Search

This paper introduces the Gilpin-Ayala equation, gives the explicit expression of the solution, and analyzes the characteristics of the solution. Based on these results, a stock growth model is constructed to study the rotation period of forests. From the viewpoint of carbon sequestration benefits, the paper explicitly expresses the optimal solution of a single rotation period of forests and studies the mathematical law of the solution. By using the monotonicity of the optimal solution, this paper proves that carbon sink unit price and rotation period modulate in the same way. In other words, if carbon sink unit price is high, then rotation period is large. This means that when carbon sink price increases, cutting day will be delayed; when carbon sink price decreases, cutting day will move forward. The impacts of timber unit price and interest rate on the rotation period are as follows: when timber price rises, cutting day will move forward; when timber price falls, cutting day will be delayed; when interest rate rises, cutting day will move forward; when interest rate falls, cutting day will be delayed. In addition, with the help of data instances, this paper demonstrates that for both singleand multi-rotation, carbon sequestration benefits postpone the cutting time. Moreover, the multi-rotation cutting occurs earlier than the single rotation cutting. Therefore, harvesting at the optimal rotation period with carbon sequestration benefits is conducive for the coordinated development of social and ecological benefits. [ABSTRACT FROM AUTHOR]

In decision making and path selection in emergency logistics, the bounded rationality of decision makers and subsequent secondary disasters have a great impact on the final plan. However, some investigation factors are difficult to express in definite quantities. In this paper, we consider the occurrence of secondary disasters and the bounded rationality of decision makers, combined with triangular fuzzy theory and prospect theory. We construct a prospect decision-making model for an emergency logistics path in a fuzzy environment and verify the rationality of the model through numerical example analysis. The research results indicate that the model adopted in this paper can obtain the priority selection order of the path according to the factors investigated and provide a reference for decision makers to make emergency logistics path selection decisions. [ABSTRACT FROM AUTHOR]

The purpose of studying inventory models is to compensate for the uncertainty in the timing and quantity of demand and supply, which is even more significant in the post-epidemic era. To solve inventory models by algebraic methods, a recent paper provided a new approach to studying the problem that had been examined by five published papers. This paper also demonstrated that his new approach can solve another transit bus model, and yet, he raised an open question. The purpose of this paper is to offer a formative answer accordingly. Moreover, we solve an open question proposed by another paper to prove identity with two cubic roots without referring to its original cubic polynomial. Meanwhile, we discuss another problem in a related paper to show that comparisons among several approximated inventory models by their minimum values are not proper that should be executed on the original model. At last, we examine a recently published paper to point out questionable results, and then offer revisions. This paper will help researchers solve their inventory models with proper solution approaches. [ABSTRACT FROM AUTHOR]

2E-MDCVRP is a multi-depot, capacity, twoechelon vehicle routing issue. Satellites make it possible to collect and sort orders. In this paper, a 2E-MDCVRP model will be designed to be applied to e-commerce. In the completion stage, the researcher developed a 2E-MDCVRP solution with two stages of completion. The first stage is finding the best route for the satellite to travel to the customer. In the first stage of work, satellites are plotted as centroids in clusters using the k-Means algorithm to obtain k-set clusters. To determine route scheduling using the RNN algorithm in determining travel routes in the second echelon. The second stage uses the trial-anderror method to determine the first echelon's routing schedule. This research aims to minimize the operational application, fuel consumption, vehicle maintenance, vehicle oil change, and handling costs. Moreover, at the end of the paper, the researcher simulate the model that has been built and test the model's effectiveness by comparing it with the classic MDCVRP model, which is similar to the Same-Day Delivery model. The test results show that the 2E-MDCVRP model can make better total distance and cost improvements than the classic MDCVRP model carried out by Same-day Delivery. [ABSTRACT FROM AUTHOR]

It is well known that the effects of the protection zone on the dynamical behavior are significantly different from the non-protection zone. In this paper, we presents a semi-linear ellipic system, which describes the predator-prey relationship with Dinosaur functional response under homogeneous Neumann boundary conditions. This paper concerns the existence of non-constant positive steady-state solutions, in the case (1): Nondegeneracy of β(x) with β(x) > 0 on Ω or case (2): Degeneracy of β(x) with β(x) = 0 in Ω0, β(x) > 0 in Ω \ Ω0. These results provide theoretical evidence for the complex spatiotemporal dynamics. [ABSTRACT FROM AUTHOR]

Sentiment analysis is one of the most important tasks in natural language processing. The goal of sentiment analysis is to classify the text-based sentiment tendencies by extracting text features. The quality of the classification can be assessed by the accuracy, precision and recall of the classification. So it is very important to design efficient tools or models to carry out the classification process. In this paper, we build a novel model, integrating the dealing method of Bidirectional Encoder Representation from Transformers (BERT), Bidirectional Long Short - Term Memory (Bi-LSTM) and the Multi-Head Attention (MHA), building an MHA based, integrating BERT and Bi-LSTM, Sentiment Analysis Model (MHA-BB-SAM). The model concludes two layers, the embedding layer and the Bi-LSTM layer. In the embedding layer, BERT is used to pre-process the text and train the required word vectors. In the Bi-LSTM layer, Bi-LSTM network is utilized to catch the order information of the context. Multi-Head Attention (MHA) is introduced to enhance the ability to capture the long-distance text features. The novel model is tested on two corpuses, SemEval-2020 corpus and College Learners' Spoken English Corpus (COLSEC). The computing results prove the high performance of the model. The approach presented in this paper can provide a reference for the sentiment analysis work. [ABSTRACT FROM AUTHOR]

In this paper, problems involving steady heat conduction in two-layered materials are considered. The problems are governed by diffusion equations. These equations may be solved analytically for a limited number of problems. Hence, a numerical method is needed to solve these problems. In this paper, a numerical method known as the Dual Reciprocity Method (DRM) is employed to solve the problems numerically. To test the accuracy of the numerical method, three problems with analytical solutions are solved numerically using the DRM. Comparisons between numerical and corresponding analytical solutions are presented and discussed. Moreover, problems with unknown analytical solutions are solved numerically by employing the method. Some numerical results are presented to verify the effect of material diversity to the heat conduction. [ABSTRACT FROM AUTHOR]

Considering the multibody method for the dynamic analysis of mechanical systems, this paper aims at constructing a simple computer model describing the dynamics of a fixed-wing aircraft performing a longitudinal motion. To this end, a simplified model of an aerial vehicle was analyzed without control surfaces, featuring an axial thrust, and having limited aerodynamic actions. By using the Digital DATCOM software, the modeling of the aerodynamic coefficients was then taken into account considering also the elevator as a control surface. First, aircraft dynamics was studied in general in the context of multibody dynamics. Then, the case study considered as the illustrative example of the paper was analyzed, namely the Cessna 172 Skyhawk aircraft. By modeling the externally applied actions and the aerodynamic coefficients, the fundamental mechanics behind the take-off phase of the flight was subsequently analyzed. In the paper, the equations of motion describing the dynamic behavior of the illustrative example were driven using a Lagrangian formulation approach. The dynamic model of the demonstrative example was then implemented in a computer code constructed in the MATLAB environment. By doing so, the goal of this process was to develop as accurately as possible a virtual model of the Cessna 172 Skyhawk aircraft. As shown in the paper using numerical simulations, the computer model of the case study analyzed in this work is able of simulating the vertical motion of the aircraft. In the conclusive part of the manuscript, a discussion about the dynamic behavior of the aircraft found in this investigation and some comments on several topics for future research are reported as well. [ABSTRACT FROM AUTHOR]

Great changes have taken place in the speed and mode of information dissemination in the digital age. In this paper, combined Word of mouth (WOM) and Internet word of mouth (IWOM), we further propose a stochastic SI1I2AD transmission model. WOM and IWOM are two channel to influence the consumers' decisions. We investigate the existence and uniqueness of the solution to the stochastic differential equations model. Then the basic reproduction numbers are obtained, and the dynamical behaviors of the individuals are investigated. In particular, we obtain the sufficient conditions of two types of information dissemination exist alone and together, respectively. Theoretical results show that greater environmental uncertainty poses a risk to the dissemination of product information, resulting in the disappearance of the disseminating population, and that lower noise intensity favours the continued presence of the product in the market. Moreover, only when, the rate at which people from WOM to IWOM, remains low, the two modes of information diffusion could coexist. [ABSTRACT FROM AUTHOR]

The efficiency of multi-objective evolutionary algorithms (MOEAs) in tackling issues with multiple objectives is examined. However, it is noted that current MOEA-based feature selection techniques often converge towards the center of the Pareto front due to inadequate selection forces. The study proposes the utilization of a novel approach known as MOEA/D, which partitions complex multi-objective problems into smaller, more feasible single-objective sub-problems. Each sub-problem may then be addressed using an equal amount of computational resources. The predetermined size of the neighborhood used by MOEA/D may lead to a delay in the algorithm's merging and reduce the effectiveness of the failure. The paper proposes the Adaptive Neighbourhood Adjustment Strategy (ANAS) as a novel approach to improve the efficiency of multi-objective optimisation algorithms in order to tackle this issue. The ANAS algorithm allows for adaptive adjustment of the subproblem neighborhood size, hence enhancing the trade-off between merging and variety. In the following section of the study, a novel feature selection technique called MOGHHNS3/DANA is introduced. This technique utilizes ANAS to expand the potential solutions for a particular subproblem. The approach evaluates the chosen features using the Regulated Extreme Learning Machine (RELM) classifier on sixteen benchmark datasets. The experimental results demonstrate that MOGHHNS3/D-ANA outperforms four commonly employed multi-objective techniques in terms of accuracy, precision, recall, F1 score, coverage, hamming loss, ranking loss, and training time, error. The APBI approach in decomposition-based multiobjective optimization focuses on handling constraints by adjusting penalty parameters to guide the search towards feasible solutions. On the other hand, the ANA approach focuses on dynamically adjusting the neighborhood size or search direction based on the proximity of solutions in the detached space to adapt the search process. The proposed approach achieves convergence by minimizing redundancy, preserving diversity in the decision space, and simultaneously enhancing classification accuracy. [ABSTRACT FROM AUTHOR]

Simpson inequalities for first-order differentiable convex functions and various fractional integrals have been studied extensively. However, Simpson type inequalities for twice-differentiable functions are researched slightly. Therefore, in the present paper, we endeavor to study fractional inequalities of Simpson type for twice-differentiable convex functions. To achieve this goal, we establish a new twicedifferentiable Simpson's identity by using tempered fractional integral operators. Based upon it, we prove several fractional Simpson type inequalities whose second derivatives in absolute value are convex. Finally, we give some examples to illustrate the correctness of the obtained results. [ABSTRACT FROM AUTHOR]

Person Re-Identification falls within the scope of computer vision, acting a technique to ascertain the presence of a specified pedestrian within a video or image library. The related research is of great significance in real-world environments such as criminal investigation and statistical analysis of commercial foot traffic and has received extensive attention from the academic community. However, traditional methods such as manual extraction cannot adapt to largescale data volumes, and deep learning-based methods at this stage suffer from interference in complex environments such as similar costumes, perspective changes, and occlusion. Therefore, in this paper, we investigate the above problems. Firstly, we expand the dataset by introducing random erasure-based preprocessing of pedestrian images to enhancing the robustness and generalization capability of neural networks. Secondly, a composite attention mechanism is introduced after the network residual layer to enhance the spatial information capability and feature expression. Finally, the union loss composed of Circle Loss, Ternary Loss, and Cross Entropy Loss was chosen for network training in the loss optimization phase. Findings from the experiments reveal that the improved method proposed in this experiment achieves 96.0%Rank-1 and 88.3%mAP in Market1501, which reflects the validity of the approach proposed in this manuscript, and provides valuable reference suggestions for Person Re-Identification related research. [ABSTRACT FROM AUTHOR]

This paper explores fundamental aspects of irresolute maps and investigates separation axioms, specifically semi-T2 and semi normal properties. The study introduces Irr-topological transformation groups, Irr*-topological transformation groups, I*-topological transformation groups and I-topological transformation groups, providing an in-depth analysis of their interrelations. Through detailed examination, the topological properties of these groups are elucidated, complemented by relevant examples and counterexamples. This comprehensive exploration aims to enhance our understanding of irresolute maps, separation axioms, and the intricate relationships within the introduced transformation groups. [ABSTRACT FROM AUTHOR]

In this paper, we advocate a new technique to determine inner bounds for the extreme eigenvalues of real symmetric matrices. Our method involves the matrix elements and compares favourably with existing methods. We also show how the bounds can be optimized. [ABSTRACT FROM AUTHOR]

In this paper, the temperature rise in the longitudinal direction, the heat release rate (HRR), the endless area temperature under the ceiling of the utility tunnel, and the root of HRR (oil and fuel type), cable type, number of cable layers and and cables per layer. The effects of airtightness, natural ventilation, and longitudinal ventilation on the fire characteristics of cable compartments were compared. The results showed that the effects of enclosed cable tunnels and natural ventilation on fire behavior near walls in cable tunnels were compared. Compared with the natural ventilation state, when placing RVVR type cables, the fire duration of the enclosed cable compartment was shorter, the ceiling temperature rise was greater. And the time that was necessary to increase the ceiling temperature to its maximum value was shorter. The HRR inside the enclosed cable compartment was very high and could almost reach its peak quickly. The contribution of this article is to reduce the social and economic costs of utility tunnels by preventing tunnel fires. [ABSTRACT FROM AUTHOR]

From the perspective of the innovation value chain and guided by the need to achieve sustained breakthroughs in major science and technology in China, this paper establishes four types of Stackelberg game models dominated by the government or the market and investigates the impact of knowledge sharing of innovative enterprises on the efficiency of supplying key core technologies and the impact of decision-making of each innovative entity on the supplying model. The results obtained show that the sharing of private knowledge by innovative enterprises can increase the benefits of the innovation value chain in the new national system model, while knowledge sharing can impair the benefits of innovative enterprises and increase the benefits of the innovation value chain in the industrial technology alliance model in the three market-dominated models. Meanwhile, innovative enterprises are not willing to share knowledge, and a knowledge compensation mechanism can increase the knowledge sharing willingness of innovative enterprises in scenarios with double-low accuracy in the potential value prediction of technological knowledge and the price of technology introduction. Additionally, the choice of knowledge sharing strategy and academic research institution supply model strategy of innovative enterprises depends on the accuracy of potential value prediction of technological knowledge, the price of technology introduction, and the cost saving of re-research institutions. [ABSTRACT FROM AUTHOR]

In order to support the learning of novice students in Java programming, the web-based Java Programming Learning Assistant System (JPLAS) has been developed. JPLAS offers several types of exercise problems to foster code reading and writing skills at different levels. In JPLAS, the code writing problem (CWP) asks a student to write a source code that will pass the test code given in the assignment where the correctness is verified by running them on JUnit. In this paper, to reduce the teacher's workload during the marking process, we present the answer code validation program that verifies all the source codes from a large number of students for each assignment and reports the number of passing tests for each source code in the CSV file. Besides, to test a source code with various input data, we implement the test data generation algorithm that identifies the data type, generates new data, and replaces it for each test data in the test code. Furthermore, to verify the correctness of the implemented procedures in the source code, we introduce the intermediate state testing in the test code. For evaluations, we applied the proposal to source codes and test codes in a Java programming course in Okayama university, Japan, and confirmed the validity and effectiveness. [ABSTRACT FROM AUTHOR]

On page 188 of his lost notebook, Ramanujan introduced distinct classes of exquisite infinite series, representing them in terms of Eisenstein series. The objective of this paper is to develop various differential identities related to Borwein's cubic theta functions and h-functions. Also, we present certain identities containing Eisenstein series of level 6 and h-functions that are used to establish relations involving class one infinite series and h-functions. Additionally, we present a straightforward approach to evaluate a discrete convolution sum by employing relationships involving Eisenstein series and h-functions. [ABSTRACT FROM AUTHOR]

Practical stability can describe qualitative behavior and quantitative properties of systems in comparison with traditional Lyapunov stability theory. In this paper, such stability problem is formulated for nonlinear delayed urban traffic systems described by uncertain differential equations which are a type of differential equations driven by Liu processes. First of all, we prove the existence and uniqueness of the solution. Then, we analyze the pth moment practically exponential stability and quasi surely globally practically uniformly exponential stability of the system by employing general It'o formula, Gronwall's inequality, Holder inequality and Borel-Cantelli lemma. Moreover, an example is presented to verify the validity of our theoretical methods. [ABSTRACT FROM AUTHOR]

This paper investigates steady infiltration problems involving periodic channels in two-layered soil with the presence of root-water uptake. The problems are governed by a set of Richards' equations accompanied by boundary interface conditions. To address these problems, we transform the system of Richards' equations, along with the corresponding boundary conditions, into a set of steady diffusion-convection equations with transformed boundary conditions. The mathematical model is then tackled through a numerical approach utilizing the Iterative Dual Reciprocity Method (IDRM). Through this numerical method, we obtain solutions that describe the distribution of soil water potential and hydraulic conductivity within the soil. The outcomes of this study shed light on the influence of soil texture and saturated hydraulic conductivity on the values of soil water potential and hydraulic conductivity. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative containment control strategy tailored for second order multi-agent systems. The primary objective is to achieve containment control, which involves the generation of containment reference signals employing a set of dynamic leaders. Subsequently, followers that directly receive these reference signals can effectively trace their generated counterparts, while the remaining followers track some designated signals to ensure the containment control. Additionally, to streamline operations and circumvent the complexities associated with multiple derivative calculations, a command filtered technique is incorporated to simplify the operations of control scheme. The stability of second order multi-agent systems is analyzed based on Lyapunov theory. An inherent advantage of the proposed scheme lies in its flexibility and allowing arbitrary adjustments to the relative positions among followers by manipulating relevant parameters. Through extensive simulations, the efficacy of the designed scheme is validated. [ABSTRACT FROM AUTHOR]

The existing heavy-backbone object detection models overlook the crucial role of cross-level interactive fusion of feature information in pyramid networks, resulting in the inability to detect occluded objects or small objects in complex scenes. In this thesis, we present a new heavy-neck object detection model called POD-YOLO based on YOLOv5s. Firstly, we propose the POD-RepC3 module to increase the model's capability to obtain the multi-layer feature. Additionally, addressing the issue of large object size span, we propose a bidirectional partial dynamic fusion module (Bi-PDC) as the detection neck of the pyramid network. This module preserves the accurate positioning signals and facilitates cross-level interactive fusion of feature information. Finally, we design Reparameterized Bi-directional Dynamic Feature Pyramid Network (RepBi-DFPN), a deep feature fusion network that integrates contextual information and enhances both feature expression and fusion capabilities of our model. The experiment results suggest that the suggested method is positive on the PASCAL VOC dataset. The mAP@0.5 and mAP@0.5:0.95 performance reached 81.3% and 58.2%, respectively, which increased by 2.4% and 4.1% compared to original algorithm YOLOv5s. Furthermore, experiment results also demonstrate that model's performance can compete with SOTA object detection models. In this paper, the algorithm optimizes the feature fusion capability of the pyramid network to effectively decrease the false detection and missing detection of the model. The model's ability to accurately detect multi-scale targets is significantly improved. [ABSTRACT FROM AUTHOR]

The (p, k)-parametrization method, as introduced by Alaca, offers an innovative approach to derive a variety of new Eisenstein series identities that incorporate Borweins' theta functions. This paper presents new Eisenstein series identities that involve Borweins' cubic theta functions, achieved through the (p, k)-parameterization technique. [ABSTRACT FROM AUTHOR]

Tomato is a globally grown vegetable and is significantly considered an economic pillar for various states. However, plants are vulnerable to a diversity of illnesses that can damage and decrease healthy plant generation, therefore, the accurate and early identification of these diseases is critically important. The utilization of cutting-edge approaches in computer vision provides a solution for these issues. This paper proposes a Generative Adversarial Network (GAN) for producing synthetic images of tomato plant leaves. The ResNet50V2 is trained with real and synthetic images of tomato leaves to categorize them into ten classes, using PlantVillage dataset. The proposed GAN-based ResNet50V2 model is evaluated on PlantVillage dataset. It attains better results with 99.75% accuracy, 99.28% precision, 99.43% recall, and 99.67% f1-score, thus, ensuring precise and timely detection of tomato leaf disease as opposed to the existing techniques like DenseNet, MobileNetV3, Inception V3 and InceptionResNetV2. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel approach to solving theorem proving problems without relying on any deduction method. We transform logical formulas into numbers, vectors and matrices, and feed the corresponding data into various machine learning algorithms to predict their satisfiability. Here, we introduce ProverX, a novel theorem prover that utilizes various binary classification algorithms, ranging from traditional machine learning to deep learning, to tackle the satisfiability (SAT) problem of propositional logic. Empirical experiments were conducted to evaluate the performance of ProverX using datasets we generated. ProverX achieved accuracy rates ranging from 81.8% to 98.7%, demonstrating a remarkable speedup of almost 180 times compared to CTL-RP, a highly efficient prover. These results demonstrate the feasibility of replacing deduction with learning in theorem proving, opening promising avenues for further exploration in more complex logics, e.g., Modal Logic, Coalition Logic, Propositional Linear-Time Temporal Logic, Computation Tree Logic, etc., provided that their resolution methods exist. We also present a detailed analysis of the best-performing machine learning approaches for this task and introduce a new algorithm, IPDA, which has the potential to further enhance performance. [ABSTRACT FROM AUTHOR]

In this paper, the clique polynomial method (CPM) is proposed for the numerical solution of Singular Lane-Emden type equations. A new operational matrix of integration concerning clique polynomials of the complete graph has been generated in its generalized representation. This operational matrix is applied to differential equations and is transformed into a system of algebraic equations that can be solved efficiently with the help of Newton's iterative solver. The efficiency of the developed method is revealed by considering illustrative examples, and the obtained results are compared favorably with the corresponding exact solution and errors. [ABSTRACT FROM AUTHOR]

Both constrained state estimation and constrained reference tracking could be formulated as optimization problems, and the symmetry between these two problems could be shown. As variables in the stochastic model, both the mean of process noise and the mean of measurement noise do not have to be zero. The main contributions of this paper are that we take the mean of process noise and the mean of measurement noise, which do not have to be zero, and show the symmetry between constrained state estimation and constrained reference tracking for this case. In other words, we study the symmetry between state estimation and reference tracking with an additional constraint on the measurement noise and the process noise. By this symmetric relationship, the constrained state estimation problem can be solved as a constrained reference tracking problem, and vice versa, despite the presence of more general process and measurement noise. [ABSTRACT FROM AUTHOR]

As the landscape of social networks evolves, the dissemination of public opinion through online channels has become increasingly dominant. Consequently, researchers have turned their attention to the impact of emotional factors introduced by communicators when conveying public opinion. This paper proposes a novel SEInIeR public opinion communication prediction model, incorporating an extreme emotion mechanism, which acknowledges the inuential nature of speech under heightened emotional states. By integrating the traditional infectious disease SEIR model with fractional order differentiation, our model not only ensures the existence and stability of the disease-free equilibrium point E0 but also allows for more accurate prediction. Finally, the model has been validated and analyzed through simulation experiments and numerical simulations. Furthermore, example data were collected for analysis and tting. The experimental results provide substantial evidence for the accuracy of our theoretical analyses and the plausibility of the proposed model. [ABSTRACT FROM AUTHOR]

In this paper we employ Sinc functions as the basis functions and call the resulting method Sinc wavelet collocation method. The main distinguishing feature of the present method, in contrast to the standard Sinc collocation method, is that we do not use a mapping to transform the Sinc functions to a finite domain on which the BVPs are defined to an infinite domain in order to apply them. This results in a much simplified algorithm which is easy to implement while maintaining the high accuracy of the traditional Sinc collocation method. We apply the Sinc collocation method to BVPs and the fractional diffusion equation. [ABSTRACT FROM AUTHOR]

In recent years, there is massive development of new metaheuristics as stochastic methods. Meanwhile, there is not any metaheuristics is powerful to handle all problems as stated in the no-free-lunch (NFL) theory. Based on this circumstance, this paper introduces a new swarm-based metaheuristics with the main strategy moving toward the resultant of better swarm members and avoiding the resultant of worse swarm members called group better-worse algorithm (GBWA). It consists of five searches: moving toward the best swarm member, moving toward the resultant of better swarm members, moving away from the resultant of worse swarm members, searching locally, and jumping to the opposite area. GBWA is then evaluated in three ways. The first evaluation is a comparative evaluation where GBWA is compared to five recent metaheuristics: coati optimization algorithm (COA), average and subtraction-based optimization (ASBO), clouded leopard optimization (CLO), total interaction algorithm (TIA), and osprey optimization algorithm (OOA). The second evaluation is the individual search evaluation. The third evaluation is hyperparameter test. The collection of 23 classic functions is chosen as the use case in all evaluations. The result of the first evaluation shows that GBWA is better than COA, ASBO, CLO, TIA, and OOA in 20, 21, 20, 21, and 21 functions consecutively. Meanwhile, the result of the second evaluation shows the equal contribution between the motion toward the best swarm member and the motion toward the resultant of better swarm members. [ABSTRACT FROM AUTHOR]

In this paper, we extend and generalize an improved bound on the unit circle jzj = 1 concerning polar derivative of a polynomial of degree n having no zero in jzj < k, 0 < k ≤ 1 to an inequality involving the maximum modulus of the polar derivative of the polynomial on circles of radii r;R with 1 ≤ r ≤ R < 1. As consequences of our result, we are able to extend and improve some known polynomial inequalities as well. [ABSTRACT FROM AUTHOR]

Foggy images suffer from severe halo disturbances, low brightness and low contrast, etc. In this paper, to solve these problems, we propose a foggy image defogging method. First, the nonSubsampled contour transform (NSCT) is employed in this method to partition the image into low and high frequency elements. In order to enhance the preservation of complex information, the Retinex approach utilises a bootstrap filter instead of a Gaussian filter. The denoising method entails the application of a threshold to the high-frequency sub-band pictures. This involves boosting coefficients that surpass the threshold and attenuating coefficients that are below it. The ultimate image is recreated following dehazing by employing the inverse transformation of NSCT. Then, the grey wolf optimization (GWO) algorithm is employed to optimise the regularisation factor of the kernel in the guided filter, hence improving the defogging process. Finally, through extensive experiments, our proposed method outperforms recent approachs under plenty of metrics in terms of visual quality. The method effectively reduces halo aberrations, preserves intricate details, and significantly enhances picture sharpness by reducing atmospheric haze. [ABSTRACT FROM AUTHOR]

The investigation into the domination problem and the associated subset problem of graphs is a focal point in graph theory research, sparking widespread interest and extensive exploration. This paper mainly studies the Italian domination of the strong product of two cycles. By constructing recursive Italian dominating functions, a well-defined bound for the Italian domination number in Cn Cm is obtained. Furthermore, through mathematical derivation and proof, the precise Italian domination number of C3 Cm is determined. [ABSTRACT FROM AUTHOR]

In this paper, we consider a problem of estimating a large loss probability of financial derivatives portfolio, which are commonly modeled as nested expectations. However, the cost of nested simulation may be too expensive and thus multilevel Monte Carlo (MLMC) method is recently used to reduce the nested simulation complexity. When using antithetic MLMC to solve the indicator function, we get the complexity of O(ϵ -5/2). To decrease the computational burden, we use a Fourier transform method to modify the form of indicator function. The new estimator is sufficiently smooth and enables the antithetic MLMC method to achieve a better complexity. In addtion, we combine quasi-Monte Carlo (QMC) with MLMC to reduce the variance of inner estimator. Numerical results show that using the Fourier transform method in both MLMC and MLQMC can attain the optimal complexity O(ϵ -2). [ABSTRACT FROM AUTHOR]

This paper delves into the global existence of weak solutions for a three-dimensional magnetoelastic interaction model. This model combines a fractional harmonic map heat flow with an evolution equation for displacement. By using the Faedo-Galerkin method, we successfully establish the global existence of weak solutions for this coupled system. [ABSTRACT FROM AUTHOR]

In this paper, we undertake a comprehensive investigation of the work presented by Çalışkan (2020). Our study serves four primary objectives. Firstly, we identify that Çalışkan (2020) derived an existing inventory model using a complex approach, and subsequently propose enhancements to the methodology. Secondly, we demonstrate the variance between Çalışkan's (2020) approximated model and the model developed by Chung and Ting (1994), which went unnoticed in Çalışkan's (2020) analysis. Thirdly, we shed light on the reason behind Çalışkan (2020) arriving at the same approximated optimal solution as previously developed by Chung and Ting (1994). Fourthly, we present an alternative approach to derive the same approximated optimal solution as Çalışkan (2020) without relying on Çalışkan's (2020) proposed objective function. Through these revisions, we aim to provide valuable insights for researchers examining Çalışkan's (2020) work. [ABSTRACT FROM AUTHOR]

The traditional modeling of dependence among branches in non-life insurance has historically rested on the assumption of independence between risks. However, recent studies have brought to light that overlooking the interdependence between risks is not prudent. An illustrative example is found in automobile insurance, where a discernible connection exists between the number of claims in the Automobile Damage branch and those in the Automobile Civil Liability branch. This paper aims to delve into the utilization of copula functions for modeling the interrelationships among claims charges across diverse branches within a non-life insurance portfolio. Moreover, it seeks to assess the consequential impacts of these dependencies on the capital requirements of AXA Insurance in the Moroccan market. Our research concentrates on four pivotal branches within AXA Insurance, namely "work accidents", "automobile civil liability", "disability", and "fire, marine, aviation, and transport". Through a thorough analysis encompassing dependency measurements, independence tests, and various graphical representations, we have substantiated the existence of interdependencies among the claim costs associated with these four risk branches. Importantly, our findings underscore that assuming independence leads to a slight underestimation of the capital requirement needed for effective risk accumulation management by the insurer. [ABSTRACT FROM AUTHOR]

This paper studies a class of fractional p-Laplacian differential equations, characterized by mixed fractional differential operators and multipoint boundary conditions at resonance. Utilizing the extension of Mawhin's continuation theorem, we establish the result of existence of solutions. [ABSTRACT FROM AUTHOR]

With a rational criterion, we developed an analytical method that proves the uniqueness for the optimal solution for stochastic vendor-buyer inventory model with defective items. Our method approach implies a vigorous approach to locate the optimal solution. The doubtful results that derived by the iterative method are demonstrated by the numerical example of the published paper is examined to indicate the virtue of our approach. Our findings offer an analytical basis for stochastic vendor-buyer inventory models with defective items that will facilitate decision-makers to realize the solution mechanism to find the minimum average cost. [ABSTRACT FROM AUTHOR]

This paper focuses on optimizing the green multimodal transportation path under the condition of transit time subjected to random distribution with dynamic changes. The study utilizes the transition matrix of a homogeneous Markov chain and the theory of time-space network to simulate the transit time subjected to random distribution with dynamic changes. A time-space network path optimization model is established and solved using a commercial solver. The solutions are compared under different transit time scenarios and carbon tax rates. It is observed that the reliability of the same solution decreases in a transportation environment with higher randomness. Additionally, new solutions that did not appear in a single situation are generated under dynamic changes in transit time. As the carbon tax rate increases, the transportation strategy gradually shifts away from road transportation. Furthermore, the time-space network model may yield better solutions compared to the physical network model. The choice of different types of distributions to simulate transit times also influences the solution results. [ABSTRACT FROM AUTHOR]

This paper established the idea of minimum evdominating matrix of semigraph and calculated its energy. The minimum ev-dominating energy EmeD(G) of a semigraph G is the sum of the absolute values of the eigenvalues of the minimum ev-dominating matrix. Here some results are also derived in connection with the energy of minimum evdominating matrix. Some lower bounds are also established. [ABSTRACT FROM AUTHOR]

Social media platforms have emerged as fertile ground for the proliferation of hate speech, which can exacerbate the dissemination of hate crimes. The Federal Bureau of Investigation UCR Program gathers data on hate crimes and disseminates annual reports to identify national patterns and inform law enforcement agencies and policymakers, these reports often fail to keep pace with urgent demands. Real-time monitoring and predictive analysis of hate crime trends are imperative for more effective prevention and response efforts. This paper presents a framework that leverages information extraction techniques to extract incidents from articles published in The New York Times, enabling accurate prediction of hate crime trends at both the federal and state levels. Experimental findings demonstrate the superiority of our approach compared to other traditional methods. By expanding forecasting approaches for federal and state levels' hate crime trends, this framework offers valuable insights for law enforcement agencies and policymakers. [ABSTRACT FROM AUTHOR]

The use of Artificial Intelligence (AI) technology is rapidly expanding across various fields, with chatbots emerging as conversational agents that provide virtual assistance. This paper presents a systematic review that examines the application of chatbots in the healthcare domain. The review aims to explore the evolution of this technology in terms of its demographic and temporal aspects trough out the past years, identify challenges faced, and investigate the factors contributing to successful chatbot implementation in the medical field. To achieve these objectives, a selection of 56 relevant articles from academic databases were analyzed. The findings from this review will provide a comprehensive insight into the utilization of chatbots in healthcare, shedding light on their potential benefits and limitations. [ABSTRACT FROM AUTHOR]

The PID controller is the most widely used controller, and the performance of each controller depends on its three gain parameters. The genetic algorithm (GA) is an optimization process that involves three types of operators: selection, crossover, and mutation. Inspired by natural evolution, a GA searches for the optimum solution automatically from a collection of potential solutions. The GA was employed for the optimization of PID controller parameters in this paper; this algorithm evolves from generation to generation until the stopping criterion is met or the optimal solution is found. The traditional GA has several shortcomings, such as the great influence of the best individual on the whole population, poor global search ability, and premature convergence; therefore, an improved cellular genetic algorithm (CGA) was proposed whose genetic operators occur between the individual and its neighbors. CGA avoids the adverse influence of the best individual and the possibility of falling into a local optimum. The simulation results show that CGA outperforms traditional GA. CGA is more efficient than GA in global search activities, and its optimum solution is better than that of GA. [ABSTRACT FROM AUTHOR]

Fault diameter is an important parameter to measure the reliability and effectiveness of interconnection networks. Strong product is an efficient method to construct large graphs from small graphs. In this paper, we study the fault diameter of strong product graph of an arbitrary connected graph and a complete graph. According to the classification of an arbitrary connected graph, we first determine the fault diameter of strong product graph of two complete graphs. Then we give the fault diameter of strong product graph of an incompletely connected graph and a complete graph, which can be denoted by the fault diameter of its incompletely factor graph. In addition, we also give a more general result about fault diameter. [ABSTRACT FROM AUTHOR]

It is very crucial for virtual enterprises to select the suitable partners when facing unprecedented opportunities and challenges. Most research in this area relies on the Analytic Hierarchy Process method. However, an alternative and potentially more effective approach is proposed by using the fuzzy number ranking score model to assess virtual enterprise partners, yet there has been limited investigation in this direction. In our paper, we establish evaluation criteria for selecting partners for virtual enterprises, including capability, efficiency, cost, risk, goal congruence, and trust level. Subsequently, we develop a ranking score model based on distance-index and area-index for these six criteria in the fuzzy environment. Additionally, the score function is calculated to rank potential virtual enterprise partners by considering defuzzification value, dispersion degree, and positive-negative area indices. Furthermore, the empirical analysis verifies the effectiveness of this method to solve the problem of partner selection in virtual enterprises. The results will help decision makers to make the right selections and decisions. [ABSTRACT FROM AUTHOR]

Unlike traditional networks, Software-defined networks (SDNs) provide an overall view and centralized control of all the devices in the network. SDNs enable the network administrator to implement the network policy by programming applications on top of the SDN controller using generic APIs. One or more controller instances can be deployed to administer the data flow by maintaining unified control of the entire network. The controller is expected to respond quickly to queries from forwarding devices. Presuming quick responses from the controller while enforcing a complex security mechanism is unreasonable. In this paper, the authors propose a unique, adaptive, lightweight, yet efficient technique called ISOLATOR to mitigate the effect of insider attacks and malfunctioning of distributed applications in an SDN-enabled cloud. The proposed security application upon detecting any suspicious activity by a virtual machine, isolates it by removing the interface to its respective shared network and reconnects it through a restricted network operating in a highly selective mode. By subjecting the data traffic to deep packet inspection, the restricted network searches for a match with a known worm pattern. The application is programmed for the OpenDayLight controller and the results show a significant improvement in identifying malicious activities with minimal latency and computational cost. [ABSTRACT FROM AUTHOR]

This paper investigates the model predictive control (MPC) for the trajectory tracking of the unmanned vehicle system with bounded disturbances and actuator saturation based on the unscented Kalman filter (UKF). In order to obtain accurate system state, the linear matrix inequality (LMI)-constrained UKF is addressed by solving the LMI optimization problem. Moreover, by expressing the saturating linear feedback law as convex hull and describing the stability of the vehicle kinematics model with bounded disturbance via the quadratic bounded theorem, a model predictive controller to achieve trajectory tracking is proposed by solving the infinite horizon optimization problem. The effectiveness of the proposed approach is verified by the co-simulation platform of Matlab/Simulink and Carsim. The results of simulations show that this approach can improve the accuracy of state estimation as well as the trajectory tracking control. [ABSTRACT FROM AUTHOR]

This paper studies about the sufficient conditions for identical synchronization in the network constructed by levels consisting of n nodes. Each node is connected to other nodes in the upper level by linear coupling and represented by a reaction-diffusion system of Hindmarsh-Rose type. With this network topology, a sufficient condition on the coupling strength is identified to achieve the desired synchronization. The result shows that when the in-degree of the nodes grows, the network becomes easier to synchronize. In addition, the coupling strength needed to synchronize a chain network grows with its diameter. [ABSTRACT FROM AUTHOR]

Chinese Named Entity Recognition (NER) for Electronic Medical Records (EMR) is a fundamental task in building a digital hospital and is widely considered to be a sequence annotation problem in the Natural Language Processing domain. (NLP). However, existing deep learning sequence annotation models cannot fully use the large amount of unannotated data for Chinese EMRs that contain a vast number of professional unregistered words, named entities, and inter-of-entity relationships carrying rich professional knowledge. Moreover, the syntactic structure of EMR sentences is complex, and the text is long; the features of the EMR documents often cannot be captured deeply. Aiming at these two problems, this paper proposes a deep learning method that combines Multi-Head Attention with a pre-trained language model. (BERTBiGRU-Att-CRF). The method uses the BERT pre-trained model to obtain dynamic word vectors combined with contextual information, extracts global semantic features through a Bidirectional Gated Recurrent Unit (BiGRU), obtains augmented semantic features by using the Multi-Head Attention. Finally, using Conditional Random Field (CRF) decoding, outputs the globally optimal label sequence with greatest probability. The model is trained using the CCKS2019 Chinese EMR dataset containing six types of entities: anatomical sites, surgeries, diseases and diagnoses, medicines, laboratory tests, and imaging tests, and good results are achieved with an F1 score of 86.97%. [ABSTRACT FROM AUTHOR]