A systematic study of Erzhu Erchen decoction against damp-heat internalized type 2 diabetes based on data mining and experimental verification

Background:Erzhu Erchen decoction(EZECD),which is based on Erchen decoction and enhanced with Atractylodes lancea and Atractylodes macrocephala,is widely used for the treatment of dampness and heat(The clinical manifestations of Western medicine include thirst,inability to drink more,diarrhea,yellow urine,red tongue,et al.)internalized disease.Nevertheless,the mechanism of EZECD on damp-heat internalized Type 2 diabetes(T2D)remains unknown.We employed data mining,pharmacology databases and experimental verification to study how EZECD treats damp-heat internalized T2D.Methods:The main compounds or genes of EZECD and damp-heat internalized T2D were obtained from the pharmacology databases.Succeeding,the overlapped targets of EZECD and damp-heat internalized T2D were performed by the Gene Ontology,kyoto encyclopedia of genes and genomes analysis.And the compound-disease targets-pathway network were constructed to obtain the hub compound.Moreover,the hub genes and core related pathways were mined with weighted gene co-expression network analysis based on Gene Expression Omnibus database,the capability of hub compound and genes was valid in AutoDock 1.5.7.Furthermore,and violin plot and gene set enrichment analysis were performed to explore the role of hub genes in damp-heat internalized T2D.Finally,the interactions of hub compound and genes were explored using Comparative Toxicogenomics Database and quantitative polymerase chain reaction.Results:First,herb-compounds-genes-disease network illustrated that the hub compound of EZECD for damp-heat internalized T2D could be quercetin.Consistently,the hub genes were CASP8,CCL2,and AHR according to weighted gene co-expression network analysis.Molecular docking showed that quercetin could bind with the hub genes.Further,gene set enrichment analysis and Gene Ontology represented that CASP8,or CCL2,is negatively involved in insulin secretion response to the TNF or lipopolysaccharide process,and AHR or CCL2 positively regulated lipid and atherosclerosis,and/or including NOD-like receptor signaling pathway,and TNF signaling pathway.Ultimately,the quantitative polymerase chain reaction and western blotting analysis showed that quercetin could down-regulated the mRNA and protein experssion of CASP8,CCL2,and AHR.It was consistent with the results in Comparative Toxicogenomics Database databases.Conclusion:These results demonstrated quercetin could inhibit the expression of CASP8,CCL2,AHR in damp-heat internalized T2D,which improves insulin secretion and inhibits lipid and atherosclerosis,as well as/or including NOD-like receptor signaling pathway,and TNF signaling pathway,suggesting that EZECD may be more effective to treat damp-heat internalized T2D.

Hybrid Recommender System Using Systolic Tree for Pattern Mining

A recommender system is an approach performed by e-commerce for increasing smooth users’experience.Sequential pattern mining is a technique of data mining used to identify the co-occurrence relationships by taking into account the order of transactions.This work will present the implementation of sequence pattern mining for recommender systems within the domain of e-com-merce.This work will execute the Systolic tree algorithm for mining the frequent patterns to yield feasible rules for the recommender system.The feature selec-tion's objective is to pick a feature subset having the least feature similarity as well as highest relevancy with the target class.This will mitigate the feature vector's dimensionality by eliminating redundant,irrelevant,or noisy data.This work pre-sents a new hybrid recommender system based on optimized feature selection and systolic tree.The features were extracted using Term Frequency-Inverse Docu-ment Frequency(TF-IDF),feature selection with the utilization of River Forma-tion Dynamics(RFD),and the Particle Swarm Optimization(PSO)algorithm.The systolic tree is used for pattern mining,and based on this,the recommendations are given.The proposed methods were evaluated using the MovieLens dataset,and the experimental outcomes confirmed the efficiency of the techniques.It was observed that the RFD feature selection with systolic tree frequent pattern mining with collaborativefiltering,the precision of 0.89 was achieved.

Research on Energy Efficiency Characteristics of Mining Shovel Hoisting and Slewing System Driven by Hydraulic-Electric Hybrid System

Mining shovel is a crucial piece of equipment for high-efficiency production in open-pit mining and stands as one of the largest energy consumption sources in mining.However,substantial energy waste occurs during the descent of the hoisting system or the deceleration of the slewing platform.To reduce the energy loss,an innovative hydrau-lic-electric hybrid drive system is proposed,in which a hydraulic pump/motor connected with an accumulator is added to assist the electric motor to drive the hoisting system or slewing platform,recycling kinetic and potential energy.The utilization of the kinetic and potential energy reduces the energy loss and installed power of the min-ing shovel.Meanwhile,the reliability of the mining shovel pure electric drive system also can be increased.In this paper,the hydraulic-electric hybrid driving principle is introduced,a small-scale testbed is set up to verify the feasibil-ity of the system,and a co-simulation model of the proposed system is established to clarify the system operation and energy characteristics.The test and simulation results show that,by adopting the proposed system,compared with the traditional purely electric driving system,the peak power and energy consumption of the hoisting electric motor are reduced by 36.7%and 29.7%,respectively.Similarly,the slewing electric motor experiences a significant decrease in peak power by 86.9%and a reduction in energy consumption by 59.4%.The proposed system expands the application area of the hydraulic electric hybrid drive system and provides a reference for its application in over-sized and super heavy equipment.

Integrative multi-omics and systems bioinformatics in translational neuroscience:A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information,with its application to neuroscience termed neuroinformatics.Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms,which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases.Importantly,integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile.In this review,we first summarize data mining studies utilizing datasets from the individual type of omics analysis,including epigenetics/epigenomics,transcriptomics,proteomics,metabolomics,lipidomics,and spatial omics,pertaining to Alzheimer's disease,Parkinson's disease,and multiple sclerosis.We then discuss multi-omics integration approaches,including independent biological integration and unsupervised integration methods,for more intuitive and informative interpretation of the biological data obtained across different omics layers.We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks.Finally,we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery,therapeutic development,and elucidation of disease mechanisms.We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Experiment and Simulation for Articulated Lifting Subsystem of 1000 m Deep Sea Mining System

The experiment system of 1000 m deep sea mining system is built up with the similarity theory. Sine mechanism is used to simulate mining ship to generate lateral shock excitation by ocean wave. Simulation and experiment of spherical joint connecting deep sea mining system has been done in band six marine conditions. The results indicate that the moment of spherical joint connecting deep sea mining is smaller than that of the thread connected ones, the lifting pipe of sphelical joint is ＂flexible pipe＂. The flexural torque of the articulated lifting pipe system in pump and buffer is generally periodic variation with some irregularity, the value is stable on 60 N·S, and it is obviously smaller than that of the fixed lifting pipe system; The total displacement exhibits cyclic variation pattern, and the periodicity of them is longer than that of sea current. The results of experiment and simulation are basically consistent. And the analysis in the paper offers theoretical foundation of 1000 m deep sea mining system in China.

A Life Evaluation System and its Application to Mining Machinery Equipment

Based on analyzing the mining equipment life evaluation theoretical system, the equipment life evaluation target system of the mining machinery is put forward taking the economic life, security life, reliability life and technical life as the targets. Then, the structure and model of the mining equipment life evaluation system are established and prepared for the realization of the system theoretically. Taking hydraulic support as the research object, the life evaluation system of hydraulic support is established and practice shows that the life evaluation system is feasible.

Engineering complex systems applied to risk management in the mining industry

Related to complexity, there is a wide diversity of concepts, ranging from ‘‘systemic" to ‘‘complex", implying a need for a unified terminology. Per different authors, the main drivers of complexity can be found in human behaviour and uncertainty. This complexity, structural or dynamic can be organizational, technological, or nested in their relationship. ISO international standard 31000:2009 definition of risk management ‘‘coordinated activities to direct and control an organization with regard to risk", when applied to economic sectors, industry, services, project, or activity, it requires the use of models or theories as guidelines. Therefore, as its basic elements comprehend human behaviour and/or uncertainty, risk management to be effective and adapted as much as possible to reality, must be operational within complex systems, as already demonstrated in different R&D environments. Risk management faces demanding challenges when approaching specific and endogenous needs, such as the mining sector. This paper presents a multivariable function analysis methodology approach based on complex system modelling and through real data corresponding to a risk management tool in the mining sector.

Dynamic Analysis of Deep-Ocean Mining Pipe System by Discrete Element Method

The dynamic analysis of a pipe system is one of the most crucial problems for the entire mining system. A discrete element method （DEM） is proposed for the analysis of a deep-ocean mining pipe system, including the lift pipe, pump, buffer and flexible hose. By the discrete element method, the pipe is divided into some rigid elements that are linked by flexible connectors. First, two examples representing static analysis and dynamic analysis respectively are given to show that the DEM model is feasible. Then the three-dimensional DEM model is used for dynamic analysis of the mining pipe system. The dynamic motions of the entire mining pipe system under different work conditions are discussed. Some suggestions are made for the actual operation of deep-ocean mining systems.

An Integrated Dynamic Model of Ocean Mining System and Fast Simulation of Its Longitudinal Reciprocating Motion

An integrated dynamic model of China＇s deep ocean mining system is developed and the fast simulation analysis of its longitudinal reciprocating motion operation processes is achieved. The seafloor tracked miner is built as a three-dimensional single-body model with six-degree-of-freedom. The track-terrain interaction is modeled by partitioning the track-terrain interface into a certain number of mesh elements with three mutually perpendicular forces, including the normal force, the longitudinal shear force and the lateral shear force, acting on the center point of each mesh element. The hydrodynamic force of the miner is considered and applied. By considering the operational safety and collection efficiency, two new mining paths for the miner on the seafloor are proposed, which can be simulated with the established single-body dynamic model of the miner. The pipeline subsystem is built as a three-dimensional multi-body discrete element model, which is divided into rigid elements linked by flexible connectors. The flexible connector without mass is represented by six spring-damper elements. The external hydrodynamic forces of the ocean current from the longitudinal and lateral directions are both considered and modeled based on the Morison formula and applied to the mass center of each corresponding discrete rigid element. The mining ship is simplified and represented by a general kinematic point, whose heave motion induced by the ocean waves and the longitudinal and lateral towing motions are considered and applied. By integrating the single-body dynamic model of the miner and the multi-body discrete element dynamic model of the pipeline, and defining the kinematic equations of the mining ship, the integrated dynamic model of the total deep ocean mining system is formed. The longitudinal reciprocating motion operation modes of the total mining system, which combine the active straight-line and turning motions of the miner and the ship, and the passive towed motions of the pipeline, are proposed and simulated with the developed 3D dynamic model. Some critical simulation results are obtained and analyzed, such as the motion trajectories of key subsystems, the velocities of the buoyancy modules and the interaction forces between subsystems, which in a way can provide important theoretical basis and useful technical reference for the practical deep ocean mining system analysis, operation and control.

Data Mining-Based Maintenance Management Framework of Multi-component System

Complex repairable system is composed of thousands of components.Some maintenance management and decision problems in maintenance management and decision need to classify a set of components into several classes based on data mining.Furthermore,with the complexity of industrial equipment increasing,the managers should pay more attention to the key components and carry out the lean management is very important.Therefore,the idea"customer segmentation"of"precise marketing"can be used in the maintenance management of the multi-component system.Following the idea of segmentation,the components of multicomponent systems should be subdivied into groups based on specific attributes relevant to maintenance,such as maintenance cost,mean time between failures,and failure frequency.For the target specific groups of parts,the optimal maintenance policy,health assessment and maintenance scheduling can be determined.The proposed analysis framework will be given out.In order to illustrate the effectiveness of this method,a numerical example is given out.

A Service Composition Approach Based on Sequence Mining for Migrating E-learning Legacy System to SOA

With the fast development of business logic and information technology, today＇s best solutions are tomorrow＇s legacy systems. In China, the situation in the education domain follows the same path. Currently, there exists a number of e-learning legacy assets with accumulated practical business experience, such as program resource, usage behaviour data resource, and so on. In order to use these legacy assets adequately and efficiently, we should not only utilize the explicit assets but also discover the hidden assets. The usage behaviour data resource is the set of practical operation sequences requested by all users. The hidden patterns in this data resource will provide users＇ practical experiences, which can benefit the service composition in service-oriented architecture （SOA） migration. Namely, these discovered patterns will be the candidate composite services （coarse-grained） in SOA systems. Although data mining techniques have been used for software engineering tasks, little is known about how they can be used for service composition of migrating an e-learning legacy system （MELS） to SOA. In this paper, we propose a service composition approach based on sequence mining techniques for MELS. Composite services found by this approach will be the complementation of business logic analysis results of MELS. The core of this approach is to develop an appropriate sequence mining algorithm for mining related data collected from an e-learning legacy system. According to the features of execution trace data on usage behaviour from this e-learning legacy system and needs of further pattern analysis, we propose a sequential mining algorithm to mine this kind of data of tile legacy system. For validation, this approach has been applied to the corresponding real data, which was collected from the e-learning legacy system; meanwhile, some investigation questionnaires were set up to collect satisfaction data. The investigation result is 90% the same with the result obtained through our approach.

Semi-real time systems for subsidence monitoring in areas affected by underground mining:the example of the Nuraxi-Figus coal district(Sardinia,Italy)

Underground mining can produce subsidence,which can be coincident with mining activities or delayed in response to the time-dependent deformation of the rocks.Therefore,in these cases,it is essential to effectively monitor the soil deformations at different times during and after mining activity.In the present work,an integrated approach based on geotechnical numeri-cal modeling and Advanced Differential Interferometric Synthetic Aperture Radar(A-DInSAR)method has been applied to detect,study and monitor the subsidence related to mining activity in the Nuraxi Figus coal district(Sardinia,Italy).Two datasets of high-resolution COSMO-Skymed images were acquired,respectively in two covering periods:from 2011 and 2014,and from 2013 to 2020.TheAA-DInSAR results show that the predominant displacement rates are located in correspond-ence with the panels.The cumulated satellite-based LoS displacements vary in the first period between-130 and+28 mm and-293 and+28.4 mm,while,during the second period between-6.9 and+1.6 mm and-8.72 and+4.33 mm in ascend-ing and descending geometries,respectively.The geotechnical numerical model allowed to obtain a value for the maximum expected.By using the vertical and horizontal components it was possible to reconstruct the kinematics of the deformation considering three phases:pre-mining,syn-mining,and post-mining activity.The temporal evolution of displacements started during the mining extraction in 2011,achieved the major values in correspondence of post-mining operations,during the period from 2013 to 2014 and continued slowly until 2020.The near real-time monitoring system applied in this study proved to be very useful for detecting subsidence during the mining activity and the post-mining period.

A virtual test and evaluation method for fully mechanized mining production system with diferent smart levels

A smart fully mechanized coal mining working face is comprised of various heterogeneous equipment that work together in unknown coal seam environments.The goal is to form a smart operational system with comprehensive perception,decisionmaking,and control.This involves many work points and complex coupling relationships,indicating it needs to be performed in stages and coordinated to address key problems in all directions and along multiple points.However,there are no existing unifed test or analysis tools.Therefore,this study proposed a virtual test and evaluation method for a fully mechanized mining production system with diferent smart levels.This is based on the concept of“real data processing–virtual scene construction–setting key information points–virtual operation and evaluation.”The actual operational data for a specifc working face geology and equipment were reasonably transformed into a visual virtual scene through a movement relationship model.The virtual operations and mining conditions of the working face were accurately reproduced.Based on the sensor and execution error analyses for diferent smart levels,the input interface for sensing,decision-making,and control was established for each piece of equipment,and an operation evaluation system was constructed.The system comprehensively simulates and tests the key points of sensing decision-making and control with various smart levels.The experimental results showed that the virtual scene constructed based on actual operational data has a high simulation degree.Users can simulate,analyze,and evaluate the overall operations of the smart mining 2.0–4.0 working face by inputting key information.The future direction for the smart development of fully mechanized mining is highlighted.

Extensible Markup Language Data Mining System Model

The existing data mining methods are mostly focused on relational databases and structured data, but not on complex structured data (like in extensible markup language(XML)). By converting XML document type description to the relational semantic recording XML data relations, and using an XML data mining language, the XML data mining system presents a strategy to mine information on XML.

Geomechanical characterization of volcanic rocks using empirical systems and data mining techniques

This paper tries to characterize volcanic rocks through the development and application of an empirical geomechanical system. Geotechnical information was collected from the samples from several Atlantic Ocean islands including Madeira, Azores and Canarias archipelagos. An empirical rock classification system termed as the volcanic rock system(VRS) is developed and presented in detail. Results using the VRS are compared with those obtained using the traditional rock mass rating(RMR) system. Data mining(DM) techniques are applied to a database of volcanic rock geomechanical information from the islands.Different algorithms were developed and consequently approaches were followed for predicting rock mass classes using the VRS and RMR classification systems. Finally, some conclusions are drawn with emphasis on the fact that a better performance was achieved using attributes from VRS.

MA-IDS: A Distributed Intrusion Detection System Based on Data Mining

Aiming at the shortcomings in intrusion detection systems (IDSs) used incommercial and research fields, we propose the MA-IDS system, a distributed intrusion detectionsystem based on data mining. In this model, misuse intrusion detection system CM1DS) and anomalyintrusion de-lection system (AIDS) are combined. Data mining is applied to raise detectionperformance, and distributed mechanism is employed to increase the scalability and efficiency. Host-and network-based mining algorithms employ an improved. Bayes-ian decision theorem that suits forreal security environment to minimize the risks incurred by false decisions. We describe the overallarchitecture of the MA-IDS system, and discuss specific design and implementation issue.

Fuzzy Inference System Design Based on Data Mining Concepts and Its Application in Time Series Forecasting

This paper adopts data mining(DM) technique and fuzzy system theory for robust time series forecasting.By introducing DM technique,the fuzzy rule extraction algorithm is improved to be more robust with the noises and outliers in time series.Then,the constructed fuzzy inference system(FIS) is optimized with a partition refining strategy to balance the system's accuracy and complexity.The proposed algorithm is compared with the WangMendel(WM) method,a benchmark method for building FIS,in comprehensive analysis of robustness.In the classical Mackey-Glass time series forecasting,the simulation results prove that the proposed method is able to predict time series with random perturbation more accurately.For the practical application,the proposed FIS is applied to predicting the time series of ship maneuvering motion.To obtain actual time series data records,the ship maneuvering motion trial is conducted in the Yukun ship of Dalian Maritime University in China.The time series forecasting results show that the FIS constructed with DM concepts can forecast ship maneuvering motion robustly and effectively.

Analysis of the rule of medication of compound Chinese traditional patent medicine for digestive system tumors based on data mining

Objective:Based on the data mining method,explore the medication rules of Chinese patent medicines for the treatment of digestive system tumors.Method:Based on the"Chinese Traditional Medicine Prescription Database"in https://db.yaozh.com/,collect Chinese patent medicines for the treatment of digestive system tumors,establish an Excel table,and use the ancient and modern medical case cloud platform(V2.2.1)to perform frequency statistics,association rules,and drugs on the data Clustering and complex network analysis.Results:A total of 36 Chinese patent medicines for the treatment of digestive system tumors were screened.The medicinal properties were mainly warm and cold,the taste was bitter and sweet,and the meridians were mainly liver and spleen meridians.High frequency Chinese medicine include Astragalus,Scutellaria-barbata,Ginseng,Curcuma,Triangle,Atractylodes,Hedyotis diffusa,etc.Correlation analysis obtained 17 drug combinations,High-frequency drug pairs include Scutellaria-barbata-Astragalus,Ginseng-Astragalus,Curcuma-Astragalus,Scutellaria-barbata-Curcuma,etc.Cluster analysis found 3 types of drugs.The core drug network is composed of 27 drugs,and the core compatibility network consists of 3 groups of drugs.Conclusion:The Chinese patent compound medicine for the treatment of digestive system tumors has the characteristics of combining cold and warming,replenishing and reducing treatment,and treating the liver and spleen at the same time.The medicine is mainly used to replenish qi and invigorate the spleen,promote blood circulation and remove blood stasis,and clear away heat and detoxification.Replenish qi,nourish yin,invigorate blood,and detoxify are mainly compatible with each other,reflecting the pathogenesis characteristics of"deficiency,stasis,and toxin"in digestive system tumors.Data mining can provide references for the prescription and compatibility of Chinese patent medicines.

Microbiotechnological Stimulation of the Root Systems of Woody Plants within the Scope of the Revitalization of Mining Works

In general, the conditions for the growth of vegetation in former mining works are unfavorable, particularly in locations where the rooting process or inadequate functional contact by the root system with underground water, creates vegetative problems. For the process of the revitalization of the disrupted areas to be successful, the correct rooting of woody plants is essential for starting the development of the vegetation, and directing it towards greater biodiversity. The addition of bio alginates, whose basic raw component is selected seaweed, to the soil ensures the stimulation of the root system, thereby significantly increasing the likelihood of the plants surviving. The article monitors, in detail, the effect of the application of these bio alginates, and the subsequent response of the plants in growth patterns.

Head-mounted display-based intuitive virtual reality training system for the mining industry

Virtual reality(VR) training technology in the mining industry is a new field of research and utilization.The successful application of VR training system is critical to mine safety and production. Through the statistics of the current research and applications of VR training systems in mining industry, all the input/output devices are classified. Based on the classifications of the input/output devices that are used in the VR system, the current VR training systems for the mining industry could be divided into three types: screen-based general type, projector-based customized type, and head-mounted display(HMD)-based intuitive type. By employing a VR headset, a smartphone and a leap motion device, an HMDbased intuitive type VR training system prototype for drilling in underground mines has been developed.Ten trainees tried both the HMD-based intuitive system and the screen-based general control system to compare the experiences and training effects. The results show that the HMD-based system can give a much better user experience and is easy to use. Three of the five components of a VR training system,namely, the user, the tasks, and software and database should be given more attention in future research.With more available technologies of input and output devices, VR engines, and system software, the VR training system will eventually yield much better training results, and will play a more important role in as a training tool for mine safety.