Research Article
A Type of Holographic Scalar Field Model and Coincidence Problem
Issue:
Volume 14, Issue 2, April 2026
Pages:
22-27
Received:
7 November 2025
Accepted:
24 November 2025
Published:
16 March 2026
DOI:
10.11648/j.sr.20261402.11
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Abstract: One of the main problems in cosmology is to resolve the problem of accelerating expansion and cosmological coincidence problem of the late universe. Many models, such as the cosmological constant model, the scalar field model, and the holographic model proposed in Einstein’s tensor gravity theory, have solved many problems in explaining these problems. The Brans-Dicke (BD) scalar-tensor theory, proposed by Brans and Dicke in 1961, is a generalization of Einstein’s tensor theory of gravity. In this theory, the scalar field can be combined with gravity, and many scalar field models have been proposed, since the observational constraints of the coupling parameters are not clear. In general formalism for scalar fields, parameterization may be relevant in some sense. For example, parameterization of parameters such as dark matter-dark energy interaction coefficient, equation of state parameter, and holographic constant, which have been proposed to solve the problem of cosmological coincidence problem, has some significance. Therefore, in this paper, we have applied the well-known Jassal-Bagular-Padmanabhann (JBP) parameterization in cosmology to the scalar field to construct a holographic scalar field model, perform cosmological verification, and consider the problem of late cosmic acceleration expansion and cosmological coincidence problem. The model parameters obtained by minimizing the chi-square function have nonzero values, and the current values of the transition red shift, equation of state parameter, deceleration parameter, and coincidence parameter are in good agreement with previous studies. We also confirmed the validity of the model and finally obtained a limit on the rate of change of the gravitational constant.
Abstract: One of the main problems in cosmology is to resolve the problem of accelerating expansion and cosmological coincidence problem of the late universe. Many models, such as the cosmological constant model, the scalar field model, and the holographic model proposed in Einstein’s tensor gravity theory, have solved many problems in explaining these probl...
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Research Article
Early Detection of Heart Disease: Enhancing Prediction Through Machine Learning Techniques
Sirage Temame Areb*
,
Mesifin Abebe
Issue:
Volume 14, Issue 2, April 2026
Pages:
28-41
Received:
7 April 2025
Accepted:
23 April 2025
Published:
19 March 2026
DOI:
10.11648/j.sr.20261402.12
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Abstract: Heart disease is the abnormal health condition that influences parts of the heart and all its parts. World Health Organization (WHO) is assured that the disease is one of the leading killer disease of the worldwide population. The prevalence of the disease is also increasing through developing countries like Ethiopia. Machine Learning (ML) is one of the key technique in the management and processing of a huge number of health data’s and it supports in diagnosis and prediction of disease at early stages. The main objective of this study is developing an early detection of Heart Disease (HD) enhancing prediction through ML technique; such as Random forest (RF), K Nearest Neighbor (KNN), Support vector Machine (SVM), Gradient Boosting (GB) and Voting Classifier with two Feature Selection (FS) methods, of Chi-Square (CFS) and Sequential Forward Feature Selection (SFFS) methods. The data used for the experimentation purpose was collected from Local Hospitals. Before FS methods are performed, all the ML algorithms are applied for the imbalanced and balanced HD dataset. Then after, the two FS methods are applied with ML techniques on these imbalanced and balanced datasets. Models are evaluated through different model evaluation metrics with two data splitting technique namely Percentage Splitting (PS) and 10-Fold-Cross Validation (10-F-CV) techniques and finally different results are registered. Thus, before FS methods are applied on the full balanced datasets, SVM and GB achieved a good accuracy score of 99.2% using PS and similarly after FS technique is applied, Both RF with CFS and VC with CFS achieved a better accuracy score of 99.4% using PS for the combined dataset, so this will helps users and experts to detect and appropriate prevention of the disease at an early stage.
Abstract: Heart disease is the abnormal health condition that influences parts of the heart and all its parts. World Health Organization (WHO) is assured that the disease is one of the leading killer disease of the worldwide population. The prevalence of the disease is also increasing through developing countries like Ethiopia. Machine Learning (ML) is one o...
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Research Article
Physical Activity Level Amongst the Academic Staffs in Delta State University Abraka
Ogbutor Udoji Godsday*,
Efienemokwu Onyeisi Kelly,
Anastacia Okwudili Ojimba,
Nwose Jephtah,
Chukwuemeka Ephraim,
Isaac Precious,
Ogbutor Emeke Godson,
Okri Favour Eloho,
Kienne Osetare Precious,
Kosin Ufoma Doris,
Ijeh Chukwunonso Basil
Issue:
Volume 14, Issue 2, April 2026
Pages:
42-55
Received:
14 February 2026
Accepted:
3 March 2026
Published:
19 March 2026
DOI:
10.11648/j.sr.20261402.13
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Abstract: Background: Physical inactivity is a global public health concern and particularly prevalent among academic professionals whose roles are predominantly sedentary. Aim: This study assessed the physical activity levels among the academic staff at Delta State University, Abraka. Materials and Methods: A sample of 300 academic staff members was selected using stratified random sampling. The Rapid Assessment of Physical Activity questionnaire was used for data collection. Data analysis was performed using SPSS version 25, employing Chi-square tests, ANOVA, and independent samples t-tests. Results: 43.3% of respondents were sedentary, 30.0% were under-active light, 18.3% were under-active moderate, and only 8.3% achieved the RAPA-defined aerobic “active” category. Based on the composite WHO operational definition (aerobic, strength, and flexibility), 37.3% met recommended physical activity guidelines, a proportion significantly lower than the 50% benchmark (z = −4.63, p < 0.001). Physical activity declined significantly with age (F = 8.76, p < 0.001), and differed across academic rank (F = 6.89, p < 0.001), with Professors recording the lowest mean score (M = 2.7) and Lecturer II staff the highest (M = 4.4). Males reported significantly higher mean activity scores than females (t = 3.21, p = 0.001), although sex was not an independent predictor after multivariable adjustment (p = 0.260). Conclusion: Majority of the academic staff do not meet recommended physical activity levels, with notable demographic disparities. Recommendation: The study highlights the urgent need for institution-led wellness interventions tailored to age, gender, and job role to foster a more active and healthier academic workforce.
Abstract: Background: Physical inactivity is a global public health concern and particularly prevalent among academic professionals whose roles are predominantly sedentary. Aim: This study assessed the physical activity levels among the academic staff at Delta State University, Abraka. Materials and Methods: A sample of 300 academic staff members was selecte...
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