This study examines the relationship between cost of governance and public infrastructure financing in Nigeria over the period 1990–2025. Rising governance costs have increasingly attracted policy concern in Nigeria due to the growing share of public resources devoted to recurrent expenditure while infrastructure deficits continue to widen. The study therefore investigates how key components of governance expenditure influence government investment in infrastructure development. Specifically, the study considers personnel cost, debt servicing expenditure, and administrative or overhead expenditure as proxies for the cost of governance, while capital expenditure on infrastructure serves as the dependent variable. The study adopts a time series research methodology and utilizes secondary data obtained from the Central Bank of Nigeria Statistical Bulletin and publications of the National Bureau of Statistics. The data were analyzed using both descriptive and econometric techniques. Trend analysis through line graphs was used to examine the behaviour of the variables over time, while descriptive statistics were employed to summarize their statistical properties. The stationarity of the series was tested using the Augmented Dickey-Fuller unit root test to avoid spurious regression results. The Autoregressive Distributed Lag (ARDL) modelling approach was then applied to examine the dynamic relationship between governance expenditure and infrastructure financing in both the short run and the long run. The empirical results reveal that components of governance expenditure exert significant dynamic effects on infrastructure financing in Nigeria. Personnel cost shows mixed but significant effects across different lag periods, indicating that rising wage bills can constrain infrastructure spending in some periods while adjustments occur over time. Administrative expenditure exhibits a positive relationship with infrastructure financing, whereas debt servicing expenditure shows a negative relationship, suggesting that rising debt obligations may reduce funds available for infrastructure development. The study concludes that the increasing cost of governance poses a challenge to sustainable infrastructure financing and recommends stronger fiscal discipline, improved expenditure management, and prioritization of capital investment in infrastructure to support long-term economic development.

In today’s language classrooms, reading comprehension remains a core literacy skill but it’s even harder to master in digital environments. Conventional text-based instructional materials often lack interactivity and multimodal support, which limit learner engagement and slow comprehension development. With the emergence of artificial intelligence and multimodal technologies, instructional resources can now be transformed into dynamic, interactive, and learner-centered materials that integrate visual, auditory, and interactive elements. Grounded in the materials development principles of Brian Tomlinson and supported by multimedia and multimodal learning perspectives, this study responds to the need for pedagogically sound, technology-enhanced resources in language instruction within the Philippine educational context. This study aimed to design, develop, and evaluate an AI-assisted multimodal e-book toolkit intended to enhance the reading comprehension and engagement of Grade 7 students. The study specifically examines the content of the multimodal features integrated into the selected e-books through AI-assisted transformation, evaluates the relevance of the developed material in supporting students’ reading comprehension and engagement, and determines the appropriateness of the multimodal elements in facilitating language learning and comprehension development. Moreover, it investigates the effects of the developed multimodal materials on students’ reading comprehension, particularly in enhancing learner engagement, understanding of texts, and overall language learning experiences. Additionally, the scaffolded activities, interactive learning tasks, and assessment components, including the pre-test and post-test, were systematically aligned with the learning competencies prescribed in the revised Department of Education curriculum to ensure instructional relevance, curriculum consistency, and pedagogical appropriateness. The study employed a Developmental Research Design approach guided by the ADDIE instructional design model, encompassing the phases of Analysis, Design, Development, Implementation, and Evaluation. The participants consisted of forty (40) Grade 7 learners from Universidad de Sta. Isabel of Pili, Inc. during the School Year 2025–2026. The developed AI-assisted e-book toolkit was pilot-tested in an authentic classroom setting to assess its usability, clarity, engagement, and instructional value. Data were collected using a researcher-developed survey questionnaire validated by experts in English education and instructional materials development, adapted from the Department of Education LRMDS Evaluation Template for non-print materials. Descriptive statistical tools, particularly the weighted mean, were utilized to analyze the data and determine the level of acceptability of the developed instructional material. The findings revealed that the multimodal features integrated into the AI-transformed e-book were highly evident, coherent, and pedagogically appropriate. Participants strongly agreed that visual supports, interactive prompts, multimedia elements, and scaffolded activities were effectively incorporated and significantly contributed to their understanding of the text. The use of the AI-assisted e-book was found to enhance students’ reading comprehension by improving their ability to interpret ideas, think critically, and engage actively with the material. Moreover, the results indicated a high level of learner engagement, with students reporting increased motivation and sustained attention during reading tasks. Among the multimodal features, visual aids, interactive questioning, and structured scaffolded activities emerged as the most helpful in supporting comprehension and higher-order thinking skills. These findings affirm that the support of artificial intelligence and integration multimodal design fosters meaningful learning experiences and supports cognitive processing. The study concludes that AI-assisted multimodal e-book toolkits are effective instructional resources that enhance reading comprehension, learner engagement, and higher-order thinking skills. The AI-assisted transformation of traditional e-books into interactive and learner-centered materials supports contemporary language learning and multimedia cognition theories. Furthermore, purposeful multimodal integration and sound pedagogical design maximize the instructional effectiveness of digital learning resources. It is therefore recommended that educators integrate AI-assisted multimodal materials into language instruction, that instructional designers continuously refine multimedia and interactive elements based on learner feedback, and that future research explore broader applications and long-term impacts of AI-enhanced instructional tools across diverse educational contexts.

The rapid adoption of microservices architecture has increased the need for efficient and reliable software design techniques. UML class diagrams are commonly used to represent system structure during the early stages of development. However, existing UML-to-code generation tools directly convert designs into code without validating design quality, often resulting in tightly coupled and poorly structured microservices. This paper proposes an AI assisted UML-driven validation and optimization framework for automated microservices code generation. The proposed system analyzes UML class diagrams using a combination of rule based validation and machine learning techniques. It identifies design issues such as tight coupling, missing relationships, and improper naming conventions. Based on the detected issues, the system provides intelligent suggestions for optimizing microservice boundaries and improving overall design quality. The optimized UML model is then used to generate microservices-based full-stack application code. The proposed approach improves scalability, maintainability, and reduces manual effort in software development.

Alphonse Mucha's recurring female figure is often treated as a unified Art Nouveau type, alternately interpreted as an erotic commodity, a decorative ideal, or an emblem of the New Woman. This article argues that the category becomes analytically unstable when it is applied to the theater posters Mucha designed for Sarah Bernhardt. Through close visual comparison of Gismonda (1894), Lorenzaccio (1896), Médée (1898), and Hamlet (1899), with JOB (1896) serving as a commercial countertype, the study develops the concept of ornamental invariance: a stable graphic system that preserves celebrity recognition while role, costume, narrative action, and gender change. Mucha's elongated format, architectural enclosure, shallow stage-like space, integrated typography, and repeated inscription of Bernhardt's proper name do not simply feminize the performer. They make her legible as a serial star persona across Byzantine noblewoman, male Renaissance conspirator, murderous mother, and Shakespearean prince. The cross-gender posters are therefore decisive. They reveal that visual continuity in the Bernhardt corpus belongs less to a generic feminine icon than to a named performer whose identity could contain unstable gendered roles. By contrast, JOB fuses an anonymous smoker with a product name, erotic pleasure, and commodity recognition. Distinguishing the named theatrical persona from the anonymous commercial type revises the gender history of Mucha's posters without presuming that visual prominence equals historical emancipation or that all viewers received the images in the same way.