Mathematical Model For Evaluation Research Articles

An extended ITL-VIKOR model using triangular fuzzy numbers for applications to water-richness evaluation

Jiaozuo coalfield is the most typical in Northern China type coalfield. When it mined the Permian coal seams, it was threatened by water inrush from the confined aquifer of thin karst fissure limestone in the lower Carboniferous. How to scientifically evaluate and predict the water-richness of thin karst fissure limestone in Carboniferous system has become the most important work in mine water control and prevention. Taking the mining of coal seam No. 21 of Shanxi Formation in Guhanshan coal mine as an example, this paper analyzed the water-richness main controlling factors of L8 Limestone Aquifer of Taiyuan Formation, and selected four evaluation indexes, i.e. water inflow of drilling, the thickness of L8 Limestone Aquifer, the water level of L8 Limestone Aquifer and fault fractal dimension. The 2-tuple linguistic weights of all factors were obtained by the use of interval 2-tuple linguistic method extended by triangular fuzzy numbers. Following the above outlines, the mathematical model for the 2-tuple linguistic evaluation of water-richness was established based on ITL-VIKOR method extended by triangular fuzzy numbers. The data of geophysical exploration were collected to test the accuracy of the model. In addition, the results of 2-tuple linguistic assessment of water-richness and geophysical exploration were used to evaluate the water-richness of L8 Limestone Aquifer, which extends the evaluation and prediction of water abundance for karst fractured aquifers to dyadic array. These results have been applied to guide the safe mining in Guhanshan coal mine, and will be further extended to the mines with similar geological conditions. It is our goal to establish an intelligent application system to ensure safe production in coal mines, which is great beneficial to the technical personnel engaged in mine water prevention and control.

The impact of patent quality on corporate performance in the context of big data and the Internet of Things

In the context of big data and the Internet of Things, this article explores the impact of corporate patent quality on corporate performance through analysis of corporate patent quality. Starting from multiple influencing factors, measure the patent value of enterprises to promote the economic benefits. Focus on the analysis of the practical conversion capacity of patents and the impact on corporate performance, establish a mathematical evaluation model, and explore the specificity of the patent quality through the method of regression analysis. This article has studied the important role of patent technology to improve the efficiency of enterprises, and explores the overall level of corporate performance management on how to maximizes the overall level of corporate performance management. The system of measures for patent technology to promote performance development under the background of big data constructed by the research has a very good reference for the sustainable development of enterprises at the current stage. From the existing research results, there are problems such as too few analysis samples and slow data updates, and these problems have been better solved in this study. The research analyzes the comprehensive impact of patent quality on enterprises through big data, highlights the positive relationship between patent quality and enterprise performance, and effectively fills the gap in current research.

The mathematical model of ecological carrying capacity evaluation in the water source area of Haikou tidal flat habitat

The mathematical model of ecological carrying capacity evaluation in the water source area of Haikou tidal flat habitat

METHOD OF ASSESSING THE EFFICIENCY OF ENSURING INFORMATION SECURITY OF THE EDUCATIONAL INFORMATION SYSTEM

In the modern conditions of the development of information technologies, the issue of ensuring information security is becoming more and more relevant. Information has become a key resource that affects the success and stability of the functioning of organizations, enterprises and institutions. Despite the fact that a significant part of efforts is aimed at creating and maintaining effective information protection systems, the issue of assessing their effectiveness remains relevant and important. The article examines a complex methodology for assessing the effectiveness of information security, which is based on the use of modern approaches and tools for analyzing the security of information systems. The proposed methodology covers various aspects of information security, including technical, organizational, procedural and human factors. The evaluation of efficiency is based on a complex analysis of multiple indicators, which allows to obtain an objective picture of the state of information security of the organization. The main part of the methodology is the development and application of mathematical models that allow quantitative assessment of the level of information security. Such models take into account various factors, including the level of technical protection of information systems, information security policy, the level of training of personnel, response to incidents and other important aspects. In addition, the article considers the issue of optimizing costs for ensuring information security by applying economic models and approaches. The proposed methodology includes several stages: determination of key security indicators, data collection and analysis, development of mathematical models for performance evaluation, interpretation of results and development of recommendations for increasing the level of protection. An important component of the methodology is a systematic approach that allows taking into account the interaction of various elements of the information security system and their impact on the overall level of protection. The practical application of the developed methodology makes it possible to timely identify vulnerabilities in the information security system, optimize the costs of its provision, increase the overall level of security of the organization and reduce the risks associated with information threats. The results of the study confirm the effectiveness of the proposed approach and demonstrate its applicability in various fields of activity, including business, public sector, education and other areas. Thus, the proposed methodology for assessing the effectiveness of information security is an important tool for organizations seeking to ensure reliable protection of their information. Its use contributes to increasing the level of information security, which, in turn, has a positive effect on the general level of functioning and stability of organizations in the face of modern challenges and threats.

Retracted: Mathematical Model of Quantitative Evaluation of Financial Investment Risk Management System

Retracted: Mathematical Model of Quantitative Evaluation of Financial Investment Risk Management System

Mathematical Model for Evaluation of Tumor Response in Targeted Radionuclide Therapy with 211At Using Implanted Mouse Tumor.

Recent introduction of alpha-emitting radionuclides in targeted radionuclide therapy has stimulated the development of new radiopharmaceuticals. Preclinical evaluation using an animal experiment with an implanted tumor model is frequently used to examine the efficiency of the treatment method and to predict the treatment response before clinical trials. Here, we propose a mathematical model for evaluation of the tumor response in an implanted tumor model and apply it to the data obtained from the previous experiment of 211At treatment in a thyroid cancer mouse model. The proposed model is based on the set of differential equations, describing the kinetics of radiopharmaceuticals, the tumor growth, and the treatment response. First, the tumor growth rate was estimated from the control data without injection of 211At. The kinetic behavior of the injected radionuclide was used to estimate the radiation dose profile to the target tumor, which can suppress the tumor growth in a dose-dependent manner. An additional two factors, including the time delay for the reduction of tumor volume and the impaired recovery of tumor regrowth after the treatment, were needed to simulate the temporal changes of tumor size after treatment. Finally, the parameters obtained from the simulated tumor growth curve were able to predict the tumor response in other experimental settings. The model can provide valuable information for planning the administration dose of radiopharmaceuticals in clinical trials, especially to determine the starting dose at which efficacy can be expected with a sufficient safety margin.

The hybrid mathematical model for the evaluation and selection of iron ore raw materials in the context of the European Green Deal

The actual scientific research on the development of the hybrid mathematical model for evaluating and selecting iron ore raw materials in the context of the EU Green Deal is carried out. The necessity of developing decision support systems within the framework of the EU Green Deal concept is substantiated, in order to increase the degree of validity of decision-making in the evaluation space, regarding the green course and achieving the goals of the green agreement. For the first time, a systematic theoretical-multiple model of the problem of evaluation and selection of iron ore raw materials, according to the proposed factors of chemical composition, cost, and environmental policy of the manufacturer. The problem of multi-criteria selection of alternatives based on the imaginary alternative of the "satisfaction point" of the decision-makers (DM) requirements using the information model of criteria for evaluating the properties of the object of study on the chemical composition of iron ore raw materials. For the first time, a hybrid mathematical model of multi-criteria evaluation of iron ore raw materials with target needs was developed: quality of chemical composition of iron ore raw materials; the cost of iron ore; environmental policy and environmental impact in the extraction and/or enrichment of iron ore raw materials. The results of a study on a real example of evaluation and selection of iron ore concentrate for six alternatives implemented by Metinvest (Ukraine) were tested. The general step-by-step algorithm of the evaluation concept is described, which can be quickly implemented in the software product for the application. The developed model will be a useful tool for various programs of state support/business assistance in the procurement of iron ore raw materials by reasonably choosing a supplier using targeted needs in the context of synchronization with the EU Green Deal initiative. Raw material extraction and subsequent transport logistics are especially important for the engineering industry, the electrical engineering industry, and the automotive industry, with strong potential for the aeronautics and aerospace industry.

Digital control by manipulator of railway track machine

Digital control of manipulator of railway track machine (RTM). It is shown, that exactness and performance of operation execution are the main characteristics, which determine quality of work RTM. Manipulator design with pyramidal structure, in which mechanism is actuated with linear drives, placed in parallel, is described. Mathematical model of exactness evaluation, when there is dry friction at actuators, is worked out. It is shown, that mechanics parallelism leads to cross-links between grasp coordinates control channels. Flowchart of manipulator under investigation, as the object under control, is given. The analytical descriptions of object under control and control algorithm, underlying Von Neumann type controller, are fulfilled with description of delays, linked with features of controller operation, namely operator-by-operator interpretation of algorithm, unfolding in physical time. For estimation of time delays at interface between controller and manipulator semi-Markov model of Von Neumann computer operation is worked out. The method of estimation lags in contours, based on transformation the ergodic semi-Markov model into non-ergodic one, and computation expectations and dispersions of time interval from-point-to-point wandering through semi-Markov process, is proposed.

An Analytical Hierarchy-Based Method for Quantifying Hydraulic Fracturing Stimulation to Improve Geothermal Well Productivity

Hydraulic fracturing (HF) has been used for years to enhance oil and gas production from conventional and unconventional reservoirs. HF in enhanced geothermal systems (EGS) has become increasingly common in recent years. In EGS, hydraulic fracturing creates a geothermal collector in impermeable or low-permeable hot dry rocks. Artificial fracture networks in the collector allow for a continuous flow of fluid in a loop connecting at least two wells (injector and producer). However, it is challenging to assess the fracability of geothermal reservoirs for EGS. Consequently, it is necessary to design a method that considers multiple parameters when evaluating the potential of geothermal development. This study proposes an improved fracability index model (FI) based on the influences of fracability-related geomechanical and petrophysical properties. These include brittle minerals composition, fracture toughness, minimum horizontal in-situ stress, a brittleness index model, and temperature effect to quantify the rock’s fracability. The hierarchical analytic framework was designed based on the correlation between the influencing factors and rock fracability. The results of the qualitative and quantitative approaches were integrated into a mathematical evaluation model. The improved fracability index model’s reliability was evaluated using well logs and 3D seismic data on low-permeable carbonate geothermal reservoirs and shale gas horizontal wells. The results reveal that the improved FI model effectively demonstrates brittle regions in the low-permeable carbonate geothermal reservoir and long horizontal section of shale reservoir. We divide the rock fracability into three levels: FI > 0.59 (the rock fracability is good); 0.59 > FI > 0.32 (the rock fracability is medium); and FI < 0.32, (the rock fracability is poor). The improved FI model can assist in resolving the uncertainties associated with fracability interpretation in determining the optimum location of perforation clusters for hydraulic fracture initiation and propagation in enhanced geothermal systems.

Design and Implementation of Community Pension Service Quality Evaluation System Based on Clustering Algorithm

The healthy development of community elderly care service is very important. Studying the quality evaluation of community elderly care services and establishing a complete, scientific, and objective community elderly care service method can establish the online reputation of enterprises and help elderly users obtain cost-effective elderly care services. This is very important for healthy development. This paper puts forward a new evaluation model for the quality of community elderly care service. The evaluation method based on the model is designed, the quality evaluation model is constructed, and the community evaluation system is developed. From the aspects of requirement analysis, design, implementation, and testing, the specific implementation process of the system is described. This paper puts forward a community evaluation model which accords with the clustering model: community elderly care service quality evaluation method. AIS-OR mathematical modeling is carried out. Under the guidance of the model, the secondary indicators are analyzed and determined, and the mathematical model of indicator evaluation is constructed. We use text mining technology and emotion analysis to determine the relationship between comment emotion and community elderly care service quality online and build a quality emotion feature library. The naive Bayesian algorithm is used to classify the online evaluation of the service quality of community elderly care services, which verified by experiments and completed the design and development of the community elderly care service quality evaluation system based on the project needs.

Mathematical Model Construction of Teaching Evaluation in Colleges and Universities Based on Convolutional Neural Network under the Background of Big Data

With the development of teacher evaluation, the main purposes of university evaluation are divided into the final purpose of ensuring teaching quality and the formative purpose of promoting professional learning. In order to effectively help teachers teach students according to their aptitude and realize personalized learning. This paper mainly analyzes and studies the mathematical model of college teaching evaluation based on convolutional neural network under the background of big data. The research analyzes the teaching evaluation under the background of big data and evaluates the teaching of big data participants. The teaching evaluation data can promote the development of education and teaching. Data acquisition processes and functions in the context of big data. Ensure the comprehensiveness and accuracy of data collection. At the same time, the function of teaching evaluation results is analyzed. The evaluation and formulation of mathematical model program in this study make full use of education big data information to provide scientific support for education decision-making.

Numerical Energy and Exergy Evaluation of a Filament Bulb Controlled Convective Heat Dryer

The mathematical model for temperature regime thermal analysis evaluation of convective heat dryer has been done. Control volume analysis was adopted in this work while taking note of all the feasible heat transfer mechanisms around the system. Mathematical expressions for the energy, exergy, entropy generation as well as destroyed exergy were derived using the control volume. The consumed energy, exergy, entropy generation and exergy destruction of the dryer were computed. The system temperature gradually increased from its initial room temperature of 270˚C to 720˚C at approximately 2700 secs. The temperature thereafter dropped and stabilized to about 670˚C at 4600 secs while the element dried gradually. The moisture removal curve followed the same trend as that of the temperature with gradual variation from 0.5 to 13.2g/mol. The peak energy from the system is approximately 958J against the useful exergy of 443J which shows that 46.24% of the expended energy was actually useful for the drying purpose and about 10.23% was lost. The energy and exergy efficiencies were also computed and results showed that the efficiencies are functions of temperature just like other influencing parameters which has to be technically controlled. Peak energy efficiency of approximately 54.6% was recorded in the dryer with the peak exergy efficiency of 23.4%. The dryer also yielded a better dried product as compared with the traditional means of drying.

Mathematical Model of Quantitative Evaluation of Financial Investment Risk Management System

The financial investment risk management system refers to an analysis and control of the intelligent system to invest in the lower financial situation so that investors quickly understand the situation in the financial industry. The purpose of this article is to use a digital model to evaluate financial investment risk management system. The investment risk value can be better evaluated by building a digital model. This paper first introduces financial investment risks and then elaborates the evaluation system and related digital models. The standards of the evaluation system are also given. The GARCH model is established to analyze the LME copper and LME aluminum case selected by this paper by investigation and analysis of the current status of corporate financial investment risks. The experimental results show that the evaluation results are often close to the reality when using the GARCH model evaluation of financial investment risk management system, and the accuracy is quite high. In addition to the EGARCH-N model, the established model is more accurate at 90% confidence level, which is more accurate and is relatively close to a given significance level.

Data-driven models for replication kinetics of Orthohantavirus infections

The Hantaviridae constitute a family of viruses harbored by mice, rats, shrews, voles, moles and bats. Intriguingly, only viruses harbored by mice and rats may cause disease in humans with up to 40% case fatality rate in the Americas. Transmission of virus from rodents to humans occurs via the respiratory route and results in replication of the virus in the microvascular endothelial cells of the lung or kidney. Understanding the replication kinetics of these viruses in various cell types and how replication is abrogated by the host is critical to the development of effective therapeutics for treatment for which there are none. We formulate several new ordinary differential equation (ODE) models to examine the replication kinetics of Prospect Hill orthohantavirus (PHV). The models are distinguished by the distribution of the viral replication delay. A new threshold, RGE, the genome equivalent replication number, is defined in terms of the model parameters. New final density relations are derived that associate RGE to the asymptotic number of virions in each model. All models are fit to real time (qRT)-PCR data of genomic RNA from PHV released from Vero E6 cells over 192 h. A sensitivity analysis of the parameters is performed and models are tested for best fit. Our findings provide a basis for future research into formulating more complex mathematical models for evaluation of the replication of hantaviruses in various cell types and sources.

Design of a high-tech system to evaluate fresh strawberry quality-based on the principal component analysis

This study was designed to investigate the differences in the physicochemical properties among twelve strawberry cultivars by using pattern recognition tools, to provide a theoretical basis for quality variation among samples. The data of 14 indicators were subjected to principal component analysis (PCA), descriptive statistical analysis, correlation analysis, and hierarchical cluster analysis, to filter core evaluation factors. Quality evaluation index weights and quality comprehensive evaluation were determined by an analytic hierarchy process. Of the 14 indicators selected as indices of fresh strawberry quality evaluation index, including a value, sugar-acid ratio, firmness, vitamin C, TAC, and TPC. The data were deployed to adjust the multivariate kinetics using Analytic Hierarchy Process (AHP), and the results were compared to those sensory score using sensory personnel. Results showed that the correlation coefficient of sensory scores and AHP comprehensive score is 0.9239. This high correlation coefficient indicates that the use of our mathematical model for strawberry quality evaluation is feasible. The information herein provides a practical strategy for the evaluation of strawberry quality.

An Evaluation of the Petroleum Investment Environment in African Oil-Producing Countries Based on Combination Weighting and Uncertainty Measure Theory

Recognizing that the evaluation of the overseas petroleum investment environment is affected by many uncertain factors and that there are problems with current evaluation methods, this paper proposes a mathematical evaluation model of an overseas oil resources investment environment, based on a combination of the weighting and uncertainty measure theory. Combining international investment environment theory with the characteristics of the petroleum industry, this paper establishes an evaluation index system for the overseas petroleum investment environment and the linear uncertainty measure function of each index. Using the subjective weight obtained using an analytic hierarchy process together with the objective weight obtained using the entropy weight method, the optimal weight of each evaluation index was obtained using minimum relative information entropy. A multi-index evaluation matrix of the top 12 oil-producing countries in Africa was calculated. Finally, the credible degree recognition criterion was used to judge the order and level of the oil investment environment. This model provides an effective method for the evaluation of the overseas petroleum investment environment. The results show that Nigeria and Angola have the best investment climate, followed by Algeria, Egypt, and Libya. In general, Africa is an important strategic partner of China and is rich in oil resources. Although Africa’s oil industry is fraught with complex challenges and headwinds, challenges also present opportunities.

Structural design of multi-body heave wave energy conversion system and analysis of energy efficiency of floating body on water surface

Abstract. Due to the defects of the internal structure and energy supply carrier, conventional deep sea unoccupied marine equipment cannot meet the requirements of low power consumption. In this paper, the whole structure of a multi-body heave wave energy conversion system was designed to capture and convert wave energy. The conversion system consists of a floating body, an underwater absorber and a power takeoff system (PTO). The dynamic model of the energy conversion system and the mathematical model of energy efficiency evaluation were established according to the dynamic analysis. Based on the real service environment in the South China Sea, the energy efficiency characteristics of floating bodies with different shapes were simulated, and the amplitude response operator (RAO), radiation damping, added mass and Froude–Krylov force of floating bodies with different shapes were compared. Then, the optimal energy efficiency parameters of surface floating body were explored. Finally, the correctness of the conclusion was verified by the energy efficiency test. The results show that, under the limitation of low power consumption and space scale, the energy conversion system of an axisymmetric rotary body with the same sea conditions, same material and the largest scale can significantly improve the conversion efficiency, and the spherical rotary body performs the best, which makes the unoccupied marine equipment have a broad prospect for development.

A novel dynamic reserve evaluation method by division between oil and water in isolated fractured-caved carbonate reservoirs

Discrete reservoirs are commonly seen in fractured-caved carbonate reservoirs. The effect of cyclic huff-n-puff in single well and water flooding in well group is varied for different wells. Some wells fail in water injection stimulation due to unforeseen high water cut. Therefore, it is necessary to estimate the displacing potential of water injection after its start and adjust the development strategy in time. The oil volume and water volume are required to be distinguished from the drainage pore volume of an isolated reservoir besides the conventional dynamic reserve evaluation. Based on the material balance principle, a mathematical model for dynamic reserve evaluation is established considering the volume ratio of oil and water. Then controlling equation describing the relationship between pressure recovery and water volume ratio in reservoir is combined with boundary conditions at time nodes before and after water injection to numerically solve the model. The dynamic reserve of crude oil and water can be accurately concluded to deepen the understanding of oil-water distribution characteristic in fractured-caved reservoirs. The method has been proved to meet the actual production after the application in Halahatang Oilfield in Tarim Basin, providing a reliable potential estimation for subsequent recovery enhancement measures.

Application of Machine Learning in the Reliability Evaluation of Pipelines for the External Anticorrosion Coating.

The purpose of this research is to enhance the analysis of the reliability status for external anticorrosive coatings. With the limitation and insufficiency of the static evaluation method, we study and construct an evaluation method of dynamic reliability for the anticorrosive layer, integrating the trend analysis of the Markov chain and the set pair theory. This method is implemented by the machine learning software of PyCharm community edition, based on Python language. The algorithm utilizes the connection degree in the set pair theory to determine the risk levels of the anticorrosive coating systems. According to the characteristics of the dynamic change of the anticorrosive layer with time, we built the mathematical evaluation model by combining it with the nonaftereffect property of the Markov chain. Therefore, we can make a dynamic and useful analysis for the reliability grade of the anticorrosive coating and assess the effectiveness grade of the changed reliability for the anticorrosive coating after some time. This method can effectively evaluate the reliability level of the anticorrosion coating through the example of big data of detection points. Under national standards, we provide the theoretical basis for pipeline maintenance within detection cycle requirements.

Comprehensive Analysis of Clinical Logistic and Machine Learning-Based Models for the Evaluation of Pulmonary Nodules

IntroductionOver the years, multiple models have been developed for the evaluation of pulmonary nodules (PNs). This study aimed to comprehensively investigate clinical models for estimating the malignancy probability in patients with PNs. MethodsPubMed, EMBASE, Cochrane Library, and Web of Science were searched for studies reporting mathematical models for PN evaluation until March 2020. Eligible models were summarized, and network meta-analysis was performed on externally validated models (PROSPERO database CRD42020154731). The cut-off value of 40% was used to separate patients into high prevalence (HP) and low prevalence (LP), and a subgroup analysis was performed. ResultsA total of 23 original models were proposed in 42 included articles. Age and nodule size were most often used in the models, whereas results of positron emission tomography-computed tomography were used when collected. The Mayo model was validated in 28 studies. The area under the curve values of four most often used models (PKU, Brock, Mayo, VA) were 0.830, 0.785, 0.743, and 0.750, respectively. High-prevalence group (HP) models had better results in HP patients with a pooled sensitivity and specificity of 0.83 (95% confidence interval [CI]: 0.78–0.88) and 0.71 (95% CI: 0.71–0.79), whereas LP models only achieved pooled sensitivity and specificity of 0.70 (95% CI: 0.60–0.79) and 0.70 (95% CI: 0.62–0.77). For LP patients, the pooled sensitivity and specificity decreased from 0.68 (95% CI: 0.57–0.78) and 0.93 (95% CI: 0.87–0.97) to 0.57 (95% CI: 0.21–0.88) and 0.82 (95% CI: 0.65–0.92) when the model changed from LP to HP models. Compared with the clinical models, artificial intelligence-based models have promising preliminary results. ConclusionsMathematical models can facilitate the evaluation of lung nodules. Nevertheless, suitable model should be used on appropriate cohorts to achieve an accurate result.