http://hdl.handle.net/123456789/390 2026-04-04T00:49:38Z 2026-04-04T00:49:38Z Guto, R http://hdl.handle.net/123456789/1242 2026-01-27T10:45:11Z 2025-09-16T00:00:00Z

A Framework For Institutionalizing Indigenous Knowledge Systems In Dispute Resolution Mechanism Among The Maasai Community In Kajiado County, Kenya Guto, R Disputes and conflicts are ubiquitous occurrences with both negative and positive effects on the social and economic development of societies. A huge number of cases are outstanding in the formal court systems despite the provision of alternate dispute resolution mechanisms. Owing to the presence of renowned indigenous dispute resolution mechanisms, there is a dearth in studies seeking to evaluate the positive contribution of the IDR mechanisms in dispute resolution among communities in Kenya. Therefore this study documented the indigenous dispute resolution mechanisms among the Maasai community and developed a framework in which IDR may be institutionalized and integrated into the formal dispute resolution mechanisms in Kajiado County, Kenya. The study had five–fold objectives: To investigate the existing utilization of indigenous knowledge in dispute resolution mechanisms among the Maasai community in Kajiado County, Kenya, evaluate the effectiveness of indigenous dispute resolution in relation to formal judicial mechanism in dispute resolution, analyse the role of non-state actors in influencing the use of IDR in dispute resolution mechanism, develop a framework for institutionalizing indigenous knowledge system in dispute resolution mechanisms and lastly to validate the developed framework. The study was anchored by three theories: The worldview theory, the frustration – aggression theory and the instrumentality theory. The study employed a mixed–method research design and the location of study was in Kajiado County, Kenya. The target population was 5,202 individuals who are 75 years and above including 171 NGAO leaders from four Sub-Counties in Kajiado County, Kenya. The sample size for the study was 371 individuals who were sampled using multi–stage sampling criteria and calculated using Yamane,(1967) formula. Data collection was done using the focus group discussion and questionnaires. The instruments were validated by piloting and reviewed by a panel of experts. Qualitative data was collected and analysed through thematic and narrative analysis while quantitative data was analysed descriptively. The study established that there is need to institutionalize a framework for indigenous dispute resolution into the formal dispute resolution mechanisms which should observe three main attributes: Legal recognition, capacity building and systematic documentation to enhance legitimacy, efficiency and sustainability.Therefore justice in plural societies is best shown through advanced hybridized frameworks that integrate cultural legitimacy from indigenous dispute resolutions with established enforceability from formal judicial mechanisms.The study recommended: There is need for legal recognition of IDRs, enhanced capa

2025-09-16T00:00:00Z Kamau, J http://hdl.handle.net/123456789/1235 2026-01-27T07:22:35Z 2025-06-05T00:00:00Z

Strategic Human Resource Management Practices And External Labour Mobility in Public Level Five Hospitals In Kenya Kamau, J External labour mobility of medical officers and nurses in Kenya has affected quality of health service delivery due to high number of exits and brain drain of human 16 resources for health and this has affected the image and the economic potential of the country. The specific objectives under this study were; strategic work-life balance practice, strategic human capital practice, strategic health and safety practice, and strategic career advancement opportunities practice. The moderating variable in this study was perceived organizational support. Ability Motivation Opportunity theory, Heinrichs Domino theory, Spillover Theory of work-life balance, Human Capital Theory and Psychological Contract Theory were used to explain the relationship between the variables under study. Mixed research design method was used in this research and it involved a variety of analytical methods. The target population of this study was 4,388 medical officers and nurses in the fourteen (14) public level five hospitals in Kenya, from which a sample of 353 respondents was selected using stratified random sampling method while purposive sampling technique was used to select the eight (8) public level five hospitals representing the former provinces in Kenya. Quantitative data was analyzed using both descriptive and inferential methods. The descriptive statistical tools include the frequencies, percentages, mean and standard deviation. Inferential statistical models included correlation coefficient and regression analysis to determine the relationship between the variables through SPSS version 22-computer program. The findings indicated that strategic health and safety was not a significant predictor of external labour mobility. The study recommended that the Public Service Commission, Ministry of Health and the County Public Service Boards (CPSB) should integrate and implement strategic human resource management practices into the organizations business strategy; The County Executive Committee Members in the department of health to develop a policy framework to ensure that there are flexi-work programs in place, childcare policies/facilities and holding rooms (nap pods) in the health care facilities; development of an employee assistance program; speedy approval of employees leave in a timely manner as per the prevailing regulatory frameworks; development of a training need assessment/analysis in the health facilities, sponsorship programs to the human resources for health; provision of personal protective equipment/gear and adequate working conditions to protect themselves against occupational diseases and loss of life; formation of health and safety committees in the health facilities; establishment of a clear succession management plan and career mentorship programs in the health facilities, promotion of socially interactive environments (interactional justice) and sharing of pertinent information to the staff members; provision of organizational rewards such as career advancement opportunities based on merit, paid holiday trips, recognition awards and certificates to the best performing medical officers and nurses and adoption of an amicable leadership style. Future researchers should focus on other strategic human resource management practices influencing external labour mobility especially on generational groups (Baby boomers, generational X, millennials, generation Z & Alpha) such as recruitment and selection, orientation/induction programs, voice, strategic involvement and participation, employee relations, human resource planning and reward management practice.

2025-06-05T00:00:00Z Mburu, F http://hdl.handle.net/123456789/1186 2025-11-10T11:48:58Z 2025-09-24T00:00:00Z

Study Of Some Curvature Tensors On Lorentzian Para-Kenmotsu Manifolds Mburu, F A comprehensive analysis of curvature tensors on Lorentzian Para Kenmotsu manifolds focusing on W3, W5 and W9 curvature tensors was done. The properties of these curvature tensors under various conditions on these manifolds were explored and examined as well as their geometric implications. The study also included investigations of W3-flat, W5 -flat and W9-flat Lorentzian Para Kenmotsu manifolds and their connections to η-Einstein, Einstein and special η-Einstein properties. Additionally, analysis of the Ricci operator’s behaviors on Lorentzian Para Kenmotsu manifolds under the conditions W3 · Q = 0, W5 · Q = 0, W9 · Q = 0 was conducted. Expressions for these curvature tensors while considering the condition Wi (ξX) · Wi = 0, where i=3, 5, 9 were derived. Proves to determine whether these manifolds are flat were provided. The findings of these studies enhance our understanding of the geometric properties of Lorentzian Para Kenmotsu manifolds in relation to Wi-curvature tensors.

2025-09-24T00:00:00Z Kipkorir P http://hdl.handle.net/123456789/1149 2025-06-25T08:59:33Z 2025-01-01T00:00:00Z

A Machine Learning-Based Packet Sniffer for Detection and Classification of the Denial of Service Attack Packets at The Network Layer Kipkorir P The research study was on modelling a packet sniffer utilizing machine learning techniques to identify denial of service (DOS) attack packets at the network layer of the OSI model. Cyber threats and attacks have continued to evolve in complexity and sophistication, posing significant risks to the network infrastructure and sensitive data's availability, confidentiality, and integrity. The necessity for sophisticated methods to improve network security is highlighted by the fact that conventional methods frequently fail to identify and mitigate these attacks. The overall purpose of the research study was to capture and interpret packets transmitted over a local area network to detect and capture the DOS threats within the Open Systems Interconnection Model (OSI) network layer. This layer is prone to several attacks for instance, denial-of-service, routing protocol attacks, Port scanning and enumeration, and fragmentation-based attacks. However, in this study, we delved into detecting and capturing the denial of service threats at the third layer of the OSI model in a local area network. Some examples of DOS attacks are UDP flood which sends a significant quantity UDP (User Datagram Protocol) packets to the targeted systems and thereby exhausting network resources, ICMP flood which transmits a significant quantity of Internet Control Message Protocol (ICMP) packets to overwhelm network devices, SYN flood which takes advantage of the TCP three-way hand-shake procedure by sending a lot of SYN requests without carrying out the necessary handshake, using server resources and blocking valid connections. Essential components extracted from Ethernet frames comprise TCP segments, ICMP packets, IPv4 packets, and associated flags. IPv4, a crucial protocol in Internet communication, enables routing and logical addressing, forming the Internet's backbone. The Internet Control Message Protocol (ICMP) facilitates error reporting and the interchange of operational information inside the Internet Protocol suite. There are header and data sections in a packet. Data bytes are sent plus a header that TCP added to the data to make up a TCP segment. Even though internet-based data transmission protocols have expanded, traditional network security measures are frequently insufficient to combat the dynamic environment of cyber threats that target networks used for data transfer. This deficiency emphasizes the need for innovative technologies to improve network security. Although antivirus programs, intrusion detection systems, and firewalls are crucial barriers against malicious attacks, they frequently fall short in detecting and halting more crafty attacks that evade their protection. The study sought to bridge this gap by providing an automated machine learning-based packet sniffer that can identify and categorize network risks. To address the research objectives thoroughly, experimental research methodology was used. This study employed the CICIDS2018 dataset. The LightGBM model was successfully trained and implemented for the task of detecting DoS attacks. We used the CICIDS2018 dataset, which provided labeled network traffic data containing both normal and attack (DoS) instances. The model was trained to classify traffic as either normal or a DoS attack based on various network features. The model's performance was evaluated using several metrics to demonstrate its ability to accurately detect threats at the network layer in a local area network including sensitivity, specificity, and accuracy. The AUC (Area Under the Curve) was particularly high, which indicated that the model was able to effectively differentiate between normal traffic and DoS attacks. Additionally, the F1-score, precision, and recall were balanced, suggesting that the model was capable of identifying attacks while minimizing false positives and false negatives. The model was successful in meeting its primary objective of detecting DoS attacks from network traffic. The performance metrics indicate that LightGBM is a strong candidate for the task, achieving a high AUC and a well-balanced F1-score. This suggested the model achieved good generalization ii capabilities, and it can effectively distinguish between normal traffic and DoS attack traffic in most cases. The main contribution of this work is the development of a LightGBM-based machine learning model for detecting DoS attacks using the CICIDS2018 dataset. The model’s ability to classify network traffic as normal or malicious will aid in enhancing network security by automating the detection of such attacks in LANs. The model will henceforth serve as a foundational step for building more advanced intrusion detection systems, especially for environments where DoS attacks are prevalent

2025-01-01T00:00:00Z