The two-phase flow in micro/mini channels is of fundamental importance for many interesting applications, such as cooling of micro-electronic components and devices by a compact heat exchanger, material processing and thin-film deposition technology, bioengineering, and biotechnology. This article discusses significant developments made in the past ten years by researchers in the fields of pool boiling and convective boiling, using water, nanofluids, and refrigerants as the working fluids. The literature's data is examined in terms of improvements and declines in the critical heat flow and nucleate boiling heat transfer.Conflicting data have been presented in the literature on the effect that nanofluids/refrigerants have on the boiling heat-transfer coefficient; however, almost all the researchers have noted an enhancement in the critical heat flux during nanofluid/refrigerant boiling. Several researchers have observed nanoparticle deposition at the heater surface, which they have related to the critical heat flux enhancement.

Objective: This study synthesizes current evidence on the role of Artificial Intelligence (AI) and, where relevant, Open Science (OS) practices in enhancing Human Resource Management (HRM) performance. It focuses on recruitment processes, ethical considerations, and employee participation. Methodology: A systematic literature review was conducted in Scopus covering the period 2019–2024, following PRISMA guidelines. The initial search yielded 1486 records. After de-duplication and screening using Rayyan, 66 studies (≈ 4.4%) met the inclusion criteria, which targeted peer-reviewed works addressing AI-supported HR decision-making. A combined content and bibliometric analysis was performed in R (Bibliometrix) to identify thematic patterns and conceptual structures. Results: Analysis revealed four thematic clusters: 1) Implementation and employee participation emphasizing human-in-the-loop approaches and effective change management; 2) ethical challenges including algorithmic bias, transparency gaps, and data privacy risks; 3) data-driven decision-making delivering higher accuracy, fewer errors, and personalized recruitment and performance assessment; 4) operational efficiency enabling faster workflows and reduced administrative workloads. AI tools consistently improved selection quality, while OS practices promoted transparency and knowledge sharing. Implications: The successful adoption of AI in HRM requires employee engagement, strong ethical safeguards, and transparent data governance. Future research should address the long-term cultural, organizational, and well-being impacts of AI integration, as well as its sustainability.