The traditional ant colony optimisation (ACO) algorithm, when applied to mobile robot path planning, faces several challenges: slow convergence, susceptibility to local optima, and the generation of paths with excessive turning points, all of which reduce the robot’s operational efficiency. To overcome these shortcomings, this paper proposes a targeted set of improvements designed to enhance algorithm performance and increase the practicality and efficiency of path planning. First, we introduce an initial pheromone enhancement mechanism based on the Bresenham algorithm. By augmenting pheromone concentration along the approximate straight-line path from the start to the goal, ants are guided to explore in the optimal direction, thereby significantly accelerating convergence. Second, we integrate a directional continuity factor into the path selection probability: by using vector dot products to strengthen the bias toward consistent directions and by coupling this with a curvature-based pheromone reward that favours straighter segments, we ensure smoother, more direct paths. Finally, we apply a spring-model-based smoothing strategy as a post-processing step to the paths generated by the ant colony, reducing path complexity and the number of turns to guarantee efficient and reliable robot motion. To validate the performance of the improved algorithm, we conduct comparative experiments on a MATLAB platform against other enhanced ACO variants reported in the literature. The results demonstrate that our proposed algorithm significantly outperforms these existing methods across all performance metrics, exhibiting superior path planning capabilities.

The article is concerned with realizability in abstract argumentation. It provides characterization theorems for the most basic types of labelling-based semantics, namely conflict-free and naive labellings. It turns out that existing characterizations for extension-based semantics are of little help in characterizing labelling-based semantics. To this end, we introduce several new criteria like L-tightness, reject-witnessing, reject-compositionality as well as the new construct of a labelling-downward-closure, which help determine whether a given set of labellings is realizable regarding conflict-free or naive semantics. Moreover, we present standard constructions and analyse their uniqueness status. Further classical concepts like ordinary and strong equivalence are studied too. Last but not least, we delve into the characterization of stable labellings. It turns out that this endeavour is a highly non-trivial task with many parallels to so-called compact realizability, an open problem for stable semantics in abstract argumentation.

Given the potential of generative artificial intelligence (GenAI) to create human clones, it is not surprising that chatbots have been implemented in politics. In a turbulent political context, these AI-driven bots are likely to be used to spread biased information, amplify polarisation, and distort our memories. Large language models (LLMs) lack ‘political memory’ and cannot accurately process political discourses that draw from collective political memory. We refer to research concerning collective political memory and AI to present our observations of a chatbot experiment undertaken during the Presidential Elections in Finland in early 2024. This election took place at a historically crucial moment, as Finland, traditionally an advocate of neutrality and peacefulness, had become a vocal supporter of Ukraine and a new member state of NATO. Our research team developed LLM-driven chatbots for all presidential candidates, and Finnish citizens were afforded the chance to engage with these chatbot–politicians. In our study, human–chatbot discussions related to foreign and security politics were especially interesting. While rhetorically very typical and believable in light of real political speech, chatbots reorganised prevailing discourses generating responses that distorted the collective political memory. In actuality, Russia’s full-scale invasion of Ukraine had drastically changed Finland’s political positioning. Our AI-driven chatbots, or ‘electobots’, continued to promote constructive dialogue with Russia, thus earning our moniker ‘Finlandised Bots’. Our experiment highlights that training AI for political purposes requires familiarity with the prevailing discourses and attunement to the nuances of the context, showcasing the importance of studying human–machine interactions beyond the typical viewpoint of disinformation.

Recent work showing the existence of conflict-free almost-perfect hypergraph matchings has found many applications. We show that, assuming certain simple degree and codegree conditions on the hypergraph $ \mathcal{H}$ and the conflicts to be avoided, a conflict-free almost-perfect matching can be extended to one covering all vertices in a particular subset of $ V(\mathcal{H})$, by using an additional set of edges; in particular, we ensure that our matching avoids all additional conflicts, which may consist of both old and new edges. This setup is useful for various applications in design theory and Ramsey theory. For example, our main result provides a crucial tool in the recent proof of the high-girth existence conjecture due to Delcourt and Postle. It also provides a black box which encapsulates many long and tedious calculations, greatly simplifying the proofs of results in generalised Ramsey theory.

The study presents a novel approach to address challenges posed by singularities in robotic arm motion, focusing on Cartesian path planning and geometric path adherence. Recognizing limitations in traditional singularity avoidance methods, the research proposes a comprehensive strategy: reconstructing motion patterns in singular regions through singularity-consistent representations, applying arc-length reparameterization to Cartesian geometric paths, and incorporating path curvature as a dynamic weighting factor for sampling interval adjustment. This method achieves a balance between joint velocity smoothness and geometric tracking accuracy in Cartesian space, significantly enhancing the robot’s ability to adhere to prescribed geometric paths, particularly near singularities. Experimental results demonstrate the efficacy of the proposed approach in facilitating smooth singularity transitions, improving joint velocity continuity, and enhancing geometric path adherence. The study contributes to robotic arm path planning by offering a practical solution for applications requiring precise trajectory following and effective singularity handling.

We consider nonmonotonic inferences from belief bases that contain conditionals enforcing some of the possible worlds to be infeasible and thus completely implausible. In contrast to belief bases satisfying the strong notion of consistency requiring every world to be at least somewhat plausible, we call such belief bases weakly consistent. First, we review the treatment of weakly consistent belief bases by the seminal approaches of p-entailment, which coincides with system P, and of system Z, which coincides with rational closure. Then we focus on c-inference, an inductive inference operator that has been shown to exhibit many desirable properties put forward for nonmonotonic reasoning. It is based on c-representations, which are a special kind of ranking model ordering worlds according to their plausibility. While c-representation is defined for strongly consistent belief bases only, in this article, we extend the notions of c-representation and of c-inference to cover also weakly consistent belief bases. We adapt a constraint satisfaction problem (CSP) characterizing c-representations to capture extended c-representations, and we show how this extended CSP can be used to characterize extended c-inference, providing a basis for its implementation. We show various properties of extended c-inference and in particular, we prove that also the extended notion of c-inference fully satisfies syntax splitting. Furthermore, we extend and evaluate credulous and weakly skeptical c-inference to weakly consistent belief bases and provide characterizations for them as CSPs.

Informative path planning (IPP) is one of key applications for unmanned aerial vehicles. It can be applied to terrain monitoring problems, which are to find the static targets from bird’s eye view. In this research, the proposed algorithm generates the cost-benefit spanning tree (CBST) to boost the IPP performance. The CBST is able to generate different tree structures based on different parameters. The proofs show that the theoretical guarantees depend on the tree structures (e.g., minimal spanning tree and shortest path tree). The simulations and experiments demonstrate that the proposed method outperforms the benchmark approaches.

Approximation fixpoint theory (AFT) is an algebraic framework designed to study the semantics of non-monotonic logics. Despite its success, AFT is not readily applicable to higher-order definitions. To solve such an issue, we devise a formal mathematical framework employing concepts drawn from category theory. In particular, we make use of the notion of Cartesian closed category to inductively construct higher-order approximation spaces while preserving the structures necessary for the correct application of AFT. We show that this novel theoretical approach extends standard AFT to a higher-order environment and generalizes the AFT setting of Charalambidis et al. (2018).

Belief network analysis (BNA) has enabled major advances in the study of belief systems, capturing Converse’s understanding of the interdependence among multiple beliefs (i.e., constraint) more intuitively than many conventional statistics. However, BNA struggles with representing political divisions that follow a spatial logic, such as the “left–right” or “liberal-conservative” ideological divide. We argue that Response Item Networks (ResINs) have important advantages for modeling political cleavage lines as they organically capture belief systems in a latent ideological space. In addition to retaining many desirable properties inherent to BNA, ResIN can uncover ideological polarization in a visually intuitive, theoretically grounded, and statistically robust fashion. We demonstrate the advantages of ResIN by analyzing ideological polarization with regard to five hot-button issues from 2000 to 2020 using the American National Election Studies (ANES), and by comparing it against an equivalent procedure using BNA. We further introduce system-level and attitude-level polarization measures afforded by ResIN and discuss their potential to enrich the analysis of ideological polarization. Our analysis shows that ResIN allows us to observe much more detailed dynamics of polarization than classic BNA approaches.

This study developed and evaluated an online English speaking training approach that integrates corpora and artificial intelligence (AI) tools. The training integrated a self-developed spoken corpus, generative AI tools, and text-to-speech AI tools. Pre- and post-test results identified improvements in participants’ speaking performances. Participants attempted to use more positive linguistic features (e.g. producing complex sentences more frequently) and avoid using negative linguistic features (e.g. reducing the number of vowel errors) after receiving the training. Participants showed positive attitudes towards this corpus-based and AI-integrated English oral ability learning approach and affirmed the importance of integrating both tools. The corpus helped raise participants’ awareness of features that influence speaking performance and offered prompt engineering and feedback-checking functions, while the generative AI tools provided useful feedback and tailor-made sample responses. Additionally, text-to-speech AI tools offered learners with tailor-made native speaker samples for imitation and helped learners learn pausing. Results also revealed that this approach helped create an interactive oral ability learning environment, and the combination of corpora and AI tools provided more accurate feedback for each subskill of speaking.

This paper presents the design and implementation of Jaeger UTFPR, an open-source, low-cost, remote-controlled tiny humanoid robot measuring just 12 cm in height. Developed with a focus on accessibility and affordability, the robot integrates 3D-printed components, cost-effective electronics, embedded systems, and wireless communication to provide real-time audio and video feedback through a virtual reality (VR) interface. Operators control Jaeger UTFPR using a VR headset and motion controllers, enabling immersive telepresence and direct manipulation of the robot’s movements. With a total cost of just a few tens of dollars, this innovative solution offers broad applications in education, entertainment, research, and remote inspection, serving as an accessible platform for robotics enthusiasts and developers. Experimental evaluations demonstrate the system’s effectiveness in balancing performance and cost, validating its potential as a tool for immersive robotics experiences.

This article examines the implications of adopting a socio-technical perspective on the design and implementation of GovTech solutions. To observe the phenomenon, it adopts a case study approach focusing on the WiseTown solution and its City Digital Twin (CDT), developed by the Italian company TeamDev. The article investigates how integrating social factors, such as urban governance, with technical elements, like data analysis and modeling, can enhance the conceptualization, design, and implementation of user-centric, data-driven digital solutions as part of a broader digital transformation strategy. The article explores an Italian best practice that is developing four dimensions of the GovTech socio-technical framework: Governance Structures, Institutional Arrangements, User and Context Understanding, and Technological Development. It critically examines and discusses the challenges and opportunities associated with the adoption of CDTs and their impact on public policy implementation. The analysis is centered on two main aspects that emerged from the case study: data integration and sharing within CDTs, and the social implications associated with data usage for decision-making. Ultimately, the article explores the role of stakeholder collaboration (public-private partnerships) and the creation of innovation ecosystems—GovTech ecosystem in this specific case—to inform and steer policymaking through and beyond the adoption of CDTs.

This study investigated the impact of familiar versus unfamiliar environments on mobile-assisted language learning (MALL) task writing performance, English as a foreign language (EFL) writing proficiency, and learner perceptions. Fifty undergraduate students were divided into an experimental group and a control group. Both groups engaged in EFL learning in the classroom and later completed writing tasks in different learning environments outside the classroom: the experimental group in familiar environments and the control group in unfamiliar ones. Using a mobile learning system on tablet PCs, students completed five writing tasks describing resources in their environments, such as objects, people, situations, and scenarios. We assessed MALL task writing performance based on factors including the amount of writing, content quality, organization, creativity, grammar, and vocabulary, and compared results between the two groups. EFL writing proficiency was evaluated through a post-test directly related to the MALL tasks, and student perceptions of the MALL experience were measured through a survey. The results indicated that the experimental group outperformed the control group in both writing tasks and the post-test. Furthermore, the experimental group reported more positive perceptions of their MALL experience, reflected in higher emotional engagement and cognitive involvement. Based on these findings, we offer both theoretical insights into the role of familiar environments in facilitating language learning and practical suggestions for EFL teachers and researchers to incorporate real-world, contextually rich environments in MALL activities.

Current design theories and models predominantly focus on evaluating innovation through design solutions, using measures of novelty and usefulness as indicators of creativity. In contrast, the assessment of creative potential of design problems has attracted far less attention. To systematically explore the creative potential of design problems, a comprehensive literature review is conducted, revealing significant gaps where existing methods have yet to be applied. To address these gaps, first, an extensive database of design problems has been constructed using data collected from design patents, surveys, and questionnaires. Three distinct quantitative methods have been developed: the first for assessing novelty using SAPPhIRE model of causality, the second for assessing usefulness using usefulness indicators, and the third for assessing creative potential. The novelty method quantifies the minimum distance between a current problem and the old problems in the database, using textual similarity at different levels of SAPPhIRE abstraction. Expert evaluation of the novelty method indicates substantial agreement with experts’ intuitive notion, in addition to higher effectiveness compared to existing methods. The first two methods have then been integrated into the third method for assessing the overall creative potential of a design problem. Statistical analyses confirmed the correlation of both novelty and usefulness with creative potential, supporting findings in the literature. To demonstrate the methods, detailed case studies have been presented, illustrating the application of the methods. This systematic approach provides a robust framework for objective assessment of creativity in design problems, facilitating better prioritization and decision-making in engineering design contexts.

In many African countries, limited population data pose a challenge for tax administrations struggling with informal economies. This study examines Uganda’s integration of national ID data into tax registration through “Instant TIN,” an interface linking the Uganda Revenue Authority (URA) with the National Identification and Registration Agency (NIRA) and the Uganda Registration Service Bureau (URSB). This reform aims to streamline taxpayer registration and improve data quality. Using a mixed-methods approach—combining interviews with government officials and administrative data analysis—we identify three key findings. First, Instant TIN registrants differ significantly from those using the conventional process. They are more likely to be individuals, female, younger, and previously informal, highlighting the reform’s role in bringing in marginalised taxpayers. Second, Instant TIN improves data quality. It reduces TIN duplications for individuals and enhances contact accuracy, decreasing invalid or missing email addresses by eight percentage points and invalid phone numbers by six. However, it worsens sector data quality, increasing missing or incorrect sector information by 12 percentage points. Third, while Instant TIN reduces duplication, manual data entry, and administrative burdens, challenges remain. Infrequent updates in external datasets and a lack of validation within the interface increase administrative costs and complicate taxpayer engagement. Additionally, mandatory in-person updates and invalid contact details add to compliance burdens. Overall, Uganda’s experience highlights both the potential and limitations of integrating national ID data for tax administration, offering insights for other countries considering similar reforms.

How can we make global sensitivity analysis accessible and viable for engineering practice? In this translation article, we present a methodology to enable sensitivity analysis for structural and geotechnical engineering for built environment design and assessment workflows. Our technique wraps computational mechanics and geomechanics finite element (FE) simulations and combines high-performance computing on public cloud with surrogate modeling using machine learning. A key question we address is: “Is there a noticeable loss in fidelity of results from the sensitivity analysis when substituting a simulation model with a surrogate model?” We answer this question for both linear and nonlinear FE simulations.