bluuebunny/crossref_metadata_2025 huggingface dataset:
Multilayer Sheets Based on Double Coatings of Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) on Paper Substrate for Sustainable Food Packaging Applications
Authors: Hernández-García Eva , Freitas Pedro A. V. , Zomeño Pedro , González-Martínez Chelo , Torres-Giner Sergio
Published: December 2022

This work reports on the development and performance evaluation of newly developed paper sheets coated, on both sides, with thin films of biodegradable poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) for applications of food packaging. For this, PHBV/paper/PHBV multilayers were first prepared by the thermo-sealing technique, optimizing the process variables of temperature and time. Thereafter, the multilayer sheets were characterized in terms of their morphological, optical, thermal, mechanical, and barrier properties and compared with equivalent paper structures double coated with high-barrier multilayer films of petrochemical polymers. The results indicated that the double coatings of PHBV successfully improved the mechanical resistance and ductility, protected from moisture, and also reduced the aroma and oxygen permeances of paper, having a minimal effect on its optical and thermal properties. Finally, the compostability of the resultant multilayer sheets was analyzed, confirming that the presence of the PHBV coatings slightly delayed the aerobic biodegradation and disintegration of paper.
Investigation on the Acoustical Transmission Path of Additively Printed Metals
Authors: Rehmet Raphael , Lorenz Swenja , Hahn Vincent , Lohrengel Armin
Published: December 2022

When additive manufacturing is used, the freedom of design plays a decisive role. It allows designers of mechanically stressed parts to generate special functional surfaces, integrate very efficient conformal cooling channels, or adapt the parts to the specific load in order to increase the stress ratio. In combination with the functional principle of particle dampers, the usage of metal additive manufacturing could be useful to implement damping directly in the transmission path of machine parts and elements. To achieve this goal, it is necessary to qualify the influence on the damping capability of the particles, outer geometry, frequency behaviour and the shape of the inner geometry. In this approach, different inner geometries are used to analyse the distinctions of damping behaviour caused by unmelted metal powder. The focus lies on the geometrical shape of the inner geometry. It is recognisable that the shape has a non-negligible impact on the dynamic behaviour of the structure. The results show that structures with a high number of sharp edges in the inner shape oriented orthogonally to the excitation or under a certain angle provide a better energy decay than structures without an inner shape or a force-parallel oriented shape.
Colour Changes of Acetal Resins (CAD-CAM) In Vivo
Authors: Gómez-Polo Cristina , Martín Casado Ana María , Quispe Norberto , Gallardo Eva Rosel , Montero Javier
Published: December 2022

To quantify the discolouration of the temporary acetal resins in vivo, based on the weeks of follow-up and the salivary pH in the three thirds of the tooth. To find out if the final CIELAB coordinates can be predicted from the initial colour coordinates, the salivary pH, the situation (in thirds) and the weeks of follow-up. Colour coordinates (L, C, and h) were recorded by spectrophotometry in 13 participants fitted with hybrid provisional complete dentures made of acetal resin. Colour recordings were made on the day of placement and after several weeks of follow-up (6 to 31 weeks). Salivary pH was also measured as a predictor variable for colour change. The ANOVA statistical test and regression models have been used. The highest colour difference according to ∆Eab* was 27.46 units after 15 weeks of follow-up and the lowest was 7.34 units after 17 weeks of follow-up. Neither in the cervical nor in the middle third any regressor variable (initial L*, initial C*, initial h*, salivary pH and weeks of follow-up) was able to significantly predict any of the final colour coordinates (p > 0.05). The colour change of the temporary acetal resins used exceeds the threshold of clinical acceptability, and it is not acceptable to maintain satisfactory aesthetics. The weeks of follow-up and the salivary pH are not capable of satisfactorily predicting the final color coordinates of the acetal resins.
Acoustic Emission and Deep Learning for the Classification of the Mechanical Behavior of AlSi10Mg AM-SLM Specimens
Authors: Barile Claudia , Casavola Caterina , Pappalettera Giovanni , Kannan Vimalathithan Paramsamy , Mpoyi Dany Katamba
Published: December 2022

In this research paper, the acoustic emission technique and a deep learning framework based on two types of pre-trained CNN models (alexNet and squeezeNet) and a new model are proposed to characterize and classify the mechanical behavior of AlSi10Mg components. Specimens are built in a Selective Laser Melting machine with different bed orientations along X, Y, Z, and 45 degrees. Tensile tests are performed, and AE signals are recorded from these tests. To characterize the elastic and plastic deformation stages, a time-frequency domain analysis was performed using CWT-based spectrograms. Three different categories of damage classification strategies were implemented, and CNN models were trained for each strategy. CNN models including AlexNet, SqueezeNet, and the new model were used. Several training modes were performed to determine the CNN model that can accurately classify AE data. Understanding the minimum set of AE signals needed to train the CNN while having 100% accuracy and understanding the parameters affecting the accuracy of a CNN and the training time for the efficient classification of AE signals are the main objectives of this work. The results obtained demonstrated that the new simplified CNN model proposed can accurately classify the AE signals in a short time compared to AlexNet and SqueezeNet.
Detection of Low Frequency Seismicity at Mt. Vesuvius Based on Coherence and Statistical Moments of Seismic Signals
Authors: Galluzzo Danilo , Manzo Roberto , La Rocca Mario , Nardone Lucia , Di Maio Rosa
Published: December 2022

Mt. Vesuvius is a high-hazard active volcano surrounded by a densely populated area. Since human activities generate high levels of seismic noise, recognizing low-amplitude seismic events in the signals recorded by the local seismic monitoring network operating at Vesuvius is very difficult. Here, we describe an automatic procedure applied to continuous data with the aim of finding low-amplitude–low-frequency events hidden in the recorded signals. The methodology is based on the computation of two spectral parameters, central frequency Ω and shape factor ẟ, at selected sites, and the coherence of the seismic signal among different sites. The proposed procedure is applied to 28 months of recordings from 2019 to 2021, tuning the search parameters in order to find low-frequency signals similar to those occasionally observed in the past at the same volcano. The results allowed us to identify 80 seismic events that have the spectral features of low-frequency earthquakes or tremor. Among these, 12 events characterized by sufficiently high signal-to-noise ratio have been classified as deep low-frequency earthquakes, most of which are not reported in the catalog. The remaining events (more than 60) are characterized by similar spectral features but with an extremely low amplitude that prevents any reliable location of the source and definitive classification. The results of this work demonstrate that the low-frequency endogenous activity at Mt. Vesuvius volcano is more frequent that previously thought.
The Use of Digital Color Imaging and Machine Learning for the Evaluation of the Effects of Shade Drying and Open-Air Sun Drying on Mint Leaf Quality
Authors: Ropelewska Ewa , Sabanci Kadir , Aslan Muhammet Fatih
Published: December 2022

The objective of this study was to reveal the usefulness of image processing and machine learning for the non-destructive evaluation of the changes in mint leaves caused by two natural drying techniques. The effects of shade drying and open-air sun drying on the ventral side (upper surface) and dorsal side (lower surface) of leaves were compared. Texture parameters were extracted from the digital color images converted to color channels R, G, B, L, a, b, X, Y, and Z. Models based on image features selected for individual color channels were built for distinguishing mint leaves in terms of drying techniques and leaf side using machine learning algorithms from groups of Lazy, Rules, and Trees. In the case of classification of the images of the ventral side of fresh and shade-dried mint leaves, an average accuracy of 100% and values of Precision, Recall, F-Measure, and MCC of 1.000 were obtained for color channels B (KStar and J48 machine learning algorithms), a (KStar and J48), b (KStar), and Y (KStar). The effect of open-air sun drying was greater. Images of the ventral side of fresh and open-air sun-dried mint leaves were completely correctly distinguished (100% correctness) for more color channels and algorithms, such as color channels R and G (J48), B, a and b (KStar, JRip, and J48), and X and Y (KStar). The classification of the images of the dorsal side of fresh and shade-dried mint leaves provided 100% accuracy in the case of color channel B (KStar) and a (KStar, JRip, and J48). The fresh and open-air sun-dried mint leaves imaged on the dorsal side were correctly classified at an accuracy of 100% for selected textures from color channels a (KStar, JRip, J48), b (J48), and Z (J48). The developed approach may be used in practice to monitor the changes in the structure of mint leaves caused by drying in a non-destructive, objective, cost-effective, and fast manner without the need to damage the leaves.
A Nonlinear Diffusion Model with Smoothed Background Estimation to Enhance Degraded Images for Defect Detection
Authors: Yang Tao , Xu Bingchao , Zhou Bin , Wei Wei
Published: December 2022

It is important to detect the defect of products efficiently in modern industrial manufacturing. Image processing is one of common techniques to achieve defect detection successfully. To process images degraded by noise and lower contrast effects in some scenes, this paper presents a new energy functional with background fitting, then deduces a novel model which approximates to estimate the smoothed background and performs the nonlinear diffusion on the residual image. Noise removal and background correction can be both successfully achieved while the defect feature is preserved. Finally, the proposed method and some other comparative methods are performed on several experiments with some classical degraded images. The numerical results and quantitative evaluation show the efficiency and advantages of the proposed method.
Application of Wavelet Transform and Fractal Analysis for Esophageal pH-Metry to Determine a New Method to Diagnose Gastroesophageal Reflux Disease
Authors: Tojza Piotr Mateusz , Doliński Łukasz , Redlarski Grzegorz , Szkopek Jacek , Dąbkowski Mariusz , Janiak Maria
Published: December 2022

In this paper, a new method for analysing gastroesophageal reflux disease (GERD) is shown. This novel method uses wavelet transform (WT) and wavelet-based fractal analysis (WBFA) on esophageal pH-metry measurements. The esophageal pH-metry is an important diagnostic tool supporting the physician’s work in diagnosing some forms of reflux diseases. Interpreting the results of 24-h pH-metry monitoring is time-consuming, and the conclusions of such an analysis can sometimes be too subjective. There is no strict procedure or reference values to follow when the impedance measurements are assessed. Therefore, an attempt was made to develop a point of reference for the assessment process, helping to distinguish healthy patients from GERD patients. In this approach, wavelet transform (WT) and wavelet-based fractal analysis (WBFA) were used to aid the diagnostic process. With this approach, it was possible to develop two efficient computer methods to classify healthy and sick patients based on the pH measurement data alone. The WT method provided a sensitivity value of 93.33%, with 75% specificity. The results of the fractal analysis confirmed that the tested signals have features that enable their automatic classification and assignment to a group of sick or healthy people. The article will be interesting for those studying the application of wavelet and fractal analysis in biomedical waveforms. The authors included in the work a description of the implementation of the fractal and wavelet analysis, the descriptions of the results of the analyses, and the conclusions drawn from them. The work will also be of interest to those who study the methods of using machine learning and artificial intelligence in computer-aided, automatic medical diagnostics.
DSVN: A Flexible and Secure Data-Sharing Model for VANET Based on Blockchain
Authors: Chen Xiaoxuan , Chen Yineng , Wang Xiayu , Zhu Xinghui , Fang Kui
Published: December 2022

Vehicular Ad Hoc Network (VANET) is an important part of the modern intelligent transportation system, which can provide vehicle communication at a certain distance. More importantly, VANET can provide route planning and autonomous driving for drivers by analyzing data. However, VANET’s data privacy and security are a huge challenge when serving drivers. In this paper, we propose a VANET data-sharing model (DSVN) that combines ciphertext-based attribute encryption (CP-ABE), blockchain, and InterPlanetary File System (IPFS). DSVN uses an outsourced and revocable ciphertext policy attribute-based encryption (ORCP-ABE) scheme, which is improved based on CP-ABE. ORCP-ABE uses key encryption key (KEK) trees to manage user attribute groups and revoke user-level attributes. It eliminates redundant attributes in the access policy by attribute-weighted access trees. Moreover, DSVN has no single point of failure. We demonstrate the indistinguishability under the chosen-plaintext attack (IND-CPA) security of DSVN by a game based on the computational Diffie–Hellman (CDH) assumption. Experimental results show that DSVN can store and share data with low overhead. Additionally, it can revoke attributes of users safely.
A Lightweight Network for Real-Time Rain Streaks and Rain Accumulation Removal from Single Images Captured by AVs
Authors: Khatab Esraa , Onsy Ahmed , Varley Martin , Abouelfarag Ahmed
Published: December 2022

In autonomous driving, object detection is considered a base step to many subsequent processes. However, object detection is challenged by loss in visibility caused by rain. Rainfall occurs in two main forms, which are streaks and streaks accumulations. Each degradation type imposes different effect on the captured videos; therefore, they cannot be mitigated in the same way. We propose a lightweight network which mitigates both types of rain degradation in real-time, without negatively affecting the object-detection task. The proposed network consists of two different modules which are used progressively. The first one is a progressive ResNet for rain streaks removal, while the second one is a transmission-guided lightweight network for rain streak accumulation removal. The network has been tested on synthetic and real rainy datasets and has been compared with state-of-the-art (SOTA) networks. Additionally, time performance evaluation has been performed to ensure real-time performance. Finally, the effect of the developed deraining network has been tested on YOLO object-detection network. The proposed network exceeded SOTA by 1.12 dB in PSNR on the average result of multiple synthetic datasets with 2.29× speedup. Finally, it can be observed that the inclusion of different lightweight stages works favorably for real-time applications and could be updated to mitigate different degradation factors such as snow and sun blare.
Laboratory Investigations of Mold Growth on Transverse and Longitudinal Wood Surfaces
Authors: Ryparová Pavla , Kopecký Pavel , Staněk Kamil , Richter Jan , Tywoniak Jan
Published: December 2022

The possible influence of anatomical sections of wood on mold growth was investigated by means of a laboratory experiment. The selected fungi, Aspergillus sp., Penicillium sp., and Alternaria sp. were inoculated by spraying on the surface of wood specimens prepared from pine (Pinus sylvestris) and spruce (Picea abies). The incubation was carried out under stable environmental conditions (temperature of 22 °C, relative humidity of 75, 87, and 95%) over three months. Mold growth was evaluated based on regular microscopic and macroscopic observations. The recorded mold coverage fractions and the qualitative indicators of mold development were later expressed by a dimensionless mold index. The differences in mold growth in the anatomical sections of wood were found to be relatively insignificant. In contrast, comparison of measured data with other experimental studies showed large differences, especially in the initial growth phase. The discrepancy is probably related to differences among the experimental protocols. It is concluded that laboratory mold growth studies would be improved if a common standardized methodology was developed and followed.
Research on the Interaction between the Pile and Shield Machine in the Process of Cutting a Reinforced Concrete Pile Foundation
Authors: Wang Xiaoyu , Yuan Dajun
Published: December 2022

As the urban underground space environment gets more complex, the cases of shield machines encountering and cutting through piles are becoming more common. The interaction between the cutter head and pile foundation directly affects the tunneling performance of the shield machine and the safety of the existing structure. To study the interaction between the pile and the shield machine, a calculation model of the interaction force is established. A field test of cutting two piles was conducted and the rationality of the model is verified by comparing the calculation results with field test data. The model is applied in the project of a shield machine cutting bridge piles in Harbin Metro Line 3, China. The shield operation parameters are predicted and compared with field test results. Besides, the impacts of cutting surface width and eccentric distance on interaction force are discussed. The study shows that there is a significant interaction between the cutter head and the piles when the shield machine cuts reinforced concrete piles, which causes obvious changes in the shield operation parameters and shield performance. The number of tools that are inside the cutting area has a significant effect on the additional torque. The additional torque fluctuates with the rotation of cutter head and increases with the increase of the number of tools. The number of these tools is determined by factors such as the layout of tools in the cutter head, cutting surface width and eccentric distance, which influence the position of each tool relative to the cutting area. As the cutting distance increase, the additional torque of the cutter head shows a trend of first increasing and then decreasing and reaches the maximum value when the cutting distance reaches the radius of the pile. Besides, the additional force and additional moment of the cutter head increase with the increase of the cutting surface width. The impacts of eccentric distance on additional force and additional moment are complicated. The results in this paper can provide reference for similar engineering.
Direct Inversion Method of Brittleness Parameters Based on Reweighted Lp-Norm
Authors: Chen Yaojie , Pan Shulin , Wu Yinghe , Wei Ze , Song Guojie
Published: December 2022

Brittleness is an important factor that indicates shale properties, as well as fracturability, and it can be well-represented using the elasticity parameter Eρ. Seismic inversion allows direct access to the Eρ ofbrittleness parameters. Seismic inversion is a typical ill-posed problem that has an enormous multiplicity of solutions. In order to invert reservoir brittleness parameters more stably and reliably, a direct inversion method for determining brittleness parameters based on a reweighted Lp-norm is proposed, and the reweighted Lp method is introduced to brittleness parameter inversion for the first time. The alternating direction method of multipliers (ADMM) is used to establish the inversion structure and to optimize the objective function in blocks, which effectively improves the convergence speed. We first introduce a reweighted Lp method and establish a pre-stack inversion objective function based on the reweighted Lp method. Then, theoretical simulation data are applied to compare the inversion outcomes of the new method with those of the traditional method, and the effect of the method in this manuscript is verified. Finally, the feasibility of this method is further verified using actual data for experimental analysis. Through an analysis of the experimental results, we find that this method can be well-applied to seismic pre-stack inversion calculation and provides a new direct inversion method for the determination of brittleness parameters for exploration geophysics.
Influence of Turbulence Intensity on the Aerodynamic Performance of Wind Turbines Based on the Fluid-Structure Coupling Method
Authors: Zheng Xing , Yao Yu , Hu Zhenhong , Yu Ziying , Hu Siyuan
Published: December 2022

The deformation and vibration of wind turbine blades in turbulent environment cannot be ignored; therefore, in order to better ensure the safety of wind turbine blades, the study of air-elastic response of wind turbine blades under turbulent wind is indispensable. In this paper, the NREL 5MW wind turbine blades are modeled with accurate 3D lay-up design, firstly, based on the joint simulation of commercial software STAR CCM+ and ABAQUS, the two-way fluid-solid coupling technology, the wind turbine under uniform wind condition is simulated, and the results from thrust, torque, structural deformation and force perspective and FAST are compared with good accuracy and consistency below the rated wind speed. Secondly, the aerodynamic performance, flow field distribution and structural response of turbulent winds with different turbulence strengths at 10 m/s were studied. The results show that the turbulence intensity has a greater impact on the amplitude of the wind turbine blade, and the stress distribution of the blade is more concentrated, which in turns affects the stability and safety of the wind turbine blade and is not conducive to the normal operation of the wind turbine.
The Impact of the Temperature Control Strategy in Steady-State Virtual Vacuum Simulation on the Spontaneous Evaporation Rate and Corresponding Evaporation Coefficient
Authors: Skarbalius Gediminas , Džiugys Algis , Misiulis Edgaras , Navakas Robertas
Published: December 2022

In the present paper, we propose a novel simulation approach that allows one to capture the steady-state evaporation into virtual vacuum state by maintaining a constant number of atoms within the liquid phase during the simulations. The proposed method was used to perform virtual vacuum simulations of argon at a temperature of 90 K in order to study the effects of the chosen simulation temperature control approach on the system’s temperature profiles, spontaneous evaporation rates, and the energetic characteristics of the evaporating atoms. The results show that the expected non-uniform temperature profile across the liquid phase can be flattened out by dividing the liquid phase into separately thermostated bins. However, the desired liquid surface temperature can be achieved only when the thermostat region boundary is placed outside the liquid phase. The obtained relationship between the surface temperature and the spontaneous evaporation rate show that the spontaneous evaporation rate and corresponding evaporation coefficient evaluation may change up to 21% when the surface temperature changes in a narrow temperature interval of 2.45 K. Furthermore, the results demonstrate that the thermostat region boundary position has no impact on the energetic characteristics of the evaporating argon atoms, even when the boundary is placed outside the liquid phase.
Production, Types, and Applications of Activated Carbon Derived from Waste Tyres: An Overview
Authors: Muttil Nitin , Jagadeesan Saranya , Chanda Arnab , Duke Mikel , Singh Swadesh Kumar
Published: December 2022

Storage of waste tyres causes serious environmental pollution and health issues, especially when they are left untreated in stockpiles and landfills. Waste tyres could be subjected to pyrolysis and activation in order to produce activated carbon, which is an effective adsorbent, and can find various applications, such as for wastewater treatment, removal of metals and dyes, energy storage devices, electrode materials, etc. Activated carbon (AC) is a non-polar and non-graphite material having high porosity and excellent adsorption capabilities, making it one of the most frequently used adsorbents in various industries. It is normally produced from carbon-rich materials such as coal, coconut shells, waste tyres, biowaste, etc. The use of waste tyres for the production of AC is a sustainable alternative to conventional sources (such as coconut shells and coal) as it supports the concept of a circular economy. Since AC sourced from waste tyres is a new area, this study reviews the methods for the preparation of AC, the types of activation, the forms of activated carbon, and the factors affecting the adsorption process. This study also reviews various applications of AC derived from waste tyres, with a specific focus on the removal of different pollutants from wastewater. Activated carbon derived from the waste tyres was found to be a versatile and economically viable carbon material, which can contribute towards safeguarding the environment and human health.
Insider Threat Detection Using Machine Learning Approach
Authors: Bin Sarhan Bushra , Altwaijry Najwa
Published: December 2022

Insider threats pose a critical challenge for securing computer networks and systems. They are malicious activities by authorised users that can cause extensive damage, such as intellectual property theft, sabotage, sensitive data exposure, and web application attacks. Organisations are tasked with the duty of keeping their layers of network safe and preventing intrusions at any level. Recent advances in modern machine learning algorithms, such as deep learning and ensemble models, facilitate solving many challenging problems by learning latent patterns and modelling data. We used the Deep Feature Synthesis algorithm to derive behavioural features based on historical data. We generated 69,738 features for each user, then used PCA as a dimensionality reduction method and utilised advanced machine learning algorithms, both anomaly detection and classification models, to detect insider threats, achieving an accuracy of 91% for the anomaly detection model. The experimentation utilised a publicly available insider threat dataset called the CERT insider threats dataset. We tested the effect of the SMOTE balancing technique to reduce the effect of the imbalanced dataset, and the results show that it increases recall and accuracy at the expense of precision. The feature extraction process and the SVM model yield outstanding results among all other ML models, achieving an accuracy of 100% for the classification model.
Deformations of Image Blocks in Photogrammetric Documentation of Cultural Heritage—Case Study: Saint James’s Chapel in Bratislava, Slovakia
Authors: Marčiš Marián , Fraštia Marek , Kovanič Ľudovít , Blišťan Peter
Published: December 2022

Structure from motion photogrammetry is currently one of the most frequently used technologies for the documentation of archaeological sites. Due to the relatively high freedom in choosing the position and orientation of the cameras, qualitatively different results for the reconstructed geometry can be achieved. Therefore, in the presented study, we focused on monitoring the changes in the geometry of point clouds obtained with different configurations of the camera network during the digitization of Saint James’s Chapel in Bratislava city. The changes of the tested photogrammetric variants were analyzed through comparison with scans from terrestrial laser scanning. The results suggest that caution should taken when striving for image recording efficiency, as insufficient connections between image blocks can lead to a decrease in relative accuracy, down to a level worse than 1:1000.
Reinforcement Learning Based Vocal Fold Localization in Preoperative Neck CT for Injection Laryngoplasty
Authors: Abdullah Al Walid , Cha Wonjae , Yun Il Dong
Published: December 2022

Transcutaneous injection laryngoplasty is a well-known procedure for treating a paralyzed vocal fold by injecting augmentation material to it. Hence, vocal fold localization plays a vital role in the preoperative planning, as the fold location is required to determine the optimal injection route. In this communication, we propose a mirror environment based reinforcement learning (RL) algorithm for localizing the right and left vocal folds in preoperative neck CT. RL-based methods commonly showed noteworthy outcomes in general anatomic landmark localization problems in recent years. However, such methods suggest training individual agents for localizing each fold, although the right and left vocal folds are located in close proximity and have high feature-similarity. Utilizing the lateral symmetry between the right and left vocal folds, the proposed mirror environment allows for a single agent for localizing both folds by treating the left fold as a flipped version of the right fold. Thus, localization of both folds can be trained using a single training session that utilizes the inter-fold correlation and avoids redundant feature learning. Experiments with 120 CT volumes showed improved localization performance and training efficiency of the proposed method compared with the standard RL method.
Analysis of Microtremor Exploration Application and Construction Monitoring in a Large-Diameter Shield Tunnel
Authors: Wang Zhe , Sheng Jianchao , Wang Rui , Li Xibin , Xiao Yuanjie , Yi Zihao
Published: December 2022

In recent years, shield tunneling has shown many advantages with the development of underground rail traffic. Geological exploration plays a significant role in tunnel engineering, and detailed geological exploration results can guide the successful construction of a tunnel. This research relies on a super large-diameter shield tunnel construction, using microtremor exploration technology to collect data onsite. Combined with a comparative analysis of the borehole surveying, the reliability of microtremor exploration technology is verified. Moreover, the monitoring result of the impact of large-diameter slurry balanced shield construction on the surrounding environment is analyzed. The results show that microtremor exploration can obtain geological details that traditional detection methods cannot obtain, which can predict the possible local geology mutation in front of the tunnel in advance. The law of surface settlement curve conforms to the Peck formula. This can be divided into five stages: micro deformation, extrusion uplift, reciprocating uplift, detachment settlement, and consolidation settlement. The surface settlement on the eccentric loads side is more prominent. The maximum pressure outside the tunnel segment appears on the lower side of the monitoring section, approximately 0.41 MPa, which will increase with the grouting pressure and become stable in five days.
Formal Matters on the Topic of Risk Mitigation: A Mathematical Perspective
Authors: Bilotta Giuseppe , Cappello Annalisa , Ganci Gaetana
Published: December 2022

How (in)formal should the classic expression describing risk as the product of hazard, exposure, and vulnerability be considered? What would be the most complete way to describe the process of risk mitigation? These are the questions we try to answer here, using a formal, mathematically sound yet abstract description of hazard, exposure, vulnerability, and risk. We highlight the elements that can be affected for the purpose of mitigation and show how this can improve the quantitative assessment of the procedural aspects of risk mitigation, both long- and short-term, down to the timescale of emergency response.
StreetScouting: A Deep Learning Platform for Automatic Detection and Geotagging of Urban Features from Street-Level Images
Authors: Charitidis Polychronis , Moschos Sotirios , Pipertzis Archontis , Theologou Ioakeim James , Michailidis Michael , Doropoulos Stavros , Diou Christos , Vologiannidis Stavros
Published: December 2022

Urban environments are evolving rapidly in big cities; keeping track of these changes is becoming harder. Information regarding urban features, such as the number of trees, lights, or shops in a particular region, can be crucial for tasks, such as urban planning, commercial campaigns, or inferring various social indicators. StreetScouting is a platform that aims to automate the process of detecting, visualizing, and exporting the urban features of a particular region. Recently, the advent of deep learning has revolutionized the way many computer vision tasks are tackled. In this work, we present StreetScouting, an extensible platform for the automatic detection of particular urban features of interest. StreetScouting utilizes several state-of-the-art computer vision approaches including Cascade R-CNN and RetinaFace architectures for object detection, the ByteTrack method for object tracking, DNET architecture for depth estimation, and DeepLabv3+ architecture for semantic segmentation. As a result, the platform is able to detect and geotag urban features from visual data. The extracted information can be utilized by many commercial or public organizations, eliminating the need for manual inspection.
Effect of the Magnetic Field Generated by a New NeFeB Cover Screw on Bone Healing around Endosseous Implants: A Case Series Report from Dental Practice
Authors: Gallusi Gianni , Strappa Enrico M. , Monterubbianesi Riccardo , Ferrante Luigi , Sampalmieri Francesco , Memè Lucia
Published: December 2022

This study aimed to investigate the effect of static magnetic field (SMF) generated by innovative cover screws made of NeFeB on early bone healing around dental implants. The study was carried out on humans in a private dental practice. Eight 3i implants (Biomet, Palm Beach, FL, USA) were placed in the posterior mandible at 3.6 and 3.7. The control implants were closed with a conventional screw (G1), while the test implants were closed with a Supercharged® screw (G2). Both groups were compared for stability at 0, 7, 14, 21, 50, and 90 days using a resonant frequency analyzer with Ostell Mentor (Osstell AB, Gothenburg, Sweden). After 50 days, the Supercharged® cover screw was removed. A significant increase in implant stability quotient (ISQ), corresponding to a lower degree of bone resorption, was observed in G2 throughout the follow-up period (p < 0.05), while an initial ISQ decrease was observed until day 21 in G1. In conclusion, the effect of SMF seems to be in the early stages of osseointegration and increases the stability of dental implants.
Mercury Contents in the Liver, Kidneys and Hair of Domestic Cats from the Warsaw Metropolitan Area
Authors: Skibniewska Ewa M. , Skibniewski Michał
Published: December 2022

Mercury is a highly toxic element subject to bioaccumulation, increasing its harmful effects on living organisms over time. In the present study, total mercury contents were determined in the liver, kidneys and hair of cats from Warsaw and its suburban areas. The study took into account the influence of the age, sex and living conditions of the animals. Samples were obtained between 2014 and 2016, and mercury contents were determined by atomic absorption spectrometry (AAS). The average mercury concentrations in the tissues studied were 0.025, 0.026 and 0.030 mg∙kg−1 in the hair, kidneys and livers of the individuals tested, respectively. Higher values were recorded in animals from the city area, and an increase in this metal with the age of the cats was also found. The average contents of mercury in the tissues studied were within the range of the recommended reference values, which in this case indicates low environmental exposure of animals to mercury.
Vision-Based Activity Classification of Excavators by Bidirectional LSTM
Authors: Kim In-Sup , Latif Kamran , Kim Jeonghwan , Sharafat Abubakar , Lee Dong-Eun , Seo Jongwon
Published: December 2022

Advancements in deep learning and vision-based activity recognition development have significantly improved the safety, continuous monitoring, productivity, and cost of the earthwork site. The construction industry has adopted the CNN and RNN models to classify the different activities of construction equipment and automate the construction operations. However, the currently available methods in the industry classify the activities based on the visual information of current frames. To date, the adjacent visual information of current frames has not been simultaneously examined to recognize the activity in the construction industry. This paper proposes a novel methodology to classify the activities of the excavator by processing the visual information of video frames adjacent to the current frame. This paper follows the CNN-BiLSTM standard deep learning pipeline for excavator activity recognition. First, the pre-trained CNN model extracted the sequential pattern of visual features from the video frames. Then BiLSTM classified the different activities of the excavator by analyzing the output of the pre-trained convolutional neural network. The forward and backward LSTM layers stacked on help the algorithm compute the output by considering previous and upcoming frames’ visual information. Experimental results have shown the average precision and recall to be 87.5% and 88.52%, respectively.
A Multi-Level Fuzzy Evaluation Method for the Reliability of Integrated Energy Systems
Authors: He Pei , Guo Yangming , Wang Xiaodong , Zhang Shiqi , Zhong Zhihao
Published: December 2022

With the increase in environmental pressure and rapid development of renewable energy technologies, an integrated energy system has been recognized as an effective approach to accommodate large-scale renewables and achieve environmental sustainability. While an integrated energy system significantly improves energy efficiency, the interaction between different energy systems may also bring multiple operational risks to its reliability, which necessitates an effective reliability assessment technique. In this paper, we proposed a multi-level fuzzy evaluation model based on combined empowerment for the reliability evaluation of an integrated energy system. The analytic hierarchy process method and entropy weight method were used to calculate the weight of each index in the evaluation model. Fuzzy evaluation matrix was constructed by the membership degree of a single factor, which was defined by the fuzzy comprehensive evaluation method. The multi-level fuzzy evaluation results were obtained based on single-level evaluation results. Finally, case studies were carried out based on a practical integrated energy system; we proposed 5 first-level indicators such as reliability and economy and 12 second-level indicators such as mean time of incapacity. The simulation results (85.15) showed the effectiveness and advantages of the proposed model.
The Role of miRNA in the Management of Localized and Advanced Renal Masses, a Narrative Review of the Literature
Authors: Napolitano Luigi , Orecchia Luca , Giulioni Carlo , Carbonara Umberto , Tavella Giovanni , Lizzio Leonardo , Fimognari Deborah , De Palma Antonio , Gheza Alberto , Grosso Antonio Andrea , Falagario Ugo , Parodi Stefano , Fasulo Vittorio , Romantini Federico , Rosiello Giuseppe , Viganò Silvia , Rabito Salvatore , Ceccato Tommaso , Pinelli Mirko , Felici Graziano , De Vita Francesco , Prata Francesco , Dibitetto Francesco , Tedde Matteo , Piramide Federico , Traunero Fabio , De Michele Mario , Morelli Michele , Piazza Pietro , Flammia Rocco Simone
Published: December 2022

Renal cell carcinoma (RCC) is the most common form of kidney cancer with 403,262 diagnoses and 170,000 deaths worldwide in 2018. Although partial or radical nephrectomy can be considered a successful treatment in early-stage or localized RCC, in advanced-stage disease, there is a high risk of metastasis or recurrence with a significantly poorer prognosis. Metastatic RCC is generally resistant to both chemotherapy and radiotherapy, and, despite several novel therapeutic agents, disease progression and mortality rates remain high. It is necessary to identify new diagnostic and therapeutic strategies for the management of this cancer. Knowledge of microRNA (miRNA) has consistently increased in the last year. miRNAs play an important role in several biological processes, such as cell proliferation, differentiation, and cell death. Due to this, miRNAs have been identified as an important key in different diseases, especially in cancer, and several studies show miRNAs as attractive tools and targets for novel therapeutic approaches. Recently several miRNAs (including miR-22, miR-203, miR-301 and miR-193a-3p) have been linked to dysregulated molecular pathways involved with the proliferation of cancerous cells and resistance to therapeutic agents. In the present study, recent data from studies assessing the application of miRNAs as biomarkers, therapeutic targets, or modulators of response to treatment modalities in RCC patients are analyzed.
A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
Authors: Cicirelli Grazia , D’Orazio Tiziana
Published: December 2022

The observation of mobility tests can greatly help neurodegenerative disease diagnosis. In particular, among the different mobility protocols, the sit-to-stand (StS) test has been recognized as very significant as its execution, both in terms of duration and postural evaluation, can indicate the presence of neurodegenerative diseases and their advancement level. The assessment of an StS test is usually done by physicians or specialized physiotherapists who observe the test and evaluate the execution. Thus, it mainly depends on the experience and expertise of the medical staff. In this paper, we propose an automatic visual system, based on a low-cost camera, that can be used to support medical staff for neurodegenerative disease diagnosis and also to support mobility evaluation processes in telehealthcare contexts. The visual system observes people while performing an StS test, then the recorded videos are processed to extract relevant features based on skeleton joints. Several machine learning approaches were applied and compared in order to distinguish people with neurodegenerative diseases from healthy subjects. Real experiments were carried out in two nursing homes. In light of these experiments, we propose the use of a quadratic SVM, which outperformed the other methods. The obtained results were promising. The designed system reached an accuracy of 95.2% demonstrating its effectiveness.
On the Characteristics of EMTR as a Correlation-Estimator-Based Fault Location Method in Transmission Lines
Authors: Nadeem Akif , Xie Yanzhao , He Shaoyin , Dong Ning , Caccavella Pietro , Saleem Maria
Published: December 2022

Methods based on electromagnetic time reversal (EMTR) have recently demonstrated promising results. In particular, EMTR, as a correlation estimator metric, has offered a quantitative definition for fault location candidates. As the correlation estimator already obtains the system transfer functions by simulating fault occurrences, these functions are subsequently correlated with the fault-generated transfer function. Therefore, it is necessary to explore the analysis related to the pre-fault processing of this metric. Firstly, the impact of fault impedance mismatch between the fault occurrence and fault estimation stages was investigated, and a simple approach is presented after observing the fault locations’ error. Secondly, it was noticed that the existing correlation estimator approach does not accurately identify fault types; therefore, a pseudo approach was developed to address this issue. Finally, the spatial step considered during the fault estimation stage was investigated. It is demonstrated that larger spatial steps result in inaccuracies in fault location. The smaller spatial steps improved the performance, but increased the memory burden on the data storage devices, especially when simple and pseudo approaches must be employed. The memory issue was resolved by presenting a hybrid approach that makes use of regression analysis. A single-phase and a three-phase lossy transmission line system were used to illustrate the proposed analyses.
Contrasting Dual Transformer Architectures for Multi-Modal Remote Sensing Image Retrieval
Authors: Rahhal Mohamad M. Al , Bencherif Mohamed Abdelkader , Bazi Yakoub , Alharbi Abdullah , Mekhalfi Mohamed Lamine
Published: December 2022

Remote sensing technology has advanced rapidly in recent years. Because of the deployment of quantitative and qualitative sensors, as well as the evolution of powerful hardware and software platforms, it powers a wide range of civilian and military applications. This in turn leads to the availability of large data volumes suitable for a broad range of applications such as monitoring climate change. Yet, processing, retrieving, and mining large data are challenging. Usually, content-based remote sensing image (RS) retrieval approaches rely on a query image to retrieve relevant images from the dataset. To increase the flexibility of the retrieval experience, cross-modal representations based on text–image pairs are gaining popularity. Indeed, combining text and image domains is regarded as one of the next frontiers in RS image retrieval. Yet, aligning text to the content of RS images is particularly challenging due to the visual-sematic discrepancy between language and vision worlds. In this work, we propose different architectures based on vision and language transformers for text-to-image and image-to-text retrieval. Extensive experimental results on four different datasets, namely TextRS, Merced, Sydney, and RSICD datasets are reported and discussed.
A Full Loading-Based MVDR Beamforming Method by Backward Correction of the Steering Vector and Reconstruction of the Covariance Matrix
Authors: Zhou Jing , Bao Changchun
Published: December 2022

In order to improve the performance of the diagonal loading-based minimum variance distortionless response (MVDR) beamformer, a full loading-based MVDR beamforming method is proposed in this paper. Different from the conventional diagonal loading methods, the proposed method combines the backward correction of the steering vector of the target source and the reconstruction of the covariance matrix. Firstly, based on the linear combination, an appropriate full loading matrix was constructed to correct the steering vector of the target source backward. Secondly, based on the spatial sparsity of the sound sources, an appropriate loading matrix was constructed to further suppress interferences. Thirdly, the spatial response power was utilized to derive a more accurate direction of arrival (DOA) of the target source, which is helpful for obtaining a more accurate steering vector of the target source and a more effective covariance matrix iteratively. The simulation results show that the proposed method can effectively suppress interferences and noise.