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This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

The primary goal of university education globally is to promote research, knowledge, and innovations instrumental for national development and societal transformation. In line with this goal, East African countries, namely Kenya, Uganda, and Tanzania have invested heavily in university education in the last 60 years since independence. The evidence of this is the increasing number of both private and public universities, and the number of students joining the institutions. While the expansion of university education is to be celebrated, it should not be taken for granted that this expansion indicates development. The purpose of this research was to assess the impact of university education on regional development in the three East African countries, that is, Kenya, Uganda, and Tanzania. Data were collected through historical analyses, database searches on electronic and printed sources, and interviews. The study found out that the three African countries have formulated the goals of education toward the achievement of development and that large budgets have been committed to educational expansion in the university in recent years. However, the impact of the universities in the development of the region expected through research and innovation is minimal because of major challenges that include expansion of access without adequate government funding; compromises quality; graduate unemployability and political interference; students’ unrest and strikes; weaknesses in earlier levels of education; regional, gender, and class inequalities; and misalignment of education, development, and contextual strengths. Proposed future strategies included the calls for governments to enhance regional consultations on development and education, the need for specialization in programs rather than duplication, enhancement of international collaborations and networking, rethinking the role of education in development within given contextual and environmental realities, and good governance and adequate funding of education.

Increased emphasis on offering quality education underscores the need for developing a rigorous process for assessing academic programs in higher education. In this chapter, we develop a practical and rigorous framework for comprehensive assessment of academic programs. This framework generates in-depth communication between the academic departments and the university administration. It provides a useful tool for advancing the university mission, setting priorities, allocating resources, and identifying future areas of potential growth. This data-driven framework covers a wide range of qualitative and quantitative variables. To ensure a smooth and efficient implementation of the assessment process we present the critical stages in the development of a successful program assessment framework − from determining the assessment criteria, establishing the organizational climate, appointing the assessment committee, preparing program self-studies, to collecting and analyzing data. We present real examples from the author’s home institution to illustrate and support the reader’s understanding of the framework.

The participation of women in contemporary non-state armed groups is an undeniable fact. In recent years, research in international politics has provided a more complex view of both the scale and nature of their contributions, as well as why women choose to engage in political violence – when they do indeed choose to do so. This chapter offers an overview of the literature in this area, while expanding the discussion towards new and emerging questions. In particular, the chapter seeks to engage a growing literature on the legal and social aspects of women's involvement in armed conflict. Discussing women's roles as perpetrators of atrocity and war crimes – including as agents of torture, sexual violence and genocide – it addresses concerns about the meaningful integration of gender into post-conflict and transitional justice processes. This includes discussions about agency and the problematic entanglement of victim/perpetrator narratives in the cases of female ex-combatants.

Prior learning and prior knowledge are among the most dependable and consistent factors in predicting students’ success (Richardson et al., 2012). Although for UK Higher Education, the traditional A-level (advanced level qualification) remains the principal qualification students use to gain entry to University, there has been a small but significant rise in alternative qualifications, such as the International Baccalaureate (IB) and vocational qualifications such as that from the Business and Technology Education Council (BTEC). The multidisciplinary nature of Psychology means students enter the degree program with a range of qualifications in differing topic areas. The current case study aimed to assess if science qualifications aided student success in the University of Liverpool’s Psychology course. Ordinal regression and correlations were used to examine the impact of prior qualifications on three first-year cohort module scores (Psychobiology, Social psychology, and Research Methods) and the overall degree mark across three cohorts of Psychology students (n = 1,072). University entry grades showed a significant overall and subject specific effect of scientific prior knowledge. However, the effects of previous qualifications were not cumulative and did not persist beyond the first year of study. These findings were strongest for Chemistry in the Psychobiology module suggesting that scientific literacy – the understanding of scientific concepts, phenomena, and processes, as well as an individual’s ability to apply such knowledge to new or non-scientific situations (Schleicher, 2019) – rather than domain-specific knowledge is driving such increase in grades. A negative relationship was seen for those holding BTEC qualifications, suggesting that vocational qualifications, specifically for this Psychology program, were of less use than academic ones, even if topic areas were similar – a finding which may also apply to other academic based courses, and warrants further study. Although the advantage of prior qualifications diminishes across the course of study, this small but distinct advantage suggests that making a science qualification a requirement for a place on a Psychology degree course would be a beneficial step for admissions tutors to consider.

Introduction: Today the ability of nations to develop and implement innovations is core for their international competitiveness. Ukraine is striving for innovation progress; however, its innovation performance is relatively low. The research problem is to find the bottlenecks, affecting Ukraine’s innovation capability.

Purpose: This study aims to research the national innovation capability profiles, based on cluster analysis, to develop an understanding of drivers and threats for the innovation capability of Ukraine.

Need of the study: The knowledge-based economy, which had already turned into one of the most efficient developmental models of the 21st century, became a key driver of international competitiveness for the leading developed countries due to their progressive structural shifts towards the growth of high-technology manufacturing and knowledge-intensive sectors. These trends are significant to capture for the sake of increasing the innovation capability of the economy of Ukraine.

Methodology: The study is based on the K-means clustering method, which is employed for identifying 10 country clusters based on the indicators of their R&D and innovation activities, which allowed us to assess the innovation capability of Ukraine in comparison with 140 countries of the world. Data selection and normalisation were based on the 2019 Global Competitiveness Report indicators.

Findings: The study showed that Ukraine’s innovation capability problems are typical for most developing countries and are prevalently connected to low R&D expenditures, patent applications, and international co-invention activities. Most countries, except for the technologically developed ones, follow the so-called ‘passive technological learning’ strategies, which usually result in low economic productivity.

Practical implications: Several innovation policy implications have been developed for the government of Ukraine based on the cluster analysis results and accounting for the problems of the national innovation system (NIS).

The son of academics Monica and Godfrey Wilson, Francis Wilson (b. 1939) was raised in a Zulu-speaking locale of rural South Africa. Despite a keen interest in history imbued by his anthropologist parents, Wilson completed his undergraduate degree in physics at the University of Cape Town (UCT) before pursuing his doctorate at Cambridge University. Fascinated by the economics of discrimination and their relationship to the Apartheid regime in South Africa, Wilson spent a year in the United States as a visiting graduate fellow at the University of Virginia’s Thomas Jefferson Center for Political Economy (TJC) in 1964.