Search Results (84)

Background: Faricimab is one of the most potent anti-vascular endothelial growth factors used in the management of diabetic macular edema (DME). However, real-world benefits regarding its timing and efficacy are still being explored. Methods: This retrospective non-randomized pilot study aimed to evaluate the efficacy of intravitreal faricimab in the treatment of DME. Eyes initially treatment-naïve for DME with a follow-up of 1 year were grouped as: group 1, where faricimab was introduced within the first six months after the start of treatment; group 2, where it was initiated six or more months after treatment with other drugs. Study parameters included changes in best corrected visual acuity (BCVA) and optical coherence tomography based structural parameters within the 6 × 6 mm optical coherence tomography (OCT) scan regions. Results: Forty-two eyes from 26 patients were analyzed. No statistically significant differences were observed between the groups in cluster-weighted proportions of intra- or sub-retinal fluid, retinal thickness or volume parameters, although group 1 showed modest numerical benefits. SRF showed a trend towards qualitative reduction in group 1, although IRF showed persistence in both groups. Adjusted linear mixed-effects modeling demonstrated no significant impact of early faricimab initiation on functional and anatomical outcomes, which appeared to be influenced by the baseline BCVA, glycemic control, and the number of injections, nullifying the benefits. Conclusions: Faricimab demonstrated modest anatomical improvements with earlier treatment in eyes initially treatment-naïve for DME. Further prospective studies are indicated to assess the treatment strategy and the timing of introduction with faricimab in such eyes. Full article

Objective: This study aimed to compare the neuromuscular control characteristics of international- and national-level squash players during forehand strokes using a multichannel surface electromyography (sEMG)-based sensing framework. By integrating wearable biosignal acquisition with muscle synergy and intermuscular coherence analyses, this study sought to identify sensor-derived markers of performance-related neuromuscular control and to provide evidence for sensor-informed squash training and athlete monitoring. Methods: Participants performed standardized forehand strokes, during which multichannel sEMG signals were synchronously collected from major upper-limb, lower-limb, and trunk muscles. The recorded sensor signals were preprocessed and analyzed using non-negative matrix factorization to extract muscle synergies, including the number of synergies, muscle weightings, and synergy activation durations. In addition, time–frequency intermuscular coherence analysis was performed on the sEMG sensor data to quantify coherence differences in the α, β, and γ frequency bands between upper-limb–trunk and lower-limb–trunk muscle pairs. Results: No significant difference was found between the two groups in the number of muscle synergies, with both groups clustering into four synergy modules. However, the sEMG sensor-based analysis revealed clear between-group differences in synergy structure and coordination patterns. International-level players showed higher muscle weightings in major proximal muscles, including the deltoid, pectoralis major, erector spinae, and gluteus maximus, and lower weightings in relatively smaller or more distal muscles such as the biceps brachii and lateral gastrocnemius. In terms of synergy timing, international-level players exhibited significantly shorter activation durations in SYN1 and SYN2, but a significantly longer activation duration in SYN3, than national-level players. For intermuscular coherence, international-level players showed significantly lower coherence in the α, β, and γ bands for multiple upper-limb–trunk and lower-limb–trunk muscle pairs. Conclusions: A multichannel sEMG sensing approach was effective in detecting performance-level differences in neuromuscular control during the squash forehand stroke. International-level players exhibited more efficient and refined neuromuscular coordination, characterized by optimized proximal muscle recruitment, more task-specific synergy timing, and reduced intermuscular coherence across selected muscle pairs. These findings highlight the value of wearable EMG sensors and sensor-based neuromuscular feature extraction for quantitative athlete assessment, movement monitoring, and the development of sensor-guided training strategies in squash. Full article

This article develops a conceptual framework to explain how theological convictions of sanctity within the Catholic Church can become institutional risk factors for unethical behavior. Existing analyses of clerical sexual abuse emphasize governance failures, clerical culture, or individual misconduct, but pay limited attention to how sacred identity shapes institutional reasoning. Integrating organizational ethics, social identity theory, and moral psychology, the article adapts the NGO halo effect to propose a three-stage model distinguishing between intrinsic sanctity, institutional sacralization (the Church halo), and the activation of moral mechanisms (the halo effect). The analysis shows how mission, moral teaching, and ordained ministry—while theologically coherent—can become amplified within institutional life in ways that alter how ethical dilemmas are weighted. It further identifies moral justification, moral superiority, and moral naivety as mechanisms through which such amplification may contribute to safeguarding failures. By analytically separating theological meaning from institutional amplification, the article advances scholarship on religious organizations and reframes clerical abuse as partly linked to the dynamics of sacralized identity. The model offers a transferable framework for examining how moral purpose and moral failure can coexist in faith-based institutions. Full article

Large language models (LLMs) are being increasingly explored to support clinical documentation, yet the influence of prompting architecture on documentation quality in complex longitudinal contexts remains insufficiently characterized. This controlled retrospective methodological study evaluated three prompting strategies—single prompt (SP), section-based prompt (SBP), and section-based prompt with writing refinement (SBP+W)—for generating inpatient rehabilitation discharge reports using OpenAI large language model (GPT-5.2). Twenty anonymized inpatient rehabilitation cases involving prolonged hospital stays and multidimensional functional documentation were processed under standardized model conditions. AI-generated reports were compared with human-authored summaries. Two blinded board-certified rehabilitation physicians independently evaluated outputs using a structured four-point ordinal scale assessing structural integrity, clinical coherence, completeness, and readability. Inter-rater reliability was estimated with quadratic weighted Cohen’s kappa and bootstrap confidence intervals. Group differences were analyzed using non-parametric testing and exploratory multivariable modeling. All LLM prompting strategies achieved significantly higher expert-rated quality scores than human-authored reports (p < 0.025). SBP demonstrated the highest median performance and strongest regression effect, although differences among LLM-based strategies were not statistically significant after correction. Prompting strategies explained more variability in expert ratings than case-level factors. Structured section-based prompting may represent a practical design lever for improving perceived quality in AI-assisted clinical documentation workflows. Larger prospective studies are needed to evaluate reliability, safety, clinical utility, and implementation in real-world workflows. Full article

Branched poly(vinyl alcohol) (PVA) was synthesized via chemical modification of linear PVA with epichlorohydrin in an alkaline aqueous medium under conditions preventing crosslinking. Branching was confirmed by IR and Heteronuclear Single Quantum Coherence (HSQC) spectroscopy, as well as by viscometric analysis. An iterative procedure is proposed for refining the branching factor (g) and the viscosity-average molecular weight of the branched macromolecules. Coil diameters determined by viscometry and dynamic light scattering showed satisfactory agreement. While an increase in the viscosity-average molecular weight of branched PVA enhances its surface activity in the low-adsorption region, the branched geometry itself hinders subsequent adsorption due to steric shielding of the interface. This correlates with wetting behavior on Teflon: lightly branched PVA requires a higher concentration to induce wetting inversion than its linear counterpart but further increase in molecular weight shifts the inversion point to lower concentrations due to a higher density of hydroxyl groups. Concurrently, the concentration dependence of the work of adhesion degenerates with increasing molecular weight. Despite their reduced adsorption capacity, the specific geometry of branched PVA macromolecules provides effective steric stabilization of micrometer-sized particles during styrene suspension polymerization. These results demonstrate that chain branching in PVA is a powerful tool for tuning its adsorption properties, stabilizing ability, and interfacial activity. Full article

User interface evaluation remains fragmented across performance metrics, subjective assessments, and user-dependent factors, limiting the comparability and interpretability of results across methodological traditions. This paper proposes a multidimensional evaluation framework that integrates these perspectives into a coherent analytical structure. The framework consists of three dimensions—Functional–Objective, Cognitive–Perceptual, and Contextual–Individual—each capturing a distinct facet of interface quality. A key feature of the proposed approach is the use of profile-dependent weighting, which enables evaluation results to reflect the specific priorities of different user groups. The framework’s operational logic is demonstrated through structured illustrative scenarios, showing how the model can be applied in practice to support more informed design and evaluation decisions. By aligning heterogeneous evaluation logics within a unified structure, the proposed approach provides a systematic basis for more consistent, transparent, and context-sensitive assessment of user interfaces. Full article

Composite hydrogen storage vessels exhibit pronounced anisotropy, multilayered winding architectures, and strong ultrasonic attenuation, which severely degrade the focusing accuracy and defect visibility of the conventional isotropic total focusing method (TFM). To address these challenges, this study proposes an enhanced TFM framework for defect inspection in composite hydrogen storage vessels by integrating anisotropic delay correction, Gray-code coded excitation, and coherence-weighted reconstruction. First, an anisotropic propagation delay model is established using forward ray tracing to compensate for beam deviation and focusing mismatch induced by the anisotropic winding structure. Then, Gray-code excitation and pulse compression are introduced to improve signal energy and echo detectability under high-attenuation conditions. Finally, coherence-weighted imaging is applied to suppress incoherent background noise and structural artifacts, thereby enhancing defect contrast and image readability. The proposed method is validated on hydrogen storage vessel specimens containing artificial defects, with CT results used as references. Experimental results show that, compared with conventional isotropic TFM, the proposed collaborative approach significantly improves defect imaging quality for defects of different sizes and depths. The signal-to-noise ratio is increased from 7.2, 12.8, 14.8, and 7.4 dB for isotropic TFM to 32.5, 29.9, 52.6, and 42.7 dB, respectively, for the combined anisotropic, coded-excitation, and coherence-weighted TFM. In addition, the defect depth estimation remains stable and agrees well with the CT references, yielding approximately 9.0–9.6 mm for shallow defects and 18.7–19.3 mm for deeper defects. These results demonstrate that the proposed method can effectively improve defect detectability, image contrast, and depth characterization for embedded delamination-like artificial defects in composite hydrogen storage vessels, providing a promising ultrasonic imaging strategy for thick-walled anisotropic composite pressure structures. Full article

Waveguide-like propagation in elongated underground environments—utility corridors, logistics tunnels—generates dense multipath that can cause the earliest or strongest resolvable channel impulse response (CIR) component to originate from a specular reflection rather than the direct line-of-sight (LOS) path. In the single-anchor CIR-tap-based implementations common to practical ultra-wideband (UWB) systems, baseline estimators such as phase-difference-of-arrival (PDOA) and MUSIC rely on selecting a single dominant CIR component, producing large angle-of-arrival (AoA) errors whenever the selected path is a reflection. We propose a multipath credibility selection (MCS) AoA estimator, MCS-AoA, that does not require explicit LOS/NLOS classification. The algorithm scores each resolvable CIR component with four credibility factors—amplitude significance, time-of-flight (TOF) consistency, inter-baseline phase–geometry agreement, and cross-baseline coherence—and fuses retained candidates into a credibility-weighted spatial covariance matrix for 2D MUSIC search. Field experiments on a custom five-channel coherent UWB platform compare MCS-AoA against six baselines—PDOA, MUSIC, MVDR/Capon, TLS-ESPRIT, PwMUSIC, and DNN-AoA. In an underground corridor (5–40 m), MCS-AoA achieves an azimuth/elevation MAE of 1.00°/1.46°, outperforming all baselines (PDOA: 2.26°/2.49°; MUSIC: 1.76°/2.40°; next-best PwMUSIC: 1.44°/2.17°); in a logistics tunnel (5–80 m), it achieves a 1.19° overall azimuth MAE. Simulations corroborate these gains, with a 0.71° azimuth RMSE at 80 m (69.3% reduction over PDOA) and 86.6% of estimates falling within 1°. Full article

Robust and transferable approaches for evaluating research capacity—whose measurable expression is reflected in research output—are essential for evidence-based science policy and strategic research management. This study develops an integrated framework to assess global scholarly capacity and regional disparities by combining semantic-similarity-based literature filtering, bibliometric mapping, dynamic performance assessment, and spatial analytical techniques into a coherent and replicable model. A Sentence-BERT model ensures thematic precision and dataset consistency, while CiteSpace 6.1.R3 is used tomap publication trajectories, thematic evolution, and influential contributors. A dynamically weighted TOPSIS model incorporates temporal variation to quantify national research capacity, and spatial analyses—including gravity center analysis, Theil index decomposition, spatial autocorrelation, gray relational analysis, and the Geographical Detector Model—identify disparity patterns and their explanatory associations. Applied to urban ecosystem security research (2001–2023), an emerging interdisciplinary field within sustainability science, the framework shows that China and the United States dominate research output, whereas European journals exert strong academic influence. The field has advanced through three stages, with increasing emphasis on ecosystem services and sustainable development. GDP, environmental pressure, and urbanization rate show the strongest explanatory associations with research capacity, and interactive effects—especially those involving GDP—exceed single-factor explanatory strength. Ecological baseline conditions such as NDVI and climate exhibit only limited associations, functioning mainly as contextual factors. Policy implications highlight four priorities: strengthening interdisciplinary and cross-regional collaboration in developing regions; promoting equity-oriented research agendas in developed regions; establishing unified definitions and validated evaluation frameworks; and advancing dynamic, systems-based approaches to ecosystem security analysis. By shifting attention from ecological status assessment to the dynamics of scientific knowledge production and research capacity, this study advances methodological foundations for research evaluation and enriches analytical approaches in urban ecosystem security, offering a generalizable framework for identifying capacity differences and supporting evidence-informed policy design. Full article

Multimodal epileptic seizure detection using physiological biosignals remains challenging due to signal noise, inter-subject variability, weak cross-modal alignment, and the limited interpretability of many machine learning models. To address these challenges, this study proposes a parameterized multimodal feature-fusion framework that unifies normalization, modality weighting, and nonlinear cross-modal interaction within a single mathematical representation. Four fusion parameters, the fusion exponent $\rho$, interaction weight $\left(\delta \right)$, normalization factor $\left(\lambda \right),$ and the cross-modal interaction term $\left(\eta \right)$, are introduced at the feature-fusion level, while all classifiers retain their original learning mechanisms. The framework is evaluated using synchronized EEG, ECG, EMG, and accelerometer signals from 120 subjects, segmented into 2 s windows at 512 Hz and analyzed using twelve classical and deep learning classifiers. Principal Component Analysis (PCA) applied to the fused feature space reveals improved class separability compared to unimodal representations, with EEG exhibiting the strongest intrinsic discrimination and peripheral modalities contributing complementary structure when fused. SHapley Additive exPlanations (SHAP) further identify entropy as the most influential feature across all modalities, followed by RMS and energy, yielding physiologically coherent attributions. Quantitative performance evaluation and ablation analysis confirm that the observed improvements arise from the proposed representation design rather than classifier-specific modifications. Unlike existing architecture-dependent fusion strategies, the proposed method introduces a mathematically parameterized feature-space formulation that enhances separability and interpretability without modifying classifier architectures, thereby establishing a representation-driven paradigm for explainable multimodal seizure detection. These results demonstrate that mathematically principled feature-space modeling can simultaneously enhance predictive performance and interpretability, providing a transparent and robust foundation for explainable multimodal seizure detection. Full article

Urban road collapses are characterized by sudden occurrence and strong spatial heterogeneity, posing substantial challenges for proactive infrastructure management. Susceptibility mapping can provide spatially explicit evidence to support targeted inspection and early-warning strategies. Using Futian District, Shenzhen (China) as a case study, a total of 315 road collapse events recorded during 2019–2023 were compiled to develop an integrated framework for urban road collapse relative susceptibility mapping based on multi-source remote sensing and urban spatial data. First, an indicator-based susceptibility index (SI) was constructed using eight conditioning factors, including PS-InSAR-derived deformation, topographic–hydrological conditions, and distance-based infrastructure variables (distance to underground utilities, metro lines, and roads). Factor weights were determined by coupling the Analytic Hierarchy Process (AHP) with the Entropy Weight Method (EWM), producing a comprehensive SI for historical collapse locations. Subsequently, a set of 17 remote-sensing predictors, including Sentinel-2 spectral bands, Sentinel-2 GLCM texture features, and Sentinel-1 SAR backscatter variables, was used to train a Random Forest model to predict SI and generate continuous susceptibility maps at the urban road-network scale. The influence of neighborhood window size on predictive performance was systematically evaluated. Results show that the Random Forest model performed best at the 5 × 5 window scale (R² = 0.70, RMSE = 0.0172, MAE = 0.0122), outperforming both pixel-based inputs (1 × 1) and larger windows. Uncertainty analysis further indicated that the 5 × 5 RF configuration yielded the most stable and spatially coherent predictions, whereas overly small windows and less robust learners produced more fragmented or higher-uncertainty susceptibility patterns. Spatiotemporal analysis indicates that susceptibility patterns remained broadly stable from 2019 to 2023, with moderate susceptibility accounting for 50.82–57.89% and high susceptibility for 21.94–23.30%, while very high susceptibility consistently remained below 1%. Overall, this study demonstrates that integrating multi-source remote sensing with scale-optimized machine learning provides an effective approach for fine-scale susceptibility mapping of urban road collapses, offering practical guidance for differentiated monitoring and risk prevention along critical road corridors. Full article

Background: The Temporomandibular Joint Ankylosis Quality of Life Questionnaire (TMJAQoL) is a disease-specific instrument designed to assess quality of life in patients with temporomandibular joint (TMJ) ankylosis. No validated Turkish version of this scale existed prior to this study. The aim of this study was to translate, culturally adapt, and evaluate the Turkish version of the TMJAQoL (TMJAQoL-TR) in patients with severe temporomandibular disorders, including a predefined ankylosis subgroup. Materials and Methods: A total of 120 patients with temporomandibular complaints were included. Test–retest reliability was evaluated in a clinically stable subsample of 72 participants with a one-week interval. Following forward–backward translation and cultural adaptation procedures, the TMJAQoL-TR was administered together with the Oral Health Impact Profile Short Form-14 (OHIP-14), the Short Form-36 (SF-36), and Visual Analog Scale (VAS) pain scores. Reliability was assessed using Cronbach’s α, item-level Weighted Cohen’s Kappa, and test–retest Intraclass Correlation Coefficients (ICC), supported by measurement error indices (Standard Error of Measurement [SEM] and Minimal Detectable Change at 95% confidence [MDC₉₅]). Construct validity was examined using Spearman correlation coefficients. Structural validity was investigated through exploratory factor analysis, followed by a confirmatory structural model in AMOS to evaluate preliminary model consistency. Floor and ceiling effects were analyzed using the 15% criterion. Results: The TMJAQoL-TR demonstrated excellent internal consistency (Cronbach’s α = 0.879) and very high test–retest reliability (ICC = 0.995; 95% CI: 0.992–0.997). Strong correlations were observed with OHIP-14 (r = 0.772, p < 0.01), and moderate correlations with VAS pain scores (r = 0.312, p < 0.01). No significant floor or ceiling effects were detected. A weak but significant negative correlation with the SF-36 physical role subscale suggests that TMJ-related quality of life impairment is associated with role limitations in daily activities, although the magnitude of this association was modest. Exploratory factor analysis supported a clinically coherent two-factor structure, and the AMOS structural model demonstrated acceptable consistency with this framework. Conclusions: The TMJAQoL-TR appears to be a valid and reliable instrument for assessing quality of life in patients with severe TMJ-related functional limitations. Findings from the ankylosis subgroup support potential applicability within the instrument’s original target population; however, further validation in larger ankylosis-specific samples is warranted. Full article

Recent advancements in AI have enabled autonomous agents to interact within complex environments, with deliberative BDI (Belief–Desire–Intention) agents standing out for their human-inspired reasoning capabilities. However, defining the initial beliefs that constitute an agent’s cognitive profile remains a significant challenge. This process often relies on manual approaches that limit scalability and validation. This study proposes the Personality–Role–Belief (P–R–B) Model for BDI agents, introducing a novel architecture for generating cognitive profiles applicable to domains such as social simulation and non-player characters (NPCs). The model translates Five-Factor Model (FFM) scores into specific social roles, assigning base beliefs to each. A key contribution is a weighting mechanism designed to resolve conflicts between beliefs when multiple roles coexist. Inspired by Cohen’s effect size conventions, this mechanism establishes an influence hierarchy that quantifies belief strength based on social roles. Consequently, this approach not only enables agents to exhibit coherent behavior consistent with their personality but also establishes a foundation for modeling ethical decision-making through role–trait alignment, thereby facilitating the creation of agents capable of navigating morally complex social contexts. Full article

This paper examines the organizational competitiveness and strategic transformation of an elite professional football entity in the Greek Super League during the period 2018–2020, using Panathinaikos as a case study within a comparative framework including Olympiacos, AEK, and PAOK. This period marked a phase of enforced reorientation for Panathinaikos due to UEFA sanctions for overdue debts and the club’s exclusion from European competitions, which resulted in extensive squad renewal and increased reliance on academy-developed players. The aim of the study is to identify the factors shaping Panathinaikos’ strategic position, diagnose the causes of its lagging performance, and suggest directions for strategic repositioning. To this end, a multi-criteria framework based on the Analytic Hierarchy Process (AHP) is employed, integrating qualitative assessments, expert judgements, and quantitative performance indicators through pairwise comparisons, weight calculations, and consistency checks. The analysis is based on a conceptually original model that defines the Football Organization as an integrated system composed of two interdependent subsystems: the Football Club and the Football Team (competitive subsystem). This approach highlights that league standings do not always reflect overall performance dynamics, as they are influenced by both organizational and on-field factors. The findings indicate that Panathinaikos is lagging behind in key areas and that a structural discontinuity between the Club and the Team limits strategic coherence and the ability to create a sustainable competitive advantage. The study concludes with proposals for restructuring and strategic repositioning, while the proposed model functions as a transferable decision-support tool for assessing organizational competitiveness, with broader applicability to complex organizational systems beyond professional football. Full article

The Upper Mono Basin Valley (UMBV) in Togo faces recurrent flooding hazards. This study assesses spatial flood susceptibility using an integrated approach combining Geographic Information Systems (GISs), Multi-Criteria Decision Making (MCDM), and the Analytic Hierarchy Process (AHP). Eight factors were weighted according to their influence: accumulation flow, annual precipitation, soil permeability, land use/land cover, slope, elevation, distance from drainage networks, and drainage network density. With a consistency ratio of 0.052, the AHP method proved coherent and enabled the development of a normalized Flood Hazard Index (FHI). Results revealed accumulation flow (weight = 0.33), distance to drainage networks (0.18), and network density (0.16) as the most critical drivers, while precipitation and soil permeability are secondary. Spatial classification revealed heterogeneity: 55% (871,046 ha) of the UMBV has very low susceptibility, while 1% (10,034 ha) is highly vulnerable, mainly in Est-Mono, Ogou, Anié, Tchamba, and Tchaoudjo. In contrast, Blitta and Sotouboua show lower vulnerability due to higher altitudes. This reveals that the UMBV is relatively less prone to flooding. The comparison of data from 28 focus groups in 14 municipalities with the flood susceptibility map shows a strong concordance between local perceptions and the mapping (r = 0.805, p < 0.001). These findings highlight the need for differentiated territorial strategies integrating physical parameters, land use dynamics, and community risk perceptions to strengthen flood risk management in the UMBV. Full article