Data Mining Analysis Research Articles

The Logical Mechanism and Practical Progress of Digital Economy Contributing to Rural Revitalization in the Context of Big Data

Abstract This paper proposes spatial data mining and clustering analysis techniques based on big data and uses four distance similarity calculation methods, namely, Euclidean distance, Manhattan distance, Ming’s distance, and Ma’s distance, to constrain spatial clustering similarity metrics. Combine the K-means algorithm and FCM clustering algorithm to calculate the center value of fuzzy clustering, use the additive synthesis method to solve the combination weights, and construct the combination weighted distance, fuzzy C-mean algorithm model. Research on the mechanism and path of digital economy-enabled rural revitalization through the C-FCM model, and propose three correlation hypotheses for this paper. Empirical experiments are conducted to verify the authenticity of the hypotheses proposed in this paper and the use effect of the constructed model. The experimental results show that the Moran index range of digital economy and rural revitalization is [0.06, 0.12], [0.05, 0.16], respectively, and there is a significant aggregation phenomenon of the two under the geographic matrix. In the analysis of the spatial spillover effect, the values of spatial autoregressive coefficients of rural industrial revitalization are all >0, with significance p>0.05. The digital economy has a positive effect on rural revitalization in neighboring areas through the spatial spillover effect. In the clustering sample extraction test, the accuracy is most stable when the number of samples is 2200, and the accuracy rate is as high as 79.66%.

Innovation and investment support for sustainable development of the agricultural sector of the economy

It is possible to ensure the country’s food security only with the sustainable development of the agricultural sector of the economy, which, in turn, is impossible without a transition to an innovative way of agricultural development. Consistent implementation of import substitution practices, improving the quality and increasing the volume of products in the long term will allow the domestic agro-industrial complex to reach a new level of its development, it is also possible only with the widespread introduction of innovative technologies and tools, thereby ensuring a high level of competitiveness of domestic agricultural products. At the same time, it is necessary to take into account the transformation of the agricultural sector of the Russian economy, which is taking place under the influence of increasing geopolitical risks, which should become a point of growth of the domestic agro-industrial complex, since, as practice shows, any economic crisis is replaced by economic growth subsequently. The aim of the study is to develop and test a methodology for determining the factors of sustainable development of the agricultural sector of the economy, based on the technology of data-mining and big-data analysis, and to conduct a subsequent assessment of trends in the identified factors.

Assessing Destination Brand Associations on Twitter: The case of Istanbul

The development of data mining has paved the way for studies that identify brand associations from user-generated content (UGC). However, the number of studies investigating destination associations with social media is limited. The aim of this study is to explore destination associations with UGC on Twitter and to show how data mining and sentiment analysis methods can be applied to destinations to elicit brand associations. In this study, 33,339 English-language tweets containing the word #Istanbul were collected over one year and analyzed using text mining (association rule analysis) and sentiment analysis. As a result of the study, a brand concept map (BCM) of what Twitter users associate with Istanbul was created and compared to other studies that measure associations using conventional methods. The main results show that users have positive associations with tourism in Istanbul. Unique and interesting associations (such as "cats") were observed compared to other previous studies that measured associations to destinations. Based on the study results, a method was proposed for measuring the image of a place brand by observing electronic word of mouth in social media.

Identification of a Gene Encoding a New Fungal Steroid 7-Hydroxylase and Its Functional Characterization in Pichia pastoris Yeast.

The hydroxylation of steroids in the C7β position is one of the rare reactions that allow the production of value-added precursors in the synthesis of ursodeoxycholic acid and other pharmaceuticals. Recently, we discovered this activity in the ascomycete Curvularia sp. VKM F-3040. In this study, the novel gene of 7-hydroxylase (P450cur) was identified as being heterologously expressed and functionally characterized in Pichia pastoris. Transcriptome data mining and differential expression analysis revealed that 12 putative genes in Curvularia sp. mycelia significantly increased their expression in response to dehydroepiandrosterone (DHEA). The transcriptional level of the most up-regulated cytochrome P450cur gene was increased more than 300-fold. A two-gene construct with a candidate P450cur gene and the gene of its natural redox partner, NADPH-cytochrome P450 reductase (CPR), which is interconnected by a T2A element, was created. Using this construct, recombinant P. pastoris strains co-expressing fungal P450cur and CPR genes were obtained. The functional activity of the recombinant P450cur was studied in vivo during the bioconversion of androstane steroids. The fungal 7-monooxygenase predominantly catalyzed the 7β-hydroxylation of androstadienedione (ADD), DHEA, and androstenediol, whereas 1-dehydrotestosterone was hydroxylated by P450cur mainly at the C7-Hα position. To our knowledge, this is the first report of a recombinant yeast capable of catalyzing the 7α/β-hydroxylation of ADD and DHEA.

Impact of Google searches and social media on digital assets’ volatility

Advanced digitalization and financial technology have of recent times become among the most crucial tools. Data mining and sentiment analysis have revealed the importance of digitalization in modern times. This study examines the influence of Google search activity on the volatility of digital assets. We analyzed six digital asset prices for Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, and Ripple from the Coinmarketcap database. We used tweets on Twitter to survey users’ sentiment by using the Twitter search Application Programming Interface and Google trend search from web searches, news searches, and YouTube searches data using RStudio software. The study spanned 1 September 2019 to 31 January 2020 and employed the Vector Autoregression (VAR) approach for analysis. The VAR estimation revealed that Google search variables have significantly influenced the volatility of Bitcoin, Ethereum, Litecoin, and Ripple, as supported by the Granger causality test and impulse response function. The results of this study could be useful for investors and policymakers in drawing up strategies to reduce market volatility. These results should thus be useful to investors in developing profitable investment strategies to mitigate the impact of market turbulence.

Data-mining analysis of media frame effects on social perception of schizophrenia renaming in Korea

BackgroundIn 2011, Korean Neuropsychiatric Association renamed schizophrenia from ‘mind split disorder’ (‘Jungshinbunyeolbyung’ in Korean) to ‘attunement disorder’ (‘Johyeonbyung’ in Korean), in a strategic way to reduce social stigma toward people with schizophrenia. However, there remains an elusive consensus that how the renaming effort has contributed to changes in the social perception of schizophrenia in Korea.MethodsWith this regard, we explored whether media frames alter the social perception, in ways of respecting or disrespecting schizophrenia patients before and after the renaming. This study extensively investigated media keywords related to schizophrenia across the time by applying both language and epidemiologic analyses.ResultsIn results, the media keywords have been negatively described for schizophrenia patients both before and after the renaming. Further, from an analysis using the regression model, a significant correlation was observed between the frequency of negative keywords and the hospitalization frequency of schizophrenia patients.ConclusionsThese findings suggest that the social perception of schizophrenia has been scarcely changed, but rather remained negatively biased against schizophrenia patients, in spite of the renaming effort. Notably, the biased media frames have been demonstrated to negatively impact on the social perception, and even on the medical use patterns of general schizophrenia patients. In conclusion, we suggest that the unbiased media frames along with the renaming effort may collectively help reduce the negative social perception of schizophrenia.Trial registrationThis study was approved from the Institute of Review Board (IRB) of the Yoing-In Mental Hospital (IRB No. YIMH-IRB-2019-02).

The dynamics of two iron-oxidizing Acidithiobacillus strains in industrial copper sulfide heap-leaching

Several species within the Acidithiobacillus (At.) genus can derive energy from oxidizing ferrous iron and sulfur. Two bacterial strains according to their 16S rRNA gene sequences closely related to At. ferridurans and At. ferrivorans were obtained from the industrial sulfide heap leaching process at Minera Escondida (SLH), named D2 and DM, respectively. We applied statistical and data mining analyses to the abundance of At. ferridurans D2 and At. ferrivorans DM taxa in the industrial process over 16 years of operation. In addition, we performed phylogenetic analysis and genome comparison of the type strains, as well as culturing approaches with representative isolates of At. ferridurans D2 and At. ferrivorans DM taxa to understand the differential phenotypic features. Throughout the 16 years, two main operational stages were identified based on the D2 and DM taxa predominance in solution samples. The better suitability of At. ferrivorans DM to grow in a wide range of temperature and in micro-oxic environments, and to oxidize S by reducing Fe(III) revealed through culturing approaches can, in a way, explain the taxa distribution in both operational stages. The isolate At. ferridurans D2 could be considered as a specialist in aerobic sulfur oxidation, while isolate At. ferrivorans DM is a specialist in iron oxidation. In addition, the results from ore samples occasionally obtained from the industrial heap suggest that At. ferridurans D2 abundance was more related to its abundance in the solution samples than At. ferrivorans DM was. This dynamic coincides with previously obtained results in in-lab cell-mineral attaching experiments with both strains. This information increases our knowledge the ecophysiology of Acidithiobacillus and of the importance of diverse physiological traits at industrial bioleaching scales.

Influence of landform, soil properties, soil Cd pollution and rainfall on the spatial variation of Cd in rice: Contribution and pathway models based on big data

Landform, soil properties, soil cadmium (Cd) pollution and rainfall are the important factors affecting the spatial variation of rice Cd. In this study, we conducted big data mining and model analysis of 150,000 rice-soil sampling sites to examine the effects by the above four factors on the spatial variation of rice Cd in Hunan Province, China. Specifically, the variable coefficient of rice Cd in space was significantly correlated with the partition scale according to the logistic fitting. The improved random forest results suggested that elevation (DEM) and pH were the two most important factors affecting the spatial variation of rice Cd, followed by relief, soil Cd content and rainfall. Typically, variance partitioning analysis (VPA) revealed that both the soil property and the interactive effects between the soil property and Cd pollution were the principal contributors to the rice-Cd variation, with the respective contributing rates of 30.5 % and 29.0 %. Meanwhile, the partial least square-structural equation modelling (PLS-SEM) elucidated 4 main paths of specific indirect effects on rice-Cd variation. They were landform → physicochemical property → soil acidity → rice-Cd variation, landform → soil acidity → rice-Cd variation, physicochemical property → soil acidity → rice-Cd variation, and soil texture → soil acidity → rice-Cd variation. This work can provide a general guidance for scientific zoning, accurate prediction and prevention of Cd pollution in paddy fields.

Economic consequences of the COVID'19 pandemic for functioning of the cruise shipping industry in the Baltic Sea Region

Cruise line owners and insurance companies failed to predict the emergence of a global pandemic that would exclude the entire global cruise fleet from operation for several months. Cruise line owners, the related service suppliers, as well as employees of shipping corporations and, finally, the passengers themselves suffered huge losses. Unfortunately, the cruise line exclusion from the market for more than a year led to a significant drop in demand for sea voyages and, in some cases, the withdrawal of some routes from the cruise timetable or the route change. Even though all cruise line operators introduced appropriate sanitary restrictions, protocols and restrictions to protect against the risk of coronavirus infection in accordance with the recommendations of international institutions, and despite large-scale information campaigns, a significant part of potential passengers still hesitate to travel by sea. This article aims to identify the economic effects that affected the cruising market as a result of the decline in demand for sea voyages in the Baltic Sea Region. The studies involved a few qualitative analysis methods, i.e. secondary data mining, desk research and comparative analysis, as well as critical analysis and deduction. The research process consisted of eight stages according to a model developed by Bell et al. [6]. The results of research may provide interesting cognitive material for seaport authorities and cruise operators useful in planning their activities, to compensate for losses related to the potential threats that may arise in the future. It also identifies several solutions for policy makers to guarantee financial security and insurance cover for businesses and society in the event of other pandemic.

A reform path for English learning based on data mining algorithms

Abstract This paper utilizes data mining algorithms to predict the evaluation value of new items by target users in an interactive learning environment. To enhance data quality, redundant data in the dataset is eliminated. To provide better learning recommendations and personalized services, prediction accuracy is assessed by calculating the deviation between predicted and actual ratings. Teachers and students can use the analysis results of feature weights, correct word cut scores, and other indicators obtained from data mining as key learning references. In the data mining analysis of students’ English learning data, the feature weight is 0.921, which helps to assess students’ knowledge mastery and learning effect more accurately.

From seeds to survival rates: investigating Linum usitatissimum’s potential against ovarian cancer through network pharmacology

Ovarian cancer is a malignant tumor that primarily forms in the ovaries. It often goes undetected until it has spread to the pelvis and abdomen, making it more challenging to treat and often fatal. Historically, natural products and their structural analogues have played a pivotal role in pharmacotherapy, especially for cancer. Numerous studies have demonstrated the therapeutic potential of Linum usitatissimum against ovarian cancer, but the specific molecular mechanisms remain elusive. This study combines data mining, network pharmacology, and molecular docking analysis to pioneer an innovative approach for ovarian cancer treatment by identifying potent phytochemicals. Findings of current study revealed that Apigenin, Vitamin E, Palmitic acid, Riboflavin, Isolariciresinol, 5-Dehydro-avenasterol, Cholesterol, Pantothenic acid, Nicotinic acid, Campesterol, Beta-Sitosterol, Stigmasterol, Daucosterol, and Vitexin suppress tumor growth by influencing AKT1, JUN, EGFR, and VEGFA. Kaplan–Meier survival analysis spotlighted AKT1, JUN, EGFR, and VEGFA as potential diagnostic and prognostic biomarkers for ovarian cancer. However, it is imperative to conduct in vivo and in vitro examinations to ascertain the pharmacokinetics and biosafety profiles, bolstering the candidacy of L. usitatissimum in ovarian cancer therapeutics.

A Large-Scale Dataset of Search Interests Related to Disease X Originating from Different Geographic Regions

The World Health Organization (WHO) added Disease X to their shortlist of blueprint priority diseases to represent a hypothetical, unknown pathogen that could cause a future epidemic. During different virus outbreaks of the past, such as COVID-19, Influenza, Lyme Disease, and Zika virus, researchers from various disciplines utilized Google Trends to mine multimodal components of web behavior to study, investigate, and analyze the global awareness, preparedness, and response associated with these respective virus outbreaks. As the world prepares for Disease X, a dataset on web behavior related to Disease X would be crucial to contribute towards the timely advancement of research in this field. Furthermore, none of the prior works in this field have focused on the development of a dataset to compile relevant web behavior data, which would help to prepare for Disease X. To address these research challenges, this work presents a dataset of web behavior related to Disease X, which emerged from different geographic regions of the world, between February 2018 and August 2023. Specifically, this dataset presents the search interests related to Disease X from 94 geographic regions. These regions were chosen for data mining as these regions recorded significant search interests related to Disease X during this timeframe. The dataset was developed by collecting data using Google Trends. The relevant search interests for all these regions for each month in this time range are available in this dataset. This paper also discusses the compliance of this dataset with the FAIR principles of scientific data management. Finally, an analysis of this dataset is presented to uphold the applicability, relevance, and usefulness of this dataset for the investigation of different research questions in the interrelated fields of Big Data, Data Mining, Healthcare, Epidemiology, and Data Analysis with a specific focus on Disease X.

An Expressway ETC Missing Data Restoration Model Considering Multi-Attribute Features

Electronic toll collection (ETC) data mining has become one of the hotspots in the research of intelligent expressway extension applications. Ensuring the integrity of ETC data stands as a critical measure in upholding data quality. ETC data are typical structured data, and although deep learning holds great potential in the ETC data restoration field, its applications in structured data are still in the early stages. To address these issues, we propose an expressway ETC missing transaction data restoration model considering multi-attribute features (MAF). Initially, we employ an entity embedding neural network (EENN) to automatically learn the representation of categorical features in multi-dimensional space, subsequently obtaining embedding vectors from networks that have been adequately trained. Then, we use long short-term memory (LSTM) neural networks to extract the changing patterns of vehicle speeds across several continuous sections. Ultimately, we merge the processed features with other features as input, using a three-layer multilayer perceptron (MLP) to complete the ETC data restoration. To validate the effectiveness of the proposed method, we conducted extensive tests using real ETC datasets and compared it with methods commonly used for structured data restoration. The experimental results demonstrate that the proposed method significantly outperforms others in restoration accuracy on two different datasets. Specifically, our sample data size reached around 400,000 entries. Compared to the currently best method, our method improved the restoration accuracy by 19.06% on non-holiday ETC datasets. The MAE and RMSE values reached optimal levels of 12.394 and 23.815, respectively. The fitting degree of the model to the dataset also reached its peak (R2 = 0.993). Meanwhile, the restoration stability of our method on holiday datasets increased by 5.82%. An ablation experiment showed that the EENN and LSTM modules contributed 7.60% and 9% to the restoration accuracy, as well as 4.68% and 7.29% to the restoration stability. This study indicates that the proposed method not only significantly improves the quality of ETC data but also meets the timeliness requirements of big data mining analysis.

Regulatory claims made by US businesses engaged in direct-to-consumer marketing of purported stem cell treatments and exosome therapies.

Aim: This study investigated whether US businesses engaged in direct-to-consumer online marketing of purported stem cell therapies and stem cell-derived exosome products made claims concerning the regulatory status of these interventions. Methods: We used data mining and content analysis of company websites to examine regulatory-related representations made by US businesses marketing stem cell treatments and exosome therapies. Results: More than two thirds of such businesses did not make explicit representations about the regulatory status of their marketed products. Businesses that made claims about the regulatory status of the stem cell and exosome products they sold used range of representations concerning the legal standing of these interventions. Conclusion: The absence of information addressing the regulatory status of stem cell interventions and exosome products and the use of what appeared to be inaccurate information concerning the regulatory status of numerous products likely complicates efforts by customers to make informed health-related decisions.

Mining and Analysis of In-use Vehicle Corrosion Data Based on Typical Environmental Conditions in China

As an important performance indicator for measuring product quality and safety, vehicle corrosion resistance has always been of great concern to industry enterprises and consumers. In this paper, we collect, processed and analysed exterior corrosion data from a total of 54 in-use vehicles (the vehicle that has been licensed and put into service) of nine models with high ownership in the coastal region of North China. Through the evaluation model of automobile corrosion resistance performance, data mining analysis was conducted on five key areas and the whole vehicle with different brands and ages (3-year and 6-year vehicles), key areas including exterior body, passenger compartment, luggage compartment, engine compartment, and chassis. It was found that the chassis and engine compartment of each vehicle model were severely corroded, with the corrosion surface of some chassis parts reaching 100%. The luggage compartment and exterior body were slightly corroded, and the luggage compartment parts of 8 models have not corroded after 3 years of use.

Exploring traditional Chinese medicine as a potential treatment for sarcopenia: A network pharmacology and data mining analysis of drug selection and efficacy.

Sarcopenia, as an increasingly pressing clinical issue, can be ameliorated through employment of traditional Chinese medicines. However, the current lack of specific pharmacological interventions for Sarcopenia necessitates further exploration of novel possibilities in traditional Chinese medicine for the treatment of this condition, utilizing advanced methodologies such as web pharmacology and data mining. Screening the essential targets of Sarcopenia, conducting matching between target and active molecules, as well as active molecules and herbs. Employing data mining techniques to analyze the screening outcomes, and molecular docking to compare the binding activities of active molecules with target proteins. The approach of using herbs for the treatment of Sarcopenia involves 13 targets, with 414 active compounds and 367 types of herbs. Data mining reveals that the herbs used in treating Sarcopenia are primarily characterized by their bitter taste, exerting their effects through dispelling dampness and promoting blood circulation. Moreover, 2 new formulas are postulated. Furthermore, molecular docking analysis indicates that the main active components of the herbs can be observed to tightly bind with the targets. Through network pharmacology and molecular docking, our findings reveal that herbs contain 15 key active components and 5 key targets, which correspond to 7 major herbs and 2 new formulas. Academically, these findings hold significant reference value for the development of novel drugs targeting Sarcopenia.

DECISION TREE PADA PENILAIAN PERFORMA LAYANAN DRIVER GOJEK DENGAN ALGORITMA C4.5

Transportation is an important means to support daily activities and operations, be it private transportation or public transportation. This study will analyze the level of customer satisfaction as a public vehicle user, in this case the author utilizes data on customers who use and utilize the gojek application as a means of transportation, using a questionnaire data collection technique, the writer will determine client satisfaction levels using the variables Tangible, Reliability, Responsiveness, Assurance, Empathy. The analytical Data mining with the is a technique employed in this investigation. concept of the Algorithm C4.5 classification which can show the results of the highest Entrophy and gain values modeled as a decision tree with the aim that the results obtained can be a guide for gojek partners to service and increase customers. Considering the outcomes of the classicization It is demonstrated by the C4.5 algorithm that the accuracy achieves 75.33% where the factors that become the assessment of satisfaction are Tangible, Reliability.

Verification of Two-Step Flow Model in the Process of City International Image Communication Based on Data Mining and Empirical Analysis

This study seeks to decipher the modus operandi of Shanghai’s image portrayal to the audience through social media platforms, particularly Twitter. Leveraging the potency of big data analysis, the study scrutinizes the applicability of the two-step flow communication model in the communication of Shanghai’s image. The findings highlight the cardinal role of opinion leaders in the image communication process, overshadowing the impact of mass media. The age-old phenomenon of two-step flow, wherein information trickles down from mass media to opinion leaders, appears to be fading away. Although mass media’s tweets can potentially reach a broad audience, they do not necessarily captivate the attention of opinion leaders. Thus, the study underscores the importance of both mass media and opinion leaders in shaping Shanghai’s image.

Water productivity maximization and ecosystem monitoring to estimate tourism economic value

Abstract Water supply from a common pool resource based on productivity indicators for different uses is one of the goals of planning in dry areas. Productivity indicators are defined based on time, geographical location and hydrological conditions in the form of food security, economic benefits and ecosystem restoration. This study was conducted in order to evaluate the contrast between economic criteria and food security in the exploitation of water resources in Lu'an city in Anhui province of China. Probabilistic modeling based on the prediction of uncertain values using the Latin hypercube technique was used for hydrological variables and water resources. The method of data mining and trend analysis of dependent variables was also simulated to estimate economic values in the water cycle. Statistical information of 32 years from 1991 to 2022 has been collected and used as an annual average per population. The results showed that the economic value of water consumption in the tourism industry has increased compared to agriculture. The total water provided for food security is equal to 6.5 m3 per person, the excess of which can be allocated to other uses through weighting indicators based on ecosystem and quality.

Involvement of toxic metals and PCBs mixture in the thyroid and male reproductive toxicity: In silico toxicogenomic data mining

Toxicological research is mostly limited to considering the effects of a single substance, even though the real exposure of people is reflected in their daily exposure to many different chemical substances in low-doses. This in silico toxicogenomic study aims to provide evidence for the selected environmental (organo)metals (lead, cadmium, methyl mercury) + polychlorinated biphenyls mixture involvement in the possible alteration of thyroid, and male reproductive system function, and furthermore to predict the possible toxic mechanisms of the environmental cocktail. The Comparative Toxicogenomic Database, GeneMANIA online software, and ToppGene Suite portal were used as the main tools for toxicogenomic data mining and gene ontology analysis. The results show that 35 annotated common genes between selected chemicals and endocrine system diseases can interact on the co-expression level. Our study highlighted the disruption of the cytokines, the cell's response to oxidative stress, and the influence of the transcription factors as the potential core of toxicological mechanisms of the discussed mixture's effects. The connected toxicological effects of the tested mixture were abnormal sperm cells, a disrupted level of testosterone, and thyroid hormones. The core mechanisms of these effects were inflammation, oxidative stress, disruption of androgen receptor signaling, and the alteration of the FOXO3-Keap-1/NRF2-HMOX1-NQO1 pathway signaling most likely controlled by the co-expression of overlapped genes among used chemicals. This in silico research can be used as a potential core for the determination of biomarkers that can be monitored in future further in vitro and in vivo experiments.