Active suspension systems can significantly enhance vehicle ride comfort and attitude stability, but often at the cost of increased energy consumption. To achieve both high dynamic performance and reduced energy usage, this study proposes an eigenstructure-oriented optimization method for active suspensions. Controller design is reformulated as a synergistic process of modal regulation and dynamic response optimization, in which partial eigenstructure assignment redistributes the dominant modes and system responses are computed using fourth-order Runge–Kutta integration. An energy-minimization optimization problem with performance constraints is then solved via the sequential quadratic programming (SQP) algorithm. Simulation results show that the proposed method markedly improves vibration performance: peak body acceleration is reduced from 3.48 m/s<sup>2</sup> to 1.70 m/s<sup>2</sup> (a 51.1% reduction), and the root mean square (RMS) acceleration decreases from 0.74 to 0.40 (a 45.6% reduction), while body displacement is also significantly suppressed. Compared with passive suspension and proportional–integral–derivative (PID) active suspension, the proposed system achieves superior performance in key indices such as body acceleration and displacement, leading to noticeably improved ride comfort and attitude stability. Furthermore, robustness analysis indicates that the method remains effective under variations in the receptance matrix, with only minor influence on system performance, demonstrating the practical applicability of the proposed control strategy.
Hacer Ozden Ayna, Aysun Yurttas Gunes, Sadik Delen
et al.
Graph energy has been the main concern of spectral graph theory in the last five decades. The classical graph energy is the sum of the absolute values of the eigenvalues of the adjacency matrix. In many research papers, different versions of graph energy by utilizing different graph matrices are introduced. For many graph types corresponding to molecular structures, the energy is determined. The theory is complete for complete bipartite graphs. For derived graphs, the problem was settled partially for line, total, double and subdivision graphs. In this paper, the more complex cases of power graphs, shadow, image and core graphs are discussed, and the adjacency matrices of these derived graph classes are formed in terms of very simple submatrices.
In this paper, we study the spreading speed and traveling wave solutions of a non-monotone reaction-diffusion system with spatio-temporal delay. By constructing a pair of auxiliary systems and using the Schauder 's fixed point theorem, the existence of the spreading speed is proved, which is consistent with the minimum wave speed of the traveling wave solution. The results show that the spreading speed and the traveling wave solution are convergent upward.
This study investigates the impact of Cabri 3D (C3D) simulations, a virtual reality-based dynamic geometry software, on Senior High students’ academic performance in 3D geometry concepts. The research addresses the persistent challenges students face in understanding abstract spatial relationships and geometric principles through traditional teaching methods. Grounded in constructivist theory, the study posits that immersive, interactive learning environments can enhance conceptual understanding, spatial visualization skills, and problem-solving abilities. A quasi-experimental design was employed, involving 122 SHS2students from a public Senior High School in Ghana, divided into an experimental group (n=69) taught usingC3D simulations and a control group (n=53) instructed via conventional methods. Pre-test and post-test assessments measured students’ performance before and after the intervention. Descriptive and inferential statistics (independent sample t-tests) were used to analyse the data. The results revealed a statistically significant improvement in the experimental group’s post-test scores (M=53.12, SD=5.635) compared to the control group (M=39.30, SD=4.432) with a p-value of 0.002 (p<0.005). This finding supports the hypothesis that C3D simulations enhance students’ comprehension of 3D geometry by fostering engagement, motivation,and spatial visualization skills. The study aligns with prior research, highlighting the efficacy of technologyintegratedlearning in mathematics education. The study recommends the adoption of Cabri 3D and similardynamic geometry tools in SHS curricula to address learning gaps in abstract mathematical concepts. Schoolsare encouraged to equip ICT labs with relevant resources and train educators to leverage these technologieseffectively. Future research could explore long-term retention and scalability of such interventions acrossdiverse educational contexts.
Propensity score is one of the most commonly used score functions in adjusting for covariates effect in statistical inference. It is important to understand the impact with propensity score in case some of the prespecified covariates are severely imbalanced. In this article, we performed simulation evaluation the empirical type 1 error and empirical power under scenario of imbalanced covariates in several nonparametric two sample tests with propensity score or with other covariate adjustments. Our results suggest common propensity score approaches might have type 1 error inflation at scenarios with severe imbalanced covariates or model is mis-specified.
Abstract Network-based time series models have experienced a surge in popularity over the past years due to their ability to model temporal and spatial dependencies, arising from the spread of infectious disease. The generalised network autoregressive (GNAR) model conceptualises time series on the vertices of a network; it has an autoregressive component for temporal dependence and a spatial autoregressive component for dependence between neighbouring vertices in the network. Consequently, the choice of underlying network is essential. This paper assesses the performance of GNAR models on different networks in predicting COVID-19 cases for the 26 counties in the Republic of Ireland, over two distinct pandemic phases (restricted and unrestricted), characterised by inter-county movement restrictions. Ten static networks are constructed, in which vertices represent counties, and edges are built upon neighbourhood relations, such as railway lines. We find that a GNAR model based on the fairly sparse Economic hub network explains the data best for the restricted pandemic phase while the fairly dense 21-nearest neighbour network performs best for the unrestricted phase. Across phases, GNAR models have higher predictive accuracy than standard ARIMA models which ignore the network structure. For county-specific predictions, in pandemic phases with more lenient or no COVID-19 regulation, the network effect is not quite as pronounced. The results indicate some robustness to the precise network architecture as long as the densities of the networks are similar. An analysis of the residuals justifies the model assumptions for the restricted phase but raises questions regarding their validity for the unrestricted phase. While generally performing better than ARIMA models which ignore network effects, there is scope for further development of the GNAR model to better model complex infectious diseases, including COVID-19.
The article considers lattices of the two-dimensional Toda type, which can be interpreted as dressing chains for spatially two-dimensional generalizations of equations of the nonlinear Schrödinger type. The well-known example of this kind of generalization is the Davey–Stewartson equation. It turns out that the finite-field reductions of these lattices, obtained by imposing cutoff boundary conditions of an appropriate type, are Darboux integrable, i.e., they have complete sets of characteristic integrals. An algorithm for constructing complete sets of characteristic integrals of finite field systems using Lax pairs and Miura-type transformations is discussed. To celebrate 120 years from the day of birth of A. Kolmogorov
In traditional quantitative trading practice, navigating the complicated and dynamic financial market presents a persistent challenge. Fully capturing various market variables, including long-term information, as well as essential signals that may lead to profit remains a difficult task for learning algorithms. In order to tackle this challenge, this paper introduces quantformer, an enhanced neural network architecture based on transformer, to build investment factors. By transfer learning from sentiment analysis, quantformer not only exploits its original inherent advantages in capturing long-range dependencies and modeling complex data relationships, but is also able to solve tasks with numerical inputs and accurately forecast future returns over a given period. This work collects more than 5,000,000 rolling data of 4,601 stocks in the Chinese capital market from 2010 to 2023. The results of this study demonstrate the model's superior performance in predicting stock trends compared with other 100-factor-based quantitative strategies. Notably, the model's innovative use of transformer-like model to establish factors, in conjunction with market sentiment information, has been shown to enhance the accuracy of trading signals significantly, thereby offering promising implications for the future of quantitative trading strategies.
Explaining the strength of arguments under gradual semantics is receiving increasing attention. For example, various studies in the literature offer explanations by computing the attribution scores of arguments or edges in Quantitative Bipolar Argumentation Frameworks (QBAFs). These explanations, known as Argument Attribution Explanations (AAEs) and Relation Attribution Explanations (RAEs), commonly employ removal-based and Shapley-based techniques for computing the attribution scores. While AAEs and RAEs have proven useful in several applications with acyclic QBAFs, they remain largely unexplored for cyclic QBAFs. Furthermore, existing applications tend to focus solely on either AAEs or RAEs, but do not compare them directly. In this paper, we apply both AAEs and RAEs, to Truth Discovery QBAFs (TD-QBAFs), which assess the trustworthiness of sources (e.g., websites) and their claims (e.g., the severity of a virus), and feature complex cycles. We find that both AAEs and RAEs can provide interesting explanations and can give non-trivial and surprising insights.
This study investigates the efficiency of a modified exponentially weighted moving average (EWMA) control method using conditional expected delay to improve its efficiency in detecting changes in a process over time. While the modified EWMA control method is commonly used for this purpose, it can sometimes experience delays in detecting changes. The proposed method aims to address this limitation by incorporating conditional expected delay. The study utilizes simulations to conduct a performance comparison between the modified EWMA control method and the conventional EWMA control, employing the metric of conditional expected delay. Simulation results demonstrate the modified EWMA control method with conditional expected delay in terms of accurately and rapidly detecting changes. Overall, this study concludes that the integration of conditional expected delay into the modified EWMA control method can increase its effectiveness in detecting changes in a process. This has significant practical implications for a variety of industries that require timely and accurate detection of changes to maintain product quality and optimize processes.
Abstract Inspired by the work of Jachymski, we slightly extend some fixed point theorems with a graph and show that some best proximity point theorems for α-ψ-contraction mappings of Jleli and Samet can be deduced by our results.
We propose a primal--dual technique that applies to infinite dimensional equality constrained problems, in particular those arising from optimal control. As an application of our general framework, we solve a control-constrained double integrator optimal control problem and the challenging control-constrained free flying robot optimal control problem by means of our primal--dual scheme. The algorithm we use is an epsilon-subgradient method that can also be interpreted as a penalty function method. We provide extensive comparisons of our approach with a traditional numerical approach.
Liliana Martirano, Lorenzo Zangari, Andrea Tagarelli
Abstract Graph representation learning has become a topic of great interest and many works focus on the generation of high-level, task-independent node embeddings for complex networks. However, the existing methods consider only few aspects of networks at a time. In this paper, we propose a novel framework, named Co-MLHAN, to learn node embeddings for networks that are simultaneously multilayer, heterogeneous and attributed. We leverage contrastive learning as a self-supervised and task-independent machine learning paradigm and define a cross-view mechanism between two views of the original graph which collaboratively supervise each other. We evaluate our framework on the entity classification task. Experimental results demonstrate the effectiveness of Co-MLHAN and its variant Co-MLHAN-SA, showing their capability of exploiting across-layer information in addition to other types of knowledge.
هدف: در کار با مجموعههای فازی شهودی بازهای-مقدار به دلیل در نظر گرفتن تابع عضویت و عدم عضویت بهصورت همزمان و همینطور به علت بازهای بودن نوع دادهها، با انعطافپذیری بسیاری برای تخصیص داده از جانب تصمیمگیرنده روبرو هستیم. با وجود اهمیت ناشی از این نوع ویژگیهای مربوط به مجموعههای فازی شهودی-بازهای مقدار، که خود از دلایل بهکارگیری روزافزون آنها در مسایل مختلف و به ویژه تصمیمگیری چند معیاره است، مقایسه بین آنها بهعنوان یکی از اولین مفاهیم در فرآیند تصمیمگیری، کار چندان سادهای به نظر نمیرسد. در ادبیات موضوعی کمتر میتوان روشی جامع و پارامتری برای رتبهبندی این نوع از اعداد یافت برای رفع این کاستی، در این مقاله با رویکردی تلفیقی، روشی کارآمد و پارامتری برای اولویتبندی بین اعداد فازی شهودی بازهای-مقدار ارائه میدهیم. سپس بهمنظور اولویتبندی بین پیمانکاران رویکرد را برای ارزیابی کیفی صلاحیت آنها به کار میبریم.روششناسی پژوهش: در این مطالعه، از مجموعه های فازی شهودی بازهای مقدار در مسئله تصمیم گیری چند معیاره استفاده شده است. ابتدا با توسیع روشی پارامتری در رتبهبندی اعداد فازی، اندیس بازهای متناظر با اعداد فازی شهودی بازهای مقدار را معرفی میکنیم. در ادامه با استفاده از رویکرد مطرحشده توسط زنگ و همکاران (2019)، تعیین ارجحیت بین بازه ها امکانپذیر شده است. در نتیجه، رویکردی ترکیبی و پارامتری در بخش 3، برای تعیین ارجحیت بین اعداد فازی شهودی بازه ای مقدار به دست آمده است (جدول 1). در مثالی کاربردی برای ارزیابی پیمانکاران بر اساس سه معیار و با در اختیار داشتن پنج گزینه (پیمانکار) شیوه ستفاده از این دیدگاه آزموده شده است.یافتهها: روشی نوین و پارامتری برای رتبهبندی اعداد فازی شهودی بازهای مقدار بهمنظور بهره گیری در ارزیابی واحدهای عملیاتی معرفی شد. چندین ویژگی برای اندیس بازه ای پیشنهادی ذکر کردیم. علاوه بر این، با ارائه یک مثال عملی ضمن توصیف عملکرد فرآیند، خروجی کار مشاهده میشود. پارامتری بودن روش بهعنوان یک مزیت میتواند مبین تاثیرگذاری نقش و دیدگاه تصمیمگیرنده در مقادیر نهایی پاسخ بر اساس سطح انتظار مطلوب باشد.اصالت/ارزش افزوده علمی: ضمن معرفی یک روش پارامتری جدید برای تعیین ارجحیت بین مجموعههای فازی شهودی بازه ای مقدار، فرآیند کارایی برای ارزیابی کیفی صلاحیت پیمانکاران ارائه شده است. علاوه بر این برخی از ویژگیها برای راستی آزمائی عملکرد اندیس بازهای مطرح شد.
In this work, a fractional evolution hemivariational inequalities driven by non-instantaneous impulses is studied. The solvability of the proposed system is obtained by fractional calculus, properties of generalized Clarke’s subdifferential and Dhage fixed point theorem. This article also presents the construction of the lower-dimensional approximation system for the proposed model, and its convergence of the mild solution is obtained. Besides, by considering suitable assumptions, the local approximation and uniform convergence results are established for the proposed system’s mild solution. Furthermore, sufficient conditions for the existence of Lagrange optimal control problem and convergence analysis of optimal control for the proposed model are formulated and proved. At last, an example is given for the illustration of invented new theoretical results.
Erik Faust, Alexander Schlüter, Henning Müller
et al.
With a sufficiently fine discretisation, the Lattice Boltzmann Method (LBM) mimics a second order Crank-Nicolson scheme for certain types of balance laws (Farag et al. [2021]). This allows the explicit, highly parallelisable LBM to efficiently solve the fundamental equations of solid mechanics: the conservation of mass, the balance of linear momentum, and constitutive relations. To date, all LBM algorithms for solid simulation - see e.g. Murthy et al. [2017], Escande et al. [2020], Schlüter et al. [2021] - have been limited to the small strain case. Furthermore, the typical interpretation of the LBM in the current (Eulerian) configuration is not easily extensible to large strains, as large topological changes complicate the treatment of boundary conditions. In this publication, we propose a large deformation Lattice Boltzmann Method for geometrically and constitutively nonlinear solid mechanics. To facilitate versatile boundary modelling, the algorithm is defined in the reference (Lagrangian) configuration.
This note provides a simple sufficient condition ensuring that solutions of stochastic delay differential equations (SDDEs) driven by subordinators are nonnegative. While, to the best of our knowledge, no simple nonnegativity conditions are available in the context of SDDEs, we compare our result to the literature within the subclass of invertible continuous-time ARMA (CARMA) processes. In particular, we analyze why our condition cannot be necessary for CARMA($p,q$) processes when $p=2$, and we show that there are various situations where our condition applies while existing results do not as soon as $p\ge 3$. Finally, we extend the result to a multidimensional setting.
Many physical, biological, and economical systems exhibit various memory effects due to which their present state depends on the history of the whole evolution. Combined with the nonlinearity of the process these phenomena pose serious difficulties in both analytical and numerical treatment. We investigate a time-fractional porous medium equation that has proved to be important in many applications, notably in hydrology and material sciences. We show that solutions of the free boundary Dirichlet, Neumann, and Robin problems on the half-line satisfy a Volterra integral equation with a non-Lipschitz nonlinearity. Based on this result we prove existence, uniqueness, and construct a family of numerical methods that solve these equations outperforming the usual finite difference approach. Moreover, we prove the convergence of these methods and support the theory with several numerical examples.