Pub Date : 2025-12-01Epub Date: 2025-09-12DOI: 10.1016/j.physo.2025.100321
J.A. Anaya-Contreras , A. Zúñiga-Segundo , I. Ramos-Prieto , H.M. Moya-Cessa
We explicitly show that a transformation based on the squeeze operator enables an exact mapping – without any approximation – of the light–matter interaction Hamiltonian, including the (diamagnetic) term, to the standard Rabi model Hamiltonian. This result underscores the novelty of our approach, as it establishes a rigorous equivalence even in the presence of terms typically excluded in conventional derivations. Additionally, we propose partner waveguide arrays that simulate both the full Hamiltonian with the diamagnetic term and the conventional Rabi Hamiltonian.
{"title":"Rabi model with diamagnetic term and its waveguide realization","authors":"J.A. Anaya-Contreras , A. Zúñiga-Segundo , I. Ramos-Prieto , H.M. Moya-Cessa","doi":"10.1016/j.physo.2025.100321","DOIUrl":"10.1016/j.physo.2025.100321","url":null,"abstract":"<div><div>We explicitly show that a transformation based on the squeeze operator enables an exact mapping – <em>without any approximation</em> – of the light–matter interaction Hamiltonian, including the <span><math><msup><mrow><mi>A</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> (diamagnetic) term, to the standard Rabi model Hamiltonian. This result underscores the novelty of our approach, as it establishes a rigorous equivalence even in the presence of terms typically excluded in conventional derivations. Additionally, we propose partner waveguide arrays that simulate both the full Hamiltonian with the diamagnetic term and the conventional Rabi Hamiltonian.</div></div>","PeriodicalId":36067,"journal":{"name":"Physics Open","volume":"25 ","pages":"Article 100321"},"PeriodicalIF":1.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-08-21DOI: 10.1016/j.physo.2025.100314
Ahmed R. Galaly , Tahani R. Aldhafeeri , Sameh M. Elghnam , Mahmoud A. Hamad
The magnetocaloric effect (MCE) of Ni50Mn35Sn15 is investigated via phenomenological model (PM) at temperatures, ranging from around 5 K–400 K, validating both inversely and conventionally MCEs, corresponding to two magnetic transitions. Magnetic entropy change (ΔSM) is maximized at the antiferromagnetic transition in martensitic state with 14.5 J/kg.K, which is similar to prior work, demonstrating that PM is a good model for studying giant inverse MCE. However, |ΔSM| is maximized with 2.5 J/kg.K at the FM transition in the austenitic state. Consequently, PM is a particularly intriguing model in which both inverse MCE and conventional MCE for a single material at different temperatures can be examined. Ni50Mn35Sn15 is an efficient material for MR technology throughout widely temperature range, particularly ambient temperature and some temperature ranges that are near ambient temperature.
通过现象模型（PM）研究了Ni50Mn35Sn15在5 K - 400 K温度下的磁热效应（MCE），验证了对应于两次磁跃迁的反向和常规MCE。磁熵变化（ΔSM）在马氏体态反铁磁跃迁时达到最大值，为14.5 J/kg。K，这与前人的工作相似，表明PM是研究巨逆MCE的一个很好的模型。然而，|ΔSM|在2.5 J/kg时达到最大值。K在奥氏体态的FM转变。因此，PM是一个特别有趣的模型，其中可以检查不同温度下单一材料的逆MCE和常规MCE。Ni50Mn35Sn15是一种适用于MR技术的高效材料，适用于广泛的温度范围，特别是环境温度和一些接近环境温度的温度范围。
{"title":"The giant and moderate magnetocaloric effect in Ni50Mn35Sn15 for room-temperature refrigeration technology","authors":"Ahmed R. Galaly , Tahani R. Aldhafeeri , Sameh M. Elghnam , Mahmoud A. Hamad","doi":"10.1016/j.physo.2025.100314","DOIUrl":"10.1016/j.physo.2025.100314","url":null,"abstract":"<div><div>The magnetocaloric effect (MCE) of Ni<sub>50</sub>Mn<sub>35</sub>Sn<sub>15</sub> is investigated via phenomenological model (PM) at temperatures, ranging from around 5 K–400 K, validating both inversely and conventionally MCEs, corresponding to two magnetic transitions. Magnetic entropy change (<em>ΔS</em><sub><em>M</em></sub>) is maximized at the antiferromagnetic transition in martensitic state with 14.5 J/kg.K, which is similar to prior work, demonstrating that PM is a good model for studying giant inverse MCE. However, |<em>ΔS</em><sub><em>M</em></sub>| is maximized with 2.5 J/kg.K at the FM transition in the austenitic state. Consequently, PM is a particularly intriguing model in which both inverse MCE and conventional MCE for a single material at different temperatures can be examined. Ni<sub>50</sub>Mn<sub>35</sub>Sn<sub>15</sub> is an efficient material for MR technology throughout widely temperature range, particularly ambient temperature and some temperature ranges that are near ambient temperature.</div></div>","PeriodicalId":36067,"journal":{"name":"Physics Open","volume":"25 ","pages":"Article 100314"},"PeriodicalIF":1.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144908546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-09-16DOI: 10.1016/j.physo.2025.100326
Khalid Reggab , Houssam Eddine Hailouf , Kingsley Onyebuchi Obodo , Mohammed Benali Kanoun , Souraya Goumri-Said
This study investigates the behavior of spinless particles under the influence of scalar and vector potentials by analytically solving the Klein-Gordon equation using the Nikiforov-Uvarov functional analysis method, coupled with the Hellmann and modified Kratzer potentials, employing the Greene-Aldrich approximation for the centrifugal term. The analytical energy eigenvalues and eigenfunctions were utilised to examine the energy spectra of specific diatomic molecules (CO, NO, N2, and CH), demonstrating the correlation of these properties with potential parameters and quantum numbers. In addition to the analytical results, machine learning techniques like Random Forest and Neural Network regressors were used to model and predict the energy spectra based on the calculated data. This made it possible to swiftly explore energy landscapes. The ML models showed great agreement with the analytical results and were better at extrapolating to new quantum numbers and molecular types. This hybrid analytical-ML approach is a strong way to speed up the study of diatomic molecular systems. It combines the rigour of quantum mechanics with data-driven predictions and makes it possible to efficiently screen molecular energy spectra in theoretical and computational chemistry.
{"title":"Klein-Gordon equation and machine learning-enhanced functional analysis: Insights into diatomic molecular systems via analytical and predictive modeling approaches","authors":"Khalid Reggab , Houssam Eddine Hailouf , Kingsley Onyebuchi Obodo , Mohammed Benali Kanoun , Souraya Goumri-Said","doi":"10.1016/j.physo.2025.100326","DOIUrl":"10.1016/j.physo.2025.100326","url":null,"abstract":"<div><div>This study investigates the behavior of spinless particles under the influence of scalar and vector potentials by analytically solving the Klein-Gordon equation using the Nikiforov-Uvarov functional analysis method, coupled with the Hellmann and modified Kratzer potentials, employing the Greene-Aldrich approximation for the centrifugal term. The analytical energy eigenvalues and eigenfunctions were utilised to examine the energy spectra of specific diatomic molecules (CO, NO, N<sub>2</sub>, and CH), demonstrating the correlation of these properties with potential parameters and quantum numbers. In addition to the analytical results, machine learning techniques like Random Forest and Neural Network regressors were used to model and predict the energy spectra based on the calculated data. This made it possible to swiftly explore energy landscapes. The ML models showed great agreement with the analytical results and were better at extrapolating to new quantum numbers and molecular types. This hybrid analytical-ML approach is a strong way to speed up the study of diatomic molecular systems. It combines the rigour of quantum mechanics with data-driven predictions and makes it possible to efficiently screen molecular energy spectra in theoretical and computational chemistry.</div></div>","PeriodicalId":36067,"journal":{"name":"Physics Open","volume":"25 ","pages":"Article 100326"},"PeriodicalIF":1.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145117648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this work, we explore a modified theory of gravity by transitioning from standard General Relativity(GR) to an f(R) gravity framework wherein the Ricci scalar is replaced by a general function . By adopting a specific Hubble parameterization , where is the present value of Hubble parameter and be the free model parameter. We investigate the dynamical evolution of the universe under this modified gravity scenario with quadratic equation of state(EoS), . The Raychaudhuri Equation is employed to analyze the focus of geodesics and provide insights into the expansion behavior of the model universe, allowing us to track deviations from the standard cosmological model. To assess the viability of our f(R) gravity model, we analyze 46 Hubble parameter observations using the Markov Chain Monte Carlo(MCMC) technique to constrain cosmological parameters. We further use the 1048 Pantheon dataset of Type Ia supernovae to enhance the statistical robustness and tighten constraints. The combined observational analysis supports the model as a viable alternative to the standard CDM framework, particularly in explaining late-time cosmic acceleration. Notably the model exhibits deviations at higher redshifts that suggest new insights into cosmic evolution. The study also develops a neural network-based machine learning model to predict the Hubble parameter H(z) across various redshifts, facilitating data-driven insights into cosmic expansion.
{"title":"Constraining f(R) gravity model through Hubble Parametrization","authors":"Kshetrimayum Govind Singh , Kangujam Priyokumar Singh , Asem Jotin Meitei","doi":"10.1016/j.physo.2025.100303","DOIUrl":"10.1016/j.physo.2025.100303","url":null,"abstract":"<div><div>In this work, we explore a modified theory of gravity by transitioning from standard General Relativity(GR) to an f(R) gravity framework wherein the Ricci scalar <span><math><mi>R</mi></math></span> is replaced by a general function <span><math><mrow><mi>f</mi><mrow><mo>(</mo><mi>R</mi><mo>)</mo></mrow><mo>=</mo><mi>R</mi><mo>+</mo><mi>α</mi><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span>. By adopting a specific Hubble parameterization <span><math><mrow><mi>H</mi><mrow><mo>(</mo><mi>z</mi><mo>)</mo></mrow><mo>=</mo><mfrac><mrow><msub><mrow><mi>H</mi></mrow><mrow><mn>0</mn></mrow></msub></mrow><mrow><msqrt><mrow><mn>2</mn></mrow></msqrt></mrow></mfrac><msup><mrow><mfenced><mrow><mn>1</mn><mo>+</mo><msup><mrow><mrow><mo>(</mo><mn>1</mn><mo>+</mo><mi>z</mi><mo>)</mo></mrow></mrow><mrow><mn>2</mn><mrow><mo>(</mo><mn>1</mn><mo>+</mo><mi>ζ</mi><mo>)</mo></mrow></mrow></msup></mrow></mfenced></mrow><mrow><mfrac><mrow><mn>1</mn></mrow><mrow><mn>2</mn></mrow></mfrac></mrow></msup></mrow></math></span>, where <span><math><msub><mrow><mi>H</mi></mrow><mrow><mn>0</mn></mrow></msub></math></span> is the present value of Hubble parameter and <span><math><mi>ζ</mi></math></span> be the free model parameter. We investigate the dynamical evolution of the universe under this modified gravity scenario with quadratic equation of state(EoS), <span><math><mrow><mi>p</mi><mo>=</mo><mi>μ</mi><msup><mrow><mi>ρ</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>−</mo><mi>ρ</mi></mrow></math></span>. The Raychaudhuri Equation is employed to analyze the focus of geodesics and provide insights into the expansion behavior of the model universe, allowing us to track deviations from the standard cosmological model. To assess the viability of our f(R) gravity model, we analyze 46 Hubble parameter observations using the Markov Chain Monte Carlo(MCMC) technique to constrain cosmological parameters. We further use the 1048 Pantheon dataset of Type Ia supernovae to enhance the statistical robustness and tighten constraints. The combined observational analysis supports the model as a viable alternative to the standard <span><math><mi>Λ</mi></math></span>CDM framework, particularly in explaining late-time cosmic acceleration. Notably the model exhibits deviations at higher redshifts that suggest new insights into cosmic evolution. The study also develops a neural network-based machine learning model to predict the Hubble parameter H(z) across various redshifts, facilitating data-driven insights into cosmic expansion.</div></div>","PeriodicalId":36067,"journal":{"name":"Physics Open","volume":"25 ","pages":"Article 100303"},"PeriodicalIF":1.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-07-31DOI: 10.1016/j.physo.2025.100297
Charlotte Rundberget
In this paper, we undertake a thorough investigation into the existence of magnetic monopoles, elusive particles theorized to possess isolated north or south magnetic poles. These hypothetical entities have long captured the imagination of physicists and have been the subject of extensive research. Our analysis, rooted in the principles of classical mechanics and electrodynamics provides a unique look into the fundamental nature of these hypothetical monopoles.
{"title":"Special solutions of coupled classical harmonic oscillators with the addition of magnetic monopoles","authors":"Charlotte Rundberget","doi":"10.1016/j.physo.2025.100297","DOIUrl":"10.1016/j.physo.2025.100297","url":null,"abstract":"<div><div>In this paper, we undertake a thorough investigation into the existence of magnetic monopoles, elusive particles theorized to possess isolated north or south magnetic poles. These hypothetical entities have long captured the imagination of physicists and have been the subject of extensive research. Our analysis, rooted in the principles of classical mechanics and electrodynamics provides a unique look into the fundamental nature of these hypothetical monopoles.</div></div>","PeriodicalId":36067,"journal":{"name":"Physics Open","volume":"25 ","pages":"Article 100297"},"PeriodicalIF":1.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144779747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-09DOI: 10.1016/j.physo.2025.100331
Taiquan Wu , Lifang Shen , Biyi Huang , Guang Liu , Wei Zhang , Yang Cui , Chen Chen , Shubin Yan
The first-principles technique has been employed to determine the structure of N-(2-hydroxyethyl)-3-mercaptopropanamide (NMPA) molecular chains, monolayers, and the adsorption system. The CASTEP calculation confirms that the NMPA monolayer forms a self-assembly system consisting of numerous parallel molecules interconnected through H-O bonds. This finding is supported by the electron density analysis. The monolayers of NMPA are composed of molecular chains arranged in either parallel or alternating configurations. Upon adsorption of the NMPA monolayer onto the Au surface, the structural parameters within the adsorption system remain consistent with those observed in the monolayer, indicating that the structure of the NMPA self-assembled monolayers is primarily governed by intermolecular interactions. The primary parameter imparted by the Au surface is the distance between adjacent molecules within the molecular chain.
{"title":"Structure of N-(2-hydroxyethyl)-3-mercaptopropanamide (NMPA) monolayer on Au surface","authors":"Taiquan Wu , Lifang Shen , Biyi Huang , Guang Liu , Wei Zhang , Yang Cui , Chen Chen , Shubin Yan","doi":"10.1016/j.physo.2025.100331","DOIUrl":"10.1016/j.physo.2025.100331","url":null,"abstract":"<div><div>The first-principles technique has been employed to determine the structure of N-(2-hydroxyethyl)-3-mercaptopropanamide (NMPA) molecular chains, monolayers, and the adsorption system. The CASTEP calculation confirms that the NMPA monolayer forms a self-assembly system consisting of numerous parallel molecules interconnected through H-O bonds. This finding is supported by the electron density analysis. The monolayers of NMPA are composed of molecular chains arranged in either parallel or alternating configurations. Upon adsorption of the NMPA monolayer onto the Au surface, the structural parameters within the adsorption system remain consistent with those observed in the monolayer, indicating that the structure of the NMPA self-assembled monolayers is primarily governed by intermolecular interactions. The primary parameter imparted by the Au surface is the distance between adjacent molecules within the molecular chain.</div></div>","PeriodicalId":36067,"journal":{"name":"Physics Open","volume":"25 ","pages":"Article 100331"},"PeriodicalIF":1.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-11-10DOI: 10.1016/j.physo.2025.100348
Ali Rehman , Mustafa Inc , Edrisa Jawo , K. Sudarmozhi
This article examines the flow of magnetohydrodynamic (MHD) nanofluids augmented by Marangoni convection (MC) over an infinite rotating inclined disk. The integration of the impacts of magnetic fields, MC, and hybrid nanofluids (HNF), utilising magnetite and silicon nanoparticles with ethylene glycol as the base fluid, on convection, flow, and heat transfer is the focus. The author employs the Homotopy analysis method (HAM), a recent approach to solving the nonlinear governing equations of the flow of a HNF, which incorporates magnetic and thermal surface tension forces. Numerically, the research demonstrates the impact of the Lorentz force, created by an increase in magnetic field strength, which in turn reduces the fluid's flow. On the contrary, the increase in the volume fractions of the nanoparticles slows the flow even further but promotes heat transfer. Even so, the MC parameter increases flow and surface temperature gradients, thereby increasing heat transfer rates. The Nusselt numbers increase as well. The residual error tables demonstrate the strong convergence of the HAM solutions, providing strong evidence of the accuracy of the developed models. Validation involves qualitative comparison with experimental and numerical studies on MHD nanofluid flows and MC. Optimization of HNF properties and the use of magnetics permits better control of flow and energy efficiency, making thermal management on turbines, electronic cooling, and solar heating systems plausible in real-time applications.
{"title":"Marangoni convection effects on heat transfer enhancement in MHD nanofluid flow over an inclined disk using magnetite and silicon nanoparticles with ethylene glycol as base fluid","authors":"Ali Rehman , Mustafa Inc , Edrisa Jawo , K. Sudarmozhi","doi":"10.1016/j.physo.2025.100348","DOIUrl":"10.1016/j.physo.2025.100348","url":null,"abstract":"<div><div>This article examines the flow of magnetohydrodynamic (MHD) nanofluids augmented by Marangoni convection (MC) over an infinite rotating inclined disk. The integration of the impacts of magnetic fields, MC, and hybrid nanofluids (HNF), utilising magnetite and silicon nanoparticles with ethylene glycol as the base fluid, on convection, flow, and heat transfer is the focus. The author employs the Homotopy analysis method (HAM), a recent approach to solving the nonlinear governing equations of the flow of a HNF, which incorporates magnetic and thermal surface tension forces. Numerically, the research demonstrates the impact of the Lorentz force, created by an increase in magnetic field strength, which in turn reduces the fluid's flow. On the contrary, the increase in the volume fractions of the nanoparticles slows the flow even further but promotes heat transfer. Even so, the MC parameter increases flow and surface temperature gradients, thereby increasing heat transfer rates. The Nusselt numbers increase as well. The residual error tables demonstrate the strong convergence of the HAM solutions, providing strong evidence of the accuracy of the developed models. Validation involves qualitative comparison with experimental and numerical studies on MHD nanofluid flows and MC. Optimization of HNF properties and the use of magnetics permits better control of flow and energy efficiency, making thermal management on turbines, electronic cooling, and solar heating systems plausible in real-time applications.</div></div>","PeriodicalId":36067,"journal":{"name":"Physics Open","volume":"25 ","pages":"Article 100348"},"PeriodicalIF":1.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-08-05DOI: 10.1016/j.physo.2025.100300
M. Faizan , Muhammad Waqar Ahmed , M. Yaqub Khan , M. Ijaz Khan
This research introduces a novel theoretical investigation into the fundamental influence of entropy on plasma dynamics, particularly its role in governing confinement and transport phenomena within magnetically confined thermonuclear fusion systems. Utilizing Braginskii's transport formalism alongside a drift approximation to incorporate entropy-driven effects, a new class of nonlinear evolution equations is derived. These equations expose previously unrecognized couplings between entropy variations and ion temperature gradient (ITG) modes. A thorough examination of the linear dispersion relation elucidates key features of wave propagation, while nonlinear analysis reveals entropy-induced transitions to chaotic behavior, reminiscent of the Lorenz-Stenflo model, a well-known representation of turbulence in plasma. This study redefines entropy as an active agent in the emergence of instability and turbulence, rather than merely a passive thermodynamic variable. The findings offer critical insights into enhancing plasma confinement and stability, potentially advancing the realization of efficient and sustainable nuclear fusion.
{"title":"Entropy-induced chaos in magnetized plasma: Insights from nonlinear dynamics","authors":"M. Faizan , Muhammad Waqar Ahmed , M. Yaqub Khan , M. Ijaz Khan","doi":"10.1016/j.physo.2025.100300","DOIUrl":"10.1016/j.physo.2025.100300","url":null,"abstract":"<div><div>This research introduces a novel theoretical investigation into the fundamental influence of entropy on plasma dynamics, particularly its role in governing confinement and transport phenomena within magnetically confined thermonuclear fusion systems. Utilizing Braginskii's transport formalism alongside a drift approximation to incorporate entropy-driven effects, a new class of nonlinear evolution equations is derived. These equations expose previously unrecognized couplings between entropy variations and ion temperature gradient (ITG) modes. A thorough examination of the linear dispersion relation elucidates key features of wave propagation, while nonlinear analysis reveals entropy-induced transitions to chaotic behavior, reminiscent of the Lorenz-Stenflo model, a well-known representation of turbulence in plasma. This study redefines entropy as an active agent in the emergence of instability and turbulence, rather than merely a passive thermodynamic variable. The findings offer critical insights into enhancing plasma confinement and stability, potentially advancing the realization of efficient and sustainable nuclear fusion.</div></div>","PeriodicalId":36067,"journal":{"name":"Physics Open","volume":"25 ","pages":"Article 100300"},"PeriodicalIF":1.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-30DOI: 10.1016/j.physo.2025.100343
Vahid Mirzaei Mahmoud Abadi
In this study, we present a deep learning framework based on Physics-Informed Neural Networks (PINNs) for solving the Schrödinger equation in neutron-nucleus scattering problems. For interaction analysis, an effective optical potential with a Woods–Saxon form factor was employed, whose parameters depend on the mass and atomic numbers of the target nucleus. The proposed framework is capable of addressing both forward problems—to predict physical quantities—and inverse problems to estimate potential parameters.
A comprehensive partial wave analysis was conducted for neutron energies up to 100 MeV. The model successfully calculated phase shifts and differential cross sections for partial waves from l = 0 to l = 6, accurately reproducing the characteristic angular distributions of each wave, consistent with Legendre polynomials. Furthermore, a systematic study on various nuclei (from 12C to 208Pb) and the tin isotopic chain confirmed the R ∼ A1/3 radius dependence and the isospin dependence of the potential depth. The total cross-section dependence on energy was also examined, revealing broad resonant structures in agreement with optical model predictions.
The findings demonstrate that PINNs offer a powerful, mesh-free, and flexible alternative to traditional numerical methods for precise analysis of nuclear reactions, with significant potential for parameter estimation and model validation tasks in nuclear physics.
{"title":"From the Schrödinger equation to cross section: A comprehensive PINN-based approach for elastic scattering analysis","authors":"Vahid Mirzaei Mahmoud Abadi","doi":"10.1016/j.physo.2025.100343","DOIUrl":"10.1016/j.physo.2025.100343","url":null,"abstract":"<div><div>In this study, we present a deep learning framework based on Physics-Informed Neural Networks (PINNs) for solving the Schrödinger equation in neutron-nucleus scattering problems. For interaction analysis, an effective optical potential with a Woods–Saxon form factor was employed, whose parameters depend on the mass and atomic numbers of the target nucleus. The proposed framework is capable of addressing both forward problems—to predict physical quantities—and inverse problems to estimate potential parameters.</div><div>A comprehensive partial wave analysis was conducted for neutron energies up to 100 MeV. The model successfully calculated phase shifts and differential cross sections for partial waves from l = 0 to l = 6, accurately reproducing the characteristic angular distributions of each wave, consistent with Legendre polynomials. Furthermore, a systematic study on various nuclei (from <sup>12</sup>C to <sup>208</sup>Pb) and the tin isotopic chain confirmed the R ∼ A<sup>1/3</sup> radius dependence and the isospin dependence of the potential depth. The total cross-section dependence on energy was also examined, revealing broad resonant structures in agreement with optical model predictions.</div><div>The findings demonstrate that PINNs offer a powerful, mesh-free, and flexible alternative to traditional numerical methods for precise analysis of nuclear reactions, with significant potential for parameter estimation and model validation tasks in nuclear physics.</div></div>","PeriodicalId":36067,"journal":{"name":"Physics Open","volume":"25 ","pages":"Article 100343"},"PeriodicalIF":1.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01Epub Date: 2025-10-10DOI: 10.1016/j.physo.2025.100332
Arun Kumar , Aziz Nanthaamornphong
Massive multiple-input multiple-output orthogonal time frequency space (M-MIMO-OTFS) is a promising waveform for beyond 5G (B5G) systems, offering high spectral efficiency and robustness in time-varying channels. However, signal detection is challenged by high-dimensional processing and severe intersymbol interference under Rayleigh fading. A Modified Deep Neural Network (M-DNN) with prior channel state information (CSI) estimation was employed to enhance detection accuracy in large-scale M-MIMO-OTFS systems. Simulation results show SNR reductions of 2.9 dB, 5.8 dB, and 7.2 dB at a BER of 10−5 for 64 × 64, 256 × 256, and 512 × 512 configurations, respectively, demonstrating improved detection with higher MIMO dimensions. For a 512 × 512 system with a 10 % CSI error variance, the performance degrades slightly to 9.9 dB, indicating robustness to CSI imperfections. Power spectral density (PSD) analysis revealed a −110 dB improvement over conventional methods, enhancing the spectral efficiency and reducing out-of-band emissions. The combination of deep-learning-based detection and CSI estimation supports reliable BER performance and optimized spectral usage, making the approach suitable for large-scale M-MIMO-OTFS deployment in B5G and 6G networks.
{"title":"Signal detection of massive MIMO-OTFS using DNN algorithm with diverse channel state estimation","authors":"Arun Kumar , Aziz Nanthaamornphong","doi":"10.1016/j.physo.2025.100332","DOIUrl":"10.1016/j.physo.2025.100332","url":null,"abstract":"<div><div>Massive multiple-input multiple-output orthogonal time frequency space (M-MIMO-OTFS) is a promising waveform for beyond 5G (B5G) systems, offering high spectral efficiency and robustness in time-varying channels. However, signal detection is challenged by high-dimensional processing and severe intersymbol interference under Rayleigh fading. A Modified Deep Neural Network (M-DNN) with prior channel state information (CSI) estimation was employed to enhance detection accuracy in large-scale M-MIMO-OTFS systems. Simulation results show SNR reductions of 2.9 dB, 5.8 dB, and 7.2 dB at a BER of 10<sup>−5</sup> for 64 × 64, 256 × 256, and 512 × 512 configurations, respectively, demonstrating improved detection with higher MIMO dimensions. For a 512 × 512 system with a 10 % CSI error variance, the performance degrades slightly to 9.9 dB, indicating robustness to CSI imperfections. Power spectral density (PSD) analysis revealed a −110 dB improvement over conventional methods, enhancing the spectral efficiency and reducing out-of-band emissions. The combination of deep-learning-based detection and CSI estimation supports reliable BER performance and optimized spectral usage, making the approach suitable for large-scale M-MIMO-OTFS deployment in B5G and 6G networks.</div></div>","PeriodicalId":36067,"journal":{"name":"Physics Open","volume":"25 ","pages":"Article 100332"},"PeriodicalIF":1.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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