Mathematics

Open AccessArticle

Apex Method: A New Scalable Iterative Method for Linear Programming

by

Leonid B. Sokolinsky

and

Irina M. Sokolinskaya

Mathematics 2023, 11(7), 1654; https://doi.org/10.3390/math11071654 (registering DOI) - 29 Mar 2023

Abstract

The article presents a new scalable iterative method for linear programming called the “apex method”. The key feature of this method is constructing a path close to optimal on the surface of the feasible region from a certain starting point to the exact [...] Read more.

The article presents a new scalable iterative method for linear programming called the “apex method”. The key feature of this method is constructing a path close to optimal on the surface of the feasible region from a certain starting point to the exact solution of a linear programming problem. The optimal path refers to a path of the minimum length according to the Euclidean metric. The apex method is based on the predictor-–corrector framework and proceeds in two stages: quest (predictor) and target (corrector). The quest stage calculates a rough initial approximation of the linear programming problem. The target stage refines the initial approximation with a given precision. The main operation used in the apex method is an operation that calculates the pseudoprojection, which is a generalization of the metric projection to a convex closed set. This operation is used both in the quest stage and in the target stage. A parallel algorithm using a Fejér mapping to compute the pseudoprojection is presented. An analytical estimation of the parallelism degree of this algorithm is obtained. AlsoAdditionally, an algorithm implementing the target stage is given. The convergence of this algorithm is proven. An experimental study of the scalability of the apex method on a cluster computing system is described. The results of applying the apex method to solve problems from the Netlib-LP repository are presented. Full article

(This article belongs to the Special Issue Parallel Computing and Applications)

Open AccessArticle

Meta-Learning for Zero-Shot Remote Sensing Image Super-Resolution

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Mathematics 2023, 11(7), 1653; https://doi.org/10.3390/math11071653 (registering DOI) - 29 Mar 2023

Abstract

Zero-shot super-resolution (ZSSR) has generated a lot of interest due to its flexibility in various applications. However, the computational demands of ZSSR make it ineffective when dealing with large-scale low-resolution image sets. To address this issue, we propose a novel meta-learning model. We [...] Read more.

Zero-shot super-resolution (ZSSR) has generated a lot of interest due to its flexibility in various applications. However, the computational demands of ZSSR make it ineffective when dealing with large-scale low-resolution image sets. To address this issue, we propose a novel meta-learning model. We treat the set of low-resolution images as a collection of ZSSR tasks and learn meta-knowledge about ZSSR by leveraging these tasks. This approach reduces the computational burden of super-resolution for large-scale low-resolution images. Additionally, through multiple ZSSR task learning, we uncover a general super-resolution model that enhances the generalization capacity of ZSSR. Finally, using the learned meta-knowledge, our model achieves impressive results with just a few gradient updates when given a novel task. We evaluate our method using two remote sensing datasets with varying spatial resolutions. Our experimental results demonstrate that using multiple ZSSR tasks yields better outcomes than a single task, and our method outperforms other state-of-the-art super-resolution methods. Full article

(This article belongs to the Special Issue Advances in Computer Vision and Machine Learning)

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Open AccessArticle

Analytical Approximations of Well Function by Solving the Governing Differential Equation Representing Unsteady Groundwater Flow in a Confined Aquifer

by

Manotosh Kumbhakar

and

Vijay P. Singh

Mathematics 2023, 11(7), 1652; https://doi.org/10.3390/math11071652 (registering DOI) - 29 Mar 2023

Abstract

A solution of the governing equation representing the drawdown in a horizontal confined aquifer, where groundwater flow is unsteady, was first provided by Theis and is famously known as the Theis solution. This solution was given in terms of an exponential integral, [...] Read more.

A solution of the governing equation representing the drawdown in a horizontal confined aquifer, where groundwater flow is unsteady, was first provided by Theis and is famously known as the Theis solution. This solution was given in terms of an exponential integral, also called the well function, for which simple and reliable approximations are preferred due to their practical applications. To that end, several approximations are available in the literature, of which some are based on series approximations for the integral, and others are numerical approximations. This study employs three kinds of homotopy-based methods, namely the homotopy analysis method (HAM), homotopy perturbation method (HPM), and optimal homotopy asymptotic method (OHAM), for analytically solving the governing partial differential equation (PDE). For convenience, the PDE is first converted to a boundary value problem (BVP) using a similarity transformation. Comparing the series approximations obtained using these methods with the Theis solution, it is found that the 10th-order HAM, and just three terms of OHAM-based solutions, provide accurate approximations. On the other hand, the HPM-based solution is found to be accurate only within a small domain. Further, the proposed approximations are compared with several series and numerical approximations available in the literature using the percentage error. The proposed methodology provides accurate approximations of the well function by directly solving the governing differential equation in a general framework and thus can be adapted to other practical situations arising in groundwater flow. Full article

(This article belongs to the Section Computational and Applied Mathematics)

Open AccessArticle

General Fractional Calculus in Multi-Dimensional Space: Riesz Form

by

Vasily E. Tarasov

Mathematics 2023, 11(7), 1651; https://doi.org/10.3390/math11071651 (registering DOI) - 29 Mar 2023

Abstract

An extension of the general fractional calculus (GFC) is proposed as a generalization of the Riesz fractional calculus, which was suggested by Marsel Riesz in 1949. The proposed Riesz form of GFC can be considered as an extension GFC from the positive real [...] Read more.

An extension of the general fractional calculus (GFC) is proposed as a generalization of the Riesz fractional calculus, which was suggested by Marsel Riesz in 1949. The proposed Riesz form of GFC can be considered as an extension GFC from the positive real line and the Laplace convolution to the m-dimensional Euclidean space and the Fourier convolution. To formulate the general fractional calculus in the Riesz form, the Luchko approach to construction of the GFC, which was suggested by Yuri Luchko in 2021, is used. The general fractional integrals and derivatives are defined as convolution-type operators. In these definitions the Fourier convolution on m-dimensional Euclidean space is used instead of the Laplace convolution on positive semi-axis. Some properties of these general fractional operators are described. The general fractional analogs of first and second fundamental theorems of fractional calculus are proved. The fractional calculus of the Riesz potential and the fractional Laplacian of the Riesz form are special cases of proposed general fractional calculus of the Riesz form. Full article

(This article belongs to the Special Issue Analytical and Computational Methods in Differential Equations, Special Functions, Transmutations and Integral Transforms)

Open AccessArticle

Predictive Modeling of the Uniaxial Compressive Strength of Rocks Using an Artificial Neural Network Approach

by

Xin Wei

,

Niaz Muhammad Shahani

and

Xigui Zheng

Mathematics 2023, 11(7), 1650; https://doi.org/10.3390/math11071650 (registering DOI) - 29 Mar 2023

Abstract

Sedimentary rocks provide information on previous environments on the surface of the Earth. As a result, they are the principal narrators of the former climate, life, and important events on the surface of the Earth. The complexity and cost of direct destructive laboratory [...] Read more.

Sedimentary rocks provide information on previous environments on the surface of the Earth. As a result, they are the principal narrators of the former climate, life, and important events on the surface of the Earth. The complexity and cost of direct destructive laboratory tests adversely affect the data scarcity problem, making the development of intelligent indirect methods an integral step in attempts to address the problem faced by rock engineering projects. This study established an artificial neural network (ANN) approach to predict the uniaxial compressive strength (UCS) in MPa of sedimentary rocks using different input parameters; i.e., dry density (ρ_d) in g/cm³, Brazilian tensile strength (BTS) in MPa, and wet density (ρ_wet) in g/cm³. The developed ANN models, M1, M2, and M3, were divided as follows: the overall dataset, 70% training dataset and 30% testing dataset, and 60% training dataset and 40% testing dataset, respectively. In addition, multiple linear regression (MLR) was performed for comparison to the proposed ANN models to verify the accuracy of the predicted values. The performance indices were also calculated by estimating the established models. The predictive performance of the M2 ANN model in terms of the coefficient of determination (R²), root mean squared error (RMSE), variance accounts for (VAF), and a20-index was 0.831, 0.27672, 0.92, and 0.80, respectively, in the testing dataset, revealing ideal results, thus it was proposed as the best-fit prediction model for UCS of sedimentary rocks at the Thar coalfield, Pakistan, among the models developed in this study. Moreover, by performing a sensitivity analysis, it was determined that BTS was the most influential parameter in predicting UCS. Full article

(This article belongs to the Special Issue Advances in Computational Intelligence in Geotechnical and Geological Engineering)

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Open AccessArticle

Fourier Graph Convolution Network for Time Series Prediction

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Mathematics 2023, 11(7), 1649; https://doi.org/10.3390/math11071649 (registering DOI) - 29 Mar 2023

Abstract

The spatio-temporal pattern recognition of time series data is critical to developing intelligent transportation systems. Traffic flow data are time series that exhibit patterns of periodicity and volatility. A novel robust Fourier Graph Convolution Network model is proposed to learn these patterns effectively. [...] Read more.

The spatio-temporal pattern recognition of time series data is critical to developing intelligent transportation systems. Traffic flow data are time series that exhibit patterns of periodicity and volatility. A novel robust Fourier Graph Convolution Network model is proposed to learn these patterns effectively. The model includes a Fourier Embedding module and a stackable Spatial-Temporal ChebyNet layer. The development of the Fourier Embedding module is based on the analysis of Fourier series theory and can capture periodicity features. The Spatial-Temporal ChebyNet layer is designed to model traffic flow’s volatility features for improving the system’s robustness. The Fourier Embedding module represents a periodic function with a Fourier series that can find the optimal coefficient and optimal frequency parameters. The Spatial-Temporal ChebyNet layer consists of a Fine-grained Volatility Module and a Temporal Volatility Module. Experiments in terms of prediction accuracy using two open datasets show the proposed model outperforms the state-of-the-art methods significantly. Full article

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Open AccessArticle

Heat and Mass Transfer Analysis for the Viscous Fluid Flow: Dual Approximate Solutions

by

Remus-Daniel Ene

,

Nicolina Pop

and

Rodica Badarau

Mathematics 2023, 11(7), 1648; https://doi.org/10.3390/math11071648 (registering DOI) - 29 Mar 2023

Abstract

The aim of this paper is to investigate effective and accurate dual analytic approximate solutions, while taking into account thermal effects. The heat and mass transfer problem in a viscous fluid flow are analytically explored by using the modified Optimal Homotopy Asymptotic Method [...] Read more.

The aim of this paper is to investigate effective and accurate dual analytic approximate solutions, while taking into account thermal effects. The heat and mass transfer problem in a viscous fluid flow are analytically explored by using the modified Optimal Homotopy Asymptotic Method (OHAM). By using similarity transformations, the motion equations are reduced to a set of nonlinear ordinary differential equations. Based on the numerical results, it was revealed that there are dual analytic approximate solutions within the mass transfer problem. The variation of the physical parameters (the Prandtl number and the temperature distribution parameter) over the temperature profile is analytically explored and graphically depicted for the first approximate and the corresponding dual solution, respectively. The advantage of the proposed method arises from using only one iteration for obtaining the dual analytical solutions. The presented results are effective, accurate and in good agreement with the corresponding numerical results with relevance for further engineering applications of heat and mass transfer problems. Full article

(This article belongs to the Special Issue Analysis and Applications of Mathematical Fluid Dynamics)

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Open AccessArticle

Distributed Fire Detection and Localization Model Using Federated Learning

by

Yue Hu

,

Xinghao Fu

and

Wei Zeng

Mathematics 2023, 11(7), 1647; https://doi.org/10.3390/math11071647 (registering DOI) - 29 Mar 2023

Abstract

Fire detection and monitoring systems based on machine vision have been gradually developed in recent years. Traditional centralized deep learning model training methods transfer large amounts of video image data to the cloud, making image data privacy and confidentiality difficult. In order to [...] Read more.

Fire detection and monitoring systems based on machine vision have been gradually developed in recent years. Traditional centralized deep learning model training methods transfer large amounts of video image data to the cloud, making image data privacy and confidentiality difficult. In order to protect the data privacy in the fire detection system with heterogeneous data and to enhance its efficiency, this paper proposes an improved federated learning algorithm incorporating computer vision: FedVIS, which uses a federated dropout and gradient selection algorithm to reduce communication overhead, and uses a transformer to replace a traditional neural network to improve the robustness of federated learning in the context of heterogeneous data. FedVIS can reduce the communication overhead in addition to reducing the catastrophic forgetting of previous devices, improving convergence, and producing superior global models. In this paper’s experimental results, FedVIS outperforms the common federated learning methods FedSGD, FedAVG, FedAWS, and CMFL, and improves the detection effect by reducing communication costs. As the amount of clients increases, the accuracy of other algorithmic models decreases by 2–5%, and the number of communication rounds required increases significantly; meanwhile, our method maintains a superior detection performance while requiring roughly the same number of communication rounds. Full article

(This article belongs to the Special Issue Trustworthy Graph Neural Networks: Models and Applications)

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Open AccessArticle

Attention and Pixel Matching in RGB-T Object Tracking

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Mathematics 2023, 11(7), 1646; https://doi.org/10.3390/math11071646 (registering DOI) - 29 Mar 2023

Abstract

Visual object tracking using visible light images and thermal infrared images, named RGB-T tracking, has recently attracted increasing attention in the tracking community. Deep neural network-based methods becoming the most popular RGB-T trackers, still have to balance the robustness and the speed of [...] Read more.

Visual object tracking using visible light images and thermal infrared images, named RGB-T tracking, has recently attracted increasing attention in the tracking community. Deep neural network-based methods becoming the most popular RGB-T trackers, still have to balance the robustness and the speed of calculation. A novel tracker with Siamese architecture is proposed to obtain the accurate object location and meet the real-time requirements. Firstly, a multi-modal weight penalty module is designed to assign different weights to the RGB and thermal infrared features. Secondly, a new pixel matching module is proposed to calculate the similarity between each pixel on the search and the template features, which can avoid bringing excessive background information versus the regular cross-correlation operation. Finally, an improved anchor-free bounding box prediction network is put forward to further reduce the interference of the background information. The experimental results on the standard RGB-T tracking benchmark datasets show that the proposed method achieves better precision and success rate with a speed of over 34 frames per second which satisfies the real-time tracking. Full article

(This article belongs to the Special Issue Advances in Artificial Intelligence: Data, Methods and Interdisciplinary Applications)

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Open AccessArticle

Diagnosing Vascular Aging Based on Macro and Micronutrients Using Ensemble Machine Learning

by

Carmen Patino-Alonso

,

Marta Gómez-Sánchez

,

Leticia Gómez-Sánchez

,

Emiliano Rodríguez-Sánchez

,

Cristina Agudo-Conde

,

Luis García-Ortiz

and

Manuel A Gómez-Marcos

Mathematics 2023, 11(7), 1645; https://doi.org/10.3390/math11071645 - 29 Mar 2023

Abstract

The influence of dietary components on vascular dysfunction and aging is unclear. This study therefore aims to propose a model to predict the influence of macro and micronutrients on accelerated vascular aging in a Spanish population without previous cardiovascular disease. This cross-sectional study [...] Read more.

The influence of dietary components on vascular dysfunction and aging is unclear. This study therefore aims to propose a model to predict the influence of macro and micronutrients on accelerated vascular aging in a Spanish population without previous cardiovascular disease. This cross-sectional study involved a total of 501 individuals aged between 35 and 75 years. Carotid-femoral pulse wave velocity (cfPWV) was measured using a Sphygmo Cor^® device. Carotid intima-media thickness (IMTc) was measured using a Sonosite Micromax^® ultrasound machine. The Vascular Aging Index (VAI) was estimated according to VAI = (LN (1.09) × 10 cIMT + LN (1.14) × cfPWV) 39.1 + 4.76. Vascular aging was defined considering the presence of a vascular lesion and the p75 by age and sex of VAI following two steps: Step 1: subjects were labelled as early vascular aging (EVA) if they had a peripheral arterial disease or carotid artery lesion. Step 2: they were classified as EVA if the VAI value was >p75 and as normal vascular aging (NVA) if it was ≤p75. To predict the model, we used machine learning algorithms to analyse the association between macro and micronutrients and vascular aging. In this article, we proposed the AdXGRA model, a stacked ensemble learning model for diagnosing vascular aging from macro and micronutrients. The proposed model uses four classifiers, AdaBoost (ADB), extreme gradient boosting (XGB), generalized linear model (GLM), and random forest (RF) at the first level, and then combines their predictions by using a second-level multilayer perceptron (MLP) classifier to achieve better performance. The model obtained an accuracy of 68.75% in prediction, with a sensitivity of 66.67% and a specificity of 68.79%. The seven main variables related to EVA in the proposed model were sodium, waist circumference, polyunsaturated fatty acids (PUFA), monounsaturated fatty acids (MUFA), total protein, calcium, and potassium. These results suggest that total protein, PUFA, and MUFA are the macronutrients, and calcium and potassium are the micronutrients related to EVA. Full article

(This article belongs to the Special Issue Current Research in Biostatistics)

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Open AccessArticle

Novel Creation Method of Feature Graphics for Image Generation Based on Deep Learning Algorithms

by

Ying Li

and

Ye Tang

Mathematics 2023, 11(7), 1644; https://doi.org/10.3390/math11071644 - 29 Mar 2023

Abstract

In this paper, we propose a novel creation method of feature graphics by deep learning algorithms based on a channel attention module consisting of a separable deep convolutional neural network and an SENet network. The main innovation of this method is that the [...] Read more.

In this paper, we propose a novel creation method of feature graphics by deep learning algorithms based on a channel attention module consisting of a separable deep convolutional neural network and an SENet network. The main innovation of this method is that the image feature of sample images is extracted by convolution operation and the key point matrix is obtained by channel weighting calculation to create feature graphics within the channel attention module. The main problem of existing image generation methods is that the complex network training and calculation process affects the accuracy and efficiency of image generation. It greatly reduced the complexity of image generation and improved the efficiency when we trained the image generation network with the feature graphic maps. To verify the superiority of this method, we conducted a comparative experiment with the existing method. Additionally, we explored the influence on the accuracy and efficiency of image generation of the channel number of the weighting matrix based on the test experiment. The experimental results demonstrate that this method highlights the image features of geometric lines, simplifies the complexity of image generation and improves the efficiency. Based on this method, images with more prominent line features are generated from the description text and dynamic graphics are created for the display of the images generated, which can be applied in the construction of smart museums. Full article

(This article belongs to the Special Issue Applied Computing and Artificial Intelligence II)

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Open AccessArticle

Applying Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication

by

Eugenia I. Toki

,

Giorgos Tatsis

,

Vasileios A. Tatsis

,

Konstantinos Plachouras

,

Jenny Pange

and

Ioannis G. Tsoulos

Mathematics 2023, 11(7), 1643; https://doi.org/10.3390/math11071643 - 29 Mar 2023

Abstract

Screening and evaluation of developmental disorders include complex and challenging procedures, exhibit uncertainties in the diagnostic fit, and require high clinical expertise. Although typically, clinicians’ evaluations rely on diagnostic instrumentation, child observations, and parents’ reports, these may occasionally result in subjective evaluation outcomes. [...] Read more.

Screening and evaluation of developmental disorders include complex and challenging procedures, exhibit uncertainties in the diagnostic fit, and require high clinical expertise. Although typically, clinicians’ evaluations rely on diagnostic instrumentation, child observations, and parents’ reports, these may occasionally result in subjective evaluation outcomes. Current advances in artificial intelligence offer new opportunities for decision making, classification, and clinical assessment. This study explores the performance of different neural network optimizers in biometric datasets for screening typically and non-typically developed children for speech and language communication deficiencies. The primary motivation was to give clinicians a robust tool to help them identify speech disorders automatically using artificial intelligence methodologies. For this reason, in this study, we use a new dataset from an innovative, recently developed serious game collecting various data on children’s speech and language responses. Specifically, we employed different neural network approaches such as Artificial Neural Networks (ANNs), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), along with state-of-the-art Optimizers, namely the Adam, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), Genetic algorithm (GAs), and Particle Swarm Optimization algorithm (PSO). The results were promising, while Integer-bounded Neural Network proved to be the best competitor, opening new inquiries for future work towards automated classification supporting clinicians’ decisions on neurodevelopmental disorders. Full article

(This article belongs to the Special Issue Advances in Fuzzy Logic and Artificial Neural Networks)

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Open AccessArticle

A Generalized Mathematical Model of Toxoplasmosis with an Intermediate Host and the Definitive Cat Host

by

Sharmin Sultana

,

Gilberto González-Parra

and

Abraham J. Arenas

Mathematics 2023, 11(7), 1642; https://doi.org/10.3390/math11071642 (registering DOI) - 28 Mar 2023

Abstract

In this paper, we construct a generalized epidemiological mathematical model to study toxoplasmosis dynamics, taking into consideration both cat and mouse populations. The model incorporates generalized proportions for the congenital transmission in the mouse and cat populations, along with the oocysts available in [...] Read more.

In this paper, we construct a generalized epidemiological mathematical model to study toxoplasmosis dynamics, taking into consideration both cat and mouse populations. The model incorporates generalized proportions for the congenital transmission in the mouse and cat populations, along with the oocysts available in the environment. We focus on determining the conditions under which toxoplasmosis can be eradicated. We conduct a stability analysis in order to reveal the dynamics of toxoplasmosis in the cat and mouse populations; moreover, we compute the basic reproduction number

R_{0}

, which is crucial for the long-term behavior of the toxoplasmosis disease in these populations as well as the steady states related to both populations. We find that vertical transmission in the cat population is essential, and affects the basic reproduction number

R_{0}

. If full vertical transmission is considered in the mouse population and

R_{0} < 1

, we find that all solutions converge to the limit set comprised by the infinitely many toxoplasmosis-free-cat steady states, meaning that toxoplasmosis would vanish from the cat population regardless of the initial conditions. On the other hand, if

R_{0} > 1

, then there is only one toxoplasmosis-endemic steady state. When full vertical transmission is not considered in the mouse population, then a unique toxoplasmosis-free equilibrium exists and toxoplasmosis can be eradicated for both the cat and mouse populations. This has important public health implications. Numerical simulations are carried out to reinforce our theoretical stability analysis and observe the repercussion of some parameters on the dynamics. Full article

(This article belongs to the Section Mathematical Biology)

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Open AccessArticle

Exploring the Dynamics of COVID-19 with a Novel Family of Models

by

Abdulaziz S. Alghamdi

and

M. M. Abd El-Raouf

Mathematics 2023, 11(7), 1641; https://doi.org/10.3390/math11071641 - 28 Mar 2023

Abstract

Much effort has recently been expended in developing efficient models that can depict the true picture for COVID-19 mortality data and help scientists choose the best-fit models. As a result, this research intends to provide a new G family for both theoretical and [...] Read more.

Much effort has recently been expended in developing efficient models that can depict the true picture for COVID-19 mortality data and help scientists choose the best-fit models. As a result, this research intends to provide a new G family for both theoretical and practical scientists that solves the concerns typically encountered in both normal and non-normal random events. The new-G distribution family is able to generate efficient continuous univariate and skewed models that may outperform the baseline model. The analytic properties of the new-G family and its sub-model are investigated and described, as well as a theoretical framework. The parameters were estimated using a classical approach along with an extensive simulation study to assess the behaviour of the parameters. The efficiency of the new-G family is discussed using one of its sub-models on COVID-19 mortality data sets. Full article

(This article belongs to the Special Issue Advances in Applied Probability and Statistical Inference)

Open AccessArticle

Kidney Transplant: Survival Analysis and Prognostic Factors after 10 Years of Follow-Up

by

Álvaro Beviá-Romero

,

Francisco Quereda-Flores

,

Javier Díaz-Carnicero

,

Francisco Gómez-Palomo

,

María Ramos-Cebrián

,

Joaquín Espinosa-Vañó

,

Dario J. Castillo-Antón

,

Enrique Broseta-Rico

,

David Vivas-Consuelo

and

Alberto Budía-Alba

Mathematics 2023, 11(7), 1640; https://doi.org/10.3390/math11071640 - 28 Mar 2023

Abstract

The aim of this work is to analyse recipient and graft survival after kidney transplant in a three-year cohort and to identify predictive factors with up to 10 years of follow-up. Methods: retrospective consecutive cohort study of 250 kidney transplant recipients operated between [...] Read more.

The aim of this work is to analyse recipient and graft survival after kidney transplant in a three-year cohort and to identify predictive factors with up to 10 years of follow-up. Methods: retrospective consecutive cohort study of 250 kidney transplant recipients operated between 2010 and 2012. Multiorganic transplants and both dead-donor and living-donor transplants were included. Data were collected from electronic health records. A survival analysis was conducted using the Kaplan-Meier method and a Cox proportional-hazards multivariate model. Results: mean follow-up was 8.1 ± 3.2 years. Graft survival at 2, 5 and 10 years was 89.0%, 85.1% and 78.4% respectively. The multivariate model identified the following risk factors for graft loss: diabetic nephropathy (HR 3.2 CI95% [1.1–9.4]), delayed graft function (3.8 [2.0–7.4]), chronic kidney rejection (3.7 [1.2–11.4]), and early surgical complications (2.6 [1.4–5.1]). Conversely, combined transplant was found to be a protective factor for graft loss (0.1 [0.0–0.5]). Recipient patient survival was 94.3%, 90.0% and 76.6% at 2, 5 and 10 years respectively. The model identified the following mortality risk factors: older recipient age (1.1 [1.1–1.2]), combined transplant (7.6 [1.7–34.5]) and opportunistic infections (2.6 [1.3–5.0]). Conclusions: 10-year recipient and graft survival were 76.6% and 78.4% respectively. Main mortality risk factors were older recipient age, opportunistic infections and multiorganic transplant. Main graft loss risk factors were diabetic nephropathy, delayed graft function, chronic kidney rejection and early surgical complications. Full article

(This article belongs to the Special Issue Mathematical Prediction Models Applied to Health Management)

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Open AccessReview

Markovian Restless Bandits and Index Policies: A Review

by

José Niño-Mora

Mathematics 2023, 11(7), 1639; https://doi.org/10.3390/math11071639 - 28 Mar 2023

Abstract

The restless multi-armed bandit problem is a paradigmatic modeling framework for optimal dynamic priority allocation in stochastic models of wide-ranging applications that has been widely investigated and applied since its inception in a seminal paper by Whittle in the late 1980s. The problem [...] Read more.

The restless multi-armed bandit problem is a paradigmatic modeling framework for optimal dynamic priority allocation in stochastic models of wide-ranging applications that has been widely investigated and applied since its inception in a seminal paper by Whittle in the late 1980s. The problem has generated a vast and fast-growing literature from which a significant sample is thematically organized and reviewed in this paper. While the main focus is on priority-index policies due to their intuitive appeal, tractability, asymptotic optimality properties, and often strong empirical performance, other lines of work are also reviewed. Theoretical and algorithmic developments are discussed, along with diverse applications. The main goals are to highlight the remarkable breadth of work that has been carried out on the topic and to stimulate further research in the field. Full article

(This article belongs to the Special Issue Advances in Markovian Dynamic and Stochastic Optimization Models in Diverse Application Areas)

Open AccessFeature PaperArticle

Cross-Server End-to-End Patient Key Agreement Protocol for DNA-Based U-Healthcare in the Internet of Living Things

by

Tuan-Vinh Le

Mathematics 2023, 11(7), 1638; https://doi.org/10.3390/math11071638 - 28 Mar 2023

Abstract

(1) Background: Third-generation sequencing (TGS) technique directly sequences single deoxyribonucleic acid (DNA) molecules, enabling real-time sequencing and reducing sequencing time from a few days to a few hours. Sequencing devices can be miniaturized and DNA-reading sensors placed on the body to monitor human [...] Read more.

(1) Background: Third-generation sequencing (TGS) technique directly sequences single deoxyribonucleic acid (DNA) molecules, enabling real-time sequencing and reducing sequencing time from a few days to a few hours. Sequencing devices can be miniaturized and DNA-reading sensors placed on the body to monitor human health and vital signs, building an “internet of living things” (IoLT) facilitating ubiquitous healthcare services. In many cases, patients may wish to directly connect to each other for purposes of sharing real-time sequencing data, medical status or trading genomic data, etc. (2) Problems: User registration for a specific service may be limited due to some reason. Registering for multiple redundant services would also result in wasted money and possible wasteful communication overhead. In addition, since medical data and health information are very sensitive, security and privacy issues in the network are of paramount importance. (3) Methods: In this article, I propose a cross-server end-to-end (CS-E2E) patient authenticated key agreement protocol for DNA-based healthcare services in IoLT networks. My work allows two patients to mutually authenticate each other through assistance of respective servers, so that they can establish a reliable shared session key for securing E2E communications. The design employs multiple cost-saving solutions and robust cryptographic primitives, including smart-card-based single sign-on, elliptic curve cryptography, biohash function, etc. (4) Results: My proposed protocol is proven to be secure against various attacks and to incur reasonable communication cost compared to its predecessor works. The protocol also provides the support for more security properties and better functionalities. (5) Conclusions: The E2E communications between the patients are properly protected using the proposed approach. This assures a secure and efficient cross-server patient conversation for multiple purposes of healthcare communication. Full article

(This article belongs to the Special Issue Frontiers in Network Security and Cryptography)

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Open AccessArticle

An Adaptive Multiple-Asset Portfolio Strategy with User-Specified Risk Tolerance

by

Yufeng Lin

,

Xiaogang Wang

and

Yuehua Wu

Mathematics 2023, 11(7), 1637; https://doi.org/10.3390/math11071637 - 28 Mar 2023

Abstract

We improve the traditional simple moving average strategy by incorporating an investor-specific risk tolerance into the method. We then propose a multiasset generalized moving average crossover (MGMA) strategy. The MGMA strategies allocate wealth between risky assets and risk-free assets in an adaptive manner, [...] Read more.

We improve the traditional simple moving average strategy by incorporating an investor-specific risk tolerance into the method. We then propose a multiasset generalized moving average crossover (MGMA) strategy. The MGMA strategies allocate wealth between risky assets and risk-free assets in an adaptive manner, with the risk tolerance specified by an investor. We derive the expected log-utility of wealth, which allows us to estimate the optimal allocation parameters. The algorithm using our MGMA strategy is also presented. As the multiple risky assets can have different variability levels and could have various degrees of correlations, this trading strategy is evaluated on both simulated data and global high-frequency exchange-traded fund (ETF) data. It is shown that the MGMA strategies could significantly increase both the investor’s expected utility of wealth and the investor’s expected wealth. Full article

(This article belongs to the Special Issue Advanced Statistical Applications in Financial Econometrics)

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Open AccessArticle

Random Forest and Whale Optimization Algorithm to Predict the Invalidation Risk of Backfilling Pipeline

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Mathematics 2023, 11(7), 1636; https://doi.org/10.3390/math11071636 - 28 Mar 2023

Abstract

The problem of backfilling pipeline invalidation has become a bottleneck restricting the application and development of backfilling technology. This study applied the whale optimization algorithm and random forest (WOA–RF) to predict the invalidation risk of backfilling pipelines based on 59 datasets from actual [...] Read more.

The problem of backfilling pipeline invalidation has become a bottleneck restricting the application and development of backfilling technology. This study applied the whale optimization algorithm and random forest (WOA–RF) to predict the invalidation risk of backfilling pipelines based on 59 datasets from actual mines. Eight influencing factors of backfilling pipeline invalidation risk were chosen as the input parameters of the WOA–RF model, and the risk level was selected as the output parameters of the WOA–RF model. Furthermore, random forest, decision tree, artificial neural network, k-nearest neighbor, and support vector machine models were also established according to the collected datasets. The prediction performance of the six classification models was compared. The evaluated results showed that the established WOA–RF hybrid model has the best prediction performance and the highest accuracy (0.917) compared to other models, with the highest kappa value (0.8846) and MCC value (0.8932). In addition, the performed sensitivity analysis showed that the deviation rate is the most important influencing factor, followed by the internal diameter of the pipeline. Eventually, the WOA–RF hybrid model was used to predict the failure risk level of the backfilling pipelines of nine actual mines in Sichuan, China. The field datasets were collected through field investigation, and engineering verification was carried out. The research results show that the WOA–RF hybrid model is reasonable and effective for backfilling pipeline invalidation risk, and it can provide a novel solution for backfilling pipeline invalidation, with good engineering practicability. Full article

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