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At IRASS Publisher, our mission is to empower authors and researchers by providing a platform for their unique perspectives. We believe in fostering creativity and promoting voices that reflect the richness of human experience.
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IRASS Publisher commits to providing open access to all its published content. Our policy ensures that research articles are freely accessible to the public without subscription fees. Authors retain copyright while allowing unrestricted distribution and reproduction in any medium, provided the original work is properly cited. By removing access barriers, IRASS aims to foster a more inclusive and collaborative scientific community.
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Latest Article
Evaluation of dosimetric indicators of routine CT scan examinations in...
5

Yene Zang Bernard Severin*, Bikoy Emmanuel Junior, Doudou Nafissatou, Ngadang Fosso Catherine Ingrid
Department of Radiology and Medical Imaging, Green Hope University / Ngaoundéré Military Hospital, Cameroon
1-4
https://doi.org/10.5281/zenodo.22667700

Radiation protection aims at an appropriate level of protection for patients, staff, populations and the environment against the adverse effects of exposure to ionizing radiation without unnecessarily limiting desirable activities that can be associated with this exposure. Objective: Evaluate the doses delivered to adult patients during routine cerebral, thoracic, abdominal and abdominopelvic computed tomography (CT) examinations performed in two health facilities in the city of Yaoundé. Methods: This was a retrospective, descriptive, cross-sectional study conducted over six months, from March to August 2025, in two healthcare facilities in Yaoundé. The records of adult patients aged 18 years and older who underwent cerebral, thoracic, abdominal, or abdominopelvic CT scans, with or without contrast injection, were included. Sociodemographic, clinical, technical, and dosimetric data were collected from registries and patient records and then analyzed using SPSS version 20. The main dosimetric indicators studied were CTDIvol and the dose-length product (DLP). Results: The sample comprised 111 examinations: 76 brain CT scans (68.5%), 16 chest CT scans (14.4%), 13 abdominal CT scans (11.7%), and 6 abdominopelvic CT scans (5.4%). Men represented 65.7% of the patients, with a male-to-female ratio of 1.9. The mean age was 45 years. The median CTDIvol was 24.75 mGy for brain CT, 11.2 mGy for chest CT, 13.6 mGy for abdominal CT, and 10.8 mGy for abdominopelvic CT. The 75th percentiles of CTDIvol were 42.15 mGy, 14.95 mGy, 37.56 mGy, and 25.22 mGy, respectively. The 75th percentiles of DLP were respectively 837, 15, 512, 65, 1808.9 and 1017.2 mGy•cm. Conclusion: The observed dose levels varied depending on the type of examination and the imaging center. Cerebral values were lower than several international reference values, while thoracic, abdominal, and abdominopelvic values were higher according to the reference values considered. The establishment of national diagnostic reference levels, combined with regular audits and the optimization of acquisition protocols, appears necessary to strengthen patient radiation protection in Cameroon.
Integrating Artificial Intelligence and IoT for Precision Farming: A R...
3

G. Swetha*1, Prof. T. Anuradha2
1*-2Department of Computer Science & Technology, Dravidan University, Kuppam
1-13
http://doi.org/10.67564/IRASSJMS.v3.i9.0186

Agriculture 4.0 marks a decisive shift from mechanized and chemically intensive farming toward a data-driven, automated, and connected model of food production. At the center of this shift lies the convergence of artificial intelligence (AI) and the Internet of Things (IoT), which together allow farms to sense, interpret, and act on field conditions with a precision that manual methods cannot match. This review synthesizes the current body of literature on AI-IoT integration in precision farming, covering the core enabling technologies — smart sensors, unmanned aerial vehicles (UAVs), geographic information systems (GIS), edge and cloud computing, and block chain-based traceability — and the layered architecture through which they combine into functioning smart-farming systems. Drawing on peer-reviewed studies published largely between 2019 and 2025, the review examines representative applications in smart irrigation, crop-disease detection, yield prediction, and supply-chain traceability, consolidates the recurring barriers reported across this literature, and provides a detailed, yearordered comparison of technique, dataset, performance metrics, and reported limitations across ten representative studies. The review concludes by outlining research gaps — particularly around affordable edge-AI models, interoperable data standards, and region-specific validation in smallholder contexts such as India — that merit attention in future work.
The Gut Microbiome as a Metabolic Organ: Biochemical and Nutritional E...
3

Udoeyop Favor1, Okorie Claribel1, Nweje-Anyalowu Paul2, Enyinnaya Blessing Oluchi3, Auwal Shehu Ali4, Banwo Faridah Mobolanle5, Shehu Muhammad Hassan6, Abdullahi Muhammad7, Usman Ibrahim8, Idakwoji Precious Adejoh*9
1Department of Medical Biochemistry, College of Medicine, Godfrey Okoye University, Ugwuomu-Nike, Enugu State, Nigeria., 2Biochemistry Programme, Department of Chemical Sciences, Faculty of Science, Clifford University, Owerrinta (Ihie campus), Abia State, Nigeria., 3Department of Public Health, Faculty of Basic Medical Sciences, Clifford University, Owerrinta, Abia State, Nigeria, 4Federal Teaching Hospital. Katsina, Katsina State, Nigeria., 5Department of Zoology, Faculty of Science, Ahmadu Bello University, Zaria, Kaduna State, Nigeria, 6Department of Biochemistry, Faculty of Life Sciences, Ahmadu Belo University, Zaria, Kaduna State, Nigeria., 7Ahmadu Belo University Distance Learning Centre and International Medical Corps, Zaria, Kaduna State, Nigeria., 8Department of Biotechnology, Modibbo Adamawa University, Yola, Adamawa State, Nigeria., 9*Department of Biochemistry, Faculty of Natural Sciences, Prince Abubakar Audu University, Anyigba, Kogi State, Nigeria
6-14
https://doi.org/10.5281/zenodo.22206897

Background: The gut microbiome functions as a dynamic metabolic system that interacts continuously with host nutrition and physiology, influencing energy balance, immune function, and disease risk. Objective: This review examines the gut microbiome as a metabolic organ, focusing on biochemical and nutritional evidence linking microbial activity to human health and disease. Methods: A narrative review approach was considered, integrating recent peer-reviewed literature on host–microbiome metabolic interactions, dietary modulation of gut microbial composition, and microbial metabolite signaling pathways. Results: Evidence indicates that gut microbes contribute to host metabolism through fermentation of dietary fibers into short-chain fatty acids, transformation of bile acids, and metabolism of amino acids and polyphenols. These microbial products act as signaling molecules influencing glucose homeostasis, lipid metabolism, inflammatory pathways, and gut barrier integrity. Dysbiosis has been associated with metabolic disorders including obesity, type 2 diabetes, non-alcoholic fatty liver disease, and cardiovascular disease. Dietary patterns rich in fiber and plant-derived compounds are consistently linked with beneficial microbial profiles and improved metabolic outcomes. Conclusion: The gut microbiome operates as a metabolically active organ that integrates dietary inputs with host biochemical pathways. Understanding these interactions provides a basis for nutritional strategies aimed at preventing and managing metabolic diseases.
School-Based Incentives and Teachers’ Commitment to Classroom Instruct...
18

Frank Ching'ali*1, Goodluck Jacob2, Edgar Nderego3
1*Nyamilama Secondary School, Kwimba, Mwanza, 2-3The Open University of Tanzania, P. O. Box 23409, Dar-es-salaam,
31-36
http://doi.org/10.67564/IRASSJAHSS.v3.i8.0264

This study examined the relationship between school-based incentives and teachers’ commitment to classroom instruction in public secondary schools in Kwimba District, Tanzania. A quantitative approach and cross-sectional survey design were employed. The target population comprised 888 teachers, of whom 88 teachers were selected using simple random samplings. Data was collected through a questionnaire and analyzed using SPSS version 27. Descriptive statistics and simple linear regression were used to analyze the data. The findings showed that recognition awards/certificates (M = 4.01), incentives for improving instruction (M = 3.95), and monetary incentives (M = 3.82) were highly rated, while leadership feedback, fair distribution, and timely incentive payments received relatively lower ratings. Teachers also demonstrated high commitment, particularly in willingness to put in extra effort (M = 4.15), sense of belonging (M = 4.08), and motivation for student success (M = 4.05). Regression results revealed a moderate positive relationship between school-based incentives and teachers’ commitment (R = 0.632), with incentives explaining 39.9% of the variation in commitment (R² = 0.399). The regression model was statistically significant, F (1,238) = 158.43, p < 0.001, while school-based incentives significantly predicted teachers’ commitment (B = 0.587, β = 0.632, t = 12.59, p < 0.001). The study concludes that well-designed school-based incentives significantly contribute to teachers’ commitment to classroom instruction. It recommends strengthening fair, transparent, timely, and diverse incentive systems alongside supportive leadership, professional development, and conducive working conditions.
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