Journal Of Moden Research Physics

Abstract Phase images obtained from holograms and interference patterns in quantitative phase microscopy often suffer from distortions, phase wrapping, and phase noise. Despite extensive efforts over the past decades and the development of various denoising methods, these challenges have not yet been completely resolved. Classical noise reduction filters typically damage image details, reduce overall quality, and weaken boundary and edge detection, thereby eliminating useful image information. A promising modern approach involves the use of machine learning algorithms. Results from image quality assessment metrics show that convolutional neural networks outperform conventional phase image denoising methods. In this approach, training data are fed into the network, and after several training stages, the network acquires the ability to reduce noise in phase images with high accuracy.

Abstract Accurate and quantitative monitoring of laboratory animals’ behavior, particularly in neuroscience models, is essential for understanding the neural-circuit function and its relation with complex behaviors. In computational ethology, novel systems and algorithms have been developed to make experiments automated, more precise, and less reliant on human observers, which could improve the reproducibility and repeatability of the results. In this research, a system composed of behavior-monitoring cameras and a miniature cranial implant for rats was designed and fabricated to record the animal’s body movement, track eye movements, and measure head orientation. The implant integrates an Inertial Measurement Unit (IMU) to measure head acceleration and angular velocity, as well as an infrared miniature camera for pupillometry; the total weight was about 4.5 g. A custom-developed software tool is designed to capture, process, and visualize the data. Sensor fusion of accelerometer and gyroscope data was used to compute the Euler angles of head motion. Classical computer-vision and deep neural-network algorithms were utilized for image analysis. Thresholding and edge-detection algorithms enabled real-time tracking of the pupil in pigmented rats and the body center, with a processing speed of 25 frames per second, which is suitable for closed-loop neural control. For body-pose estimation and pupil tracking in albino rats, the DeepLabCut networks were used along with data augmentation and transfer learning methods. This approach reduced the pupil detection error to 3.31 pixels after training on 448 labeled images for 30,000 iterations. While deep learning methods provide high accuracy, they impose substantial computational demands; therefore, the suitable algorithm should be chosen based on the experimental objective and available hardware resources. The proposed behavioral monitoring system can be used simultaneously with optogenetics and electrophysiological recordings, which provides a versatile and beneficial tool for advancing research in cognitive neuroscience.

Abstract Background and Aim: Treatment planning systems (TPSs) should be prepared to identify various tissues of body. This process requires a CT-ED curve as an input to TPS. In this process, a phantom with various inhomogeneities is scanned with CT and then the CT numbers of corresponding materials of the phantom are obtained. By knowing the electron density of these materials, The CT-ED curve can be entered into the TPS and the system can identify the material of different tissues and perform dose calculations based on the tissue type using this curve. The purpose of this study is to design and fabricate a phantom with 6 different inhomogeneities to be used in the process of commissioning TPSs.
Material and Methods: The electron density phantom with code M062 made by CIRS company of America was chosen as the reference phantom. The body of the new phantom was designed using Solidworks software. A cylindrical phantom with height of 15cm and diameter of 20cm was designed by considering various inhomogeneities. The CT images were obtained from two phantoms under same conditions. After determining the CT numbers and the electron densities of the materials, the CT-ED curves were obtained and compared with each other using Excel software.
Results: The results obtained from both phantoms were close to each other and subsequently the CT-ED curves of both phantoms had the same structure with great similarity to each other.
Conclusion: The fabricated phantom is equivalent to the reference phantom and has all three main parts: lung, soft tissue and bone and can be used in radiotherapy centers instead of the reference phantom for commissioning TPSs.
Keywords: Phantom, Electron density, Treatment Planning System, Radiotherapy.

Abstract Based on the density functional theory and the GGA approximation, by applying the improved potential of TB-mbJ the structural, electronic, optical, and thermodynamic properties of the XVSi semiconductor compounds (X = Co, Rh) and its [001] films Were studied. These two heusler compounds with the non-magnetic semiconductor behavior are stable in the MgAgAs-type cubic structure with F4-3m space group. Due to the good responses of the real and imaginary parts of the dielectric function for CoVSi and RhVSi in the visible spectrum range and the low electronic loss function, these two heuslers will be suitable for optical applications in this energy range. An examination of the stability phase diagram of [001] films showed that all 6 of its possible terminations would be thermodynamically stable. The electronic structure of these films indicates the emergence of half-metallic magnetic behavior only for two terms of V-Si: CoVSi [001] and V-Si: RhVSi[001]. The responses of the dielectric function, as well as the absorption spectra of the two terms, are similar to those of the Bulk state, but with less intensity, while the electron loss in these two films is greater than that of the Bulk.

Abstract In this research, energy band gap of specific materials has been predicted using machine learning approach. We try by using mixing some usual machine learning methods to presenting an efficient machine learning method to improve material band gap prediction. Based on Gradient Boosting Decision Tree, Light Gradient Boosting, Random Forest and Extreme Gradient Boosting, we presented Stacking method by mixing all mentioned methods as an efficient machine learning method.

Abstract Classical plasma physics primarily addresses regimes characterized by high temperatures and low densities, where quantum mechanical effects are essentially negligible. In recent years, however, advances in technology—particularly in semiconductor engineering and the development of nanoscale structures—have opened the way for studying and forecasting practical applications of plasma physics under conditions where the quantum nature of particles plays a pivotal role. In this study, several approaches to modeling quantum effects in collisionless electrostatic plasmas are explored. The most comprehensive kinetic description of these phenomena is based on the Wigner equation, the quantum analogue of the classical Vlasov equation. The Wigner formalism is remarkable in that it frames quantum theory within the familiar classical phase space, though it introduces the challenge of distribution functions that may take on negative values. From an equivalent standpoint, the Wigner model can be reformulated in terms of N single-particle Schrödinger equations coupled with the Poisson equation—an approach known as the Hartree formalism—which closely parallels the multi-flow method in classical plasma physics. To manage the complexity inherent in these kinetic frameworks, a quantum fluid model can be obtained by taking velocity-space moments of the Wigner equation. These reduced models facilitate the investigation of collective particle dynamics with reasonable accuracy and within a more tractable theoretical structure. Furthermore, in specific regimes characterized by high excitation energies, semiclassical kinetic models of the Vlasov–Poisson type can be utilized, provided that the initial ground state is determined according to quantum mechanical principles. The models discussed herein have been validated and cross-compared in both linear and nonlinear scenarios. Their outcomes indicate that integrating quantum and classical formulations yields a richer and more nuanced understanding of plasma behavior at microscopic scales and under extreme physical conditions.

Abstract One of the effective methods for identifying high-mass neutron stars is to study binary systems containing millisecond pulsars. In such systems, the companion star is affected by intense pulsar irradiation, which alters its apparent brightness and allows for the estimation of the neutron star’s mass. This irradiation also displaces the optical center of the companion relative to its center of mass, which in turn increases the uncertainty in precisely determining the orbital parameters and the neutron star mass. In this study, we investigated the binary system PSR J2215+5135 using Eclipsing Light Curve (ELC) modeling along with combined photometric and spectroscopic data, and estimated the mass of the neutron star through comprehensive modeling of the system’s irradiation. The binary system lies at an estimated distance of ~ 3 kpc from Earth. Its neutron star is a rapidly rotating millisecond pulsar with a spin period of 2.61 ms, while the binary orbit has a period of 4.14 hr. To achieve this, we employed a physical model of the irradiated companion star and simultaneously fitted light curves in three different bands as well as radial velocity curves from two distinct spectral groups. Our results yield a center-of-mass velocity for the companion star of K1 = 414.6−2.6 +4.6 km/s and an orbital inclination angle of i = 64.1°. The neutron star’s mass was determined to be M2 = 2.28−0.11 +0.12 M☉, and the companion star’s mass was estimated at M1 = 0.32±0.10 M☉. In part of this work, the effect of hot spots on the companion's surface was examined and compared to models excluding such features. To avoid exclusivity in analyzing data from compact-object binaries, the use of accessible and general-purpose modeling tools is essential, as it enables independent reproduction and validation of results by various research groups. The significance of this study lies in the fact that, unlike many proprietary tools, the ELC model is publicly available to the scientific community. Achieving results with over 99% consistency compared to specialized codes underscores the credibility and reliability of this model for future studies—including projects related to the Iranian National Observatory. Moreover, the identification of a neutron star with such a high mass places stringent constraints on the equation of state of dense matter and may prompt revisions of existing theoretical models.