bluuebunny/crossref_metadata_2025 huggingface dataset:
The effect of fasting in Ramadan on patients with heart disease
Authors: Awad Abdul ameer J.
Published: December 2023

Background: Knowledge about the clinical problems during the fast of Ramadan is important in order to opens the way to further research aimed at discovering the significance of Ramadan fasting in patients with heart disease.Patients and Methods: Eighty-six outpatients with heart disease with intention to fast were studied in the month of Ramadan 2010 (1430 H) at the Ibn Al-bitar Hospital. Detailed clinical and biochemical assessments were performed within 3 days before the start of Ramadan and then on the last day of Ramadan.Results: There were 54 (62.8%) males and 32 (37.2%) females with a mean age of 56.3 years (range, 17-84 ). Forty-six patients (53%) had coronary artery disease, 23 patients (27%) had valvular heart disease, 13 patients (15%) had congestive heart failure and 4 patients (5%) were treated for arrhythmia. Sixty-two patients (72%) were in New York Heart Association (NYHA) Class I, 18 patients (21%) in Class II, and 6 patients (7%) were in Class III. Seventy-four patients (86%) managed to fast during the entire Ramadan, 9 patients (10.4%) missed the fasting for up to 7 days, and 3 patients (3.5%) could not fast. There were no significant changes in the NYHA Class (p=0.12). No significant changes occurred in any of the hematological or biochemical parameters during the fasting of Ramadan.Conclusion: The effects of fasting during Ramadan on stable patients with cardiac disease are minimal. The majority of patients with stable cardiac disease can fast during Ramadan without significant detrimental effects.
Varus/valgus stability in imageless robotic-assisted total knee arthroplasty applying three-dimensional assessment of varus/valgus stress X-rays
Authors: Hijikata Hiroki , Mochizuki Tomoharu , Maeda Keisuku , Tanifuji Osamu , Omori Go , Yamamoto Noriaki , Kawashima Hiroyuki
Published: December 2023

BACKGROUND: The postoperative varus/valgus stability assessment in stress X-rays has been established as an evaluation index. However, it is performed by the two-dimensional (2D) method rather than the three-dimensional (3D) method. OBJECTIVE: This study aimed to identify the precision and reproducibility of measuring varus/valgus stress X-rays three-dimensionally and to examine varus/valgus stability under anesthesia in imageless robotic assisted total knee arthroplasty (rTKA). METHODS: This prospective study analyzed 52 consecutive rTKAs (five males, 67 ± 5.3 years; 47 females, 74 ± 5.9 years). Postoperative varus/valgus stress X-rays in knee extension under anesthesia at manual maximum stress were three-dimensionally assessed by 2D–3D image matching technique using the 3D bone and component models. Varus/valgus angle between components (VV angle) in no stress, valgus stress, varus stress, medial joint opening (MJO), and lateral joint opening (LJO) were evaluated, clarifying this method’s precision and reproducibility and valgus/varus stability. RESULTS: All parameters’ precision and reproducibility had <1° mean differences and high intra- and inter-class correlation coefficients. Bland–Altman plots showed no fixed and proportional bias. Non-stress VV angle, valgus VV angle, varus VV angle, MJO, and LJO were 3.6 ± 1.2°, 1.0 ± 1.4°, 7.1 ± 1.9°, 1.5 ± 1.0 mm, and 2.8 ± 2.7 mm, respectively. CONCLUSION: This prospective study demonstrated that (1) the three-dimensional measurement method provided sufficient precision and reproducibility, and (2) the rTKAs could achieve good postoperative varus/valgus stability with a small standard deviation.
Image Retrieval-Based Product Identification for Automatic Checkout Systems
Authors: Lu Haitao
Published: December 2023

To solve the problem of low accuracy of commodity identification due to frequent update of commodity library in automatic checkout system, an image retrieval-based commodity identification method for automatic checkout system is proposed. The YOLOv5 algorithm is used to detect commodities in the checkout image, generate a set of localization frames and segment the targets. For the problem that YOLOv5 is insensitive to small targets, a detection layer is added to the YOLOv5 network structure, and the loss function and bounding box localization error are optimized to improve the model detection performance. On this basis, the SCDA algorithm is used to match the registered commodity map in the database with the segmented commodity map to complete the commodity detection and recognition. The experimental results show that the algorithm in this paper improves the accuracy rate by 3.75% relative to DPNet and achieves 80.79% mAP on the whole test set.
Design and Implementation of Intelligent Potted Plant Management System Based on Internet of Things
Authors: Wang Yujie , Huang Langbing , Li Jianshan , Sun Tang
Published: December 2023

To solve the problem of difficult cultivation of precious potted plants, this paper designed an intelligent potted plant cabinet which can be monitored remotely. Embedded control system was developed based on Keil environment, Wireless communication and real-time monitoring devices are brought together, IP address is created in Alibaba Cloud server, data center is established, cloud middleware is set up, B/S version and mobile App access system are developed, and automatic plant of plant cabinet and intelligent monitoring of potted growth status are realized through temperature and humidity control device, light intensity control device, watering control device and real-time monitoring device. The growth and flowering period of jasmine was taken as the experimental object to verify the plant effect. The intelligent plant cabinet controlled the temperature deviation within ±0.5 °C, the air humidity deviation within ±1.5% RH, the light intensity deviation within ±23 Lx, the soil humidity deviation within ±4.1% RH, and the flowering period was prolonged by 4-5 days. The plant cabinet can intelligently control environmental factors according to the growth habits of jasmine, and the system has fast response and stable operation, which can be extended to intelligent cultivation of various potted plants.
The Application of VR Technology in Agricultural Cultural Restoration Projects
Authors: Wang Xiaodong , Li Yan
Published: December 2023

This article aims to introduce the overall design and specific implementation of the agricultural cultural heritage restoration project based on VR technology. We will expound on the overall design of agricultural culture restoration, including content planning, software selection, and technical routes for system implementation, and provide a detailed introduction to the agricultural element modeling based on Blender software, including the establishment of the basic scene and model elements of farming, the production of scene elements for farming display mode, the production of scene elements for farming interaction mode, and the design and model export of interactive farming tools. Finally, we introduce the implementation of virtual reality scenes, including the construction of virtual reality environments in the Unreal Engine5, the implementation of model import, lighting, reflection, and post-effects, the implementation of roaming and interactive functions, and the publication as an EXE file.
Automatic Resource Scaling Management System on Electric Power Supercomputing Clouds
Authors: Gan Rundong , Li Xun , Wei Wei , Su Haibin , Zhan Zhu
Published: December 2023

With the advent of the big data era, the explosive growth of data volume has put enormous computing and storage pressure on electric power companies. As a powerful technical means, supercomputing clouds are widely used in data processing, storage, and online services. However, critical services in supercomputing clouds are often deployed with overprovisioned resources to ensure the quality of service for users, resulting in significant energy consumption and additional costs. At the same time, insufficient resources for service provisioning can lead to performance degradation and service violations. To this end, this paper proposes an automatic resource scaling management system on electric power supercomputing clouds. Specifically, the proposed system is based on Transformer’s long-sequence prediction model to predict the future load intensity of the service and calculate the number of instances required by the service in the future through the runtime service requirement estimation component, thus automatically scaling resources and minimizing resource costs. Experimental results show that our system achieves the best scaling behavior based on performance metrics and the lowest cost overhead compared to strong baselines.
Research on Displacement Monitoring of Hydraulic Structure with Machine Vision
Authors: Hou Wenang , Shi Rong , Yan Zhikun
Published: December 2023

Hydraulic structure has a long history, which varies in many kinds. Its damage will have a great impact on people’s life and property safety. Therefore, relevant personnel should be able to immediately discover the damage of hydraulic buildings and focus on it, and at the same time, it can immediately carry out relevant warning and treatment of the safety risks. The traditional displacement measurements are mainly contact, which have lots of disadvantages, such as insufficient coverage area, many monitoring blind areas and high monitoring cost. With the rapid development of high definition camera and artificial intelligence, machine vision is a non-contact, high precision, low cost, all-weather monitoring system has received more and more attention. The machine vision system can not only eliminate the shortcomings of the contact measurement method, but also realize the multi-azimuth full coverage monitoring of hydraulic buildings. This paper will from the machine vision system composition and commonly used computer vision methods, matters to pay attention to three aspects of the current machine vision-based hydraulic building displacement monitoring methods are discussed, aimed at relevant researchers through machine vision to improve the monitoring of comprehensiveness, accuracy and real-time, help to better protect the health, safety, smooth operation of hydraulic buildings.
Based on Improved YOLOv7 Small Target Detection
Authors: Wang Ruohan , Li Yu
Published: December 2023

In recent years, deep learning-based object detection has developed rapidly, but its performance in the small object detection field is not ideal compared to the natural scene image domain. Hence, this paper proposes an improved small object detection algorithm based on YOLOV7 algorithm. Firstly, based on Ghost model, the ELAN module in backbone structure is improved to Gmodel which effectively reduces computation and improves accuracy. Secondly, this paper introduces a Triplet Attention-improved small object attention module Amodel in YOLOV7’s head structure; through Amodel’s cross-latitude interaction function, it enhances the feature detection performance for small objects. Experiments were conducted on RSOD dataset and our method increased yolov7’s AP50 by 1.65mAP and AP50-95 by 1.88mAP while also reducing FLOPs by 0.2G, making it more suitable for dense small target scenes for object detection.
Robust Skeleton-Based Action Recognition Based on Multi-Stream Gradient Activation Graph Convolution Network
Authors: Wang Xiaotian , Hu Qibin , Hu Yanzhe , Wang Qian
Published: December 2023

Current skeleton-based human action recognition methods usually apply to complete skeletons. However, in real scenarios, capturing incomplete or noisy skeletons is inevitable. When some joints information is occluded or interfered, it may significantly reduce the performance of current methods. To improve the robustness of action recognition models, a multi-stream dynamic graph convolutional network (GCN) is proposed to explore sufficient discriminative features distributed on all skeleton joints. By a multi-stream structure, gradient information of the graph structure is aggregated in a progressive manner. We introduce class activation map (CAM) techniques to extract the joints with the maximum amount of information in each stream. The activation maps in each stream are input to the next stream as mask matrices so that the new stream can explore the unactivated parts. Meanwhile, we set up a depth module so that we can still distinguish the characteristic values between nodes after multiple aggregations. Experiments prove that our model achieves the state-of-the-art performance on the NTU-RGB+D dataset. At the same time, it also shows strong robustness on the jittering dataset.
Interactive Device of Digital Heritage Visualization Based on Metaverse
Authors: Tang Zichun
Published: December 2023

In the era of big data, people store a large amount of information or assets on the Internet, where the left traces of data are difficult to be eliminated. Digital heritage plays a unique and valuable role for individuals, countries and societies, yet currently there are many dilemmas about its preservation. The author studied and concluded that it is easier to solve the problem of digital heritage in the context of the metaverse, which will gain distinctive advantages in the future. Based on the above-mentioned social problems and research background, the interactive device themed “Digital Heritage Visualization”, designed by the author, can build a bridge between digital heritage, metaverse and new media interactive devices. The existing gesture sensing technology, 3D modeling technology and programming software contribute to the device’s completion.
Moving Object Detection and Tracking in Video Frame Based on OpenCV
Authors: Yang Yueting , Hu Shaolin , Wang Guogang , Ke Ye
Published: December 2023

Local occlusion may be developed during the target motion, such that it is urgent to solve the problem of video tracking loss caused by moving target occlusion. In this paper, the computer vision library OpenCV is used to preprocess the motion video frame. Two algorithms are combined to solve the problem of tracking loss due to target-background similarity and occlusion: one is the Camshift algorithm (which is used to track the moving target); the other is the Kalman filter (which is used to predict the target position). Comparing with Meanshift algorithms in the same experimental environment, the results show that the proposed Camshift-based method can effectively filter out the background interference in the tracking process based on accurate detection and tracking of moving targets, and make the motion information of occluded targets more prominent. Good performance has been achieved in the interference and occlusion environment, and it has certain real-time performance and effectiveness.
Application of BP Neural Network in the Prediction of Residents’ Consumption Level
Authors: Han Hong , Duan Yuehua
Published: December 2023

Household consumption is an important part of stimulating domestic demand, developing the economy and promoting the steady growth of China’s GDP. In this paper, the consumption levels of residents in cities in Henan Province are obtained through factor analysis. There are obvious differences among regions, which can be divided into three levels according to the comprehensive score: high-consumption cities, medium-consumption cities and low-consumption cities. Secondly, the BP neural network is used to train the model using MATLAB software, with 8 evaluation indicators as the input layer and resident consumption level as the output layer. The prediction results show that the prediction accuracy of BP neural network has reached 83.33%, which is highly scientific and reasonable. It can add a new perspective to predict the consumption level of residents, provide scientific research data for urban consumption departments, improve the residents’ awareness of reasonable expenditure in Henan Province, and drive the economic development of Henan Province.
Research on Transaction Mechanism and Game Theory for Energy Management of Virtual Power Plants
Authors: Lu Yutao , Ma Zhenyu , Ma Tongtao , Li Changjun , Wang Weixian
Published: December 2023

In response to the problem of low enthusiasm for energy efficiency transformation of high energy consuming enterprises under the traditional fixed electricity pricing model, a differential pricing mechanism is adopted in the process of power reform to promote energy conservation and emission reduction. Virtual power plants aggregate multiple distributed resources, providing energy-saving equipment transformation for high energy consuming enterprises while also providing power guarantee. This not only alleviates the power supply shortage problem of power supply companies, but also reduces their operating income, This has led to a cooperative and competitive relationship between virtual power plants and power supply companies. This article constructs a two-layer optimization model for this scenario, simulates and analyzes the decision-making game between the virtual power plant and the power supply company, obtains the final optimized results, and compares the user benefits and social comprehensive benefits before and after the user transformation.
Neonatal Seizure Detection Combining Sparse Representation and Deep Learning
Authors: Gao Guangmiao , Liu Jie , Yuan Qi , Nie Weiwei
Published: December 2023

Neonatal seizures are a common emergency in neonatal intensive care unit (NICU). Neonatal epilepsy detection is generally the doctor through the naked eye reading electroencephalogram (EEG) to judge, this way is more subjective easy to produce differences. In this paper, a novel algorithm combining sparse representation and deep learning for automatic detection of neonatal seizures is proposed. After the EEG data is preprocessed, the features are extracted and sent to the Deep Sparse Representation Classification (DSRC) network. The performance of the algorithm was evaluated by sensitivity (sen) and specificity (spe) values in a publicly available dataset of 36 neonates with continuous EEG signals. Testing is done using cross-validation, so performance accurately represents the ability to classify architectures and test generalizations in a clinical setting. The sensitivity and specificity of cross detection were 89.33 and 90.2, respectively.
Quadruple Extraction of Aspect-Category-Emotion-Opinion Through Comparative Learning
Authors: Chen Yuanchao , Luan Cuiju
Published: December 2023

In recent years, significant advances have been made in novel modelling tasks centred on aspectual sentiment analysis. This task shows relatively good results by extracting aspect-category-opinion-sentiment (ACOS) quaternions. To further improve the performance of the model in ACOS quadruplet extraction, this study proposes an integrated approach combining encoder-decoder networks, multi-level perceptrons (MLP), supervised contrast loss (SCL) and representation structure (ES) metrics. The integration of these techniques aims to improve the generation of more informative features by propagating aggregated representations from the encoder to the multilevel perceptron. In addition, contrast learning methods are used to encourage models to generate discriminable input representations on key attributes, thereby facilitating distinguishable input representations that facilitate the task of second-level prediction. In addition, an expression structure output target was introduced which was combined into an autoregressive encoder-decoder model to generate quartets, and empirical results confirmed the excellent performance of the model on three ACOS datasets. The experiment effectively established the feasibility of the task and highlighted its strengths in generating aspects for sentiment analysis.
Multi-Modal Sarcasm Detection via Cross-Modal Attention Mechanism
Authors: Li Yueying , Cao Hui , Xia Xiaotian , Song Quan
Published: December 2023

With the popularity of social media, the sarcasm figure of speech has become a common phenomenon on social media platforms, and many studies have utilized text and visual information for multimodal sarcasm detection. This paper use a method based on cross modal attention mechanism. Specifically, the paper extract multimodal features firstly, use attention mechanism to focus on the inconsistent information between modalities, and then use the inconsistent information for prediction. The experimental results show that the performance of this paper is improved on the Twitters sarcasm dataset.
Research on Personalized Recommendation System of New Media Short Video Based on Big Data Mining
Authors: Xiong Huayong
Published: December 2023

In the era of mobile internet, the new media video entertainment industry has developed rapidly, and entertainment information is showing an explosive growth trend. The problems of information overload and knowledge redundancy are becoming increasingly severe. How to efficiently and accurately obtain interesting entertainment information has become a difficult problem in people’s daily lives. Recommendation systems are one of the important applications of data mining and an important way to solve information overload. The recommendation system mainly includes login registration module, personal center module, management permission module, video recommendation module, video browsing module, video editing module, and comment and like module. This article analyzes the specific requirements of the video service platform, designs the system architecture and specific modules, and develops and tests the system. After testing, all parts are currently running well and meet user expectations.
Design and Development of Regional Smart Pension Service Platform Based on Cloud Technology
Authors: Cao Yufei
Published: December 2023

With the aging of our society deepening year by year, the contradiction between supply and demand of social pension services is becoming increasingly serious. Developing regional smart pension has become an important way to optimize the pattern of social pension and improve the quality of social pension services. In this regard, based on the problems existing in the current residents’ pension system, such as the imbalance of resource allocation, insufficient combination of medical care and nursing care, and low service efficiency, this paper puts forward a set of regional smart pension service platform construction scheme to promote data sharing and business collaboration, and promote the transformation and development of traditional social pension services to modern smart pension services. The platform is based on cloud architecture server. The upstream of the platform relies on wearable technology to obtain user data, and NB-LoT cellular technology completes the data transmission. The downstream uses Javaweb technology to build the corresponding application server and interactive interface, and uses artificial intelligence algorithms to assist in the analysis and processing of data information. After the simulation test, the functions of the system run smoothly, which can meet the actual needs of regional smart pension services and provide reference for building a new form of social pension services.
Dynamic Load Analysis Software Design of Vehicle Radar Structure
Authors: Xu Jinkang , Zhou Xiaolong
Published: December 2023

In recent years, the military electronic industry has developed rapidly, and the discrete simulation analysis models have been integrated into system-level digital simulation for performing full-cycle simulation analysis of the radar system. Based on QT platform, this paper mainly builds a software system for dynamic simulation and fatigue life evaluation of vehicle radar structure through secondary development of the Nastran kernels, realizing the automation and customization functions of CAE analysis, simplifying the analysis process and providing friendly GUI for non-professional CAE users. Meanwhile, inside the software a problem that the date transmission efficiency is relatively low. At the end, several methods for optimizing the operation efficiency are proposed.
Different Embedding Frameworks of Reversible Information Hiding in Encrypted Domain
Authors: Kong Yongjun , Zhang Minqing , Xiong Zhang , Song Zhechao
Published: December 2023

Reversible data hiding in encrypted domain takes encrypted data as the carrier to be embedded, which can embed secret information for lossless transmission on the basis of protecting data content. The reversibility of this technology can ensure that the original data can be recovered lossless after the secret information is extracted, which has important research significance for sensitive carrier data fields such as military, medical and judicial forensics. To better carry out related research of reversible data hiding in encrypted domain, different embedding frameworks of this technology are summarized in this paper.
Design of Oil Field Data Fusion Integration Platform
Authors: Song Caili , Liang Bin , Xu Yuanzhi , He Zhizhou
Published: December 2023

With the deepening of oilfield digitization process, oilfield data sharing can improve data utilization and is conducive to mining oilfield production laws. Due to historical reasons, there are great differences in the formats of databases and data tables used in different oil fields. Under the condition of keeping the data storage environment and format of each oil field unchanged, the establishment of an integrated data fusion platform can improve the access speed of data and enhance the utilization effect. The platform integrates the data in Oracle, SQL Server 2008, SQL Server 2012 and other databases in the form of data mapping. The integrated platform adopts SQL Server 2012. In the process of data fusion, data integrity and legitimacy directly affect the accuracy of data mining and data analysis. The system belong to B / S architecture, uses HTML and C# language to jointly carry out front and back-end development in vs 2015 environment, and realizes data fusion of different databases. Taking the information of 10000 wells of Chuanqing drilling company as the test data, the system can extract and display the data as required, and remind the error of unreasonable data, so that users can check and calibrate the data, so as to achieve the goal of data availability.
Research on E-Commerce Purchase Prediction Model Based on Machine Learning
Authors: Lin Zaixin , Huang Zihui , He Huiyan , Liang Jiazi , Zheng Xinbei , Huang Jingde
Published: December 2023

To improve the sales volume of electronic commerce platforms, predicting user purchasing behavior has become an important research area. This paper addresses the issue of user purchase prediction in e-commerce platforms. Firstly, a machine learning-based user purchase prediction model is proposed. Secondly, a machine learning decision model based on random forest algorithm is established, which extracts features from various aspects such as user historical purchase data to determine customer purchasing behavior. Finally, the trained machine learning model is used to predict customer purchasing behavior. Experimental results show that the proposed model exhibits excellent performance in terms of prediction accuracy, fitness, and has practicality and promotion value.
A Study of Data Governance Methods Based on Non-Linear Evaluation of K-Domains
Authors: Li Yajie , Sun Kaining , Ming Tao , Chen Anqing
Published: December 2023

The current traditional data governance methods mainly ensure the overall quality of data through data quality inspection, and the lack of data fusion processing leads to poor data governance results. In this regard, a data governance method based on nonlinear evaluation of K domain is proposed. By formulating data cleaning rules, data are processed such as weight reduction, gap filling and deletion, and data fusion operations are performed according to the fusion table attributes by configuring fusion rules. Finally, the data consistency as well as wholeness is checked, and the actual effect of data governance is determined by the data quality score results. In the experiments, the proposed method is validated for the governance effect. The analysis of the experimental results shows that the proposed method takes significantly shorter time for data fusion and has better data governance performance when the proposed method is used to govern multiple sources of data.
Research on the Application of Domestic Autonomous Cryptography Algorithms in the Field of Ecological Environment Information Security
Authors: Yu Ying , Zhao Kangping , Yu Zhiyang , Lang Zhiyu
Published: December 2023

Passwords are not simply the crystallization of scientific and technological progress, but also a sharp tool for maintaining network security and order, and an indispensable strategic resource for safeguarding national security. Ecological and environmental informatization is not only an important component of national cyberculture and informatization construction, but also the core content and support guarantee of ecological and environmental protection work, which is related to the practical performance ability of ecological and environmental departments at all levels. The rapid development of ecological environment informatization construction not only brings efficient and fast office experience, but also enhances the security risks of network information. The ecological environment protection work involves a variety of types and a large amount of multi-source heterogeneous data, and there are many practical operations such as data collection, data analysis, data storage, and data application. The frequent occurrence of high-risk risks in information security, including data theft and vulnerability attacks, has led to numerous security risks in the construction of ecological environment informatization, seriously affecting the security, scientifically, and standardization of ecological environment protection work, and even threatening national security. This article focuses on the network information security issues exposed in the process of ecological environment informatization construction, focusing on the security issues faced by data storage and transmission, and conducting research on the application of domestically produced autonomous password algorithms in the field of ecological environment information security. On the basis of continuously improving the standardized storage of data, this article effectively ensures the security of various types of data during transmission. It has played a positive role in promoting the improvement of information security in the field of ecological environment.
Optimisation of New Media Diplomacy Based on Big Data Algorithms
Authors: Tian Yuan
Published: December 2023

With the rapid development of today’s society, the constant innovation of new media technologies and the emergence of new communication tools, internet culture is becoming increasingly prominent as a new form of culture in social life. New media technology provides a series of technical platforms for human life, gradually integrating into all aspects and areas of human society and life, reflecting more comprehensively the real life of our national network society, which has stimulated new vitality for spreading positive energy and enriching the content of online culture, and is an indispensable and important part of people’s cultural life. The innovation of new media technology has changed the concept of human life, consumption habits and behaviour, and has had an important impact on the development of the cultural industry and the change in the way culture is disseminated. The dissemination of a positive online culture for today’s times promotes the progress of human society. Based on the intelligent big data foundation of new media, this paper combines the analysis and research of the new media development requirements in the new era, laying a new development approach and development foundation for the later new media development.
Modeling the Pork Price Cycle in China Based on the Age Structure of Hogs
Authors: Peng Shicheng
Published: December 2023

Pork price is essential in Chinese household consumption. Pork price prediction can advise the government to control the macroeconomy. Because of the explicitly periodic feature for pork price, previous researchers used fancy functions to fit the price cycle and predict it. However, only few researchers have tried to solve the pork price cycle based on the actual production of hogs. In this paper, a model is constructed using the most straightforward optimization and age structure of hogs to simulate the production process in pig farms and the pork price trend. The results match the claim that the price has a five-year cycle, and the model does well in empirical testing. Finally, some advice on controlling inflation is given based on the model prediction.
Composition Analysis and Identification Model of Ancient Glass Products
Authors: Yi Pingchuan
Published: December 2023

Ancient glass objects are valuable physical evidence of the existence of the Silk Road. Due to the long preservation time, the glass is highly susceptible to weathering and resulting in chemical composition changes, which in turn affects the identification of this artifact. Based on the relevant data of a given batch of ancient glass, a model for analyzing and identifying the composition of ancient glass products was established and sensitivity analysis was conducted Spearman’s rank correlation coefficient, chi-square analysis and stepwise regression.
Key Technologies of Talent Portrait Based on Big Data Analysis
Authors: Zhang Yang , Sun Qiao , Du Changyu , Ji Peng , Liu Dan , Zhao Lei
Published: December 2023

This paper studies a talent comprehensive quality platform based on big data analysis, including: unified data collection layer, which gathers talent data from various data sources to HDFS storage in an adaptive way; At the basic data platform layer, the talent data from the unified data collection layer is analyzed with big data to form a talent comprehensive quality evaluation report, which provides publishing and query; The application display layer displays the talent comprehensive quality evaluation report published or queried by the basic data platform layer. This paper realizes the comprehensive quality evaluation and management of talents, with high accuracy, convenience and security. In addition, this paper proposes a multimodal information fusion method, including image, text, and audio data, to comprehensively describe and predict the characteristics of talents. The experimental results show that the user portrait modeling method based on multi-modal information in this paper has achieved better performance in the talent feature prediction task.
The use of blockchain technology in the scientific research workflow
Authors: Lawlor Bonnie
Published: December 2023

This paper is based upon a lightening talk that was given on February 14, 2023 at the 2023 NISO Plus conference. The presentation described the research for the creation of a white paper on the use of blockchain technology along the scientific research workflow that will be released in early 2024 by the International Union of Pure and Applied Chemistry (IUPAC).

What the three-year study found is that the technology is indeed being used in almost all the steps in the scientific research workflow - from hypothesis development through to publication - by commercial organizations as well as by non-profits and across all market sectors, even governments. For example, the U.S Department of Health and Human Services (HHS) uses blockchain technology in a pilot program, the Grant-recipient Digital Dossier (GDD), to manage their grant program more efficiently. As of July 2021, GDD had reduced the time required to complete grant assessment tasks from four-plus-hours to a fifteen-minute process.

This paper briefly summarizes the findings, discusses the pros/cons of the technology, and provides a glimpse of how the technology is impacting the future of scientific research.
Research on effectiveness evaluation of corporate culture construction based on the neutrosophic cubic number multiple attribute decision making
Authors: Cui Tong , Sun Peixi , Liu Xiao
Published: December 2023

Corporate culture has its own development laws and may play a significant role in a short period of time, but its development and improvement are a relatively long-term task. The construction of corporate culture is a systematic project that varies depending on the enterprise. For enterprises, the construction of corporate culture has very important practical significance for the re integration of the enterprise team and the enhancement of competitive strength. Culture is a productive force, and the role of corporate culture is more direct. Therefore, enterprises should focus on their own development goals, create their own unique corporate culture based on learning and reference, and meet market challenges with a new look and strong strength. The effectiveness evaluation of corporate culture construction is a classical multiple attribute decision making (MADM). Recently, the TODIM and VIKOR method has been used to cope with MADM issues. The neutrosophic cubic sets (NCSs) are used as a tool for characterizing uncertain information during the effectiveness evaluation of corporate culture construction. In this manuscript, the neutrosophic cubic number TODIM-VIKOR (NCN-TODIM-VIKOR) method is built to solve the MADM under NCSs. In the end, a numerical case study for effectiveness evaluation of corporate culture construction is given to validate the proposed method. The research aim of the paper is summarized: (1) the NCN-TODIM-VIKOR is proposed for MADM problem with NCSs; (2) The attributes weight information is obtained through information entropy; (3) the NCN-TODIM-VIKOR method is designed for effectiveness evaluation of corporate culture construction and were compared with some existing methods; (4) Through the comparison, it is found that NCN-TODIM-VIKOR method for effectiveness evaluation of corporate culture construction proposed are effective.
Classifying flexible measures in two-stage network DEA and DEA-RA using novel slacks-based models
Authors: Hosseini Monfared Seyede Nasrin , Hosseinzadeh Lotfi Farhad , Mozaffari Mohammad Reza , Rostamy malkhalifeh Mohsen
Published: December 2023

In conventional DEA models it has been assumed that each measure status is considered input or output. However, a performance measure in some cases can have input role for some DMUs and output role for others and is known as flexible measure. In this paper new slacks-based FNSBM models are proposed in general two-stage network DEA to determine the relative efficiency of units and the role of flexible measures. Then new radial FNDEA-R models and new slacks-based FNSBM-DEA-R models are developed in the presence of flexible measures based on the ratio of input components to output components or vice versa in the input and output orientation under constant returns to scale in general two-stage network. In our proposed models, flexible measures are determined as input or output to improve performance to maximize the relative efficiency of the DMU under evaluation. The FNDEA-R and FNSBM-DEA-R models versus FNSBM models prevent efficiency underestimation and pseudo inefficiency issues. The status of one flexible measure in the input-oriented and output-oriented FNDEA-R and FNSBM-DEA-R models may have different conclusions. The radial FNDEA and FNDEA-R models have unitsinvariant and the objective function of the FNSBM and FNSBM-DEA-R models are invariant with respect to the units of data. A numerical example is used to illustrate the procedures.
Availability evaluation of solar photovoltaic systems using markov modeling and cuckoo search algorithm
Authors: Kumar Ashish , Maan Vijay Singh , Choudhary Ravi , Saini Monika
Published: December 2023

The main objective of present investigation is to evaluate and optimize the operational availability of the solar photovoltaic systems. As the solar energy is a prominent source of renewal energy and contribute a lot in global development having less environmental impacts but the safety and reliability issues of these systems also observed during the operational phase. Availability is an effective tool that is used to discourse the safety and performance issues of renewal energy sources especially solar photovoltaic systems. Here, a stochastic model is developed for solar photovoltaic system having solar photovoltaic plates, solar charger, solar battery, and inverter. The Markov birth-death process is applied for development of the mathematical model of the proposed system. The chapman-Kolmogorov differential difference equations of the proposed solar photovoltaic system used to predict the steady state availability of system. On the basis of literature, the failure and repair rates of all components of solar photovoltaic system are considered as exponentially distributed. In addition, an effort is also made to predict the optimum availability of solar photovoltaic system using well-known optimization technique cuckoo search algorithm. It is revealed that, the predicted availability of the solar photovoltaic system is 0.9988799 at population size 60 after 700 iterations. The estimated parametric values of the failure and repair rates also derived. To highlight the importance of the study the numerical and graphical results are presented and shared with the system designers and maintenance engineers.
An integrated methodology for enterprise financial management capability evaluation based on EDAS technique and group decision making
Authors: Mu Li
Published: December 2023

The financial management capability of enterprises, as an important component of their soft power, has a decisive impact on the success or failure of their operations. In the increasingly fierce market competition, enterprises must continuously improve their financial management capabilities in order to ensure efficient operation and achieve better economic benefits. Insufficient financial management capabilities in enterprises can seriously affect the stability of production and operation, hinder the realization of profits, and hinder the long-term development of enterprises. In order to better improve the financial management level of enterprises and promote the standardization of financial management, it is necessary to use scientific techniques to evaluate the financial management ability of enterprises, so as to accurately grasp the key links in the financial management process of enterprises and implement targeted effective measures. The enterprise financial management capability evaluation is a classical multiple attribute group decision making (MAGDM). In recent years, the MAGDM problem has become an important research field in modern decision science. This paper extends the EDAS technique to the 2-tuple linguistic Pythagorean fuzzy sets (2TLPFSs). On the basis of the original EDAS technique, 2-tuple linguistic Pythagorean fuzzy number EDAS (2TLPFN-EDAS) technique based on cosine similarity measure (CSM) and Hamming distances is managed for MAGDM. Finally, a case study for enterprise financial management capability evaluation and some comparative analysis with the other techniques show that the new technique proposed in this paper is effective, reasonable and accurate. The main contribution of the paper is summarized: (1) the 2TLPFN-EDAS technique based on CSM and Hamming distances is managed for MAGDM under 2TLPFSs; (2) The entropy is employed to manage the attribute weight based on cosine similarity measure(CSM) and Hamming distances under 2TLPFSs; (3) the 2TLPFN-EDAS technique is employed for enterprise financial management capability evaluation and were compared with some existing techniques; (4) Through the comparison, it is found that 2TLPFN-EDAS technique for enterprise financial management capability evaluation proposed are effective.
Detection of mapping function location observation with displacement in organising model by using deep learning
Authors: Pradeep M. , Sivaji U. , Nithya B. , Kadiravan G. , Preethi D. , Painam Ranjith Kumar
Published: December 2023

The mapping function must identify the reference model and detect coordinate arrangement by observing a repository with deep learning. Progression model with coordinate arrangement composition should have various positional displacements from one location to another. A prerogative classification model is an evolution of factor accomplishment in a repository method. Coordinate arrangement with calculation method must formulate a model locality twirl in classification method of a reference in dominance factor of perpetuity position observation by procession of reference localities. In a procession model observation by location, tendency method should be rotated from locality position into another coordinate method, with a PDD factor measuring DPA of cadent RFT with an origin of 92.6, a cadent DS intermediate factor of 95.2, culmination factor of cadent RFT of 94.1. The docile exploratory arrangement of heuristic parameters is used in existing system to perceive phenomena such as sprout, enrollment discernment, demeanour, gravest perforation measure, Model of a heretic in apprehension method by premonition incongruity. Annotation should identify classification process using a proposed model to obtain massive measure of imputation function, In PDD measure of DPA in Cadent DS, with inception of 96.1, intercession of Cadent RFT in 92.6, with crowning of Cadent RFT in 96.4, 93.2
Stock market prediction-COVID-19 scenario with lexicon-based approach
Authors: Ayyappa Yalanati , Siva Kumar A.P.
Published: December 2023

Stock market forecasting remains a difficult problem in the economics industry due to its incredible stochastic nature. The creation of such an expert system aids investors in making investment decisions about a certain company. Due to the complexity of the stock market, using a single data source is insufficient to accurately reflect all of the variables that influence stock fluctuations. However, predicting stock market movement is a challenging undertaking that requires extensive data analysis, particularly from a big data perspective. In order to address these problems and produce a feasible solution, appropriate statistical models and artificially intelligent algorithms are needed. This paper aims to propose a novel stock market prediction by the following four stages; they are, preprocessing, feature extraction, improved feature level fusion and prediction. The input data is first put through a preparation step in which stock, news, and Twitter data (related to the COVID-19 epidemic) are processed. Under the big data perspective, the input data is taken into account. These pre-processed data are then put through the feature extraction, The improved aspect-based lexicon generation, PMI, and n-gram-based features in this case are derived from the news and Twitter data, while technical indicator-based features are derived from the stock data. The improved feature-level fusion phase is then applied to the extracted features. The ensemble classifiers, which include DBN, CNN, and DRN, were proposed during the prediction phase. Additionally, a SI-MRFO model is suggested to enhance the efficiency of the prediction model by adjusting the best classifier weights. Finally, SI-MRFO model’s effectiveness compared to the existing models with regard to MAE, MAPE, MSE and MSLE. The SI-MRFO accomplished the minimal MAE rate for the 90th learning percentage is approximately 0.015 while other models acquire maximum ratings.