged in online English learning. To address this issue, the present study investigates 233 Chinese secondary school students attending online English courses during the pandemic and explores the degree to which learners are behaviorally, cognitively, emotionally, and socially engaged in synchronous online English courses. Three basic psychological needs (autonomy, competence, and relatedness) are examined as antecedents of learning engagement, and the behavioral intention is hypothesized as a learning outcome. This study takes a mixed-methods approach, integrating quantitative data from questionnaires and qualitative data from semi-structured interviews. A partial least squares (PLS)-structural equation modeling (SEM) technique was used to test hypotheses and the proposed research model. The quantitative findings indicate that, firstly, whereas the basic psychological needs predict students’ four dimensions of online learning engagement, competence is confirmed to be the strongest predicting factor. Secondly, behavioral intention is significantly influenced by students’ cognitive engagement and emotional engagement. Thirdly, thematic analysis of the qualitative data shows that students tend to have a lower level of engagement compared with a face-to-face classroom learning enviroment, and a more interesting and interactive online course design is crucial to the fulfillment of learners’ psychological needs of autonomy and relatedness in synchronous online English learning.
Research on Coupled Cooperative Operation of Medium- and Long-Term and Spot Electricity Transaction for Multi-Energy System: A Case Study in China
Authors: Wang Kaiyan , Wang Xueyan , Jia Rong , Dang Jian , Liang Yan , Du Haodong
Published: August 2022

Due to the intermittent and anti-peak shaving characteristics of the new energy generator sets, the phenomenon of power abandonment hinders direct participation in the electricity market transactions. The hybrid electricity market can use spot market transactions to absorb renewable energy to a large extent. The multi-energy complementary operation coupling of the hybrid electricity market transactions can exploit the complementation and substitution between different energy sources, realize flexible energy production, consumption, storage, and transmission, and optimize the allocation of resources on a larger scale. In this paper, a mid-long-term spot transaction coordination scheduling (MTCS) model for a multi-energy system is constructed by considering the medium- and long-term electricity market uncertainty and the trial operation characteristics of the spot power market in China. A two-stage solution method is introduced to solve the complex multi-agent, multi-period, and multi-energy model. The results of testing this model on the Gansu region, one of the first eight spot pilot areas in China, are presented and discussed in detail. The results showed that this MTCS model could reduce the opening of thermal power units to a more considerable extent, prioritize the consumption of new energy power generation, and reduce the output uncertainty of new energy through the hybrid power market.
The Dynamics of Water-Based Nanofluid Subject to the Nanoparticle’s Radius with a Significant Magnetic Field: The Case of Rotating Micropolar Fluid
Authors: Ali Bagh , Ahammad N. Ameer , Awan Aziz Ullah , Oke Abayomi S. , Tag-ElDin ElSayed M. , Shah Farooq Ahmed , Majeed Sonia
Published: August 2022

This article investigates the significance of varying radius of copper nanoparticles for non-Newtonian nanofluid flow due to an extending sheet in the presence of a magnetic field and porous medium. The modern technological applications of non-Newtonian nanofluids have attracted researchers in the current era. So, the impacts of the radius of nanoparticles with micropolar fluid have been taken into consideration. Three-dimensional leading equations (PDEs) for momentum, concentration, and temperature are transformed into ODEs by applying the appropriate similarity transformation. The numerical approach bvp4c is applied to obtain the problem’s solution numerically. The influence of the nanoparticles’ radius and various physical parameters on the microrotation, velocity, and temperature profile are analyzed. The velocity profile decreases against the magnetic field (M), rotational parameter (Γ), and Forchheimer number (Fr), but the temperature distribution has increasing behavior for these parameters, and the microrotation is augmented for rising inputs of the magnetic parameter and boundary parameter (β). It is also observed that the temperature reduces against the material parameter (∇) and Forchheimer number (Fr). The skin friction coefficients and Nusselt number decrease against the growing strength of the Forchheimer number (Fr). At the stretching surface, the skin friction factor and Nusselt number are numerically and graphically calculated.
Comprehensive Water Inrush Risk Assessment Method for Coal Seam Roof
Authors: Sun Zhenming , Bao Wenpeng , Li Mei
Published: August 2022

In order to prevent coal mine water inrush accidents, it is necessary to appropriately assess the water abundance of coal mines based on drilling and geophysical data. This paper studied a comprehensive risk assessment method of water inrush. First, a water inrush risk index was proposed based on the analytic hierarchy process-entropy method (AHP-EM) and the water-rich structure index was proposed based on the geological data coupled calculation, then weighted two indices above which established the comprehensive water inrush risk assessment method. Secondly, eight factors were chosen as risk control factors of water inrush: core recovery, aquifer thickness, distance from the indirect aquifer to the coal seam, aquiclude thickness, height of water-conducting fracture zone, sand-mud ratio, total layers of aquifer and aquiclude, and the equivalent thickness of sandstone. Finally, the No. 2 coal seam of Dahaize coal mine was taken as the research object, the factors were calculated, and a comprehensive water inrush assessment model was constructed. With site investigation and observation, the water inrush risk assessment model of the No.2 coal seam roof is consistent with the actual mining situation, which verifies the validity of the model. In addition, this method was used to evaluate the water-richness of the weathered bedrock fractured aquifer in the Zhangjiamao coal mine. The practical application of the two mines has verified the generality of the approach. The research could provide scientific assistance for mine water hazard mitigation and mining safety.
A Study on the Status of Safety and Health for Golf Course Caddies and Improvement of Protective Measures in South Korea
Authors: Lee Ja-Young , Kang Chankyu
Published: August 2022

Due to the rapid expansion of the leisure industry, there were about 32,000 golf caddies in South Korea in 2020, an increase of 18.5% compared to 2016. Consequently, they face an increasing industrial accident rate, which is presumed to be the result of exposure to various harmful factors. Through a survey and oral interview of 221 caddies across more than 20 golf courses, health protective measures, protective measures for caddies, preparation for golf cart operation, physical burden, compliance with golf cart safety during games, and golf course responses to emotional labor were investigated in this study to identify safety and health problems of caddies and suggest prevention measures. Preliminary interviews were conducted to confirm golf courses’ safety and health status and participants’ characteristics. Golf caddies’ health and safety were confirmed using frequency analysis, independent sample t-test, one-way analysis of variance (ANOVA), Pearson correlation analysis, and multiple regression analysis. The results showed that caddies’ workplaces were relatively vulnerable to safety and health issues and caddies were exposed to various harmful risk factors. In addition, it was confirmed that golf caddy protective measures, golf cart safety compliance, physical burden, and health protection affect golf courses’ response level to caddies’ emotional labor.
Agricultural Price Prediction Based on Combined Forecasting Model under Spatial-Temporal Influencing Factors
Authors: Guo Yan , Tang Dezhao , Tang Wei , Yang Senqi , Tang Qichao , Feng Yang , Zhang Fang
Published: August 2022

Grain product price fluctuations affect the input of production factors and impact national food security. Under the influence of complex factors, such as spatial-temporal influencing factors, price correlation, and market diversity, it is increasingly important to improve the accuracy of grain product price prediction for agricultural sustainable development. Therefore, successful prediction of the agricultural product plays a vital role in the government’s market regulation and the stability of national food security. In this paper, the price of corn in Sichuan Province is taken as an example. Firstly, the apriori algorithm was used to search for the spatial-temporal influencing factors of price changes. Secondly, the Attention Mechanism Algorithm, Long Short-term Memory (LSTM), Autoregressive Integrated Moving Average (ARIMA), and Back Propagation (BP) Neural Network models were combined into the AttLSTM-ARIMA-BP model to predict the accurate price. Compared with the other seven models, the AttLSTM-ARIMA-BP model achieves the best prediction effect and possesses the strongest robustness, which improves the accuracy of price forecasting in complex environments and makes the application to other fields possible.
Urban Surface Deformation Management: Assessing Dangerous Subsidence Areas through Regional Surface Deformation, Natural Factors, and Human Activities
Authors: Hu Bo , Chen Bangxin , Na Jing , Yao Jianqun , Zhang Zhimin , Du Xiangfeng
Published: August 2022

Geological disasters caused by surface deformation are common, especially in urban areas, which seriously impede urbanization’s sustainable development. Monitoring and analysis with high spatial and temporal resolution are particularly important to assess the risk of geological disasters caused by urban deformation. This study uses Sentinel-1A satellite imagery to obtain the surface deformation time series of Nanchang City based on SBAS-InSAR and PS-InSAR techniques and is combined with wavelet period analysis and gray correlation analysis to determine the correlation between deformation area and climate environment. This study shows that there was a large-scale subsidence trend in the central urban area of Nanchang in those two years, and an uplift trend in the agro-ecological areas in the southeast. A periodic analysis further shows that the areas with larger changes in surface deformation are more affected by changes in precipitation. This study, integrated with external data, examines the possibility of subsidence disasters occurring along subway lines in areas with large deformation magnitudes from multiple angles.
Framework for Spatio-Temporal Distribution of Disasters and Influencing Factors: Exploratory Study of Tianjin, China
Authors: Yang Weichao , Hu De , Jiang Xuelian , Dun Xuebo , Hou Bingtao , Zheng Chuanxing , Chen Caixia , Zhuang Rong
Published: August 2022

Facing the increased frequency of disasters and resulting massive damages, many countries have developed their frameworks for disaster risk management (DRM). However, these frameworks are highly regional and few of them suggest corresponding methods for specific analysis content, which makes them less practical and operational. In this paper, we investigate an exploratory framework for spatio-temporal distribution of disasters and analysis of influencing factors, including suggested methods for all the content. Time series analysis, geographical difference analysis, direction distribution analysis, and spatial autocorrelation analysis are selected as the content of spatio-temporal distribution of disasters part. Mann–Kendall test method, Lorentz curve, standard deviation ellipse method, and exploratory spatial data analysis method are introduced to analyze them. The impact analysis part was carried out on individual disaster and multiple disasters, which are suggested as the two aspects of influencing factors analysis, by means of a geographic detector and a novel comprehensive impact evaluation model that was developed. Then, the framework is applied to analyze disasters including flood, storm surge, and earthquake in Tianjin of China. The research results help to understand the spatial and temporal distribution of disasters and the disaster mechanism in Tianjin. Further, the superiority and limitations of the framework are discussed. Finally, the framework developed is expected to be widely applied in other districts for better DRM in the future.
Effect of Recycled Aggregate Modification on the Properties of Permeable Asphalt Concrete
Authors: Lei Bin , Xiong Qianghui , Tang Zhuo , Yao Zhimin , Jiang Jianguo
Published: August 2022

This study aims to evaluate the pavement performance of permeable asphalt concrete (PAC) produced with modified recycled coarse aggregate (RCA) based on six types of single modification methods and three types of double modification methods. Firstly, the water absorption, apparent density, and crushing value of the RCA before and after modification were tested. Subsequently, the performance of PAC with these types of modified RCA was investigated, including the optimum asphalt-aggregate ratio, water stability, high-temperature performance, and low-temperature crack. The test results show that the physical properties of RCA were improved to some extent owing to the modification methods adopted, except that the water absorption of RCA-C and RCA-PC increased by 33.5% and 32.6%, respectively, compared with that of RCA. This increase in the water absorption was attributed to the high water absorption of the cement mortar. As a whole, various modification methods have different effects on the road performance of asphalt mixtures, and soaking RCA with slag powder and silane coupling agent was prominent among them. Compared with the PAC with RCA, the Marshall stability, Marshall stability of immersion, residual stability, TSR, dynamic stability and low-temperature tensile strength of PAC with RCA-SLSC were increased by 31.8%, 41.0%, 7.0%, 5.8%, 280.7% and 17.2%, respectively.
Rock Mass Classification Method Based on Entropy Weight–TOPSIS–Grey Correlation Analysis
Authors: Dai Bing , Li Danli , Zhang Lei , Liu Yong , Zhang Zhijun , Chen Shirui
Published: August 2022

The accurate and reliable classification of rock mass is the basis of a reasonable engineering design. In the Xishan mining region of Sanshandao Gold Mine, three conventional rock mass classification methods of Tunneling Quality Index (Q), Rock Mass Rating (RMR) and China National Standard-basic quality (BQ), were compared in the burial depth area above 780 m, and it was discovered that the classification results of different rock mass classification methods had a low coincidence rate in the deep area; Therefore, this paper adopted entropy weight method, TOPSIS method and grey correlation analysis method to calculate the entropy weight and relative closeness of different methods in different middle sections. The study’s findings revealed that in the deep area, the relative closeness between each classification mass was: RMR > Q > BQ; Based on the above results, the IRMR method with modified RMR was selected for comprehensive analysis, and the concept of importance degree of evaluation index was defined; it was found that the importance degree of evaluation index of in-situ stress loss was the highest, while the importance degree of joint direction was the lowest; The “ETG” rock mass classification method based on “site-specific” is established, which provides a reference for the establishment of deep rock mass classification method.