Search Results (951)

The peak–end rule proposes that retrospective evaluations depend on the emotional peak and the end of an experience rather than on its duration. Two short, controlled vocabulary-learning experiments tested whether optimizing these moments improves retrospective emotional valence. Study 1 (N = 32) manipulated task length (4 vs. 8 words). Retrospective emotional valence did not differ significantly between groups (p = 0.459, d = 0.27), a result consistent with duration neglect under this short task–episode manipulation but not a strong test of pure temporal duration neglect. Retrospective emotional valence correlated more strongly with the peak–end mean than with the mean of reconstructed page-level ratings (r = 0.761 vs. r = 0.314; Steiger’s Z = 3.03, p = 0.002). Study 2 (N = 56) used a 2 × 2 design to optimize the candidate peak-related completion page and the structurally defined end check-in page through color and anthropomorphic graphics. Both peak (ηp² = 0.18) and end (ηp² = 0.22) optimization enhanced retrospective emotional valence, with a significant non-additive interaction (ηp² = 0.09): the effect of optimizing one node was reduced when the other node had already been optimized. For learning accuracy, the main effect of peak optimization was significant (F(1, 52) = 4.44, p = 0.040), but only the combined peak-and-end optimization significantly outperformed the control condition (p = 0.041, d = 1.11); neither single-optimization condition significantly differed from the control condition after correction. The findings provide preliminary evidence for a peak–end-consistent evaluation pattern in brief, controlled vocabulary-learning tasks, identify a non-additive interaction in peak–end optimization, and offer guidance for designing key interactive moments within similarly short, task-based learning episodes. Full article

Palaeolithic engraved portable art provides a valuable record for investigating the technical aspects of Palaeolithic graphic production. In this study, we analyse an engraved portable art object from the Epigravettian sequence of Grotta Paglicci (southern Italy): a Bos primigenius mandible bearing a figurative representation interpreted as a waterbird, possibly a swan. The analysis combines 3D digital microscopy and geometric morphometrics to reconstruct the sequence of engraving gestures and to quantitatively characterise the morphological variability of the incisions. Archaeological engravings are compared with experimentally produced marks obtained using different lithic tools displaying similar trihedral active edges (burins and unretouched flakes). In addition, experimental and archaeological cut marks from the same Epigravettian context are included for comparative purposes. The results allow the reconstruction of the sequence of gestures involved in the production of the figure, revealing a structured execution comprising contour engraving, internal filling and the addition of secondary elements. Morphological and morphometric analyses show low variability among the engravings, pointing to a high degree of motor control throughout the engraving process. The predominance of U-shaped cross-sections in the archaeological sample, compared with the experimental engravings, is consistent with the use of a previously used and/or partially smoothed cutting edge. These results highlight the potential of integrated technological and morphometric approaches for investigating gestures, technical choices and operational organisation underlying the production of Upper Palaeolithic portable art. Full article

Conducted electromagnetic interference (EMI) in multilevel power converters is governed by nonlinear interactions among passive filter components, operating conditions, and resonance-sensitive spectral behavior, making analytical prediction and trial-and-error tuning insufficient for systematic compliance-oriented design. This study presents an experimentally grounded, data-driven framework for predicting and optimizing conducted EMI in an IGBT-based, SVPWM-controlled three-level neutral-point-clamped (NPC) inverter equipped with an active harmonic filter. A dataset of 1000 conducted-emission measurements was constructed from 250 filter parameter combinations evaluated under four operating scenarios: constant-current average, constant-current peak, standby average, and standby peak, over the 10 kHz–30 MHz range. Four surrogate architectures were trained and evaluated: a multilayer perceptron (ANN), a convolutional neural network (CNN), a deep neural network (DNN), and a physics-informed neural network (PINN). Model reliability was assessed through nested cross-validation, standard 5-fold cross-validation, Monte Carlo resampling, and SHAP-based interpretability analysis. Among the tested architectures, the CNN achieved the most consistent predictive performance and stability, whereas the PINN provided smoother and more physically disciplined spectral reconstructions in several load-related conditions. The trained surrogates were embedded in a Python 3.11-based graphical user interface and further employed within a compliance-oriented optimization framework to identify filter parameter sets capable of satisfying legal conducted-emission limits. Experimental verification confirmed that surrogate-guided optimized designs achieved positive worst-case legal margins between 7.26 and 11.50 dBµV. Relative to the best measured pre-optimization combination, which still exhibited a worst-case margin of −3.7 dBµV, the best experimentally validated optimized design improved the worst-case legal margin by 15.20 dBµV. These results demonstrate that experimentally trained surrogate models can support not only high-resolution EMI prediction but also regulation-aware filter design and practical engineering decision making. Full article

Soft story irregularity poses a critical seismic risk to existing building stocks. While current seismic codes define stiffness irregularity factors to detect this vulnerability, they are typically evaluated based solely on initial elastic properties. This study investigates the evolution of these code-defined factors (ASCE/SEI-7, UBC, NBC, TBEC-2018, and BSL) within the post-elastic range to examine how structural damage affects soft story irregularity. The methodology comprises two phases: a low-strength RC plane frame (Case A) and a parametric study on a 3D RC building with incrementally increased ground story heights (Case B). Nonlinear pushover analyses were conducted to track the variation in irregularity factors at each pushover step and examined graphically. Results demonstrate that soft story behavior is not a static characteristic; irregularity factors deteriorate significantly as plastic hinges form. Crucially, several models that initially satisfied code limits in the elastic range eventually exceeded irregularity thresholds under inelastic behavior. This indicates that relying solely on initial stiffness may mask latent irregularities emerging during seismic actions. Consequently, to capture the true severity of soft story mechanisms, it is recommended that stiffness irregularity factors be evaluated at target displacement levels corresponding to the design earthquake. Full article

Physics-based simulation of debris flows over complex terrain is essential for hazard assessment, but repeated numerical integration is costly when many scenarios must be explored. We develop a general deep-learning surrogate modelling framework for two-dimensional (2D) debris-flow propagation, here applied to the Morino–Rendinara area (central Italy) using a three-dimensional (3D) Fourier Neural Operator (FNO) trained on synthetic simulations generated by a validated in-house finite-volume shallow-water solver. The solver reproduces debris-flow propagation over complex terrain and is specifically developed for artificial intelligence (AI) applications. It is based on a depth-averaged 2D formulation using the Harten–Lax–van Leer–Contact (HLLC) approximate Riemann solver, hydrostatic reconstruction, positivity-preserving wet–dry treatment, and Voellmy-type basal friction, and was verified through analytical benchmarks, numerical tests, and back-analyses of real events. The dataset was built from four site-specific release settings derived from real topography, combining different released volumes and bulk densities while preserving local geomorphological and rheological characteristics. Each simulation was stored as a full spatio-temporal tensor and used to train an FNO conditioned on coordinates, topography, friction parameters, bulk density, and initial release thickness. Training used a novel loss to emphasize active-flow areas and improve velocity reconstruction, and was performed using a graphics processing unit (GPU). The surrogate shows effective generalization to within-distribution validation samples, with global relative mean squared errors of 5.49% for flow thickness, 5.34% for velocity component u, and 2.60% for v, and mean $R^2$ values of 0.95, 0.94, and 0.97. For a representative sample, the surrogate predicts the full spatio-temporal solution in $0.52$ s, versus about 47 s for the first-order finite-volume solver, corresponding to a speed-up of about $91\times$, with an even larger gap expected for higher-order solvers, since, whilst the computation time of the solver increases as its complexity increases, the computation time of the FNO remains essentially unchanged. These results indicate that the proposed FNO is a reliable site-specific surrogate for rapid approximation of 2D debris-flow dynamics over real terrain, with potential for uncertainty propagation, Monte Carlo analysis, large-ensemble simulation, and hazard-oriented scenario assessment. Full article

In recent years, the generation and animation of avatars in virtual reality (VR) have undergone a definitive paradigm shift, transitioning from pre-rendered, manually rigged meshes to autonomous, AI-driven digital entities. While individual algorithms have been extensively studied, there is a critical lack of comprehensive synthesis regarding how these generative models impact the broader sociotechnical ecosystem of Spatial Computing. To address this gap, this systematic literature review, conducted in accordance with PRISMA guidelines, analyzed 48 primary studies to evaluate the intersection of Generative AI, hardware architecture, human psychology, and digital ethics. The synthesis reveals a deeply interdependent ecosystem. While advanced neural rendering and diffusion models (RQ1) successfully bypass traditional 3D authoring bottlenecks, their pursuit of absolute visual fidelity severely antagonizes the thermal and latency constraints of standalone mobile hardware (RQ2). The literature demonstrates that failing to mitigate these bottlenecks through hardware–software co-design (e.g., specialized ASICs, gaze-contingent foveation) inevitably shatters the user’s sensorimotor loop, collapsing the sense of agency and triggering the Kinematic Uncanny Valley (RQ3). Furthermore, as these hyper-realistic avatars achieve kinematic autonomy, they introduce unprecedented sociotechnical vulnerabilities regarding spatial privacy, dataset bias, and post-mortem digital identity (RQ4). Ultimately, this review concludes that realizing a compelling and inclusive AI-driven Metaverse is no longer an isolated computer graphics challenge; it demands a rigorous, interdisciplinary paradigm shift where algorithms, silicon architectures, and cognitive psychology are inextricably co-designed under a foundational framework of digital ethics. Full article

In this article, we study the homotopy aspects of intersection graphs of topological semigroups. We begin by defining the top intersection graph $T{G}_{\mathbb{X}}$ and investigating how the algebraic and topological properties of a topological semigroup are reflected in the global structure of this graph. In particular, we characterize when $T{G}_{\mathbb{X}}$ is totally disconnected, bipartite, or planar in terms of the order and factorization of the underlying semigroup. We then introduce the notions of $HTG$-semigroups, graphical homomorphisms, and graphical homotopy relations, thereby developing a graphical homotopy framework. Within this setting, we study $Gr$-homotopy equivalences, $Gr$-contractible spaces, and retraction phenomena, including $DGr$-retracts and homotopy extension properties. Finally, we introduce graphical total semigroups and equip the set of $Gr$-path homotopy classes $\left[{\mathbb{X}}_{pe}\right]$ with a natural $\Delta$-uniform topology. We show that this topology is compatible with the induced semigroup operation, yielding a topological semigroup structure. Overall, this work provides a unified algebraic, topological, and graph-theoretic perspective, and opens the door to further applications of homotopy theory in the study of intersection graphs of topological semigroups. Full article

There are numerous benefits associated with creating digital copies of anatomical structures, which can be used during patient diagnosis. Such models can be used not only for visualization, but also in order to assess the condition of the patient. As advances in both medical imaging and 3D graphics are made, it is necessary to determine areas of application of the known reconstruction algorithms. Specifically, it is crucial to find advantages and disadvantages of known approaches to mesh generation, depending on the properties of the object and compare the quality of their results. In order to provide reliable ground-truth data, three 3D models with features resembling those identified in anatomical structures have been created. Based on these meshes, sets of CT-like DICOM images have been generated. Five different reconstruction approaches were proposed: using 3D occupancy information directly, two ways of obtaining point clouds and two methods that utilize Signed Distance Field. A neural network architecture for the SDF upsampling has also been presented. The obtained results justify the popularity of the Marching Cubes algorithm, as it produced accurate reconstructions most reliably. However, for certain scenarios, promising alternatives have been found. The presented outcomes make it clear that the approach to reconstruction must be tailored to the specific problem. Full article

The internationalization of university research is driven by multiple, overlapping rationales that operate across institutional and individual dimensions and condition how international engagement becomes feasible in practice. This study examines how Honduran university faculty constructs and articulates the reasons for internationalizing their research activities; it identifies the discursive positions that emerge from these accounts. A qualitative–structural approach was used to analyze the discursive data produced in the group meetings convened through with an ad hoc structural sample. The analysis identified and graphically represented five discursive positions configured as a relational structure: (A) Professional Development, (B) Ethical–Political Commitment, (C) Financial Acquisition, (D) Academic Prestige, and (E) Sociocultural Engagement. Together, these positions capture distinct yet interrelated motivations by which faculty members pursue research internationalization. By mapping this relational configuration, the study contributes to the literature by showing how faculty rationales are combined and hierarchized in situated academic practice and provides empirically grounded insights for designing, planning, and managing strategies that align institutional priorities with the diversity of faculty rationales, thereby leveraging their tensions and complementarities to strengthen the effectiveness and sustainability of research internationalization within the university context. Full article

This paper presents a lightweight MATLAB-based framework with a graphical interface for modeling, 3D simulation, trajectory generation, and experimental validation of a 6-DOF industrial robot. The platform integrates kinematic modeling using the rigidBodyTree structure, animated visualization, and both predefined and user-defined trajectory planning within a unified environment. A central aspect of the proposed approach is the implementation of a ROS-compatible TCP/IP communication protocol that avoids the need for a full ROS core installation while preserving compatibility with ROS-Industrial standards. This enables bidirectional data exchange between MATLAB and the robot controller within a simplified architecture. Communication performance tests indicate round-trip latency in the tens-of-milliseconds range and consistent StateServer update rates, supporting monitoring, trajectory execution, and digital twin synchronization in non-real-time conditions. Experiments conducted on an ABB IRB120 robot demonstrate a close correspondence between simulated and real motion, with RMSE below 0.0075 rad and MAE below 0.0065 rad across all joints. All data are stored in JSON format to support reproducibility and further analysis. By integrating simulation and real robot execution within a modular architecture, the proposed framework provides a practical tool for education, rapid prototyping, and experimental research in industrial robotics, while offering a basis for future extensions toward advanced control strategies and digital twin applications. Full article

In this work, we construct a homotopy theory for a class of intersection graphs arising from topological monoids. We introduce the M-intersection graph of a ${\tau }_e$-monoid, where the vertices correspond to proper ${\tau }_e$-submonoids and adjacency is defined by trivial intersection. Several structural properties of the graph, including total disconnectedness, bipartiteness and planarity, are investigated and shown to be closely related to the algebraic structure and decomposition of finite ${\tau }_e$-monoids. Based on this framework, we develop a graphical homotopy theory by introducing graphical ${\tau }_e$-monoids, graphical homomorphisms, and graphical homotopies. We study graphical homotopy equivalence, graphical contractibility, and path monoids, and examine retraction properties through graphical retracts, D-graphical retracts and graphical homotopy extension properties. Furthermore, we present an example of graphical comprehensive monoids and construct a $\theta$-homogeneous topology on the set of graphical path homotopy classes. We show that this topology is compatible with the induced monoid operation, yielding a well-behaved functorial topological monoid structure. Full article

In this paper, we study the dynamical analysis and solutions of the fractional Benjamin–Bona–Mahony–Burger equation. We demonstrate various derived solutions using different definitions of fractional derivatives, namely the $\beta$-derivative, conformable derivative, and M-truncated derivative, to examine their kinetic characteristics. Firstly, we find the solution of the fractional Benjamin–Bona–Mahony–Burger equation using two different approaches. We then discuss the effects of the fractional derivative on the solutions using 3D graphical discussion. Finally, we discuss the dynamical analysis using sensitivity and chaos analysis. We also discuss the chaos analysis using permutation entropy, 2D and 3D phase portrait, fractal dimension, time analysis, return map, Lyapunov exponent, and multistability through Poincare map and basins of attraction. To explore a diverse range of phenomena across the fields of physical science and engineering, this study highlights the computational strength and flexibility of the proposed method. Full article

Reconstructing photorealistic and animatable whole-body avatars from monocular videos is a hot topic in computer vision and computer graphics. However, existing methods still face challenges due to the limited frequency response of single-scale geometry encodings and the instability of appearance modeling without an explicit surface anchor. In this paper, we present H²Avatar, a real-time framework that builds on a mesh-embedded 3D Gaussian representation guided by SMPL-X and disentangles geometry and appearance into hierarchical and hybrid components. For geometry, we propose a semantic-aware hierarchical encoding based on a multi-scale tri-plane pyramid, where features at different resolutions capture both global structure and high-frequency surface details such as clothing wrinkles. For appearance, we introduce a hybrid rendering strategy that anchors canonical colors using a learnable UV texture map, and complements it with a neural residual color branch conditioned on tri-plane features, pose embedding, and surface normals to model pose- and view-dependent shading variations. This design improves temporal stability and preserves identity details while enhancing photorealism under complex motions. Experiments on the NeuMan dataset demonstrate that H²Avatar consistently outperforms representative baselines across multiple sequences, outperforming ExAvatar by up to 0.66 dB in PSNR and reducing LPIPS by up to 16.3%. These results validate the effectiveness of hierarchical geometry encoding and texture-anchored hybrid appearance modeling. Full article

In this work, we provide a convolution type operator ${\mathsf{\Lambda }}_{\nu ,b}$ that is produced by the generalized Marcum Q-function and examine how it maps to various Janowski-type subclasses of harmonic univalent functions. Since the Marcum Q-function has an integral form via the lower incomplete gamma function, the convolution operator ${\mathsf{\Lambda }}_{\nu ,b}$ can be understood as a fractional type integral operator operating on the coefficients of harmonic mappings. Applying ${\mathsf{\Lambda }}_{\nu ,b}$ to harmonic mappings $f=h+\overline{g}$ in the unit disk $\mathbb{D}$, we derive coefficient inequalities, and inclusion relations for various subclasses of harmonic and analytic univalent functions. In particular, we give sufficient conditions for ${\mathsf{\Lambda }}_{\nu ,b}\left(f\right)$ to belong to Janowski-starlike families such as ${\mathcal{S}}_H^{\ast }(F,G)$, ${\mathcal{K}}_H^0$, and ${\mathcal{R}}_H(F,G)$. Closure properties of the Janowski class under the proposed operator are also established. Numerical tables and examples confirm the inclusion results, and graphical plots illustrate how the operator reshapes the image domains for different parameter pairs $(\nu ,b)$. Numerical illustrations are provided to visualize the geometric steering effect induced by the Marcum Q-function and its fractional-order damping behavior. Full article

This article examines the Unité Pédagogique d’Architecture in Toulouse (UPAT) as a paradigmatic example of the palimpsestic architectures that characterize many contemporary university campuses. Conceived in the immediate aftermath of May 1968, the school emerged at a moment when pedagogical reform, political commitment, and architectural experimentation became closely intertwined. These conditions gave rise to a singular spatial organization based on a combinatory grid, intended to give architectural form to a democratic ideal of education grounded in openness, flexibility, and collective agency. The study adopts a historical–critical methodology based on the systematic analysis of primary and secondary sources, complemented by original graphic interpretations. This approach makes it possible to read the UPAT simultaneously as a didactic instrument and as an ideological manifesto, one whose ambitions were inherently marked by internal tensions and contradictions. A diachronic examination of subsequent extensions and transformations reveals how these founding intentions were progressively reinterpreted, constrained, or displaced in response to changing institutional, social, and cultural conditions. Taken as a whole, the evolving trajectory of this “manifesto school” illuminates the ways in which architectural ideals—particularly the pursuit of openness—are negotiated over time, offering a critical perspective on the reciprocal shaping of architecture, pedagogy, and institutional identity within the history of university buildings. Full article