Past Issue [Vol. 11, No. 03] [March 2025]

With the increasing adoption of microservices architecture and the distributed nature of modern projects, the role of a technical lead has become particularly significant. This study examines key aspects related to service coordination, adherence to architectural standards, and the implementation of Agile management methodologies (Agile, Scrum, Kanban) within development teams. Based on an analysis of theoretical sources and case studies of large-scale projects, mentorship methods that facilitate the adoption of new tools and practices are explored, along with strategies for enhancing specialist motivation. It is demonstrated that a leader who possesses not only technical expertise but also strong communication skills contributes to shaping a unified product vision and improving interaction between autonomous microservices modules. Special attention is given to the distribution of responsibility within teams and the psychological factors affecting productivity, from managing change to fostering a conducive learning environment. The proposed practical recommendations aim to mitigate risks associated with system scaling and enhance the overall quality of the final product. This study is intended for project managers, technical leads, and professionals working with microservices systems and Agile management approaches.
Keywords: microservices architecture, technical lead, Agile methodologies, mentorship, distributed teams, architectural coordination, DevOps, motivation.

This paper presents a comprehensive investigation into cloud-based Manufacturing Execution Systems (MES) and their role in real-time production optimization within the broader context of Industry 4.0. Drawing upon recent advancements in advanced planning and scheduling (APS) technology, the study addresses the limitations of traditional on-premises MES, particularly those linked to hierarchical automation pyramids and fragmented data flows. By consolidating theoretical frameworks and empirical findings from case studies in the automotive and electronics sectors, the paper highlights how cloud-based platforms facilitate dynamic scheduling, reduce cycle times, and enhance overall equipment effectiveness (OEE). Key enablers include the integration of Internet of Things (IoT) devices, modular microservices architectures, and standardized communication protocols—capable of delivering actionable insights to stakeholders in near real-time. Emphasis is also placed on practical deployment considerations, covering gradual (phase-based) versus comprehensive (big-bang) implementation strategies, data security, and organizational readiness. The research concludes with a set of recommendations for enterprises of varying scales to maximize the benefits of cloud-driven MES/APS solutions while mitigating operational, security, and cost-related risks. In doing so, it contributes to an emerging body of knowledge on the design and execution of agile, data-centric production systems that align with contemporary market demands for responsiveness and flexibility.
Keywords: Cloud-based MES, Advanced Planning and Scheduling, Industry 4.0, Real-time Production Optimization, IoT Integration, Data-driven Manufacturing

Artificial Intelligence (AI) is redefining the paradigm of software test automation, offering innovative capabilities that transcend traditional script-based approaches. This article provides an in-depth exploration of AI-powered testing by synthesizing two key sources—(1) a systematic review of 55 AI-assisted test automation tools and (2) empirical evaluations of real-world applications in finance and healthcare. The study highlights major features of AI-driven solutions, such as self-healing scripts, AI-based test generation, and visual AI testing. Empirical evidence suggests that these innovations significantly enhance test coverage, reduce maintenance effort, and improve defect detection rates. Nevertheless, AI-driven testing faces challenges including overgeneration of test cases, limited domain context, and false positives in visual checks. Looking ahead, advances in large language models, deeper predictive analytics, and a ―human-in-the-loop‖ model are anticipated to mitigate these constraints, paving the way for more intelligent, context-aware, and trustworthy AI solutions.
Keywords: Artificial Intelligence, Test Automation, Self-Healing Scripts, Visual AI, Predictive Analytics, Human-in-the-Loop.

The purpose of this research was to enhance solar output efficiency of a solar panel through an automated cleaning design, with specific focus to a rural set up. The panel’s efficiency was measured first without dust particles on clear days. The results were then compared to those measured on dusty conditions. The panel’s efficiencies were recorded. Air quality sensor GP2Y1014AUOF, Class A dust particles measuring device PCE-MPC10; Microcontroller, software designs and other Hardware systems were used. Results indicated a clear correlation between dust accumulation and decreased power output, underscoring the necessity of automated cleaning. Lessons learnt emphasise the importance of precise sensor calibration and the efficacy of our cleaning mechanism. The developed solution holds particular significance amid the global shift towards renewable energy, catering to both urban and rural communities. The system represents a significant step towards realising smart rural villages where solar PV arrays need to consistently operate at peak efficiency so as to contribute to long-term sustainability. This innovation aligns with the broader objectives of fostering technology-enabled research and innovation for sustainable development, particularly in ICT4D activities. Research limitations were use of some equipment that are not rugged.
Keywords: Smart Village, Renewable Energy, Sustainability, Efficiency

Modern digital technologies are significantly transforming construction project management by increasing efficiency, reducing costs, and minimizing project risks. Building Information Modeling (BIM) plays a crucial role in integrating data throughout the entire lifecycle of a construction project. The combination of BIM with artificial intelligence (AI), the Internet of Things (IoT), and cloud-based platforms enhances the processes of design, construction, and building operation. However, the implementation of digital solutions faces several challenges, including high costs, the need for data standardization, and a shortage of skilled professionals. This paper analyzes the impact of BIM and other digital technologies on construction project management, highlighting their advantages, challenges, and future.
Keywords: BIM (Building Information Modeling), construction digitalization, AI (artificial intelligence), IoT (Internet of Things), project management, construction technologies.