bluuebunny/crossref_metadata_2025 huggingface dataset:
An Alternative to the Log-Skew-Normal Distribution: Properties, Inference, and an Application to Air Pollutant Concentrations
Authors: Arrué Jaime , Arellano-Valle Reinaldo Boris , Venegas Osvaldo , Bolfarine Heleno , Gómez Héctor W.
Published: November 2022

In this study, we consider an alternative to the log-skew-normal distribution. It is called the modified log-skew-normal distribution and introduces greater flexibility in the skewness and kurtosis parameters. We first study several of the main probabilistic properties of the new distribution, such as the computation of its moments and the non-existence of the moment-generating function. Parameter estimation by the maximum likelihood approach is also studied. This approach presents an overestimation problem in the shape parameter, which in some cases, can even be infinite. However, as we demonstrate, this problem is solved by adapting bias reduction using Firth’s approach. We also show that the modified log-skew-normal model likewise inherits the non-singularity of the Fisher information matrix of the untransformed model, when the shape parameter is null. Finally, we apply the modified log-skew-normal model to a real example related to pollution data.
Three-Parameter Estimation Method of Multiple Hybrid Weibull Distribution Based on the EM Optimization Algorithm
Authors: Dong Xiaowei , Sun Feng , Xu Fangchao , Zhang Qi , Zhou Ran , Zhang Liang , Liang Zhongwei
Published: November 2022

The hybrid Weibull distribution model can describe the failure rules of electromechanical products more accurately than the single Weibull distribution model, and it can improve the accuracy of reliability analysis. However, the hybrid Weibull distribution model is also more complex, and the multi-parameter estimation is more difficult. In this paper, a reliability mathematical model based on the two-fold three-parameter hybrid Weibull distribution model was established, an EM optimization algorithm was derived for its solution, and a practical initial parameter selection scheme was designed. The validity of the model and the algorithm were verified, and goodness-of-fit tests were conducted through an arithmetic example. The results showed that the initial value selection scheme proposed in this paper and the corresponding solution algorithm could solve all the parameters and weight coefficients to be estimated for each sub distribution, and the obtained failure probability fitting curve had a high fit with the actual sample data, which effectively solved the multi-parameter estimation problem of the multiple mixed Weibull distribution model.
A Differential Privacy Budget Allocation Algorithm Based on Out-of-Bag Estimation in Random Forest
Authors: Li Xin , Qin Baodong , Luo Yiyuan , Zheng Dong
Published: November 2022

The issue of how to improve the usability of data publishing under differential privacy has become one of the top questions in the field of machine learning privacy protection, and the key to solving this problem is to allocate a reasonable privacy protection budget. To solve this problem, we design a privacy budget allocation algorithm based on out-of-bag estimation in random forest. The algorithm firstly calculates the decision tree weights and feature weights by the out-of-bag data under differential privacy protection. Secondly, statistical methods are introduced to classify features into best feature set, pruned feature set, and removable feature set. Then, pruning is performed using the pruned feature set to avoid decision trees over-fitting when constructing an ϵ-differential privacy random forest. Finally, the privacy budget is allocated proportionally based on the decision tree weights and feature weights in the random forest. We conducted experimental comparisons with real data sets from Adult and Mushroom to demonstrate that this algorithm not only protects data security and privacy, but also improves model classification accuracy and data availability.
Homogeneity Test of Multi-Sample Covariance Matrices in High Dimensions
Authors: Sun Peng , Tang Yincai , Cao Mingxiang
Published: November 2022

In this paper, a new test statistic based on the weighted Frobenius norm of covariance matrices is proposed to test the homogeneity of multi-group population covariance matrices. The asymptotic distributions of the proposed test under the null and the alternative hypotheses are derived, respectively. Simulation results show that the proposed test procedure tends to outperform some existing test procedures.
Optimal Route Design for Construction Waste Transportation Systems: Mathematical Models and Solution Algorithms
Authors: Wang Haoqing , Yi Wen , Liu Yannick
Published: November 2022

A huge amount of construction waste is generated in construction sites every day that needs to be transported by vehicle to disposal facilities for processing. Unlike in most typical transportation problems, once these vehicles are loaded with construction waste, they must travel directly to the disposal facility. Moreover, there are different types of construction waste that may require handling by different disposal facilities. In this paper, we develop a model and algorithm for identifying the optimal transportation routes specific to construction waste transportation. Our results can not only minimize the overall costs for both the logistics company and the contractor but also minimize the distance traveled, thus reducing urban traffic emissions.
Quasi Efficient Solutions and Duality Results in a Multiobjective Optimization Problem with Mixed Constraints via Tangential Subdifferentials
Authors: Jennane Mohsine , Kalmoun El Mostafa , Lafhim Lahoussine , Houmia Anouar
Published: November 2022

We take up a nonsmooth multiobjective optimization problem with tangentially convex objective and constraint functions. In employing a suitable constraint qualification, we formulate both necessary and sufficient optimality conditions for (local) quasi efficient solutions in terms of tangential subdifferentials. Furthermore, under generalized convexity assumptions, we state strong, weak and converse duality relations of Wolfe and Mond–Weir types. We give a number of examples to illustrate the new concepts and main results of this paper.
Adaptive Neural Control for an Uncertain 2-DOF Helicopter System with Unknown Control Direction and Actuator Faults
Authors: Wu Bing , Wu Jiale , He Weitian , Tang Guojian , Zhao Zhijia
Published: November 2022

In accordance with the rapid development of smart devices and technology, unmanned aerial vehicles (UAVs) have been developed rapidly. The two-degree-of-freedom helicopter system is a typical UAV that is susceptible to uncertainty, unknown control direction and actuator faults. Hence, a novel adaptive neural network (NN), fault-tolerant control scheme is proposed in this paper. Firstly, to compensate for the uncertainty, a radial-basis NN was developed to approximate the uncertain, unknown continuous function in the controlled system, and a novel weight-adaptive approach is proposed to save on computational cost. Secondly, a class of Nussbaum functions was chosen to solve the unknown-control-direction issue to prevent the effect of an unknown sign for the control coefficient. Subsequently, in response to the actuator faults, an adaptive parameter was designed to compensate for the performance loss of the actuators. Through rigorous Lyapunov analyses, the designed control scheme was proven to enable the states of the closed-loop system to be semi-globally uniformly bounded and the controlled system to be stable. Finally, we conducted a numerical simulation on Matlab to further verify the validity of the proposed scheme.
Convexity of the Capacity of One-Bit Quantized Additive White Gaussian Noise Channels
Authors: Lee Sungmin , Min Moonsik
Published: November 2022

In this study, the maximum error-free transmission rate of an additive white Gaussian noise channel with a symmetric analog-to-digital converter (ADC) was derived as a composite function of the binary entropy function, Gaussian Q-function, and the square root function, assuming that the composite function was convex on the set of all non-negative real numbers. However, because mathematically proving this convexity near zero is difficult, studies in this field have only presented numerical results for small values in the domain. Because the low-signal-to-noise (SNR) regime is considered to be a major application area for one-bit ADCs in wireless communication, deriving a concrete proof of the convexity of the composite function on small SNR values (non-negative values near zero) is important. Therefore, this study proposes a novel proof for convexity, which is satisfied for all non-negative values, based on the continuity of the involved functions.
Risk Diffusion and Control under Uncertain Information Based on Hypernetwork
Authors: Yu Ping , Wang Zhiping , Sun Yanan , Wang Peiwen
Published: November 2022

During the height of the COVID-19 epidemic, production lagged and enterprises could not deliver goods on time, which will bring considerable risks to the supply chain system. Modeling risk diffusion in supply chain networks is important for prediction and control. To study the influence of uncertain information on risk diffusion in a dynamic network, this paper constructs a dynamic evolution model based on a hypernetwork to study risk diffusion and control under uncertain information. First, a dynamic evolution model is constructed to represent the network topology, which includes the addition of links, rewiring of links, entry of nodes, and the exit of outdated nodes that obey the aging principle. Then, the risk diffusion scale is discussed with the Microscopic Markovian Chain Approach (MMCA), and the risk threshold is analyzed. Finally, the consistency of Monte Carlo (MC) simulation and MMCA is verified by MATLAB, and the influence of each parameter on the risk diffusion scale and risk threshold is tested. The results show that reducing the cooperation and production during the risk period, declining the attenuation factor, enhancing the work efficiency of the official media, and increasing the probability of the exit of outdated nodes in the supply chain networks will increase the risk threshold and restrain the risk diffusion.
Approximation and Analysis of Natural Data Based on NARX Neural Networks Involving Wavelet Filtering
Authors: Mandrikova Oksana , Polozov Yuryi , Zhukova Nataly , Shichkina Yulia
Published: November 2022

Recurrent neural network (RNN) models continue the theory of the autoregression integrated moving average (ARIMA) model class. In this paper, we consider the architecture of the RNN with embedded memory—«Process of Nonlinear Autoregressive Exogenous Model» (NARX). Though it is known that NN is a universal approximator, certain difficulties and restrictions in different NN applications are still topical and call for new approaches and methods. In particular, it is difficult for an NN to model noisy and significantly nonstationary time series. The paper suggests optimizing the modeling process for a complicated-structure time series by NARX networks involving wavelet filtering. The developed procedure of wavelet filtering includes the application of the construction of wavelet packets and stochastic thresholds. A method to estimate the thresholds to obtain a solution with a defined confidence level is also developed. We introduce the algorithm of wavelet filtering. It is shown that the proposed wavelet filtering makes it possible to obtain a more accurate NARX model and improves the efficiency of the forecasting process for a natural time series of a complicated structure. Compared to ARIMA, the suggested method allows us to obtain a more adequate model of a nonstationary time series of complex nonlinear structure. The advantage of the method, compared to RNN, is the higher quality of data approximation for smaller computation efforts at the stages of network training and functioning that provides the solution to the problem of long-term dependencies. Moreover, we develop a scheme of approach realization for the task of data modeling based on NARX and anomaly detection. The necessity of anomaly detection arises in different application areas. Anomaly detection is of particular relevance in the problems of geophysical monitoring and requires method accuracy and efficiency. The effectiveness of the suggested method is illustrated in the example of processing of ionospheric parameter time series. We also present the results for the problem of ionospheric anomaly detection. The approach can be applied in space weather forecasting to predict ionospheric parameters and to detect ionospheric anomalies.
Address Privacy of Bluetooth Low Energy
Authors: Sun Dazhi , Tian Yangguang
Published: November 2022

Bluetooth low energy (LE) devices have been widely used in the Internet of Things (IoT) and wireless personal area networks (WPAN). However, attackers may compromise user privacy by tracking the addresses of the LE device. The resolvable private address (RPA) mechanism provides address privacy protection for the LE device. Similar to Zhang and Lin’s work in CCS 2022, we investigate the privacy of the RPA mechanism in this paper. Our contributions are threefold. First, we discover that the RPA mechanism has a privacy weakness. The attacker can track the targeted device by exploiting the runs of the RPA mechanism when he intercepts the targeted device’s obsolete RPA value. Second, we propose an improved RPA mechanism to overcome the privacy weakness in the RPA mechanism. The improved RPA mechanism leads to a small amount of extra overheads without requiring modification to the basic cryptographic tools used in the standard specification. Third, we formalize a privacy model to capture the address privacy of the RPA mechanisms. Our improved RPA mechanism provides enhanced privacy guarantees to Bluetooth LE devices in wireless personal applications.
Unsupervised Image Translation Using Multi-Scale Residual GAN
Authors: Zhang Yifei , Li Weipeng , Wang Daling , Feng Shi
Published: November 2022

Image translation is a classic problem of image processing and computer vision for transforming an image from one domain to another by learning the mapping between an input image and an output image. A novel Multi-scale Residual Generative Adversarial Network (MRGAN) based on unsupervised learning is proposed in this paper for transforming images between different domains using unpaired data. In the model, a dual generater architecture is used to eliminate the dependence on paired training samples and introduce a multi-scale layered residual network in generators for reducing semantic loss of images in the process of encoding. The Wasserstein GAN architecture with gradient penalty (WGAN-GP) is employed in the discriminator to optimize the training process and speed up the network convergence. Comparative experiments on several image translation tasks over style transfers and object migrations show that the proposed MRGAN outperforms strong baseline models by large margins.
From Fuzzy Information to Community Detection: An Approach to Social Networks Analysis with Soft Information
Authors: Gutiérrez Inmaculada , Gómez Daniel , Castro Javier , Espínola Rosa
Published: November 2022

On the basis of network analysis, and within the context of modeling imprecision or vague information with fuzzy sets, we propose an innovative way to analyze, aggregate and apply this uncertain knowledge into community detection of real-life problems. This work is set on the existence of one (or multiple) soft information sources, independent of the network considered, assuming this extra knowledge is modeled by a vector of fuzzy sets (or a family of vectors). This information may represent, for example, how much some people agree with a specific law, or their position against several politicians. We emphasize the importance of being able to manage the vagueness which usually appears in real life because of the common use of linguistic terms. Then, we propose a constructive method to build fuzzy measures from fuzzy sets. These measures are the basis of a new representation model which combines the information of a network with that of fuzzy sets, specifically when it comes to linguistic terms. We propose a specific application of that model in terms of finding communities in a network with additional soft information. To do so, we propose an efficient algorithm and measure its performance by means of a benchmarking process, obtaining high-quality results.
Recovery of Inhomogeneity from Output Boundary Data
Authors: Kravchenko Vladislav V. , Khmelnytskaya Kira V. , Çetinkaya Fatma Ayça
Published: November 2022

We consider the Sturm–Liouville equation on a finite interval with a real-valued integrable potential and propose a method for solving the following general inverse problem. We recover the potential from a given set of the output boundary values of a solution satisfying some known initial conditions for a set of values of the spectral parameter. Special cases of this problem include the recovery of the potential from the Weyl function, the inverse two-spectra Sturm–Liouville problem, as well as the recovery of the potential from the output boundary values of a plane wave that interacted with the potential. The method is based on the special Neumann series of Bessel functions representations for solutions of Sturm–Liouville equations. With their aid, the problem is reduced to the classical inverse Sturm–Liouville problem of recovering the potential from two spectra, which is solved again with the help of the same representations. The overall approach leads to an efficient numerical algorithm for solving the inverse problem. Its numerical efficiency is illustrated by several examples.
Modified Sand Cat Swarm Optimization Algorithm for Solving Constrained Engineering Optimization Problems
Authors: Wu Di , Rao Honghua , Wen Changsheng , Jia Heming , Liu Qingxin , Abualigah Laith
Published: November 2022

The sand cat swarm optimization algorithm (SCSO) is a recently proposed metaheuristic optimization algorithm. It stimulates the hunting behavior of the sand cat, which attacks or searches for prey according to the sound frequency; each sand cat aims to catch better prey. Therefore, the sand cat will search for a better location to catch better prey. In the SCSO algorithm, each sand cat will gradually approach its prey, which makes the algorithm a strong exploitation ability. However, in the later stage of the SCSO algorithm, each sand cat is prone to fall into the local optimum, making it unable to find a better position. In order to improve the mobility of the sand cat and the exploration ability of the algorithm. In this paper, a modified sand cat swarm optimization (MSCSO) algorithm is proposed. The MSCSO algorithm adds a wandering strategy. When attacking or searching for prey, the sand cat will walk to find a better position. The MSCSO algorithm with a wandering strategy enhances the mobility of the sand cat and makes the algorithm have stronger global exploration ability. After that, the lens opposition-based learning strategy is added to enhance the global property of the algorithm so that the algorithm can converge faster. To evaluate the optimization effect of the MSCSO algorithm, we used 23 standard benchmark functions and CEC2014 benchmark functions to evaluate the optimization performance of the MSCSO algorithm. In the experiment, we analyzed the data statistics, convergence curve, Wilcoxon rank sum test, and box graph. Experiments show that the MSCSO algorithm with a walking strategy and a lens position-based learning strategy had a stronger exploration ability. Finally, the MSCSO algorithm was used to test seven engineering problems, which also verified the engineering practicability of the proposed algorithm.
Analysis of the Accident Propensity of Chinese Bus Drivers: The Influence of Poor Driving Records and Demographic Factors
Authors: Zheng Lili , He Xinyu , Ding Tongqiang , Li Yanlin , Xiao Zhengfeng
Published: November 2022

Previous studies have shown that bus drivers are a major contributing factor to bus accidents. The aim of this study is to explore the factors that contribute to the presence of accident propensity among bus drivers, as well as the relative importance of each influencing factor and the mechanism of influence. To this end, a C5.0 decision tree model was developed to determine the relative importance as well as rank the importance of the impact of poor driving records and demographic factors on accident propensity, and a binary logistic regression model was developed to analyze the relationship between accident propensity and the different values of each essential influencing factor. Based on our results, we found that: (1) the number of violations had the most significant effect on bus drivers’ accident propensity, followed by age, driving age, and number of alarms; (2) violations and alarms are positively related to bus driver accident propensity; age and driving age are inversely related to bus driver accident propensity; and (3) men have a higher accident risk probability than women. This study’s findings will help bus companies and traffic management authorities to implement more targeted improvements to their bus driver management programs.
Two-Stage Optimization Methods to Solve the DNA-Sample Allocation Problem
Authors: Noceda-Davila Diego , Lorenzo-Freire Silvia , Carpente Luisa
Published: November 2022

This paper deals with new methods capable of solving the optimization problem concerning the allocation of DNA samples in plates in order to carry out the DNA sequencing with the Sanger technique. These methods make it possible to work with independent subproblems of lower complexity, obtaining solutions of good quality while maintaining a competitive time cost. They are compared with the ones introduced in the literature, obtaining interesting results. All the comparisons among the methods in the literature and the laboratory results have been made with real data.
On the BiHom-Type Nonlinear Equations
Authors: Wu Hui , Zhang Xiaohui
Published: November 2022

In this paper, the Heisenberg doubles and Long dimodules of a BiHom-Hopf algebra are introduced. Then, we discussed the relation between BiHom-Hopf equation and BiHom-pentagon equation, and we obtain the solutions of BiHom-Hopf equation from Heisenberg doubles. We also showed that the parametric generalized Long dimodules can provide the solutions of BiHom-Yang-Baxter equation and generalized D-equation.
Probability and Certainty in the Performance of Evolutionary and Swarm Optimization Algorithms
Authors: Ivković Nikola , Kudelić Robert , Črepinšek Matej
Published: November 2022

Reporting the empirical results of swarm and evolutionary computation algorithms is a challenging task with many possible difficulties. These difficulties stem from the stochastic nature of such algorithms, as well as their inability to guarantee an optimal solution in polynomial time. This research deals with measuring the performance of stochastic optimization algorithms, as well as the confidence intervals of the empirically obtained statistics. Traditionally, the arithmetic mean is used for measuring average performance, but we propose quantiles for measuring average, peak and bad-case performance, and give their interpretations in a relevant context for measuring the performance of the metaheuristics. In order to investigate the differences between arithmetic mean and quantiles, and to confirm possible benefits, we conducted experiments with 7 stochastic algorithms and 20 unconstrained continuous variable optimization problems. The experiments showed that median was a better measure of average performance than arithmetic mean, based on the observed solution quality. Out of 20 problem instances, a discrepancy between the arithmetic mean and median happened in 6 instances, out of which 5 were resolved in favor of median and 1 instance remained unresolved as a near tie. The arithmetic mean was completely inadequate for measuring average performance based on the observed number of function evaluations, while the 0.5 quantile (median) was suitable for that task. The quantiles also showed to be adequate for assessing peak performance and bad-case performance. In this paper, we also proposed a bootstrap method to calculate the confidence intervals of the probability of the empirically obtained quantiles. Considering the many advantages of using quantiles, including the ability to calculate probabilities of success in the case of multiple executions of the algorithm and the practically useful method of calculating confidence intervals, we recommend quantiles as the standard measure of peak, average and bad-case performance of stochastic optimization algorithms.
On Focal Borel Probability Measures
Authors: García-Pacheco Francisco Javier , Rivero-Dones Jorge , Villegas-Vallecillos Moisés
Published: November 2022

The novel concept of focality is introduced for Borel probability measures on compact Hausdorff topological spaces. We characterize focal Borel probability measures as those Borel probability measures that are strictly positive on every nonempty open subset. We also prove the existence of focal Borel probability measures on compact metric spaces. Lastly, we prove that the set of focal (regular) Borel probability measures is convex but not extremal in the set of all (regular) Borel probability measures.
Detection of River Floating Garbage Based on Improved YOLOv5
Authors: Yang Xingshuai , Zhao Jingyi , Zhao Li , Zhang Haiyang , Li Li , Ji Zhanlin , Ganchev Ivan
Published: November 2022

The random dumping of garbage in rivers has led to the continuous deterioration of water quality and affected people’s living environment. The accuracy of detection of garbage floating in rivers is greatly affected by factors such as floating speed, night/daytime natural light, viewing angle and position, etc. This paper proposes a novel detection model, called YOLOv5_CBS, for the detection of garbage objects floating in rivers, based on improvements of the YOLOv5 model. Firstly, a coordinate attention (CA) mechanism is added to the original C3 module (without compressing the number of channels in the bottleneck), forming a new C3-CA-Uncompress Bottleneck (CCUB) module for improving the size of the receptive field and allowing the model to pay more attention to important parts of the processed images. Then, the Path Aggregation Network (PAN) in YOLOv5 is replaced with a Bidirectional Feature Pyramid Network (BiFPN), as proposed by other researchers, to enhance the depth of information mining and improve the feature extraction capability and detection performance of the model. In addition, the Complete Intersection over Union (CIoU) loss function, which was originally used in YOLOv5 for the calculation of location score of the compound loss, is replaced with the SCYLLA-IoU (SIoU) loss function, so as to speed up the model convergence and improve its regression precision. The results, obtained through experiments conducted on two datasets, demonstrate that the proposed YOLOv5_CBS model outperforms the original YOLOv5 model, along with three other state-of-the-art models (Faster R-CNN, YOLOv3, and YOLOv4), when used for river floating garbage objects detection, in terms of the recall, average precision, and F1 score achieved by reaching respective values of 0.885, 90.85%, and 0.8669 on the private dataset, and 0.865, 92.18%, and 0.9006 on the Flow-Img public dataset.
Smart Patrolling Based on Spatial-Temporal Information Using Machine Learning
Authors: Guevara Cesar , Santos Matilde
Published: November 2022

With the aim of improving security in cities and reducing the number of crimes, this research proposes an algorithm that combines artificial intelligence (AI) and machine learning (ML) techniques to generate police patrol routes. Real data on crimes reported in Quito City, Ecuador, during 2017 are used. The algorithm, which consists of four stages, combines spatial and temporal information. First, crimes are grouped around the points with the highest concentration of felonies, and future hotspots are predicted. Then, the probability of crimes committed in any of those areas at a time slot is studied. This information is combined with the spatial way-points to obtain real surveillance routes through a fuzzy decision system, that considers distance and time (computed with the OpenStreetMap API), and probability. Computing time has been analized and routes have been compared with those proposed by an expert. The results prove that using spatial–temporal information allows the design of patrolling routes in an effective way and thus, improves citizen security and decreases spending on police resources.
A New Fractal-Fractional Version of Giving up Smoking Model: Application of Lagrangian Piece-Wise Interpolation along with Asymptotical Stability
Authors: Etemad Sina , Shikongo Albert , Owolabi Kolade M. , Tellab Brahim , Avcı İbrahim , Rezapour Shahram , Agarwal Ravi P.
Published: November 2022

In this paper, a new kind of mathematical modeling is studied by providing a five-compartmental system of differential equations with respect to new hybrid generalized fractal-fractional derivatives. For the first time, we design a model of giving up smoking to analyze its dynamical behaviors by considering two parameters of such generalized operators; i.e., fractal dimension and fractional order. We apply a special sub-category of increasing functions to investigate the existence of solutions. Uniqueness property is derived by a standard method based on the Lipschitz rule. After proving stability property, the equilibrium points are obtained and asymptotically stable solutions are studied. Finally, we illustrate all analytical results and findings via numerical algorithms and graphs obtained by Lagrangian piece-wise interpolation, and discuss all behaviors of the relevant solutions in the fractal-fractional system.
Neural Network-Based Bitcoin Pricing Using a New Mutated Climb Monkey Algorithm with TOPSIS Analysis for Sustainable Development
Authors: Mohanty Samuka , Dash Rajashree
Published: November 2022

Bitcoin is yet to be assumed as a worthy cryptocurrency and rewarding asset in the global market. As polynomial-based neural networks (PBNNs) are very robust and more accurate in modeling stock price prediction, their advantage in Bitcoin pricing needs to be analyzed. In this study, the robustness of PBNNs, based on Chebyshev (CPBNN) and Legendre (LPBNN), is blended with the proposed algorithm, coined as the mutated climb monkey algorithm (MCMA), to control the estimation of network parameters to accurately predict the one-day-ahead Bitcoin price. The performance was evaluated by a comparative analysis of the testing of both CPBNN and LPBNN with each of the six algorithms under consideration on three different datasets collected within the same time interval. As the use of a few evaluation criteria will not be able to identify an efficient predictor model, this study also proposes the use of a Multi-Criteria Decision-Making (MCDM) framework to rank all models using 15 different evaluation criteria. The ranking of the models clearly indicates that the proposed MCMA algorithm outperforms all other algorithms under study. The convergence plots of the top two models for the datasets also indicate that the PBNN using MCMA for learning predicts better results.
Relaxed Variable Metric Primal-Dual Fixed-Point Algorithm with Applications
Authors: Huang Wenli , Tang Yuchao , Wen Meng , Li Haiyang
Published: November 2022

In this paper, a relaxed variable metric primal-dual fixed-point algorithm is proposed for solving the convex optimization problem involving the sum of two convex functions where one is differentiable with the Lipschitz continuous gradient while the other is composed of a linear operator. Based on the preconditioned forward–backward splitting algorithm, the convergence of the proposed algorithm is proved. At the same time, we show that some existing algorithms are special cases of the proposed algorithm. Furthermore, the ergodic convergence and linear convergence rates of the proposed algorithm are established under relaxed parameters. Numerical experiments on the image deblurring problems demonstrate that the proposed algorithm outperforms some existing algorithms in terms of the number of iterations.
Deep Learning-Based Cyber–Physical Feature Fusion for Anomaly Detection in Industrial Control Systems
Authors: Du Yan , Huang Yuanyuan , Wan Guogen , He Peilin
Published: November 2022

In this paper, we propose an unsupervised anomaly detection method based on the Autoencoder with Long Short-Term Memory (LSTM-Autoencoder) network and Generative Adversarial Network (GAN) to detect anomalies in industrial control system (ICS) using cyber–physical fusion features. This method improves the recall of anomaly detection and overcomes the challenges of unbalanced datasets and insufficient labeled samples in ICS. As a first step, additional network features are extracted and fused with physical features to create a cyber–physical dataset. Following this, the model is trained using normal data to ensure that it can properly reconstruct the normal data. In the testing phase, samples with unknown labels are used as inputs to the model. The model will output an anomaly score for each sample, and whether a sample is anomalous depends on whether the anomaly score exceeds the threshold. Whether using supervised or unsupervised algorithms, experimentation has shown that (1) cyber–physical fusion features can significantly improve the performance of anomaly detection algorithms; (2) the proposed method outperforms several other unsupervised anomaly detection methods in terms of accuracy, recall, and F1 score; (3) the proposed method can detect the majority of anomalous events with a low false negative rate.
A New Simple Chaotic System with One Nonlinear Term
Authors: Bouteraa Yassine , Mostafaee Javad , Kchaou Mourad , Abbassi Rabeh , Jerbi Houssem , Mobayen Saleh
Published: November 2022

In this research article, a simple four-dimensional (4D) chaotic dynamic system with uncomplicated structure and only one nonlinear term is introduced. The features of the proposed design have been conducted with some standard nonlinear dynamic analysis and mathematical tools which show the chaotic nature. One of the most important indicators for detecting complexity of the chaotic systems is the Kaplan-York dimension of the system. Moreover, one of the main criteria of chaotic systems is its simplicity due to the reduction of operating costs. Therefore, it seems necessary to design a system as simple as possible and with high complexity. In this research, a comparison has been made between the proposed system and similar chaotic systems, which has given noticeable results. For the practical implementation of the proposed design, the circuit analysis using Multisim software has been employed. The proposed scheme has been used in the application of image encryption to show the efficiency of the proposed chaotic system and standard encryption tests have been performed. The rest of the numerical results have been conducted using MATLAB/Simulink software.
Land Plots Evaluation for Agriculture and Green Energy Projects: How to Overcome the Conflict Using Mathematics
Authors: Ilin Igor , Laskin Mikhail , Logacheva Irina , Sarygulov Askar , Tick Andrea
Published: November 2022

Seventeen sustainable development goals were formulated to create a harmonious world order for the benefit of different nations and peoples. At the same time, economic practice provides a lot of examples of conflicts of an economic nature between individual sustainable development goals. One of these conflicts is the need for environmental imperatives and economic growth when a massive assessment of land used for crop production and green energy projects is needed. The present paper considers a non-traditional approach to the mass evaluation of land plots on the condition that geographic information systems provide the main source of information, such as the case of land allocation for green energy facilities and evaluation of agricultural plots. The novelty of the proposed approach firstly means the development of a comparative approach, which receives much less attention in the valuation literature than cost and income approaches, as it can give an adequate picture of the current state of the market. The model includes the study of the entire dataset, the selection of model distributions and the construction of estimates based on model distributions. The methodology of multivariate lognormal distribution of factors and prices of analogues is used. The peculiarity of the market evaluation of land plots in such cases is, as a rule, the absence of rank predictors and sufficient number of continuous predictors, which provides a base for the application of a novel approach. The method of express testing of hypotheses about joint normality of logarithms of values of pricing factors and prices is proposed. The market value is estimated as an estimate of the modal value of conditional lognormal price distribution. Secondly, the problem of market valuation is solved in case of the almost complete absence of information about price-forming factors in the areas being assessed, and thirdly, the factors are determined based on geoinformation databases (distance to the nearest large city, regional center, federal highway, large rivers, lakes, and solid waste landfills), which allow for market assessment in the absence of information on pricing factors for land plots, except for the offer price and the plot area. The research was necessitated by the claim to determine on a specific date the cadastral value of agricultural land for the purposes of taxation, corresponding to the market value, in the almost complete absence of information on pricing factors in the assessed areas. The value of land reflects a complex combination of factors, so the use of the proposed mathematical toolkit allows for building a consistent model for the evaluation of land where improvements are absent or have no value in terms of land acquisition purposes.
Accelerated Randomized Coordinate Descent for Solving Linear Systems
Authors: Wang Qin , Li Weiguo , Bao Wendi , Zhang Feiyu
Published: November 2022

The randomized coordinate descent (RCD) method is a simple but powerful approach to solving inconsistent linear systems. In order to accelerate this approach, the Nesterov accelerated randomized coordinate descent method (NARCD) is proposed. The randomized coordinate descent with the momentum method (RCDm) is proposed by Nicolas Loizou, we will provide a new convergence boundary. The global convergence rates of the two methods are established in our paper. In addition, we show that the RCDm method has an accelerated convergence rate by choosing a proper momentum parameter. Finally, in numerical experiments, both the RCDm and the NARCD are faster than the RCD for uniformly distributed data. Moreover, the NARCD has a better acceleration effect than the RCDm and the Nesterov accelerated stochastic gradient descent method. When the linear correlation of matrix A is stronger, the NARCD acceleration is better.
Neoagaro-Oligosaccharides Ameliorate Chronic Restraint Stress-Induced Depression by Increasing 5-HT and BDNF in the Brain and Remodeling the Gut Microbiota of Mice
Authors: Zhuang Yan , Zeng Runying , Liu Xiao , Yang Longhe , Chan Zhuhua
Published: November 2022

Neoagaro-oligosaccharides (NAOs) belong to the algae oligosaccharides. NAOs have been found to have diverse biological activities. However, the effects of NAOs on depression and their underlying mechanism have not been thoroughly studied. A chronic restraint stress (CRS)-induced C57BL/6J mouse model was used to assess the antidepressant effects of NAOs. Anxiety and depression behaviors were assessed by open field tests (OFT) and forced swimming tests (FST), while interleukin 18 (IL-18), 5-hydroxytryptamine (5-HT) and brain-derived neurotrophic factor (BDNF) were the molecular biomarkers of depression. Fecal microbiota transplantation (FMT) was performed. The results showed that NAO treatment significantly improved the body weight of depressed mice and reduced the central area time in the OFT and immobility time in the FST. NAO treatment decreased the levels of IL-18 in the serum and increased the levels of 5-HT in the serum and whole brain and of BDNF in the whole brain. NAO treatment mitigated the gut microbiota dysbiosis in the depressed mice and reversed the decreased levels of short-chain fatty acids (SCFAs) in the cecum of the depressed mice. FMT indicated that the gut microbiota is, indeed, linked to depression, which was reflected in the changes in weight gain and behaviors. In a word, NAOs effectively reversed the CRS-induced mice model of depression, which depended on the changes in the gut microbiota and SCFAs, as well as its modulation of 5-HT and BDNF.
Diastolic versus Systolic Left Ventricular Dysfunction as Independent Predictors for Unfavorable Postoperative Evolution in Patients with Aortic Regurgitation Undergoing Aortic Valve Replacement
Authors: Iliuta Luminita , Andronesi Andreea Gabriella , Diaconu Camelia Cristina , Moldovan Horatiu , Rac-Albu Marius , Rac-Albu Madalina-Elena
Published: November 2022

Background and Objectives: Chronic severe aortic valve disease is associated with important changes in left ventricle (LV) performance associated with eccentric or concentric LV hypertrophy. We aimed to assess the immediate prognostic implications of the type of the LV diastolic filling pattern (LVDFP) compared with LV systolic performance in patients with severe aortic regurgitation (AR) undergoing aortic valve replacement (AVR) and to define the independent echographic predictors for the immediate and long-term prognoses. Materials and Methods: We performed a prospective study enrolling 332 AR patients undergoing AVR, divided into two groups: Group A—201 pts with normal LV systolic function, divided into two subgroups (A1: 129 pts with a nonrestrictive LVDFP and A2: 72 pts with restrictive LVDFP), and Group B—131 pts with LV systolic dysfunction (LV ejection fraction LVEF < 50%), divided into two subgroups (B1: 83 pts with a nonrestrictive LVDFP and B2: 48 pts with restrictive LVDFP). Results: The early postoperative mortality rate was higher in patients with a restrictive LVDFP (11.12% in A2 and 12.5% in B2) compared with normal LV filling (2.32% in A1 and 7.63% in B1, p < 0.0001), regardless of the LVEF. The restrictive LVDFP—defined by at least one of the following echographic parameters: an E/A > 2 with an E wave deceleration time (EDt) < 100 ms; an isovolumetric relaxation time (IVRT) < 60 ms; or an S/D ratio < 1 in the pulmonary vein flow—was an independent predictor for early postoperative mortality, increasing the relative risk by 8.2-fold. Other independent factors associated with early poor prognosis were an LV end-systolic diameter (LVESD) > 58 mm, an age > 75 years, and the presence of comorbidities (chronic obstructive pulmonary disease-COPD or diabetes mellitus). On a medium-term, an unfavorable evolution was associated with: an age > 75 years (RR = 8.1), an LV end-systolic volume (LVESV) > 95 cm3 (RR = 6.7), a restrictive LVDFP (RR = 9.8, p < 0.0002), and pulmonary hypertension (RR = 8.2). Conclusions: The presence of a restrictive LVDFP in patients with AR undergoing AVR is associated with both increased early and medium-term mortality rates. The LV diastolic function is a more reliable parameter for prognosis than LV systolic performance (RR 9.2 versus 2.1). Other independent predictors for increased early postoperative mortality rate were: an age > 75 years, an LVESD > 58 mm, and comorbidities (diabetes mellitus, COPD), and for unfavorable evolution at 2 years postoperatively: an age > 75 years, an LVESV > 95 cm3, and severe pulmonary hypertension.
Assessing the Safety of Total Intravenous Anesthesia with Remimazolam in General Anesthesia for Transcatheter Aortic Valve Implantation of Severe Aortic Valve Stenosis: A Case Series
Authors: Kim Yu-Yil , Heo Hyun-Joo , Lee Ji-Hye , Cho Hyung-Gu , Kim Geonbo
Published: November 2022

Background and Objectives: In patients with severe aortic stenosis (sAS), it is crucial to maintain hemodynamic stability during the induction and maintenance of general anesthesia for transcatheter aortic valve implantation (TAVI). In this study, we assessed the efficacy and safety of remimazolam in maintaining hemodynamic stability during anesthetic induction and maintenance. Cases: TAVI was performed on seven patients with sAS, and remimazolam was administered for total intravenous anesthesia (TIVA) of general anesthesia with induction (3.0 mg/kg/h) and maintenance (1.0 mg/kg/h). All patients underwent TAVI without major hemodynamic concerns and later recovered. Conclusions: Remimazolam can be safely used for induction and maintenance of general anesthesia in patients with sAS when performing TAVI.
Behavioral and Dietary Habits That Could Influence Both COVID-19 and Non-Communicable Civilization Disease Prevention—What Have We Learned Up to Now?
Authors: Veljković Milica , Pavlović Dragana R. , Stojanović Nikola M. , Džopalić Tanja , Popović Dragonjić Lidija
Published: November 2022

The massive expansion of the new coronavirus SARS-CoV-2 has urged countries to introduce lockdowns and set restrictive actions worldwide. The focus of the studies was to determine how COVID-19 induces damage to the lungs in order to find an alternative or adjuvant therapy that could lead to preventing COVID-19 or at least ameliorating it. This paper aims to survey the literature and provide new insights into behavioral and dietary habits that could influence the prevention of COVID-19. Maintaining an adequate mental health status, sleep, and taking moderate exercise are often disrupted in the conditions of lockdown and are followed by weakened immunity. Mediterranean and vegetarian diets are superior to other eating patterns in terms of immunity boosting and fighting COVID-19. Our study showed how adequate hydration, green tea intake, and supplementation with vitamins D, C, and E can increase our chances of avoiding the infection and even help us sleep better. Another focus of the research was on determining what level of hygiene really increases one’s chances of not contracting SARS-CoV-2, but this seems a little counter-intuitive at first. Since an immunocompromised state is a familiar predisposing factor for all contagious diseases, maintaining healthy behavioral and dietary habits could be a crucial step in boosting immunity and preventing COVID-19.
Discovery of the Potentiator of the Pore-Forming Ability of Lantibiotic Nisin: Perspectives for Anticancer Therapy
Authors: Chernyshova Dayana N. , Tyulin Alexander A. , Ostroumova Olga S. , Efimova Svetlana S.
Published: November 2022

This study was focused on the action of lantibiotic nisin on the phospholipid membranes. Nisin did not produce ion-permeable pores in the membranes composed of DOPC or DOPE. The introduction of DOPS into bilayer lipid composition led to a decrease in the threshold detergent concentration of nisin. An addition of nisin to DOPG- and TOCL-enriched bilayers caused the formation of well-defined ion pores of various conductances. The transmembrane macroscopic current increased with the second power of the lantibiotic aqueous concentration, suggesting that the dimer of nisin was at least involved in the formation of conductive subunit. The pore-forming ability of lantibiotic decreased in the series: DOPC/TOCL ≈ DOPE/TOCL >> DOPC/DOPG ≥ DOPE/DOPG. The preferential interaction of nisin to cardiolipin-enriched bilayers might explain its antitumor activity by pore-formation in mitochondrial membranes. Small natural molecules, phloretin and capsaicin, were found to potentiate the membrane activity of nisin in the TOCL-containing membranes. The effect was referred to as changes in the membrane boundary potential at the adsorption of small molecules. We concluded that the compounds diminishing the membrane boundary potential should be considered as the potentiator of the nisin pore-forming ability that can be used to develop innovative formulations for anticancer therapy.