Search Results (26,908)

Automatically mapping written Kazakh to dictionary-linked Kazakh Sign Language (KSL) lexical signs is difficult because inflected forms often do not match canonical entries in limited digital lexicons. Surdo Med combines Kazakh natural-language processing, Dimskis notation, reusable avatar animation components, and a web interface for medical-domain lexical learning. This study evaluates morphology-aware lexical concept recovery rather than complete Kazakh-to-KSL translation. The primary benchmark uses naturally occurring inflected tokens from the official UD Kazakh-KTB test split that map uniquely to a frozen medical concept inventory. Across 373 supported inflected tokens, direct lookup recovered 1.61% of target concepts, a rule-based baseline recovered 45.31%, and contextual Stanza lemmatization recovered 97.86% (sentence-cluster bootstrap 95% CI: 96.29–99.19%). Recovery remained 96.85% after deduplicating surface–lemma pairs and 94.80% when macro-averaged across 52 lexical concepts. An external analysis using published KSL lexical material showed 20.0% overlap with the medical inventory; within supported items, Stanza recovered 6/6 root tokens versus 3/6 for direct lookup. These results support morphology-aware normalization for dictionary-linked KSL concept recovery. Avatar linguistic quality and educational effectiveness require separate human evaluation. Full article

Structure-borne vibration transmitted through mechanical interfaces is an important contributor to aircraft cabin noise, while conventional isolators offer limited flexibility for adapting stiffness to application-specific requirements. This study investigates additive manufacturing as an enabler for parameterized vibration isolators, linking application requirements, design principles, simulation, manufacturing, and experimental assessment. Seven isolator design variants were investigated: four fused deposition modeling (FDM)-based and three stereolithography (SLA)-based design variants. Four TPU hardness grades were explored within the FDM-based variants as a separate material dimension; one grade did not yield specimens suitable for dynamic characterization. Finite element analysis was used for pre-screening, followed by axial transmissibility measurements on an electrodynamic shaker over 30–2000 Hz against a commercial elastomer isolator as reference. Experimental validation is limited to the axial direction; anisotropic stiffness is targeted by design but was not independently confirmed through radial measurements. Measurements indicate that geometric variations, particularly wall thickness and internal architecture, influence resonance location and isolation-region behavior across the investigated AM variants. A Parametric Design Map links design, process, simulation, and measurement data, demonstrating the feasibility of a measurement-supported parametric development approach for future aircraft cabin isolator applications. Full article

This study proposes a traceable conceptual framework for analysing and synthesising automotive product visual identity. It integrates Design for Six Sigma (DFSS) through the DMADV cycle (Define, Measure, Analyse, Design, Validate), TRIZ, and a parameterised Shape Grammar. The BMW 3 Series was selected because its seven generations provide a long, visually continuous lineage within the premium D-segment. This study converts secondary consumer evidence and author-generated decision matrices into design requirements, maps technical contradictions to three morphological domains (volume, surfaces, and distinctive elements), and expresses the styling process as an ordered set of geometric construction rules. The framework was applied to the hypothetical BMW H10 concept through sketching, 2D CAD, and 3D surface modelling. The principal result is a documented chain from requirements and constraints to formal decisions, together with an internal rule-compliance check of the resulting concept. The H10 retained the selected proportional construction targets and recurring BMW cues while accommodating proposed sensor-integration and packaging responses. No independent expert or user-perception study, physical prototype, CFD/FEM analysis, or vehicle-performance test was conducted. Accordingly, the results demonstrate design-process traceability and internal geometric verification, not engineering or perceptual validation. Transferability is presently limited to automotive exterior concept design and requires testing on further vehicle families. Full article

Accurate mapping of building damage after destructive natural disasters is essential for emergency response and recovery planning. However, annotated post-disaster samples are often scarce, and conventional supervised models usually generalize poorly across scenes and hazard types. These constraints limit the operational use of remote sensing-based damage assessment during emergency response. To address this problem, this study proposes SCTSC-VLM, a training-free framework for damage grading of pre-localized building instances, implemented using a locally deployed Q4_K_M 4-bit quantized version of the Qwen3-VL-30B vision–language model. The framework has three main components. First, an instance-level visual input construction strategy integrates independent assessment of building instances localized by the supplied footprint data without a dedicated instance segmentation model. Second, a hierarchical reasoning mechanism decomposes the assessment into four progressive stages: multi-view structured feature extraction, independent visual description, cross-validation, and rule-constrained classification. This design is intended to reduce hallucination and visually ungrounded reasoning. Third, a multi-sampling ensemble decision mechanism mitigates the randomness of autoregressive generation. The framework was evaluated using post-disaster optical imagery from the 2025 Dingri earthquake and the 2025 Eaton Fire in California. On the full Dingri evaluation dataset comprising Areas A–C (n = 398), SCTSC-VLM achieved a Macro-F1 score of 88.2% and a Kappa coefficient of 0.806. On the separate full California evaluation dataset (n = 386), it achieved a Macro-F1 score of 83.8% and a Kappa coefficient of 0.800. Separate ablation experiments were conducted on explicitly defined analysis populations to examine the contributions of the framework components. In both full-dataset evaluations, the fully damaged and undamaged classes obtained higher F1-scores than the partially damaged class. These results provide case-level evidence that structured reasoning can support training-free damage grading of pre-localized buildings, although broader generalization and large-area operational efficiency remain to be established. Full article

Background/Objectives: Primary plasma cell leukemia (pPCL) is a rare and highly aggressive plasma cell malignancy with poor prognosis and limited therapeutic options. This study aimed to characterize global publication and citation trends, leading contributors, and collaboration patterns in pPCL research. Methods: Scopus was searched on 8 September 2026 using a phrase-restricted TITLE-ABS-KEY query for the disease name and orthographic variants. English-language records classified as “Article” and published from 1961 through 2026 were eligible. All retrieved records underwent author-led relevance screening and duplicate assessment. Analyses were performed using bibliometrix through the Biblioshiny interface. Results: Of 219 records retrieved, nine were excluded because pPCL was mentioned only incidentally or pPCL-specific findings were not separately identifiable; 210 articles published between 1983 and 2026 were included. The articles appeared in 110 sources and involved 1681 authors. The annual publication growth was 5.95%, although 2026 was incomplete, and the mean citation count per article was 30.02. Acta Haematologica was the most productive journal (n = 10), followed by Blood (n = 9) and the Indian Journal of Hematology and Blood Transfusion (n = 8). Among 152 articles with corresponding-author country data, China, the United States, and India contributed the most articles, whereas France, the United States, and Spain had the highest total citation counts. International co-authorship accounted for 10.95% of articles, and the author network contained five collaboration clusters. Conclusions: This analysis maps the development of pPCL research and identifies its principal publication sources and contributors. The limited proportion of internationally co-authored articles supports broader multicenter and international collaboration in this rare disease. Full article

High-speed mass-production environments require reliable surface inspection because metal surface defects directly affect product reliability and process stability. Recent automatic surface inspection systems have increasingly adopted RGB image-based deep learning detectors to identify defects such as scratches, dents, and contamination. However, in practical inspection settings, the position and pose of small metal components can vary from frame to frame, and the reflective nature of metallic surfaces can make background texture visually similar to defect patterns. This study presents a cross-modal Region of Interest (ROI) generation framework that combines Time-of-Flight (ToF) intensity images with RGB images to define a stable component-level ROI before defect detection. The proposed method exploits signal attenuation and dropout patterns in ToF intensity images, caused by specular reflection on metallic surfaces, as structural cues for object localization. Foreground support regions extracted from the ToF intensity image are separated into object candidates using connected-component analysis and then projected onto the RGB coordinate system through a pre-calibrated homography. An object-adaptive ROI is generated from the projected region and provided to a YOLO-based defect detector. Experimental results show that the proposed method improves mAP@50 from 0.585 to 0.796 compared with the baseline YOLO26 model, demonstrating that ToF-guided object-adaptive ROI generation effectively reduces background interference and improves localization stability in metal surface defect detection. Full article

Invasive albizia (Falcataria falcata (L.) Greuter & R. Rankin) trees pose an increasing threat to infrastructure, transportation networks, and public safety in Hawai’i because their rapid growth and shallow root systems make them susceptible to windthrow during severe weather events. However, comprehensive spatial information on albizia distribution remains limited, constraining invasive-species management, hazard mitigation, and sustainable infrastructure planning. This study evaluates the applicability of a GeoAI-based framework for identifying invasive-tree-related infrastructure exposure at a regional planning scale. This study develops a GeoAI framework using a U-Net convolutional neural network with a ResNet-34 backbone to detect and map albizia canopy from 0.6 m National Agriculture Imagery Program (NAIP) aerial imagery. Training data from two geographically distinct areas, Mānoa and Kahalu’u, were used for model development and iterative refinement. The final model achieved a precision of 0.76, compared with 0.64 in the initial iteration, and was applied to estimate potential tree-fall exposure through spatial proximity analysis of roads and buildings. Following manual quality control, approximately 2 km² of albizia canopy were identified within the study area. More than 53 km of roads and 2300 buildings were located within the potential tree-fall exposure zone, including portions of major transportation corridors such as Pali Highway, Likelike Highway, and Interstate H-3. By integrating scalable invasive-tree detection with infrastructure exposure assessment, the framework provides a spatial decision-support tool for prioritizing vegetation management, hazard mitigation, and infrastructure maintenance. The approach contributes to sustainability by supporting more targeted use of management resources, reducing potential disruption to critical infrastructure and essential access, and strengthening long-term community and infrastructure resilience. Full article

Safety-control conditions in open-pit quarrying are shaped by interactions among mine-system configuration and the linked sequences of drilling, blasting, excavation/loading, and haulage operations. Here, we develop a process-informed comparative decision-support framework for assessing safety-control conditions in construction-stone quarries. Twenty technical, human, organizational, and environmental criteria were assessed using technical reports, occupational safety and health (OSH) documentation, field observations, and structured expert judgment. Criterion priorities were computed using the analytic hierarchy process (AHP), which confirmed the internal consistency of the aggregated expert-judgment matrix (C_R = 0.032). Mining system configuration and control (C1) and hazard identification/risk assessment (C11) criteria received the highest priorities (0.146 each), followed by blasting control (C3; 0.124). The AHP priority structure was mapped to four study-specific ordinal priority bands, with assigned functional coefficients of 4, 3, 2, and 1, respectively, to determine the composite safety index: I_a = 4T₁ + 3T₂ + 2T₃ + T₄. When applied to 16 construction-stone quarries operating in Binh Duong Province, Vietnam, I_a ranged from 84 to 207, corresponding to normalized safety-control condition scores of 21.7–90.0%. Among the process-mapped criteria, blasting control (C3) received the highest individual AHP priority. Sensitivity analysis showed that quarry rankings remained internally stable under the tested ±10% and ±20% one-at-a-time perturbations of the functional coefficients, with a minimum Spearman correlation of 0.9985. The framework is intended for comparative engineering decision support under the adopted assessment protocol and does not estimate accident probability, frequency, severity, or future safety outcomes. Full article

The present study provides a useful overview of the state of innovation and eco-innovation in the European Union. It does so by studying and understanding the performance, trends, descriptive spatial patterns and convergence of the main indicators adopted to this purpose, them being the European Innovation Scoreboard (EIS), also inclusive of the Regional Innovation Scoreboard (RIS) and the Eco-Innovation Index (EI). While the analysis involves all 27 EU members, it also provides a focus on four countries (Italy, Spain, Greece and Lithuania). These are illustrative project-driven case studies, as they have recently launched a common research effort aimed at promoting innovation-based resilience in the EU’s agrifood sector, named the SOSFood Project, facing the topic of innovation from a multidisciplinary and multiregional perspective. To better understand innovation trends, the paper presents an elaborate descriptive analysis and mapping of the most representative sub-indicators at a national and regional (NUTS-2) level, plus a convergence analysis through the adoption of the Phillips–Sul method. The analysis shows multifaceted results suggesting a differentiated catching up (where, by “catching up”, we refer to the achievement of more homogeneous innovation standards in many areas of the continent), resulting in a club convergence of EU members, where countries tend to be clustered based on regional location, although substantial differences arise depending on the specific indicator (innovation or eco-innovation) and sub-indicator subject to the analysis. Our work provides a clear visualisation of recent innovation and eco-innovation trends that can be of good use for analysts, researchers and policy makers interested in understanding where, how much and in what form innovation is most consolidated and opens an avenue to further research aimed at understanding where it is most needed. Full article

This study addresses cross-domain recommendation when source-domain users and target-domain items are structurally disjoint. We propose an adaptive framework that embeds source behavior and target-item information in a shared semantic space. Its shared core combines semantic similarity, keyword mapping, preference compatibility, similar-user retrieval, and behavior transfer, while a domain-aware adaptive layer activates scenario-specific signals only when the user supplies the corresponding preference. Post-ranking explanations are generated by a separate language model. The framework was evaluated in hobby-to-volunteer and consumption-to-travel scenarios using human and LLM-based relevance judgments and eight baselines, including content-based hybrids with and without transfer and a direct LLM ranker. In Case A, the revised cold-start score outperformed the earlier content hybrid with transfer (Precision@10: 0.420 vs. 0.258; Holm-adjusted p < 0.001; Cohen’s dz = 0.90) but did not differ significantly from semantic similarity alone. The transfer term did not yield a statistically detectable incremental ranking gain in either scenario. In Case B, a two-by-two factorial analysis identified the adaptive layer as the main source of improvement (+0.363 Precision@10; p < 0.001; dz = 1.04), while a control without the adaptive layer showed that part of the association between active signals and performance reflected profile specificity. An exploratory Case A control found no significant explanation-associated change in automated scores. Human–LLM agreement was positive but limited, supporting automated evaluation as a scalable complement rather than a replacement for human judgment. Full article

Finite-precision chaotic generators are deterministic finite-state systems whose structure is not established by continuous-domain chaos indicators or by ciphertext statistics. We study a map on ${(\mathbb{Z}/2^w\mathbb{Z})}^d$ coupled to a Weyl counter and prove that every eventual joint cycle length is divisible by the counter period, while the image fraction and the normalized indegree distribution are preserved exactly. A simple instance attains this period floor and is predictable from two words of its own output. We then audit fifteen specified quantized realizations, thirteen from recent image-encryption designs and classical maps and two validation controls, under two quantizers at reduced widths, measuring reachability, recurrence, cycle-length distributions and output-equivalence classes. An exponential enhancer that improves every dynamical indicator its authors report lowers the image fraction for three of its four base maps and raises it for the fourth. Across the sampled one-dimensional cases the section-cycle multiplier is at most three, while the two- and three-dimensional examples reach the tens; a permutation counterexample rules out a universal dimension-based claim. Under the common post-update decimal readout, the non-affine one-dimensional realizations approach the image-size bound within six output symbols. These findings motivate reporting finite-state structure alongside conventional tests. The measurements characterize the specified reduced-width realizations; they neither establish native-precision behaviour nor prove or disprove the security of the source ciphers. Full article

Expanding solar power plants requires strategic planning that accounts for future climatic, environmental, and topographic conditions. This study develops an integrated Geographic Information Systems (GIS), multi-criteria decision analysis (MCDA), and machine-learning framework to identify priority locations for solar PV development in Iraq under the ACCESS-CM2 SSP2-4.5 climate projection for 2050. Support vector machine (SVM), random forest (RF), and gradient-boosted trees (GBT) classifiers were evaluated against MCDA-derived reference classes using five-fold spatial block cross-validation as the primary validation procedure; the fixed stratified 70:30 split was retained only for comparison. Their spatial outputs were integrated through GIS-based intersection to identify consensus suitability zones. Under the adopted temperature- and dust-correction model and its reference parameter assumptions, the modeled mean PV-efficiency indicator was about 14.3%, and the modeled mean daily energy-yield indicator was about 872 Wh/m²/day at a mean projected air temperature of 33.3 °C and a mean DUEXTTAU index of approximately 0.20. These quantities are scenario-dependent screening-model outputs, not observed or field-calibrated measurements. The mean spatial-validation accuracies were 91.46 ± 2.90% for SVM, 84.03 ± 2.14% for RF, and 82.22 ± 4.47% for GBT; these values quantify spatially separated agreement with the MCDA-derived reference classes and do not constitute independent validation of physical site suitability. Accordingly, the resulting maps should be interpreted as a spatial screening and prioritization framework whose quantitative outputs remain conditional on the adopted climate projection, MCDA weighting scheme, PV-performance assumptions, and the absence of independent field-based ground truth. Full article

Multidomain vulnerability in older adults is often assessed across separate domains, despite important interactions among cognitive, frailty-related, functional, nutritional, swallowing–airway, respiratory, medication-related, and socioeconomic factors. This study developed and internally evaluated a machine learning-informed dynamic Fuzzy Cognitive Map (FCM) model for prioritizing geriatric vulnerability. The analysis included data from 77 participants: 59 from a hospital outpatient setting and 18 from a nursing home. The model comprised 22 concepts: 18 inputs, three pathway outputs, and one global output. An expert-defined topology represented clinically informed relationships across domains. Dynamic simulation used an input-preserving updating rule, while ridge regression was used to calibrate selected readout weights using fuzzy targets derived from the transformed model inputs. These targets were not independent clinical outcomes. Internal analyses examined site-stratified contribution profiles, expert-only versus calibrated outputs, leave-one-out calibration, dense input-preservation sensitivity (α = 0.50–0.90), alternative missing value imputation, frailty-scale holdout comparisons, and perturbation of manually specified modeling choices. All participant simulations converged. Expert-only and calibrated review classifications agreed for 73/77 participants (94.8%), and the swallowing–airway watchlist was unchanged; leave-one-out calibration remained an internal audit because its targets were derived from model inputs. The nursing home subgroup had higher mean scores than the hospital subgroup across all four outputs. Under the common FCM, stratified contribution analysis identified larger realized contributions from functional dependence, clinical frailty, cognitive impairment, mobility impairment, and respiratory/cough-related burden in the nursing home subgroup. These findings were considered descriptive differences within this cohort and were not interpreted as effects of care setting. The global geriatric vulnerability score classified 22 participants as baseline/low, 32 as elevated, 21 as moderate-high, and 2 as high. The swallowing–airway watchlist identified 46 participants: 11 assigned to priority review, 15 to targeted review, 15 to routine follow-up, and five to monitor. The model transformed multidomain information into interpretable pathway scores, a global priority score, and a swallowing–airway review watchlist. The findings demonstrate internal computational stability and sensitivity characteristics rather than independent clinical validity; external and prospective validation is required before clinical implementation. Full article

Avian metapneumovirus (aMPV) is an emerging viral pathogen causing many outbreaks in chickens all over the world. There are several subtypes of the MPV circulating in chickens in the US. The current circulating subtypes of the virus in the US chicken are A, B, and C. Despite the availability of some aMPV vaccines in the US, most of these vaccines are based on foreign strains, particularly European aMPV-A/B strains. Their protective efficacy against contemporary U.S. aMPV isolates has not been fully established. The main goal of this study is to integrate the most recent aMPV genome sequencing data and the machine learning tools to design a novel aMPV. The machine learning tools such as epitope mapping, molecular docking, and immune simulation were used to design the multiepitope DNA vaccine based on the top-ranked epitopes of two major surface proteins of the virus (F and G). The top-ranked seventeen epitopes representing B cells, CD4 and CD8 epitopes were linked using linkers, with IL-18 added as an adjuvant. The selected epitopes showed high antigenicity, no toxicity and no allergenicity values among the screened epitopes. The molecular docking analysis of the designed vaccine construct showed a high binding affinity to the MHC class I and II epitopes to chicken alleles. The immune simulation analysis of this vaccine provides an in silico proof of concept assessment through modeling of the vaccine potential to elicit humoral and cell-mediated immunity. Further functional studies are required to test the immunogenicity and the efficacy of this novel vaccine before applications using chickens and turkey. Full article

Financial sustainability has become a central concern in contemporary banking, particularly with the growing integration of ESG principles and regulatory reforms. Although both Islamic and conventional banking systems are frequently examined in relation to sustainability, the intellectual structure and comparative evolution of this research field remain insufficiently clarified. Existing studies often focus on specific dimensions such as governance, performance, or green finance without systematically mapping how sustainability-related knowledge has developed across banking models. This study addresses this gap through a bibliometric comparative analysis of 629 Scopus-indexed publications published between 2010 and 2025. Using VOSviewer version 1.6.20, we analysed publication trends, influential contributors, and keyword co-occurrence networks to identify dominant research themes in financial sustainability within Islamic and conventional banking literature. The results reveal five major thematic clusters and highlight differences in research emphasis between the two models, particularly regarding ethical finance, ESG, and financial stability. The model-specific networks further show that conventional banking research is broader and more thematically differentiated, whereas Islamic and participative banking research forms a smaller but denser network centered on Shariah-compliant instruments, social finance, financial inclusion, and ethical sustainability. By mapping the intellectual structure of this field, the study provides a clearer understanding of how sustainability research is evolving across banking systems and identifies directions for future research. Full article