Optimal Theory Research Articles

A Comprehensive Method for Example-Based Color Transfer with Holistic–Local Balancing and Unit-Wise Riemannian Information Gradient Acceleration

Color transfer, an essential technique in image editing, has recently received significant attention. However, achieving a balance between holistic color style transfer and local detail refinement remains a challenging task. This paper proposes an innovative color transfer method, named BHL, which stands for Balanced consideration of both Holistic transformation and Local refinement. The BHL method employs a statistical framework to address the challenge of achieving a balance between holistic color transfer and the preservation of fine details during the color transfer process. Holistic color transformation is achieved using optimal transport theory within the generalized Gaussian modeling framework. The local refinement module adjusts color and texture details on a per-pixel basis using a Gaussian Mixture Model (GMM). To address the high computational complexity inherent in complex statistical modeling, a parameter estimation method called the unit-wise Riemannian information gradient (uRIG) method is introduced. The uRIG method significantly reduces the computational burden through the second-order acceleration effect of the Fisher information metric. Comprehensive experiments demonstrate that the BHL method outperforms state-of-the-art techniques in both visual quality and objective evaluation criteria, even under stringent time constraints. Remarkably, the BHL method processes high-resolution images in an average of 4.874 s, achieving the fastest processing time compared to the baselines. The BHL method represents a significant advancement in the field of color transfer, offering a balanced approach that combines holistic transformation and local refinement while maintaining efficiency and high visual quality.

Cumulative Effect of Debt and Tax on Firm Value: Optimal Capital Structure Theories in the Light of EMM

Cumulative Effect of Debt and Tax on Firm Value: Optimal Capital Structure Theories in the Light of EMM

Optimal scheduling of virtual power plants with reversible solid oxide cells in the electricity market

Virtual power plants (VPP) can act as an independent entity to aggregate different distributed resources to participate in the electricity market, which is an inevitable trend in building green and low-carbon power systems. To investigate the beneficial effect of bi-directional electric-hydrogen conversion on market trading and optimal scheduling of VPP, this paper presents an optimal scheduling method for VPP with reversible solid oxide cells (RSOC) in the electricity market. Firstly, according to the Butler-Volmer equation and Faraday law, the relationship between the hydrogen flow rate and the electric power is established, and the equivalent physical models of the two RSOC working modes are developed based on the relationship. Then, the optimal scheduling model of the VPP participating in the energy and reserve joint market is proposed based on the two-stage robust optimization theory. The system operator reserve demand is used to describe the uncertainty of the transactions between VPP and reserve market. Finally, the column and constraint generation (C&CG) algorithm is used to solve the two-stage robust scheduling model. The simulation results show that RSOC can effectively promote renewable energy consumption and improve the VPP operating profit. Where, the operating profit can be increased by 46.5 % and the wind power consumption rate can be increased by 3.4 %, compared with the non-hydrogen VPP. Furthermore, compared with the traditional conversion equipment, RSOC can increase the VPP operating profit by 2.3 % and the wind power consumption rate by 1.85 %.

A novel hybrid forecasting system for crude oil futures prices: A dual perspective of deterministic forecasting and uncertainty analysis

The crude oil market occupies a crucial place in the composition of the global economy, however, the high level of uncertainty in crude oil prices makes it a challenging task to construct reliable forecasting techniques to obtain reliable expected results. Therefore, to effectively cope with the high uncertainty and the technical limitations of existing forecasting studies, this study constructs a hybrid forecasting system from the dual perspectives of deterministic forecasting and uncertainty analysis. With the concept of “denoising-integration” as the core, the system integrates several denoising techniques, econometric methods, and artificial intelligence methods, and establishes a hybrid forecasting model with high accuracy and robustness with the assistance of multi-objective optimization theory. Furthermore, by introducing fuzzy theory onto the basis of deterministic prediction results, an interval forecasting model with a narrower interval width and better comprehensive performance is constructed under the premise of ensuring that the coverage of prediction intervals meets the requirements of the confidence level. In this study, the forecasting system is applied to four global core crude oil futures markets, and the empirical study finds that the performance is improved by more than 50 % compared with the benchmark model, which has an impressive performance advantage.

Optimizing Pronunciation Instruction for Vietnamese Learners of English: Applying Optimality Theory and Phonetic Principles in Language Teaching

This study explores the perceived benefits of using phonetic principles in teaching English pronunciation from both teacher and student perspectives. The research examines how incorporating phonetic instruction and Optimality Theory can improve pronunciation accuracy and phonological awareness. Using a mixed-methods approach, the study collected quantitative data through surveys from 57 participants (6 teachers and 51 students) and qualitative insights from focus group interviews with teachers. The results showed that teachers rated the benefits of phonetic instruction higher (mean score of 4.24) compared to students (mean score of 3.31). Teachers highlighted how phonetic principles offer a systematic way to identify and correct pronunciation errors, making teaching more efficient. Students, while recognizing the advantages, faced challenges in grasping some technical aspects, such as using phonetic symbols. However, they acknowledged that their pronunciation and listening skills improved with practice. The study concludes that while phonetic instruction is valuable, more practical and accessible approaches are needed to better support students' learning. These findings provide useful insights for educators looking to enhance pronunciation teaching and suggest areas for further exploration.

Detecting marine heatwaves below the sea surface globally using dynamics-guided statistical learning

Extreme warm water events, known as marine heatwaves, cause a variety of adverse impacts on the marine ecosystem. They are occurring more and more frequently across the global ocean. Yet monitoring marine heatwaves below the sea surface is still challenging due to the sparsity of in situ temperature observations. Here, we propose a statistical learning method guided by ocean dynamics and optimal prediction theory, to detect subsurface marine heatwaves based on the observable sea surface temperature and sea surface height. This dynamics-guided statistical learning method shows good skills in detecting subsurface marine heatwaves in the oceanic epipelagic zone over many parts of the global ocean. It outperforms both the classical ordinary least square regression and popular deep learning methods that do not effectively exploit ocean dynamics, with clear dynamical interpretation for its outperformance. Our study provides a useful statistical learning method for near real-time monitoring of subsurface marine heatwaves at a global scale and highlights the importance of exploiting ocean dynamics for enhancing the efficiency and interpretability of statistical learning.

A learning-based account of local phonological processes

Abstract Phonological processes tend to involve local dependencies, an observation that has been expressed explicitly or implicitly in many phonological theories, such as the use of minimal symbols in SPE and the inclusion of primarily strictly local constraints in Optimality Theory. I propose a learning-based account of local phonological processes, providing an explicit computational model. The model is grounded in experimental results that suggest children are initially insensitive to long-distance dependencies and that as their ability to track non-adjacent dependencies grows, learners still prefer local generalisations to non-local ones. The model encodes these results by constructing phonological processes starting around an alternating segment and expanding outward to incorporate more phonological context only when surface forms cannot be predicted with sufficient accuracy. The model successfully constructs local phonological generalisations and exhibits the same preference for local patterns that humans do, suggesting that locality can emerge as a computational consequence of a simple learning procedure.

Optimizing environmental regulation policies for digital transformation in energy enterprises in China: An evolutionary game theory approach

In response to the global consensus on achieving carbon reduction, China has introduced a series of policies aimed at accelerating the digital transformation of energy enterprises. However, these policies have revealed shortcomings such as deficiency in regulation methods and insufficient integration of regulation with technologies. This study applies evolutionary game theory (EGT) to evaluate the impacts of different environmental regulatory policies on the digital transformation and verifies the effectiveness of the theory in policy optimization. Utilizing modified real-world data, the study quantitatively examines the effects of alterations in various parameter combinations on players' strategic choices. The results indicate that: (1) increasing the regulatory intensity above 0.8, the carbon tax rate and penalties can promote the digital transformation, stimulating the low-carbon development in energy sector; (2) an interesting finding is that tax incentives such as carbon tax refund ratio can exacerbate enterprises’ reliance on government compensation, thereby slowing down their transition process; (3) this study highlights the optimal service provision intensity for technological service providers (TSP) is 0.9, which can expedite system evolution towards the ideal state and foster the construction of a favorable digital regulatory environment. The study provides valuable references for optimizing regulatory policies and promoting digital transformation to realize the decarbonization goal.

A Fractional-Order Model Predictive Control Strategy with Takagi–Sugeno Fuzzy Optimization for Vehicle Active Suspension System

Aiming at the problem of system controller performance failure caused by improperly setting the value of each weighting coefficient of the model predictive control (MPC), a fractional-order MPC strategy with Takagi–Sugeno fuzzy optimization (T–SFO MPC) is proposed for a vehicle active suspension system. Firstly, the fractional-order model predictive control framework for active suspension systems is designed based on a 1/4 vehicle model. Then, we analyze the influence of different weighting coefficients on the suspension performance and introduce the Takagi–Sugeno fuzzy optimization theory to adaptively adjust the weighting coefficients of the fractional-order MPC controller. Finally, the system responses of the T–SFO MPC, traditional MPC, linear quadratic regulator (LQR), and passive suspension control are numerically analyzed under various road conditions. Simulation results show that suspension response with the T–SFO MPC is significantly improved compared with passive suspension control, traditional MPC control, and LQR control, and the weight coefficients of the T–SFO MPC can be adaptively adjusted according to the dynamic changes of suspension response. Compared with passive suspension, the root mean square (RMS) value of the vertical acceleration of the T–SFO MPC under various roads decreased by a maximum of 37.97%, and the RMS value of suspension dynamic deflection and tire dynamic load decreased by a maximum of 32.94% and 37.8%, respectively. These results validate that the proposed control method can achieve coordinated optimization of vehicle comfort and handling stability.

New applications of linear semi-infinite optimization theory in copositive optimization

This paper provides a review and update of the theory of linear semi-infinite optimization, followed by its direct application to derive existence theorems, results on the geometry of the feasible set, duality theorems, and optimality conditions for copositive optimization problems.

Optimal and simultaneous synthesis of fractional order QFT controllers and prefilters for non-minimum phase hydro power systems

Abstract Ideally, the operating frequency of the power units, working in parallel, must not deviate from its nominal value. However, due to the dynamic nature of the load, operating frequency varies throughout the day; thus, the control of governors in the prime movers is essential to balance out the demand, and prevent any frequency deviation. To address this, present study investigates the design of optimal and robust quantitative feedback theory based (QFT) controllers and pre-filters for single area load frequency control of hydro power systems. Hydropower system model considered in the present study features a right-hand side zero, thus exhibiting non-minimum phase; these characteristics make the control more challenging. Also, the work investigates the optimal design of the QFT controllers and prefilters simultaneously by automating the loop shaping procedure, which else follows a sequential manual process on Nichols charts. The proposed automation also empowers the control designer to predefine the controller structure. In the present work the design of fractional order QFT controllers and pre-filters is considered. The study also compares the proposed work with the methods proposed in the existing literature as well as the optimal controllers obtained using the time domain indices. The obtained results establishes that the developed fractional QFT controller offers good tracking, stability, and robustness to disturbances when compared to existing approaches. The proposed fractional order QFT controllers offer the elimination of the overshoot and the undershoot from the closed loop response of the system; outperforms the existing controllers in terms of disturbance rejection; offers 0% steady-state deviation, while providing improved phase margin (60.1°) for enhanced stability compared to existing controllers. Also, the proposed QFT-GA attains the ideal values for both the peak complementary sensitivity functions; thus, establishing excellent robustness. Also, the proposed controllers ensure adequate tolerance to frequency deviation in case of load change.

Wasserstein task embedding for measuring task similarities

Measuring similarities between different tasks is critical in a broad spectrum of machine learning problems, including transfer, multi-task, continual, and meta-learning. Most current approaches to measuring task similarities are architecture-dependent: (1) relying on pre-trained models, or (2) training networks on tasks and using forward transfer as a proxy for task similarity. In this paper, we leverage the optimal transport theory and define a novel task embedding for supervised classification that is model-agnostic, training-free, and capable of handling (partially) disjoint label sets. In short, given a dataset with ground-truth labels, we perform a label embedding through multi-dimensional scaling and concatenate dataset samples with their corresponding label embeddings. Then, we define the distance between two datasets as the 2-Wasserstein distance between their updated samples. Lastly, we leverage the 2-Wasserstein embedding framework to embed tasks into a vector space in which the Euclidean distance between the embedded points approximates the proposed 2-Wasserstein distance between tasks. We show that the proposed embedding leads to a significantly faster comparison of tasks compared to related approaches like the Optimal Transport Dataset Distance (OTDD). Furthermore, we demonstrate the effectiveness of our embedding through various numerical experiments and show statistically significant correlations between our proposed distance and the forward and backward transfer among tasks on a wide variety of image recognition datasets.

Implications of Constraints on the Language of Emerging Aided Communicators and Intervention Decisions: A Tutorial

This tutorial offers speech-language pathologists (SLPs) an opportunity to consider the theoretical constructs of language acquisition theory and the multifactorial determinants that can impinge on the language acquisition process for children with complex communication needs. The system of constraints observed through the lens of optimality theory is discussed alongside intrinsic and extrinsic barriers associated with children who acquire their first language simultaneous to their introduction to aided augmentative and alternative communication (AAC) systems. This examination calls into question whether the language competence of emerging aided communicators may be masked by their ability to produce language that accurately represents their understanding of their native language. Specific to this question is whether vocabularies available to emerging aided communicators, opportunities to access aided language models, and intervention decisions made by SLPs may be implicated in the language acquisition and development process. Suggestions for clinical practice are offered as a means of mitigating adverse outcomes related to extrinsic constraints.

An Enhanced Algorithm for Hybrid Flow Shop Scheduling Using Discrete Sparrow Optimization and Rough Set Theory

Aiming at the shortcomings of the sparrow search algorithm, such as it is easy to fall into local optimum and unable to solve discrete optimization problems, an improved discrete sparrow search algorithm is proposed. Firstly, the position update formula of the original sparrow search algorithm is abstracted, a new discrete heuristic position update strategy is designed according to the different identities of individuals, and the encoding and decoding methods are designed for the hybrid flow shop scheduling problem; Secondly, the rough data-deduction theory is introduced, and the feasibility and rationality of the above theory are explained by mathematical proofs, which provides theoretical support for the algorithm and improves the interpretability; Then, the nature of upper approximation is adopted to expand the search space, improve the population diversity, avoid prematurity of the algorithm, combine division and rough data-deduction to propose three strategies to promote information sharing among populations, regulate the exploitation ability and exploration ability of populations, and reduce the probability of the algorithm falling into local optimum; Finally, the improved discrete sparrow search algorithm is used to solve the hybrid flow shop scheduling problem. Simulation experiments are carried out on three small-scale practical examples and Liao's classic test set to verify the feasibility of the improved discrete sparrow search algorithm to solve the hybrid flow shop scheduling problem, and to prove the superiority of the proposed algorithm and the effectiveness of the improved strategy through comparison experiments with other algorithms.

Modeling and optimization of two-stage emergency supply chain network under uncertain environment

In order to formulate an effective emergency supply chain network planning scheme, improve the efficiency of emergency organization rescue and realize the reasonable allocation of resources and materials, considering the uncertainty of supply and demand, a distribution mode combining multiple transportation modes is adopted, and the optimization objectives are to minimize network response time, cost and carbon emissions. A two-stage emergency supply chain mixed integer programming model is constructed. At the same time, an adjustable robust optimization model is constructed based on robust optimization theory to enhance the network's ability to cope with uncertain factors, and the constraints with uncertain parameters are transformed through linear duality theory. In order to improve the solution effect of the model, an optimize cuckoo search (OCS) algorithm is proposed, and a benchmark example is introduced to verify the superiority and applicability of the OCS algorithm in solving multi-objective functions. Finally, the emergency material distribution data during the COVID-19 epidemic in Wuhan is used to study the emergency supply chain network decision-making problem with uncertain parameters, and the effective suppression of the robust control coefficient on uncertain disturbances is proved through sensitivity analysis.

On the inverse problem of parameter identification in the steady Oseen model with unilateral and frictional‐type boundary conditions

AbstractRecently, Migórski‐Dudek investigated a steady Oseen flow for a generalized Newtonian incompressible fluid with unilateral and frictional‐type boundary conditions. They established an existence theorem for the steady Oseen model when the divergence‐free convection field and acting volume force are known. However, prior knowledge of and is often impossible in practical engineering applications. This leads to the question of whether the divergence‐free convection field and acting volume force can be determined. The main objective of this paper is to provide a positive response to this challenging and intriguing question. Specifically, we aim to formulate an inverse problem for identifying and , characterized by a nonlinear and nonsmooth regularized optimization problem. Initially, we introduce a variational selection mapping for the steady Oseen model. We then establish the boundedness and generalized continuity of this variational selection. Finally, by leveraging optimization theory, convex analysis, and nonsmooth analysis, we develop an abstract regularization framework for the considered inverse problem and establish the existence of a solution.

Multidisciplinary Design Optimization of Alignment and Whirling Vibration Characteristics of a Submarine Propulsion Shafting Using Kriging Surrogate Model

To improve the performance indexes, such as safety, reliability and acoustic stealth, of a submarine, it is significant to optimize the dynamic characteristics of its propulsion shafting. The alignment state of a shafting has a coupling effect on its whirling vibration characteristics, and the multidisciplinary design optimization (MDO) theory can fully consider the coupling relationships between different disciplines like this, which is a scientific and effective method to achieve the design optimization of shafting dynamics. However, the iterative calculation of high-precision numerical models greatly restricts the optimization efficiency of this method. Aiming at this problem, in this paper, an MDO model was established based on the coupling dynamic analysis of submarine propulsion shafting, and the Kriging surrogate model was used to predict the state variables within each subdiscipline. Along with the reduction of computational expense, the MDO of the alignment and whirling vibration characteristics of the shafting was achieved. The studied results can be applied to the design process of submarine propulsion shafting, which can provide technical and theoretical support for improving the optimization efficiency of its coupling dynamics.

EEG microstate transition cost correlates with task demands.

The ability to solve complex tasks relies on the adaptive changes occurring in the spatio-temporal organization of brain activity under different conditions. Altered flexibility in these dynamics can lead to impaired cognitive performance, manifesting for instance as difficulties in attention regulation, distraction inhibition, and behavioral adaptation. Such impairments result in decreased efficiency and increased effort in accomplishing goal-directed tasks. Therefore, developing quantitative measures that can directly assess the effort involved in these transitions using neural data is of paramount importance. In this study, we propose a framework to associate cognitive effort during the performance of tasks with electroencephalography (EEG) activation patterns. The methodology relies on the identification of discrete dynamical states (EEG microstates) and optimal transport theory. To validate the effectiveness of this framework, we apply it to a dataset collected during a spatial version of the Stroop task, a cognitive test in which participants respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. The Stroop task is a cognitive test where participants must respond to one aspect of a stimulus while ignoring another, often conflicting, aspect. Our findings reveal an increased cost linked to cognitive effort, thus confirming the framework's effectiveness in capturing and quantifying cognitive transitions. By utilizing a fully data-driven method, this research opens up fresh perspectives for physiologically describing cognitive effort within the brain.

Penalized Bayesian D-Optimal Designs for Regression Models of Continuous Response

We propose extending the use of desirability functions in Bayesian optimal designs for regression models. This technique generates experimental designs with good statistical inference properties according to Bayesian optimal design theory and practical features, as defined by an investigator. These practical features are defined by a penalty function, using an overall desirability function, which is added to a Bayesian $D$-optimal design criterion to penalize impractical experimental designs. This methodology is illustrated by two examples of regression models: quadratic and exponential. Then, we compare designs obtained for different prior distributions of unknown parameters by efficiency calculations and simulation study. Results show that the $D$-efficiencies of the penalized designs relative to the non-penalized Bayesian D-optimal designs are competitive.

Dynamics of capital structure determinants: empirical evidence from GCC countries

AbstractThe study empirically examines the factors affecting the financing decisions of non-financial listed companies in Gulf Cooperation Council (GCC) countries. Using static and dynamic two-step generalized method of moments techniques, it analyzes unbalanced panel data from 364 non-financial companies across six GCC countries from 2011 to 2021. The findings partially support optimal capital structure theories, highlighting significant internal factors such as profitability, market-to-book ratio, firm size, earnings volatility, and growth opportunities that influence financing decisions. While no single theory fully explains the financing choices, the association of internal factors with book and market leverage is consistent. The study provides robust and generalizable results, aiding financial institutions and policymakers in formulating pro-development policies and regulations. This research facilitates better coordination between corporate managers and financial institutions, supporting the region’s economic transition. It is the first study to use extensive data from GCC non-financial firms to investigate financing decisions, offering valuable insights for investors and a basis for further analysis of capital structure choices in the region and beyond.