Open Cloud (O-Cloud) is the Open Radio Access Network (O-RAN) computing infrastructure spanning edge to cloud sites defined by the O-RAN Alliance to support and coordinate RAN infrastructure management shared among multiple Mobile Network Operators (MNO). In the standard definition, O-Cloud ensures reliable connectivity, active coordination, and efficient distribution of the Radio Access Network (RAN) deployments. Thanks to these properties, O-Cloud can support Mobile Network and Infrastructure operators to achieve fully automated infrastructure management, self-service MNO portals, enhanced security measures, and O-Cloud infrastructure-agnostic management. In this paper, we present TORNADO: TOSCA-enabled Orchestration for RAN in Next-Generation Networks Automating DevOps in O-Cloud, an O-Cloud automation infrastructure designed to ease DevOps deployment and operation phases of RAN network functions for multiple MNOs. Our solution introduces infrastructure-agnostic automation for multi-site, multi-MNO RAN components, offering high-level, secure self-service MNO portals for defining RAN deployments. Performance evaluation of our solution for various RAN network functions demonstrated the capability of TORNADO to automate the deployment in a multi-site, multi-MNO heterogeneous infrastructure.
The increasing reliance on the judiciary to secure access to healthcare in Brazil has created a vast corpus of legal rulings that contain insights into healthcare delivery. However, extracting actionable information from these unstructured texts remains a significant challenge. This study evaluates the performance of state-of-the-art large language models (LLMs) for healthcare-related named entity recognition (NER) in Brazilian judicial decisions. Using our release dataset, LexCare.BR, a manually annotated gold-standard dataset of 1,200 legal rulings, we assessed 10 predefined healthcare entities across multiple LLMs, including both open-source and closedsource models. Our results show that larger models, such as GPT-4o and Llama 3.1, achieved the highest overall F1-scores (0.739 and 0.694, respectively), demonstrating robust capabilities in extracting clinically and policy-relevant entities. However, we observed significant variations in performance across entity types, with higher precision for standardized codes, such as ICD-10, but lower recall for context-dependent categories like dietary supplements. These findings highlight the potential of LLMs to automate the extraction of structured healthcare data from judicial texts, thereby enabling real-time monitoring of healthcare judicialization and informing targeted policy interventions. Data is available at https://github.com/eliasjacob/lexcare.br
This study presents a fine-tuning of the BERTimbau model for Named Entity Recognition (NER) in Electronic Health Records (EHR), focusing on oncology data in Brazilian Portuguese. The proposed approach employs an annotated dataset formatted in the Inside-Outside-Beginning (IOB) scheme, enabling precise extraction of clinical entities. The model was trained using the BERTimbau Base Tokenizer, with hyperparameters optimized through the Hugging Face library. Experimental results demonstrate robust performance, achieving F1scores between 0.83 and 0.85 across disease, procedure, and medication categories. When applied to a dataset of 125,825 new clinical records, the model achieved a correct identification rate exceeding 92%, underscoring its potential for medical decision support. Key challenges include enhancing classification accuracy for less frequent entities and adapting the model to diverse clinical contexts. Future directions involve dataset expansion, data augmentation techniques, exploration of more advanced transformer-based models, and integration into hospital decision support systems to further improve healthcare analytics.
Public key X. 509 certificates play a powerful role in promoting effective electronic identification, but some significant practical issues still affect their scalability. Every time a public key certificate is used, it must be validated by the system or application relying on it for security services, generically called also relying party. The validation involves several processing steps and checks, and it has been measured that many applications (still) perform it incompletely. Furthermore, privacy issues may occur when validating certificates, for example a website visited by a user could be revealed to external parties. We propose TPValCert, an architecture tailoring a trusted proxy to provide privacy-preserving certificate validation service to the relying parties. By exploiting TPValCert, a desktop, IoT, or mobile system that needs to validate a public-key certificate may execute a transaction with a trusted proxy, which performs validation by considering certificate policy parameters, privacy, and validation options received from the client and returns the validation status. Besides reducing complexity on the client, exploiting such trusted validation parties may also bring privacy benefits. To communicate with the clients, we consider the SCVP (Server-based Certificate Validation Protocol) or DVCS (Data Validation and Certification Server) protocols, even though, depending on the context, lighter formats could be considered. Our implementation efforts emphasize the possibility of pursuing a tradeoff between timeliness, privacy, and computational resource usage, via dynamic selection of several configurable options.
Multiomics is an emerging biological analysis approach in which the datasets come from multiple “omics”, such as genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics. Nowadays, the convergence of Deep Learning and multiomics sciences presents an unprecedented opportunity to dissect the intricate interplay of biological processes. Specifically, multiomics data integration, propelled by Deep Learning methodologies, has revolutionised biological research, enabling a more holistic understanding of complex biological systems and disease mechanisms. This paper explores the current landscape of Deep Learning applications in multiomics, highlighting state-of-the-art techniques, emerging research areas, and the challenges that lie ahead. In particular, we delve into the application areas and computational methods that have been considered so far, offering guidance to researchers navigating this intricate field.
