This paper addresses the limited attention given to blind users as content creators in Content Management Systems (CMS), a gap that remains under-explored in web accessibility research. For blind authors, effective interaction with CMS platforms requires more than technical compliance; it demands interfaces designed with semantic clarity, predictable navigation, and meaningful feedback for screen reader users. This study investigates the accessibility barriers blind users face when performing key tasks, such as page creation, menu editing, and image publishing, using CMS platforms. A two-fold evaluation was conducted using automated tools and manual usability testing with three blind and one sighted participant, complemented by expert analysis based on the Barrier Walkthrough method. Results showed that block-based interfaces were particularly challenging, often marked as accessible by automated tools but resulting in critical usability issues during manual evaluation. The use of a text-based editor, the integration of AI-generated image descriptions, and training aligned with screen reader workflows, significantly improved usability and autonomy. These findings underscore the limitations of automated assessments and highlight the importance of user-centered design practices. Enhancing CMS accessibility requires consistent navigation structures, reduced reliance on visual interaction patterns, and the integration of AI tools that support blind content authors throughout the content creation process.

Several Artificial Intelligence based heuristic and metaheuristic algorithms have been developed so far. These algorithms have shown their superiority towards solving complex problems from different domains. However, it is necessary to critically validate these algorithms for solving real-world constrained optimization problems. The search behavior in those problems is different as it involves large number of linear, nonlinear and non-convex type equality and inequality constraints. In this work a 57 real-world constrained optimization problems test suite is solved using two constrained metaheuristic algorithms originated from a socio-based Cohort Intelligence (CI) algorithm. The first CI-based algorithm incorporates a self-adaptive penalty function approach i.e., CI-SAPF. The second algorithm combines CI-SAPF with the intrinsic properties of the physics-based Colliding Bodies Optimization (CBO) referred to CI-SAPF-CBO. The results obtained from CI-SAPF and CI-SAPF-CBO are compared with other constrained optimization algorithms. The superiority of the proposed algorithms is discussed in details followed by future directions to evolve the constrained handling techniques.

User Experience (UX) evaluation methods that are commonly used with hearing users may not be functional or effective for Deaf users. This is because these methods are primarily designed for users with hearing abilities, which can create limitations in the interaction, perception, and understanding of the methods for Deaf individuals. Furthermore, traditional UX evaluation approaches often fail to address the unique accessibility needs of Deaf users, resulting in an incomplete or biased assessment of their user experience. This research focused on analyzing a set of UX evaluation methods recommended for use with Deaf users, with the aim of validating the accessibility of each method through findings and limitations. The results indicate that, although these evaluation methods presented here are commonly recommended in the literature for use with Deaf users, they present various limitations that must be addressed in order to better adapt to the communication skills specific to the Deaf community. This research concludes that evaluation methods must be adapted to ensure accessible software evaluation for Deaf individuals, enabling the collection of data that accurately reflects their experiences and needs.