Over 193 countries have signed at least one of more than 500 multilateral treaties addressing critical global issues, such as human rights, environmental protection, and trade. Ratifying a treaty obligates a country, as a “State Party,” to report to the United Nations on its progress toward implementing the treaty’s provisions. These reports and their associated review processes generate a wealth of textual data. Effectively monitoring, reviewing, and assessing national, regional, and global progress toward these treaty commitments is crucial for ensuring compliance and realizing the benefits of international cooperation. The UN Convention on the Rights of Persons with Disabilities (CRPD), which has been ratified by 191 countries, exemplifies this challenge. With over 1.3 billion people worldwide living with disabilities, the CRPD aims to promote a shift from a charity-based “medical model” that views disability as an individual deficiency, to a rights-based “social justice model” that emphasizes societal barriers and inclusivity. Each State Party submits periodic reports to the Committee on the Rights of Persons with Disabilities detailing their implementation efforts. This study analyzed all available CRPD State Reports (N = 170) using text mining, Natural Language Processing, and GenerativeAI tools to assess global progress, identify regional variations, and explore the factors influencing successful implementation. The findings reveal evidence of widespread CRPD implementation, growing support for social justice and economic inclusion, and the importance of civil society engagement. Hybrid data analysis approach of this study offers a promising framework for harnessing the power of textual data to advance the realization of treaty commitments worldwide.

Program equivalence checking is the task of confirming that two programs have the same behavior on corresponding inputs. We develop a calculus based on symbolic execution and coinduction to check the equivalence of programs in a non-strict functional language. Additionally, we show that our calculus can be used to derive counterexamples for pairs of inequivalent programs, including counterexamples that arise from non-termination. We describe a fully automated approach for finding both equivalence proofs and counterexamples. Our implementation, nebula, proves equivalences of programs written in Haskell. We demonstrate nebula’s practical effectiveness at both proving equivalence and producing counterexamples automatically by applying nebula to existing benchmark properties.

The population-based structural health monitoring paradigm has recently emerged as a promising approach to enhance data-driven assessment of engineering structures by facilitating transfer learning between structures with some degree of similarity. In this work, we apply this concept to the automated modal identification of structural systems. We introduce a graph neural network (GNN)-based deep learning scheme to identify modal properties, including natural frequencies, damping ratios, and mode shapes of engineering structures based on the power spectral density of spatially sparse vibration measurements. Systematic numerical experiments are conducted to evaluate the proposed model, employing two distinct truss populations that possess similar topological characteristics but varying geometric (size and shape) and material (stiffness) properties. The results demonstrate that, once trained, the proposed GNN-based model can identify modal properties of unseen structures within the same structural population with good efficiency and acceptable accuracy, even in the presence of measurement noise and sparse measurement locations. The GNN-based model exhibits advantages over the classic frequency domain decomposition method in terms of identification speed, as well as against an alternate multilayer perceptron architecture in terms of identification accuracy, rendering this a promising tool for PBSHM purposes.

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.