In complex work environments, improving efficiency and stability is an important issue in robot path planning. This article proposes a new path optimization algorithm based on pseudospectral methods. The algorithm includes an adaptive weighting factor in the objective function, which automatically adjusts the quality of the path while satisfying the performance indicators of the shortest time. It also considers kinematic, dynamic, boundary, and obstacle constraints, and applies the Separating Axis Theorem collision detection method to improve computational efficiency. To discretize the continuous path optimization problem into a nonlinear programming problem, the algorithm utilizes Chebyshev polynomials for the interpolation of state and control variables, along with the adoption of the Lagrange interpolation polynomial to approximate the curve. Finally, it solves the nonlinear programming problem numerically using CasADi, which supports automatic differentiation. The results of the simulation demonstrate that the path optimized by the adaptive-weight pseudospectral method can satisfy various constraints and optimization objectives simultaneously. Experimental verification confirms the efficiency and feasibility of the proposed algorithm.

Robot hands are essential components of robots; however, the hand of more complex spatial mechanisms with coupling chains is rarely proposed. This paper proposes a hybrid hand with three underactuated finger plane limbs connected by a flexible closed-loop chain. The degree of freedom (DOF) of the hybrid hand is equal to the number of motors before grasping the object. When the contact force appears between the fingertips and the object, the flexible linkages deform, allowing the hybrid hand to maintain adaptability during contact. As the three fingers make contact with the object, the hybrid hand forms a closed-loop chain with the object, ensuring that the overall DOF remains consistent with the number of motors. Firstly, the hybrid hand’s structural characteristics and DOF are analyzed. Secondly, the kinematics of the hybrid hand are derived, and the relationships among the spring deformation, the kinematics of the fingertip and the input of the hybrid hand are obtained according to the geometric constraints. Thirdly, based on the kinematic results and the principle of virtual work method, the coupling dynamics formula of the hybrid hand is established, and the relationship between the dynamic driving force, dynamic constrained force, spring force and the force acting on the object is solved. Finally, the simulation model of the hybrid hand is constructed in MATLAB to validate the theoretical solution, and the merits of the hybrid hand were confirmed by prototype experiments. This paper aims to support a theoretical foundation for the intelligent control of novel hybrid hands.

Following the large-scale Russian invasion in February 2022, policymakers and humanitarian actors urgently sought to anticipate displacement flows within Ukraine. However, existing internal displacement data systems had not been adapted to contexts as dynamic as a full-fledged war marked by uneven trigger events. A year and a half later, policymakers and practitioners continue to seek forecasts, needing to anticipate how many internally displaced persons (IDPs) can be expected to return to their areas of origin and how many will choose to stay and seek a durable solution in their place of displacement. This article presents a case study of an anticipatory approach deployed by the International Organization for Migration (IOM) Mission in Ukraine since March 2022, delivering nationwide displacement figures less than 3 weeks following the invasion alongside near real-time data on mobility intentions as well as key data anticipating the timing, direction, and volume of future flows and needs related to IDP return and (re)integration. The authors review pre-existing mobility forecasting approaches, then discuss practical experiences with mobility prediction applications in the Ukraine response using the Ukraine General Population Survey (GPS), including in program and policy design related to facilitating durable solutions to displacement. The authors focus on the usability and ethics of the approach, already considered for replication in other displacement contexts.

In this work, we focus on stochastic modeling for sustainable systems and introduce the family of r-modified reliability systems. This new family generalizes classical reliability systems studied in the literature by considering the components in the system to exhibit a kind of dependence that relaxes the component operating requirements and provides energy and resource efficiency. From a theoretical viewpoint, such a dependence is modeled with the use of a modified binary sequence. We then derive the reliability of two members of the family, i.e., the r-modified-k-out-of-n:F system and the r-modified-consecutive-k-out-of-n:F system, under different assumptions on the component reliabilities by using a variety of approaches, including Markov chains, combinatorial methods, and simple probabilistic arguments. We finally give some examples of real-life systems wherein the developed models and results are applicable and present the corresponding numerical results.

Automatic visual localization of electric vehicle (EV) charging ports presents significant challenges in uncertain environments, such as varying surface textures, reflections, lighting and observation distance. Existing methods require extensive real-world training data and well-focused images to achieve robust and accurate localization. However, both requirements are difficult to meet under variable and unpredictable conditions. This paper proposes a 2-stage vision-based localization approach. Firstly, the image synthesis technique is used to reduce the cost of real-world data collection. A task-oriented parameterization protocol (TOPP) is proposed to optimize the quality of the synthetic images. Secondly, an autofocus and servoing strategy is proposed. A hybrid detector is employed to enhance sharpness assessment performance, while a visual servoing method based on single exponential smoothing (SES) is developed to enhance stability and efficiency during the search process. Experiments were conducted to evaluate image synthesis efficiency, detection accuracy, and servoing performance. The proposed method achieved 99% detection accuracy on the real-world port images, and guided the robot to the optimal imaging position within 16 s, outperforming comparable approaches. These results highlight its potential for robust automated charging in real-world scenarios.

With the increasing manufacturing of electric vehicles, car battery recycling is crucial for environmental sustainability. The disassembly of car batteries includes critical health hazards for the operator, due to potential chemical reactions or physical injuries. These reasons make robots particularly interesting for automatic disassembly. This paper proposes a systematic approach to automation and human–robot cooperation in car battery disassembly tasks with a case study on screw removal. A novel parameter is proposed to evaluate whether a human operator or a robot is more appropriate for each specific task, considering both performance and associated risks. The proposed metrics are validated with an experimental example, in which the performance of a robot and a human on a screw-removal task is compared numerically using statistical methods. The advantages and disadvantages of both options are examined through the application and show how the new performance criterion effectively provides insights into the distribution of tasks between humans and robots.

Inequality is a critical global issue, particularly in the United States, where economic disparities are among the most pronounced. Social justice research traditionally studies attitudes towards inequality—perceptions, beliefs, and judgments—using latent variable approaches. Recent scholarship adopts a network perspective, showing that these attitudes are interconnected within inequality belief systems. However, scholars often compare belief systems using split-sample approaches without examining how emotions, such as anger, shape these systems. Moreover, they rarely investigate Converse’s seminal idea that changes in central attitudes can lead to broader shifts in belief systems. Addressing these gaps, we applied a tripartite analytical strategy using U.S. data from the 2019 ISSP Social Inequality module. First, we used a mixed graphical model to demonstrate that inequality belief systems form cohesive small-world networks, with perception of large income inequality and belief in public redistribution as central nodes. Second, a moderated network model revealed that anger towards inequality moderates nearly one-third of network edges, consolidating the belief system by polarizing associations. Third, Ising model simulations showed that changes to central attitudes produce broader shifts across the belief system. This study advances belief system research by introducing innovative methods for comparing structures and testing dynamics of attitude change. It also contributes to social justice research by integrating emotional dynamics and highlighting anger’s role in structuring inequality belief systems.

The human hand’s exceptional dexterity and compliance, derived from its rigid-soft coupling structure and tendon-driven interphalangeal coordination, inspire robotic grippers capable of versatile grasping and force adaptation. Traditional rigid manipulators lack compliance for delicate tasks, while soft robots often suffer from instability and low load capacity. To bridge this gap, we propose a biomimetic multi-joint composite finger integrating a 3D-printed rigid phalanges (46–51 mm) with dual fabric-reinforced pneumatic bladders, mimicking human finger biomechanics. This hybrid design combines hinge-jointed rigidity and anisotropic fabric constraints, enabling two rotational degrees of freedom with higher radial stiffness, achieving 2.18× higher critical burst pressure (240 kPa) than non-reinforced bladders, while preserving axial compliance. Experimental validation demonstrates a 4.77 N maximum fingertip force at 200 kPa and rapid recovery (< 2s) post-impact. The composite finger exhibits human-like gestures (enveloping, pinching, flipping) and adapts to irregular/fragile objects (e.g., eggs, screws) through coordinated bladder actuation. Assembled into a modular gripper, it sustains 1 kg payloads and executes thin-object flipping via proximal-distal joint synergy. This rigid-soft coupling design bridges compliance and robustness, offering high environmental adaptability for applications in industrial automation, human–robot interaction, and delicate manipulation.

Implementing changes to digital health systems in real-life contexts poses many challenges. Design as a field has the potential to tackle some of these. This article illustrates how design knowledge, through published literature, is currently referenced in relation to the implementation of digital health. To map design literature’s contribution to this field, we conducted a scoping review on digital health implementation publications and their use of references from nine prominent design journals. The search in Scopus and Web of Science yielded 382 digital health implementation publications, of which 70 were included for analysis. From those, we extracted data on publication characteristics and how they cited the design literature. The 70 publications cited 58 design articles, whose characteristics were also extracted. The results show that design is mainly cited to provide information about specific design methods and approaches, guidelines for using them and evidence of their benefits. Examples of referenced methods and approaches were co-design, prototyping, human-centered design, service design, understanding user needs and design thinking. The results thus show that design knowledge primarily contributed to digital health implementation with insights into methods and approaches. In addition, our method showcases a new way for understanding how design literature influences other fields.

We define the generalized equilibrium distribution, that is the equilibrium distribution of a random variable with support in $\mathbb{R}$. This concept allows us to prove a new probabilistic generalization of Taylor’s theorem. Then, the generalized equilibrium distribution of two ordered random variables is considered and a probabilistic analog of the mean value theorem is proved. Results regarding distortion-based models and mean-median-mode relations are illustrated as well. Conditions for the unimodality of such distributions are obtained. We show that various stochastic orders and aging classes are preserved through the proposed equilibrium transformations. Further applications are provided in actuarial science, aiming to employ the new unimodal equilibrium distributions for some risk measures, such as Value-at-Risk and Conditional Tail Expectation.

Design-by-analogy (DbA) is a powerful method for product innovation design, leveraging multidomain design knowledge to generate new ideas. Previous studies have relied heavily on designers’ experiences to retrieve analogical knowledge from other domains, lacking a structured method to organize and understand multidomain analogical knowledge. This presents a significant challenge in recommending high-quality analogical sources, which needs to be addressed. To tackle these issues, a knowledge graph-assisted DbA approach via structured analogical knowledge retrieval is proposed. First, an improved function-effect-structure ontology model is constructed to extract functions and effects as potential analogical sources, and six semantic matching rules are established to output entity triplets, and the DbA knowledge graph (DbAKG) is developed. Second, based on the knowledge of semantic relationships in DbAKG, the domain distance and similarity between the design target and the analogical sources are introduced to establish an analogical value model, ensuring the novelty and feasibility of analogical sources. After that, with function as the design target, analogical sources transfer strategy is formed to support innovative solution solving, and TRIZ theory is used to solve design conflicts. Finally, a pipeline inspection robot case study is further employed to verify the proposed approach. Additionally, a knowledge graph-assisted analogical design system has been developed to assist in managing multidomain knowledge and the analogical process, facilitate the adoption of innovative design strategies, and assist companies in providing more competitive products to seize the market.

We show that on every affine Weyl group natural random walks are noise sensitive in total variation.

Underwater robots conducting inspections require autonomous obstacle avoidance capabilities to ensure safe operations. Training methods based on reinforcement learning (RL) can effectively develop autonomous obstacle avoidance strategies for underwater robots; however, training in real environments carries significant risks and can easily result in robot damage. This paper proposes a Sim-to-Real pipeline for RL-based training of autonomous obstacle avoidance in underwater robots, addressing the challenges associated with training and deploying RL methods for obstacle avoidance in this context. We establish a simulation model and environment for underwater robot training based on the mathematical model of the robot, comprehensively reducing the gap between simulation and reality in terms of system inputs, modeling, and outputs. Experimental results demonstrate that our high-fidelity simulation system effectively facilitates the training of autonomous obstacle avoidance algorithms, achieving a 94% success rate in obstacle avoidance and collision-free operation exceeding 5000 steps in virtual environments. Directly transferring the trained strategy to a real robot successfully performed obstacle avoidance experiments in a pool, validating the effectiveness of our method for autonomous strategy training and sim-to-real transfer in underwater robots.

This study investigates the applicability of generative artificial intelligence (AI) in early-stage architectural design by evaluating the daylight performance of AI-generated sustainable housing plans across five distinct climate zones. A three-phase methodology was implemented: (1) Plan generation using text-to-image diffusion models (ChatGPT, Copilot, and LookX); (2) digital reconstruction in AutoCAD; and (3) daylight simulation via Velux Daylight Visualizer. Climate-adaptive prompts were formulated to guide the AI tools in producing context-specific floor plans with passive strategies. Out of 31 initial plans, eight valid outputs (five from ChatGPT and three from Copilot) were reconstructed in AutoCAD and simulated. Quantitative simulations were conducted on equinox and solstice dates, and average illuminance values were analyzed for key interior spaces (living room, kitchen, and bedroom). ChatGPT-generated plans demonstrated higher spatial clarity and more balanced daylight performance, whereas Copilot outputs varied significantly, and LookX was excluded due to insufficient architectural legibility. Results revealed that none of the models consistently integrated solar orientation or seasonal lighting considerations, indicating a gap between generative representation and environmental logic. The research contributes a replicable workflow that bridges generative AI and performance-based evaluation, offering critical insight into the current limitations and future potential of AI-assisted architectural design. The findings underscore the need for next-generation AI systems capable of semantic, spatial, and climatic reasoning to support environmentally responsive design practices.

The Grothendieck construction establishes an equivalence between fibrations, a.k.a. fibred categories and indexed categories and is one of the fundamental results of category theory. Cockett and Cruttwell introduced the notion of fibrations into the context of tangent categories and proved that the fibres of a tangent fibration inherit a tangent structure from the total tangent category. The main goal of this paper is to provide a Grothendieck construction for tangent fibrations. Our first attempt will focus on providing a correspondence between tangent fibrations and indexed tangent categories, which are collections of tangent categories and tangent morphisms indexed by the objects and morphisms of a base tangent category. We will show that this construction inverts Cockett and Cruttwell’s result, but it does not provide a full equivalence between these two concepts. In order to understand how to define a genuine Grothendieck equivalence in the context of tangent categories, inspired by Street’s formal approach to monad theory we introduce a new concept: tangent objects. We show that tangent fibrations arise as tangent objects of a suitable $2$-category and we employ this characterisation to lift the Grothendieck construction between fibrations and indexed categories to a genuine Grothendieck equivalence between tangent fibrations and tangent indexed categories.

This text accompanies the performance A Foot, A Mouth, A Hundred Billion Stars, which premiered at the Lapworth Museum of Geology in the United Kingdom on 18 March 2023, as part of the Flatpack film festival. It includes both the text and a film version, developed during a residency at the museum. Over 18 months, I had full access to the collection and archives, selecting objects that served as prompts for stories about time and memory. A central theme of the work is slippage – misremembering and misunderstanding – as a generative methodology for exploring the connection between the collection, our past, and possible futures.

A Foot, A Mouth, A Hundred Billion Stars combines analogue media and digital technologies to examine our understanding of remembering and forgetting. I used a live digital feed and two analogue slide projectors to explore the relationships between image and memory. This article does not serve as a guide to the performance but instead reflects on the process and the ideas behind the work. My goal is to share my practice of rethinking memory through direct engagement with materials. In line with the performance’s tangential narrative, this text weaves together diverse references, locations, thoughts, and ideas, offering a deeper look into the conceptual framework of the work.

Earth’s forests play an important role in the fight against climate change and are in turn negatively affected by it. Effective monitoring of different tree species is essential to understanding and improving the health and biodiversity of forests. In this work, we address the challenge of tree species identification by performing tree crown semantic segmentation using an aerial image dataset spanning over a year. We compare models trained on single images versus those trained on time series to assess the impact of tree phenology on segmentation performance. We also introduce a simple convolutional block for extracting spatio-temporal features from image time series, enabling the use of popular pretrained backbones and methods. We leverage the hierarchical structure of tree species taxonomy by incorporating a custom loss function that refines predictions at three levels: species, genus, and higher-level taxa. Our best model achieves a mean Intersection over Union (mIoU) of 55.97%, outperforming single-image approaches particularly for deciduous trees where phenological changes are most noticeable. Our findings highlight the benefit of exploiting the time series modality via our Processor module. Furthermore, leveraging taxonomic information through our hierarchical loss function often, and in key cases significantly, improves semantic segmentation performance.