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Research Article
Improved Forage as Supplemental Feed Source and Its Utilization System in Yem Special Woreda of Central Ethiopia
Kedir Adem*
Issue:
Volume 11, Issue 2, June 2025
Pages:
20-27
Received:
1 January 2025
Accepted:
10 March 2025
Published:
29 April 2025
Abstract: This study was conducted to assess the improved forage production and Utilization. For this study, three kebeles were selected purposively based on their livestock potential and 160 households were selected from selected kebeles randomly. The major feed resource in the study area was crop residue and pasture land grazing. The dominant forage species adopted in the area were Desho and elephant grass. Primary problem for livestock production was the shortage of feed resources. Majority (66%) of the households were in the active productive age (31-45) about 60% of household heads were literate (primary school and above). The average land cultivated per household in the study area was 0.25 hectares. Crop production was the principal source of cash income in the region, followed by cattle production in second place and sheep production in third. Almost all households in the study area had experience in cultivating improved forage, particularly elephant and desho grasses. The main challenges related to livestock production identified in the area were primarily diseases, ranked first, followed by feed shortages in second place, and water shortages in third, along with issues related to poor breed performance to some extent. Among the main feed sources identified, grazing, crop residues, and desho grass were ranked first, second, and third, respectively, in the study area.
Abstract: This study was conducted to assess the improved forage production and Utilization. For this study, three kebeles were selected purposively based on their livestock potential and 160 households were selected from selected kebeles randomly. The major feed resource in the study area was crop residue and pasture land grazing. The dominant forage specie...
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Research Article
Numerical Inference on the Inverse Weibull Model Parameters Based on Dual Generalized Hybrid Progressive Censoring Data
Mohamed Maswadah*,
Alia A. Alkhathami
Issue:
Volume 11, Issue 2, June 2025
Pages:
28-44
Received:
23 January 2025
Accepted:
10 February 2025
Published:
9 May 2025
Abstract: In parameter estimation techniques, several methods exist for estimating the distribution parameters in life data analysis. However, some of them are less efficient than Bayes’ method, despite its subjectivity to prior information other than data that can mislead subsequent inferences. Thus, the main objective of this study is to present optimal numerical iteration techniques, such as the Picard and the Runge-Kutta methods, which are more efficient than Bayes’ method. The proposed methods have been applied to the inverse Weibull distribution parameters and compared to the Bayes’ method based on the informative gamma prior and the non-parametric kernel and characteristic priors, via an extensive Monte Carlo simulation study through the absolute average bias and the mean squared errors for the parameter estimators. The simulation results indicated that the Picard and Runge-Kutta methods provide better estimates and outperform the Bayes’ method based on the dual generalized progressive hybrid censoring data. Finally, it has been shown that the inverse Weibull distribution gives a good fit to new areas of dataset applications, such as flood data and reactor pump data. We have analyzed and illustrated the proposed methods using these datasets to confirm the simulation results.
Abstract: In parameter estimation techniques, several methods exist for estimating the distribution parameters in life data analysis. However, some of them are less efficient than Bayes’ method, despite its subjectivity to prior information other than data that can mislead subsequent inferences. Thus, the main objective of this study is to present optimal nu...
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Research Article
Assessment of Teachers’ Job Satisfaction and Professional Commitment at Sekota College of Teachers’ Education
Issue:
Volume 11, Issue 2, June 2025
Pages:
45-55
Received:
6 March 2025
Accepted:
18 April 2025
Published:
19 May 2025
Abstract: This study investigates job satisfaction and professional commitment among teachers at Sekota College of Teachers’ Education, recognizing that teacher effectiveness and involvement are crucial for positive educational reforms. Given the belief that happier employees are more engaged, the research aims to assess teachers' feelings, behaviors, and performance in their roles. Employing a quantitative methodology, the researchers utilized a descriptive survey design, gathering primary data through questionnaires distributed to 65 teachers, of which 60 completed the survey, resulting in a 92% return rate. Secondary data were also collected from published literature. The data were analyzed using SPSS version 25.0, employing descriptive statistics to summarize participant characteristics and a one-sample t-test to evaluate levels of job satisfaction and professional commitment. Results indicated that teachers reported low job satisfaction across various dimensions, including workload, working conditions, income, promotional opportunities, and relationships with colleagues. On the other hand, teachers expressed high degree of professional commitment: affective, continuance and normative commitment. To summarize, the study suggests that improving overall job satisfaction is key to boosting retention, and suggests methods such as matching salaries to inflation and living costs, delivering suitable compensation and benefits, upgrading work environments and nurturing good relations among staff. This is an important step toward improving the low job satisfaction of teachers and strengthening their professional commitment to their work, which in turn is highly beneficial to the education system.
Abstract: This study investigates job satisfaction and professional commitment among teachers at Sekota College of Teachers’ Education, recognizing that teacher effectiveness and involvement are crucial for positive educational reforms. Given the belief that happier employees are more engaged, the research aims to assess teachers' feelings, behaviors, and pe...
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Research Article
On the Weighted 2-Parameter Rayleigh Distribution
Adetunji Kolawole Ilori*,
Awogbemi Clement Adeyeye,
Damilare Oladimeji,
Toyosi Adebambo,
Adebisi Michael
Issue:
Volume 11, Issue 2, June 2025
Pages:
56-65
Received:
7 May 2025
Accepted:
19 May 2025
Published:
6 June 2025
DOI:
10.11648/j.ijsda.20251102.14
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Abstract: The role of Weighted Distribution in statistical modeling is influenced by a particular weighting mechanism. This paper introduces the Weighted Two-Parameter Rayleigh (W2R) distribution, an extension of the Rayleigh distribution, achieved by extending the baseline distribution by using an inverted weight function with an additional parameter. This modification provides greater flexibility, making the W2R distribution more suitable for diverse applications in reliability analysis and survival studies modeling. Theoretical and statistical properties of the new weighted distribution such as survival function, hazard function, reversed hazard function, moments, coefficient of variation, coefficient of skewness, coefficient of kurtosis, harmonic mean, moment generating function, mean-deviation, Rényi entropy, and order statistics were explicitly derived. This was to assess the flexibility and applicability of W2RD, moments, and the associated measures of W2RD distribution. The new weighted Rayleigh distribution parameters were estimated using the Maximum Likelihood Estimation (MLE) performance evaluators. A comparative analysis of W2R distribution with other existing distributions using remission time analysis was applied to two real-life datasets to evaluate its effectiveness. The models performances were assessed using Log-Likelihood and Akaike Information Criterion (AIC) and the results indicated that the W2R distribution provides a superior fit to real-world data compared to competing distributions. The study therefore highlights the potential of the W2R distribution as a more robust and versatile alternative for statistical modeling in various fields.
Abstract: The role of Weighted Distribution in statistical modeling is influenced by a particular weighting mechanism. This paper introduces the Weighted Two-Parameter Rayleigh (W2R) distribution, an extension of the Rayleigh distribution, achieved by extending the baseline distribution by using an inverted weight function with an additional parameter. This ...
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Research Article
On the Use of Bayesian Probability Networks with Hypothesized Malaria Influence Diagrams
Emmanuel Segun Oguntade*,
Awogbemi Clement Adeyeye,
Ilori Adetunji Kolawole
Issue:
Volume 11, Issue 2, June 2025
Pages:
66-73
Received:
12 May 2025
Accepted:
26 May 2025
Published:
6 June 2025
DOI:
10.11648/j.ijsda.20251102.15
Downloads:
Views:
Abstract: Bayesian Belief Network (BBN) is an emerging modeling technique that provides a decision support framework for problem relating to uncertainty, complexity and probabilistic cognitive. The specification of Bayesian network is made up of graph structure of networks (qualitative part) and the specification of the conditional probability distributions (quantitative part). The technique conceptualizes a system of interest as a network of connected nodes and linkages. In spite of the versatile and general acceptability of estimation of disease cases from various available methods in literature, incorporating model uncertainty remains an open issue. In this article, we derived a probability based graphical model using expert opinions in related studies on malaria and its hypothesized predictors with a BBN. This approach is well applied in ecological studies and other environmental sciences in recent times for various estimations and predictions based on Bayesian reasoning. The study therefore examines the application of BBN with a view to estimate the model parameters by deriving a probability based networks applicable to malaria epidemics. While Markov Chain principles were explored as they relates to a BBN formulation and useful guidelines for developing the preliminary structure of the network, the topology of a BBN was derived as a directed acyclic graph with malaria predictors as network nodes.
Abstract: Bayesian Belief Network (BBN) is an emerging modeling technique that provides a decision support framework for problem relating to uncertainty, complexity and probabilistic cognitive. The specification of Bayesian network is made up of graph structure of networks (qualitative part) and the specification of the conditional probability distributions ...
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