Detection of Knowledge on Social Media Using Data Mining Techniques

In light of the rapid growth and development of social media, it has become the focus of interest in many different scientific fields. They seek to extract useful information from it, and this is called (knowledge), such as extracting information related to people’s behaviors and interactions to analyze feelings or understand the behavior of users or groups, and many others. This extracted knowledge has a very important role in decision-making, creating and improving marketing objectives and competitive advantage, monitoring events, whether political or economic, and development in all fields. Therefore, to extract this knowledge, we need to analyze the vast amount of data found within social media using the most popular data mining techniques and applications related to social media sites.

Linking Competitors’ Knowledge and Developing Innovative Products Using Data Mining Techniques

In this article, the relationship between the knowledge of competitors and the development of new products in the field of capital medical equipment has been investigated. In order to identify the criteria for measuring competitors’ knowledge and developing new capital medical equipment products, marketing experts were interviewed and then a researcher-made questionnaire was compiled and distributed among the statistical sample of the research. Also, in order to achieve the goals of the research, a questionnaire among 100 members of the statistical community was selected, distributed and collected. To analyze the gathered data, the structural equation modeling (SEM) method was used in the SMART PLS 2 software to estimate the model and then the K-MEAN approach was used to cluster the capital medical equipment market based on the knowledge of actual and potential competitors. The results have shown that the knowledge of potential and actual competitors has a positive and significant effect on the development of new products in the capital medical equipment market. From the point of view of the knowledge of actual competitors, the market of “MRI”, “Ultrasound” and “SPECT” is grouped in the low knowledge cluster;“Pet MRI”, “CT Scan”, “Mammography”, “Radiography, Fluoroscopy and CRM”, “Pet CT”, “SPECT CT” and “Gamma Camera” markets are clustered in the medium knowledge. Finally, “Angiography” and “CBCT” markets are located in the knowledge cluster. From the perspective of knowledge of potential competitors, the market of “angiography”, “mammography”, “SPECT” and “SPECT CT” in the low knowledge cluster, “CT scan”, “radiography, fluoroscopy and CRM”, “pet CT”, “CBCT” markets in the medium knowledge cluster and “MRI”, “pet MRI”, “ultrasound” and “gamma camera” markets in the high knowledge cluster are located.

Comparative Analysis of the Factors Influencing Metro Passenger Arrival Volumes in Wuhan, China, and Lagos, Nigeria: An Application of Association Rule Mining and Neural Network Models

This study explores the factors influencing metro passengers’ arrival volume in Wuhan, China, and Lagos, Nigeria, by examining weather, time of day, waiting time, travel behavior, arrival patterns, and metro satisfaction. It addresses a significant research gap in understanding metro passengers’ dynamics across cultural and geographical contexts. It employs questionnaires, field observations, and advanced data analysis techniques like association rule mining and neural network modeling. Key findings include a correlation between rainy weather, shorter waiting times, and higher arrival volumes. Neural network models showed high predictive accuracy, with waiting time, metro satisfaction, and weather being significant factors in Lagos Light Rail Blue Line Metro. In contrast, arrival patterns, weather, and time of day were more influential in Wuhan Metro Line 5. Results suggest that improving metro satisfaction and reducing waiting times could increase arrival volumes in Lagos Metro while adjusting schedules for weather and peak times could optimize flow in Wuhan Metro. These insights are valuable for transportation planning, passenger arrival volume management, and enhancing user experiences, potentially benefiting urban transportation sustainability and development goals.

Multi-disciplinary Conceptual Design Knowledge of Multi-stage Hybrid Rocket Using Data Mining Technique

This paper deals with the application of data mining techniques to the conceptual design knowledge for a LV （launch vehicle） with a HRE （hybrid rocket engine）. This LV is a concept of the space transportation, which can deliver micro-satellite to the SSO （sun-synchronous orbit）. To design the higher performance LV with HRE, the optimum size of each component, such as an oxidizer tank containing liquid oxidizer, a combustion chamber containing solid fuel, a pressurizing tank and a nozzle, should be acquired. The Kriging based ANOVA （analysis of variance） and SOM （self-organizing map） are employed as data mining techniques for knowledge discovery. In this study, the paraffin （FT-0070） is used as a propellant of HRE. Then, the relationship among LV performances and design variables are investigated through the analysis and the visualization. To calculate the engine performance, the regression rate is computed based on an empirical expression. The design knowledge is extracted for the design knowledge of the multi-stage LV with HRE by analysis using ANOVA and SOM. As a result, the useful design knowledge on the present design problem is obtained to design HRE for space transportation.

Green coal mining technique integrating mining-dressing-gas draining-backfilling-mining

Aiming to address the following major engineering issues faced by the Pingdingshan No. 12 mine:(1) difficulty in implementing auxiliary lifting because of its depth(i.e., beyond 1000 m);(2) highly gassy main coal seam with low permeability;(3) unstable overlying coal seam without suitable conditions for implementing conventional mining techniques for protective coal seam; and(4) predominant reliance on ‘‘under three" coal resources to ensure production output. This study proposes an integrated, closed-cycle mining-dressing-gas draining-backfilling-mining(MDGBM) technique. The proposed approach involves the mining of protective coal seam, underground dressing of coal and gangue(UDCG), pressure relief and gas drainage before extraction, and backfilling and mining of the protected coal seam. A system for draining gas and mining the protective seam in the rock stratum is designed and implemented based on the geological conditions. This system helps in realizing pressure relief and gas drainage from the protective seam before extraction. Accordingly, another system, which is connected to the existing production system, is established for the UDCG based on the dense medium-shallow trough process. The mixed mining workface is designed to accommodate both solid backfill and conventional fully mechanized coal mining, thereby facilitating coal mining, USCG, and backfilling. The results show that: The mixed mining workface length for the Ji15-31010 protected seam was 220 m with coal production capacity 1.2 million tons per year, while the backfill capacity of gangue was 0.5 million tons per year. The gas pressure decreased from 1.78 to 0.35 MPa, and the total amount of safely mined coal was 1.34 million tons. The process of simultaneously exploiting coal and draining gas was found to be safe, efficient, and green.This process also yielded significant economic benefits.

A probe into“mining technique in the condition of floor failure”for coal seam above longwall goafs

Targeting at the coal seam with useful value discarded above goafs,attempted to explore the feasibility of'mining technique in the condition of floor failure' from theoretical point of view,and predicted.It indicated that mining technique in the condition of floor failure used above Longwall Goafs in Baijiazhuang Mining is totally feasible.At law,the deformation of the floor in the mining technique by means of probability-integral method.And it is discov- ered that deformed basin can emerge in the footwall of No.6 coal seam and its maximum subsidence was possibly 1 633 mm or so and its maximum positive curvature is 61.74/10^(-3). At last,it therefore suggests appropriate ground pressure control measures as strengthening observation of ground pressure and adopting false slope for exploitation and strengthening support for reasonable push and slide based on the adverse ground pressure behaviors possibly occurring in the mining technique.This serves to gather data and lay sturdy founda- tion for further probe into the mining technique,and offers theoretical and technical grounds for concrete implementation of the mining technique.

Theories and techniques of coal bed methane control in China

Coal bed methane control with low permeability is a hot issue at present. The current status of coal bed methane control in China is introduced. The government-support policies on coal bed methane control are presented. This paper proposes the theories of methane control in depressurized mining, including methane extraction in depressurized mining, simultaneous mining technique of coal and methane without coal pillar, and circular overlying zone for high-efficiency methane extraction in coal seams with low permeability. The techniques of methane control and related instruments and equipments in China are introduced. On this basis, the problems related to coal bed methane control are addressed and further studies are pointed out.

Challenges and new insights for exploitation of deep underground metal mineral resources

Long-term and continuous large-scale exploitation has increasingly exhausted shallow metal mineral resources,and deep mining has become inevitable.The current global status of deep mining of metal mineral resources was presented,a series of engineering challenges faced by deep mining were systematically analyzed,and some progress and future innovation focus in key engineering technologies,such as the prediction and prevention of rockburst,cooling techniques,rock support techniques,deep hoisting techniques,and several nontraditional deep mining techniques,were highlighted.Meanwhile,new insights into development strategies of deep mining technology were proposed.The integration of these forward-looking key innovative technologies will form the overall framework of an innovative technology system for the deep mining of metal minerals.This technology system will help to achieve safe,efficient,and green exploitation of deep underground metal mineral resources and ensure the sustainable development of the metal mining industry.

Oilfield analogy and productivity prediction based on machine learning: Field cases in PL oilfield, China

In the early time of oilfield development, insufficient production data and unclear understanding of oil production presented a challenge to reservoir engineers in devising effective development plans. To address this challenge, this study proposes a method using data mining technology to search for similar oil fields and predict well productivity. A query system of 135 analogy parameters is established based on geological and reservoir engineering research, and the weight values of these parameters are calculated using a data algorithm to establish an analogy system. The fuzzy matter-element algorithm is then used to calculate the similarity between oil fields, with fields having similarity greater than 70% identified as similar oil fields. Using similar oil fields as sample data, 8 important factors affecting well productivity are identified using the Pearson coefficient and mean decrease impurity(MDI) method. To establish productivity prediction models, linear regression(LR), random forest regression(RF), support vector regression(SVR), backpropagation(BP), extreme gradient boosting(XGBoost), and light gradient boosting machine(Light GBM) algorithms are used. Their performance is evaluated using the coefficient of determination(R^(2)), explained variance score(EV), mean squared error(MSE), and mean absolute error(MAE) metrics. The Light GBM model is selected to predict the productivity of 30 wells in the PL field with an average error of only 6.31%, which significantly improves the accuracy of the productivity prediction and meets the application requirements in the field. Finally, a software platform integrating data query,oil field analogy, productivity prediction, and knowledge base is established to identify patterns in massive reservoir development data and provide valuable technical references for new reservoir development.