Editorial

4 pages, 172 KiB

Open AccessEditorial

Preface to the Special Issue on “Computational and Mathematical Methods in Information Science and Engineering”

by Wen Zhang, Xiaofeng Xu, Jun Wu and Kaijian He

Mathematics 2023, 11(14), 3187; https://doi.org/10.3390/math11143187 - 20 Jul 2023

Viewed by 627

With the emergence of big data and the resulting information explosion, computational and mathematical methods provide effective tools to handle the vast amounts of data and information used in big data analytics, knowledge discovery and distillation, and decision-making for solving complex problems in [...] Read more.

With the emergence of big data and the resulting information explosion, computational and mathematical methods provide effective tools to handle the vast amounts of data and information used in big data analytics, knowledge discovery and distillation, and decision-making for solving complex problems in the world [...] Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

Research

Jump to: Editorial

14 pages, 1331 KiB

Open AccessArticle

On Study of the Occurrence of Earth-Size Planets in Kepler Mission Using Spatial Poisson Model

by Hong-Ding Yang, Yun-Huan Lee and Che-Yang Lin

Mathematics 2023, 11(11), 2508; https://doi.org/10.3390/math11112508 - 30 May 2023

Cited by 1 | Viewed by 847

Abstract

The problem of determining the occurrence rate for Earth-size planets orbiting Sun-like stars is emerging in the universe. We propose a methodology based on a spatial Poisson regression model with model parameters being inferred by the Bayesian framework to investigate this occurrence rate. [...] Read more.

The problem of determining the occurrence rate for Earth-size planets orbiting Sun-like stars is emerging in the universe. We propose a methodology based on a spatial Poisson regression model with model parameters being inferred by the Bayesian framework to investigate this occurrence rate. We analyzed an exoplanet sample and its corresponding survey completeness data. Our results suggest that 46% of Sun-like stars have an Earth-size (i.e., 1–2 times Earth radii) planet with an orbital period of 5–100 days. Furthermore, we are also interested in the occurrence rate of Earth analogs hosted by GK dwarf stars (i.e., orbital period of 200–400 days and size 1–2 times Earth radii). After completeness correction, we obtained an occurrence rate of 0.18% based on the proposed methodology. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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13 pages, 3393 KiB

Open AccessArticle

Deviance and Pearson Residuals-Based Control Charts with Different Link Functions for Monitoring Logistic Regression Profiles: An Application to COVID-19 Data

by Maryam Cheema, Muhammad Amin, Tahir Mahmood, Muhammad Faisal, Kamel Brahim and Ahmed Elhassanein

Mathematics 2023, 11(5), 1113; https://doi.org/10.3390/math11051113 - 23 Feb 2023

Cited by 2 | Viewed by 1578

Abstract

In statistical process control, the control charts are an effective tool to monitor the process. When the process is examined based on an exponential family distributed response variable along with a single explanatory variable, the generalized linear model (GLM) provides better estimates and [...] Read more.

In statistical process control, the control charts are an effective tool to monitor the process. When the process is examined based on an exponential family distributed response variable along with a single explanatory variable, the generalized linear model (GLM) provides better estimates and GLM-based charts are preferred. This study is designed to propose GLM-based control charts using different link functions (i.e., logit, probit, c-log-log, and cauchit) with the binary response variable. The Pearson residuals (PR)- and deviance residuals (DR)-based control charts for logistic regression are proposed under different link functions. For evaluation purposes, a simulation study is designed to evaluate the performance of the proposed control charts. The results are compared based on the average run length (ARL). Moreover, the proposed charts are implemented on a real application for COVID-19 death monitoring. The Monte Carlo simulation study and real applications show that the performance of the model-based control charts with the c-log-log link function gives a better performance as compared to model-based control charts with other link functions. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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22 pages, 735 KiB

Open AccessArticle

Robust Online Support Vector Regression with Truncated ε-Insensitive Pinball Loss

by Xian Shan, Zheshuo Zhang, Xiaoying Li, Yu Xie and Jinyu You

Mathematics 2023, 11(3), 709; https://doi.org/10.3390/math11030709 - 30 Jan 2023

Cited by 2 | Viewed by 1211

Abstract

Advances in information technology have led to the proliferation of data in the fields of finance, energy, and economics. Unforeseen elements can cause data to be contaminated by noise and outliers. In this study, a robust online support vector regression algorithm based on [...] Read more.

Advances in information technology have led to the proliferation of data in the fields of finance, energy, and economics. Unforeseen elements can cause data to be contaminated by noise and outliers. In this study, a robust online support vector regression algorithm based on a non-convex asymmetric loss function is developed to handle the regression of noisy dynamic data streams. Inspired by pinball loss, a truncated

ε

-insensitive pinball loss (TIPL) is proposed to solve the problems caused by heavy noise and outliers. A TIPL-based online support vector regression algorithm (TIPOSVR) is constructed under the regularization framework, and the online gradient descent algorithm is implemented to execute it. Experiments are performed using synthetic datasets, UCI datasets, and real datasets. The results of the investigation show that in the majority of cases, the proposed algorithm is comparable, or even superior, to the comparison algorithms in terms of accuracy and robustness on datasets with different types of noise. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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23 pages, 981 KiB

Open AccessArticle

Optimal Decision of Dynamic Bed Allocation and Patient Admission with Buffer Wards during an Epidemic

by Chengliang Wang, Feifei Yang and Quan-Lin Li

Mathematics 2023, 11(3), 687; https://doi.org/10.3390/math11030687 - 29 Jan 2023

Cited by 2 | Viewed by 1404

Abstract

To effectively prevent patients from nosocomial cross-infection and secondary infections, buffer wards for screening infectious patients who cannot be detected due to the incubation period are established in public hospitals in addition to isolation wards and general wards. In this paper, we consider [...] Read more.

To effectively prevent patients from nosocomial cross-infection and secondary infections, buffer wards for screening infectious patients who cannot be detected due to the incubation period are established in public hospitals in addition to isolation wards and general wards. In this paper, we consider two control mechanisms for three types of wards and patients: one is the dynamic bed allocation to balance the resource utilization among isolation, buffer, and general wards; the other is to effectively control the admission of arriving patients according to the evolution process of the epidemic to reduce mortality for COVID-19, emergency, and elective patients. Taking the COVID-19 pandemic as an example, we first develop a mixed-integer programming (MIP) model to study the joint optimization problem for dynamic bed allocation and patient admission control. Then, we propose a biogeography-based optimization for dynamic bed and patient admission (BBO-DBPA) algorithm to obtain the optimal decision scheme. Furthermore, some numerical experiments are presented to discuss the optimal decision scheme and provide some sensitivity analysis. Finally, the performance of the proposed optimal policy is discussed in comparison with the other different benchmark policies. The results show that adopting the dynamic bed allocation and admission control policy could significantly reduce the total operating cost during an epidemic. The findings can give some decision support for hospital managers in avoiding nosocomial cross-infection, improving bed utilization, and overall patient survival during an epidemic. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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27 pages, 11542 KiB

Open AccessArticle

Defects Classification of Hydro Generators in Indonesia by Phase-Resolved Partial Discharge

by Chun-Yao Lee, Nando Purba and Guang-Lin Zhuo

Mathematics 2022, 10(19), 3659; https://doi.org/10.3390/math10193659 - 06 Oct 2022

Cited by 2 | Viewed by 2187

Abstract

This paper proposed a phase-resolved partial discharge (PRPD) shape method to classify types of defect generator units by using offline partial discharge (PD) measurement instruments. In this paper, the experimental measurement was applied to two generators in the Inalum hydropower plant, located in [...] Read more.

This paper proposed a phase-resolved partial discharge (PRPD) shape method to classify types of defect generator units by using offline partial discharge (PD) measurement instruments. In this paper, the experimental measurement was applied to two generators in the Inalum hydropower plant, located in North Sumatera, Indonesia. The recorded PRPD using the instrument MPD600 can illustrate the PRPD patterns of generator defects. The proposed PRPD shape method is used to mark auxiliary lines on the PRPD patterns. Moreover, four types of defects refer to the IEC 60034-27 standard, which are microvoid (S1), delamination tape layer (S2), slot defect (S3), and internal delamination (S4) and are used to classify the defect types of the generators. The results show that the proposed method performs well to classify types of defect generator units. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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17 pages, 505 KiB

Open AccessArticle

Filter-GAN: Imbalanced Malicious Traffic Classification Based on Generative Adversarial Networks with Filter

by Xin Cao, Qin Luo and Peng Wu

Mathematics 2022, 10(19), 3482; https://doi.org/10.3390/math10193482 - 23 Sep 2022

Cited by 6 | Viewed by 1644

Abstract

In recent years, with the rapid development of Internet services in all walks of life, a large number of malicious acts such as network attacks, data leakage, and information theft have become major challenges for network security. Due to the difficulty of malicious [...] Read more.

In recent years, with the rapid development of Internet services in all walks of life, a large number of malicious acts such as network attacks, data leakage, and information theft have become major challenges for network security. Due to the difficulty of malicious traffic collection and labeling, the distribution of various samples in the existing dataset is seriously imbalanced, resulting in low accuracy of malicious traffic classification based on machine learning and deep learning, and poor model generalization ability. In this paper, a feature image representation method and Adversarial Generative Network with Filter (Filter-GAN) are proposed to solve these problems. First, the feature image representation method divides the original session traffic into three parts. The Markov matrix is extracted from each part to form a three-channel feature image. This method can transform the original session traffic format into a uniform-length matrix and fully characterize the network traffic. Then, Filter-GAN uses the feature images to generate few attack samples. Compared with general methods, Filter-GAN can generate more efficient samples. Experiments were conducted on public datasets. The results show that the feature image representation method can effectively characterize the original session traffic. When the number of samples is sufficient, the classification accuracy can reach 99%. Compared with unbalanced datasets, Filter-GAN has significantly improved the recognition accuracy of small-sample datasets, with a maximum improvement of 6%. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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16 pages, 1298 KiB

Open AccessArticle

Dynamic Model Selection Based on Demand Pattern Classification in Retail Sales Forecasting

by Erjiang E, Ming Yu, Xin Tian and Ye Tao

Mathematics 2022, 10(17), 3179; https://doi.org/10.3390/math10173179 - 03 Sep 2022

Cited by 6 | Viewed by 2438

Abstract

Many forecasting techniques have been applied to sales forecasts in the retail industry. However, no one prediction model is applicable to all cases. For demand forecasting of the same item, the different results of prediction models often confuse retailers. For large retail companies [...] Read more.

Many forecasting techniques have been applied to sales forecasts in the retail industry. However, no one prediction model is applicable to all cases. For demand forecasting of the same item, the different results of prediction models often confuse retailers. For large retail companies with a wide variety of products, it is difficult to find a suitable prediction model for each item. This study aims to propose a dynamic model selection approach that combines individual selection and combination forecasts based on both the demand patterns and the out-of-sample performance for each item. Firstly, based on both metrics of the squared coefficient of variation (CV²) and the average inter-demand interval (ADI), we divide the demand patterns of items into four types: smooth, intermittent, erratic, and lumpy. Secondly, we select nine classical forecasting methods in the M-Competitions to build a pool of models. Thirdly, we design two dynamic weighting strategies to determine the final prediction, namely DWS-A and DWS-B. Finally, we verify the effectiveness of this approach by using two large datasets from an offline retailer and an online retailer in China. The empirical results show that these two strategies can effectively improve the accuracy of demand forecasting. The DWS-A method is suitable for items with the demand patterns of intermittent and lumpy, while the DWS-B method is suitable for items with the demand patterns of smooth and erratic. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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12 pages, 341 KiB

Open AccessArticle

Tourist Arrival Forecasting Using Multiscale Mode Learning Model

by Kaijian He, Don Wu and Yingchao Zou

Mathematics 2022, 10(16), 2999; https://doi.org/10.3390/math10162999 - 19 Aug 2022

Cited by 5 | Viewed by 1740

Abstract

The forecasting of tourist arrival depends on the accurate modeling of prevalent data patterns found in tourist arrival, especially for daily tourist arrival, where tourist arrival changes are more complex and highly nonlinear. In this paper, a new multiscale mode learning-based tourist arrival [...] Read more.

The forecasting of tourist arrival depends on the accurate modeling of prevalent data patterns found in tourist arrival, especially for daily tourist arrival, where tourist arrival changes are more complex and highly nonlinear. In this paper, a new multiscale mode learning-based tourist arrival forecasting model is proposed to exploit different multiscale data features in tourist arrival movement. Two popular Mode Decomposition models (MD) and the Convolutional Neural Network (CNN) model are introduced to model the multiscale data features in the tourist arrival data The data patterns at different scales are extracted using these two different MD models which dynamically decompose tourist arrival into the distinctive intrinsic mode function (IMF) data components. The convolutional neural network uses the deep network to further model the multiscale data structure of tourist arrivals, with the reduced dimensionality of key multiscale data features and finer modeling of nonlinearity in tourist arrival. Our empirical results using daily tourist arrival data show that the MD-CNN tourist arrival forecasting model significantly improves the forecasting reliability and accuracy. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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21 pages, 698 KiB

Open AccessArticle

Research on Hybrid Multi-Attribute Three-Way Group Decision Making Based on Improved VIKOR Model

by Jiekun Song, Zeguo He, Lina Jiang, Zhicheng Liu and Xueli Leng

Mathematics 2022, 10(15), 2783; https://doi.org/10.3390/math10152783 - 05 Aug 2022

Cited by 7 | Viewed by 1290

Abstract

In the era of internet connection and IOT, data-driven decision-making has become a new trend of decision-making and shows the characteristics of multi-granularity. Because three-way decision-making considers the uncertainty of decision-making for complex problems and the cost sensitivity of classification, it is becoming [...] Read more.

In the era of internet connection and IOT, data-driven decision-making has become a new trend of decision-making and shows the characteristics of multi-granularity. Because three-way decision-making considers the uncertainty of decision-making for complex problems and the cost sensitivity of classification, it is becoming an important branch of modern decision-making. In practice, decision-making problems usually have the characteristics of hybrid multi-attributes, which can be expressed in the forms of real numbers, interval numbers, fuzzy numbers, intuitionistic fuzzy numbers and interval-valued intuitionistic fuzzy numbers (IVIFNs). Since other forms can be regarded as special forms of IVIFNs, transforming all forms into IVIFNs can minimize information distortion and effectively set expert weights and attribute weights. We propose a hybrid multi-attribute three-way group decision-making method and give detailed steps. Firstly, we transform all attribute values of each expert into IVIFNs. Secondly, we determine expert weights based on interval-valued intuitionistic fuzzy entropy and cross-entropy and use interval-valued intuitionistic fuzzy weighted average operator to obtain a group comprehensive evaluation matrix. Thirdly, we determine the weights of each attribute based on interval-valued intuitionistic fuzzy entropy and use the VIKOR method improved by grey correlation analysis to determine the conditional probability. Fourthly, based on the risk loss matrix expressed by IVIFNs, we use the optimization method to determine the decision threshold and give the classification rules of the three-way decisions. Finally, an example verifies the feasibility of the hybrid multi-attribute three-way group decision-making method, which provides a systematic and standard solution for this kind of decision-making problem. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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17 pages, 1830 KiB

Open AccessArticle

A Two-Stage Model with an Improved Clustering Algorithm for a Distribution Center Location Problem under Uncertainty

by Jun Wu, Xin Liu, Yuanyuan Li, Liping Yang, Wenyan Yuan and Yile Ba

Mathematics 2022, 10(14), 2519; https://doi.org/10.3390/math10142519 - 20 Jul 2022

Cited by 5 | Viewed by 1987

Abstract

Distribution centers are quite important for logistics. In order to save costs, reduce energy consumption and deal with increasingly uncertain demand, it is necessary for distribution centers to select the location strategically. In this paper, a two-stage model based on an improved clustering [...] Read more.

Distribution centers are quite important for logistics. In order to save costs, reduce energy consumption and deal with increasingly uncertain demand, it is necessary for distribution centers to select the location strategically. In this paper, a two-stage model based on an improved clustering algorithm and the center-of-gravity method is proposed to deal with the multi-facility location problem arising from a real-world case. First, a distance function used in clustering is redefined to include both the spatial indicator and the socio-economic indicator. Then, an improved clustering algorithm is used to determine the optimal number of distribution centers needed and the coverage of each center. Third, the center-of-gravity method is used to determine the final location of each center. Finally, the improved method is compared with the traditional clustering method by testing data from 12 cities in Inner Mongolia Autonomous Region in China. The comparison result proves the proposed method’s effectiveness. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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20 pages, 7143 KiB

Open AccessArticle

Mining Plan Optimization of Multi-Metal Underground Mine Based on Adaptive Hybrid Mutation PSO Algorithm

by Yifei Zhao, Jianhong Chen, Shan Yang and Yi Chen

Mathematics 2022, 10(14), 2418; https://doi.org/10.3390/math10142418 - 11 Jul 2022

Cited by 2 | Viewed by 1690

Abstract

Mine extraction planning has a far-reaching impact on the production management and overall economic efficiency of the mining enterprise. The traditional method of preparing underground mine production planning is complicated and tedious, and reaching the optimum calculation results is difficult. Firstly, the theory [...] Read more.

Mine extraction planning has a far-reaching impact on the production management and overall economic efficiency of the mining enterprise. The traditional method of preparing underground mine production planning is complicated and tedious, and reaching the optimum calculation results is difficult. Firstly, the theory and method of multi-objective optimization are used to establish a multi-objective planning model with the objective of the best economic efficiency, grade, and ore quantity, taking into account the constraints of ore grade fluctuation, ore output from the mine, production capacity of mining enterprises, and mineral resources utilization. Second, an improved particle swarm algorithm is applied to solve the model, a nonlinear dynamic decreasing weight strategy is proposed for the inertia weights, the variation probability of each generation of particles is dynamically adjusted by the aggregation degree, and this variation probability is used to perform a mixed Gaussian and Cauchy mutation for the global optimal position and an adaptive wavelet variation for the worst individual optimal position. This improved strategy can greatly increase the diversity of the population, improve the global convergence speed of the algorithm, and avoid the premature convergence of the solution. Finally, taking a large polymetallic underground mine in China as a case, the example calculation proves that the algorithm solution result is 10.98% higher than the mine plan index in terms of ore volume and 41.88% higher in terms of economic efficiency, the algorithm solution speed is 29.25% higher, and the model and optimization algorithm meet the requirements of a mining industry extraction production plan, which can effectively optimize the mine’s extraction plan and provide a basis for mine operation decisions. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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19 pages, 691 KiB

Open AccessArticle

SEPSI: A Secure and Efficient Privacy-Preserving Set Intersection with Identity Authentication in IoT

by Bai Liu, Xiangyi Zhang, Runhua Shi, Mingwu Zhang and Guoxing Zhang

Mathematics 2022, 10(12), 2120; https://doi.org/10.3390/math10122120 - 17 Jun 2022

Cited by 2 | Viewed by 1621

Abstract

The rapid development of the Internet of Things (IoT), big data and artificial intelligence (AI) technology has brought extensive IoT services to entities. However, most IoT services carry the risk of leaking privacy. Privacy-preserving set intersection in IoT is used for a wide [...] Read more.

The rapid development of the Internet of Things (IoT), big data and artificial intelligence (AI) technology has brought extensive IoT services to entities. However, most IoT services carry the risk of leaking privacy. Privacy-preserving set intersection in IoT is used for a wide range of basic services, and its privacy protection issues have received widespread attention. The traditional candidate protocols to solve the privacy-preserving set intersection are classical encryption protocols based on computational difficulty. With the emergence of quantum computing, some advanced quantum algorithms may undermine the security and reliability of traditional protocols. Therefore, it is important to design more secure privacy-preserving set intersection protocols. In addition, identity information is also very important compared to data security. To this end, we propose a quantum privacy-preserving set intersection protocol for IoT scenarios, which has higher security and linear communication efficiency. This protocol can protect identity anonymity while protecting private data. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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22 pages, 495 KiB

Open AccessArticle

Markovian Demands on Two Commodity Inventory System with Queue-Dependent Services and an Optional Retrial Facility

by K. Jeganathan, M. Abdul Reiyas, S. Selvakumar, N. Anbazhagan, S. Amutha, Gyanendra Prasad Joshi, Duckjoong Jeon and Changho Seo

Mathematics 2022, 10(12), 2046; https://doi.org/10.3390/math10122046 - 13 Jun 2022

Cited by 4 | Viewed by 1496

Abstract

The use of a Markovian inventory system is a critical part of inventory management. The purpose of this study is to examine the demand for two commodities in a Markovian inventory system, one of which is designated as a major item (Commodity-I) and [...] Read more.

The use of a Markovian inventory system is a critical part of inventory management. The purpose of this study is to examine the demand for two commodities in a Markovian inventory system, one of which is designated as a major item (Commodity-I) and the other as a complimentary item (Commodity-II). Demand arrives according to a Poisson process, and service time is exponential at a queue-dependent rate. We investigate a strategy of

(s, Q)

type control for commodity-I with a random lead time but instantaneous replenishment for commodity-II. If the waiting hall reaches its maximum capacity of N, any arriving primary client may enter an infinite capacity orbit with a specified ratio. For orbiting consumers, the classical retrial policy is used. In a steady-state setting, the joint probability distributions for commodities and the number of demands in the queue and the orbit, are derived. From this, we derive a waiting time analysis and a variety of system performance metrics in the steady-state. Additionally, the physical properties of various performance measures are evaluated using various numerical assumptions associated with diverse stochastic behaviours. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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9 pages, 1607 KiB

Open AccessArticle

Equation for Egg Volume Calculation Based on Smart’s Model

by Yu-Kai Weng, Cheng-Han Li, Chia-Chun Lai and Ching-Wei Cheng

Mathematics 2022, 10(10), 1661; https://doi.org/10.3390/math10101661 - 12 May 2022

Cited by 5 | Viewed by 7000

Abstract

In the egg industry, it is necessary to estimate the egg volume accurately when estimating egg quality or freshness in a non-destructive method. Egg volume and weight could obtain egg density and could be used to determine egg freshness. Therefore, the egg geometric [...] Read more.

In the egg industry, it is necessary to estimate the egg volume accurately when estimating egg quality or freshness in a non-destructive method. Egg volume and weight could obtain egg density and could be used to determine egg freshness. Therefore, the egg geometric must be obtained first to establish a volume equation with a geometric shape. This research proposes an innovative idea to derive the mathematical model and volume equation of egg shape, calculate its volume, and verify the accuracy of the mathematical equation proposed using the volume displacement method. Using the proposed equation, the minimum error between the calculated egg volume) and actual egg volume is 0.01%. The maximum volume error does not exceed 2%. The egg shape equation can accurately draw the outer contour curve of the egg by the half-length of the maximum long axis and maximum breadth of the short axis, and the distance from the center point of the egg to the maximum breadth (x_m). Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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22 pages, 5998 KiB

Open AccessArticle

Reservoir Characterization and Productivity Forecast Based on Knowledge Interaction Neural Network

by Yunqi Jiang, Huaqing Zhang, Kai Zhang, Jian Wang, Shiti Cui, Jianfa Han, Liming Zhang and Jun Yao

Mathematics 2022, 10(9), 1614; https://doi.org/10.3390/math10091614 - 09 May 2022

Cited by 1 | Viewed by 1998

Abstract

The reservoir characterization aims to provide the analysis and quantification of the injection-production relationship, which is the fundamental work for production management. The connectivity between injectors and producers is dominated by geological properties, especially permeability. However, the permeability parameters are very heterogenous in [...] Read more.

The reservoir characterization aims to provide the analysis and quantification of the injection-production relationship, which is the fundamental work for production management. The connectivity between injectors and producers is dominated by geological properties, especially permeability. However, the permeability parameters are very heterogenous in oil reservoirs, and expensive to collect by well logging. The commercial simulators enable to get accurate simulation but require sufficient geological properties and consume excessive computation resources. In contrast, the data-driven models (physical models and machine learning models) are developed on the observed dynamic data, such as the rate and pressure data of the injectors and producers, constructing the connectivity relationship and forecasting the productivity by a series of nonlinear mappings or the control of specific physical principles. While, due to the “black box” feature of machine learning approaches, and the constraints and assumptions of physical models, the data-driven methods often face the challenges of poor interpretability and generalizability and the limited application scopes. To solve these issues, integrating the physical principle of the waterflooding process (material balance equation) with an artificial neural network (ANN), a knowledge interaction neural network (KINN) is proposed. KINN consists of three transparent modules with explicit physical significance, and different modules are joined together via the material balance equation and work cooperatively to approximate the waterflooding process. In addition, a gate function is proposed to distinguish the dominant flowing channels from weak connecting ones by their sparsity, and thus the inter-well connectivity can be indicated directly by the model parameters. Combining the strong nonlinear mapping ability with the guidance of physical knowledge, the interpretability of KINN is fully enhanced, and the prediction accuracy on the well productivity is improved. The effectiveness of KINN is proved by comparing its performance with the canonical ANN, on the inter-well connectivity analysis and productivity forecast tasks of three synthetic reservoir experiments. Meanwhile, the robustness of KINN is revealed by the sensitivity analysis on measurement noises and wells shut-in cases. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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21 pages, 1601 KiB

Open AccessArticle

Structural Balance under Weight Evolution of Dynamic Signed Network

by Zhenpeng Li, Ling Ma, Simin Chi and Xu Qian

Mathematics 2022, 10(9), 1441; https://doi.org/10.3390/math10091441 - 25 Apr 2022

Cited by 2 | Viewed by 1614

Abstract

The mutual feedback mechanism between system structure and system function is the ‘hot spot’ of a complex network. In this paper, we propose an opinions–edges co-evolution model on a weighted signed network. By incorporating different social factors, five evolutionary scenarios were simulated to [...] Read more.

The mutual feedback mechanism between system structure and system function is the ‘hot spot’ of a complex network. In this paper, we propose an opinions–edges co-evolution model on a weighted signed network. By incorporating different social factors, five evolutionary scenarios were simulated to investigate the feedback effects. The scenarios included the variations of edges and signed weights and the variations of the proportions of positive and negative opinions. The level of balance achieved depends on the connection weight and the distribution of negative edges/opinions on the signed graph. This paper sheds light on the analysis of constraints and opportunities of social and cognitive processes, helping us understand the real-world opinions polarization process in depth. For example, the results serve as a confirmation of the imperfect balance theory, i.e., even if the system evolves to a stable state, the signed network still cannot achieve perfect structural balance. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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31 pages, 4872 KiB

Open AccessArticle

The Sustainable Supply Chain Network Competition Based on Non-Cooperative Equilibrium under Carbon Emission Permits

by Peiyue Cheng, Guitao Zhang and Hao Sun

Mathematics 2022, 10(9), 1364; https://doi.org/10.3390/math10091364 - 19 Apr 2022

Cited by 6 | Viewed by 1283

Abstract

Under the background of a circular economy, this paper examines multi-tiered closed-loop supply chain network competition under carbon emission permits and discusses how stringent carbon regulations influence the network performance. We derive the governing equilibrium conditions for carbon-capped mathematical gaming models of each [...] Read more.

Under the background of a circular economy, this paper examines multi-tiered closed-loop supply chain network competition under carbon emission permits and discusses how stringent carbon regulations influence the network performance. We derive the governing equilibrium conditions for carbon-capped mathematical gaming models of each player and provide the equivalent variational inequality formulations, which are then solved by modified projection and contraction algorithms. The numerical examples empower us to investigate the effects of diverse carbon emission regulations (cap-and-trade regulation, mandatory cap policy, and cap-sharing scheme) on enterprises’ decisions. The results reveal that the cap-sharing scheme is effective in coordinating the relationship between system profit and carbon emission abatement, while cap-and-trade regulation loses efficiency compared with the cap-sharing scheme. The government should allocate caps scientifically and encourage enterprises to adopt green production technologies, especially allowing large enterprises to share carbon quotas. This study can also contribute to the enterprises’ decision-making and revenue management under different carbon emissions reduction regulations. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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18 pages, 3242 KiB

Open AccessArticle

Mining Campus Big Data: Prediction of Career Choice Using Interpretable Machine Learning Method

by Yuan Wang, Liping Yang, Jun Wu, Zisheng Song and Li Shi

Mathematics 2022, 10(8), 1289; https://doi.org/10.3390/math10081289 - 13 Apr 2022

Cited by 7 | Viewed by 2422

Abstract

The issue of students’ career choice is the common concern of students themselves, parents, and educators. However, students’ behavioral data have not been thoroughly studied for understanding their career choice. In this study, we used eXtreme Gradient Boosting (XGBoost), a machine learning (ML) [...] Read more.

The issue of students’ career choice is the common concern of students themselves, parents, and educators. However, students’ behavioral data have not been thoroughly studied for understanding their career choice. In this study, we used eXtreme Gradient Boosting (XGBoost), a machine learning (ML) technique, to predict the career choice of college students using a real-world dataset collected in a specific college. Specifically, the data include information on the education and career choice of 18,000 graduates during their college years. In addition, SHAP (Shapley Additive exPlanation) was employed to interpret the results and analyze the importance of individual features. The results show that XGBoost can predict students’ career choice robustly with a precision, recall rate, and an F1 value of 89.1%, 85.4%, and 0.872, respectively. Furthermore, the interaction of features among four different choices of students (i.e., choose to study in China, choose to work, difficulty in finding a job, and choose to study aboard) were also explored. Several educational features, especially differences in grade point average (GPA) during their college studying, are found to have relatively larger impact on the final choice of career. These results can be of help in the planning, design, and implementation of higher educational institutions’ (HEIs) events. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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16 pages, 330 KiB

Open AccessArticle

Information Acquisition for Product Design in a Green Supply Chain

by Mengli Fan, Yi Huang and Wei Xing

Mathematics 2022, 10(7), 1160; https://doi.org/10.3390/math10071160 - 03 Apr 2022

Cited by 1 | Viewed by 1350

Abstract

This paper studies the interaction between the product development mode and the acquisition of consumers’ environmental awareness (CEA) information in a two-echelon green supply chain. Our study shows that when the downstream manufacturer achieves the CEA information superiority, the in-house mode improves the [...] Read more.

This paper studies the interaction between the product development mode and the acquisition of consumers’ environmental awareness (CEA) information in a two-echelon green supply chain. Our study shows that when the downstream manufacturer achieves the CEA information superiority, the in-house mode improves the total environmental quality and is better for supply chain members than the outsourcing mode. In contrast, when the upstream supplier achieves the information advantage, the green product development modes affect neither the decisions nor the performance of supply chain members because the supplier discloses its CEA information through pricing and/or green level decisions. We further find that under the outsourcing mode, the supplier has more incentive to achieve CEA information superiority, which always improves the total environmental quality and may benefit the manufacturer; however, under the in-house mode, the supplier’s superior information benefits the manufacturer and itself as well as total environmental quality only under certain conditions. Finally, we show that the downstream CEA information disclosure under the outsourcing mode helps supply chain members achieve a Pareto improvement and increases the total environmental quality; this finding is contrary to the extant literature that focuses on demand intercept information disclosure. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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17 pages, 5215 KiB

Open AccessArticle

Lightweight Image Super-Resolution Based on Local Interaction of Multi-Scale Features and Global Fusion

by Zhiqing Meng, Jing Zhang, Xiangjun Li and Lingyin Zhang

Mathematics 2022, 10(7), 1096; https://doi.org/10.3390/math10071096 - 29 Mar 2022

Cited by 3 | Viewed by 1538

Abstract

In recent years, computer vision technology has been widely applied in various fields, making super-resolution (SR), a low-level visual task, a research hotspot. Although deep convolutional neural network has made good progress in the field of single-image super-resolution (SISR), its adaptability to real-time [...] Read more.

In recent years, computer vision technology has been widely applied in various fields, making super-resolution (SR), a low-level visual task, a research hotspot. Although deep convolutional neural network has made good progress in the field of single-image super-resolution (SISR), its adaptability to real-time interactive devices that require fast response is poor due to the excessive amount of network model parameters, the long inference image time, and the complex training model. To solve this problem, we propose a lightweight image reconstruction network (MSFN) for multi-scale feature local interaction based on global connection of the local feature channel. Then, we develop a multi-scale feature interaction block (FIB) in MSFN to fully extract spatial information of different regions of the original image by using convolution layers of different scales. On this basis, we use the channel stripping operation to compress the model, and reduce the number of model parameters as much as possible on the premise of ensuring the reconstructed image quality. Finally, we test the proposed MSFN model with the benchmark datasets. The experimental results show that the MSFN model is better than the other state-of-the-art SR methods in reconstruction effect, computational complexity, and inference time. Full article

(This article belongs to the Special Issue Computational and Mathematical Methods in Information Science and Engineering)

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