Global Convergence Research Articles

Performance Optimization Algorithm for Motor Design with Adaptive Weights Based on GNN Representation

Motor design involves multiple complex parameters, and traditional methods rely on experience and experimentation, which are inefficient and difficult to optimize. With the rapid development of emerging industries such as electric vehicles and intelligent manufacturing, the performance requirements for motors are constantly increasing. Optimizing design under multi-objective and multi-constraint conditions has become a key challenge. To address this, this paper proposes a motor design performance optimization algorithm based on Graph Neural Networks (GNN) representation and adaptive weighting. GNN, as a deep learning model capable of handling complex structured data, can model the multi-parameter relationships in motor design and automatically extract key features through its feature propagation mechanism, thus overcoming the difficulty of capturing parameter dependencies with traditional methods. At the same time, Mixed-Integer Linear Programming (MILP) provides a powerful global optimization tool that can find the global optimal solution when dealing with complex decision variables and constraints, overcoming the shortcomings of traditional optimization algorithms in terms of global convergence. Moreover, the adaptive weighting mechanism allows the optimization algorithm to dynamically adjust the weights according to the influence of parameters on motor performance, ensuring the accuracy and adaptability of optimization results in different scenarios. Through the organic combination of these three methods, this paper aims to solve the problems of low efficiency, poor global convergence, and inability to dynamically adjust the importance of design parameters in traditional motor design optimization. By introducing advanced machine learning models and optimization algorithms, this work constructs an efficient motor design performance optimization framework.

Local Convergence Study for an Iterative Scheme with a High Order of Convergence

In this paper, we address a key issue in Numerical Functional Analysis: to perform the local convergence analysis of a fourth order of convergence iterative method in Banach spaces, examining conditions on the operator and its derivatives near the solution to ensure convergence. Moreover, this approach provides a local convergence ball, within which initial estimates lead to guaranteed convergence with details about the radii of domain of convergence and estimates on error bounds. Next, we perform a comparative study of the Computational Efficiency Index (CEI) between the analyzed scheme and some known iterative methods of fourth order of convergence. Our ultimate goal is to use these theoretical findings to address practical problems in engineering and technology.

Optimized trajectory tracking control with disturbance resistance for wheeled mobile robot

When unavoidable disturbances exist in practical applications, the unicycle wheeled mobile robot (WMR) with non-holonomic constraint is challenging to be accurately controlled in finite time. An optimized dual closed-loop strategy is proposed in this paper to solve the trajectory tracking control problem of the unicycle WMR. The control strategy with fast time convergence and anti-disturbance properties consists of kinematic and dynamics controllers and an optimization algorithm. The kinematic controller is designed with an improved backstepping method to eliminate the non-holonomic constraints. The robust sliding mode control (SMC) method with fixed-time convergence is used to construct the dynamics controller. The optimization algorithm enhances the performance of the strategy and achieves accurate control. The global convergence and robustness of the strategy are theoretically demonstrated. The effectiveness of the strategy is verified by comparison in simulations.

Analysis of crystallographic phase retrieval using iterative projection algorithms.

For protein crystals in which more than two thirds of the volume is occupied by solvent, the featureless nature of the solvent region often generates a constraint that is powerful enough to allow direct phasing of X-ray diffraction data. Practical implementation relies on the use of iterative projection algorithms with good global convergence properties to solve the difficult nonconvex phase-retrieval problem. In this paper, some aspects of phase retrieval using iterative projection algorithms are systematically explored, where the diffraction data and density-value distributions in the protein and solvent regions provide thesole constraints. The analysis is based on the addition of random error to thephases of previously determined protein crystal structures, followed by evaluation of the ability to recover the correct phase set as the distance from the solution increases. The properties of the difference-map (DM), relaxed-reflect-reflect (RRR) and relaxed averaged alternating reflectors (RAAR) algorithms are compared. All of these algorithms prove to be effective for crystallographic phase retrieval, and the useful ranges of the adjustable parameter which controls their behavior are established. When these algorithms converge to the solution, the algorithm trajectory becomes stationary; however, the density function continues to fluctuate significantly around its mean position. It is shown that averaging over the algorithm trajectory in the stationary region, following convergence, improves the density estimate, with this procedure outperforming previous approaches for phase or density refinement.

Promoting Equitable and Affordable Patient Access to Safe and Effective Innovations in Donation and Transplantation of Substances of Human Origin and Derived Therapies.

Innovation is a hallmark of organ, tissue, and cell transplantation. The development of new treatments derived from these substances of human origin (SoHO) has rapidly evolved in recent years. Despite the great benefits that these innovative therapies could bring to patients, significant difficulties have arisen in making them equitably and widely accessible. Herein, we identify and address 4 challenges to promote innovation in this field in a collaborative, sustainable, and transparent manner and propose some concrete solutions applicable to SoHO-derived treatments, ranging from cell therapies to solid organ transplantation. Regulators, health policymakers, and government officials are recommended to incorporate specific elements into the regulatory frameworks of their respective jurisdictions, although regulatory convergence and equivalent quality and safety standards applicable to SoHO at a global level would be needed. An innovation-driven regulatory environment, respectful with the human origin and in accordance with the altruistic donation of SoHO, should be encouraged to improve the safety, effectiveness, accessibility, and affordability of SoHO and to promote collaboration between countries and between public and private sectors. This overview is the outcome of a working group focused on "Innovation in the donation and clinical application of SoHO" as part of the international Summit "Towards Global Convergence in Transplantation: Sufficiency, Transparency and Oversight" convened by the Organización Nacional de Trasplantes under the Spanish Presidency of the Council of the European Union in November 2023 and cosponsored by the Council of Europe, the World Health Organization, the Transplantation Society, and the European Society for Organ Transplantation.

Developing and Expanding Deceased Organ Donation to its Maximum Therapeutic Potential: An Actionable Global Challenge From the 2023 Santander Summit.

On November 9 and 10, 2023, the Organización Nacional de Trasplantes (ONT), under the Spanish Presidency of the Council of the European Union, convened in Santander a Global Summit entitled "Towards Global Convergence in Transplantation: Sufficiency, Transparency and Oversight." This article summarizes two distinct but related challenges elaborated at the Santander Summit by Working Group 2 that must be overcome if we are to develop and expand deceased donation worldwide and achieve the goal of self-sufficiency in organ donation and transplantation. Challenge 1: the need for a unified concept of death based on the permanent cessation of brain function. Working group 2 proposed that challenge 1 requires the global community to work toward a uniform, worldwide definition of human death, conceptually unifying circulatory and neurological criteria of death around the cessation of brain function and accepting that permanent cessation of brain function is a valid criterion to determine death. Challenge 2: reducing disparities in deceased donation and increasing organ utilization through donation after the circulatory determination of death (DCDD). Working group 2 proposed that challenge 2 requires the global community to work toward increasing organ utilization through DCDD, expanding DCDD through in situ normothermic regional perfusion, and expanding DCDD through ex situ machine organ perfusion technology. Recommendations for implementation are described.

Expanding Opportunities for Living Donation: Recommendations From the 2023 Santander Summit to Ensure Donor Protections, Informed Decision Making, and Equitable Access

A strategic vision toward global convergence in transplantation must encourage and remove barriers to living organ donation and transplantation. Here, we discuss deliberations of a working group of the 2023 Santander Summit charged with formulating recommendations for the safe expansion of living donor kidney transplantation and living donor liver transplantation worldwide. Living donor kidney transplantation has grown to be the preferred treatment for advanced kidney failure. Living donor liver transplantation emerged more recently as a strategy to reduce waitlist mortality, with adoption influenced by cultural factors, regional policies, clinical team experience, and the maturity of regional deceased donor transplant systems. Barriers to living donor transplantation span domains of education, infrastructure, risk assessment/risk communication, and financial burden to donors. Paired donor exchange is a growing option for overcoming incompatibilities to transplantation but is variably used across and within countries. Effectively expanding access to living donor transplantation requires multifaceted strategies, including improved education and outreach, and measures to enhance efficiency, transparency, and shared decision making in donor candidate evaluation. Efforts toward global dissemination and vigilant oversight of best practices and international standards for the assessment, informed consent, approval, and monitoring of living donors are needed. Fostering greater participation in paired exchange requires eliminating disincentives and logistical obstacles for transplant programs and patients, and establishing an ethical and legal framework grounded in World Health Organization Guiding Principles. Sharing of best practices from successful countries and programs to jurisdictions with emerging practices is vital to safely expand the practice of living donation worldwide and bring the field together globally.

Santander Global Summit in Transplantation: Supporting Global Convergence in the Shared Goals of Sufficiency, Transparency, and Oversight

Santander Global Summit in Transplantation: Supporting Global Convergence in the Shared Goals of Sufficiency, Transparency, and Oversight

Dynamic Random Intersection Graph: Dynamic Local Convergence and Giant Structure

ABSTRACTRandom intersection graphs containing an underlying community structure are a popular choice for modeling real‐world networks. Given the group memberships, the classical random intersection graph is obtained by connecting individuals when they share at least one group. We extend this approach and make the communities dynamic by letting them alternate between an active and inactive phase. We analyse the new model, delivering results on degree distribution, local convergence, largest connected component, and maximum group size, paying particular attention to the dynamic description of these properties. We also describe the connection between our model and the bipartite configuration model, which is of independent interest.

Isolated Calmness of Perturbation Mappings and Superlinear Convergence of Newton-Type Methods.

In this paper, we characterize Lipschitzian properties of different multiplier-free and multiplier-dependent perturbation mappings associated with the stationarity system of a so-called generalized nonlinear program popularized by Rockafellar. Special emphasis is put on the investigation of the isolated calmness property at and around a point. The latter is decisive for the locally fast convergence of the so-called semismooth* Newton-type method by Gfrerer and Outrata. Our central result is the characterization of the isolated calmness at a point of a multiplier-free perturbation mapping via a combination of an explicit condition and a rather mild assumption, automatically satisfied e.g. for standard nonlinear programs. Isolated calmness around a point is characterized analogously by a combination of two stronger conditions. These findings are then related to so-called criticality of Lagrange multipliers, as introduced by Izmailov and extended to generalized nonlinear programming by Mordukhovich and Sarabi. We derive a new sufficient condition (a characterization for some problem classes) of nonexistence of critical multipliers, which has been also used in the literature as an assumption to guarantee local fast convergence of Newton-, SQP-, or multiplier-penalty-type methods. The obtained insights about critical multipliers seem to complement the vast literature on the topic.

Distinct clubs or one big party? Global value chain convergence: Supply concentration and susceptibility to supply shocks

Research background: Recent events, such as the COVID-19 pandemic or Russia’s aggression on Ukraine, raise important questions about the stability of supply chains and their resilience to supply shocks. Purpose of the article: This paper investigates convergence clubs in global value chains, focusing on supply concentration measured with the Herfindahl-Hirschman Index. Methods: Using country-sector pairs from the OECD’s ICIO database, we employed the Phillips-Sul (2007) algorithm with the Schnurbus-Haupt-Meier (2017) augmentation to distinguish convergence clubs in the global trade network. Findings & value added: Our main policy-related finding is that there are two main convergence clubs — one covering roughly 80% of all units and the other one close to 20% (the number of divergent sectors is practically negligible). The dominant club is characterised by high and growing concentration, which poses a risk to the trade network, as, over time, it makes trading partners more susceptible to supply-side shocks. Since the global economy has proven to be surprisingly homogenous in terms of concentration patterns, it seems justified that growing concentration could be considered a systemic feature for future theoretical international trade models. Our main methodological finding is that the WIOD database, which is still used as a standard for such analyses, may be misleading due to its limitations. While the results based on the ICIO and WIOD databases do not align, ICIO seems to be superior in terms of coverage.

New Generalized Derivatives for Solving Variational Inequalities Using the Nonsmooth Newton Methods

AbstractVariational inequality (VI) generalizes many mathematical programming problems and has a wide variety of applications. One class of VI solution methods is to reformulate a VI into a normal map nonsmooth equation system, which is then solved using nonsmooth equation-solving techniques. In this article, we propose a first practical approach for furnishing B-subdifferential elements of the normal map, which in turn enables solving the normal map equation system using variants of the B-subdifferential-based nonsmooth Newton method. It is shown that our new method requires less stringent conditions to achieve local convergence than some other established methods, and thus guarantees convergence in certain cases where other methods may fail. We compute a B-subdifferential element using the LD-derivative, which is a recently established generalized derivative concept. In our new approach, an LD-derivative is computed by solving a sequence of strictly convex quadratic programs, which can be terminated early under certain conditions. Numerical examples are provided to illustrate the convergence properties of our new method, based on a proof-of-concept implementation in Python.

Evolving the Whale Optimization Algorithm: The Development and Analysis of MISWOA.

This paper introduces an enhanced Whale Optimization Algorithm, named the Multi-Swarm Improved Spiral Whale Optimization Algorithm (MISWOA), designed to address the shortcomings of the traditional Whale Optimization Algorithm (WOA) in terms of global search capability and convergence velocity. The MISWOA combines an adaptive nonlinear convergence factor with a variable gain compensation mechanism, adaptive weights, and an advanced spiral convergence strategy, resulting in a significant enhancement in the algorithm's global search capability, convergence velocity, and precision. Moreover, MISWOA incorporates a multi-population mechanism, further bolstering the algorithm's efficiency and robustness. Ultimately, an extensive validation of MISWOA through "simulation + experimentation" approaches has been conducted, demonstrating that MISWOA surpasses other algorithms and the Whale Optimization Algorithm (WOA) and its variants in terms of convergence accuracy and algorithmic efficiency. This validates the effectiveness of the improvement method and the exceptional performance of MISWOA, while also highlighting its substantial potential for application in practical engineering scenarios. This study not only presents an improved optimization algorithm but also constructs a systematic framework for analysis and research, offering novel insights for the comprehension and refinement of swarm intelligence algorithms.

Sequential Task Allocation of More Scalable Artificial Dragonfly Swarms Considering Dubins Trajectory

With the rapid advancement of UAV technology and the increasing complexity of tasks, multi-UAV systems face growing challenges in task execution. Traditional task allocation algorithms often perform poorly when dealing with issues such as local optima, slow convergence speed, and low convergence accuracy, making it difficult to meet the demands for efficiency and practicality in real-world applications. To address these problems, this paper focuses on collaborative task allocation technology for multi-UAV. It proposes a collaborative task allocation strategy for multi-UAV in a multi-target environment, which comprehensively considers various complex constraints in practical application scenarios. The strategy utilizes Dubins curves for trajectory planning and constructs a multi-UAV collaborative task allocation model, with targets including the shortest total distance index, the minimum time index, and the trajectory coordination index. Each UAV is set as an artificial dragonfly by modifying the traditional dragonfly algorithm, incorporating differential evolution algorithms and their crossover, mutation, and selection operations to bring UAV swarms closer to the characteristics of biological dragonflies. The modifications can enhance the global scalability of artificial dragonfly swarms (ADSs), including wider search capacity, wider speed range, and more diverse search accuracy. Meanwhile, potential solutions with global convergence properties are stored to better support real-time adjustments to task allocation. The simulation results show that the proposed strategy can generate a conflict-free task execution scheme and plan the trajectory, which has advantages in changing the data scale of the UAV and the target and improves the reliability of the system to a certain extent.

Chinese Accounting Standards Convergence with International Financial Reporting Standards

As the global economy becomes more integrated, Chinese Accounting Standards (CAS) are gradually moving towards convergence with International Financial Reporting Standards (IFRS). So far, CAS and IFRS do still have a number of variations including content, format and setup mechanisms. This paper analyzes three main differences in the content between CAS and IFRS regarding the financial instrument, biological asset, and lease measurement. These differences may cause problems for international practitioners and investors. In addition, the paper discusses the influences of the global convergence of CAS and the challenges faced by the technology industry, listed companies, and government. It is recommended that China need to consider the domestic economic situation and policies when adopting IFRS, and properly adjust the content of the standard accordingly, so as to better meet the domestic needs and development prospects. And industries should balance convergence and market challenges and work together to shape a practical and comprehensive China accounting system to enable China to develop globally further.

NTK-Guided Few-Shot Class Incremental Learning.

The proliferation of Few-Shot Class Incremental Learning (FSCIL) methodologies has highlighted the critical challenge of maintaining robust anti-amnesia capabilities in FSCIL learners. In this paper, we present a novel conceptualization of anti-amnesia in terms of mathematical generalization, leveraging the Neural Tangent Kernel (NTK) perspective. Our method focuses on two key aspects: ensuring optimal NTK convergence and minimizing NTK-related generalization loss, which serve as the theoretical foundation for cross-task generalization. To achieve global NTK convergence, we introduce a principled meta-learning mechanism that guides optimization within an expanded network architecture. Concurrently, to reduce the NTK-related generalization loss, we systematically optimize its constituent factors. Specifically, we initiate self-supervised pre-training on the base session to enhance NTK-related generalization potential. These self-supervised weights are then carefully refined through curricular alignment, followed by the application of dual NTK regularization tailored specifically for both convolutional and linear layers. Through the combined effects of these measures, our network acquires robust NTK properties, ensuring optimal convergence and stability of the NTK matrix and minimizing the NTK-related generalization loss, significantly enhancing its theoretical generalization. On popular FSCIL benchmark datasets, our NTK-FSCIL surpasses contemporary state-of-the-art approaches, elevating end-session accuracy by 2.9% to 9.3%.

Optimization of vehicle conceptual design problems using an enhanced hunger games search algorithm

Abstract Electric vehicles have become a standard means of transportation in the last 10 years. This paper aims to formalize design optimization problems for electric vehicle components. It presents a tool conceptual design technique with a hunger games search optimizer that incorporates dynamic adversary-based learning and diversity leader (referred to as HGS-DOL-DIL) to overcome the local optimum trap and low convergence rate limitations of the Hunger Games search algorithm to improve the convergence rate. The performance of the proposed algorithms is studied on six widely used engineering design problems, complex constraints, and discrete variables. For the HGS-DOL-DIL practical feasibility analysis, a case study of shape optimization of an electric car suspension arm from the industry is carried out. Overall, the inclusion of the OL strategy has proven its superiority in solving real-world problems, especially in solving real-world problems such as shape optimization of an electric vehicle automobile suspension arm, showing that the algorithm improves the search space improves the solution quality, and reflects its potential to find global optimum solutions in a well-balanced exploration and exploitation phase.

Distributed robust tracking control for multiple unmanned aerial vehicles: theory and experimental verification

The distributed trajectory tracking problem for multiple UAVs under unknown external disturbance is investigated. A new nonlinear robust trajectory tracking strategy for multiple UAVs is proposed. The dynamic model for the UAVs' formation is illustrated in the Tangent frame. For the trajectory tracking problem, a Robust formation tracking control strategy based on robust integral of the signum of error (RISE) is designed to compensate for the effects of unknown external disturbances, which improves the robustness of the UAVs' formation system. Based on the Lyapunov stability analysis, it is proved that the global asymptotic convergence of the coordination errors and the semi-global asymptotic convergence of the UAVs' position tracking errors are achieved. The proposed formation control strategy is validated via real-time experiments that are performed on the self-build UAVs' formation flight control testbed. Flights without wind disturbance and flights with disturbance are all performed. The performance comparison experiment with the conventional sliding mode control algorithm is performed. The experimental results show that the designed control strategy can achieve good coordinated trajectory tracking of multiple UAVs, and has better control performance compared with normal sliding mode control law.

Energy-Efficient Passivity-Based Sliding Mode Controller for Small-Scale Quadrotor UAV for Trajectory Tracking

Quadrotor unmanned aerial vehicles (UAVs) have emerged as versatile platforms applicable to various fields such as surveillance, reconnaissance, and mapping. However, the limited energy capacity inherent in these vehicles poses a significant challenge. This limitation is a critical concern hindering their widespread adoption across diverse applications. Despite various proposed technological solutions addressing the energy consumption issue, the central challenge for the control community lies in developing controllers that ensure stability, robustness, and energy efficiency. In this paper, we present a passivity-based sliding mode controller (P-SMC) designed specifically for quadrotor UAVs. The suggested controller capitalizes on the robustness of the SMC and the energy efficiency of passivity-based control theory. Feedback passification is employed to create a sliding surface with passivity, ensuring stability. The inclusion of a noncontinuity term in the proposed P-SMC guarantees global asymptotic convergence to the sliding surface. To address the multi-faceted nature of the problem, a multi-objective optimization criterion is introduced. This criterion, which combines tracking error and control energy, is solved using the ant colony optimization (ACO) algorithm. The optimization process aims to identify control parameters that strike a balance between tracking accuracy and energy efficiency. Additionally, a conventional sliding mode method is developed to respond to conditions of attempted reach and sliding. Finally, the effectiveness of the proposed method is demonstrated through simulation results considering piecewise constant external disturbances. The outcomes based on the proposed control strategy indicate a notable reduction in energy consumption (ranging from [Formula: see text] to [Formula: see text]), faster reaching times (5–8 times), higher accuracy approximately ([Formula: see text]), and reduced chattering compared to the conventional sliding mode technique.

Hybridized Brazilian–Bowein type spectral gradient projection method for constrained nonlinear equations

This paper proposes a hybridized Brazilian and Bowein derivative-free spectral gradient projection method for solving systems of convex-constrained nonlinear equations. The method avoids solving any subproblems in each iteration. Global convergence is established under appropriate assumptions on the functions involved. Additionally, numerical experiments are conducted to evaluate the algorithm’s performance, providing evidence of its efficiency compared to similar algorithms from the existing literature. The results demonstrate that the method outperforms some existing approaches in terms of the number of iterations, function evaluations, and time required to obtain a solution based on the examples considered.