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  1. 1621

    Food and nutrition security of smallholder farmers in Eastern Ethiopia: A comparative analysis of shifters and non-shifters from coffee to khat by Ahmed Mohammed Abdurehim, Jema Haji, Abdi Khalil Edriss, Kedir Jemal

    Published 2025-06-01
    “…The ordered probit model indicated that nutrition security significantly influenced by family size, shift to khat, off/non-farm activities, credit access, on-farm income, clean water, and road distance. The findings suggest that policymakers and development partners aiming to reduce food and nutrition insecurity in the study area should focus on the identified variables.…”
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    Article
  2. 1622

    Internal Control Systems and Financial Management in Uganda: A Case of Rukungiri District Local Government. by Turyamusiima, Judith

    Published 2024
    “…Pearson correlation was used to establish the relationship between the independent and dependent variables. The results of the study revealed that some district staff do not comply with the accounting and financial policies of the district, resulting in poor financial management practices. …”
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    Thesis
  3. 1623

    Use of multiple indicators multiple causes (MIMIC) method to investigate quantitative inference in socioeconomic determinants on motorcyclist stress by Iqra Mona Meilinda, Sugiarto Sugiarto, Sofyan M. Saleh, Ashfa Achmad

    Published 2025-06-01
    “…Such strategies are particularly vital in developing countries to reduce stress and improve road safety. This research provides a foundation for developing practical solutions aimed at minimizing driving stress and enhancing the well-being of motorcyclists in high-risk environments. • A MIMIC model was applied to analyze the relationship between stress variables in the time and frequency domains based on HRV data. • The model identified significant causal relationships, emphasizing the pivotal role of socioeconomic factors in influencing motorcyclists' driving stress. • The model demonstrated strong statistical performance with key indicators: chi-square = 38.749, GFI = 0.958, CFI = 0.982, AGFI = 0.893, TLI = 0.961, and RMSEA = 0.057, confirming its robustness and reliability.…”
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    Dynamic cross-domain transfer learning for driver fatigue monitoring: multi-modal sensor fusion with adaptive real-time personalizations by S. S. Aravinth, G. Muni Nagamani, Chanumolu Kiran Kumar, Ayodele Lasisi, Quadri Noorulhasan Naveed, A. Bhowmik, Wahaj Ahmad Khan

    Published 2025-05-01
    “…Abstract Driver fatigue is one of the most common causes of road accidents, which means that there is a great need for robust and adaptive monitoring systems. …”
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    Article
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