The growth of the Internet of Things (IoT) devices has introduced significant security challenges due to energy efficiency and limited hardware resources. This research paper presents a preliminary review of security techniques applied in IoT systems, focusing on mechanisms for authentication, encryption, and intrusion detection as well as lightweight cryptographic approaches, and device-level security capabilities like Secure Boot (SB) and Trusted Platform Module (TPM). This review highlights current trends and common vulnerabilities, aiming to provide an improved understanding of IoT security strategies and challenges.

The Goal of this paper is to review software quality in agile environments as well as software quality monitoring in agile working environments. An overview of ISO standards for different quality aspects has also been reviewed, as it proposes the international standardization scope for quality evaluation. Besides functional and non-functional aspects of software quality during the software development lifecycle, psychological factors were also considered.

In this work, a brief analysis of related work and comparation of programming languages in development of embedded systems has been presented. Focus of this paper is to show different programming languages used in various embedded systems as well as their popularity and efficiency in terms of speed and processing required data

Onboarding in software teams requires the simultaneous acquisition of technical, domain, and organisational knowledge, while the progress of new employees is often monitored through informal and “difficult-to-compare” assessments. This paper presents an integrated, metric-based model for recording and reviewing indicators specified in an onboarding plan and preliminarily examines its functional and computational feasibility using a software prototype. The research was conducted using the Design Science Research (DSR) approach, with the integrated model serving as the central research artefact incorporating selected planning and evaluation concepts from the previously developed OnMod model. The model integrates a conceptual domain model, a functional and structural specification based on the Unified Modeling Language (UML), and an operational model for measurement and score calculation. The prototype supports versioned onboarding plans, a hierarchical metric tree, four measurement specifications, two additional recording methods, periodicity, normalisation, and fixed-weight aggregation. The contribution lies in defining the links between the plan version, measurement specifications, period-specific records, and hierarchically calculated results. Demonstration and preliminary evaluation were conducted in a controlled, simulated scenario with one expert participant in a small software company. The scenario simulated two weeks of onboarding. An additional sensitivity analysis examined the effects of changes in weights and selected inputs on the same metric tree. The analytical root node score is 0.829375. For the examined endpoint combinations of local weights modified by $\pm 20\%$, with the weights of the children of each node renormalised to sum to 1, the score ranges from 0.805012 to 0.857072. The preliminary evaluation shows that the specified measurement, recording, and score aggregation procedures can be executed in the prototype within a controlled scenario.

Generative artificial intelligence (GenAI) is increasingly available to schools, but evidence on GenAI acceptance and intention to use it among subject teachers in rural primary education remains limited. This study examined behavioral intention to use GenAI among 202 subject teachers teaching Grades 5–8 in rural Serbian primary schools through an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework and semi-structured interviews with 20 survey participants. The initial composite-score regression explained 10.5% of Behavioral Intention variance (R² = 0.105). Performance Expectancy was marginally positive (β = 0.136, p = 0.049) and Facilitating Conditions showed a nominal, unadjusted negative association (β = −0.187, p = 0.009); however, the Performance Expectancy coefficient was no longer nominally significant with HC3 robust standard errors (p = 0.054), and neither coefficient survived Holm correction applied to the HC3 hypothesis-test p-values. Prior GenAI use (83.7%) did not materially change the model. A post hoc PLS-SEM sensitivity analysis, using theory-guided indicator refinement and 10,000 bootstrap resamples, showed composite reliability and convergent-validity indices of CR = 0.820–0.928 and AVE = 0.560–0.865, with low HTMT values (maximum = 0.482). Unadjusted bootstrap intervals suggested a positive Effort Expectancy path and a negative Facilitating Conditions path, but none of the eight hypothesized paths survived family-wise multiplicity correction. Facilitating Conditions also showed severe floor restriction (M = 1.34, maximum = 2.50/5; 52.0% at the minimum), while interviews described infrastructure as a barrier—a qualitative pattern that diverged from the negative coefficient. The findings therefore indicate limited and measurement-sensitive support for transferring an extended UTAUT model to this context and emphasize the need for stronger prospective measurement validation, context-specific constructs, and longitudinal research.

This paper proposes a practical, program-level framework for integrating generative AI into engineering education while safeguarding academic integrity and improving learning outcomes and retention. Drawing on recent evidence from engineering and STEM education, the framework aligns curriculum outcomes with ABET expectations, embeds AI-supported active learning (e.g., retrieval practice, automated feedback), and redesigns assessment to prioritize process evidence and oral/interactive defenses. We describe governance mechanisms, recommended tool chains, and analytics for continuous improvement. A comparative table and implementation roadmap illustrate how departments can sequence adoption across courses and years of study. The approach is intended for resource-constrained programs seeking measurable, ethically grounded gains in performance, persistence, and fairness.