Abstract
As organizations increasingly transition from monolithic systems to microservices, they aim to achieve higher availability, automatic scaling, simplified infrastructure management, enhanced collaboration, and streamlined deployments. However, this migration process remains largely manual and labour-intensive. While existing literature offers various strategies for decomposing monoliths, these approaches primarily focus on architecture-level guidance, often overlooking the code-level challenges and dependencies that developers must address during the migration. This article introduces a catalogue of seven refactorings specifically designed to support the transition to a microservices architecture with a focus on handling dependencies. The catalogue provides developers with a systematic guide that consolidates refactorings identified in the literature and addresses the critical gap in systematizing the process at the code level. By offering a structured, step-by-step approach, this work simplifies the migration process and lays the groundwork for its potential automation, empowering developers to implement these changes efficiently and effectively.

Abstract

Abstract
Background: It is well known that the university period is an important stage for young adults, involving significant academic and psychosocial adjustments. Students with greater Mental Health Literacy (MHL), which is defined as the knowledge, beliefs, and skills individuals have regarding mental health and mental illness, are better able to identify difficulties, seek help, and adopt healthier coping strategies. This study aims to describe the MHL levels of undergraduate health students and identify associated factors related to academic life, mental health and psychological state. Methods: A cross-sectional, self-administered, web-based survey was conducted using a non-probability sampling strategy among undergraduate students in health-related degrees at a Portuguese higher-education institution. Data was collected using a general characterization questionnaire and the following instruments: MHL Questionnaire, Academic Life Satisfaction, Subjective Happiness Scale, Psychological Well-Being Scale (PWBS), and Depression Anxiety Stress Scale. Bivariate and linear regression analyses were employed to identify factors associated with MHL. Results: A total of 306 students (79% female, mean age = 21.6 years; 59% nursing students) participated. The median MHL score was 70 (range: 30-80). The linear regression model explained 17.5% of the variance in MHL. Higher MHL levels were associated with having the course as a first choice, holding a previous degree, reporting taking psychotropic medication use (which may reflect previous mental health service utilization), and higher levels of psychological well-being. Conclusions: This study provides evidence on factors associated with MHL among undergraduate health students, suggesting that higher MHL is associated with greater psychological well-being, highlighting the potential importance of integrating strategies to promote MHL and psychological well-being in health and nursing education. However, these findings should be interpreted with caution due to the single-institution convenience sample, potential self-selection and reporting biases, and cross-sectional design, which limits causal inferences.

Abstract
Breakthroughs in sequencing technologies led to an exponential growth of genomic data, providing novel biological insights and therapeutic applications. However, analyzing large amounts of sensitive data raises key data privacy concerns, specifically when the information is outsourced to untrusted third-party infrastructures for data storage and processing (e.g., cloud computing). We introduce Gyosa, a secure and privacy-preserving distributed genomic analysis solution. By leveraging trusted execution environments (TEEs), Gyosa allows users to confidentially delegate their GWAS analysis to untrusted infrastructures. Gyosa implements a computation partitioning scheme that reduces the computation done inside the TEEs while safeguarding the users' genomic data privacy. By integrating this security scheme in Glow, Gyosa provides a secure and distributed environment that facilitates diverse GWAS studies. The experimental evaluation validates the applicability and scalability of Gyosa, reinforcing its ability to provide enhanced security guarantees.

Abstract

Abstract
The semiconductor industry commits capital years ahead of returns while the boundary of best practice keeps moving, so locating that boundary and tracking movement relative to it is a recurring problem. Porter's productivity frontier names the boundary, and the reading adopted here is that it admits several efficient positions at once and is displaced over time, yet neither property has been observed over a long horizon at the scale of a whole industry. This study estimates the financial efficiency frontier of the global semiconductor industry with data envelopment analysis (DEA). Twenty companies holding about 78% of sector market capitalization, in five clusters, were observed over 2005 to 2025, giving 415 observations. Input-oriented models under constant and variable returns to scale were estimated on annual industry frontiers, the Malmquist index separated catch-up from frontier displacement, and cumulative change was measured against a pooled metafrontier. Productivity change came from frontier displacement rather than from catch-up, with technological change at 1.016 against efficiency change at 0.990, a decomposition that holds under every specification tested. Cumulative change of 1.063 for the industry conceals a dispersion from 1.78 for Broadcom to 0.61 for Intel, so frontier proximity and frontier advance are empirically distinct. Technical underperformance outweighs scale misalignment as the source of frontier distance in three of five clusters, and scale dominates in Foundries and IDM Logic. The study renders the two structural properties of Porter's productivity frontier observable in one industry over twenty-one years and delivers a cluster-calibrated diagnostic separating scale-driven from capability-driven disadvantage.