The quantum superposition principle is reexamined and reformulated based on the adiabatic theorem of quantum mechanics, nonadiabatic dressed states and experimental evidences. The collapse of the wave function and the quantum measurement problem are explained within the reformulated quantum superposition principle.

The dynamics of the toroidal wheel with loaded mass that rolls without slipping is studied. The reduced equations of motion for a dynamic system of dimension five, depending on three angular rates, the leaning angle and the rolling angle, are derived. The dynamic system is controlled by the tilt and throttle torque.

In this paper, we consider a nonlinear boundary value problem for the vibrations of a nonlinear continuous model string, which describes the standing vibrations of a finite, continuous, and nonlinear string. Under the assumption that the nonlinear string is proportional to the derivative of the stress with respect to the strain, exact solutions of this problem are provided. To obtain the solution, the given nonlinear partial differential equation is transformed into a first-order nonlinear system by introducing the dependent variables transformation $u = y_x$, $v = y_t$. The modified method of separation of variables is applied to the system and the solutions for $t$ and $x$ are written as general implicit formulas. It is shown that the technique used here offers advantages in finding solutions of such problems.

A spatial symmetric generalized Kadomtsev-Petviashvili (KP) model in (2+1)-dimensions is taken into consideration in the present study. Advanced computer algebra system software is employed for symbolic calculations, enabling the construction of diverse multiwave interaction solutions for this model. Moreover, by appropriately setting the associated constants, the profiles of some acquired solutions are plotted. This graphical representation effectively clarifies the structural characteristics of waves, providing valuable insights into the underlying physical phenomena.

Gamma-ray detectors are used for radioactive sources calibration and testing in ionizing radiation metrology laboratories, as more than 90% of radionuclides are gamma-ray emitters. When measuring the activity of radioactive sources with complex geometries and matrices for which standard reference sources are unavailable, an accurate understanding of efficiency transfer factors is required. This is the case in testing laboratories where cotton wipes/smears are widely used to assess non-fixed radioactive surface contamination. The efficiency transfer factors were determined by simulating the full-energy peak efficiency using Monte Carlo codes such as FLUKA, MCNP, and GESPECOR.

For precise measurements of High Purity Germanium (HPGe) detector and in order to estimate the Full Energy Peak Efficiency (FEPE), a theoretical procedure using the Monte Carlo simulation method was established in the Central Laboratory for Environmental Radioactivity Measurements Inter-comparison and Training (CLERMIT), as a continuation of previous work. A set of standard point sources (¹³³Ba, ¹³⁷Cs, ⁶⁰Co and ²²Na) was used, employing a 8 cm distance from the detector and appropriate detection geometry. MCNP5 input file was constructed and the detector was simulated. The detector efficiency curve was normalized to the volume efficiency curve using a KCl solution. Experimental results were compared with those simulated by MCNP5. Uncertainty analyses were performed to determine the effect of each operational and design parameter on the efficiency curve. Another set of point sources with different activities was used for method verification. For the method validation, an IAEA-326 certified reference sample was used for verification. A good agreement between the two methods was achieved, with discrepancies of less than 5% (ranging from -1.10 to 2.63%). Some selected environmental samples with different matrices were measured and the natural radioactivity concentration for ²³⁸U-series, ²³²Th-series and ⁴⁰K were determined.

Gap equations at finite temperature are deduced in the pure isoscalar pairing case ($T=0$). Expressions of the nuclear statistical quantities, i.e. the energy, the entropy and the heat capacity are deduced. A numerical study is then performed using the schematic one-level model.
It is found that the behavior of the isoscalar neutron-proton gap parameter $\Delta^{T}_{np} = 0$ as a function of the temperature is similar to that of $\Delta_{pp}$ and $\Delta_{nn}$ in the conventional Finite Temperature BCS (FTBCS) approach. The presence of a critical temperature value beyond which $\Delta^{T}_{np} = 0$ is null is noted.
Dealing with the nuclear statistical quantities, their behavior as a function of the temperature is similar to that obtained using the conventional FTBCS theory in the pairing between like-particles case. An increase in the energy value is noted compared with other types of pairing.

Silver nanoparticles were prepared by chemical reduction method. Silver nitrate was taken as metal precursor, chondroitin sulfate as stabilizing agent and glucose as reducing agent. The formation of AgNPc was monitored by UV-VIS absorption spectroscopy. UV-VIS spectroscopy revealed the formation of AgNPc by showing typical surface plasmon absorption maxima at 420-429 nm in the UV-VIS spectrum. Comparison of theoretical (Mie light scattering theory) and experimental data showed that the diameter of AgNPc in colloidal solution is about 20 nm. X-ray diffraction (XRD), FTIR spectroscopy and UV-VIS spectroscopy were used to characterize the obtained nanoparticles. The peaks in the XRD curves are in good agreement with the standard cubic shape values of metallic silver (JCPDS No. 040783) and no peaks of other crystalline impurity phases were detected.

Our work focused to prepare diatomite-silica that can be reduced to silicon, Algerian diatomite was synthesized through a chemical method. This research aimed to study the influence of acid treatment and grinding on diatomite’s impurity level. An analysis of the X-ray photoelectron spectroscopy (XPS) showed a decrease in the concentration of impurities in the diatomite.
A hydrochloric acid (HCl) concentration that’s 4 mol/L resulted into a soft leaching of most organics such as carbon, but still retained impurities. The X-ray diffraction (XRD) analysis also confirmed that the chemical configuration of SiO2 was barely altered. In comparison, grinding led to an invasion of diatomite by impurities and to structural disorders. These findings are relevant for encouraging work on purification steps which facilitate the extraction of non-pertaining silica while producing solar silicon for solar cells.

In this work, we conducted a numerical study of cavitating nanofluid flow through a Venturi. The objective is to investigate the influence of nanoparticles in the base fluid on the cavitation phenomenon. The computational fluid dynamics code (CFD) was selected with a cavitation model. The mixture model for multiphase flow and the k-ω SST turbulence model were adopted. Three fluids were chosen: water, Cu/water and TiO₂/water with different volume franctions of nanoparticle (0%, 10%, 20%, 30%). The simulation was conducted with inlet and outlet pressures set at 700 kPa and atmosphere pressure respectively. The numerical results are compared with the previous experimental and numerical data for flow without nanoparticle. The obtained results found that, the presence of the nanoparticles in the base fluid lead to a slight increase in the static pressure, the position of pressure recovery a significant decrease in fluid velocity and an increase in the vapor fraction formation in the flow. Also, the increase of the nanoparticle volume fractions φ results a decrease in the pressure recovery position, fluid velocity and an increase in the vapor fraction formation. Therefore, the presence of nanoparticles in the base fluid promotes the phenomenon of cavitation.

Due to the tolerances given by the linear accelerator (LINAC) producer regarding the variability of mechanical movements, this work is focused on the influences of the mechanical components position in relative dose measurements such as percentage depth-dose distributions (PDD) and dose profiles. Those measurements represent an essential role in modelling the radiation beam accurately in the treatment planning system (TPS), such that the radiation therapy treatments can be delivered as intended. The main aspects of this work are to estimate the measurement uncertainty itself by statistical means and to provide a practical workflow to be performed prior to starting the dosimetric measurements.

Anomaly detection in time-series data is critical for ensuring stability in high-power laser systems, where deviations can indicate potential failures. This study optimizes a moving average-based methodology for anomaly detection accuracy by evaluating window sizes (3, 6, 9, 12, and 15) and threshold multipliers (1.0, 1.5, and 2.0). The analysis integrates Mean Squared Error (MSE), correlation analysis, and graphical evaluations, including anomaly distribution, moving average trends, and parameter sensitivity plots. Results indicate that smaller window sizes effectively detect short-term fluctuations but are more susceptible to noise, while larger windows smooth trends but may overlook minor anomalies. Threshold multipliers significantly impact detection, with lower values capturing more anomalies, potentially increasing false positives, and higher values reducing sensitivity but minimizing false alarms. MSE trends suggest a trade-off between sensitivity and robustness, where smaller windows better fit the data but risk overfitting noise, while larger windows reduce responsiveness but enhance stability. Correlation analysis scatterplots reveal a strong dependency between window size and MSE, while anomaly counts exhibit a nonlinear relationship with threshold multipliers. Anomaly detection plots and MSE vs. window size comparisons highlight detection efficiency. The study bridges statistical anomaly detection techniques with real-world laser monitoring, ensuring computational efficiency, robustness, and enhanced fault detection. These findings lay the groundwork for adaptive parameter tuning and machine learning integration in real-time anomaly detection for high-power laser systems.

The paper is devoted to the presentation of a method able to calculate kinematic ratios, torque and power flow distribution of epicyclic gear trains having more than 2DOF (two Degrees of Freedom). The method is based on the transformation of the compound epicyclic gear train into a structure consisting of an epicyclic gear train with 2DOF, preserving the DOF (number of Degrees of Freedom) of the overall structure as for the compound epicyclic gear trains. Consequently, it become possible to use analytical tools, including the computer. The method is exemplified on the most frequently used compound epicyclic gear trains such as Ravigneaux.

Real-time gas detection using a multiparameter data acquisition system has been widely adopted for many gas detection applications. It is attractive for its accurate, non-invasive, and fast determination of critical gas parameters such as concentration, temperature, and humidity. To perform real-time gas detection, data acquisition, and processing must be implemented. The aim is to simultaneously measure the responses of the gas sensor and those of humidity and temperature sensors integrated within the gas test cell to enable the evolution of relative humidity and ambient air temperature to be monitored in parallel with the gas detection process. In this work, a simple and cost-effective up to 16-channel real-time data acquisition, visualization, and storage system with a programmable voltage generator, calibrated sensors, and customized visualization software are designed and developed for gas detection applications. Experimental results show that this data acquisition system can accurately measure a wide range of sensor responses, and is suitable for measuring multiple sensors based on different materials or under different activation conditions. Dedicated software also enables users to easily capture, analyze, and save measurement data. In addition, the framework designed for a multi-parameter system is based on a real-time object-oriented methodology, enabling the implementation of hardware-software functions. The system is similar to the concept of the electronic nose, based on a network of sensors. This is one of the most viable approaches to solving the main problems encountered by most families of gas sensors, namely their lack of selectivity. By simultaneously measuring the response of sensors under different gases and environmental parameters, such as temperature, humidity, and pressure, it is possible to improve the accuracy and reliability of detection. This connection not only enables better analysis of sensor signals but also the identification of environmental influences on measurements, providing a better understanding of evaluation conditions.