bluuebunny/crossref_metadata_2025 huggingface dataset:
Vibration Control of Large Wind Turbine Blades with Unidirectional Cable Pendulum Damper
Authors: Li Wanrun , Qiao Lei , Li Ganggang , Du Yongfeng
Published: October 2022

A unidirectional cable pendulum damper is proposed to mitigate vibration responses of large-scale wind turbine blades in the edgewise direction, and the effectiveness of the proposed device for vibration response control is investigated. First, the configuration and working principles of the vibration damper are introduced. Second, the Euler–Lagrange equation of the single blade vibration damping system with the vibration damper is derived based on the Hamilton’s principle. At last, considering the effect of blade rotation, the wind load that applied on the blade was calculated using the rotating sample spectrum. The dynamic responses of blades are also calculated using the MATLAB/Simulink platform. The peak value and standard deviation of blade responses were taken as evaluation indexes to evaluate the vibration control efficiency of the device with varying the parameters such as the mass ball pendulum length, the fixed pulley block installation position, the stiffness and the pre-stress of the flexible cable. The optimal parameters of the vibration damper are investigated and obtained based on the assumption that no pounding happens between the mass ball of the vibration damper and the inner wall of the blade. Results show that the length of the mass ball pendulum and the installation position of the fixed pulley block, the values of which are within a reasonable range, can ensure the optimal performance of the vibration damper. The displacement of the mass sphere can be effectively reduced by increasing the stiffness of the flexible cable, and the pre-stress shows marginal beneficial effect on the damping performance and the displacement control of the mass sphere. Using the optimum parameters, the vibration damper can keep the mass ball within a safe running distance. The reduction of the peak value and the standard deviation of the blade displacement can reach 89.36% and 89.83%, respectively. The proposed device space can effectively reduce the blade tip response of the wind turbine by occupying less space. Moreover, the size of the device installation space can be changed by adjusting the weight ball swing length, which is suitable for the vibration control of the wind turbine blade edgewise direction.
The algebraic structure of additive codes over 𝔽2𝔽8
Authors: Cheng Xiangdong
Published: October 2022

In this paper, we investigate the algebraic structure of [Formula: see text]-additive codes, where [Formula: see text] and [Formula: see text] are nonnegative integers, [Formula: see text] (respectively, [Formula: see text]) denotes the finite field of order 2 (respectively, [Formula: see text]). We first give the generator polynomials of additive cyclic codes over [Formula: see text] and then the generator polynomials of additive cyclic codes over [Formula: see text] is also given. In addition, we introduce a linear map [Formula: see text], and study its properties. What’s more, the dual of additive cyclic codes over [Formula: see text] are investigated as well. And we get that the duals of any additive cyclic codes over [Formula: see text] are also additive cyclic codes. Finally, separable [Formula: see text]-additive cyclic codes are investigated.
COVIDDCGAN: Oversampling Model Using DCGAN Network to Balance a COVID-19 Dataset
Authors: Javadi-Moghaddam Seyyed-Mohammad , Gholamalinejad Hossain , Fard Hamid Mohammadi
Published: October 2022

The COVID-19 infection was announced as a pandemic in late 2019. Due to the high speed of the spread, rapid diagnosis can prevent the virus outbreak. Detection of the virus using prominent information from CT scan images is a fast, cheap, and accessible method. However, these image datasets are imbalanced due to the nature of medical data and the lack of coronavirus images. Consequently, the conventional classification algorithms classify this data unsuitably. Oversampling technique is one of the most well-known methods that try to balance the dataset by increasing the minority class of the data. This paper presents a new oversampling model using an improved deep convolutional generative adversarial network (DCGAN) to produce samples that improve classifier performance. In previous DCGAN structures, the feature extraction took place only in the convolution layer, while in the proposed structure, it is done in both the convolution layer and the pooling layer. A Haar transform layer as the pooling layer tries to extract better features. Evaluation results on two hospital datasets express an accuracy of 95.8 and a loss criterion of 0.5354 for the suggested architecture. Moreover, compared to the standard DCGAN structure, the proposed model has superiority in all classification criteria. Therefore, the new model can assist radiologists in validating the initial screening.
Provenance Framework for Multi-Depth Querying Using Zero-Information Loss Database
Authors: Rani Asma , Goyal Navneet , Gadia Shashi K.
Published: October 2022

Data provenance is a kind of metadata that describes the origin and derivation history of data. It provides the information about various direct and indirect sources of data and different transformations applied on it. Provenance information are beneficial in determining the quality, truthfulness, and authenticity of data. It also explains how, when, why, and by whom this data are created. In a relational database, fine-grained provenance captured at different stages (i.e., multi-layer provenance) is more significant and explanatory as it provides various remarkable information such as immediate and intermediate sources and origin of data. In this paper, we propose a novel multi-layer data provenance framework for Zero-Information Loss Relational Database (ZILRDB). The proposed framework is implemented on top of the relational database using the object relational database concepts to maintain all insert, delete, and update operations efficiently. It has the capability to capture multi-layer provenance for different query sets including historical queries. We also propose Provenance Relational Algebra (PRA) as an extension of traditional relational algebra to capture the provenance for ASPJU (Aggregate, Select, Project, Join, Union) queries in relational database. The framework provides a detailed provenance analysis through multi-depth provenance querying. We store the provenance data in both relational and graph database, and further evaluate the performance of the framework in terms of provenance storage overhead and average execution time for provenance querying. We observe that the graph database offers significant performance gains over relational database for executing multi-depth queries on provenance. We present two use case studies to explain the usefulness of proposed framework in various data-driven systems to increase the understandability of system’s behavior and functionalities.
The geometric universe Sir Roger Penrose’s mathematical physics works and insights
Authors: Kouneiher Joseph , Szczeciniarz Jean-Jacques
Published: October 2022

The aim of this paper is to explore the conceptual and mathematical physics foundations of the work of Sir Roger Penrose. This is a transcript of an interview a that took place on 15 November 2021.

a The text of the interview was enriched by complements and figures to illustrate the answers and the thought of Roger Penrose. They are taken from the excellent book The Road to Reality: A Complete Guide to the Laws of the Universe, Jonathan Cape, London [4, 7] others references. With courtesy of Sir Roger Penrose.
Role of constant jerk parameter in f(R,T) gravity
Authors: Tiwari Rishi Kumar , Kumar Sushil , Dubey Vipin Kumar , Sofuoğlu Değer
Published: October 2022

In this study, a homogeneous and anisotropic Bianchi type-I universe model is considered taking the cosmological constant as a variable in the context of f (R, T) gravity. The presented model relies on a unique condition of constant jerk parameter as j = 1, which corresponds to a flat [Formula: see text]CDM model that leads to a variable deceleration parameter. The exact solution of the field equations is obtained under that condition, and the physical and geometrical features of the model are discussed through the paper. The model has been shown to be successful in explaining recent cosmological observations such as the universe’s expansion decelerates in the early universe and accelerates in the late universe, in agreement with their current data.
A Perforated High-Order Element for Fracture Mechanics Problems Using the Hybrid Strain Method
Authors: Ramezani Mohammadreza , Ghalehnovi Mansour , Rezaiee-Pajand Mohammad
Published: October 2022

In this paper, a novel hybrid approach with complex and conformal mapping functions is proposed for the first time. This strain-based approach is based on the special trial functions and the assumed natural strain (ANS) theory, which provides a unified framework for solving fracture problems. In the utilized method, the proposed super-element is divided into perforated and solid domains. For the perforated domain, Muskhelishvili–Kolosov potential function is employed to simulate the perforated discontinuity. For the solid domain, the higher-order strain field is assumed as the enriched interpolation functions. Moreover, the rigid body terms are imposed into the kinematic variables to enhance the convergence ability and the element performance. Numerical examples demonstrate that the proposed element (PHSM20) produces high precision results for both displacement and stresses.
On certain Kampé de Fériet-like hypergeometric matrix functions
Authors: Dwivedi Ravi , Sahai Vivek
Published: October 2022

In this paper, we introduce certain Kampé de Fériet-like matrix functions and investigate their regions of convergence and recurrence relations satisfied by them. The importance of studying these matrix functions stems from the fact that the derivatives of arbitrary integral order of confluent hypergeometric matrix function and Gauss hypergeometric matrix function with respect to their matrix parameters can be expressed in the form of these Kampé de Fériet-like matrix functions. Integral representations of these matrix functions in a special case are also given.
Unveiling Phase Transitions in 1D Systems with Short-Range Interactions
Authors: Ferreira L. S. , Jorge L. N. , DaSilva Cláudio J. , Neto Minos A. , Caparica A. A.
Published: October 2022

The statement that any phase transition is related to the appearance or disappearance of long-range spatial correlations and precludes a finite transition temperature in one-dimensional (1D) systems is rooted in condensed matter theory. In this paper, we demonstrate that the 1D Ising model with short-range exchange interactions exhibits a second-order phase transition at a finite temperature relying on the proper choice of the order parameter. We combined analytical calculations and high-precision entropic sampling simulations and chose a slightly different order parameter, namely, the modulus of the magnetization. Notably, we detected a phase transition with a corresponding critical temperature around 15 K, which is in excellent agreement with experimental results. Our study indicates that the incorrect choice of the order parameter may mask phase transitions in 1D systems.
Designing Superalkali Metals-Doped Sumanene-Based Highly Efficient Nonlinear Optical Materials for Cutting-Edge Optoelectronic Applications
Authors: Asif Azka , Mustafa Ghulam , Yaqoob Junaid , Khan Muhammad Usman , Assiri Mohammed A. , Imran Muhammad
Published: October 2022

Nonlinear optical (NLO) materials have attracted bounteous scientific attention in the modern era because of their optoelectronic and biological applications. In this respect, an attempt is made to present thermodynamically stable superalkali metals (Li3N, Li3O, Li3S and Li3F)-doped sumanene (C[Formula: see text]H[Formula: see text])-based complexes with fine NLO response properties. Nine isomers (I–III of Li3N@Sumanene, I–II of Li3O@Sumanene, I–II of Li3S@Sumanene and I–II of Li3F@Sumanene) are proposed, and their geometric, thermodynamic, electronic and NLO properties are explored by using density functional theory (DFT) calculations. Computational results reveal that the [Formula: see text] gap is reduced up to 0.56[Formula: see text]eV for doped complexes. The maximum hyperpolarizability response is calculated [Formula: see text][Formula: see text]a.u. for isomer II of the Li3F@Sumanene series. The participation of distinct fragments, type of interaction, and charge transfer are computed by the corresponding TDOS and PDOS, NCI and NBO analysis. For UV–Vis analysis and crucial excitation state, TD-DFT calculations are carried out, which exhibits that all doped complexes are transparent in the UV region. NCI analysis confirmed the Van-der Waals interactions as an important mode of adsorption between superalkalis and sumanene. This report provides an efficient superalkali doping technique for creating highly effective future NLO systems and recommends superalkali-doped sumanene systems as ideal NLO prospects for future NLO applications.
Accelerating Graph Computations on 3D NoC-Enabled PIM Architectures
Authors: Choudhury Dwaipayan , Xiang Lizhi , Rajam Aravind , Kalyanaraman Anantharaman , Pande Partha Pratim
Published: October 2022

Graph application workloads are dominated by random memory accesses with the poor locality. To tackle the irregular and sparse nature of computation, ReRAM-based Processing-in-Memory (PIM) architectures have been proposed recently. Most of these ReRAM architecture designs have focused on mapping graph computations into a set of multiply-and-accumulate (MAC) operations. ReRAMs also offer a key advantage in reducing memory latency between cores and memory by allowing for PIM. However, when implemented on a ReRAM-based manycore architecture, graph applications still pose two key challenges—significant storage requirements (particularly due to wasted zero cell storage), and significant amount of on-chip traffic. To tackle these two challenges, in this article, we propose the design of a 3D NoC-enabled ReRAM-based manycore architecture. Our proposed architecture incorporates a novel crossbar-aware node reordering to reduce ReRAM storage requirements. Secondly, its 3D NoC-enabled design reduces on-chip communication latency. Our architecture outperforms the state-of-the-art in ReRAM-based graph acceleration by up to 5× in performance while consuming up to 10.3× less energy for a range of graph inputs and workloads.
Availability-aware Provision of Service Function Chains in Mobile Edge Computing
Authors: Li Jinxi , Guo Deke , Xie Junjie , Chen Sheng
Published: October 2022

With the advent of Network Function Virtualization (NFV) and Mobile Edge Computing (MEC), outsourcing network functions (NFs) to the MEC is becoming popular among network service providers (NSPs), since it brings the scalability and flexibility for NF deployment and maintenance. Each user’s request will go through a service function chain (SFC), which consists of several virtual network functions (VNFs, software substitutions for traditional hardware-based middleboxes) in a specific order, and then get a response. Unlike conventional hardware-based middleboxes, VNFs are not very reliable due to potential software faults and host malfunctions. Thus, a sensible way is to add redundancy for the primary VNFs of an SFC to enhance its availability. Nevertheless, which MEC node to place each VNF, and how many backup instances are enough to ensure the availability requirement of each SFC? These issues have not yet been resolved.

In this article, we present the availability-aware provision of SFC (APoS) in the MEC environment with the primary goal of maximizing the number of served requests while meeting the requirements and reliability expectations of SFCs. For the APoS, we have primarily addressed the following two fundamental challenges: (i) First, how to efficiently map these primary and backup VNFs to meet the availability requirements of SFCs? At this point, we formulate it as an integer nonlinear programming (INLP) under the limitation of each MEC node’s resources. This issue is NP-hard, and a novel binary N-back search method is proposed to derive the optimal solution for the primary and backup VNFs mapping; (ii) Second, how can we reduce the latency for users to access their desired SFCs? Then, we investigate how to minimize the average delay for all requests in each time slot. To solve this problem, we design an online service switching (OSS) method, which jointly considers the queuing delay, communication delay, and switching delay. It achieves the optimal solution with a theoretical guarantee. Finally, we evaluate the proposed methods with real-world datasets. The results demonstrate that, compared with the benchmarks, our practices can achieve approximately 20% request acceptance gain and up to 30% delay reduction, on average.
The Side-channel Metrics Cheat Sheet
Authors: Papagiannopoulos Kostas , Glamočanin Ognjen , Azouaoui Melissa , Ros Dorian , Regazzoni Francesco , Stojilović Mirjana
Published: October 2022

Side-channel attacks exploit a physical observable originating from a cryptographic device in order to extract its secrets. Many practically relevant advances in the field of side-channel analysis relate to security evaluations of cryptographic functions and devices. Accordingly, many metrics have been adopted or defined to express and quantify side-channel security. These metrics can relate to one another, but also conflict in terms of effectiveness, assumptions, and security goals. In this work, we review the most commonly used metrics in the field of side-channel analysis. We provide a self-contained presentation of each metric, along with a discussion of its limitations. We practically demonstrate the metrics on examples of relevant implementations of the Advanced Encryption Standard (AES), and make the software implementation of the presented metrics available to the community as open source. This work, being beyond a survey of the current status of metrics, will allow researchers and practitioners to produce a well-informed security evaluation through a better understanding of its supporting and summarizing metrics.
Facilitating Serverless Match-based Online Games with Novel Blockchain Technologies
Authors: Wu Feijie , Yuen Ho Yin , Chan Henry , Leung Victor C. M. , Cai Wei
Published: October 2022

Applying
peer-to-peer (P2P)
architecture to online video games has already attracted both academic and industrial interests, since it removes the need for expensive server maintenance. However, there are two major issues preventing the use of a P2P architecture, namely how to provide an effective distributed data storage solution, and how to tackle potential cheating behaviors. Inspired by emerging blockchain techniques, we propose a novel consensus model called
Proof-of-Play (PoP)
to provide a decentralized data storage system that incorporates an anti-cheating mechanism for P2P games, by rewarding players that interact with the game as intended, along with consideration of security measures to address the Nothing-at-stake Problem and the Long-range Attack. To validate our design, we utilize a game-theory model to show that under certain assumptions, the integrity of the PoP system would not be undermined due to the best interests of any user. Then, as a proof-of-concept, we developed a P2P game (
Infinity Battle
) to demonstrate how a game can be integrated with PoP in practice. Finally, experiments were conducted to study PoP in comparison with
Proof-of-Work (PoW)
to show its advantages in various aspects.
A Content-adaptive Visibility Predictor for Perceptually Optimized Image Blending
Authors: Fukiage Taiki , Oishi Takeshi
Published: October 2022

The visibility of an image semi-transparently overlaid on another image varies significantly, depending on the content of the images. This makes it difficult to maintain the desired visibility level when the image content changes. To tackle this problem, we developed a perceptual model to predict the visibility of the blended results of arbitrarily combined images. Conventional visibility models cannot reflect the dependence of the suprathreshold visibility of the blended images on the appearance of the pre-blended image content. Therefore, we have proposed a visibility model with a content-adaptive feature aggregation mechanism, which integrates the visibility for each image feature (i.e., such as spatial frequency and colors) after applying weights that are adaptively determined according to the appearance of the input image. We conducted a large-scale psychophysical experiment to develop the visibility predictor model. Ablation studies revealed the importance of the adaptive weighting mechanism in accurately predicting the visibility of blended images. We have also proposed a technique for optimizing the image opacity such that users can set the visibility of the target image to an arbitrary level. Our evaluation revealed that the proposed perceptually optimized image blending was effective under practical conditions.
Two-round Multiparty Secure Computation from Minimal Assumptions
Authors: Garg Sanjam , Srinivasan Akshayaram
Published: October 2022

We provide new two-round multiparty secure computation (MPC) protocols in the dishonest majority setting assuming the minimal assumption that two-round oblivious transfer (OT) exists. If the assumed two-round OT protocol is secure against semi-honest adversaries (in the plain model) then so is our two-round MPC protocol. Similarly, if the assumed two-round OT protocol is secure against malicious adversaries (in the common random/reference string model) then so is our two-round MPC protocol. Previously, two-round MPC protocols were only known under relatively stronger computational assumptions.
A Data-Driven Analysis of Behaviors in Data Curation Processes
Authors: Han Lei , Chen Tianwa , Demartini Gianluca , Indulska Marta , Sadiq Shazia
Published: October 2022

Understanding how data workers interact with data, and various pieces of information related to data preparation, is key to designing systems that can better support them in exploring datasets. To date, however, there is a paucity of research studying the strategies adopted by data workers as they carry out data preparation activities. In this work, we investigate a specific data preparation activity, namely
data quality discovery
, and aim to (i) understand the behaviors of data workers in discovering data quality issues, (ii) explore what factors (e.g., prior experience) can affect their behaviors, as well as (iii) understand how these behavioral observations relate to their performance. To this end, we collect a multi-modal dataset through a data-driven experiment that relies on the use of eye-tracking technology with a purpose-designed platform built on top of iPython Notebook. The experiment results reveal that: (i) ‘copy–paste–modify’ is a typical strategy for writing code to complete tasks; (ii) proficiency in writing code has a significant impact on the quality of task performance, while perceived difficulty and efficacy can influence task completion patterns; and (iii) searching in external resources is a prevalent action that can be leveraged to achieve better performance. Furthermore, our experiment indicates that providing sample code within the system can help data workers get started with their task, and surfacing underlying data is an effective way to support exploration. By investigating data worker behaviors prior to each search action, we also find that the most common reasons that trigger external search actions are the need to seek assistance in writing or debugging code and to search for relevant code to reuse. Based on our experiment results, we showcase a systematic approach to select from the top best code snippets created by data workers and assemble them to achieve better performance than the best individual performer in the dataset. By doing so, our findings not only provide insights into patterns of interactions with various system components and information resources when performing data curation tasks, but also build effective and efficient data curation processes through data workers’ collective intelligence.
Environmental Linguistics
Authors: Harrison K. David
Published: October 2022

Environmental linguistics is an emerging field at the intersection of linguistics and natural sciences. It recognizes the mutual relationship between cultural and ecological diversity, documenting linguistic structures and verbal practices by which speakers conceptualize, encode, and transmit knowledge about the natural world. It surpasses the largely metaphorical and narrative program of ecolinguistics to position language as the preeminent conceptual framework and channel for environmental knowledge. Natural phenomena—as Indigenous experts explain—cannot be understood apart from the languages that encode them, and vice versa. Language diversity is thus the key to safeguarding biodiversity and a balanced human relationship with nature. Environmental linguistics helps decolonize linguistics as our field evolves to prioritize knowledge coproduction over data extraction. Examples from my fieldwork in Tuva cover six domains of knowledge: landscapes, lifeforms, time, sound, memory, and survival. This article reviews recent literature from many cultures, emphasizing works by Indigenous authors.
Health Misinformation Exposure and Health Disparities: Observations and Opportunities
Authors: Southwell Brian G. , Otero Machuca Jessica , Cherry Sabrina T. , Burnside Melissa , Barrett Nadine J.
Published: October 2022

The concepts of health misinformation and health disparities have been prominent in public health literature in recent years, in part because of the threat that each notion poses to public health. How exactly are misinformation proliferation and health disparities related, however? What roles might misinformation play in explaining the health disparities that we have documented in the United States and elsewhere? How might we mitigate the effects of misinformation exposure among people facing relatively poor health outcomes? In this review, we address such questions by first defining health disparities and misinformation as concepts and then considering how misinformation exposure might theoretically affect health decision-making and account for disparate health behavior and health outcomes. We alsoassess the potential for misinformation-focused interventions to address health disparities based on available literature and call for future research to address gaps in our current evidence base.
Influence of sex and age on the relationship between aerobic fitness and muscle sympathetic nerve activity in healthy adults
Authors: Badrov Mark B. , Keir Daniel A. , Notarius Catherine F. , O’Donnell Emma , Millar Philip J. , Kimmerly Derek S. , Shoemaker J. Kevin , Floras John S.
Published: October 2022

Our data reveal for the first time that associations between aerobic fitness and resting muscle sympathetic nerve activity are sex and age specific; inverse relationships are evident in younger males (<50 yr) and older females (≥50 yr), but absent in younger females (<50 yr) and older males (≥50 yr).
Noninvasive electrophysiological differences between women and men: differences in body size not an explanation
Authors: Bergfeldt Lennart , Noor Baloch Adnan , Lundahl Gunilla , Gransberg Lennart , Bergström Göran
Published: October 2022

We sought explanations for sex-related differences in >20 cardiac electrophysiology parameters including conventional conduction intervals in 319 (158 women) apparently healthy 50- to 64-yr-old subjects using Frank vectorcardiography, a novelty. Our hypothesis that body size was partly explanatory for such differences had to be refuted apart from QRS duration and QRSarea. Scaling (indexing) electrophysiological parameters for body size is therefore not an option and explanations for electrophysiological sex-related differences are to be sought elsewhere.
Application of Industrial Internet of Things (IIoT) in Crude Oil Production Optimization Using Pump Efficiency Control
Authors: Allahloh Ali S. , Sarfraz Mohammad , Alqahtani Mejdal , Ahmad Shafiq , Huda Shamsul
Published: October 2022

The collapse of oil prices in mid-2014 and early 2016 was the biggest in modern history that witnessed more than a 70% drop in the price to around $40 barrel. It prompted companies to think seriously to maintain profitability. Most companies were able to survive partly by simplifying their operations. Price recovery in 2021 is only 70% of its peak value. Companies are focusing on reducing the cost of operations, increasing production simultaneously, and finding new and different strategies to survive. The current scenario is witnessing strong research focusing on the development of process control for oil and gas upstream and downstream to improve the control and preventive maintenance to reduce operating costs and increase production. This paper presents the Industrial Internet of Things (IIoT) practical solution that improves the oil production rate from the well and increases the average pump efficiency (fillage) to 90%. This paper proposes a mechanism for collecting, storing, and analyzing all required parameters to build valuable charts. These charts’ data help optimize the values and parameters for controller setpoints to prevent pump gas lock problems. An artificial lift is required to lift the oil from the well. In this paper, the sucker rod pump is driven by a gas engine fed by the well’s gas. At the same time, SCADAPack 535E remote terminal unit collects all pump and well parameters such as hydraulic pressure, casing pressure, tubing pressure, and pump speed in stroke per minute (SPM). The remote terminal unit sends the data through a wireless network using a 5 GHz antenna to the main control room. The IIoT platform is designed using a visual basic programming language. Microsoft DDE (Dynamic Data Exchange) and Kepware OPC server were used to work on the received data to monitor, generate charts, and apply the controllers.
[Retracted] Clinical Characteristics and Risk Factors among Patients with Positive COVID‐19 Test Admitted to ICU
Authors: Alanazi Asma , Alturki Abdulaziz , Alosaimi Mohammed , Alnajmi Nawaf , Alrajeh Mohammed , Kentab Abdullah , Habib Farida
Published: October 2022

Background. Studies that show common characteristics among ICU‐admitted patients due to COVID‐19 are available on the net, but such studies in Saudi Arabia are limited. Methods. A descriptive cross‐sectional study establishing common comorbidities and risk factors among critically ill patients who tested positive for COVID‐19 at the National Guard Hospital from March 2, 2020, to March 20, 2021. The data were obtained from the BEST Care System of King Abdulaziz Medical City, computed, and analyzed using SPSS. Results. Three hundred eighty‐five COVID‐19 patients admitted to the intensive care unit (ICU) were included in this study. The mean age was 60.85 ± 20.46, 60.85% were males, and 39.2% were females. There was statistically significant positive relationship between severity of the symptoms and age (P = 0.002). The mean duration of hospital stay in the sample was 21.85 ± 28.47. More than one‐third (37.4%) of cases admitted to the hospital died while about two‐thirds of the cases were discharged after complete recovery. Two hundred ninety (75.3%) of the patients who were admitted to the National Guard Health Affairs (Riyadh, Saudi Arabia) had respiratory disease. Two hundred twelve patients (55.1%) had diabetes mellitus, while the number of hypertensive patients was 203 (52.7%). There was a significant positive relation among patients with gastrointestinal tract infection (GIT) risk factors and the severity of the symptoms of COVID‐19 (P = 0.000). In addition, there was a strong significant relation between hypertension patients and the severity of the COVID‐19 symptoms (P = 0.017). Conclusion. COVID‐19 patients who have GIT and hypertension have been found to be at an increased risk of COVID‐19 symptom severity. Old age was also found to have an increased risk for COVID‐19 symptom severity.
Performance Art Video Action Management Oriented to 6G Wireless Transmission Technology
Authors: Wang Yong , Cho Nam-gyu
Published: October 2022

With the development of 5G communication networks, the development prospects, capability requirements, and key technologies of the next-generation communication system 6G have become new hotspots. This article expands the coverage of the communication system through 6G, realizes the wireless transmission technology, builds the ideal of an intelligent mobile society everywhere, and analyzes the development trend of 6G technology, the challenges it faces, and the possible wireless transmission technology. Wireless transmission technology is widely used in various fields of production and life. Its various technical requirements and terminal energy limitations restrict the application and development of communications. This article focuses on the high-efficiency optimization scheme that combines 6G wireless transmission technology to improve the utilization efficiency of network resources. In addition, this thesis also discusses video action management in public performance art. Based on public performance art, the research on video action recognition can be summarized into three levels from simple to complex according to the content of the action, namely mobile vision, action vision, and action vision. Video action vision is currently being studied. In the recognition process, most of the action frame information in the video is repetitive or has a low correlation with action recognition, which not only increases the complexity of calculation but also affects the accuracy of action recognition. In the process of feature selection and understanding, the main methods include characterization fusion and research based on gesture features. Based on the introduction of 6G wireless transmission technology, this article introduces the technological development of 6G network, which promotes the rapid development of public performance art video action management.
Preschool Cyber Security Management System Based on Intelligent Agents
Authors: Song Jing
Published: October 2022

As information and communication technologies create an ever-increasing complexity in interconnected systems and devices, cybersecurity and privacy issues are constantly at the fore, highlighting the need to strengthen the protection and resilience of these systems against the ever-evolving threats of modern cyberspace. This particular work, taking into account that preschool children now have significant needs to ensure their digital identity and, in general, their protection from their contacts with the internet, aspires to provide an understandable and practical guide to strengthen the security of information systems and information from both public and private school agencies. Specifically, a preschool cybersecurity management system based on intelligent agents is proposed. Using sophisticated, intelligent techniques, it aims to improve the ability of preschools to resist modern threats adequately, respond to cyber-attack incidents with the least possible impact, and protect their critical systems, services offered, and the personal data they hold and process. The system intends to link and control distributed systems that currently exist, as well as to solve issues that are beyond the knowledge and skills of a single agent. This novel research idea has never been offered in the relevant literature, and we think it has the potential to advance the state of the art in cybersecurity significantly.
Study on Pore Structure and Fractal Characteristics of Tar-Rich Coal during Pyrolysis by Mercury Intrusion Porosimetry (MIP)
Authors: Wang Zhendong , Jiang Pengfei , Yang Fu , Kong Yuanyuan , Ma Li , Xie Bohan , Duan Zhonghui
Published: October 2022

This study aims to show how fractal analysis can be effectively used to characterize the pore structure of porous tar-rich coal. In this study, tar-rich coal pores were obtained by mercury intrusion porosimetry (MIP). The results showed that the sample had a high porosity and large pore diameter after pyrolysis, and the porosity of tar-rich coal was more than 35% at 600°C. The pore-throat ratio at high temperature was large, resulting in the high mercury retention rate. The pore distribution curves of samples at pyrolysis temperatures of 500 and 600°C were unimodal, and those of samples at room temperature and 150, 300, and 400°C were bimodal. The models for calculating fractal dimension based on MIP include Menger sponge model and thermodynamic model. Experiments show that the Menger sponge method is more reasonable when the pore size is less than 50 μm and greater than 350 nm. For the fractal dimension calculation of the whole pore curve section, the thermodynamic method was more reasonable. The use of fractal analysis in conjunction with the results of classical characterization methods leads to a better understanding of pore evolution in the pyrolysis process of tar-rich coal. The average fractal dimension could also be used as a valid parameter to monitor the textural evolution of the coals.
A Mathematical Programming Approach to Supply Chain Network Design considering Shareholder Value Creation
Authors: Biglar Abbas , Hamta Nima , Rad Mona Ahmadi
Published: October 2022

One of the main goals of supply chain management is to ensure proper flows of products and information through all nodes to supply them in the right place at the right time. To achieve this objective, it is very important to consider flows of products and finances among supply chain nodes. Traditionally, operational and financial processes have been optimized as separate problems. The developed model addresses the problem of designing a supply chain network and tries to integrate both areas of operations and financial aspects to maximize the value created and measured by the Shareholder Value Analysis (SVA). The results show that with appropriate financial decisions, creating more value for the company and its shareholders is achievable. The developed model with a new financial approach is able to improve the total created shareholder value as much as 0.7% larger than the SVA obtained without financial aspects and 0.93% larger than the value created by the basic model. The main reason for an increase in value creation is due to new operational and financial aspects, which mainly show the possibility of closing facilities and bank debt repayments. To validate and show the applicability of the proposed model, it was solved by GAMS‐BARON solver with data provided from the literature. Sensitivity analyses on financial parameters were performed to evaluate the results.
Upregulation of miR-664a-3p Ameliorates Calcific Aortic Valve Disease by Inhibiting the BMP2 Signaling Pathway
Authors: Jiang Yun , Ji Wei , Zhu Jiaqi , Shen Zihao , Chen Jianle
Published: October 2022

The development of calcific aortic valve disease (CAVD) is a complex process of ectopic calcification involving various factors that lead to aortic valve stenosis, hemodynamic changes, and, in severe cases, even sudden death. Currently, aortic valve replacement is the only effective method. The osteogenic differentiation of aortic valve interstitial cells (AVICs) is one of the key factors of valve calcification. Emerging evidence suggests that bone morphogenetic protein 2 (BMP2) can induce the proosteogenic activation of AVICs. However, the regulatory mechanism underlying this activation in AVICs is unclear. In the present study, we elucidated through high-throughput RNA sequencing and RT-qPCR that miR-664a-3p was evidently downregulated in the calcific aortic valve. We also proved that miR-664a-3p was involved in regulating osteogenic differentiation in AVICs. Target prediction analysis and dual-luciferase reporter gene assay confirmed that miR-664a-3p is preferentially bound to BMP2. Furthermore, the effect of the miR-664a-3p/BMP2 axis on osteogenic differentiation in AVICs was examined using the gain- and loss-of-function approach. Finally, we constructed a mouse CAVD model and verified the effect of the miR-664a-3p/BMP2 axis on the aortic valve calcification leaflets in vivo. In conclusion, miR-664a-3p regulates osteogenic differentiation in AVICs through negative regulation of BMP2, highlighting that miR-664a-3p may be a potential therapeutic target for CAVD.
Optimization and Prediction of Concrete with Recycled Coarse Aggregate and Bone China Fine Aggregate Using Response Surface Methodology
Authors: Gour Chandra Prakash , Dhurvey Priyanka , Shaik Nagaraju
Published: October 2022

Construction recycled material is crucial for protecting natural resources and promoting sustainable human development in a rapidly industrializing world. Many administrations worldwide accepted that it is beneficial to use demolition waste in the concrete building industry to reduce manufacturing costs and minimize the use of virgin aggregates. However, control measures should be done as their mechanical properties are poorer than traditional aggregates. To overcome this problem, pozzolanic materials like bone chine can be incorporated, providing extra CSH gel, which improves mechanical strength. Therefore, this research is aimed at producing eco‐friendly concrete, which can be used for medium‐grade strength, using recycled construction waste (RCA) as coarse and bone china fine aggregate (BCA) as fine aggregate. Workability, density, compressive, split tensile, and flexural strength are used to compare the fresh and hardened properties of the concrete. Experimental and statistical research is employed in the current study to evaluate the impact of RCA and BCA on the performance of concrete. To simulate all measurable responses, including workability, density, compressive, flexural, and split strength, RSM (response surface methodology) was utilized. The CCD (Central Composite Design) approach in RSM was used to create and analyze mixes in an experiment. Based on the experiment’s results, mathematical models were designed and assessed using the analysis of variance test (ANOVA). The analysis of variance results demonstrated the statistical significance of each constructed model. Three‐dimensional response surface plots created using established regression models were used to investigate the interaction between the respective variables and to optimize the mixing ratio. The results indicate that the optimum utilization of RCA is up to 40% and BCA up to 60% as coarse and fine aggregate replacement in concrete, respectively, which not only helps to reduce costs but also offers sustainability. Finally, it was concluded that the generated models might be employed by obtaining the maximum tested features of concrete to assure a quick mix design approach. To conduct the microstructure study, thin section techniques were used to observe a strong aggregate‐matrix interaction.
Reinforcement Learning Based Mobile Underwater Localization for Silent UUV in Underwater Acoustic Sensor Networks
Authors: Liao Ruiheng , Su Wei , Wu Xiurong , Cheng En
Published: October 2022

Unmanned underwater vehicles (UUVs) that are widely utilized for underwater cooperative combat, underwater environment detection and underwater resource exploration have to be localized by underwater acoustic sensor networks (UASNs). However, the localization accuracy is hard to guarantee due to the limited bandwidths, long propagation latency, and limited energy resources of the UASNs. In this paper, we propose a reinforcement learning (RL) and neural network based mobile underwater localization scheme to optimize the anchor nodes selection in the UASNs to localize the target precisely. More specifically, this scheme applies SqueezeNet to select the line-of-sight (LOS) anchor nodes based on the received signals. In addition, an RL-based approach is further proposed to make further selection from the LOS anchor nodes without knowing the underwater environment model. The Dyna architecture is applied to reduce the convergence time of the anchor nodes selection. Simulation results based on a nonisovelocity geometry-based underwater acoustic channel model show that the proposed schemes significantly improve the localization accuracy and reduce energy consumption of the UASN to achieve trajectory correction.
Recommendation Algorithm of Web-Based Learning Resources considering Timeliness and Popularity
Authors: Han Xiaolu
Published: October 2022

The optimized learner evaluation matrix and similarity model are essential methods to deal with the challenges of “data sparsity” and “cold start” in the process of learning resource recommendation on the online learning platform. Accordingly, an improved collaborative filtering algorithm (TRCP) is proposed to improve the accuracy of learning resource recommendation. The TRCP algorithm generates the learner evaluation matrix for recommended projects by classifying learning resources. It comprehensively considers the influence of learners’ online learning behavior, learning time, and popularity of learning resources on learners’ interest and optimizes the sample data in the evaluation matrix. The experimental results on the school online teaching platform verified that this method has achieved obvious and effective results in both the accuracy and satisfaction rate of learning resource recommendation.
Generalized Fubini Apostol-Type Polynomials and Probabilistic Applications
Authors: Gomaa Rabab S. , Magar Alia M.
Published: October 2022

The paper aims to introduce and investigate a new class of generalized Fubini-type polynomials. The generating functions, special cases, and properties are introduced. Using the generating functions, various interesting identities, and relations are derived. Also, special polynomials are obtained from the general class of polynomials. Finally, probabilistic applications and some probabilistic properties are obtained.
The Bifurcation Curves of a Category of Dirichlet Boundary Value Problems
Authors: Qin Huizeng , Lu Youmin
Published: October 2022

We study the Dirichlet boundary value problemu″t+λfut=0,−1<t<1,u−1=u1=0,generally and develop a schema for determining the relationship between the values of its parameters and the number of positive solutions. Then, we focus our attention on the special cases whenfu=σ−uexp−K/1+uandfu=∏i=1mai—u, respectively. We prove first that all positive solutions of the first problem are less than or equal toσ, obtain more specific lower and upper bounds for these solutions, and compute a curve in theσK-plane with accuracy up to10−6, below which the first problem has a unique positive solution and above which it has exactly three positive solutions. For the second problem, we determine its number of positive solutions and find a formula for the value ofλthat separates the regions ofλ, in which the problem has different numbers of solutions. We also computed the graphs for some special cases of the second problem, and the results are consistent with the existing results. Our code in Mathematica is available upon request.