Search Results (1,435)

Mode conversion effects in Fibre Bragg Gratings (FBGs) are widely exploited in applications such as sensing and fibre lasers. However, when FBGs are inscribed into Few-mode optical Fibres (FMFs), the mode interactions become highly complex due to the increased number of guided modes, rendering their practical use difficult. In this study, we investigate whether the addition of a spatial mode multiplexer, used to selectively excite specific fibre modes, can simplify the interpretation and utility of few-mode FBGs (FM-FBGs). We focus on point-by-point (PbP)-inscribed FBGs, localised with respect to the transverse cross-section of the fibre core, and study their interaction with a range of Hermitian Gauss input modes. We present a comprehensive numerical study supported by experimental validation, examining the mechanisms of mode coupling induced by localised FBGs and its implications, with a focus on sensing applications. Our results show that the introduction of a spatial mode multiplexer leads to slight simplification of the FBG transmission spectrum. Nevertheless, significant simplification of the reflection spectrum is achievable after modal filtering occurs as the reflected light re-traverses the spatial mode multiplexer, potentially enabling WDM monitoring of FM-FBGs. Notably, we report a novel approach to multiplexing FBGs based on their transverse location within the fibre core and the modal content initially coupled into the fibre. To the best of our knowledge, this multiplexing technique is yet to be reported. Full article

Background and Objectives: Children with autism spectrum disorder (ASD) often experience visual problems, yet their ophthalmic health remains underexplored due to testability challenges and limited-service access. This study evaluated ophthalmic screening outcomes in children with ASD and examined whether autism severity influenced ocular findings or cooperation during examinations. Materials and Methods: This cross-sectional study included 210 children with ASD (mean age 8.18 ± 4.99 years; 83.3% male). Examinations were conducted in an autism education center using non-contact methods: stereopsis (LANG I stereotest; LANG-STEREOTEST AG, Küsnacht, Switzerland), cover–uncover, and Hirschberg tests for strabismus, Spot Vision Screener (Welch Allyn Inc., Skaneateles Falls, NY, USA) for refractive errors, and Brückner test for red reflex. Autism severity was assessed with the Turkish version of the Adapted Autism Behavior Checklist (AABC). Results: Refractive errors were identified in 22.3% of participants: astigmatism in 15.2%, myopia in 5.2% (including 3 high myopia), and hyperopia in 1.9%. Strabismus was present in 11.9%, most commonly intermittent exotropia. Nearly half (49.5%) could not complete stereopsis testing, and a weak positive correlation was observed between AABC scores and the higher absolute spherical equivalent (SE) value between the two eyes (r = 0.173, p = 0.044). Children unable to complete stereopsis testing had significantly higher AABC scores (22.66 ± 9.69 vs. 13.39 ± 9.41, p < 0.001). Notably, 50 children (23.8%) had never undergone an eye examination prior to this study. Conclusions: Ophthalmic findings, particularly astigmatism and strabismus, are common in children with ASD. Greater autism severity was associated with reduced testability and modestly worse refractive error status. These findings suggest that tailored, accessible eye-care approaches and systematic vision screening may help to reduce overlooked visual problems and support more equitable care for children with ASD. Full article

Cultural heritage is increasingly mobilized as a tool of international engagement, yet the diplomatic uses of heritage remain conceptually underdeveloped and analytically fragmented. This paper introduces the Heritage Diplomacy Spectrum, a multidimensional framework that maps how states and affiliated actors use heritage—both tangible and intangible—to pursue strategic, symbolic, and normative goals in cross-border contexts. Drawing on critical heritage studies, international relations, and memory politics, this study identifies six analytical dimensions (e.g., proactive vs. reactive, cultural vs. historical, strategic vs. moral) and develops seven ideal types of heritage diplomacy, ranging from soft power projection to post-dependency and corrective diplomacy. These ideal types, constructed in the Weberian tradition, serve as heuristic tools to illuminate the varied motivations and diplomatic postures underlying heritage-based engagement. A central matrix is presented to illustrate how each type aligns with different strategic logics and affective registers. This study argues that heritage diplomacy constitutes a distinct modality of heritage governance—one that transcends soft power narratives and encompasses conflict, reconciliation, symbolic redress, and identity assertion. The framework contributes both to theory-building and policy analysis, offering a diagnostic lens through which the ethical, political, and communicative dimensions of heritage diplomacy can be more systematically understood. Full article

Introduction: The educational inclusion of students with Autism Spectrum Disorder (ASD) in Spain has been promoted through regulations such as LOMCE and LOMLOE. However, its effective implementation depends on teachers’ attitudes and perceptions. This study analyzes teachers’ feelings, attitudes, and concerns regarding the inclusion of students with ASD. Methods: A quantitative, descriptive, and cross-sectional study was conducted with a sample of 2310 teachers from different educational stages in Spain. The SACIE-R and INTEA questionnaires were used to assess teachers’ perceptions of inclusion. ANOVA tests and Spearman correlations were applied for statistical analysis. Results: The results show that the variable “Attitudes” follows a normal distribution, indicating a stable perception of inclusion. In contrast, the variables “Feelings” and “Concerns” present an inverse relationship: the greater the concern, the fewer positive feelings toward inclusion. Significant differences were found based on gender, type of school, educational stage, and teaching specialty. Discussion: Positive attitudes toward inclusion are associated with greater training and specialization in diversity. Special Education teachers show better perceptions, while in Secondary Education, concerns and negative feelings prevail. Continuous training emerges as a key factor in improving teachers’ perceptions. Conclusions: The study highlights the importance of training programs and support strategies to promote effective inclusion. Strengthening support networks and teacher training is recommended to improve attitudes toward the inclusion of students with ASD. Full article

Background: Mitral annular plane systolic excursion (MAPSE) is a simple and widely used M-mode echocardiographic marker of left-ventricular longitudinal function that correlates well with left ventricular ejection fraction (LVEF). Conventional chronic right ventricle (RV) pacing is associated with left ventricle (LV) dysfunction, inducing heart failure (HF) and leading to the development of pacing-induced cardiomyopathy (PiCM). The aim of this study is to ascertain the clinical usefulness of MAPSE in the assessment of LV function in patients with permanent RV pacing. Methods: We performed a cross-sectional association analysis, enrolling consecutive patients with pacemakers and chronic RV pacing burdens over 20% (Vp > 20%) from 2021 to 2024. All patients were assessed by standard transthoracic echocardiography (TTE) with LVEF and MAPSE among other parameters being assessed. We performed a correlation test using linear regression and plotted an ROC curve. Results: 409 patients (mean age = 68.7 year) were included, 225 men (55%) and 245 (59.9%) with dual-chamber pacemakers. The mean follow-up period was 18 ± 2 months, with HF incidence in the study group being 23.2%. The results showed that average, septal, and lateral MAPSE all correlate well with LVEF, but septal values seemed to provide the strongest correlation (r = 0.90, p < 0.001), and that a septal MAPSE cut off value of <10 mm (sensitivity 99.4, specificity 42.1, AUC = 0.89) was associated with impaired LVEF (<50%). Conclusions: MAPSE seems to corelate well with LVEF across the spectrum of HF in pts with chronic RV conventional pacing. Septal MAPSE shows the strongest correlation with LVEF, and a value of <10 mm is a cut-off for altered LVEF, making it a potentially useful marker of cardiac function in these pts. Full article

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with diverse behavioral, genetic, and structural characteristics. Due to its heterogeneous nature, early diagnosis of ASD is challenging, and conventional unimodal approaches often fail to capture cross-modal dependencies. To address this, this study introduces an adaptive multimodal fusion framework that integrates behavioral, genetic, and structural MRI (sMRI) data, addressing the limitations of unimodal approaches. Each modality undergoes a dedicated preprocessing and feature optimization phase. For behavioral data, an ensemble of classifiers using a stacking technique and attention mechanism is applied for feature extraction, achieving an accuracy of 95.5%. The genetic data is analyzed using Gradient Boosting, which attained a classification accuracy of 86.6%. For the sMRI data, a Hybrid Convolutional Neural Network–Graph Neural Network (Hybrid-CNN-GNN) architecture is proposed, demonstrating a strong performance with an accuracy of 96.32%, surpassing existing methods. To unify these modalities, fused using an adaptive late fusion strategy implemented with a Multilayer Perceptron (MLP), where adaptive weighting adjusts each modality’s contribution based on validation performance. The integrated framework addresses the limitations of unimodal approaches by creating a unified diagnostic model. The transfer learning framework achieves superior diagnostic accuracy (98.7%) compared to unimodal baselines, demonstrating strong generalization across heterogeneous datasets and offering a promising step toward reliable, multimodal ASD diagnosis. Full article

Background/Objectives: Streptococcus pyogenes (Group A Streptococcus, GAS) is a major human pathogen that causes a wide spectrum of diseases. While mild infections like pharyngitis and impetigo are common, severe and invasive infections, though less frequent, pose significant health risks, particularly in children. In recent years, the re-emergence of hypervirulent GAS strains has heightened global concern. Nowadays, the absence of universally accepted guidelines compels clinicians to rely on a combination of clinical judgment, microbiological data and available evidence to manage these infections effectively. Our aim was to assess the current management of invasive GAS (iGAS) infections in Italy and the variability in therapeutic and preventive approaches. Methods: A web-based current clinical practice survey about invasive and severe GAS infections was designed according to the Checklist for Reporting of Survey Studies (CROSS) methodology and circulated among the members of the Italian Society of Pediatric Infectious Diseases (SITIP). Results: The survey reveals that while many practices are commonly shared among clinicians, particularly regarding first-line therapies (penicillin or ceftriaxone depending on the infection site), significant uncertainties remain, particularly about the use of combined antibiotic regimens and supportive treatments. The use of combined antibiotic regimens was considered appropriate as first-line therapy for STSS, NF and brain abscesses. Clindamycin was the preferred agent for combination with beta-lactam for most infections, except for brain abscesses, where linezolid was favored. However, there was disagreement regarding the optimal timing for de-escalation to beta-lactam monotherapy. Responses varied widely concerning the indications and dosages for IVIG, as well as the use of corticosteroids. Conclusions: Addressing the burden of invasive GAS (iGAS) infections in children requires enhanced surveillance, early recognition, prompt treatment and preventive strategies. Further work to increase surveillance, e.g., developing national registries, and to standardize the management of the disease, e.g., developing country-specific guidelines, is essential to build solid evidence on the most effective approaches. Full article

Background/Objectives: Acute myocardial infarction (MI) is a major cause of death worldwide, and it imposes a heavy burden on health care systems. Although diagnostic methods have improved, detecting the disease early and accurately is still difficult. Recently, AI has demonstrated increasing capability in improving ECG-based MI detection. From this perspective, this scoping review aimed to systematically map and evaluate AI applications for detecting MI through ECG data. Methods: A systematic search was performed in Ovid MEDLINE, Ovid Embase, Web of Science Core Collection, and Cochrane Central. The search covered publications from 2015 to 9 October 2024; non-English articles were included if a reliable translation was available. Studies that used AI to diagnose MI via ECG were eligible, and studies that used other diagnostic modalities were excluded. The review was performed per the PRISMA extension for scoping reviews (PRISMA-ScR) to ensure transparent and methodological reporting. Of a total of 7189 articles, 220 were selected for inclusion. Data extraction included parameters such as first author, year, country, AI model type, algorithm, ECG data type, accuracy, and AUC to ensure all relevant information was captured. Results: Publications began in 2015 with a peak in 2022. Most studies used 12-lead ECGs; the Physikalisch-Technische Bundesanstalt database and other public and single-center datasets were the most common sources. Convolutional neural networks and support vector machines predominated. While many reports described high apparent performance, these estimates frequently came from relatively small, single-source datasets and validation strategies prone to optimism. Cross-validation was reported in 57% of studies, whereas 36% did not specify their split method, and several noted that accuracy declined under inter-patient or external validation, indicating limited generalizability. Accordingly, headline figures (sometimes ≥99% for accuracy, sensitivity, or specificity) should be interpreted in light of dataset size, case mix, and validation design, with risks of spectrum/selection bias, overfitting, and potential data leakage when patient-level independence is not enforced. Conclusions: AI-based approaches for MI detection using ECGs have grown quickly. Diagnostic performance is limited by dataset and validation issues. Variability in reporting, datasets, and validation strategies have been noted, and standardization is needed. Future work should address clinical integration, explainability, and algorithmic fairness for safe and equitable deployment. Full article

This study presents a flutter answer analysis of the Romanian IAR-99 HAWK advanced trainer aircraft equipped with multiple external store configurations. A high-fidelity finite element model (FEM) of the complete aircraft, including pylons and external stores, was coupled with a Doublet Lattice Method (DLM) aerodynamic model. The aeroelastic framework was validated against Ground Vibration Test (GVT) data to ensure structural accuracy. Four representative configurations were assessed: (A) RS-250 drop tanks on inboard pylons and PRN 16 × 57 unguided rocket launchers on outboard pylons; (B) four B-250 bombs; (C) eight B-100 bombs mounted on twin racks; and (D) a hybrid layout with B-100 bombs inboard and PRN 32 × 42 launchers outboard. Results show that spanwise distribution governs aeroelastic stability more strongly than total carried mass. Distributed stores lower wing-bending frequencies and densify the modal spectrum, producing critical pairs and subsonic crossings near M ≈ 0.82 at sea level, whereas compact heavy loads remain subsonic-stable. A launcher-specific modal family around ≈29.8 Hz is also identified in the hybrid layout. The validated FEM–DLM framework captures store-driven mode families (≈4–7 Hz) and provides actionable guidance for payload placement, certification, and modernization of the IAR-99 and similar platforms. Full article

This study has compiled the first impact-crater dataset for Mercury with diameters greater than 400 m by a multimodal deep-learning pipeline. We present an enhanced deep learning framework for large-scale planetary crater detection, extending the YOLOLens architecture through the integration of multimodal inputs: optical imagery, digital terrain models (DTMs), and hillshade derivatives. By incorporating morphometric data, the model achieves robust detection of impact craters that are often imperceptible in optical imagery alone, especially in regions affected by low contrast, degraded rims, or shadow-dominated illumination. The resulting catalogs LU6M371TGT for the Moon and ME6M300TGT for Mercury constitute the most comprehensive automated crater inventories to date, demonstrating the effectiveness of multimodal learning and cross-planet transfer. This work highlights the critical role of terrain information in planetary object detection and establishes a scalable, high-throughput pipeline for planetary surface analysis using modern deep learning tools. To validate the pipeline, we compare its predictions against the manually annotated catalogs for the Moon, Mercury, and several regional inventories, observing close agreement across the full diameter spectrum, revealing a high level of confidence in our approach. This work presents a spatial density analysis, comparing the spatial density maps of small and large craters highlighting the uneven distribution of crater sizes across Mercury. We explore the prevalence of kilometer-scale (1–5 km range) impact craters, demonstrating that these dominate the crater population in certain regions of Mercury’s surface. Full article

Background: Oxytocin (OXT), a neuropeptide involved in social bonding, has been proposed as a potential biomarker for autism spectrum disorder (ASD) due to its role in modulating social behaviors. However, prior studies on peripheral OXT levels in individuals with ASD have yielded inconsistent results, partly due to methodological and developmental variability. This study aimed to compare baseline salivary OXT concentrations between children with ASD and typically developing controls. Methods: In this cross-sectional, case–control study, salivary OXT levels were measured in 35 children aged 6–9 years (18 with ASD, 17 controls) using a standardized ELISA protocol. Samples were collected under controlled conditions and analyzed in duplicate. Between-group differences in raw and log-transformed OXT levels were examined using t-tests. Subgroup analyses were conducted by sex, and correlations with autism symptom severity (Aberrant Behavior Checklist, ABC) were assessed within the ASD group. Results: Children with ASD showed higher mean salivary OXT levels than controls (21.5 pg/mL vs. 14.0 pg/mL), but the difference was not statistically significant (Welch’s t = −1.79, p = 0.088). Log transformation of OXT values confirmed the non-significant group difference (t = 1.68, p = 0.102). Female participants with ASD had significantly higher OXT than female controls (p = 0.048), while no difference was observed among males. OXT levels did not significantly correlate with autism severity (r = −0.04, p = 0.88). Conclusions: Baseline salivary OXT levels do not significantly differ between children with and without ASD and do not correlate with behavioral symptom severity. However, elevated OXT in females with ASD warrants cautious interpretation and further investigation. Salivary OXT may not be a reliable standalone diagnostic biomarker but could have exploratory value for understanding sex-specific neurobiological profiles in autism. Full article

Background/Objectives: Soluble ST2 (sST2) has gained recognition as a clinically relevant biomarker across a spectrum of inflammatory, cardiovascular, and respiratory conditions. However, the lack of assay standardization raises concerns about result comparability across platforms and studies. Methods: This study systematically evaluated serum sST2 concentrations measured with two ELISA systems—DuoSet and Quantikine—produced by the same manufacturer (R&D Systems, Minneapolis, MN, USA). Results: Using archived serum samples from healthy volunteers and marathon runners, we identified marked discrepancies: serum sST2 concentrations using the DuoSet recombinant standard were on average 4.3-fold higher than those using Quantikine (median 308.3 [106.6–608.6] vs. 71.5 [41.8–115.6] ng/mL). On the pre-coated Quantikine plate, using the DuoSet recombinant standard increased calculated concentrations 4.3-fold compared with the native Quantikine standard (median 308.3 [106.6–608.6] vs. 71.5 [41.8–115.6] ng/mL). On the manually coated DuoSet plate, the DuoSet standard yielded higher medians than the Quantikine standard (8.0 [5.6–11.3] vs. 5.0 [3.7–7.4] ng/mL). Furthermore, between-lot variability within the same ELISA platform resulted in concentration shifts from 0.09 [0.07–0.10] ng/mL (2016) to 1.17 [0.81–3.23] ng/mL (2023) using the same sample. Previously published studies also exhibited wide inter-study variability among healthy cohorts. Conclusions: These findings emphasize that current ELISA systems for sST2 are not standardized and that cross-study comparisons should be interpreted with caution. Until universal standardization is implemented, sST2 should primarily be used for within-study comparisons. This variability may limit the reliability of longitudinal sST2 assessment even in clinical settings. Full article

This study proposes a novel SSA-EMS framework that integrates Singular Spectrum Analysis (SSA) with Effect-Matched Spatial Filtering (EMS), combining the noise-reduction capability of SSA with the dynamic feature extraction advantages of EMS to optimize cross-subject EEG-based emotion feature extraction. Experiments were conducted using the SEED dataset under two evaluation paradigms: “cross-subject sample combination” and “subject-independent” assessment. Random Forest (RF) and SVM classifiers were employed to perform pairwise classification of three emotional states—positive, neutral, and negative. Results demonstrate that the SSA-EMS framework achieves RF classification accuracies exceeding 98% across the full frequency band, significantly outperforming single frequency bands. Notably, in the subject-independent evaluation, model accuracy remains above 96%, confirming the algorithm’s strong cross-subject generalization capability. Experimental results validate that the SSA-EMS framework effectively captures dynamic neural differences associated with emotions. Nevertheless, limitations in binary classification and the potential for multimodal extension remain important directions for future research. Full article

A disc-shaped double-layer shell structure reinforced by stiffeners is introduced for underwater gliders. Based on the finite element method integrated with automatic matching layer (FEM/AML) technology and the direct boundary element method (DBEM), the acoustic response of a disc-shaped double-layer shell with six longitudinal ribs within the frequency range of 10–500 Hz is obtained. The resonant frequencies of the sound pressure level (SPL) correlate with the structural–acoustic modes. At resonance frequencies, the acoustic directivity and spatial sound pressure distribution of the double-layer shell exhibit symmetry relative to the mid-cross-section. The influence of longitudinal rib counts on vibro-acoustic behavior is investigated. The analysis results of frequency–spatial spectrum for radiated sound pressure reveal that the resonant frequencies migrate to the mid-high frequency with increases in the longitudinal rib quantity. Full article

In Nature-Based Solution (NBS) projects, stakeholder mapping is not merely a methodological step but a strategic process that enables meaningful engagement, co-creation, and the building of trust among diverse actors. This study describes the stakeholder mapping approach adopted in the NBSINFRA project, paying particular attention to methods designed to strengthen participation and co-creation. The process followed three inter-related steps: (1) stakeholder identification; (2) stakeholder analysis, filtering, and prioritization; and (3) stakeholder understanding. Drawing on a cross-methodological approach, including interviews, focus groups, direct observation, and on-site observations, the project engaged a wide spectrum of stakeholders, involving representatives of the local community. The findings point out that stakeholder mapping functioned as a catalyst for social engagement, co-design, informal collaborations, and the development of trustful and transparent relationships between team members and the community. The process made it possible to identify regional and national stakeholders, thereby opening avenues for international collaboration in later stages of the project. Finally, this study highlights persistent challenges that require attention, including information gaps, limited opportunities for participation due to time constraints, and the enduring prevalence of top-down decision-making. Full article