Electricity theft comes with various disadvantages for power utilities, governments, businesses, and the general public. This continues despite the various solutions employed to detect and prevent it. Some of the disadvantages of electricity theft include revenue loss and load shedding, leading to a disruption in business operations. This study aimed to conduct a systematic literature review to identify what technology solutions have been offered to solve electricity theft and the effectiveness of those solutions by considering peer-reviewed empirical studies. The systematic literature review was undertaken following the guidelines for conducting a literature review in computer science to assess potential bias. A total of 11 journal articles published from 2012 to 2022 in SCOPUS, Science Direct, and Web of Science were analysed to reveal solutions, the type of theft addressed, and the success and limitations of the solutions. The findings show that the focus in research is channelled towards solving electricity theft in Smart Grids (SGs) and Advanced Metering Infrastructure (AMI); moreover, there is a neglect in the recent literature on finding solutions that can prevent electricity theft in countries that do not have SG and AMI installed. Although the results reported in this study are confined to the analysed research papers, the leading limitation in the selected studies, lack of real-life data for dishonest users. This study’s contribution is to show what technology solutions are prevalent in solving electricity theft in recent years and the effectiveness of such solutions.

The increasing demand for sustainable livestock products necessitates a re-evaluation of animal production and breeding practices. Contemporary breeding programs now integrate animal phenotypic behaviors due to their considerable influence on productivity, health, and welfare, which ultimately impact industry yield and economic outcomes. Monitoring animal behavior manually is challenging and subjective, especially in continuous or large-scale operations, as it is time-consuming and labor-intensive. Consequently, computer vision technology has attracted attention for its objectivity, non-invasiveness, and capacity for continuous monitoring. However, recognizing livestock behavior using computer vision remains difficult due to complex scenes and varying conditions, hindering its widespread adoption in the industry. Deep learning technology has emerged as a promising solution, mitigating some of these challenges and enhancing the recognition of livestock behaviors. This paper reviews recent advancements in computer vision methods for detecting behaviors in livestock such as cattle with an emphasis on behaviors critical for health, welfare, and productivity. It investigates the development of both traditional computer vision and deep learning techniques for image segmentation, identification, and behavior recognition. The review explores the development of research trends in livestock behavior recognition, focusing on improvements in reliable identification algorithms, the analysis of behaviors at different growth stages, the measurement of behavioral data, and the design of systems to evaluate welfare, health, growth, and development.

Software project teams (SPTs) are critical stakeholders. Their contribution in successful delivery of software projects is unquestionable. However, the empirical evidence of their importance seems to exist on paper only, as software project managers (SPMs) and scholars in the project management field ignore their individual needs, and as a result, SPTs remain the most neglected stakeholder group in the software industry. In endeavoring to address the neglect of SPTs by SPMs, the authors of this study developed a model aimed at assisting SPMs to pay due consideration to the needs of this important stakeholder group. At the heart of the model’s functionality is the Project Management Office (PMO), which intends to enforce and standardize the gathering and addressing of software project team needs and interests by SPMs. The aim of the research study is to investigate how the functions of the PMO can be applied to operationalize the enforcement and standardization of the overall function of the model. Since the study is practical-oriented, the pragmatic interpretive approach was considered a suitable methodology. Through the interpretative methodology, several appropriate functions of the PMO, such as «Project management methodology, standards, and tooling», «Monitoring and controlling project performance», «Human resource management» and «Development of project management competencies» as established from project management literature were utilized to achieve the study’s purpose. Even though the interpretation process was guided by literature, the inference was also influenced, to a certain extent, by the researcher opinion as «interested observer». Therefore, the approach presents a limitation to the study. Future studies should include the validation of the feasibility of the study’s claim in a real-world project setup.

Recent research studies indicate a gap in research regarding descriptive and diagnostic analytics across project management knowledge areas. As a result there is mismatch between the information needed by project managers for good decision making and what the current tools provide. This research study purports that a project management intelligence (PMInt) tool which is aimed at assisting project manager to make informed decisions attempts to close this gap. Furthermore, the research proposes additional functional capabilities for the PMInt tool which enables it to fully provide project managers with insightful information (such as trends and patterns of sentiments over time, duration of expressed sentiments) and not just inform managers about sentiment polarity, namely, positive or negative or neutral. The proposed improvements have been incorporated in the prototype of the PMInt tool. The preliminary test results obtained from testing the prototype indicate that the tool will provide project managers with insightful information which will assist project managers to arrive at informed decisions regarding project team members' concerns and views and hence help in stabilizing project teams.

Traffic congestion is a major challenge in resource-constrained urban environments, where number of vehicles are exceeding the capacity of existing road infrastructure. Existing models are often failing to capture the complex spatial and temporal relationships inherent in traffic data, resulting in reduced prediction accuracy, poor model transferability, and limited reliability across heterogeneous urban roads. Accurate traffic congestion prediction is therefore essential for effective traffic management. To address these challenges, this study developed a hybrid model by integrating Persistent Homology (PH) framework with an ensemble stacking method. Real-time traffic data was collected from TomTom via traffic data APIs from Maputo Maseru and Pretoria. Hybrid model achieved an overall prediction accuracy of 98%, outperforming all baseline models and attained recall scores of 93% for Maputo, 90% for Maseru and 96% for Pretoria during peak hour. While during non-peak hours, the model attained recall score of 97% for Maputo, 90% for Maseru and 90% for Pretoria. The prediction confidence levels reached 80% for Maputo, 70% for Maseru and 100% for Pretoria. PH computational analysis revealed that the Pretoria CBD road networks exhibited more persistent H0 and H1 topological features and higher death values compared to Maputo and Maseru, indicating stronger structural persistence, greater route redundancy, and more complex road network integration patterns. These resulting PH features enabled the hybrid model to better capture complex spatial-temporal traffic dynamics, improving congestion prediction performance. Future study should focus on models that are: adaptable, scalable, computationally efficient, and balance performance with practical deployability in real-world, multi-environment contexts.