Concept Of Mining Research Articles

Toward circular and socially just urban mining in global societies and cities: Present state and future perspectives

This study evaluates the perspectives of urban mining in the framework of the circular economy (CE) and starts with a brief analysis of the size of global and urban metabolism and the role that plays materials and waste streams such as construction and demolition waste (C&DW) and waste from electronic and electrical equipment (WEEE). These can be considered as temporary stocks or deposits to be mined in the future, thus shedding light on the concept of recycling potential, end-of-life functional recycling, and material concentration. The recycling potential could be very variable as in the case of metals. The average concentration of some metals (e.g., gold) in WEEE shows that it is higher per ton of electronic product compared to the amount in mining ores. This explains the importance of the concept of urban mining in the circular economy (CE) transition, given that the CE concept was born to address the challenges of high resources consumption rates and worsening environmental problems. The urban mining phenomenon becomes timely and extremely important for cities as they are relevant hubs of materials and energy consumption and source of environmental and social impacts in external areas due to mining and extraction activities. This study points to the need for creating and establishing strong synergies between the concept of CE and urban mining and the role of cities as innovators in finding circular solutions by incorporating more socially just urban mining activities to improve urban resource management, land use, and local and global wellbeing.

Fault Detection of Mechanical Equipment Failure Detection Using Intelligent Data Analysis

Poor maintenance of machinery in manufacturing plants has always been an important link in the production process. In addition to computer technology, artificial intelligence technologies and various intelligent sensors are widely used in manufacturing industries. The amount of data generated by production machines and equipment at all stages of the production process is also growing rapidly, and it is particularly important to analyze the data generated by these devices in order to detect and even predict malfunctions. Intelligent data mining provides advanced data analysis techniques for this purpose. This article introduces the basic concepts of data mining, its processes, the main data mining technologies, and provides recommendations for applying data mining to detect failures in devices.

Analysis of the Development Trend of Sports Research in China and Taiwan Using Natural Language Processing

Background: A digital text abstract presents the essential information of an article, and we can find the trend and value of the research by analyzing it rigorously and digging up knowledge. Therefore, this study focuses on the abstracts of index journals in China and Taiwan from July 2010 to June 2020 (a total of 3283 abstracts). Methods: Through the concepts of text mining and natural language processing (NLP), it constructs processes such as text retrieval, text segmentation and word cloud analysis, TF-IDF weight analysis, co-word analysis, network analysis, and trend analysis, and analyses a large amount of text data. Results: The results show that the scope of research in China covers the fields of social sports and sports science, and research in Taiwan covers both natural and social sciences. The network diagram highlights the richness of sports-related research fields in the two regions, but research on sports philosophy is relatively rare. Conclusions: It is suggested that all disciplines/departments should re-allocate the same resources, so as to show a balanced development trend and help expand a new chapter in the sports academic field.

RoofKIT - Circular Construction and Solar Energy Use in Practice at the Solar Decathlon Europe 21/22

The contribution of the RoofKIT team to the SDE 21/22 competition is the extension for an existing café in Wuppertal, Germany, to create new functions and living space for the building with simultaneous energetic upgrading. The energy concept targets all renewable resources available on and in the building for energy supply: mainly solar energy which is used via PVT collectors, as well as waste heat from ventilation and grey water which is recovered for pre-heating. As part of the competition, a demonstration unit will be built representing a small cut-out of the extension. An integral building and energy concept combines physical properties of the building with adapted building services technologies to achieve maximum indoor comfort – particularly considering possible overheating of the lightweight construction during summer – and minimum CO2 emissions. The latter extends to the whole lifecycle of the building unit and one of the major goals of the project is to realize an almost completely mono-fraction and circular building construction as a contribution to the urban mining concept.

Unpacking the design, implementation and uptake of community-integrated health care services: a critical realist synthesis

IntroductionCommunity-integrated care initiatives are increasingly being used for social and health service delivery and show promising outcomes. Nevertheless, it is unclear what structures and underlining causal agents (generative mechanisms) are...

Modeling and feature assessment of the sleep quality among chronic kidney disease patients

Chronic Kidney Disease (CKD) is a progressive and irreversible loss of kidney function. Data mining concepts may be used in assessing and predicting CKD-related issues to obtain hidden clinical information for a reliable and effective decision-making process. These advanced learning methods would identify the relationships and patterns that will help classify factors that affect the poor sleep quality of CKD patients. Poor sleep quality is a critical issue for CKD individuals, negatively affecting immunity, cognitive functions, and emotional demonstrations. This study aims to find the factors affecting the sleep quality of CKD patients. Decision tree-based methods are used to identify the impact of each feature to predict sleep quality. The predictive results are compared with different classification models as well. Furthermore, two re-sampling techniques, Synthetic Minority Oversampling and Random Oversampling, are also used to reduce the impact of the imbalanced nature of the data set. We further discuss how these results agree with the clinically relevant features determined by the physicians.

Application of Data Mining in Predictive Analysis of Network Security Model

In order to improve the application effect of data mining in the predictive analysis of network security models, this paper starts with the concept of data mining and data sources, then introduces related technologies from data mining technology and security setting collection technology, and finally introduces the computer network security maintenance system based on data mining. The results show that data mining technology plays an important role in the predictive analysis of network security models. The data show that by the end of 2021, the number of security vulnerabilities collected by my country’s information security vulnerability sharing platform has reached 14,871, an increase of 46.6% compared with 2020. Among them, there are 5,567 high-risk vulnerabilities, an increase of about 1,400 vulnerabilities compared with last year. It can be seen that the number of vulnerabilities discovered every year and the number of high-risk vulnerabilities are basically increasing year by year. Therefore, it is recommended to strengthen the promotion of data mining technology in the predictive analysis of network security models, so that it can play a greater role.

Road Accidents Prediction and Classification

Abstract: Road accidents leads to death, disability and hospitalization of people across world which leads to loss of potential income of individual and also affects the economy of the country. For every 10 people killed during road accidents across world one person belongs to India. In year 2020, total of 3,66,138 road accidents occurred leading to loss of 1,31,714 persons lives, injuring 3,48,279 persons. Number of road accidents and damage caused by it can be reduced by identifying the factors leading to it. In this project, we are applying the concepts of data mining and machine learning to identify the various factors that affect road accidents and its severity. The application will take variety inputs such as age of vehicle, light condition, road surface condition, speed limit etc. and will use random forest machine learning algorithm to calculate the severity of a possible accident. The severity of a possible accident will be displayed on a scale of 1 to 3, 1 being the highest and 3 being the least severe so, that they drive safely and take precautions. This data can be used in future to analyze inputs and improves the accuracy of the system output. In case of severity 1 which is the case of possibility of fatal accident an alert message will be sent to police so that they can take any preventive measures and therefore this application can prove to be very helpful in reducing accident fatality rates in the country.

Psychological Mechanism and Exercise Intervention of College Students’ Problematic Internet Use Based on IoT Technology

Dangerous Internet use alludes to unreasonable utilization of the Internet that unfavorably influences individual emotional well-being, relational correspondence, social transformation, social turn of events, learning, and work. This paper aims to study how to analyze and study the psychological mechanism and exercise intervention of problematic Internet use based on the Internet of Things technology, and describe it. It addresses the question of problematic web usage, and this question is based on IoT technology. This paper then elaborates on the concept of data mining and related algorithms. It designs and analyzes the psychological mechanism and exercise intervention of college students’ problematic Internet use. The experimental results showed that in the incidence of Internet addiction, gender, grade, family economic environment, and single-parent family will be affected. Moderate-intensity (50%–80% VO2 max) aerobic and anaerobic exercise for more than 30 minutes per year has a positive effect on college students’ Internet addiction.

P-Matrix Reasoning and Information Intelligent Mining

Abstract P-sets (P stand for Packet) and P-matrix are novel and effective mathematical tools for studying dynamic information systems. In this paper, the concept of P-information mining is given by using the dynamic characteristics of P-sets and P-matrix structure. In addition, the reasoning theorem of P-matrix and the reasoning structure are given. Moreover, the information intelligent mining method under the condition of P-matrix reasoning is obtained. As the application, intelligent recognition of information image was shown.

Three-Dimensional Landscape Rendering and Landscape Spatial Distribution of Traditional Villages Based on Big Data Information System

Traditional villages refer to the villages that formed earlier, are rich in traditional resources, and have various values and must be protected. This paper aims to study how to draw three-dimensional landscapes of traditional villages based on the big data information system. This paper puts forward the problem of 3D landscape rendering, which is based on big data and modeling, and then elaborates around the concept of data mining and related algorithms and designs and systematically tests 3D landscape rendering. The experimental results show that when the number of rendered patches is less than 120193 between 120193 and 242029, the system can run smoothly. It can achieve acceptable and relatively smooth operation; when it exceeds 242029, the system is obviously stuck. The design scheme of the three-dimensional landscape geographic information system of the traditional villages is reasonable and feasible, the operation is stable, and the expected research goals are achieved.

Review of Data Mining Concept and its Techniques

Nowadays, some of the intersting roles of human life are data, information, and knowledge. Analyzing and modelling of big data have been required by data massive storehouses together with the rapid technologies growth to predict and analyze the future trends of information. Methodologies and techniques, which are employed into diverse information systems scope, are needed for detection of knowing in the databases. The technology which extracts advantageous information to discover knowledge is called Data Mining. Data mining, it has been defined as discovery of knowledge in data (KDD), it is the disclosure of modalities procedures and other valuable information from considerable sets of data. It has been a tremendous progress in machine learning, artificial agent systems, and decision-making in the expert systems. In the last decades, most of the techniques and applications has been surveyed via the researchers. Those techniques and applications are utilized in distinct areas in daily life like industrialization, education, engineering, commerce and business. Searching last years researches about the review of the most techniques and trends of data mining in multiple areas was the method which is followed in this paper. It has discovered in the learning field as diffusing data mining for educating activities, improvement quality of tasks into manufacturing field, text mining as a technique into research databases and so on. This study collectes a summary of information about the basci concept of Data Mining and its technques which other researchers may need to start their studies in Data Mining field.

Concept learning consistency under three-way decision paradigm

Concept Mining is one of the main challenges both in Cognitive Computing and in Machine Learning. The ongoing improvement of solutions to address this issue raises the need to analyze whether the consistency of the learning process is preserved. This paper addresses a particular problem, namely, how the concept mining capability changes under the reconsideration of the hypothesis class. The issue will be raised from the point of view of the so-called Three-Way Decision (3WD) paradigm. The paradigm provides a sound framework to reconsider decision-making processes, including those assisted by Machine Learning. Thus, the paper aims to analyze the influence of 3WD techniques in the Concept Learning Process itself. For this purpose, we introduce new versions of the Vapnik-Chervonenkis dimension. Likewise, to illustrate how the formal approach can be instantiated in a particular model, the case of concept learning in (Fuzzy) Formal Concept Analysis is considered.

Semantic Segmentation-Based Adaptive Mining Algorithm for Ceramic Cultural Resource Data

Ceramic culture as Chinese culture has a long history and is the Chinese people’s spiritual home. The ceramic culture resource base contains a large number of images, videos, and other resources, so the collection of image category of data mining, as well as the finishing processing, is very critical. Thousands of years of ceramic data accumulation and impatient information demand have created a new point of contradiction for ceramic cultural resources. Therefore, in order to address this issue, we carried out a research study based on the concept of big data mining of ceramic cultural resource data, which is based on data fusion and feature extraction methods. We also considered semantic segmentation processing methods, which are used for data information management, scheduling, identification, collection, statistics, and aggregation of heterogeneous ceramic cultural big data. Further, a fine mining method for ceramic culture big data based on semantic segmentation is also proposed. As a result, the distributed storage of information and detection capability of ceramic cultural resource data are improved. The experimental results reveal that the proposed method performed better than the earlier approaches.

A literature review on the analysis of symptom-based clinical pathways: Time for a different approach?

Breathlessness is a common clinical presentation, accounting for a quarter of all emergency hospital attendances. As a complex undifferentiated symptom, it may be caused by dysfunction in multiple body systems. Electronic health records are rich with activity data to inform clinical pathways from undifferentiated breathlessness to specific disease diagnoses. These data may be amenable to process mining, a computational technique that uses event logs to identify common patterns of activity. We reviewed use of process mining and related techniques to understand clinical pathways for patients with breathlessness. We searched the literature from two perspectives: studies of clinical pathways for breathlessness as a symptom, and those focussed on pathways for respiratory and cardiovascular diseases that are commonly associated with breathlessness. The primary search included PubMed, IEEE Xplore and ACM Digital Library. We included studies if breathlessness or a relevant disease was present in combination with a process mining concept. We excluded non-English publications, and those focussed on biomarkers, investigations, prognosis, or disease progression rather than symptoms. Eligible articles were screened before full-text review. Of 1,400 identified studies, 1,332 studies were excluded through screening and removal of duplicates. Following full-text review of 68 studies, 13 were included in qualitative synthesis, of which two (15%) were symptom and 11 (85%) disease focused. While studies reported highly varied methodologies, only one included true process mining, using multiple techniques to explore Emergency Department clinical pathways. Most included studies trained and internally validated within single-centre datasets, limiting evidence for wider generalisability. Our review has highlighted a lack of clinical pathway analyses for breathlessness as a symptom, compared to disease-focussed approaches. Process mining has potential application in this area, but has been under-utilised in part due to data interoperability challenges. There is an unmet research need for larger, prospective multicentre studies of patient pathways following presentation with undifferentiated breathlessness.

A pragmatic approach to recover access time of Apriori algorithm by applying intersection on CSS for redefining FIS through matrix implementation in textual data

Nowadays data analytics OLAP (online Analytical Processing) is mostly accepted domain of current researchers and the concept of data mining serves better for the same. There are so many data mining methodologies defined for data analytics. Mining Association rule is widely used in data mining methods for data categorization. Apriori Algorithm is popular method for defining n-element. Frequent item set form k number of huge transactional data set online transaction processing (OLTP) using Association Mining rule (AMR). In this paper, researchers executed original Apriori on transactional data set containing 35039 number of transactions, divided into three data sets DS-1 to DS-3 with 20039, 12000, 5000 number of transactions with variable length with minimum support of 30%, 60% and 80% respectively. Researchers carried out experimental work and compared results of Apriori Algorithm with our proposed algorithm (enhanced version of Apriori algorithm) on the same perimeter and state improvement with 11%, 30% and 27% of Rate of Improvements in DS-1 to Ds-3 respectively for 30% minimum support. Our proposed algorithm is working far much better then Apriori algorithm at each parameter which was included to conclude the results.

Developing Conception about Differences between OLAP And Data Mining

The following study has been based on the concept of data mining and OLAP and the differences between these aspects. At the beginning, the procedures of data mining an OLAP have been served with a brief introduction. The discussion have focused on the section namely the introductory section, the methodology section, results section, discussion and the conclusion part. The results part has given a detailed account about in ways the data mining is different from the OLAP. Both the technology has been emplaced separates atha tthe finally into result section key difference has been analysis thoroughly. Data is a emerging technological tool that was going o be a revolutionary in the future technology sector but into the current the use of data mining is complex and it is difficult to make the most as the technology is costly as well sales expertise is available in the market. Due to this it can be said that the technology is at a developing stage and it needs further improvement. OLAP is the type of technological tool that is often used to make the process more and more convenient with the analysis of past data. Bothe the technology has its own significance and differences that has been given in the research study.

Personalized Learning Path Recommendation Based on Weak Concept Mining

Discovering valuable learning path patterns from learner online learning data can provide follow-up learners with effective learning path reference and improve their learning experience and learning effects. In this paper, a personalized learning path recommendation method based on weak concept mining is proposed. Firstly, according to the degree of concept mastery of historical learners, concept maps of different types of learners are generated by clustering and association rule mining algorithms. A set of weak concept learning paths are then automatically generated through topological sorting algorithm. Secondly, the long short-term memory neural network based on the attention mechanism (LSTM+attention) is trained to predict the learning effect of the weak concept learning path. Finally, the personalized weak concept path that meets the expected learning effect is selected from the path prediction results. In the experiment, the proposed method is not only compared with the traditional recommendation method, but also, a comparative experiment on the impact of different learning effect prediction models is carried out. The experiment results show that our proposed method has obvious advantages in recommendation performance.

Pyrolysis and in-line catalytic decomposition of excavated landfill waste to produce carbon nanotubes and hydrogen over Fe- and Ni-based catalysts – Investigation of the catalyst type and process temperature

Undeniably, non-sanitary landfills existing worldwide pose considerable environmental risks related to air, water and soil pollution. Despite that, the landfill mining concept does not spread swiftly around the world. To prevent its fading into oblivion, it is necessary to transform the perception of landfills as waste to seeing them as stocks of valuable materials. Guided by this idea, this novel study investigates the possibility of producing carbon nanotubes (CNTs) and hydrogen-rich gas, materials crucial for our transition towards a more sustainable future, from excavated waste as these. To the best of our knowledge, this is the first study on catalytic pyrolysis of excavated waste. For this purpose, excavated waste was subjected to pyrolysis followed by in-line catalytic decomposition of the produced pyrovapours. The impact of the catalyst type and catalyst bed temperature on the process performance was analysed. Six types of monometallic and bimetallic Ni- and Fe- based catalysts, synthesised using two methods (the sol–gel and the impregnation method), were considered. Three catalyst bed temperatures were taken into account, namely 700 °C, 800 °C, and 900 °C. The results showed that the bimetallic catalyst prepared by using the sol–gel method (FeNi/Al_Sg) outperformed the other analysed catalysts, yielding 9 mmol/gsample_daf of H2 and 76 mg/gsample_daf of CNTs at 800 °C. The product yields and quality were comparable to those reported in the open literature for homogeneous plastic waste pyrolysis. Eventually, the future research directions were discussed.

The Vision for the Future of Asteroid Mining

In recent years, the contradictions between various energy problems, environmental problems and human development have become more and more intensive, and the imbalance distribution of natural resources in various countries has become severe. In order to address these problems, the concept of asteroid mining came into being, which brings people the hope of obtaining resources from outer space. In this paper, we model global equity in the context of asteroid mining, and ask how people's lives could be improved. What will happen to the fairness of the world? First, in order to build a model to measure global equity, we select some indicators according to Maslow’s Hierarchy of human needs theory, including happy-planet-index, youth employment rate, life expectancy, infant mortality rate, and the proportion of female students to male students in higher education in 2021 of eight zones to construct the EWM-TOPSIS model, results illustrate that UK is the most unfair country and Japan is the fairest country. The equity indexes is classified into three levels, and are used to achieve the clustering of the fairness degree of each country in the world. Second, we describe the current status of asteroid mining, and realize that although the asteroid mining project is developing rapidly at the technical level, there is still a lack of global unified regulation and constraints at the institutional level. On this basis, we design a mature and complete global asteroid mining system, and envision the distribution of the mined resources according to the nature endowments.