bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance Analysis of Speculative Parallel Adaptive Local Timestepping for Conservation Laws
Authors: Bremer Maximilian , Bachan John , Chan Cy , Dawson Clint
Published: September 2022

Stable simulation of conservation laws, such as those used to model fluid dynamics and plasma physics applications, requires the satisfaction of the so-called Courant-Friedrichs-Lewy condition. By allowing regions of the mesh to advance with different timesteps that locally satisfy this stability constraint, significant work reduction can be attained when compared to a time integration scheme using a single timestep size. However, parallelizing this algorithm presents considerable difficulty. Since the stability condition depends on the state of the system, dependencies become dynamic and potentially non-local. In this article, we present an adaptive local timestepping algorithm using an optimistic (Timewarp-based) parallel discrete event simulation. We introduce waiting heuristics to limit misspeculation and a semi-static load balancing scheme to eliminate load imbalance as parts of the mesh require finer or coarser timesteps. Last, we outline an interface for separating the physics of the specific conservation law from the temporal integration allowing for productive adoption of our proposed algorithm. We present a misspeculation study for three conservation laws, demonstrating both the productivity of the local timestepping API, for which 74% of the lines of code are reused across different conservation laws, and the robustness of the waiting heuristics—at most 1.5% of element updates are rolled back. Our performance studies demonstrate up to a 2.8× speedup versus a baseline unoptimized local timestepping approach, a 4x improvement in per-node throughput compared to an MPI parallelization of synchronous timestepping, and scalability up to 3,072 cores on NERSC’s Cori Haswell partition.
User Cold-Start Recommendation via Inductive Heterogeneous Graph Neural Network
Authors: Cai Desheng , Qian Shengsheng , Fang Quan , Hu Jun , Xu Changsheng
Published: September 2022

Recently, user cold-start recommendations have attracted a lot of attention from industry and academia. In user cold-start recommendation systems, the user attribute information is often used by existing approaches to learn user preferences due to the unavailability of user action data. However, most existing recommendation methods often ignore the sparsity of user attributes in cold-start recommendation systems. To tackle this limitation, this article proposes a novel Inductive Heterogeneous Graph Neural Network (IHGNN) model, which utilizes the relational information in user cold-start recommendation systems to alleviate the sparsity of user attributes. Our model converts new users, items, and associated multimodal information into a Modality-aware Heterogeneous Graph (M-HG) that preserves the rich and heterogeneous relationship information among them. Specifically, to utilize rich and heterogeneous relational information in an M-HG for enriching the sparse attribute information of new users, we design a strategy based on random walk operations to collect associated neighbors of new users by multiple times sampling operation. Then, a well-designed multiple hierarchical attention aggregation model consisting of the intra- and inter-type attention aggregating module is proposed, focusing on useful connected neighbors and neglecting meaningless and noisy connected neighbors to generate high-quality representations for user cold-start recommendations. Experimental results on three real datasets demonstrate that the IHGNN outperforms the state-of-the-art baselines.
An
Intermediate-Centric
Dataflow for Transposed Convolution Acceleration on FPGA
Authors: Ma Zhengzheng , Dai Tuo , Wei Xuechao , Luo Guojie
Published: September 2022

Transposed convolution has been prevailing in convolutional neural networks (CNNs), playing an important role in multiple scenarios such as image segmentation and back-propagation process of training CNNs. This mainly benefits from the ability to up-sample the input feature maps by interpolating new information from the input feature pixels. However, the
backward-stencil computation
constrains its performance and hindered its wide application in diverse platforms. Moreover, in contrast to the efforts on accelerating the convolution, there is a rare investigation on the acceleration of transposed convolution that is identically compute-intensive as the former.

For acceleration of transposed convolution, we propose an
intermediate-centric
dataflow scheme, in which we decouple the generation of the intermediate patch from its further process, aim at efficiently performing the
backward-stencil computation
. The
intermediate-centric
dataflow breaks the transposed convolution into several phases/stages, achieving feeding the input feature maps and performing the
backward-stencil computation
in a pipelining manner. It also provides four-degree computation parallelism and efficient data reuse of input feature maps/weights. Furthermore, we also theoretically analyze the irregular data dependence leveraging the polyhedral model, which constrains the parallel computing of transposed convolution. Additionally, we devise an optimization problem to explore the design space and automatically generate the optimal design configurations for different transposed convolutional layers and hardware platforms. By selecting the representative transposed convolutional layers from DCGAN, FSRCNN, and FCN, we generate the corresponding accelerator arrays of
intermediate-centric
dataflow on the Xilinx Alveo U200 platform and reach the performance of 3.92 TOPS, 2.72 TOPS, and 4.76 TOPS, respectively.
An Enhanced Battery Aging Model Based on a Detailed Diffusing Mechanism in the SEI Layer
Authors: Lanjan Amirmasoud , Srinivasan Seshasai
Published: September 2022

The impetus for this study is the lack of a detailed knowledge on the formation mechanism of the solid electrolyte interface (SEI) layer and the diffusion mechanisms within this layer that impacts the predictive abilities of the current mathematical models. Specifically, most models continue to employ a constant value of diffusion coefficient along with several lumped fitting parameters, instead of a variable formulation that is dependent on the temperature and concentration of Li-ions, to characterize Li-ion batteries (LIBs). As a result, the current models fail in predicting the capacity fading accurately. In overcoming this gap, we have employed a previously proposed temperature and concentration-dependent diffusion equation to present a modified mathematical model that is capable of accurately predicting the capacity fading and SEI growth rate as a function of temperature, concentration, and time, with just two significantly simplified temperature dependent fitting parameters. Further, these parameters need to be adjusted only for new temperatures. Our enhanced model is validated with respect to the experimental data for different operational conditions, including open circuit condition with different initial state of charges (SOCs) as well as cycling with a constant current.
News Text Classification Method Based on the GRU_CNN Model
Authors: Deng Lujuan , Ge Qingxia , Zhang Jiaxue , Li Zuhe , Yu Zeqi , Yin Tiantian , Zhu Hanxue
Published: September 2022

The convolutional neural network can extract local features of text but cannot capture structure information or semantic relationships between words, and a single CNN model’s classification performance is low, whereas GRU can effectively extract semantic information and global structure relationships of text. To address this problem, this paper proposes a news text classification method based on the GRU_CNN model, which combines the advantages of CNN and GRU. The model first trains word vectors as the embedding layer with the Word2vec model and then extracts semantic information from text sentences with the GRU model. Following that, this model employs the CNN model to extract crucial semantic information features and finally completes the classification through the Softmax layer. The experimental results reveal that the GRU_CNN hybrid model outperforms single CNN, LSTM, and GRU models in terms of classification effect and accuracy.
Packaging Decorative Pattern Design Based on Genetic Algorithm
Authors: Zhu Yunming , Gao Tong
Published: September 2022

The science, art, and technology of covering or protecting things for distribution, storing, selling, and usage are known as packaging. Decorative design is the process of applying color, line, texture, or pattern to an object. Decorative design can be used to complement a structural design, or it can simply be a piece of art. Designers not only have the necessary design knowledge and expertise but they should also have certain observation, inventiveness, and other conditions. When it comes to packaging materials, most businesses want their products to be unique and attractive. The inventive design has entirely relied on people’s thinking, and there is no technology or approach that can substitute human creative thinking. A genetic algorithm (GA) is a metaheuristic motivated by natural selection that relates to the wider family of evolutionary algorithms (EA) in computer science and operations research. Therefore, this paper first introduces the theoretical basis of genetic algorithms and then describes the generation process of packaging decorative patterns. Following the generation of the first population by coding the control points of the initial design line, the genetic algorithm is used to perform genetic operations on it in order to obtain multiple design solutions. The study found that this approach can alter the basic design scheme and provides an efficient way for the inventive creation of packaging decorative patterns, assisting designers in improving design efficiency and developing design ideas.
[Retracted] Enterprise Human Resource Optimization Algorithm Using PSO Model in Big Data and Complex Environment
Authors: Wang Xiang , Zhang Ying
Published: September 2022

The distribution of human resources has a direct impact on the HR utilization rate in businesses, which in turn has an impact on the profitability and labor productivity of those businesses. As a result, this article develops an enterprise HR optimal allocation model based on PSO. The concepts of HR and HR allocation are introduced, and a programme for implementing optimal HR allocation in businesses is provided from the perspectives of scale prediction, structure analysis, and implementation. An HR configuration optimization model is established, providing a specific method of quantitative management for HR configuration optimization, at the same time starting from operability, based on the methods of system analysis and quantitative evaluation, and an improved PSO is created to address this issue. Results from numerical simulations demonstrate this algorithm’s effectiveness. According to the experimental findings, the improved PSO has a quick convergence rate and a roughly 5% lower average error rate than the conventional algorithm. Moreover, this algorithm’s accuracy is roughly 94%. This method offers some targeted tactics for optimizing HR configuration.
Researches on Stem and Progenitor Cells in Intervertebral Discs: An Analysis of the Scientific Landscape
Authors: Cheng Yunzhong , Yang Honghao , Hai Yong , Liu Yuzeng
Published: September 2022

Low back pain (LBP) is a common clinical symptom, and the prevalence is ranged from 60% to 70%. With the deepening of basic research, the development of intervertebral disc regeneration-oriented cell therapy, especially stem and progenitor cells therapy, showed good research prospects and was expected to become new methods of treatment for LBP. Our study is aimed at analyzing the scientific output of stem and progenitor cells in intervertebral discs and at driving future research into new publications. Researches focused on this file were searched from the Science Citation Index Expanded (SCI-E) of the Web of Science (WOS) core collection database and were screened according to inclusion criteria. We evaluated and visualized the results, including annual publications, citations, authors, organizations, countries, research directions, funds, and journals by bibliometric website, VOSviewer, and Citespace softwares on May 27, 2022. A total of 450 original articles and reviews were included, and the overall trend of the number of publications rapidly increased. In worldwide, China and the USA were the leading countries for research production. The retrieved 450 publications received 14322 citations, with an average of 31.83 citations and an H-index of 62. The most high-yield author, organization, country, research directions, funds, and journals were Chen QX from Zhejiang University, Zhejiang University, China, Cell Biology, National Natural Science Foundation of China, and Spine, respectively. Keywords cluster analysis showed the research hotspots in the future, including “human intervertebral disc”, “adipose-derived mesenchymal stem cell”, “intervertebral disc degeneration”, “degenerative disc model”, “nucleus pulposus regeneration”, “human cartilage”, “3d culture”, “shrinkage-free preparation”, and “polylactide disc”. Furthermore, with accumulating evidence demonstrating the role of stem and progenitor cells in intervertebral discs, “microenvironment”, “activation”, “intervertebral disc degeneration”, and “oxidative stress” are becoming the research frontiers and trends.
[Retracted] Comparison of Hepatectomy and Hemangiomas Stripping on Patients with Giant Hepatic Hemangiomas
Authors: Yang Zhiqi , Zhang Chunyan , Li Zhiwen , Wu Lang , Li Minghao
Published: September 2022

Objective. To compare the value of a hepatectomy and hemangioma stripping on patients with giant hepatic hemangiomas. Methods. Seventy‐four patients with giant hepatic hemangiomas were retrospectively analyzed from data collected from their hepatobiliary surgeries performed from June 2010 to June 2015 at the People’s Hospital of Ningxia and the general hospital affiliated with Ningxia Medical University. The patients were divided into a hepatectomy group (37 patients) and a hemangioma‐stripping group (37 patients). Conditions of each group were compared before and after surgery and comprised of surgery duration, intraoperative blood loss, blood transfusion, duration of hepatic blood occlusion, and hospital stay. Any complications after surgery, such as pleural effusions, bile leakage, and abdominal hemorrhage, were also observed. Results. In the hemangioma‐stripping group, the surgery time was 2.38 ± 0.93 h, intraoperative blood loss was 889.19 ± 756.37, blood transfusion amount was 723.78 ± 801.14, the duration of hepatic blood occlusion 26.84 ± 17.30 min, and hospital stay was 16.19 ± 5.01 d. In the hepatectomy group, surgery time was 3.26 ± 1.16 h, intraoperative blood loss was 1551.35 ± 1755.88 mL blood transfusion amount was 1693.24 ± 2117.72 mL, duration of hepatic blood occlusion was 26.84 ± 17.30 min, and hospital stay was 16.19 ± 5.01 d. The difference between the groups was statistically significant (P < 0.05). The pleural effusion incident rate in the former group was lower than that of the latter group, and the difference was statistically significant. Conclusions. Hemangiomas stripping is an effective method by which to cure hepatic hemangioma, with the advantages being a relatively easy surgery with less patient trauma, rapid recovery, and fewer complications. This method should be used more often in clinical settings.
Data Analysis and Optimization of English Reading Corpus Based on Feature Extraction
Authors: Fan Nengwei
Published: September 2022

In order to solve the difficulties faced by English reading teaching, this paper proposes a feature extraction-oriented method for data analysis and optimization of English reading corpus. This method specifically includes the evaluation of vocabulary coverage, comparing the value and vocabulary distribution evaluation of the two sets of textbooks in helping students master English core vocabulary and adapt to the English reading environment as soon as possible, testing whether the teaching of the three sets of textbooks can enable learners to reach the level of free reading corresponding English materials, and testing whether the gradient of the textbooks can be in line with the average language level required for reading corresponding English materials. The experimental results show that, compared with the blog-1565 based on the four authoritative core word lists, the English core word coverage of the two tutorials has reached 91.8% and 93.7%, respectively, and can be improved to 96% and 97.4%, respectively, after optimization. Conclusion. It is proved that the data analysis and optimization of English reading corpus based on feature extraction can play a directional role in the current English reading-related textbooks.
Multislice Computed Tomography Angiography Imaging Diagnosis of Lower Extremity Arteriosclerosis in Patients with Hypertension and Its Correlation with the Level of High-Sensitivity C-Reactive Protein
Authors: Huo Jing , Wu Zhongyin , Jiang Haijun , Zhang Hong
Published: September 2022

The aim of this study was to investigate the relationship between multislice computed tomography (CT) angiography (MSCTA) imaging and high-sensitivity C-reactive protein (hs-CRP) in patients with hypertension and lower extremity arteriosclerosis. 68 hypertensive patients with lower extremity arteriosclerosis were selected as the observation group, and 68 healthy volunteers were selected as the control group to compare the differences in hs-CRP. According to the degree of stenosis, the patients were further divided into five grades: no obvious stenosis, mild stenosis, moderate stenosis, severe stenosis, and occlusion. The correlation between the degree of stenosis and the content of hs-CRP was compared. The changes of hs-CRP content before and after treatment were compared, and the difference of images before and after surgical treatment and the difference of hs-CRP expression in patients with occlusion were compared. Compared with the control group, the content of hs-CRP in the observation group was significantly higher (

P
<
0.05

), and the degree of stenosis was positively correlated with the content of hs-CRP. After two weeks of treatment, the hs-CRP levels of patients with severe stenosis and occlusion were significantly lower than those before treatment (

P
<
0.01

). The level of hs-CRP in patients with occlusion after arterial stent intervention was significantly lower than before, and the images also showed that the blood vessels were significantly expanded. The degree of stenosis in patients with lower extremity arteriosclerosis diagnosed by MSCTA imaging was closely related to the expression of hs-CRP in the patient, and a sustained high concentration of hs-CRP corresponded to a more severe degree of vascular occlusion. In conclusion, the hs-CRP can be used as one of the factors to predict and evaluate the occurrence of cardiovascular and cerebrovascular diseases.
Financial Shared Services Empower the Real Economy: The Evidence from China
Authors: Chen Xue
Published: September 2022

As a product of digital technology, financial shared services can be deeply integrated into enterprise operation and management, which is significant to accelerate the financial transformation of real enterprises and achieve high-quality development. Based on this, this paper examines whether and how financial shared services empower the real economy, using data from listed companies in China from 2008 to 2017 as a sample. The research results show that financial shared services can significantly improve enterprise value and thus empower the real economy, and the optimization of operational efficiency is an important path for financial shared services to empower the real economy. Further research finds that the empowering effect of financial shared services on the real economy mainly exists in enterprises with many subsidiaries, large scale, and high labor intensity. This paper not only bridges the gap between financial shared services and micro-enterprise related research but also provides references and lessons for how to implement financial shared services in the real economy.
A Multitime Window Parallel Scheduling System for Large-Scale Offshore Platform Project
Authors: Yang Boxin , Yin Wenhao , Li Jinghua , Zhou Qinghua
Published: September 2022

In order to complete the offshore platform project scheduling intelligently, an improved scheduling optimization system based on the parallel genetic algorithm was proposed. An optimal model for the large-scale offshore platform project scheduling problem (LSOPPSP) was built and produced the mathematic model of LSOPPSP, based on the characteristics of the abundance of activities, long duration, high uncertainty, and frequent changes. In long-term unsteady manufacturing, this model can provide good robustness. In addition, the essential steps of the multitime window parallel genetic algorithm were proposed. An improved population initialization algorithm was designed, as well as the coevolution strategy among populations was also proposed in parallel computing. These two strategies can increase population variety while also speeding up convergence. Finally, the suggested parallel scheduling system was deployed in our self-developed schedule optimization software for offshore platform enterprises, and the outperformance of the improved algorithm was proven by simulated examples and practical application.
A Novel Method for COVID-19 Detection Based on DCNNs and Hierarchical Structure
Authors: Li Yuqin , Zhang Ke , Shi Weili , Jiang Zhengang
Published: September 2022

The worldwide outbreak of the new coronavirus disease (COVID-19) has been declared a pandemic by the World Health Organization (WHO). It has a devastating impact on daily life, public health, and global economy. Due to the highly infectiousness, it is urgent to early screening of suspected cases quickly and accurately. Chest X-ray medical image, as a diagnostic basis for COVID-19, arouses attention from medical engineering. However, due to small lesion difference and lack of training data, the accuracy of detection model is insufficient. In this work, a transfer learning strategy is introduced to hierarchical structure to enhance high-level features of deep convolutional neural networks. The proposed framework consisting of asymmetric pretrained DCNNs with attention networks integrates various information into a wider architecture to learn more discriminative and complementary features. Furthermore, a novel cross-entropy loss function with a penalty term weakens misclassification. Extensive experiments are implemented on the COVID-19 dataset. Compared with the state-of-the-arts, the effectiveness and high performance of the proposed method are demonstrated.
Oral and Lower Extremity Ulcers as the Initial Presentation of Granulomatosis with Polyangiitis
Authors: Al Salihi Mohammed Omar , Dominguez Bianca , Mohanlal Viresh , Carlan S. J.
Published: September 2022

Background. Granulomatosis with polyangiitis (GPA) is a small vessel vasculitis characterized by lung and kidney involvement. It is typically a disease of white females and has a poor prognosis with the average life expectancy of 5 months for a patient without treatment. Oral and skin ulcers are considered to be rare presentations. Case. A 39-year-old black male presented to the hospital with oral and skin ulcers and was diagnosed with GPA based on the biopsies of both cutaneous lesions and kidney. He was started on rituximab with minimal improvement. Later he was admitted to the ICU and had plasmapheresis, and he gradually improved and was discharged home 8 days after admission. Conclusion. GPA is an aggressive vascular disorder resulting in possible organ system damage and failure. The role of the sickle cell trait in this patient is undefined, but this combination of gender, race, and presenting symptoms in GPA is extremely unusual.
Practical Exploration on the Construction of Theoretical Courses of Composition Technology in the Age of Artificial Intelligence
Authors: Luo Luo
Published: September 2022

The theory of composition technology is to study the basic knowledge and skills of musicology, instrumental music, composition, and composition technology, including harmony, polyphony, musical form, orchestration, and so on, and then to analyze, create, and edit music. The current theory of composition technology has resulted in the phenomenon of relatively single form. The current method is the traditional way of composing music through the creation of composers. The defect is that various elements cannot be integrated together, and the meaning of music cannot be perfectly presented. In order to solve these problems, this paper proposes the use of recurrent neural network algorithm and backpropagation algorithm in artificial intelligence algorithm. It aims to study how to innovatively integrate the composition technology theory course with the current network technology. And it utilizes recurrent neural networks in artificial intelligence to help design part of the analysis of musical characteristics, through the evaluation of the music effect generated by automatic composition. The results show that the accuracy of note prediction obtained by the automatic composition method on the basis of objective evaluation is 81.93%, 90.15%, and 92.62%, respectively, on Top1, Top2, and Top3, which basically meet the current basic requirements for composition technology theory.
Network Traffic Obfuscation against Traffic Classification
Authors: Liu Likun , Yu Haining , Yu Shilin , Yu Xiangzhan
Published: September 2022

In recent years, tremendous progress has been made in network traffic classification and its use has become ubiquitous in many emerging applications, such as Internet censorship in many countries and ISP traffic engineering. However, the traffic analysis of intermediaries brings the risk of privacy disclosure to users. This paper presents a network traffic obfuscation technology to resist traffic classification. It deceives the machine learning and deep learning models by generating adversarial samples. The adversarial samples generation algorithm includes a white-box attack algorithm based on fuzzy strategy and a black-box attack algorithm based on smote data enhancement. Experiments based on the ISCXTor2016 public data set show that the MIM algorithm has the best performance in white-box attacks, and the obfuscation success rate of DNN and LSTM models is 90%. In the black-box attack, the obfuscation effect of LSTM is the best, while CNN has stronger robustness.
Folk Music Audio Enhancement Method Combining Internet of Things and GA
Authors: Ming Cheng
Published: September 2022

The current methods of music sound enhancement mainly use the way of enhancing the intensity of the sound source, which ignores the spatial noise cancellation of the human ear, resulting in poor sound enhancement effect. Therefore, this paper proposes an audio enhancement method for folk music combining Internet of Things and GA. After acquiring folk music audio by Internet of Things technology, the postprocessing algorithm of wavelet packet decomposition combined with Wiener filter is used to suppress the noise in music. On the basis of HRTF, genetic algorithm is used to reduce the output error of left and right sound channels and realize the enhancement of music sound effect. The simulation experiment verifies the method of application research. The reverberation is established at 65 ms and lasts for a long time, which has good effect of sound enhancement.
Analysis of the Dilemmas and Countermeasures Brought by Data Analysis Based on Short Online Videos on Civic and Political Work
Authors: Xie Junhong
Published: September 2022

In the new era of Internet development, short video media has emerged and become the second largest network application in China. While bringing opportunities for the propaganda work of civic and political science, short video also brings difficulties and challenges such as complex network information interfering with the attention of netizens, fragmented communication method not conducive to systematic communication, and algorithmic recommendation technology causing barriers to the reception of information by netizens. In the case that the media short video and the political work need to be constantly updated, we should deal with the short video challenge brought by the media, effectively avoid its negative impact, actively deal with the period of time since the media, and make use of the short video to carry out various forms of educational reform. Facing these challenges, this paper focuses on helping the development of political work in the new era from the aspects of talent training, content optimization, brand building, algorithm standardization, etc.
[Retracted] Construction of Network Ideological and Political Management Platform in Institution of Higher Learning Based on Information Technology under the New Media Environment
Authors: Li Xingchen
Published: September 2022

The resources used by network ideological and political educators to achieve their goals make up the entire collection of materials and components that make up network ideological. Some academic institutions, such as Nanjing University of Aeronautics and Astronautics, rely on new media to develop novel ideological techniques, such as “Xuchuan’s political work practices,” which have had a significant impact on young college students. WeChat, Weibo, and Twitter are just a few of the new media platforms that have emerged as a result of China’s ongoing process of digitalization. The development of new media information technology has subtly altered how political and ideological management is conducted in academic institutions. This study of the network ideological in higher education institutions is based on information technology and the new media environment. The study reveals that the highest rate of knowing very well is 14.36%, and the highest rate of knowing well is 25.65%, according to the trend of understanding new media information technology. Only 3.35% said they were unsure. According to the experiment, there is a rising trend in understanding and understanding unity as the number of samples increases. In this instance, it makes sense to use new media to advance the ideological goals of a higher education institution. The network ideological and political management platform of the institution of higher learning uses information technology to support online discussion and analysis, which is crucial for fostering students’ ethical values and a healthy way of thinking.
The WoLF-MFGA Algorithm for Equilibrium Analyses of Multiuser Power Control in Wireless Network
Authors: Liu Luping , Jia Wensheng
Published: September 2022

The main goal of this paper is to explore strategic decision-making of multiuser power control in wireless network from the perspective of mean field game theory. First, we formulate the multiuser power control problem as a

n

-person noncooperative game and prove the existence of Nash equilibrium for multiuser power control game. Then, we design a new mean field gradient ascent with win or learn fast (WoLF-MFGA) algorithm by introducing mean field term into gradient ascent with the WoLF criterion algorithm. Subsequently, we investigate a sufficient condition of convergence for the proposed WoLF-MFGA algorithm. Furthermore, the WoLF-MFGA algorithm is used to achieve the Nash equilibrium of multiuser power control game, and some simulation results are given. Finally, for the sake of analyzing the convergence of the proposed algorithm, we investigate the sensitivity results of the WoLF-MFGA algorithm.
Revealing the Role of Social Support on Cognitive Deficits in Fibromyalgia Syndrome
Authors: Galvez-Sánchez Carmen M. , Reyes del Paso Gustavo A. , Montoro Casandra I.
Published: September 2022

Despite the relevance of cognitive deficits in fibromyalgia syndrome (FMS) and the attempts to elucidate the influence of the disorder symptoms in the cognitive decline reported by patients, no studies have explored the specific role of social support on cognition in FMS. Social support has been shown to be an essential modulator factor on cognitive performance in other diseases. Sixty-four women with FMS and 32 healthy women participated in the study and completed questionnaires pertaining to anxiety, depression, fatigue, insomnia, clinical pain, and social support, along with a neuropsychological battery assessing verbal memory, organization, strategic and planning abilities, self-regulation, processing speed, attention, and cognitive flexibility. Results showed that FMS patients exhibited lower values in all social support dimensions in comparison with healthy individuals, especially in the socializing dimension. Despite the lower social support observed in FMS, all social support dimensions showed a positive impact on verbal memory, organization and planning abilities, strategic planning, self-regulation, processing speed, attention, and cognitive flexibility in these patients. In fact, social support was associated with greater correct responses and processing speed and minor number of errors in all the neuropsychological battery tests. Socializing was the main predictor of organization and planning abilities, strategic planning, and self-regulation. In sum, results suggest that social support may be a key factor in buffering the cognitive decline observed in FMS. Designing psychoeducation programs and intervention programs directed not only to FMS patients but also relatives, health care workers, and the general population might be essential to improve the social support of FMS patients and positively impact on patient’s cognitive status.
The Effect of Cell Salvage on Bleeding and Transfusion Needs in Cardiac Surgery
Authors: Tachias Frixos , Samara Evangelia , Petrou Anastasios , Karakosta Agathi , Siminelakis Stavros , Apostolakis Efstratios , Tzimas Petros
Published: September 2022

Introduction. Cell salvaging is well established in the blood management of cardiac patients, but there remain some concerns about its effects on perioperative bleeding and transfusion variables. This randomized controlled study investigated the potential effects of the centrifuged end-product on bleeding, transfusion rates, and other transfusion-related variables in adult cardiac surgery patients submitted to extracorporeal circulation. Materials and Methods. Patients were randomly chosen to receive (cell-salvage group, 99 patients) or not to receive (control group, 110 patients) the centrifuged product of a cell salvage apparatus. Bleeding and transfusion rates according to the universal definition of perioperative bleeding (UDPB) classification, postoperative hemoglobin, coagulation, and oxygenation indices were recorded and compared between the groups. Results. Both groups had almost identical bleeding and transfusion rates (median value: 2 units of red blood cells (RBC) and no units of fresh frozen plasma (FFP) and platelets (PLT) for both groups,

p
>
0.05

). Patients in the cell-salvage group presented slightly higher hemoglobin concentrations (10.6 ± 1.1 vs. 10.1 ± 1.7 g/dL,

p
<
0.05

, respectively) and a tendency towards better oxygenation indices (PaO2/FiO2: 241 ± 94 vs. 207 ± 84,

p
=
0.013

) in the postoperative period albeit with a tendency for prolongation of prothrombin time (INR: 1.31 ± 0.18 vs. 1.26 ± 0.12,

p
=
0.008

). Conclusion. Within the study’s constraints, the perioperative use of the cell salvage concentrate does not seem to affect bleeding or transfusion variables, although it could probably ameliorate postoperative oxygenation in adult cardiac surgery patients. A tendency to promote coagulation disturbances was detected.
Construction of Early Warning Model for Financial Economic Cycle Using Genetic Algorithm
Authors: Liu Lulu
Published: September 2022

Economic crises occur when the economy experiences drastic fluctuations, which in turn cause heavy losses to society and the economy. Economic crises happen when there are significant losses to society and the economy as a result of the economy experiencing abrupt changes or deep recessions. The term “economic cycle early warning mechanism” refers to a collection of theories and techniques for keeping track of, assessing, forecasting, and choosing policies regarding finances in light of the particular economic phenomenon known as economic cycle fluctuations. The traditional early warning mechanism has been unable to keep up with the demands of economic cycle early warning due to the growth of economic globalisation, and the phenomenon of incomplete early warning and poor early warning accuracy frequently occurs. A genetic algorithm simulates the natural selection and genetic mechanisms of Darwin’s theory of biological evolution. It is a computational model of the biological evolution process. It is a technique for looking for the best answer by simulating the course of natural evolution. Genetic algorithms have advanced incredibly quickly in recent years. It has been widely applied in machine learning, neural networks, control system optimization, and the social sciences as an effective, useful, and reliable optimization technique. In order to optimize the early warning mechanism and increase the thoroughness and accuracy of early warning, this paper investigates the early warning mechanism of financial and economic cycles by combining genetic algorithms. A mathematical model of the economic cycle was built during the experiment and tested in accordance with the genetic algorithm’s basic operating principle. The findings demonstrate that the genetic algorithm-based early warning system for the financial economic cycle is more complete and accurate, with a 6.4% increase in accuracy.
WSNs-Based Data Transmission Bandwidth Allocation Method for Smart Campus Communication Network in Colleges and Universities
Authors: Guo Dun , Xia Liping
Published: September 2022

Real-time reliability allocation of communication channels is carried out in the environment of limited bandwidth and strong interference, so as to improve the anti-interference ability and communication quality of data transmission in WSNs of smart campus communication in colleges and universities. A real-time adaptive allocation algorithm of data transmission bandwidth in WSNs of smart campus communication in colleges and universities based on Porter’s interval equalization and channel error compensation is proposed. We construct the data transmission channel model of WSNs of smart campus communication in colleges and universities, carry out the optimal distribution interval balance control of the data time series of smart campus communication network according to the attenuation of the data transmission bandwidth of the communication network, adopt the band-limited multipath channel balance control method to suppress the interference in the data transmission channel of smart campus communication WSNs in colleges and universities, and realize the frequency spectrum suppression of data transmission of smart campus communication WSNs in colleges and universities. Time-varying channel and delay spread equalization method are adopted for channel equalization scheduling, and combined with channel error compensation, real-time adaptive allocation of data transmission bandwidth in WSNs of smart campus communication in colleges and universities is realized. The simulation results show that the reliability of data transmission bandwidth allocation in WSNs for smart campus communication in colleges and universities is good, the real-time channel allocation ability and channel balance are improved, the error rate of data transmission in smart campus communication network in colleges and universities is low, and the data transmission quality of smart campus communication network in colleges and universities is improved.
Correlation Analysis and Application of Respiratory and Lung Diseases in Pediatrics of Traditional Chinese Medicine Based on Factor Analysis Method
Authors: Sun Shengnan , Zhuang Lingling , Cao Ming
Published: September 2022

Many scholars have studied the influencing factors of children’s lung infection, whether it is the region or the environment, or the living air quality of the mother during pregnancy. Western medicine is the most frequently used medicine, but Chinese medicine has more remarkable characteristics in treating children’s lung diseases. For viral invasive diseases, people often use antibiotics to treat them. Children’s lung conditions are too fragile, and taking antibiotics will lead to the damage of Staphylococcus in the lungs, resulting in pulmonary respiratory insufficiency. Although the conditioning time of traditional Chinese medicine is longer than that of western medicine, traditional Chinese medicine will not cause secondary damage to the lungs. In this paper, we introduce factor analysis and principal component analysis and compare the performance of the three analysis methods by using data such as cure rate, improvement rate, mortality rate, and drug taking frequency as evaluation indexes. In the model comparison, the accuracy rate of factor analysis method is over 97%, while the error rate is below 5%. Compared with the other two analysis methods, this method has a better application effect. Finally, we compare the comprehensive scores of eigenvalues of the three analysis methods. From 2016 to 2021, the comprehensive scores of factor analysis gradually increased.
A Secure Cloud Storage Algorithm for University Financial Data Based on the Blockchain Technology
Authors: Zhang Zhuyun , Geng Yanjun
Published: September 2022

In order to increase the secure storage capacity of university financial system process operation data under the blockchain environment, a secure cloud storage algorithm of university financial data based on the blockchain technology is proposed. The blockchain storage structure model of university financial system process operation data is first constructed, and then the mapping method of Atlas features is adopted. Finally, the blockchain equilibrium configuration parameter analysis model of university financial system process operation data is established. According to the outcomes of the feature extraction process of cloud resource storage Atlas of university financial system process operation data, the fuzzy clustering method is implemented to comprehend the rational planning of cloud storage space. The resource cloud storage structure configuration of university financial system process operation data in cloud environment is realized by using the method of block link fusion and channel equilibrium configuration. The multi-layer modal structure decomposition and fuzzy clustering processing are carried out on university financial system process operation data storage information in blockchain environment by using the empirical mode decomposition method. According to the data graph clustering results, the cloud resource graph of university financial system process operation data becomes smooth in the adjacent wave domain through cloud information fusion and block clustering, which effectively reduces the data storage overhead and improves the secure cloud storage capability of university financial data. The simulation outcomes indicate that this approach can significantly increase the storage performance of process operation data of university financial system under blockchain environment, with better data classification storage, internal structure information fusion performance of university financial data, and lower storage overhead than other methods. We observed that this improvement, in terms of storage overhead costs, can be as high as 43.67% higher than the wavelet method and 30.45% higher than the mode decomposition approach.
Study on Ring-Opening Copolymerization of Trioxymethylene and Second Monomer Initiated by Phosphotungstic Acid
Authors: Li Yanhong , Jin Yushun , Li Hongjuan , Li Xiang , Wang Yatao , Liu Ruofan , Wu Yibo
Published: September 2022

In this study, a series of polyoxymethylene copolymers are synthesized by bulk cationic ring-opening polymerization by 1,3,5-trioxane (TOX) with 1,3-dioxolane (DOX), octamethylcyclotetrasiloxane (D4), and cyclohexane oxide (CHO) as the second monomer using phosphotungstic acid (PTA) as an initiator. The polymer products were characterized by hydrogen nuclear magnetic resonance (1H-NMR), infrared spectroscopy (IR), thermogravimetry (TG), and differential scanning calorimetry (DSC). And the copolymerization energy barrier was calculated at the b3lyp/6-31g(d) calculation level using density functional theory (DFT) to explore the copolymerization ability of the second monomer with 1,3,5-trioxane. The results showed that CHO as the second monomer more easily participated in the copolymerization reaction, and the copolymers showed better thermal stability.
Feature Extraction-Based Fitness Characteristics and Kinesiology of Wushu Sanda Athletes in University Analysis
Authors: Lei Zhe , Lv Wu
Published: September 2022

This paper designs a feature extraction-based physical characteristics and kinematic analysis of collegiate martial arts Sanda athletes by profoundly studying the model of athletes’ action feature extraction and constructing physical characteristics and kinematic model of collegiate martial arts Sanda athletes. This paper provides feature extraction category support by describing the data types of clustered objects, calculating behavioral operator distances, identifying multiple behaviors occurring in collegiate martial arts Sanda tactics, and delineating the correct thresholds for operator identification. A comparison experiment is designed to verify that the proposed method can improve the accuracy of feature extraction for athletes’ technical and tactical use and is more applicable to the comprehensive assessment of athletes’ behaviors. The underlying features of the athletes’ motion technique forming image features are analyzed, the image features are regionally divided, the feature sets of different levels of developing actions are obtained, and the image features are allowed to map to the corresponding feature dimension space. The AdaBoost algorithm filters out the feature data of the athletes’ action images that contribute most to the intelligent visual analysis. It is used as training samples for training and recognition to complete the extraction of athlete action image features. The action times of the five different phases of side kick up, knee lift, kick strike, and strike: recovery is 0.2986 s, 0.1819 s, 0.1322 s, 0.0708 s, and 0.9986 s, respectively, which reflects that the time of different action phases of athletes with extra body weight is more related to the athletes’ weight, strength, flexibility, and coordination.
Construction and Implementation Path of College English Teaching Mode in Big Data Environment
Authors: Liu Xing
Published: September 2022

As a basic course for cultivating innovative and applied foreign language talents in the new era, college English audio-visual courses are facing huge opportunities and challenges in the era of big data. The transformation of the teaching environment under the background of big data is mainly reflected in the role of teachers, the dissemination of the information age, and the improvement of students’ self-learning ability. The role of students in teaching has been strengthened, and the education system has undergone certain changes. This paper analyzes the current teaching status of college English audio-visual courses. According to the actual situation, a feasible reform plan is proposed to reconstruct the teaching mode of college English audio-visual courses, to improve the teaching quality of college English audio-visual courses, and to promote the improvement of students’ comprehensive ability of college English.