Abstracts

Community participation in water resource management through water users associations in the Wami-Ruvu Basin, Tanzania

2026-01-01T00:00:00Z

Community participation in water resource management through water users associations in the Wami-Ruvu Basin, Tanzania Zemba, Janeth I.; Katundu, Mangasini A.; Komba, Cyril K. Community participation through Water User Associations (WUAs) is intended to support sustainable water resource management in Tanzania, yet its effectiveness remains uncertain, particularly in the Wami-Ruvu Basin. This study investigates the extent and quality of participation across five stages of water management: identification, planning, implementation, monitoring, and evaluation. Using Arnstein's Ladder of Citizen Participation and Participatory Development Theory, a cross-sectional design was applied to 385 WUA members, supported by household surveys, focus group discussions, and key informant interviews. Data were analysed using a Linear Mixed Model. Findings indicate that participation is generally low and largely symbolic rather than empowering. Engagement is highest during identification activities but declines significantly in planning, implementation, monitoring, and evaluation stages (p < 0.001). Major barriers include limited time, weak communication systems, restricted opportunities for input, and elite capture by dominant individuals. The results reveal a gap between policy expectations of participatory governance and actual practice within WUAs. The study concludes that WUAs, while structurally designed for inclusivity, do not yet facilitate meaningful or equitable participation. Strengthening institutional capacity, improving communication, and adapting interventions to local socio-cultural and economic contexts are recommended to enhance effectiveness and transform WUAs into genuine platforms for community-driven water governance systems. Full text is available at International Journal of River Basin Management

Mobile-based application for assessing and reporting social-engineering vulnerabilities in higher learning institutions:

2025-01-01T00:00:00Z

Mobile-based application for assessing and reporting social-engineering vulnerabilities in higher learning institutions: Mjema, Lucas H. This study developed a mobile-based application for assessing and reporting social-engineering vulnerabilities in Tanzanian higher learning institutions (HLIs). Social-engineering attacks such as phishing, smishing, vishing, baiting and pretexting, continue to threaten campus data security, yet existing detection and reporting mechanisms are slow and fragmented. Guided by the theories of reasoned action, protection motivation and technology acceptance, a mixedmethods approach was used: a targeted literature review, a questionnaire survey of 395 students, academic and administrative staff, and semi-structured interviews with ten cybersecurity experts informed the functional requirements. An Agile development cycle produced a bilingual Android application that integrates interactive self-assessment quizzes, instant incident-reporting forms and tailored awareness content. Results showed that 74% of respondents recognised social-engineering threats, but only 38% had received formal training, and just 32% of incidents were officially reported. Pilot deployment in four stratified institutions yielded 95% overall user satisfaction and 100% successful login/registration, demonstrating strong acceptance of the mobile solution. All ten critical system-test cases passed on first run (100% task-success, zero functional errors), the mean System-Usability-Scale score reached 85.4/100, and Locust stress-tests sustained 1000 concurrent writes with sub-250 ms latency, quantitatively confirming the app’s reliability, usability and scalability. This is an Abstract

Menstrual Cycle Effects on Mental Health Outcomes:

2024-01-01T00:00:00Z

Menstrual Cycle Effects on Mental Health Outcomes: Ara, Nelofar.; V K, Aswathy.; Kura, Sani M.; Ahmada, Halima A.; Khatoon, Nargis.; Ally, Tariq A. Menstruation causes the body to go through a cycle of changes each month. Premenstrual syndrome, or PMS, is the term used to describe the variety of symptoms that many women experience in the weeks before and during their periods. Some women do not at all suffer from PMS symptoms. However, some women’s symptoms may be crippling. We, therefore, examined the relationship between menstruation and mental health in this study and how one person can help the other. According to the current study based on a qualitative approach, 9% to 17% of women experience irritability, upset, or anxiety during their periods. Women report sleeping issues of 6% to 11%, mood swings of 9% to 16%, and bloating and pain of 9% to 16%. Between 8% and 15% of women get headaches around menstruation. Depression and period anxiety are both very typical. Premenstrual syndrome frequently includes these symptoms (PMS). Physical, emotional, and behavioural symptoms known as PMS are present during the premenstrual stage of the cycle and disappear once the period starts. In addition to food cravings, headaches, Mood swings, social withdrawal, exhaustion, and sore breasts, PMS can also cause sadness before and during periods. However, premenstrual dysphoric disorder may be to blame for significant depression before or during periods (PMDD). This is an Abstract

Machine learning-optimized titanium-based broadband absorber with high-efficiency performance across visible and infrared wavelengths

2025-01-01T00:00:00Z

Machine learning-optimized titanium-based broadband absorber with high-efficiency performance across visible and infrared wavelengths Wekalao, Jacob.; Muheki, Jonas.; Mandela, ,Ngaira.; Kabarokole, Pelluce.; Makoye, Paschal. This study introduces an advanced broadband absorber design featuring titanium-based square ring resonators on silicon dioxide substrates, optimized for superior absorption performance across visible and infrared wavelengths. The proposed absorber leverages a metal–insulator–metal configuration with a titanium resonator layer, SiO2 substrate, and tungsten ground layer, achieving over 94% absorption across the 0.7–4 μm wavelength range, with peak efficiency surpassing 99% at 2.142 μm. Unlike conventional designs relying on noble metals, the proposed absorber utilizes titanium, offering a cost-effective, thermally stable, and scalable solution suitable for high-temperature applications. The key novelty of this work lies in integrating machine learning, specifically K-Nearest Neighbour (KNN) regression, to predict and optimize the absorption characteristics, achieving R2 values of up to 0.99. This approach facilitates rapid design iterations, ensuring robust performance under varying structural and environmental conditions. Furthermore, the absorber demonstrates exceptional angular and polarization independence, maintaining high efficiency under both transverse electric (TE) and transverse magnetic (TM) polarizations. These attributes make the proposed design an innovative and versatile solution for applications in solar energy harvesting, thermal management, and broadband photonic sensing. This is an Abstract