bluuebunny/crossref_metadata_2025 huggingface dataset:
Prompt Tuning for Multi-Label Text Classification: How to Link Exercises to Knowledge Concepts?
Authors: Wei Liting , Li Yun , Zhu Yi , Li Bin , Zhang Lejun
Published: October 2022

Exercises refer to the evaluation metric of whether students have mastered specific knowledge concepts. Linking exercises to knowledge concepts is an important foundation in multiple disciplines such as intelligent education, which represents the multi-label text classification problem in essence. However, most existing methods do not take the automatic linking of exercises to knowledge concepts into consideration. In addition, most of the widely used approaches in multi-label text classification require large amounts of training data for model optimization, which is usually time-consuming and labour-intensive in real-world scenarios. To address these problems, we propose a prompt tuning method for multi-label text classification, which can address the problem of the number of labelled exercises being small due to the lack of specialized expertise. Specifically, the relevance scores of exercise content and knowledge concepts are learned by a prompt tuning model with a unified template, and then the multiple associated knowledge concepts are selected with a threshold. An Exercises–Concepts dataset of the Data Structure course is constructed to verify the effectiveness of our proposed method. Extensive experimental results confirm our proposed method outperforms other state-of-the-art baselines by up to 35.53% and 41.78% in Micro and Macro F1, respectively.
ViT-Cap: A Novel Vision Transformer-Based Capsule Network Model for Finger Vein Recognition
Authors: Li Yupeng , Lu Huimin , Wang Yifan , Gao Ruoran , Zhao Chengcheng
Published: October 2022

Finger vein recognition has been widely studied due to its advantages, such as high security, convenience, and living body recognition. At present, the performance of the most advanced finger vein recognition methods largely depends on the quality of finger vein images. However, when collecting finger vein images, due to the possible deviation of finger position, ambient lighting and other factors, the quality of the captured images is often relatively low, which directly affects the performance of finger vein recognition. In this study, we proposed a new model for finger vein recognition that combined the vision transformer architecture with the capsule network (ViT-Cap). The model can explore finger vein image information based on global and local attention and selectively focus on the important finger vein feature information. First, we split-finger vein images into patches and then linearly embedded each of the patches. Second, the resulting vector sequence was fed into a transformer encoder to extract the finger vein features. Third, the feature vectors generated by the vision transformer module were fed into the capsule module for further training. We tested the proposed method on four publicly available finger vein databases. Experimental results showed that the average recognition accuracy of the algorithm based on the proposed model was above 96%, which was better than the original vision transformer, capsule network, and other advanced finger vein recognition algorithms. Moreover, the equal error rate (EER) of our model achieved state-of-the-art performance, especially reaching less than 0.3% under the test of FV-USM datasets which proved the effectiveness and reliability of the proposed model in finger vein recognition.
Early Ventricular Fibrillation Prediction Based on Topological Data Analysis of ECG Signal
Authors: Ling Tianyi , Zhu Ziyu , Zhang Yanbing , Jiang Fangfang
Published: October 2022

Early ventricular fibrillation (VF) prediction is critical for prevention of sudden cardiac death, and can improve patient survival. Generally, electrocardiogram (ECG) signal features are extracted to predict VF, a process which plays an important role in prediction accuracy. Therefore, this study first proposes a novel feature based on topological data analysis (TDA) to improve the accuracy of early ventricular fibrillation prediction. Firstly, the heart activity is regarded as a cardiac dynamical system, which is described by phase space reconstruction. Then the topological structure of the phase space is characterized with persistent homology, and its statistical features are further extracted and defined as TDA features. Finally, 60 subjects (30 VF, 30 healthy) from three public ECG databases are used to validate the prediction performance of the proposed method. Compared to heart rate variability features and box-counting features, TDA features achieve a superior accuracy of 91.7%. Additionally, the three types of features are combined as fusion features, achieving the optimal accuracy of 95.0%. The fusion features are then ranked, and the first seven components are all from the TDA features. It follows that the proposed features provide a significant effect in improving the predictive performance of early VF.
Hemorheological Alterations and Physical Activity
Authors: Ivanov Ivan
Published: October 2022

Elucidating the complex processes that determine the ability of the human body to adapt to specific intense training programs is critical to improving athletic performance in elite athletes. The present work aims to describe the main hemorheological changes as a result of physical exercises of different intensity, frequency, duration and modes of loading. Sport and physical exertion induce a hemorheological response of different nature and degree, structured in the present paper as follows: erythrocyte aggregation; change in the concentration of basic plasma components; changes in blood flow; changes in blood volume; changes in the endothelial cells of the vascular walls; changes in blood pressure; changes resulting from tissue hypoxia; interacting, different in nature hemorheological consequences. The studied and described original models of hemorheological response can contribute to a more successful adaptation of the training programs—In order to improve the health status of the participants and to optimize the sports form of the elite athletes. Physical loads of different frequencies, intensities and durations induce a different hemorheological response in athletes. The effect of “regular, moderate and properly dosed physical activity” during training is also strictly individual. This fact suggests approaching the training regime of each elite athlete personally, practicing specific to his preparation, well-chosen, balanced physical exercises. This will help to achieve the so-called state of hemorheological fitness. Extensive future research is needed to elucidate the cellular, tissue and molecular mechanism of hemorheological changes in blood, blood cells, and blood plasma. It is essential to study the vascular characteristics and biomechanical properties of blood under physical loads with different regimes and variable parameters, taking into account the individual, professional, biological and mechanical profile of each athlete.
Determination of Polypropylene Fiber-Reinforced Concrete Compressive Strength and Elasticity Modulus via Ultrasonic Pulse Tests
Authors: Del Savio Alexandre Almeida , La Torre Esquivel Darwin , Carrillo Julian , Chi Yep Emilio
Published: October 2022

Compressive strength and elasticity modulus are the main mechanical properties of concrete. The non-destructive ultrasound pulse test can be used to determine these properties without compromising the structure’s integrity. This study seeks to assess whether a correlation exists (1) between the Reinforcement Index (RI) and the mechanical properties, (2) between the RI and the dynamic properties, and (3) among the dynamic properties of polypropylene fiber-reinforced concrete. The RI was modified through fiber volume fraction (0, 0.4, 0.8 and 1.2%) and fiber length (40, 50 and 60 mm). The dynamic properties were assessed through dynamic elasticity modulus and ultrasonic pulse velocity (UPV), which were determined by direct, semi-direct, and indirect prospect methods. Finally, compressive strength, static elasticity modulus, and Poisson’s ratio were assessed through destructive tests. Their relationship with UPV and the dynamic elasticity modulus is also subsequently studied. The results reveal a correlation between RI and compressive strength and UPV; however, the static elasticity modulus only exhibits a correlation with UPV in one of its measurement methods. Finally, empirical models were developed for predicting compressive strength, elasticity modulus as a function of ultrasonic pulse velocity and RI, and dynamic elasticity modulus as a function of compressive strength and RI.
A Comparative Analysis of the Dynamic Strength Properties of the Long Guides of Intelligent Machines for a New Method of the Thermal Spraying of Polymer Concrete
Authors: Zhetessova Gulnara , Nikonova Tatyana , Gierz Łukasz , Berg Alexandra , Yurchenko Vassiliy , Zharkevich Olga , Alexey Kalinin
Published: October 2022

The possibility of using polymer concrete for metal cutting machine beds is analyzed. A comparison of the structures of the machine beds made of polymer concrete and cast iron is made. The frequency of the body of the machine beds made of polymer concrete and cast iron is determined. An analysis of the stress–strain state under static loads is carried out. To increase resistance to wear, it is proposed to spray polymer concrete frames are proposed by the gas–thermal method. The installation of thermal spraying for guide machine beds is given. The optimal parameters for spraying the guides of the machine beds made of polymer concrete are established calculated using the finite element method for the guide beds of metal cutting machine tools using polymer concrete with gaseous coating. The manufacture of the foundation bed from polymer concrete increases the vibration resistance of the machine by 1.4 times. At the same time, the metal consumption of metal cutting machines will decrease by 60%. To increase the wear resistance of the frame guides, it is proposed to use thermal spraying with certain modes typical for polymer concrete. The installation of thermal spraying for bed guides is given. Calculating using the finite element method for the guide beds of metal cutting machines showed that the use of polymer concrete with a wear-resistant coating is justified.
An Abstract Summarization Method Combining Global Topics
Authors: Duan Zhili , Lu Ling , Yang Wu , Wang Jinghui , Wang Yuke
Published: October 2022

Existing abstractive summarization methods only focus on the correlation between the original words and the summary words, ignoring the topics’ influence on the summaries. To this end, an abstract summarization method combining global topic information, ACGT, is proposed. A topic information extractor, based on Latent Dirichlet Allocation, is constructed to extract key topic information from the original text, and an attention module is built to fuse key topic information with the original text representation. The summary is then generated by combining a pointer generation network and coverage mechanism. With evaluation metrics of ROUGE-1, ROUGE-2, and ROUGE-L, the experimental results of ACGT in the English dataset CNN/Daily Mail are 0.96%, 2.44%, and 1.03% higher than the baseline model, respectively. In the Chinese dataset, LCSTS, ACGT shows a higher performance than the baseline method by 1.19%, 1.03%, and 0.85%, respectively. Our results demonstrate that the performance of summaries is significantly correlated with the number of topics that are introduced. Case studies show that the introduction of topic information can improve both the coverage of original text topics and the fluency of summaries.
Automatic Correction of Indonesian Grammatical Errors Based on Transformer
Authors: Musyafa Ahmad , Gao Ying , Solyman Aiman , Wu Chaojie , Khan Siraj
Published: October 2022

Grammatical error correction (GEC) is one of the major tasks in natural language processing (NLP) which has recently attracted great attention from researchers. The performance of universal languages such as English and Chinese in the GEC system has improved significantly. This could be attributed to the large number of powerful applications supported by neural network models and pretrained language models. Referring to the satisfactory results of the universal language in the GEC task and the lack of research on the GEC task for low-resource languages, especially Indonesian, this paper proposes an automatic model for Indonesian grammar correction based on the Transformer architecture which can be applied to other low-resource language texts. Furthermore, we build a large corpus of the Indonesian language that can be utilized for evaluating the next Indonesian GEC task. We evaluate the models in this dataset, and the results show that the Transformer-based automatic error correction model achieved significant and satisfactory results compared with the results of previous research models.
Sentence Graph Attention for Content-Aware Summarization
Authors: Siragusa Giovanni , Robaldo Livio
Published: October 2022

Neural network-based encoder–decoder (ED) models are widely used for abstractive text summarization. While the encoder first reads the source document and embeds salient information, the decoder starts from such encoding to generate the summary word-by-word. However, the drawback of the ED model is that it treats words and sentences equally, without discerning the most relevant ones from the others. Many researchers have investigated this problem and provided different solutions. In this paper, we define a sentence-level attention mechanism based on the well-known PageRank algorithm to find the relevant sentences, then propagate the resulting scores into a second word-level attention layer. We tested the proposed model on the well-known CNN/Dailymail dataset, and found that it was able to generate summaries with a much higher abstractive power than state-of-the-art models, in spite of an unavoidable (but slight) decrease in terms of the Rouge scores.
BCI Wheelchair Control Using Expert System Classifying EEG Signals Based on Power Spectrum Estimation and Nervous Tics Detection
Authors: Pawuś Dawid , Paszkiel Szczepan
Published: October 2022

The constantly developing biomedical engineering field and newer and more advanced BCI (brain–computer interface) systems require their designers to constantly develop and search for various innovative methods used in their creation. In response to practical requirements and the possibility of using the system in real conditions, the authors propose an advanced solution using EEG (electroencephalography) signal analysis. A BCI system design approach using artificial intelligence for the advanced analysis of signals containing facial expressions as control commands was used. The signals were burdened with numerous artifacts caused by simulated nervous tics. The proposed expert system consisted of two neural networks. The first one allowed for the analysis of one-second samples of EEG signals from selected electrodes on the basis of power spectrum estimation waveforms. Thus, it was possible to generate an appropriate control signal as a result of appropriate facial expression commands. The second of the neural networks detected the appearance and type of nervous tics in the signal. Additionally, the participants were affected by interference such as street and TV or radio sound, Wi-Fi and radio waves. The system designed in such a way is adapted to the requirements of the everyday life of people with disabilities, in particular those in wheelchairs, whose control is based on BCI technology.
Surface Engineering of Woodworking Tools, a Review
Authors: Warcholinski Bogdan , Gilewicz Adam
Published: October 2022

The wide range of applications of wood are due to its strength properties. The mechanical properties of wood in various parts or directions are different. The complex structure of wood and its hygroscopicity prevent the use of coolants and lubricants, resulting in rapid tool wear disproportionate to the hardness of the processed material. This significantly affects machining efficiency and the quality of the processed surface. It seems that an effective method of reducing tool wear is its modification with a thin hard coating produced by the Physical Vapor Deposition or Chemical Vapor Deposition methods. The article presents tool materials used for woodworking, areas for improving the efficiency of their work, and the impact of thin hard coatings on the increase in tool durability, including binary coatings and also doping with various elements and multilayer coatings. Scientific centers dealing with the above-mentioned subject are also mentioned. A brief review of the effects of surface modifications of woodworking tools in the context of their durability is presented. It was found that the most promising coatings on tools for woodworking were multilayer coatings, especially based on chromium. Higher wear resistance was demonstrated by coatings with a lower coefficient of friction. This value was more important than hardness in predicting the service life of the coated tool.
Sewage Sludge Quality and Management for Circular Economy Opportunities in Lombardy
Authors: Domini Marta , Bertanza Giorgio , Vahidzadeh Reza , Pedrazzani Roberta
Published: October 2022

From the perspective of a circular economy that prioritizes resource reuse and recovery, sewage sludge could be a source of nutrients for agricultural soils or a source of energy, depending on its characteristics. Lombardy is the region with the highest quantity of sludge production and management in Italy. A methodology was developed to: extract and analyze quantitative data on sewage sludge (EWC 190805) production and management (2017–2018); collect and analyze qualitative data from publicly available documents in tender processes for sewage sludge management (2014–2020). Sludge from Lombardy’s wastewater treatment facilities displayed average qualities that were useful for recovery in agriculture after additional stabilization treatments. Sludge showed generally low heating values and elevated water content and should require additional treatments to be used in mono-combustion. The study discovered that there is still work to be done in sludge recovery in agriculture in Lombardy, taking biosolid quality into account. Sludge, on the other hand, can be converted into energy. The methodology for collecting and analyzing site-specific data presented here can be applied to other areas. The findings can assist and guide decision-makers in developing future regional sewage sludge management strategies.
Effect of Vertical Pressure on Temperature Field Distribution of Bulk Paddy Grain Pile
Authors: Ge Mengmeng , Chen Guixiang , Liu Chaosai , Zheng Deqian , Liu Wenlei
Published: October 2022

Grain storage pressure is an important factor affecting grain pile temperature, and its influencing mechanism needs to be studied further. The distribution and variation of the temperature field of a bulk grain pile under different vertical pressures and a temperature difference of 25 °C are studied by a model test and numerical simulation. Initially, the temperature change and heat transfer law at different points in the bulk grain pile space are studied under different vertical pressures using a self-made test device. Thereafter, a multi-field coupling software platform COMSOL is used to simulate and study the distribution law of the temperature field in the bulk grain pile under different vertical pressures. The influence mechanism of vertical pressure on the temperature field of the grain pile is discussed based on the micro-airflow velocity field obtained by numerical simulation. The results show that the numerical simulation and experimental results are in good agreement. With the increase in vertical pressure, the heat transfer rate of the grain pile increases gradually, the convective heat transfer in the grain pile is hindered, and the temperature distribution gradually attains uniformity. When the vertical pressure increases from 50 kPa to 200 kPa, the temperature of the grain pile decreases by approximately 0.6–2.7 °C, and the rate of change of the temperature gradient reaches 7.4%. Under different vertical pressures, the proportion of the high-temperature area decreases linearly with the storage duration. The micro-airflow velocity field affects the temperature transfer in the bulk grain pile, resulting in the temperature at the top of the storage structure being higher than that at the bottom. The research methods and conclusions in this study can provide theoretical support and reference for the multi-field coupling research on bulk grain pile storage.
A Two-Stage Structural Damage Detection Method Based on 1D-CNN and SVM
Authors: Jiang Chenhui , Zhou Qifeng , Lei Jiayan , Wang Xinhong
Published: October 2022

Deep learning has been applied to structural damage detection and achieved great success in recent years, such as the popular structural damage detection methods based on structural vibration response and convolutional neural networks (CNN). However, due to the limited number of vibration response samples that can be acquired in practice for damage detection, the CNN-based models may not be fully trained; thus, their performance for identifying different damage severity as well as the damage locations may be reduced. To solve this issue, in this paper, we follow the strategy of "divide-and-conquer" and propose a two-stage structural damage detection method. Specifically, in the first stage, a 1D-CNN model is constructed to extract the damage features automatically and identify the damage locations. In the second stage, a support vector machine (SVM) model and wavelet packet decomposition technique are combined to further quantify the damage. Experiments are conducted on an eight-level steel frame structure, and the accuracy of the experimental results is greater than 99%, which demonstrates the superiority of the proposed method compared to the state-of-the-art approaches.
A Development of Optimal Algorithm for Integrated Operation of UGVs and UAVs for Goods Delivery at Tourist Destinations
Authors: Ko Young Kwan , Park Ju Hyeong , Ko Young Dae
Published: October 2022

Although the actual use of delivery robots like UGVs and UAVs has not yet been generalized, they are also used for additional purposes like fun and enjoyment in some limited areas such as tourist destinations. In this study, an optimal algorithm is proposed that operates a delivery service through an integrated system of UGVs and UAVs at certain tourism destination. It is assumed that both UGVs and UAVs or only one means could be used depending on the type of goods delivered and the topographical characteristics. The mathematical model-based optimization technique is applied to generate the delivery service route of both UGVs and UAVs that can maximize total customer satisfaction. The developed mathematical model is solved through CPLEX and genetic algorithm, and the results are compared by dividing into case 1 in which UAVs move freely and case 2 in which UAVs can move only in a limited path since there is a risk of accidental falling when moving. As a result, when UAVs move freely, the total customer satisfaction is higher while the total complete time increases. However, it is suggested that an appropriate operation policy should be determined considering the risk of accidental falling.
Analysis of Confinement in Dual Spherical-Tapered Ended Fiber Optical Trap
Authors: Feng Haining , Jin Shilong , An Hui , Chen Xinlin , Tan Zhongqi , Luo Hui , Xiao Guangzong
Published: October 2022

In dual-fiber optical traps, two counter-propagating laser beams emitted from opposing laser fibers trap and manipulate particles. We describe the operation and performance of a dual-fiber optical trap created using spherical-tapered ended fiber pigtailed to 1436 nm laser diodes. Compared with the dual flat ended fiber optical trap, the dual spherical-tapered ended fiber optical trap increased the axial stiffness from 0.44 pN/µm to 0.99 pN/µm, and increased the lateral stiffness from 1.68 pN/µm to 1.76 pN/µm. The dual-fiber optical trap fabricated by spherical-tapered ended fiber enhanced the trapping efficiency of the optical trap. It expanded the application range and reliability of the dual-fiber optical trap. Additionally, we integrated the dual-fiber optical trap into an optical chip, thereby improving the stability of the system.
Techno-Economics and the Identification of Environmental Barriers to the Development of Concentrated Solar Thermal Power Plants in India
Authors: Praveenkumar Seepana , Agyekum Ephraim Bonah , Kumar Abhinav , Ampah Jeffrey Dankwa , Afrane Sandylove , Amjad Fahd , Velkin Vladimir Ivanovich
Published: October 2022

India is endowed with a lot of solar radiation as a result of its location. The Indian government therefore intends to maximize the usage of its solar energy resources through the development of solar power plants across the country. The concentrated solar power plant (CSP) is one of the technologies that rely on solar energy for its electricity generation. The type of condenser model in the CSP technology has the potential to affect its techno-economic viability. In this paper, a 100 MW solar tower power plant (STPP) with two different condenser models, i.e., the dry-cooled STPP and wet-cooled STPP models, are studied using the System Advisor Model (SAM) at six different geographical areas in India. The study employed the optimization of the thermal energy storage and the solar field size to identify the minimum levelized cost of electricity (LCOE) for all six locations. Results from the simulation show that the LCOE will range between 13 and 17 cents/kWh under the optimization conditions for the STPP dry-cooled condenser model, while that of the wet-cooled condenser model will range between 12.40 and 12.96 USD cents/kWh for the study locations. It was also observed that the optimized solar multiple (SM) for the dry-cooled STPP model ranges between 1.4 and 1.8, whereas that of the wet-cooled model ranges between 1 and 1.8. The study identified Bhopal as the best location for installing the STPP plant for both condenser models. In addition, this paper also discusses major potential barriers and government policies that are needed to develop CSP technologies in India. The outcome of the study is expected to help both government and other stakeholders in decision making and policy formulation for the sector.
Experimental Study of Rock-like Materials with Two Unequal Parallel Preexisting Fissures under Uniaxial Compression
Authors: Fan Pingyang , Liu Jianxin , Zuo Wengui , Wang Min , Zhang Daoyong
Published: October 2022

Fissures substantially impact the engineering stability of rock masses, making research on rock masses with preexisting fissures essential for engineering activities. The development of specimens with different preexisting fissure types was examined in this study. Three different types of specimens with preexisting fissures can be broadly categorized: specimens with few preexisting fissures, specimens with multiple preexisting fissures, and specimens with preexisting fissures and other flaws. Uniaxial compression tests of specimens with preexisting fissures of two unequal lengths were conducted, and the influence of preexisting fissure angle, the distance between the two fissures and fissure length on the peak strength and coalescence mode of specimens were analyzed. A large preexisting fissure length significantly influenced the peak strength and coalescence, and the uniaxial compressive strength (UCS) values of the rock-like materials decreased. In contrast, increasing the long preexisting fissure length influenced the crack propagation and coalescence more extensively in the case of a long preexisting fissure. The preexisting fissure angle and two preexisting fissure distances had a negligible effect on the UCS values of the rock-like materials. However, with increasing long preexisting fissure length, the short preexisting fissure was subject to crack propagation and coalescence.
Improving Air Pollution Prediction System through Multimodal Deep Learning Model Optimization
Authors: Ko Kyung-Kyu , Jung Eun-Sung
Published: October 2022

Many forms of air pollution increase as science and technology rapidly advance. In particular, fine dust harms the human body, causing or worsening heart and lung-related diseases. In this study, the level of fine dust in Seoul after 8 h is predicted to prevent health damage in advance. We construct a dataset by combining two modalities (i.e., numerical and image data) for accurate prediction. In addition, we propose a multimodal deep learning model combining a Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN). An LSTM AutoEncoder is chosen as a model for numerical time series data processing and basic CNN. A Visual Geometry Group Neural Network (VGGNet) (VGG16, VGG19) is also chosen as a CNN model for image processing to compare performance differences according to network depth. The VGGNet is a standard deep CNN architecture with multiple layers. Our multimodal deep learning model using two modalities (i.e., numerical and image data) showed better performance than a single deep learning model using only one modality (numerical data). Specifically, the performance improved up to 14.16% when the VGG19 model, which has a deeper network, was used rather than the VGG16 model.
Value Chain Comparison of Additively and Conventionally Manufactured Multi-Cavity Tool Steel Inserts: An Injection Molding Industrial Case Study for High-Volume Production
Authors: Moshiri Mandaná , Raza Mohsin , Sahlab Mohamed , Malik Ali Ahmad , Bilberg Arne , Tosello Guido
Published: October 2022

The development of injection molding tools is an expensive, time-consuming, and resource-intensive process offering little to no flexibility to adapt to variations in product design. Metal additive manufacturing can be used to produce these tools in a cost-effective way. Nevertheless, in an industrial context, effective methods are missing for the selection of the most suitable technology for the given tooling project. This paper presents a method to compare process chains based on additive and conventional subtractive technologies for the manufacturing of metal tooling for injection molding. The comparison is based on a technology focused-performance analysis (TFPA) through computer simulation performed using Tecnomatix Plant Simulation developed by Siemens Digital Industries Software combined with a customized cost–benefit economic analysis tool. The analysis of the technology comparison highlights potential bottlenecks for production, such as the printing phase and the heat treatment. It also gives a deeper understanding of the technology maturity level of conventional milling machines against laser powder bed fusion machines. The result is that the total costs for an insert made by AM and CM are indeed rather similar (the cost difference between the two tooling process chains is lower than 5%). The cost analysis reveals major costs drivers in the production of high-performance molding tools, such as the cutting tools employed for the milling steps and their changeover frequency. The industrial case of a 32-cavity mold insert for plastic injection molding is used to perform the study, develop the analysis, and validate the results.
ZIF-75 under Pressure: Negative Linear Compressibility and Pressure-Induced Instability
Authors: Colmenero Francisco , Timón Vicente
Published: October 2022

The behavior of the crystal structure of the zeolitic imidazolate framework ZIF-75 under pressure was studied by means of periodic density functional theory methods. Experimentally, it was shown that this material is tetragonal, space group I41/a at room temperature. However, according to the calculations, at zero temperature this material is monoclinic, space group C2/c. Irrespective of the symmetry of the material, the results show that ZIF-75 exhibits a negative linear compressibility effect and is unstable under relatively small applied pressures of the order of 0.1 GPa.
Crashworthiness Analysis and Multi-Objective Optimization for Concave I-Shaped Honeycomb Structure
Authors: Wang Tingting , Li Mengchun , Qin Dongchen , Chen Jiangyi , Wu Hongxia
Published: October 2022

Due to their superior structural and mechanical properties, materials with negative Poisson’s ratio are of increasing interest to research scholars, especially in fuel-efficient vehicles. In this work, a new concave I-shaped honeycomb structure is established by integrating the re-entrant hexagon and the I-shaped beam structure, and its negative Poisson’s ratio characteristics and energy absorption properties are investigated. The effect of structural parameters on the energy absorption characteristics is analyzed using the finite element model. The results show that both the specific energy absorption and peak impact force decrease with the increase in cellular length and vertical short cellular height, and increase with the increase in horizontal short cellular length and cellular thickness. To obtain a smaller peak impact force and larger specific energy absorption with smaller mass, the four cell sizes were optimized by using Latin hypercube sampling, Gaussian radial basis function, and non-dominated sorting genetic algorithm II (NSGA-II). Compared with the original design, the SEA increased by 44.175%, and the PCF increased by 25.857%. Meanwhile, the mass decreased by 31.140%. Hence, the optimal structure has better crashworthiness.
IoT-Based Intelligent Monitoring System Applying RNN
Authors: Shin Moonsun , Hwang Seonmin , Kim Byungcheol , Seo Sungbo , Kim Junghwan
Published: October 2022

In this paper, we propose an intelligent monitoring framework based on the Internet of Things (IoT) by applying a Recurrent Neural Network (RNN) for the predictive maintenance of a biobanking system. RNN, which is one of the deep learning models, is used for time series data. It is called a sequence model because it processes inputs and outputs in sequence units. The proposed framework measures the internal temperature of the cryogenic freezer and the temperature of each component simultaneously, monitors the internal temperatures of internal and middle layers in real time, sends the sensing temperature data to the server, and performs predictive learning. Thus, it is possible to support the intelligent predictive maintenance of the biobank by performing a time series data analysis of the temperature sensor using RNN. Among RNN methods, a simple RNN has a longer-term dependency problem; therefore, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), which have higher learning performance, are selected. To support the intelligent predictive maintenance of the biobank, both the LSTM and GRU models were constructed, and comparative experiments were performed. The proposed system can ensure the safety of bio-resources by performing predictive maintenance using RNN and provide an accurate status of the biobank in real-time. In addition, before an abnormal situation occurs, it is possible to respond immediately to emergencies that may damage biological resources.
Wi-GC: A Deep Spatiotemporal Gesture Recognition Method Based on Wi-Fi Signal
Authors: Dang Xiaochao , Bai Yanhong , Hao Zhanjun , Liu Gaoyuan
Published: October 2022

Wireless sensing has been increasingly used in smart homes, human–computer interaction and other fields due to its comprehensive coverage, non-contact and absence of privacy leakage. However, most existing methods are based on the amplitude or phase of the Wi-Fi signal to recognize gestures, which provides insufficient recognition accuracy. To solve this problem, we have designed a deep spatiotemporal gesture recognition method based on Wi-Fi signals, namely Wi-GC. The gesture-sensitive antennas are selected first and the fixed antennas are denoised and smoothed using a combined filter. The consecutive gestures are then segmented using a time series difference algorithm. The segmented gesture data is fed into our proposed RAGRU model, where BAGRU extracts temporal features of Channel State Information (CSI) sequences and RNet18 extracts spatial features of CSI amplitudes. In addition, to pick out essential gesture features, we introduce an attention mechanism. Finally, the extracted spatial and temporal characteristics are fused and input into softmax for classification. We have extensively and thoroughly verified the Wi-GC method in a natural environment and the average gesture recognition rate of the Wi-GC way is between 92–95.6%, which has strong robustness.
Topp–Leone Modified Weibull Model: Theory and Applications to Medical and Engineering Data
Authors: Alyami Salem A. , Elbatal Ibrahim , Alotaibi Naif , Almetwally Ehab M. , Okasha Hassan M. , Elgarhy Mohammed
Published: October 2022

In this article, a four parameter lifetime model called the Topp–Leone modified Weibull distribution is proposed. The suggested distribution can be considered as an alternative to Kumaraswamy Weibull, generalized modified Weibull, extend odd Weibull Lomax, Weibull-Lomax, Marshall-Olkin alpha power extended Weibull and exponentiated generalized alpha power exponential distributions, etc. The suggested model includes the Topp-Leone Weibull, Topp-Leone Linear failure rate, Topp-Leone exponential and Topp-Leone Rayleigh distributions as a special case. Several characteristics of the new suggested model including quantile function, moments, moment generating function, central moments, mean, variance, coefficient of skewness, coefficient of kurtosis, incomplete moments, the mean residual life and the mean inactive time are derived. The probability density function of the Topp–Leone modified Weibull distribution can be right skewed and uni-modal shaped but, the hazard rate function may be decreasing, increasing, J-shaped, U-shaped and bathtub on its parameters. Three different methods of estimation as; maximum likelihood, maximum product spacing and Bayesian methods are used to estimate the model parameters. For illustrative reasons, applications of the Topp–Leone modified Weibull model to four real data sets related to medical and engineering sciences are provided and contrasted with the fit reached by several other well-known distributions.
An Attention-Based Deep Convolution Network for Mining Airport Delay Propagation Causality
Authors: Tan Xianghua , Liu Yan , Liu Dandan , Zhu Dan , Zeng Weili , Wang Huawei
Published: October 2022

The airport network is a highly dynamic and complex network connected by air routes, and it is difficult to study the impact of delays at one airport on another airport by means of human intervention. Due to the delay propagation law contained in the delay time series, some studies have used Granger causality and transfer entropy to explore whether there is a causal relationship between any two airports. However, no research has yet established a delay causal network from the perspective of the airport network as a whole. To this end, an attention mechanism is introduced into the deep convolutional network architecture, and a deep temporal convolution prediction model considering the attention mechanism is proposed, so as to establish the relationship between different airport delay time series under the same network architecture. According to the attention factor score, the delay propagation causality between airports is preliminarily screened, and the direct causality is verified based on a t-test and propagation delay analysis. Taking China’s civil airport network as an example, the method proposed in this paper can not only discover the causal relationship of delays between airports but also characterize the strength of the relationship. Further analysis found that each airport is affected by an average of six airports, and airports with small delays are more likely to be affected by other airports.
Study of In-Plane Mechanical Properties of Novel Ellipse-Based Chiral Honeycomb Structure
Authors: Wang Wei , Wang Jianjie , Hai Hong , Xu Weikai , Yu Xiaoming
Published: October 2022

In this paper, we propose an elliptical anti-tetrachiral honeycombs structure (E-antitet) with in-plane negative Poisson’s ratio (NPR) and orthogonal anisotropy. The analytical and numerical solutions of the in-plane Poisson’s ratio and Young’s modulus are given by theoretical derivations and finite element method (FEM) numerical simulations and are verified experimentally by a 3D printed sample. Finally, we analyzed the influences of different parameters on the in-plane Poisson’s ratio and Young’s modulus of E-antitet. The results show that the proposed E-antitet can achieve a smaller Poisson’s ratio and larger Young’s modulus in the desired direction compared with the anti-tetrachiral honeycombs structure (antitet), and moreover, the E-antitet has a more flexible means of regulation than the antitet. The analytical results of this paper provide meaningful guidance for the design of chiral honeycomb structures.
Research on Optimization Design of Geometric Parameters of a Novel Frame-Embedded Track (NFET)
Authors: Zeng Zhiping , Xiao Yancai , Huang Xudong , Wang Weidong , Wang Di , Qahtan Ayoub Abdullah Senan , Yuan Weidong , Houdou Saidi Boumedienne
Published: October 2022

A novel frame-embedded track (NFET) with additional beams between the prefabricated rail seats before was proposed, but its geometric parameters need to be further optimized, in order to solve the problem of difficulty in adjustment, due to the independence of the two prefabricated rail seats during the construction of tram. Based on the finite element method, the geometric parameters of the NFET structure are systematically studied and optimized. The research shows: (1) As the width of the beams and the thickness of the lower slab increases, the mechanical characteristics of the NFET structure does not change significantly; therefore, the recommended design reference value for these two are 240 and 80 mm, respectively. (2) When considering the cable and the drainage facilities, the stress state of the NFET structure is less affected (or even improved), which proves that the layout of the cable and the drainage facilities is feasible. (3) According to the analysis during different construction stages, the stress of the NFET rail seats is generally greater than the stress of the cast-in-place concrete. It is recommended that the intensity of the cast-in-place concrete should be greater than that of the prefabricated frame structure.
A Novel Mixed-Attribute Fusion-Based Naive Bayesian Classifier
Authors: Ou Guiliang , He Yulin , Fournier-Viger Philippe , Huang Joshua Zhexue
Published: October 2022

The Naive Bayesian classifier (NBC) is a well-known classification model that has a simple structure, low training complexity, excellent scalability, and good classification performances. However, the NBC has two key limitations: (1) it is built upon the strong assumption that condition attributes are independent, which often does not hold in real-life, and (2) the NBC does not handle continuous attributes well. To overcome these limitations, this paper presents a novel approach for NBC construction, called mixed-attribute fusion-based NBC (MAF-NBC). It alleviates the two aforementioned limitations by relying on a mixed-attribute fusion mechanism with an improved autoencoder neural network for NBC construction. MAF-NBC transforms the original mixed attributes of a data set into a series of encoded attributes with maximum independence as a pre-processing step. To guarantee the generation of useful encoded attributes, an efficient objective function is designed to optimize the weights of the autoencoder neural network by considering both the encoding error and the attribute’s dependence. A series of persuasive experiments was conducted to validate the feasibility, rationality, and effectiveness of the designed MAF-NBC approach. Results demonstrate that MAF-NBC has superior classification performance than eight state-of-the-art Bayesian algorithms, namely the discretization-based NBC (Dis-NBC), flexible naive Bayes (FNB), tree-augmented naive (TAN) Bayes, averaged one-dependent estimator (AODE), hidden naive Bayes (HNB), deep feature weighting for NBC (DFW-NBC), correlation-based feature weighting filter for NBC (CFW-NBC), and independent component analysis-based NBC (ICA-NBC).
Ultrasound-Assisted Extraction of an Extract with Dermatological and Nosocomial Activity from Agave nuusaviorum, a Mexican Endemic Plant
Authors: García-Sánchez Edgar , Chávez-Ruiz Priscila , Hernández-Gama Regina , Solano-Gómez Rodolfo , Lagunez-Rivera Luicita
Published: October 2022

The revolutionary development of green technology, such as ultrasound-assisted extraction, has had a significant impact on the extraction methods of natural compounds with pharmacological potential. These methods are considered green because they are environmentally friendly, minimizing the use of toxic solvents, energy consumption, and CO2 generation. Antimicrobial resistance is a global public health problem. Pathogens of dermatological and nosocomial relevance are more frequent because they are resistant to antimicrobials. Agave nuusaviorum leaves are used in traditional medicine to treat onychomycosis and inflammatory processes. Here, the antimicrobial activity of hydroethanolic extract of A. nuusaviorum leaves obtained by ultrasound-assisted extraction was evaluated. The antimicrobial activity was tested against 14 pathogens with dermatological and nosocomial relevance by the agar diffusion technique. The extract exhibited an antimicrobial effect on all microorganisms tested, showing inhibition zones of 9–16 mm for Staphylococcus aureus, Candida albicans, Candida parapsilosis, and eleven isolates of Trichophyton. The extract’s minimum inhibitory concentration ranged between 0.5 mg/mL for Staphylococcus aureus, 0.25–1 mg/mL for yeast, and 0.4–1.25 mg/mL for molds. The phytochemical screening revealed the presence of terpenes, phenolic acids, tannins, carbohydrates, and steroids in the extract. The results showed that A. nuusaviorum leaves have broad-spectrum antimicrobial potential.
New Actuation Control for Hybrid Electromagnetic Valve Train
Authors: Shiao Yaojung , Kantipudi Mahendra Babu , Weng Chang-Bo
Published: October 2022

Nowadays, vehicle industries are trying to introduce actively controllable variable valve trains to achieve maximumly efficient internal combustion engines. The electromagnetic valve train (EMV) is one of the promising valve actuators. A traditional electromagnet valve needs to be continuously supplied with current and consumes more energy during valve opening and closing, and the permanent magnet-assisted valves have a demagnetization issue. Thus, this study presents a hybrid permanent magnet electromagnetic valve (PMEMV), which needs a power supply only for a short interval of time during valve opening or closing; eventually, this PMEMV consumes much less energy than conventional EMVs. This paper proposed an improved control approach for this hybrid PMEMV to achieve variable valve actuation. Magnetic stimulation was performed on the proposed valve train to analyze the direction of the magnetic circuit during the valve actuation. An improved magnetic circuit control method was introduced to achieve the release and attraction of the armature. This innovative magnetic circuit control can make the armature effectively attract at each apex, so that the PMEMV can be effectively and completely actuated. The prototype of the valve train and the experimental platform were developed to test and validate the real-time performance of the composite EM valve. Peripheral sensor components were used to measure the valve displacement. The experimental results proved that the concept of the innovative magnetic-circuit drive and control can enable the successful operation of the hybrid compound EM valve.