Data-based Analysis Research Articles

Construction of financial data management and analysis system based on big data

To solve financial data management, the author proposes research on the framework of big data analysis system. In the activities of various enterprises, massive high-frequency data will be generated, and these data are often characterized by infinity, suddenness, disorder, and volatility. Due to the above characteristics of the financial data related to the production activities of the enterprise, the financial data of the enterprise is often different from the actual situation, and the access is not standardized. To solve the above problems, it is necessary to improve the efficiency of enterprise financial management. With the application of big data in the enterprise industry as the background, the author builds a big data-based enterprise financial data analysis system, the construction of the financial data analysis system of electric enterprises is described from three aspects: big data storage, big data preprocessing and financial sharing center construction, to help enterprises improve the accuracy of financial data and the efficiency of financial management.

A Data-based Critical Discourse Analysis of Attitude Resources in News Reports on COVID-19 in China Daily

COVID-19 has been going on in recent three years, and this phenomenon has attracted much attention from all walks of life since its first appearance in 2020. News discourse achieves the purpose of highlighting, or hiding certain facts. Different news media may hold quite different, or even opposite attitudes towards the same issue based on different ideologies they want to convey. With Martin and White’s attitude resources of Appraisal Theory as the theoretical framework for discourse analysis, this paper adopts both quantitative and qualitative methodologies to analyze the attitudes towards COVID-19 in news discourses selected from China Daily. The findings have indicated that Judgement resource is the most frequent, while Affect and Appreciation share similar minor proportions. With regard to the overall attitude polarity, positive attitudes outweigh the negative. The sub-systems of different attitude resources also vary in distribution and predisposition. These differences are interpreted with regard to China Daily’s underlying national interests towards Chinese government and people’s combating behavior as well as the pandemic situation in different regions of China.

The role of badges to spur frequent travelers to write online reviews

研究目的在线旅游评论平台已成为创新的信息系统, 这也是由于结合了复杂的游戏化元素, 例如具有视觉吸引力的徽章。本研究旨在分析升级徽章后评论的三个特征：评论长度（字数）、情绪评分和两次连续评论之间的时间段（距离撰写下一次评论的天数）。研究设计/方法/途径本研究使用数据挖掘方法分析了由 100 位常旅客和贡献者撰写的总共 77,000 条在线 TripAdvisor 评论。然后进行基于数据的敏感性分析 (DSA), 以提供对数据挖掘训练模型的理解。研究发现结果表明, 具有自我自豪感（“徽章护照”）和同行认可（“徽章有帮助”）的徽章在在线评论的整个生命周期中具有显着影响, 而仅通过计算贡献来授予徽章几乎没有影响。研究原创性本研究首次分析了经验丰富的旅行者在获得徽章和积分时受到的影响。奖励标准贡献徽章的内在动机因素几乎不会影响用户行为。需要设计徽章来奖励那些并非微不足道且不完全取决于用户的成就。

An integrated model for international market and entry mode selections for Chinese contractors

PurposeGiven their interrelationship, international market selection (IMS) and entry mode selection (EMS) must be considered jointly if an optimal entry strategy is to be realized. However, researchers in the field of international construction have the tendency to consider IMS and EMS independently or sequentially. Therefore, this paper aims to explore a holistic framework that can accommodate IMS and EMS concurrently and test it using empirical data.Design/methodology/approachhis study includes theoretical and empirical research. In theoretical part, an integrated decision model of IMS and EMS is proposed adopting literature review and theoretical derivation, then hypotheses are developed for the impact of decision-making factors. In the latter part, the IMS and EMS of 54 Chinese contractors in 67 countries were investigated, empirical data are collected according to hypotheses, an ordinal logistic regression model is established for statistics analysis. Finally, findings are drawn by comparing literature-based hypotheses with data-based analysis results.FindingsResults show that empirical data fit theoretical model well. Findings are: IMS and EMS can be integrated into a holistic decision-making framework when be properly sequenced. When IMS and EMS are determined simultaneously, the decision can benefit from a sharing of common information. And the roles of at least 13 common factors are empirically demonstrated in this study.Research limitations/implicationsThe integrated decision sequence proposed in this study is applicable for a specific market, and cannot compare multiple alternative markets directly. The decision-making factors identified in this paper do not cover the enterprise strategic objectives and some other factors. Empirical data and some theoretical assumptions are based on the international market entry strategy of Chinese contractors. Therefore, the conclusions may not be completely applicable to global contractors though have certain reference value.Originality/valueBased on the idea of holistic decision-making of IMS and EMS, this study proposes an international market entry strategy (IMES) sequence and an explicit model for determinants, then tests them with empirical data. This paper provides a new idea to manage IMS and EMS concurrently, which can improve the efficiency of IMES decision-making and avoid missing optimal alternatives. This study paves the way for a practical model and provides reference for contractors' international market entry strategy.

GIS and remotely sensed data-based morphometric elements analysis for determination of Bengal Basin evolution

GIS and remotely sensed data-based morphometric elements analysis for determination of Bengal Basin evolution

On the Evaluation Scheme for Large Scale Innovation Competition

Large-scale innovation contests are becoming increasingly popular globally, with many innovators and enterprises actively participating. Work allocation optimization and fair judging are two significant concerns in innovation contests. This academic paper aims to recommend an optimized "cross-assignment" program by leveraging the optimal objective function method to improve the comparability of scores given by various judges. We conducted data-based descriptive statistical analysis and concluded that the two-stage and weighted evaluation schemes are more beneficial than the traditional judging scheme. Nonetheless, there are still some shortcomings that require addressing. To enhance fairness, we propose an improved twostage evaluation scheme. In the first stage, we normalize the scores with a normal distribution. In the second stage of the process, we implement a system utilizing the Borda sorting technique to categorize submissions into five distinct groups for judges to evaluate based on their perceptions. We also detail a method for weighting tied scores to determine the final rankings. Testing indicates that this approach yields a Normalized Discounted Cumulative Gain (NDCG) of 0.8667, implying greater fairness and precision in the assessment of submissions.

Implementation of Preventive and Corrective measures in SCADA based Wind Turbines

Wind energy is becoming increasingly important in addressing energy demand and environmental sustainability. It is driven by the outstanding offshore wind resource and the need to minimize carbon emissions from energy generation. A growing interest in wind turbine condition monitoring, which allows condition based rather than reactive or scheduled maintenance, stems from the difficulty of accessing and maintaining wind turbines offshore. Vibration analysis and oil debris detection are currently used wind turbine condition monitoring methods. The extra expenditures might be enormous when considering the number of turbines normally put in offshore wind farms, and specialist interpretation is usually required. However, extending SCADA data-based analysis could bring significant value to condition monitoring at little or no expense to the wind farm operator. This Research Paper focuses on wind turbine condition monitoring using only SCADA data. An early detection of operational defects or breakdowns allows for regular maintenance or repair, decreasing downtime and potentially preventing further damage. Comparing incoming operational SCADA data with results for relevant variables, like component temperature, produced from applicable models trained on SCADA data from a healthy wind turbine, yields useful component condition indicators. Anomalies in the variables of interest in the SCADA data indicate impending breakdowns. This method is used to derive machine-to-machine comparison of component condition Compared to developing separate turbine models, this saves significant computing work and model complexity. A real-time wind turbine power curve is also created using SCADA data and compared to a reference power curve to quickly discover abnormal behavior based on slight variations in the power curve.

A systematic review study on online distance teacher education

Academic research on the use of technology and distance learning to facilitate teacher education has generated various insights and knowledge, which are the contributions of research on online distance teacher education. This research has explored topics such as teaching strategies and learners, distance education and information technologies, prospective teachers and collaboration, science, mathematics, and teacher education. The main purpose of this study is to examine the contributions of research on online distance teacher education and to conduct a bibliometric mapping analysis. Ninety-eight articles published from 1998 to 2021 were included in the Web of Science (WOS) database, which was accessed from a total of 178 publications. These articles underwent text-based mapping and bibliometric data-based analysis for the study. The results indicate that universities on the African continent have conducted more research on this topic. Van Wyk, Barquero, and Blignaut emerged as the most impactful authors in the field, while Garrison, Anderson, Darling-Hammond, Moore, and Perraton garnered the highest number of citations. The findings of this study provide guidance for those who want to conduct research in this field.

Material flow data-based analysis on nitrogen flow and use efficiency in Japan’s food and feed supply system and livestock sector during the last four decades

Material flow data-based analysis on nitrogen flow and use efficiency in Japan’s food and feed supply system and livestock sector during the last four decades

GeoAI 기반 환경정책 수립 지원 체계 구축 : 서울특별시의 토지피복과 LST 관계 분석

It is necessary to establish environmental policies based on data-based analysis in order to solve complex and diverse environmental problems that occur continuously. However, simple analysis of environmental spatial information and data has limitations in establishing effective environmental policies and solving environmental problems. It is necessary to utilize objective analysis using artificial intelligence techniques such as probability/statistics, machine learning, and deep learning through convergence and complex connection with new technologies such as sensors, images, and drones. In particular, location-based spatial information can provide tools and technologies to understand, analyze, and visualize phenomena that appear differently depending on the location of an actual phenomenon. In addition, the convergence of spatial information and various data enables value creation through new knowledge. Therefore, in this study, it is proposed to establish a GeoAI-based environmental policy establishment support system that can converge and link spatial information based on location information and various data. And in order to examine the applicability of this support system, meaningful results could be derived by analyzing the relationship between LST and land use/cover for Seoul. The valuable new knowledge derived through this GeoAI-based environmental policy establishment support system is expected to be utilized as various basic data for future city and environmental planning.

Unsteady RANS-based DMD analysis of airfoil NACA0015 with Gurney flap

The present investigation summarizes the numerical prediction and DMD analysis of the flow in the vicinity of a Gurney flap set on an airfoil NACA0015 at Re = 10,300 tested in a water tunnel with time-resolved Particle Image Velocimetry. A series of two-dimensional U-RANS simulations has been accomplished using an incompressible, time-accurate solver of OpenFOAM. A purely data-based DMD analysis has followed to extract the spatio-temporal coherent structures from the time-evolving flowfields, to mimic the experiments. It is stressed that this work focuses on the analysis of this kind of flap by applying DMD to datasets generated with numerical simulations, which has not been previously investigated, but using experimental datasets. Under this approach, the comparison of the numerics with the experimental counterpart shows a remarkable agreement for both the structures and major frequencies, which stresses the suitability of the incompresible U-RANS approach in this class of studies. Special attention has been focused on the modelling of the experimental facility, and a quantification of the prediction accuracy is provided by assessing the deviation of the spectral content. The simulations clearly show the key role of having a good characterization of the water tunnel to be successful at capturing the spectrum and flow unsteadiness. Furthermore, they reveal quite promising for those industrial oriented simulations, which deal with lift-enhancement devices and lift-to-drag ratio optimization of airfoils since they permit an accurate prediction.

Using Precision Agriculture (PA) Approach to Select Suitable Final Disposal Sites for Energy Generation

Severe environmental pollution and disease exposure are caused by poor waste management, specifically in urban areas due to urbanization. Additionally, energy shortage has threatened almost all parts of human life in the world. To overcome this problem, a precision agriculture approach using spatial mapping based on social environmental factors and sustainability principles can be used to find the variability of sites with respect to their suitability for waste disposal and energy generation. Therefore, this study aimed to develop a system for selecting suitable areas for municipal waste disposal and energy generation based on several structured criteria as hierarchical weighted factors. The system prototype was developed and tested in a case study conducted in an Indonesian Megapolitan area. The suitability map produced by the system for waste disposal and energy generation had an accuracy of 84.3%. Furthermore, validation was carried out by ground-checking at 102 location points. A future application of the proposed system is to provide spatial data-based analysis to improve regional planning and policy-making for waste disposal and energy generation in certain areas, particularly in Indonesia.

Groundwaters and deep-seated fluid circulation around Aso Volcano, Southwest Japan, revealed by multivariate statistical analysis of the geochemical data

To understand the fluid circulation under volcanoes and their surrounding area, geochemical compositions of 590 groundwater and spring/river water samples from the central Kyusyu in SW Japan, including hot and mineral spring waters around Aso and Kuju Volcanoes, have been analyzed for 12 major solutes as well as oxygen‑hydrogen and helium isotopic ratios. To extract independent processes and sources involved in formation of the groundwaters, whitened data-based k-means cluster analysis (KCA) has been applied to the data set of 12 major solutes. Eight clusters have been identified by KCA, which were mapped geographically and were compared with the isotopic compositions, temperature, and pH that were not included in KCA. By the geographical mapping, a concentric zonal structure of the clusters emerges in the Aso caldera, which resulted broadly from two fluid cycling systems from caldera rim and central cones. In the entire study area including Aso and Kuju Volcanoes, the mapping also shows geographical provenance, and the distribution of each cluster shows characteristics related to geological features, such as upwelling of deep-seated fluids along the prominent tectonic lines. On the other hand, the Piper diagram-based classification is significantly different from the KCA results and exhibits neither a concentric zonal structure within the Aso caldera nor a regional geographical provenance. These results suggest that multivariate statistical analysis such as KCA is potentially useful for investigating the origins of groundwaters and shows the advantages over classical Piper diagram-based evaluations.

Data-Based Analysis of Environmental Attractiveness towards Low-Carbon Development in Seaside Cities

Data-based technologies have been implemented in urban planning projects and environmental sciences. However, in the field of the environmental attractiveness analysis of seaside urban space, these technologies have not been fully studied. This paper critically assesses the attractiveness using data-based technologies with a focus on Chinese seaside cities’ low-carbon development. The analysis addresses the research question: How to use data-based technologies and their instruments to analyze environmental attractiveness of seaside cities towards low-carbon development? Methodologies include a case study of Dalian (China), field investigation, observation, and heatmapping. Results indicate that data-based technologies can support analysis of behavior and activity interests of inhabitants, as well as heatmapping with attractiveness consideration. The results provide a rational foundation for decision making during urban planning of seaside cities. Findings include insights and principles of planning seaside urban areas for smart sustainable development.

Mitigating Subsynchronous Torsional Interaction Using Geometric Feature Extraction Method

This paper proposes a method to mitigate subsynchronous torsional interaction detected during power system operation. This innovative method employs the delay reconstruction of the damping controller of a thyristor-controlled series compensator. This addresses the need to detect and manage stability and electromagnetic transients in power systems caused by the increasing use of fast-response power electronics. Previously, severe oscillation conditions could be avoided via analysis of the subsynchronous torsional interaction scenarios during the planning stage, enabling the suppression of oscillations. However, planning, modeling, and analysis for various scenarios becomes more difficult as the complexity of the power system increases, owing to the use of renewable energy and the incorporation of topology changes. Therefore, interest in measurement data-based real-time oscillation analysis has increased. The first step of the mitigation strategy proposed herein reconstructs nonlinear time-series data to detect subsynchronous torsional interaction in real time and generate alert signals. The second step of the strategy is that the controller mitigates oscillations by controlling the firing angle using the geometric feature extraction method. In this paper, the relaxation of the frequency oscillation in the subsynchronous region of about 22 Hz and about 18 Hz was verified through two simulation cases.

Elucidating genetic diversity and population structure in jamun [Syzygium cumini (L.) Skeels] using morpho-physiological traits and CAAT box-derived polymorphism

• Assessment of morpho-physiological and molecular diversity in Syzygium cumini . • Population structure analysis using CBDP markers. • CBDP markers were highly informative in detecting the polymorphism. • Evidence for high levels of gene flow and less genetic differentiation. The use of molecular markers has received little attention in jamun [ Syzygium cumini (L.) Skeels], an underutilized fruit crop highly valued for its medicinal and nutraceutical properties. In this study, 50 jamun accessions from four different populations were analyzed using 21 morpho-physiological traits and 22 polymorphic gene-targeted CAAT box-derived polymorphism (CBDP) markers. The morpho-physiological variability was fairly high among the jamun accessions. The UPGMA analysis based on morpho-physiological traits produced three clusters with a cophenetic correlation of 0.75, reflecting a good fit between the original matrix and the clustering algorithm. The average polymorphism of 91.17% and the wide-ranging heterozygosity values (0.282-0.499) for the CBDP markers revealed considerable genetic variation among jamun genotypes. The average PIC of 0.352 evinced a high discriminatory power of the CBDP markers in distinguishing the accessions. CDBP data-based UPGMA clustering and population structure analysis identified four distinct populations; albeit with overlaps among them. This, together with the existence of admixed types in all four populations, indicated a fairly high degree of gene flow within jamun populations sampled in this study. It is therefore likely that despite widely differing geographic origins, some accessions in all the populations have a shared ancestral origin. Analysis of Molecular Variance also supports this assumption, as an exceedingly higher within-population variance (84%) than among-population variance (16%) reflected a high degree of gene flow and low genetic differentiation for the Jamun populations examined. Our findings improve the existing understanding vis-à-vis genetic diversity and population structure in jamun, and are expected to increase the use of CBDP and similar marker techniques in cultivar development programs.

Investigation of Lipid Metabolism Dysregulation in Chronic Lymphocytic Leukemia and the Role of ENPP2 in Tumorigenesis of Chronic Lymphocytic Leukemia

Introduction Chronic lymphocytic leukemia (CLL) is the most common adult leukemia in the Western world, and although the advent of novel targeted drugs has greatly improved the prognosis of CLL patients, CLL remains incurable at present. Improving the risk stratification of CLL patients and finding new CLL therapeutic targets are urgent issues to be addressed. Methods We performed untargeted metabolomic analysis using LC-MS on sera from CLL patients and healthy controls (HC). Statistical analysis was used to screen for differential metabolites (DMs). Differentially expressed genes (DEGs) were also screened for enrichment pathway enrichment relying on transcriptome data analysis through the GEO database. Finally, DMs and DEGs were analyzed together to explore CLL metabolic alteration pathways. In this study, we targeted the gene ENPP2. Peripheral blood samples from 82 CLL patients were collected at the Department of Hematology in Shandong Provincial Hospital. Expressions levels of ENPP2 in CLL patients were determined by quantitative RT-PCR. In addition, cell viability and apoptosis were assessed by cell counting kit-8 and annexin V-PE/7AAD staining. Results A total of 52 differential metabolites were annotated, 40 of them were up-regulated and 12 were down-regulated. There were significant differences in sphingolipids (SP), glycerolipids (GL), glycerophospholipids (GP), and fatty acids (FA) levels in CLL patients compared to health controls. Fifty-two differential metabolites between samples from CLL patients and HC were analyzed by pathway analysis using metaboanalyst v5.0. KEGG enrichment showed that differential metabolites in CLL patients and HC were mainly annotated and enriched in human diseases, metabolism, and organismal systems (Figure 1A). Furthermore, we obtained differential expression information from the GEO database for CLL patients with HC. A total of 539 DEGs were screened, of which 279 were up-regulated and 260 were down-regulated. A total of 257 KEGG pathways were enriched. In the current work, a joint analysis of the two histological data was performed to validate each other. A total of 20 KEGG pathways were repeatedly obtained from metabolomics and transcriptomics data-based pathway analysis, including pathways in cancer, insulin resistance, long-term depression, regulation of lipolysis in adipocytes metabolic pathways, glycerophospholipid metabolism, glycerolipid metabolism, sphingolipid metabolism, arachidonic acid metabolism, ether lipid metabolism, inositol phosphate metabolism, choline metabolism in cancer, phospholipase D signaling pathway. A PPI network was constructed for the identified significantly different genes, from which we also screened for the gene ENPP2, a secreted lysophospholipase D, that we desired to investigate further (Figure 1B). We validated the difference in ENPP2 expression between CLL patients and healthy donors in the GEO database and its guiding effect on CLL prognosis. The results showed that elevated expression of ENPP2 in CLL patients and relatively high expression in CLL patients implied a worse prognosis. In addition, to validate the role of ENPP2 in CLL, we did the further investigation in CLL patients in our own center. Aberrantly increased expression of ENPP2 were detected in CLL primary cells and cell line (MEC1) at mRNA compared with normal B cells. To investigate the biological processes of ENPP2 involving in CLL progression, functional assays were performed. The survival rate of CLL cells treated with ENPP2 inhibitor PF-8380 was significantly decreased, while apoptosis was increased. In addition, we performed a combination of ibrutinib with PF8380, which showed a further decrease in cell viability and showed better anti-CLL effects. Conclusion Taken together, the present work provides evidence that the identified metabolic biomarkers can be used for CLL diagnosis and screening. The combined analysis of metabolomics and transcriptomics data provides a comprehensive understanding of the metabolic pathways in CLL. Besides, this study was the first investigation on the role of ENPP2 in CLL. ENPP2 expression was increased in CLL cells, and the ENPP2 inhibitor PF-8380 inhibited cell proliferation and induced apoptosis, with therapeutic potential. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

A Data-Based Reliability Analysis of ESP Failures in Oil Production Wells

Electrical Submersible Pumps (ESPs) are one of the most widely used artificial lift methods in the petroleum industry. However, ESP failures are unanticipated and common occurrences with significant financial impacts for the operators. Analysis of the ESP performance and failures are essential in its design and optimization. This paper presents a statistical approach for diagnosing and evaluating the root causes of ESP failures. The analysis is based on the field data gathered from the surface and downhole ESP monitoring equipment over five years of production of 10 wells. Electrical failures are the most common general cause of ESP failures, accounting for 61% of all failures, followed by motor failure and gas locking. Specifically, power failure, under-voltage, voltage unbalance, and motor underload are the most common occurrences. The data trends are analyzed for the two weeks before each specific failure, and conclusions are made on the warning signs to predict failures. In addition, a Weibull statistical analysis model is constructed to evaluate the reliability features and estimate the ESP failure probability, allowing operators to perform preventive maintenance. The results provide guidelines for ESP operations and contribute to reducing or preventing ESP downtimes and operating costs.

Uncovering the Key Targets and Therapeutic Mechanisms of Qizhen Capsule in Gastric Cancer through Network Pharmacology and Bioinformatic Analyses.

Objective. This study is aimed at screening out effective active compounds of Qizhen capsule (QZC) and exploring the underlying mechanisms against gastric cancer (GACA) by combining both bioinformatic analysis and experimental approaches. Weighted gene coexpression network analysis (WGCNA), network pharmacology, molecular docking simulation, survival analysis, and data-based differential gene and protein expression analysis were employed to predict QZC's potential targets and explore the underlying mechanisms. Subsequently, multiple experiments, including cell viability, apoptosis, and protein expression analyses, were conducted to validate the bioinformatics-predicted therapeutic targets. The results indicated that luteolin, rutin, quercetin, and kaempferol were vital active compounds, and TP53, MAPK1, and AKT1 were key targets. Molecular docking simulation showed that the four abovementioned active compounds had high binding affinities to the three main targets. Enrichment analysis showed that vital active compounds exerted therapeutic effects on GACA through regulating the TP53 pathway, MAPK pathway, and PI3K/AKT pathway. Furthermore, data-based gene expression analysis revealed that TP53 and JUN genes were not only differentially expressed between normal and GACA tissues but also correlated with clinical stages. In parallel, in vitro experimental results suggested that QZC exerted therapeutic effects on GACA by decreasing IC50 values, downregulating AKT expression, upregulating TP53 and MAPK expression, and increasing apoptosis of SGC-7901 cells. This study highlights the potential candidate biomarkers, therapeutic targets, and basic mechanisms of QZC in treating GACA, providing a foundation for new drug development, target mining, and related animal studies in GACA.

Real-time data-based performance analysis of a large-scale building applied PV system

Solar photovoltaic (PV) is one of the fastest-growing renewable technologies. Building applied photovoltaic systems can significantly help reduce the energy and environmental loads of buildings. This study analyzes the performance of a 425 kW building applied photovoltaic system by investigating important technical, economic, and environmental parameters. These analyses have been carried out with the help of year-long real-time performance data. The technical performance indicators examined in this study include direct current and alternating current power output, array yield, specific (final) yield, references yield, array losses, module and overall PV conversion efficiency, capacity factor, and performance ratio. The main strengths of the study include the large size of the BAPV system, and a comprehensive techno-economic and environmental analysis based upon real-time monitored data. The PV system has been found to annually produce 835.60 MWh and 814.88 MWh of DC and AC power output, respectively. The performance ratio varies from 74.03% in August to 84.62% in January with an annual figure of 78.09%. The annual capacity factor is determined to be 21.85%. For a project life of 25 years, the simple payback period and Net Present Value have been calculated to be 6.92 years and USD 866,565.24, respectively. Over the project lifetime, the savings of CO2, CH4 and N2O are calculated to be 15,666 tons, 483.75 kg, and 83.25 kg respectively.