With the rapid growth of Internet of Things (IoT) systems, ensuring robust security measures has become paramount. Microservices Architecture (MSA) has emerged as a promising approach for enhancing IoT systems security, yet its adoption in this context lacks comprehensive analysis. This systematic review addresses this research gap by examining the incorporation of MSA in IoT systems from 2010 to 2024. From an initial pool of 4388 studies, selected articles underwent thorough quality assessment with weighted critical appraisal questions and a defined inclusion threshold. This study represents the first comprehensive systematic review to investigate the potential of microservices in IoT, with a particular focus on security aspects. The review explores the merits of MSA, highlighting twelve benefits, eight key challenges, and eight security risks. Additionally, the eight best practices for implementing MSA in IoT systems are extracted. The findings underscore MSA’s utility in fortifying IoT security while also acknowledging complexities and potential vulnerabilities. Moreover, the study calls attention to the importance of incorporating complementary technologies including blockchain and machine learning to address identified gaps effectively. Finally, we propose a taxonomic classification for Microservice-based IoT security patterns, facilitating the categorization and organization of security measures in this context. Such a review can help researchers and practitioners identify existing gaps, highlight potential research directions, and provide guidelines for designing secure and efficient microservice-based IoT systems.

This paper investigates how microservices, a transformative paradigm in cloud and enterprise applications, can adapt to the challenges presented by the Internet of Things (IoT). Microservices architecture, identified by the disintegration of monolithic software into distributed and decoupled services, presents a solution that enhances scalability and maintainability. While traditionally top-down, microservices align with IoT's need for flexible, vendor-independent service combinations. Identifying challenges in traditional IoT architectures, this research addresses a gap in the lack of a comprehensive review on the integration of microservices. Objectives include analyzing characteristics, identifying benefits and challenges, exploring best practices, and evaluating adaptability in terms of flexibility, scalability, data management, security, privacy, performance, and efficiency. We aim, through this work, to add to the existing knowledge base on microservices and IoT systems and offer valuable perspectives for practitioners and researchers in the field.

It is not always easy to predict the outcome of a surgery. Peculiarly, when talking about the risks associated to a given intervention or the possible complications that it may bring about. Thus, predicting those potential complications that may arise during or after a surgery will help minimize risks and prevent failures to the greatest extent possible. Therefore, the objectif of this article is to propose an intelligent system based on machine learning, allowing predicting the complications related to a flexible uretero-renoscopy with laser lithotripsy for the treatment of kidney stones. The proposed method achieved accuracy with 100% for training and, 94.33% for testing in hard voting, 100% for testing and 95.38% for training in soft voting, with only ten optimal features. Additionally, we were able to evaluted the machine learning model by examining the most significant features using the shpley additive explanations (SHAP) feature importance plot, dependency plot, summary plot, and partial dependency plots.

Effective time and resource management is crucial not only in the operating room but also in healthcare supply chains. Healthcare supply chains involve the movement of medical supplies, equipment, and medications from manufacturers to healthcare providers. Effective management is crucial to ensuring that patients receive the care they need promptly. In the operating room, it is essential to have an information process in place to effectively manage time and resources during the current surgical procedure. This paper focuses on developing a predictive model for the operating time of flexible ureteroscopy for kidney stones. The model can forecast surgical and preoperative time based on patient characteristics and surgeon experience. The model can assist in planning ureteroscopy procedures and preventing surgical complications, which is crucial not only for the operating room but also for healthcare supply chains. The paper presents a study that compares different feature selection methods and regression techniques. The study found that sequential backward selection combined with the extra tree regressor was the most effective approach.

As DevOps pipelines accelerate release cycles from weeks to hours, testing practices face a fundamental misalignment: most AI-driven approaches automate isolated tasks rather than orchestrating testing activities across the delivery lifecycle. Although generative AI has broadened the capabilities of software testing, this task-centric focus leaves a critical autonomy gap unaddressed. To characterize this gap, this study presents a systematic literature review (SLR) of AI-driven software testing in DevOps environments. Through a structured and reproducible methodology that assessed study quality, 31 articles were selected from Scopus and Web of Science. The SLR evaluates the level of autonomy achieved by existing approaches and examines their integration within continuous integration and continuous delivery (CI/CD) pipelines. Within the journal-based corpus captured by the predefined review protocol, 27 of the 31 articles (87%) operate at task-level assistance, 3 (10%) demonstrate stage-level integration, only 1 (3%) exhibits partially agentic behavior within a bounded domain, and none demonstrates lifecycle-wide, goal-driven orchestration. Two additional shortcomings emerge from the literature: the absence of standardized criteria for evaluating testing autonomy and the limited empirical validation of proposed approaches in operational CI/CD environments. Based on these findings, this study introduces a four-level autonomy taxonomy for AI-driven testing and outlines the architectural, operational, and governance conditions required for future Agentic TestOps systems, understood as goal-driven and self-adaptive testing systems operating across delivery pipelines. Together, these contributions help define a research agenda for advancing DevOps testing toward more autonomous testing approaches.