bluuebunny/crossref_metadata_2025 huggingface dataset:
Real-time Multi-person Pose Tracking Method Using Deep Reinforcement Learning
Authors: Jia Baojian , Ren Jie
Published: August 2022

To address the problem of low tracking accuracy caused by many recognized objects in the existing methods, we propose a real-time multi-person pose tracking method using deep reinforcement learning. First, the convolutional neural network (CNN) is used to predict the human key points and center vector in grid mode, make the human key points point to the human center according to the center vector, group the human key points according to the distance from the human key points to the human center, complete the multi-person pose estimation, and obtain the human pose sequence diagram. Then, the human pose sequence diagram is input into the deep reinforcement learning network, and the pose label and category label are output by the supervised learning and training stage. The best pose tracking strategy obtained in the reinforcement learning and training stage is applied to online tracking. Finally, CNN is used to predict the rectangular frame position of the pose instead of the target pose, and the tracking is completed when the pose stops. At this time, the rectangular frame position is the result of multi-person pose tracking. The results show that the maximum expected average overlap (EAO) of the proposed method is 0.53. When the root mean square error (RMSE) of the position component threshold reaches 8, the accuracy has been stable at 0.98%. Therefore, the proposed method has high tracking accuracy. In the future, it can be applied to smart home scenarios to realize smart home human pose tracking, effectively identify human dangerous pose and ensure residents’ life safety.
A Deep Learning-Based Decision Support System for Mobile Performance Marketing
Authors: Matos Luís Miguel , Cortez Paulo , Mendes Rui , Moreau Antoine
Published: August 2022

In Mobile Performance Marketing (MPM), monetary compensation only occurs when an advertisement results in a conversion (e.g., sale of a product or service). In this work, we propose an intelligent decision support system (IDSS) to automatically select mobile marketing campaigns for users. The IDSS is based on a computationally efficient mobile user conversion prediction model that assumes a novel Percentage Categorical Pruning (PCP) categorical preprocessing and an online deep multilayer perceptron (MLP) reuse model (MLPr). Using private (nonpublicly available) business MPM data provided by a marketing company, the MLPr model outperformed an offline multilayer perceptron and a logistic regression, obtaining a high quality class discrimination when applied to sampled (85% to 92%) and complete (90% to 94%) data. In addition, the MLPr compared favorably with other machine learning (ML) models (e.g., Random Forest, XGBoost), as well as with other deep neural networks (e.g., diamond shaped). Moreover, we designed two strategies (A — best campaign selection; and B — random selection among the top candidate campaigns) to build the IDSS, in which the predictive deep learning model is used to perform a real-time selection of advertisement campaigns for mobile users. Using recently collected big data (with millions of redirect events) from a worldwide MPM company, we performed a realistic IDSS evaluation that considered three criteria: response time, potential profit and advertiser diversity. Overall, competitive results were achieved by the IDSS B strategy when compared with the current marketing company ad assignment method.
鲁迅木刻思想的马来亚传播
Authors: 胡 星灿
Published: August 2022

1930年，马来亚青年戴隐郎负笈北上，求学于上海艺术专科学校西画科。在校期间，他与鲁迅结识，并在其感召下积极参与“新兴木刻运动”。归返南洋后，他将鲁迅木刻思想刊载于《南洋商报》的副刊《文漫界》和《今日艺术》，“马来亚木刻运动”随之展开。可以说，鲁迅从未到过南洋，却无意中促成了马来亚现代木刻的萌芽，他被称为“马来亚木刻之父”可谓当之无愧。然而，戴隐郎在引介鲁迅时，虽对其有所继承，却亦会根据本土情境进行“反叛”。在继承与“反叛”之间，鲁迅木刻思想的马来亚传播全貌得以呈现，“马来亚”的主体性与能动性也对鲁迅的选择、修改、塑造中得以凸显。
A System for Automated Industrial Test Laboratory Scheduling
Authors: Danzinger Philipp , Geibinger Tobias , Janneau David , Mischek Florian , Musliu Nysret , Poschalko Christian
Published: August 2022

Automated scheduling solutions are tremendously important for the efficient operation of industrial laboratories. The
Test Laboratory Scheduling Problem (TLSP)
is an extension of the well-known
Resource
Constrained Project Scheduling Problem (RCPSP)
and captures the specific requirements of such laboratories. In addition to several new scheduling constraints, it features a grouping phase, where the jobs to be scheduled are assembled from smaller units. In this work, we introduce an innovative scheduling system that allows the efficient and flexible generation of schedules for TLSP. It features a new Constraint Programming model that covers both the grouping and the scheduling aspect, as well as a hybrid Very Large Neighborhood Search that internally uses the CP model. Our experimental results on generated and real-world benchmark instances show that good results can be obtained even compared to settings which have a good grouping already provided, including several new best known solutions for these instances. Our algorithms for TLSP have been successfully implemented in a real-world industrial test laboratory. We provide a detailed description of the deployed system as well as additional useful soft constraints supported by the solvers and general lessons learned. This includes a discussion of the choice of soft constraint weights, with an analysis on the impact and relation of different objectives to each other. Our experiments show that some soft constraints complement each other well, while others require explicit trade-offs via their relative weights.
Making Sense of the Unknown: How Managers Make Cyber Security Decisions
Authors: Shreeve Benjamin , Gralha Catarina , Rashid Awais , Araújo João , Goulão Miguel
Published: August 2022

Managers rarely have deep knowledge of cyber security and yet are expected to make decisions with cyber security implications for software-based systems. We investigate the decision-making conversations of seven teams of senior managers from the same organisation as they complete the Decisions & Disruptions cyber security exercise. We use grounded theory to situate our analysis of their decision-making and help us explore how these complex socio-cognitive interactions occur. We have developed a goal-model (using iStar 2.0) of the teams’ dialogue that illustrates what cyber security goals teams identify and how they operationalise their decisions to reach these goals. We complement this with our model of cyber security reasoning that describes how these teams make their decisions, showing how each team members’ experience, intuition, and understanding affects the team’s overall shared reasoning and decision-making.

Our findings show how managers with little cyber security expertise are able to use logic and traditional risk management thinking to make cyber security decisions. Despite their lack of cyber security–specific training, they demonstrate reasoning that closely resembles the decision-making approaches espoused in cyber security–specific standards (e.g., NIST/ISO). Our work demonstrates how organisations and practitioners can enrich goal modelling to capture not only
what
security goals an organisation has (and
how
they can operationalise them) but also
how
and
why
these goals have been identified. Ultimately, non–cyber security experts can develop their cyber security model based on their current context (and update it when new requirements appear or new incidents happen), whilst capturing their reasoning at every stage.
Feminist Ecologies
Authors: Ojeda Diana , Nirmal Padini , Rocheleau Dianne , Emel Jody
Published: August 2022

In times of devastating ecological crisis, where can we find a route map to collectively halt current trends of destruction? In this review, we examine feminist studies’ recent contributions to activism and theorizing regarding extraction, emerging ecologies, and multispecies justice. By bringing in salient research from the fields of feminist political ecology, ecofeminism, and decolonial/anticolonial feminisms, we point to the ways in which feminist thought and action has opened up spaces for recognizing, envisioning, and making life-affirming ecologies rather than extractive systems of destruction. We refer to the former as emergent and emancipatory ecologies, that is, ecologies always in the process of becoming and capable of defying and subverting oppression based on gender, race, ethnicity, class, sexuality, caste, ability, species and other forms of discrimination—and, thus, capable of protecting and defending life and living worlds.
Kindleberger Cycles: Method in the Madness of Crowds?
Authors: Morck Randall
Published: August 2022

Corporate R&D has a social return far above its internal rate of return to the innovating corporation, and so it is chronically underfunded from a social perspective. Kindleberger cycles, irregularly recurring stock market manias, panics, and crashes that are prominent in financial history, are also a major problem for mainstream economics. If manias inundating hot new technologies with capital sufficiently counter chronic underinvestment in innovation, economy-level selection may favor institutions and behavioral norms conducive to Kindleberger cycles despite individual agents’ losses in panics and crashes.
Distinct distribution patterns of exercise-induced natural killer cell mobilization into the circulation and tumor tissue of patients with prostate cancer
Authors: Schenk Alexander , Esser Tobias , Belen Sergen , Gunasekara Nadira , Joisten Niklas , Winker Matteo Thomas , Weike Lea , Bloch Wilhelm , Heidenreich Axel , Herden Jan , Löser Heike , Oganesian Sabine , Theurich Sebastian , Watzl Carsten , Zimmer Philipp
Published: August 2022

The mobilization and activation of natural killer (NK) cells have been proposed as key mechanisms promoting anti-oncogenic effects of physical exercise. Although mouse models have proven that physical exercise recruits NK cells to tumor tissue and inhibits tumor growth, this preclinical finding has not been transferred to the clinical setting yet. In this first-in-human study, we found that physical exercise mobilizes and redistributes NK cells, especially those with a cytotoxic phenotype, in line with preclinical models. However, physical exercise did not increase NK cell tumor infiltrates. Future studies should carefully distinguish between acute and chronic exercise modalities and should be encouraged to investigate more immune-responsive tumor entities.
An urban diet differentially alters the gut microbiome and metabolomic profiles compared with a seed diet in mourning doves
Authors: Mohr Alex E. , Basile Anthony J. , Sweazea Karen L.
Published: August 2022

Urbanization influences food quality and availability for many avian species, with increased access to human refuse and food subsidies in built environments. In relation to such nutritional intakes and their presumed impact on microbes harbored in the intestinal tract and metabolic profiles of host physiological systems, our overall knowledge of the role of gut microbiome (GM) and metabolomic expression in the avian host lags far behind our understanding of mammals. Therefore, the objective of this investigation was to examine the potential differential effect of an urban modeled versus control (i.e., bird seed) diet on the GM, the metabolic profiles of plasma, liver, adipose, kidney, and muscle tissues, and circulating endotoxin and inflammatory factors in urban-caught mourning doves ( Zenaida macroura). We hypothesized that the urban diet would differently impact the profiles of the GM and tissue metabolomes and increase plasma lipopolysaccharide (LPS) and proinflammatory factors compared with animals fed a seed diet. After a 4-wk-diet period, contents of the large intestine were sequenced to profile the microbiome, metabolomic analyses were performed on plasma and tissue homogenates, and circulating LPS and inflammatory markers were assessed. The composition of the GM was significantly dissimilar between diets, with greater abundance of Erysipelatoclostridiaceae, Sanguibacteraceae, Oribacterium, and Sanguibacter and decreased circulating LPS in the urban-fed birds. These differences were largely not reflected in the surveyed metabolomes and plasma inflammatory markers. This research supports the notion that the microbial composition in urban doves is impacted by diet, though may only weakly associate with host physiology.
Application and Deconstruction of Exercise Prescription Formulation Based on K-Means Algorithm in the Prevention and Treatment of Chronic Diseases
Authors: Zhang Quancheng
Published: August 2022

Chronic diseases, also known as chronic noncommunicable diseases, have the characteristics of a long period of symptoms, complex and diverse causes, relatively large damage to human health, and a relatively wide impact on the overall safety of society. This study mainly discusses the application of exercise prescription formulation based on K-means algorithm in the prevention and treatment of chronic diseases. Aiming at different groups of people with different physical conditions in different environments, this study established a comprehensive exercise prescription library and feedback channels. By comparing and analyzing the effects of different exercise exercises, people can provide scientifically standardized and suitable exercise and fitness programs for people with chronic diseases. The feasibility of the K-means algorithm in chronic disease prediction is confirmed by experiments, and the experimental time of the improved algorithm and the traditional algorithm is compared, and the efficiency of the improved algorithm is confirmed. Aiming at the privacy, complexity, missing data values, and other issues of chronic disease medical examination data, we have carried out perfect data preprocessing research. After 12 weeks of exercise intervention, the vital capacity of the exercise group increased significantly (5.83%) (

P
<
0.01

). The subendocardial myocardial viability rate increased by 9.74% (

P
<
0.01

), and the radial artery reflected wave growth index and systolic blood pressure decreased slightly (

P
<
0.05

). This indicates that the 12-week exercise training intervention program designed in this study can effectively enhance the cardiopulmonary function of the exercise group, thereby further improving the body’s aerobic capacity (

P
<
0.05

). This study lays the foundation for the smooth development of chronic disease prevention and treatment.
Design of a Resident Physical Fitness Data Monitoring System Based on the Sensor and Fuzzy Algorithm
Authors: Ma Jun , Ma Mingxiao
Published: August 2022

This article first introduces the working principle and research progress of motion sensors and analyzes the advantages and disadvantages of motion tracking sensors to provide a reference for in-depth research on motion sensing technology. By combining the fuzzy judgment method to identify the kinematic parameters, this paper establishes the statistical identification of features to predict the kinematic parameters and uses the fuzzy identification method to model the human behavioral characteristics. At the same time, the article designs a set of real-time data monitoring system for athletes’ real-time monitoring behavior under actual sports conditions, which is used to obtain residents’ sports data. Then, based on such requirements, the overall system architecture was designed to integrate the data acquisition module, data storage and processing module, and real-time data presentation module. The motion monitor can initiate a real-time monitoring request for a specific system group, the server can monitor individual groups to add real-time individual motion status, and the system will issue an alert if the system monitors a potentially dangerous situation.
Development and Utilization of Aesthetic Education Resources Using Collaborative Filtering Model
Authors: Chen Shuanzhen
Published: August 2022

In students’ education, other educational methods cannot take the place of aesthetic education. This study investigates the creation and use of aesthetic education resources and develops a recommendation model for these resources based on the CF algorithm. The conventional CF algorithm is also enhanced by this study. One by one, the solutions are presented with a focus on the traditional CF algorithm’s cold start and data sparseness issues. Additionally, the system’s requirements, business process, and functional structure are examined in the section on system design. The front-end diagnostic test, the background instructional resource management system, and the recommendation of instructional resources are all realized in the realization section. According to the experimental findings, this algorithm’s 95.8% recommendation accuracy is 5.87% higher than the user-based recommendation algorithm and 6.03% higher than the item-based recommendation algorithm. The outcomes demonstrate the accuracy and dependability of this algorithm. It can suggest appropriate resources for aesthetic education, fostering the beneficial and healthy growth of students’ aesthetic education.
Design of Table Tennis Training Competition Knowledge Interaction Platform Integrating Improved Swarm Intelligence Algorithm
Authors: Li Deqi
Published: August 2022

Table tennis is China ’s national game and the proudest sport in China’s sports field. During the research and technology service work of the Chinese table tennis team for many years, it has accumulated a large amount of valuable data on the analysis of skills and tactics of training and matches, match video, training monitoring, and so on. This paper discusses the relevant theory of swarm intelligence algorithm processing big data on the table tennis training competition knowledge interaction platform system, as well as the technical support of Nginx and Tomcat, and determines the technical basis of the table tennis training competition knowledge interaction platform. Through the establishment of the firefly algorithm model, the resource search ability is enhanced, and the traditional firefly algorithm is improved. From the results of the system performance test, it can be found that the improved swarm intelligence algorithm adopted in this paper improves the global convergence, and the load balancing degree gradually decreases with the increase of time. The improved firefly algorithm shows good performance when the bandwidth is low, and the resource occupancy rate is greatly reduced. When the bandwidth is 20, it is reduced by 12.55%. It solves the shortcomings of long time and low success rate, so as to verify the convenience of the system operation and the power of functions and make the platform more intelligent and efficient.
Complementary and Integrative Approaches to Cancer: A Pilot Survey of Attitudes and Habits among Cancer Patients in Italy
Authors: Bonucci Massimo , Geraci Andrea , Pero Dina , Villivà Cristina , Cordella Daniele , Condello Maria , Meschini Stefania , del Campo Laura , Tomassi Franco , Porcu Alessandro , De Lorenzo Francesco , Lozupone Francesco
Published: August 2022

Background. Cancer patients are among the main consumers of traditional, complementary, integrative, and alternative medicine (TCIM) such as natural products (herbals, integrators, etc.) and mind and body practices (yoga, acupuncture, etc.). Methods. A questionnaire on TCIM was submitted to 415 Italian cancer patients. The questionnaire consisted of three sections: (i) biographical and clinical information; (ii) use of natural substances; and (iii) use of mind-body practices. Results. 406 patients completed the questionnaire. The prevalence of TCIM use was 72.3%. Of them, 75.6% started to use TCIM after a tumor diagnosis. The main reasons for using TCIM were to mitigate side effects (65.0%), to regain physical and mental balance (35.9%), to relieve pain (18.3%), and to improve the efficacy of cancer therapy (16.0%). 44.7% of patients taking natural products used them during conventional therapies (chemotherapy, radiotherapy, etc.), and in 67.5% of cases without consulting a doctor. As a consequence, only about 50% of patients taking natural substances used these compounds appropriately, and the most common errors were related with the purpose of reducing the side effects of the therapy (52.3%) and for boosting immune system (32.1%). Conclusions. There is an impelling need to provide patients with scientifically validated information to raise awareness about the benefits and risks of using TCIM.
A Case of Ruptured Decidualized Ovarian Endometrioma: Usefulness of Serial MRI for Determining Adequate Management
Authors: Yamamoto Saki , Kikuchi Tomohiro , Fujii Hiroyuki , Otake Yuko , Matsuki Mitsuru , Narumi Risa , Endo Masashi , Fujiwara Hiroyuki , Mori Harushi
Published: August 2022

Decidualization can originate in ovarian endometrioma by elevated serum progesterone levels during pregnancy, which mimics malignancy on ultrasonography. Moreover, decidualized ovarian endometrioma may rupture and cause acute abdominal pain during pregnancy. Magnetic resonance imaging (MRI) is reportedly useful in differentiating decidualized ovarian endometriomas from malignancies. However, to our knowledge, serial MRI of decidualized ovarian endometrioma before and after rupture has not been reported. Herein, we report the case of a 39-year-old woman with a ruptured decidualized ovarian endometrioma in which serial MRI was useful for adequate management. She had a history of right ovarian endometrioma. Transvaginal ultrasonography at 20 weeks of gestation showed the known right ovarian endometrioma with mural nodules that were not evident before pregnancy. MRI for further evaluation showed ovarian endometrioma with mural nodules with signals similar to those of the placenta. Based on the MRI findings, we diagnosed a decidualized ovarian endometrioma. At 27 weeks of gestation, she complained of sudden abdominal pain, for which MRI was performed. MRI showed disappearance of the ovarian endometrioma and bloody ascites, based on which we diagnosed a ruptured ovarian endometrioma. The abdominal pain subsided immediately, and a conservative observational treatment approach was taken. At 37 weeks of gestation, right ovarian cystectomy was performed simultaneously with an elective cesarean section, which revealed a ruptured decidualized ovarian endometrioma. Our findings demonstrate that the accurate diagnosis of a ruptured decidualized ovarian endometrioma on serial MRI can contribute to its management.
Optimization Mold and Algorithm of Risk Control for Power Grid Corporations Based on Collaborative Filtering Technology
Authors: Chen Longxing , Han Ping
Published: August 2022

With the ever-changing internal and external environmental factors of enterprises, various uncertainties and risks faced by enterprises are increasing, and the feasibility of financial meltdown is increasing. Research on financial meltdown early warning can help enterprises to prevent the occurrence of peril in advance and take resultful measures to ensure the healthy development of enterprises. If a serious financial meltdown leads to the bankruptcy of enterprises, the financial meltdown is not sudden, but a gradual process. The occurrence of financial meltdown is not only a harbinger, but also predictable. Therefore, it is an urgent question to be solved for listed corporations in China that how to mine the message with early warning function from a large amount of financial data generated in the business process of enterprises. The continuous maturity of data mining technique just solves this question. Based on collaborative filtering technique, this paper analyzes the risk control optimization mold and algorithm of power grid corporations, which is of great signification. After research, this algorithm is 30% better than the traditional algorithm, and it is suitable to be proverbially used.
Research on the Semantic Analysis Method of Translation Corpus Based on Natural Language Processing
Authors: Yue Xin
Published: August 2022

Accurate recognition and analysis of semantics is the most important research field in the process of English translation with the help of natural language processing technology. This paper proposes an English semantic analysis method based on the neural network. First, the idea of model transfer is used to construct a topic segmentation model and the topic granularity segmentation of the translated text is carried out. Then, in order to obtain all the information in the English text, the recursive neural network is selected to recognize the word model. In order to recognize English texts with different sentence patterns, the long-term and short-term memory network is selected to extract the useful information of the text. Through the experimental data measurement and analysis results, compared with the traditional sentence analysis methods, the accuracy of the proposed method is as high as 95.8% and the model occupies less hardware resources.
Cognitive-Based High Robustness Frequency Hopping Strategy for UAV Swarms in Complex Electromagnetic Environment
Authors: Xue Rui , Zhao Mingfei
Published: August 2022

Unmanned aerial vehicles (UAVs) confront various interference in the process of missions, and frequency hopping (FH) technology is one of the effective means of anti-interference for UAV. The FH system can avoid interference frequency points and possess certain anti-interference ability by making the carrier frequency continuously popping. However, the increasingly complex electromagnetic environment of UAV swarms requires more efficient anti-interference measure. To further improve the anti-interference ability of UAVs, this paper proposes a cognitive-based high robustness FH strategy. By adding a cognitive module to FH system, UAVs can avoid the interference frequency points adaptively and sensitively. The proposed system can identify typical suppressed interference and filter the interference frequency points. Cognitive FH system can avoid interference points completely with accurate detections and enhance the robustness for UAV swarms in complex electromagnetic environment compared to conventional FH system. Simulation results have shown that BER performance of proposed cognitive FH strategy is better than conventional FH strategy for typical suppressed interference.
Research on Application of Naive Bayes Algorithm Based on Attribute Correlation to Unmanned Driving Ethical Dilemma
Authors: Liu Guoman , Luo Yufeng , Sheng Jing
Published: August 2022

At present, unmanned driving technology has made great progress, while those research on its related ethical issues, laws, and traffic regulations are relatively lagging. In particular, it is still a problem how unmanned vehicles make a decision when they encounter ethical dilemmas where traffic collision is inevitable. So it must hinder the application and development of unmanned driving technology. Firstly, 1048575 survey data collected by Moral Machine online experiment platform is analyzed to calculate the prior probability that the straight being protector or sacrificer in ethical dilemmas with single feature. Then, 116 multifeature ethical dilemmas are designed and surveyed. The collected survey data are analyzed to determine decision-making for these ethical dilemmas by adopting the majority principle and to calculate correlation coefficient between attributes, then an improved Naive Bayes algorithm based on attribute correlation (ACNB) is established to solve the problem of unmanned driving decision in multifeature ethical dilemmas. Furthermore, these ethical dilemmas are used to test and verify traditional NB, ADOE, WADOE, CFWNB, and ACNB, respectively. According to the posterior probability that the straight being protector or sacrificer in those ethical dilemmas, classification and decision are made in these ethical dilemmas. Then, the decisions based on these algorithms are compared with human decisions to judge whether these decisions are right. The test results show that ACNB and CFWNB are more consistent with human decisions than other algorithms, and ACNB is more conductive to improve unmanned vehicle’s decision robustness than NB. Therefore, applying ACNB to unmanned vehicles has a good role, which will provide a new research point for unmanned driving ethical decision and a few references for formulating and updating traffic laws and regulations related to unmanned driving technology for traffic regulation authorities.
[Retracted] Effects of Core Stabilization Training on the Cobb Angle and Pulmonary Function in Adolescent Patients with Idiopathic Scoliosis
Authors: Qi Kexin , Fu Haidong , Yang Zhen , Bao Lingqi , Shao Yinxin
Published: August 2022

Objective. To observe the effects of core stabilization training on the Cobb angle, respiratory muscle strength (maximum inspiratory pressure, MIP; maximal expiratory pressure, MEP), and pulmonary function (forced vital capacity, FVC; forced expiratory volume, FEV1.0; FEV1.0/FVC%) in adolescent patients with idiopathic scoliosis (AIS) and offer practical‐based evidence for the rehabilitation treatment for AIS patients. Methods. 36 AIS patients were assigned to the core stability training (CST) group (n = 18) and control group (n = 18); the CST group participated in three sessions of core stabilization exercise per week for 12 weeks and the control group did not perform regular physical training during 12 weeks of study. Then, the Cobb angle, respiratory muscle strength (MIP and MEP), and pulmonary function (FVC, FEV1.0, and FEV1.0/FVC%) were measured before and after core stabilization training. Results. After 12 weeks of core stabilization training, compared with the pretest, the Cobb angle showed a significant decrease, FVC, FEV1, MIP, and MEP a significant increase (P < 0.01 respectively), and there was no statistical difference in FEV1/FVC in the CST group; there was no significant difference (P > 0.05 respectively) before and after an experiment in the control group except MEP decreased significantly (P < 0.01, P < 0.05). After 12 weeks of core stabilization training, compared with the control group, the Cobb angle significantly decreased (P < 0.01), FVC, FEV1, MIP, and MEP significantly increased (P < 0.05 respectively) in the CST group, but there was no significant difference (P > 0.05, respectively) in FEV1/FVC between the control group and CST group. Conclusions. Core stabilization exercise can be considered to have a positive effect on the normal physiological curvature of the spine in AIS patients, as it decreases the Cobb angle and strengthens respiratory muscle strength and pulmonary function.
Tourism Consumer Demand Forecasting under the Background of Big Data
Authors: Li Fang , Li Tao
Published: August 2022

In recent years, the tourism industry has grown rapidly around the world as an emerging force, especially in China, which has become the world’s leading tourism country in recent years, and its tourism revenue also occupies a good weight in the country’s total income. The number of tourists every year shows a very high growth rate, which not only improves the tourism economy but also brings great pressure to the management of various tourist attractions. In this context, many tourist attractions are actively developing innovative applications of new technologies, hoping to effectively improve their management efficiency. In these experiments, the speech big data analysis technology has achieved remarkable achievements, so it is necessary to combine the speech big data analysis technology with the demand analysis of the tourism industry. The purpose of this paper is to study the effective model of tourism consumer demand prediction using big data analysis. In the process of completing this paper, we consulted a large number of research results of big data analysis technology, tourism-related books, and demand prediction models in HowNet, VIP, and other network databases as well as campus libraries, summarized the related concepts of tourism, and used big data analysis technology to predict the demand of tourism consumers. Understand the needs of tourism consumers on major tourism websites, and extract the indicators that will affect the forecast results of consumer demand, establish a demand forecast model based on the indicators, and analyze its forecast effects through comparative analysis to understand its advantages and disadvantages, in order to establish a tourism demand forecast models providing actionable advice. Through the practical application case of the demand forecasting model, this paper puts forward the development strategy of tourism. The experimental results show that the mean square error of the neural network model is less than 2.5, which is more suitable for predicting the number of tourists, indicating that different models are suitable for predicting different indicators. The main contribution of this research lies in the modeling and analysis of regional tourism characteristics and tourists’ willingness, so as to achieve accurate prediction of tourists in scenic spots and formulate targeted plans.
The Application of Nano-Calcium Carbonate in the Technology of Improving Road Petroleum Asphalt
Authors: Wang Qiuyun
Published: August 2022

Nano-calcium carbonate is a new type of nanomaterial, which has great influence in many fields. In order to explore the influence of nano-calcium carbonate on petroleum asphalt, an experiment was designed to analyze the application of nano-calcium carbonate in improving road petroleum asphalt technology. This article explores the methods of preparing nano-calcium carbonate and petroleum pitch, understands the characteristics of the two materials, and then draws two methods of mixing nano-calcium carbonate and petroleum pitch: one is freezing and disintegrating, and the other is stepwise mixing fusion. In this paper, the fusion-modified asphalt is subjected to grouping experiments on the prediction model and “three indicators.” The experimental results show that with the increase in nano-calcium carbonate, the ductility of the modified asphalt decreases, the softening point increases, and the penetration is 15% reaching the maximum value. Nano-calcium carbonate is very suitable for improving road petroleum asphalt.
Computer Vision Process Development regarding Worker’s Safety Harness and Hook to Prevent Fall Accidents: Focused on System Scaffolds in South Korea
Authors: Lim Jeeyoung , Jung Dae Gyo , Park Chansik , Kim Dae Young
Published: August 2022

In South Korea, industrial accidents continue to increase in frequency, with construction accidents accounting for more than a third of all industrial accidents. Specifically, by preventing fall accidents, the death rate from accidents can be reduced by 50%. Fall protection is required to prevent fall accidents, and investigating the reinforcement of the worker’s safety harness and hook fastening becomes imperative. This requires automation of computer vision confirmation of the safety harness and hook fastening. As the accident risk can be reduced by an effective safety culture in the system, it is necessary to monitor safety on site through a construction safety automation system. Therefore, the objective of this study is to develop a computer vision process for safety harness and hook for preventing fall accidents in South Korea’s construction industry. This study focuses on system scaffolds that are widely used at construction sites. The application of this methodology to sample sites and field cases established its applicability. The proposed computer vision application methodology will serve as the foundation for visualization research in the construction industry, and image recognition will help reduce the safety accident rate. Accidents caused by failure to use a safety harness and hook can be reduced in South Korea as well as globally. Additionally, this methodology is applicable to roof construction, tower crane installation and dismantling work, as well as steel tower installation and dismantling.
Multimedia Drama Imaging Technology Based on Big Data Information System
Authors: Li Na
Published: August 2022

Opera is a unique comprehensive stage art form in China. As a traditional Chinese drama style, it is known as the three ancient drama cultures in the world together with the tragicomedy of ancient Greece and the Sanskrit drama of India. However, in the trend of the new era, Chinese opera is facing a severe existential crisis. How to innovate the opera and make people fall in love with the opera again is a problem worthy of research and analysis at present. This study takes the research of multimedia opera image technology as the object, aims to improve the perception and experience of opera, and studies how to use modern technology to improve opera image. First, this study briefly expounds on the presentation methods and current situation of traditional opera video technology. This study then describes in detail the imaging technology that combines opera and modern technology and then establishes the algorithm model of virtual reality imaging technology. And it then describes in detail the video technology that combines Chinese opera with multimedia technologies such as projection and virtual reality interaction. It then established a virtual reality technology algorithm model for opera images. This study then experiments, analyzes, and improves the multimedia opera video technology based on virtual reality. Finally, by means of a questionnaire survey, it investigates and analyzes the viewing attitude of opera images based on virtual reality technology. Through the research of virtual reality image technology and the investigation of citizens, this study obtains the methods and models to improve traditional opera images and effectively innovates opera. In a survey of citizens, it was found that nearly 70% of the respondents held a positive and optimistic attitude toward the virtual reality-based opera video technology, indicating that people are willing to accept the combination of traditional culture and modern technology. In the element loss detection experiment, it was found that nearly 70% of the interviewees were very interested in the method of combining traditional opera with modern technology. The model of virtual opera scene construction was improved so that the element loss rate was lower than 4%. The element representation delay is less than 3%, which improves the smoothness of the picture and maintains a good viewing experience.
Network Interactive Application of High-Performance Computing Platform for Japanese Teaching
Authors: Zhang Su
Published: August 2022

With the development of network technology, Japanese teaching is increasingly showing the characteristics of network, and information interaction is becoming more and more frequent. Interactive teaching is a new teaching mode under the network environment. However, traditional interactive teaching cannot meet the flexibility and dynamics of resource allocation in the network environment. In the daily Japanese teaching process, the contradiction between the students’ growing need for learning resources and the unbalanced and insufficient distribution of resources is becoming more and more acute. High-performance computing is a model of swarm computing, which can solve the contradiction between complex student needs and resource allocation. Based on this, this paper proposes a Japanese interactive teaching method based on a high-performance computing platform. Experiments show that the interactive Japanese teaching method based on HPC can effectively improve students’ Japanese performance. Among them, the scores of high-level students increased by 23.3%, and the proportion of middle-level students accounted for more than 70.9%.
Practical Application of Artificial Intelligence Technology in Glaucoma Diagnosis
Authors: Gong Di , Hu Man , Yin Yue , Zhao Tong , Ding Tong , Meng Fan , Xu Yongli , Chen Yi
Published: August 2022

Purpose. By comparing the performance of different models between artificial intelligence (AI) and doctors, we aim to evaluate and identify the optimal model for future usage of AI. Methods. A total of 500 fundus images of glaucoma and 500 fundus images of normal eyes were collected and randomly divided into five groups, with each group corresponding to one round. The AI system provided diagnostic suggestions for each image. Four doctors provided diagnoses without the assistance of the AI in the first round and with the assistance of the AI in the second and third rounds. In the fourth round, doctor B and doctor D made diagnoses with the help of the AI and the other two doctors without the help of the AI. In the last round, doctor A and doctor B made diagnoses with the help of AI and the other two doctors without the help of the AI. Results. Doctor A, doctor B, and doctor D had a higher accuracy in the diagnosis of glaucoma with the assistance of AI in the second (

p
=
0.036

,

p
=
0.003

, and

p
≤
0.000

) and the third round (

p
=
0.021

,

p
≤
0.000

, and

p
≤
0.000

) than in the first round. The accuracy of at least one doctor was higher than that of AI in the second and third rounds, in spite of no detectable significance (

p
=
0.283

,

p
=
0.727

,

p
=
0.344

, and

p
=
0.508

). The four doctors’ overall accuracy (

p
=
0.004

and

p
≤
0.000

) and sensitivity (

p
=
0.006

and

p
≤
0.000

) as a whole were significantly improved in the second and third rounds. Conclusions. This “Doctor + AI” model can clarify the role of doctors and AI in medical responsibility and ensure the safety of patients, and importantly, this model shows great potential and application prospects.
A Deep Learning Model for ERP Enterprise Financial Management System
Authors: Zhang Hui
Published: August 2022

With the advent of the information age, the need for information technology construction is beginning to be realized when working in corporate financial management. The application of ERP systems to financial management has become a major trend in the development of modern society. This can help companies collect financial information in real time and analyze and process the obtained information. This paper first gives the significance and models of the ERP financial management system. Then, a financial risk prediction model based on a deep learning model is designed. The method proposes an improved temporal convolutional network-long and short-term memory network (TCN_LSTM) structure and introduces an optimization algorithm to optimize the parameters of the deep learning model. Finally, several benchmark models and evaluation methods are used for comparative study. The experimental results show that the deep learning risk prediction model has significant superiority in prediction accuracy and stability. The proposed model can help enterprises organize their limited resources, realize the scientific allocation of enterprise resources, and create more benefits for enterprises.
Equilibrium Analysis of Price Discrimination Economic Phenomenon Based on Big Data Monitoring of Netizen Sensors
Authors: Liu Jie , Wang Chenxi , Lu Jinzhong
Published: August 2022

With the wide application of sensors, people's behavior in life has been transformed into data resources. The emergence of big data can more effectively deal with a large amount of information generated. Network big data is increasingly applied to the analysis of economic problems. By analyzing the common phenomenon of price discrimination in life, we can better solve the differences between buyers and sellers. Because both businesses care about the number of vested interests, it is necessary to find a way to balance the two psychologically. Sellers feel that they have made a profit, while buyers feel that they have not lost, and then they will feel that this is a fair deal. In fact, this is not absolutely fair, only their own ideas. The main purpose of this paper is to analyze the economic phenomenon of price discrimination. On this basis, this paper proposes to use the combination of big data that netizens pay attention to and network sensors to detect price changes under the background of big data. Through the analysis of the collected sample data and related economic models, the experimental data show that when

p
>
v

, if buyers choose to trade at this time, they will bring negative material returns to themselves. There was a significant negative correlation between second-class buyers and whether they accepted the transaction

P
<
0.01

. In the case of bringing negative material returns to themselves, vulnerable buyers are more likely to choose to refuse the transaction.
Analysis of the Effectiveness of SMILE, FS‐LASIK, and SBK in Myopic Patients and the Impact in UCVA and Tear Film Stability
Authors: Ji Yan , Wan Wenjuan , Zhang Qi , Xu Mei , Yang Xin , Xia Jiuyi
Published: August 2022

The aim of the present study was to investigate the effect of femtosecond laser small incision stromal lens extraction (SMILE), femtosecond laser‐assisted excimer laser keratomileusis (FS‐LASIK), and anterior elastic sublaminar laser keratomileusis (SBK) in myopic patients, and their effects on uncorrected visual acuity (UCVA) and tear film stability. 600 myopic patients admitted to our hospital from January 2020 to December 2021 were selected for the study and divided into SMILE group (200 patients, SMILE treatment), FS‐LASIK group (200 patients, FS‐LASIK treatment), and SBK group (200 patients, SBK treatment) according to the random number table method. Uncorrected visual acuity (UCVA), tear breakup time (BUT), tear secretion function test (Sit), and corneal higher‐order image aberration global higher‐order aberration (HOAS) were compared among the three groups. The UCVA values before operation, 2 weeks after operation, 1 month after operation, and 3 months after operation in the three groups were similar, and the BUT, Sit value, and HOAS of corneal higher‐order image difference in the three groups were similar, and the differences were not statistically significant (P > 0.05). The BUT, Sit value, and HOAS of corneal higher‐order image difference at 1 and 3 months after surgery in the SMILE group were higher than those in the FS‐LASIK and SBK groups, and the differences were statistically significant (P < 0.05). SMILE, FS‐LASIK, and SBK are effective in the treatment of myopia, which can effectively improve the uncorrected visual acuity and help the patients to recover their normal visual ability, but SMILE is more effective in tear film stability and corneal higher‐order aberrations.
Semantic Trajectory Frequent Pattern Mining Method with Fuzzy Stay Time Constraint
Authors: Li Aiguo , Fu Jiahao , Gao Ruifang , Yang Jie
Published: August 2022

In the security system, transforming a large number of collected target trajectories into semantic trajectories with a less volume and high quality and mining their frequent patterns are helpful to analyze the target behavior patterns, identify hazard sources, and enhance the internal prevention, and control of the security system. Aiming at the limitation of semantic trace frequent pattern mining method defined by precise stay time in practical application scenarios, a fuzzy semantic trace frequent pattern mining method is proposed. Firstly, the membership function of fuzzy stay time is defined, so the stay time of the target at the stay point is fuzzified, and the fuzzy semantic trajectory is obtained. Then, a fuzzy semantic trajectory frequent pattern mining algorithm FST-FPM (fuzzy semantic trajectory frequent pattern mining) is proposed. The FST-FPM algorithm is experimentally verified on the Geolife public dataset and the self-collected RFID positioning dataset. The experimental results show that FST-FPM algorithm can mine frequent patterns of fuzzy semantic trajectories on Geolife dataset and RFID positioning dataset, and the running time is reduced by more than 10% compared with classical PrefixSpan algorithm, PrefixSpan-x algorithm, and LFFT2 algorithm.