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Participation of people from culturally and linguistically diverse (CALD) communities in public health research is often limited by challenges with recruitment, retention and second-language data collection. Consequently, people from CALD communities are at risk of their needs being marginalised in public health interventions. This paper presents intrinsic case analyses of two studies which were adapted to increase the cultural competence of research processes. Both cases were part of the Optimise study, a major mixed methods research study in Australia which provided evidence to inform the Victorian state government’s decision-making about COVID-19 public health measures. Case study 1 involved the core Optimise longitudinal cohort study and Case study 2 was the CARE Victorian representative survey, an Optimise sub-study. Both case studies engaged cultural advisors and bilingual staff to adjust the survey measures and research processes to suit target CALD communities. Reflexive processes provided insights into the strengths and weaknesses of the inclusive strategies. Selected survey results are provided, demonstrating variation across CALD communities and in comparison to participants who reported speaking English at home. While in most cases a gradient of disadvantage was evident for CALD communities, some patterns were unexpected. The case studies demonstrate the challenge and value of investing in culturally competent research processes to ensure research guiding policy captures a spectrum of experiences and perspectives.

IoT devices include RFID tags, microprocessors, sensors, readers, and actuators. Their main characteristics are their limited resources and computing capabilities, which pose critical challenges to the reliability and security of their applications. Encryption is necessary for security when using these limited-resource devices, but conventional cryptographic algorithms are too heavyweight and resource-demanding to run on IoT infrastructures. This paper presents a lightweight version of AES (called LAES), which provides competitive results in terms of randomness levels and processing time, operating on GF(24). Detailed mathematical operations and proofs are presented concerning LAES rounds design fundamentals. The proposed LAES algorithm is evaluated based on its randomness, performance, and power consumption, it is then compared to other cryptographic algorithm variants, namely Present, Clefia, and AES. The design of the randomness and performance analysis is based on six measures developed with the help of the NIST test statistical suite of cryptographic applications. The performance and power consumption of LAES on a low-power, 8-bit microcontroller unit were evaluated using an Arduino Uno board. LAES was found to have competitive randomness levels, processing times, and power consumption compared to Present, Clefia, and AES.