As digital welfare systems expand in local governments worldwide, understanding their implications is crucial for safeguarding public values like transparency, legitimacy, accountability, and privacy. A lack of political debate on data-driven technologies risks eroding democratic legitimacy by obscuring decision-making and impeding accountability mechanisms. In the Netherlands, political discussions on digital welfare within local governments are surprisingly limited, despite evidence of negative impacts on both frontline professionals and citizens. This study examines what mechanisms explain if and how data-driven technologies in the domain of work and income are politically discussed within the municipal government of a large city in the Netherlands, and its consequences. Using a sequential mixed methods design, combining automated text-analysis software ConText (1.2.0) and text-analysis software Atlas.ti (9), we analyzed documents and video recordings of municipal council and committee meetings from 2016 to 2023. Our results show these discussions are rare in the municipal council, occurring primarily either in reaction to scandals, or in reaction to criticism. Two key discursive factors used to justify limited political discussion are: (1) claims of lacking time and knowledge among council members and aldermen, and (2) distancing responsibility and diffusing accountability. This leads to a ‘content chopping’ mechanism, where issues are chopped into small content pieces, for example technical, ethical, and political aspects, and spreading them into separate documents and discussion arenas. This fragmentation can obscure overall coherence and diffuse critical concerns, potentially leading to harmful effects like dehumanization and stereotyping.

A multi-modal embodied robot framework was developed and evaluated to support English as a Second Language (ESL) learning in preschoolers through physical interaction and adaptive engagement. The system integrates a 4-DOF OpenManipulator-X robot with a tablet-based educational application, forming a unified instructional platform that delivers synchronized auditory, visual, and kinesthetic stimuli. Designed to improve lexical retention and motivation in early learners, the framework enables task-based interaction through pick-and-place vocabulary reinforcement, collaborative drawing, and tablet-mediated language tasks, coupled with a real-time emotion recognition module to adjust instructional cues.

An experimental design within the subject was used with 21 Korean preschool children (ages 4–8), comparing robot-assisted language learning (RALL) with traditional teacher-led language learning (TLLL) in matched tasks involving vocabulary learning, math reasoning, color categorization, and spelling recall. Each session was conducted under controlled classroom conditions and analyzed using both quantitative and qualitative metrics, including engagement frequency, task precision, and structured post-session surveys.

The results demonstrate significantly higher participation and task completion rates in the RALL condition, with vocabulary acquisition outcomes comparable to TLLL (p > 0.05). Children exhibited increased motivation and sustained interaction when guided by the robot and the application, suggesting that embodied adaptive systems can effectively support early second language learning. The study contributes validated design principles for integrating physical embodiment, affective responsiveness, and multi-modal instructional delivery in educational robotics. Implications are discussed for the scalable deployment of robot-assisted systems in preschool contexts, emphasizing child-centered interaction and developmental appropriateness within RALL environments.

One of the natural problems of operational semantics is to characterise the relationship between eager and lazy evaluation. In the context of $\lambda$-calculus, this is expressed by the classic theorem that call-by-value evaluation of a program to (weak-head) normal form can always be simulated by a call-by-name evaluation. While the statement and intuition behind it are simple and clear, naive attempts at proof famously fail: the result is usually established as a consequence of the more complex standardisation theorem. In this work, we develop and formalise a novel and lightweight inductive approach to tackle the problem of simulation between two semantics for a single calculus, but with different evaluation orders. We exercise our method on the classic call-by-value and call-by-name example and report on methodological takeaways suggested by our approach, in particular what effect the flavour of semantics chosen has on the proof.

The Pósa–Seymour conjecture determines the minimum degree threshold for forcing the $k$th power of a Hamilton cycle in a graph. After numerous partial results, Komlós, Sárközy, and Szemerédi proved the conjecture for sufficiently large graphs. In this paper, we focus on the analogous problem for digraphs and for oriented graphs. We asymptotically determine the minimum total degree threshold for forcing the square of a Hamilton cycle in a digraph. We also give a conjecture on the corresponding threshold for $k$th powers of a Hamilton cycle more generally. For oriented graphs, we provide a minimum semi-degree condition that forces the $k$th power of a Hamilton cycle; although this minimum semi-degree condition is not tight, it does provide the correct order of magnitude of the threshold. Turán-type problems for oriented graphs are also discussed.

This is the foreword for the special issue “Differential Structures in Computer Science and Mathematics.” We dedicate this special issue to Phil Scott (1947–2023), who initially organized and then invited us to serve as editors of this special issue.

Reliably identifying and understanding temporal precursors to extreme wind gusts is crucial for early warning and mitigation. This study proposes a simple data-driven approach to extract key predictors from a dataset of historical extreme European winter windstorms and derive simple equations linking these precursors to extreme gusts over land. A major challenge is the limited training data for extreme events, increasing the risk of model overfitting. Testing various mitigation strategies, we find that combining dimensionality reduction, careful cross-validation, feature selection, and a nonlinear transformation of maximum wind gusts informed by Generalized Extreme Value distributions successfully reduces overfitting. These measures yield interpretable equations that generalize across regions while maintaining satisfactory predictive skill. The discovered equations reveal the association between a steady drying low-troposphere before landfall and wind gust intensity in Northwestern Europe.

Underactuated Cable-Driven Parallel Robots (UACDPRs) typically rely on relative internal sensors to estimate the end-effector (EE) state. Therefore, at startup, the reference values of the quantities measured by these sensors are unknown, and so is the initial pose of the EE. The problem of determining the reference values of the internal sensors is called initial-pose self-calibration. The latter is often formulated as an overdetermined system of nonlinear equations and solved using nonlinear weighted least-squares methods, minimizing the error between modeled and measured variables, and its effectiveness is highly influenced by the choice of measurement configurations, as well as the motion planning and control strategy used to reach them. This work presents two practical data acquisition methods for initial-pose self-calibration of UACDPRs, aiming to reduce the overall time required by the procedure and enhance process automation. The first method is slower but richer in data, as it relies on equilibrium poses and, therefore, can leverage cable-tension data, whereas the second method is faster and is based on geometric constraints only. The performance of the methods is evaluated in terms of acquisition time, number of measurements, and calibration accuracy on a 4-cable UACDPR prototype. The results highlight the merits and shortcomings of both methods, namely, trade-offs between the velocity of data collection and the precision of pose estimation.

We study the complexity of deterministic and probabilistic inversions of partial computable functions on the reals.

For $\ell \geq 3$, an $\ell$-uniform hypergraph is disperse if the number of edges induced by any set of $\ell +1$ vertices is 0, 1, $\ell$, or $\ell +1$. We show that every disperse $\ell$-uniform hypergraph on $n$ vertices contains a clique or independent set of size $n^{\Omega _{\ell }(1)}$, answering a question of the first author and Tomon. To this end, we prove several structural properties of disperse hypergraphs.

Artificial Intelligence (AI) has reached memory studies in earnest. This partly reflects the hype around recent developments in generative AI (genAI), machine learning, and large language models (LLMs). But how can memory studies scholars handle this hype? Focusing on genAI applications, in particular so-called ‘chatbots’ (transformer-based instruction-tuned text generators), this commentary highlights five areas of critique that can help memory scholars to critically interrogate AI’s implications for their field. These are: (1) historical critiques that complicate AI’s common historical narrative and historicize genAI; (2) technical critiques that highlight how genAI applications are designed and function; (3) praxis critiques that centre on how people use genAI; (4) geopolitical critiques that recognize how international power dynamics shape the uneven global distribution of genAI and its consequences; and (5) environmental critiques that foreground genAI’s ecological impact. For each area, we highlight debates and themes that we argue should be central to the ongoing study of genAI and memory. We do this from an interdisciplinary perspective that combines our knowledge of digital sociology, media studies, literary and cultural studies, cognitive psychology, and communication and computer science. We conclude with a methodological provocation and by reflecting on our own role in the hype we are seeking to dispel.

This study investigates unintended information flow in large language models (LLMs) by proposing a computational linguistic framework for detecting and analyzing domain anchorage. Domain anchorage is a phenomenon potentially caused by in-context learning or latent “cache” retention of prior inputs, which enables language models to infer and reinforce shared latent concepts across interactions, leading to uniformity in responses that can persist across distinct users or prompts. Using GPT-4 as a case study, our framework systematically quantifies the lexical, syntactic, semantic, and positional similarities between inputs and outputs to detect these domain anchorage effects. We introduce a structured methodology to evaluate the associated risks and highlight the need for robust mitigation strategies. By leveraging domain-aware analysis, this work provides a scalable framework for monitoring information persistence in LLMs, which can inform enterprise guardrails to ensure response consistency, privacy, and safety in real-world deployments.

In firefighting missions, human firefighters are often exposed to high-risk environments such as intense heat and limited visibility. To address this, firefighting robots can serve as valuable agents for autonomous navigation and flame perception. This paper proposes a novel visual Simultaneous Localization and Mapping (SLAM) framework, Fire SLAM, tailored for firefighting scenarios. The system integrates a flame detection and tracking thread-based on the YOLOv8n network and Kalman filtering-to achieve real-time flame detection, tracking, and 3D localization. By leveraging the detection results, dynamic flame regions are excluded from the SLAM front-end, allowing static features to be used for robust pose estimation and loop closure. To validate the proposed system, multiple datasets were collected from real-world and simulated fire environments. Experimental results demonstrate that Fire SLAM improves localization accuracy and robustness in fire scenes with flame disturbances, showing promise for autonomous firefighting robot deployment.

The tail recursion modulo cons transformation can rewrite functions that are not quite tail-recursive into a tail-recursive form that can be executed efficiently. In this article, we generalize tail recursion modulo cons (TRMc) to modulo context (TRMC) and calculate a general TRMC algorithm from its specification. We can instantiate our general algorithm by providing an implementation of application and composition on abstract contexts and showing that our context laws hold. We provide some known instantiations of TRMC, namely modulo evaluation contexts (CPS), and associative operations, and further instantiations not so commonly associated with TRMC, such as defunctionalized evaluation contexts, monoids, semirings, exponents, and fields. We study the modulo cons instantiation in particular and prove that an instantiation using Minamide’s hole calculus is sound. We also calculate a second instantiation in terms of the Perceus heap semantics to precisely reason about the soundness of in-place update. While all previous approaches to TRMc fail in the presence of nonlinear control (e.g., induced by call/cc, shift/reset, or algebraic effect handlers), we can elegantly extend the heap semantics to a hybrid approach which dynamically adapts to nonlinear control flow. We have a full implementation of hybrid TRMc in the Koka language, and our benchmark shows the TRMc transformed functions are always as fast or faster than using manual alternatives.

Social relationships provide opportunities to exchange and obtain health advice. Not only close confidants may be perceived as sources of health advice, but also acquaintances met in places outside a closed circle of family and friends, e.g., in voluntary organizations. This study is the first to analyze the structure of complete health advice networks in three voluntary organizations and compare them with more commonly studied close relationships. To this end, we collected data on multiple networks and health outcomes among 143 middle-aged and older adults (mean age = 53.9 years) in three carnival clubs in Germany. Our analyses demonstrate that perceived health advice and close relationships overlap only by 34%. Moreover, recent advances in exponential random graph models (ERGMs) allow us to illustrate that the network structure of perceived health advice differs starkly from that of close relationships. For instance, we found that advice networks exhibited lower transitivity and greater segregation by gender and age in comparison to networks of close relationships. We also found that actors with poor physical health perceive less individuals as health advisors than those with good physical health. Our findings suggest that community settings, such as voluntary associations, provide a unique platform for exchanging health advice and information among both close and distant network members.

In the fields of meal-assisting robotics and human–robot interaction (HRI), real-time and accurate mouth pose estimation is critical for ensuring interaction safety and improving user experience. The complexity arises from the diverse opening degrees of mouths, variations in orientation, and external factors such as lighting conditions and occlusions, which pose significant challenges for real-time and accurate posture estimation of mouths. In response to the above-mentioned issues, this paper proposes a novel method for point cloud fitting and posture estimation of mouth opening degrees (FP-MODs). The proposed method leverages both RGB and depth images captured from a single viewpoint, integrating geometric modeling with advanced point cloud processing techniques to achieve robust and accurate mouth posture estimation. The innovation of this work lies in the hypothesis that different states of mouth openings can be effectively described by distinct geometric shapes: closed mouths are modeled by spatial quadratic surfaces, half-open mouths by spatial ellipses, and fully open mouths by spatial circles. Then, based on these hypotheses, we developed algorithms for fitting geometric models to point clouds obtained from mouth regions, respectively. Specifically, for the closed mouth state, we employ an algorithm based on least squares optimization to fit a spatial quadratic surface to the point cloud data. For the half-open or fully open mouth states, we combine inverse projection methods with least squares fitting to model the contour as a spatial ellipse and circle, respectively. Finally, to evaluate the effectiveness of the proposed FP-MODs method, extensive actual experiments were conducted under varying conditions, including different orientations and various types of mouths. The results demonstrate that the proposed FP-MODs method achieves high accuracy and robustness. This study can provide a theoretical foundation and technical support for improving HRI and food delivery safety in the field of robotics.

In dynamic environments, moving objects pose a great challenge to the accuracy and robustness of visual simultaneous localization and mapping (VSLAM) systems. Traditional dynamic VSLAM methods rely on hand-designed feature frames, and these methods usually make it difficult to fully utilize feature information in dynamic regions. To this end, this paper proposes a SLAM system (GAF-SLAM) that combines gray area feature points, weighted static probabilities, and spatio-temporal constraints. This method realizes the efficient fusion of key point detection and target detection by introducing YOLO-Point to extract gray area feature points from dynamic regions. These feature points are located within the detection frame and have potentially static feature point properties. By combining the reprojection error and polar geometry constraints, potential static feature points are effectively screened out and the identification of these gray area feature points is further optimized. Subsequently, a novel static probabilistic computational framework is designed to assign weights to these gray area feature points and dynamically adjust their influence on the optimization results during the attitude estimation process. By combining static probability with temporal continuity and spatial smoothness constraints, the system achieves significantly improved localization accuracy and robustness in dynamic environments. Finally, the proposed method was evaluated on the TUM RGB-D dataset. The experimental results demonstrate that GAF-SLAM significantly improves pose estimation accuracy and exhibits strong robustness and stability in dynamic indoor environments.

Model-based systems engineering (MBSE) is increasingly used across industries for the integrated modeling of complex systems to support model-based development and provide enhanced traceability between requirements and verification and validation of the system. This paper seeks to strengthen the function modeling methodology in MBSE by introducing an approach based on flow heuristics guided by the System State Flow Diagram schema. This provides integrated function architectures with an enhanced integrity in MBSE. The approach is illustrated with a case study of an electric bicycle implemented in the MathWorks System Composer environment.

This article investigates “livingness” at the convergence of design, human–computer interaction (HCI) and synthetic biology, emphasising the evolving role of materialism. It examines living artefacts – objects designed with life-like qualities that utilise natural, engineered or programmable materials. The study thoroughly reviews theoretical underpinnings, highlighting new materialism’s focus on the agency of matter and HCI’s material turn, underscoring the value of physical interaction with digital systems. It also discusses recent advancements in living organisms as integral elements in design, aimed at reducing environmental impact and creating new user experiences. Through a systematic literature review and an in-depth analysis of case studies, the article proposes an extended definition of “livingness” across the three disciplines, advancing the understanding of the functions of living artefacts, how life-like capabilities can be integrated into them, and the implications for regenerative design. The findings invite a reimagined relationship between humans, materials and technology, fostering sustainable and interactive design practices.

Australian public sector agencies want to improve access to public sector data to help conduct better informed policy analysis and research and have passed legislation to improve access to this data. Much of this public sector data also contains personal information or health information and is therefore governed by state and federal privacy law which places conditions on the use of personal and health information. This paper therefore analyses how these data sharing laws compare with one another, as well as whether they substantially change the grounds on which public sector data can be shared. It finds that data sharing legislation, by itself, does not substantially change the norms embedded in privacy and health information management law governing the sharing of personal and health information. However, this paper notes that there can still be breaches of social licence even where data sharing occurs lawfully. Further, this paper notes that there are several inconsistencies between data sharing legislation across Australia. This paper therefore proposes reform, policy, and technical strategies to resolve the impact of these inconsistencies.