Natural disasters are an inevitable part of the world that we inhabit. Human casualties and financial losses are concomitants of these natural disasters. However, by an efficient crisis management program, we can minimize their physical and social damages. The real challenge in crisis management is the inability to timely receive the information from the stricken areas. Technology has come to the aid of crisis management programs to help find an answer to the problem. One of these technologies is wireless sensor network. With recent advances in this field, sensor nodes can independently respond to the queries from the users. This has transformed the Processing of the queries into one of the most useful chapters in sensor networks. Without requiring any infrastructure, the sensor network can easily be deployed in the stricken area. And with the help of spatial Query Processing, it can easily provide managers with the latest information. The main problem, however, is the irregular shape of the area. Since these areas require many points to present them, the transmission of the coordinates by sensor nodes necessitates an increase in the number of data packet transmissions in the sensor network. The high number of packets considerably increases energy consumption. In related previous works, to solve this problem, line simplification algorithm s, such as Ramer-Douglas-Peucker (RDP), were used. These algorithms could lessen energy consumption by reducing the number of points in the shape of the area. In this article, we present a new algorithm to simplify packet shapes which can reduce more points with more accuracy. This results in decreasing the number of transmitted packets in the network, the concomitant reduction of energy consumption, and, finally, increasing network lifetime. Our proposed method was implemented in different scenarios and could on average reduce network’s energy consumption by 72.3%, while it caused only 4.5% carelessness which, when compared to previous methods, showed a far better performance.

Nowadays, with the advancement of database information technology, databases has led to large-scale distributed databases. According to this study, database management systems are improved and optimized so that they provide responses to customer questions with lower cost. Query Processing in database management systems is one of the important topics that grabs attentions. Until now, many techniques have been implemented for Query Processing in database system. The purpose of these methods is to optimize Query Processing in the database. The main topics that is interested in Query Processing in the database makes run-time adjustments of Processing or summarizing topics by using the new approaches. The aim of this research is to optimize Processing in the database by using adaptive methods. Ant Colony Algorithm (ACO) is used for solving optimization problems. ACO relies on the created pheromone to select the optimal solution. In this article, in order to make adaptive hybrid Query Processing. The proposed algorithm is fundamentally divided into three parts: separator, replacement policy, and Query similarity detector. In order to improve the optimization and frequent adaption and correct selection in queries, the Ant Colony Algorithm has been applied in this research. In this algorithm, based on Versatility (adaptability) scheduling, Queries sent to the database have been attempted be collected. The simulation results of this method demonstrate that reduce spending time in the database. According to the proposed algorithm, one of the advantages of this method is to identify frequent queries in high traffic times and minimize the time and the execution time. This optimization method reduces the system load during high traffic load times for adaptive Query Processing and generally reduces the execution runtime and aiming to minimize cost. The rate of reduction of Query cost in the database with this method is 2. 7%. Due to the versatility of high-cost queries, this improvement is manifested in high traffic times. In the future Studies, by adapting new system development methods, distributed databases can be optimized

Query expansion as one of Query adaptation approaches, improves retrieval effectiveness of information retrieval. Pseudo-relevance feedback (PRF) is a Query expansion approach that supposes top-ranked documents are relevant to the Query concept, and selects expansion terms from top-ranked documents. However, Existing of irrelevant document in top-ranked documents is possible. Many approaches have been proposed for selecting relevant documents and ignoring irrelevant ones, which use clustering or classification of documents. Important issue in Query expansion approaches is using relevant documents for selecting expansion terms. In this paper, we propose clustering of pseudo-relevant documents based on Query sensitive similarity, which is efficient for placing similar documents together. Query sensitive similarity obtained good results in document retrieval rather than term-based similarity, is the reason for using in this paper. Clusters are ranked based on inner similarity, and some top ranked ones are selected for Query expansion. Then, we extract expansion terms from documents of selected clusters based on Term Frequency- Inverse document frequency (TF-IDF) scoring function. Conducted experiments over Medicine dataset (MED) shows that retrieval results for expanded queries with selected documents from clusters is better than basic retrieval (VSM) and Pseudo-relevance feedback. In addition, the effectiveness of retrieval is raised.

In a verifiable database scheme (VDB), a client with limited storage resources securely outsources its very large and dynamic database to an untrusted server such that any attempt to tamper with the data, or even any unintentional changes to the data, can be detected by the client with high probability. The latest work in this area has tried to add the secure search feature of single keyword and multiple keywords. In this paper, we intend to add a range Query to the features of this database. The scheme presented in this article provides the requirements of a secure search, namely the completeness of the search result, the proof of the empty search result, the lack of additional information leakage and the freshness of the search results, as well as the database with public verifiability. In the proposed scheme, the computational complexity of the client is not changed significantly compared with the previous scheme, but the computational and storage complexity of the server has increased which is justifiable by its rich resources.

In this paper, an alternative approach in operational modal analysis is presented, utilizing image Processing technique and transmissibility functions. Imaging sensors do not impose additional mass on the structure due to their non-contact nature, while transmissibility functions, independent of excitation type, can directly extract mode shapes. The innovation of this research lies in combining these two techniques to record dynamic responses and identify modal properties. To capture the temporal response history from video signals, the block-matching method with sub-pixel accuracy was employed. Validation was conducted by recording the response of the tip of a cantilevered steel beam subjected to impact excitation, using a high-speed camera and a laser vibrometer, simultaneously. The RMSE plots in the time domain and the PSD in the frequency domain indicate high accuracy of this method. Using this approach, the displacement time histories of various points on the structure were extracted from the video signals, and the modal properties, including natural frequencies, damping ratios, and mode shapes, were identified using the transmissibility matrix method. The results obtained from the proposed method were compared with the stochastic subspace identification (SSI) method and analytical solutions. The findings reveal the accuracy of the modal identification approach introduced in this article. The highest relative error in estimating the natural frequencies of the first and second modes, compared to the values from the laser method, are 0.19% and 0.13%, respectively, and in comparison to the analytical values, they are 0.34% and 1.5%, respectively.