Speculative design is widely used in research contexts across multiple disciplines, emphasising problem-finding over problem-solving, and involves methods for exploring possibilities that challenge ingrained assumptions. This systematic literature review analyses speculative design methods used in 52 studies within disciplines such as human–computer interaction, fashion, urban planning, and healthcare, among other fields. It presents results about the common phases and methods of speculative design that are utilised in these studies. It identifies and characterises four core phases that appear to be common within speculative design processes, namely select, explore, transform, and provoke. It shares examples of how these phases are used to achieve the goals of speculative design. The discussion section considers the process of speculative design, leading to the synthesis of a framework that visually and conceptually organises these findings to facilitate their comprehension and application. This paper contributes to the understanding of speculative design by providing a clear process that addresses gaps in its theoretical and methodological foundations.

This study synthesized 65 (quasi-)experimental studies published between 2010 and 2024 that examined the use of mobile applications to develop language learners’ vocabulary learning. Bayesian meta-analysis was adopted to assess (1) overall effect size; (2) subgroup analyses (i.e. education level, vocabulary knowledge, aspects of vocabulary learning, learning environment, sample size, mobile application type, gender, and cultural background); and (3) publication bias. A large effect size of 1.28 was found for the overall effectiveness of using mobile applications for vocabulary learning when we restricted the studies to long-term treatment duration of 10 weeks or above. Each moderator was analyzed and discussed, and implications for language teaching and research were provided.

This paper considers two supercritical branching processes with immigration in different random environments, denoted by $\{Z_{1,n}\}$ and $\{Z_{2,m}\}$, with criticality parameters µ1 and µ2, respectively. Under certain conditions, it is known that $\frac{1}{n} \log Z_{1,n} \to \mu_1$ and $\frac{1}{m} \log Z_{2,m} \to \mu_2$ converge in probability as $m, n \to \infty$. We present basic properties about a central limit theorem, a non-uniform Berry–Esseen’s bound, and Cramér’s moderate deviations for $\frac{1}{n} \log Z_{1,n} - \frac{1}{m} \log Z_{2,m}$ as $m, n \to \infty$. To this end, applications to construction of confidence intervals and simulations are also given.

Let $K^r_n$ be the complete $r$-uniform hypergraph on $n$ vertices, that is, the hypergraph whose vertex set is $[n] \, :\! = \{1,2,\ldots ,n\}$ and whose edge set is $\binom {[n]}{r}$. We form $G^r(n,p)$ by retaining each edge of $K^r_n$ independently with probability $p$. An $r$-uniform hypergraph $H\subseteq G$ is $F$-saturated if $H$ does not contain any copy of $F$, but any missing edge of $H$ in $G$ creates a copy of $F$. Furthermore, we say that $H$ is weakly $F$-saturated in $G$ if $H$ does not contain any copy of $F$, but the missing edges of $H$ in $G$ can be added back one-by-one, in some order, such that every edge creates a new copy of $F$. The smallest number of edges in an $F$-saturated hypergraph in $G$ is denoted by ${\textit {sat}}(G,F)$, and in a weakly $F$-saturated hypergraph in $G$ by $\mathop {\mbox{$w$-${sat}$}}\! (G,F)$. In 2017, Korándi and Sudakov initiated the study of saturation in random graphs, showing that for constant $p$, with high probability ${\textit {sat}}(G(n,p),K_s)=(1+o(1))n\log _{\frac {1}{1-p}}n$, and $\mathop {\mbox{$w$-${sat}$}}\! (G(n,p),K_s)=\mathop {\mbox{$w$-${sat}$}}\! (K_n,K_s)$. Generalising their results, in this paper, we solve the saturation problem for random hypergraphs $G^r(n,p)$ for cliques $K_s^r$, for every $2\le r \lt s$ and constant $p$.

Based on the characteristics of the variable pivot gait during the human load-carrying, this paper proposes a double-leg coordination assistance principle for load-carrying: assisting support of the guiding leg at the heel-pivot stage by the spring to reduce the collision, which can reduce the ankle moment of the following leg that is performing the push-off at the toe-pivot stage. A novel unpowered load-carrying exoskeleton (ULE) with a double-support closed-chain configuration is designed, and the theoretical verification is carried out. Five subjects participate in the load-carrying and metabolic cost experiments for assisting and energy-saving effect evaluation, and the angle and moment of human joints, plantar pressure, spring compression and human net metabolic rate are analyzed. Compared with carrying load by the human alone, wearing the novel ULE with spring reduces the human peak ankle moment performing the push-off by up to 11.9 ± 1.6% (Mean±SE, 10 kg), average ankle moment over the support phase by up to 36.8 ± 9.1% (Mean±SE, 5 kg) and the average vertical plantar pressure by up to 8.1 ± 1%% (Mean±SE, 15 kg). Meanwhile, wearing the novel ULE reduces the human net metabolic rate by 5.6 ± 0.5% (Mean±SE, 10 kg), 4.1 ± 0.7% (Mean±SE, 15 kg) and 5.9 ± 1.6% (Mean±SE, 20 kg). The results show that the novel ULE can provide support and joint moment assistance over the whole support phase while reducing human net metabolic rate. This study can also be applied to the powered load-carrying exoskeleton, providing a new avenue.

Neural network (NN)-based control policies have proven their advantages in cyber-physical systems (CPS). When an NN-based policy fails to fulfill a formal specification, engineers leverage NN repair algorithms to fix its behaviors. However, such repair techniques risk breaking the existing correct behaviors, losing not only correctness but also verifiability of initial state subsets. That is, the repair may introduce new risks, previously unaccounted for. In response, we formalize the problem of Repair with Preservation (RwP) and develop Incremental Simulated Annealing Repair (ISAR). ISAR is an NN repair algorithm that aims to preserve correctness and verifiability—while repairing as many failures as possible. Our algorithm leverages simulated annealing on a barriered energy function to safeguard the already-correct initial states while repairing as many additional ones as possible. Moreover, formal verification is utilized to guarantee the repair results. ISAR is compared to a reviewed set of state-of-the-art algorithms, including (1) reinforcement learning-based techniques (STLGym and F-MDP), (2) supervised learning-based techniques (MIQP and minimally deviating repair) and (3) online shielding techniques (tube MPC shielding). Upon evaluation on two standard benchmarks, OpenAI Gym mountain car and an unmanned underwater vehicle, ISAR not only preserves correct behaviors from previously verified initial state regions, but also repairs 81.4% and 23.5% of broken state spaces in the two benchmarks. Moreover, the signal temporal logic (STL) robustness of the ISAR-repaired policies is higher than the baselines.

This research examines whether a machine, specifically artificial intelligence (AI), can be creative by comparing design solutions for a practical competition – a light fixture for a pediatric waiting room – among AI, collaboration efforts and a human designer. Amazon Mechanical Turk and Prolific workers observed the design solutions throughout the design process, from sketches ($ S $) to three-dimensional renderings ($ 3D $) to fully developed models in virtual waiting rooms ($ VR $). Using the well-established Creative Product Semantic Scale (CPSS), the workers rated each design solution in three distinctive stages – $ S $, $ 3D $ and $ VR $ – on three criteria – novelty (freshness or newness), resolution (relevance and logic) and style (craftsmanship and desirability). Despite some demographic discrepancies, the workers expressed general senses of happiness and calmness, resonating with the competition’s requirements. Statistical results of CPSS ratings revealed that while AI excelled in style for $ 3D $, the human designer outperformed in novelty for both $ S $ and $ VR $. Collaboration efforts surprisingly finished last. Such findings challenge current assumptions of AI’s creative ability in design research and highlight the need to be agile in the age of disruptive technologies. This research also offers guidance for product and interior designers and educators on thoughtfully integrating AI into the design process.

This study investigated an 18‑week teacher education model grounded in technological pedagogical content knowledge (TPACK). Known as CATERR (comprehending, analyzing, teaching, evaluating, reflecting, and refining), this teacher education model cultivated the computer-assisted language learning (CALL) competencies of 43 content and language integrated learning (CLIL) preservice teachers (PSTs) from Taiwan. The model promotes peer coaching, where participants collaborate, reflect, and refine their teaching over three rounds. The study utilized a multi-method case study and triangulated the quantitative and qualitative data. Quantitative data refers to the TPACK-CLIL questionnaire administered before and after the teacher education model. Qualitative data included lesson plans, self-analysis, teaching demonstration videos, revised lesson plans, classroom discussion records, peer evaluations, and reflection notes. Data analysis involved paired-samples t-tests and descriptive statistics for the coding framework, thematic analysis for qualitative data, and a repeated measures ANOVA to compare three total scores across three rounds using scoring rubrics. Results showed that the CATERR teacher education model enhanced CLIL PSTs’ self-perceived and observed CALL competencies. Specifically, as “digital native” PSTs with high levels of technological knowledge (TK), they successfully transferred their TK into TPACK by adding pedagogical values and contextualizing the ICT tools in their CLIL lessons. Meanwhile, their ability to use ICT tools to facilitate interaction and students’ autonomous learning substantially improved. The theoretical and pedagogical implications for CALL teacher education research and practice are discussed.

The Kock-Lawvere axiom has two formulations that are equivalent in the usual models of Synthetic Differential Geometry. We show that, in the classifier of integral rigs, and some of its pre-cohesive subtoposes, the generic model satisfies one version of the axiom but not the other.

Three-translation (3T) redundant actuated Delta/Par4-like manipulators with closed-loop units (CLU) are widely used in logistics sorting, industrial packaging, and other applications due to their superior load-carrying capacity and dynamic performance. However, compared to conventional 3T parallel mechanisms (PMs), there are fewer configurations of 3T redundant actuated PMs with CLU, and the existing CLU are of a single configuration. This paper introduces a method for synthesizing a 3T redundant actuated PM with CLU based on the atlas method. First, the degrees of freedom (DOF) line diagrams and constraint line diagrams of the moving platform are determined sequentially. By applying the dual rule, the constraint diagrams are decomposed, yielding six dual DOF spaces. The equivalent DOF line diagrams are expanded using the principle of line equivalence, which synthesizes a variety of novel 4- and 5-DOF branched chains with CLU. Second, considering the geometrically symmetric distribution criterion of the branched chains, two assembly schemes for redundant actuated 3T PMs with CLU are proposed, yielding at least 432 and 324 novel configurations, respectively. Finally, two representative novel mechanisms are selected, and their DOF properties are verified using screw theory, which demonstrates the feasibility and applicability of the synthesized method. This method systematically synthesizes novel redundant actuated 3T PMs with CLU, laying the foundation for further research into innovative CLU configurations.

Flapping-wing robots, inspired by natural flyers, have gained significant attention for surveillance and environmental monitoring applications. This study presents the design and analysis of a bat-inspired flapping-wing robot with foldable wings, aiming to enhance flight efficiency and maneuverability. The robot features silicone-based, stretchable membrane wings, with a wingspan of 1.4 m and a total mass of 620 g. A one-degree-of-freedom (DOF) revolute-spherical-spherical-revolute mechanism is used to reproduce the flapping motion, while a one-DOF Watt six-bar linkage mechanism enables dynamic wing folding, allowing adaptive wing shape modulation during flight. Explicit solutions for joint angle of the wing were expressed through analytical method. Flight tests were conducted to validate the effectiveness of the flapping-folding mechanism. Results show that the robot successfully replicates bat wing kinematics, with folding during the upstroke and unfolding during the downstroke. This research offers insights into bio-inspired wing designs for next-generation flapping-wing robots.

Aiming at the issues of traditional ant colony algorithm (ACO) in mobile robot path planning, including initial search blindness, susceptibility to local optima, and slow convergence, this paper proposes a multi-strategy improved ant colony algorithm (MS-ACO). Firstly, dynamic non-uniform distribution of initial pheromones is implemented by integrating the repulsive field from artificial the potential field method. Secondly, the heuristic information is enhanced to improve global search capability while constraining unnecessary path turns. Thirdly, an improved pheromone update strategy is developed by adopting distinct updating mechanisms for different evolutionary phases. Finally, dynamic parameter adaptation is achieved through optimized weight coefficients and volatility coefficients that coordinate with the pheromone update strategy, better aligning with the iterative characteristics of ant colony optimization. Experimental results demonstrate that MS-ACO effectively addresses the limitations of traditional ACO. Under identical experimental conditions, it achieves a 30.4% reduction in path length, 37.8% decrease in pathfinding time, and 71% fewer turns compared to conventional methods, verifying the feasibility and superiority of the proposed algorithm.

Sit-to-stand (STS) motion is an essential daily activity. However, this motion becomes increasingly difficult for older adults as their muscle strength declines with age. To assist individuals in standing up while maximizing their muscle strength based on the assist-as-needed (AAN) strategy, assistive devices must detect early STS intent, specifically before the buttocks leave the chair, to ensure timely assistance. This study proposes a novel method for detecting STS intent by applying external mechanical stimuli to the toes and analyzing the resulting changes in heel and toe-reaction forces. Moreover, a structured detection framework was developed by utilizing predefined thresholds for the change rate and magnitude of the heel and toe-reaction forces to detect STS intent. Offline tests for threshold setting of STS-intent detection were established in the offline tests: change rate and magnitude of the reaction forces on the heel and toes. The thresholds for each criterion were determined using the Pareto optimization method. Using the determined thresholds, these criteria were then applied in online tests to evaluate the performance of the proposed intent detection method. The results demonstrated that mechanical stimuli improved the performance of STS-intent detection, providing accurate and stable detection. This method can be applied to STS-assistive devices to effectively implement AAN functionality for standing assistance.