New Research in Physics

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 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 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 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 paper, the interaction between two Lambda-type three-level atoms and a single-mode field is investigated in the presence of detuning parameters and dissipation. Assuming that the two atoms are initially prepared in their excited states and the field is in a coherent state, the state vector of the total system is obtained analytically under resonance conditions in the absence of dissipation, and numerically under off-resonant conditions in the presence of dissipation. The entanglement between the atoms and the field is quantified using the linear entropy, while the entanglement between the two atoms is evaluated by means of the negativity measure. The numerical results show that the behavior of the entanglement measures strongly depends on the system parameters, and that both the maximum value and the persistence of entanglement can be controlled by adjusting the resonance conditions and the strength of dissipation.

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 In this paper electronic structure, magnetic and optical properties of pure SnO₂ and Sn_1-xCo_xO₂ (x= 6.25%, 12.5%, 18.75% and 25%) samples and effect of Oxygen vacancies on those were investigated using density functional theory (DFT). Density of states and band structure curves show Co doping causes increase in energy surfaces near to the Fermi level. The results of this study showed the ground states of the 12.5% and 25% samples were ferromagnetic. The study of the effects of Oxygen vacancy on 12.5%Co doped- sample revealed that the introducing of vacancies causes increase in magnetic moment of the Co ions. The results were also showed that by increasing the Co concentration up to 12.5% the optical band gap decreases and then by more increasing of Co it increases. The intensity of the first peak in absorption curve increases and the intensity of the other peak above the 6eV decreases. The red shift of peak positions was also observed on absorption curves with increasing Co concentration to 12.5%.