bluuebunny/crossref_metadata_2025 huggingface dataset:
Correlation between Commercial Anti-RBD IgG Titer and Neutralization Titer against SARS-CoV-2 Beta Variant
Authors: Poon Rosana Wing-Shan , Lu Lu , Fong Carol Ho-Yan , Ip Tak-Chuen , Chen Lin-Lei , Zhang Ricky Rui-Qi , Yip Cyril Chik-Yan , Cheng Vincent Chi-Chung , Chan Kwok-Hung , Yuen Kwok-Yung , To Kelvin Kai-Wang
Published: November 2021

Objectives: The emergence of SARS-CoV-2 variants of concern (VOCs) have diminished the effectiveness of vaccines and are associated with a rebound in the number of COVID-19 cases globally. These variants contain mutations at the spike (S) protein receptor binding site (RBD), which affect antibody binding. Current commercially available antibody assays were developed before the VOCs emerged. It is unclear whether the levels of these commercially available antibody assays can predict the neutralizing antibody titers against the VOCs. In this study, we sought to determine the correlation between the binding antibody concentration and microneutralization antibody titer against the beta variant. Methods: This study included 58 COVID-19 patients. The concentrations of IgG against the SARS-CoV-2 spike protein RBD and nucleocapsid (N) protein were measured using the Abbott SARS-CoV-2 IgG II Quant assay and the SARS-CoV-2 IgG assay, respectively. The neutralization antibody titer against the wild type lineage A SARS-CoV-2 and against the beta variant (B.1.351) was determined using a conventional live virus neutralization test. Results: The geometric mean MN titer (GMT) against the beta variant was significantly lower than that against the wild type lineage A virus (5.6 vs. 47.3, p < 0.0001). The anti-RBD IgG had a better correlation with the neutralizing antibody titer than that of the anti-N IgG assay against the wild type lineage A virus (Spearman rho, 0.5901 vs. 0.3827). However, the correlation between the anti-RBD or the anti-N IgG and the MN titer against the beta variant was poor. Conclusions: Currently available commercial antibody assays may not predict the level of neutralizing antibodies against the variants. A new generation of antibody tests specific for variants are required.
Evaluation of the Practicability of Biosynex Antigen Self-Test COVID-19 AG+ for the Detection of SARS-CoV-2 Nucleocapsid Protein from Self-Collected Nasal Mid-Turbinate Secretions in the General Public in France
Authors: Tonen-Wolyec Serge , Dupont Raphaël , Awaida Natalio , Batina-Agasa Salomon , Hayette Marie-Pierre , Bélec Laurent
Published: November 2021

Due to their ease-of-use, lateral flow assay SARS-CoV-2 antigen-detecting rapid diagnostic tests could be suitable candidates for antigen-detecting rapid diagnostic self-test (Ag-RDST). We evaluated the practicability of the Ag-RDST BIOSYNEX Antigen Self-Test COVID-19 Ag+ (Biosynex Swiss SA, Freiburg, Switzerland), using self-collected nasal secretions from the turbinate medium (NMT), in 106 prospectively included adult volunteers living in Paris, France. The majority of the participants correctly understood the instructions for use (94.4%; 95% confidence interval (CI): 88.3–97.4), showing a great ability to perform the entire self-test procedure to obtain a valid and interpretable result (100%; 95% CI: 96.5–100), and demonstrated the ability to correctly interpret test results (96.2%; 95% CI: 94.2–97.5) with a high level of general satisfaction. About one in eight participants (# 15%) needed verbal help to perform or interpret the test, and only 3.8% of test results were misinterpreted. By reference to multiplex real-time RT-PCR, the Ag-RDST showed 90.9% and 100% sensitivity and specificity, respectively, and high agreement (98.1%), reliability (0.94), and accuracy (90.9%) to detect SARS-CoV-2 antigen. Taken together, our study demonstrates the high usability and accuracy of BIOSYNEX Antigen Self-Test COVID-19 Ag+ for supervised self-collected NMT sampling in an unselected adult population living in France.
Reservoir Computing with Delayed Input for Fast and Easy Optimisation
Authors: Jaurigue Lina , Robertson Elizabeth , Wolters Janik , Lüdge Kathy
Published: November 2021

Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.
Thermodynamic Performance of a Brayton Pumped Heat Energy Storage System: Influence of Internal and External Irreversibilities
Authors: Pérez-Gallego David , Gonzalez-Ayala Julian , Calvo Hernández Antonio , Medina Alejandro
Published: November 2021

A model for a pumped thermal energy storage system is presented. It is based on a Brayton cycle working successively as a heat pump and a heat engine. All the main irreversibility sources expected in real plants are considered: external losses arising from the heat transfer between the working fluid and the thermal reservoirs, internal losses coming from pressure decays, and losses in the turbomachinery. Temperatures considered for the numerical analysis are adequate for solid thermal reservoirs, such as a packed bed. Special emphasis is paid to the combination of parameters and variables that lead to physically acceptable configurations. Maximum values of efficiencies, including round-trip efficiency, are obtained and analyzed, and optimal design intervals are provided. Round-trip efficiencies of around 0.4, or even larger, are predicted. The analysis indicates that the physical region, where the coupled system can operate, strongly depends on the irreversibility parameters. In this way, maximum values of power output, efficiency, round-trip efficiency, and pumped heat might lay outside the physical region. In that case, the upper values are considered. The sensitivity analysis of these maxima shows that changes in the expander/turbine and the efficiencies of the compressors affect the most with respect to a selected design point. In the case of the expander, these drops are mostly due to a decrease in the area of the physical operation region.
Scheduling to Minimize Age of Incorrect Information with Imperfect Channel State Information
Authors: Chen Yutao , Ephremides Anthony
Published: November 2021

In this paper, we study a slotted-time system where a base station needs to update multiple users at the same time. Due to the limited resources, only part of the users can be updated in each time slot. We consider the problem of minimizing the Age of Incorrect Information (AoII) when imperfect Channel State Information (CSI) is available. Leveraging the notion of the Markov Decision Process (MDP), we obtain the structural properties of the optimal policy. By introducing a relaxed version of the original problem, we develop the Whittle’s index policy under a simple condition. However, indexability is required to ensure the existence of Whittle’s index. To avoid indexability, we develop Indexed priority policy based on the optimal policy for the relaxed problem. Finally, numerical results are laid out to showcase the application of the derived structural properties and highlight the performance of the developed scheduling policies.
Introduction to Semi-Classical Analysis for Digital Errors of Qubit in Quantum Processor
Authors: Hirota Osamu
Published: November 2021

In recent years, remarkable progress has been achieved in the development of quantum computers. For further development, it is important to clarify properties of errors by quantum noise and environment noise. However, when the system scale of quantum processors is expanded, it has been pointed out that a new type of quantum error, such as nonlinear error, appears. It is not clear how to handle such new effects in information theory. First of all, one should make the characteristics of the error probability of qubits clear as communication channel error models in information theory. The purpose of this paper is to survey the progress for modeling the quantum noise effects that information theorists are likely to face in the future, to cope with such nontrivial errors mentioned above. This paper explains a channel error model to represent strange properties of error probability due to new quantum noise. By this model, specific examples on the features of error probability caused by, for example, quantum recurrence effects, collective relaxation, and external force, are given. As a result, it is possible to understand the meaning of strange features of error probability that do not exist in classical information theory without going through complex physical phenomena.
Bayesian and Classical Inference under Type-II Censored Samples of the Extended Inverse Gompertz Distribution with Engineering Applications
Authors: Elshahhat Ahmed , Aljohani Hassan M. , Afify Ahmed Z.
Published: November 2021

In this article, we introduce a new three-parameter distribution called the extended inverse-Gompertz (EIGo) distribution. The implementation of three parameters provides a good reconstruction for some applications. The EIGo distribution can be seen as an extension of the inverted exponential, inverse Gompertz, and generalized inverted exponential distributions. Its failure rate function has an upside-down bathtub shape. Various statistical and reliability properties of the EIGo distribution are discussed. The model parameters are estimated by the maximum-likelihood and Bayesian methods under Type-II censored samples, where the parameters are explained using gamma priors. The performance of the proposed approaches is examined using simulation results. Finally, two real-life engineering data sets are analyzed to illustrate the applicability of the EIGo distribution, showing that it provides better fits than competing inverted models such as inverse-Gompertz, inverse-Weibull, inverse-gamma, generalized inverse-Weibull, exponentiated inverted-Weibull, generalized inverted half-logistic, inverted-Kumaraswamy, inverted Nadarajah–Haghighi, and alpha-power inverse-Weibull distributions.
Dynamic Risk Measures for Anticipated Backward Doubly Stochastic Volterra Integral Equations
Authors: Miao Liangliang , Liu Zhang , Hu Yijun
Published: November 2021

Inspired by the consideration of some inside and future market information in financial market, a class of anticipated backward doubly stochastic Volterra integral equations (ABDSVIEs) are introduced to induce dynamic risk measures for risk quantification. The theory, including the existence, uniqueness and a comparison theorem for ABDSVIEs, is provided. Finally, dynamic convex risk measures by ABDSVIEs are discussed.
Optimal Passive Source Localization for Acoustic Emissions
Authors: Prete Carlos A. , Nascimento Vítor H. , Lopes Cássio G.
Published: November 2021

Acoustic emission is a non-destructive testing method where sensors monitor an area of a structure to detect and localize passive sources of elastic waves such as expanding cracks. Passive source localization methods based on times of arrival (TOAs) use TOAs estimated from the noisy signals received by the sensors to estimate the source position. In this work, we derive the probability distribution of TOAs assuming they were obtained by the fixed threshold technique—a popular low-complexity TOA estimation technique—and show that, if the sampling rate is high enough, TOAs can be approximated by a random variable distributed according to a mixture of Gaussian distributions, which reduces to a Gaussian in the low noise regime. The optimal source position estimator is derived assuming the parameters of the mixture are known, in which case its MSE matches the Cramér–Rao lower bound, and an algorithm to estimate the mixture parameters from noisy signals is presented. We also show that the fixed threshold technique produces biased time differences of arrival (TDOAs) and propose a modification of this method to remove the bias. The proposed source position estimator is validated in simulation using biased and unbiased TDOAs, performing better than other TOA-based passive source localization methods in most scenarios.
Statistical Physics of Evolving Systems
Authors: Annila Arto
Published: November 2021

Evolution is customarily perceived as a biological process. However, when formulated in terms of physics, evolution is understood to entail everything. Based on the axiom of everything comprising quanta of actions (e.g., quanta of light), statistical physics describes any system evolving toward thermodynamic balance with its surroundings systems. Fluxes of quanta naturally select those processes leveling out differences in energy as soon as possible. This least-time maxim results in ubiquitous patterns (i.e., power laws, approximating sigmoidal cumulative curves of skewed distributions, oscillations, and even the regularity of chaos). While the equation of evolution can be written exactly, it cannot be solved exactly. Variables are inseparable since motions consume driving forces that affect motions (and so on). Thus, evolution is inherently a non-deterministic process. Yet, the future is not all arbitrary but teleological, the final cause being the least-time free energy consumption itself. Eventually, trajectories are computable when the system has evolved into a state of balance where free energy is used up altogether.
A Generalization of the Concavity of Rényi Entropy Power
Authors: Guo Laigang , Yuan Chun-Ming , Gao Xiao-Shan
Published: November 2021

Recently, Savaré-Toscani proved that the Rényi entropy power of general probability densities solving the p-nonlinear heat equation in Rn is a concave function of time under certain conditions of three parameters n,p,μ, which extends Costa’s concavity inequality for Shannon’s entropy power to the Rényi entropy power. In this paper, we give a condition Φ(n,p,μ) of n,p,μ under which the concavity of the Rényi entropy power is valid. The condition Φ(n,p,μ) contains Savaré-Toscani’s condition as a special case and much more cases. Precisely, the points (n,p,μ) satisfying Savaré-Toscani’s condition consist of a two-dimensional subset of R3, and the points satisfying the condition Φ(n,p,μ) consist a three-dimensional subset of R3. Furthermore, Φ(n,p,μ) gives the necessary and sufficient condition in a certain sense. Finally, the conditions are obtained with a systematic approach.
Embodied Simulations of Forces of Nature and the Role of Energy in Physical Systems
Authors: Fuchs Hans U. , Corni Federico , Pahl Angelika
Published: November 2021

We experience (perceive, act upon and react to, and conceptualize) dynamical processes in nature as agentive. Expressed differently, we experience events as resulting from activities and interactions of Forces of Nature (such as wind, light, heat, fluids, electricity, substances, and motion) that are conceived of as powerful agents acting and interacting in physical environments. An example would be sunlight creating heat in the Earth’s surface layers, and this heat using the atmosphere as a heat engine whose output are the winds on our planet. In the physics of dynamical systems, these forces are characterized in terms of intensive and extensive quantities (i.e., electric potential and electric charge in the case of electricity). The aspect of power is formalized with the help of a generalized energy principle and the rules relating power/energy to intensive and extensive physical quantities. Concrete processes depend upon properties of physical materials (in and through which forces are active) such as (thermal, electrical, etc.) capacity or conductivity. In this paper, we demonstrate how we can create Embodied Simulations and Forces-of-Nature Theater performances, where children act as forces such as water, heat, electricity, and motion. The embodied logic of the physical play teaches children about the logic of our explanations of physical processes.
Objects Detection Using Sensors Data Fusion in Autonomous Driving Scenarios
Authors: Bocu Razvan , Bocu Dorin , Iavich Maksim
Published: November 2021

The relatively complex task of detecting 3D objects is essential in the realm of autonomous driving. The related algorithmic processes generally produce an output that consists of a series of 3D bounding boxes that are placed around specific objects of interest. The related scientific literature usually suggests that the data that are generated by different sensors or data acquisition devices are combined in order to work around inherent limitations that are determined by the consideration of singular devices. Nevertheless, there are practical issues that cannot be addressed reliably and efficiently through this strategy, such as the limited field-of-view, and the low-point density of acquired data. This paper reports a contribution that analyzes the possibility of efficiently and effectively using 3D object detection in a cooperative fashion. The evaluation of the described approach is performed through the consideration of driving data that is collected through a partnership with several car manufacturers. Considering their real-world relevance, two driving contexts are analyzed: a roundabout, and a T-junction. The evaluation shows that cooperative perception is able to isolate more than 90% of the 3D entities, as compared to approximately 25% in the case when singular sensing devices are used. The experimental setup that generated the data that this paper describes, and the related 3D object detection system, are currently actively used by the respective car manufacturers’ research groups in order to fine tune and improve their autonomous cars’ driving modules.
A Shared Cyber Threat Intelligence Solution for SMEs
Authors: van Haastrecht Max , Golpur Guy , Tzismadia Gilad , Kab Rolan , Priboi Cristian , David Dumitru , Răcătăian Adrian , Baumgartner Louis , Fricker Samuel , Ruiz Jose , Armas Esteban , Brinkhuis Matthieu , Spruit Marco
Published: November 2021

Small- and medium-sized enterprises (SMEs) frequently experience cyberattacks, but often do not have the means to counter these attacks. Therefore, cybersecurity researchers and practitioners need to aid SMEs in their defence against cyber threats. Research has shown that SMEs require solutions that are automated and adapted to their context. In recent years, we have seen a surge in initiatives to share cyber threat intelligence (CTI) to improve collective cybersecurity resilience. Shared CTI has the potential to answer the SME call for automated and adaptable solutions. Sadly, as we demonstrate in this paper, current shared intelligence approaches scarcely address SME needs. We must investigate how shared CTI can be used to improve SME cybersecurity resilience. In this paper, we tackle this challenge using a systematic review to discover current state-of-the-art approaches to using shared CTI. We find that threat intelligence sharing platforms such as MISP have the potential to address SME needs, provided that the shared intelligence is turned into actionable insights. Based on this observation, we developed a prototype application that processes MISP data automatically, prioritises cybersecurity threats for SMEs, and provides SMEs with actionable recommendations tailored to their context. Subsequent evaluations in operational environments will help to improve our application, such that SMEs are enabled to thwart cyberattacks in future.
Applications of Integrated IoT-Fog-Cloud Systems to Smart Cities: A Survey
Authors: Mohamed Nader , Al-Jaroodi Jameela , Lazarova-Molnar Sanja , Jawhar Imad
Published: November 2021

Several cities have recently moved towards becoming smart cities for better services and quality of life for residents and visitors, with: optimized resource utilization; increased environmental protection; enhanced infrastructure operations and maintenance; and strong safety and security measures. Smart cities depend on deploying current and new technologies and different optimization methods to enhance services and performance in their different sectors. Some of the technologies assisting smart city applications are the Internet of Things (IoT), fog computing, and cloud computing. Integrating these three to serve one system (we will refer to it as integrated IoT-fog-cloud system (iIFC)) creates an advanced platform to develop and operate various types of smart city applications. This platform will allow applications to use the best features from the IoT devices, fog nodes, and cloud services to deliver best capabilities and performance. Utilizing this powerful platform will provide many opportunities for enhancing and optimizing applications in energy, transportation, healthcare, and other areas. In this paper we survey various applications of iIFCs for smart cities. We identify different common issues associated with utilizing iIFCs for smart city applications. These issues arise due to the characteristics of iIFCs on the one side and the requirements of different smart city applications on the other. In addition, we outline the main requirements to effectively utilize iIFCs for smart city applications. These requirements are related to optimization, networking, and security.
Security Analysis of DDoS Attacks Using Machine Learning Algorithms in Networks Traffic
Authors: Alzahrani Rami J. , Alzahrani Ahmed
Published: November 2021

The recent advance in information technology has created a new era named the Internet of Things (IoT). This new technology allows objects (things) to be connected to the Internet, such as smart TVs, printers, cameras, smartphones, smartwatches, etc. This trend provides new services and applications for many users and enhances their lifestyle. The rapid growth of the IoT makes the incorporation and connection of several devices a predominant procedure. Although there are many advantages of IoT devices, there are different challenges that come as network anomalies. In this research, the current studies in the use of deep learning (DL) in DDoS intrusion detection have been presented. This research aims to implement different Machine Learning (ML) algorithms in WEKA tools to analyze the detection performance for DDoS attacks using the most recent CICDDoS2019 datasets. CICDDoS2019 was found to be the model with best results. This research has used six different types of ML algorithms which are K_Nearest_Neighbors (K-NN), super vector machine (SVM), naïve bayes (NB), decision tree (DT), random forest (RF) and logistic regression (LR). The best accuracy result in the presented evaluation was achieved when utilizing the Decision Tree (DT) and Random Forest (RF) algorithms, 99% and 99%, respectively. However, the DT is better than RF because it has a shorter computation time, 4.53 s and 84.2 s, respectively. Finally, open issues for further research in future work are presented.
Trajectory Tracking Control for Intelligent Vehicles Based on Cut-In Behavior Prediction
Authors: Chen Chongpu , Guo Jianhua , Guo Chong , Li Xiaohan , Chen Chaoyi
Published: November 2021

For intelligent vehicles, trajectory tracking control is of vital importance. However, due to the cut-in possibility of adjacent vehicles, trajectory planning of intelligent vehicles is challenging. Therefore, this paper proposes a trajectory tracking control method based on cut-in behavior prediction. A method of cut-in intention recognition is adopted to judge the possibility of adjacent vehicle and the driver preview model is used to predict the trajectory of the cut-in vehicle. The three driving scenarios are divided to manage trajectory planning under different cut-in behaviors. At the same time, the safety distance model is established as the basis for scene conversion. Taking the predicted trajectory of the cut-in vehicle as a reference, the model predictive control (MPC) method is used to plan and control the driving trajectory of the subject vehicle, so as to realize the coordinated control of the subject vehicle and the cut-in vehicle. Finally, the simulation shows that the subject vehicle can effectively recognize the cut-in intention of the adjacent vehicle and predict its trajectory. Facing with the cut-in vehicle, the subject vehicle can take appropriate control actions in advance to ensure the safety. Finally, a smoother coordinate control process is obtained between the subject vehicle and the cut-in vehicle.
MBMQA: A Multicriteria-Aware Routing Approach for the IoT 5G Network Based on D2D Communication
Authors: Tilwari Valmik , Hindia MHD , Dimyati Kaharudin , Jayakody Dushantha , Solanki Sourabh , Sinha Rashmi , Hanafi Effariza
Published: November 2021

With the rapid development of future wireless networks, device-to-device (D2D) technology is widely used as the communication system in the Internet of Things (IoT) fifth generation (5G) network. The IoT 5G network based on D2D communication technology provides pervasive intelligent applications. However, to realize this reliable technology, several issues need to be critically addressed. Firstly, the device’s energy is constrained during its vital operations due to limited battery power; thereby, the connectivity will suffer from link failures when the device’s energy is exhausted. Similarly, the device’s mobility alters the network topology in an arbitrary manner, which affects the stability of established routes. Meanwhile, traffic congestion occurs in the network due to the backlog packet in the queue of devices. This paper presents a Mobility, Battery, and Queue length Multipath-Aware (MBMQA) routing scheme for the IoT 5G network based on D2D communication to cope with these key challenges. The back-pressure algorithm strategy is employed to divert packet flow and illuminate the device selection’s estimated value. Furthermore, a Multiple-Attributes Route Selection (MARS) metric is applied for the optimal route selection with load balancing in the D2D-based IoT 5G network. Overall, the obtained simulation results demonstrate that the proposed MBMQA routing scheme significantly improves the network performance and quality of service (QoS) as compared with the other existing routing schemes.
Reinforcement Learning Aided UAV Base Station Location Optimization for Rate Maximization
Authors: Gopi Sudheesh Puthenveettil , Magarini Maurizio
Published: November 2021

The application of unmanned aerial vehicles (UAV) as base station (BS) is gaining popularity. In this paper, we consider maximization of the overall data rate by intelligent deployment of UAV BS in the downlink of a cellular system. We investigate a reinforcement learning (RL)-aided approach to optimize the position of flying BSs mounted on board UAVs to support a macro BS (MBS). We propose an algorithm to avoid collision between multiple UAVs undergoing exploratory movements and to restrict UAV BSs movement within a predefined area. Q-learning technique is used to optimize UAV BS position, where the reward is equal to sum of user equipment (UE) data rates. We consider a framework where the UAV BSs carry out exploratory movements in the beginning and exploitary movements in later stages to maximize the overall data rate. Our results show that a cellular system with three UAV BSs and one MBS serving 72 UE reaches 69.2% of the best possible data rate, which is identified by brute force search. Finally, the RL algorithm is compared with a K-means algorithm to study the need of accurate UE locations. Our results show that the RL algorithm outperforms the K-means clustering algorithm when the measure of imperfection is higher. The proposed algorithm can be made use of by a practical MBS–UAV BSs–UEs system to provide protection to UAV BSs while maximizing data rate.
Optimal Load Determination of Capacitor–Inductor Compensated Capacitive Power Transfer System with Curved-Edge Shielding Layer
Authors: Zhang Kehan , Du He , Luo Bo , Mai Ruikun , Song Baowei , Hu Aiguo Patrick
Published: November 2021

Due to the natural low permittivity in vacuum, the voltage stresses on compensation capacitors and inductances in the capacitive power transfer (CPT) system are very high, which brings challenges to the design of CPT systems in practical applications. This paper used a three-cell structure analysis method for the CPT system to determine the optimal load for achieving the maximum power transfer or maximum efficiency transfer, through considering the maximum withstand voltage of the capacitor or inductor. A shielding layer with edge bending is designed to reduce the range of dangerous areas markedly. The simulation and experimental results verified the above conclusion. The prototype of the CPT system with transfer 3.1 kW across a 13 cm air gap and DC-DC transfer efficiency of 91.4% is built.
Optimizing Prediction of YouTube Video Popularity Using XGBoost
Authors: Nisa Meher UN , Mahmood Danish , Ahmed Ghufran , Khan Suleman , Mohammed Mazin Abed , Damaševičius Robertas
Published: November 2021

YouTube is a source of income for many people, and therefore a video’s popularity ultimately becomes the top priority for sustaining a steady income, provided that the popularity of videos remains the highest. Analysts and researchers use different algorithms and models to predict the maximum viewership of popular videos. This study predicts the popularity of such videos using the XGBoost model, considering features selection, fusion, min-max normalization and some precision parameters such as gamma, eta, learning_rate etc. The XGBoost gives 86% accuracy and 64% precision. Moreover, the Tuned XGboost also shows enhanced accuracy and precision. We have also analyzed the classification of unpopular videos for a comparison with our results. Finally, cross-validation methods are also used to evaluate certain combination of parameter’s values to validate our claims. Based on the obtained results, it can be said that our proposed models and techniques are very useful and can precisely and accurately predict the popularity of YouTube videos.
An Electrochemical Platform for the Carbon Dioxide Capture and Conversion to Syngas
Authors: Mezza Alessio , Pettigiani Angelo , Monti Nicolò B. D. , Bocchini Sergio , Farkhondehfal M. Amin , Zeng Juqin , Chiodoni Angelica , Pirri Candido F. , Sacco Adriano
Published: November 2021

We report on a simple electrochemical system able to capture gaseous carbon dioxide from a gas mixture and convert it into syngas. The capture/release module is implemented via regeneration of NaOH and acidification of NaHCO3 inside a four-chamber electrochemical flow cell employing Pt foils as catalysts, while the conversion is carried out by a coupled reactor that performs electrochemical reduction of carbon dioxide using ZnO as a catalyst and KHCO3 as an electrolyte. The capture module is optimized such that, powered by a current density of 100 mA/cm2, from a mixture of the CO2–N2 gas stream, a pure and stable CO2 outlet flow of 4–5 mL/min is obtained. The conversion module is able to convert the carbon dioxide into a mixture of gaseous CO and H2 (syngas) with a selectivity for the carbon monoxide of 56%. This represents the first all-electrochemical system for carbon dioxide capture and conversion.
Wind Tunnel Experiments on Interaction between Two Closely Spaced Vertical-Axis Wind Turbines in Side-by-Side Arrangement
Authors: Jodai Yoshifumi , Hara Yutaka
Published: November 2021

This study aimed to determine the optimal rotor spacing of two vertical-axis wind turbines, which are simulated by miniature models arranged side-by-side with a relatively low aspect ratio. Wind tunnel experiments with a pair of 3-D printed model rotors were conducted at a uniform velocity. A series of experiments were conducted involving both incremental adjustments to the rotor gaps, g, and the rotational direction of each rotor. Increases in the power and the related flow patterns were observed in all three arrangements: Co-Rotating (CO), Counter-Up (CU), and Counter-Down (CD). The maximum phase-synchronized rotational speed occurs at the narrowest gap in the CD arrangement. Meanwhile, local maxima arise in the CO and CU arrangements at g/D < 1, where D is the rotor diameter. From an engineering perspective, the optimal rotor spacing is g/D = 0.2 with the CO arrangement, using the same two rotors rotating in the same direction. Based on flow visualization using a smoke-wire method at a narrower gap opening of 0.2D, the wake width in the case of the CU arrangement was remarkably narrower than those obtained in the CO and CD arrangements. In the CU arrangement, a movement towards the center of the rotor pair of the nominal front-stagnation point of each rotor was confirmed via flow visualization. This finding explains a reduction tendency in the rotational speed of the rotors via a reduction in the lift in the CU arrangement.
A Technical Analysis of Solid Recovered Fuel from Torrefied Jatropha Seed Residue via a Two-Stage Mechanical Screw Press and Solvent Extraction Process
Authors: Yuan Min-Hao , Chang Chia-Chi , Hsu Tsung-Chi , Shie Je-Lueng , Chen Yi-Hung , Chang Ching-Yuan , Lin Cheng-Fang , Yu Chang-Ping , Wu Chao-Hsiung , Do Manh Van , Lin Far-Ching , Lee Duu-Jong , Liu Bo-Liang , Chen Yen-Hau , Huang Michael
Published: November 2021

This study investigated the torrefaction of de-oiled Jatropha seed residue after a two-stage sequential process consisting of mechanical screw pressing and solvent extraction using n-hexane (denoted as JMS). The optimal torrefaction temperature (Tr) and torrefaction time (tr) were determined in the ranges of 260–300 °C and 10–60 min, respectively, so to achieve a better heating value and satisfactory energy densification (ED) with acceptable mass loss. Thermogravimetric analysis was employed to elucidate the thermal decomposition behaviors of JMS. By comparison with the torrefaction of Jatropha seed residue after mechanical oil extraction by screw pressing only (namely, JMET), the results indicated that the ED of the torrefaction of JMS yielding the torrefied product JMST (two-stage product) was higher than that of the torrefaction of JME giving the torrefied product JMET (single-stage product). Further, it was found that JMET contained some tar, which was attributed to a thermal reaction in the residual oil in JME during torrefaction. The tar/oil content of JMET was about 1.0–1.8 wt.% in the determined optimal conditions. Thus, the enhanced recovery of the residual oil is advantageous not only because it allows obtaining more oil from Jatropha seed residue with a positive net energy gain but also because it prevents the formation of tar in torrefied biomass products.
Novel Method of the Seal Aerodynamic Design to Reduce Leakage by Matching the Seal Geometry to Flow Conditions
Authors: Joachimiak Damian
Published: November 2021

This paper presents a novel method of labyrinth seals design. This method is based on CFD calculations and consists in the analysis of the phenomenon of gas kinetic energy carry-over in the seal chambers between clearances. The design method is presented in two variants. The first variant is designed for seals for which it is impossible to change their external dimensions (length and height). The second variant enables designing the seal geometry without changing the seal length and with a slight change of the seal height. Apart from the optimal distribution of teeth, this variant provides for adjusting chambers geometry to flow conditions. As the result of using both variants such design of the seal geometry with respect to leakage is obtained which enables achieving kinetic energy dissipation as uniform as possible in each chamber of the seal. The method was developed based on numerical calculations and the analysis of the flow phenomena. Calculation examples included in this paper show that the obtained reduction of leakage for the first variant ranges from 3.4% to 15.5%, when compared with the initial geometry. The relation between the number of seal teeth and the leakage rate is also analyzed here. The second variant allows for reduction of leakage rate by 15.4%, when compared with the geometry with the same number of teeth. It is shown that the newly designed geometry reveals almost stable relative reduction of leakage rate irrespective of the pressure ratio upstream and downstream the seal. The efficiency of the used method is proved for various heights of the seal clearance.
A Case Study on the Closed-Type Barrier Effect on Debris Flows at Mt. Woomyeon, Korea in 2011 via a Numerical Approach
Authors: Choi Shin-Kyu , Kwon Tae-Hyuk
Published: November 2021

Debris flows are capable of flowing with high velocities and causing significant economic and infrastructural damage. As a hazard mitigation measure, physical barriers are frequently installed to dissipate the energy of debris flows. However, there is a lack of understanding on how barriers affect and interact with debris-flow behavior (e.g., velocity and volume). This study investigated the changes in debris-flow characteristics depending on the installation location of barriers. Mt. Woomyeon, which is located in Seoul, Korea, was the site of a major debris-flow event in 2011. This study modeled this event using DAN3D, numerical software based on smoothed particle hydrodynamics (SPH). Our numerical approach assessed changes in debris-flow behavior, including velocity and volume, as the debris flow interacts with four closed-type barriers installed at separate points along the flow path. We used DAN3D to model the barriers via terrain elevation modifications. The presence of a closed-type barrier results in the reduction in the debris-flow velocity and volume compared to when no barrier is present. Most notably, the closer a barrier is installed to the debris source, the greater the velocity decrease. By contrast, a barrier that is constructed further downstream allows the debris flow to undergo entrainment-driven growth before confronting the barrier, resulting in a larger debris deposition volume that can often cause overflow, as shown at our particular study site. The presented results highlight the effectiveness of barriers as a method of hazard mitigation by providing insight into how such installations can alter debris-flow behavior. In addition, the findings can provide a reference for future debris-flow barrier designs, increasing the effectiveness and efficiency of such barrier systems.
Wireless Rechargeable Sensor Networks
Authors: Yu Chang-Wu
Published: November 2021

Wireless sensor networks have attracted much attention recently due to their various applications in many fields [...]
Analyses and Comparisons of Generic and User Writing Models of HVDC System Considering Transient DC Current and Voltages
Authors: Kwon Dohoon , Kim Youngjin
Published: November 2021

Analyses and comparisons of generic models and a novel modeling method of line-commutated converter (LCC)-based high voltage direct current (HVDC) systems are presented for the power system simulator for engineering (PSS/E) simulation tool. The main purposes are to describe the dynamics of the DC voltage and current of HVDC system and to improve estimation of the maximum values using the generic and proposed HVDC models when AC line-to-ground faults are occurred. For the generic HVDC models, the characteristics and limitations as well as parameter investigations are conducted. Three modules are also are developed for the proposed HVDC model, which are composed of (a) AC/DC conversion; (b) controller selection; and (c) DC line model. Case studies were conducted considering the real-operated HVDC system in Korea under the PSS/E and power systems computer aided design (PSCAD) simulation environments. The simulation results were compared with measured data from the real-operated HVDC system and the results from the PSCAD HVDC models considering single- and three-phase line-to-ground faults. The case study results show that a specific generic HVDC model in PSS/E can simulate the dynamics of the DC voltage and current after the AC line-to-ground faults. The case studies also showed that the proposed modeling method is effectively improves the estimation of the maximum DC voltage and current values for the AC line-to-ground faults.
Behavioral Modeling Paradigm for DC Nanogrid Based Distributed Energy Systems
Authors: Saad Muhammad , Ju Yongfeng , Ali Husan , Jan Sami Ullah , Awan Dawar , Khan Shahbaz , Wadood Abdul , Khan Bakht Muhammad , Ali Akhtar , Khurshaid Tahir
Published: November 2021

The remarkable progress of power electronic converters (PEC) technology has led to their increased penetration in distributed energy systems (DES). Particularly, the direct current (dc) nanogrid-based DES embody a variety of sources and loads, connected through a central dc bus. Therefore, PECs are required to be employed as an interface. It would facilitate incorporation of the renewable energy sources and battery storage system into dc nanogrids. However, it is more challenging as the integration of multiple modules may cause instabilities in the overall system dynamics. Future dc nanogrids are envisioned to deploy ready-to-use commercial PEC, for which designers have no insight into their dynamic behavior. Furthermore, the high variability of the operating conditions constitute a new paradigm in dc nanogrids. It exacerbates the dynamic analysis using traditional techniques. Therefore, the current work proposes behavioral modeling to perform system level analysis of a dc nanogrid-based DES. It relies only on the data acquired via measurements performed at the input–output terminals only. To verify the accuracy of the model, large signal disturbances are applied. The matching of results for the switch model and its behavioral model verifies the effectiveness of the proposed model.
Geological Controls on Mineralogical Characteristic Differences of Coals from the Main Coal Fields in Shaanxi, North China
Authors: Yuan Wei , Li Jing , Zhuang Xinguo , Yang Guanghua , Pan Lei
Published: November 2021

Shaanxi is among the provinces with abundant coal resources in North China. These enormous coal resources (approx. 4143 Gt) are widely distributed in the Ordos Basin and its marginal fold belts. The main coal-bearing strata consist of the late Carboniferous Taiyuan Formation, the early Permain Shanxi Formation, the late Triassic Wayaobao Formation, and the middle Jurassic Yan’an Formation, which were respectively deposited in coastal plains and a lagoon environment, a continental environment, an inland open lake and a confined lake environment. The Permo-Carboniferous coals are low volatile bituminous and characterized by relatively high vitrinite content, which decreases from south to north, and from the lower coal seams upwards. By contrast, the late Triassic and middle Jurassic coals are highly volatile bituminous, but are respectively characterized by relatively high vitrinite and high inertinite content. Minerals in the Permo-Carboniferous coals, the late Triassic coals, and the middle Jurassic coals, are respectively dominated by kaolinite and calcite, quartz and kaolinite, and quartz and calcite. Furthermore, contemporary coals deposited in different coal fields or even different mines of the same coal field present different mineral characteristics. The Permain Shanxi Formation coals from the Shanbei C-P coalfield in the north of Shaanxi Province are characterized by higher kaolinite and lower carbonate contents compared to those from the Weibei C-P coalfield in the south of Shaanxi Province. The distinctive mineralogical characteristics of coals formed in different coalfields and different geological ages were ascribed to integrated influences of different terrigenous detrital input from sediment provenance, sedimentary settings (e.g., subsidence rate, sea transgression, and regression process), and hydrothermal activities.
CFD Analysis of the Influence of the Front Wing Setup on a Time Attack Sports Car’s Aerodynamics
Authors: Szudarek Maciej , Piechna Janusz
Published: November 2021

In time attack races, aerodynamics plays a vital role in achieving short track times. These races are characterized by frequent braking and acceleration supported by aerodynamic downforce. Usually, typical cars are modified for these races by amateurs. Adjusting the aerodynamic solutions to work with bodies developed for other flow conditions is difficult. This paper presents the results of a numerical analysis of the effects of installing a straight wing in front of or above the body on the modified vehicle system’s aerodynamic characteristics, particularly on the front wheels’ aerodynamic downforce values. The paper presents the methodology and results of calculations of the aerodynamic characteristics of a car with an additional wing placed in various positions in relation to the body. The numerical results are presented (Cd, Cl, Cm, Clf, Clr), as well as exemplary pressure distributions, pathlines, and visualizations of vortex structures. Strong interactions between the wing operation and body streamline structure are shown. An interesting and unexpected result of the analysis is that the possibility of obtaining aerodynamic downforce of the front wheels is identified, without an increase in aerodynamic drag, by means of a wing placed in a proper position in front of the body. A successful attempt to balance the additional downforce coming from the front wing on the front axle is made using a larger spoiler. However, for large angles of attack, periodically unsteady flow is captured with frequency oscillations of ca. 6–12 Hz at a car speed of 40 m/s, which may interfere with the sports car’s natural suspension frequency.
Oil Pressure Monitoring for Sealing Failure Detection and Diagnosis of Power Transformer Bushing
Authors: Li Jiansheng , Li Zhi , Chen Judong , Bie Yifan , Jiang Jun , Yang Xiaoping
Published: November 2021

Power transformer bushings withstand great electrical and mechanical stress during high voltage and high current working conditions. Sealing failure poses a great threat to the long-term and reliable operation of the bushing and power transformer; however, the criterion to evaluate the sealing status of a bushing caused by mechanical problems is still lacking. In this paper, a transformer bushing model is established to gain theoretical insight into the relationship between temperature and pressure of a compact multilayer bushing. To evaluate the bushing mechanical status, different sealing conditions are tested based on the temperature and pressure monitoring within the physical 110 kV bushing. The results show that mechanical sealing failure can be diagnosed when the fluctuation of the oil pressure value exceeds the theoretical curve in steady state by 3 kPa. With different reliability coefficients, gas leakage and oil leakage are available to be further determined. The primary and auxiliary criteria based on oil pressure and its gradient are proposed to evaluate comprehensively the actual sealing condition of the bushing, and a wireless oil pressure module is developed at the bottom valve, which is quite beneficial to field online application. It is promising to extend the online mechanical monitoring and diagnosis to oil-immersed power equipment.