Urban traffic congestion is a major challenge in cities like Dar es Salaam. Traditional fixed-time traffic light systems fail to respond to real-time traffic changes, causing long waits, fuel wastage, and pollution. This study proposes a hybrid traffic light control model for Mwenge intersection using Long Short-Term Memory (LSTM) and Deep Q-Network (DQN) models. The LSTM predicts short-term traffic volumes, which regulate vehicle flow in the SIMULATION OF URBAN MOBILITY (SUMO) simulation environment. At the same time, the Deep Q-Network (DQN) adaptively adjusts signal phases based on real-time traffic states. Traffic data collected over 240 hours was used for model training and simulation. The LSTM achieved an average Test Mean Absolute Error (MAE) of 3 vehicles/min across all directions, indicating accurate traffic prediction. The Deep Q-Network (DQN) improved intersection performance with Average Wait Time (21.6s), Queue Length (9.2 vehicles), and Throughput (888 vehicles), outperforming the traditional model (38.4s, 17.1 vehicles, and 712 vehicles, respectively). Overall, the hybrid LSTM-DQN model reduced wait times by 43.8%, queue lengths by 46.2%, and increased throughput by 25%. The proposed approach offers an adaptive, cost-effective solution for optimising traffic signals in resource-constrained cities like Dar es Salaam, supporting smarter, more sustainable urban mobility. Policymakers are encouraged to adopt this model for busy intersections in Tanzania.

This study investigates the utilization of Subscribed Scholarly Resources in Tanzanian higher education libraries, emphasizing the challenges and factors influencing their adoption. The research combines quantitative surveys and qualitative case studies using a convergent parallel mixed-methods design. A sample of 353 master's students and 18 library staff from Tanzanian higher education institutions were surveyed, and stratified random sampling was used for sample selection. Data were collected primarily through questionnaires and interviews. Findings reveal that while general awareness of Subscribed Scholarly Resources is moderately high, it remains superficial mainly, lacking the critical depth needed for sustained academic integration. Key factors influencing Subscribed Scholarly Resources usage include usability, adaptability, content trustworthiness, and intention to reuse, with technical infrastructure, training gaps, and institutional culture posing significant barriers. Regression analysis confirms that awareness, accessibility, and satisfaction are major predictors of usage benefits. Moreover, inadequate advocacy, poor ICT environments, low policy clarity, and fragile funding structures were identified as persistent impediments. The study concludes that isolated interventions are insufficient; a coherent institutional strategy integrating awareness campaigns, infrastructure development, capacity-building, and legal frameworks is vital. Recommendations target institutional reform, national policy alignment, and donor-supported sustainable funding to transform into indispensable academic infrastructures supporting research, teaching, and innovation.

This study analyzes the performance of a network, specifically on advanced encryption standard and Triple data encryption standard when used with secure hash algorithm and Message digest-5 hashing algorithms, when the communication between two communicating nodes is aided by virtual private network connectivity. The study involved two cases; one with AES and the other with 3DES at which each case had a set of two test scenarios which simulated the performance of the selected protocols to simulate the intended scenarios. The simulation scenarios were configuring AES256 with SHA256, AES256 with MD5 and 3DES with SHA256 as well as 3DES with MD5. The simulations carried out measured the latency that impacted the VPN network. Data was collected using network simulation tool “the Ping tool” in an Ubuntu 20.0.1 environment. It is founded that the hashing algorithm SHA256 experienced high Latency compared to MD5 which is contributed by the capacity of processing the hash value by SHA256. Sha256 Produces a hash value that is 266 bits long while MD5 produces a hash value that is 128 bits long, the longer the hash value the longer time is taken to process it. This causes more latency in the network