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Quarrying may provide rural cash income while also involving occupational, environmental, and economic risks. This study examines whether the characteristics associated with reported quarry participation differ from those associated with reported positive monthly quarry-related income in Kinyenze and Lugono villages, Mvomero District, Tanzania. The survey included 142 households; 137 provided direct participation responses, and 8 of the 87 participants had no recorded income. The questionnaire recorded an estimated amount in Tanzanian shillings; the analysis treats it as self-reported monthly quarry-related income, although the recall period, costs, and net profit were not separately documented. A two-part strategy estimates participation with Probit and the log of observed positive income with robust ordinary least squares in separate equations. Education and farming experience were negatively associated with participation. Farming experience was negatively associated with income among participants with observed positive amounts; estimates for age and land access were imprecise, and the adjusted village contrast was not statistically distinguishable from zero. The small cross-sectional sample and missing income responses limit precision and generalisability. These are adjusted associations, not causal effects or measures of welfare. Future surveys should define the income recall period, document sampling and consent procedures, and record revenues, costs, labour inputs, and seasonality separately.

Quarrying can contribute to rural cash income, but participation is self-selected and expenditure comparisons do not measure all livelihood costs. This study examined the association between directly reported quarry participation and household consumption using the June 2026 survey in Kinyenze and Lugono villages, Mvomero District, Tanzania. Of 142 household records, 137 contained direct participation responses; the primary analytic sample comprised 86 participants and 50 nonparticipants with observed food expenditure. Reported expenditure components were aggregated and divided by household size; blank non-food components were coded as zero. Augmented inverse-probability weighting (AIPW) combined participation and outcome models adjusting for age, sex, marital status, education, household size, birthplace and village. The adjusted consumption difference was TZS 11,243 per person per month (95% percentile bootstrap confidence interval: TZS 549–20,764; normal-approximation p = 0.022). The food-expenditure estimate was positive, whereas log consumption and the asset/access index were imprecise. Sensitivity point estimates remained positive, but the nonlinear specification’s percentile interval included zero. Residual covariate imbalance, a weighted control effective sample size of 30.3, uncertain expenditure reporting and limited bootstrap replications qualify the findings. AIPW does not establish temporal order or eliminate unmeasured confounding. The results are adjusted cross-sectional associations, not causal effects or evidence of comprehensive welfare improvement. Longitudinal research with documented sampling, complete expenditure measurement and direct assessment of occupational and environmental costs is needed.

Tanzania’s improved primary curriculum for 2023 emphasizes learner participation, practical activities, continuous assessment, and contemporary skills, but schools vary in staffing, materials, and infrastructure. This study examines how teachers in Gairo District, Morogoro Region, experience and manage curriculum implementation, focusing on workload, professional learning, resource and curricular alignment, adaptation, morale, and perceived benefits. This qualitative secondary analysis covers 12 unique semi-structured teacher-interview records from six schools, five subjects, and Grades 3 and 4. Hybrid thematic analysis combined deductive coding of workload, alignment, learning, coping, morale, and fatigue with inductive coding of workload mechanisms and negative cases. Teachers described cumulative work: dense timetables, large or combined classes, assessment and examination duties, remedial teaching, substitute-material preparation, and learning during implementation. One teacher reported about 70 teaching periods weekly; this is an individual account, not a verified timetable or district estimate. Uneven formal preparation made peer exchange and school-based learning important. Improvisation, grouping, borrowing resources, using personal devices, and extra time helped sustain instruction, but could shift implementation costs onto teachers. Evidence of curricular layering was limited: one teacher described a competency in current planning documents but absent from new books and potentially examinable, prompting use of an older text; another described studying difficult Grade 7 content associated with the former curriculum. The records do not establish that two complete curricula were generally delivered. No participant directly linked exhaustion, cynicism, resistance, or disengagement to repeated reform. The corpus supports implementation strain and some morale vulnerability, but does not demonstrate reform fatigue. As a bounded secondary analysis without a pre-reform baseline, it cannot estimate prevalence or attribute longstanding capacity constraints to the reform. Findings point to alignment between documents and assessments, protected professional learning, workload-sensitive planning, and realistic provision of materials and infrastructure.

As adversarial AI systems are constantly evolving, and new attacks are being developed, they create a serious threat for cybersecurity, especially for the detection of complex, adaptive, and unseen threats. This paper introduces a quantum augmented AI system to extract knowledge from vast amounts of cybersecurity data and to build knowledge graph dynamically for enhancing adversarial threat detection. The framework takes advantage of recent developments in quantum utility by mapping high-dimensional and complex security data into quantum Hilbert spaces using Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) to uncover hidden relationships and polymorphic attack patterns. The proposed approach leverages the ability of quantum machine learning algorithms to detect subtle adversarial behaviors, anomalous interactions, and zero-day vulnerabilities that could escape traditional machine learning defenses, by integrating data mining and knowledge graph analytics with AI. The framework offers a scalable approach to harnessing cybersecurity intelligence from large and diverse data sources and improves the security systems' strength and flexibility. The experimental analysis shows that the use of quantum-assisted models can enhance the representation of threats, pattern recognition, and proactive cyber defense, making quantum machine learning a potential direction for next-generation adversarial cyber defense solutions.

The swift development of cyber threats, especially, sophisticated and distributed attacks on software-defined networks (SDNs) have necessitated smart and proactive threat-hunting solutions. This paper suggests a Hybrid Classical-Quantum Neural Network (C-QNN) model that combines classical machine learning algorithms with the near-term quantum computing capacity to improve predictive cyber threat detection. The proposed framework uses classical data mining techniques to extract features, reduce dimensionality and preprocess traffic pattern prior to passing optimized representations to variational quantum circuits to perform more sophisticated pattern recognition. The hybrid pipeline combines classical optimization techniques and quantum embedding strategies to overcome the drawbacks of existing quantum hardware and exploit quantum-enhanced learning capabilities. Empirical analysis shows better detection accuracy and predictive capabilities in detecting complex attack patterns in high dimensional network settings. The results emphasize the prospects of hybrid quantum-classical AI systems as a viable solution to deploy scalable, intelligent cybersecurity systems without depending on full fault-tolerant quantum processors. This study helps to establish the basis of embedding quantum-enhanced analytics into the next-generation proactive systems of cyber defense.