This paper explores the integration of Large Language Models (LLMs) and Software-Defined Resources (SDR) as innovative tools for enhancing cloud computing education in university curricula. The study emphasizes the importance of practical knowledge in cloud technologies such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS), Software as a Service (SaaS), DevOps, and cloud-native environments. It introduces Lean principles to optimize the teaching framework, promoting efficiency and effectiveness in learning. By examining a comprehensive educational reform project, the research demonstrates that incorporating SDR and LLMs can significantly enhance student engagement and learning outcomes, while also providing essential hands-on skills required in today’s dynamic cloud computing landscape. A key innovation of this study is the development and application of the Entropy-Based Diversity Efficiency Analysis (EDEA) framework, a novel method to measure and optimize the diversity and efficiency of educational content. The EDEA analysis yielded surprising results, showing that applying SDR (i.e., using cloud technologies) and LLMs can each improve a course’s Diversity Efficiency Index (DEI) by approximately one-fifth. The integrated approach presented in this paper provides a structured tool for continuous improvement in education and demonstrates the potential for modernizing educational strategies to better align with the evolving needs of the cloud computing industry.

Falling is one of the most critical outcomes of loss of consciousness during triage in emergency department (ED). It is an important sign requires an immediate medical intervention. This paper presents a computer vision-based fall detection model in ED. In this study, we hypothesis that the proposed vision-based triage fall detection model provides accuracy equal to traditional triage system (TTS) conducted by the nursing team. Thus, to build the proposed model, we use MoveNet, a pose estimation model that can identify joints related to falls, consisting of 17 key points. To test the hypothesis, we conducted two experiments: In the deep learning (DL) model we used the complete feature consisting of 17 keypoints which was passed to the triage fall detection model and was built using Artificial Neural Network (ANN). In the second model we use dimensionality reduction Feature-Reduction for Fall model (FRF), Random Forest (RF) feature selection analysis to filter the key points triage fall classifier. We tested the performance of the two models using a dataset consisting of many images for real-world scenarios classified into two classes: Fall and Not fall. We split the dataset into 80% for training and 20% for validation. The models in these experiments were trained to obtain the results and compare them with the reference model. To test the effectiveness of the model, a t-test was performed to evaluate the null hypothesis for both experiments. The results show FRF outperforms DL model, and FRF has same accuracy of TTS.

While the notion of the smart city has grown in popularity, the backlash against smart urban infrastructure in the context of changing state-public relations has seldom been examined. This article draws on the case of Hong Kong’s smart lampposts to analyse the emergence of networked dissent against smart urban infrastructure during a period of unrest. Deriving insights from critical data studies, dissentworks theory, and relevant work on networked activism, the article illustrates how a smart urban infrastructure was turned into both a source and a target of popular dissent through digital mediation and politicisation. Drawing on an interpretive analysis of qualitative data collected from multiple digital platforms, the analysis explicates the citizen curation of socio-technic counter-imaginaries that constituted a consent of dissent in the digital realm, and the creation and diffusion of networked action repertoires in response to a changing political opportunity structure. In addition to explicating the words and deeds employed in this networked dissent, this article also discusses the technopolitical repercussions of this dissent for the city’s later attempts at data-based urban governance, which have unfolded at the intersections of urban techno-politics and local contentious politics. Moving beyond the common focus on neoliberal governmentality and its limits, this article reveals the underexplored pitfalls of smart urban infrastructure vis-à-vis the shifting socio-political landscape of Hong Kong, particularly in the digital age.

This paper provides a unique empirical analysis of the effects of political factors on the adoption of PPP contracts in Brazil. As such, it innovates along two different lines: first, political factors behind the adoption of PPPs have been largely ignored in the vast body of empirical literature, and second, there is scant work done on the motives of any kind behind the adoption of PPPs in Brazil. Various economic and financial reasons have been evoked to justify the use of PPPs in general. These include the goal of promoting socio-economic development in a tight public budgetary framework or of improving the quality of public services through the use of economically efficient and cost-effective mechanisms. Any possible underlying political motives, however, have been overlooked in the PPP research. And yet, there is abundant literature suggesting a link between the adoption of PPPs and the ideology of the governing body or the political cycles associated with elections. This study examines the impact of ideological commitment and opportunistic political behavior on the process of PPP contracting in Brazil, including the stages of public consultation, the publication of tender, and the signature of the contract, using federative-level data for the period between 2005 and 2022. Consistent with the outstanding literature, the two hypotheses are tested: first, conservative parties tend to celebrate more PPP contracts than left-leaning parties, and second, the electoral calendar has a significant effect in the process, allowing for opportunistic behaviors. Empirical results suggest that there is little evidence for the relevance of ideological leanings in the process of adopting PPPs in Brazil. Additionally, regardless of ideology, parties significantly choose to enter PPPs at specific points in the electoral cycle, suggesting decisions are influenced by political considerations and electoral strategy rather than by purely financial or ideological considerations. This may pose severe constraints on the efficiency and cost-effectiveness of the contracts, negatively impacting public governance and leading to protracted costs for taxpayers.

As the complexity and scale of software applications increase, the challenges associated with testing these systems grow correspondingly, necessitating innovative and sustainable testing strategies. This paper explores a multifaceted approach aimed at addressing the intricate challenges inherent in testing large-scale software applications. Through a comprehensive examination of current industry practices and emerging trends, this study introduces a novel framework that integrates advanced testing techniques with state-of-the-art tools. This framework not only mitigates the challenges posed by the complexity and size of modern applications but also enhances the efficiency and effectiveness of the testing process. Key aspects of this research include a detailed exploration of test methodologies suited for large-scale applications, an evaluation of advanced tools designed for complex test scenarios, and an analysis of the impact of the test environment on sustainability. The findings offer valuable insights and actionable strategies for software development and testing professionals aiming to optimize testing processes and improve the quality and sustainability of their software in a rapidly evolving technological landscape.

Work is reported on thermal-induced redshifts of quantum particle plasmon. The redshifts are predicted to be caused indirectly by the quantum size effects. The particles are enlarged when temperature increases, and consequently, quantum size effects modify the plasmon but not the band structure. It has been modeled for metallic quantum particles. The results are also instructive to other quantum systems, such as complex molecules. Every electron inside the quantum particle is taken into account. Tiny quantum size effects are harvested, and the redshift becomes significant. Experimental evidence is also given for the spectral redshift. Faujasite zeolites were synthesized. Optical spectroscopy has been carried out, and the resulting spectra showed a significant redshift with the increase in temperature.