Digital life does not flow evenly across the calendar—instead, it fluctuates and surges in line with the rhythms of work, rest, vacation, and digital disengagement. By examining longitudinal data from the .CZ top-level domain, we demonstrate that domain registrations display pronounced seasonal patterns: they fall during the summer and December holidays, and rebound in early spring and late autumn. Using passenger air-travel and hotel-stay statistics as proxies for vacation intensity, our modeling results suggest significant associations between leisure periods and reduced registration activity. We conclude that digital infrastructures, far from being insulated from offline rhythms, are actively shaped by seasonal patterns of digital disengagement, challenging the view of digital activity as smooth and continuous. This calls for models that recognize human seasonality at the core of digital ecosystems.
This paper explores the concept of web service composition with focus on the distinction between static and dynamic approaches. It discusses how modern service-oriented architectures rely on the ability to integrate independent, modular components to achieve complex functionality and maintain adaptability within dynamic environments. The study incorporates informal participant observation (as the author's professional experience in web service development and business collaboration provides practical insights into real-world implementation challenges) and literature review. The paper further analyses multi-objective optimization techniques, including scalarization and evolutionary algorithms, as mechanisms for improving the efficiency and adaptability of dynamic composition. Two illustrative use cases are presented. The findings emphasize that the optimal composition strategy depends on contextual business and organizational factors rather than technical considerations.
Microservice architecture has become a dominant paradigm for building scalable and maintainable enterprise applications, particularly in large organizations adopting agile methodologies. Simultaneously, observability has emerged as a key requirement for maintaining Quality of Service (QoS), resilience, and efficient debugging in distributed systems. The aim of this study is to identify, categorize, and evaluate observability patterns specifically tailored for Java-based microservices built with the Spring Boot framework. To achieve this, a review of both academic and technical industry literature was conducted – though not as a formal systematic review – focusing on recurring strategies for metrics collection, logging, tracing, and alerting. The results present a systematized set of observability patterns, along with insights into their practical applications and associated trade-offs. The implications of this work suggest the need for more consistent adoption of design-level observability practices. By providing a consolidated and context-specific overview of observability strategies, this paper contributes original value to both academic discourse and industry practice in the field of microservices engineering.
The calculation of greenhouse gas emissions represents a fundamental component of environmental reporting and the assessment of a company's carbon footprint. Although the resulting emissions are typically reported in metric tons of carbon dioxide equivalent, their determination is based on the use of emission factors and global warming potential values. The utilisation of these parameters facilitates the conversion of data pertaining to energy and fuel consumption, along with other activities, into comparable greenhouse gas emission values. The objective of this article is to analyse the significance of emission factors and GWP values in quantifying greenhouse gas emissions and to elucidate their role in the context of ESG reporting requirements. The focus of this paper is twofold: firstly, to examine the principles of their use in calculating emissions and, secondly, to consider the availability of input data, the differences between individual sources and their impact on the resulting reported emission values. The article goes on to examine the relationship between emission factors and GWP values, highlighting the fact that an inappropriate choice of these parameters can significantly affect the results of environmental reporting as a whole.
The operational energy performance of residential buildings consistently falls 15–50% below design projections, a discrepancy that neither improved simulation fidelity nor tighter envelope specifications have resolved across three decades of building science research. The root cause lies not in measurement error but in the structural incompatibility between the deterministic assumptions of conventional building energy models and the stochastic reality of occupant behavior, envelope aging, and equipment drift in service. This paper argues that adaptive distributed control architectures, comprising per-zone sensor-actuator nodes operating over mesh communication networks and guided by physics-informed predictive algorithms, address this incompatibility at its source, and that thermal regulation and structural health monitoring are functionally inseparable at the system design level rather than parallel independent fields. The argument is developed through analysis of three engineering systems documented in recent applied research and patent literature, alongside a synthesis of published work in model predictive control, reinforcement learning, and structural monitoring. Empirically observed outcomes include a 37.6% reduction in HVAC energy consumption relative to conventional thermostat control, a 76% improvement in power supply reliability under distributed micro-grid management, and envelope airtightness at 0.42 ACH50, below passive house certification thresholds. The convergence of these results across independent system implementations supports the proposition that behavioral model quality, rather than hardware specification alone, constitutes the binding constraint on built environment performance.
This study aims to determine the role which the three factors perceived value, customer satisfaction and brand perceptions play in consumer trust in South Africa’s private higher education institutions. A large-scale quantitative research design used a structured questionnaire to collect data on a 5-point Likert scale from 399 alumni (376 usable responses) of South African private higher education institutions. Confirmatory factor analysis identified and tested the significance of relationships. Perceived value (β=0.14; p<0.05), Customer satisfaction (β=0.71; p<0.001) and Brand perception (β=0.14; p<0.05) all have a significant direct effect on Students Behavioral trust. Likewise, Perceived value (β=0.36; p<0.001) and Brand perception (β=0.50; p<0.001) have a significant direct effect on Customer satisfaction as a mediating variable. Customer satisfaction (β=0.71; p<0.001) has a strong significant effect on Behavioral trust. The model contributes 88.9% of the variance in behavioral trust. Management of private higher education institutions can fruitfully apply the findings to improve students’ behavioral trust in their brand, while future researchers can further refine the factors and improve the measurement model.
South African private higher education institutions (PHEIs) face several challenges when integrating artificial intelligence (AI) into their academic quality management frameworks. These challenges revolve around safeguarding academic integrity, acting ethically, and staying up to date with regulatory changes. To respond, the study takes a conceptual lens to examine how artificial intelligence can be embedded within the Quality Management System (QMS) of South African private higher education institutions. The intention is to connect everyday institutional practice with a framework that supports quality assurance while remaining sufficiently flexible to support future research and policy development. Drawing on existing theory and current scholarship, the framework demonstrates how AI can be integrated into sensitive areas, such as student assessment, personalised learning, and research. In doing so, it extends theoretical understanding while providing practical guidance for educational leaders and policymakers. The resulting framework bridges international approaches to AI integration with South Africa’s quality-assurance environment, providing a foundation for subsequent empirical testing in emerging higher-education contexts.
Work life balance is managing one’s work and non-work life. This includes managing relationships with family and friends, extracurricular activities such as sport or hobbies. Many employees are experiencing abridged quality of work life which is noticeable in the work -life imbalance. Work and personal life have difficulties, inconsistent job responsibilities and family responsibilities. This impacts on both male and females, all professionals working across all levels and industries around the world. Therefore, the objective of this study was to determine how employees achieve work life balance. This was a cross-sectional study conducted amongst metal industry workers. Results showed that majority of employees felt that they were able to balance their work and non-work life. More than a third of the participants reported that shift work seldom interfered with domestic life in the past few weeks. There was a statistically significant relationship between employees that work shifts during non- working hours. Majority of employees agreed to achieving and maintaining work-life balance. This also included spending enough time at home with the family during non-working hours. Some employees spent their time relaxing at home, watching movies, whilst a few enjoyed exercising by walking, or going to the gym.
The South African financial sector faces challenges with big data analytics (BDA), including understanding its role, the risk of replacing managerial decision-making and identifying necessary institutional competencies (ICs). Globally, BDA is crucial for performance and competitive advantage, emphasising management’s need to comprehend BDA and related ICs. Research with 10 senior management individuals highlighted five fundamental ICs for BDA implementation: leadership, business acumen, data science knowledge, new job roles and functional governance. The theoretical frameworks included resource-based theory, social cognitive theory and the diffusion of innovation theory. The findings stress the importance of enhanced leadership engagement, governance frameworks, business acumen and stronger data science roles alongside the development of new positions in BDA. Continuous development of suitable ICs is vital for improving organisational performance in the evolving BDA landscape.
This review article deals with the issue of process modelling, a fundamental tool in process optimization. The aim of the article is to provide a structured overview of the most common methods and tools used in process mapping, analysis, and optimization. The article includes a description of process model creation in clearly defined steps, followed by an explanation of the basic terminology. The article also includes a list of methods for process mapping, discussing the advantages and disadvantages of each method based on the interpreted data. Selected process plotting software tools are shown, and the reader is also introduced to the advantages and disadvantages of process optimisation.
The evolution of project management methodologies reflects the increasing complexity and diversity of modern industries. Traditional project management approaches, rooted in sequential planning and control, have long been the cornerstone of project execution. However, as industries face rapid technological change and evolving customer expectations, agile methodologies have emerged as a flexible alternative, emphasizing adaptability, collaboration, and iterative processes. Hybrid models integrate the strengths of both traditional and agile frameworks to address diverse project needs. This research investigates the comparative impacts of traditional, agile, and hybrid approaches on project success. Success is examined through a multidimensional lens, including project efficiency, team impact, customer satisfaction, business outcomes, and future preparedness. Data collected from 227 global project professionals reveals that agile and hybrid methodologies significantly outperform traditional approaches in enhancing team dynamics and preparing organizations for long-term adaptability. The findings provide actionable insights for practitioners and scholars in optimizing project management strategies across varied contexts.
Agile methodologies have fundamentally transformed software development paradigms by emphasizing adaptability, collaborative synergy, and iterative enhancement. However, their successful integration necessitates a nuanced understanding of contextual variables, intra-team dynamics, and project-specific considerations. This paper meticulously examines Agile methodologies through a technical lens, evaluating their ramifications on human resources, procedural workflows, and project execution. We delve into the intrinsic advantages and potential pitfalls, alongside strategies for risk mitigation. By offering actionable insights, this paper aims to equip project managers and organizations with the requisite tools to refine their Agile practices.
Generative Artificial Intelligence (AI) has emerged as a transformative force in the technological landscape, revolutionizing various industries and reshaping the way businesses operate. This trend shows the power of AI in transforming business and industrial processes. The advent of Generative AI, in particular, has revolutionized business operations by enabling machines to create content, predict trends, and automate complex tasks. Despite the growing adoption of AI, a notable gap exists in the literature concerning the detailed exploration of the day-to-day use of generative AI among tech entrepreneurs. Entrepreneurs, especially in the tech space, are vital drivers of innovation and economic growth. They create new products and services and push the boundaries of what is possible with technology, often leading to the development of entirely new industries. To understand how entrepreneurs adopt the use of generative AI technologies, we employed a qualitative research design. The study involved in-depth interviews with 15 tech entrepreneurs who actively use generative AI tools in their businesses. The findings reveal that generative AI significantly enhances content creation, design, software development, customer engagement, and prototyping processes. However, challenges such as quality control, originality, and the need for human oversight were also identified. The findings suggest that while generative AI offers substantial benefits, including time and cost savings, the technology also requires careful management to ensure alignment with business goals and ethical standards. These insights underscore the importance of a balanced approach that combines automation with human expertise, providing valuable guidance for tech entrepreneurs and policymakers in leveraging AI for sustainable growth and innovation.
This research explores the role of project risk management (PRM) in influencing project performance. Specifically, it investigates the adoption and diffusion of risk management practices across Brazilian industries. The study employed a survey methodology, analyzing 415 projects of varying complexity from different industrial sectors and states in Brazil. The findings indicate a significant positive correlation between the implementation of risk management practices and project success. Furthermore, the presence of a dedicated risk manager enhances this success. Methodological limitations include reliance on non-probability sampling and perception-based data collection via questionnaires. From a practical perspective, the results underscore the importance of addressing uncertainties, employing systematic risk management techniques, and cultivating a profound understanding of business environments. These factors emerge as critical success elements, necessitating the active engagement of both project and risk managers. The findings also highlight the importance of soft skills in effective risk management.
This paper offers an analysis of the transformative roles undertaken by BRICS nations, with a specific focus on South Africa, in redefining the landscape of global health governance during pandemics. Set against the backdrop of post-Cold War and post-9/11 dynamics, the study elucidates the evolution from traditional to more inclusive health security paradigms. It investigates how BRICS+ countries, including South Africa, have actively contributed to reshaping global health governance, transcending conventional institutional frameworks, especially evident in responses to crises like COVID-19, SARS, and MERS. Employing a multi-faceted approach drawing on case studies, policy analyses, and empirical evidence, this research assesses the strategies, collaborations, and policy interventions employed by South Africa within the broader BRICS+ framework to combat transnational health challenges. It examines the significance, effectiveness, limitations, and future potential of these initiatives in advancing a more adaptable and participatory global health governance architecture. Specifically, it delves into South Africa's unique role as a BRICS+ member, considering its diplomatic engagements, policy contributions, and regional leadership in fostering a collective approach to global health crises. Additionally, the paper explores the implications of these collaborative endeavors on the evolving international health landscape and highlights the imperative for sustained cooperation, innovation, and inclusivity in global health governance frameworks. Through this examination, this paper aims to provide insights into the dynamics shaping the contemporary discourse on health diplomacy and the collective response to global health challenges within the South African context and the broader BRICS+ framework.
The construction industry, like the market as a whole, is facing new challenges in the area of non-financial reporting. For 2025, all major construction companies, the largest design firms, leading developers, many manufacturers and sellers of building materials and supplies, and some real estate investors will be required to publish sustainability reports or ESG data. Much larger groups of companies will be indirectly affected. Large construction companies will also have to report data for their supply chain, which is often very complex and long. The aim of this article was to determine, based on a survey of the construction industry in the Czech Republic, 1) whether the largest construction companies in the Czech Republic publish sustainability reports (ESG), and 2) how they present this data. The results show that all surveyed construction companies publish at least some sustainability information on their websites. These are mainly certifications related to environmental protection or occupational health and safety. There is no lack of a code of ethics, a reporting channel or social responsibility. Up to three-fifths of companies publish their ESG data through their own or their parent company's annual report. Large foreign companies are one step ahead. Their sustainability reports are prepared in accordance with international GRI standards and have been reporting detailed data for several years, including GHG Protocol emissions and taxonomic indicators.
This research aims to create and apply efficient sentiment analysis methods for the Bengali language. It also aims to investigate how people in Bangladesh communicate their feelings and mental health issues on social media platforms with a particular emphasis on depression and suicidal thoughts. The process of applying deep learning models to sentiment analysis of suicidal and depressing writing in Bangla entails a few thorough stages. First a dataset of 1076 data points is created by carefully classifying data from a variety of sources including news articles, Facebook, YouTube, and any other online resources into three categories: depressive, non-depressive, and suicidal. Tokenization, stop word removal, and stemming are important preprocessing techniques that help to improve the text. The dataset is split into training and testing sets to train various algorithms. Confusion metrics are used for evaluation and LSTM has the best accuracy (92.01%). This study advances the understanding of sentiment analysis in Bengali by exploring various methodologies and addressing specific challenges in this area. The usefulness of LSTM models is notably highlighted, and it shows that deep learning may be used to achieve accurate sentiment classification. The study compares the simplicity of use of machine learning with the superior performance of deep learning in managing contextual information. The goals are to employ sentiment analysis more widely in interdisciplinary fields and to improve existing methods.
Although the decision whether to use the BIM method for a project has many factors, the purpose of this work is to describe financial benefits that comes with using the method in the field of project coordination in design phase. On the following pages it will be described how the tools of the BIM method can be used to detect collisions in comparison to performing classical coordination only using 2D coordination and visual inspection. The cost of removing the collisions will be determined for the case in which it would not have been detected during the design phase, but the collision is dealt with during construction. The difference between BIM and classical detections will be determined as the added value of using BIM method.
The integration of e-governance into project management has emerged as a transformative solution for addressing modern challenges such as inefficiencies in decision-making and suboptimal resource allocation. By leveraging advanced digital tools, including Artificial Intelligence (AI), Blockchain, and Dashboards, e-governance enhances transparency, accountability, and strategic alignment within project lifecycles. This paper explores the dual facets of e-governance—its potential benefits and its inherent risks—through a case study of prominent organizations in Dubai, namely the Dubai Police, the Road and Transport Authority (RTA), and the Dubai Health Authority (DHA). The findings highlight the significant value of integrating human expertise with advanced technologies, enabling real-time governance and improving project outcomes. Nevertheless, the challenges of system security, scalability, and cultural adaptation must be addressed for successful implementation. This research provides actionable insights for organizations transitioning toward e-governance frameworks and establishes a foundation for further exploration into its long-term implications.
Project management turnover, the departure of key project managers, is a pervasive issue with significant implications for project performance and organizational success. This research explores the multifaceted dimensions of project management turnover, focusing on its causes, timing, and impacts. A comprehensive review of literature reveals critical drivers, including organizational factors, project characteristics, and individual motivations, while emphasizing the pivotal role of turnover timing in project success. The study finds that both early and late turnover events disrupt knowledge continuity, delay deliverables, and compromise quality. Recommendations include proactive succession planning, robust knowledge transfer mechanisms, and fostering a supportive organizational culture to mitigate the adverse effects of turnover. By addressing these challenges, organizations can ensure smoother leadership transitions, sustain team morale, and enhance project outcomes.
