Background: Nowadays we are facing significant advances in technology. Virtual reality, which is considered as one of these novel technologies, has been entered into many different areas of our lives, from video games to various industrial uses as well as education, travel, healthcare, entertainment and research. Despite the benefits of virtual reality, the attitudes of patients toward using it are still unclear. Therefore, the aim of this study was to design a questionnaire to assess accepting virtual reality technology among patients with mental disorders. Methods: This design and evaluation of tools study was conducted in two phases including design of the questionnaire and evaluating its validity and reliability in 1400. In this study, CVR and CVI coefficients were used to evaluate the content validity and its external reliability was determined using Cronbach's alpha method. Results: In the current study, the content validity index was calculated to be 0. 8, which is an acceptable value for this questionnaire. Also, Cronbach's alpha was calculated to be 0. 7 and the correlation between the questions shown to be acceptable at a significant level of 0. 0001 Conclusions: According to the results, it was found that the questionnaire has high validity and reliability to assess the acceptance of virtual reality technology among patients with mental disorders.

In cavity opto-mechanical cooling, driving laser optical field, exerts extra decay to the mechanical mode of cavity, and cause a return to the ground state. In this study, quantum cooling of an opto-mechanical cavity in contact with a thermal bath, has been simulated by using Quantum Toolbox in Python “QuTiP”. Simulation results show that, the cavity cooling process, takes more time with increasing the decay rate of optical modes and go faster with increasing the decay rate of mechanical modes. Also this process takes less time with the increasing coupling constant of optical and mechanical modes. By adding a qubit to the system, simulation shows that the cooling process could happen faster in compare to the previous.

Multipactor(MP) is an unwanted phenomenon which is occurred in the radio frequency vacuum devices. Research to find a convenient method to lower the MP threshold or move it away from the operational range is an important key in the design of microwave components. In this regard, the accuracy of predicting the MP threshold is essential. In the simulation of MP, the secondary electron yield model is typically built in the commercial code; CST code. The accuracy of the results of MP simulation is mainly determined by the variation of secondary electron yield model. Therefore, in this paper, we calculate the effect of the variation of the secondary electron yield on the results of the MP simulation. For this purpose, the generalized polynomial chaos method is employed using Python programming. This method is based on an orthogonal polynomial expansion. Here, MP in the rectangular waveguide, Cornell Electron Storage Ring input coupler, is studied.

In standard gravimetric correction methods, after the raw gravity data sets were corrected for drift, tide, latitude, and free-air effects to obtain free air anomalies, the effect of the mass between the reference surface and ground surface is eliminated in two steps including Bouguer and terrain corrections. But this study removes this effect in one step through the forward modeling method. To do this, two things are necessary for finding more accurate answers. First, how is the underground discretization, and to what extent a network of Digital Terrain Model (DTM) is available? Quad tree mesh accessible in Simulation and Parameter Estimation in Geophysics (SimPEG) is a very accurate and advanced meshing algorithm to discretize subsurface based on our requirements. This meshing system can choose the size of cells in the desired locations. Hence, using this flexible discretization, it is possible to define the smaller cells in borders, near the topographic region, which helps a for more precise answers. Having a dense DTM, the SRTM GeoTiff pictures are downloaded from USGS Earth explorer with 1 arc-second (90 m) resolution (https://doi.org/10.5066/F7PR7TFT), and then height information is extracted from these pictures through GeoToolkit (http://toolkit.geosci.xyz) script. Assuming a flat geoid for our study area, topography is extracted from the SRTM and the pictures are interpolated to estimate the elevation at the gravity observation points. The gravity effect of the model space (the space between the reference surface and topography) is computed via numerical forward modeling assuming a constant density (2.67 gr/cm3). This procedure is done by the Simulation module in SimPEG and is considered as the Bouguer and terrain corrections simultaneously. These corrections are subtracted from the free-air anomalies, which yields the complete Bouguer anomaly. This method is powerful in contrast to other standard methods. In standard methods, Bouguer correction considers Bouguer slab approximation. Therefore, accuracy is lost. Also, in large-scale problems, curvature correction becomes necessary. Also, terrain correction for removing the effects of the mass between the lowlands and heights of the region is inevitable. Terrain correction considers two approximations. First, it uses average height. Hence this procedure has a low precision. Secondly it divides the surrounding area into three zones (near, middle, and far) and computes the effects of middle and far zones with lower precision. Therefore, it decreases the accuracy of the results. The mentioned method is tested on 399 ground gravity data with a grid spacing of about 5 km prepared by the National Cartographical Center of Iran (NCC) in an area of about 200 km in 200 km located in parts of Central Zagros and Central Iran. The results obtained from this one-stage correction method are more accurate and less complicated in doing compared to the results of the usual procedure. Because in this method, we have no simplifying assumptions such as infinite Bouguer slab in Bouguer correction or using relative heights in terrain correction that exist in standard methods.

پژوهش حاضر با هدف سنتز ادبیات مربوط به دانشگاه مهارت محور در راستای شناسایی مولفه های دانشگاه مهارت محور در نظام آموزش عالی کشور انجام شده است. این پژوهش از نظر هدف کاربردی و با روش کیفی فراترکیب انجام شده است. جامعه آماری، کلیه مطالعات انجام شده در بازه زمانی 1380 الی 1401 است. در این راستا 38پژوهش در زمینه موضوع موردنظر ارزیابی و درنهایت 22 پژوهش به صورت هدفمند انتخاب گردید و با تحلیل محتوا و ترکیب ادبیات مربوط، درمجموع 126 کد،12 مفهوم، 7 مقوله کلیدی طی فرایند جست وجو و ترکیب نظام مند متون شناسایی و مورد تحلیل قرار گرفت و اعتبار آن از طریق آزمون کاپای کوهن تأیید گردید. نتایج تحلیل نشان داد، که مهم ترین مولفه های کلیدی شناسایی شده شامل: دولت، ماموریت و رسالت های دانشگاه، پژوهش، آموزش، ارتباط با صنعت، فرهنگ و تعامل با محیط بین الملل است. این پژوهش ازآن جهت که درک عمیقی از ادبیات موجود در مورد موضوع پژوهش با شناسایی مولفه های دانشگاه مهارت محور ارائه می دهد، می تواند در سیاست گذاری های نظام آموزش عالی کشور در راستای توسعه مهارت محوری در نظام آموزش عالی کشور مورداستفاده قرار گیرد.

Machine learning methods are widely used today to estimate petrophysical data. In this study, an attempt has been made to calculate shear sonic log (DTS) from other petrophysical data using machine learning methods and compare it with the sonic data obtained from the core. For this purpose, computational methods such as Standard Deviation, Isolation Forest, Min. Covariance, and Outlier Factors were used to normalize the data and were compared. Given the amount of missing data and box plots, the Standard Deviation method was selected for normalization. The machine learning methods used include Random Forest, Multiple Regression, Boosted Regression, Support Vector Regression, K-Nearest Neighbor, and MLP Regressor. Multiple regression had the lowest evaluation index (R2=0.94), while Random Forest regression had the highest correlation between the estimated shear sonic log and the original shear sonic log with an evaluation index of 0.98. Therefore, Random Forest regression was used for the final estimation, and to prevent data generalization or overfitting, the GridSearchCV function was used to calculate optimal hyperparameters and final estimation. The estimated sonic log showed a very high similarity with the core data.

One of the Principles of water resources management is the optimal use of the reservoirs as the main sources of surface water, and this issue has a special importance in the science of water engineering. In this research, the new K-means clustering method to discretize reservoir inflow has been presented for the Stochastic Dynamic Programming(SDP). In addition, the Moran's method is used to discretize the reservoir storage. By the programming in the Python environment, the historical reservoir inflow in each season is classified to different clusters and obtained the best inflow cluster for each season. The effects of this clustering is also considering in the SDP of Jamishan reservoir. In general, the change in inflow classification will lead to a fundamental change in the transition probability matrix. Thus, the use of K-means method for the reservoir inflow discretization, due to the possibility of optimizing the number of clusters in each time period, can be very useful for the SDP. finally, it is strongly recommended to use K-means method to discretize reservoir inflow for reservoir operation by SDP.The k-means clustering algorithm was first used by James McQueen in 1967. K-means is an object-based algorithm that selects representative clusters from the data itself rather than averaging them. Actually, K-means of a cluster is the most central element of a cluster. The purpose of this method is to reduce sensitivity to large values in the data set. In this algorithm, each cluster is introduced with one of the data close to the center. In this algorithm, according to the number of data categories (k), the value of the least squares function is minimized and the data are categorized in the best way. In addition, the Moran's method is used to discretize the reservoir storage. In this method, the upper and lower limit of the range of changes and the upper limit of each category are used as indicators of discretization of the reservoir volume. The study area includes Jamishan reservoir sub-basin with an area of 527.07 km2 located in the southwest of Sanghar city near the Pirsalman hydrometric station. The annual average of rainfall, evaporation and temperature are 441 mm, 1534 mm and 10 degrees Celsius, respectively.Evaluating the performance of the K-means model in 4 different seasons, showed that among the 19 considered clusters, the best result in seasonal classification is obtained by the 5 inflow clusters according to the performance rate in fall, winter, spring and summer seasons - 142.57, -176.90, -475.36 and -2.10, respectively. In order to investigate the effect of classification of flow in the results of the first order Markov chain, the possible values are given in Table 1 for 4 seasons in 5 clusters, and in this table, the specified numbers indicate the probability of moving each cluster for each season.In the following, using the backward recursive function, the calculations are continued until reaching the stationary state condition. Finally, the value of l* was obtained for all 4 periods and for different combinations of k and i.The results of steady state condition showed that l* happened mostly in spring up to 5 clusters of the reservoir storage and the least happened in summer with one cluster. Then, the calculations of the reservoir release probability in each period for each class of inflow and storage have been made. The highest value has occurred for reservoir storage class 4 in autumn, winter, and spring seasons but in summer season, due to less inflow and high water demand, it has happened in reservoir storage class 5.In this research, Stochastic Dynamic Programming(SDP) of Jamishan dam reservoir is discussed using K-means method in classifying the inflow discharge seasonally for the 41 years’ historical data. Moran's method is also used to classify the storage volume of the reservoir by 7 classes. To calculate the transition probability matrix during the first-order Markov chain process, it is necessary to have the flow class in each period. For this purpose, the k-means method is used. The reservoir inflow in each season is classified from 2 to 20 classes by programming in the Python environment and especially with Scikit-learn library. Evaluating the performance of the K-means model in 4 different seasons, showed that among the 19 considered clusters, the best result in seasonal classification is obtained by the 5 inflow clusters. Changing in the number of inflow cluster leads to changes in the transition probability matrix and this process would change the results of reservoir operation. It can be said that the use of this flow classification method can have a significant impact on the management and optimization of dam reservoir performance. In general, the use of new classification methods such as K-means method in the discretization of reservoir inflow for the reservoir stochastic dynamic programming can be very beneficial and effective

Graphics statics is a geometric method for analyzing and calculating the internal forces of structural members under external loads. In this method, two diagrams of structural form and internal forces in the members are determined and drawn graphically. The process of parameterization of this method in both form production and internal force calculations can lead to various designs with the least amount of time. In such circumstances, Architects and Civil engineers can make the best choice based on the criteria and objectives of the structure, form and geometric topology. In this paper, the parametric graphics statics analysis of various truss designs, derived from the popular species Warren, Pratt, Howe, and Fink, or any related free-form, is performed in the Python programming language in the grasshopper Plugin. The results of the analysis performed with the results in the available sources show that the proposed parametric graphics statics method has a high performance in calculating the internal forces of members of conventional trusses or free-form with any desired load.