Abstract Despite abundant studies on motion events and mental simulation in first languages (L1s), research on how cross-linguistic dis/similarity – whether an L1 shares constructions with a second language (L2) – affects mental simulation during incremental L2 processing remains limited. This study used a novel self-paced reading task with video verification to investigate L1 influence on mental imagery of the dual (directional/locational) interpretation of locative prepositions. Participants included native English speakers and advanced L2 English learners whose L1s were either similar (Dutch) or dissimilar (Japanese) to English. Results revealed an L1 dis/similarity effect on the reaction times for the directional interpretation, but not for the locational interpretation, which was readily accessible across all L1 groups. Factors such as L2 proficiency and onset age of L2 acquisition were found to be constrained by L1, suggesting that L1–L2 constructional correspondence limits the influence of learner factors. These findings support the simulation-based model of L2 sentence processing.

This phenomenological study explores the lived experiences of residents in Benin City's self-help housing settlements, examining how subjective meanings and daily practices shape housing satisfaction. Through in-depth interviews with 10 purposively selected residents and observational field notes, the research employs Interpretative Phenomenological Analysis to uncover five key themes: housing as autonomy and identity, cultural anchors of satisfaction, contextual definitions of adequacy, place-ballets of daily routines, and community embeddedness. The findings challenge conventional housing assessment metrics by demonstrating how residents derive satisfaction from incremental construction processes, cultural continuity, and social networks rather than formal infrastructure standards. Grounded in David Seamon's place-ballet theory and Kim Dovey's concept of home as paradox, the study argues for housing policies that recognize the phenomenological dimensions of dwelling. The research contributes to urban studies literature by centering resident experiences in informal settlement discourse and suggests new directions for culturally attuned housing policy in Nigeria's rapidly urbanizing context.

Rethinking interactive image matting as incremental Gaussian process regression problems

Similarity-based prototype reconstruction and feature reorganization for non-exemplar class incremental learning.

This study presents a high-frequency heat-assisted incremental bending process for the high-efficiency, high-precision forming of medium-thickness (≥3 mm) double-curved metal plates, addressing the limitations of traditional stamping and line heating methods in aerospace and marine applications. A minimum energy loading path strategy is proposed to optimize the forming trajectory and reduce residual stress. A coupled thermomechanical finite element model was developed, incorporating high-frequency induction heating, temperature-dependent material properties, and Coulomb friction. The model was validated through experiments on Q235 steel plates. Results show that the proposed process reduces the peak forming force and decreases the number of forming points compared to conventional cold incremental bending. Springback is reduced, and the final shape accuracy reaches within 3 mm deviation from the target geometry. Double-curvature sail and saddle-shaped plates were successfully fabricated, demonstrating the feasibility and effectiveness of the method. This work provides a promising solution for low-cost, flexible manufacturing of complex medium-thickness components.

Technology has transformed commerce through the rise of e-commerce and digital marketplaces. However, many academic institutions, including Babcock University, lack structured platforms, forcing students and vendors to rely on fragmented and inefficient channels. This study addresses this gap by developing a digital marketplace tailored to the university community. The existing system was analyzed using direct observation, and the new system was designed with UML diagrams. The incremental process model guided development, enabling flexibility and continuous improvement. Implementation utilized HTML, CSS, JavaScript, PHP, and MySQL. Results indicate that the platform streamlines on-campus commerce, supports student entrepreneurship, and enhances the buying and selling experience. SHA-512 cryptographic hashing ensures data security, while service scheduling and real-time notifications improve efficiency and communication. User feedback highlighted the platform’s intuitive interface, convenience, and reliability, and performance assessments confirmed its effectiveness. Future enhancements should incorporate AI-driven features and expanded payment options to further optimize the user experience.

Given a collection of points in $\mathbb {R}^{3}$, KD-Tree and R-Tree are well-known nearest neighbor search (NNS) algorithms that rely on spatial partitioning and indexing techniques. However, when the query point is far from the data points or the data points inherently represent a 2-manifold surface, their query performance may degrade. To address this, we propose a novel dynamic programming technique that precomputes a Directed Acyclic Graph (DAG) to encode the proximity structure between data points. More specifically, the DAG captures how the proximity structure evolves during the incremental construction of the Voronoi diagram of the data points. Experimental results demonstrate that our method achieves a speed increase of 1-10x. Furthermore, our algorithm demonstrates significant practical value in diverse applications. We validated its effectiveness through extensive testing in four key applications: Point-to-Mesh Distance Queries, Iterative Closest Point (ICP) Registration, Density Peak Clustering, and Point-to-Segments Distance Queries. A particularly notable feature of our approach is its unique ability to efficiently identify the nearest neighbor among the first $k$ points in the point cloud, a capability that enables substantial acceleration in low-dimensional applications like Density Peak Clustering. As a natural extension of our incremental construction process, our method can also be readily adapted for farthest-point sampling tasks. These experimental results across multiple domains underscore the broad applicability and practical importance of our approach.

Language difficulties are common in autism, with several theoretical perspectives proposing that difficulties in forming and updating predictions may underlie the cognitive profile of autism. However, research examining prediction in the language domain among autistic children remains limited, with inconsistent findings regarding prediction efficiency and insufficient investigation of how autistic children incrementally integrate multiple semantic elements during language processing. This study addresses these gaps by investigating both prediction efficiency and incremental processing strategy during spoken language comprehension in autistic children compared to neurotypical peers. Using the visual world paradigm, we compared 45 autistic children (3-8 years) with 52 age-, gender-, and verbal IQ-matched neurotypical children. Participants viewed arrays containing a target object and three semantically controlled distractors (agent-related, action-related, and unrelated) while listening to subject-verb-object structured sentences. Eye movements were recorded to analyze fixation proportions. We employed cluster-based permutation analysis to identify periods of sustained biased looking, growth curve analysis to compare fixation trajectories, and divergence point analysis to determine the onset timing of predictive looking. Both groups demonstrated predictions during spoken language comprehension and employed similar incremental processing strategies, showing increased fixations to both target objects and action-related distractors after verb onset despite the latter's incompatibility with the agent. However, autistic children exhibited reduced prediction efficiency compared to neurotypical peers, evidenced by significantly lower proportions of and slower growth rates in fixations to target objects relative to unrelated distractors, and delayed onset of predictive looking. Reduced prediction efficiency was associated with higher levels of autism symptom severity in the autistic group and increased autistic traits across both groups, with autism-related communication difficulties showing the most robust associations. Our sample included only autistic children without language impairments, limiting generalizability to the broader autism spectrum. The task employed only simple sentence structures in controlled experimental settings, which may not fully capture language processing patterns in naturalistic communication contexts. While autistic children employ similar incremental processing strategies to neurotypical peers during language comprehension, they demonstrate reduced prediction efficiency. Autism symptom severity and autistic traits varied systematically with prediction efficiency, with autism-related communication difficulties showing the strongest associations. These findings enhance our understanding of language processing mechanisms in autism and suggest that interventions targeting language development might benefit from addressing prediction efficiency, such as providing additional processing time and gradually increasing the complexity of semantic integration tasks.

Both classic thought experiments and recent empirical evidence suggest that children frequently encounter new words whose meanings are underdetermined by the extralinguistic contexts in which they occur. The role that these referentially ambiguous events play in children's word learning is central to ongoing debates in the field. Do children learn words from referentially ambiguous events via an incremental learning process? Or, do children learn words primarily from the rare referentially transparent events they experience? Across two experiments with adults as model word learners, the current work asks whether the answer to these questions depends in part on how word learning is assessed. Participants were asked to learn the meanings of novel words solely from their referentially ambiguous contexts. When learning was assessed by asking participants to identify the exact meanings of those novel words, participants struggled mightily. However, when learning was assessed by asking the same participants to identify which of two new contexts the novel word most likely occurred in, even those who failed the exact meaning assessment succeeded. These data suggest that although referentially ambiguous events may fall short in allowing learners to identify a word's exact meaning, they nevertheless lead learners into the right regions of semantic space. These findings are a reminder of the pervasiveness of partial word learning effects in vocabulary acquisition and highlight that the resolution to the debate over the role of referentially ambiguous events in learning may depend on how learning is defined.

Incremental processing of postverbal negation: ERP evidence from Korean

This paper explores the existential dimension of Monica Ali’s Brick Lane through the lens of Jean-Paul Sartre’s philosophy, particularly his concepts of radical freedom and responsibility. Central to Sartre’s existentialism is the belief that individuals are condemned to be free, meaning they must continuously choose and, in doing so, define their essence. Within Brick Lane, Nazneen’s journey from passive submission to active self-definition encapsulates this existential struggle. The research focuses on key moments where Nazneen and other characters, such as Chanu, Karim, and Razia, confront meaningful choices that redefine their sense of self and agency. These small acts of choice—from Nazneen deciding to stop writing letters to her sister, to her final rejection of Karim’s proposal—illustrate an incremental process of existential awakening. By interpreting these moments through Sartre’s framework, the paper argues that Brick Lane presents not only a narrative of cultural and gendered displacement but also an existential narrative of becoming. The analysis reveals how choice functions as both a burden and a vehicle for self-creation, illuminating the existential tensions inherent in immigrant life and female subjectivity. Ultimately, Brick Lane demonstrates that even in constrained conditions, the act of choosing can be a profound assertion of being.

This research explores the concept of open building (OB) in the context of low-cost housing, focusing on its historical applications in Egypt during the 1980s. By evaluating past experiences, the study aims to extract key lessons that can inform the design and implementation of contemporary social housing projects. The goal is to foster resilience and diversity in housing typologies to ensure they align with the evolving needs of residents. To achieve these objectives, the research employed a multi-dimensional strategy, beginning with a comprehensive literature review of the open building movement (OB); then, the study traced the evolution of the OB movement in Egypt using a qualitative analysis approach, which involved analyzing its implementation in low-cost housing projects over the past four decades. Through this historical lens, the study identifies design principles and strategies that can enhance social housing projects by applying OB. Considering the life cycle cost, OB enables an incremental process that would align with users’ financial capacities. The research revealed the substantial capacity of open building (OB) to address Egypt’s social housing challenges, primarily by fostering user-driven flexibility in housing unit design and area selection. This empowers occupants to choose spaces perfectly suited to their family’s evolving needs. Moreover, the findings provide a roadmap for revitalizing the OB movement by analyzing and overcoming past implementation difficulties, consequently balancing the initial cost and long-term economics for citizens and significantly reducing the governmental sector’s expenditure.

Self-adaptive data-driven evolutionary algorithm based on random forest feature selection and incremental Gaussian process regression on personalized antidepressant medication research

Abstract Based on the erasable FPGA, a dual-header incremental photoelectric encoder processing system is designed, including the hardware circuit part and the FPGA software part. The system improves the resolution by software subdivision. Through the FPGA real-time inverse trigonometric function algorithm, the 11-bit subdivision of the single precision code period is realized, and the data output of 22 bits of high resolution is achieved. The eccentricity error can be eliminated, and the accuracy of the photoelectric encoder can be improved by averaging the data of the dual read heads. According to actual measurements, the accuracy is ± 9 arc seconds, which meets the system design requirements and can be used in high-resolution and high-precision occasions.

The majority of research in computational psycholinguistics on sentence processing has focused on word-by-word incremental processing within sentences, rather than holistic sentence-level representations. This study introduces two novel computational approaches for quantifying sentence-level processing: sentence surprisal and sentence relevance. Using multilingual large language models (LLMs), we compute sentence surprisal through three methods, chain rule, next sentence prediction, and negative log-likelihood, and apply a "memory-aware" approach to calculate sentence-level semantic relevance based on convolution operations. The sentence-level metrics developed are tested and compared to validate whether they can predict the reading speed of sentences, and, further, we explore how sentence-level metrics take effects on human processing and comprehending sentences as a whole across languages. The results show that sentence-level metrics are highly capable of predicting sentence reading speed. Our results also indicate that these computational sentence-level metrics are exceptionally effective at predicting and explaining the processing difficulties encountered by readers in processing sentences as a whole across a variety of languages. The proposed sentence-level metrics offer significant interpretability and achieve high accuracy in predicting human sentence reading speed, as they capture unique aspects of comprehension difficulty beyond word-level measures. These metrics serve as valuable computational tools for investigating human sentence processing and advancing our understanding of naturalistic reading. Their strong performance and generalization capabilities highlight their potential to drive progress at the intersection of LLMs and cognitivescience.

In this paper, I provide a general response to the points made in the commentaries. One of the major questions probed is whether gradient, variable output is diagnostic of gradient mental representations. I argue that this is not necessarily the case, and look to aspects of the learning theory (such as input processing, restructuring, and cue reweighting), as well as the architecture of the phonology/phonetics interface to account for such variation. I argue for a conservative, incremental restructuring process as the basis of the transition theory of Ln developmental paths. I reiterate the nature of the projection problem at various levels of the prosodic hierarchy when it comes to the input underdetermining the cues to abstract, algebraic phonological constituent labels. The question of whether Ln grammars are consistent with the structural properties of natural languages (i.e. constrained by UG) is discussed. I conclude with a presentation of the idea that linguistic representations can be considered wavelike superstates. This has the potential of capturing what has been described as the fuzziness of representations, as well as the benefit of unifying our treatment of mental and physical objects.

Seizure often manifests in various forms across diverse patient groups. Various machine learning (ML) models have been proposed to automate seizure type classification (STC). However, ML models especially deep networks (DNs) learn from datasets randomly supplied in mini-batches leading to overfitting and poor generalization. Additionally, computational demands for DNs especially a convolutional neural network (CNN) can be overwhelming. Therefore, a curriculum learning (CL) framework has been proposed in this study to solve the abovementioned challenges. CL is an incremental learning process where the model learns to generalize better by increasing the difficulty of learning tasks. Hence, we have divided the publicly available Temple University Hospital (TUH) dataset into various levels of difficulty. We aim to have a computationally efficient model capable of generalizing to unseen datasets. Therefore, we schedule binary classifications for easy tasks, ternary classifications for medium tasks, and multiclass for a hard task of STC. By training the CNN using the proposed CL, we achieved improved performance with precision, recall, F1-score, and accuracy of 84.94%, 80.29%, 82.33%, and 80.29% respectively, with an improvement of 2.02%, 2.65%, 2.58%, and 2.65% when compared with the traditional training method. Moreover, the implementation of CL allows the training time to be reduced by 170.88 s.Clinical Relevance- This demonstrates the effectiveness of CL in the precise classification of STC to improve clinical epilepsy diagnosis.

This introduction to the special issue critically explores the pervasive logic of solutionism in infrastructure-led urban development and planning – a logic marked not only by the strong belief in the transformative power of infrastructures but also by a tendency to reframe how urban problems are prioritised and governed. Although infrastructures are increasingly positioned as key tools for urban decarbonisation, circularity, resilience or smartness, this introduction critically questions dominant solutionist approaches to complex urban problems. Drawing on recent urban scholarship, it explores infrastructures as ongoing, relational, and contested sociotechnical processes, rethinking transformative urban change as a situated, incremental, and ambiguous process shaped by local politics, materiality, and everyday repair and patching. Contributions to this issue highlight how infrastructural initiatives, even when partial or unrealised, can challenge dominant interests and practices and open space for alternative urban futures. Rather than repudiating infrastructural solutions, therefore, we suggest that the special issue foregrounds infrastructures’ contested potential to enable progressive, transformative change. We pull out four transversal themes from the papers, around rethinking governance, repoliticising infrastructure development, embracing incremental and context-sensitive approaches and expanding conceptions of justice. In doing so, we call for approaches to infrastructural transformation that remain open to uncertainty, friction and possibility.

The aim of this paper is to identify the most suitable material model for the numerical simulation of the incremental forming of polymeric materials using the finite element method. The analysis program used was Ls-Dyna, and two material models, namely material 24 (Piecewise Linear Plasticity) and material 89 (Plasticity Polymer), were chosen for comparison from the library of the program. A comparison was made between two polymeric materials, polyamide PA 6.6 and polyethylene HDPE 1000, with the following dimensions of the forming tools: punch diameter, Dp = 6 mm; die length, Ld = 190 mm; die radius, Rd = 5 mm; die corner radius, Rcorner = 10 mm; and blankholder length, Lbl = 190 mm. The simulation using the finite element method was performed with the Ls-Dyna software, and the experimental research was carried out using the Kuka KR210-2 robot. The strains were measured with the Aramis 2M optical system. Experimental investigations were carried out simultaneously, and the results obtained were compared in terms of main strains, thickness reduction, and forces on three directions. Close results were obtained between theoretical and experimental research for both material models.

This phenomenological study explores the lived experiences of residents in Benin City's self-help housing settlements, examining how subjective meanings and daily practices shape housing satisfaction. Through in-depth interviews with 10 purposively selected residents and observational field notes, the research employs Interpretative Phenomenological Analysis to uncover five key themes: housing as autonomy and identity, cultural anchors of satisfaction, contextual definitions of adequacy, place-ballets of daily routines, and community embeddedness. The findings challenge conventional housing assessment metrics by demonstrating how residents derive satisfaction from incremental construction processes, cultural continuity, and social networks rather than formal infrastructure standards. Grounded in David Seamon's place-ballet theory and Kim Dovey's concept of home as paradox, the study argues for housing policies that recognize the phenomenological dimensions of dwelling. The research contributes to urban studies literature by centering resident experiences in informal settlement discourse and suggests new directions for culturally attuned housing policy in Nigeria's rapidly urbanizing context.