This article investigates global patterns of facilitation and interference among identities—socially recognizable categories that shape individuals’ sense of who they are and carry cultural expectations (e.g., mother, worker). While identity theory suggests that identities interact in structured ways, existing research often examines identities in isolation or conventional roles, limiting the ability to observe broader patterns. This study adopts a relational approach to explore how identities facilitate or interfere with each other. By drawing on sociological identity theory, I formulate hypotheses about these interactions. Using original survey data, I construct identity networks where nodes represent identities and ties indicate the prevalence of facilitation or interference. Blockmodeling techniques are then employed to characterize the global structure of these networks. The findings reveal distinct positions within the network, largely aligning with theoretical expectations.

The enhanced computing power of the onboard flight control system and the low flapping frequency have made real-time position and attitude control possible for large flapping-wing flying robots (LFWFRs). Therefore, it is necessary to design an efficient flapping load calculation method to provide the load situation of the flapping wings. To address this problem, we establish a three-dimensional aeroelastic model by coupling the finite element method and state-space airloads theory. This model considers the interaction between aerodynamic loads, inertial loads, and flapping-wing structural elasticity during the flapping motion, which could quickly calculate the instantaneous aerodynamic loads and inertial loads of flapping wings under different flight conditions. The accuracy of the model was verified through vacuum and wind tunnel experiments. Experiments under various flight conditions demonstrate the effectiveness and reliability of the proposed method, and the method could be used to guide the rapid iterative upgrade and control law design of LFWFRs.

Industrial mobile robots as service units will be increasingly used in the future in factories with Industry 4.0 production cells in an island-like manner. The differences between the mobile robots available on the market make it necessary to help the optimal selection and use of these robots. In this article, we present a concept that focuses on the mobile robot as a way to investigate the manufacturing system. This approach will help to find the optimal solution when selecting robots. With the parameters that can be included, the robot can be characterized in the manufacturing system environment, making it much easier to express and compute capacity, performance, and efficiency characteristics compared to previous models. In this article, we also present a case study based on the outlined method, which investigates the robot utilization as a function of battery capacity and the number of packages to be transported.

This paper explores a principled approach to calculating abstract machines and associated compilers, starting from an intrinsically typed interpreter. After deriving a compiler for a simple expression language in some detail, the first steps of this calculation are repeated to derive an optimizing evaluator for the simply typed lambda calculus.

We present an explicit subset $A\subseteq \mathbb{N} = \{0,1,\ldots \}$ such that $A + A = \mathbb{N}$ and for all $\varepsilon \gt 0$,

\begin{equation*}\lim _{N\to \infty }\frac {\big |\big \{(n_1,n_2): n_1 + n_2 = N, (n_1,n_2)\in A^2\big \}\big |}{N^{\varepsilon }} = 0.\end{equation*}

This answers a question of Erdős.

In the context of urban traffic control, traffic signal optimisation is the problem of determining the optimal green length for each signal in a set of traffic signals. The literature has effectively tackled such a problem, mostly with automated planning techniques leveraging the PDDL + language and solvers. However, such language has limitations when it comes to specifying optimisation statements and computing optimal plans. In this paper, we provide an alternative solution to the traffic signal optimisation problem based on Constraint Answer Set Programming (CASP). We devise an encoding in a CASP language, which is then solved by means of clingcon 3, a system extending the well-known ASP solver clingo. We performed experiments on real historical data from the town of Huddersfield in the UK, comparing our approach to the PDDL+ model that obtained the best results for the considered benchmark. The results showed the potential of our approach for tackling the traffic signal optimisation problem and improving the solution quality of the PDDL + plans.

Partial correctness of imperative or functional programming divides in logic programming into two notions. Correctness means that all answers of the program are compatible with the specification. Completeness means that the program produces all the answers required by the specifications. We also consider semi-completeness – completeness for those queries for which the program does not diverge. This paper presents an approach to systematically construct provably correct and semi-complete logic programs, for a given specification. Normal programs are considered, under Kunen’s 3-valued completion semantics (of negation as finite failure) and the well-founded semantics (of negation as possibly infinite failure). The approach is declarative, it abstracts from details of operational semantics, like, for example, the form of the selected literals (“procedure calls”) during the computation. The proposed method is simple and can be used (maybe informally) in actual everyday programming.

The multi-agent path finding (MAPF) problem aims to find plans for multiple agents in an environment within a given time, such that the agents do not collide with each other or obstacles. Motivated by the execution and monitoring of these plans, we study dynamic MAPF (D-MAPF) problem, which allows changes such as agents entering/leaving the environment or obstacles being removed/moved. Considering the requirements of real-world applications in warehouses with the presence of humans, we introduce (1) a general definition for D-MAPF (applicable to variations of D-MAPF), (2) a new framework to solve D-MAPF (utilizing multi-shot computation and allowing different methods to solve D-MAPF), and (3) a new answer set programming-based method to solve D-MAPF (combining advantages of replanning and repairing methods, with a novel concept of tunnels to specify where agents can move). We have illustrated the strengths and weaknesses of this method by experimental evaluations, from the perspectives of computational performance and quality of solutions.

The Stable Roommates problems are characterized by the preferences of agents over other agents as roommates. A solution is a partition of the agents into pairs that are acceptable to each other (i.e., they are in the preference lists of each other), and the matching is stable (i.e., there do not exist any two agents who prefer each other to their roommates and thus block the matching). Motivated by real-world applications, and considering that stable roommates problems do not always have solutions, we continue our studies to compute “good-enough” matchings. In addition to the agents’ habits and habitual preferences, we consider their networks of preferred friends and introduce a method to generate personalized solutions to stable roommates problems. We illustrate the usefulness of our method with examples and empirical evaluations.

Novel utility computing paradigms rely upon the deployment of multi-service applications to pervasive and highly distributed cloud-edge infrastructure resources. Deciding onto which computational nodes to place services in cloud-edge networks, as per their functional and non-functional constraints, can be formulated as a combinatorial optimisation problem. Most existing solutions in this space are not able to deal with unsatisfiable problem instances, nor preferences, i.e., requirements that DevOps may agree to relax to obtain a solution. In this article, we exploit Answer Set Programming optimisation capabilities to tackle this problem. Experimental results in simulated settings show that our approach is effective on lifelike networks and applications.

Today’s field of spatialisation in acousmatic music is very heterogeneous. Composers tend to develop their own technologies and techniques for spatialisation, and often the differences in how multichannel systems are addressed may influence both the musical appreciation and the future reproducibility of a piece. Moreover, the analytical and musicological perspectives of spatialisation are both fragmented and underdeveloped, with a lack of a shared framework for their study. This article focuses on these problems and tries to give a coherent and consistent view of spatialisation practice, from both technological and musicological perspectives. It will also act as a bedrock for the development of the musicological side of spatialisation, an aspect too often overlooked. ‘Spatial reduced listening’ and ‘spatial relativism’ will be introduced as analytical perspectives to shine a light on the composed spatial traits of sound, and not only on its spectromorphological and technological features.

Higher-order constructs enable more expressive and concise code by allowing procedures to be parameterized by other procedures. Assertions allow expressing partial program specifications, which can be verified either at compile time (statically) or run time (dynamically). In higher-order programs, assertions can also describe higher-order arguments. While in the context of (constraint) logic programming ((C)LP), run-time verification of higher-order assertions has received some attention, compile-time verification remains relatively unexplored. We propose a novel approach for statically verifying higher-order (C)LP programs with higher-order assertions. Although we use the Ciao assertion language for illustration, our approach is quite general, and we believe is applicable to similar contexts. Higher-order arguments are described using predicate properties – a special kind of property which exploits the (Ciao) assertion language. We refine the syntax and semantics of these properties and introduce an abstract criterion to determine conformance to a predicate property at compile time, based on a semantic order relation comparing the predicate property with the predicate assertions. We then show how to handle these properties using an abstract interpretation-based static analyzer for programs with first-order assertions by reducing predicate properties to first-order properties. Finally, we report on a prototype implementation and evaluate it through various examples within the Ciao system.

The emergence of large language models (LLMs) provides an opportunity for AI to operate as a co-ideation partner during the creative processes. However, designers currently lack a comprehensive methodology for engaging in co-ideation with LLMs, and there is a limited framework that describes the process of co-ideation between a designer and ChatGPT. This research thus aimed to explore how LLMs can act as codesigners and influence creative ideation processes of industrial designers and whether the ideation performance of a designer could be improved by employing the proposed framework for co-ideation with custom GPT. A survey was first conducted to detect how LLMs influenced the creative ideation processes of industrial designers and to understand the problems that designers face when using ChatGPT to ideate. Then, a framework which based on mapping content to guide the co-ideation between humans and custom GPT (named as Co-Ideator) was promoted. Finally, a design case study followed by a survey and an interview was conducted to evaluate the ideation performance of the custom GPT and framework compared with traditional ideation methods. Also, the effect of custom GPT on co-ideation was compared with a non-artificial intelligence (AI)-used condition. The findings indicated that if users employed co-ideation with custom GPT, the novelty and quality of ideation outperformed by using traditional ideation.

In this paper, we study ordering properties of vectors of order statistics and sample ranges arising from bivariate Pareto random variables. Assume that $(X_1,X_2)\sim\mathcal{BP}(\alpha,\lambda_1,\lambda_2)$ and $(Y_1,Y_2)\sim\mathcal{BP}(\alpha,\mu_1,\mu_2).$ We then show that $(\lambda_1,\lambda_2)\stackrel{m}{\succ}(\mu_1,\mu_2)$ implies $(X_{1:2},X_{2:2})\ge_{st}(Y_{1:2},Y_{2:2}).$ Under bivariate Pareto distributions, we prove that the reciprocal majorization order between the two vectors of parameters is equivalent to the hazard rate and usual stochastic orders between sample ranges. We also show that the weak majorization order between two vectors of parameters is equivalent to the likelihood ratio and reversed hazard rate orders between sample ranges.

Asymptotic dimension and Assouad–Nagata dimension are measures of the large-scale shape of a class of graphs. Bonamy, Bousquet, Esperet, Groenland, Liu, Pirot, and Scott [J. Eur. Math. Society] showed that any proper minor-closed class has asymptotic dimension 2, dropping to 1 only if the treewidth is bounded. We improve this result by showing it also holds for the stricter Assouad–Nagata dimension. We also characterise when subdivision-closed classes of graphs have bounded Assouad–Nagata dimension.