Search Results (6,112)

The multivariate Vasicek model is commonly used to capture mean-reverting dynamics typical for short rates, asset price stochastic log-volatilities, etc. Reparametrizing the discretized problem as a VAR(1) model, the parameters are oftentimes estimated using the multivariate least squares (MLS) method, which can be susceptible to outliers. To account for potential model violations, a maximum trimmed likelihood estimation (MTLE) approach is utilized to derive a system of nonlinear estimating equations, and an iterative procedure is developed to solve the latter. In addition to robustness, our new technique allows for reliable recovery of the long-term mean, unlike existing methodologies. A set of simulation studies across multiple dimensions, sample sizes and robustness configurations are performed. MTLE outcomes are compared to those of multivariate least trimmed squares (MLTS), MLE and MLS. Empirical results suggest that MTLE not only maintains good relative efficiency for uncontaminated data but significantly improves overall estimation quality in the presence of data irregularities. Additionally, real data examples containing daily log-volatilities of six common assets (commodities and currencies) and US/Euro short rates are also analyzed. The results indicate that MTLE provides an attractive instrument for interest rate forecasting, stochastic volatility modeling, risk management and other applications requiring statistical robustness in complex economic and financial environments. Full article

Currently, certain complex issues are classified as NP-hard problems, for which there is no exact solution, or they cannot be solved in a reasonable amount of time. As a result, metaheuristic algorithms have been developed as an alternative. These algorithms aim to approximate the optimal solution rather than providing a definitive one. Over recent years, these algorithms have gained considerable attention from the research community. Nature and its inherent principles serve as the primary inspiration for the development of metaheuristic algorithms. A notable subgroup of these algorithms is evolutionary algorithms, which are modeled based on the behavior of social and intelligent animals and organisms. However, each metaheuristic algorithm typically excels only with specific types of problems. As a result, researchers continuously endeavor to develop new algorithms. This study introduces a novel metaheuristic algorithm known as the Farmer Ants Optimization Algorithm (FAOA). The algorithm is inspired by the life of farmer ants, which cultivate mushrooms for food, protect them from pests, and nourish them as they grow. These behaviors, based on their social dynamics, serve as the foundation for the proposed algorithm. Experiments conducted on various engineering and classical problems have demonstrated that the FAOA provides acceptable solutions for discrete optimization problems. Full article

To address the challenges of carbon emission reduction in the global shipping industry and the requirements of the International Maritime Organization (IMO)’s Carbon Intensity Indicator (CII) rating, this paper takes China’s commuter ships as an example to study the dynamic optimization of ship routes based on CII implementation requirements. In response to the existing research gap in the collaborative optimization of routes and carbon emissions under CII constraints, this paper constructs a mixed-integer programming model that comprehensively considers CII limits, port throughput capacity, channel capacity, and the stochastic demand for spot cargo. The objective is to minimize the operating costs of shipping companies, and an adaptive genetic algorithm is designed to solve the dynamic route scheduling problem. Numerical experiments demonstrate that the model can reasonably plan routes under different sequences of spot cargo arrivals, ensuring compliance with CII ratings while reducing total costs and carbon emissions. The results indicate that the proposed method provides efficient decision-making support for dynamic ship scheduling under CII constraints, contributing to the green transformation of the shipping industry. Future work will extend the model to scenarios involving multiple ship types and complex maritime conditions, further enhancing its applicability. Full article

The welded beam design problem represents a real-world engineering challenge in structural optimization. The objective is to determine the optimal dimensions of a steel beam and weld length to minimize cost while satisfying constraints related to shear stress ($\tau$), bending stress ($\sigma$), critical buckling load ($Pc$), end deflection ($\delta$), and side constraints. The structural analysis of this problem involves the following four design variables: weld height ($x_1$), weld length ($x_2$), beam thickness ($x_3$), and beam width ($x_4$), which are commonly denoted in structural engineering as $h,l,t,b$ respectively. The structural formulation of this problem leads to a nonlinear objective function, which is subject to five nonlinear and two linear inequality constraints. The optimal solution lies on the boundary of the feasible region, with a very small feasible-to-search-space ratio, making it a highly challenging problem for classical optimization algorithms. This paper explores the application of quantum computing to solve the welded beam optimization problem, utilizing the unique properties of quantum computers for constrained optimization in engineering problems. Specifically, we employ the D-Wave quantum computing system, which utilizes quantum annealing and is particularly well-suited for solving constrained optimization problems. The study presents a detailed formulation of the problem in a format compatible with the D-Wave system, ensuring the efficient encoding of constraints and objective functions. Furthermore, we analyze the performance of quantum computing in solving this problem and compare the obtained results with classical optimization methods. The effectiveness of quantum computing is evaluated in terms of computational efficiency, accuracy, and its ability to navigate complex, constrained search spaces. This research highlights the potential of quantum algorithms in tackling real-world engineering optimization problems and discusses the challenges and limitations of current quantum hardware in solving practical industrial application issues. Full article

Background/Objectives: The subject of this work is the reconstruction of the inner mechanics of Götz von Berlichingen’s second iron hand. The complex inner mechanics were unknown until Christian von Mechel published a detailed description in 1815. In this artificial hand, each finger can be engaged individually in its three joints and the thumb in one joint. Methods: Based on this description, the individual components were reconstructed at an enlarged scale of 2:1 using computer-aided design (CAD) software and a three-dimensional (3D) printer for the mechanisms. In addition, a finite element method (FEM) analysis was carried out for the components exposed to the greatest stress in order to identify critical areas. Results: By making some adjustments to the mechanics, it was possible to reproduce the mechanisms on a scale of 2:1 on the basis of the index finger. However, when the model was rescaled to 1:1, the internal plastic components were too fragile. This problem was caused by the properties of the 3D printing materials and could be solved by manufacturing the springs from steel. Conclusions: This work aims to make a valuable contribution to the preservation and understanding of the historical artificial second iron hand of Götz von Berlichingen. It once again demonstrates the very precise and detailed craftsmanship of goldsmiths of that time. Full article

This paper presents a Problem-Based Learning (PBL) electrochemistry course contextualized within a real-world problem of wastewater treatment, designed to enhance students’ subject matter knowledge. The sample was a group of chemistry and chemical engineering undergraduate students who were taking an electrochemical course. The research outlines various activities and analyzes five cases of team learning outcomes using Atlas.ti(TM) 22 software. The analysis identifies and describes eight categories of scientific knowledge and practices derived from student reports. The results are represented using a Sankey diagram to show the complexity of students’ thinking after solving their problem. The findings indicate significant progress in students’ conceptual understanding of electrochemistry, the development of complex thinking, and the recognition of its relevance in solving everyday problems. Full article

Distribution networks are an indispensable component of modern economic societies. Against the background of building new power systems, the rapid growth of distributed renewable energy sources, such as photovoltaic and wind power, has introduced many challenges for distribution network planning (DNP), including different source-load compositions, complex network topologies, and varied application scenarios. Traditional heuristic algorithms are limited in scalability and struggle to address the increasingly complex optimization problems of DNP. The emergence of new artificial intelligence provides a new way to solve this problem. Based on the above discussion, this paper proposes a DNP method based on deep reinforcement learning (DRL). By defining state space and action space, a Markov decision process model tailored for DNP is formulated. Then, a multi-objective optimization function and a corresponding reward function including construction costs, voltage deviation, renewable energy penetration, and electricity purchase costs are designed to guide the generation of network topology schemes. Based on the proximal policy optimization algorithm, an actor-critic-based autonomous generation and adaptive adjustment model for DNP is constructed. Finally, the representative test case is selected to verify the effectiveness of the proposed method, which indicates that the proposed method can improve the efficiency of DNP and promote the digital transformation of DNP. Full article

Accurately fitting bimodal wave spectra is crucial for understanding complex ocean conditions and promoting ocean-related research. In this context, this paper aims to solve the problem of reconstructing bimodal wave spectra in domestic island and reef areas. Taking measured data from the Jiangsu Xiangshui station in August 2017 and the Xisha Sea area on 1–3 August 2014 as case studies, the researchers selected three types of original bimodal wave spectra. After obtaining the sample spectra through fast Fourier transform and wave spectrum non-dimensionalization, this paper selected a novel wave spectrum—the rational fractional unimodal spectrum—and two classical wave spectra—the Jonswap spectrum and the Neumann spectrum. Three bimodal wave spectra were constructed by superimposing the low-frequency sub-spectrum and the high-frequency sub-spectrum. After using the improved PSO algorithm to optimize the parameters of these three bimodal wave spectra, the specific parameters were obtained. Comparisons were made between the above three bimodal wave spectra and three high-precision double-peak fitting spectra, the Huang Peiji six-parameter spectrum, the Ochi-Hubble spectrum, and the Shen Zhichun fitting spectrum, and the fitting effects were analyzed. The results demonstrated that when fitting the bimodal spectrum dominated by wind waves and the bimodal spectrum with comparable wind and swell energy, the combination of the rational fractional unimodal spectrum and the Neumann spectrum can achieve a fitting accuracy of up to 99%. When fitting the bimodal spectrum dominated by swell waves, the combination of the rational fractional unimodal spectrum and the Jonswap spectrum can also achieve a fitting accuracy of 99%. The findings of this paper provide valuable references for the study of other types of double-peak wave spectra in China. Full article

The solution to the problem of insufficient accuracy in determining the position and speed of human movement during interaction with a treadmill-based training complex is considered. Control command generation based on the training complex user’s actions may be performed with a delay, may not take into account the specificity of movements, or be inaccurate due to the error of the initial data. The article introduces a technology for improving the accuracy of predicting a person’s position and speed on a running platform using machine learning and computer vision methods. The proposed technology includes analysing and processing data from the tracking system, developing machine learning models to improve the quality of the raw data, predicting the position and speed of human movement, and implementing and integrating neural network methods into the running platform control system. Experimental results demonstrate that the decision tree (DT) model provides better accuracy and performance in solving the problem of positioning key points of a human model in complex conditions with overlapping limbs. For speed prediction, the linear regression (LR) model showed the best results when the analysed window length was 10 frames. Prediction of the person’s position (based on 10 previous frames) is performed using the DT model, which is optimal in terms of accuracy and computation time relative to other options. The comparison of the control methods of the running platform based on machine learning models showed the advantage of the combined method (linear control function combined with the speed prediction model), which provides an average absolute error value of 0.116 m/s. The results of the research confirmed the achievement of the primary objective (increasing the accuracy of human position and speed prediction), making the proposed technology promising for application in human-machine systems. Full article

The microgrid is a small-scale, independent power system that plays a crucial role in the transition to carbon-neutral energy systems. Combined heat and power (CHP) systems with energy storage reduce energy waste within microgrids, enhancing energy utilization efficiency. The key challenge for a microgrid integrated with a combined heat and power system is determining the optimal configuration and operation duration under different scenarios to meet users’ electricity and heat demands while minimizing both economic and environmental costs. Thus, this paper presents a bi-objective mathematical model to solve the optimal scheduling problem of the microgrid. The Long Short-Term Memory–Parallel Multi-Objective Energy Valley Optimizer (LSTM-PMOEVO) framework incorporates energy load prediction using LSTM and scheduling planning solved via PMOEVO. These strategies address the challenges posed by unpredictable energy load fluctuations and the complexity of solving such systems. Finally, a public dataset was utilized for the experiments to verify the performance of the proposed algorithm. Comparisons and discussions show that the proposed optimization strategies significantly improve the performance of PMOEVO, demonstrating marked advantages over six classical algorithms. In conclusion, the PMOEVO developed in this paper performs excellently in solving the Scheduling Problem of Biomass-Hybrid microgrids with energy storage considering uncertainty. The work presented in this paper provides a new solution framework for the microgrid-scheduling problem considering uncertainty. In future research, this solution framework will be further advanced for application in real-world scenarios. Full article

Due to increasing energy consumption, green scheduling in the manufacturing industry has attracted great attention. In distributed manufacturing involving heterogeneous plants, accounting for complex work sequences and energy consumption poses a major challenge. To address distributed heterogeneous green hybrid flowshop scheduling (DHGHFSP) while optimising total weighted delay (TWD) and total energy consumption (TEC), a deep reinforcement learning-based evolutionary algorithm (DRLBEA) is proposed in this article. In the DRLBEA, a problem-based hybrid heuristic initialization with random-sized population is designed to generate a desirable initial solution. A bi-population evolutionary algorithm with global search and local search is used to obtain the elite archive. Moreover, a distributional Deep Q-Network (DQN) is trained to select the best local search strategy. Experimental results on 20 instances show a 9.8% increase in HV mean value and a 35.6% increase in IGD mean value over the state-of-the-art method. The results show the effectiveness and efficiency of the DRLBEA in solving DHGHFSP. Full article

Representing domain knowledge extracted from unstructured texts using knowledge graphs supports knowledge reasoning, enabling the extraction of accurate factual information and the generation of interpretable results. However, reasoning with knowledge graphs is challenging due to their complex logical structures, which require deep semantic understanding and the ability to address uncertainties with common sense. The rapid development of large language models makes them an option for solving this problem, with good complementary capabilities regarding the determinacy of knowledge graph reasoning. However, the use of large language models for knowledge graph reasoning also has challenges, including structural understanding challenges and the balance of semantic density sparsity. This study proposes a domain knowledge graph reasoning method based on a large model prompt learning metapath (DKGM-path), discussing how to use large models for the preliminary induction of reasoning paths and completing reasoning on knowledge graphs based on iterative queries. The method has made significant progress on several public reasoning question answering benchmark datasets, demonstrating multi-hop reasoning capabilities based on knowledge graphs. It utilizes structured data interfaces to achieve accurate and effective data access and information processing and can intuitively show the reasoning process, with good interpretability. Full article

Computational Thinking (CT) and programming encompass a range of skills that are essential in everyday life and play a crucial role in addressing social and environmental challenges. They facilitate the analysis and understanding of global issues, the evaluation of viable solutions, and the formulation of strategic decisions that contribute to Education for Sustainable Development and the achievement of the Sustainable Development Goals. The primary objective of this study was to examine pre-service teachers’ perceptions of these areas. A quantitative study was conducted with 134 university students from the Faculty of Education and Tourism at the University of Salamanca. The findings indicate that CT and programming significantly contribute to enhancing digital competence, fostering essential skills for the effective use of technological tools, developing problem-solving strategies, and increasing self-confidence in identifying and refining solutions to complex problems. Regarding gender differences, significant differences were observed, with women scoring higher on average in various aspects. These included the ability to actively seek, compare, and select digital information from diverse sources and contexts, assess the potential risks associated with digital tools—such as security and identity concerns—and demonstrate confidence in accessing the necessary resources and training to integrate CT and programming into education. Full article

This article presents two novel mixed-integer nonlinear programming (MINLP) formulations in the complex variable domain to address the optimal phase-balancing problem in asymmetric three-phase distribution networks. The first employs a matrix-based load connection model (M-MINLP), while the second uses a compact vector-based representation (V-MINLP). Both integrate the power flow equations through the current injection method, capturing the nonlinearities of Delta and Wye loads. These formulations, solved via an interior-point optimizer and the branch-and-cut method in the Julia software, ensure global optima and computational efficiency. Numerical validations on 8-, 25-, and 37-node feeders showed power loss reductions of 24.34%, 4.16%, and 19.26%, outperforming metaheuristic techniques and convex approximations. The M-MINLP model was 15.6 times faster in the 25-node grid and 2.5 times faster in the 37-node system when compared to the V-MINLP approach. The results demonstrate the robustness and scalability of the proposed methods, particularly in medium and large systems, where current techniques often fail to converge. These formulations advance the state of the art by combining exact mathematical modeling with efficient computation, offering precise, scalable, and practical tools for optimizing power distribution networks. The corresponding validations were performed using Julia (v1.10.2), JuMP (v1.21.1), and AmplNLWriter (v1.2.1). Full article

In response to the limitations of reinforcement learning and Evolutionary Algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution. This systematic review aims to provide a comprehensive analysis of EvoRL, examining the symbiotic relationship between EAs and reinforcement learning algorithms and identifying critical gaps in relevant application tasks. The review begins by outlining the technological foundations of EvoRL, detailing the complementary relationship between EAs and reinforcement learning algorithms to address the limitations of reinforcement learning, such as parameter sensitivity, sparse rewards, and its susceptibility to local optima. We then delve into the challenges faced by both reinforcement learning and EvoRL, exploring the utility and limitations of EAs in EvoRL. EvoRL itself is constrained by the sampling efficiency and algorithmic complexity, which affect its application in areas like robotic control and large-scale industrial settings. Furthermore, we address significant open issues in the field, such as adversarial robustness, fairness, and ethical considerations. Finally, we propose future directions for EvoRL, emphasizing research avenues that strive to enhance self-adaptation, self-improvement, scalability, interpretability, and so on. To quantify the current state, we analyzed about 100 EvoRL studies, categorizing them based on algorithms, performance metrics, and benchmark tasks. Serving as a comprehensive resource for researchers and practitioners, this systematic review provides insights into the current state of EvoRL and offers a guide for advancing its capabilities in the ever-evolving landscape of artificial intelligence. Full article