Search Results (3,209)

This paper presents a qualitative analysis of multilingual past-tense constructions produced by Italian–Peruvian speakers in Turin (Italy). Drawing on data from the Stra-ParlaTO corpus, the study aims both to describe the multilingual practices of this community and to assess the explanatory potential of Diasystematic Construction Grammar for language-contact phenomena. After reviewing code-mixing, translanguaging, and second-language acquisition approaches, the paper argues that these frameworks converge in recognising the role of structural similarity in the emergence of hybrid productions, but leave underspecified the nature of the abstract linguistic knowledge that makes them possible. The analysis shows that speakers generalise across Italian passato prossimo and Spanish pretérito perfecto compuesto, developing an abstract language-unspecific construction that licenses both monolingual and mixed realisations. These findings suggest that Diasystematic Construction Grammar provides a cognitively plausible account of multilingual constructions and can be fruitfully extended to recent migration settings. Full article

Background: Cervical cancer remains a significant health issue in Indonesia, where structural barriers, fragmented information, and sociocultural norms continue to hinder timely diagnosis and treatment. Families play a central role throughout the illness journey, yet their perspectives are often overlooked in the development of digital patient navigation systems. This study explored family experiences, caregiving challenges, and expectations for a family-centered digital navigation model, DIVA.ID, by integrating Digital Health frameworks and Family Systems Theory. Methods: A qualitative descriptive approach was employed through semi-structured, in-depth interviews with 18 purposively selected family caregivers of women with cervical cancer at a major referral hospital in West Java. Participants were selected because they were directly involved in daily care, treatment decisions, logistical support, or emotional assistance. Interviews were conducted between August and October 2025 and continued until thematic saturation was reached, as indicated by repetition of categories and the absence of new major codes in the final interviews. Data were analyzed using inductive–deductive content analysis guided by Elo and Kyngäs, with five researchers conducting independent coding, iterative code comparison, consensus meetings, and theoretical mapping. Results: Four main themes emerged: (1) family involvement in decision-making, including collective discussion, shifting authority roles, and patient autonomy; (2) caregiver burden, involving physical exhaustion, psychological distress, social restriction, stigma, financial pressure, and employment disruption; (3) psycho-spiritual coping mechanisms, including emotional sharing, prayer, crying, patience, and surrender to God; and (4) digital healthcare needs, covering BPJS guidance, treatment information, scheduling, communication pathways, shelter support, and mental–spiritual support. Mapping these themes to Digital Health frameworks and Family Systems Theory clarified how DIVA.ID could translate family experiences into practical navigation functions. Conclusions: This study provides empirical foundations for a culturally sensitive, family-centered digital navigation model in Indonesia. Rather than demonstrating effectiveness, the findings identify design requirements for DIVA.ID that should be tested in subsequent feasibility, usability, and intervention studies. Full article

Fire is the core safety threat to the survival and development of timber-framed buildings, and passive fire prevention intervention is the core foundation of fire protection systems for timber-framed buildings. Existing reviews suffer from limitations such as incomplete scenario coverage, insufficient breakdown of intervention mechanisms, and a lack of methodological standardization. This study strictly followed the PRISMA 2020 systematic review guidelines, searching the relevant literature from January 2016 to April 2026 on the Web of Science, Scopus, and Science Direct databases. After standardized screening, 89 valid articles were finally included and a systematic study was conducted through bibliometric analysis, keyword visualization, and multi-dimensional classification coding. The results show that the number of publications in this field has been continuously increasing from 2016 to 2025, with China accounting for 31.46% of the total, ranking first globally. The study constructed a core intervention mechanism system for passive fire prevention in timber-framed buildings, covering four categories: intrinsic flame-retardant modification, isolation protection, structural optimization, and spatial control. The working principles, application effects, advantages and disadvantages, and engineering application scenarios of each mechanism were clarified. This study systematically sorts out the core intervention mechanisms of passive fire prevention in timber-framed buildings, clarifies the research status and development trends in this field, and can provide evidence-based support for the design optimization, technology development, and engineering practice of passive fire protection for timber buildings. Full article

Smart labels are emerging as a key enabling technology for product traceability, environmental monitoring, and user interaction within Internet of Things (IoT) ecosystems. This work presents the design and experimental validation of a low-power smart label platform integrating Bluetooth Low Energy (BLE) communication, temperature sensing, and dynamic e-paper visualization based on the HY0020 System-on-Chip (SoC). This platform was implemented on a custom Printed Circuit Board (PCB) designed around a 1.02-inch monochrome e-paper display and incorporates a TXS0108E interface to support reliable display communication. The developed prototype enables wireless user interaction, dynamic QR code rendering, and ambient temperature monitoring while maintaining low average power consumption. Experimental evaluation included BLE communication testing, display operation validation, temperature monitoring assessment using the integrated HY0020 sensor, and energy consumption characterization. Experimental results confirmed reliable BLE connectivity, stable temperature monitoring performance under normal environmental conditions, and an estimated battery lifetime of approximately 54 days under the evaluated operating profile. The presented platform demonstrates the feasibility of integrating sensing, wireless communication, and electrophoretic display technology within a compact battery-powered smart label device. The proposed architecture provides a practical proof-of-concept foundation for future applications involving product traceability, digital information management, and Digital Product Passport (DPP)-oriented services. Full article

Existing AISI S100 provisions for cold-formed steel (CFS) screw connections lack codified strength equations for screw shear and net section fracture, and traditional machine learning (ML) models struggle to predict these minority failure modes due to imbalanced experimental datasets. This study proposes a cascaded ML framework that first classifies the failure mode and then predicts strength using mode-specific regressors. Two cascade strategies are evaluated: a Hard Classification Cascade (HC-C) and a novel Probability-Weighted Cascade (PW-C) that weights predictions by class probabilities to mitigate error propagation from misclassification. The predictive performance of the two cascaded models is benchmarked against a single regressor without classification. The superior PW-C model is then compared with AISI S100, and its resistance factor ϕ is subsequently calibrated in accordance with LRFD. Results show that the proposed cascaded models outperform the direct regression model, with PW-C improving the R² for minority-class screw shear from 0.765 to 0.933 and for net section fracture from 0.784 to 0.912. Compared with AISI S100 provisions, PW-C extends coverage to the currently unaddressed failure modes and effectively captures screw group effects on shear strength based on a database of 564 tests. Reliability analysis yields an overall ϕ_c of 0.64 for the PW-C model, with a recommended divisor of 1.15 for direct application within the AISI design framework. This work provides a practical, data-driven pathway for updating design codes to cover failure modes beyond current specification limits. Full article

The rapid emergence of generative AI tools is transforming software development. Consequently, software engineering education must adapt to ensure that students not only learn traditional development methods but also understand how to use these new technologies effectively and responsibly. In particular, project-based courses provide an effective setting in which to explore and evaluate the integration of AI assistance into real-world development practices. This paper presents our approach and a user study conducted in the context of a university programming project in which students collaboratively developed computer games. The study investigates how participants used generative AI tools across different phases of the software development process, identifies the tasks for which these tools were perceived as most useful, and analyzes the challenges students encountered. Building on these insights, we further examine a repository-aware, locally deployed large language model (LLM) assistant designed to provide project-contextualized support. The system employs retrieval-augmented generation (RAG) to ground its responses in relevant documentation and source code, thereby enabling a qualitative analysis of model behavior, parameter sensitivity, and common failure modes. These findings deepen our understanding of context-aware AI support in educational software projects and inform the future integration of AI-based assistance into software engineering curricula. Full article

Small and medium enterprises routinely face a paradox in financial monitoring: their accounting documents exist, but the cost of converting heterogeneous PDFs into timely financial signals is prohibitive without dedicated analytical staff or specialized software. This paper presents a two-layer artifact, designed under Design Science Research, that bridges this gap using only public-web large language models (LLMs) and a parsimonious multi-criteria decision routine. Layer 1 implements a structured LLM-driven workflow that extracts account–value pairs from annual tax balance sheets without code, APIs, or fine-tuning. Layer 2 reconstructs auditable accounting aggregates and ranks yearly financial condition through TOPSIS with min–max normalization—a deliberate replacement for classical vector normalization, which fails when profitability indicators are negative, as routinely occurs in distress years. To avoid size effects and algebraic redundancy, the decision matrix uses only three criteria spanning liquidity, profitability, and solvency. The artifact is demonstrated in a four-year case study of an anonymized construction SME (2021–2024), with accountant-verified document-level match rates of 0.810, 0.998, 0.950, and 0.909. Equal weighting is the only weighting configuration used; a supplementary entropy-based dispersion diagnostic yields the same ordinal ranking—2024 > 2023 > 2021 > 2022—and 10,000 Monte Carlo replications, with uncertainty injected at the reconstructed-aggregate level, confirm that the extreme ranks are invariant across all runs. The contribution is methodological and practical: a transparent, low-infrastructure pipeline that brings first-pass financial screening within reach of SMEs operating under severe data and budget constraints. Full article

Background: Infectious diseases remain a leading cause of mortality in South Africa, compounded by a high HIV prevalence. This study aimed to delineate the spectrum and clinicopathological characteristics of fatal infectious diseases through a postmortem audit to inform clinical practice and public health strategy. Methods: A retrospective, cross-sectional descriptive study was conducted on all autopsies with a final cause of death attributed to infectious disease at a National Health Laboratory Service, in Northern Pretoria, Gauteng, South Africa, from 2012 to 2021. Using the Systematised Nomenclature of Medicine Clinical Terms (SNOMED) code and word search engines codes, 55 cases were identified. Data on demographics, clinical presentation, HIV status, antiretroviral therapy (ART), comorbidities, and final autopsy diagnosis were extracted from the laboratory information system. Histological confirmation was performed using standard stains. Descriptive statistical analysis was conducted using STATA-18. Results: The cohort (n = 55) had a median age of 31 years (IQR 19–45) and was predominantly female (67%). HIV prevalence was 35%, with 68% of those on ART. The leading cause of death was multilobar pneumonia (36%), followed by bronchopneumonia (22%). AIDS-defining illnesses were present in 27% of cases, with disseminated tuberculosis being the most common (46%). Septic shock was identified in 18% of decedents. A significant proportion (60%) of the cohort was HIV-negative. Conclusions: This autopsy series reveals a high burden of fatal community-acquired pneumonias and HIV-associated opportunistic infections, with a notable proportion of deaths occurring in HIV-negative individuals. The findings underscore diagnostic gaps and highlight the critical role of autopsy in accurate mortality surveillance, advocating for enhanced antemortem diagnostic protocols and targeted public health interventions. Full article

Emotional well-being has become a central concern in contemporary educational research, particularly in contexts shaped by social and cultural diversity. However, dominant approaches to educational assessment continue to prioritize cognitive outcomes, often overlooking the affective dimensions of children’s everyday experiences. In this context, play emerges as a key yet underexplored process through which emotional well-being is constructed in childhood. This study aimed to analyze the role of play in the configuration of emotional well-being in sociocultural educational contexts from a sociocultural and relational perspective. A qualitative multiple-case study was conducted in two rural schools located in Mapuche territories in southern Chile, involving students, teachers, caregivers, and Mapuche knowledge holders (kimches). Data were generated through semi-structured interviews and focus groups and analyzed using inductive coding procedures supported by qualitative data analysis software. The findings indicate that play operates as a socioemotional ecology through which children participate in collective forms of life, construct relationships, and experience emotional well-being in interaction with others, territory, and culturally meaningful practices. Three interconnected dimensions emerged. First, play was experienced as a relational, territorialized, and culturally situated practice sustained through participation, collective interaction, and intergenerational transmission. Second, emotional well-being emerged through enjoyment, companionship, belonging, and opportunities for social participation. Third, well-being appeared as a situated experience dependent on access to meaningful spaces, material conditions, cultural repertoires, and opportunities for play. Participants also identified tensions associated with technological change, the reduction in free play opportunities, and transformations in community life, while highlighting the potential role of schools in revitalizing culturally significant play practices such as palín and linao. These findings suggest that emotional well-being is not simply an individual psychological state but a relational and sociocultural accomplishment emerging through participation in meaningful play practices. The study contributes to interdisciplinary debates on childhood, emotional well-being, intercultural education, and sociocultural approaches to development by proposing the concept of play as a socioemotional ecology. Full article

Background/Objectives: Deep learning models for digital mammography sensor data are increasingly deployed across hospitals using different X-ray detector technologies and patient populations. Whether models trained on one sensor platform and population maintain accuracy when transferred to another has not been tested for the latest generation of mammography-specific foundation models under one controlled protocol. Methods: We fine-tuned five backbone architectures (ResNet-50, DINOv2-B14, Rad-DINO, Mammo-CLIP B5, and Mammo-FM) on CBIS-DDSM (film-digitized, USA, n = 714 validation) with three seeds, ablated a density-aware focal loss across three auxiliary weights, and evaluated transfer to three external sensor cohorts: CMMD (full-field digital, China, n = 1032), DMID (mixed digital, India, n = 509), and MIAS (film-digitized, UK, n = 322). Significance used paired DeLong z-tests with Benjamini–Hochberg FDR correction; temperature scaling tested post hoc recalibration at all transfer targets. Results: Within this single-source three-seed evaluation, ResNet-50 outperformed all four foundation models on CBIS-DDSM (AUC 0.867 vs. 0.847, 0.846, 0.813, and 0.703; all gaps p_adj < 0.05). The density-aware focal loss degraded both AUC and calibration at every weight tested. At transfer, every model lost 0.165 to 0.320 AUC points relative to in-distribution performance, with sensitivity at 95% specificity collapsing from 0.31 to 0.47 in-distribution to 0.11 to 0.22 across the three external targets. A per-seed Stouffer meta-analysis confirms that Mammo-CLIP B5 and Mammo-FM significantly outperformed ResNet-50 on DMID and Mammo-CLIP on CMMD, after BH-FDR; MIAS comparisons remained directional only. In the extremely dense subgroup (BI-RADS D4), Mammo-FM reached AUC 0.870 versus ResNet-50 at 0.842, a directional observation whose 95% CIs overlap heavily at the n = 140 sample size and which we do not interpret as a statistically supported advantage. Conclusions: In this single training-source, three-seed protocol, mammography-specific pretraining did not deliver the in-distribution AUC premium reported in the originating papers, and no architecture reached a level at which transfer deployment without local validation would be defensible. We frame these as observations specific to the present protocol rather than as broader conclusions about foundation models for mammography classification. The findings argue for sensor-stratified and population-stratified external validation and for local recalibration as practical prerequisites before clinical use. Code and weights are released under MIT license. Full article

This study investigates the comparative effectiveness of Lead Rubber Bearing (LRB) and Friction Pendulum System (FPS) isolation units under varying seismic hazard levels and soil classes, within the framework of the Turkish Building Earthquake Code (TBEC 2018). The assessment was conducted in two stages. First, keeping the site class constant, multiple locations characterized by different seismic hazard levels are examined. Second, a fixed geographical location is considered to evaluate the influence of different site classes on isolator response. The performance of the isolation systems is evaluated in terms of displacement demand, base shear ratio, and code-based verification criteria. Additional sensitivity checks were performed using selected limit values to better understand the response trends under changing hazard and soil parameters. The findings highlight how soil amplification effects and seismic intensity levels influence the relative advantages of LRB and FPSs. The results provide practical insight for the selection of seismic isolation systems in hazard-prone regions, contributing to improved performance-based decision-making in earthquake-resistant design. The isolator parameter choices were set based on average catalogue values provided by manufacturers to make this research an example. As a result of the analysis of the isolators’ performance, it was concluded that the FPS-type isolator performed better as acceleration values increased. Full article

This study examines how German textbooks provide learning-behavioral affordances for sustainability-oriented intercultural competence development. Drawing on Klieme’s competence-model logic, ESD, intercultural competence research, learning behavior theory, and affordance theory, it treats “sustainable intercultural competence” not as a standardized construct but as a working shorthand for the sustainability-oriented development of intercultural competence. Methodologically, the study adopts a directed qualitative content analysis supplemented by descriptive frequency aggregation. All 37 units across the four volumes of Meilenstein were coded on a 0–2 scale across three affordance dimensions: cognitive-understanding affordance, reflective value-judgment affordance, and interaction-action affordance. The findings show that the series provides substantial but uneven affordances. Interaction-action received the highest aggregated score, followed by cognitive-understanding, whereas reflective value-judgment remained substantially lower. Units on family, identity, sustainability, and civic engagement offer the most balanced affordance structures, whereas everyday practical units privilege communicative action and disciplinary units privilege cognitive understanding. The study argues that textbook-based intercultural learning should be examined not only through topic inclusion but also through how texts, prompts, and tasks organize opportunities for comparison, reflection, judgment, negotiation, and action. Full article

Reasoning-intensive multimodal retrieval suffers from a counter-intuitive bottleneck: on MM-BRIGHT multimodal-to-text (Query+Image → Documents), the strongest dense multimodal encoder reaches only 27.6 nDCG@10 and the rest of the dense vision–language retrievers cluster between 10.0 and 23.0. The visual signal, encoded as a dense vector, adds noise rather than evidence; even augmenting strong text retrievers with raw image captions degrades performance by up to 12.0 points. We propose VISA, a Visual Symbolic Agent that re-casts multimodal-to-text as text retrieval over three parallel streams. A Vision LLM is dispatched in three roles via separate prompts: a zero-shot router that classifies the query image into up to three parser types from a fixed taxonomy of nine (chart, circuit, equation, screenshot, code, figure, diagram, map, photograph); typed parsers that extract structured text per type; and a holistic captioner. The agent constructs three text streams (raw query, query ⊕ symbolic, query ⊕ caption), scores each with a single frozen 4B-parameter retrieval LLM, and fuses the per-document scores via Reciprocal Rank Fusion or a confidence-weighted linear combination. The whole agent contains no trainable parameters. The key novelty is a change of substrate: rather than projecting the query image into a dense multimodal vector that competes with text, VISA is, to our knowledge, the first retrieval system to convert the image into typed symbolic text and keep retrieval entirely text-side, so that a frozen text retriever can match the literal tokens (axis values, variable names, function signatures) that answering documents actually contain. Across all 29 MM-BRIGHT multimodal-to-text domains, VISA achieves 32.4 nDCG@10, an absolute improvement of +4.8 over the strongest dense multimodal encoder and substantially larger margins over the remaining six dense vision–language baselines. Per-domain analysis shows VISA maintains its margin across STEM and software domains where image content is structure-heavy. In practical terms, VISA is training-free and model-agnostic: it requires no fine-tuning, reuses any off-the-shelf vision LLM and text retriever, caches all per-image parsing so re-runs cost only three query encodes, and can therefore be dropped into an existing text-retrieval stack to add reasoning-intensive multimodal capability without building or training a multimodal encoder. Full article

Determining the 3D position of a vehicle from a 2D image plays a key role in video surveillance, autonomous driving, and spatial localization. However, localization accuracy can significantly degrade in conditions of incomplete or synthetic measurement noise and keypoint jitter. In this paper, we propose a new iterative 3D position estimation algorithm (KGA). This algorithm includes geometric correction and calibration steps for converting from 2D to 3D coordinates; trajectory prediction and correction using a Kalman filter; and adaptive tuning of the filter parameters using the Goldschmidt algorithm. Experiments confirm that KGA outperforms the standard (FK) and modified (MFK) Kalman filters in accuracy and convergence speed, demonstrating robustness to various camera angles and noise levels. The novelty of this approach lies in the integration of the Goldschmidt algorithm into the Kalman filter to create an adaptation mechanism that dynamically adjusts the measurement noise covariance based on instantaneous innovation magnitude. Unlike end-to-end deep learning trackers or nonlinear filters (EKF/UKF), KGA is designed as a lightweight post-processing stage that can be seamlessly integrated into existing detection pipelines while maintaining the low computational footprint required for UAV-based edge deployment. The algorithm is of practical value for computer vision systems requiring accurate and robust tracking under varying observational conditions, with current implementation suitable for offline or buffered processing, and clear pathways to real-time deployment through code optimization. The algorithm is of practical value for computer vision systems requiring accurate and robust tracking under varying observational conditions. Full article

Linearly homomorphic signatures (LHSs) are widely used in scenarios such as network coding and the Internet of Things, but their security faces the serious threat of key leakage. To address this issue, this paper introduces a forward secure mechanism into LHSs, aiming to construct a linearly homomorphic signature (LHS) scheme that can resist the risk of key leakage. By combining the binary tree minimal cover set mechanism with lattice-based extension algorithms, we construct an LHS scheme that supports time-period key updates. We prove its forward secure unforgeability under the standard model (SM) by reducing it to the Short Integer Solution (SIS) problem. To the best of our knowledge, this scheme is the first provably secure lattice-based forward secure linearly homomorphic signature (FSLHS) scheme in the SM, filling a theoretical gap in existing research. Furthermore, we apply this scheme to a smart grid data acquisition system and verify its practicality through concrete performance analysis. Full article