bluuebunny/crossref_metadata_2025 huggingface dataset:
Rearing Conditions and Automated Feed Distribution Systems for Zebrafish (Danio rerio)
Authors: Del Vecchio Gianmarco , Mazzei Aurora , Schiavone Roberta , Gomes Ana S. , Frangelli Giovanni , Sala Tommaso , Fantino Stefania , Brocca Marco G. A. , Barca Amilcare , Rønnestad Ivar , Verri Tiziano
Published: October 2022

Zebrafish (Danio rerio) is a well-established animal model, used in a number of research areas. In the last decade, it has also emerged as a tool to evaluate the effects of diets and dietary components and to test novel paradigms in nutrigenomics, nutrigenetics, and nutritional physiology. Despite its worldwide use, the standardization of the zebrafish rearing conditions, including daily nutritional and good feed management practices, is not yet achieved. This is surprising when compared with what is available for other reared animals, such as rodents or other (e.g., commercial) fishes. To date, a major applicative goal in zebrafish nutritional physiology research is to define common, standard, and reproducible protocols of rearing and feeding conditions to generate reliable and comparable results among research laboratories. This review aims to focus on limitations and disadvantages of the current rearing and feeding practices and on some recent technological solutions provided by research groups and/or biotech companies in the field of facility design, with emphasis on automated feeding distribution systems. A general overview of some common schemes of zebrafish husbandry is also given.
Advances in Artificial Intelligence Methods Applications in Industrial Control Systems: Towards Cognitive Self-Optimizing Manufacturing Systems
Authors: Carpanzano Emanuele , Knüttel Daniel
Published: October 2022

Industrial control systems play a central role in today’s manufacturing systems. Ongoing trends towards more flexibility and sustainability, while maintaining and improving production capacities and productivity, increase the complexity of production systems drastically. To cope with these challenges, advanced control algorithms and further developments are required. In recent years, developments in Artificial Intelligence (AI)-based methods have gained significantly attention and relevance in research and the industry for future industrial control systems. AI-based approaches are increasingly explored at various industrial control systems levels ranging from single automation devices to the real-time control of complex machines, production processes and overall factories supervision and optimization. Thereby, AI solutions are exploited with reference to different industrial control applications from sensor fusion methods to novel model predictive control techniques, from self-optimizing machines to collaborative robots, from factory adaptive automation systems to production supervisory control systems. The aim of the present perspective paper is to provide an overview of novel applications of AI methods to industrial control systems on different levels, so as to improve the production systems’ self-learning capacities, their overall performance, the related process and product quality, the optimal use of resources and the industrial systems safety, and resilience to varying boundary conditions and production requests. Finally, major open challenges and future perspectives are addressed.
Clear Declining Behaviors and Causes in Atmospheric Polycyclic Aromatic Hydrocarbon Concentrations at the West End of Japan from 2017 to 2021
Authors: Kubo Takashi , Bai Wenzhi , Nagae Masaki , Takao Yuji
Published: October 2022

In order to determine recent behaviors in atmospheric polycyclic aromatic hydrocarbon (PAH) concentrations at the west end of Japan and to reveal the causes of these behaviors, atmospheric PAH concentrations were measured in suburban and forest sites of Nagasaki, Japan from 2017 to 2021. The results showed that the total concentration of PAHs decreased considerably by 60% and 57% in suburban and forest sites, respectively, over this period. When analyzed by season, the rate of decrease in winter was markedly high. Therefore, the decreasing behavior in PAH concentrations in Nagasaki in recent years was considered to be mainly due to less PAHs originating from cold continental regions such as northern China. In particular, the reduction in coal and biomass combustion for winter heating in households, the efforts to improve air quality, and the limitation of economic activities in response to COVID-19 were likely responsible for the decrease in atmospheric PAH concentrations. In addition, although the PAH concentrations decreased, there was no significant change in the breakdown of the number of benzene rings in the PAH or in the attributes of their sources.
Application of Generative Adversarial Networks and Shapley Algorithm Based on Easy Data Augmentation for Imbalanced Text Data
Authors: Wu Jheng-Long , Huang Shuoyen
Published: October 2022

Imbalanced data constitute an extensively studied problem in the field of machine learning classification because they result in poor training outcomes. Data augmentation is a method for increasing minority class diversity. In the field of text data augmentation, easy data augmentation (EDA) is used to generate additional data that would otherwise lack diversity and exhibit monotonic sentence patterns. Generative adversarial network (GAN) models can generate diverse sentence patterns by using the probability corresponding to each word in a language model. Therefore, hybrid EDA and GAN models can generate highly diverse and appropriate sentence patterns. This study proposes a hybrid framework that employs a generative adversarial network and Shapley algorithm based on easy data augmentation (HEGS) to improve classification performance. The experimental results reveal that the HEGS framework can generate highly diverse training sentences to form balanced text data and improve text classification performance for minority classes.
Functional and Sustainable Application of Natural Antioxidant Extract Recovered from Olive Mill Wastewater on Shelf-Life Extension of “Basil Pesto”
Authors: De Bruno Alessandra , Gattuso Antonio , Romeo Rosa , Santacaterina Simone , Piscopo Amalia
Published: October 2022

A natural antioxidant extract obtained from oil mill wastewater was used for the formulation of basil pesto sauce, with the aim to improve quality and stability during storage. The antioxidant extract was added to traditional ingredients (basil, cheese, oil, etc.) and after preparation, packaging, and thermal treatment it was submitted to storage (monitored for 90 days). Fresh samples were stored at 4 °C and pasteurized samples were stored at room temperature. The effect of natural antioxidant addition on basil pesto sauce was evaluated for the main qualitative attributes, such as: physicochemical, microbiological, and antioxidant parameters. The principal results showed that the addition of a natural phenolic extract led to an evident reduction in pH, attaining food safety values under pH 4. The high oxidative stability observed in the basil pesto sauces fortified with the phenolic extract suggests that the incorporation of phenolic compounds delays the propagation phase of lipid oxidation.
An Explainable and Lightweight Deep Convolutional Neural Network for Quality Detection of Green Coffee Beans
Authors: Hsia Chih-Hsien , Lee Yi-Hsuan , Lai Chin-Feng
Published: October 2022

In recent years, the demand for coffee has increased tremendously. During the production process, green coffee beans are traditionally screened manually for defective beans before they are packed into coffee bean packages; however, this method is not only time-consuming but also increases the rate of human error due to fatigue. Therefore, this paper proposed a lightweight deep convolutional neural network (LDCNN) for a quality detection system of green coffee beans, which combined depthwise separable convolution (DSC), squeeze-and-excite block (SE block), skip block, and other frameworks. To avoid the influence of low parameters of the lightweight model caused by the model training process, rectified Adam (RA), lookahead (LA), and gradient centralization (GC) were included to improve efficiency; the model was also put into the embedded system. Finally, the local interpretable model-agnostic explanations (LIME) model was employed to explain the predictions of the model. The experimental results indicated that the accuracy rate of the model could reach up to 98.38% and the F1 score could be as high as 98.24% when detecting the quality of green coffee beans. Hence, it can obtain higher accuracy, lower computing time, and lower parameters. Moreover, the interpretable model verified that the lightweight model in this work was reliable, providing the basis for screening personnel to understand the judgment through its interpretability, thereby improving the classification and prediction of the model.
Coal and Gas Outburst Risk Prediction and Management Based on WOA-ELM
Authors: Miao Dejun , Ji Jiaqi , Chen Xiujie , Lv Yueying , Liu Lu , Sui Xiuhua
Published: October 2022

A gas outburst risk level prediction method, based on the Whale Optimization Algorithm (WOA) Improved Extreme Learning Machine (ELM), is proposed to predict the coal and gas outburst hazard level more accurately. Based on this method, recommendations are given according to the gas outburst risk level with the help of the Case-Based Reasoning (CBR) method. Firstly, we analyze the accident reports of gas outburst accidents, select the gas outburst risk prediction index, and construct the gas outburst risk prediction index system by combining the gas outburst prevention and control process. The WOA-ELM model was used to predict the gas outburst risk level by selecting data from 150 accident reports from 2008 to 2021. Again, based on the coal and gas outburst risk level, CBR is used to match the cases and give corresponding suggestions for different levels of gas outburst risk conditions to help reduce the gas outburst risk. The results show that the WOA-ELM algorithm has better performance and faster convergence than the ELM algorithm, when compared in terms of accuracy and the error of gas outburst hazard prediction. The use of CBR to manage prediction results can be helpful for decision-makers.
Recycling Waste Paver Blocks in the Manufacture of New Concrete Paver Blocks and Building Bricks
Authors: Kim Jeonghyun , Kim Namho
Published: October 2022

This study investigates the feasibility of recycling various waste paving blocks as raw materials for concrete. The recycling rate of waste blocks in Korea remains around 11%. Therefore, three types of waste paving blocks, i.e., recycled clay-, granite-, and concrete block, were used as partial replacements for sand in a concrete mixture at 10%, 20%, and 30% replacement ratios. According to the Korean standards for concrete pavers and building bricks, compressive strength, flexural strength, and water absorption tests were conducted. In addition, UPV and unit weight tests were performed. The results showed that the performance of concrete mixtures decreased with the increasing replacement ratio of waste clay and concrete, while the addition of waste granite improved the properties of the mixtures. All the prepared concrete mixtures met the requirements specified in the standards, demonstrating that concrete incorporating waste blocks can be used as paving blocks and building bricks under limited conditions.
Hydraulic Performance of Howell–Bunger and Butterfly Valves Used for Bottom Outlet in Large Dams under Flood Hazards
Authors: Kyriakopoulos Grigorios L. , Aminpour Younes , Yamini Omid Aminoroayaie , Movahedi Azin , Mousavi S. Hooman , Kavianpour Mohammad Reza
Published: October 2022

Floods control equipment in large dams is one of the most important requirements in hydraulic structures. Howell–Bunger valves and butterfly valves are two of these types of flow controls that are commonly used in bottom outlet dams. The optimal longitudinal distance (L) between the two Howell–Bunger and butterfly valves is such that the turbulence of the outlet flow from the butterfly valve should be dissipated before entering the outlet valve. Subsequently, the flow passing through the butterfly valves must have a fully developed flow state before reaching the Howell–Bunger valve. Therefore, the purpose of this study was to evaluate the optimal longitudinal distance between the Howell–Bunger and butterfly valves. For this purpose, different longitudinal distances were investigated using the Flow-3D numerical model. The ideal longitudinal distance obtained from the numerical model in the physical model was considered and tested. Based on the numerical study, the parameters of flow patterns, velocity profiles and vectors, turbulence kinetic energy, and formation of flow vorticity were investigated as criteria to determine the appropriate longitudinal distance. In addition, the most appropriate distance between the butterfly valve and the Howell–Bunger valve was determined, and the physical model was evaluated based on the optimal distance extracted from the numerical simulation. A comparison of the results from the numerical and the laboratory models showed that the minimum distance required in Howell–Bunger valves and butterfly valves should be equal to four times the diameter of the pipe (L=4D) so as not to adversely affect the performance of the bottom outlet system.
Can Gestural Filler Reduce User-Perceived Latency in Conversation with Digital Humans?
Authors: Kum Junyeong , Lee Myungho
Published: October 2022

The demand for a conversational system with digital humans has increased with the development of artificial intelligence. Latency can occur in such conversational systems because of natural language processing and network issues, which can deteriorate the user’s performance and the availability of the systems. There have been attempts to mitigate user-perceived latency by using conversational fillers in human–agent interaction and human–robot interaction. However, non-verbal cues, such as gestures, have received less attention in such attempts, despite their essential roles in communication. Therefore, we designed gestural fillers for the digital humans. This study examined the effects of whether the conversation type and gesture filler matched or not. We also compared the effects of the gestural fillers with conversational fillers. The results showed that the gestural fillers mitigate user-perceived latency and affect the willingness, impression, competence, and discomfort in conversations with digital humans.
High-Quality Coherent Plane-Wave Compounding Using Enhanced Covariance-Matrix-Based Statistical Beamforming
Authors: Wang Yuanguo , Zheng Chichao , Peng Hu , Wang Yadan
Published: October 2022

Coherent plane-wave compounding (CPWC) enables high-frame-rate ultrasound imaging, but the imaging quality is mainly determined by the beamforming method. Covariance-matrix-based statistical beamforming (CMSB) was previously proposed for synthetic aperture ultrasound imaging, which provides notable improvements in resolution and contrast over conventional delay-and-sum (DAS). However, the speckle quality is inadequate in the phantom experiment, and there exists a tradeoff between the contrast and speckle preservation of CMSB due to the constant diagonal reducing factor. In this paper, we applied CMSB in CPWC ultrasound imaging and propose an enhanced CMSB approach for CPWC to enhance the image quality. First, we introduced lag-one coherence (LOC) as an adaptive weighting factor for CMSB to suppress incoherent noise. Then, we propose adaptive diagonal reducing for CMSB using the coherence factor and amplitude standard deviation, with the aim to further improve the speckle quality. Finally, the combination of LOC weighting and adaptive diagonal reducing is proposed for CMSB to simultaneously improve the contrast and speckle quality. A simulation, experiments, and carotid studies were used to validate the imaging performance of the proposed methods. Results from the experiments show that LOC-weighted CMSB (LOCw-CMSB) with adaptive diagonal reducing improves the average contrast, generalized contrast-to-noise ratio (gCNR), and speckle signal-to-noise ratio (sSNR) by 59.9%, 53.6%, and 77.7%, respectively, in comparison with DMAS. The contrast and sSNR of the LOCw-CMSB with adaptive diagonal reducing were improved by 32.3% and 33.1%, respectively, compared to CMSB. In addition, LOCw-CMSB with adaptive diagonal reducing improves the contrast by 176.6% compared with SLSC in the in vivo carotid study, while it obtains a comparable gCNR. These results demonstrate that the proposed methods are effective in improving the image quality of CPWC imaging.
Zeolitization of Diatomite Residues by a Simple Method
Authors: Moreno-Maroto José Manuel , Alonso-Azcárate Jacinto , Martínez-García Carmen , Romero Maximina , López-Delgado Aurora , Cotes-Palomino Teresa
Published: October 2022

The possibility of transforming a diatomite-rich waste from the brewing industry into synthetic zeolites has been investigated. After precalcination at 550 °C to eliminate the retained organic matter, the clean diatomite (Dt; with a Si/Al molar ratio of 17.4), was hydrothermally treated for 24 h with continuous stirring in a 3M NaOH solution at 80 °C. The results of mineralogical characterization by X-ray diffraction with Rietveld refinement have shown a crystallization of 55% of zeolite P, which was neoformed from the amorphous phase, opal-CT and quartz of the starting sample. The spectra obtained by Fourier Transform Infrared Spectrometry have corroborated such zeolitization. N2 adsorption–desorption isotherms have shown that the zeolitized material (Dt-Z) is mesoporous, with almost 60% more specific surface area than Dt (62.6 m2/g vs. 39.4 m2/g), greater microporosity and 40% smaller average pore size than Dt (71 Å vs. 118 Å). This study is a first approximation to know the potential of diatomite wastes as zeolite precursors, for which additional research including an aluminum source will be required.
A Two-Stage Semi-Supervised High Maneuvering Target Trajectory Data Classification Algorithm
Authors: Li Qing , He Xintai , Chen Kun , Ouyang Qicheng
Published: October 2022

Labeled data in insufficient amounts and missing categories are two observable features for high maneuvering target trajectory data. However, the existing research achievements are insufficient for solving these two problems simultaneously during data classification. This study proposed a two-stage semi-supervised trajectory data classification algorithm. By pre-training the autoencoder and combining it with the Siamese network, a two-stage joint training was formed, which enabled the model to deal with missing categories by clustering and maintaining the classification ability under the missing label categories. The experimental simulation results showed that the performance of this algorithm was better than the classical semi-supervised algorithm label propagation and transferred learning when the amount of various labeled data was as low as 1–5. The two-stage training model also had a good effect on the problem of missing categories. When 75% of the types were missing, the purity could still reach 82%, which was about eight percentage points higher than the directly trained network. When two problems appeared simultaneously, compared with the directly trained network, the performance improved by about three percentage points on average, and the purity was consistently higher than the clustering results. In summary, this algorithm was more tolerant of the problems of labeled datasets, so it was more practical.
On Ghost Imaging Studies for Information Optical Imaging
Authors: Hu Chenyu , Han Shensheng
Published: October 2022

Since the birth of information theory, to understand, study, and optimize optical imaging systems from the information–theoretic viewpoint has been an important research subfield of optical imaging, accompanied by a series of corresponding advances. However, since the “direct point-to-point” image information acquisition mode of traditional optical imaging systems, which directly performs one-to-one signal mapping from the object to the detection plane, lacks a “coding–decoding” operation on the image information, related studies based on information theory are more meaningful in the theoretical sense, while almost acting as icing on the cake for the optimization and design of practical systems and contributing little to substantive breakthroughs in further imaging capabilities. With breakthroughs in modern light-field modulation techniques as well as ghost imaging techniques, which establish point-to-point image signal reproduction based on high-order correlation of light fields, currently, it is able to encode the image information with controllable spatiotemporal light-field fluctuations during the ghost imaging process. Combined with modern digital photoelectric detection technologies, ghost imaging systems behave more in line with the modulation–demodulation information transmission mode compared to traditional optical imaging. This puts forward imperative demands and challenges for understanding and optimizing ghost imaging systems from the viewpoint of information theory, as well as bringing more development opportunities for the research field of information optical imaging. This article will briefly review the development of information optical imaging since the birth of information theory, overview its current research status by combining with latest related progresses in ghost imaging, and discuss the potential developing tendency of this research topic.
Data-Driven Model-Free Adaptive Sliding Mode Control Based on FFDL for Electric Multiple Units
Authors: Zhou Liang , Li Zhongqi , Yang Hui , Fu Yating , Yan Yue
Published: October 2022

The electric multiple units (EMUs) have become a very convenient and powerful means of transportation in our daily life. Safe and punctual trajectory tracking control is the key to improve the performance of the EMUs system, but it is difficult to realize due to the influence of environmental uncertainty, coupling and nonlinearity. In this paper, a model-free adaptive sliding mode control (MFASMC) method is proposed for the EMUs. This method can solve the dependence of the model-based control method on the train model and eliminate the influence of external disturbances on the robust performance of the system. In this method, the running process of the EMUs is equivalent to a full format dynamic linearization (FFDL) data model, and a model-free adaptive controller (MFAC) is designed based on the data model. Then, to reduce the influence of measurement disturbance and improve the robustness of the system, a discrete sliding mode control (SMC) algorithm is introduced. Furthermore, to prevent the control input from being too large, the parameter estimation error is introduced as an additional correction term of the algorithm. In the end, the simulation experiment is carried out with CRH380A EMUs as the object. Compared with the traditional MFAC and the traditional SMC, the speed tracking effect of each power unit of the MFASMC algorithm is more effective, the change of control force is stable, the acceleration meets the requirements of driving, and has a strong inhibitory effect on external disturbances.
Temporal Logic Planning and Receding Horizon Control for Signal Source Localization
Authors: Chen Xingtong , Lu Qiang , Chen Dilong , Geng Boyuan
Published: October 2022

This article copes with signal source localization by employing a receding horizon control approach with temporal logic planning in the light of a single mobile robot. First, a temporal logic planning approach is proposed such that the task requirements from the temporal logic specifications can be effectively dealt with based on the product automaton in an offline fashion. Second, in order to label the key nodes of the product automaton, a particle filter is utilized to predict the source positions as the key nodes. Third, on the basis of the product automaton, a receding horizon control approach with temporal logic planning is developed to produce the robot’s trajectory that satisfies a given linear temporal logic specification. Finally, the effectiveness of the proposed control approach is illustrated for signal source localization.
Transformer-Based Hybrid Forecasting Model for Multivariate Renewable Energy
Authors: Galindo Padilha Guilherme Afonso , Ko JeongRyun , Jung Jason J. , de Mattos Neto Paulo Salgado Gomes
Published: October 2022

In recent years, the use of renewable energy has grown significantly in electricity generation. However, the output of such facilities can be uncertain, affecting their reliability. The forecast of renewable energy production is necessary to guarantee the system’s stability. Several authors have already developed deep learning techniques and hybrid systems to make predictions as accurate as possible. However, the accurate forecasting of renewable energy still is a challenging task. This work proposes a new hybrid system for renewable energy forecasting that combines the traditional linear model (Seasonal Autoregressive Integrated Moving Average—SARIMA) with a state-of-the-art Machine Learning (ML) model, Transformer neural network, using exogenous data. The proposal, named H-Transformer, is compared with other hybrid systems and single ML models, such as Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN), using five data sets of wind speed and solar energy. The proposed H-Transformer attained the best result compared to all single models in all datasets and evaluation metrics. Finally, the hybrid H-Transformer obtained the best result in most cases when compared to other hybrid approaches, showing that the proposal can be a useful tool in renewable energy forecasting.
A Message Passing Approach to Biomedical Relation Classification for Drug–Drug Interactions
Authors: Zaikis Dimitrios , Karalka Christina , Vlahavas Ioannis
Published: October 2022

The task of extracting drug entities and possible interactions between drug pairings is known as Drug–Drug Interaction (DDI) extraction. Computer-assisted DDI extraction with Machine Learning techniques can help streamline this expensive and time-consuming process during the drug development cycle. Over the years, a variety of both traditional and Neural Network-based techniques for the extraction of DDIs have been proposed. Despite the introduction of several successful strategies, obtaining high classification accuracy is still an area where further progress can be made. In this work, we present a novel Knowledge Graph (KG) based approach that utilizes a unique graph structure in combination with a Transformer-based Language Model and Graph Neural Networks to classify DDIs from biomedical literature. The KG is constructed to model the knowledge of the DDI Extraction 2013 benchmark dataset, without the inclusion of additional external information sources. Each drug pair is classified based on the context of the sentence it was found in, by utilizing transfer knowledge in the form of semantic representations from domain-adapted BioBERT weights that serve as the initial KG states. The proposed approach was evaluated on the DDI classification task of the same dataset and achieved a F1-score of 79.14% on the four positive classes, outperforming the current state-of-the-art approach.
A Multi-Branch Multi-Scale Deep Learning Image Fusion Algorithm Based on DenseNet
Authors: Dong Yumin , Chen Zhengquan , Li Ziyi , Gao Feng
Published: October 2022

Infrared images have good anti-environmental interference ability and can capture hot target information well, but their pictures lack rich detailed texture information and poor contrast. Visible image has clear and detailed texture information, but their imaging process depends more on the environment, and the quality of the environment determines the quality of the visible image. This paper presents an infrared image and visual image fusion algorithm based on deep learning. Two identical feature extractors are used to extract the features of visible and infrared images of different scales, fuse these features through specific fusion methods, and restore the features of visible and infrared images to the pictures through the feature restorer to make up for the deficiencies in the various photos of infrared and visible images. This paper tests infrared visual images, multi-focus images, and other data sets. The traditional image fusion algorithm is compared several with the current advanced image fusion algorithm. The experimental results show that the image fusion method proposed in this paper can keep more feature information of the source image in the fused image, and achieve excellent results in some image evaluation indexes.
Influence of Gap Width on Temporal Nonlinear Behaviors in CO2 Dielectric Barrier Discharges under Martian Conditions
Authors: Wang Xucheng , Gao Shuhan , Zhang Yuantao
Published: October 2022

In recent years, the in situ resource utilization of CO2 on Mars for oxygen and carbon monoxide production has attracted increasing attention. Dielectric barrier discharges (DBDs) have great potential for large-scale industrial application of CO2 decomposition, and the nonlinear behaviors of DBDs are directly related to the discharge stability. In this paper, a fluid model is built to investigate the influence of gap width on temporal nonlinear behaviors in CO2 DBDs driven by tailored voltages under Martian conditions (the pressure and temperature are 4.5 Torr and 210 K, respectively). The simulation results show that, with the increase in the gap width, the discharge evolves from period-one state into period-two state, then changes into chaos, and finally undergoes an inverse period-doubling bifurcation from reverse period-two discharge to period-one discharge. After the CO2 discharge is extinguished, the electron density drops rapidly, and the dominant charged particles in the discharge region are heavy CO3− and CO2+ ions. As the gap width increases, the heavy ions produced by the previous discharge cannot be completely dissipated and stay in the sheath region, which makes the subsequent discharge easy to be ignited and reduces the breakdown voltage, leading to the evolution from period-one discharge to period-two discharge. When the gap width is increased to 5 mm, a lot of charged particles stay in the discharge gap, and these charged particles, especially electrons, are driven to the electrodes by the applied voltage, forming a reverse electric field, which inhibits the development of positive discharge and facilitates the formation of negative discharge. Then, as the gap width continues to increase, the density and spatial distribution of residual ions in the sheath region at the beginning of the negative discharge for two consecutive voltage periods are gradually equal, resulting in the discharge evolution from reverse period-two state to reverse period-one state. This study could deepen the understanding of the underpinning physics of nonlinear behaviors, and provide a groundwork for actively regulating the evolution of nonlinear behaviors.
Security Analysis of the MQTT-SN Protocol for the Internet of Things
Authors: Roldán-Gómez José , Carrillo-Mondéjar Javier , Castelo Gómez Juan Manuel , Ruiz-Villafranca Sergio
Published: October 2022

The expansion of the Internet of Things (IoT) paradigm has brought with it the challenge of promptly detecting and evaluating attacks against the systems coexisting in it. One of the most recurrent methods used by cybercriminals is to exploit the vulnerabilities found in communication protocols, which can lead to them accessing, altering, and making data inaccessible and even bringing down a device or whole infrastructure. In the case of the IoT, the Message Queuing Telemetry Transport (MQTT) protocol is one of the most-used ones due to its lightness, allowing resource-constrained devices to communicate with each other. Improving its effectiveness, a lighter version of this protocol, namely MQTT for Sensor Networks (MQTT-SN), was especially designed for embedded devices on non-TCP/IP networks. Taking into account the importance of these protocols, together with the significance that security has when it comes to protecting the high-sensitivity data exchanged in IoT networks, this paper presents an exhaustive assessment of the MQTT-SN protocol and describes its shortcomings. In order to do so, seven different highly heterogeneous attacks were designed and tested, evaluating the different security impacts that they can have on a real MQTT-SN network and its performance. Each one of them was compared with a non-attacked implemented reference scenario, which allowed the comparison of an attacked system with that of a system without attacks. Finally, using the knowledge extracted from this evaluation, a threat detector is proposed that can be deployed in an IoT environment and detect previously unmodeled attacks.
An Adaptive Launch Control for Balloon-Borne UAVs with Large Wingspans
Authors: Hu Yanpeng , Hu Yanfei , Ding Xiaomiao , Zeng Guannan , Guo Jin
Published: October 2022

Near space has attracted major countries’ attention due to the fact that it is a new cognitive space of Earth and acts as an important national security space. Near-space solar-powered unmanned aerial vehicles (UAVs) are becoming a focus of research in the aviation field. However, it is difficult for solar-powered UAVs to climb such high heights and achieving optimal cruising levels is challenging. A balloon-borne-based aircraft that rises with the help of a balloon avoids difficult climbing processes and initiates a new research direction in the near-space aviation domain. Simultaneously, the special mode of taking off poses a great challenge for the pull-up control of balloon-borne aircraft, especially for large wingspan aircraft. In this paper, we propose an adaptive launch control for the pull-up process of large-scale balloon-borne-based aircraft. First, the flight control of the pull-up process for a large-scale balloon-borne-based aircraft is analyzed. Then, a flight dynamic model considering elastic deformation is established. Finally, an adaptive aircraft pull-up control algorithm is proposed. We evaluate it by performing simulation experiments and comparing it with the latest control algorithm utilized in physical experiments. The experiment’s results demonstrate the effectiveness of the proposed algorithm with respect to overcoming challenges in controlling pull-up processes and its superiority compared to the latest control algorithm.
Considering the Effect of Non-Propagating Cracks on Fatigue Limit Prediction in the Critical Distance Method Framework
Authors: Zhou Zhuo , Guan Deqing
Published: October 2022

Although the material has developed micro-scale cracks, the micro-cracks stop propagating and transform into non-propagating cracks (NPCs) under fatigue limit loading. The movement of the crack tip position caused by the non-propagating crack will generate a small change in the notch geometry, which easily triggers the geometric size effect. Since the critical distance method focuses on evaluating the limit of fatigue in terms of the material’s cracking conditions, the present study attempted to develop a notched fatigue analysis model to consider the effect of non-propagating cracks on fatigue limit prediction in the critical distance method framework. The effectiveness and capability of the proposed model were demonstrated by the fatigue experimental data of Q345qD low carbon steel.
A Deep Learning Approach for Credit Scoring Using Feature Embedded Transformer
Authors: Wang Chongren , Xiao Zhuoyi
Published: October 2022

In this paper, we introduce a transformer into the field of credit scoring based on user online behavioral data and develop an end-to-end feature embedded transformer (FE-Transformer) credit scoring approach. The FE-Transformer neural network is composed of two parts: a wide part and a deep part. The deep part uses the transformer deep neural network. The output of the deep neural network and the feature data of the wide part are concentrated in a fusion layer. The experimental results show that the FE-Transformer deep learning model proposed in this paper outperforms the LR, XGBoost, LSTM, and AM-LSTM comparison methods in terms of area under the receiver operating characteristic curve (AUC) and the Kolmogorov–Smirnov (KS). This shows that the FE-Transformer deep learning model proposed in this paper can accurately predict user default risk.
A Novel Method for Testing the Effect of Base Post-Grouting of Super-Long Piles
Authors: Hu Haibo , Jin Qiqin , Yang Feng , Zhou Jiajin , Ma Junjie , Gong Xiaonan , Guo Jin
Published: October 2022

The base post-grouting technology is widely used in pile foundation engineering to improve its behavior. For super-long piles, due to the large compression of pile shaft, conventional static load tests cannot effectively test the effect of post-grouting at the pile base. This study introduced a novel method to test the effect of base post-grouting of three 88 m long piles. A total of three test piles were grouted at the bottom of the piles. The grouting amount of the No. 1 pile was 500 kg, the No. 2 pile was 250 kg, and the No. 3 pile was 0 kg. The transmitted wave method in the pile and dynamic penetration method were used in combination to quantitatively evaluate the effect of base post-grouting on the soil below the pile base. The influencing range of base post-grouting was firstly evaluated by the transmitted wave method in the pile, and the strength of the soil before and after grouting under the same pile base was measured by the dynamic penetration method. The test results showed that: the depth of the pile base reinforcement was 2 m for No. 1 pile, and 1 m for No. 2 pile, which was consistent with the grouting amount; the elastic modulus of the soil below the pile base after base post-grouting was about twice that of the soil before base post-grouting; the strength of the soil below pile base was largely improved after grouting; and the bearing capacity of the base soil layer increased from 35.5% to 41.7% when the grouting amount increased from 250 kg to 500 kg.
A Novel Cementing Quality Evaluation Method Based on Convolutional Neural Network
Authors: Fang Chunfei , Wang Zheng , Song Xianzhi , Zhu Zhaopeng , Yang Donghan , Liu Muchen
Published: October 2022

The quality of cement in cased boreholes is related to the production and life of wells. At present, the most commonly used method is to use CBL-VDL to evaluate, but the interpretation process is complicated, and decisions associated with significant risks may be taken based on the interpretation results. Therefore, cementing quality evaluation must be interpreted by experienced experts, which is time-consuming and labor-intensive. To improve the efficiency of cementing interpretation, this paper used VGG, ResNet, and other convolutional neural networks to automatically evaluate the cementing quality, but the accuracy is insufficient. Therefore, this paper proposes a multi-scale perceptual convolutional neural network with kernels of different sizes that can extract and fuse information of different scales in VDL logging. In total, 5500 datasets in Tarim Oilfield were used for training and validation. Compared with other convolutional neural network algorithms, the multi-scale perceptual convolutional neural network algorithm proposed in this paper can evaluate cementing quality more accurately by identifying VDL logging. At the same time, this model’s time and space complexity are lower, and the operation efficiency is higher. To verify the anti-interference of the model, this paper added 3%, 6%, and 9% of white noise to the VDL data set for cementing evaluation. The results show that, compared with other convolutional neural networks, the multi-scale perceptual convolutional neural network model is more stable and more suitable for the identification of cementing quality.
Trident: A Deep Learning Framework for High-Resolution Bridge Vibration Monitoring
Authors: Sajedi Seyedomid , Liang Xiao
Published: October 2022

Bridges are the essential components in lifeline transportation systems, and their safe operation is of great importance. Information on structural damage could assist in timely repairs and reduce downtime. With the latest advancements in sensing technology, collecting vibration data from bridges has become more accessible. However, effective vibration processing is still a challenge, given the high dimensionality and massive size of vibration data. Existing studies have shown that machine/deep learning techniques can be valuable tools for this task. However, the learning and computational capacities of these models are challenged in the presence of large sensor arrays. We propose Trident as a novel deep learning framework that enables automatic damage feature extraction by simultaneously learning from temporal and three-dimensional (3D) spatial variations of 6D input data in instrumented bridges. Trident is equipped with 3 ConvLSTM3D branches to achieve this goal. A 3D steel truss bridge subject to dynamic traffic loads is monitored for its vibrations to evaluate Trident’s robustness in finding damaged elements. A damage dataset of 52,800 vehicle passing simulations is generated leveraging a database of 528 passenger vehicles in the United States, obtained from the National Highway and Traffic Safety Administration. Bayesian optimization is utilized to tune the model’s hyperparameters, achieving a test Node Average Geometric Mean Accuracy of 86%. This level of performance is promising given the high dimensionality and complexities of the output space in vibration-based monitoring. Trident’s concept can be extended to other vibration monitoring tasks with different time series data and damage labeling strategies.
Lane-Changing Strategy Based on a Novel Sliding Mode Control Approach for Connected Automated Vehicles
Authors: Wang Chengmei , Du Yuchuan
Published: October 2022

Safe and efficient autonomous lane changing is a key step of connected automated vehicles (CAVs), which can greatly reduce the traffic accident rate and relieve the traffic pressure. Aiming at the requirements of the smoothness and efficiency of the lane-changing trajectory of CAVs, it is necessary to design the lane changing controller to integrate the sensing, decision-making, and control tasks in the driving process. Firstly, based on the vehicle dynamics model, this paper proposes a vehicle lane-changing control strategy based on NNTSMC method (neural network enhanced non-singular fast terminal sliding mode control). The designed lane-changing controller can well realize the designed path tracking, and both lateral position and yaw angle can well track the expected value. This method enables the vehicle to control the front wheel steering angle intelligently, and the lateral acceleration during steering changes in the small scope, which ensures the steering stability of the vehicle. In this study, an improved adaptive RBF neural network with bounded mapping is designed to estimate the upper bound of the total disturbance of the system, which effectively reduces the chattering phenomenon of the control force. The Lyapunov function constructed in this study proves that the designed controller can ensure the stability of the controlled system. Finally, a comparative experiment is performed by the MATLAB/Simulink-CarSim co-simulation. Compared with SMC and TSMC (non-singular fast terminal sliding mode control), the proposed method has a performance improvement of at least 58.0% and 34.1%, respectively. The effectiveness and superiority of the proposed control method were confirmed by the experiments on the co-simulation platform.
Dynamic Generation of Knowledge Graph Supporting STEAM Learning Theme Design
Authors: Ke Qingchao , Lin Jian
Published: October 2022

Instructional framework based on a knowledge graph makes up for the interdisciplinary theme design ability of teachers in a single discipline, to some extent, and provides a curriculum-oriented theme generation path for STEAM instructional design. This study proposed a dynamic completion model of a knowledge graph based on the subject semantic tensor decomposition. This model can be based on the tensor calculation of multi-disciplinary curriculum standard knowledge semantics to provide more reasonable STEAM project-based learning themes for teachers of those subjects. First, the STEAM multi-disciplinary knowledge semantic dataset was generated through the course’s standard text and open-source encyclopedia data. Next, based on the semantic tensor decomposition of specific STEAM topics, the dynamic generation of knowledge graphs was realized, providing interdisciplinary STEAM learning topic sequences for teachers of a single discipline. Finally, the application experiment of generating STEAM learning themes proved the effectiveness of our model.
Fabrication of a Conductive Pattern on a Photo-Polymerized Structure Using Direct Laser Sintering
Authors: Jo Jung-Hoe , Park Min-Soo
Published: October 2022

Three-dimensional (3D)-printed electronic technology is considered to have great potential as it can be utilized to make electronic products with complex 3D shapes. In this study, based on a 3D printer with single UV laser equipment, we continuously performed photo-polymerization (PP) and selective metal powder sintering to fabricate a conductive pattern. For this, 3D structures were printed at a low energy using a 355 nm DPSS laser with a galvanometer scanner, which are widely used in PP-type 3D printing, and then the selective sintering of metal powders was performed with a high energy. In order to obtain a high-conductivity pattern by laser sintering, a circuit pattern that could actually be operated was fabricated by experimenting with various condition changes from mixing the metal composite resin to the laser process. As a result, it was found that the optimal result was to irradiate a 0.8 W UV laser with a beam spot size of 50 µm to 50 vol% aluminum composite resin. At this time, an optimal conductive pattern with a resistance of 0.33 Ω∙cm−1 was obtained by setting the pulse repetition rate, scan path interval, and scanning speed to 90 kHz, 10 μm, and 50 mm/s, respectively. This suggested process may be of great help in the manufacturing of practical 3D sensors or functional products in the future.
Estimates of Quantum Tunneling Effects for Hydrogen Diffusion in PuO2
Authors: Goldman Nir , Zepeda-Ruiz Luis , Mullen Ryan G. , Lindsey Rebecca K. , Pham C. Huy , Fried Laurence E. , Belof Jonathan L.
Published: October 2022

We detail the estimation of activation energies and quantum nuclear vibrational tunneling effects for hydrogen diffusion in PuO2 based on Density Functional Theory calculations and a quantum double well approximation. We find that results are relatively insensitive to choice of exchange correlation functional. In addition, the representation of spin in the system and use of an extended Hubbard U correction has only a small effect on hydrogen point defect formation energies when the PuO2 lattice is held fixed at the experimental density. We then compute approximate activation energies for transitions between hydrogen interstitial sites seeded by a semi-empirical quantum model and determine the quantum tunneling enhancement relative to classical kinetic rates. Our model indicates that diffusion rates in H/PuO2 systems could be enhanced by more than one order of magnitude at ambient conditions and that these effects persist at high temperature. The method we propose here can be used as a fast screening tool for assessing possible quantum nuclear vibrational effects in any number of condensed phase materials and surfaces, where hydrogen hopping tends to follow well defined minimum energy pathways.