bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization Convolutional Neural Network for Automatic Skin Lesion Diagnosis Using a Genetic Algorithm
Authors: Salih Omran , Duffy Kevin Jan
Published: March 2023

Examining and predicting skin cancer from skin lesion images is challenging due to the complexity of the images. Early detection and treatment of skin lesion disease can prevent mortality as it can be curable. Computer-aided diagnosis (CAD) provides a second opinion for dermatologists as they can classify the type of skin lesion with high accuracy due to their ability to show various clinical identification features locally and globally. Convolutional neural networks (CNNs) have significantly improved the performance of CAD systems for medical image segmentation and classifications. However, tuning CNNs are challenging since the search space of all possible hyperparameter configurations is substantially vast. In this paper, we adopt a genetic algorithm to automatically configure a CNN model for an accurate, reliable, and robust automated skin lesion classification for early skin lesion diagnosis. The optimized CNN model uses four public datasets to train and be able to detect abnormalities based on skin lesion features in different orientations. The model achieves the best scores for each of the DICE coefficients, precision measure, and F-score. These scores compare better than other existing methods. Considering the success of this optimized model, it could be a valuable method to implement in clinical settings.
Image Enhancement Method in Underground Coal Mines Based on an Improved Particle Swarm Optimization Algorithm
Authors: Dai Lili , Qi Peng , Lu He , Liu Xinhua , Hua Dezheng , Guo Xiaoqiang
Published: March 2023

Due to the poor lighting conditions and the presence of a large amount of suspended dust in coal mines, obtained video has problems with uneven lighting and low differentiation of facial features. In order to address these problems, an improved image enhancement method is proposed. Firstly, the characteristics of underground coal mine images are analyzed, and median filtering is selected for noise removal. Then, the gamma function and fractional order operator are introduced, and an image enhancement algorithm based on particle swarm optimization is proposed. Finally, several experiments are conducted, and the results show that the proposed improved algorithm outperforms classical image enhancement algorithms, such as MSR, CLAHE and HF. Compared with the original image, the evaluation metrics of the enhanced Yale face images, including average local standard deviation, average gradient, information entropy and contrast, are improved by 113.1%, 63.8%, 22.8% and 24.1%, respectively. Moreover, the proposed algorithm achieves a superior enhancement effect in the simulated coal mine environment.
Real-Time Semantic Segmentation of Point Clouds Based on an Attention Mechanism and a Sparse Tensor
Authors: Wang Fei , Yang Yujie , Wu Zhao , Zhou Jingchun , Zhang Weishi
Published: March 2023

A 3D point cloud is one of the main data sources for robot environmental cognition and understanding. Due to the limited computation and memory capacities of the robotic platform, existing semantic segmentation models of 3D point clouds cannot meet the requirements of real-time applications. To solve this problem, a lightweight, fully convolutional network based on an attention mechanism and a sparse tensor is proposed to better balance the accuracy and real-time performance of point cloud semantic segmentation. On the basis of the 3D-Unet structure, a global feature-learning module and a multi-scale feature fusion module are designed. The former improves the ability of features to describe important areas by learning the importance of spatial neighborhoods. The latter realizes the fusion of multi-scale semantic information and suppresses useless information through the task correlation learning of multi-scale features. Additionally, to efficiently process the large-scale point clouds acquired in real time, a sparse tensor-based implementation method is introduced. It is able to reduce unnecessary computation according to the sparsity of the 3D point cloud. As demonstrated by the results of experiments conducted with the SemanticKITTI and NuScenes datasets, our model improves the mIoU metric by 6.4% and 5%, respectively, over existing models that can be applied in real time. Our model is a lightweight model that can meet the requirements of real-time applications.
Efficiency Analysis of Roadway Lighting Replacement in a Selected Polish Municipality
Authors: Zima Krzysztof , Ciepłucha Wojciech
Published: March 2023

The paper addresses the problem of the efficiency of road lighting replacement in a selected municipality. The following question arose: can the replacement of lighting bring measurable financial benefits in the short or long term, and does increasing the comfort of road use in terms of lighting involve higher costs? During the global energy crisis, reducing the operating costs of road lighting has become the goal of many analyses and studies. Professionally selected lighting of streets and sidewalks is a factor that significantly affects the safety of road users. The problem of many studies is the omission of safety considerations, lighting comfort and budgetary possibilities of municipalities. The authors conducted comparative analyses of solution variants, examining both road lighting comfort and costs as an innovative element of research. The DIALux application was used to analyse the changes in road lighting depending on the luminaires used and the changes in pole spacing influenced by the height of the light point. Variant and scenario analyses were incorporated into the efficiency analyses, while the calculations themselves were based on detailed cost analyses with reference to Polish catalogues of material inputs and market prices. The authors conducted cost analyses of lighting dismantling and installation, including the subsequent operating costs over 20 years for seven variants of poles with their systems and four variants of luminaires. The results were compared with the existing lighting system in use. An original element of the study is the use of BIM analyses with design variants, combined with analyses of the technical condition of the existing lighting network and an illuminance analysis with estimates of the height and spacing of poles. Numerous studies indicate that it is very cost-effective to replace old lighting systems, especially those based on high-intensity discharge (HID) sodium lamps, with more modern LED lamps additionally equipped with twilight dimmers. The analyses also demonstrated that cost-efficient lighting replacement could go hand in hand with improved road lighting comfort without the need to incur additional costs, which is often overlooked in various studies. The analysis performed for a model lighting network indicates that savings of nearly 60% are possible over 20 years.
Experiment and Analysis on Friction Damage and Energy Loss Characteristics of Potatoes
Authors: Guo Yaping , Xie Shengshi , Deng Weigang , Li Junru , Shang Zhenyu , Lu Kailiang , Gai Xiuzhi , Ji Xinjie
Published: March 2023

In view of the problem that the research of potato friction damage is not sufficiently systematic and the characteristics of potato friction damage and energy loss are not clear, this paper investigates the initial height, potato mass, potato varieties, friction materials and temperature as experimental factors and the epidermal damage proportion as the evaluation index, filtering for significant factors by a factorial test. On this basis, the proportion of epidermal damage and the ratio of energy consumption loss were used as evaluation indicators to conduct a single factor test for significant factors. Thereby, the influence of these factors on friction damage and energy loss characteristics of potato were further investigated. The results show that the initial height, potato mass and potato varieties were the significant factors affecting the proportion of epidermal damage of potatoes. With the increase in initial height, the proportion of epidermal damage and ratio of energy consumption loss gradually increased. With the increment in potato mass, the proportion of epidermal damage gradually increased, and the energy consumption loss ratio gradually decreased. The epidermal damage proportion and energy consumption loss ratio of potato varieties were ranked as Xisen 1 > Jizhang 12 > Xisen 6 > Xuechuanhong > Netherlands 15, and Netherlands 15 > Xisen 6 > Xisen 1 > Xuechuanhong > Jizhang 12, respectively. The energy loss ratio and epidermal damage proportion were highly correlated at initial height and potato mass. A mathematical model for predicting the proportion of epidermal damage using energy consumption loss ratio is proposed: η1 = 0.8861ζ − 0.2062 (R2 = 0.8626), η2 = −0.5813ζ + 0.2394 (R2 = 0.8537).
Multi-Information Fusion Indoor Localization Using Smartphones
Authors: Yan Suqing , Wu Chunping , Luo Xiaonan , Ji Yuanfa , Xiao Jianming
Published: March 2023

Accurate indoor localization estimation has important social and commercial values, such as indoor location services and pedestrian retention times. Acoustic-based methods can achieve high localization accuracies in specific scenarios with special equipment; however, it is a challenge to obtain accurate localization with general equipment in indoor environments. To solve this problem, we propose a novel fusion CHAN and the improved pedestrian dead reckoning (PDR) indoor localization system (CHAN-IPDR-ILS). In this system, we propose a step length estimation method that adds the previous two steps for extracting more accurate information to estimate the current step length. The maximum influence factor is set for the previous two steps to ensure the importance of the current step length. We also propose a heading direction correction method to mitigate the errors in sensor data. Finally, pedestrian localization is achieved using a motion model with acoustic estimation and dynamic improved PDR estimation. In the fusion localization, the threshold and confidence level of the distance between estimation base-acoustic and improved PDR estimation are set to mitigate accidental and cumulative errors. The experiments were performed at trial sites with different users, devices, and scenarios, and experimental results demonstrate that the proposed method can achieve a higher accuracy compared with the state-of-the-art methods. The proposed fusion localization system manages equipment heterogeneity and provides generality and flexibility with different devices and scenarios at a low cost.
Evaluation of the Effects of a 50 Hz Electric Field on Brain Tissue by Immunohistochemical Method, and on Blood Tissue by Biochemical, Physiological and Comet Method
Authors: Şenol Nurgül , Kaya Erşan , Coşkun Özlem , Aslankoç Rahime , Çömlekçi Selçuk
Published: March 2023

The aim of this study was to evaluate the possible effects of a 50 Hz electric field on brain tissue and the positive effects of juglone (5-hydroxy-1,4-naphthoquinone) antioxidant activity, using the immunohistochemical technique on male Wistar-Albino rats. The effects on blood tissue were also examined using biochemical, physiological and comet methods. Animals were randomly divided into three groups (eight in each group): group I: control, group II: electric field, group III: 50 Hz electric field + juglone (5-hydroxy-1,4-naphthoquinone)/300 ppm. Juglone was applied per day by gavage over 30 days. At the end of the experimental procedure, animals were sacrificed and brain tissue was subjected to routine histologic and immunohistochemical processes. As a result of histophatological examination, the brain tissue of rats with 50 Hz electric field exposure showed severe histopathological changes. The differences between groups were statistically significant according to total comet score (p = 0.001). For the antioxidant parameters on the blood, SOD activity in the electric field group was significantly higher among the other groups, although we did not find significant differences in MDA, CAT activity level.
The Role of Competence Profiles in Industry 5.0-Related Vocational Education and Training: Exemplary Development of a Competence Profile for Industrial Logistics Engineering Education
Authors: Pacher Corina , Woschank Manuel , Zunk Bernd M.
Published: March 2023

Industry 5.0 and the associated transformation into Society 5.0 require a complete realignment of the skills required of the engineers of tomorrow. Thereby, the pre-dominant and policy-driven drivers of digitalization, and sustainability in particular, demand a complex variety of enhanced special competencies (e.g., lean thinking, data science skills) and transversal competencies from engineers working in manufacturing companies which must be systematically developed and continuously expanded. Since methodical approaches to systematic competence development in the environment of Industry 5.0-related engineering education are scarce, this article develops a competence profile for industrial logistics engineering education as an example of Industry 5.0-related vocational education and training initiatives. After elaborating on the relevant theoretical aspects of systematic competence development in adult education, an exemplary competence profile for industrial logistics engineering education is developed. Moreover, this paper presents a preliminary investigation of the impact of competencies on job performance and job satisfaction. The research results serve as a basis for the development of new teaching and learning concepts as well as for the investigation of causal relationships between competence-orientation and individual performance for industrial engineers in manufacturing enterprises.
An Emotional Model Based on Fuzzy Logic and Social Psychology for a Personal Assistant Robot
Authors: Fernández-Blanco Martín Gema , Matía Fernando , García Gómez-Escalonilla Lucía , Galan Daniel , Sánchez-Escribano M. Guadalupe , de la Puente Paloma , Rodríguez-Cantelar Mario
Published: March 2023

Personal assistants and social robotics have evolved significantly in recent years thanks to the development of artificial intelligence and affective computing. Today’s main challenge is achieving a more natural and human interaction with these systems. Integrating emotional models into social robotics is necessary to accomplish this goal. This paper presents an emotional model whose design has been supervised by psychologists, and its implementation on a social robot. Based on social psychology, this dimensional model has six dimensions with twelve emotions. Fuzzy logic has been selected for defining: (i) how the input stimuli affect the emotions and (ii) how the emotions affect the responses generated by the robot. The most significant contribution of this work is that the proposed methodology, which allows engineers to easily adapt the robot personality designed by a team of psychologists. It also allows expert psychologists to define the rules that relate the inputs and outputs to the emotions, even without technical knowledge. This methodology has been developed and validated on a personal assistant robot. It consists of three input stimuli, (i) the battery level, (ii) the brightness of the room, and (iii) the touch of caresses. In a simplified implementation of the general model, these inputs affect two emotions that generate an externalized emotional response through the robot’s heartbeat, facial expression, and tail movement. The three experiments performed verify the correct functioning of the emotional model developed, demonstrating that stimuli, independently or jointly, generate changes in emotions that, in turn, affect the robot’s responses.
TSDNet: A New Multiscale Texture Surface Defect Detection Model
Authors: Dong Min , Li Dezhen , Li Kaixiang , Xu Junpeng
Published: March 2023

Industrial defect detection methods based on deep learning can reduce the cost of traditional manual quality inspection, improve the accuracy and efficiency of detection, and are widely used in industrial fields. Traditional computer defect detection methods focus on manual features and require a large amount of defect data, which has some limitations. This paper proposes a texture surface defect detection method based on convolutional neural network and wavelet analysis: TSDNet. The approach combines wavelet analysis with patch extraction, which can detect and locate many defects in a complex texture background; a patch extraction method based on random windows is proposed, which can quickly and effectively extract defective patches; and a judgment strategy based on a sliding window is proposed to improve the robustness of CNN. Our method can achieve excellent detection accuracy on DAGM 2007, a micro-surface defect database and KolektorSDD dataset, and can find the defect location accurately. The results show that in the complex texture background, the method can obtain high defect detection accuracy with only a small amount of training data and can accurately locate the defect position.
A Digital Twin-Based State Monitoring Method of Gear Test Bench
Authors: Li Jubo , Wang Songlin , Yang Jianjun , Zhang Huijie , Zhao Hengbo
Published: March 2023

The gear test bench is important equipment for analyzing gear performance, detecting gear quality, and providing basic data for gear design and machining. In order to monitor the running state of the gear test bench, predict its running performance both timely and effectively, and guarantee its stable operation, a digital twin-based state monitoring method of the gear test bench is proposed in this paper. The state monitoring system of the gear test bench based on the digital twin model is constructed to simulate its normal running state in real time. On this basis, through the acquisition of physical information of the gear test bench, the developing of the state monitoring system, and the simulation of the digital twin model, the real-time state monitoring of the normal operation gear test bench is realized. The test results of the closed power flow gear test bench show that the digital twin simulation results of the gear test bench are basically consistent with it. The proposed state monitoring system can map the normal running state of the gear test bench, so as to realize the running state monitoring of the gear test bench.
Cross-Platform Gait Analysis and Fall Detection Wearable Device
Authors: Chang Ming-Hung , Wu Yi-Chao , Niu Hsi-Yu , Chen Yi-Ting , Juang Shu-Han
Published: March 2023

Since the fall was often occurred in elders daily, this paper focused on gait analysis with fall detection to develop a wearable device. To ensure that the mobile application, APP, could be used in different platform of mobile phone, such Android or iOS, the designed wearable device also could be used in cross-platform in mobile phone. Therefore, a cross-platform gait analysis and fall detection wearable device (CPGAFDWD) was proposed. Since CPGAFDWD APP was used in web browser without limiting to platform, it could be used for different platforms of mobile phone. The gait analysis could be detected at home. The fall detection also could be executed in any place immediately. The patients and medical staff all could query the status of rehabilitation in any place and any time via the Internet. The experimental results showed that the correct rate of gait analysis and fall detection could be up to 90% in cross-platform of mobile phone. In the future, CPGAFDWD will be planned to be verified by Institutional Review Board, IRB, for clinical treatment.
A New Approach to Nonlinear State Observation for Affine Control Dynamical Systems
Authors: Azar Ahmad Taher , Smait Drai Ahmed , Muhsen Sami , Jassim Moayad Abdullah , AL-Salih Asaad Abdul Malik Madhloom , Hameed Ibrahim A. , Jawad Anwar Ja’afar Mohamad , Abdul-Adheem Wameedh Riyadh , Cocquempot Vincent , Sahib Mouayad A. , Kamal Nashwa Ahmad , Ibraheem Ibraheem Kasim
Published: March 2023

In this work, a Nonlinear Higher Order Extended State Observer (NHOESO) is presented to replace the Linear Extended State Observer (LESO) used in Conventional Active Disturbance Rejection Control (C-ADRC) solutions. In the NHOESO, the standard LESO is completed with a two-term smooth nonlinear function with saturation-like characteristics. The proposed novel NHOESO enables precise observation of the generalized disturbances with higher-order derivatives. The stability of the NHOESO is examined with the aid of the Lyapunov method. A simulation of an uncertain nonlinear Single-Input–Single-Output (SISO) system with time-varying external disturbances confirms that the proposed NHOESO copes well with the generalized disturbance, which is not true for other ESOs.
GhostNeXt: Rethinking Module Configurations for Efficient Model Design
Authors: Hong Kiseong , Kim Gyeong-hyeon , Kim Eunwoo
Published: March 2023

Despite the continuous development of convolutional neural networks, it remains a challenge to achieve performance improvement with fewer parameters and floating point operations (FLOPs) as a light-weight model. In particular, excessive expressive power on a module is a crucial cause of skyrocketing the computational cost of the entire network. We argue that it is necessary to optimize the entire network by optimizing single modules or blocks of the network. Therefore, we propose GhostNeXt, a promising alternative to GhostNet, by adjusting the module configuration inside the Ghost block. We introduce a controller to select channel operations of the module dynamically. It holds a plug-and-play component that is more useful than the existing approach. Experiments on several classification tasks demonstrate that the proposed method is a better alternative to convolution layers in baseline models. GhostNeXt achieves competitive recognition performance compared to GhostNet and other popular models while reducing computational costs on the benchmark datasets.
Effect of Three-Dimensional (3D) Scanning Factors on Minimizing the Scanning Errors Using a White LED Light 3D Scanner
Authors: Raza Syed Farhan , Amjad Muhammad , Ishfaq Kashif , Ahmad Shafiq , Abdollahian Mali
Published: March 2023

The largest problem with scanning real objects involves bearing the huge costs of scanning and the low quality of point cloud data for a scanned object, thereby increasing the cost and lead time. Therefore, a need exists to improve the quality of scanning to save time, costs, and computational resources. In this research, the levels of optimal factors associated with a three-dimensional (3D) scanner were investigated, improving the quality of 3D scanning data. Optimizing the 3D scanner factors could help us acquire errorless digital scanned data that accurately resemble a 3D physical object and which may be further used in various engineering applications, e.g., additive manufacturing and non-engineering applications. For this study, four modes of 3D scanning (A, B, C, and D) were utilized with five crucial 3D scanning factors namely texture, watertightness, simplification, and alternate deployments of smoothness and sharpness. This research was divided into two stages. The former stage involved the 3D scanning of two samples with simple and complex geometrical intricacies and the later stage involved checking the scanned objects for any dimensional errors. A coordinate measuring machine (CMM) was used to measure the dimensional details of the real objects. For virtual metering, Solidworks was utilized. With reference to the limited literature in the current context, 3D scanning errors were highly reduced for the first time up to 0.1% for the complex sample when compared to the errors found for the simple sample.
New Approach of the Variable Fractional-Order Model of a Robot Arm
Authors: Bąkała Marcin , Duch Piotr , Ostalczyk Piotr
Published: March 2023

This paper proposes a simple mathematical model based on the variable fractional-order difference equation of a robot arm. The model of the described arm does not consider the impact of the movement of the mobile platform, it was assumed that all degrees of freedom would be taken away from it. The implementation of the task was divided into two stages. First, a mechanical model was developed. In order to estimate the torques of nodal propulsion motors, a description of the components of the Lagrange equation for the considered system, i.e., energy, power, and external interactions, and derivation of the equations of motion of the tested manipulator based on the Lagrange equation was made. An additional criterion was also considered in the selection of drives in the kinematic nodes of the links, which was to set the manipulator in a vertical position at a specific time. Processing the measured data of a robot arm, model parameters were selected, and the order function was chosen. The second stage was a simulation, whose results were compared with the collected data.
Smart Tool Wear Monitoring of CFRP/CFRP Stack Drilling Using Autoencoders and Memory-Based Neural Networks
Authors: Caggiano Alessandra , Mattera Giulio , Nele Luigi
Published: March 2023

The drilling of carbon fiber-reinforced plastic (CFRP) materials is a key process in the aerospace industry, where ensuring high product quality is a critical issue. Low-quality of final products may be caused by the occurrence of drilling-induced defects such as delamination, which can be highly affected by the tool conditions. The abrasive carbon fibers generally produce very fast tool wear with negative effects on the hole quality. This suggests the need to develop a method able to accurately monitor the tool wear development during the drilling process in order to set up optimal tool management strategies. Nowadays, different types of sensors can be employed to acquire relevant signals associated with process variables which are useful to monitor tool wear during drilling. Moreover, the increasing computational capacity of modern computers allows the successful development of procedures based on Artificial Intelligence (AI) techniques for signal processing and decision making aimed at online tool condition monitoring. In this work, an advanced tool condition monitoring method based on the employment of autoencoders and gated recurrent unit (GRU) recurrent neural networks (RNN) is developed and implemented to estimate tool wear in the drilling of CFRP/CFRP stacks. This method exploits the automatic feature extraction capability of autoencoders to obtain relevant features from the sensor signals acquired by a multiple sensor system during the drilling process and the memory abilities of GRU to estimate tool wear based on the extracted sensor signal features. The results obtained with the proposed method are compared with other neural network approaches, such as traditional feedforward neural networks, and considerations are made on the influence that memory-based hyperparameters have on tool wear estimation performance.
The Correlation between Surface Integrity and Operating Behaviour of Slide Burnished Components—A Review and Prospects
Authors: Maximov Jordan , Duncheva Galya
Published: March 2023

This review paper analyses and summarises the results found while studying the slide burnishing (SB) of metal components refracted through a prism during the surface engineering (SE) process, over the period of January 2019 to January 2023. According to the classification of SE processes defined in the article, SB as a technique in the scope of SE that belongs to the group of static surface cold working (SCW) processes, based on severe surface plastic deformation, and is realised under the condition of sliding friction contact with the treated surface. When the deforming element is natural or artificial diamond, SB is known as diamond burnishing (DB). SB is especially suited for axes, shafts, and holes with circular cross-sections but can also be implemented on flat-face and complex surfaces. SB is eco-friendly and a very economical method for producing mirror-like surface finishes on a wide range of ferrous and non-ferrous machined surfaces, but it can also be realised as a hardening and mixed process with the aim of significantly increasing the fatigue strength and wear resistance of the treated components. Based on a literature review of the results of the theory and practice of SB, an analysis on different criteria was carried out, and graphic visualizations of the statistical results were made. Additionally, the results were analysed using the integrated approach of SE to study the correlations between the apexes of the triangle: SB—surface integrity (SI)—operational behaviour (OB). On this basis, relevant conclusions were drawn, and promising directions for future investigations of SB were outlined.
Special Issue on Complexation of Metals in Natural Fluids: Simulations, Experiments and Applications
Authors: Cassone Giuseppe , Foti Claudia , Giuffrè Ottavia , Saija Franz
Published: March 2023

The complexation of metals in natural fluids, such as natural waters and biological fluids, is a key process in the context of environmental and biological phenomena [...]
Temperature Variation of Rock during Deformation and Fracturing: Particle Flow Modeling Method and Mechanism Analyses
Authors: Jiao Xiaojie , Cheng Cheng , Song Yubing , Wang Gang , He Linjuan
Published: March 2023

The rock deformation and failure characteristics and mechanisms are very important for stability evaluation and hazard control in rock engineering. The process of rock deformation and failure is often accompanied by temperature changes. It is of great significance to study the characteristics and mechanism of temperature variation in rock under deformation and fracturing for a better understanding of rock failure and to obtain some probable precursor information for guiding the prediction of the mechanical behavior of rock. However, most of the studies are based on observations in the field and laboratory tests, while it is still required to develop an effective method for modeling and calculating the temperature variation of rock during the deformation and failure processes. In this paper, a particle flow modeling method based on energy analyses is proposed for simulating the temperature variation of rocks, considering four temperature effects, including the thermoelastic effect, friction effect, damping effect, and heat conduction effect. The four effects are analyzed, and the theoretical equations have been provided. On this basis, the numerical model is built and calibrated according to the laboratory uniaxial compressive experiment on a marble specimen, and a comparison study has been conducted between the laboratory and numerical experiment results. It is found that the numerical model can well simulate the average value and distribution of the temperature variation of rock specimens, so this method can be applied for studying the mechanism of temperature variation more comprehensively during the whole process of rock deformation and fracturing compared with the continuous modeling methods. With this method, it is shown that the temperature change has three different stages with different characteristics during the uniaxial compression experiments. In the different stages, the different effects play different roles in temperature variation, and stress distribution and crack propagation have obvious influences on the local distribution of temperature. Further investigations have also been conducted in a series of sensitive analyses on the influences of four factors, including the thermal conductivity, friction coefficient, thermal expansion coefficient, and particle size ratio. The results show that they have different influences on the thermal and mechanical behaviors of the rock specimens during the deformation and failure process, while the thermal expansion coefficient and the particle size ratio have more significant impacts than the other two factors. These findings increase our knowledge on the characteristics and mechanism of temperature variation in rock during the deformation and fracturing process, and the proposed modeling method can be used in more studies for deformation and fracturing analyses in rock experiments and engineering.
Study on Denoising Method of Vibration Signal Induced by Tunnel Portal Blasting Based on WOA-VMD Algorithm
Authors: Liu Ze , Peng Yaxiong
Published: March 2023

Because of the impact of the complex environment of tunnel portals, the measured blasting vibration signals in a tunnel portal contains a lot of high-frequency noise. To achieve effective noise reduction, a novel method of noise reduction for blasting vibration signals based on the whale optimization algorithm (WOA) optimized with the variational mode decomposition (VMD) algorithm was proposed. The WOA algorithm is used to optimize globally for the mode number K and penalty factor α of VMD for measured signals and to determine the optimal parameters of [K, α], and to obtain the intrinsic mode function (IMF). Multi-scale permutation entropy (MPE) was used to identify and remove noise components in IMF, and then the reserved IMF was reconstructed to achieve a denoised signal. The method is applied to the blasting vibration analysis of the Xiali tunnel of the Jixin expressway in the Henan Province. Results indicate that the novel method can acquire the optimal decomposition mode number and identify the high frequency noise. Its denoising effect is better than the conventional VMD algorithm and the complete ensemble empirical mode decomposition with adaptive ability (CEEMDAN), which verifies the self-adaptivity and effectiveness of the WOA-VMD denoising method.
The Material Matters: Sorption/Desorption Study of Selected Estrogens on Common Tubing or Sampling Materials Used in Water Sampling, Handling, Analysis or Treatment Technologies
Authors: Odehnalová Klára , Přibilová Petra , Zezulka Štěpán , Maršálek Blahoslav
Published: March 2023

Plastic/rubber materials used as fasteners in equipment for analyzing or removing organic pollutants in water treatment technologies form an essential part of the device. Micropollutants in water are typically present at very low concentrations (ng/L to µg/L). Therefore, when designing, for example, units for advanced oxidation processes (AOPs) or planning sample handling, it is necessary to assess whether the material is compatible with the usually hydrophobic nature of the pollutants. As a model example, the possible interactions of estrogens, namely, estrone (E1), 17β-estradiol (E2), estriol (E3) and 17α-ethinylestradiol (EE2) with six commonly used plastic and rubber materials were investigated at environmentally relevant concentrations (100–500 ng/L). In the first phase, we proved that polyvinyl chloride (PVC), polytetrafluoroethylene (PTFE), polyvinylidene fluoride (PVDF) and ethylene propylene diene monomer (EPDM) materials adsorbed only negligible amounts of estrogens, while significant amounts of E1, E2 and EE2 were adsorbed onto Tygon S3™ material. Another unsuitable material was styrene butadiene rubber (SBR), sorbing a considerable quantity of estrone. A detailed test of EPDM at higher concentrations (300 and 500 ng/L) and prolonged soaking time showed significant sorption of EE2 after 12 h of soaking in both deionized and tap water matrices. Thus, EPDM, PTFE and PVDF are suitable materials for sample handling or producing devices for AOP treatment due to their chemical inertness and mechanical flexibility. The results suggest that plastic materials that come into contact with contaminated water must be carefully selected, especially when working at environmentally relevant concentrations.
Investigation of Ultrasonic Velocity and Transmission Losses in Graphite Rods Based on Numerical Simulation and Experiment
Authors: Li Mi , He Jianjun
Published: March 2023

The depth of electrodes inserted into the charge plays a vital role in controlling the submerged arc furnace. Therefore, we used ultrasound waves reflected from the electrode tip to estimate the depth of electrodes inserted into the charge. However, graphite’s ultrasonic velocity and transmission loss are the basis for establishing an ultrasonic measurement system. Thus, we expected to improve our understanding of them through numerical simulations and experimental measurements. First, we proposed an ultrasonic detection method to estimate the electrode length by embedding graphite rods in Söderberg electrodes. Then, we developed a 3D finite element model in COMSOL for wave transmission in the graphite rod. The wave transmission through 20 and 40 cm graphite rods was simulated using finite element models. The transmission loss, sound pressure, intensity, and displacement distribution of the sound wave passing through the graphite electrode were calculated. To verify the simulation calculation results, we further conducted an acoustic experiment. The results showed that transmission loss varies significantly with frequency. When the frequency was between 25 and 55 kHz, the transmission loss of the graphite rod was slight. At 47 kHz, the transmission loss was 1.837 dB/m.
Optimal Power Flow Solutions for Power System Considering Electric Market and Renewable Energy
Authors: Nguyen Thang Trung , Nguyen Hung Duc , Duong Minh Quan
Published: March 2023

The paper applies jellyfish search algorithm (JSA) for reaching the maximum profit of IEEE 30-node and IEEE 118-node transmission power networks considering electrical market and wind turbines (WTs). JSA is compared with the particle swarm optimization (PSO), genetic algorithm (GA), moth swarm algorithm (MSA), salp swarm algorithm (SSA), and water cycle algorithm (WCA) for three study cases. The same and different electric prices for all nodes are, respectively, considered in Case 1 and Case 2, whereas Case 3 considers different prices and the placement of one WT. As a result, JSA can reach higher profit than MSA, SSA, WCA, PSO, and GA by 1.2%, 2.44%, 1.7%, 1.3%, and 1.02% for Cases 1, 2, and 3. Then, JSA is applied for optimizing the placement of from two to four WTs for the first system, and from zero to five wind farms (WF) for the second systems. Comparison of profits from the study cases indicates that the network can reach higher profit if more WTs and WFs are optimally placed. The placement of four WTs can support the two systems to reach higher profit by $130.3 and $34770.4, respectively. The greater profits are equivalent to 2.6% and 97.2% the profit of the base system. On the other hand, the obtained results also reveal the important order of location for installing wind power generators. The important order of nodes is, respectively, Nodes 5, 2, 1, and 10 for the first system, as well as Nodes 29, 31, 71, 45, and 47 for the second system. Thus, it is recommended that renewable energies are very useful in improving profit for transmission power systems, and the solutions of installing renewable energy-based generators should be determined by high performance algorithms, such as JSA.
Performance Appraisal of Urban Street-Lighting System: Drivers’ Opinion-Based Fuzzy Synthetic Evaluation
Authors: Alharbi Fawaz , Almoshaogeh Meshal I. , Ibrahim Anwar H. , Haider Husnain , Elmadina Abd Elaziz M. , Alfallaj Ibrahim
Published: March 2023

Saudi Arabian urban roads and highways have witnessed a large number of traffic crashes. Road lighting is one of the most important factors influencing drivers’ safety during the nighttime. Street-lighting design (e.g., spacing and height), visibility (e.g., lane marking and oncoming vehicles), and drivers’ satisfaction (e.g., glare effect on eyes and overall ambiance) are primary criteria affecting the performance of an urban street-lighting system (USLS). The present study presents a methodology for the performance appraisal of USLS in Qassim, Saudi Arabia. An online questionnaire survey was developed to obtain drivers’ opinions on nine sub-criteria (three under each primary criterion). The responses were translated into a five-scale subjective rating system from very low to very high. Fuzzy synthetic evaluation (FSE) effectively aggregated the statistically diverse (p-value < 0.001) responses obtained on the three primary criteria. The study found that electronic billboards’ positioning, oncoming vehicle lights, and poor lighting in the course of bad weather (mainly dust) are mainly affecting the performance of USLS in the view of road users. The performance levels ranged between “medium” and “high”, with no criteria or sub-criteria achieving a “very high” level, suggesting a need for upgrades, such as conversion to LED lights and smart lighting control systems. The proposed methodology benefits the transportation ministries to identify lacking components of USLSs in different regions of Saudi Arabia. The methodology provides the opportunity to include additional or site-specific factors for appraising the performance of USLS before (during planning and design) or after the implementation of improvement actions.
A Chinese Few-Shot Text Classification Method Utilizing Improved Prompt Learning and Unlabeled Data
Authors: Hu Tingkai , Chen Zuqin , Ge Jike , Yang Zhaoxu , Xu Jichao
Published: March 2023

Insufficiently labeled samples and low-generalization performance have become significant natural language processing problems, drawing significant concern for few-shot text classification (FSTC). Advances in prompt learning have significantly improved the performance of FSTC. However, prompt learning methods typically require the pre-trained language model and tokens of the vocabulary list for model training, while different language models have different token coding structures, making it impractical to build effective Chinese prompt learning methods from previous approaches related to English. In addition, a majority of current prompt learning methods do not make use of existing unlabeled data, thus often leading to unsatisfactory performance in real-world applications. To address the above limitations, we propose a novel Chinese FSTC method called CIPLUD that combines an improved prompt learning method and existing unlabeled data, which are used for the classification of a small amount of Chinese text data. We used the Chinese pre-trained language model to build two modules: the Multiple Masks Optimization-based Prompt Learning (MMOPL) module and the One-Class Support Vector Machine-based Unlabeled Data Leveraging (OCSVM-UDL) module. The former generates prompt prefixes with multiple masks and constructs suitable prompt templates for Chinese labels. It optimizes the random token combination problem during label prediction with joint probability and length constraints. The latter, by establishing an OCSVM model in the trained text vector space, selects reasonable pseudo-label data for each category from a large amount of unlabeled data. After selecting the pseudo-label data, we mixed them with the previous few-shot annotated data to obtain brand new training data and then repeated the steps of the two modules as an iterative semi-supervised optimization process. The experimental results on the four Chinese FSTC benchmark datasets demonstrate that our proposed solution outperformed other prompt learning methods with an average accuracy improvement of 2.3%.
Centralized Co-Operative Detection Algorithm Based on Double Thresholds for Underwater Acoustic Networks
Authors: Zhang Jing , Wang Qiqi , Zhang Rui
Published: March 2023

Spectrum-sensing algorithms are one of the effective solutions to the problem of the underwater spectrum resource constraint. However, because the underwater acoustic channel is one of the most complex channels, it has many characteristics, such as a limited communication bandwidth, multipath effect, and ocean noise, all of which render the spectrum detection more difficult. As basic spectrum-sensing algorithms, energy detection algorithms are widely used in underwater acoustic communication and radio. However, most of the existing dual-threshold energy detection methods do not judge the signals with energy values between the thresholds or discard them directly. In this paper, a double-threshold centralized co-operative detection algorithm is proposed to solve this problem. In this algorithm, each sensing user makes a judgment independently, and if the historical energy statistics are between the thresholds, the number of sampling points is increased, and the judgment is made again. In the centralized collaborative sensing algorithm, each sensing user’s results are sent to the fusion center, which uses the OR judgment criterion to make decisions. Simulation results show that this algorithm can improve the detection performance and reduce the error rate.
Assessment of Challenges to the Adoption of IOT for the Safety Management of Small Construction Projects in Malaysia: Structural Equation Modeling Approach
Authors: Waqar Ahsan , Khan Muhammad Basit , Shafiq Nasir , Skrzypkowski Krzysztof , Zagórski Krzysztof , Zagórska Anna
Published: March 2023

The Internet of Things is a comprehensive system of connected computing devices and sensors that provide extensive data sharing capability for any specific purpose. For the construction industry, the applications of the Internet of Things have been increasing over the past few years, and it is because technology can provide full support to construction projects in attaining significant efficiency. The most critical part of construction products where the internet of things can be adopted is safety management because hundreds of accidents happen every year that result in significant injuries to construction workers and even death in some cases. For small construction projects, the situation is much worse, as there are never enough resources to adopt the latest technology, such as the Internet of Things. This study is structured with the aim of identifying the critical implementation barriers of the internet of things that affect small construction projects in Malaysia specifically. A mixed methodology study design is followed in which, after identifying the implementation barriers of the internet of things from existing literature, they are filtered with expert opinion. A pilot survey was conducted on which exploratory factor analysis was applied to further identify the significant barriers relating to the Internet of Things in small construction projects in Malaysia. A main survey was conducted afterwards, on which the structural equation modelling was done to develop the model involving the final 16 barriers divided into 5 formative constructs. The most critical barriers are found to be related to databases and technology, while the least impact is created by management barriers. Positive theoretical and managerial implications are indicated for future researchers and construction workers, respectively, by which they can improve the implementation of internet of things in small construction projects in Malaysia.
Does Context Matter? Effective Deep Learning Approaches to Curb Fake News Dissemination on Social Media
Authors: Alghamdi Jawaher , Lin Yuqing , Luo Suhuai
Published: March 2023

The prevalence of fake news on social media has led to major sociopolitical issues. Thus, the need for automated fake news detection is more important than ever. In this work, we investigated the interplay between news content and users’ posting behavior clues in detecting fake news by using state-of-the-art deep learning approaches, such as the convolutional neural network (CNN), which involves a series of filters of different sizes and shapes (combining the original sentence matrix to create further low-dimensional matrices), and the bidirectional gated recurrent unit (BiGRU), which is a type of bidirectional recurrent neural network with only the input and forget gates, coupled with a self-attention mechanism. The proposed architectures introduced a novel approach to learning rich, semantical, and contextual representations of a given news text using natural language understanding of transfer learning coupled with context-based features. Experiments were conducted on the FakeNewsNet dataset. The experimental results show that incorporating information about users’ posting behaviors (when available) improves the performance compared to models that rely solely on textual news data.