Research/Technical Note
Study of Octupole Transitions in Xe, Ba, Ce and Nd Nuclei within Interacting Boson Model
Tuqa Abdulbaset Hashim,
Saad Naji Abood*
Issue:
Volume 10, Issue 2, April 2024
Pages:
18-24
Received:
12 January 2024
Accepted:
30 January 2024
Published:
28 April 2024
Abstract: For nuclei with 54≤Z≤60 and 86≤N≤94, the results regarding the excitation spectra at low energies, both in positive and negative parity, as well as the transition probabilities for electric dipole (B(E1)), quadrupole (B(E2)), and octupole transitions (B(E(3)), indicate the emergence of significant octupole behavior. These observations have been made using the Interacting Boson Model-1 (IBM-1). The study examines the onset of octupole deformation and its impact on the spectroscopic characteristics in even-even neutron-rich lanthanide isotopes, specifically in the Ba and Nd nuclei. The investigation compares the results obtained from the Interacting Boson Model-1 (IBM-1) with the existing experimental data. The focus is on understanding how the addition of neutrons influences the development of octupole deformation and its manifestation in the observed spectroscopic features. The onset of strong octupolarity for Z≈ 56 and N ≈ 88 nuclei is indicated by the results obtained for the electric dipole, quadrupole, and octupole transition probabilities, as well as the low-energy positive and negative-parity excitation spectra. Conversely, discrepancies between the spectroscopic data and the IBM results suggest that the mapping quality needs to be evaluated in order to determine if the mapped boson Hamiltonian or the fermionic calculations are the source of the issues.
Abstract: For nuclei with 54≤Z≤60 and 86≤N≤94, the results regarding the excitation spectra at low energies, both in positive and negative parity, as well as the transition probabilities for electric dipole (B(E1)), quadrupole (B(E2)), and octupole transitions (B(E(3)), indicate the emergence of significant octupole behavior. These observations have been mad...
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Research Article
Wind Speed Prediction in Jerusalem Using Machine Learning Algorithms: A Case Study of Using ANFIS and KNNR
Khalil Abuayyash,
Husain Alsamamra,
Musa Abu Teir,
Hazem Doufesh*
Issue:
Volume 10, Issue 2, April 2024
Pages:
25-37
Received:
29 April 2024
Accepted:
17 May 2024
Published:
30 May 2024
Abstract: Wind energy is acknowledged for its status as a renewable energy source that offers several advantages, including its low cost of electricity generation, abundant availability, high efficiency, and minimal environmental impact. The prediction of wind speed using machine learning algorithms is crucial for various applications, such as wind energy planning and urban development. This paper presents a case study on wind speed prediction in Palestine Jerusalem city using the Adaptive Neuro-Fuzzy Inference System (ANFIS) and K-Nearest Neighbors Regression (KNNR) algorithms. The study evaluates their performance using multiple metrics, including root mean square (RMSE), bias, and coefficient of determination R2. ANFIS demonstrates good accuracy with lower RMSE (0.196) and minimal bias (0.0003). However, there is room for improvement in capturing overall variability (R2 = 0.15). In contrast, KNNR exhibits a higher R2 (0.4093), indicating a better fit, but with a higher RMSE (1.4209). These results demonstrated the potential of machine learning algorithms in wind speed prediction, which can lead to optimize the wind energy generation at specific site, and reducing the cost of energy production. This study provides insights into the applicability of ANFIS and KNNR in wind speed prediction for Jerusalem and suggests future research directions. The outcomes have practical implications for wind energy planning, urban development, and environmental assessments in similar regions.
Abstract: Wind energy is acknowledged for its status as a renewable energy source that offers several advantages, including its low cost of electricity generation, abundant availability, high efficiency, and minimal environmental impact. The prediction of wind speed using machine learning algorithms is crucial for various applications, such as wind energy pl...
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