Large-Language-Model (LLM) functionality is rapidly becoming a cornerstone of Telemedicine-as-a-Service (PGaaS) platforms. Recent Q1 studies demonstrate that even minuscule training-set or parameter perturbations can introduce persistent back-doors, while inference pipelines leak protected health information (PHI) if left unguarded. Building on the NIST AI Risk Management Framework (AI RMF), this paper proposes and implements a zero-trust, multi-cloud security architecture that couples (i) knowledge-graph–driven data-integrity validation, (ii) containerised fine-tuning isolation, (iii) AI-RMF–centred governance and continuous risk registers, (iv) a privacy-preserving response-sanitisation gateway enhanced with one-time-password (OTP) and KYC identity binding, and (v) remote-attestation-backed zero-knowledge-proof (ZKP) integrity challenges for model weights at runtime. An extensive multi-cloud evaluation shows that the framework detects 94.6 % of tainted samples before ingestion and blocks 91.3 % of unsafe outputs, with a median latency overhead of 66 ms—well below clinical tele-consultation thresholds.

Serverless computing has revolutionized application development but faces a significant challenge: cold starts, which introduce delays when a function is called after a period of inactivity. Addressing these delays is crucial because they affect efficiency, performance, cost, and scalability. Existing mitigation strategies come with trade-offs, such as increased resource overhead and the need for precise resource management predictions. Also, optimizing the function startup process requires detailed knowledge of the runtime characteristics and the isolation technique used, such as using a container-based or a micro virtual machine setup. This work presents PARSEC, a comprehensive solution for cold start issues in serverless computing. By focusing on reducing initialization latency in idle containers, this research seeks to preserve scalability and ease of deployment features of serverless computing while overcoming cold start limitations. The proposed architecture improved cold start by streamlining the initialization of containers to reduce overhead. This involves minimizing unnecessary operations and customizing launches for serverless needs, aiming for a faster and more efficient setup. It also enhances the provisioning of Zygotes to speed up sandbox launches. The results show better performance for PARSEC when compared with other architectures, particularly at shorter wait times, suggesting effective cold start management. The strategic management of Zygotes and their provisioning scaling plays a critical role in managing large numbers of packages and instances, thereby enhancing the performance of package management. The cache system also evolves to become more selective, reducing overhead by focusing on essential packages.

The ethnic diversity of the Colombian Caribbean is reflected in its rich cultural manifestations, which are at risk due to digital divides in the artisanal sector. While some argue that technology threatens artisanal practices, others highlight the lack of knowledge transmission and documentation as the real threats. Public and community initiatives aim to preserve ancestral knowledge, opening opportunities for design and other disciplines to support traditional crafts. This study focuses on the Chimichagua community in Cesar, Colombia, analyzing cultural heritage promotion strategies to identify findings and methodologies that integrate social marketing, collaborative design, and heritage conservation. An ethnographic approach is used to understand community needs and motivations, enabling the development of an ICT-based strategy to promote material culture. The research includes a foresight process using the "Three Tomorrows of Postnormal Times" methodology, horizon scanning for future forces, and the "Pace Layering" framework to understand ecosystem changes over time. The study prioritizes four forces—Education, Demographics, Media & ICTs, and Geopolitics—to strengthen digital capacities and prevent the loss of artisanal vocation, proposing strategies to stimulate local development through culture and innovation.

This paper presents the development and evaluation of a multi-platform communication application designed for children with nonverbal autism. Utilizing Python and deep learning models, the application, named Conectando Voces, aims to facilitate communication for children with autism by providing a user-friendly interface and voice generation capabilities. The application employs the Flet framework for building the UI, enabling deployment across various devices including mobile phones, tablets, and PCs. Voice generation is achieved through Google’s text-to-speech model, with a fallback to pyttsx3 for offline use. The validation study involved five children with varying degrees of autism and comorbidities, highlighting the app’s potential effectiveness in promoting speech. Results indicate that children with mild to moderate autism benefit significantly, while those with severe autism or additional comorbidities require further tailored support. The study underscores the importance of personalized adaptations and comprehensive support for maximizing the app’s utility in enhancing communication skills in children with autism.

Abstract The increasing frequency of technological events has resulted in significant damage to the environment, human health, social stability, and economy, driving ongoing scientific development and interest in emergency management (EM). Traditional EM approaches are often inadequate because of incomplete and imprecise information during crises, making fast and effective decision-making challenging. Computational Intelligence techniques (CI) offer decision-supporting capabilities that can effectively address these challenges. However, there is still a need for deeper integration of emerging computational intelligence techniques to support evidence-based decision-making while also addressing gaps in metrics, standards, and protocols for emergency response and scalability. This study presents a coordinated decision-making system for multiple types of emergency case scenarios for technological disaster management based on CI techniques, including an Improved Genetic Algorithm (IGA), and Multi-objective Particle Swarm Optimization (MOPSO). The IGA enhances emergency performance by optimizing the task assignment for multiple agents involved in emergency response with coordination mechanisms, resulting in an approximately 15% improvement compared to other state-of-the-art methods. Ultimately, this study offers a promising foundation for future research to develop effective strategies for mitigating the impact of technological disasters on society and the environment.

This paper aims to compare and evaluate several approaches from the state-of- the-art literature for developing a Multi-objective Particle Swarm Optimization (MOPSO) Algorithm that aims to classify victims in emergency response for evacuation and generate decision support information for agents during emergency response. The paper compares the results of the proposed approach with those obtained from other state-of-the-art approaches with similar objectives to evaluate their performance and improvements. One of the critical metrics for evaluating the MOPSO algorithm is the response time of agents, which is critical in disaster situations. Other metrics may include the accuracy of victim classification, efficiency of the algorithm, and ease of implementation. Moreover, ways in which the proposed MOPSO algorithm can be adapted to other disaster events of similar nature are explored. Overall, this paper aims to provide a comprehensive evaluation of the proposed MOPSO algorithm and highlight its potential usefulness in emergency response situations.