The cumulative residual extropy has been proposed recently as an alternative measure of extropy to the cumulative distribution function of a random variable. In this paper, the concept of cumulative residual extropy has been extended to cumulative residual extropy inaccuracy (CREI) and dynamic cumulative residual extropy inaccuracy (DCREI). Some lower and upper bounds for these measures are provided. A characterization problem for the DCREI measure under the proportional hazard rate model is studied. Nonparametric estimators for CREI and DCREI measures based on kernel and empirical methods are suggested. Also, a simulation study is presented to evaluate the performance of the suggested measures. Simulation results show that the kernel-based estimator performs better than the empirical-based estimator. Finally, applications of the DCREI measure for model selection are provided using two real data sets.

The virtual model control (VMC) method establishes a direct correlation model between the end-effector and the main body by selecting appropriate virtual mechanical components. This approach facilitates direct force control while circumventing the necessity for complex dynamic modeling. However, the simplification inherent in this modeling can result in inaccuracies in the calculation of joint driving torques, ultimately diminishing control precision. Moreover, VMC typically depends on predefined models for control, which constrains its adaptability in dynamically complex environments and under varying movement conditions. To address these limitations, this paper proposes the BP-VMC method, which is based on a backpropagation neural network (BPNN). Initially, a quadruped robot model was established through kinematic analysis. Subsequently, a decomposed VMC model was developed, and BPNN was introduced to facilitate the adaptive tuning of virtual parameters. This approach resulted in the creation of a virtual mechanical component model with adaptive capabilities, compensating for errors arising from simplified modeling. Finally, a simulation control system was constructed based on the BP-VMC control framework to validate the optimization of control performance. Simulation experiments demonstrated that, in comparison to traditional VMC methods, the BP-VMC method exhibits enhanced control accuracy and stability, achieving a 30% reduction in trajectory tracking error, a 40% reduction in velocity tracking error, and a 20–30% improvement in instability indices across various working conditions. This evidence underscores the BP-VMC method’s robust adaptability in dynamic environments.

Recursive types and bounded quantification are prominent features in many modern programming languages, such as Java, C#, Scala, or TypeScript. Unfortunately, the interaction between recursive types, bounded quantification, and subtyping has shown to be problematic in the past. Consequently, defining a simple foundational calculus that combines those features and has desirable properties, such as decidability, transitivity of subtyping, conservativity, and a sound and complete algorithmic formulation, has been a long-time challenge.

This paper shows how to extend $F_{\le}$ with iso-recursive types in a new calculus called $F_{\le}^{\mu}$. $F_{\le}$ is a well-known polymorphic calculus with bounded quantification. In $F_{\le}^{\mu}$, we add iso-recursive types and correspondingly extend the subtyping relation with iso-recursive subtyping using the recently proposed nominal unfolding rules. In addition, we use so-called structural folding/unfolding rules for typing iso-recursive expressions, inspired by the structural unfolding rule proposed by Abadi et al. (1996). The structural rules add expressive power to the more conventional folding/unfolding rules in the literature, and they enable additional applications. We present several results, including: type soundness; transitivity; the conservativity of $F_{\le}^{\mu}$ over $F_{\le}$; and a sound and complete algorithmic formulation of $F_{\le}^{\mu}$. We study two variants of $F_{\le}^{\mu}$. The first one uses an extension of the $\textrm{kernel}~F_{\le}$ (a well-known decidable variant of $F_{\le}$). This extension accepts equivalent rather than equal bounds and is shown to preserve decidable subtyping. The second variant employs the $\textrm{full}~F_{\le}$ rule for bounded quantification and has undecidable subtyping. Moreover, we also study an extension of the kernel version of $F_{\le}^{\mu}$, called $F_{\le\ge}^{\mu\wedge}$, with a form of intersection types and lower bounded quantification. All the properties from the kernel version of $F_{\le}^{\mu}$ are preserved in $F_{\le\ge}^{\mu\wedge}$. All the results in this paper have been formalized in the Coq theorem prover.

This paper provides a consistent first-order theory solving the knower paradoxes of Kaplan and Montague, with the following main features: 1. It solves the knower paradoxes by providing a faithful formalization of the principle of veracity (that knowledge requires truth), using both a knowledge and a truth predicate. 2. It is genuinely untyped i.e., it is untyped not only in the sense that it uses a single knowledge predicate applying to all sentences in the language (including sentences in which this predicate occurs), but in the sense that its axioms quantify over all sentences in the language, thus supporting comprehensive reasoning with untyped knowledge ascriptions. 3. Common knowledge predicates can be defined in the system using self-reference. These facts, together with a technique based on Löb’s theorem, enables it to support comprehensive reasoning with untyped common knowledge ascriptions (without having any axiom directly addressing common knowledge).

We propose Rényi information generating function (RIGF) and discuss its properties. A connection between the RIGF and the diversity index is proposed for discrete-type random variables. The relation between the RIGF and Shannon entropy of order q > 0 is established and several bounds are obtained. The RIGF of escort distribution is derived. Furthermore, we introduce the Rényi divergence information generating function (RDIGF) and discuss its effect under monotone transformations. We present nonparametric and parametric estimators of the RIGF. A simulation study is carried out and a real data relating to the failure times of electronic components is analyzed. A comparison study between the nonparametric and parametric estimators is made in terms of the standard deviation, absolute bias, and mean square error. We have observed superior performance for the newly proposed estimators. Some applications of the proposed RIGF and RDIGF are provided. For three coherent systems, we calculate the values of the RIGF and other well-established uncertainty measures, and similar behavior of the RIGF is observed. Further, a study regarding the usefulness of the RDIGF and RIGF as model selection criteria is conducted. Finally, three chaotic maps are considered and then used to establish a validation of the proposed information generating function.

This work studies the reliability function of K-out-of-N systems with a general repair time distribution and a single repair facility. It introduces a new repair mechanism using an effort function, described by a nonlinear ordinary differential equation. Three theoretical results are obtained: regularity properties preventing simultaneous failures and repairs, derivation of a Kolmogorov forward system for micro-state and macro-state probabilities, and comparison of reliability functions of two K-out-of-N systems. An additional hypothesis on the model’s parameters allows us to obtain an ordering relation between the reliability functions. A numerical example demonstrates the model’s practical application and confirms the theoretical results.

The various global refugee and migration events of the last few years underscore the need for advancing anticipatory strategies in migration policy. The struggle to manage large inflows (or outflows) highlights the demand for proactive measures based on a sense of the future. Anticipatory methods, ranging from predictive models to foresight techniques, emerge as valuable tools for policymakers. These methods, now bolstered by advancements in technology and leveraging nontraditional data sources, can offer a pathway to develop more precise, responsive, and forward-thinking policies.

This paper seeks to map out the rapidly evolving domain of anticipatory methods in the realm of migration policy, capturing the trend toward integrating quantitative and qualitative methodologies and harnessing novel tools and data. It introduces a new taxonomy designed to organize these methods into three core categories: Experience-based, Exploration-based, and Expertise-based. This classification aims to guide policymakers in selecting the most suitable methods for specific contexts or questions, thereby enhancing migration policies.

This study is predicated on the limited scholarly exploration of the connection between logos and the architectural spaces associated with these brands. The primary objective of this paper is to investigate the relationship between a brand’s corporate identity and its architectural structures through a holistic approach, leveraging artificial intelligence (AI) as a design tool. To achieve this, this study conducts an interdisciplinary literature review, synthesizing existing works in both architecture and branding. The research methodology follows a qualitative, exploratory framework, focusing on the formal and aesthetic evaluations of AI-driven visual outputs. In this context, the central aim of this study is to explore the use of contemporary technologies as a design instrument within the architectural domain. Another key objective is to examine the application of AI as a methodological tool for architectural design within the context of corporate identity. To this end, architectural forms were visually generated using text-to-image and image-to-image, with the resulting products assessed in terms of architectural presentation techniques, visual quality, and aesthetic strategies. For the study’s empirical component, brands ranked at the top of the 2023 Best Global Brands report were selected as the sample, and AI-driven architectural productions were created based on their logos. The findings suggest that AI, with its diverse styles and capabilities, can serve as a design parameter within architectural practice. This study contributes to the discourse on the evolving intersection of AI, branding, and architectural design, proposing new perspectives on the integration of these domains in the design process.

Language is central to issues of displacement and education. This paper examines how English language teachers in refugee settings negotiated and exercised autonomy in teaching and learning in the context of the COVID-19 pandemic. It draws on the notion of autonomy and its dynamics in language classrooms in refugee settings. The paper focuses on one displacement context – Jordan’s refugee settings – to offer a fine-grained analysis of teachers’ accounts to synthesise how teachers negotiated the transition to online teaching and developed practices and relations across different sites. The study recognises teachers’ rights in contributing their own experience and expertise and draws on the Participatory Ethnographic Evaluation Research (PEER) methodology, which involved working closely with a group of six language teachers as peer researchers, who conducted in-depth interviews with two of their peers. The analysis examines the ways in which autonomy was exercised, mobilised, resourced, constrained and shaped by contextual factors during the pandemic and thus provides a nuanced understanding of teachers’ experiences. The study points to the importance of understanding teacher autonomy in the context of language teaching in technology-poor environments. By providing critical insights into the dynamics of teacher autonomy in unique professional settings, it contributes to the broader discourse on digital language learning and agency, roles and skills needed by teachers to support crisis preparedness for the future.

In this paper, a cellular robot for space trusses is structured so that it can perform tasks such as moving the truss and assembling the truss. There may be some spatial operating mechanisms on the space truss that cause obstacles to the robot’s movement, especially other mobile mechanical devices that are working, which are dynamic obstacles, so a suitable path planning for the robot is needed. In path planning, A-star algorithm has the advantages of efficient searching speed and good optimization effect, but it can’t deal with the path planning problem with dynamic obstacles, so this paper improves Lifelong Planning A-star (LPA-star) algorithm so that the improved algorithm satisfies the dynamic path planning task. Then a three-dimensional truss mathematical model is established, a dynamic obstacle environment is set up, the improved LPA-star algorithm is used for path planning, and the unimproved LPA-star algorithm and the improved A-star algorithm are used to compare with it. The simulation results show that in the environment set up in this paper, the optimal path length of the improved LPA-star algorithm is shortened by about 25% and the algorithm search time is shortened by about 55% compared with the improved A-star algorithm; while the unimproved LPA-star algorithm is unable to accomplish the dynamic path planning task. Therefore, the improved LPA-star algorithm can reduce the robot’s moving distance and time consumption.

Onboard localization for multi-robot systems stands as a critical area of research with wide-ranging applications. This paper introduces an innovative framework for multi-robot localization, uniquely characterized by its onboard capability, thereby negating the dependency on external infrastructure. Our approach harnesses the inherent capabilities of each robot, enabling them to localize and synchronize their movements independently. The integration of cooperative localization algorithms with formation control mechanisms empowers a group of robots to sustain a predefined formation while following a linear trajectory. The efficacy of our framework is substantiated through comprehensive simulations and real-world experimental validations. We rigorously assess the system’s resilience to localization inaccuracies and external disturbances, demonstrating its adaptability and consistency in maintaining formation under diverse conditions. Furthermore, we explore the scalability of our approach, highlighting its potential to manage varying numbers of robots and its applicability in tasks such as collaborative transportation.

Walking mechanisms offer advantages over wheels or tracks for locomotion but often require complex designs. This paper presents the kinematic design and analysis of a novel overconstrained spatial a single degree-of-freedom leg mechanism for walking robots. The mechanism is generated by combining spherical four-bar linkages into two interconnecting loops, resulting in an overconstrained design with compact scalability. Kinematic analysis is applied using recurrent unit vector methods. Dimensional synthesis is performed using the Firefly optimization algorithm to achieve a near-straight trajectory during the stance phase for efficient walking. Constraints for mobility, singularity avoidance, and transmission angle are also implemented. The optimized design solution is manufactured using 3D printing and experimentally tested. Results verify the kinematic properties including near-straight-line motion during stance. The velocity profile shows low perpendicular vibrations. Advantages of the mechanism include compact scalability allowing variable stride lengths, smooth motion from overconstraint, and simplicity of a single actuator. The proposed overconstrained topology provides an effective option for the leg design of walking robots and mechanisms.

In this paper, we investigate the number of customers that overlap or coincide with a virtual customer in an Erlang-A queue. Our analysis starts with the fluid and diffusion limit differential equations to obtain the mean and variance of the queue length. We then develop precise approximations for waiting times using fluid limits and the polygamma function. Building on this, we introduce a novel approximation scheme to calculate the mean and variance of the number of overlapping customers. This method facilitates the assessment of transient overlap risks in complex service systems, offering a useful tool for service providers to mitigate significant overlaps during pandemic seasons.

Regression is a fundamental prediction task common in data-centric engineering applications that involves learning mappings between continuous variables. In many engineering applications (e.g., structural health monitoring), feature-label pairs used to learn such mappings are of limited availability, which hinders the effectiveness of traditional supervised machine learning approaches. This paper proposes a methodology for overcoming the issue of data scarcity by combining active learning (AL) for regression with hierarchical Bayesian modeling. AL is an approach for preferentially acquiring feature-label pairs in a resource-efficient manner. In particular, the current work adopts a risk-informed approach that leverages contextual information associated with regression-based engineering decision-making tasks (e.g., inspection and maintenance). Hierarchical Bayesian modeling allow multiple related regression tasks to be learned over a population, capturing local and global effects. The information sharing facilitated by this modeling approach means that information acquired for one engineering system can improve predictive performance across the population. The proposed methodology is demonstrated using an experimental case study. Specifically, multiple regressions are performed over a population of machining tools, where the quantity of interest is the surface roughness of the workpieces. An inspection and maintenance decision process is defined using these regression tasks, which is in turn used to construct the active-learning algorithm. The novel methodology proposed is benchmarked against an uninformed approach to label acquisition and independent modeling of the regression tasks. It is shown that the proposed approach has superior performance in terms of expected cost—maintaining predictive performance while reducing the number of inspections required.

This study explored the effects of interacting with ChatGPT 4.0 on L2 learners’ motivation to write English argumentative essays. Conducted at a public university in a non-English-speaking country, the study had an experimental and mixed-methods design. It utilized both quantitative and qualitative data analyses to inform the development of effective AI-enhanced tailored interventions for teaching L2 essay writing. Overall, the results revealed that interacting with ChatGPT 4.0 had a positive lasting effect on learners’ motivation to write argumentative essays in English. However, a decline in their motivation at the delayed post-intervention stage suggested the need to maintain a balance between utilizing ChatGPT as a writing support tool and enhancing their independent writing capabilities. Learners attributed the increase in their motivation to several factors, including their perceived improvement in essay writing skills, the supportive learning environment created by ChatGPT as a tutor, positive interactions with it, and the development of meta-cognitive awareness by addressing their specific writing issues. The study highlights the potential of AI-based tools in enhancing L2 learners’ motivation in English classrooms.

We consider the count of subgraphs with an arbitrary configuration of endpoints in the random-connection model based on a Poisson point process on ${\mathord{\mathbb R}}^d$. We present combinatorial expressions for the computation of the cumulants and moments of all orders of such subgraph counts, which allow us to estimate the growth of cumulants as the intensity of the underlying Poisson point process goes to infinity. As a consequence, we obtain a central limit theorem with explicit convergence rates under the Kolmogorov distance and connectivity bounds. Numerical examples are presented using a computer code in SageMath for the closed-form computation of cumulants of any order, for any type of connected subgraph, and for any configuration of endpoints in any dimension $d{\geq} 1$. In particular, graph connectivity estimates, Gram–Charlier expansions for density estimation, and correlation estimates for joint subgraph counting are obtained.

We apply moral foundations theory (MFT) to explore how the public conceptualizes the first eight months of the conflict between Ukraine and the Russian Federation (Russia). Our analysis includes over 1.1 million English tweets related to the conflict over the first 36 weeks. We used linguistic inquiry word count (LIWC) and a moral foundations dictionary to identify tweets’ moral components (care, fairness, loyalty, authority, and sanctity) from the United States, pre- and post-Cold War NATO countries, Ukraine, and Russia. Following an initial spike at the beginning of the conflict, tweet volume declined and stabilized by week 10. The level of moral content varied significantly across the five regions and the five moral components. Tweets from the different regions included significantly different moral foundations to conceptualize the conflict. Across all regions, tweets were dominated by loyalty content, while fairness content was infrequent. Moral content over time was relatively stable, and variations were linked to reported conflict events.