Purpose: This research seeks to examine identity tensions in IT project managers involved in developing Agile Information Systems Development (ISD). It looks at the transition from the hierarchical functional PM structures to the non-hierarchical decentralised decision-making forms of Agile. These tensions relate to how the organisational tensions affected the roles of project managers. The third study concerns role tensions leading to conflicts and identity work about traditional and agile paradigms. Materials and Methods: The study employs a qualitative research technique involving semi-structured interviews among the participants, the IT project managers. It gathers data on the project managers and explores the nature of their conflicts and how managers resolve these conflicts regarding tensions concerning compliance and cooperation, role definition, and leadership adaptations in the Agile context. Findings: The research identifies three primary areas of tension for IT project managers; Lack of coordination between attempts to enforce bureaucracy (laws and policies) and coordination (work incorporation). Ambiguity of the roles played in various organisational settings Failure of the organisational structures to provide clear roles that individuals play during their operations. Agile leadership challenges moving from decision-making authority to a more coordinated leadership position. Organisational officials and project managers use measures including endorsing peripheral positions, engaging peers and enrolling for organisational development to deal with such opposing forces. Importance is placed on the organisational climate, resources and training within the study to focus on organisation adaptability and identity work within Agile frameworks. Implications to Theory, Practice and Policy: The study suggests practical measures for management organisations to improve individual and organisational outcomes in Agile ISD settings; Develop suitable training courses as a measure to help people adapt to the Agile type with effectiveness. Make networks and peer support systems for project managers. This increases role conflict, which should be minimised by establishing a clear structure of responsibilities and tasks within the company. The findings also advance theory by discussing how professional identity develops and the psychological and social changes that occur when transitioning to Agile culture.

Purpose: In today's world, data is gathered without any particular objective in mind; every action taken by a computer, or a human being is documented, and the data is studied in the future, if it is considered important to do so. The data will be subjected to several steps for analysis by a variety of parties, which raises the issue of trust in this context. There is a risk that the organizations involved in the analytical stages may abuse the data, which might contain private or sensitive information. Consequently, data privacy considerations should be carefully considered at this time. Methodology/Findings: A definition of "data privacy" is the practice of limiting access to information according to how important it is. People are usually very comfortable giving out their names to strangers, but they'll wait to give out their mobile phone numbers until they're more familiar with the individual. In this era of digital technology, important personal information is often the target of individuals' efforts to protect their data. From a business's point of view, data privacy encompasses more than just employees' and consumers' private information. Data privacy issues are often believed to be a barrier to the widespread adoption of AI and ML-driven technology. The reason for this is that ML can only be trained and tested on very large data sets. Implications to Theory, Practice and Policy: Imagine a world where trust is impossible to establish; here is where Blockchain technology might be useful. Blockchain uses the data anonymously. In this study, we provide a solution that ensures data security by integrating Blockchain technology with machine learning (Alfandi et al., 2020).

Purpose: The purpose of this study is to address the limited use of transfer learning techniques in radio frequency machine learning and to propose a customized taxonomy for radio frequency applications. The aim is to enable performance gains, improved generalization, and cost-effective training data solutions in this specific domain.
 Methodology: The research design employed in this study involves a comprehensive review of existing literature on transfer learning in radio frequency machine learning. The researchers collected relevant papers from reputable sources and analyzed them to identify patterns, trends, and insights. The method of data collection primarily relied on examining and synthesizing existing literature. Data analysis involved identifying key findings and developing a customized taxonomy for radio frequency applications.
 Findings: The study's findings highlight the limited utilization of transfer learning techniques in radio frequency machine learning. While transfer learning has shown significant performance improvements in computer vision and natural language processing, its potential in the wireless communications domain has yet to be fully explored. The customized taxonomy proposed in this study provides a consistent framework for analyzing and comparing existing and future efforts in this field.
 Recommendations: Based on the findings, the study recommends further research and experimentation to explore the potential of transfer learning techniques in radio frequency machine learning. This includes investigating performance gains, improving generalization capabilities, and addressing concerns related to training data costs. Additionally, collaborations between researchers and practitioners in the field are encouraged to facilitate knowledge exchange and foster innovation. Practice: To practitioners in the field of radio frequency machine learning, this study emphasizes the potential benefits of incorporating transfer learning techniques. It encourages practitioners to explore the application of transfer learning in their specific domain, leveraging prior knowledge to enhance performance and address training data challenges. It also highlights the importance of staying informed about the latest developments and collaborating with experts in the field. Policy: To policy makers, the study underscores the need for supportive policies that promote research and development in radio frequency machine learning. It recommends creating an environment that fosters innovation, encourages collaborations between academia and industry, and provides resources and incentives for further exploration of transfer learning techniques. Policy makers should consider the potential impact of transfer learning on the wireless communications industry and support initiatives that enhance its adoption and implementation.

Introduction: Bring your own device (BYOD) is a paradigm where employees use personal devices for organizational related activities. Various benefits accrued by both employees and organization. However, BYOD leads to risks and threats during their usage.
 Purpose: The paper aimed at exploring benefits, risks, and suggested controls based on a systematic review of literature.
 Methodology: A systematic review methodology was adopted for the study. A search using keywords was conducted to select peer-reviewed journal papers from 2010 to 2020 in ACM Digital Library, Emerald Insight, IEEE Explorer, Science Direct, and Taylor and Francis. Inclusion and exclusion criteria was applied, followed by quality appraisal on the selected articles, and then the data was extracted.
 Results: According to the search results, BYOD research is on the rise. Benefits, risks, and controls associated with BYOD were also identified based on a layered approach. Findings indicate that user management is the weakest layer.
 Originality/value: This paper adds to previous research on BYOD practices by highlighting key risks and suggesting practices that organizations can use to manage security and privacy risks in BYOD environments using a layered approach.

Objective: Radon levels in structures (interior environment) were measured in many dwellings and hostels in Ayeduase Township, Ashanti Region, Ghana. The purpose of this study was to quantify radon toxicity in the Ayeduase community, which houses approximately half of the students at Kwame Nkrumah University of Science and Technology (KNUST), Kumasi, and to determine the risk levels for individuals (categorized as infants, children, and adults) living in the community's various suburbs. Materials and Methods: Households in Ayeduase had an average of four people per room. The radon in air concentration was measured with a continuous radon monitor using continuous active sampling approach. Multiple measurements were taken at tight space-time intervals over a period of 24 hours. The Continuous Radon Monitor was installed in dwelling rooms in zones where the monitor could record fresh air entering the room. The yearly effective dose was computed using the Mondal 3 Software. Results: The continuous sample air radon concentration in Ayeduase Township was found to be in the range of 8.510 0.05607 Bq/m3 to 11.100 0.6379 Bq/m3. The manually computed effective annual dose rate for indoor air radon was 0.3494 mSv/year to 0.4558 mSv/year for babies, 0.0683 mSv/year to 0.0891 mSv/year for children, and 0.0559 mSv/year to 0.0729 mSv/year for adults. The comparative results from the Mondal 3 Software showed that the effective annual dose rate for indoor air ranged from 0.32 mSv/year to 0.37 mSv/year, with an average of 0.36 mSv/year for infants, 0.11 mSv/year to 0.14 mSv/year, with an average of 0.12 mSv/year for children, and 0.084 mSv/year to 0.097 mSv/year, with an average of 0.94 mSv/year for adults. Conclusion: Ayeduase's radon toxicity profile in terms of air showed distinct features. The maximum value of indoor air radon concentration level determined was lower than the global average value of 40 Bq/m3 of indoor radon concentration level and also lower than the reference level of 100 Bq/m3. In Ayeduase, the estimated average effective dosage over a year for babies, children, and adults were lower than the 1.2 mSv/year indicated by ICRP Publication 126. Recommendation: Radon toxicity due to water and soil sources are required to access the total toxicity levels in the township. Reliable evaluation of the potential for human exposure to radon depends in part on the reliability of supporting analytical data from environmental samples and biological specimens is highly recommended, as well as, concentrations of radon in unpolluted atmospheres and in pristine surface waters are typically within the limits of current analytical methods.

Purpose: The main target of this paper was to examine the significance of Artificial Intelligence and Machine Learning and their effect on the transportation business.
 Methodology: This hypothesis was a survey of the significant machine learning calculations and their applications in the field of big data. This paper try to attempt to exhibit the need to remove significant data from the huge measure of enormous information as traffic data available in this day and age and recorded diverse machine learning strategies that can be utilized to separate this information needed to encourage better dynamic for transportation applications.
 Findings: This paper present an investigation of the different Artificial Intelligence (AI) methods that have been actualized to improve Intelligent Transportation Systems (ITS). Specifically, this paper assembled them into three main territories relying upon the main field where they were applied: Vehicle control, Traffic control and prediction, and Road security and accident prediction. The aftereffects of this examination uncover that the mix of various AI methodologies is by all accounts promising, particularly to oversee and investigate the huge measure of data created in transportation