Current Issue : July-September Volume : 2026 Issue Number : 3 Articles : 5 Articles
Quantum computing is one of the research areas progressing rapidly toward practical deployment, yet the engineering of scalable and reliable quantum software remains underdeveloped. Current quantum software engineering (QSE) practices are largely toolsdriven and ad hoc that providing limited support for managing probabilistic execution, hybrid quantum–classical workflows, noise sensitivity, and hardware constraints. This study proposed a structured QSE lifecycle that integrates quantum-specific characteristics with disciplined software engineering practices and principles. The proposed lifecycle organizes development into six phases, encompassing quantum requirements engineering, formal modeling, architecture and circuit design, hybrid integration, noiseaware testing, and deployment with monitoring. Each phase is supported by explicit artifacts and quantitative criteria to enable systematic progression and iterative refinement. The QSE is validated through expert assessment and simulation-based experimentation using representative variational quantum algorithms under the realistic noise conditions. The results show improved fidelity convergence, reduced resource overhead, enhanced development stability (DS), and more reliable validation compared with unstructured workflows, demonstrating the value of lifecycle-driven engineering for quantum software systems....
As a field,web development is roughly 30 years old, and during this period, it has been transformed several times already as it has moved from static websites to dynamic web applications. Now, with the introduction of Artificial Intelligence (AI), the field is again at the cusp of a transformation as the latest AI tools might change how to develop for theweb yet again. The objective of this study is to look into this phenomenon and understand how AI is changing web development. To achieve this task, we chose to use the sequential qualitative–quantitative design method that combines interviews with a survey to validate and expand our findings from the interviews.We found that AI is used by web developers to increase their development efficiency, as even the current tools are easy to use and access, although they come with several minor downsides, including AI not being able to understand complex logic, the need for validation of AI output, and suggested code that could potentially lead to security issues. While there are clear benefits to using AI tools for web development and AI proficiency is a vital skill for web developers, there are still open questions related to the quality of code produced by AI tools....
Creating with artificial intelligence (AI) is fundamental to AI literacy through effective teaching methods and programmes. The aim is to suggest strategies for AI literacy education using LearningML software, based on machine learning, which allows users to create artificial intelligence models to recognise text and images without the need for programming knowledge. The study method is based on a systematic review of different databases that have integrated LearningML since 2020, their authors, countries, affiliations, keywords, associated resources, objectives, study methods, and conclusions, in order to determine the impact of the LearningML tool for integrating and developing AI literacy in teachers, students, and any user. The results were 48 documents that position LearningML software as a resource that can be integrated into curricula to promote AI literacy from primary education (K‐8) to university, and even for any citizen. The main conclusions position this software in STEM fields, such as medicine, which recommend this software for understanding the fundamentals of AI. This tool helps address the challenge of preparing citizens for the future and making decisions about the use of AI....
Accurate identification of dynamic parameters, specifically natural frequency and damping ratio, is critical for optimizing the disturbance rejection performance of laser level self-leveling mechanisms. However, traditional Finite Element Analysis (FEA) often struggles to quantify micro-friction damping, while contact measurement methods introduce added mass interference. To address these challenges, this paper proposes an integrated framework combining Pulse-Window Software Lock-in (PWSL) sensing with a data-driven model updating strategy. Initially, a rigid-body dynamic model theoretically predicted a natural frequency (f sim) of 2.987 Hz and a damping ratio (ζsim) of 0.1255. To acquire authentic responses, a non-contact Position Sensitive Detector (PSD) system was developed. The custom PWSL algorithm leverages the laser’s 10 kHz carrier to extract highfidelity displacement signals, effectively suppressing broadband noise despite embedded hardware limitations. Experimental results demonstrated that the measured frequency (f exp = 2.861 Hz) aligned well with predictions (4.22% error). In contrast, the measured damping ratio (ζexp = 0.1435) exceeded the simulation value by 14.34%, quantitatively revealing the energy dissipation caused by unmodeled bearing friction. Based on this disparity, the FEA model was inversely updated by introducing an equivalent friction coefficient, successfully reducing the damping prediction error to 0.97%. This study establishes a high-fidelity updated model, providing a reliable basis for the refined design of precision pendulum instruments....
Software Engineering (SE) faces simultaneous pressure from AI automation (reducing code production costs) and hardware/energy constraints (amplifying failure costs). We position that SE must redefine itself around human discernment—intent articulation, architectural control, and verification—rather than code construction. This shift introduces ’accountability collapse’ as a central risk and requires fundamental changes to research priorities, educational curricula, and industrial practices.We argue that Software Engineering, as traditionally defined around code construction and process management, is no longer sufficient. Instead, the discipline must be redefined around intent articulation, architectural control, and systematic verification. This redefinition shifts Software Engineering from a production-oriented field to one centered on human judgment under automation, with profound implications for research, practice, and education....
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