bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of the Spreadability of Butter and Butter Substitutes
Authors: Ziarno Małgorzata , Derewiaka Dorota , Florowska Anna , Szymańska Iwona
Published: February 2023

There are many types of butter, soft margarine, and blends, e.g., a mixture of butter and vegetable fats, on the market as bread spreads. Among these, butter and blends of butter with vegetable fats are very popular. The consumer’s choice of product is often determined by functional properties, such as texture, and the physicochemical composition of butter and butter substitutes. The aim of this study was to compare sixteen market samples of butter and butter substitutes in terms of spreadability and other selected structural (spreadability, hardness, adhesive force, and adhesiveness) and physicochemical parameters (water content, water distribution, plasma pH, color, acid value, peroxide number, saponification number, and instrumentally measured fatty acid profile) to investigate their correlation with spreadability. The parameters determined here were correlated with factors such as the type of sample, measuring temperature, and physicochemical composition. The statistical analysis revealed a very strong positive correlation between hardness and spreadability for all samples tested at 4 °C, as well as between hardness and spreadability for all samples tested 30 min after removal from the refrigerator; however, the interpretation of the results was different if the butter and butter substitute samples were subjected to a multivariate analysis separately.
Plant-Inspired Soft Growing Robots: A Control Approach Using Nonlinear Model Predictive Techniques
Authors: El-Hussieny Haitham , Hameed Ibrahim A. , Zaky Ahmed B.
Published: February 2023

Soft growing robots, which mimic the biological growth of plants, have demonstrated excellent performance in navigating tight and distant environments due to their flexibility and extendable lengths of several tens of meters. However, controlling the position of the tip of these robots can be challenging due to the lack of precise methods for measuring the robots’ Cartesian position in their working environments. Moreover, classical control techniques are not suitable for these robots because they involve the irreversible addition of materials, which introduces process constraints. In this paper, we propose two optimization-based approaches, combining Moving Horizon Estimation (MHE) with Nonlinear Model Predictive Control (NMPC), to achieve superior performance in point stabilization, trajectory tracking, and obstacle avoidance for these robots. MHE is used to estimate the entire state of the robot, including its unknown Cartesian position, based on available configuration measurements. The proposed NMPC approach considers process constraints by relying on the estimated state to ensure optimal performance. We perform numerical simulations using the nonlinear kinematic model of a vine-like robot, one of the newly introduced plant-inspired growing robots, and achieve satisfactory results in terms of reduced computation times and tracking error.
Application of Thermoelectric Generators for Low-Temperature-Gradient Energy Harvesting
Authors: Liu Yuming , Riba Jordi-Roger , Moreno-Eguilaz Manuel , Sanllehí Josep
Published: February 2023

Thermoelectric generators (TEGs) convert a temperature difference into useful direct current (DC) power. TEGs are solid-state semiconductor devices that are generating a lot of interest for energy harvesting purposes in Internet of Things (IoT) applications. This paper analyzes the behavior of state-of-the-art TEGs designed for low temperature gradient operation, with special emphasis on IoT systems for health monitoring for high-voltage alternating current (AC) and DC applications. In such applications, the energy harvesting unit plays a leading role in supplying wireless sensors (WSs). An application example is also presented with the aim to monitor the health condition of devices installed in the tubular busbars found in electrical substations. Since substation busbars heat up due to the Joule effect, there is a small thermal gradient between the busbar and the ambient, so the TEG can convert this heat flow into useful DC energy to supply low-power WSs. This paper assesses the performance of different TEG devices for this application, where very low temperature gradients are expected. The results presented show that with temperature gradients as low as 5 °C it is possible to supply WSs.
Water Extraction in PolSAR Image Based on Superpixel and Graph Convolutional Network
Authors: Wan Haoming , Tang Panpan , Tian Bangsen , Yu Hongbo , Jin Caifeng , Zhao Bo , Wang Hui
Published: February 2023

The timely detection and mapping of surface water bodies from Polarimetric Synthetic Aperture Radar (PolSAR) images are of great significance for emergency management and post-disaster restoration tasks. Though various methods have been proposed in previous years, there are still some inherent flaws. Thus, this paper proposes a new surface water extraction method based on superpixels and Graph Convolutional Networks (GCN). First, the PolSAR images are segmented to generate superpixels as the basic unit of classification, and the graph structure data are established according to their connection to superpixels. Then, the features of each superpixel are extracted. Finally, a GCN is used to classify each superpixel unit using node features and their relationships. This study conducted experiments on a sudden flooding event due to heavy rain and a lake in the city. Detailed verification was carried out. Compared to traditional methods, the recall was improved by 3% while maintaining almost 100% accuracy in complex flood areas. The results show that the proposed method of surface water extraction from PolSAR images has great advantages, acquiring higher accuracy and better boundary adherence in cases of fewer samples. This paper also illustrates the advantage of using GCN to mine the contextual information of classification objects.
Evaluation of Cutting Performance of a TBM Disc Cutter and Cerchar Abrasivity Index Based on the Brittleness and Properties of Rock
Authors: Jeong Hoyoung , Choi Seungbeom , Lee Yong-Ki
Published: February 2023

The brittleness of rock is known to be an important property that affects the fragmentation characteristics of rock in mechanized rock cutting. As the interaction between the cutting tool and the rock (i.e., cutter forces, cutting efficiency, s/p ratio, and abrasivity) during mechanical rock cutting is strongly influenced by the characteristics of rock fragmentation, the cutting tools (i.e., disc cutter and pick cutter) experience different cutting behaviors depending on the rock brittleness. In this study, the relationships between the rock brittleness and the abrasivity of rock, and the cutting efficiency of a Tunnel Boring Machine (TBM) disc cutter were investigated for Korean rock types. The brittleness was calculated by the mathematical relations between the uniaxial compressive and Brazilian tensile strengths of the rock. The cutting efficiency and abrasivity were evaluated by the cutter forces and specific energy from the linear cutting machine (LCM) test and the Cerchar abrasivity index (CAI) test, respectively. The results show that rock brittleness is significantly correlated with cutting efficiency and CAI values. Consequently, some prediction models for cutter forces, specific energy, and the CAI were proposed as functions of the rock brittleness.
Development of Potato Mass Estimation System Based on Deep Learning
Authors: Jang Sung-Hyuk , Moon Seok-Pyo , Kim Yong-Joo , Lee Sang-Hee
Published: February 2023

Potato is one of the world’s four major food crops as an important resource cultivated in about 150 countries. As precision agriculture has recently attracted increasing attention for its role in improving productivity, interest in yield monitoring is also increasing. Yield monitoring is a precision agriculture technology, and it can help farmhouse business management in the future by contributing to variable fertilization and supply and demand control. The present study was carried out to develop and evaluate a system that uses machine vision and deep learning technologies to estimate potato mass to monitor potato yield. The system performs object classification using the YOLOv5 algorithm to sort out potatoes among various foreign substances, object tracking using the DeepSORT algorithm to track the sorted potatoes, and volume calculation using the lengths of the major axis and minor axis of the tracked potatoes. The results of analyzing the function of the developed yield monitoring system showed an object detection rate of 95.2% and a weight measurement error of 9%, indicating that the computation load must be reduced through algorithm optimization to improve the accuracy and that error correction needs to be performed based on the potato position within the view angle.
Automatic Identification System for Rock Microseismic Signals Based on Signal Eigenvalues
Authors: Chen Junzhi , Li Hongbo , Ren Chunfang , Hu Fan
Published: February 2023

The microseismic signals of rock fractures indicate that the rock mass in a particular area is changing slowly, and the microseismic signals of rock blasting indicate that the rock mass in a particular area is changing violently. It is of great significance to accurately distinguish rock fracture signals and rock microseismic signals for analyzing the changes in the rock mass in the area where the signal occurs. Considering the microseismic signals of the Dahongshan Iron Mine, the time domain, frequency domain, energy characteristic distribution, and fractal features of each signal were analyzed after noise reduction of the original signal. The results demonstrate that the signal duration and maximum amplitude of the signal could not accurately distinguish the two types of signals. However, the main frequency of the rock fracture signal after noise reduction is distributed above 500 HZ, and the main frequency of the rock blasting signal is mainly distributed below 500 HZ. After the denoised signal is decomposed by the ensemble empirical simulation decomposition, the energy of the IMF1 frequency band of the rock fracture signal occupies an absolute dominant position, and the sum of the energy of the IMF2–IMF4 frequency bands of the rock blasting signal occupies a dominant position. The fractal box dimension of the rock fracture signal is mainly below 1.1, and the fractal box dimension of the rock blasting signal is mainly above 1.25. According to the above research results, an automatic signal recognition system based on the BP neural network is established, and the recognition accuracy of the rock blasting and rock fracture signals reached 93% and 94% respectively, when this system was used.
Coastal Adaptation to Climate Change and Sea Level Rise: Ecosystem Service Assessments in Spatial and Sectoral Planning
Authors: Schernewski Gerald , Konrad Alena , Roskothen Jannik , von Thenen Miriam
Published: February 2023

In this paper, we critically reflect on the ability of spatial and sectoral planning approaches to deal with regional climate change, especially sea level rise, and analyze the potential of ecosystem service assessments in regional coastal climate change adaptation planning. We present two concrete but contrasting case studies that focus on coastal realignment and wetland restoration as well as seaside tourism and resort development on the German Baltic Sea coast. The ecosystem service assessments compare past, present and potential future local development scenarios. Complementary, we apply the InVest and the Matrix ecosystem service assessment approaches. Despite some shortcomings, the proactive German hierarchical planning system seems suitable to deal with climate change and provide a framework for adaptation measures in coastal zones. It largely prevents a shift of climate change risks between municipalities and stakeholders. Comparative ecosystem service assessments seem well suited to play a role in the planning process by raising awareness about problems, possible solutions and consequences. On a local level, they can provide comprehensive insight into consequences of alternative measures. Our examples show that, in principle, potential future situations can be addressed with comparative ecosystem service approaches. We discuss whether the results of hypothetical future scenarios, with inherent uncertainties, might be misleading and counterproductive.
Joint Task Offloading, Resource Allocation, and Load-Balancing Optimization in Multi-UAV-Aided MEC Systems
Authors: Elgendy Ibrahim A. , Meshoul Souham , Hammad Mohamed
Published: February 2023

Due to their limited computation capabilities and battery life, Internet of Things (IoT) networks face significant challenges in executing delay-sensitive and computation-intensive mobile applications and services. Therefore, the Unmanned Aerial Vehicle (UAV) mobile edge computing (MEC) paradigm offers low latency communication, computation, and storage capabilities, which makes it an attractive way to mitigate these limitations by offloading them. Nevertheless, the majority of the offloading schemes let IoT devices send their intensive tasks to the connected edge server, which predictably limits the performance gain due to overload. Therefore, in this paper, besides integrating task offloading and load balancing, we study the resource allocation problem for multi-tier UAV-aided MEC systems. First, an efficient load-balancing algorithm is designed for optimizing the load among ground MEC servers through the handover process as well as hovering UAVs over the crowded areas which are still loaded due to the fixed location of the ground base stations server (GBSs). Moreover, we formulate the joint task offloading, load balancing, and resource allocation as an integer problem to minimize the system cost. Furthermore, an efficient task offloading algorithm based on deep reinforcement learning techniques is proposed to derive the offloading solution. Finally, the experimental results show that the proposed approach not only has a fast convergence performance but also has a significantly lower system cost when compared to the benchmark approaches.
GA-StackingMD: Android Malware Detection Method Based on Genetic Algorithm Optimized Stacking
Authors: Xie Nannan , Qin Zhaowei , Di Xiaoqiang
Published: February 2023

With the rapid development of network and mobile communication, intelligent terminals such as smartphones and tablet computers have changed people’s daily life and work. However, malware such as viruses, Trojans, and extortion applications have introduced threats to personal privacy and social security. Malware of the Android operating system has a great variety and updates rapidly. Android malware detection is faced with the problems of high feature dimension and unsatisfied detection accuracy of single classification algorithms. In this work, an Android malware detection framework GA-StackingMD is presented, which employs Stacking to compose five different base classifiers, and Genetic Algorithm is applied to optimize the hyperparameters of the framework. Experiments show that Stacking could effectively improve malware detection accuracy compared with single classifiers. The presented GA-StackingMD achieves 98.43% and 98.66% accuracies on CIC-AndMal2017 and CICMalDroid2020 data sets, which shows the effectiveness and feasibility of the proposed method.
Evaluation of Bridge Maintenance Priorities in Megacities
Authors: Lee Jongeok , Choo Seungyeon , Yang Jun-Mo , Chang Chunho
Published: February 2023

Bridges are key social overhead capital facilities with a direct impact on citizen safety and the socioeconomic development of a country. Many bridges in South Korea were completed during the nation’s period of rapid economic growth in the 1970s and 1980s, meaning that bridge aging is now accelerating and should be of real concern. Based on records of other highly developed countries not having adequate maintenance systems for old bridges and consequently experiencing a number of tragic bridge collapses, Korea is at risk of following suit. Being able to measure the efficiency of bridge management considering economic and social values is therefore critical. In this work, a qualitative bridge maintenance priority evaluation model using open-source bridge information was developed. The model was evaluated by applying it to eight actual bridges located in Daegu Metropolitan City, Korea. It is concluded that aging bridge maintenance and management can be performed more efficiently and reliably if the model proposed in this work is used.
New Contributions for Damping Assessment on Filler-Beam Railway Bridges Framed on In2Track EU Projects
Authors: Silva Artur , Ribeiro Diogo , Montenegro Pedro Aires , Ferreira Gonçalo , Andersson Andreas , Zangeneh Abbas , Karoumi Raied , Calçada Rui
Published: February 2023

Structural damping is an important characteristic in railway bridges, which affects the performance of the structure, especially for bridges with train speeds higher than 200 km/h. The accurate evaluation of damping must be performed properly to correctly assess the structural performance of the bridge under dynamic loading conditions. The present article introduces an alternative methodology that contributes to the assessment of damping coefficients with application to railway bridges. The methodology is based in the Prony method with an energy-sorting technique for the identification of dominant frequencies of a free vibration signal of a passing train. The numerical validation of the method is based on a sensitivity analysis of the free vibration periods of signals through the evaluation of influence lines of displacement and numerically simulated receptance tests, and in the estimation of the damping coefficient from the free vibration period obtained in a train-bridge interaction dynamic analysis with a known imposed value. Finally, and in the scope of the In2Track2 and In2Track3 projects, the experimental assessment of damping coefficients using this methodology was carried out, considering four filler-beam bridges from the Portuguese Railway Network. The ambient vibration tests allowed the evaluation of the main frequencies and damping in these bridges, and the dynamic tests under railway traffic allowed the definition of the dynamic response of these bridges and subsequent application of the Prony method for two types of trains. The results of this work allow a new update of the database for damping coefficients of filler-beam railway bridges, contributing to future revisions of EN1991-2.
Towards Self-Reinforced PLA Composites for Fused Filament Fabrication
Authors: Yadav Neha , Richter Tim , Löschke Oliver , Abali Bilen Emek , Auhl Dietmar , Völlmecke Christina
Published: February 2023

Aligned with the Sustainability Development Goals (SDGs), we present the complete methodology of preparing bio-based polymer filaments to be used in additive manufacturing, specifically by means of so-called Fused Filament Fabrication (FFF) in 3D printing. Filament production and 3D printing were both developed and optimised in this work. First, we focused on the steps of producing and optimising the extrusion process of unreinforced polylactic acid (PLA) composite filaments. Second, we studied the resulting material properties by discussing the selection of a specimen geometry and the international standards adequate for FFF 3D printing. Moreover, we investigated the process parameters in order to achieve reliable structures. Based on the reinforcement material (stereocomplex fibres (Sc-PLA fibre) and bi-component fibres (bi-co PLA fibre), base-matrices were selected for producing un-reinforced filaments. In this way, we present the complete preparation approach by identifying problems and pitfalls for fostering studies of bio-based polymer filaments.
Heads Up! A Biomechanical Pilot Investigation of Soccer Heading Using Instrumented Mouthguards (iMGs)
Authors: Sokol-Randell Darek , Stelzer-Hiller Oscar W. , Allan David , Tierney Gregory
Published: February 2023

Soccer players purposefully head the ball, raising concerns about reduced tolerance to concussion and potential long-term brain health. By combining qualitative video analysis with custom-fit instrumented mouthguards (iMGs), we aimed to categorize header kinematics (peak linear acceleration (PLA) and peak angular acceleration (PAA)) by header type and ball delivery method. iMGs were fitted to 10 male collegiate players for twelve matches. A total of 133 headers were verified and contextualized via video review. The most common header type (38.7%), as well as the preceding ball delivery method (47.4%), was found to be a pass. Approximately one-quarter of header impacts (27.0%) occurred below 10 g. For header type, there were no significant differences in kinematics, with shot attempts having the highest median PLA and PAA. For ball delivery methods, goal kicks had significantly greater PAA than long balls and pass attempts. The current study highlights the utility of qualitative video analysis in combination with real-time head kinematic data from iMGs to understand the mechanism and severity of header impacts. The pilot findings indicate that high-speed ball delivery methods result in higher head kinematics and should be a focus of future mitigation strategies.
Alkaline-Activation Technique to Produce Low-Temperature Sintering Activated-HAp Ceramic
Authors: Ibrahim Wan Mohd Arif W. , Abdullah Mohd Mustafa Al Bakri , Jamil Noorina Hidayu , Mohamad Hasmaliza , Salleh Mohd Arif Anuar Mohd , Sandu Andrei Victor , Vizureanu Petrica , Baltatu Madalina Simona , Sukmak Patimapon
Published: February 2023

The fabrication of hydroxyapatite (HAp) ceramics prepared by existing conventional sintering requires high-temperature sintering of 1250 °C to 1300 °C. In this paper, the activated metakaolin (MK)/HAp specimens were prepared from varied mix design inputs, which were varied solid mixtures (different amounts of MK loading in HAp) and liquid-to-solid (L/S) ratios, before being pressed and sintered at 900 °C. Phase analysis, thermal analysis, surface morphology, and tensile strength of the specimens were investigated to study the influences of the Al, Si, Fe, Na, and K composition on the formation of the hydroxyapatite phase and its tensile strength. XRD analysis results show the formation of different phases was obtained from the different mix design inputs HAp (hexagonal and monoclinic), calcium phosphate, sodium calcium phosphate silicate and calcium hydrogen phosphate hydrate. Interestingly, the specimen with the addition of 30 g MK prepared at a 1.25 L/S ratio showed the formation of a monoclinic hydroxyapatite phase, resulting in the highest diametrical tensile strength of 12.52 MPa. Moreover, the increment in the MK amount in the specimens promotes better densification when sintered at 900 °C, which was highlighted in the microstructure study. This may be attributed to the Fe2O3, Na2O, and K2O contents in the MK and alkaline activator, which acted as a self-fluxing agent and contributed to the lower sintering temperature. Therefore, the research revealed that the addition of MK in the activated-HAp system could achieve a stable hydroxyapatite phase and better tensile strength at a low sintering temperature.
A Collection of Formulae for the Design of Compton Back-Scattering X-ray Sources
Authors: Dattoli Giuseppe , Di Palma Emanuele , Petrillo Vittoria
Published: February 2023

Along with the development of FEL X-ray sources, the design and the construction of Compton Back-Scattering (CBS) devices for different applications are being pursued. These sources are designed to provide monochromatic, high peak brightness tunable light beams, with photon energies above 10 KeV. The source brightness is the most significant figure of merit, which specifies the suitability of X-ray beams for their application in a specific field of interest and also reflects the quality of the electron beam itself, which in turn depends on the charge, emittance, energy spread, etcetera. This article is intended as an introductory review to the field and we embed a few theoretical considerations and analytical formulae to develop simple tools for the design of CBS X-ray sources. The present attempt is analogous to previous efforts, put forward to model Free Electron Laser devices, using scaling relations accounting for the entanglement between the different physical quantities contributing to the output beam performances. We comment on the reliability of our analysis by a comparison with the wealth of results (either numerical and analytical) reported in the dedicated literature.
Comparison of Artificial Neural Network and Regression Models for Filling Temporal Gaps of Meteorological Variables Time Series
Authors: Dyukarev Egor
Published: February 2023

Continuous meteorological variable time series are highly demanded for various climate related studies. Five statistical models were tested for application of temporal gaps filling in time series of surface air pressure, air temperature, relative air humidity, incoming solar radiation, net radiation, and soil temperature. A bilayer artificial neural network, linear regression, linear regression with interactions, and the Gaussian process regression models with exponential and rational quadratic kernel were used to fill the gaps. Models were driven by continuous time series of meteorological variables from the ECMWF (European Centre for Medium-range Weather Forecasts) ERA5-Land reanalysis. Raw ECMWF ERA5-Land reanalysis data are not applicable for characterization of specific local weather conditions. The linear correlation coefficients (CC) between ERA5-Land data and in situ observations vary from 0.61 (for wind direction) to 0.99 (for atmospheric pressure). The mean difference is high and estimated at 3.2 °C for air temperature and 3.5 hPa for atmospheric pressure. The normalized root-mean-square error (NRMSE) is 5–13%, except for wind direction (NRMSE = 49%). The linear bias correction of ERA5-Land data improves matching between the local and reanalysis data for all meteorological variables. The Gaussian process regression model with an exponential kernel based or bilayered artificial neural network trained on ERA5-Land data significantly shifts raw ERA5-Land data toward the observed values. The NRMSE values reduce to 2–11% for all variables, except wind direction (NRMSE = 22%). CC for the model is above 0.87, except for wind characteristics. The suggested model calibrated against in situ observations can be applied for gap-filling of time series of meteorological variables.
Review: A Survey on Objective Evaluation of Image Sharpness
Authors: Zhu Mengqiu , Yu Lingjie , Wang Zongbiao , Ke Zhenxia , Zhi Chao
Published: February 2023

Establishing an accurate objective evaluation metric of image sharpness is crucial for image analysis, recognition and quality measurement. In this review, we highlight recent advances in no-reference image quality assessment research, divide the reported algorithms into four groups (spatial domain-based methods, spectral domain-based methods, learning-based methods and combination methods) and outline the advantages and disadvantages of each method group. Furthermore, we conduct a brief bibliometric study with which to provide an overview of the current trends from 2013 to 2021 and compare the performance of representative algorithms on public datasets. Finally, we describe the shortcomings and future challenges in the current studies.
Analysis of the Consistency of Prerequisites and Learning Outcomes of Educational Programme Courses by Using the Ontological Approach
Authors: Nazyrova Aizhan , Milosz Marek , Bekmanova Gulmira , Omarbekova Assel , Mukanova Assel , Aimicheva Gaukhar
Published: February 2023

The article presents the results of the application of an ontological approach to the description of the structure and content of the educational programme, and its subsequent analysis for the consistency of prerequisites and learning outcomes of courses. The practical result of the work is an ontology approach implemented in the Protégé 5.5.0 editor, which reflects the studied disciplines in terms of the skills they form and the entrance requirements (prerequisites) for the qualification of the student. The curriculum model includes sequences of semesters and courses of study (academic year) related by time relationships. The developed ontology approach is filled with data from the educational programme “Software Engineering”. The authors have earned queries in DL Query and SPARQL languages, which, using logical inference procedures, make it possible to analyse an educational programme for consistency of disciplines in terms of input requirements and the skills of the learner formed during the training period. The developed ontology and rules of logical inference can be used as a part of the educational process management information systems and educational programme designers, for the intellectual analysis of programme integrity and the consistency of learning prerequisites and outcomes in disciplines.
Accuracy of Three-Dimensional Printed Dental Models Based on Ethylene Di-Methacrylate-Stereolithography (SLA) vs. Digital Light Processing (DLP)
Authors: Vlasa Alexandru , Bocanet Vlad I. , Muntean Mircea H. , Bud Anamaria , Dragomir Bogdan Radu , Rosu Sorana Nicoleta , Lazar Luminita , Bud Eugen
Published: February 2023

Additive manufacturing is a technology that has many uses across a variety of fields. Its usage spans many fields, including the fields of art, design, architecture, engineering and medicine, including dentistry. The study aims to evaluate and compare the accuracy of three-dimensional printed dental models based on ethylene di-methacrylate using the SLA and DLP techniques. For evaluation, a reference model containing 16 maxillary permanent molars was chosen. An ATOS Capsule 3D scanner was used to scan the reference model. Using a photo-cured liquid resin, eight three-dimensional printed models were obtained using the reference model as benchmark. Four of the models (A1–A4) were obtained using SLA printing technology and four models (B1–B4) were manufactured using DLP printing technology. A standard best fit method was used to pre-align the reference and the printed model surfaces. The height of the teeth, and the mesial–distal and buccal–lingual distances were analyzed. The assessment of the two manufacturing methods was achieved by using non-parametric tests to compare the mean ranks for the assessed features. The results show that models obtained through DLP had a higher precision but also a higher bias. Both methods still are within the required accuracy range for dental models.
Influence of Headlight Level on Object Detection in Urban Traffic at Night
Authors: Erkan Anil , Hoffmann David , Singer Timo , Schikowski Julia Maria , Kunst Korbinian , Peier Markus Alexander , Khanh Tran Quoc
Published: February 2023

The purpose of this work is to determine the influence of the low beam intensity of motor vehicle headlights on detection conditions in urban traffic. For this purpose, studies with fourteen subjects are conducted on three differently illuminated test roads, in which the low beam intensity is dimmed from off to fully on. At each dimming level, the subjects indicate whether or not they have detected the object, which is realized by a flat target and occurs at sixteen different positions in front of the vehicle. In addition, considerations of the contrast curve and the visibility level are made in order to determine the influence of switched off and fully switched on headlights. The results show that the negative contrast created by the existing street lighting creates detection conditions at least as good as full low beam intensity in almost all cases. The results further indicate that the influence of the low beam intensity increases with decreasing distance to the object and decreasing illumination levels. The results of this work show that an increase in low beam intensity initially leads to poorer detection conditions; thus, the option of reducing low beam intensity should be considered in urban traffic space.
Review of Fault-Tolerant Control Systems Used in Robotic Manipulators
Authors: Milecki Andrzej , Nowak Patryk
Published: February 2023

Control systems that ensure robot operation during failures are necessary, particularly when manipulators are operating in hazardous or hard-to-reach environments. In such applications, fault-tolerant robot controllers should detect failures and, using fault-tolerant control methods, be able to continue operation without human intervention. Fault-tolerant control (FTC) is becoming increasingly important in all industries, including production lines in which modern robotic manipulators are used. The use of fault-tolerant systems in robotics can prevent the production line from being immobilized due to minor faults. In this paper, an overview of the current state-of-the-art methods of fault-tolerant control in robotic manipulators is provided. This review covers publications from 2003 to 2022. The article pays special attention to the use of artificial intelligence (AI), i.e., fuzzy logic and artificial neural networks, as well as sliding mode and other control methods, in the FTC of robotic manipulators. The cited and described publications were mostly found using Google Scholar.
Mechanical Characterization of a Polymer/Natural Fibers/Bentonite Composite Material with Implementation of a Continuous Damage Model
Authors: Fernández Meylí Valin , Rivera José Luis Valin , Rodríguez Frank Pérez , Losada Henry Figueredo , Abreu María Elena Fernández , Diaz Francisco Rolando Valenzuela , Soto Angel Rodríguez , Alvarez Alexander Alfonso , Quinteros Roberto , Ketterer Cristobal Galleguillos , García del Pino Gilberto
Published: February 2023

A characterization of composite materials of a polyester matrix reinforced with natural fibers of Cuban henequen and organophilized Cuban bentonite was carried out, the experimental results of which are used to formulate, implement, and validate a scalar damage model. The fibers were added on a weight basis of 15, 20, and 25% as a reinforcing material, and organophilized Cuban bentonite, in amounts of 3, 5, and 7% by weight, was added. Samples containing only polymer and fibers showed the best mechanical performance for 25% of fiber, with increases of 51% in the tensile strength and 169% on the impact resistance. Samples containing polymer and clay showed the best results for 5% of bentonite, with increases of 89% in the tensile strength and 83% on the impact resistance, and samples containing 25% fiber and 5% clay were also prepared and achieved a 98% increase in the tensile strength and 219% in the impact strength. The scalar damage model deals with an internal variable, which corresponds to the damage variable, together with a failure surface that allows identifying the mechanical state of the material dependent on the mechanical stress, obtaining an adequate correlation with the results of the experimental tests.
Sustainable Approach to Nonurban Road Network Maintenance Management: Herzegovina-Neretva County, B&H Case Study
Authors: Jajac Nikša , Bošnjak Ana
Published: February 2023

This paper presents a systematic and sustainable approach to nonurban road network maintenance management based on the integration of multicriteria decision-making methods and methods of monitoring and control in the service of planning, implementation and monitoring and control as the basic management processes. This approach is based on the developed maintenance management model for nonurban road network, which consists of a weighting sub-model based on the AHP method, a priority road ranking sub-model based on TOPSIS method and a monitoring and control sub-model based on the combination of TOPSIS and Earned Value Analysis (EVA). The described model with all its supporting sub-models is tested on the case study of the regional road network of Herzegovina-Neretva County. The analysis of the obtained results shows the effectiveness of the presented approach to road maintenance management and the possibility of its practical application.
Evidencing Fluid Migration of the Crust during the Seismic Swarm by Using 1D Magnetotelluric Monitoring
Authors: Vargas Carlos A. , Caneva Alexander , Solano Juan M. , Gulisano Adriana M. , Villalobos Jaime
Published: February 2023

We applied multi–temporal 1D magnetotelluric (MT) surveys to identify space–time anomalies of apparent resistivity (ρa) in the upper lithosphere in the Antarctic Peninsula (the border between the Antarctic and the Shetland plates). We used time series over several weeks of the natural Earth’s electric and magnetic fields registered at one MT station of the Universidad Nacional de Colombia (RSUNAL) located at Seymour–Marambio Island, Antarctica. We associated resistivity anomalies with contrasting earthquake activity. Anomalies of ρa were detected almost simultaneously with the beginning of a seismic crisis in the Bransfield Strait, south of King George Island (approximately 85.000 events were reported close to the Orca submarine volcano, with focal depths < 20 km and MWW < 6.9). We explained the origin of these anomalies in response to fluid migration near the place of the fractures linked with the seismic swarm, which could promote disturbances of the pore pressure field that reached some hundreds of km away.
On the Uniaxial Compression Testing of Metallic Alloys at High Strain Rates: An Assessment of DEFORM-3D Simulation
Authors: Bodunrin Michael , Obiko Japheth , Klenam Desmond
Published: February 2023

In this study, the challenges associated with conducting high-strain rate uniaxial compression testing experiments are highlighted. To address these challenges, DEFORM-3D simulation was explored as an alternative approach to experimental testing. Previously established constitutive constants obtained from experimental low strain rate uniaxial compression testing of three titanium (α + β) alloys were used as input codes. From the results, the peak flow stress values obtained from the DEFORM-3D simulation were close to the values obtained experimentally at low (0.1 to 10/s) and high (20 and 50/s) strain rates. For the alloys considered in this study, a discrepancy of ~20% in the peak flow stress was obtained at a 10/s strain rate. The difference in peak flow stress for strain rates less than 10/s or higher (20 and 50/s) is within acceptable limits. The limitations of using DEFORM-3D simulations for high strain rate uniaxial compression testing are highlighted.
Static and Dynamic Cyclic Fatigue Resistance of Nickel-Titanium Rotary Instruments in a Double-Curved Stainless Steel Artificial Canal
Authors: Le Hoang-Lan-Anh , Tran Thuan-Loc , Nguyen Thu-Thuy , Pham Tran-Lan-Khue , Pham Van-Khoa
Published: February 2023

The present study aims to measure the number of cycles leading to fracture (NCF) of instruments in static and dynamic cyclic fatigue situations under body temperature in stainless steel double-curved canals. The framework was constructed to establish the movement of instruments occurring at a stable body temperature. A step motor, a holding system for an endodontic handpiece, created the movement in and out of the artificial canal of the file mounted on the handpiece. A total of 30 instruments of ProTaper Universal and ProTaper Next (Dentsply Sirona, Maillefer, Ballaigues, Switzerland) were divided into three groups of 10 per group. For group 1 (10 PTU F2), files were rotated in static cycles. For groups 2 (10 PTU F2) and 3 (10 PTN X2), files were rotated in dynamic cycles. Files were rotated using proprietary programs, and the times the files were rotated before fracture were recorded. Data were analyzed using survival probabilities and regression with life data. The ProTaper Next in the dynamic cycles had the largest NCF, and the ProTaper Universal in the static condition had the least. New modes of rotation, material, and design have affected the cyclic fatigue resistance of the instrument.
Novel Mechanical Flux-Weakening Design of a Spoke-Type Permanent Magnet Generator for Stand-Alone Power Supply
Authors: Jiang Mingyuan , Niu Shuangxia
Published: February 2023

In this paper, a novel mechanical flux-weakening design of a spoke-type permanent magnet generator for a stand-alone power supply is proposed. By controlling the position of the adjustable modulator ring mechanically, the total induced voltage, i.e., the amplitude of the back EMF vector sum can be effectively adjusted accordingly by the modulation effect. Consequently, the variable-speed constant-amplitude voltage control (VSCAVC) with a large speed range can be achieved. Compared to the electrical flux-weakening method, the mechanical flux-weakening method is easier to operate without the risk of PM demagnetization. The analytical model is presented, and the operation principles are illustrated. To analyze the performance of different combinations of stator/rotor pole pairs, four cases are optimized and analyzed using the finite element method for comparison. The characteristics of VSCAVC are analyzed.
RNA Sequences-Based Diagnosis of Parkinson’s Disease Using Various Feature Selection Methods and Machine Learning
Authors: Kim Jingeun , Park Hye-Jin , Yoon Yourim
Published: February 2023

Parkinson’s disease is a neurodegenerative disease that is associated with genetic and environmental factors. However, the genes causing this degeneration have not been determined, and no reported cure exists for this disease. Recently, studies have been conducted to classify diseases with RNA-seq data using machine learning, and accurate diagnosis of diseases using machine learning is becoming an important task. In this study, we focus on how various feature selection methods can improve the performance of machine learning for accurate diagnosis of Parkinson’s disease. In addition, we analyzed the performance metrics and computational costs of running the model with and without various feature selection methods. Experiments were conducted using RNA sequencing—a technique that analyzes the transcription profiling of organisms using next-generation sequencing. Genetic algorithms (GA), information gain (IG), and wolf search algorithm (WSA) were employed as feature selection methods. Machine learning algorithms—extreme gradient boosting (XGBoost), deep neural network (DNN), support vector machine (SVM), and decision tree (DT)—were used as classifiers. Further, the model was evaluated using performance indicators, such as accuracy, precision, recall, F1 score, and receiver operating characteristic (ROC) curve. For XGBoost and DNN, feature selection methods based on GA, IG, and WSA improved the performance of machine learning by 10.00% and 38.18%, respectively. For SVM and DT, performance was improved by 0.91% and 7.27%, respectively, with feature selection methods based on IG and WSA. The results demonstrate that various feature selection methods improve the performance of machine learning when classifying Parkinson’s disease using RNA-seq data.
LSTM Network-Assisted Binocular Visual-Inertial Person Localization Method under a Moving Base
Authors: Xu Zheng , Su Zhong , Dai Dongyue
Published: February 2023

In order to accurately locate personnel in underground spaces, positioning equipment is required to be mounted on wearable equipment. But the wearable inertial personnel positioning equipment moves with personnel and the phenomenon of measurement reference wobble (referred to as moving base) is bound to occur, which leads to inertial measurement errors and makes the positioning accuracy degraded. A neural network-assisted binocular visual-inertial personnel positioning method is proposed to address this problem. Using visual-inertial Simultaneous Localization and Mapping to generate ground truth information (including position, velocity, acceleration data, and gyroscope data), a trained neural network is used to regress 6-dimensional inertial measurement data from the IMU data fragment under the moving base, and a position loss function is constructed based on the regressed inertial data to reduce the inertial measurement error. Finally, using vision as the observation quantity, the point feature and inertial measurement data are tightly coupled to optimize the mechanism to improve the personnel positioning accuracy. Through the actual scene experiment, it is verified that the proposed method can improve the positioning accuracy of personnel. The positioning error of the proposed algorithm is 0.50%D, and it is reduced by 92.20% under the moving base.
Hybrid ML-Based Technique to Classify Malicious Activity Using Log Data of Systems
Authors: Mostafa Almetwally M. , Altheneyan Alaa , Alnuaim Abeer , Alhadlaq Aseel
Published: February 2023

A computer system generates logs to record all relevant operational data about the system and all operations performed in such systems. System log examination is crucial in the identification of network- and system-level assaults. In comparison to established hazards, new technological advancements and better connections pose a greater degree of risk. Several machine learning algorithms that continually monitor logs of data have been created in the past to defend the system against many threats. The majority of earlier anomaly detection methods need a priori knowledge and are not intended for spotting recent or impending threats. Additionally, the growing volume of logs creates fresh difficulties for anomaly identification. In this study, we developed a machine learning technique that identifies abnormalities in the system log with higher accuracy and efficiency. In our suggested strategy, we started with three log features, preprocessed them, and then got more than 10 features for the model. We performed studies to test the effectiveness of our suggested approach, and the criteria for that included F1 scores, ROCs, accuracy, sensitivity, and specificity. We also evaluated how well our suggested technique performs in comparison to other methods. Our suggested solution has a greater rate of anomaly identification than any previously published algorithms as per the experimental findings based on the dataset obtained from the Blue Green/l supercomputer system.
A Comparative Study of Damage-Sensitive Features for Rapid Data-Driven Seismic Structural Health Monitoring
Authors: Reuland Yves , Martakis Panagiotis , Chatzi Eleni
Published: February 2023

Rapid post-earthquake damage assessment forms a critical element of resilience, ensuring a prompt and functional recovery of the built environment. Monitoring-based approaches have the potential to significantly improve upon current visual inspection-based condition assessment that is slow and potentially subjective. The large variety of sensing solutions that has become available at affordable cost in recent years allows the engineering community to envision permanent-monitoring applications even in conventional low-to-mid-rise buildings. When combined with adequate structural health monitoring (SHM) techniques, sensor data recorded during earthquakes have the potential to provide automated near-real-time identification of earthquake damage. Near-real time building assessment relies on the tracking of damage-sensitive features (DSFs) that can be directly and rapidly derived from dynamic monitoring data and scaled with damage. We here offer a comprehensive review of such damage-sensitive features in an effort to formally assess the capacity of such data-driven indicators to detect, localize and quantify the presence of nonlinearity in seismic-induced structural response. We employ both a parametric analysis on a simulated model and real data from shake-table tests to investigate the strengths and limitations of purely data-driven approaches, which typically involve a comparison against a healthy reference state. We present an array of damage-sensitive features which are found to be robust with respect to noise, to reliably detect and scale with nonlinearity, and to carry potential to localize the occurrence of nonlinear behavior in conventional structures undergoing earthquakes.