Search Results (24,052)

Polyamide 6 (PA6) is limited in its application in precision and high-temperature fields due to its high moisture absorption, low heat resistance, and poor barrier properties. To overcome these intrinsic deficiencies, a rigid 9-(carboxyphenyl)carbazole-based diacid monomer (CzIPA) was incorporated into the PA6 backbone via one-step melt polycondensation. Structural analyses confirmed successful copolymer formation and effective modulation of hydrogen-bonding interactions and chain rigidity. The introduction of the bulky carbazole units markedly enhanced the thermal and physical properties of PA6. The glass transition temperature increased by up to 35.5 °C, while the maximum decomposition temperature rose by 23.8 °C, reflecting the reduced chain mobility and strengthened thermal resistance. The decreased amide-group density led to a 15% reduction in water absorption, improving dimensional stability. The Young’s modulus, flexural strength, and flexural modulus of the prepared copolymers were significantly improved compared to PA6, while the toughness was slightly reduced. Furthermore, oxygen and water-vapor permeabilities were simultaneously reduced by 30–35%, attributed to restricted diffusion pathways in the modified microstructure. Despite the increased rigidity, the copolymers maintained good melt processability with clear shear-thinning behavior. This study demonstrates CzIPA copolymerization as an efficient structural design strategy for producing high-performance PA6 materials with enhanced thermal stability, lower hygroscopicity, and superior barrier properties. Full article

Down syndrome (DS) is associated with persistent language impairments that extend beyond early childhood, yet evidence from agglutinative languages remains limited. While morphosyntactic weaknesses have been well-documented in Indo-European languages, less is known about how such difficulties are manifested in Turkish, a language in which grammatical relations are primarily marked through morphology. In addition, short-term memory (STM) limitations, particularly in verbal domains, are characteristic of DS and may contribute to language outcomes. This study examined the interaction between morphosyntax and STM in Turkish-speaking children and adolescents with DS. A cross-sectional observational design was employed, including 12 monolingual Turkish-speaking participants with DS (aged 6;7–15;11) and 10 TD peers matched on nonverbal mental age. Participants completed standardized assessments of syntax and morphology, spontaneous language sampling, and STM tasks assessing verbal and visual memory. Children with DS performed significantly below controls on syntactic comprehension and production as well as morphological measures, with larger effects observed for syntax. Noun morphology was less accurate than verb morphology, likely reflecting increased morphophonological complexity. Regression analyses indicated that auditory digit span predicted sentence comprehension, whereas nonword repetition predicted morphological production indexed by mean length of utterance in morphemes. Substantial inter-individual variability was observed within the DS group. These findings suggest that morphosyntactic outcomes in Turkish-speaking children with DS are closely linked to verbal STM capacities and vary considerably across individuals, underscoring the importance of integrated assessment and individualized intervention planning. Future research with larger samples is warranted to confirm and extend these preliminary findings. Findings should be interpreted cautiously due to the limited sample size and are presented as preliminary descriptive evidence. This study provides initial data on Turkish-speaking individuals with Down syndrome. Full article

Asymmetric error characteristics in spin-transfer torque magnetic random-access memory (STT-MRAM), particularly the imbalance between logical ‘0’ and ‘1’ error probabilities, can significantly degrade system reliability under conventional modulation and error-correcting schemes. This issue is especially critical in sensor network applications, where STT-MRAM is widely adopted for its non-volatility, low standby power, and robustness under energy-constrained and intermittently active operation. Existing approaches typically optimize the detection threshold under the assumption of a fixed or equiprobable bit distribution, while sparse coding techniques impose a predefined imbalance without explicitly accounting for its interaction with threshold detection. In this paper, we formulate the bit error rate (BER) minimization problem as a joint optimization of the codeword bit distribution and the detection threshold over an asymmetric cascaded STT-MRAM channel. Analytical results reveal that the minimum BER is achieved when the error probabilities associated with transmitted ‘0’ and ‘1’ bits are balanced, which induces an intrinsic coupling between the optimal detection threshold and the codeword composition. Motivated by this insight, we propose a new family of threshold-matched probability codes (TMPCs), in which the proportion of logical ‘1’s in each codeword is explicitly designed to match the optimal detection threshold of the underlying channel. The proposed coding framework generalizes conventional sparse modulation by enabling adjustable bit distributions while preserving low-complexity linear encoding and syndrome-based decoding. Numerical evaluations demonstrate that the TMPC achieves consistently lower BERs than existing sparse and fixed-distribution coding schemes across a wide range of STT-MRAM operating conditions, particularly under severe write asymmetry and resistance variation. These results indicate that the proposed joint design offers a principled and flexible approach for improving reliability in STT-MRAM-based sensor networks and non-volatile memory systems. Full article

Progressive collapse is characterized by disproportionate structural failure triggered by localized damage, such as column loss under extreme loading conditions. The objective of this study is to develop a simplified analytical model that is applicable in engineering practice without the need for high-fidelity nonlinear finite element analysis. Although current design guidelines (GSA and DoD) provide analytical procedures and acceptance criteria, they do not explicitly address the tensile resistance of girders after the acceptance criteria are satisfied, particularly under large deformation and connection failure. To address this limitation, this study proposes a simplified theoretical load–displacement model for a fixed-end girder subjected to three concentrated loads, considering the effects of secondary beams and focusing on the local girder response under a column-removal scenario. The proposed model incorporates moment–axial force interactions at plastic sections in the large-deformation range. Based on one-dimensional finite element analysis results, an early-developed axial force of 0.15Fₚ at the onset of the transition stage and a residual bending moment of 0.3Mₚ during the catenary action stage are explicitly introduced to better represent actual structural behavior. The girder response is idealized using five characteristic points: yielding (Y), full plasticity (P), transition initiation (T), pure catenary action initiation (C), and collapse governed by connection failure (F_conn). Stress distributions at plastic sections are analyzed using three-dimensional finite element models to establish stress-based formulations and a rational procedure for estimating axial force at collapse. The validity of the proposed model is verified through comparisons with finite element analysis results for girders with different span-to-depth ratios. The results demonstrate reasonable agreement in terms of collapse load and displacement, particularly for slender girders, confirming the applicability of the proposed model for progressive collapse assessment. Full article

The rapid integration of generative AI in education often frames teachers as technology users who primarily need technical training. Existing prompt engineering frameworks offer technical guidance but have limited grounding in theories of teacher professional development or reflective practice. This misses a key feature of prompt engineering: prompting can externalize pedagogical thinking, making AI interaction a process of knowledge externalization. Through systematic conceptual analysis, this paper proposes a reconceptualization of prompt engineering from a technical competency to a reflective professional practice. The methodology integrates three theoretical traditions: Schön’s reflective practice theory (for externalizing tacit knowledge), Wiggins and McTighe’s backward design (for structuring instructional decisions), and Celik’s AI-TPACK framework (as integrated knowledge base). This synthesis suggests that effective prompting can be understood as an act of pedagogical externalization requiring integrated professional knowledge. The paper develops a seven-strategy framework (RPE framework) as an analytic lens for examining prompt engineering sophistication. This theoretical framework offers theory-derived hypotheses that require future empirical validation rather than presenting verified outcomes. Ultimately, the RPE framework offers a conceptual basis for potentially shifting the focus from technical training to teacher professional development by repositioning educators as AI-assisted instructional designers rather than mere AI users. Full article

The prevalence of ground fissures in deformation-affected areas has intensified, presenting serious risks to both operational safety and the local natural environment. Fissures in these disturbed terrains are typically characterized by elongated morphologies and large-scale variations, which pose substantial challenges to accurate feature extraction. To address these complexities, this paper proposes a semantic segmentation network termed MGF-UNet. In the shallow layers, we integrate multi-scale feature sensing (MFS) and grouped efficient multi-scale attention (EMA) to sharpen anisotropic textures and boundary details under high-resolution representations. For the deeper layers, a Token-Selective Context Transformer (TSCT) is designed to perform selective global modeling on high-level semantic features, effectively capturing long-range dependencies while preserving the structural integrity of elongated fissures. Meanwhile, we employ feature-wise linear modulation (FiLM) to derive pixel-wise affine parameters from shallow structures, which pre-modulate deep features and strengthen cross-level interactions. In the decoder, a Fourier transform-based adaptive feature fusion (AFF) module suppresses background noise and enhances boundary contrast, followed by cross-scale aggregation for final prediction.Benchmark tests conducted on the mining-area fissure dataset (MFD) and road-based datasets demonstrate that MGF-UNet achieves an accuracy of 78.2%, a Dice score of 81.4%, and an IoU of 68.6%, outperforming existing mainstream networks. The results confirm that MGF-UNet provides an effective solution for automatic fissure extraction in deformation-prone environments, offering significant potential for geohazard monitoring and ecological restoration. Full article

Recycled carbon fibres frequently exhibit degraded surface functionality owing to prior matrix removal processes, limiting their compatibility with contemporary epoxy resin systems. This study proposes a nanobubble-based surface treatment route designed to restore and enhance the surface characteristics of recycled carbon fibres without aggressive chemical oxidation. The study generated ozone and carbon dioxide nanobubbles in aqueous media and experimentally investigated the effects of nanobubble treatment on the surface properties and adhesive behaviour of recycled carbon fibres. Surface chemical changes were examined using X-ray photoelectron spectroscopy, which revealed an increase in oxygen-containing functional groups due to the nanobubble treatment, indicating improved surface polarity and potential for chemical interaction with epoxy networks. The practical effectiveness of the treatment was assessed via a pinhole pull-out test that served as an indirect measure of interfacial adhesion with epoxy resin, especially the combination of ozone nanobubbles and recycled carbon fibres. Notably, the nanobubble-treated recycled carbon fibres exhibited an increase in the adhesion compared with untreated recycled carbon fibres, rising from 84.5 ± 11.5 MPa to 138.5 ± 14.8 MPa, reflecting enhanced wetting behaviour and stronger fibre–matrix interfacial bonding. Overall, the proposed nanobubble processing route offers a mild, scalable, and environmentally favourable method for restoring surface reactivity in recycled carbon fibres, supporting their reintegration into high-performance composite applications. Full article

Digital transformation (DT) is reshaping manufacturing, with core enterprises (CEs) leveraging their resources to build industrial Internet platforms (IIPs) that support ordinary enterprises (OEs) in adopting DT. Differences in enterprise roles lead to varying impacts of government subsidies, necessitating careful policy design. Crucially, IIP adoption involves higher-order, multi-player interactions beyond conventional pairwise relationships—a dimension often overlooked in existing quantitative studies. This research employs hypergraph theory to model these complex interactions on IIPs and applies evolutionary game theory to analyze how enterprise decisions and government subsidies shape DT dynamics in manufacturing supply chains. The findings reveal that: (1) The network effect is the primary driver for DT via IIPs, but its promotional impact exhibits diminishing marginal returns. (2) Governments should prioritize subsidizing CEs for platform establishment, as subsidies directed at OEs for DT adoption are less effective. (3) Before withdrawing subsidies, governments must ensure a sufficiently high IIP adoption rate to sustain DT autonomously. This study introduces a novel methodology for examining DT and offers theoretical insights to guide enterprise strategy and policy implementation. Full article

Motivation: Accurate spectral reconstruction of printed halftone images is essential for achieving high-fidelity color reproduction and robust color management across modern printing systems. However, traditional physics-based models, such as the Yule–Nielsen and Clapper–Yule formulations, rely on simplified empirical assumptions and often fail to capture the complex nonlinear and asymmetric interactions induced by multi-ink overlays and substrate light scattering. Meanwhile, existing data-driven approaches based on single learning models exhibit limited capability in modeling the complementary and symmetrical characteristics inherent in halftone structures, resulting in suboptimal prediction accuracy and generalization performance. Method: To address these limitations, we propose a Stacking Ensemble Spectral Prediction (SESP) framework. The proposed method adopts a two-layer stacking architecture that integrates heterogeneous base regressors, including Support Vector Regression (SVR), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost 3.0.3), with Ridge Regression employed as the meta-learner for optimal prediction aggregation. This ensemble design enables effective modeling of both halftone pattern symmetry and complex substrate scattering behavior. Results: Extensive experiments conducted on printed halftone image datasets demonstrate the superior performance of the proposed SESP framework. Compared with the best-performing reference method (PCA-IPSO-DNN), SESP achieves relative reductions in RMSE and CIEDE2000 of 12.8% and 6.8% under illuminant A, 9.5% and 6.9% under D50, and 12.2% and 7.2% under D65, respectively. In addition, SESP consistently outperforms traditional physics-based models, including Yule–Nielsen and Clapper–Yule, in terms of both spectral prediction accuracy and colorimetric fidelity. These results confirm the effectiveness of the proposed framework in modeling the intricate nonlinear and asymmetric relationships between CMYK halftone patterns and spectral reflectance. Full article

The article examines how framing and actor identity structure attention in short-video politics using a country-level corpus from Ecuador. It assembles 4612 public TikTok videos from official accounts and politically salient hashtags, extracts multimodal text via automatic speech recognition and on-screen OCR, and constructs two continuous indices: a quality index (programmatic, efficacy-oriented content) and a populism index (antagonistic, people-versus-elite cues). Engagement is modeled as a fractional response (binomial GLM with logit link), with robustness checks using OLS on logit(ER) and Poisson counts with an offset for log(plays + 1). Models include affect (positive sentiment and anger), hour/day controls, and actor fixed effects (leader, creator, institution, party, and media). The indices display construct validity: quality aligns with positive/joyful tone and populism with anger. Net of controls, populism is positively and consistently associated with engagement across estimators; quality is small and often null or negative. Effects are heterogeneous: leaders gain under both frames, creators primarily under populism, and media modestly under populism, while institutions face penalties under both, and parties show limited returns. Monthly series reveal event-linked intensification of populism, and hashtag networks are modular, mapping onto institutional, partisan, and creator ecosystems. A design analysis identifies a non-populist pathway—benefit-first micro-explanations, concise captions, targeted hashtags, and joyful/efficacy affect—that raises engagement without antagonism. The study contributes a reproducible, open-source pipeline for survey-free, multimodal framing measurement and clarifies how persona × frame interactions and meso-level discursive structure jointly organize attention in short-video politics. Full article

This study investigates the irradiation response of two L1₂-strengthened HEAs, (Ni₂Co₂FeCr)₉₂Ti₄Al₄ (TiHEA) and (Ni₂Co₂FeCr)₉₂Nb₄Al₄ (NbHEA), subjected to 6.4 MeV Fe³⁺ irradiation at 500 °C up to 30 dpa. Transmission electron microscopy (TEM) and atom probe tomography (APT) consistently showed that the Ti-containing HEA maintains L1_2-ordered structure and compositional stability better than Nb-containing alloys under irradiation. This difference is attributed to the distinct solute–defect interactions. Ti imposes a weaker hindering effect on vacancy mobility, allowing vacancies to remain mobile and participate in thermal reordering processes that counteract ballistic mixing, whereas Nb acts as a strong vacancy trap, suppressing the diffusion required for structural recovery. Irradiation-induced dislocation loops in the two alloys further exhibited different characteristics. TiHEA showed larger loops at lower number density, and NbHEA exhibited a higher density of smaller loops, consistent with their respective stacking fault energies and loop mobility. Nanoindentation results indicated that TiHEA exhibited a slightly higher irradiation hardening rate (27%) than NbHEA (23%), likely associated with a stronger order-strengthening contribution, given the better preservation of precipitate order in TiHEA under irradiation. These findings show the critical role of solute addition in designing radiation-tolerant high-entropy alloys. Full article

This paper presents BPMN Assistant, a tool that leverages Large Language Models for natural language-based creation and editing of BPMN diagrams. While direct XML generation is common, it is verbose, slow, and prone to syntax errors during complex modifications. We introduce a specialized JSON-based intermediate representation designed to facilitate atomic editing operations through function calling. We evaluate our approach against direct XML manipulation using a suite of state-of-the-art models, including GPT-5.1, Claude 4.5 Sonnet, and DeepSeek V3. Results demonstrate that the JSON-based approach significantly outperforms direct XML in editing tasks, achieving higher or equivalent success rates across all evaluated models. Conformance checking evaluation confirms that generated models preserve executable semantics, with JSON achieving an average F1 score of 0.72 compared to 0.69 for XML, though frontier models like GPT-5.1 and Claude 4.5 Sonnet demonstrated superior precision with direct XML generation. Furthermore, despite requiring more input context, our approach reduces generation latency by approximately 43% and output token count by over 75%, offering a more reliable and responsive solution for interactive process modeling. Full article

Urban air pollution from road traffic remains a major public health concern, with its spatial variability at neighbourhood scales strongly influenced by urban morphology. This study investigates how urban form affects the dispersion of traffic-related PM_2.5 in four Italian cities (Lecce, Bari, Milan and Rome) representing diverse climatic and morphological contexts. Seasonal simulations were conducted using the ADMS-Roads dispersion model, integrating detailed road geometries, standardized traffic emissions, and city-level meteorological data for 2019–2021. Urban morphology was characterized at 100 m resolution using building plan area fraction (λ_p), street-canyon aspect ratio and mean building height derived from GIS analyses. Statistical analysis combined random forest regression with partial dependence plots and quantile regression to explore both average and distributional effects. Results reveal a generally negative association between λ_p and PM_2.5 in Lecce, Milan, and Rome, particularly at higher concentration quantiles, suggesting that denser urban fabrics may mitigate extreme pollution episodes. Bari exhibits a weaker and more heterogeneous response, highlighting the influence of local wind regimes and traffic distribution. Wind speed and temperature consistently reduce PM_2.5 across all cities, while street geometry effects are non-linear and season-dependent. These findings demonstrate the importance of considering urban morphology alongside traffic and meteorology when designing strategies to reduce exposure. Importantly, the methodological framework presented here, combining high-resolution dispersion modelling with interpretable machine-learning analyses, is transferable to other urban contexts, providing a robust approach to assess morphology–pollution interactions beyond the studied cities. Full article

Nanoplastics (NPs) have emerged as pervasive aquatic pollutants due to their small size, high surface activity, and potential ecological and health risks. Although sludge-derived biochar is a sustainable adsorbent for NP removal, the relative importance of coexisting adsorption mechanisms remains poorly quantified. Here, iron-modified sludge biochar (FeBC) was synthesized and evaluated for NP removal from water. Batch experiments showed that FeBC significantly outperformed pristine biochar, achieving a maximum removal efficiency of 96.09%. Adsorption was strongly pH-dependent, with enhanced removal under acidic conditions due to surface protonation and strengthened electrostatic attraction toward negatively charged NPs. SEM, BET, FTIR, and XPS analyses indicated that electrostatic interactions, hydrogen bonding, π–π interactions, and pore adsorption jointly contributed to NP capture. Importantly, structural equation modeling quantitatively disentangled these mechanisms, revealing electrostatic interactions as the dominant driver (52.6%), followed by hydrogen bonding (23%), pore adsorption (16.6%), and π–π interactions (7.9%), and further identified synergistic and antagonistic relationships among them. These results demonstrate that surface charge regulation governs NP adsorption efficiency, providing a quantitative mechanistic basis for the rational design of biochar-based adsorbents. This study advances a multi-mechanistic framework for understanding and optimizing NP removal while promoting sludge resource valorization. Full article

This study examines individual-centric multimodal sensory experiences in the museum context, where multimodality is defined as the interplay among sensory modalities. Focusing on visual and haptic experiences, the research aims to investigate the role of museum lighting in shaping sensory perception at the Istanbul Museum of Painting and Sculpture. We asked how local museum visitors aged 18–26 (primarily university students and frequent museum-goers) perceive and engage with the museum atmosphere beyond visual stimuli, particularly through lighting. Data were collected through sensorywalks (n = 16), a sensory-spatial research method, and interviews (n = 10) with local museum visitors. Findings indicated that lighting enhances multimodal sensory interactions during museum visits and enhances visitors’ awareness of spatial scale, materiality, and atmosphere. The discussion highlighted the significance of sensory-based museum design, including the sensory museum models and toolkits, in rethinking how young adults engage with museum environments. Understanding the multimodal experiences offers valuable insights for advancing both research and practice in museum studies. Full article