The issue of policy changes to support teacher professional development is an important factor shaping the career trajectory, efficacy, and ultimately the success of Junior Reserve Officer Training Corps (JROTC) instructors and the performance of the secondary students they serve and whose lives they affect. Although a rich body of research associated with policies regarding teacher preparation and professional development exists, a more closely related area of research focused specifically on the policies regarding preparation and professional development of JROTC instructors is limited. This lack of research presents a unique opportunity to explore the experiences of JROTC instructors and their perspectives on policies affecting teacher preparation and professional development. This qualitative exploratory single-case study can help to advance understanding of the complexities and nuances of teacher preparation and professional development policies supporting the JROTC instructors serving in high schools across the United States and overseas. One-on-one interviews with 14 JROTC personnel who had completed required teacher preparation requirements and professional development initiatives were conducted. Data analysis revealed 11 themes. Recommendations for improving policies concerning JROTC instructor preparation and professional development, including placing greater emphasis on the unique requirements, as well as suggestions for future research, are provided.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

The Moroccan economy has undergone significant structural changes since the 1980s. Attracting Foreign Direct Investment (FDI) has been a key strategy for the country’s economic growth and development, particularly in some specific high value-added sectors, such as the automotive supply industry. This paper uses the results of a survey to examine the reasons why multinational enterprises (MNEs) in the automotive supply sector set up in Morocco. Our findings show that proximity to Europe and labor costs and skills are the most important considerations for investing in this sector in Morocco. However, some institutional issues are still of concern to these MNEs.

The principal objective of this article is to gain insight into the biases that shape decision-making in contexts of risk and uncertainty, with a particular focus on the prospect theory and its relationship with individual confidence. A sample of 376 responses to a questionnaire that is a replication of the one originally devised by Kahneman and Tversky was subjected to analysis. Firstly, the aim is to compare the results obtained with the original study. Furthermore, the Cognitive Reflection Test (CRT) will be employed to ascertain whether behavioural biases are associated with cognitive abilities. Finally, in light of the significance and contemporary relevance of the concept of overconfidence, we propose a series of questions designed to assess it, with a view to comparing the various segments of respondents and gaining insight into the profile that reflects it. The sample of respondents is divided according to gender, age group, student status, professional status as a trader, status as an occasional investor, and status as a behavioural finance expert. It can be concluded that the majority of individuals display a profile of underconfidence, and that the hypotheses formulated by Kahneman and Tversky are generally corroborated. The low frequency of overconfident individuals suggests that the results are consistent with prospect theory in all segments, despite the opposite characteristics, given the choice of the less risk-averse alternative. These findings are useful for regulators to understand how biases affect financial decision making, and for the development of financial literacy policies in the education sector.

The primary objective of this paper is to explore the impact of household policies in both Saudi Arabia and Nigeria towards achieving efficient and sustainable economic growth in the 21st century. Fundamentally, the objective of the study was sparked by the basic factors of comparison the importance of culture in international relations, challenges related to terrorism which impede adequate implementations of economic policies, trade facilitation and logistics to enhance economic growth and cross-border movement of goods and services. Systematic literature review (SLR) and content analysis (CA) were used as methodological approaches of the paper. The articles explored for review were accessed using visualization of similarities (VOS) by exploring different database such as: journals, core collection of Web of Science (WOS), peer review sources and library sources. The findings demonstrated that Saudi Arabia and Nigeria have different policies regarding households in achieving sustainable economic growth. On one hand, in Saudi Arabia, the focus is on the economic burden associated with chronic non-communicable diseases (NCDs) and the out-of-pocket spending among individuals diagnosed with these diseases. In addition, the study found that households with older and more educated members, an employed head of household, higher socioeconomic status, health insurance coverage, and urban residency had significantly higher out-of-pocket expenditure in achieving sustainable economic development. On the other hand, Nigeria’s policy is centered around trade liberalization and its impact on household welfare as an integral part of sustainable economic development. The policies implemented in Saudi Arabia and Nigeria have implications for the well-being of their citizens. In Saudi Arabia, the household policies have significantly impacted the quality of life (QoL) of households, particularly those with low income, large size, male-led, urban, and with elderly heads. In Nigeria, trade liberalization policies have mixed welfare implications for households in the aspects of real income, they also induce unemployment in key sectors, such as agriculture and industry. To mitigate negative effects, it is suggested that Saudi Arabia should effectively address chronic non-communicable diseases (NCDs) among the households while Nigeria should efficiently pursue trade liberalization on a sectorial basis, focusing on sectors that do not severely undermine household welfare.