bluuebunny/crossref_metadata_2025 huggingface dataset:
E-Eye-Based Approach for Traceability and Annuality Compliance of Lentils
Authors: Foschi Martina , Di Maria Valerio , D’Archivio Angelo Antonio , Marini Federico , Biancolillo Alessandra
Published: January 2023

In recent years, thanks to their numerous nutritional benefits, legumes have been rediscovered and have attracted interest from many consumers. However, these products, the most valuable ones traditionally produced in smaller communities in particular, can be objects of fraud; this is the case of Italian lentils, which, being a dry product, have a fairly long shelf life, but, due to the minimal visual changes that can affect them, it is possible that expired lentils may be sold alongside edible ones. The present work aims at creating a non-destructive method for classifying Italian lentils according to their harvest year and origin, and for discriminating between expired and edible ones. In order to achieve this goal, Red-Green-Blue (RGB) imaging, which could be considered as a sort of e-eye and represents a cutting-edge, rapid, and effective analytical method, was used in combination with a discriminant classifier (Sequential Preprocessing through ORThogonalization-Linear Discriminant Analysis, SPORT-LDA) to create novel testing models. The SPORT-LDA models built to discriminate the different geographical origins provided an average correct classification rate on the test set of about 88%, whereas an overall 90% accuracy was obtained (on the test samples) by the SPORT-LDA model built to recognize whether a sample was still within its expiry date or not.
AAL-Net: A Lightweight Detection Method for Road Surface Defects Based on Attention and Data Augmentation
Authors: Zhang Cheng , Li Gang , Zhang Zekai , Shao Rui , Li Min , Han Delong , Zhou Mingle
Published: January 2023

The pothole is a common road defect that seriously affects traffic efficiency and personal safety. Road evaluation and maintenance and automatic driving take pothole detection as their main research part. In the above scenarios, accuracy and real-time pothole detection are the most important. However, the current pothole detection methods can not meet the accuracy and real-time requirements of pothole detection due to their multiple parameters and volume. To solve these problems, we first propose a lightweight one-stage object detection network, the AAL-Net. In the network, we design an LF (lightweight feature extraction) module and use the NAM (Normalization-based Attention Module) attention module to ensure the accuracy and real time of the pothole detection process. Secondly, we make our own pothole dataset for pothole detection. Finally, in order to simulate the real road scene, we design a data augmentation method to further improve the detection accuracy and robustness of the AAL-Net. The metrics F1 and GFLOPs show that our method is better than other deep learning models in the self-made dataset and the pothole600 dataset and can well meet the accuracy and real-time requirements of pothole detection.
Sentiment Analysis of Text Reviews Using Lexicon-Enhanced Bert Embedding (LeBERT) Model with Convolutional Neural Network
Authors: Mutinda James , Mwangi Waweru , Okeyo George
Published: January 2023

Sentiment analysis has become an important area of research in natural language processing. This technique has a wide range of applications, such as comprehending user preferences in ecommerce feedback portals, politics, and in governance. However, accurate sentiment analysis requires robust text representation techniques that can convert words into precise vectors that represent the input text. There are two categories of text representation techniques: lexicon-based techniques and machine learning-based techniques. From research, both techniques have limitations. For instance, pre-trained word embeddings, such as Word2Vec, Glove, and bidirectional encoder representations from transformers (BERT), generate vectors by considering word distances, similarities, and occurrences ignoring other aspects such as word sentiment orientation. Aiming at such limitations, this paper presents a sentiment classification model (named LeBERT) combining sentiment lexicon, N-grams, BERT, and CNN. In the model, sentiment lexicon, N-grams, and BERT are used to vectorize words selected from a section of the input text. CNN is used as the deep neural network classifier for feature mapping and giving the output sentiment class. The proposed model is evaluated on three public datasets, namely, Amazon products’ reviews, Imbd movies’ reviews, and Yelp restaurants’ reviews datasets. Accuracy, precision, and F-measure are used as the model performance metrics. The experimental results indicate that the proposed LeBERT model outperforms the existing state-of-the-art models, with a F-measure score of 88.73% in binary sentiment classification.
Module-Level Performance Evaluation for a Smart PV System Based on Field Conditions
Authors: Feng Li , Amin Nowshad , Zhang Jingwei , Ding Kun , Hamelmann Frank U.
Published: January 2023

This study presents an approach with a simple structure, low complexity and low costs to evaluate the real-time status and localize the faults of a smart PV system at module level based on field conditions. The performance evaluation approach of a PV system is developed through the defined performance indicators, a complex data matrix to track module locations and a thermal model to determine the module temperature. The generalization potential of the proposed approach has been demonstrated through the successful experiment validation. The results show that the performance indicators are greatly corrected by the estimated module temperature with great linear agreement in R2 of 0.922 compared to actual measured temperature under same conditions. Due to the effective performance indicators capturing more performance differences caused by faults of cracks in 0.22 of PV_ΔV, partial shading in 0.47 of PV_ΔV, broken sensors in 0.17 of PI_ΔI and 1 of PV_ΔV separately, the proposed approach is very effective in evaluating the performance of PV modules at module level. Meanwhile, the faulty modules are diagnosed and located through these findings and the data matrix in the smart PV system. Additionally, the sensitivity of the proposed approach to fault in cracks is much higher than that of monitoring only the power.
Application of Interpolating Matrix Method to Study Dynamics of Axially Moving Beams Made of Functionally Graded Materials
Authors: Wang Jing-Ping , Ge Ren-Yu , Tang Ye
Published: January 2023

In this paper, the divergent instability and coupled flutter characteristics of axially moving beams made of functionally graded materials (FGM) are studied using the interpolation matrix method. The material property of the beam is designed to change smoothly and continuously along the thickness direction. In considering the Euler-Bernoulli beam theory, Hamilton’s principle is used to derive the differential equation of the transverse vibration kinematics of axially moving FGM beams. In addition, the calculation model for solving the complex frequency of the beam based on the interpolation matrix method has been established. The presented solutions are compared with those in the literature to illustrate the effectiveness of the interpolation matrix method. The results show that the divergence and flutter velocities of axially moving FGM beams tend to decrease with the increase of the material gradient index, and there is a very narrow stability region between the first static instability region (divergence) and the first dynamic instability region (first- and second-order coupled flutter).
Differential Effects of White Wine and Ethanol Consumption on Survival of Rats after a Myocardial Infarction
Authors: Boban Danica , Dželalija Ana Marija , Gujinović Diana , Benzon Benjamin , Ključević Nikola , Boban Zvonimir , Mudnić Ivana , Grković Ivica
Published: January 2023

Studies of the cardioprotective effects of wine are mainly focused on red wines, due to their much higher content of bioactive compounds relative to white wines. Although some studies indicate a cardioprotective effect of white wine, there is no clear consensus on the existence of additional benefits of white wine over ethanol. The aim of this study was to determine and compare the effects of moderate consumption of white wine and ethanol on the survival of rats subjected to surgically induced myocardial infarction (MI). Male Sprague Dawley rats (n = 74) were randomized into three groups: water only, white wine or a 13% v/v ethanol/water solution. After a four-week drinking period, MI was induced by ligating the left anterior descending artery. The survival rate was highest in the wine group (72.2%), and lowest in the water only group (47.8%). There was no statistically significant difference in survival between the ethanol and water groups. An analysis linking drinking volumes to survival outcomes revealed that lower ethanol consumption was more prevalent in rats that survived, indicating an upper limit for the protective effects of ethanol. An opposite finding was noticed in the wine group, where no deaths occurred in rats with an average daily white wine consumption of approximately 10 mL or more. We conclude that moderate consumption of white wine has a positive effect on survival after a myocardial infarction, which cannot be attributed only to ethanol, but also to other white wine constituents.
Special Issue on Ultrasound Technology in Industry and Medicine
Authors: Opieliński Krzysztof J.
Published: January 2023

As early as 1774, the application of ultrasound in the animal world was noted by the Italian naturalist Lazzaro Spallanzani, who discovered that bats move using ultrasonic waves [...]
Passive Cooling of PV Modules Using Heat Pipe Thermosiphon with Acetone: Experimental and Theoretical Study
Authors: Kaneesamkandi Zakariya , Almalki Mohammed Jarallah , Sayeed Abdul , Haidar Zeyad A.
Published: January 2023

The effect of heat pipe thermosiphon in reducing the operating temperature of a photovoltaic panel has been analyzed theoretically and experimentally in this paper. Copper heat pipe thermosiphon with acetone as a working fluid was used. The theoretical study involved a heat balance analysis of the panel with cylindrical heat pipe with surface contact with the panel bottom. The experimental study involved recording temperature variations, with and without a heat pipe, which had very good agreement with the theoretical results of 2.61%. Additionally, the optimum quantity of acetone was 50 mL, with a maximum reduction in panel temperature of 10 °C.
Internal Aerodynamic Performance Enhancement for Aircraft with High Maneuver by Designing a Distributed Submerged Inlet
Authors: Zhang Junyao , Mi Baigang
Published: January 2023

Submerged inlet has been widely used in UAVs and cruise missiles due to its good stealth characteristics, but it also brings the disadvantage of poor aerodynamic characteristics. Especially in large maneuvering flight, the flow field near the fuselage has a strong unsteady effect, and the total pressure recovery coefficient and distortion characteristics have deteriorated sharply. In order to investigate the steady and transient aerodynamic characteristics of the submerged inlet in large maneuver flight and improve its maneuver envelope, a design scheme of a distributed submerged inlet for large maneuver flight is proposed in this paper. Taking a cruise missile as the research object, the steady and transient analysis of the conventional submerged inlet and the distributed submerged inlet is carried out using CFD numerical method. The results show that the distributed submerged inlet can significantly improve the inlet performance and enhance the sideslip limit of the submerged inlet during large sideslip maneuver flight. When the sideslip angle is 30°, compared with the conventional submerged inlet configuration, the outlet total pressure recovery coefficient of the distributed submerged inlet configuration is increased by 44.2%, and the total pressure distortion index is reduced by 66.3%.
Numerical Method of Increasing the Critical Buckling Load for Straight Beam-Type Elements with Variable Cross-Sections
Authors: Botis Marius , Imre Lajos , Conțiu Mircea
Published: January 2023

Structural expressionism resembles the use of slender structural elements, in particular beam-type elements. To satisfy structural, functional, and also architectural requirements a comprehensive structural analysis must be performed. The main issue of this study is the buckling analysis of beam-type elements, concerning Cavalieri’s principle. The present study is divided into two separate sections. The first part is a theoretical study, in which a variable cross-section beam-type element is modeled. The stability analysis is performed by an indirect variational method and the stiffness of the support connections is also introduced. The numerical simulation highlights 6 cases defined by the restraints of the support connections. The case study follows the modification of the critical buckling load of the variable cross-section beam-type element. Prior to the case study, a novel verification method is proposed to achieve a realistic cross-section for the beam-type element. The study revealed that with ideal characteristics of the stiffness coefficients of the restrains significant increase of the critical buckling load is obtained, and further if an actual situation is considered with finite values of the stiffness of the restrains, the variable cross-section for the beam-type element is a recommended and rational choice to make, to eliminate stability issues.
Making a Case for Hybrid GFRP-Steel Reinforcement System in Concrete Beams: An Overview
Authors: Devaraj Rajeev , Olofinjana Ayodele , Gerber Christophe
Published: January 2023

Ageing concrete infrastructures are known to be facing deterioration, especially regarding the corrosion of their reinforcing steel. As a solution, glass fibre-reinforced plastic (GFRP) bars are now considered a reinforcement alternative to conventional steel, and design codes now exist for designing GFRP-RC structures. However, there is a need to improve on addressing the limited plastic yield in GFRPs. Consequently, it is suggested that a hybrid steel–GFRP RC system can enhance the mechanical performance of flexure beams up to the required standard and, at the same time, address the durability concerns of steel-only RC beams. This overview presents the studies conducted to enhance the performance of hybrid GFRP–steel RC beams by reviewing the analytical models proposed to improve the various aspects of reinforcement design. The models consider mechanical effects such as ductility, crack width, flexure and shear, and the physical effects such as thermal stability when exposed to the temperature. Though the evidence reviewed supports the viability of the hybrid GFRP–steel reinforcing system to address ductility, much is still required in the area of research, as highlighted in the future outlook.
Dementia Classification Using Deep Reinforcement Learning for Early Diagnosis
Authors: Hashmi Arshad , Barukab Omar
Published: January 2023

Neurodegeneration and impaired neuronal transmission in the brain are at the root of Alzheimer’s disease (AD) and dementia. As of yet, no successful treatments for dementia or Alzheimer’s disease have indeed been found. Therefore, preventative measures such as early diagnosis are essential. This research aimed to evaluate the accuracy of the Open Access Series of Imaging Studies (OASIS) database for the purpose of identifying biomarkers of dementia using effective machine learning methods. In most parts of the world, AD is responsible for dementia. When the challenge level is high, it is nearly impossible to get anything done without assistance. This is increasing due to population growth and the diagnostic period. Two current approaches are the medical history and testing. The main challenge for dementia research is the imbalance of datasets and their impact on accuracy. A proposed system based on reinforcement learning and neural networks could generate and segment imbalanced classes. Making a precise diagnosis and taking into account dementia in all four stages will result in high-resolution sickness probability maps. It employs deep reinforcement learning to generate accurate and understandable representations of a person’s dementia sickness risk. To avoid an imbalance, classes should be evenly represented in the samples. There is a significant class imbalance in the MRI image. The Deep Reinforcement System improved trial accuracy by 6%, precision by 9%, recall by 13%, and F-score by 9–10%. The diagnosis efficiency has improved as well.
Functionally Graded Additive Manufacturing: Bridging the Gap between Design and Material Extrusion
Authors: Leoni Francesco , Dal Fabbro Pierandrea , Rosso Stefano , Grigolato Luca , Meneghello Roberto , Concheri Gianmaria , Savio Gianpaolo
Published: January 2023

Nowadays, the use of 3D printing is becoming a key process for on-demand and customized manufacturing. One of the most flexible 3D printing techniques is fused deposition modeling (FDM), where the combination of multiple materials was recently introduced. A quantum leap in part design is possible by integrating local variations between materials that allow for expanded functionality to be built into a single part. Therefore, the process of co-extrusion and material mixing is becoming more and more popular. The process of management and design of the engineered part are still complicated, and there are no commercially available tools that follow the process from design to production of these highly engineered products. This paper proposes a methodology to fill this gap and allow any designer to be able to produce multi-material parts by editing a G-code (computer numerical control programming language) with engineered gradients for FDM technology. More specifically, the proposed approach is based on the modification of the G-code according to a volumetric model describing the local combination of two or more materials. This original aspect allows for a wide extension of the current software capabilities. To explain and test the method, a simple test case was investigated, in which two components of an earphone are consolidated and developed gradually by combining polylactic acid and thermoplastic polyurethane. The results show the effectiveness of the proposed approach within the limits of the material coextrusion additive manufacturing process.
Simulation-Based Optimization of Transport Efficiency of an Urban Rail Transit Network
Authors: Wang Liyu , Lu Fang , Han Baoming , Zhang Qi , Zhang Chen
Published: January 2023

It has been proven that developing urban rail transit (URT) is the key to solving the traffic problems in big cities, and transport efficiency is essential for scientific line planning and construction, operation management and sustainable development of urban rail transit. This paper focuses on the transport efficiency of the URT network and adopts shadow efficiency theory creatively to combine factors of passengers, capacity, and efficiency effectively. A quantitative calculation method of system utility based on “double time penalty” is proposed. According to the characteristics of Beijing URT, this paper puts forward a single optimization strategy and combinatorial optimization strategy and analyzes the results with DEA. The simulation of Beijing subway network proves the availability of the method and its optimization effects.
Frequency-Based Adaptive Interpolation Filter in Intra Prediction
Authors: Lim Su-Yeon , Choi Min-Kyeong , Lee Yung-Lyul
Published: January 2023

This paper proposes a method to improve the fractional interpolation of reference samples in the Versatile Video Coding (VVC) intra prediction. The proposed method uses additional interpolation filters which use more integer-positioned reference samples for prediction according to the frequency information of the reference samples. In VVC, a 4-tap Discrete Cosine Transform-based interpolation filter (DCT-IF) and 4-tap Smoothing interpolation filter (SIF) are alternatively performed on the block size and block directional prediction mode for reference sample interpolation. This paper uses four alternative interpolation filters such as 8-tap/4-tap DCT-IFs, and 4-tap/8-tap SIFs and an interpolation filter selection method using a high-frequency ratio calculated from one-dimensional (1D) transform of the reference samples are proposed. The proposed frequency-based Adaptive Filter allows to achieve the overall Bjøntegaard Delta (BD) rate gains of −0.16%, −0.13%, and −0.09% for Y, Cb, and Cr components, respectively, compared with VVC.
Effect of Environmental Parameters on Structural Health Status Assessment Using OMA Techniques
Authors: Bertagnoli Gabriele , Ferrara Mario , Lucà Francescantonio , Cigada Alfredo
Published: January 2023

The data from a laboratory test on two aluminum rods are analyzed in this work. The rods are subjected to environmental excitation in an uncontrolled environment, attempting to replicate real operational conditions of structural health monitoring with external disturbances. Different damage levels are simulated on one of the two tension rods. Three of the most frequently used OMA methods are applied to evaluate the effects of the simulated damage on the dynamic behavior of the system. The complexity of the three applied OMA methods is gradually increased. The difference between the results from the different approaches is assessed. The aim of this work is to assess the performance of the proposed OMA methods, to understand their limits, and to assess the role of environmental disturbance parameters, such as temperature, in the evaluation of the health status of structures.
Numerical and Experimental Studies on the Aerodynamics of NACA64 and DU40 Airfoils at Low Reynolds Numbers
Authors: Wu Chieh-Hsun , Chen Jing-Zong , Lo Yuan-Lung , Fu Chung-Lin
Published: January 2023

The aerodynamics of airfoils can be seen in a wide range of applications. To obtain the aerodynamic loads, geometrically-scaled airfoil sections are tested in wind tunnels. However, due to the limited space of the wind tunnel, the mismatch of Reynolds numbers may lead to different aerodynamic loads. Previous works showed that decreased lifts and increased drag coefficients are associated with lower Reynolds numbers, which are accompanied by the changes in ambient flow, such as increased sizes of the separation bubbles and wake vortices. Although insightful, few direct connections between loads, pressures, and ambient flow were presented, leaving a critical knowledge gap for aerodynamic modifications to improve the aerodynamic performances at low Reynolds numbers. To bridge this gap, this work utilizes numerical simulations and wind tunnel experiments to study the aerodynamics of a thin airfoil (NACA64) and a thick airfoil (DU40), at two chord Reynolds numbers, i.e., 4000 and 60,000. The two-dimensional (2D) vortex particle method (VPM) with varying-sized particles is used to simulate the unsteady flow and compared to the steady-state simulations by XFOIL. As the Reynolds number increases, it reveals that the higher lift coefficients are associated with the increased upstream suction and positive pressures on the upper and lower surfaces of the airfoils, respectively. These changes are explained by the increased and decreased normalized wind speeds on the upper and lower surfaces of the airfoils, respectively. Stronger pressure recoveries observed downstream of the reattachment points are the main cause of drag reductions at higher Reynolds numbers. The smaller and more irregular vortices in the roll-up shear layers and wakes observed at the higher Reynolds number are similar to the previous experimental findings, which are shown in this work to make the force fluctuations more irregular at higher frequencies. Possibly due to missing 3D effects, the results obtained from the 2D VPM are observed to ‘overestimate’ the effects of increasing the Reynolds number at ReC = 60,000. Furthermore, both VPM and XFOIL are found to work best in explaining the physics at low angles of attacks, i.e., −10°≤α≤10°, which are similar to the previous numerical works utilizing 2D methods.
Characterization of Pyrolysis Products of Forest Residues and Refuse-Derived Fuel and Evaluation of Their Suitability as Bioenergy Sources
Authors: Vamvuka Despina , Esser Katerina , Marinakis Dimitrios
Published: January 2023

The products generated from a fixed bed pyrolysis unit of solid waste materials were quantitatively characterized, and their energy potential was determined, in order to evaluate their suitability as energy sources. An elemental analyzer, a bomb calorimeter, an X-ray fluorescence spectrometer, a Couette viscometer and a TG-MS (thermogravimetric-mass spectrometry) analyzer were employed for the measurements. Biochars obtained at 450 °C were enriched in carbon; their calorific value was high (20–39 MJ/kg) and exceeded that of raw materials. These biochars can be utilized for energy production, preferably at temperatures below 1000 °C, to avoid slagging/fouling phenomena. The bio-oils of pinecones and forest residue obtained at 450 °C, with a density of 0.93–0.94 kg/m3, a pH of 2.1–3, a dynamic viscosity of 1.5–7 cP and a calorific value of 22–27 MJ/kg, were superior to typical flash pyrolysis oil and could be used in static applications for heat or electricity generation after a de-oxygenation process. The quality of RDF bio-oil was lower. The higher heating value of gases from pinecones and RDF fuels at 450 °C was satisfactory for the energy requirements of the process (13.6–13.8 MJ/m3); however, that of forest residue gas was moderately low.
An Ensemble Approach Based on Fuzzy Logic Using Machine Learning Classifiers for Android Malware Detection
Authors: Atacak İsmail
Published: January 2023

In this study, a fuzzy logic-based dynamic ensemble (FL-BDE) model was proposed to detect malware exposed to the Android operating system. The FL-BDE model contains a structure that combines both the processing power of machine learning (ML)-based methods and the decision-making power of the Mamdani-type fuzzy inference system (FIS). In this structure, six different methods, namely, logistic regression (LR), Bayes point machine (BPM), boosted decision tree (BDT), neural network (NN), decision forest (DF) and support vector machine (SVM) were used as ML-based methods to benefit from their scores. However, through an approach involving the process of voting and routing, the scores of only three ML-based methods which were more successful in classifying either the negative instances or positive instances were sent to the FIS to be combined. During the combining process, the FIS processed the incoming inputs and determined the malicious application score. Experimental studies were performed by applying the FL-BDE model and ML-based methods to the balanced dataset obtained from the APK files downloaded in the Drebin database and Google Play Store. The obtained results showed us that the FL-BDE model had a much better performance than the ML-based models did, with an accuracy of 0.9933, a recall of 1.00, a specificity of 0.9867, a precision of 0.9868, and an F-measure of 0.9934. These results also proved that the proposed model can be used as a more competitive and powerful malware detection model compared to those of similar studies in the literature.
Game Theory-Based Load-Balancing Algorithms for Small Cells Wireless Backhaul Connections
Authors: Polgar Zsolt Alfred , Varga Mihaly
Published: January 2023

5G wireless networks have as one of the main characteristics the large-scale deployment of small cells (microcells, picocells, etc.), which is expected to bring several advantages in what concerns the high speed and low latency connectivity of the users. This large-scale deployment of small cells also raises several technical challenges, provisioning the backhaul connectivity being one of them. The paper considers the situations when small cells are deployed temporarily or are deployed in a vehicle transporting many passengers, situations when the traditional wired or wireless backhaul solutions could be too costly to be used. The paper proposes, as an alternative solution, the use as backhaul connections of the wireless links set up in the macro cells which cover the location of the small cell. The paper proposes several Game Theory (GT)-based Load-Balancing (LB) algorithms for distributing the traffic of the small cell users over the macro cell links. The proposed LB algorithms are evaluated by computer simulations and are compared with “classical” LB algorithms considered as references. The performed computer simulations show that the auction-based algorithms have the best performance in terms of delay suffered by the transmitted data packets, while the selfish routing type algorithm has weaker performance, even behaving poorly than some of the reference non-GT-based algorithms. The paper also considers the situation when several small cell APs are deployed in a limited area or a vehicle and the user groups that attach to different APs should be identified. The paper proposes two GT-based user clustering algorithms, and the performance of these algorithms are evaluated by computer simulations. These simulations show that even simple clustering algorithms could improve the distribution of the traffic over the neighbor small cell APs and reduce the delay experienced by the data packets in the transmission system.
Design and Implementation of Simulation System for Multi-UAV Mission
Authors: Qi Duo , Cai Ming , Jiao Zhiqiang , He Xingyu , Ren Xiaoyue , Hou Yiming
Published: January 2023

Multi-UAV (unmanned aerial vehicle) mission collaboration is one of the current research hotspots in automation, artificial intelligence, and other fields. The difficulty and high cost of real flight verification have led to the problem that related knowledge learning focuses on theoretical derivation and ignores technical practice. In this paper, a multi-UAV mission simulation system is designed to show the greatest advantage of its collaborative mission planning results simulated and tested in 3D scenarios. It simultaneously reflects and records changes in UAV position, velocity, status, and other state values. The simulation program can be directly applied to real flight with only a few settings. First, the general system framework is presented. Second, according to the simulation requirements, the key modules involved in the software in the loop are given. Finally, through a path planning test for two UAVs, the effect of the system is demonstrated. The results show that the simulation verification based on the system is consistent with the real flight results in terms of functional implementation. It is applicable to the teaching process of related majors and can also provide support for the realization of many complex tasks in the future.
Vumark’s Method of Production Layout Designing
Authors: Kováč Juraj , Malega Peter , Rudy Vladimír , Svetlík Jozef
Published: January 2023

The paper deals with the issue of the mixed-reality usage in the design of production systems, its changes during expansion, or technological changes in the production, where it is necessary to flexibly and quickly verify the integration of a new machine into the existing layout and eliminate collision situations even before the installation of a physical machine in production. This is realized through Vumark’s design methodology, which was verified and applied in the conditions of the production environment of the Innovation and Prototyping Centre in the Faculty of Mechanical Engineering at the Technical University of Kosice. The individual parts of the paper describe the Vumark deployment procedure in the production area and the software and hardware resources that the user can apply. Three production machines and one robotic device selected from the Factory design software database were chosen for the experiment. The chosen method enables us to verify during the experiment several variants of deployment the machines in the layout and thus to find the optimal location in a very short time. The experiment showed that the chosen method is applicable in practice and brings savings of time, costs, and energy especially when changing the layout or replacing the machine in the production hall.
Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes
Authors: Jia Xianguang , Zhou Xinbo , Bao Jing , Zhai Guangyi , Yan Rong
Published: January 2023

To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence algorithm to fall into a local optimum when solving complex problems. Lastly, simulation experiments were conducted to compare the designed algorithm with PSO, GA, WOA, GWO, and other cutting-edge optimization algorithms in cross-sectional simulations, and the results show that the algorithm had excellent global optimization finding and convergence capabilities. The simulation results show that the research in this paper can provide effective decisions for the dwell time of trains at multiple stations, and promote the intelligent operation of the train system.
Experimental Investigation on Local and Global Texture Evolution in Drawing Seamless Copper Tubes
Authors: Khani Somayeh , Palkowski Heinz , Carradò Adele , Foadian Farzad
Published: January 2023

Mass flow inequality in the initial stage of tube processing can lead to eccentricity and micro- and nano-structural changes that affect residual stress and texture development. In this study, the macro- and micro-texture development of copper tubes drawn with a tilted die was investigated using three methods: synchrotron, neutron diffraction, and electron backscatter diffraction, in the positions of maximum and minimum wall thickness of the tubes. Understanding how a tilted die can affect the texture development in copper tubes is the main aim of this study. The micro-texture results of EBSD examinations showed the same behavior at the maximum and minimum sides of the as-received tube, as observed using the synchrotron diffraction method as well as macro-texture measurements. The cube texture component was found to be the predominant orientation in the as-received tube. However, it almost disappeared after drawing with −5° tilting. By contrast, the Cu texture component increased significantly. Before drawing, the cube component varied strongly across the wall thickness. After drawing, however, there was no noticeable texture gradient across the wall thickness. The analyses showed that tilting is not creating an inhomogeneous texture development over the circumference.
Proton and Electron Irradiations of CH4:H2O Mixed Ices
Authors: Mifsud Duncan V. , Herczku Péter , Sulik Béla , Juhász Zoltán , Vajda István , Rajta István , Ioppolo Sergio , Mason Nigel J. , Strazzulla Giovanni , Kaňuchová Zuzana
Published: January 2023

The organic chemistry occurring in interstellar environments may lead to the production of complex molecules that are relevant to the emergence of life. Therefore, in order to understand the origins of life itself, it is necessary to probe the chemistry of carbon-bearing molecules under conditions that simulate interstellar space. Several of these regions, such as dense molecular cores, are exposed to ionizing radiation in the form of galactic cosmic rays, which may act as an important driver of molecular destruction and synthesis. In this paper, we report the results of a comparative and systematic study of the irradiation of CH4:H2O ice mixtures by 1 MeV protons and 2 keV electrons at 20 K. We demonstrate that our irradiations result in the formation of a number of new products, including both simple and complex daughter molecules such as C2H6, C3H8, C2H2, CH3OH, CO, CO2, and probably also H2CO. A comparison of the different irradiation regimes has also revealed that proton irradiation resulted in a greater abundance of radiolytic daughter molecules compared to electron irradiation, despite a lower radiation dose having been administered. These results are important in the context of the radiation astrochemistry occurring within the molecular cores of dense interstellar clouds, as well as on outer Solar System objects.
Manifestations of Rabi Dynamics in the Photoelectron Energy Spectra at Resonant Two-Photon Ionization of Atom by Intense Short Laser Pulses
Authors: Simonović Nenad S. , Popović Duška B. , Bunjac Andrej
Published: January 2023

We study the Rabi flopping of the population between the ground and excited 2p states of the hydrogen atom, induced by intense short laser pulses of different shapes and of carrier frequency ω=0.375 a.u. which resonantly couples the two states, and manifestations of this dynamics in the energy spectra of photoelectrons produced in the subsequent ionization of the atom from the excited state. It is found that, for Gaussian, half-Gaussian and rectangular pulses, characterized by the same pulse area, the final populations take the same values and the spectra consist of similar patterns having the same number of peaks and approximately the same separation between the prominent edge (Autler–Townes) peaks. The additional analysis in terms of dressed states showed that the mechanism of formation of multiple-peak structures during the photoionization process is the same regardless of the pulse shape. These facts disprove the hypothesis proposed in earlier studies with Gaussian pulse, that the multiple-peak pattern appears due to dynamic interference of the photoelectrons emitted with a time delay at the rising and falling sides of the pulse, since the hypothesis is not applicable to either a half-Gaussian pulse that has no rising part or a rectangular pulse whose intensity is constant.
Parameters Estimation in Non-Negative Integer-Valued Time Series: Approach Based on Probability Generating Functions
Authors: Stojanović Vladica , Ljajko Eugen , Tošić Marina
Published: January 2023

This manuscript deals with a parameter estimation of a non-negative integer-valued (NNIV) time series based on the so-called probability generating function (PGF) method. The theoretical background of the PGF estimation technique for a very general, stationary class of NNIV time series is described, as well as the asymptotic properties of the obtained estimates. After that, a particular emphasis is given to PGF estimators of independent identical distributed (IID) and integer-valued non-negative autoregressive (INAR) series. A Monte Carlo study of the thus obtained PGF estimates, based on a numerical integration of the appropriate objective function, is also presented. For this purpose, numerical quadrature formulas were computed using Gegenbauer orthogonal polynomials. Finally, the application of the PGF estimators in the dynamic analysis of some actual data is given.
A Study of Monotonicity Analysis for the Delta and Nabla Discrete Fractional Operators of the Liouville–Caputo Family
Authors: Mohammed Pshtiwan Othman , Goodrich Christopher S. , Srivastava Hari Mohan , Al-Sarairah Eman , Hamed Y. S.
Published: January 2023

In the present article, we explore the correlation between the sign of a Liouville–Caputo-type difference operator and the monotone behavior of the function upon which the difference operator acts. Finally, an example is also provided to demonstrate the application and the validation of the results which we have proved herein.
Exponential Stability of Hopfield Neural Network Model with Non-Instantaneous Impulsive Effects
Authors: Ma Rui , Fečkan Michal , Wang Jinrong
Published: January 2023

We introduce a non-instantaneous impulsive Hopfield neural network model in this paper. Firstly, we prove the existence and uniqueness of an almost periodic solution of this model. Secondly, we prove that the solution of this model is exponentially stable. Finally, we give an example of this model.
Research on Fuzzy Temporal Event Association Mining Model and Algorithm
Authors: Zhu Aihua , Meng Zhiqing , Shen Rui
Published: January 2023

As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the temporal dimension. After restructuring temporal data and extracting fuzzy features out of information, a fuzzy temporal event association rule mining model as well as an algorithm was constructed. The proposed algorithm can fully extract the data features at each granularity level while preserving the original information and reducing the amount of computation. Furthermore, it is capable of efficiently mining the possible rules underlying different temporal data streams. In experiments, by comparing and analyzing stock trading data in different temporal granularities, the model and algorithm identify association events in disorder trading. This not only is valuable in identifying stock anomalies, but also provides a new theoretical tool for dealing with complex irregular temporal data.
High-Performance Anodes Made of Metallic Lithium Layers and Lithiated Silicon Layers Prepared by Vacuum Technologies
Authors: Saager Stefan , Decker Ludwig , Kopte Torsten , Scheffel Bert , Zimmermann Burkhard
Published: January 2023

Replacing conventional electrode materials is one of the most pressing challenges for next-generation lithium-ion batteries since state-of-the-art systems have almost reached their limitations for performance gains. For anodes, ambitious candidates include lithium and silicon because of their extremely high capacity. In this paper, a physical vapor deposition process for the preparation of pure metallic lithium layers and lithiated silicon layers in the layer thickness range of 1–20 µm is demonstrated. The lithium layers were deposited by thermal evaporation. Static coating rates up to 120 nm/s and dynamic deposition rates up to 1 µm·m/min were realized. Furthermore, the deposition of lithiated silicon alloy layers with various compositions was performed via the co-evaporation of lithium and silicon, where silicon was evaporated by an electron beam. The process was characterized regarding the deposition rate, heat loads, and effects of substrate pre-treatment. To achieve a porous microstructure, the layer morphology needed to be manipulated by adapting process parameters. Stripping experiments revealed high electrochemical activity of the lithium up to 85 %. The innovative approach carried out via vacuum processing showed capabilities for overcoming the current bottlenecks experienced with high-capacity anode materials in combination with the potential for upscaling to high throughput production.
2D/3D Non-Rigid Image Registration via Two Orthogonal X-ray Projection Images for Lung Tumor Tracking
Authors: Dong Guoya , Dai Jingjing , Li Na , Zhang Chulong , He Wenfeng , Liu Lin , Chan Yinping , Li Yunhui , Xie Yaoqin , Liang Xiaokun
Published: January 2023

Two-dimensional (2D)/three-dimensional (3D) registration is critical in clinical applications. However, existing methods suffer from long alignment times and high doses. In this paper, a non-rigid 2D/3D registration method based on deep learning with orthogonal angle projections is proposed. The application can quickly achieve alignment using only two orthogonal angle projections. We tested the method with lungs (with and without tumors) and phantom data. The results show that the Dice and normalized cross-correlations are greater than 0.97 and 0.92, respectively, and the registration time is less than 1.2 seconds. In addition, the proposed model showed the ability to track lung tumors, highlighting the clinical potential of the proposed method.
Computational Study of Hemodynamic Field of an Occluded Artery Model with Anastomosis
Authors: Parissis Panagiotis , Romeos Alexandros , Giannadakis Athanasios , Kalarakis Alexandros , Peroulis Michail
Published: January 2023

In this research work, the hemodynamic field of an occluded artery with anastomosis by means of computational simulation has been studied. The main objective of the current study is the investigation of 3D flow field phenomena in the by-pass region and the effect of the bypass graft to stenosis volume flow ratio on their formation. The anastomosis type was end-to-side with a 45° angle, while stenosis imposed a 75% area blockage of the aorta vessel and the total volume flow was 220 lt/h. The computational study of the flow field was utilized via a laminar flow model and three turbulence models (k—ε RNG, standard k—ω, and k—ω SST). Numerical results were compared qualitatively with experimental visualizations carried out under four different flow conditions, varying according to the flow ratio between the stenosis and the anastomotic graft. Comparison between computational results and experimental visualization findings exhibited a good agreement. Results showed that SST k—ω turbulence models reproduce better visually obtained flow patterns. Furthermore, cross-sectional velocity distributions demonstrated two distinct flow patterns down the bypass graft, depending on the flow ratio. Low values of flow ratio are characterized by fluid rolling up, whereas for high values fluid volume twisting was observed. Finally, areas with low wall shear stresses were mapped, as these are more prone to postoperative degradation of the bypass graft due to the development of subendothelial hyperplasia.
A Framework for Susceptibility Analysis of Brain Tumours Based on Uncertain Analytical Cum Algorithmic Modeling
Authors: Ur Rahman Atiqe , Saeed Muhammad , Saeed Muhammad Haris , Zebari Dilovan Asaad , Albahar Marwan , Abdulkareem Karrar Hameed , Al-Waisy Alaa S. , Mohammed Mazin Abed
Published: January 2023

Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients’ susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals.