The growing number of older adults online has spurred interest in improving web accessibility for this demographic. To evaluate the current state of this field, we conducted a systematic review of studies published between 2014 and 2023. Our research aimed to identify the challenges older adults face online and effective solutions for accessible web design. From 4,052 articles, we identified 25 types of accessibility issues, 104 improvements to enhance website accessibility, 24 technological resources to design these accessibility enhancements, and 20 evaluation methods. Despite notable progress enhancing web accessibility for older people, our findings underscore the need for ongoing improvements for a truly inclusive web, considering the differences within this heterogeneous group and understanding old age not as a problem but as an opportunity.

Automated surface inspection of balsa wood panels is challenging because defects may be small, elongated, weakly contrasted, or visually similar to natural grain patterns. This study proposes YOLOv11–BiFPN–DAAF, an enhanced object-detection architecture that combines bidirectional multi-scale feature fusion with adaptive dual-attention feature refinement. The task was formulated as single-class detection, with all anomalous surface regions labeled as Defect. The dataset comprised 508 manually annotated RGB images, including independent internal and external production test sets. A preliminary screening identified YOLOv11-m512 as the reference configuration, followed by a controlled $2\times 2$ factorial ablation comprising the baseline, BiFPN, DAAF, and their combined integration. Each configuration was trained using five independent random seeds under identical experimental conditions. On the validation set, the combined architecture achieved a precision of $0.893\pm 0.004$, recall of $0.848\pm 0.006$, mAP@0.5 of $0.897\pm 0.004$, and mAP@0.5:0.95 of $0.389\pm 0.004$. Relative to the baseline, the largest improvement was obtained for mAP@0.5:0.95, with a relative gain of 9.89%, indicating improved localization under stricter IoU thresholds. The improvement was retained on the independent internal test set, where the proposed architecture reached mAP@0.5 and mAP@0.5:0.95 values of $0.892\pm 0.005$ and $0.384\pm 0.006$, respectively. On the external production test set, the corresponding values were $0.865\pm 0.007$ and $0.358\pm 0.008$, representing absolute improvements of 0.034 and 0.042 over the original YOLOv11 baseline. Under the matched experimental protocol, YOLOv11–BiFPN–DAAF also achieved the highest principal detection metrics among the evaluated representative detectors. Although BiFPN and DAAF introduced a moderate computational overhead, the architecture maintained an inference time of $9.6\pm 0.3$ ms per image. These findings support the potential of the proposed architecture for automated balsa wood panel inspection, while broader multi-site and hardware-level validation remains necessary before large-scale industrial deployment.

Balsa wood defects can resemble natural grain, making successful detection insufficient to establish precise localization or robustness to acquisition changes. This study investigates these limitations using the previously published YOLOv11–BiFPN–DAAF framework as a reference for analyzing adaptive multiscale attention. On a 508-image industrial dataset, five training seeds support selected-IoU evaluation, geometric box-error analysis, stratification by defect size, contrast, and elongation, and controlled comparisons of within-scale attention and mixing operators. Robustness is assessed under 15 brightness, contrast, noise, blur, and compression conditions and an external production campaign. Relative to YOLOv11, the complete framework increases internal-test AP from 0.865 to 0.892 at IoU 0.50, from 0.385 to 0.442 at IoU 0.75, and from 0.082 to 0.125 at IoU 0.90, while reducing mean geometric errors among matched detections. It improves mAP@0.5:0.95 by 0.035 (paired-seed 95% CI: 0.03061–0.03939) and exhibits smaller mean degradation under every tested perturbation, while external mAP@0.5:0.95 decreases from the internal value of 0.384 to 0.358. In the within-scale ablation, learned gating exceeds fixed mixing by 0.003 in mean mAP@0.5:0.95 (0.384 versus 0.381), with additional trainable capacity. Composite spatial-attention maps yield a concentration ratio of $4.8\pm 0.7$, indicating higher mean weights inside annotation boxes without establishing a causal suppression mechanism. Paired statistical analyses distinguish training-seed variability from test-panel uncertainty. The findings identify localization gains, acquisition sensitivities, and the observed contribution of adaptive refinement within the established architecture. They support further evaluation of difficult defects and independent production domains rather than unrestricted generalization claims.

El artículo aborda las dificultades de navegación que presentan las páginas web de universidades ecuatorianas respecto a la accesibilidad. El objetivo de este artículo es determinar el grado de aplicación de las pautas WCAG 2.1, en las universidades ecuatorianas. Para ello se aplicaron varias herramientas de validación. Del total de 61 páginas web analizadas, solo 11 presentaban un botón para ajustes visuales. Utilizando TAW (Test de Accesibilidad Web) y un análisis de dispersión, se determinó que la mayoría de las universidades tienen hasta 26.8 errores en el principio “Perceptible”; 46.8 errores en el principio “Operable”; 9,4 errores en el principio “Comprensible” y 9.4 errores en el principio “Robusto”. Utilizando Markup Validation Service se encontraron hasta 80 errores de marcado. La aplicación del CSS Validation Service evidenció hasta 143 errores en las páginas web. Con estos resultados, los desarrolladores web podrán enfocar sus mejoras, considerando las pautas de accesibilidad.