Traditional Problems Research Articles

«Кризис поступка» героя в повести А. Д. Галахова «Старое зеркало»

The purpose of the article is to characterize the literary hero of the story “The Old Mirror”, written by the author, who appeared under the pseudonym Sto-odin on the pages of the magazine “Otechestvennye Zapiski” in 1845. The study of the Russian novels of the forties of the XIX century, in particular the novels of A.D. Galakhov, are relevant in the context of the search for a model of the hero in the Russian literature. Results. The article deals with the problem of tradition and innovation in the depiction of the Russian literary hero. The issue of the appearance of the “incomprehensible hero” in the world of Russian novels of the second third of the XIX century is being discussed. The situation on the “crisis of the deed” is interpreted as a result of the ontological uncertainty of the hero in the world.

A many-to-many pick-up and delivery problem under stochastic battery depletion of electric vehicles

ABSTRACT The study extends the traditional pick-up and delivery problems (PDPs) to address the specific challenges of urban logistics and electric vehicle (EV) adoption. These challenges include the limited range of EVs, energy consumption along the route, and uncertainty in traffic conditions. To overcome the limited range of EVs, the study includes battery swapping stations to ensure sufficient energy to complete delivery routes. Vehicle energy consumption is considered to reduce range anxiety and optimize energy use. The study also considers the unpredictability of traffic conditions that affect energy consumption and delivery schedules. To address these concerns, the study proposes an approximate Quadratic Chance-Constrained Mixed-Integer Programming (QC-MIP) model with a linear approximation, a constructive heuristic and a meta-heuristic. These quantitative models incorporate comprehensive EV energy estimation approaches, enabling more accurate energy predictions. The proposed approaches provide valuable insights and strategies for improving energy efficiency and delivery performance in urban logistics environments.

Energy-efficient non-anchor channel allocation in NB-IoT cellular networks

In Narrowband Internet of Things (NB-IoT), a base station can provide multiple non-anchor channels to release the burden of anchor channels. NB-IoT devices can use non-anchor channels to execute data transmission to save energy. However, we creatively observe a new important property in the NB-IoT; allocating more non-anchor channels to a base station may even cause an increase in the energy consumption of devices during the data transmission procedure. Unlike traditional channel assignment problems, this new property in the NB-IoT leads to the non-anchor channel allocation being a non-convex problem. This article studies the energy-efficient non-anchor channel allocation in the NB-IoT to minimize the energy consumption of devices to finish their data requests. Because of the non-convex property, this article proposes a dynamic programming algorithm without considering frequency reuse to determine how many non-anchor channels each base station should use. We prove that the dynamic programming algorithm can get an optimal solution. Then, we propose an energy-efficient non-anchor channel reuse algorithm to minimize the energy consumption of devices. Compared with two baselines, the simulation results show that the proposed algorithms reduce the energy consumption by 66%, and blind decoding consumes the most energy of a device.

Private International Law as Art

Private international law stands apart in the legal system due to its special subject and method. Currently, the potential of private international law is being revealed in a new way due to the emergence of a new type of “space” — cyberspace, and many traditional institutions and problems of private international law are finding opportunities for a new interpretation or solution. At its core, private international law is the right of the avant-garde, in connection with which the idea arose of considering its individual institutions through the prism of art. And it was the work of the Belgian artist Rene Magritte that, according to the author, turned out to be most in tune with many of the sought-after issues of private international law. This article is an exclusively author’s attempt to combine legal matter with painting, aimed at illustration with the help of an artistic form of legal content.

Examining the Effect of Teaching with Ethnomathematics on Students' Problem-Solving Skills on Transformation Geometry

This research aims to examine the impact of teaching with ethnomathematics on students' problem-solving skills in transformation geometry and their skills in problem-solving steps. The study was carried out with an experimental design, one of the quantitative research methods. The sample was determined as 42 eighth-grade students in two classes using the purposive sampling method. One of these classes was randomly assigned as the experimental group and the other as the control group. In the research, the "Problem-Solving Test (PST)" was used as a data-gathering tool, and the "Problem-Solving Skill Scoring Key" put forward by Baki (2006) was used in the analysis of the data. According to this key, student responses were graded and the results were noted for the data analysis phase. The results obtained were analyzed with the SPSS20 program. It was seen that teaching using ethnomathematics elements improved students' skills in the steps of understanding the problem, preparing a plan, and plan implementing at the end of the research. The instruction did not have a significant effect on student skills at the evaluation stage. At the same time, teaching using ethnomathematical elements increased students' problem-solving skills and solution skills in daily life problems and traditional problems. In this context, it is recommended to integrate cultural elements into the secondary school mathematics curriculum, and in this way, it is thought that it can contribute to the development of students' problem-solving skills.

Mean-square output consensus for heterogeneous multi-agent systems over nonidentical packet loss channels

In this paper, we study the cooperative output consensus problem of discrete-time heterogeneous multi-agent systems over nonidentical channel fading networks. Different from the traditional output consensus problem, the communication channels among agents are perturbed by stochastic multiplicative noises, which can also describe the packet loss phenomenon. The perturbed channels can result in a significant challenge for consensus analysis since their effect cannot be separated linearly from the communication topology. Based on the distributed feedforward control method, a novel distributed dynamic output feedback controller is developed to ensure output synchronization for each agent in the mean-square sense. To facilitate the consensus analysis, we utilize the relationship between the graph Laplacian matrix and the incidence matrix and matrix transformation technique, and find sufficient solvability conditions relying on each agent’s dynamics, the maximum eigenvalue of communication graph and the packet loss probability for the scenarios of undirected graph and directed graph, respectively. Finally, simulation results are given to verify the theoretical analysis.

Promoting the Digital Transformation of The Payment Industry Based on Blockchain Technology

In the era of digital economy, the digital transformation of the payment industry has become a general trend. However, digital transformation can bring development opportunities to the payment industry, but it also faces some challenges. Blockchain technology is regarded as the key core technology to support the future development and application of the Internet. The development of blockchain has become the focus of financial technology. Its decentralization, simple process, openness, transparency, and non-tampering characteristics can effectively solve traditional payment problems. problems existing in. It is precisely because of the characteristics of decentralization that the payment method is more information symmetric and transparent, which greatly improves the security of the transaction process. Commercial banks use the blockchain consensus mechanism to ensure that the data of the platform, upstream and downstream, and other participants reach a consensus across the entire network to ensure information symmetry and transparency; the secure interaction of information is achieved through encryption algorithms, which solves the possibility of information being tampered with; blocks The timestamp feature of the chain ensures the integrity of historical data. This topic is based on the research on the role of blockchain technology in the digital transformation of the payment industry. It is mainly used in the following three aspects:1.This technology has the intelligent features of payment settlement and fund management. Ensure the safe interaction of transaction information by improving payment efficiency and the security of fund transactions. 2.This technology has the characteristics of high efficiency and low cost. It efficiently and low-costly obtains true and reliable data in the system, optimizes the credit creation mechanism, forms a new trade financing model, and effectively reduces financing risks. 3.This technology has safe and low-risk characteristics. Improve the level of data governance, improve the intelligence level of KYC, risk control strategies, and anti-money laundering, and reduce the risks of both banks and the industrial Internet.

Correlation Recurrent Units: A Novel Neural Architecture for Improving the Predictive Performance of Time-Series Data.

Time-series forecasting (TSF) is a traditional problem in the field of artificial intelligence, and models such as recurrent neural network, long short-term memory, and gate recurrent units have contributed to improving its predictive accuracy. Furthermore, model structures have been proposed to combine time-series decomposition methods such as seasonal-trend decomposition using LOESS. However, this approach is learned in an independent model for each component, and therefore, it cannot learn the relationships between the time-series components. In this study, we propose a new neural architecture called a correlation recurrent unit (CRU) that can perform time-series decomposition within a neural cell and learn correlations (autocorrelation and correlation) between each decomposition component. The proposed neural architecture was evaluated through comparative experiments with previous studies using four univariate and four multivariate time-series datasets. The results showed that long- and short-term predictive performance was improved by more than 10%. The experimental results indicate that the proposed CRU is an excellent method for TSF problems compared to other neural architectures.

The application of heterogeneous graph neural network and deep reinforcement learning in hybrid flow shop scheduling problem

The hybrid flow shop scheduling problem (HFSP) is a fundamental optimization problem in the process industries. HFSP needs to consider both job selection and machine allocation. The existing HFSP solution methods represented by deep reinforcement learning (DRL) are challenging to extract complex state features and lack certain adaptability in dealing with large-scale HFSP. To solve HFSP with a large scale and relatively strong flexibility, we propose an end-to-end DRL architecture based on the heterogeneous graph neural network to learn an optimal scheduling policy. First of all, by adding a set of machine nodes, the disjunctive graph of the traditional flexible scheduling problem is extended and transformed into a heterogeneous graph, which intuitively represents the allocation relationship between operations and machines, and it is regarded as the scheduling state at different times; secondly, a new HFSP heterogeneous graph neural network (HGNN) algorithm is established to embed potential relationships between different types of nodes and edges, and an embedding of the entire graph is generated; finally, we use the graph-level representation vectors of heterogeneous graphs as input to the proximal policy optimization (PPO) algorithm for learning the optimal scheduling policy. The experimental results show that compared with several well-known heuristic algorithms, the scheduling policy obtained by the architecture through learning can have good generalization performance in simple and complex problems, respectively, and has a relatively high solution efficiency.

정수 선형 분수 계획법 문제에 대한 파라메트릭 접근법

In this paper, we focus on the integer linear fractional optimization problem, a special case of fractional programming where all functions in the objective function and constraints are linear and all variables are bounded and discrete. To derive the optimal solution, the parametric algorithms are considered, and the efficiency of the algorithms is investigated computationally. Unlike traditional parametric algorithms such as Newton's method, which create a unidirectional sequence approaching the optimal function value, our proposed algorithm generates both upper and lower bounds converging to this value. To demonstrate its effectiveness across various production and operations management problems, the suggested algorithms are used to solve the fractional knapsack problem by comparison to other algorithms (e.g., Newton’s method) under the randomized experimental conditions. The relative practical performance measured by the number of function calls demonstrates that the proposed algorithms are fast and robust for solving the linear fractional programs with discrete variables. Leveraging this algorithm holds the potential to overcome situations in traditional production and operations problems where non-fractional objective functions were previously unconsidered, thereby expecting to derive new outcomes and significance.

Visual analytics for process monitoring: Leveraging time-series imaging for enhanced interpretability

In the era of big data driven by the advent of the Internet of Things (IoT), process industries face the challenge of analyzing massive and complex data to extract relevant information for effective process monitoring. Despite exploring various approaches, scalability and interpretability continue to present practical limitations. To address these limitations, we propose a new framework called visual analytics. Visual analytics offers a new perspective on solving process monitoring problems. It involves transforming historical process data into visual clues, thereby converting traditional fault detection problems into image classification problems. This approach allows process experts to visually analyze patterns and textures within the data, making interpretation much easier compared to traditional time domain analysis. Moreover, by treating process data as images, visual analytics can leverage a wide range of computer vision techniques, including convolutional neural networks (CNNs), to accurately classify and detect faults. By integrating human visual perception with advanced computer vision techniques, visual analytics enables the effective analysis of massive and complex process data. To empirically validate the proposed visual analytics approach, we conduct experiments on both the simulated continuous stirred tank heater (CSTH) benchmark and the industrial arc loss benchmark. The experimental results from both benchmarks demonstrate that the proposed visual analytics approach yields competitive performance in predicting process faults while enhancing interpretability by providing meaningful and informative visual representations.

Exploring the couple stress flow of sodium alginate-based casson tetra hybrid nanofluid between inclined parallel microplates using Fourier analysis and Fick's laws

Compared to a regular fluid like water, the Couple Stress Fluid (CSF) has an additional material constant that manages both couple stress and lubricant viscosity. However, because this material constant creates a fourth-order derivative term in the momentum equation, there has been little research done on this type of fluid, even for traditional fluid problems. In this article, we analyze the flow of a tetra hybrid nanofluid consisting of Casson (sodium alginate-based) couple stress between two parallel microplates that are inclined infinitely. Fourier and Fick's laws are used to investigate the flow, where one plate remains stationary and the other oscillates at a constant speed. The flow is driven by the buoyant force generated by heat transfer and free convection, which results in the uniform dispersion of all spherical dust particles throughout the fluid. The system of partial differential equations (PDEs) is expanded using the Caputo-Fabrizio (CF) fractional derivative to simulate fluid flow. The problem is tackled by using finite sine Fourier and Laplace transforms to obtain closed-form solutions for temperature and velocity profiles. CF time fractional derivatives are used to provide CSF (Couette flow with suction or injection) results for various stimulating fluid parameters and examine their impact on the system. Graphical representations of the CSF, dust particle, and temperature profiles are presented, and skin friction and Nusselt number are calculated using Mathcad-15. It is found that an increase in the couple stress parameter leads to a retardation of both velocity profiles.

An image fusion-based method for recovering the 3D shape of roll surface defects

Most of the existing studies on roll surface defects focus on qualitative detection and lack quantitative analysis, while the commonly used methods for detecting the three-dimensional shape of small objects such as defects are the stylus method, laser scanning method, and structured light scanning method, but these methods are difficult to accurately measure the complex defect variations on the roll surface. In this paper, we propose a method for recovering the 3D shape of roll surface defects based on image fusion. The traditional 3D reconstruction problem is transformed into a 2D image fusion problem using a focusing method. The non-subsampled shear wave transform is used as the base algorithm for image fusion, combined with an enhanced fusion strategy called modified multi-state pulse-coupled neural network to obtain a fully focused image. The method achieves 3D shape recovery of defects by modeling the relationship between the defect depth, the fully focused image, and the original image. To evaluate the performance of the method, experiments were carried out using data involving craters and scratches on the roll surface. This method significantly improves the quality of defect detection images, with a 98% better gradient and a 28% increase in overall image quality. Additionally, it keeps 3D reconstruction errors under 4%, ensuring high accuracy and noise resistance.

A Novel Method for Finding the Shortest Path With Two Objectives Under Trapezoidal Intuitionistic Fuzzy Arc Costs

The Shortest Path Problem is a core problem in network optimization, with applications in various scientific and engineering fields, such as communication, transportation, routing, scheduling, and computer networks. Many studies and algorithms have been proposed to solve the traditional shortest path problem, but they often fail to provide optimal solutions when dealing with the uncertainties and vagueness that exist in real-world situations. This study aims to address the Bi-objective Shortest Path Problem using intuitionistic fuzzy arc numbers. The main goal is to find the path that minimizes both cost and time between a given source node and destination node. To handle the complexities introduced by trapezoidal intuitionistic fuzzy numbers, an accuracy function is used. The study suggests a simple yet effective method to solve this problem and shows its efficiency through a numerical example. The research tries to offer innovative solutions for optimizing paths in scenarios where cost and time factors are important, navigating the complex landscape of uncertainty inherent in practical applications.

Dynamic modeling and nonlinear analysis for lateral–torsional coupling vibration in an unbalanced rotor system

Imbalance is a common problem in rotating machinery. In addition to the unbalanced force, lateral–torsional coupling vibration can also be induced by the mass imbalance. However, the related nonlinear behaviors and coupling mechanism are still not well understood, especially when lateral-angular motion is incorporated in a model. In this paper, a model for coupling lateral and torsional vibrations of an unbalanced rotor is developed by Lagrangian dynamics, in which the coupling of lateral-angular and torsional motions is first considered. Based on the model, the lateral responses, torsional responses, coupling excitation characteristics and coupling mechanisms are investigated in detail. The results show that mass imbalance can lead to nonlinear time-varying characteristics in the dynamic equations. When the imbalance is small, the lateral vibration can be degraded to a traditional imbalance problem. Meantime, very small transient torsional vibration with torsional natural frequency ft in frequency domain can be observed at the beginning. The coupling excitation applying on the torsional DOF caused by the lateral synchronous whirling is demonstrated to behave the feature of static torque, consequently, there actually exists no steady-state torsional vibration in small unbalanced rotor. Under largely unbalanced conditions, both resonant and unstable behavior can be observed in the lateral and torsional responses. The combined resonance is induced by the lateral translational–torsional coupling, while the instability is caused by lateral angular–torsional coupling. By increasing the lateral damping, the instability phenomenon can be suppressed, and the lateral amplitude in the resonance region can be effectively reduced.

A lightweight deep learning based bowel sounds segmentation algorithm for gastrointestinal (GI) monitoring

Segmentation of bowel sounds (BS) events is a significant task of automatic BS monitoring. Recently, deep learning (DL) has been utilized to realize the segmentation of BS events. However, most researchers treat BS segmentation as a traditional classification problem, which causes that more precise locations for the occurrence of BS events are unable to obtained. Besides, the performance of segmentation of BS events is easily affected by thresholds of model output in practical applications. To tackle these issues, in this paper, a lightweight DL-based BS segmentation algorithm is proposed. The one-dimensional convolution layers and bidirectional gate recurrent unit (GRU) layers are adopted to enhance the ability of feature extraction. A loss function named shape loss is proposed to reduce the sensitivity of model to thresholds. Moreover, a portable BS monitoring device is developed to realize data acquisition, display and algorithm deployment. Based on this device, experiments on human and rats are conducted to verify the effectiveness of our proposed approach. Experimental results shows that the proposed method outperforms other comparison methods with the higher f1-scores and lower sensitivity to thresholds.

Estimation of pollution sources and hydraulic conductivity field in a coastal aquifer under tidal effects

In the past decades, most of the efforts have often been invested in the joint identification of contamination and hydraulic conductivity field in inland subsurface aquifers. However, the booming economy and dense population around the coast have brought a new challenge to the forefront: the serious pollution of coastal aquifers. Coastal aquifers are highly vulnerable ecological areas, which are affected by inland pollutants carried by groundwater during its entry into the sea and are also influenced by seawater intrusion. This article addresses the inverse problem of coastal aquifer pollution, which is subject to tidal effects, using the Iterative Local Updating Ensemble Smoother (ILUES) algorithm. The adopted ILUES algorithm is leveraged to efficiently extract pollutant concentration and salinity data for the identification of the source release history and characterization of the hydraulic conductivity field of a site with multiple pollutant sources. This study is based on a variant of the traditional benchmark problem, analyzing the influence of algorithm parameters, tidal effects, and heterogeneity on data assimilation. The article further demonstrates the great potential of ILUES to address new challenges. In addition, the significance of multi-level monitoring wells for the estimation of hydraulic conductivity in coastal multi-layered heterogeneous aquifers is explained.

A Barrier-Certificated Reinforcement Learning Approach for Enhancing Power System Transient Stability

Increasing integration of renewable resources brings more flexibility and poses new challenges to modern power systems, leading to highly nonlinear and complex dynamics. This paper aims to provide a general solution framework to traditional control problems, such as frequency control and voltage control, which attempt to maintain the stability of either synchronous generators-governed or inverter-governed systems when subjected to a disturbance and simultaneously guarantee operational constraints, providing a complete complement to existing works on control design. Building on reinforcement learning (RL) and control barrier functions, the framework includes two subsystems, i.e., a model-free controller and a barrier-certification system, which discover RL-based control actions and sequentially filter them using a barrier certificate to satisfy operational constraints. Calculating a barrier function is generally challenging for a complex power system. This is addressed by representing the barrier function using neural networks (NNs) and data-based approaches. An adaptive method is introduced to certify the neural barrier function that perseveres barrier conditions, which is more compatible with online implementation. The proposed framework synthesizes a stabilizing controller that satisfies predefined safety regions. The effectiveness of the proposed framework is demonstrated via several comparative case studies.

Noisy Label Learning With Provable Consistency for a Wider Family of Losses.

Deep models have achieved state-of-the-art performance on a broad range of visual recognition tasks. Nevertheless, the generalization ability of deep models is seriously affected by noisy labels. Though deep learning packages have different losses, this is not transparent for users to choose consistent losses. This paper addresses the problem of how to use abundant loss functions designed for the traditional classification problem in the presence of label noise. We present a dynamic label learning (DLL) algorithm for noisy label learning and then prove that any surrogate loss function can be used for classification with noisy labels by using our proposed algorithm, with a consistency guarantee that the label noise does not ultimately hinder the search for the optimal classifier of the noise-free sample. In addition, we provide a depth theoretical analysis of our algorithm to verify the justifies' correctness and explain the powerful robustness. Finally, experimental results on synthetic and real datasets confirm the efficiency of our algorithm and the correctness of our justifies and show that our proposed algorithm significantly outperforms or is comparable to current state-of-the-art counterparts.

Cooperative search for moving targets with the ability to perceive and evade using multiple UAVs

This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.