Traditional Problems Research Articles

Research on Practical Innovation of Elderly Education Service System in the Era of Artificial Intelligence

Abstract The trend of our country into an aging society is becoming more and more obvious, and the elderly education service has become the focus of social attention. The integration of artificial intelligence into elderly education is one of the inspiring ideas in which the personalized recommendation algorithm can recommend educational resources according to the characteristics of older people, and it has the prospect of application. In this paper, we first provide a CTransD-GAT recommendation model based on a knowledge graph, which improves traditional problems such as data sparsity by setting weight preference and feature aggregation. A dynamic preference-capturing method is proposed based on contextual interaction to capture the variable user learning interests more accurately and flexibly. This paper examines the practical utility of personalized recommendation methods for educational resources based on these two improved techniques. The post-test mean score of each knowledge module test of the experimental group is improved by 1.83 points compared with the pre-test, 11.46 points improve the score of teaching ability, and the scores of perceived usefulness, ease of use, and intention to use are 3.82, 3.89, and 3.97, respectively. It shows that the improved educational resource recommendation model has an excellent effect on improving knowledge structure and teaching ability, and it is characterized by simplicity and ease of use.

Design and Implementation of a Model Predictive Formation Tracking Control System for Underwater Multiple Small Spherical Robots

Due to the characteristics of good concealment ability and strong mobility, multiple, small spherical underwater robot formations play an important role in near coast defense missions, such as cruising, reconnaissance, surveillance, and sensitive target capturing. Referring to the formation problem for underwater small spherical robots with limited energy, perception, and computation abilities, a trajectory tracking-based formation strategy that transforms the complex formation tracking problem into a simple trajectory tracking problem of a single robot is provided. Two layers are designed in the formation tracking strategy. The upper layer is a virtual structure-based formation algorithm. The bottom layer is a tracking controller based on model predictive control (MPC). The formation algorithm is in charge of calculating reference trajectory for each robot in the formation according to the global formation path. The MPC-based dynamic controller for each robot is designed to track the self reference trajectory. Compared with the model predictive control method used for the traditional trajectory tracking problem of a single robot, this paper additionally considers the formation constraints and the internal collision avoidance. In addition, the extended state observer (ESO) is utilized to estimate the lumped disturbance composed of environment disturbance and the inaccurate dynamic model of a small spherical robot. Not only are the numerical simulations based on MATLAB v.2015a, but physical simulations based on self-building multi-spherical robot formation platform are also carried out. Furthermore, through using two small spherical robots, a formation tracing experiment is conducted. All of the results prove that the proposed formation method is feasible and practical for small spherical robots.

A General Deep Learning Point–Surface Fusion Framework for RGB Image Super-Resolution

Hyperspectral images are usually acquired in a scanning-based way, which can cause inconvenience in some situations. In these cases, RGB image spectral super-resolution technology emerges as an alternative. However, current mainstream spectral super-resolution methods aim to generate continuous spectral information at a very narrow range, limited to the visible light range. Some researchers introduce hyperspectral images as auxiliary data. But it is usually required that the auxiliary hyperspectral images have the same spatial range as RGB images. To address this issue, a general point–surface data fusion method is designed to achieve the RGB image spectral super-resolution goal in this paper, named GRSS-Net. The proposed method utilizes hyperspectral point data as auxiliary data to provide spectral reference information. Thus, the spectral super-resolution can extend the spectral reconstruction range according to spectral data. The proposed method utilizes compressed sensing theory as a fundamental physical mechanism and then unfolds the traditional hyperspectral image reconstruction optimization problem into a deep network. Finally, a high-spatial-resolution hyperspectral image can be obtained. Thus, the proposed method combines the non-linear feature extraction ability of deep learning and the interpretability of traditional physical models simultaneously. A series of experiments demonstrates that the proposed method can effectively reconstruct spectral information in RGB images. Meanwhile, the proposed method provides a framework of spectral super-resolution for different applications.

Using critical pedagogy and legal design methodology to develop the traditional problem question

ABSTRACT This article attempts to locate legal design methodology in the problem-based learning spectrum. In doing so it considers which taxonomy ought to be used to assess legal problem-based learning questions; it also ranks the two different forms of legal problem-based learning questions against Jonassen and Hung’s paradigm for good problem design. It concludes that by adopting a critical approach and minimising academic voice within the question we centre our students’ unique socio-cultural voice. In doing so we may produce more innovative solutions to legal problems.

Hybrid Deep Learning and Sensitivity Operator-Based Algorithm for Identification of Localized Emission Sources

Hybrid approaches combining machine learning with traditional inverse problem solution methods represent a promising direction for the further development of inverse modeling algorithms. The paper proposes an approach to emission source identification from measurement data for advection–diffusion–reaction models. The approach combines general-type source identification and post-processing refinement: first, emission source identification by measurement data is carried out by a sensitivity operator-based algorithm, and then refinement is done by incorporating a priori information about unknown sources. A general-type distributed emission source identified at the first stage is transformed into a localized source consisting of multiple point-wise sources. The second, refinement stage consists of two steps: point-wise source localization and emission rate estimation. Emission source localization is carried out using deep learning with convolutional neural networks. Training samples are generated using a sensitivity operator obtained at the source identification stage. The algorithm was tested in regional remote sensing emission source identification scenarios for the Lake Baikal region and was able to refine the emission source reconstruction results. Hence, the aggregates used in traditional inverse problem solution algorithms can be successfully applied within machine learning frameworks to produce hybrid algorithms.

«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.