In this paper, recommended spiral passive micromixer was designed and simulated. spiral design has the potential to create and strengthen the centrifugal force and the secondary flow. A series of simulations were carried out to evaluate the effects of channel width, channel depth, the gap between loops, and flowrate on the micromixer performance. These features impact the contact area of the two fluids and ultimately lead to an increment in the quality of the mixture. In this study, for the flow rate of 25 μl/min and molecular diffusion coefficient of 1×10-10 m2/s, mixing efficiency of more than 90% is achieved after 30 (approximately one-third of the total channel length). Finally, the optimized design fabricated using proposed 3D printing method.

In this research, in order to investigate the effect of the piezoelectric patch which is used as a sensor or actuator in rotating flexible structures such as a helicopter blade, the free vibrations of the rotating rectangular sheet with and without the piezoelectric patch have been presented. First-order shear deformation theory is considered for plate displacement and piezoelectric field. Considering the effect of Coriolis acceleration, centrifugal acceleration and centrifugal in-plane forces, the equations of motion are derived from Hamilton's principle and the electromechanical couple equation is obtained from Maxwell's equation. For piezoelectric, two electrical conditions, open circuit and closed circuit, which are used in sensors and actuators, respectively, have been considered. The equations are discretized with the help of the Numerical method of generalized differential squares and the matrices of inertia mass, eccentricity, Coriolis and stiffness matrix are obtained. Natural frequency values for beam and rotating plate have been compared in Abaqus software. Also, the values obtained from the Numerical solution in MATLAB have been verified with articles and ABAQUS, which have high accuracy. The effect of parameters such as hub radius, rotation speed, sheet thickness, aspect ratio, piezoelectric patch thickness and applied voltage on the natural frequency of the system has also been investigated.

Plastic hinge properties play a crucial role in predicting the nonlinear response of structural elements. The plastic hinge region of reinforced concrete normal beams has been previously studied experimentally and analytically. The main objective of this research is to evaluate the behavior of the plastic hinge region of reinforced concrete deep beams and its comparison with normal beams through finite element simulation. To do so, ten beams contain six deep beams, and four normal beams, under concentrated and uniformly distributed loading, are investigated. Lengths in the plastic hinge region involving curvature localization, rebar yielding, and concrete crushing zones are studied. The results indicate that the curvature localization zone is not suitable for the prediction of plastic hinge length in reinforced concrete deep beams. Based on the results it can be stated that in simply supported normal beams the concrete crushing zone is focused on the middle span, but in simply supported deep beams by creating a compression strut between loading place and support, the concrete crushing zone spreads along the compression trajectory. The rebar yielding zone of simply supported beams increases as the loading type is changed from the concentrated load at the middle to the uniformly distributed load.

We address the throughput maximization problem for downlink transmission in DF-relay-assisted cognitive radio networks (CRNs) based on simultaneous wireless information and power transfer (SWIPT) capability. In this envisioned network, multiple-input multiple-output (MIMO) relay and secondary user (SU) equipment are designed to handle both radio frequency (RF) signal energy harvesting and SWIPT functional tasks. Additionally, the cognitive base station (CBS) communicates with the SU only via the MIMO relay. Based on the considered network model, several combined constraints of the main problem complicate the solution. Therefore, in this paper, we apply heuristic guidelines within the convex optimization framework to handle this complexity. First, consider the problem of maximizing throughput on both sides of the relay separately. Second, each side progresses to solve the complex problem optimally by adopting strategies for solving sub-problems. Finally, these optimal solutions are synthesized by proposing a heuristic iterative power allocation algorithm that satisfies the combinatorial constraints with short convergence times. The performance of the optimal proposed algorithm (OPA) is evaluated against benchmark algorithms via Numerical results on optimality, convergence time, constraints’ compliance, and imperfect channel state information (CSI) on the CBS-PU link.

Calculation the rate of change or differentiation of digital signal has always been one of the most important challenges in Digital Signal Processing field. Differentiating digital signals is essential in various applications. In many applications، even well-known methods aren’ t useful and cause sharp increase in error and noise up to 10 times. One of the applications that is focused on in this paper is determining velocity from noisy and discrete data. Various aspects of this issue is investigated and the best derivation algorithm for the aforementioned application is designed and proposed. It was seen that Kalman filter method is the best approach for minimizing least square error for determining derivative from GPS data. It was also seen that Kalman filter method has not good transient response. This problem is somewhat improved with modification Kalman filter and using adaptive Kalman filter with appropriate weighting of old data. Another important aspect، which is also discussed in this paper، is adequate classification of various differentiation algorithms that is designed and referring the practical application of each algorithms. In order to depict the advantages of the methods، some practical results are given based on real GPS data for extracting instantaneous velocity vector.

Tumor detection and isolation in magnetic resonance imaging (MRI) is a significant consideration, but when done manually by people, it is very time consuming and may not be accurate. Also, the appearance of the tumor tissue varies from patient to patient, and there are similarities between the tumor and the natural tissue of the brain. In this paper, we have tried to provide an automated method for diagnosing and displaying brain tumors in MRI images. Images of patients with glioblastoma were used after applying pre-processing and removing areas that have no useful information (such as eyes, scalp, etc.). We used a bounding box algorithm, to create a projection for to determining the initial range of the tumor in the next step, an artificial bee colony algorithm, to determine an initial point of the tumor area and then the Grow cut algorithm for, the exact boundary of the tumor area. Our method is automatic and extensively independent of the operator. comparison between results of 12 patients in our method with other similar methods indicate a high accuracy of the proposed method (about 98%) in comparison s.

In this paper for solving Toeplitz system by PCG, two parallel algorithms based on mesh and hypercube are given. We have shown that using these algorithms reduce of arithmetical operations. Also, we investigate speed up and efficiency for these algorithms.

Among various types of wave energy converters, the oscillating water column (OWC) has attracted significant research attention. In this paper, a 1: 10 scale OWC with dimensions of 100×100×160 cm, variable inlet height and draft was Numerically studied. Based on the tests conducted, it was found that the wave amplitude in the range of Caspian Sea waves decreased with the increase of wave frequency, to the extent that at the sloshing frequency, the system efficiency dropped significantly. To solve this problem, changes in the geometry of the device were studied, and Numerical simulations were performed at the highest frequency using OpenFOAM software. Using Reynolds-averaged Navier-Stokes (RANS) equations, Numerical simulations were performed in 3D, two-phase, and turbulent flow conditions. Changing the geometry was initially investigated by adjusting the height of the OWC inlet duct, and then by adding an inlet at the different angles of 0, 20, and 40 degrees. The results showed that by increasing the height of the inlet by 10 cm while keeping the water depth and wave conditions constant, the maximum output power of the system increased by 54%. However, after the optimization of the inlet duct, it was found that the best angle for an inlet duct is 30°, compared to the case without an inlet, which increased the maximum output power by up to 13% and slightly reduced the sloshing by more than 50%.

Automatic topic detection seems unavoidable in social media analysis due to big text data which their users generate. Clustering-based methods are one of the most important and up-to-date categories in topic detection. The goal of this research is to have a wide study on this category. Therefore, this paper aims to study the main components of clustering-based-topic-detection, which are embedding methods, distance metrics, and clustering algorithms. Transfer learning and consequently pretrained language models and word embeddings have been considered in recent years. Regarding the importance of embedding methods, the efficiency of five new embedding methods, from earlier to recent ones, are compared in this paper. To conduct our study, two commonly used distance metrics, in addition to five important clustering algorithms in the field of topic detection, are implemented by the authors. As COVID-19 has turned into a hot trending topic on social networks in recent years, a dataset including one-month tweets collected with COVID-19-related hashtags is used for this study. More than 7500 experiments are performed to determine tunable parameters. Then all combinations of embedding methods, distance metrics and clustering algorithms (50 combinations) are evaluated using Silhouette metric. Results show that T5 strongly outperforms other embedding methods, cosine distance is weakly better than other distance metrics, and DBSCAN is superior to other clustering algorithms.

Introduction temporal and spatial change of meteorological and environmental variables is very important. These changes can be predicted by Numerical prediction models over time and in different locations and can beprovided as spatial zoning maps with interpolation methods such as geostatistics (16, 6). But these maps are comparable to each other as visual, qualitative and univariate for a limited number of maps (15). To resolve this problem the similarity algorithm is used. This algorithm is a simultaneous comparison method to a large number of data (18). Numerical prediction models such as MM5 were used in different studies (10, 22, and 23). But alittle research is done to compare the spatio-temporal similarity of the models with real data quantitatively. The purpose of this paper is to integrate geostatistical techniques with similarity algorithm to study the spatial and temporal MM5 model predicted results with real data.Materials and Methods The study area is north east of Iran.55 to 61 degrees of longitude and latitude is 30to 38 degrees. Monthly and annual temperature and precipitation actual data for the period of 1990-2010 was received from the Meteorological Agency and Department of Energy. MM5 Model Data, with a spatial resolution0.5 × 0.5 degree were downloaded from the NASA website (5). GS+ and ArcGis software were used to produce each variable map. We used multivariate methods co-kriging and kriging with an external drift by apply in gtopography and height as a secondary variable via implementing Digital Elevation Model. (6, 12, 14). Then the standardize and similarity algorithms (9, 11) was applied by programming in MATLAB software to each map gridpoint. The spatial and temporal similarities between data collections and model results were obtained by Fvalues. These values are between 0 and 0.5 where the value below 0.2 indicates good similarity and above 0.5shows very poor similarity. The results were plotted on maps by MATLAB software.Results Discussion In this study the similarity and geostatistical algorithm were combined to compare and evaluate spatio-temporal of predicted temperature and precipitation data by MM5 model with actual data. The analysis of the similarity map is based on the F values, the area and also the uniformity of distribution over the area. The similarity between predicted and actual data is higher when F values are low and distributed more uniform. The temperature similarity maps showed that F values are between 0.0 - 0.2 in cold seasons. It was shown that the values had spatial continuity and uniform distribution. A large part of area (almost 80%) is covered by lowest F value (F 0.1), which shows very high similarity among temperature datasets. The high estvalues (0.15<F<0.2) occurred in the central of the study area. In the warm seasons F values were between 0.0- 0.4. These values had spatial continuity and uniform distribution which is lower than cold season. The area ofgood similarity values (0.0 F 0.1) is almost 45% of the whole region. The highest values (F>0.3) in the centralregion indicate errors in the model predictions data. But generally prediction of model in both seasons for thetemperature was good. In annual time scale, F values are between 0.0 - 0.25. The area of good similarity value(0.0 F 0.1) is almost 65% of the whole region with spatial continuity and uniform distribution. Accuracy of the model declined from temperature of the cold season to annual and then warm season respectively. The precipitation similarity maps showed that in cold season F values changes between 0.05 - 0.4. These values hadless spatial continuity than temperature. In more than half of the area (60%) there was fairly good similaritywhere 0.05<F<0.15. The maximum values (0.3<F<0.35) occur in mountainous regions of the study area. Inwarm seasons F values are between 0.1- 0.45. These values are not uniformly distributed and dispersed. The areaof good similarity values (0.0 F 0.1) is zero percent. The highest values (F>0.3) in the central mount ainousarea and south part of region suggests the low similarity in the model predictions. Similarity between the coldseasons is much higher than the warm seasons, which is due to the variability of precipitation during the seasons.In the annual time scale, F values are between 0.05 - 0.3. F values (0.0­ F­ 0.1) are almost 40% of the whole region with uniform distribution. Overall, the higher uniform distribution of annual similarity values showed that prediction of model for annual precipitation data is better than seasonal. The maximum F values identified theareas with modeling error for various reasons. In this study the central and the southern parts had maximum Fvalues at different time steps. Plotted mean monthly values of similarity indicated minimum and maximum temperature F values were occurred in January and July while for precipitation was taken place in January and September respectively. This shows that MM5 model prediction was good in January.Conclusion: In this paper, the similarity algorithm discovered spatial and temporal similarities between the predicted and actual data for temperature and precipitation variables. According to the obtained F values, the model predicts temperature was better than precipitation. Due to the upward movement of the convective zoneand the effects of topography for both variables, the similarity between predicted and actual data is low in warm seasons. In small areas of the south and the central region of the study area, F values are between 2.0 and 4.0, respectively, which could be considered as a weak similarity. The area with high f values (F>0.45) can be seenon every precipitation map, which suggests a large error values related to reporting of the station data.