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This study aims to focus on the surface mount technology (SMT) mass production process of Sn-9Zn-2.5Bi-1.5In solder. It explores it with some components that will provide theoretical support for the industrial SMT application of Sn-Zn solder.

This study evaluates the properties of solder pastes and selects a more appropriate reflow parameter by comparing the microstructure of solder joints with different reflow soldering profile parameters. The aim is to provide an economical and reliable process for SMT production in the industry.

Solder paste wettability and solder ball testing in a nitrogen environment with an oxygen content of 3,000 ppm meet the requirements of industrial production. The printing performance of the solder paste is good and can achieve a printing rate of 100–160 mm/s. When soldering with a traditional stepped reflow soldering profile, air bubbles are generated on the surface of the solder joint, and there are many voids and defects in the solder joint. A linear reflow soldering profile reduces the residence time below the melting point of the solder paste (approximately 110 s). This reduces the time the zinc is oxidized, reducing solder joint defects. The joint strength of tin-zinc joints soldered with the optimized reflow parameters is close to that of Sn-58Bi and SAC305, with high joint strength.

This study attempts to industrialize the application of Sn-Zn solder and solves the problem that Sn-Zn solder paste is prone to be oxidized in the application and obtains the SMT process parameters suitable for Sn-9Zn-2.5Bi-1.5In solder.

Rural Australian patients continue to receive inadequate medical attention. One potential solution to this is to train Indigenous Australians to become medical doctors and return to their community to serve their people. The study aims to examine whether Indigenous medical students have a stronger intention to practice in underserved communities.

A sample of Indigenous (N = 17) and non-Indigenous students (N = 188) from a medical program in Sydney was surveyed about their medical self-concept and motivation. Confirmatory factor analysis (CFA) was conducted, group differences were tested, and correlation patterns were examined.

CFA found seven distinct factors – three medical self-concepts (affective, cognitive, and cultural competence), one motivation factor, and three work-related variables – intention to serve underserved communities (intention), understanding of Indigenous health (understanding), and work-related anxiety (anxiety). Indigenous medical students were higher in cultural competence, intention, and understanding. Both the affective and cognitive components of medical self-concept were more highly correlated with intention and understanding for Indigenous students than for non-Indigenous students.

It is important to examine medical students’ self-concepts as well as their cultural characteristics and strengths that seed success in promoting service to underserved Indigenous communities.

The findings show that Indigenous medical students tended to understand Indigenous health issues better and to be more willing to serve underserved Indigenous communities. By enhancing both the affective and cognitive components of medical self-concepts, the “home-grown” medical education program is more likely to produce medical doctors to serve underserved communities with a good understanding of Indigenous health.

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.