bluuebunny/crossref_metadata_2025 huggingface dataset:
A Novel Second-Order and Unconditionally Energy Stable Numerical Scheme for Allen–Cahn Equation
Authors: Lin Shimin , Song Fangying , Sun Tao , Zhang Jun
Published: April 2022

We propose a novel time-stepping scheme for solving the Allen–Cahn equation. We first rewrite the free energy into an equivalent form and then obtain a new Allen–Cahn equation by energy variational formula of

L

2

-gradient flow. Using leapfrog formula, a new linear scheme is obtained, and we prove that the numerical scheme is unconditionally energy stable and uniquely solvable, and the discrete energy is in agreement with the original free energy. In addition, we also discuss the uniform boundedness and error estimate of numerical solution, the results show that the numerical solution is uniformly bounded in

H

2

-norm, and error estimate shows that the time-stepping scheme can achieve second-order accuracy in time direction. At last, several numerical tests are illustrated to verify the theoretical results. The numerical strategy developed in this paper can be easily applied to other gradient flow models.
Research on Blockchain Technology and Media Industry Applications in the Context of Big Data
Authors: Peng Chen , Liu Zhikun , Wen Fang , Lee Jong-Yoon , Cui Feng
Published: April 2022

With the everyday use of big data technology, using big data resources effectively has become a key area of research. Media consumption creates enormous amounts of data in people’s digital lives. This data is not only very valuable to the company, but it also presents a number of concerns, including privacy breaches and data exploitation. Along with the continuous improvement of blockchain technology, the rational use of big data resources has become possible. Blockchain technology has expanded from the initial Bitcoin to the financial field and has become more widely used in the legal and media industries. This study catches the actual cases of blockchain technology in the media industry through Qingbo Big Data, analyzes the current global blockchain and media industry’s overall development status and the existing problems, and puts forward corresponding countermeasures to accelerate the integration and development of blockchain and media industry in the context of big data. The results show that the technical features of blockchain can solve the painful problems of copyright risk, algorithmic black box, and weak trust mechanism in the media industry, and the combination of the two can form a new burst of media productivity. The article discusses the broad application prospects of blockchain in the media industry, which is essential for big data and blockchain research. This paper composes the pioneering literature on the blockchain, catches the information on blockchain by keywords, analyzes the application prospect of blockchain in the media industry, and promotes the research and application of blockchain technology.
Redactable Blockchain Trust Scheme Based on Reputation Consensus for MEC
Authors: Tang YongLi , Wu Shuai , Wang XiaoJun
Published: April 2022

Blockchain technology can build trust, reduce costs, and accelerate transactions in the mobile edge computing (MEC) and manage computing resources using the smart contract. However, the immutability of blockchain also poses challenges for the MEC, such as the smart contract with bugs cannot be modified or deleted. We propose a redactable blockchain trust scheme based on reputation consensus and a one-way trapdoor function in response to the problem that data on the blockchain, which is an error or invalid needs to be modified or deleted. The scheme calculates each user’s reputation based on their currency age and behavior. The SM2 asymmetric cryptography algorithm is used as the one-way trapdoor function to construct a new Merkle tree structure, which guarantees the legitimacy of the modification or deletion after verification and vote. The simulation experiments show that the modification or deletion does not change the existing blockchain structure and the links of blocks. Furthermore, the consensus verification accurately passes after the modification or deletion operations, which indicates the proposed scheme is feasible.
Correlation between RASSF1A Methylation in Cell-Free DNA and the Prognosis of Cancer Patients: A Systematic Review and Meta-Analysis
Authors: Chen Shuchen , Duan He , Zhang Dewei , Sun Gongping
Published: April 2022

Background. Although the effects of methylation of the Ras association domain-containing protein 1 isoform A (RASSF1A) gene in cell-free DNA on the outcomes of patients with different types of cancer have been reported, the results are inconsistent. Objective: To explore the relationships between RASSF1A methylation in cell-free DNA and the outcomes of cancer patients. Methods. The PubMed, Embase, and Web of Science databases were searched for papers related to this topic on December 8, 2021. The retrieved articles were screened by two independent researchers, following which the methodological quality of the selected studies was evaluated using the Newcastle-Ottawa Scale. Additionally, hazard ratios were calculated, and publication bias of the studies was determined using Egger’s test. Results. Nine relevant publications involving a combined total of 1254 patients with different types of cancer were included in this study. The combined results of the random effects models yielded a hazard ratio of 1.73 (95% confidence interval: 1.31, 2.29;

P
<
0.001

), which suggested there was a significant association between RASSF1A methylation and overall survival, and patients with an RASSF1A methylation status had a significantly increased risk of total death. Moreover, the Egger test result suggested there was no significant publication bias among the included studies. Conclusions. The methylation of RASSF1A in cell-free DNA in cancer patients was observably associated with an increased risk of poor overall survival.
Multiobjective Algorithm for Urban Land Spatial Layout Optimization
Authors: He Li , Zhang Xueyun
Published: April 2022

In order to explore a quantitative and multiobjective optimization method of land use spatial allocation, this paper proposes a multiobjective algorithm for urban land spatial layout optimization. In this paper, the optimal multiobjective particle swarm optimization (MSO) algorithm is used to construct the optimal land use allocation model, and the variation characteristics of the optimized land use allocation scheme in quantity structure and spatial layout are analyzed. The results show that the total running time of the MSO model and the ordinary genetic algorithm spatial optimal allocation model is 8.57 h and 3.31 h, respectively, and the running efficiency of the mosolua model is 61.38% higher than that of the ordinary genetic algorithm spatial optimal allocation model. The configuration was optimized by using the model of land use spatial pattern from the plaque compactness, adjacency, aggregation degree, environmental compatibility, and the overall degree of resource-saving and environmental friendliness than the ordinary genetic algorithm model of optimal configuration results, and the model of overall fitness model compared with the ordinary genetic algorithm improved by 12.57%.
Effects of High-Concentration CO2 on Ignition Delay Time of 70% n-Heptane/30% Toluene Mixtures
Authors: Yao Shengzhuo , Zhang Yuewei , Liu Yongfeng , Bi Guijun , Zhang Lu , Wei Ping , Song Jinou , Sun Hua
Published: April 2022

In order to research the high-concentration CO2 effects on ignition delay time (IDT) of diesel surrogate fuel (70% n-heptane/30% toluene), a carbon dioxide effect (CDE) model is established, which considers fuel and ambient gas concentration, density, and temperature influence on autoignition under CO2/O2 atmosphere. Firstly, a chemical model of n-heptane/toluene is established, and the coupling, reduction, and simulation processes are carried out in chemical kinetic software with the IDT as the target parameter. Secondly, a constant volume combustion chamber (CVCC) visualization platform is built by incorporating a high-speed camera system and different working conditions are set in the CO2 volume fraction range (40%-70%) at 3.0 MPa and 850 K for an autoignition experiment. Thirdly, experiment and simulation results are discussed in air, 60% CO2/40% O2, 50% CO2/50% O2, and 40% CO2/60% O2 atmospheres, including the IDT, CO2 effects, temperature sensitivity, and OH radical rate of production (ROP). The results show that the CDE model well predicts the 70% n-heptane/30% toluene IDT under the CO2/O2 atmosphere and the average error in 60% CO2/40% O2 atmosphere is 5.29%. Besides, when the CO2 volume fraction increases from 40% to 60%, the CO2 thermal effect plays a leading role in the IDT prolongation and the OH radical ROP peak of R4 (O+H2O⟶2OH) decreases by 180%.
Generalized Cut Functions and n‐Ary Block Codes on UP‐Algebras
Authors: Koam Ali N. A. , Haider Azeem , Ansari Moin A.
Published: April 2022

In this paper, the work is comprised of n‐ary block codes for UP‐algebras and their interrelated properties. n‐ary block codes for a known UP‐algebra is constructed and further it is shown that for each n‐ary block code U, it is easy to associate a UP‐algebra U in such a way that the newly constructed n‐ary block codes generated by U, i.e., Ux, contain the code U as a subset. We define a UP‐algebra valued function on a set say X, then we prove that for every n‐ary block‐code U, a generalized UP‐valued cut function exists that determines U. We have also proved that the UP‐algebras associated to an n‐ary block code are not unique up to isomorphism.
Adsorptive Removal of Methylene Blue Dye Using Biowaste Materials: Barley Bran and Enset Midrib Leaf
Authors: Mekuria Dereje , Diro Abebe , Melak Fekadu , Asere Tsegaye Girma
Published: April 2022

In this study, several biowaste materials are screened for adsorptive removal of methylene blue (MB) from synthetic water. Among the tested adsorbents, barley (Hordeum vulgare) bran (BB) and enset (Ensete ventricosum midrib leaf, EVML) were selected for further evaluation of MB (a model cationic dye) adsorption. Batch MB adsorption performance of BB and EVML adsorbents was significantly high in a wide pH range (4-9). The well fitting of experimental data with pseudosecond-order kinetic model suggests a monolayer adsorption of MB. The MB adsorption onto both adsorbents was fit well with the Langmuir isotherm model with maximum MB adsorption capacities of 63.2 mg/g (BB) and 35.5 mg/g (EVML). The biowaste materials exhibit considerable adsorption capacity for cationic dye (MB), perform well under acidic and basic conditions, and are reusable. Therefore, the use of these materials as adsorbents may have an environmental benefit in terms of the conversion of wastes into valuable materials. Further studies are suggested to investigate the performance of these adsorbents in a continuous mode using real wastewater.
Comparison of Blackhole and Wormhole Attacks in Cloud MANET Enabled IoT for Agricultural Field Monitoring
Authors: Safdar Malik Tauqeer , Siddiqui Muhammad Nasir , Mateen Muhammad , Malik Kaleem Razzaq , Sun Song , Wen Junhao
Published: April 2022

In Mobile Ad hoc Network (MANET) enabled Internet of Things (IoT) agricultural field monitoring, sensor devices are automatically connected and form an independent network that serves as a cloud for many services such as monitoring, securing, and properly maintaining. Cloud-based services in MANET models can prove to be an extremely effective way of smart agricultural functionalities for device-to-device information exchange. Security is a serious issue with Cloud-MANET-based IoT since nodes are scattered, mobile, and lacking centralized administrator, which makes it possible for data tampering and illegal actions on cloud servers. Therefore, these types of networks are more vulnerable to Denial of Service (DoS) attacks such as Blackhole and Wormhole. The MANET Enabled IoT-Agricultural Field Monitoring environment is deployed through a case study. The effect of Blackhole and Wormhole attacks is analyzed using the Ad hoc On-demand Distance Vector (AODV) routing protocol with the help of Network Simulator 3 (NS-3) in order to determine which has the most impact on network performance. We computed performance constraints such as throughput, packet delivery ratio (PDR), end-to-end delay (EED), and Jitter-Sum of preprocessed data gathered with the flow-monitor module of NS-3. The effect of attacks on MANET Enabled IoT-Agricultural Field Monitoring is compared on the varying number of nodes participating in the Cloud-MANET-based IoT network. The throughput and goodput capability of every node is computed through the trace metric package. This method is also highly useful for future Cloud-MANET-Based IoT smart agricultural field security research.
Deep Learning for Ocular Disease Recognition: An Inner-Class Balance
Authors: Khan Md Shakib , Tafshir Nafisa , Alam Kazi Nabiul , Dhruba Abdur Rab , Khan Mohammad Monirujjaman , Albraikan Amani Abdulrahman , Almalki Faris A.
Published: April 2022

It can be challenging for doctors to identify eye disorders early enough using fundus pictures. Diagnosing ocular illnesses by hand is time-consuming, error-prone, and complicated. Therefore, an automated ocular disease detection system with computer-aided tools is necessary to detect various eye disorders using fundus pictures. Such a system is now possible as a consequence of deep learning algorithms that have improved image classification capabilities. A deep-learning-based approach to targeted ocular detection is presented in this study. For this study, we used state-of-the-art image classification algorithms, such as VGG-19, to classify the ODIR dataset, which contains 5000 images of eight different classes of the fundus. These classes represent different ocular diseases. However, the dataset within these classes is highly unbalanced. To resolve this issue, the work suggested converting this multiclass classification problem into a binary classification problem and taking the same number of images for both classifications. Then, the binary classifications were trained with VGG-19. The accuracy of the VGG-19 model was 98.13% for the normal (N) versus pathological myopia (M) class; the model reached an accuracy of 94.03% for normal (N) versus cataract (C), and the model provided an accuracy of 90.94% for normal (N) versus glaucoma (G). All of the other models also improve the accuracy when the data is balanced.
Triazoles Synthesis & Applications as Nonsteroidal Aromatase Inhibitors for Hormone-Dependent Breast Cancer Treatment
Authors: Rashdan Huda R. M. , Shehadi Ihsan A.
Published: April 2022

In the last few years, nonsteroidal aromatase inhibitors (AIs) have been emerged as promising agents for treating hormone-dependent breast cancer in postmenopausal women because of their inhibitory effect on estrogen synthesis. Indeed, these compounds can block the activity of aromatase, the enzyme that intervenes in the last steps of estrogen production pathway. Triazoles are the core structures of nonsteroidal AIs. The nitrogen atom of the triazole moiety plays a fundamental role in the aromatase functionality by interacting with the iron ions of the heme group. In general, AIs possess numerous advantages as they quench the last step of estrogen synthesis without any inhibitory effects on the production of other steroids produced via the same pathway. Some AIs as anastrozole, letrozole, and vorozole have already been approved by the Food and Drug Administration in the treatment of breast cancer. The previously mentioned compounds present severe and adverse effects as polycystic ovary syndrome (PCOS), resistance onset on long-term treatments, and a higher risk of bone fractures. This review focuses intensively on the role of AIs in the treatment of hormone-sensitive types of cancers, especially the role of triazoles as nonsteroidal AIs. Also, the review provides an overview about the chemistry of triazoles along with the different methods by which the

v

-triazoles and s-triazoles are synthesized.
Study on the Effect and Mechanism of Huaji Jianpi Decoction on Simple Obesity
Authors: Zhu Mengmeng , Zhang Baixia , Zhang Jinghui , Ge Shaoqin , Liu Meiyun
Published: April 2022

Background. As the major type of obesity in clinical, simple obesity has gained increasing attention in recent years. Depending on the etiology and pathogenesis of simple obesity and combined with clinical practice experience, Huaji Jianpi decoction (HJJPD) was established to invigorate the spleen and eliminate dampness; however, the underlying molecular mechanism is yet unclear. Materials and Methods. A simple obesity mouse model was established by feeding a high-fat diet to the animals, and the related indexes were analyzed. The mice were divided into the normal, positive control (orlistat), and HJJPD high-dose, medium-dose, and low-dose groups. After 6 weeks of administration, the curative effect of HJJPD was observed. Simple obesity is associated with leptin resistance. The leptin signal transduction pathways mainly include the JAK2-STAT3, AMPK-ACC, LepRb-IRS-PI3K-PDE3B-cAMP, and LepRb-SHP2-MAPKs (ERK1/2) pathways. Therefore, the networks of HJJPD acting on these four pathway-related targets were constructed using the network pharmacology method, and the key nodes were identified. Results. After 6 weeks of drug intervention, we found a good therapeutic effect of HJJPD on simple obesity in the mouse model. The biological network analysis showed that HJJPD plays a role in treating leptin resistance in simple obesity by acting on multiple targets in the JAK2-STAT3 pathway via various components. Also, HJJPD can improve leptin resistance in mice by enhancing the binding force of LEP and LEPRB and activating the LEP-mediated JAK2-STAT3 signaling pathway. Conclusion. In this study, animal experiments, network pharmacology, and molecular biology were combined to establish a mouse model of simple obesity, confirm the role of HJJPD in the treatment of simple obesity, and preliminarily reveal the related mechanism. Relevant research results will provide a basis for the treatment of simple obesity and the drug discovery.
The Behaviour and Influence Mechanism of the Subject of IUR Collaborative Innovation: A Simulation Study Based on NetLogo
Authors: Wang Yanrong , Yao Xiaochao , Song Lei , Zhang Shujing
Published: April 2022

This paper simulates the behavior mechanism of each subject in the equipment manufacturing industry‐university‐research (IUR) collaborative innovation system based on NetLogo, and constructs the input‐output model of each subject. Secondly, the reasonable value of each parameter is determined through expert consultation, and finally 6 scenarios are selected to simulate the evolution of the IUR collaborative innovation system. The results show that different decision‐making behaviors of each subject will lead to different operating states of the innovation collaborative system. When the conditions of each subject are exactly the same, the innovation system can achieve stable and continuous operation. However, if the initial conditions are different, the IUR collaborative innovation system needs an authoritative intermediary to coordinate the behavioral strategies of each subject, strengthen the willingness to cooperate, and ensure its stable operation. Reasonable reward and punishment measures are also an effective and important measure, which can mobilize the enthusiasm of all subjects for collaborative innovation. This paper is of great significance for the IUR collaborative innovation in equipment manufacturing industry.
Platelet-Rich Plasma and Adipose-Derived Mesenchymal Stem Cells in Association with Arthroscopic Microfracture of Knee Articular Cartilage Defects: A Pilot Randomized Controlled Trial
Authors: Venosa Michele , Calafiore Francesco , Mazzoleni Manuel , Romanini Emilio , Cerciello Simone , Calvisi Vittorio
Published: April 2022

Background. This study aims to compare the effects of platelet-rich plasma (PRP) alone or in combination with adipose-derived mesenchymal stem cells (AD-MSCs) in patients affected by cartilage defects, undergoing knee arthroscopic microfracture. Methods. Thirty-eight patients diagnosed with a knee monocompartmental cartilage defect (Outerbridge grade IV) on the MRI, underwent an arthroscopic procedure. After the confirmation of the lesion, they all received the same bone marrow stimulation technique (microfracture) and were randomized into two groups: the first one had additional PRP injection (group A), while the second received PRP and AD-MSC injection (group B). Knee assessment and pain score were documented with Knee Injury Osteoarthritis Outcome Score (KOOS), International Knee Documentation Committee (IKDC) score, Short-Form (SF) 12, and Visual Analogue Scale (VAS) before the treatment and at 1, 3, 6, and 12 months of follow-up postoperatively. An additional arthroscopic procedure, performed in four patients for a subsequent meniscal lesion, let us evaluate cartilage evolution by performing a macro/microscopical assessment on cartilage biopsy specimens. Results. At the 12-month follow-up, both groups showed a comparable functional improvement. The scores on the IKDC form, KOOS, pain VAS, and SF-12 significantly improved from baseline (

p
<
0.05

) to 12 months postoperatively in both treatment groups. The four second-look arthroscopies showed a complete repair of the articular defects by smooth solid cartilage layer, with a good chondrocytic population, in both groups. A thick smooth hyaline-like cartilage with a predominantly viable cell population and normal mineralization (a form closely resembling native tissue) was observed in group B. Conclusions Modern regenerative medicine techniques, such as PRP and AD-MSC, associated with traditional arthroscopic bone marrow stimulating techniques, seem to enhance cartilage restoration ability. The preliminary results of this pilot study encourage the synergic use of these regenerative modulating systems to improve the quality of the regenerated cartilage.
Bone Marrow-Derived Cells Contribute to the Maintenance of Thymic Stroma including TECs
Authors: Chakrabarti Shami , Hoque Mohammed , Jamil Nawshin Zara , Singh Varan J. , Pollacksmith Daniel , Meer Neelab , Pezzano Mark T.
Published: April 2022

In paradox to critical functions for T-cell selection and self-tolerance, the thymus undergoes profound age-associated atrophy and loss of T-cell function, further enhanced by cancer therapies. Identifying thymic epithelial progenitor populations capable of forming functional thymic tissue will be critical in understanding thymic epithelial cell (TEC) ontogeny and designing strategies to reverse involution. We identified a new population of progenitor cells, present in both the thymus and bone marrow (BM) of mice, that coexpress the hematopoietic marker CD45 and the definitive thymic epithelial marker EpCAM and maintain the capacity to form functional thymic tissue. Confocal analysis and qRT-PCR of sorted cells from both BM and thymus confirmed coexpression of CD45 and EpCAM. Grafting of C57BL/6 fetal thymi under the kidney capsule of H2BGFP transgenic mice revealed that peripheral CD45+ EpCAM+ GFP-expressing cells migrate into the developing thymus and contribute to both TECs and FSP1-expressing thymic stroma. Sorted BM-derived CD45+ EpCAM+ cells contribute to reaggregate thymic organ cultures (RTOCs) and differentiate into keratin and FoxN1-expressing TECs, demonstrating that BM cells can contribute to the maintenance of TEC microenvironments previously thought to be derived solely from endoderm. BM-derived CD45+ EpCAM+ cells represent a new source of progenitor cells that contribute to thymic homeostasis. Future studies will characterize the contribution of BM-derived CD45+ EpCAM+ TEC progenitors to distinct functional TEC microenvironments in both the steady-state thymus and under conditions of demand. Cell therapies utilizing this population may help counteract thymic involution in cancer patients.
Life Cycle Assessment of Construction and Demolition Waste from Railway Engineering Projects
Authors: Huang Jianling , Yin Yang , Zheng Lei , Zhang Sibo , Zhao Qingyun , Chen Huihua
Published: April 2022

Railway engineering generates large amounts of construction and demolition waste (CDW). To quantify the amount of CDW generated from railway engineering projects throughout the whole life cycle, a process-based life cycle assessment model is proposed in this paper. The life-cycle CDW is divided into four parts: CDW from off-site transportation of construction materials (OSTCM), CDW from site operation wastage of construction materials (SOWCM), discard ballast from roadbeds, stationyard, bridges and tunnels (DB), and CDW from reparation and renewal of aging components (RRAC). Yun-Gui Railway is selected as a case study to validate the developed model, and an uncertainty analysis is conducted with Oracle Crystal Ball software. The results show that between 175 and 311 million tons of CDW is generated throughout the whole life cycle of Yun-Gui Railway. DB is the largest component of the life-cycle CDW from railway engineering projects. This indicates the negative environmental impacts of railway construction can be significantly mitigated by optimizing the location of ballast disposal sites and developing suitable landfill proposals. Also, the CDW generated by wastage of construction materials during off-site construction and site operation is important in waste management in railway engineering projects, in which rubble, sand, and cement have the high potential for waste reduction. Findings from this study can contribute to the knowledge body as well as the engineering practice in green railways.
Search for the Charmed Baryonium and Dibaryon Structures via the QCD Sum Rules
Authors: Wang Xiu-Wu , Wang Zhi-Gang
Published: April 2022

In the present work, we construct eight six-quark currents to study the

Σ

c

Σ
¯

c

baryonium and

Σ

c

Σ

c

dibaryon states via the QCD sum rules. For either

Σ

c

Σ
¯

c

baryonium or

Σ

c

Σ

c

dibaryon states, we construct four currents with the

J

P

=

1

−

,

1

+

,

0

+

and

0

−

. Except the current

1

+

of

Σ

c

Σ

c

, we find the Borel windows for the other seven. There are two possible

Σ

c

Σ
¯

c

molecular states or compact six-quark states with the

J

P

=

1

−

and

0

−

. The results of the current

0

+

of

Σ

c

Σ

c

show a possible

Σ

c

Σ

c

molecular state with 20 MeV binding energy. We find two

Σ

c

Σ
¯

c

baryonium resonances states with the

J

P

=

1

+

,

0

+

and two

Σ

c

Σ

c

dibaryon resonances states with the

J

P

=

0

−

,

1

−

, respectively. Pole residues of the seven states are also calculated.
Fitness-Based Grey Wolf Optimizer Clustering Method for Spam Review Detection
Authors: Shringi Sakshi , Sharma H. , Suthar D. L.
Published: April 2022

Customers nowadays rely heavily on online reviews when making buying decisions. Various internet websites, including Amazon, Yelp, Google Plus, BookMyShow, Facebook, Twitter, and others, allow users to generate massive amounts of data. The information is gathered through feedback/reviews, comments, and tweets. Companies can leverage this information to improve the quality of their products. Spam reviews are created pretentiously by some businesses and people to promote or degrade the popularity of any product, organization, or person due to their reliance on these online reviews. Thus, identifying spam or nonspam review by the naked eye is nearly impossible. Classifying the reviews manually is also highly speculative. Hence, to overcome this issue, a fitness-based Grey Wolf Optimizer (FGWOK) clustering method is proposed in this paper to identify spam reviews. The fitness-based grey wolf optimization (GWO) is used to obtain the optimal cluster heads in the proposed method. In the fitness-based GWO, the position of the grey wolves is updated in two phases. In the first phase, all the search agents update their position using the contemporary GWO, and in the second phase, the fitness is evaluated to update the position. To prove that the proposed strategy is effective, three spam data sets, namely synthetic spam reviews, movie reviews, and Yelp hotel and restaurant reviews, have been used in our work. The reported results are compared with the existing state-of-art metaheuristic clustering methods like a genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), cuckoo search (CS), k-Means, and artificial bee colony clustering (ABCK) method. The experimental and statistical analysis results show that the proposed FGWOK algorithm outperforms current methodologies.
H−/L∞Fault Detection Observer Design for Uncertain Lipschitz Nonlinear Systems
Authors: Qin Yufeng , Shi Xianjun , Long Yufeng , Lv Jiapeng
Published: April 2022

This paper deals with the robust fault detection problem in the finite frequency domain for a class of Lipschitz nonlinear systems with uncertain parameters. The design conditions ofH−/L∞fault detection observers are proposed. These conditions make the error estimation system stable, and the residual hasL∞disturbance attenuation performance for external disturbances and uncertain parameters. In addition, considering that fault occurs in the finite frequency domain, the residual also hasH−fault sensitivity performance. To ease of solution, the observer design conditions are further transformed into linear matrix inequalities. On this basis, a dynamic threshold design method based onL∞analysis is presented. Finally, simulation results demonstrate the superiority of the proposed method.
Analysis of Logistics Linkage by Digital Twins Technology and Lightweight Deep Learning
Authors: Qiao Liang , Cheng Ying
Published: April 2022

The present work expects to meet the personalized needs of the continuous development of various products and improve the joint operation of the intraenterprise Production and Distribution (P-D) process. Specifically, this paper studies the enterprise’s P-D optimization. Firstly, the P-D linkage operation is analyzed under dynamic interference. Secondly, following a literature review on the difficulties and problems existing in the current P-D logistics linkage, the P-D logistics linkage-oriented decision-making information architecture is established based on Digital Twins. Digital Twins technology is mainly used to accurately map the P-D logistics linkage process’s real-time data and dynamic virtual simulation. In addition, the information support foundation is constructed for P-D logistics linkage decision-making and collaborative operation. Thirdly, a Digital Twins-enabled P-D logistics linkage-oriented decision-making mechanism is designed and verified under the dynamic interference in the linkage process. Meanwhile, the lightweight deep learning algorithm is used to optimize the proposed P-D logistics linkage-oriented decision-making model, namely, the Collaborative Optimization (CO) method. Finally, the proposed P-D logistics linkage-oriented decision-making model is applied to a domestic Enterprise H. It is simulated by the Matlab platform using sensitivity analysis. The results show that the production, storage, distribution, punishment, and total costs of linkage operation are 24,943 RMB, 3,393 RMB, 2,167 RMB, 0 RMB, and 30,503 RMB, respectively. The results are 3.7% lower than the nonlinkage operation. The results of sensitivity analysis provide a high reference value for the scientific management of enterprises.
The Application of PSO-SVM Algorithm in the Evaluation System of Sports Competition Events
Authors: Zhang Zhenglei
Published: April 2022

With the development of society and the improvement of residents’ living standards, sports competition events have attracted much attention as a large-scale event that is popular with the people. Based on the process of holding sports competitions, this paper obtains 14 evaluation indicators from four dimensions of policy, social environment, economy, and natural environment, uses the PSO-SVM algorithm for a comprehensive evaluation, and finally selects Henan Province as the empirical research object. Empirical research shows that the PSO-SVM algorithm has certain applicability in evaluating SCE and can provide a reference for the government and event organizers. The evaluation index system and evaluation model constructed in this paper have certain practical value, but the training data are insufficient and the model still needs to be further improved.
Screening Biomarkers and Constructing a Predictive Model for Symptomatic Urinary Tract Infection and Asymptomatic Bacteriuria in Patients Undergoing Cutaneous Ureterostomy: A Metagenomic Next-Generation Sequencing Study
Authors: Yuan Qian , Huang Rong , Tang Liping , Yuan Lijuan , Gao Li , Liu Yang , Cao Ying
Published: April 2022

Objectives. To investigate the clinical diagnostic value of differential flora as biomarkers in patients with symptomatic urinary tract infection (UTI) and asymptomatic bacteriuria (ASB) undergoing cutaneous ureterostomy based on metagenomic next-generation sequencing and construct predictive models to provide a scientific reference for clinical diagnosis and treatment. Material and Methods. According to standard procedures, samples were taken from each patient for routine tests (urine, ureteral stent, and skin swab around the stoma). Cytokine levels in the blood were also detected. Urinary microflora were measured by mNGS, and potential biomarkers for distinguishing UTI and ASB were identified by differential flora. Finally, we generated the predictive models for ASB and UTI using the Lasso method and cytokine levels. Results. Urine culture was performed for 50 patients with cutaneous ureterostomy; 44 of these patients developed bacteriuria. The incidence of symptomatic bacteriuria was 54.55%. Biomarker analysis showed that Propionimicrobium lymphophilum, Staphylococcus haemolyticus, Stenotrophomonas maltophilia, Ralstonia insidiosa, and Aspergillus sydowii all had good predictive performance and were combined in a single model. The predictive model exhibited good prediction performance (area under the curve

AUC

=
0.8729

,

sensitivity
=
80
%

,

specificity
=
83.3
%

, and

cutoff
=
1.855

). We also identified a significant negative correlation between the weight sum of the abundance for these five characteristic pathogens (Sum_weighted_Reads) and levels of the cytokine IL-6 and IL-1β (

P
<
0.05

). Conclusion. mNGS had a higher positive detection rate for pathogens in urine samples. The selected differential bacteria can be used as biomarkers of ASB and UTI, and the prediction model has good predictive performance. Analysis also showed that the occurrence of symptoms was related to individual immunity. Combined with the Sum_weighted_Reads cutoff and cytokine levels (IL-6 and IL-1β) of differential flora, it was possible to judge the severity of symptoms in cutaneous ureterostomy patients with bacteriuria and provide new insights for the treatment and intervention of ASB and UTI.
Productivity Prediction of Fractured-Vuggy Reservoirs under Time-Varied Flow Rates and Bottom Hole Flow Pressures
Authors: Tao Shan , Xu Yandong , Zou Ning , Du Xin , Hao Youzhi
Published: April 2022

Fracture-vuggy oil reservoirs contain micrometer-sized dissolved secondary pores, fractures, and meter-sized karst caves. Generally, the researches on the flow mechanism of fractured-vuggy reservoirs are based on the assumption of karst cave-fracture-matrix triple medium that gives a partial differential equation which the pressure satisfies. This triple medium flow equation lays the basics for the well testing theory of fractured-vuggy reservoirs. However, the triple medium equation cannot reflect the actual existence of the karst cave volume in geology. Recently, we proposed a theory of fracture-cave seepage flow coupled with vug pressure wave which takes the vug size into consideration and thus can better describe the fracture-vuggy reservoirs. On the basis of this theory, this work combines the change of formation pressure with time, Duhamel’s theorem, and well testing theory to obtain the mathematical expression of time-varied bottom hole pressure and flow rate in Laplace space. Then, the dynamic inflow performance relationship (IPR) curve of fractured-vuggy reservoir is obtained by inverse Laplace transformations. This dynamic IPR curve can predict the productivity for fractured-vuggy reservoirs using parameters from well test interpretations of pressure recovery. The correctness of this productivity prediction method is verified by filed fractured-vuggy oil well. This productivity prediction method not only utilizes and expands the application of well test interpretation data but also shortens the test time and reduces test costs, which is important for oil development.
MRI Reconstruction with Separate Magnitude and Phase Priors Based on Dual-Tree Complex Wavelet Transform
Authors: He Wei , Zhao Linman
Published: April 2022

The methods of compressed sensing magnetic resonance imaging (CS-MRI) can be divided into two categories roughly based on the number of target variables. One group devotes to estimating the complex-valued MRI image. And the other calculates the magnitude and phase parts of the complex-valued MRI image, respectively, by enforcing separate penalties on them. We propose a new CS-based method based on dual-tree complex wavelet (DT CWT) sparsity, which is under the frame of the second class of CS-MRI. Owing to the separate regularization frame, this method reduces the impact of the phase jumps (that means the jumps or discontinuities of phase values) on magnitude reconstruction. Moreover, by virtue of the excellent features of DT CWT, such as nonoscillating envelope of coefficients and multidirectional selectivity, the proposed method is capable of capturing more details in the magnitude and phase images. The experimental results show that the proposed method recovers the image contour and edges information well and can eliminate the artifacts in magnitude results caused by phase jumps.
Resection and Abdominal Wall Reconstruction for Cesarean Scar Endometriosis
Authors: Takaya Kento , Shido Hirokazu , Yamazaki Shun
Published: April 2022

Introduction. Currently, there are few reports describing the use of reconstructive techniques in the treatment of cesarean scar endometriosis (CSE). Here, we report a case of CSE, a rare form of endometriosis caused by scars from obstetric and gynecological surgeries. Case Report. A 50-year-old woman became aware of a painful, deep scar mass in her lower abdomen during her menstrual period 10 years after her second cesarean section. This was diagnosed as CSE after the biopsy. Under general anesthesia, the mass, a portion of the rectus abdominis, and a 1 cm tumor-free margin were resected as a whole, and the abdominal wall was reconstructed with a soft artificial mesh. Results. No obvious recurrence or subjective symptoms were observed postoperatively or reported in the 1-year follow-up period. Discussion. Endometriosis appearing in a cesarean scar is rare; it is chiefly triggered by intraoperative mechanical implantation. In cases of surgical scar masses with a history of gynecological surgery and associated menstrual symptoms, this syndrome should be considered during diagnosis and treatment.
Performance of Various Voltage Stability Indices in a Stochastic Multiobjective Optimal Power Flow Using Mayfly Algorithm
Authors: Kyomugisha Rebeccah , Muriithi Christopher Maina , Nyakoe George Nyauma
Published: April 2022

The performance of voltage stability indices in the multiobjective optimal power flow of modern power systems is presented in this work. Six indices: the Voltage Collapse Proximity Index (VCPI), Line Voltage Stability Index (LVSI), Line Stability Index (Lmn), Fast Voltage Stability Index (FVSI), Line Stability Factor (LQP), and Novel Line Stability Index (NLSI) were considered as case studies on a modified IEEE 30-bus consisting of thermal, wind, solar and hybrid wind-hydro generators. A multiobjective evaluation using the multiobjective mayfly algorithm (MOMA) was performed in two operational scenarios: normal and contingency conditions, using the MATLAB–MATPOWER toolbox. Fuzzy Decision-Making technique was used to determine the best compromise solutions for each Pareto front. To evaluate the computational efficiency of the case studies, a preference selection index was used. The results indicate that VCPI and NLSI yielded the best-optimized system performance in minimizing generation costs, transmission loss reduction, and simulation time for normal and contingency conditions. The best-case studies also promoted the most scheduled reactive power generation from renewable energy sources (RES). On average, the VCPI index contributed the highest penetration level from RES (13.40%), while the Lmn index had the lowest. Overall, VCPI and Lmn index provided the best and worst average performance in both operating scenarios, respectively. Also, the MOMA algorithm demonstrated superior performance against the multiobjective harris hawks algorithm (MHHO), multiobjective Jaya algorithm (MOJAYA), multiobjective particle swarm algorithm (MOPSO), and nondominated sorting genetic algorithm III (NSGA-III) algorithms. In all, the proposed approach yields the lowest system cost and loss compared to other methods.
ResNet-50 for 12-Lead Electrocardiogram Automated Diagnosis
Authors: Sakli Nizar , Ghabri Haifa , Soufiene Ben Othman , Almalki Faris. A. , Sakli Hedi , Ali Obaid , Najjari Mustapha
Published: April 2022

Nowadays, the implementation of Artificial Intelligence (AI) in medical diagnosis has attracted major attention within both the academic literature and industrial sector. AI would include deep learning (DL) models, where these models have been achieving a spectacular performance in healthcare applications. According to the World Health Organization (WHO), in 2020 there were around 25.6 million people who died from cardiovascular diseases (CVD). Thus, this paper aims to shad the light on cardiology since it is widely considered as one of the most important in medicine field. The paper develops an efficient DL model for automatic diagnosis of 12-lead electrocardiogram (ECG) signals with 27 classes, including 26 types of CVD and a normal sinus rhythm. The proposed model consists of Residual Neural Network (ResNet-50). An experimental work has been conducted using combined public databases from the USA, China, and Germany as a proof-of-concept. Simulation results of the proposed model have achieved an accuracy of 97.63% and a precision of 89.67%. The achieved results are validated against the actual values in the recent literature.
A Comparative Study of Total Knee Arthroplasty and Unicondylar Knee Arthroplasty in the Treatment of Knee Osteoarthritis
Authors: Wang Lin , Wang Qiang , Li Qiuwei , Song Fahuan
Published: April 2022

Objective. To compare the clinical efficacy of total knee arthroplasty (TKA) and unicondylar knee arthroplasty (UKA) in the treatment of knee osteoarthritis (KOA). Methods. A retrospective analysis was conducted on 30 patients admitted to the Department of Orthopaedics of Yijishan Hospital from 2020 to 2021. The patients were divided into UKA group (n = 15) and TKA group (control, n = 15). The intraoperative situation and postoperative clinical indicators of patients in the two groups were collected and compared, such as operation time, intraoperative blood loss, length of hospital stay, postoperative complications, and postoperative functional recovery. Postoperative functional recovery was investigated by the visual analogue pain scale (VAS), knee score scale (HSS), and knee range of motion (ROM) scores 5 days after surgery. Results. Perioperative indexes in the UKA group were significantly lower than those in the TKA group, including operation time, intraoperative blood loss, first time going to the ground, and length of hospital stay. VAS, HSS, and ROM scores in the two groups were significantly improved after surgery compared with those before surgery. However, ROM scores in the UKA group were significantly better than in the TKA group. In terms of early postoperative complications, there was one case of venous thrombosis of lower limbs in the UKA group, while in the TKA group there was one case of delayed wound healing due to diabetes, and one case of deep infection. Conclusion. Both UKA and TKA are very successful options for the treatment of KOA, but the use of UKA can promote the recovery of postoperative knee function, reduce postoperative complications, and achieve more satisfactory than expected results.
Wind Power Forecasting by the BP Neural Network with the Support of Machine Learning
Authors: Tian Weihua , Bao Yan , Liu Wei
Published: April 2022

The goal of the research is to increase the accuracy of wind power forecasts while maintaining the power system’s stability and safety. First, the wireless sensor network (WSN) is used to collect the meteorological data of wind power plants in real time. Second, the real-time data collected by WSN are combined with the meteorological forecast data of some meteorological organizations. Then, the fruit fly optimization algorithm (FOA) is improved, and the improved fruit fly optimization algorithm (IFOA) and back propagation neural network (BPNN) are combined to construct the wind power forecast model. Finally, the signal reception of the WSN and the error of wind power forecast under different receiving distances and different antenna heights are tested. The results show that with the increase of receiving and transmitting distance, the signal strength decreases, the packet loss rate increases, and the electromagnetic wave of wind plants will cause some interference to the signal strength. The fly optimization algorithm-back propagation (IFOA-BP) wind power forecast model has a better effect than other models in wind power prediction and can better fit the actual tested wind power. Its root mean square error (RMSE) and mean absolute error (MAE) are 0.16 and 0.11, respectively. The research results provide a reference for improving the forecast accuracy of wind power.
User Capacity Analysis of Space-Based Internet of Things with Multibeam Techniques
Authors: Zhang Zhenhua , Sun Siyue , Hou Huiling , Zhao Lulu , Liang Guang , Yu Jinpei
Published: April 2022

Space-based Internet of Things (S-IoT) utilizes the characteristics of satellite network coverage, strong antidestructiveness, and full-time work to realize large-scale sensing and interconnection of everything in the sky, land, sea, and air. The user capacity of S-IoT satellites is mainly determined by beam design, communication schemes, satellite-to-earth links, and communication protocols, which directly affect the system capacity and operational capabilities of S-IoT. This paper proposes an S-IoT satellite user capacity analysis model with multibeam and spread spectrum technology. Taking into account the randomness of user distribution, the difference in transmit power, and different spreading sequences, the user capacity of S-IoT satellites was simulated and compared in different scenarios. Based on these results, this article analyzes the key factors affecting user capacity, which will provide a valuable reference for S-IoT design, link optimization, and power selection.