Utilizing IoT technologies for monitoring large-scale smart facilities such as power, water and gas distribution networks has been the subject of many studies recently. The aim is to detect anomalous events in the network due to elements’ failure, bad designs, attacks or abuses of the network and alert the network operators in a timely manner. As the centralized cloud-based approaches are impractical in time-critical and real-time anomaly detection applications due to 1) high sensor-to-cloud transmission latency 2) high communication cost and 3) high energy consumption at the sensor nodes, the Distributed anomaly detection methods based on Deep Neural Networks (DNN) have been applied in past studies vastly. In these methods, in order to detect anomalies in real-time, copies of the anomaly detection model are placed at the sensor nodes (rather than placing one at the cloud node) reducing the sensor-to-cloud transmissions significantly. Nevertheless, new normal samples collected at the sensor nodes still need to be transmitted to the cloud node at predefined intervals to re-train the Distributed anomaly detection DNNs. In order to minimize these sensor-to-cloud transmissions during the retraining process, in this paper, two well-known lossless coding algorithms: Huffman Coding and Arithmetic Coding were studied and it was observed that the Huffman and Arithmetic Coding were able to reduce the transmission traffic up to 50% and 75% respectively using two IoT benchmark datasets of pipeline measurements. Besides, the Huffman Coding shown to be computationally feasible on resource limited sensors and resulted in up to 10% saving in energy consumption on each sensor resulting in longer network longevity. Moreover, the experimental results showed that the auto-encoder DNN could outperform the one-class SVM in the iterative Distributed anomaly detection method.

Aim: The aim of this study is to propose an approach for modeling spatiotemporal changes in rainfall that can be used as input for rainfall-runoff models.Research Method: To achieve this, rainfall data from four rain gauge stations in the Paskouhak catchment were used. Five parameters, including elevation, slope, aspect, longitude, and latitude, were identified. The different combinations of these five parameters were prioritized using the gamma test in WinGammaTM software. After the use of different regression models, the best model was selected based on evaluation criteria such as R2, RMSE, and the Taylor diagram. A raster map of a selected rainfall event was drawn in the Arc GIS environment. Finally, using the proposed approach of relative equations, the spatiotemporal changes in rainfall were modeled.Results: The results showed that using a second-degree nonlinear model and parameters of elevation and latitude, it is possible to accurately obtain the spatial distribution of rainfall in the form of a regular pixel grid (100 square meters) with high precision (R2=0.917 and RMSE=0.2277).Conclusion: In different rainfall events in small catchment areas, the variation in rainfall in each pixel is almost constant relative to other pixels, including the rain gauge station, the proposed approach in this study can model the spatiotemporal changes of each rainfall event as a three-dimensional matrix in the study area. The approach can be valuable in predicting potential flood events and in water resource management and planning. However, further research is required to validate the results and test the approach in other areas.

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.

Evaluating the cutting rate (CR) of stones is important in the cost estimation and the planning of the stone processing plants. This research used regression models to estimate the stones’ CR based on their physico-mechanical characteristics. Stone processing factories in Mahallat City (Markazi province, Iran) were visited, and the CR of diamond circular saws was recorded on six different travertine stones. Next, the stone block samples were collected from the quarries for laboratory tests. Stones’ porosity (n), uniaxial compressive strength (UCS), and Schmidt hammer hardness (SH) were determined in the laboratory as their physico-mechanical characteristics. Correlation relationships of CR with physico-mechanical characteristics were evaluated using simple and multiple regression analyses, and estimator models were developed. Results showed that multiple regression models are more reliable than simple regression for estimating the stones’ CR. The validity of the developed multiple regression models was verified with the published data of one researcher. The findings indicated that these models are accurate enough for estimating the CR of stones. Consequently, the multiple regression models provide practical advantages for estimating the CR and save time and cost during the planning and design of the stone processing factories.

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.

In this paper, a fully Distributed model for a single pole single throw traveling wave switch is introduced and important parameters of the switch such as insertion loss, isolation, and reflection coefficient are presented based on the lossy transmission line model of switch. The results of fully Distributed model are compared with the semiDistributed model’s results and have good agreement with them. By applying the fully Distributed model and calculating various switch’s parameters as a function of the switch length and operating frequency, the optimum switch length and operating frequency are obtained versus the parameters of switch, especially the reflection coefficient and isolation in OFF and ON conditions.