bluuebunny/crossref_metadata_2025 huggingface dataset:
Protective Effect of the Hydrophilic Extract of Polypodium leucotomos, Fernblock®, against the Synergistic Action of UVA Radiation and Benzo[a]pyrene Pollutant
Authors: Gallego-Rentero María , Nicolás-Morala Jimena , Alonso-Juarranz Miguel , Carrasco Elisa , Portillo-Esnaola Mikel , Rodríguez-Luna Azahara , González Salvador
Published: November 2022

Oxidative stress is a harmful effect induced on the skin by polycyclic aromatic hydrocarbons (PAH), including benzo[a]pyrene (BaP) air pollutants. This effect is amplified by the additive damaging effect of the sun, especially through the UVA light component. Besides being one of the main compounds that make up air pollution, BaP can also be found in tar, tobacco smoke, and various foods. In addition to its direct carcinogenic potential, BaP can act as a photosensitizer absorbing sunlight in the UVA range and thus generating ROS and 8-hydroxy-2′-deoxyguanosine (8-OHdG). Fernblock® (FB) is an aqueous extract from the leaves of Polypodium leucotomos that has been proven to exert photoprotective and antioxidant effects on skin cells. In this study, we evaluate the potential of FB to prevent the damage induced by a combination of BaP and UVA light on human keratinocyte and mouse melanocyte cell lines (HaCaT and B16-F10, respectively). In particular, we have analyzed the capacity of FB to counteract the alterations caused on cellular morphology, viability, oxidative stress and melanogenic signaling pathway activation. Our data indicate that FB prevented cell damage and reduced oxidative stress and melanogenic signaling pathway activation caused by a combination of BaP and UVA light irradiation. Altogether, our findings support the fact that FB is able to prevent skin damage caused by the exposure to a combination of UVA and the air pollutant BaP.
The Effect Citrox BCL on Legionella pneumophila Mechanisms of Biofilm Formation, Oxidative Stress and Virulence
Authors: Butucel Eugenia , Balta Igori , McCleery David , Popescu Cosmin Alin , Iancu Tiberiu , Pet Ioan , Marcu Adela , Horablaga Nicolae-Marinel , Stef Lavinia , Corcionivoschi Nicolae
Published: November 2022

Legionella pneumophila is responsible for causing Legionnaires’ disease and Pontiac fever, also known as legionellosis. The aim of this study was to investigate the mechanistic effect of a mixture of natural antimicrobials (Citrox BCL) in preventing L. pneumophila biofilm formation and reducing its in vitro virulence. The minimum inhibitory concentrations were detected at 0.06%, and the MBC was established at 0.125%. Based on the growth curve profile, the sub-inhibitory concentration of 0.02% was further used to study the mechanistic implications in the absence of a cytotoxic effect on A549 cells. At 24 h post-infection, Citrox BCL reduced (p = 0.005) the intracellular growth of L. pneumophila when the A549 cells or the bacteria were pre-treated with 0.02% Citrox BCL. This result was replicated when Citrox BCL was added during the 24 h infection assay leading to a reduction in intracellular growth (p = 0.003). Herein we show that at the sub-inhibitory concentration of 0.02%, Citrox CBL lowers the ROS levels in infected A549 cells and causes a 45% reduction in L. pneumophila EPS production, a reduction associated with the decline in biofilm formation. Overall, our results corroborate the low c-di-GMP production with the decrease in biofilm formation and low EPS levels. The low EPS levels seemed to be caused by the downregulation of the tatB and tatC gene expressions. Moreover, inhibition of pvcA and pvcB gene expressions, leading to lower siderophore levels, suggests that Citrox BCL reduces the ability of L. pneumophila to sequester iron and reduce biofilm formation through iron starvation.
Oxidative Stress-Induced Cellular Senescence in Aging Retina and Age-Related Macular Degeneration
Authors: Terao Ryo , Ahmed Tazbir , Suzumura Ayana , Terasaki Hiroko
Published: November 2022

Aging leads to a gradual decline of function in multiple organs. Cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration (AMD) are age-related ocular diseases. Because their pathogenesis is unclear, it is challenging to combat age-related diseases. Cellular senescence is a cellular response characterized by cell cycle arrest. Cellular senescence is an important contributor to aging and age-related diseases through the alteration of cellular function and the secretion of senescence-associated secretory phenotypes. As a driver of stress-induced premature senescence, oxidative stress triggers cellular senescence and age-related diseases by inducing senescence markers via reactive oxygen species and mitochondrial dysfunction. In this review, we focused on the mechanism of oxidative stress-induced senescence in retinal cells and its role in the pathogenesis of AMD.
Therapeutic Effects of Heterotrigona itama (Stingless Bee) Bee Bread in Improving Hepatic Lipid Metabolism through the Activation of the Keap1/Nrf2 Signaling Pathway in an Obese Rat Model
Authors: Zakaria Zaida , Othman Zaidatul Akmal , Suleiman Joseph Bagi , Mustaffa Khairul Mohd Fadzli , Jalil Nur Asyilla Che , Ghazali Wan Syaheedah Wan , Zulkipli Ninie Nadia , Mohamed Mahaneem , Kamaruzaman Khaidatul Akmar
Published: November 2022

Bee bread (BB) has traditionally been used as a dietary supplement to treat liver problems. This study evaluated the therapeutic effects of Heterotrigona itama BB from Malaysia on obesity-induced hepatic lipid metabolism disorder via the regulation of the Keap1/Nrf2 pathway. Male Sprague Dawley rats were fed with either a normal diet or high-fat diet (HFD) for 6 weeks to induce obesity. Following 6 weeks, obese rats were treated either with distilled water (OB group), BB (0.5 g/kg body weight/day) (OB + BB group) or orlistat (10 mg/kg body weight/day) (OB + OR group) concurrent with HFD for another 6 weeks. BB treatment suppressed Keap1 and promoted Nrf2 cytoplasmic and nuclear translocations, leading to a reduction in oxidative stress, and promoted antioxidant enzyme activities in the liver. Furthermore, BB down-regulated lipid synthesis and its regulator levels (SIRT1, AMPK), and up-regulated fatty acid β-oxidation in the liver of obese rats, being consistent with alleviated lipid levels, improved hepatic histopathological changes (steatosis, hepatocellular hypertrophy, inflammation and glycogen expression) and prevented progression to non-alcoholic steatohepatitis. These results showed the therapeutic potentials of H. itama BB against oxidative stress and improved lipid metabolism in the liver of obese rats possibly by targeting the Keap1/Nrf2 pathway, hence proposing its role as a natural supplement capable of treating obesity-induced fatty liver disease.
Honey and Its Biomimetic Deep Eutectic Solvent Modulate the Antioxidant Activity of Polyphenols
Authors: Dimitriu Luminița , Constantinescu-Aruxandei Diana , Preda Daniel , Nichițean Andra-Lavinia , Nicolae Cristian-Andi , Faraon Victor Alexandru , Ghiurea Marius , Ganciarov Mihaela , Băbeanu Narcisa Elena , Oancea Florin
Published: November 2022

Honey is a highly valued natural product with antioxidant, antimicrobial and anti-inflammatory properties. However, its antioxidant activity (AOA) is not as high as that of other honeybee products, such as propolis. Several polyphenol—honey formulations have been proposed up to now, most of them using maceration of biomass in honey or mixtures with liquid extracts, which either limit polyphenols bioavailability or destroy the characteristics of honey. To improve the health benefits of honey by increasing AOA and keeping its structural and sensory properties, we propose its enrichment in a polyphenol extract of raspberry after solvent evaporation. A honey-biomimetic natural deep eutectic solvent (NaDES) was prepared and compared with honey. The main polyphenols found in the raspberry extract were tested in combination with honey and NaDES, respectively. The AOA was determined by DPPH, ABTS, CUPRAC, and FRAP methods. The AOA behaviour of honey—polyphenol mixtures varied from synergism to antagonism, being influenced by the AOA method, polyphenol type, and/or mixture concentration. The honey-biomimetic NaDES resulted in similar AOA behaviour as with honey mixed with polyphenols. Honey seems to have additional properties that increase synergism or reduce antagonism in some cases. Honey and its biomimetic NaDES modulate AOA of polyphenols extract.
New RED-Type TCP-AQM Algorithms Based on Beta Distribution Drop Functions
Authors: Giménez Angel , Murcia Miguel A. , Amigó José M. , Martínez-Bonastre Oscar , Valero José
Published: November 2022

In recent years, Active Queue Management (AQM) mechanisms to improve the performance of TCP/IP networks have acquired a relevant role. In this paper, we present a simple and robust RED-type algorithm together with a couple of dynamical variants with the ability to adapt to the specific characteristics of different network environments, as well as to the user’s needs. We first present a basic version called Beta RED (BetaRED), where the parameters can be tuned according to the specific network conditions. The aim is to introduce control parameters that are easy to interpret and provide a good performance over a wide range of values. Secondly, BetaRED is used as a framework to design two dynamic algorithms, which we will call Adaptive Beta RED (ABetaRED) and Dynamic Beta RED (DBetaRED). In those new algorithms, certain parameters are dynamically adjusted so that the queue length remains stable around a predetermined reference value and according to changing network traffic conditions. Finally, we present a battery of simulations using the Network Simulator 3 (ns-3) software with a two-fold objective: to guide the user on how to adjust the parameters of the BetaRED mechanism, and to show a performance comparison of ABetaRED and DBetaRED with other representative algorithms that pursue a similar objective.
Research on the Development Law of Water-Conducting Fracture Zone in the Combined Mining of Jurassic and Carboniferous Coal Seams
Authors: Lu Cunjin , Xu Jinpeng , Li Qiang , Zhao Hui , He Yao
Published: November 2022

The accurate prediction of the height of the water-conducting fracture zone is essential for the prevention of roof damage by water disasters in coal mines. The development law of water-conducting fracture zone in combined mining of Jurassic and Carboniferous coal seams is different from that of previous research results. This study constructed an engineering geomechanics model to carry out material simulation and numerical simulation. The changes of stress, displacement, and fracture propagation were analyzed and compared with the results of formula calculation and field measurement, revealing the combined action of Jurassic and Carboniferous coal seams on the development law of water-conducting fracture zone. The results show that: (1) stress concentration is formed in the middle of the goaf in Jurassic coal seam, resulting in the high height of water-conducting fracture zone and the fracture “closed”; (2) the mining of Carboniferous coal seams caused the second subsidence of Jurassic goaf, and closed fracture “activated”; (3) the height of the water-conducting fracture zone obtained by the empirical formula is small, which is quite different from the actual situation. These research results are of significance for determining the height of the water-conducting fracture zone in Jurassic and Carboniferous coal seams during combined mining and the prevention of coal roof water hazards.
Digital Transformation of Beekeeping through the Use of a Decision Making Architecture
Authors: Huet Jean-Charles , Bougueroua Lamine , Kriouile Yassine , Wegrzyn-Wolska Katarzyna , Ancourt Corinne
Published: November 2022

The use of information and communication technologies (ICT) in agriculture is far from their potential. In this article, we consider how to facilitate and systematize the process of transforming traditional agriculture into digital agriculture; Agriculture 4.0. Among the different technologies, we focus on the IoT aspects. In the article, we propose a new approach for the design of intelligent agricultural management and supervision systems. The proposed approach is illustrated as an example of application in the beekeeping sector. Indeed, this sector is affected by a crisis due to the disappearance of bees and the different actors need support to make their decisions. As an example of decisions that can be made, we can cite: treatment planning or policy planning. An architecture based on sensors and open data is proposed to help them make decisions. An implementation of it is shown; it is based on a device with sensors, as well as an interface to collect the data on beehives and show notifications and alerts to beekeepers. The proposed architecture is flexible, and it can be used in the context of different levels of technology maturity. The final objective is to develop a reusable architecture for Agriculture 4.0.
Attention-Based Background/Foreground Monocular Depth Prediction Model Using Image Segmentation
Authors: Chiang Ting-Hui , Chiang Meng-Hsiu , Tsai Ming-Han , Chang Che-Cheng
Published: November 2022

While many monocular depth estimation methods have been proposed, determining depth variations in outdoor scenes remains challenging. Accordingly, this paper proposes an image segmentation-based monocular depth estimation model with attention mechanisms that can address outdoor scene variations. The segmentation model segments images into foreground and background regions and individually predicts depth maps. Moreover, attention mechanisms are also adopted to extract meaningful features from complex scenes to improve foreground and background depth map prediction via a multi-scale decoding scheme. From our experimental results, we observed that our proposed model outperformed previous methods by 27.5% on the KITTI dataset.
Deep Learning-Based Method for Accurate Real-Time Seed Detection in Glass Bottle Manufacturing
Authors: Bereciartua-Perez Arantza , Duro Gorka , Echazarra Jone , González Francico Javier , Serrano Alberto , Irizar Liher
Published: November 2022

Glass bottle-manufacturing companies produce bottles of different colors, shapes and sizes. One identified problem is that seeds appear in the bottle mainly due to the temperature and parameters of the oven. This paper presents a new system capable of detecting seeds of 0.1 mm2 in size in glass bottles as they are being manufactured, 24 h per day and 7 days per week. The bottles move along the conveyor belt at 50 m/min, at a production rate of 250 bottles/min. This new proposed method includes deep learning-based artificial intelligence techniques and classical image processing on images acquired with a high-speed line camera. The algorithm comprises three stages. First, the bottle is identified in the input image. Next, an algorithm based in thresholding and morphological operations is applied on this bottle region to locate potential candidates for seeds. Finally, a deep learning-based model can classify whether the proposed candidates are real seeds or not. This method manages to filter out most of false positives due to stains in the glass surface, while no real seeds are lost. The F1 achieved is 0.97. This method reveals the advantages of deep learning techniques for problems where classical image processing algorithms are not sufficient.
An ICS Traffic Classification Based on Industrial Control Protocol Keyword Feature Extraction Algorithm
Authors: Yu Changhong , Zhang Ze , Gao Ming
Published: November 2022

Industrial control protocol feature extraction is an important way to improve the accuracy and speed of industrial control protocol traffic classification. This paper firstly proposes a keyword feature extraction method for industrial control protocol, and then designs and implements an industrial control system (ICS) traffic classification based on this method. The proposed method utilizes the characteristics of the relatively fixed format of the industrial control protocol and the periodicity of the protocol traffic in ICS. The keyword features of the industrial control protocol can be accurately extracted after data preprocessing, data segmentation, redundant data filtering, and feature byte mining. A feature dataset is then formed. The designed ICS traffic classifier adopts decision tree and is trained with the feature dataset. Experiments are carried out on the open-source dataset. The results show that the proposed method achieves 99.99% classification accuracy, and the classification precision and classification recall rate reach 99.98% and 99.93%, respectively. The training time and predicting time of classifier are 0.34 s and 0.264 s, respectively, which meets the requirements of high precision and low latency of industrial control system.
The Shortest Verification Path of the MHT Scheme for Verifying Distributed Data
Authors: Kim Daeyoub , Lee Jihoon
Published: November 2022

One of the most common approaches for enhancing network performance is to retrieve data from nearby data holders that have previously obtained the desired data, not only from the original data source itself. In this case, since a data receiver cannot identify a practical data sender, it is necessary to verify both the received data and the data sender. Moreover, a data sender generally fragments the data into several small segments and sends them. Therefore, if these segments are retrieved from multiple unknown senders, the receiver must verify every segment to safely use the data. MHT (Merkle hash tree) is suitable for efficiently verifying the set of segments shared in the network. NDN (named-data networking) and Bitcoin utilize MHT to verify transmitted data. However, a data authentication scheme based on the MHT has an inefficient factor that repeatedly computes the same node values of the MHT and are repeatedly computed. The larger the size of the MHT is, the greater the number of calculation iterations. Therefore, as a result, the authentication scheme’s inefficiency is also more severe. When a sender transmits data consisting of many segments through NDN, the data authentication time may take longer than the data transmission time. Hence, in this paper, the degree of the MHT’s inefficiency and the pattern of the iterated operation of the MHT are analyzed first. The proposed improvement is to find repeatedly used node values, store them internally, and use the stored node values without recalculation when required to reuse them. For that process, a rule to select such node values is given. Additionally, when verifying the leaf node value of the MHT, the MHT-based authentication scheme asks a verifier to compute all node values on the path from the leaf node to the root node of the MHT. This paper demonstrates the proposed shortest path selection for verifying the leaf node value. The proposed scheme, using saved node values and the shortest path, reduces the computational overhead of the MHT and improves service latency. It has been proven from performance evaluations that the proposed scheme decreases the computational overhead by more than one-third if the number of segments is more than 1024.
A Method of Predicting Critical Derailment Speed and Analysis of Derailment Process
Authors: Yu Ruodan , Ma He , Zhang Jun , Niu Yan
Published: November 2022

In order to study the derailment process and dynamic performance-changing rules of vehicles on a curved track, a method of calculating critical derailment speed is proposed, which is the progressive trial method. In terms of their theoretical basis and calculation results, compared with the two traditional methods (root locus method and limit cycle method) for calculating linear and nonlinear critical speed, the advantages of this method in studying derailment are proved, and its applicable conditions are explained. Taking CRH3 as an example, the critical derailment speed on different radius curves is calculated by the progressive trial method. At the critical derailment speed, the variation laws of wheel lifting, wheel–rail attack angle and dynamic index are analyzed. The discrete elastic contact analysis is used to observe the changes in the wheel–rail contact position and contact spot during derailment. The results show that with the increase in the curve radius and critical speed of derailment, the absolute values of lateral force, vertical forces and derailment coefficient increase at the same position of the line; when the impact angle between the front wheel of the front bogie and the rail increases to more than 0.06 rad, the train is in a critical derailment state.
Small Object Detection in Infrared Images: Learning from Imbalanced Cross-Domain Data via Domain Adaptation
Authors: Kim Jaekyung , Huh Jungwoo , Park Ingu , Bak Junhyeong , Kim Donggeon , Lee Sanghoon
Published: November 2022

Deep learning-based object detection is one of the most popular research topics. However, in cases where large-scale datasets are unavailable, the training of detection models remains challenging due to the data-driven characteristics of deep learning. Small object detection in infrared images is such a case. To solve this problem, we propose a YOLOv5-based framework with a novel training strategy based on the domain adaptation method. First, an auxiliary domain classifier is combined with the YOLOv5 architecture to compose a detection framework that is trainable using datasets from multiple domains while maintaining calculation costs in the inference stage. Secondly, a new loss function based on Wasserstein distance is proposed to deal with small-sized objects by overcoming the problem of the intersection over union sensitivity problem in small-scale cases. Then, a model training strategy inspired from domain adaptation and knowledge distillation is presented. Using the domain confidence output of the domain classifier as a soft label, domain confusion loss is backpropagated to force the model to extract domain-invariant features while training the model with datasets with imbalanced distributions. Additionally, we generate a synthetic dataset in both the visible light and infrared spectrum to overcome the data shortage. The proposed framework is trained on the MS COCO, VEDAI, DOTA, ADAS Thermal datasets along with a constructed synthetic dataset for human detection and vehicle detection tasks. The experimental results show that the proposed framework achieved the best mean average precision (mAP) of 64.7 and 57.5 in human and vehicle detection tasks. Additionally, the ablation experiment shows that the proposed training strategy can improve the performance by training the model to extract domain-invariant features.
Investigation of Gold Gilding Materials and Techniques Applied in the Murals of Kizil Grottoes, Xinjiang, China
Authors: Yang Jie , Zhou Zhibo , Lu Tianji , Shen Ling
Published: November 2022

The present work reports the identification of inorganic and organic materials applied in gildings of Kizil Grottoes mural paintings, located in the western part of China. A total of 10 gold gilding samples were collected from caves dated in the fourth–seventh century AD. In this research, optical microscopy, scanning electron microscopy (SEM) with energy-dispersive X-ray spectroscopy (EDX), UV fluorescence microscopy, and pyrolysis gas chromatography–mass spectrometry (Py-GC/MS) analyses were carried out to clarify the mordant materials and the coating materials on the foils. This research reports some new finds of the gilding technique in Kizil grottoes, such as oil and lac resin being used as mordant to provide a binding medium for gold gilding, gold powder probably being used as pigment, and some organic dyes being well preserved under gold foil. These interesting findings not only provide scientific evidence for us to understand the materials and techniques of gold gildings on Kizil Grottoes murals, but also illustrate material trade and technique exchanges along the Silk Road in the past.
Induced Emotion-Based Music Recommendation through Reinforcement Learning
Authors: De Prisco Roberto , Guarino Alfonso , Malandrino Delfina , Zaccagnino Rocco
Published: November 2022

Music is widely used for mood and emotion regulation in our daily life. As a result, many research works on music information retrieval and affective human-computer interaction have been proposed to model the relationships between emotion and music. However, most of these works focus on applications in a context-sensitive recommendation that considers the listener’s emotional state, but few results have been obtained in studying systems for inducing future emotional states. This paper proposes Moodify, a novel music recommendation system based on reinforcement learning (RL) capable of inducing emotions in the user to support the interaction process in several usage scenarios (e.g., games, movies, smart spaces). Given a target emotional state, and starting from the assumption that an emotional state is entirely determined by a sequence of recently played music tracks, the proposed RL method is designed to learn how to select the list of music pieces that better “match” the target emotional state. Differently from previous works in the literature, the system is conceived to induce an emotional state starting from a current emotion instead of capturing the current emotion and suggesting certain songs that are thought to be suitable for that mood. We have deployed Moodify as a prototype web application, named MoodifyWeb. Finally, we enrolled 40 people to experiment MoodifyWeb, employing one million music playlists from the Spotify platform. This preliminary evaluation study aimed to analyze MoodifyWeb’s effectiveness and overall user satisfaction. The results showed a highly rated user satisfaction, system responsiveness, and appropriateness of the recommendation (up to 4.30, 4.45, and 4.75 on a 5-point Likert, respectively) and that such recommendations were better than they thought before using MoodifyWeb (6.45 on a 7-point Likert).
Experimental and Numerical Methods for the Evaluation of Sound Radiated by Vibrating Panels Excited by Electromagnetic Shakers in Automotive Applications
Authors: Tira Anna , Pinardi Daniel , Farina Angelo , Figuretti Alessio , Palmieri Davide
Published: November 2022

Numerical simulations are increasingly employed in the automotive industry to optimize the design stage, reduce prototype testing, and shorten the time to market. The aim of the presented research is the development of a fast and reliable method for the prediction of the sound field generated outside a vehicle by vibrating panels under electromagnetic shaker excitation. Despite that multi-physics numerical simulation software already link mechanical vibrations to their acoustic effect, they show a drawback when calculating the exterior sound field produced by a vibrating panel: the presence of a car model to separate front and rear radiations avoiding the acoustic short circuit, and an air volume surrounding it are required, thus increasing the model complexity and calculation time. Both problems can be overcome with the presented methodology: only the mechanical vibration of the panel is solved numerically, and the radiated sound field is then calculated postprocessing, relying on Rayleigh’s integral. At first, the method’s validation is presented through laboratory experiments; then, a real vehicle panel is analyzed. Comparisons between the finite element method (FEM) simulations and experimental measurements showed very good agreement while keeping the calculation time low for both the laboratory and on-vehicle tests.
Gamified Digital Game-Based Learning as a Pedagogical Strategy: Student Academic Performance and Motivation
Authors: Camacho-Sánchez Rubén , Rillo-Albert Aaron , Lavega-Burgués Pere
Published: November 2022

Digital game-based learning (DGBL) and Gamification are emerging methodological strategies in education. This research aims to analyze the effects on academic performance and motivation after an experience combining DGBL and Gamification in university students. The sample comprises 126 students, of whom 38 were in the experimental group. Three measurement instruments have been used: an evaluative test to measure academic performance, the Questionnaire on Motivation for Cooperative Playful Learning Strategies (CMELAC) and a questionnaire with three open-ended questions, which complement the measurement of motivation. This analysis is conducted using independent sample t-tests. We undertook a Bonferroni adjustment to the alpha level (new p = 0.017). The results show significant differences in academic performance between the control and experimental groups. Motivation shows high values among all participants. No significant differences were found between the two experimental subgroups when the competition was added to the dynamics. In conclusion, the results of the present study support the gamified DGBL method as an exciting teaching tool that corresponds to students’ active learning and provide valuable immediate feedback on students’ attempts, improvements in academic performance and a high level of motivation.
Retweet Prediction Based on Heterogeneous Data Sources: The Combination of Text and Multilayer Network Features
Authors: Meštrović Ana , Petrović Milan , Beliga Slobodan
Published: November 2022

Retweet prediction is an important task in the context of various problems, such as information spreading analysis, automatic fake news detection, social media monitoring, etc. In this study, we explore retweet prediction based on heterogeneous data sources. In order to classify a tweet according to the number of retweets, we combine features extracted from the multilayer network and text. More specifically, we introduce a multilayer framework for the multilayer network representation of Twitter. This formalism captures different users’ actions and complex relationships, as well as other key properties of communication on Twitter. Next, we select a set of local network measures from each layer and construct a set of multilayer network features. We also adopt a BERT-based language model, namely Cro-CoV-cseBERT, to capture the high-level semantics and structure of tweets as a set of text features. We then trained six machine learning (ML) algorithms: random forest, multilayer perceptron, light gradient boosting machine, category-embedding model, neural oblivious decision ensembles, and an attentive interpretable tabular learning model for the retweet-prediction task. We compared the performance of all six algorithms in three different setups: with text features only, with multilayer network features only, and with both feature sets. We evaluated all the setups in terms of standard evaluation measures. For this task, we first prepared an empirical dataset of 199,431 tweets in Croatian posted between 1 January 2020 and 31 May 2021. Our results indicate that the prediction model performs better by integrating multilayer network features with text features than by using only one set of features.
Wave Diffraction by Metamaterial-Coated Wedges: The UAPO Solution for Skew Incidence
Authors: Riccio Giovanni , Gennarelli Gianluca , Ferrara Flaminio , Gennarelli Claudio , Guerriero Rocco
Published: November 2022

The interaction between an electromagnetic plane wave and a metallic wedge coated with a uniform layer made of a double-negative metamaterial is studied by means of the uniform asymptotic physical optics method in the high-frequency range, e.g., at microwave and optical frequencies. The apex angle of the structure is arbitrarily chosen, and the wave is at a skew incidence with respect to the external edge, which is formed by the metamaterial layers covering the wedge faces. The proposed method is built on the scattering integral involving electric and magnetic surface currents as physical optics equivalent sources, and it takes advantage of analytic evaluations. The last step uses a uniform asymptotic procedure to obtain a closed form expression of the diffracted field to be added to the geometrical optics one for computing the total field at the observation point in the neighboring free space. The study also includes a numerical validation of the method.
Facile Synthesis of Hafnium Oxide Nanoparticle Decorated on Graphene Nanosheet and Its Photocatalytic Degradation of Organic Pollutants under UV-Light Irradiation
Authors: Jayaraman Venkatachalam , Mahalingam Shanmugam , Chinnathambi Shanmugavel , Pandian Ganesh N. , Prakasarao Aruna , Ganesan Singaravelu , Ramasamy Jayavel , Ayyaru Sivasankaran , Ahn Young-Ho
Published: November 2022

The HfO2 nanoparticles and the nanocomposites of HfO2-graphene (10, 30, and 50 wt%) were prepared via precipitation and simple mixing method. The XRD pattern confirmed the presence of monoclinic HfO2 and hexagonal graphene in the nanocomposite. Raman spectroscopy studies revealed the formation of HfO2-graphene nanocomposite. According to SEM and TEM images the HfO2, NPs are spherical, and their size is less than 10 nm, anchored on the surface of the graphene sheets. The EDX spectrum shows carbon, oxygen, and HfO2 and reveals the formation of the HfO2-graphene nanocomposite. The UV-vis absorption spectra show the optical properties of synthesized HfO2-graphene nanocomposite. The study examines the influence of different ratios of the addition of graphene on the photocatalytic activity of HfO2-graphene. It was found that the HfO2-graphene (50 wt%) 40 mg nanocomposite exhibits enhanced photocatalytic activity than the bare HfO2 towards the methylene blue photodegradation, an aromatic pollutant in water under UV light irradiation, which can be applied optimally for individually wastewater management system. The HfO2-graphene (50 wt%) photocatalyst degrades 81 ± 2% of tetracycline in 180 min, implying that tetracycline can be degraded more efficiently under UV light. The enhancement in photocatalytic activity under UV light illumination can be attributed to the effective separation of photogenerated electrons, inhibiting recombination in the HfO2-graphene composite.
Impact of Vacuum Assisted Oven Drying of Kurut on Product Quality and Drying Characteristics
Authors: Anli Elif Ayse
Published: November 2022

Kurut is an ancient flavor of central Asia, produced by sun-drying of yogurt. Kurut is used in preparation of pasta, soup and also in the form of yogurt by mixing with water. Alternative to sun drying, this study was aimed at conducting Kurut production by use of vacuum-oven drying (VOD) and oven drying (OD) techniques at two different temperatures (35 °C and 45 °C). Samples were coded as KV35, KV45 and K35, K45 for VOD and OD techniques, respectively. All samples were concentrated by drying in proximate composition (p > 0.05). Less progress of acidity, lower solubility ratio (p > 0.05) and lower hydroxymethyl furfural (HMF) values were maintained by VOD than OD (p < 0.05) in the 1st day. All Kurut samples were very distinctly different (ΔE ˃ 3), more yellowish (b*) and less bright (L*) than strained yogurt. Redness (a*) was observed only in OD-applied samples (p < 0.05). Yagcioglu, Wang and Singh, Midilli and two-term models were best fitting models for predicting drying behavior of KV35, KV45, K35 and K45, respectively. Drying time for VOD and OD were determined as 2 days and 8 days, respectively. Difference in means of drying temperature was found statistically significant in terms of HMF, L*, a* and b* in the OD technique (p < 0.05). In the industrial aspect use of VOD, especially with the establishment of continuous systems, Kurut production in a shorter time with better preservation of quality is possible.
Contribution Analysis of Assembled Brake System to Reduce Squealing
Authors: Kwon Seong-Jin , Kim Chan-Jung
Published: November 2022

Brake noise is a problem that is still being studied owing to continuous issues in the vehicle industry, and many countermeasures have been suggested using experimental and simulation approaches. The assembled brake system comprises several subparts, and a contribution analysis is an efficient solution for selecting the subpart with the most influence on squealing. In this study, a finite-element model of the assembled brake system was verified with modal test results for each part. Then, a forced response analysis of the assembled brake system model was conducted to obtain the spectral response vector of the subparts under white-noise excitation. A contribution analysis was formulated by calculating the similarity between the total and partial response vectors of each part and deriving the contribution index of all subparts of interest. The brake pad was selected as the target of design modification, and the feasibility was validated using an experimental chassis dynamometer test.
Laser-Induced Graphene Film and Its Applications in Flexible Electronics
Authors: Liu Huilong , Chen Yun
Published: November 2022

Laser-induced graphene (LIG) films and their derivatives have been regarded as one of the most outstanding functional flexible electrodes in the past decade, which will transform society and enable new devices and developments. The aim of this Special Issue is to provide a scientific platform for scholars in the LIG field to present their recent research towards a deeper understanding of forming mechanism, structure/ morphology, properties and behaviors of LIG films. This Special Issue gives readers the possibility to gain new insights into the applications of LIG films in flexible electronics, including mechanical/temperature/gas/electrochemical sensors, micro-supercapacitors, actuators, electrocatalysis, solid-state triboelectric nanogenerators, Joule heater, etc. We believe that the papers published in this Special Issue will provide a useful guidance for the manufacturing of nanostructured LIG electrodes in flexible electronics.
Contact Analysis and Friction Prediction of Non-Gaussian Random Surfaces
Authors: Ren Jinzhao , Yuan Huiqun
Published: November 2022

Engineering surfaces exhibit asymmetrical height distributions due to certain types of surface finishing and running-in process. This non-Gaussian surface reflects different contact performances and tribological properties. In this paper, the influence of non-Gaussian surface parameters on contact performance and friction is investigated. First, the computer program for generating rough surfaces with given parameters is developed; then, contact analysis for rough surfaces are conducted through a deterministic contact model; finally, friction coefficient can be derived from friction model using the results of the contact model. The simulation analyses indicate that the skewness and amplitude of non-Gaussian surface have significant effects on contact performance and friction. The contact characteristic parameters and friction coefficient become slightly changed when kurtosis becomes relatively large.
Influence of the Mesh Topology on the Accuracy of Modelling Turbulent Natural and Excited Round Jets at Different Initial Turbulence Intensities
Authors: Wawrzak Karol , Tyliszczak Artur
Published: November 2022

The paper is aimed at an assessment of the importance of the coordinate system (Cartesian vs. cylindrical) assumed for simulations of free-round jets. The research is performed by applying the large eddy simulation method with spatial discretisation based on high-order compact difference schemes. The results obtained for natural and excited jets at three different turbulence intensity levels, Ti=0.01%,0.1% and 1.0%, are compared. In the case of the natural jet, it is found that both instantaneous and time-averaged results are significantly dependent on the coordinate system only for the lowest Ti. In this case, in the Cartesian coordinate system, the errors introduced by an azimuthal non-uniformity of the mesh seem to have a larger impact on the solutions than the disturbances generated at the nozzle exit. The azimuthal non-uniformity of the mesh also has a substantial influence on the results of the modelling of the excited jets. In this case, the excitation is introduced as time-varying forcing, with the frequency corresponding to half of the preferred mode frequency and the amplitude equal to 5% of the jet velocity. Such an excitation leads to the formation of the so-called side-jets being revealed as inclined streams of fluid ejected outside the main jet stream. Primary attention is paid to the mechanism of the formation of the side-jets, their number and location. The results obtained on Cartesian meshes show that for very low turbulence intensity levels (Ti=0.01%), the number and direction of the side-jets are dependent on the non-uniform distribution of the mesh nodes along the azimuthal direction of the jet. On the other hand, when the cylindrical coordinate system is used, the number of the side-jets and their locations are random and dependent only on inlet parameters. It has been demonstrated that the mechanism of side-jet formation is the same in both coordinate systems; however, its random nature can only be predicted when the cylindrical coordinate system is used.
Maritime Traffic Evaluation Using Spatial-Temporal Density Analysis Based on Big AIS Data
Authors: Kim Yoon-Ji , Lee Jeong-Seok , Pititto Alessandro , Falco Luigi , Lee Moon-Suk , Yoon Kyoung-Kuk , Cho Ik-Soon
Published: November 2022

For developing national maritime traffic routes through the coastal waters of Korea, the customary maritime traffic flow must be accurately identified and quantitatively evaluated. In this study, the occupancy time of ships in cells was calculated through a density analysis based on automatic identification system data. The density map was statistically created by logarithmically transforming the density values and adopting standard deviation-based stretch visualization to increase the normality of the distribution. Many types of traffic routes such as open-sea, coastal, inland, and coastal access routes were successfully identified; moreover, the stretch color ramp ratio was reduced to identify routes having relatively high density. Adopting a single standard deviation and demonstrating the top 25% of color ramps, the analysis afforded the main routes through which customary traffic flows. This novel density analysis method and statistical visualization method is expected to be used for developing national maritime traffic routes and should ultimately contribute to maritime safety. Moreover, it provides a scientific means and simulator for determining the navigation area and analyzing conflicts with other activities in marine spatial planning.
Do Psychogenic Erectile Dysfunction and Premature Ejaculation Share a Neural Circuit? Evidence from a fMRI Systematic Review and Meta-Analysis
Authors: Monteiro Jéssica , Castelhano João , Pignatelli Duarte , Tartaro Armando , Cera Nicoletta
Published: November 2022

Background: Several functional magnetic resonance imaging (fMRI) studies investigated the brain correlates of psychogenic erectile dysfunction (PED) and premature ejaculation (PE), representing the most common sexual dysfunctions in men. These studies allowed a wide set of brain regions in PED and PE patients when compared to healthy men. In the present meta-analysis, we aim at assessing the presence of homogeneity in the cerebral underpinnings of PED and PE. Methods: Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and after the electronic search, duplicate removal and the application of pre-exclusion criteria, nine PED and ten PE studies were considered eligible with a Cohen’s k of 0.84 and 0.75, respectively. The effect sizes of the sociodemographic and psychological/urological dimensions were calculated. We extracted brain clusters from the retrieved studies, comparing patients and controls, and we calculated brain maps with Seed-Based D Mapping software. Results: We found a homogenous involvement of the frontal gyrus and insula in both dysfunctions, suggesting a common network. Conclusions: The anterior insula plays a key role in the processing of emotional features of stimuli, while the posterior insula in interoceptive information is relevant for sexual response. The prefrontal and inferior frontal cortices are important for sexual inhibition/disinhibition.
Issues and Solutions in Deep Learning-Enabled Recommendation Systems within the E-Commerce Field
Authors: Almahmood Rand Jawad Kadhim , Tekerek Adem
Published: November 2022

In recent years, especially with the (COVID-19) pandemic, shopping has been a challenging task. Increased online shopping has increased information available via the World Wide Web. Finding new products or identifying the most suitable products according to customers’ personalization trends is the main benefit of E-commerce recommendation systems, which use different techniques such as rating, ranking, or reviewing. These recommendations can be formed using different techniques and approaches, particularly using the technology of intelligent agents, and specific interfaces or personal agents can be used to model this type of system. These agents usually use the techniques and algorithms of Artificial Intelligence internally. A recommendation system is a prediction system that has been created to help the user to select the proper product for them, and to reduce the effort spent in the search process using advanced technology such as deep learning techniques. We investigate all studies using a standard review process for collecting and retrieving data from previous studies and illustrate their relevant accuracy and interpretability along with pros and cons helpful to business firms to adopt the most legitimate approach. The study’s findings revealed that recommendation problems are solved better by using deep learning algorithms such as CNN, RNN, and sentiment analysis, especially for popular problems such as cold start and sparsity.
A Comparison of the Music Key Detection Approaches Utilizing Key-Profiles with a New Method Based on the Signature of Fifths
Authors: Kania Michalina , Łukaszewicz Tomasz , Kania Dariusz , Mościńska Katarzyna , Kulisz Józef
Published: November 2022

This paper compares approaches to music key detection based on popular key-profiles with a new key detection method that utilizes the concept of the signature of fifths. The signature of fifths is a geometrical music harmonic-content descriptor. Depending on the scenario, it may reflect either the multiplicities of occurrences or the aggregated durations of individual pitch-classes of the chromatic scale in a given fragment of music. In this study, we compare the efficacies of a few strictly correlational key recognition approaches based on music key-profiles (i.e., Krumhansl–Kessler, Temperley, and Albrecht–Shanahan) with a new method that implements the concept of the signature of fifths. All the experiments were performed in a collection of music pieces comprised of preludes by J. S. Bach (The Well-Tempered Clavier-Book I), preludes by F. Chopin (Op. 28), and songs by The Beatles (from the album A Hard Day’s Night). In the scenario implementing the aggregate durations of individual pitch-classes, based on the analysis of the shortest initial fragments of music for which the key was indicated in all the considered approaches, the key detection efficacy obtained with the method using the signature of fifths was greater than the efficacies obtained with the strictly correlational approaches utilizing key-profiles (on average by 9.27 pp). In the case of the analogous analysis carried out for the scenario implementing the multiplicities of occurrences of individual pitch-classes, on average, greater efficacy was observed for the strictly correlational approaches based on key-profiles (by 2.7 pp). The conducted experiments confirmed the new key-detecting method offers advantages in computational simplicity, stability of decision making, and the ability to successfully determine the key based on a very short fragment of music.