This paper presents the development of a graph autoencoder architecture capable of performing projection-based model-order reduction (PMOR) using a nonlinear manifold least-squares Petrov–Galerkin (LSPG) projection scheme. The architecture is particularly useful for advection-dominated flows modeled by unstructured meshes, as it provides a robust nonlinear mapping that can be leveraged in a PMOR setting. The presented graph autoencoder is constructed with a two-part process that consists of (1) generating a hierarchy of reduced graphs to emulate the compressive abilities of convolutional neural networks (CNNs) and (2) training a message passing operation at each step in the hierarchy of reduced graphs to emulate the filtering process of a CNN. The resulting framework provides improved flexibility over traditional CNN-based autoencoders because it is readily extendable to unstructured meshes. We provide an analysis of the interpretability of the graph autoencoder’s latent state variables, where we find that the Jacobian of the decoder for the proposed graph autoencoder provides interpretable mode shapes akin to traditional proper orthogonal decomposition modes. To highlight the capabilities of the proposed framework, which is named geometric deep least-squares Petrov–Galerkin (GD-LSPG), we benchmark the method on a one-dimensional Burgers’ model with a structured mesh and demonstrate the flexibility of GD-LSPG by deploying it on two test cases for two-dimensional Euler equations that use an unstructured mesh. The proposed framework is more flexible than using a traditional CNN-based autoencoder and provides considerable improvement in accuracy for very low-dimensional latent spaces in comparison with traditional affine projections.

Central to drawn representations of activism and memory are ideas of embodiment and trace. From DIY protest signs to craftivism, the articulation of protest and memory is connected to the handmade trace of a witnessing individual present in time and place. This is reflected in comics scholarship through the notion of the drawn line conveying subjective experience through the trace of the body.

This article will consider the relationship between witnessing, truth claims, autographic drawing, and memory at a moment when AI image-generation tools have called into question the connection of drawn traces to their origin in time, space, materiality, and the body.

Although a combination of critical AI theory and comics studies, this article will outline ways in which generative AI presents a challenge to these ideas. Through comparison of Joe Sacco’s graphic reportage with recent AI images of conflict and history, the article considers the truth claims of images that are the products of computational and algorithmic processes considered broadly.

Comics scholarship has been slow to critically respond to these new conditions, and the task of disentangling the human/non-human in ontologies of trace is now compounded by generative drawings, which represent the outcome of archival reappropriation defined by opaque algorithmic parameters. This article will explore theoretical assumptions around authenticity and truth claims in analogue, computational, algorithmic, and generative drawing practice and ask what kinds of theory and practice are appropriate if activist graphic memoir is to endure as documents of political memory.

This article examines the short-lived Marvel comic Misty (1985–1986), created by feminist cartoonist Trina Robbins, as a case study in how comics can invite and depend on reader participation. We draw on an archival collection of over 1,000 fan letters and fashion designs submitted to Misty, along with recent communications with former readers, to explore how children and young adults influenced both the published comic and its surrounding culture. We argue that readers’ contributions – ranging from clothing designs to story ideas – constituted a form of activism: they challenged corporate publishing practices, promoted new story directions, and built local fan communities. Highlighting the recent memories of Misty’s reader contributors, we show how engaging in the comic’s participatory culture could, in turn, have lasting effects on readers, shaping their confidence, career paths, and creative philosophies. By reframing Misty’s collective participatory culture as activism and placing it in conversation with readers’ personal memories, this study contributes to scholarship on comics, fandom, and memory: even small acts of reader engagement can transform both cultural texts and individual lives.

We study exponentiable functors in the context of synthetic $\infty$-categories. We do this within the framework of simplicial homotopy type theory of Riehl and Shulman. Our main result characterizes exponentiable functors. In order to achieve this, we explore Segal type completions. Moreover, we verify that our result is semantically sound.

Design Science is the discipline that studies the creation of artifacts – products, services, and systems and their embedding in our physical, virtual, psychological, economic, and social environments. This editorial is a collective effort of the Design Science Journal’s editorial board members, past and present. The journal’s inaugural 2015 editorial, “Design Science: Why, What and How,” reflected the thoughts and vision of that first editorial board for the new journal and the discipline it represented. The present contribution offers the reflections of editors who served the journal in the past 10 years. The individual contributions were not primed and are presented here unedited for conformity or consistency. Differently from the 2015 editorial, there is no effort to synthesize the individual contributions, leaving the task to our readers, who can draw their own conclusions about the Design Science Journal and community accomplishments to date, and the challenges ahead.

Rotterdam, a city in the Netherlands, experienced significant bombing in its city centre during the Second World War. Despite the trauma associated with this event, in 1948, the city adopted a new motto: ‘Sterker Door Strijd’, translating as ‘Stronger Through Struggle’. This motto remains visible today under the city’s coat of arms, symbolising the resilience and strength of its inhabitants as they rebuilt their city. ‘Sterker Door Strijd’ has become a central aspect of Rotterdam’s development, particularly in its architecture and urban planning. It showcases a shift in the city’s memory from pain to pride and hope for the future. The motto beautifully embodies Rigney’s ‘memory–activism nexus’ from a spatial perspective, reconstructing the city’s traumatic memory of destruction into a narrative of resistance. The motto is widely known and felt by every Rotterdammer, including foreigners who live and work in the city, like me. The visual essay ‘From Struggle to Strength’ poetically focuses on the city of Rotterdam and its motto. It intimately follows my personal artistic journey and my embodiment in the city. The story unfolds as I walk and draw around the city. Additionally, I interviewed inhabitants focusing on the challenges of social housing issues in the city, such as displacement and demolition and considering how the residents are actively resisting these issues. Through these interactions, the visual essay reflects on the transformative power of memory and activism in shaping the city’s past, present and future.

In this article, we identify the comics of the Real Cost of Prisons Project as graphic memory work that denaturalises ‘penal common sense’ and engages in graphic witnessing. To show how the United States’ ‘crime problem’ established a seemingly natural link between crime and incarceration, we first review the criminological aspects of American comics memory. Then, we demonstrate how The Real Cost of Prisons Comix reworks the historical and social dynamics of the American carceral regime through its abolitionist framework. We discuss the importance of the image–text form for abolitionist pedagogy by reflecting on the position of comics in carceral textual cultures and the use of these comics in activist education. Finally, we emphasise that the comics created by the Real Cost of Prisons Project should be understood as pedagogical tools in a broader abolitionist movement whereby the historical and social education initiated by memory work aims to ignite collaborative praxis. In this sense, we show that their activist memory work is a means to demystify the historical processes of carceral expansion, enabling its audience to develop historical consciousness.

This paper focuses on six-degree-of-freedom (six-DOF) spatial cable-suspended parallel robots with eight cables (8-6 CSPRs) because the redundantly actuated CSPRs are relevant in many applications, such as large-scale assembly and handling tasks, and pick-and-place operations. One of the main concerns for the 8-6 CSPRs is the stability because employing cables with strong flexibility and unidirectional restraint operates the end-effector of the robot under external disturbances. As a consequence, this paper attempts to address two key issues related to the 8-6 CSPRs: the force-pose stability measure method and the stability sensitivity analysis method. First, a force-pose stability measure model taking into account the poses of the end-effector and the cable tensions of the 8-6 CSPR is presented, in which two cable tension influencing factors and two position influencing factors are developed, while an attitude influence function representing the influence of the attitudes of the end-effector on the stability of the robot is constructed. And furthermore, a new type of workspace related to the force-pose stability of the 8-6 CSPRs is defined and generated in this paper. Second, a force-pose stability sensitivity analysis method for the 8-6 CSPRs is developed with the gray relational analysis method, where the relationship between the force-pose stability of the robot and the 14 influencing factors (the end-effector’s poses and cable tensions) is investigated to reveal the sequence of the 14 influencing factors on the force-pose stability of the robot. Finally, the proposed force-pose stability measure method and stability sensitivity analysis method for the 8-6 CSPRs are verified through simulations.

Low-dimensional representation and clustering of network data are tasks of great interest across various fields. Latent position models are routinely used for this purpose by assuming that each node has a location in a low-dimensional latent space and by enabling node clustering. However, these models fall short through their inability to simultaneously determine the latent space dimension and number of clusters. Here we introduce the latent shrinkage position cluster model (LSPCM), which addresses this limitation. The LSPCM posits an infinite-dimensional latent space and assumes a Bayesian nonparametric shrinkage prior on the latent positions’ variance parameters resulting in higher dimensions having increasingly smaller variances, aiding the identification of dimensions with non-negligible variance. Further, the LSPCM assumes the latent positions follow a sparse finite Gaussian mixture model, allowing for automatic inference on the number of clusters related to non-empty mixture components. As a result, the LSPCM simultaneously infers the effective dimension of the latent space and the number of clusters, eliminating the need to fit and compare multiple models. The performance of the LSPCM is assessed via simulation studies and demonstrated through application to two real Twitter network datasets from sporting and political contexts. Open-source software is available to facilitate widespread use of the LSPCM.

Accurate 3D deformation control of deformable soft tissues is of paramount importance in robotic-assisted surgeries. Selecting optimal grasping points is a fundamental challenge, as the deformation behavior is highly dependent on the applied forces and their locations. This paper presents an efficient grasping point selection algorithm using optimization-based inverse finite element method for tissue manipulation tasks. We propose a method for the automatic identification of optimal grasping points that minimize feature or shape errors during deformation tasks. Specifically, we formulate the grasping task as a quadratic programming problem while considering the complex mechanical coupling within the tissue structure. Our method effectively accommodates both discrete key points and point clouds as input, and can simultaneously determine multiple optimal grasping points in one optimization process. We validate the proposed method in simulation on a tissue and liver model, demonstrating its feasibility and efficiency in various scenarios. Real-world experiments are conducted on a silicone liver phantom to further validate the effectiveness of our proposed method.

This research proposes an Internet of Things (IoT)-enabled adaptive robotic navigation framework tailored for smart campuses and urban mobility systems. It aims to overcome critical limitations in existing systems that rely on static data, lack real-time adaptability, and perform poorly in dynamic or adverse environments. The proposed system uniquely integrates heterogeneous real-time data sources including traffic, obstacle, and weather captured from IoT sensors into a unified decision-making architecture. It combines a graph neural network for dynamic environmental modeling, a convolutional neural network for obstacle mapping, and a multilayer perceptron for weather-aware path assessment. A proximal policy optimization-based reinforcement learning (RL) controller then computes continuous control actions. A novel multi-objective reward function adaptively adjusts priorities between travel time, energy efficiency, collision risk, and terrain stability based on the current IoT context, enabling fine-grained, scenario-aware optimization. The system is deployed on resource-constrained edge hardware (Jetson Nano), proving its feasibility for real-time embedded applications. Simulations across diverse scenarios including urban traffic congestion, dynamic obstacle handling, and adverse weather demonstrate 95% navigation accuracy, 98% obstacle detection precision, and near-optimal route selection. The framework sustains real-time operation with 10 Hz decision throughput and sub-300 ms latency, outperforming traditional static and rule-based systems while sustaining over 92% performance consistency under adverse weather. This work introduces a first-of-its-kind modular framework that fuses IoT sensory data, adaptive RL control, and edge deployment for robust, efficient navigation. It establishes a scalable baseline for real-world autonomous mobility in smart city ecosystems.

Effectively controlling systems governed by partial differential equations (PDEs) is crucial in several fields of applied sciences and engineering. These systems usually yield significant challenges to conventional control schemes due to their nonlinear dynamics, partial observability, high-dimensionality once discretized, distributed nature, and the requirement for low-latency feedback control. Reinforcement learning (RL), particularly deep RL (DRL), has recently emerged as a promising control paradigm for such systems, demonstrating exceptional capabilities in managing high-dimensional, nonlinear dynamics. However, DRL faces challenges, including sample inefficiency, robustness issues, and an overall lack of interpretability. To address these challenges, we propose a data-efficient, interpretable, and scalable Dyna-style model-based RL framework specifically tailored for PDE control. Our approach integrates Sparse Identification of Nonlinear Dynamics with Control within an Autoencoder-based dimensionality reduction scheme for PDE states and actions (AE+SINDy-C). This combination enables fast rollouts with significantly fewer environment interactions while providing an interpretable latent space representation of the PDE dynamics, facilitating insight into the control process. We validate our method on two PDE problems describing fluid flows—namely, the 1D Burgers equation and 2D Navier–Stokes equations—comparing it against a model-free baseline. Our extensive analysis highlights improved sample efficiency, stability, and interpretability in controlling complex PDE systems.

Most exoskeletons are designed with the shoulder joint’s instantaneous center of rotation (ICR) in mind as a fixed joint, often also known as the center of the shoulder joint. In fact, shoulder ICR changes during shoulder abduction–adduction and flexion–extension. Abduction–adduction causes the ICR to move in the frontal plane, which is caused by the joint movement of the shoulder joint, including depressed elevation and horizontal translation, while the flexion–extension movement of the sagittal plane produces the shoulder extension movement. If the change in shoulder ICoR movements is not compensated for in the exoskeleton design, they can create discomfort and pain for the robot’s wearer. Although conventional exoskeletons typically treat the shoulder joint as a three degree of freedom spherical joint, this study incorporates a more sophisticated understanding of shoulder kinematics. The developed scapulohumeral rhythm compensation mechanism successfully compensates for shoulder joint motion, with simulation results confirming kinematics that closely match ergonomic shoulder movement patterns. First, the complex kinematics of the shoulder joint are analyzed. To meet the demand for mismatch compensation, a shoulder exoskeleton based on a winding mechanism is designed. A mismatch compensation model is established, and theoretical analysis and simulation verify that the designed shoulder exoskeleton has a mismatch compensation function. While solving the mismatch problem, the human–machine coupling model is established through OpenSim software. The simulation results show that the designed exoskeleton has a good assisting effect from the perspective of muscle force generation and shoulder torque.

Grasp detection is a significant research direction in the field of robotics. Traditional analysis methods typically require prior knowledge of the object parameters, limiting grasp detection to structured environments and resulting in suboptimal performance. In recent years, the generative convolutional neural network (GCNN) has gained increasing attention, but they suffer from issues such as insufficient feature extraction capabilities and redundant noise. Therefore, we proposed an improved method for the GCNN, aimed at enabling fast and accurate grasp detection. First, a two-dimensional (2D) Gaussian kernel was introduced to re-encode grasp quality to address the issue of false positives in grasp rectangular metrics, emphasizing high-quality grasp poses near the central point. Additionally, to address the insufficient feature extraction capabilities of the shallow network, a receptive field module was added at the neck to enhance the network’s ability to extract distinctive features. Furthermore, the rich feature information in the decoding phase often contains redundant noise. To address this, we introduced a global-local feature fusion module to suppress noise and enhance features, enabling the model to focus more on target information. Finally, relevant evaluation experiments were conducted on public grasping datasets, including Cornell, Jacquard, and GraspNet-1 Billion, as well as in real-world robotic grasping scenarios. All results showed that the proposed method performs excellently in both prediction accuracy and inference speed and is practically feasible for robotic grasping.

Robotic manufacturing systems offer significant advantages, including increased flexibility and reduced costs. However, challenges in trajectory planning, error compensation, and system integration hinder their broader application in additive manufacturing. To address these issues, this paper proposes a generalized non-parametric trajectory planning method tailored for robotic additive manufacturing. The proposed trajectory planner incorporates chord error and speed continuity constraints and integrates the look-ahead planning with real-time interpolation in a parallel structure to ensure smooth transitions in the robot’s trajectory. Additionally, a real-time path tracking control method is introduced, combining RBF neural network-based dynamic feedforward control with visual servoing-based feedback control. This control strategy significantly improves the robot’s tracking accuracy, particularly for complex additive manufacturing paths that involve multiple short connected line segments and frequent speed variations. The effectiveness of the proposed methods is validated through experiments on a robotic additive manufacturing platform. The experimental results (line segment, circular arc segment, and continuous path tracking) show that the robot’s tracking error remains within $\pm$0.15 mm and $\pm 0.05^{\circ }$.