Capturing dynamic targets is particularly challenging for either rigid or soft grippers, as impact buffering should be completed in a short time to ensure the reliability of the robotic system. At collision onset, to deal with relatively low contact forces, adopting low stiffness and damping can effectively mitigate the rebound of the dynamic targets. As the contact area and forces increase, employing high stiffness and damping becomes necessary for absorbing high energy. This paper proposed a novel robotic gripper whose stiffness and damping follow a predefined profile “low stiffness and damping for low impact and high stiffness and damping for high impact.” The variable effects of impact buffering and energy dissipation in a collision process were modeled and analyzed. Then, a passive variable stiffness and damping regulator (P-VSDR) was developed where tendons and pulleys are used to generate a nonlinear motion from a linear spring-damper unit. The contact dynamics model of the robotic gripper equipped with P-VSDR was established. Simulated and experimental results show that this gripper enables reliable capture of dynamic targets with different velocities.

While LGBTQIA+ identities are already mostly invisible in the Italian education system, the current anti-gender policies proposed by right-wing and far-right politicians risk further hindering an inclusive education. However, recent Italian graphic novels pave the way for a multifaceted representation of the LGBTQIA+ community and an alternative form of education. For instance, Nicoz Balboa’s Play with Fire (2020) and Alec Trenta’s Barba (2022) are two autofictional graphic novels that depict the authors’ discovery of their trans identity and their experiences in the cis-heteronormative society. The article argues that the two works by Balboa and Trenta are not just examples of autofiction but also constitute an archive of memory and activism. First, the article traces the damaging effects of a lack of education around LGBTQIA+ themes. Then, it explores how Balboa and Trenta understand their lives by reading LGBTQIA+ stories and histories. Crucially, the article investigates how both authors become a point of reference themselves by representing their own bodies and including explanations about gender and sexuality topics. Documenting the way Balboa and Trenta build a counter-educational space in their graphic novels and chart a literary queer and trans genealogy, the article ultimately suggests that their works are a form of activist practice.

The effectiveness of robotic grippers is critical for the secure and damage-free manipulation of objects with diverse geometries and material properties. This paper presents the design, analysis, and experimental evaluation of a novel reconfigurable four-finger robotic gripper. The proposed design incorporates two stationary fingers fixed to a circular base and two movable fingers repositioned and reoriented via a face gear mechanism, enabling multiple finger configurations to enhance adaptability. A single geared motor drives the opening and closing motions of all four fingers, simplifying the actuation mechanism. The robotic gripper was fabricated using 3D printing technology, ensuring cost-effective and precise manufacturing. Experimental tests were conducted to evaluate the robotic gripper’s reconfigurability and grasping performance across a range of objects, demonstrating its effectiveness in various configurations. Additionally, a closed-loop force control system was implemented to assess the grasping performance of a soft reconfigurable variant. Grasping force measurements were performed on three distinct objects, yielding a grasping curve that confirmed successful adaptation and secure handling. While the results validate the robotic gripper’s performance, further refinement of the control algorithm is recommended to optimize its capabilities. Compared to conventional three-finger designs, the proposed robotic gripper offers superior reconfigurability and adaptability, making it suitable for a broader range of industrial and research applications. The innovative face gear mechanism and modular design expand the robotic gripper’s functionality, positioning it as a versatile tool for advanced robotic manipulation tasks.

We developed a cloud microphysics parameterization for the icosahedral nonhydrostatic modeling framework (ICON) model based on physics-informed machine learning (ML). By training our ML model on high-resolution simulation data, we enhance the representation of cloud microphysics in Earth system models (ESMs) compared to traditional parameterization schemes, in particular by considering the influence of high-resolution dynamics that are not resolved in coarse ESMs. We run a global, kilometer-scale ICON simulation with a one-moment cloud microphysics scheme, the complex graupel scheme, to generate 12 days of training data. Our ML approach combines a microphysics trigger classifier and a regression model. The microphysics trigger classifier identifies the grid cells where changes due to the cloud microphysical parameterization are expected. In those, the workflow continues by calling the regression model and additionally includes physical constraints for mass positivity and water mass conservation to ensure physical consistency. The microphysics trigger classifier achieves an F1 score of 0.93 on classifying unseen grid cells. The regression model reaches an $ {R}^2 $ score of 0.72 averaged over all seven microphysical tendencies on simulated days used for validation only. This results in a combined offline performance of 0.78. Using explainability techniques, we explored the correlations between input and output features, finding a strong alignment with the graupel scheme and, hence, physical understanding of cloud microphysical processes. This parameterization provides the foundation to advance the representation of cloud microphysical processes in climate models with ML, leading to more accurate climate projections and improved comprehension of the Earth’s climate system.

Reasoning about dynamic systems with a fine-grained temporal and numeric resolution presents significant challenges for logic-based approaches like Answer Set Programming (ASP). To address this, we introduce and elaborate upon a novel temporal and constraint-based extension of the logic of Here-and-There and its nonmonotonic equilibrium extension, representing, to the best of our knowledge, the first approach to nonmonotonic temporal reasoning with constraints specifically tailored for ASP. This expressive system is achieved by a synergistic combination of two foundational ASP extensions: the linear-time logic of Here-and-There, providing robust nonmonotonic temporal reasoning capabilities, and the logic of Here-and-There with constraints, enabling the direct integration and manipulation of numeric constraints, among others. This work establishes the foundational logical framework for tackling complex dynamic systems with high resolution within the ASP paradigm.

This paper presents four new monolithic continuum robot designs that can be 3D printed in a single piece and with TPU or similar elastic filaments for either educational or experimental applications. Similar tendon-driven continuum robots are usually made of a flexible backbone (often in NiTi alloys) and rigid vertebrae, with tens of components in a robot segment resulting in time-consuming manual assembly and high costs. Conversely, the proposed designs achieve equivalent functionality while avoiding the manufacturing challenges. Additionally, by removing the need for coupled features for assembly and 3D-printing backbones and vertebrae as a single part, new geometries are possible and can be explored to tailor robot performance to specific requirements. To validate the proposed design, four sample prototypes have been manufactured and experimentally tested. The obtained results, when compared to the piecewise constant curvature model, demonstrate a 3.06% tip positioning error and limited reduction of the workspace area of 23.07%, which compares favorably to similar but more expensive and complex tendon-driven robots.

This paper makes a twofold contribution to the study of expressivity. First, we introduce and study the novel concept of conditional expressivity. Taking a universal logic perspective, we characterize conditional expressivity both syntactically and semantically. We show that our concept of conditional expressivity is related to, but different from, the concept of explicit definability in Beth’s definability theorem. Second, we use the concept to explore inferential relations between collective deontic admissibility statements for different groups. Negative results on conditional expressivity are stronger than standard (unconditional) inexpressivity results: we show that the well-known inexpressivity results from epistemic logic on distributed knowledge and on common knowledge only concern unconditional expressivity. By contrast, we prove negative results on conditional expressivity in the deontic logic of collective agency. In particular, we consider the full formal language of the deontic logic of collective agency, define a natural class of sublanguages of the full language, and prove that a collective deontic admissibility statement about a particular group is conditionally expressible in a sublanguage from the class if and only if that sublanguage includes a collective deontic admissibility statement about a supergroup of that group. Our negative results on conditional expressivity may serve as a proof of concept for future studies.

The control of shipborne stabilisation platforms is challenging due to the effects of platform dynamic characteristics and unpredictable wave disturbances in operational environments. This paper proposes an integrated control strategy that combines dynamic feedforward and fuzzy gain control. Based on the derived dynamic model of the shipborne stabilisation platform, a dynamic feedforward controller is designed to mitigate the effects of platform dynamics on motion accuracy. In the fuzzy gain control design, scaling modules are proposed to enhance the fuzzy controller’s adaptability to varying operating conditions and unpredictable wave disturbances. The motion of the stabilisation platform is simulated by taking the motion of the lower platform calculated based on the wave fluctuations in marine environments as the input. The prototype experiment is conducted by using a large-scale parallel mechanism to simulate the wave environments. Simulation and experimental results indicate that the proposed control strategy achieves real-time disturbance compensation without precise mathematical modelling or pre-training, and demonstrates good adaptability.

Climate change poses an existential threat, necessitating effective climate policies to enact impactful change. Decisions in this domain are incredibly complex, involving conflicting entities and evidence. In the last decades, policymakers increasingly use simulations and computational methods to guide some of their decisions. Integrated Assessment Models (IAMs) are one of such methods, which combine social, economic, and environmental simulations to forecast potential policy effects. For example, the UN uses outputs of IAMs for their recent Intergovernmental Panel on Climate Change (IPCC) reports. Traditionally these have been solved using recursive equation solvers, but have several shortcomings, e.g. struggling at decision making under uncertainty. Recent preliminary work using Reinforcement Learning (RL) as an alternative to traditional solvers shows promising results in decision making in uncertain and noisy scenarios. We extend on this work by introducing multiple interacting RL agents as a preliminary analysis on modelling the complex interplay of socio-interactions between various stakeholders or nations that drives much of the current climate crisis. Our findings show that cooperative agents in this framework can consistently chart pathways towards more desirable futures in terms of reduced carbon emissions and improved economy. However, upon introducing competition between agents, for instance by using opposing reward functions, desirable climate futures are rarely reached. Modelling competition is key to increased realism in these simulations, as such we employ policy interpretation by visualizing what states lead to more uncertain behavior, to understand algorithm failure. Finally, we highlight the current limitations and avenues for further work to ensure future technology uptake for policy derivation.

The development of intelligent control-oriented solutions for building energy systems is a promising research field. The development of effective systems relies on seldom available large data sets or on simulation environments, either for training or execution phases. The creation of simulation environments based on thermal models is a challenging task, requiring the usage of third-party solutions and high levels of expertise in the energy engineering field, which poses relevant restrictions to the development of control-oriented research.

In this work, a training workbench is presented, integrating an accurate but lightweight lumped capacitance model with proven accuracy to represent the thermal dynamics of buildings, engineering models for energy systems in buildings, and user behavior models into an overall building energy performance forecasting model. It is developed in such a way that it can be easily integrated into control-oriented applications, with no requirements to use complex, third-party tools.

In this paper, we compare four different semantics for disjunction in Answer Set Programming that, unlike stable models, do not adhere to the principle of model minimality. Two of these approaches, Cabalar and Muñiz’ Justified Models and Doherty and Szalas’ Strongly Supported Models, directly provide an alternative non-minimal semantics for disjunction. The other two, Aguado et al’s Forks and Shen and Eiter’s Determining Inference (DI) semantics, actually introduce a new disjunction connective, but are compared here as if they constituted new semantics for the standard disjunction operator. We are able to prove that three of these approaches (Forks, Justified Models and a reasonable relaxation of the DI-semantics) actually coincide, constituting a common single approach under different definitions. Moreover, this common semantics always provides a superset of the stable models of a programme (in fact, modulo any context) and is strictly stronger than the fourth approach (Strongly Supported Models), that actually treats disjunctions as in classical logic.

This paper continues an established line of research about the relations between argumentation theory, particularly assumption-based argumentation, and different kinds of logic programs. In particular, we extend known result of Bondarenko, Dung, Kowalski and Toni, and of Caminada and Schulz, by showing that assumption-based argumentation can represent not only normal logic programs, but also disjunctive logic programs under the stable model semantics. For this, we consider some inference rules for disjunction that the core logic of the argumentation frameworks should respect, and show the correspondence to the handling of disjunctions in the heads of the logic programs’ rules.

Answer Set Programming (ASP) provides a powerful declarative paradigm for knowledge representation and reasoning. Recently, counting answer sets has emerged as an important computational problem with applications in probabilistic reasoning, network reliability analysis, and other domains. This has motivated significant research into designing efficient ASP counters. While substantial progress has been made for normal logic programs, the development of practical counters for disjunctive logic programs remains challenging. We present $\mathsf{sharpASP}$-$\mathcal{SR}$, a novel framework for counting answer sets of disjunctive logic programs based on subtractive reduction to projected propositional model counting. Our approach introduces an alternative characterization of answer sets that enables efficient reduction while ensuring the intermediate representations remain polynomial in size. This allows $\mathsf{sharpASP}$-$\mathcal{SR}$ to leverage recent advances in projected model counting technology. Through extensive experimental evaluation on diverse benchmarks, we demonstrate that $\mathsf{sharpASP}$-$\mathcal{SR}$ significantly outperforms existing counters on instances with large answer set counts. Building on these results, we develop a hybrid counting approach that combines enumeration techniques with $\mathsf{sharpASP}$-$\mathcal{SR}$ to achieve state-of-the-art performance across the full spectrum of disjunctive programs. The extended version of the paper is available at: https://arxiv.org/abs/2507.11655.

VR sketching tools have matured to a practical level, enabling use across various 3D design disciplines. Studies into VR sketching in design report beneficial affordances but are based on brief testing of tools in simulated tasks. Consequently, there is a knowledge deficit in understanding how to effectively integrate VR sketching into design projects. We address this gap with a case study on the sustained use of VR sketching in 10 automotive concept design projects over 10 months. In analysing designers’ logbooks, which captured design development, and post-study reflections, we show how the affordances of VR sketching outlined in literature manifest in practice. Specifically, we show how and when designers can exploit the precedence of 3D geometry embodied in VR sketches to advance the design process in terms of several dimensions of design fidelity. We highlight where process advantages are realised through (1) increased spatial fidelity, reducing the time required to iterate 2D sketches, (2) operational fidelity supporting dynamic testing of concept functionality via animation and (3) environmental fidelity supporting contextualising components and storytelling. As such, our findings highlight how and when practitioners can realise the comparative benefits of VR sketching alongside traditional sketching and 3d modelling during the concept design process.

Can we quantify over absolutely every set? Absolutists typically affirm, while relativists typically deny, the possibility of unrestricted quantification (in set theory). In the first part of this article, I develop a novel and intermediate philosophical position in the absolutism versus relativism debate in set theory. In a nutshell, the idea is that problematic sentences related to paradoxes cannot be interpreted with unrestricted quantifier domains, while prima facie absolutist sentences (e.g., “no set is contained in the empty set”) are unproblematic in this respect and can be interpreted over a domain containing all sets. In the second part of the paper, I develop a semantic theory that can implement the intermediate position. The resulting framework allows us to distinguish between inherently absolutist and inherently relativist sentences of the language of set theory.

The operating room scheduling (ORS) problem deals with the optimization of daily operating room surgery schedules. It is a challenging problem subject to many constraints, like to determine the starting time of different surgeries and allocating the required resources, including the availability of beds in different department units. Recently, solutions to this problem based on answer set programming (ASP) have been delivered. Such solutions are overall satisfying but, when applied to real data, they can currently only verify whether the encoding aligns with the actual data and, at most, suggest alternative schedules that could have been computed. As a consequence, it is not currently possible to generate provisional schedules. Furthermore, the resulting schedules are not always robust. In this paper, we integrate inductive and deductive techniques for solving these issues. We first employ machine learning algorithms to predict the surgery duration, from historical data, to compute provisional schedules. Then, we consider the confidence of such predictions as an additional input to our problem and update the encoding correspondingly in order to compute more robust schedules. Results on historical data from the ASL1 Liguria in Italy confirm the viability of our integration.

Visible satellite imagery (VIS) is essential for monitoring weather patterns and tracking ground surface changes associated with climate change. However, its availability is limited during nighttime. To address this limitation, we present a discrete variational autoencoder (VQVAE) method for translating infrared satellite imagery to VIS. This method departs from previous efforts that utilize a U-Net architecture. By removing the connections between corresponding layers of the encoder and decoder, the model learns a discrete and rich codebook of latent priors for the translation task. We train and test our model on mesoscale data from the Geostationary Operational Environmental Satellite (GOES) West Advanced Baseline Imager (ABI) sensor, spanning 4 years (2019 to 2022) using the Conditional Generative Adversarial Nets (CGAN) framework. This work demonstrates the practical use of a VQVAE for meteorological satellite image translation. Our approach provides a modular framework for data compression and reconstruction, with a latent representation space specifically designed for handling meteorological satellite imagery.

The rise of visually driven platforms like Instagram has reshaped how information is shared and understood. This study examines the role of social, cultural, and political (SCP) symbols in Instagram posts during Taiwan’s 2024 election, focusing on their influence in anti-misinformation efforts. Using large language models (LLMs)—GPT-4 Omni and Gemini Pro Vision—we analyzed thousands of posts to extract and classify symbolic elements, comparing model performance in consistency and interpretive depth. We evaluated how SCP symbols affect user engagement, perceptions of fairness, and content spread. Engagement was measured by likes, while diffusion patterns followed the SEIZ epidemiological model. Findings show that posts featuring SCP symbols consistently received more interaction, even when follower counts were equal. Although political content creators often had larger audiences, posts with cultural symbols drove the highest engagement, were perceived as more fair and trustworthy, and spread more rapidly across networks. Our results suggest that symbolic richness influences online interactions more than audience size. By integrating semiotic analysis, LLM-based interpretation, and diffusion modeling, this study offers a novel framework for understanding how symbolic communication shapes engagement on visual platforms. These insights can guide designers, policymakers, and strategists in developing culturally resonant, symbol-aware messaging to combat misinformation and promote credible narratives.

We investigate a system of modal semantics in which $\Box \phi $ is true if and only if $\phi $ is entailed by a designated set of formulas by a designated logics. We prove some strong completeness results as well as a natural connection to normal modal logics via an application of some lattice-theoretic fixpoint theorems. We raise a difficult problem that arises naturally in this setting about logics which are identical with their own ‘meta-logic’, and draw a surprising connection to recent work by Andrew Bacon and Kit Fine on McKinsey’s substitutional modal semantics.

In this paper, we consider an approach introduced in term rewriting for the automatic detection of non-looping non-termination from patterns of rules. We adapt it to logic programing by defining a new unfolding technique that produces patterns describing possibly infinite sets of finite rewrite sequences. We present an experimental evaluation of our contributions that we implemented in our tool NTI (Non-Termination Inference).