bluuebunny/crossref_metadata_2025 huggingface dataset:
IEALL: Dam Deformation Prediction Model Based on Combination Model Method
Authors: Xu Guoyan , Lu Yuwei , Jing Zixu , Wu Chunyan , Zhang Qirui
Published: April 2023

The accuracy of dam deformation prediction is a key issue that needs to be addressed due to the many factors that influence dam deformation. In this paper, a dam deformation prediction model based on IEALL (IGWO-EEMD-ARIMA-LSTM-LSTM) is proposed for a single-point scenario. The IEALL model is based on the idea of a combination model. Firstly, EEMD is used to decompose the dam deformation data, and then the ARIMA and LSTM models are selected for prediction. To address the problem of low prediction accuracy caused by simple linear addition of prediction results from different models in traditional combination models, the LSTM model is used to learn the combination relationship of different model prediction results. The problem of neural network parameters falling into local optima due to random initialization is addressed by using the improved gray wolf optimization (IGWO) to optimize multiple parameters in the IEALL combination model to obtain the optimal parameters. For the multi-point scenario of dam deformation, based on the IEALL model, a dam deformation prediction model based on spatio-temporal correlation and IEALL (STAGCN-IEALL) is proposed. This model introduces graph convolutional neural networks (GCN) to extract spatial features from multi-point sequences, increasing the model’s ability to express spatial dimensions. To address the dynamic correlation between different points in the deformation sequence at any time and the dynamic dependence on different points at any given time, spatio-temporal attention mechanisms are introduced to capture dynamic correlation from both spatial and temporal dimensions. Experimental results showed that compared to ST-GCN, IEALL reduced the RMSE, MAE, and MAPE by 16.06%, 14.72%, and 21.19%. Therefore, the proposed model effectively reduces the prediction error and can more accurately predict the trend of dam deformation changes.
Discontinuous Yielding of Fe E420 under High Strain Rate Loading
Authors: Bruno Matteo , Esposito Luca , Iannitti Gianluca , Scherillo Fabio
Published: April 2023

The discontinuous yielding behaviour of Fe E420 steel was experimentally investigated through tensile tests conducted on laminated sheets. In order to assess the response under both quasi-static and dynamic loading conditions, uniaxial tensile tests were performed according to ASTM E8 standards, and dynamic tests were conducted on the Hopkinson bar using a specially designed flat specimen. Three nominal strain rates (2×10−4, 500, and 1000) s−1 were considered. The effect of heat treatment on the material was also considered. For this purpose, the proposed experimental campaign was carried out on both the as-is material and on one treated by water quenching. All dynamic tests were performed with the direct tensile split Hopkinson pressure bar and were recorded via a high frame rate camera. The post failure fracture surfaces were investigated using scanning electron microscopy technique.
Optimal Graph Convolutional Neural Network-Based Ransomware Detection for Cybersecurity in IoT Environment
Authors: Khalid Alkahtani Hend , Mahmood Khalid , Khalid Majdi , Othman Mahmoud , Al Duhayyim Mesfer , Osman Azza Elneil , Alneil Amani A. , Zamani Abu Sarwar
Published: April 2023

The fast development of the Internet of Things (IoT) and widespread utilization in a large number of areas, such as vehicle IoT, industrial control, healthcare, and smart homes, has made IoT security increasingly prominent. Ransomware is a type of malware which encrypts the victim’s records and demands a ransom payment for restoring access. The effective detection of ransomware attacks highly depends on how its traits are discovered and how precisely its activities are understood. In this article, we propose an Optimal Graph Convolutional Neural Network based Ransomware Detection (OGCNN-RWD) technique for cybersecurity in an IoT environment. The OGCNN-RWD technique involves learning enthusiasm for teaching learning-based optimization (LETLBO) algorithms for the feature subset selection process. For ransomware classification, the GCNN model is used in this study, and its hyperparameters can be optimally chosen by the harmony search algorithm (HSA). For exhibiting the greater performance of the OGCNN-RWD approach, a series of simulations were made on the ransomware database. The simulation result portrays the betterment of the OGCNN-RWD system over other existing techniques with an accuracy of 99.64%.
The Arrangement of the Osteons and Kepler’s Conjecture
Authors: Zedda Marco
Published: April 2023

The studies of bone tissue have mainly highlighted the morphometrical characteristics of the osteons, rather than their spatial distribution. This work aimed to verify if the topographical distribution of the osteons responds to geometrical order. From an analysis of hundreds of bone sections of domestic and wild mammals collected over 60 years, it is evident that the spatial distribution of osteons varies from a random arrangement in the irregular Haversian tissue to an ordered geometric arrangement in the dense Haversian tissue. In this work, a new method of classification of Haversian bone tissue was introduced based on the number of points of contact that the perimeter of an osteon has with neighboring osteons. When the functional commitment of the bone is maximum to resist biomechanical stresses, the osteons are smaller and crammed adjacent to each other as if to occupy less space. Their spatial arrangement, in this case, reminds us of Kepler’s conjecture, which predicts the ideal arrangement that spheres must have to occupy as little space as possible. The conjecture was elaborated by Kepler in the Seventeenth Century to solve the practical problem linked to the need to transport the largest number of cannonballs in warships.
A Numerical Comparison between Preisach, J-A and D-D-D Hysteresis Models in Computational Electromagnetics
Authors: De Santis Valerio , Di Francesco Antonio , D’Aloia Alessandro G.
Published: April 2023

The incorporation of hysteresis models in computational electromagnetic software is of paramount importance for the accurate prediction of the ferromagnetic devices’ performance. The Preisach and Jiles-Atherton (J-A) models are frequently used for this purpose. The former is more accurate and can represent a broad range of magnetic materials, but it is computationally expensive. The latter is more efficient but can accurately model only soft ferromagnetic materials. In this paper, a recently proposed hysteresis model, referred to as the D’Aloia-Di Francesco-De Santis (D-D-D) model, is shown to have the best trade-off between accuracy and computational burden. For the first time, a numerical comparison between the Preisach, J-A and D-D-D models is provided for a large class of hysteresis loops including soft, semi-hard and hard ferromagnetic materials.
A New Instrument Monitoring Method Based on Few-Shot Learning
Authors: Zhang Beini , Li Liping , Lyu Yetao , Chen Shuguang , Xu Lin , Chen Guanhua
Published: April 2023

As an important part of the industrialization process, fully automated instrument monitoring and identification are experiencing an increasingly wide range of applications in industrial production, autonomous driving, and medical experimentation. However, digital instruments usually have multi-digit features, meaning that the numeric information on the screen is usually a multi-digit number greater than 10. Therefore, the accuracy of recognition with traditional algorithms such as threshold segmentation and template matching is low, and thus instrument monitoring still relies heavily on human labor at present. However, manual monitoring is costly and not suitable for risky experimental environments such as those involving radiation and contamination. The development of deep neural networks has opened up new possibilities for fully automated instrument monitoring; however, neural networks generally require large training datasets, costly data collection, and annotation. To solve the above problems, this paper proposes a new instrument monitoring method based on few-shot learning (FLIMM). FLIMM improves the average accuracy (ACC) of the model to 99% with only 16 original images via effective data augmentation method. Meanwhile, due to the controllability of simulated image generation, FLIMM can automatically generate annotation information for simulated numbers, which greatly reduces the cost of data collection and annotation.
Strata Movement Characteristics in Underground Coal Gasification (UCG) under Thermal Coupling and Surface Subsidence Prediction Methods
Authors: Liu Xiaopeng , Xu Liangji , Zhang Kun
Published: April 2023

As a green, safe, and efficient method of coal development, underground coal gasification (UCG) technology has gradually moved from the experimental stage to the industrial production stage. This technology plays one of the key roles in the sustainable development of resources and energy. However, underground mining will inevitably lead to strata movement and surface subsidence, which will have certain impacts on the surface environment and buildings. Currently, limited research results on strata movement and surface subsidence under high-temperature environments hardly support the further development of the UCG technology. Hence, this study aims at the key problems of UCG strata movement and surface subsidence prediction. The study established a numerical model to analyze the effects of thermal stress and coal–rock burnt on strata movement and surface subsidence. Results show that coal–rock burnt caused by high temperature has greatly changed the characteristics of UCG strata movement and surface subsidence and is the main controlling factor for aggravating the strata movement and surface subsidence of UCG. The coordinated deformation calculation method of the UCG cavity roof-coal pillar-floor is formed. Moreover, the cooperative subsidence space is regarded as the mining space. A prediction model of surface subsidence based on continuous-discrete medium theory is also established using the probability integral method. The reliability of the predicted model is proved by comparing the measured value with the predicted value.
Biogas Production and Metagenomic Analysis in a New Hybrid Anaerobic Labyrinth-Flow Bioreactor Treating Dairy Wastewater
Authors: Zieliński Marcin , Kisielewska Marta , Dębowski Marcin , Rusanowska Paulina , Nowicka Anna , Dudek Magda
Published: April 2023

Increasing worldwide milk manufacturing and dairy processing resulted in producing more effluents, and thus effective management of wastewater is now the most important issue. This study used a new design of a pilot plant-scale hybrid anaerobic labyrinth-flow bioreactor (AL-FB) to increase the efficiency of anaerobic biodegradation and biogas productivity and improve anaerobic microflora performance. In addition, effluent recirculation was used to boost the treatment of dairy wastewater. Metagenomic analyses of the anaerobic microbial community were performed. It was found that an organic loading rate (OLR) of 4.0–8.0 g COD/L·d contributed to the highest CH4 yield of 0.18 ± 0.01–0.23 ± 0.02 L CH4/g COD removed, which corresponded to a high COD removal of 87.5 ± 2.8–94.1 ± 1.3%. The evenest distribution of the microorganisms’ phyla determined the highest biogas production. In all tested samples, Bacteroidetes and Firmicutes abundance was the highest, and Archaea accounted for about 4%. Metagenomic studies showed that methane was mainly produced in acetoclastic methanogenesis; however, higher OLRs were more favorable for enhanced hydrogenotrophic methanogenesis. Effluent recirculation enhanced the overall treatment. Thus, at OLR of 10.0 g COD/L·d, the highest COD removal was 89.2 ± 0.4%, and methane production yield achieved 0.20 ± 0.01 L CH4/g COD removed, which was higher by 25% compared to the achievements without recirculation.
T* Revise Attenuation Tomography for Q Estimation
Authors: Jin Ziqi , Wang Ruoteng , Shi Ying
Published: April 2023

Seismic attenuation is often calculated by attenuated travel time tomography. The accuracy of this method is controlled by the precision of attenuated travel time. In this paper, a novel T* revise in attenuated travel time tomography method for Q inversion was developed. The attenuated travel time was calculated from seismic data by using a logarithmic spectral ratio inversion strategy. In the inversion process, multiple offset traces were used for multiple attenuated travel time calculations. The proposed method produced more accurate results compared to those of the conventional approach without the requirement of choosing an optimistic frequency band. The accuracy of the proposed method was improved by avoiding the effect of overburden. Both synthetic and real data examples prove the viability and effectiveness of the proposed method.
Design and Assessment of an Interactive Role-Play System for Learning and Sustaining Traditional Glove Puppetry by Digital Technology
Authors: Wang Chao-Ming , Tseng Shih-Mo
Published: April 2023

As an ancient performing art, the puppet show was popular entertainment for early civilians. However, with the advance of media technology, traditional puppetry declined gradually, and old puppets became relics displayed in museums. In this study, an interactive role-play system for learning and sustaining traditional glove puppetry is proposed. Constructed with RFID and multimedia techniques to replace the traditional static displays of puppetry, the proposed system allows in-person experiencing of operating real puppets of famous roles. Statistical analyses of the comments collected from expert interviews and the users’ answers to a questionnaire survey lead to the following findings: (1) it is easy to understand and operate the puppets as physical interfacing with the system; (2) the interactive system design conforms to the 3E indicators of easiness, effectiveness, and enjoyableness; (3) the users’ experiences of role-plays emulating experts’ puppet shows help learn the knowledge and skills of the traditional puppetry; (4) in-person operations of real puppets and experiences of RFID-based interactive interfacing bring the users feelings of pleasure and senses of achievement as puppet performers; and (5) the content designs and operations of the puppet characters can turn into a fine material for learning the traditional puppetry.
A Non-Invasive Optical Multimodal Photoplethysmography-Near Infrared Spectroscopy Sensor for Measuring Intracranial Pressure and Cerebral Oxygenation in Traumatic Brain Injury
Authors: Roldan Maria , Kyriacou Panicos A.
Published: April 2023

(1) Background: Traumatic brain injuries (TBI) result in high fatality and lifelong disability rates. Two of the primary biomarkers in assessing TBI are intracranial pressure (ICP) and brain oxygenation. Both are assessed using standalone techniques, out of which ICP can only be assessed utilizing invasive techniques. The motivation of this research is the development of a non-invasive optical multimodal monitoring technology for ICP and brain oxygenation which will enable the effective management of TBI patients. (2) Methods: a multiwavelength optical sensor was designed and manufactured so as to assess both parameters based on the pulsatile and non-pulsatile signals detected from cerebral backscatter light. The probe consists of four LEDs and three photodetectors that measure photoplethysmography (PPG) and near-infrared spectroscopy (NIRS) signals from cerebral tissue. (3) Results: The instrumentation system designed to acquire these optical signals is described in detail along with a rigorous technical evaluation of both the sensor and instrumentation. Bench testing demonstrated the right performance of the electronic circuits while a signal quality assessment showed good indices across all wavelengths, with the signals from the distal photodetector being of highest quality. The system performed well within specifications and recorded good-quality pulsations from a head phantom and provided non-pulsatile signals as expected. (4) Conclusions: This development paves the way for a multimodal non-invasive tool for the effective assessment of TBI patients.
The New Role of the Dental Assistant and Nurse in the Age of Advanced Artificial Intelligence in Telehealth Orthodontic Care with Dental Monitoring: Preliminary Report
Authors: Surovková Jana , Haluzová Sára , Strunga Martin , Urban Renáta , Lifková Michaela , Thurzo Andrej
Published: April 2023

This paper explores the impact of Artificial Intelligence (AI) on the role of dental assistants and nurses in orthodontic practices, as there is a gap in understanding the currently evolving impact on orthodontic treatment workflows. The introduction of AI-language models such as ChatGPT 4 is changing patient-office communication and transforming the role of orthodontic nurses. Teledentistry is now heavily reliant on AI implementation in orthodontics. This paper presents the proof of a novel concept: an AI-powered orthodontic workflow that provides new responsibilities for an orthodontic nurse. It also provides a report of an assessment of such a workflow in an orthodontic practice that uses an AI solution called Dental Monitoring over a period of three years. The paper evaluates the benefits and drawbacks of daily automated assessments of orthodontic treatment progress, the impact of AI on personalized care, and the new role of a dental assistant. The paper concludes that AI will improve dental practice through more precise and personalized treatment, bringing new roles and responsibilities for trained medical professionals but raising new ethical and legal issues for dental practices.
Metal Levels in Serranus atricauda and Sparisoma cretense from the North-Eastern Atlantic Ocean—Contribution to Risk Assessment
Authors: Gutiérrez Alberto , Lozano-Bilbao Enrique , Gutiérrez-Fernández Ángel J. , Paz-Montelongo Soraya , González-Weller Dailos , Rubio-Armendáriz Carmen , Niebla-Canelo Daniel , Alejandro-Vega Samuel , Hardisson Arturo
Published: April 2023

The objective of this study was to study whether the metal concentrations in Sparisoma cretense and Serranus atricauda differ between different coastal areas around the island of Tenerife, Canary Islands and to study whether these species are good bioindicators of pollution. Thirty samples of each species were collected from three parts of the coastline around the island, and samples of muscle and liver tissue were taken from the collected specimens. The determination of the metal content (Al, Cd, Pb, Ca, K, Mg, Na, B, Ba, Cr, Cu, Fe, Li, Mn, Mo, Ni, Zn) was performed by inductively coupled plasma optical emission spectrometry (ICP-OES) before conducting a PERMANOVA analysis. The mean metal concentration was significantly higher in the liver tissue than in the muscle tissue of the two species studied. S. atricauda specimens had a larger number of metals with a higher concentration, and the samples from the northern and eastern zones were found to have a higher concentration of elements than those from the southern zone. The northern and eastern zones were found to have a higher concentration of metals and trace elements than the southern zone, which could be explained by the fact that these zones are more polluted due to their higher population density.
Time Series Prediction Model of Landslide Displacement Using Mean-Based Low-Rank Autoregressive Tensor Completion
Authors: Wang Chenhui , Zhao Yijiu
Published: April 2023

Landslide displacement prediction is a challenging research task that can help to reduce the occurrence of landslide disasters. The frequent occurrence of extreme weather increases the probability of landslides, and the subsequent increase in the superimposed economic development level exacerbates disaster losses, emphasizing the importance of landslide prediction. The collection of landslide monitoring data is the foundation of landslide displacement prediction, but the lack of various data severely limits the effectiveness of the landslide monitoring system. To address the issue of missing data during the landslide monitoring process, this paper proposes a time series prediction model of landslide displacement using mean-based low-rank autoregressive tensor completion (MLATC). Firstly, the reasons for the missing data of landslide displacement are analyzed, and the corresponding dataset of missing data is designed. Then, according to the characteristics and internal correlation of landslide displacement monitoring data, the establishment process of mean-based low-rank tensor completion prediction model is introduced. Finally, the proposed method is used to complete and predict the missing data for the random missing and non-random missing landslide displacement. The results show that the data completion and prediction results of the model are essentially consistent with the original displacement monitoring data of the landslide, and the accuracy and precision are relatively high. It shows that the model has good landslide displacement completion and prediction effects, which can provide a certain reference value for the missing data processing and landslide displacement prediction.
A Novel Opportunistic Network Routing Method on Campus Based on the Improved Markov Model
Authors: Cao Yumei , Li Peng , Liang Tianmian , Wu Xiaojun , Wang Xiaoming , Cui Yuanru
Published: April 2023

Opportunities networks’ message transmission is significantly impacted by routing prediction, which has been a focus of opportunity network research. The network of student nodes with smart devices is a particular type of opportunity network in the campus setting, and the predictability of campus node movement trajectories is also influenced by the regularity of students’ social mobility. In this research, a novel Markov route prediction method is proposed under the campus background. When two nodes meet, they share the movement track data of other nodes stored in each other’s cache in order to predict the probability of two nodes meeting in the future. The impact of the node within the group is indicated by the node centrality. The utility value of the message is defined to describe the spread degree of the message and the energy consumption of the current node, then the cache is managed according to the utility value. By creating a concurrent hash mapping table of delivered messages, the remaining nodes are notified to delete the delivered messages and release the cache space in time after the messages are delivered to their destinations. The method suggested in this research can successfully lower the packet loss rate, minimize transmission latency and network overhead, and further increase the success rate of message delivery, according to experimental analysis and algorithm comparison.
Semantic-Based Multi-Objective Optimization for QoS and Energy Efficiency in IoT, Fog, and Cloud ERP Using Dynamic Cooperative NSGA-II
Authors: Reffad Hamza , Alti Adel
Published: April 2023

Regarding enterprise service management, optimizing business processes must achieve a balance between several service quality factors such as speed, flexibility, and cost. Recent advances in industrial wireless technology and the Internet of Things (IoT) have brought about a paradigm shift in smart applications, such as manufacturing, predictive maintenance, smart logistics, and energy networks. This has been assisted by smart devices and intelligent machines that aim to leverage flexible smart Enterprise Resource Planning (ERP) regarding all the needs of the company. Many emerging research approaches are still in progress with the view to composing IoT and Cloud services for meeting the expectation of companies. Many of these approaches use ontologies and metaheuristics to optimize service quality of composite IoT and Cloud services. These approaches lack responsiveness to changing customer needs as well as changes in the power capacity of IoT devices. This means that optimization approaches need an effective adaptive strategy that replaces one or more services with another at runtime, which improves system performance and reduces energy consumption. The idea is to have a system that optimizes the selection and composition of services to meet both service quality and energy saving by constantly reacting to context changes. In this paper, we present a semantic dynamic cooperative service selection and composition approach while maximizing customer non-functional needs and quickly selecting the relevant service drive with energy saving. Particularly, we introduce a new QoS energy violation degree with a cooperative energy-saving mechanism to ensure application durability while different IoT devices are run-out of energy. We conduct experiments on a real business process of the company SETIF IRIS using different cooperative strategies. Experimental results showed that the smart ERP system with the proposed approach achieved optimized ERP performance in terms of average service quality and average energy consumption ratio equal to 0.985 and 0.057, respectively, in all simulated configurations compared to ring and maser/slave methods.
Theoretical Evaluation for Soil Thrust of Single-Track System over Clay Slope via Upper Bound Analysis
Authors: Na Seon Hong , Woo Sang Inn
Published: April 2023

This study aimed to theoretically evaluate soil thrust on a clay slope as a reaction force associated with the motion of an off-road tracked vehicle. The existing concept of the potential failure modes of a clay block on flat ground has extended to determine the soil thrust of sloped clay ground. Based on the upper-bound limit analysis, the soil thrust under the most critical failure was derived for three potential failure modes: block, triangular-wedge, and trapezoidal-wedge failures. Specifically, the influence of the slope angle, the shear strength of clay, the weight of a vehicle, and the geometry of a track system on the soil thrust was investigated. Only the block and triangular wedge failure modes were able to occur, and the geometry of a single-track system could lead to different failure modes. Under the block failure mode, the soil thrust decreased as the slope became stiff, and the vehicle weight increased. On the other hand, the soil thrust decreased as the slope angle decreased under the triangular-wedge failure mode.
Diversified Kernel Latent Variable Space and Multi-Objective Optimization for Selective Ensemble Learning-Based Soft Sensor
Authors: Peng Lijun , Gu Lichen , He Lin , Shi Yuan
Published: April 2023

The improvement of data-driven soft sensor modeling methods and techniques for the industrial process has strongly promoted the development of the intelligent process industry. Among them, ensemble learning is an excellent modeling framework. Accuracy and diversity are two key factors that run through the entire stage of building an ensemble learning-based soft sensor. Existing base model generating methods or ensemble pruning methods always consider the two factors separately, which has limited the development of high-performance but low-complexity soft sensors. To work out this issue, a selective ensemble learning-based soft sensor modeling method based on multi-kernel latent variable space and evolutionary multi-objective optimization is proposed, referred to as MOSE-MLV-VSPLS. This method designs a multiple diversity enhancement mechanism in the base model generation stage. Diversified input variable subspaces are first constructed using the maximum information coefficient on the bootstrapping random resampling subset. Then a set of base models that combine accuracy and diversity are generated on supervised latent variable subspaces under multiple kernel function perturbations. Further, two quantifiable parameters are designed for accuracy and diversity, and the multi-objective gray wolf optimization algorithm is used to select the base models that maximize these two important parameters to achieve effective ensemble pruning at the model ensemble stage. The MOSE-MLV-VSPLS method is applied to two typical industry processes, and the experimental results show that the method is effective and superior in selective ensemble-based soft sensor modeling.
Forming Difficulty Evaluation for Curved Hull Plates Based on Grey Relational Analysis
Authors: Wang Shun , Xu Zhikang , Wang Ji , Li Rui , Wang Yibing
Published: April 2023

To solve the problem that the lack of reference for hull plate division leads to the difficulty and low efficiency of curved plate forming, the forming difficulty evaluation of complex curved hull plates is researched. Considering the characteristics in curved plate forming process, the forming difficulty evaluation model of complex curved hull plates based on grey relational analysis is established. The evaluation process of forming difficulty for curved plates is designed. The influence law of parameters and forming parameters on the forming difficulty of curved plates are revealed. The weight coefficients of different influencing factors on the forming difficulty of curved plates are quantified. Taking the real hull plate of a container ship as an example, the evaluation results for the forming difficulty of curved plates are calculated based on the evaluation model. The rationality of the evaluation model is verified by comparing the evaluation results of curved plate forming and the process time data. The research can provide a reference for the reasonable division of hull plates and reduce the forming difficulty of curved plates.
Satellite-Derived Bathymetry for Selected Shallow Maltese Coastal Zones
Authors: Darmanin Gareth , Gauci Adam , Deidun Alan , Galone Luciano , D’Amico Sebastiano
Published: April 2023

Bathymetric information has become essential to help maintain and operate coastal zones. Traditional in situ bathymetry mapping using echo sounders is inefficient in shallow waters and operates at a high logistical cost. On the other hand, lidar mapping provides an efficient means of mapping coastal areas. However, this comes at a high acquisition cost as well. In comparison, satellite-derived bathymetry (SDB) provides a more cost-effective way of mapping coastal regions, albeit at a lower resolution. This work utilises all three of these methods collectively, to obtain accurate bathymetric depth data of two pocket beaches, Golden Bay and Għajn Tuffieħa, located in the northwestern region of Malta. Using the Google Earth Engine platform, together with Sentinel-2 data and collected in situ measurements, an empirical pre-processing workflow for estimating SDB was developed. Four different machine learning algorithms which produced differing depth accuracies by calibrating SDBs with those derived from alternative techniques were tested. Thus, this study provides an insight into the depth accuracy that can be achieved for shallow coastal regions using SDB techniques.
Education Mobility as a Service: A Study of the Features of a Novel Mobility Platform
Authors: Gawlik-Kobylińska Małgorzata , Kabashkin Igor , Misnevs Boriss , Maciejewski Paweł
Published: April 2023

The European Union’s Erasmus+ program strongly supports mobility for finding training or work opportunities. However, in the era of pandemics, there is a need to find remedies for restricted movement. One solution is to create a platform for collaboration in the spheres of work and training. This article aims to identify the crucial factors for the development of a novel ecosystem for the European education mobility. This mixed-method study employed interviews with 140 respondents. The authors utilized the NVivo software, specifically its Word Frequency Query functionality, to identify relevant features in the four main categories: competence acquisition, pedagogy, organization, and technology. Findings suggest that the acquisition of competencies should be oriented towards the future labor market, students prefer gamified activities, the platform should be organized to allow for easy job and training searches, and 2D and 3D solutions should be combined for versatility. Overall, future-oriented content, motivation, and technologically diverse activities are positively perceived by students and can be seen as a major trend in creating similar tools. This type of user experience research could also be extended to a group of teachers and administrative personnel engaged in education mobility as service activities. This research was conducted as part of the ERASMUS+ project eMediator.
Serial Maximum a Posteriori Detection of Two-Dimensional Generalized Partial Response Target for Holographic Data Storage Systems
Authors: Nguyen Thien An , Lee Jaejin
Published: April 2023

Holographic data storage (HDS) is an emerging technology that promises to revolutionize digital data storage and access. Unlike traditional storage media such as hard drives and flash memory, HDS uses light to write and read page-oriented two-dimensional (2D) data from volume media. This allows for significantly higher densities and faster data transfer rates in HDS systems. However, 2D interference is a serious issue in HDS due to hologram dispersion during the reading process. Therefore, we present a novel detection algorithm based on maximum a posteriori (MAP) detection to mitigate 2D interference. In our proposed model, we inherited the structure of the serial generalized partial response target to design the serial structure for MAP detection. The simulation results show that the proposed model can achieve a high bit error rate performance.
Development of Hydrophobic-Modified Nanosilica for Pressure Reduction and Injection Increase in an Ultra-Low-Permeability Reservoir
Authors: Lai Hao , Shi Wei , Wang Junqi , Tang Lei , Lai Nanjun
Published: April 2023

A low-permeability reservoir contains many fine pore throat structures, which result in excessive injection pressure of the water injection well and difficult water injection in the production process of a low-permeability reservoir. In this study, a new silane coupling agent was synthesized via the ring-opening reaction between dodecyl amine and KH-560 (γ-propyl trimethoxysilane). The modified KH-560 was reacted with nano-SiO2 to synthesize the modified nano-SiO2 as an antihypertensive additive. Fourier infrared spectroscopy, thermogravimetric analysis and laser scattering were used to characterize this modified nano-SiO2. The results show that the particle size of the modified nano-SiO2 is less than 60 nm. The test results of the water contact angle show that the dispersion system can increase the rock contact angle from 37.34° to 136.36°, which makes the rock surface transform from hydrophilicity to hydrophobicity and reduce the binding effect of rock with water. The dispersion test shows that the modified nano-SiO2 has good dispersion stability under alkaline conditions with TX-100 (Polyethylene glycol octylphenyl ether) as the dispersant. The antiswelling test results show that the antiswelling rate of this modified nano-SiO2 is 42.9%, which can efficiently prevent the clay expansion in the formation to reduce the injection pressure. The core displacement test results show that its depressurization rate reaches 49%. The depressurization rate still maintains 46% at a 20 PV water flow rate, indicating that its depressurization effect is remarkable and it has excellent erosion resistance.
Design of Unilateral Complete Presurgical Nasoalveolar Molding (PNAM) Corrector Based on Feature Points Extraction of Complex 3D Surface
Authors: Li Li , Liu Tao , Fang Dongshen
Published: April 2023

Cleft lip and palate is a congenital maxillofacial deformity. Unilateral complete cleft lip and palate is one of the most common clinical types. Nasal alveolar molding (PNAM) is a recognized strategy for the treatment of cleft lip and palate. However, the current design of PNAM devices mainly relies on the subjective experience of doctors. The purpose of this paper is to describe the design and manufacture of a new computer-aided design appliance, which can be applied to the presurgical nasoalveolar molding of unilateral complete cleft lip and palate, eliminate individual differences, and improve production efficiency. In this paper, seven feature points on the healthy side and the affected side are extracted by the method of Gaussian curvature and ridge line extraction, and the healthy side rotation and built-in model are designed by using these seven feature points, which can quickly generate eight treatment stages of PNAM. The correction effects of the PNAM appliance designed in this paper were compared with the original maxillary model and the clinical PNAM appliance (hand-made by subjective experience) from the aspects of alveolar fissure width and symmetry. The PNAM appliance designed in this paper can effectively improve the symmetry of patients with unilateral complete cleft lip and palate (morphological similarity: t = 3.250, p ≤ 0.01; length similarity: t = 1.559, p = 0.150) and reduce the width of alveolar cleft (t = 8.330, p < 0.01). This can fully achieve the therapeutic effect of PNAM appliances prepared by experienced doctors and is more efficient. The method based on complex 3D surface feature point extraction can provide the basis for the design and evaluation of a unilateral complete PNAM correction model, improve the design and production efficiency of unilateral complete cleft lip and palate appliance, eliminate the design problems caused by individual differences, and reduce the burden of doctors.
Experimental and Mathematical Study of Flexible–Rigid Rail Vehicle Riding Comfort and Safety
Authors: Sharma Sunil Kumar , Sharma Rakesh Chandmal , Choi Yeongil , Lee Jaesun
Published: April 2023

This paper analyses the dynamic behavior of a rail vehicle using experimental and simulation analysis on a multi-rigid–flex body model. The mathematical models are developed considering the car body, bogie frame, and wheel axle for rail vehicles of rigid–flexible and multi-rigid formulations, taking the car body as rigid for the rigid body analysis and the flexible car body for flex–rigid analysis. A finite element model of the car body was developed in ANSYS, and substructure and modal analyses were performed. The mathematical model is validated through an experiment conducted by the Research Design and Standards Organization. Then, the validated model is further analyzed to evaluate the running comfort, using the Sperling ride index and the running safety, by investigating the derailment coefficient and wheel load reduction rate. The impact of flexibility on the vehicle’s running stability is investigated using the rigid body dynamics model and experimental data. Compared to experimental data, the simulation results reveal that elastic vibration cannot be neglected in vehicle dynamics, since the rigid–flexible coupling model is slightly more significant than the rigid-body model for ride comfort and safety.
Numerical Simulation and Wind Tunnel Test of a Variable Geometry Auxiliary Inlet for a Wide-Body Aircraft Environmental Control System
Authors: Li Zhimao , Liu Meinan , Jiang Yanlong , Pei Houju
Published: April 2023

A variable geometry auxiliary inlet for a wide-body aircraft environmental control system with moveable deflectors operating in a large mass flow rate range is studied through numerical simulation and wind tunnel tests, which yields a design method for the variable geometry auxiliary inlet with high performance. The characteristics of the flow field are studied by numerical simulation. The results show that the favorable pressure gradient and the roll-up vortices are the major impetus that inhales the incoming flow into the inlet. The law of regulation and the performance variation under different conditions are obtained by wind tunnel test. The flow coefficient increases first but then decreases with the increase in the inlet opening, and the pressure rise ratio and total pressure recovery coefficient increase first and then decrease with the increase in the mass flow rate. In general, under the condition of a high Mach number (Ma > 0.4), the inlet opening of this test configuration should not exceed 50%. The deflectors can maintain the normal work of the environmental control system by moving properly to control the mass flow rate of the auxiliary inlet.
Automated Detection of Periodontal Bone Loss Using Deep Learning and Panoramic Radiographs: A Convolutional Neural Network Approach
Authors: Ryu Jihye , Lee Dong-Min , Jung Yun-Hoa , Kwon OhJin , Park SunYoung , Hwang JaeJoon , Lee Jae-Yeol
Published: April 2023

(1) Background: The accurate diagnosis of periodontal disease typically involves complex clinical and radiologic examination. However, recent studies have demonstrated the potential of deep learning in improving diagnostic accuracy and reliability through the development of computer-aided detection and diagnosis algorithms for dental problems using various radiographic sources. This study focuses on the use of panoramic radiographs, which are preferred due to their ability to assess the entire dentition with a single radiation dose. The objective is to evaluate whether panoramic radiographs are a reliable source for the detection of periodontal bone loss using deep learning, and to assess its potential for practical use on a large dataset. (2) Methods: A total of 4083 anonymized digital panoramic radiographs were collected using a Proline XC machine (Planmeca Co., Helsinki, Finland) in accordance with the research ethics protocol. These images were used to train the Faster R-CNN object detection method for detecting periodontally compromised teeth on panoramic radiographs. (3) Results: This study demonstrated a high level of consistency and reproducibility among examiners, with overall inter- and intra-examiner correlation coefficient (ICC) values of 0.94. The Area Under the Curve (AUC) for detecting periodontally compromised and healthy teeth was 0.88 each, and the overall AUC for the entire jaw, including edentulous regions, was 0.91. (4) Conclusions: The regional grouping of teeth exhibited reliable detection performance for periodontal bone loss using a large dataset, indicating the possibility of automating the diagnosis of periodontitis using panoramic radiographs.
Genetic Algorithm as a Tool for the Determination of the Self-Stress States of Tensegrity Domes
Authors: Obara Paulina , Solovei Maryna , Tomasik Justyna
Published: April 2023

The aim of the paper is to find the appropriate self-stress state of the tensegrity structures. The first approach provides exact solutions but is suitable for simple structures. In the second approach proposed in this research, it is assumed that the forces of the self-stressed state are a set of randomly selected values, which are then optimized by a genetic algorithm. This procedure is intended for more elaborate structures, for which the spectral analysis identifies many self-stress states that need to be superimposed. Two approaches are used, i.e., the spectral analysis of the compatibility matrix and the genetic algorithm. The solution procedures are presented on the example of a simple two-dimensional truss. Next, three different tensegrity domes are considered, i.e., Geiger, Levy and Kiewitt. The significant difference between these domes lies in the cable system. The obtained results are compared with those documented in the literature. It follows from the considerations that the self-stressed states found in the literature are not always accurate (forces do not balance themselves). The presented results confirm the effectiveness of the genetic algorithm for finding self-balanced forces of the existing structures. The method is relatively simple and provides sufficiently accurate results.
Multizone Leak Detection Method for Metal Hose Based on YOLOv5 and OMD-ViBe Algorithm
Authors: Chen Renshuo , Wu Zhijun , Zhang Dan , Chen Jiaoliao
Published: April 2023

It is necessary to determine the location and number of leaks in a pipeline in time to repair it, thus reducing economic losses. A multizone leakage detection method based on the YOLOv5 and OMD-ViBe algorithm is proposed to detect the metal hose’s location and leakage rate. The deep learning model of YOLOv5 is used to accurately recognize the zone of the metal hose for the region of interest rectification. The multiframe averaging method is applied to construct the initial background of the video frames. The OTSU algorithm based on the background difference method and the adaptive threshold of the maximum intraclass and interclass variance ratio method is used to improve the recognition rate of bubbles and reduce the influence of illumination change. In a comparison with the existing algorithms, the experimental results showed that OMD-ViBe improves the F-measure by 1.79–16.41% and the percentage of misclassification by 0.003–0.165%. Analysis of the pressure data indicated a comprehensive leakage error reduction of 1.53–25.19%, which can meet the requirements of metal hose leakage detection.
HiTIM: Hierarchical Task Information Mining for Few-Shot Action Recognition
Authors: Jiang Li , Yu Jiahao , Dang Yuanjie , Chen Peng , Huan Ruohong
Published: April 2023

Although the existing few-shot action recognition methods have achieved impressive results, they suffer from two major shortcomings. (a) During feature extraction, few-shot tasks are not distinguished and task-irrelevant features are obtained, resulting in the loss of task-specific critical discriminative information. (b) During feature matching, information critical to the features within the task, i.e., self-information and mutual information, is ignored, resulting in the accuracy being affected by redundant or irrelevant information. To overcome these two limitations, we propose a hierarchical task information mining (HiTIM) approach for few-shot action recognition that incorporates two key components: an inter-task learner (Kinter) and an attention-matching module with an intra-task learner (Kintra). The purpose of the Kinter is to learn the knowledge of different tasks and build a task-related feature space for obtaining task-specific features. The proposed matching module with Kintra consists of two branches: the spatiotemporal self-attention matching (STM) and correlated cross-attention matching (CM), which reinforce key spatiotemporal information in features and mine regions with strong correlations between features, respectively. The shared Kintra can further optimize STM and CM. In our method, we can use either a 2D convolutional neural network (CNN) or 3D CNN as embedding. In comparable experiments using two different embeddings in the five-way one-shot and five-way five-shot task, the proposed method achieved recognition accuracy that outperformed other state-of-the-art (SOTA) few-shot action recognition methods on the HMDB51 dataset and was comparable to SOTA few-shot action recognition methods on the UCF101 and Kinetics datasets.