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. 

Feature reduction is widely used in machine learning based phishing URL detection to lower model complexity while retaining detection performance. However, conclusions about how much reduction is acceptable may depend on how training and testing data are partitioned. This study examines whether conclusions about feature reduction remain stable when evaluation changes from random stratified to domain disjoint cross validation. Experiments were conducted on URL-Phish Version 2 using Mutual Information ranking within each training fold and four predefined feature budgets, from 22 to 5 features. Logistic Regression and Random Forest were evaluated under both protocols, with PR AUC as the primary metric. PR AUC decreased as the feature budget was reduced for both classifiers under both evaluation protocols, so the qualitative conclusion about feature reduction remained consistent within this experimental setting. However, the size of the protocol gap varied across classifiers and feature budgets: it was clearest for Random Forest under the smallest feature budget, whereas the Logistic Regression gaps were less clearly separated from fold-level variability. Domain disjoint evaluation also produced greater fold-level variability. The results show that claims about compact phishing URL representations should be interpreted together with the evaluation protocol used. Future work should examine whetherthis pattern persists across additional datasets, classifiers, and domain grouping rules.

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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This study proposes a new robust blind video watermarking technique for copyright protection that combines the Fourier transform, entropy analysis, Hessenberg decomposition, and lifting wavelet transform (LWT). The host video is divided into frames, the RGB channel of each frame is then isolated, and the 2-level LWT is applied to the R channel. The FFT coefficients are divided into 4x4 non-overlapping blocks after the FFT is applied to the high-frequency sub-band of the 2-level LWT. The watermark is then hidden in the first row and first column element of the upper Hessenberg matrix after each selected block has been subjected to Hessenberg decomposition. The proposed technique is extremely invisible and resilient to attacks, as demonstrated by numerous simulated experiments. Comparisons with similar scheme show that the proposed watermarking scheme outperforms the others in terms of invisibility and robustness

• Several types of steroids are a class of medications used worldwide to treat pain. Despite significant differences in their use across various age groups, particularly among young people, including university students, steroids offer a significant health benefit when used without a prescription and with guidance from a pharmacist. 425 new Libyan students from various colleges in Gharyan University (Medicine, Pharmacy, Science, Arts, etc.). The data were collected using a structured self-designed questionnaire consisting of 32 items. Descriptive (frequencies and percentages) and inferential statistics (Cronbach's alpha coefficient) were applied to assess internal consistency, and the Chi-squared test and ANOVA. The analysis revealed that 90.4% of students do not use non-steroidal anti-inflammatory drugs for pain, 94.1% do not use them for headaches, and 94.6% do not use them without a prescription. 6.1% reported experiencing stomach pain after use, and 1.6% indicated that they needed a larger dose to achieve the same effect. The Cronbach's alpha coefficient was 0.81, indicating good internal consistency. This study found significant differences in eight question items (29.6%) between genders and in three items between age groups. Thus, students at Gharyan University have a high level of awareness regarding the safe use of non-steroidal anti-inflammatory drugs and avoiding their use without a prescription.