The rapid growth of Internet of Things (IoT) applications has increased the number of connected devices operating in shared wireless environments. IoT systems rely on sensors, actuators, embedded electronics, software, and internet connectivity to collect, exchange, and process data across sectors such as smart homes, healthcare, transportation, industrial systems, energy systems, and public infrastructure. However, the dense deployment of wireless IoT devices can create significant Electromagnetic Compatibility (EMC) challenges, especially when multiple transmitters and receivers operate in proximity or within the same or adjacent frequency bands. These conditions may lead to Electromagnetic Interference (EMI), which can degrade communication performance, reduce device reliability, and affect safety in critical environments. This study uses a regulatory review and conceptual framework development, drawing on literature synthesis, regulatory requirement extraction, analytical classification, regulatory mapping, and risk-based EMC categorisation. It identifies key EMC and EMI challenges in IoT applications, particularly in dense wireless and low-power communication environments. It analyses immunity requirements in critical sectors such as vehicular networks, healthcare, industry, and public infrastructure. It also develops a regulatory framework that defines stakeholder roles and classifies IoT connection methods, application areas, and device structures to support safer deployment and reduce interference risks.

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Abstract

The effects of annealing temperature and holding time on recrystallization and grain growth in Nb-stabilized AISI 347H austenitic stainless steel containing 0.55 wt.% Nb were investigated under isothermal conditions. Specimens were annealed at 1000°C and 1200°C for 5, 10, and 30 min. Recrystallization at 1000°C was characterized by scanning electron microscopy–electron backscatter diffraction (SEM–EBSD), while grain size was quantified by optical metallography using ImageJ software. At 1000°C, the material was approximately 65% recrystallized after 5 min and approximately 95% recrystallized after 10 min; after 30 min, the microstructure was fully recrystallized and exhibited substantial grain growth. At 1200°C, rapid transition from precipitate-controlled boundary pinning to rapid grain growth where fully recrystallized structure was obtained within the first 5 min, followed by pronounced grain coarsening with increasing holding time. The measured mean grain sizes at 1200°C were 57, 85, and 235 μm after 5, 10, and 30 min, respectively. The accelerated grain growth at 1200°C is consistent with increased grain-boundary mobility and reduced effectiveness of Nb containing precipitates as boundary-pinning agents. A power-law grain-growth model was applied to the 1200°C data, giving a growth exponent of 1.597 and predicting a grain size of approximately 162 μm after 20 min. The results demonstrate the strong influence of annealing temperature and time on recrystallization and subsequent grain-boundary migration in AISI 347H. 

Missing data are common in clinical prediction studies, yet their handling may affect not only predictive performance but also which model is judged best. This study examined the stability of classifier selection when missing data handling was changed under controlled, paired evaluation conditions. Using SUPPORT2 data from 9,105 patients, Logistic Regression, Random Forest, and XGBoost were evaluated with median, K nearest neighbor, and iterative imputation. The analysis retained natural missingness, added nested missing completely at random (MCAR) perturbations of 5%, 10%, 20%, and 30%, and included a separate 20% missing at random (MAR) condition. Repeated stratified five fold cross validation used the same patient partitions, model seeds, and artificial missingness masks across corresponding comparisons. Ranking stability was assessed through condition level winner changes, paired rank reversals, and agreement across receiver operating characteristic area under the curve (ROC AUC), Average Precision, and Brier Score. The condition level winner remained stable under natural and mild additional missingness, but became dependent on imputation at higher missingness. Winner changes occurred in 6 of 12 imputation comparisons and 9 of 15 missingness comparisons, while pairwise rank reversals occurred in about 44% of matched repeat comparisons. All three metrics selected the same winner in 8 of 18 conditions. Because competing winners were separated by small ROC AUC margins, the results indicate sensitivity of model selection rather than large performance advantages. Reporting ranking stability alongside conventional performance estimates may therefore provide a more cautious basis for comparative clinical prediction studies.

ABSTRACT

The fatigue behavior of 2024 aluminum alloy is strongly influenced by heat treatment conditions, particularly T6 and retrogression and re-ageing (RRA) processes. In this study, specimens obtained from 2024 alloy plates were solution heat-treated at 520°C /2 h and subsequently aged to the T4 condition at 120°C/24 h. All samples were then subjected to a natural ageing (shelf life) period of 15 days before undergoing retrogression at approximately 220°C/3 min, followed by re-ageing at 165°C for 2h. The heat-treated samples were evaluated using tensile testing, electrical conductivity in %IACS and high cycle fatigue testing after all proposed heat treatment cycles. The samples were further tested under high-cycle fatigue conditions. The results indicate that both tensile properties and electrical conductivity were adversely affected after the 15-day shelf life. Distinct differences in fatigue behavior were observed between the retrogression and T6 (re-ageing) treated alloys. The retrogression treatment for a short time for the samples after 15 days, exhibited an improvement of mechanical strength and electrical conductivity. However, re-aging of these samples for 165/2hr shows beak strength with decrease in electrical conductivity. Although, T6 condition provides superior strength, the Retrogression treatment offers high fatigue performance by improving crack growth resistance, making it a more suitable choice for applications requiring enhanced fatigue durability. Keywords: Retrogression and re-aging, Fatigue, Electrical conductivity, crack growth, Strength