Network Data Research Articles

Auricular Therapy to Control Pain in Women With Breast Cancer: Protocol for Systematic Review and Meta-Analysis.

The increased incidence of breast cancer implies the appearance of frequent symptoms associated with disease and treatments, such as pain. For the management of this issue, auricular therapy has been used in a complementary manner, especially for its safety and analgesic action. This systematic review aims to summarize available evidence on the effects of auricular therapy on pain in women undergoing breast cancer treatment. This is a systematic review that includes randomized controlled trials that evaluated the effects of auricular therapy on pain in women with breast cancer, as compared with other interventions (sham or placebo auricular therapy, other nonpharmacological interventions, and routine pain treatments) during the treatment of the disease. Pain, whether induced or not by cancer treatments, is the main outcome to be evaluated. The search for the studies was performed in the following databases: MEDLINE through PubMed, CINAHL, CENTRAL, Embase, Web of Science, Scopus, VHL, TCIM Americas Network, CNKI, and Wanfang Data. The reviewers have independently evaluated the full texts, and in the near future, they will extract the data and assess the risk of bias in the included studies. The certainty of the evidence will be assessed using the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE), and a meta-analysis will be carried out to evaluate the intervention, considering the homogeneity of the results, using the Cochran Q test and quantified by the Higgins inconsistency index. The guidelines of the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) have been respected in the elaboration of this protocol. The records screening stage has been completed, and the synthesis and meta-analysis were conducted in February 2024. We hope to have finished the preparation of the paper for publication by September 2024. Review reporting will follow standard guidelines for reporting systematic reviews. The results will be published in peer-reviewed scientific journals. This review will compile the strength of evidence for the use of auricular therapy in the management of pain in women with breast cancer during the treatment of the disease, identifying gaps in the available evidence as well as assisting health professionals in indicating the intervention for clinical practice. PROSPERO CRD42022382433; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=382433. DERR1-10.2196/55792.

Adaptive Strategies and Sustainable Innovations of Chinese Contractors in the Belt and Road Initiative: A Social Network and Supply Chain Integration Perspective

As global economic integration and rapid technological advancements transform international business, international engineering contracting has become essential for achieving sustainable development goals (SDGs). This paper investigates the significant impact of China’s strategic initiatives, notably the “Going Global” strategy and the Belt and Road Initiative (BRI), on the operational practices of Chinese enterprises involved in overseas investments. Central to this transformation is the Engineering, Procurement, and Construction (EPC) model, which emphasizes the integration of supply chain management and stakeholder collaboration to enhance performance in international EPC projects and underscores the crucial role of these elements in promoting sustainability. Incorporating insights from social network data analysis, this study reveals that contractors collaborating with various stakeholders—such as owners/consulting engineers, domestic and foreign customs departments, and group headquarters/design parties—exhibit a high degree of similarity in personnel profiles. This suggests that the internal organizational structure and personnel allocation of contractors could be optimized to enhance operational efficiency, aligning with the collaborative patterns identified. This study addresses a critical research gap by exploring how effective supply chain management and collaborative stakeholder engagement within multinational EPC projects contribute to sustainable outcomes. Employing advanced social network analysis software, the research examines the complex interactions among stakeholders and their influence on procurement dynamics. Findings indicate that strong relational networks and strategic collaborations significantly enhance procurement efficiency and project success, underscoring the importance of supply chain integration. Ultimately, integrating supply chain management principles into the EPC model not only offers innovative perspectives for advancing sustainability in international projects but also provides actionable insights for improving project outcomes within the BRI framework. This research underscores the pivotal role of supply chain organization and stakeholder cooperation in achieving sustainability objectives, thereby enriching the discourse on sustainable enterprise operation and supply chain management in the context of global initiatives.

Improved identification of network anomalies through optimal CURE clustering

Abstract In this paper, we propose an advanced network anomaly behavior identification framework to overcome the constraints inherent in conventional rule- or signature-based approaches, which often struggle with emerging and previously unknown threats. Central to our framework is an Enhanced CURE (Cluster Updating and REfining) clustering algorithm, meticulously tailored and refined to incorporate a density-based methodology. This enhancement enables the algorithm to discern subtle shifts in network anomaly patterns with heightened precision. The implementation workflow commences with the application of the optimized CURE algorithm to analyze network data, followed by the deployment of a sophisticated anomaly degree ranking mechanism. This mechanism, through meticulous calculation of individual data points' anomaly degrees and subsequent ranking, effectively isolates those deviating significantly from standard behavioral norms, incorporating a strategic threshold to filter out false positives. To validate our methodology's efficacy and its superiority over existing techniques, experiments were conducted utilizing a substantial real-world network dataset. These tests affirm not only a marked increase in the accuracy of abnormal behavior recognition and a reduction in computational intricacy but also demonstrate the adaptability across diverse network ecosystems. Our approach has proven successful in pinpointing a wide array of network anomalies, encompassing malicious cyberattacks, fraudulent activities, unauthorized intrusions, and breaches of security protocols, thereby highlighting its comprehensive capability in bolstering network defense strategies. Despite the notable advancements and successful identification of various network anomalies, our framework currently lacks integration with real-time learning capabilities, limiting its immediate responsiveness to rapidly evolving attack patterns and necessitating ongoing research for dynamic updates and adaptive learning mechanisms.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

The galactic cosmic ray intensity fluctuations during perturbations of the solar wind in early November 2021

In order to develop methods for predicting negative manifestations of space weather, the dynamics of fluctuations in the intensity of galactic cosmic rays during geophysical disturbances in early November 2021 is studied. The obtained results point to the possibility of real-time short-term space weather forecasting based on the measurement data of the Russian national ground-based network of cosmic ray stations.

Space weather monitoring with a system of ground-based cosmic ray detectors

For remote identification of the processes of powerful solar activity in the form of CME directed towards the Earth, a technique of flicker noise spectroscopy has been developed. The results of the analysis of geo-effective events ( G1) are presented according to the data of the international network of neutron monitors and the multidirectional muon hodoscope URAGAN, which indicate the appearance of predictors (with an outstrip of 1–2 days) of the CME approach to the Earthʼs orbit.

BYOD Security Policy Model: A Systematic Literature Review

In education sector, Bring Your Own Device (BYOD) Security Policy (BYOD-SP) was established as earlier in 2015 in foreign universities with the purpose to safeguard campus’s network, system and confidential data and information. BYOD-SP is one of the strategic planning launched, aims to achieve maximum security for personally owned device through BYOD phenomenon. BYOD-SP acts as an official documentation that describes the detailed explanation of each component for the BYOD users to abide to all the components’ requirement in BYOD-SP. In BYOD practice in education field, BYOD-SP must meet the education agenda where BYOD-SP need to be tailored following education strategic plans and eliminate potential attacks that might cause the universities in loss if BYOD-SP is launched without having a robust model to refer of its components. Hence, it is crucial as this will welcome unauthorized parties to get access, gain and steal campus corporate data in BYOD environment. This study aims to identify what the necessary components that could assist top management and policy makers to formulate the comprehensive BYOD-SP in future and BYOD can be one of mobile computing that being accepted to be practiced in secured environment. Existing studies were executed in many research designs, where the review fulfilled the Preferred Reporting Items for Systematic reviews and MetaAnalyses (PRISMA) publishing standard. Two common online databases, World of Science (WOS) and Scopus were utilized to observe articles for this study purposes where a total of 16 articles published from 2013-2022 were reviewed to answer the formulated research questions on BYOD-SP. This review includes a topic based on the thematic analysis which is the components in drafting BYOD-SP documentation. The findings from this study proved four main components to be emphasized in tailoring BYOD-SP which are Device, Process, People and Jurisdiction/Law. The research’s findings here may drive the organization to have a reference model of BYOD-SP components and come out with the sturdy BYOD-SP to be adhered by all individuals in education premises. The top management in the organization need to involve in the procedures in each component of BYOD-SP to alleviate the case of security breach in BYOD activities.

Network mutual information measures for graph similarity

A wide range of tasks in network analysis, such as clustering network populations or identifying anomalies in temporal graph streams, require a measure of the similarity between two graphs. To provide a meaningful data summary for downstream scientific analyses, the graph similarity measures used for these tasks must be principled, interpretable, and capable of distinguishing meaningful overlapping network structure from statistical noise at different scales of interest. Here we derive a family of graph mutual information measures that satisfy these criteria and are constructed using only fundamental information theoretic principles. Our measures capture the information shared among networks according to different encodings of their structural information, with our mesoscale mutual information measure allowing for network comparison under any specified network coarse-graining. We test our measures in a range of applications on real and synthetic network data, finding that they effectively highlight intuitive aspects of network similarity across scales in a variety of systems.

Inconsistency in ferritin reference intervals across laboratories: a major concern for clinical decision making.

Iron deficiency anemia is a significant global health concern, diagnosed by measuring hemoglobin concentrations in combination with plasma ferritin concentration. This study investigated the variability in ferritin reference intervals among laboratories in the Netherlands and examined how this affects the identification of iron-related disorders. Ferritin reference intervals from 52 Dutch ISO15189-certified medical laboratories were collected. Ferritin, hemoglobin and mean corpuscular volume data of non-anemic apparently healthy primary care patients, measured by four laboratory platforms (Beckman, Abbott, Siemens, and Roche), were collected (n=397,548). Median ferritin levels were determined per platform, stratified by sex and age. The proportion of ferritin measurements outside of the reference interval was calculated using the reference intervals from the 52 laboratories (using a total of n=1,093,442 ferritin measurements). Lastly, ferritin data from 3,699 patients as captured in general practitioner (GP) data from the PHARMO Data Network were used to assess the variation of abnormal ferritin measurements per GP. Median plasma ferritin concentrations were approximately four times higher in men and twice as high in postmenopausal women compared to premenopausal women. Moreover, there are substantial differences in the median plasma ferritin concentration between the four platforms. However, even among laboratories using the same platform, ferritin reference intervals differ widely. This leads to significant differences in the percentages of measurements classified as abnormal, with the percentage of ferritin measurements below the reference limit in premenopausal women ranging from 11 to 53 %, in postmenopausal women from 3 to 37 %, and in men from 2 to 19 %. The percentage of ferritin measurements above the reference limit in premenopausal women ranged from 0.2 to 11 %, in postmenopausal women from 3 to 36 % and in men from 7 to 32 %. The lack of harmonization in ferritin measurement and the disagreement in plasma ferritin reference intervals significantly impact the interpretation of the iron status of patients and thereby the number of iron disorder diagnoses made. Standardization or harmonization of the ferritin assays and establishing uniform reference intervals and medical decision limits are essential to reduce the substantial variability in clinical interpretations of ferritin results.

Artificial neural networking for computational assessment of ternary hybrid nanofluid flow caused by a stretching sheet: implications of machine-learning approach

Researchers are mainly interested in using soft computing artificial intelligence (AI) methods due to their broad applications in analysis, modelling and simulations. Backpropagation neural networks, one of the supervised learning algorithms, is commonly used to train data networks by optimizing the error between actual and predicted values. To optimize this process of training data, Levenberg-Marquardt algorithm is applied; particularly beneficial for solving nonlinear fluid flow problems. Little knowledge is known about the ternary-hybrid nanofluid flow caused by a stretching surface with heat generation, viscous dissipation, magnetic effect and porosity etc. This article presents a novel machine-learning approach using backpropagation neural networks augmented with the Levenberg-Marquardt procedure to figure out the ternary-hybrid nanofluid flow generated by a stretching sheet. It uniquely examines the mixture of copper, Iron oxide and silicon dioxide nanoparticles inside a single base fluid within a magnetic field, tackling the research gaps in the effects of heat generation, viscous dissipation, porosity, and magnetic effects on fluid flow system and heat transfer. Shooting numerical technique (RK-5th) is used for solving the governing ordinary equations. Graphical illustration, error analysis, mean squared error, histograms and regression analysis justify the proposed method, showing better performance for ternary nanofluids.

How Can Overinvestment in Farms Affect Their Technical Efficiency? A Case Study from Poland

Farm overinvestment is highly dangerous in how it affects productivity and profitability. Therefore, it imperatively needs to be measured in the context of investment support offered under the Common Agricultural Policy. In turn, technical efficiency is one of the best methods for measuring farm investment. This paper analyzes the technical efficiency of 3273 Polish farms based on unpublished FADN (Farm Accountancy Data Network) data from 2010–2019. The purpose of this study was to indicate the level of, and changes in, technical efficiency of Polish farms in function of their investment levels, with particular emphasis on overinvested holdings. Technical efficiency was examined using stochastic frontier analysis (SFA). The study proved a decline in technical efficiency in underinvested farms and in those affected by absolute overinvestment (i.e., ones where the assets-to-labor ratio grows while labor productivity drops).

Socio-Economic Characteristics of Producers and Economic Size Classes of Enterprises According To Typologies and Risk Attitudes in TR21 Region

The aim of this study was to determine the typologies of agricultural enterprises operating in the TR21 Region according to the Farm Accounting Data Network methodology and the risk attitudes of the producers. According to the typologies and risk attitudes of the enterprises, the socio-economic characteristics of the producers and the economic size classes of the enterprises were determined. As a result of sampling based on a 95% confidence interval and a 10% margin of error, the number of enterprises surveyed was determined as 334. While 63.77% of the enterprises were specialized in field crops, 25.45% engaged in mixed livestock and plant production. Additionally, 7.49% were specialized in pasture livestock and 3.29% were in the perennial plant farming group. In the enterprises examined, 55.09% of the producers were classified as risk-averse and 44.91% as risk-lover. Of the average land holdings, 71.25% was property and 28.75% was rented land. It was noteworthy that the land rental rate in risk-lover enterprises was higher than in risk-averse enterprises. It was concluded that risk-lover enterprises had higher production value in both plant production and animal production, and risk-averse enterprises were engaged with product and animal diversity as they avoided taking risks. On average, in risk-lover enterprises, 53.64% of the operating expenses were vegetative special expenses and 46.36% were animal special expenses. In risk-averse enterprises, the share of vegetative special expenses in total expenses was 47.15%, and the share of animal special expenses was 52.85%. Compared to the average of enterprises and all enterprise typologies, standard gross profit was found to be higher in risk-lover enterprises. In enterprise typology groups, the average ESU values of the enterprises varied between 10.88 and 33.80 ESU. According to enterprise typologies, risk-averse agricultural enterprises in specialized field crop farming and perennial plant farming were in the below-medium enterprise class, while other enterprise groups were in the above-medium enterprise class.

DESIGN AND BUILD A STUDENT ATTENDANCE APPLICATION AT MADRASAH ALIYAH POMPANUA BONE DISTRICT BASED ON ANDROID

This research aims to determine the results of the design and effectiveness of the application as well as determine the responses of users of the Android-based student attendance application at Madrasah Aliyah Pompanua, Bone Regency. This research uses an R&D (Research and Development) type of research that uses the waterfall development model. The data collection techniques used were observation, interviews and questionnaires. This application testing uses the ISO/IEC 25010 quality standard which covers 4 aspects including functional suitability, performance efficiency, usability and protability. The application testing results used ISO/IEC 25010 quality, namely the functional suitability aspect testing was carried out by two system experts, and it was found that this application was in the very good category to use. Performance efficiency aspect testing was carried out using the Android Studio profiler by measuring real-time data for CPU, memory and network when the application was used, and the test results obtained good values. Usability aspect testing was carried out by teachers at Madrasah Aliyah Pompanua Bone Regency using a questionnaire and it was found that this application was in the very good category. Protability aspect testing is carried out by running the application using an Android device in various Android versions. Based on these data, it can be concluded that the Android-based student attendance application at Madrasah Aliyah Pompanua, Bone Regency is acceptable for use as a medium that makes it easier for teachers to take attendance and recap student absences at Madrasah Aliyah Pompanua, Bone Regency

A modular curriculum to teach undergraduates ecological forecasting improves student and instructor confidence in their data science skills

Abstract Data science skills (e.g., analyzing, modeling, and visualizing large data sets) are increasingly needed by undergraduates in the life sciences. However, a lack of both student and instructor confidence in data science skills presents a barrier to their inclusion in undergraduate curricula. To reduce this barrier, we developed four teaching modules in the Macrosystems EDDIE (for environmental data-driven inquiry and exploration) program to introduce undergraduate students and instructors to ecological forecasting, an emerging subdiscipline that integrates multiple data science skills. Ecological forecasting aims to improve natural resource management by providing future predictions of ecosystems with uncertainty. We assessed module efficacy with 596 students and 26 instructors over 3 years and found that module completion increased students’ confidence in their understanding of ecological forecasting and instructors’ likelihood to work with long-term, high-frequency sensor network data. Our modules constitute one of the first formalized data science curricula on ecological forecasting for undergraduates.

Retraction Note: Design and analysis of photovoltaic solar based longer transmission of data in ADHOC networks

Retraction Note: Design and analysis of photovoltaic solar based longer transmission of data in ADHOC networks

Network pharmacology combined with experimental validation show that apigenin as the active ingredient of Campsis grandiflora flower against Parkinson's disease by inhibiting the PI3K/AKT/NF-κB pathway.

The exploration of novel natural products for Parkinson's disease (PD) is a focus of current research, as there are no definitive drugs to cure or stop the disease. Campsis grandiflora (Thunb.) K. Schum (Lingxiaohua) is a traditional Chinese medicine (TCM), and the exact active constituents and putative mechanisms for treating PD are unknown. Through data mining and network pharmacology, apigenin (APi) was identified as the main active ingredient of Lingxiaohua, and key targets (TNF, AKT1, INS, TP53, CASP3, JUN, BCL2, MMP9, FOS, and HIF1A) of Lingxiaohua for the treatment of PD were discovered. The primary routes implicated were identified as PI3K/AKT, Apoptosis, TNF, and NF-κB pathways. Subsequently, therapeutic potential of APi in PD and its underlying mechanism were experimentally evaluated. APi suppressed the release of mediators of inflammation and initiation of NF-κB pathways in MES23.5 cells induced by MPP+. APi suppressed caspase-3 activity and apoptosis and elevated p-AKT levels in MES23.5 cells. Pretreatment with LY294002, a PI3K inhibitor, resulted in APi treatment blocking the activation of NF-κB pathway and expression of inflammatory factors in MES23.5 cells by activating the PI3K/AKT pathway. In conclusion, APi protects dopaminergic neurons by controlling the PI3K/AKT/NF-κB pathway, giving novel insights into the pharmacological mechanism of Lingxiaohua in treating PD.

Analysis and Research on Intelligent Logistics Data under Internet of Things and Blockchain

ABSTRACT To explore the security performance of logistics data transmission in an Intelligent Logistics System (ILS) under the Internet of Things (IoT) and Blockchain (BC), IoT is introduced to optimize the logistics transportation process. First, BC is adopted to improve the ILS under IoT, and ILS under IoT combined with BC is established. Next, an analysis of the relationship between the information service provider and the transportation provider in the logistics transaction process is conducted to perform data analysis and to establish an incentive mechanism for logistics transportation under BC. Finally, the simulation analysis of the constructed model is implemented. The results reveal that the constructed model is highly efficient. It can effectively solve the traceability problem of logistics transaction information, and the calculation cost is stable within 1,000 ms. In the comparative analysis of system transmission performance, the model exhibits outstanding results in transmission latency, consistently maintaining an average delay of approximately 350 ms. This stability is primarily attributed to the seamless integration of BC technology, which optimizes data packet transmission paths and mitigates waiting time. Furthermore, the system efficiently manages high data volumes while upholding robust data processing capabilities, owing to the optimization of smart contracts and decentralized storage mechanisms. As for energy consumption, the model notably reduces overall energy usage within the logistics network by curtailing unnecessary data transmission and computation, particularly in mobile nodes and wireless communication channels. The proposed algorithm excels across various transmission metrics, including transmission delay, data successful acceptance rate, network throughput, and energy consumption are all shown to be the best transmission performance of the proposed algorithm, in which the data successful acceptance rate is close to 1. As a result, the developed ILS model not only guarantees low latency performance but also demonstrates high data transmission security, facilitating more efficient and precise real-time information transmission. In this study, within the ILS framework, the relationship between information service providers and transportation service providers is established through BC, and an incentive mechanism is constructed. However, some shortcomings in this incentive mechanism are recognized. For instance, during the initial stages of BC integration, there are issues of information asymmetry concerning factors such as credit, transportation capacity, and effort level. Moreover, the open access policy of couriers in the model raises concerns, as the access policy itself constitutes sensitive information, which may result in privacy breaches. Hence, as part of future work, the introduction of attribute encryption schemes is planned to obfuscate access policies, thereby safeguarding the privacy of logistics personnel’s access policies.

Large language models recover scientific collaboration networks from text

Science is a collaborative endeavor. Yet, unlike co-authorship, interactions within and across teams are seldom reported in a structured way, making them hard to study at scale. We show that Large Language Models (LLMs) can solve this problem, vastly improving the efficiency and quality of network data collection. Our approach iteratively applies filtering with few-shot learning, allowing us to identify and categorize different types of relationships from text. We compare this approach to manual annotation and fuzzy matching using a corpus of digital laboratory notebooks, examining inference quality at the level of edges (recovering a single link), labels (recovering the relationship context) and at the whole-network level (recovering local and global network properties). Large Language Models perform impressively well at each of these tasks, with edge recall rate ranging from 0.8 for the highly contextual case of recovering the task allocation structure of teams from their unstructured attribution page to 0.9 for the more explicit case of retrieving the collaboration with other teams from direct mentions, showing a 32% improvement over a fuzzy matching approach. Beyond science, the flexibility of LLMs means that our approach can be extended broadly through minor prompt revision.

Research on the tourist destination image of the Huaiyang Canal based on network data

The Huaiyang Canal, a significant section of the Grand Canal, boasts representative tourist attractions. This study analysis of online reviews from Ctrip and Mahive using R language, Gephi, ROST CM, and SPSS has provided insights into tourists’ perceptions of the Huaiyang Canal’s image. Key findings include: (1) Dominant landscape images encompass gardens, canals, and buildings, emphasizing the historical and cultural assets. Both cultural and natural landscapes equally captivate tourists. (2) The canal’s tourism image perception follows a “garden-history-canal” hierarchy with the canal as the central space and history expanding its tourism features. (3) The perceptions can be categorized into historical and cultural landscapes, man-made projects, and attraction perception. Despite varying tourist numbers in Huaian and Yangzhou, scenic spot experiences are similar. The overall perception of tourists is largely positive, but some express concerns about service attitudes and travel time planning.

Prediction of basic turnout rail straightening with a neural network.

The main factors affecting the rebound of a switch rail in the straightening process are analysed and used as the input data of a neural network. An experimental group is designed, finite element simulation is carried out, and the data results are used to train the designed neural network to obtain a model with high accuracy. Taking the U71Mn rail as an example, the parameters are selected according to a certain interval, the test dataset is obtained via finite element simulation, and the selected parameters are predicted by the neural network. The error between the detection prediction data and the simulation data is small, and the prediction results are divided to obtain the parameter range that meets the requirements. This method provides a reference for the automatic straightening of a basic turnout rail.