bluuebunny/crossref_metadata_2025 huggingface dataset:
Fast Method Based on Fuzzy Logic for Gaussian-Impulsive Noise Reduction in CT Medical Images
Authors: Arnal Josep , Súcar Luis
Published: October 2022

To remove Gaussian-impulsive mixed noise in CT medical images, a parallel filter based on fuzzy logic is applied. The used methodology is structured in two steps. A method based on a fuzzy metric is applied to remove the impulsive noise at the first step. To reduce Gaussian noise, at the second step, a fuzzy peer group filter is used on the filtered image obtained at the first step. A comparative analysis with state-of-the-art methods is performed on CT medical images using qualitative and quantitative measures evidencing the effectiveness of the proposed algorithm. The parallel method is parallelized on shared memory multiprocessors. After applying parallel computing strategies, the obtained computing times indicate that the introduced filter enables to reduce Gaussian-impulse mixed noise on CT medical images in real-time.
A Generalized Approach of the Gilpin–Ayala Model with Fractional Derivatives under Numerical Simulation
Authors: Amdouni Manel , Alzabut Jehad , Samei Mohammad Esmael , Sudsutad Weerawat , Thaiprayoon Chatthai
Published: October 2022

In this article, we study the existence and uniqueness of multiple positive periodic solutions for a Gilpin–Ayala predator-prey model under consideration by applying asymptotically periodic functions. The result of this paper is completely new. By using Comparison Theorem and some technical analysis, we showed that the classical nonlinear fractional model is bounded. The Banach contraction mapping principle was used to prove that the model has a unique positive asymptotical periodic solution. We provide an example and numerical simulation to inspect the correctness and availability of our essential outcomes.
Joint Models for Incomplete Longitudinal Data and Time-to-Event Data
Authors: Takeda Yuriko , Misumi Toshihiro , Yamamoto Kouji
Published: October 2022

Clinical studies often collect longitudinal and time-to-event data for each subject. Joint modeling is a powerful methodology for evaluating the association between these data. The existing models, however, have not sufficiently addressed the problem of missing data, which are commonly encountered in longitudinal studies. In this paper, we introduce a novel joint model with shared random effects for incomplete longitudinal data and time-to-event data. Our proposed joint model consists of three submodels: a linear mixed model for the longitudinal data, a Cox proportional hazard model for the time-to-event data, and a Cox proportional hazard model for the time-to-dropout from the study. By simultaneously estimating the parameters included in these submodels, the biases of estimators are expected to decrease under two missing scenarios. We estimated the proposed model by Bayesian approach, and the performance of our method was evaluated through Monte Carlo simulation studies.
A Flexible Robust Possibilistic Programming Approach toward Wood Pellets Supply Chain Network Design
Authors: Abusaq Zaher , Habib Muhammad Salman , Shehzad Adeel , Kanan Mohammad , Assaf Ramiz
Published: October 2022

Increasing energy demand and the detrimental environmental impacts of fossil fuels have led to the development of renewable energy sources. Rapid demand growth for wood pellets over the last decade has established wood pellets as a potential renewable energy source in a globally competitive energy market. Integrated decision making including all stakeholders in the wood pellet supply chain (WPSC) is essential for a smooth transition to commercially viable wood pellet production. In this aspect, this study aims to suggest a decision support system for optimizing biomass-based wood pellet production supply chain network design (WPP-SCND). The WPP-SCND decision system minimizes the total supply chain (SC) cost of the system while also reducing carbon emissions associated with wood pellet SC activities. All objective parameters, including biomass availability at the supply terminals, market demand, and biomass production, are considered fuzzy to account for epistemic uncertainty. A fuzzy flexible robust possibilistic programming (fuzzy-FRPP) technique is developed for solving the suggested uncertain WPP-SCND model. The case findings show that the imprecise nature of the parameters has a significant impact on the strategic and tactical decisions in the wood pellet SC. By investing almost 10% of the total cost, robust decisions within the wood pellet SC can be obtained. It is established that the fuzzy-FRPP technique successfully provides robust decisions and achieves a balance between transportation costs, emissions costs, and economies of scale when making capacity decisions. Although the suggested decision support system is used to manage the production and distribution of wood pellets, the insights and solution methodology may be extended to the production of other biofuels. The proposed research may be valuable to authorities involved in planning large-scale wood pellet-related production-distribution projects.
Generalization of the Brusov–Filatova–Orekhova Theory for the Case of Variable Income
Authors: Brusov Peter , Filatova Tatiana
Published: October 2022

To expand the applicability in practice of the modern theory of cost and capital structure, the theory of Brusov–Filatova–Orekhova (BFO), which is valid for companies of arbitrary age, is generalized for the case of variable income. The generalized theory of capital structure can be successfully applied in corporate finance, business valuation, banking, investments, ratings, etc. income. A generalized Brusov–Filatova–Orekhova formula for the weighted average cost of capital, WACC, is derived using a formula in MS Excel, where the role of the discount rate shifts from WACC to WACC–g (here g is the growth rate) for financially dependent companies and k0–g for financially independent companies is shown. A decrease in the real discount rates of WACC–g and k0–g with g ensures an increase in the company’s capitalization with g. The tilt of the equity cost curve, ke(L), increases with g. Since the cost of equity justifies the amount of dividends, this should change the dividend policy of the company. It turns out that for the growth rate g < g*, the tilt of the curve ke(L) becomes negative. This qualitatively new effect, discovered here for the first time, can significantly change the principles of the dividend policy of the company. The obtained results are compared with the results of the MM theory with variable income.
Designing of Optimal Reinsurance Indemnity
Authors: Skvarciany Viktorija , Lapinskaitė Indrė
Published: October 2022

This paper contributes to relevant research in the area of optimal reinsurance indemnity and deals with the risk measures that are used in reinsurance. The research aims at finding optimal reinsurance contracts under different risk levels. The paper has demonstrated that the method of calculating the indemnity of the reinsurance contract discussed in the aforementioned article—the reduction of the square of excess of loss—can be generalised and is valid in all instances where p ∈ (0; 1) ∪ (1; +∞). The results could be useful for the insurance companies calculating indemnities for different cases, as they could state the degrees that fit their needs most.
Hybrid Particle Swarm and Whale Optimization Algorithm for Multi-Visit and Multi-Period Dynamic Workforce Scheduling and Routing Problems
Authors: Punyakum Voravee , Sethanan Kanchana , Nitisiri Krisanarach , Pitakaso Rapeepan
Published: October 2022

This paper focuses on the dynamic workforce scheduling and routing problem for the maintenance work of harvesters in a sugarcane harvesting operation. Technician teams categorized as mechanical, hydraulic, and electrical teams are assumed to have different skills at different levels to perform services. The jobs are skill-constrained and have time windows. During a working day, a repair request from a sugarcane harvester may arrive, and as time passes, the harvester’s position may shift to other sugarcane fields. We formulated this problem as a multi-visit and multi-period dynamic workforce scheduling and routing problem (MMDWSRP) and our study is the first to address the workforce scheduling and routing problem (WSRP). A mixed-integer programming formulation and a hybrid particle swarm and whale optimization algorithm (HPSWOA) were firstly developed to solve the problem, with the objective of minimizing the total cost, including technician labor cost, penalty for late service, overtime, travel, and subcontracting costs. The HPSWOA was developed for route planning and maintenance work for each mechanical harvester to be provided by technician teams. The proposed algorithm (HPSWOA) was validated against Lingo computational software using numerical experiments in respect of static problems. It was also tested against the current practice, the traditional whale optimization algorithm (WOA), and traditional particle swarm optimization (PSO) in respect of dynamic problems. The computational results show that the HPSWOA yielded a solution with significantly better quality. The HPSWO was also tested against the traditional genetic algorithm (GA), bat algorithm (BA), WOA, and PSO to solve the well-known CEC 2017 benchmark functions. The computational results show that the HPSWOA achieved more superior performance in most cases compared to the GA, BA, WOA, and PSO algorithms.
Short-Term Online Forecasting for Passenger Origin–Destination (OD) Flows of Urban Rail Transit: A Graph–Temporal Fused Deep Learning Method
Authors: Zheng Han , Chen Junhua , Huang Zhaocha , Yang Kuan , Zhu Jianhao
Published: October 2022

Predicting short-term passenger flow accurately is of great significance for daily management and for a timely emergency response of rail transit networks. In this paper, we propose an attention-based Graph–Temporal Fused Neural Network (GTFNN) that can make online predictions of origin–destination (OD) flows in a large-scale urban transit network. In order to solve the key issue of the passenger hysteresis in online flow forecasting, the proposed GTFNN takes finished OD flow and a series of features, which are known or observable, as the input and performs multi-step prediction. The model is constructed from capturing both spatial and temporal characteristics. For learning spatial characteristics, a multi-layer graph neural network is proposed based on hidden relationships in the rail transit network. Then, we embedded the graph convolution into a Gated Recurrent Unit to learn spatial–temporal features. For learning temporal characteristics, a sequence-to-sequence structure embedded with the attention mechanism is proposed to enhance its ability to capture both local and global dependencies. Experiments based on real-world data collected from Chongqing’s rail transit system show that the metrics of GTFNN are better than other methods, e.g., the SMAPE (Symmetric Mean Absolute Percentage Error) score is about 14.16%, with a range from 5% to 20% higher compared to other methods.
On Solving the Problem of Finding Kinetic Parameters of Catalytic Isomerization of the Pentane-Hexane Fraction Using a Parallel Global Search Algorithm
Authors: Barkalov Konstantin , Gubaydullin Irek , Kozinov Evgeny , Lebedev Ilya , Faskhutdinova Roza , Faskhutdinov Azamat , Enikeeva Leniza
Published: October 2022

This article is devoted to the problem of developing a kinetic model of a complex chemical reaction using a parallel optimization method. The design of the kinetic model consists of finding the kinetic parameters of the reaction, which cannot be calculated analytically, and since the chemical reaction involves many stages, the optimization problem is multiextremal. As a chemical reaction, the process of catalytic isomerization of the pentane-hexane fraction is considered, which is now important due to the switch of the oil refining industry to the production of gasoline corresponding to the Euro-5 standard. On the basis of known industrial data on the concentrations of reaction components and the temperature at the outlet of the third reactor, the activation energies and pre-exponential factors of each reaction stage were calculated. To solve the optimization problem, the authors developed a parallel global search algorithm and a program based on Lipschitz optimization. The kinetic parameters found made it possible to develop a mathematical model of the process, which is in good agreement with industrial data. The developed mathematical model in future works will make it possible to study the dynamics of the gas–liquid flow in the reactor unit, taking into account diffusion and heat exchange processes through the catalyst layer.
Identification of Nonlinear State-Space Systems via Sparse Bayesian and Stein Approximation Approach
Authors: Zhang Limin , Li Junpeng , Zhang Wenting , Yang Junzi
Published: October 2022

This paper is concerned with the parameter estimation of non-linear discrete-time systems from noisy state measurements in the state-space form. A novel sparse Bayesian convex optimisation algorithm is proposed for the parameter estimation and prediction. The method fully considers the approximation method, parameter prior and posterior, and adds Bayesian sparse learning and optimization for explicit modeling. Different from the previous identification methods, the main identification challenge resides in two aspects: first, a new objective function is obtained by our improved Stein approximation method in the convex optimization problem, so as to capture more information of particle approximation and convergence; second, another objective function is developed with L1-regularization, which is sparse method based on recursive least squares estimation. Compared with the previous study, the new objective function contains more information and can easily mine more important information from the raw data. Three simulation examples are given to demonstrate the proposed algorithm’s effectiveness. Furthermore, the performances of these approaches are analyzed, including parameter estimation of root mean squared error (RMSE), parameter sparsity and prediction of state and output result.
20 Years of Particle Swarm Optimization Strategies for the Vehicle Routing Problem: A Bibliometric Analysis
Authors: Reong Samuel , Wee Hui-Ming , Hsiao Yu-Lin
Published: October 2022

This study uses bibliometric analysis to examine the scientific evolution of particle swarm optimization (PSO) for the vehicle routing problem (VRP) over the past 20 years. Analyses were conducted to discover and characterize emerging trends in the research related to these topics and to examine the relationships between key publications. Through queries of the Web of Science and Scopus databases, the metadata for these particle swarm optimization (PSO) and vehicle routing problem (VRP) solution strategies were compared using bibliographic coupling and co-citation analysis using the Bibliometrix R software package, and secondly with VOSViewer. The bibliometric study’s purpose was to identify the most relevant thematic clusters and publications where PSO and VRP research intersect. The findings of this study can guide future VRP research and underscore the importance of developing effective PSO metaheuristics.
Clustering and Modelling of the Top 30 Cryptocurrency Prices Using Dynamic Time Warping and Machine Learning Methods
Authors: Šťastný Tomáš , Koudelka Jiří , Bílková Diana , Marek Luboš
Published: October 2022

Cryptocurrencies are a new field of investment opportunities that has experienced a significant growth in the last decade. The crypto market was capitalized at more than USD 3000 bn, having grown from USD 10 m over the period 2011–2021. Generating high returns, investments in cryptocurrencies have also shown high levels of price volatility. By comparing the performance of cryptocurrencies (measured by the crypto index) and standard equities (included in the S&P 500 index), we found that the former has outperformed the latter 14 times over the last two years. In the present paper, we analyzed the 2012–2022 global crypto market developments and main constituents. With a focus on the top 30 cryptocurrencies and their prices, as of 9 April 2022, covering data of the two major market stress events—outbreaks of the COVID-19 pandemic (February 2020) and the Russian invasion of Ukraine (February 2022). We applied the dynamic time warping method including barycentre averaging and k-Shape clustering of time series. The use of the dynamic time warping has been essential for the preparation of data for subsequent clustering and forecasting. In addition, we compared performance of cryptocurrencies and equities. Cryptocurrency time series are rather short, sometimes involving high levels of volatility and including multiple data gaps, whereas equity time series are much longer and well-established. Identifying similarities between them allows analysts to predict crypto prices by considering the evolution of similar equity instruments and their responses to historical events and stress periods. Moreover, we tested various forecasting methods on the 30 cryptocurrencies to compare traditional econometric methods with machine learning approaches.
A Fixed Point Approach to Lattice Fuzzy Set via F-Contraction
Authors: Kanwal Shazia , Azam Akbar , Gulzar Muhammad , Santos-García Gustavo
Published: October 2022

In this work, we design and demonstrate the occurrence of L-fuzzy common fixed points of L-fuzzy mappings (L-FM) meeting contractive criteria in the framework of complete b-metric spaces (b-MS) employing F-contractions and a certain class of continuous functions. We also conduct a case study to determine the implementation of our derived principles. A few other concepts which are the direct consequences of our findings are explained in this paper.
Fluid–Structure Interaction Modeling of Structural Loads and Fatigue Life Analysis of Tidal Stream Turbine
Authors: Zhang Yuquan , Liu Zhiqiang , Li Chengyi , Wang Xuemei , Zheng Yuan , Zhang Zhi , Fernandez-Rodriguez Emmanuel , Mahfoud Rabea Jamil
Published: October 2022

Developing reliable tidal-energy turbines of a large size and capacity links to preservation of the structural safety and stability of the blades. In this study, a bidirectional fluid–structure coupling method was applied to analyze the hydrodynamic performance and structural characteristics of the blade of a tidal-stream turbine. Analyses were conducted on the transient and stable structural stresses, fatigue, and deformations under the influence of water depth and turbine rotational speed. The performance predictions with and without fluid–structure coupling are similar to measurements. The water-depth change has little effect on the stress and deformation change of the blade, while the turbine-speed change has the most significant effect on it. When the turbine just starts, the blade will be subject to a sudden change load. This is due to the increase in turbine speed, resulting in the sudden load. Similar to the trend of blade stress, the blade safety factor is lower near the root of the blade, and the turbine-speed change has a more significant impact on the blade structure’s safety. However, the number of stress cycles in the blade at different rotational speeds is within the safety range.
Improved Hille-Type Oscillation Criteria for Second-Order Quasilinear Dynamic Equations
Authors: Hassan Taher S. , Cesarano Clemente , El-Nabulsi Rami Ahmad , Anukool Waranont
Published: October 2022

In this work, we develop enhanced Hille-type oscillation conditions for arbitrary-time, second-order quasilinear functional dynamic equations. These findings extend and improve previous research that has been published in the literature. Some examples are given to demonstrate the importance of the obtained results.
Modeling Air Pollution Using Partially Varying Coefficient Models with Heavy Tails
Authors: Jeldes Nicole , Ibacache-Pulgar Germán , Marchant Carolina , López-Gonzales Javier Linkolk
Published: October 2022

The increase in air pollution levels in recent decades around the world has caused a negative impact on human health. A recent investigation by the World Health Organization indicates that nine out of ten people on the planet breathe air containing high levels of pollutants and seven million people die each year from this cause. This problem is present in several cities in South America due to dangerous levels of particulate matter present in the air, particularly in the winter period, making it a public health problem. Santiago in Chile and Lima in Peru are among the ten cities with the highest levels of air pollution in South America. The location, climate, and anthropogenic conditions of these cities generate critical episodes of air pollution, especially in the coldest months. In this context, we developed a semiparametric model to predict particulate matter levels as a function of meteorological variables. For this, we discuss estimation and diagnostic procedures using a Student’s t-based partially varying coefficient model. Parameter estimation is performed through the penalized maximum likelihood method using smoothing splines. To obtain the parameter estimates, we present a weighted back-fitting algorithm implemented in R-project and Matlab software. In addition, we developed local influence techniques that allowed us to evaluate the potential influence of certain observations in the model using four different perturbation schemes. Finally, we applied the developed model to real data on air pollution and meteorological variables in Santiago and Lima.
A Weld Surface Defect Recognition Method Based on Improved MobileNetV2 Algorithm
Authors: Ding Kai , Niu Zhangqi , Hui Jizhuang , Zhou Xueliang , Chan Felix T. S.
Published: October 2022

Traditional welding quality inspection methods for pipelines and pressure vessels are time-consuming, labor-intensive, and suffer from false and missed inspection problems. With the development of smart manufacturing, there is a need for fast and accurate in-situ inspection of welding quality. Therefore, detection models with higher accuracy and lower computational complexity are required for technical support. Based on that, an in-situ weld surface defect recognition method is proposed in this paper based on an improved lightweight MobileNetV2 algorithm. It builds a defect classification model with MobileNetV2 as the backbone of the network, embeds a Convolutional Block Attention Module (CBAM) to refine the image feature information, and reduces the network width factor to cut down the number of model parameters and computational complexity. The experimental results show that the proposed weld surface defect recognition method has advantages in both recognition accuracy and computational efficiency. In summary, the method in this paper overcomes the limitations of traditional methods and achieves the goal of reducing labor intensity, saving time, and improving accuracy. It meets the actual needs of in-situ weld surface defect recognition for pipelines, pressure vessels, and other industrial complex products.
Structured Compression of Convolutional Neural Networks for Specialized Tasks
Authors: Gabbay Freddy , Salomon Benjamin , Shomron Gil
Published: October 2022

Convolutional neural networks (CNNs) offer significant advantages when used in various image classification tasks and computer vision applications. CNNs are increasingly deployed in environments from edge and Internet of Things (IoT) devices to high-end computational infrastructures, such as supercomputers, cloud computing, and data centers. The growing amount of data and the growth in their model size and computational complexity, however, introduce major computational challenges. Such challenges present entry barriers for IoT and edge devices as well as increase the operational expenses of large-scale computing systems. Thus, it has become essential to optimize CNN algorithms. In this paper, we introduce the S-VELCRO compression algorithm, which exploits value locality to trim filters in CNN models utilized for specialized tasks. S-VELCRO uses structured compression, which can save costs and reduce overhead compared with unstructured compression. The algorithm runs in two steps: a preprocessing step identifies the filters with a high degree of value locality, and a compression step trims the selected filters. As a result, S-VELCRO reduces the computational load of the channel activation function and avoids the convolution computation of the corresponding trimmed filters. Compared with typical CNN compression algorithms that run heavy back-propagation training computations, S-VELCRO has significantly fewer computational requirements. Our experimental analysis shows that S-VELCRO achieves a compression-saving ratio between 6% and 30%, with no degradation in accuracy for ResNet-18, MobileNet-V2, and GoogLeNet when used for specialized tasks.
A New Approach in Heat Transfer Analysis: Reduced-Scale Straight Bars with Massive and Square-Tubular Cross-Sections
Authors: Turzó Gábor , Száva Ildikó-Renáta , Dancsó Sándor , Száva Ioan , Vlase Sorin , Munteanu Violeta , Gălățanu Teofil , Asztalos Zsolt
Published: October 2022

This paper reports experimental and theoretical result derived from research on steel structural elements’ fire-protection with intumescent paint. The experimental results were obtained by means of an original testing bench, briefly described below and some basic cases, i.e., horizontally and vertically disposed, massive and square-tubular cross-sectioned, reduced-scale straight bars heated at one end. By means of the thermocouples mounted along the bars, the temperature distribution laws were monitored, depending on the heated end’s nominal temperature. The paper describes an original approach to the temperature distribution evaluation by means of some new parameters, based on the temperature distribution laws experimentally obtained with reduced-scale models. We involved the least-square method (LSM) and the curve-fitting one in order to obtain a more accurate temperature distribution law compared to the experimentally obtained ones. We also introduced some new parameters in order to define the amount of heat loss in a more accurate way. Based on the results obtained, the authors suggest that this approach to the temperature distribution law can be efficiently applied in further thermal analyses, for both 2D and 3D structures. The paper also includes a thorough analysis of “m” variation along the square-tubular-cross-section, reduced-scale straight bars, and similar new approaches are proposed by the authors. The sub-goals of this investigation were (1) to obtain useful correlations between the magnitudes of the massivity ζ=P/A and the parameter “m” along the bar, and (2) to analyze, on reduced-scale models, the heat distribution laws on unprotected and intumescent-paint-protected 2D and 3D steel structures.
Steady-State Solutions for Two Mixed Initial-Boundary Value Problems Which Describe Isothermal Motions of Burgers’ Fluids: Application
Authors: Fetecau Constantin , Ahammad N. Ameer , Vieru Dumitru , Shah Nehad Ali
Published: October 2022

Steady-state solutions of two mixed initial-boundary value problems are presented in equivalent forms. They describe isothermal permanent motions of incompressible Burgers’ fluids over an infinite flat plate that applies time-dependent shear stresses to the fluid. More exactly, they are the first exact solutions for motions of Burgers’ fluids with differential expressions of the shear stress or velocity on the boundary. The obtained results are designed to make equivalent solutions for motions caused by an infinite plate moving in its plane at velocities that seem to be similar to previous shear stresses. It is simple to limit all results for the purpose of providing efficient results for incompressible Oldroyd-B, Maxwell, second grade and Newtonian fluids undergoing comparable motions. They may also be used to estimate how long it will take to get to a steady or permanent state.
Fixed-Time Output-Constrained Synchronization of Unknown Chaotic Financial Systems Using Neural Learning
Authors: Yao Qijia , Jahanshahi Hadi , Batrancea Larissa M. , Alotaibi Naif D. , Rus Mircea-Iosif
Published: October 2022

This article addresses the challenging problem of fixed-time output-constrained synchronization for master–slave chaotic financial systems with unknown parameters and perturbations. A fixed-time neural adaptive control approach is originally proposed with the aid of the barrier Lyapunov function (BLF) and neural network (NN) identification. The BLF is introduced to preserve the synchronization errors always within the predefined output constraints. The NN is adopted to identify the compound unknown item in the synchronization error system. Unlike the conventional NN identification, the concept of indirect NN identification is employed, and only a single adaptive learning parameter is required to be adjusted online. According to the stability argument, the proposed controller can ensure that all error variables in the closed-loop system regulate to the minor residual sets around zero in fixed time. Finally, simulations and comparisons are conducted to verify the efficiency and benefits of the proposed control strategy. It can be concluded from the simulation results that the proposed fixed-time neural adaptive controller is capable of achieving better synchronization performance than the compared linear feedback controller.
Negativity of Green’s Functions to Focal and Two-Point Boundary Value Problems for Equations of Second Order with Delay and Impulses in Their Derivatives
Authors: Domoshnitsky Alexander , Malev Sergey , Raichik Vladimir
Published: October 2022

We consider the second-order impulsive differential equation with impulses in derivative and without the damping term. Sufficient conditions that a nontrivial solution of the homogeneous equation having a zero of its derivative does not have a zero itself are obtained. On the basis of the obtained results on differential inequalities, which can be considered as analogues of the Vallee–Poussin theorems, new sufficient conditions on the negativity of Green’s functions are obtained.
Member Tampering Attack on Burmester-Desmedt Group Key Exchange Protocol and Its Countermeasure
Authors: Sun Da-Zhi , Tian Yangguang
Published: October 2022

With the rapid development of cloud computing and mobile networks, more and more application scenarios require a secret group key for secure communication. Group Key Exchange (GKE) protocol provides a secret group key for three or more members. Burmester and Desmedt presented an influential GKE protocol, which has a broadcast version and a cyclic version. In this paper, we investigate the security weaknesses of the Burmester-Desmedt protocol. We report that both the broadcast version and the cyclic version of the Burmester-Desmedt protocol suffer member tampering attacks if the two members that belong to both group A and group B are corrupted. That is, two corrupted members can add some unknowing members of group A to group B and trick the legal members of group B to believe that these unknowing members share the secret group key with them after a protocol run. Furthermore, to defeat the member tampering attack, we propose digital signature-based improvements on the broadcast version and the cyclic version of the Burmester-Desmedt protocol. We hope our research results will encourage the development of more robust and effective GKE protocols that stand rigorous security analysis.
Development of a Model for Spoofing Attacks in Internet of Things
Authors: Khan Faheem , Al-Atawi Abdullah A. , Alomari Abdullah , Alsirhani Amjad , Alshahrani Mohammed Mujib , Khan Jawad , Lee Youngmoon
Published: October 2022

Internet of Things (IoT) allows the integration of the physical world with network devices for proper privacy and security in a healthcare system. IoT in a healthcare system is vulnerable to spoofing attacks that can easily represent themselves as a legal entity of the network. It is a passive attack and can access the Medium Access Control address of some valid users in the network to continue malicious activities. In this paper, an algorithm is proposed for detecting spoofing attacks in IoT using Received Signal Strength (RSS) and Number of Connected Neighbors (NCN). Firstly, the spoofing attack is detected, located and eliminated through Received Signal Strength (RSS) in an inter-cluster network. However, the RSS is not useful against intra-cluster spoofing attacks and therefore the NCN is introduced to detect, identify and eliminate the intra-cluster spoofing attack. The proposed model is implemented in Network Simulator 2 (NS-2) to compare the performance of the proposed algorithm in the presence and absence of spoofing attacks. The result is that the proposed model increases the detection and prevention of spoofing.
Dual Attention Multiscale Network for Vessel Segmentation in Fundus Photography
Authors: Yin Pengshuai , Fang Yupeng , Wan Qilin
Published: October 2022

Automatic vessel structure segmentation is essential for an automatic disease diagnosis system. The task is challenging due to vessels’ different shapes and sizes across populations. This paper proposes a multiscale network with dual attention to segment various retinal blood vessels. The network injects a spatial attention module and channel attention module on a feature map, whose size is one-eighth of the input size. The network also uses multiscale input to receive multi-level information, and the network uses the multiscale output to gain more supervision. The proposed method is tested on two publicly available datasets: DRIVE and CHASEDB1. The accuracy, AUC, sensitivity, and specificity on the DRIVE dataset are 0.9615, 0.9866, 0.7709, and 0.9847, respectively. On the CHASEDB1 dataset, the metrics are 0.9800, 0.9892, 0.8215, and 0.9877, respectively. The ablative study further shows effectiveness for each part of the network. Multiscale and dual attention mechanism both improve performance. The proposed architecture is simple and effective. The inference time is 12 ms on a GPU and has potential for real-world applications. The code will be made publicly available.
Classification of Complete Degenerate Stationary Surfaces in R3,1 Whose Gauss Maps Are Injective
Authors: Ou Li , Yang Ling
Published: October 2022

In this paper, we classify the complete degenerate stationary surfaces in R3,1 with injective Gauss maps.
A Boundary Shape Function Method for Computing Eigenvalues and Eigenfunctions of Sturm–Liouville Problems
Authors: Liu Chein-Shan , Chang Jiang-Ren , Shen Jian-Hung , Chen Yung-Wei
Published: October 2022

In the paper, we transform the general Sturm–Liouville problem (SLP) into two canonical forms: one with the homogeneous Dirichlet boundary conditions and another with the homogeneous Neumann boundary conditions. A boundary shape function method (BSFM) was constructed to solve the SLPs of these two canonical forms. Owing to the property of the boundary shape function, we could transform the SLPs into an initial value problem for the new variable with initial values that were given definitely. Meanwhile, the terminal value at the right boundary could be entirely determined by using a given normalization condition for the uniqueness of the eigenfunction. In such a manner, we could directly determine the eigenvalues as the intersection points of an eigenvalue curve to the zero line, which was a horizontal line in the plane consisting of the zero values of the target function with respect to the eigen-parameter. We employed a more delicate tuning technique or the fictitious time integration method to solve an implicit algebraic equation for the eigenvalue curve. We could integrate the Sturm–Liouville equation using the given initial values to obtain the associated eigenfunction when the eigenvalue was obtained. Eight numerical examples revealed a great advantage of the BSFM, which easily obtained eigenvalues and eigenfunctions with the desired accuracy.
Signal Identification of Wire Breaking in Bridge Cables Based on Machine Learning
Authors: Li Guangming , Ding Heming , Li Yaohan , Li Chun-Yin , Lee Chi-Chung
Published: October 2022

With the booming development of bridge construction, bridge operation and maintenance have always been major issues to ensure the safety of the community. Affected by the long-term service of bridges and natural factors, the safety and durability of cables can be threatened. Cables are critical stress-bearing elements of large bridges such as cable-stayed bridges. Realizing the health monitoring of bridge cables is the key to ensuring the normal operation of bridges. Acoustic emission (AE) is a dynamic nondestructive testing method that is increasingly used in the local monitoring of bridge cables. In this paper, a testbed is described for generating the acoustic emission signals for signal identification testing with machine learning (ML) models. Owing to the limited number of measured signals being available, an algorithm is proposed to simulate acoustic emission signals for model training. A multi-angle feature extraction method is proposed to extract the acoustic emission signals and construct a comprehensive feature vector to characterize the acoustic emission signals. Seven ML models are trained with the simulated acoustic emission signals. Long short-term memory (LSTM) has been specially applied for deep learning demonstration which requires a large amount of training data. As all machine learning models (including LSTM) provide desired performance, it shows that the proposed approach of simulating acoustic emission signals can be effective.
Density Peak Clustering Based on Relative Density under Progressive Allocation Strategy
Authors: Liu Yongli , Zhao Congcong , Chao Hao
Published: October 2022

In traditional density peak clustering, when the density distribution of samples in a dataset is uneven, the density peak points are often concentrated in the region with dense sample distribution, which is easy to affect clustering accuracy. Under the progressive allocation strategy, a density peak clustering algorithm based on relative density is proposed in this paper. This algorithm uses the K-nearest neighbor method to calculate the local density of sample points. In addition, in order to avoid the domino effect during sample allocation, a new similarity calculation method is defined, and a progressive allocation strategy from near to far is used for the allocation of the remaining points. In order to evaluate the effectiveness of this algorithm, comparative experiments with five algorithms were carried out on classical artificial datasets and real datasets. Experimental results show that the proposed algorithm can achieve higher clustering accuracy on datasets with uneven density distribution.
Investigation of the Genotoxic Potential of the Marine Toxin C17-SAMT Using the In Vivo Comet and Micronucleus Assays
Authors: Marzougui Zeineb , Huet Sylvie , Blier Anne-Louise , Hégarat Ludovic Le , Tounsi-Kettiti Haïfa , Kharrat Riadh , Marrouchi Riadh , Fessard Valérie
Published: October 2022

The contaminant responsible for the atypical toxicity reported in mussels from Bizerte Lagoon (Northern Tunisia) during the last decade has been characterized as C17-sphinganine analog mycotoxin (C17-SAMT). This neurotoxin showed common mouse toxic symptoms, including flaccid paralysis and severe dyspnea, followed by rapid death. For hazard assessment on human health, in this work we aimed to evaluate the in vivo genotoxic effects of this marine biotoxin using the classical alkaline and modified Fpg comet assays performed to detect DNA breaks and alkali-labile sites as well as oxidized bases. The micronucleus assay was used on bone marrow to detect chromosome and genome damage. C17-SAMT induces a statistically insignificant increase in DNA tail intensity at all doses in the duodenum, and in the spleen contrary to the liver, the percentage of tail DNA increased significantly at the mid dose of 300 µg/kg b.w/d. C17-SAMT did not affect the number of micronuclei in the bone marrow. Microscopic observations of the liver showed an increase in the number of mitosis and hepatocytes’ cytoplasm clarification. At this level of study, we confirm that C17-SAMT induced DNA damage in the liver but there was no evidence of effects causing DNA oxidation or chromosome and genome damage.
Seven New Alkaloids Isolated from Marine Flavobacterium Tenacibaculum discolor sv11
Authors: Wang Lei , Marner Michael , Mettal Ute , Liu Yang , Schäberle Till F.
Published: October 2022

Marine flavobacterium Tenacibaculum discolor sv11 has been proven to be a promising producer of bioactive nitrogen-containing heterocycles. A chemical investigation of T. discolor sv11 revealed seven new heterocycles, including the six new imidazolium-containing alkaloids discolins C-H (1–6) and one pyridinium-containing alkaloid dispyridine A (7). The molecular structure of each compound was elucidated by analysis of NMR and HR-ESI-MS data. Furthermore, enzymatic decarboxylation of tryptophan and tyrosine to tryptamine and tyramine catalyzed by the decarboxylase DisA was investigated using in vivo and in vitro experiments. The antimicrobial activity of the isolated compounds (1–7) was evaluated. Discolin C and E (1 and 3) exhibited moderate activity against Gram-positive Bacillus subtilis DSM10, Mycobacterium smegmatis ATCC607, Listeria monocytogenes DSM20600 and Staphylococcus aureus ATCC25923, with MIC values ranging from 4 μg/mL to 32 μg/mL.
Marine Compounds, Mitochondria, and Malignancy: A Therapeutic Nexus
Authors: Fakhri Sajad , Abdian Sadaf , Moradi Seyed Zachariah , Delgadillo Blake E. , Fimognari Carmela , Bishayee Anupam
Published: October 2022

The marine environment is important yet generally underexplored. It contains new sources of functional constituents that can affect various pathways in food processing, storage, and fortification. Bioactive secondary metabolites produced by marine microorganisms may have significant potential applications for humans. Various components isolated from disparate marine microorganisms, including fungi, microalgae, bacteria, and myxomycetes, showed considerable biological effects, such as anticancer, antioxidant, antiviral, antibacterial, and neuroprotective activities. Growing studies are revealing that potential anticancer effects of marine agents could be achieved through the modulation of several organelles. Mitochondria are known organelles that influence growth, differentiation, and death of cells via influencing the biosynthetic, bioenergetic, and various signaling pathways related to oxidative stress and cellular metabolism. Consequently, mitochondria play an essential role in tumorigenesis and cancer treatments by adapting to alterations in environmental and cellular conditions. The growing interest in marine-derived anticancer agents, combined with the development and progression of novel technology in the extraction and cultures of marine life, led to revelations of new compounds with meaningful pharmacological applications. This is the first critical review on marine-derived anticancer agents that have the potential for targeting mitochondrial function during tumorigenesis. This study aims to provide promising strategies in cancer prevention and treatment.
Bioactive Peptides from Skipjack Tuna Cardiac Arterial Bulbs: Preparation, Identification, Antioxidant Activity, and Stability against Thermal, pH, and Simulated Gastrointestinal Digestion Treatments
Authors: Cai Wei-Wei , Hu Xiao-Meng , Wang Yu-Mei , Chi Chang-Feng , Wang Bin
Published: October 2022

Cardiac arterial bulbs of Skipjack tuna (Katsuwonus pelamis) are rich in elastin, and its hydrolysates are high quality raw materials for daily cosmetics. In order to effectively utilizing Skipjack tuna processing byproducts-cardiac arterial bulbs and to prepare peptides with high antioxidant activity, pepsin was selected from six proteases for hydrolyzing proteins, and the best hydrolysis conditions of pepsin were optimized. Using ultrafiltration and chromatographic methods, eleven antioxidant peptides were purified from protein hydrolysate of tuna cardiac arterial bulbs. Four tripeptides (QGD, PKK, GPQ and GLN) were identified as well as seven pentapeptides (GEQSN, GEEGD, YEGGD, GEGER, GEGQR, GPGLM and GDRGD). Three out of them, namely the tripeptide PKK and the pentapeptides YEGGD and GPGLM exhibited the highest radical scavenging activities on 2,2-diphenyl-1-picrylhydrazyl (DPPH), hydroxyl, 2,2′-azino-bis-3-ethylbenzothiazoline-6-sulfonic acid (ABTS) and superoxide anion assays. They also showed to protect plasmid DNA and HepG2 cells against H2O2-induced oxidative stress. Furthermore, they exhibited high stability under temperature ranged from 20-100 °C, pH values ranged from 3-11, and they simulated gastrointestinal digestion for 240 min. These results suggest that the prepared eleven antioxidant peptides from cardiac arterial bulbs, especially the three peptides PKK, YEGGD, and GPGLM, could serve as promising candidates in health-promoting products due to their high antioxidant activity and their stability.
Therapeutic Potential of Microalgae-Derived Bioactive Metabolites Is Influenced by Different Large-Scale Culture Strategies
Authors: Garcia-Parra Jezabel , Fuentes-Grünewald Claudio , Gonzalez Deyarina
Published: October 2022

Microalgae have been identified as one of the most promising sources of novel bioactive compounds for biomedical applications, the food industry, and cosmetics. In the last decade, several biotechnological developments have facilitated the identification of a growing number of compounds as well as the study of optimal microalgae culture conditions for the production of biomass enriched in specific molecules of interest. In this study, two common commercial marine microalgae (Nannochloropsis oculata and Porphyridium purpureum) were cultured in standard and nutrient-stressed conditions and the obtained biomass extracts were assessed for their potential to inhibit cancer cell proliferation and migration as well as their antioxidant activity. Results from viability in 2D and 3D cancer cell models showed an enhancement of the antitumour activity of P. purpureum in the 3D model compared to 2D, together with a greater capacity to reduce the migration capacity of cancer cells with the biomass from nutrient-stressed conditions, whereas the antioxidant activity of N. oculata decreased when exposed to nutrient-stressed conditions. To date, this is one of the few studies that proves that controlled changes in large-scale culturing conditions such as nutrient depletion have a relevant impact in the bioactivity of the biomass on cancer cells.