Introduction: Using focus group to collect data is a valuable method for qualitative researchers. This method is being used increasingly in nursing research. It can provide rich information about a special topic through group dynamics. This article aims to provide a comprehensive review on characteristics of and implementing focus group as a data collection method.Content: A focus group is a semi-structured group session which is moderated by a group leader and held in an informal setting to collect information about a designated topic. The main characteristic of a focus group is the presentation of information and knowledge through interaction between the moderator and the group, as well as group members. Open-ended questions result in extended, in-depth and rich information. Also, participants' non-verbal responses can complete their verbal responses. Focus groups are used to study several qualitative subjects in the fields of politics, consumers' satisfaction, health subjects, quality of care evaluation, designing instruments and so on.Main components of a focus group include skilled moderator, proper participants, appropriate place and time, and correct implementation of the process. The moderator is responsible not only for guiding the participants through the discussion, but also for looking after the group dynamics to ensure that all participants dominate the discussion. Ideally, two people will be needed to conduct each focus group, one as the moderator and the other as note-taker. Using a discussion guide can help in effective data collection and the researcher can also use probing questions to reach in-depth information. Selecting proper participants is necessary, and sampling is usually purposive in which individuals with common experience about the phenomena under investigation, are selected. Time and the place of performing a group discussion must be proportionate to the subject and participants` condition. Tape recording and verbatim transcription along with field notes are usual methods of documenting data in focus groups.Conclusion: Focus group is a carefully planned series of discussions, designed to obtain perceptions on a defined area of interest in a permissive, non-threatening environment. A well-organized and guided group discussion results in rich and in-depth information about the phenomena at interest. However, this method has its own strengths and weaknesses which must be considered.

In the last decade, to reduce the costs of environmental monitoring, the data aggregation based on the joint dictionary learning and Compressive Sensing (CS) technique in Wireless Sensor Networks (WSNs) has been considered. It has been shown that when the dictionary obtained with the learning technique based on principal component analysis is used for CS-based data gathering of environmental signals, using a deterministic node selection method for data collection in WSNs can outperform random node selection ones. In this article, a deterministic and non-random sampling design for use in the CS-based data aggregation method is presented. This method is based on estimating the amount of mutual information of sensor data and is obtained by sampling all of them in a short part of the data collection round named the training phase. In the next and main stage of the data collection period, only the nodes that provide the most information about the non-sampled nodes are scheduled to sample. Simulation results for real signals in MATLAB software environment show that when the number of sampling sensors comprises still about 25% of the total network nodes, average energy savings of more than 12% can be achieved over a reference sampling method.

Nowadays, wireless sensor networks (WSNs) have found many applications in a variety of topics. The main purpose of these networks is to measure environmental phenomena and to send read data in multi-hop paths to the sink to be exploited by users. The most important challenge in WSNs is to minimize energy consumption in sensor batteries and increase network lifetime. One of the most important techniques for reducing energy consumption in WSNs is the Compressive sensing (CS) technique. CS reduces network energy consumption by reducing data transmission in the network and increasing the network lifetime. The use of CS technique in a WSN results in the production of different models of CS signals. These models are based on spatial, temporal and spatio-temporal sensors readings. On the other hand, in order to overcome the challenge of energy consumption, the exact recognition of energy resources in the network is essential. Energy consumption in a sensor node can be divided into two parts: (a) the energy used for computing, and (b) the energy consumed by the communication. The energy used for the computing consists of three components: 1. sensor energy consumption (data reading), 2. background energy consumption, and 3. energy consumption for processing. The power consumption of the communication includes the following: 1. energy consumption for data transmission, 2. energy consumption for data receiving, 3. energy consumption for sending messages, and 4. energy consumption for receiving messages. Hence, the existence of a model for analyzing energy consumption in a CS-based WSN is necessary. Several models have been developed to analyze energy consumption in a WSN, but there is not a complete model for analyzing energy consumption in a CS-based WSN. In this paper, we study all energy consumption components mentioned above in a CS-based WSN and present a complete model for energy consumption analysis. This model can optimize the design of CS-based WSNs energy efficiency improvement approach. To evaluate the proposed model, we use this model to analyze energy consumption in the Compressive data gathering technique which is a CS-based data aggregation method. Using this model can optimize the design of CS-based WSNs.

The Delay Tolerant Mobile Sensor Networks (DTMSNs) distinguish themselves from conventional sensor networks by means of some features such as loose connectivity, node mobility, and delay tolerability. It needs to be acknowledged that traditional end-to-end routing protocols cannot be applied usefully in such challenging network conditions because of intermittent connections and/or long delays. Hence, this research is intended to propose a Unicast Tree-based data gathering protocol (UTDG) to resolve this problem. A UTDG includes 3 phases: tree formation phase, data collection and data transmission phase, and finally the updating phase. The proposed protocol constructs a tree in each community on the basis of transmission ranking, contact probability and the link expiration time. The selection of the next-hop node is based on the tree structure rather than forwarding the message to the neighbor node directly. Each node unicasts the data to its parent in the related community, and the root of the tree successively sends the data to the sink node. The authors contend, based on the simulation results of the study, that the proposed protocol can gain significantly higher message delivery rates with lower transmission overhead and also lower delay in data delivery than the other existing DTMSNs routing protocols in some applications.

This paper investigates the problem of data gathering in rechargeable Wireless Sensor Networks (WSNs). The low energy harvesting rate of rechargeable nodes necessitates effective energy management in these networks. The existing schemes did not comprehensively examine the important aspects of energy-aware data gathering including sleep scheduling, and energy-aware clustering and routing. Additionally, most of them proposed greedy algorithms with poor performance. As a result, nodes run out of energy intermittently and temporary disconnections occur throughout the network. In this paper, we propose an energy-efficient data gathering algorithm namely Energy-aware data gathering in Rechargeable wireless sensor networks (EDGR). The proposed algorithm divides the original problem into three phases namely sleep scheduling, clustering, and routing, and solves them successively using particle swarm optimization algorithm. As derived from the simulation results, the EDGR algorithm improves the average and standard deviation of the energy stored in the nodes by 17% and 5. 6 times, respectively, compared to the previous methods. Also, the packet loss ratio and energy consumption for delivering data to the sink of this scheme is very small and almost zero.

Background and Aim: ZOE has been used in different fields of dentistry for many years. A locally produced component (Zoliran) has been recently introduced to the marketwith similar characteristic to the original Zonalin. Because of a lower cost involved to use Zoliran cement and its availability, confirm reliability of its physical properties. This investigation was designed to assess the Compressive strength of Zoliran cement in comparison to Zonalin cement as the standard material. Materials and Methods: Five samples with dimension of 4mmx6mm of each cement were provided and stored in distilled water in 370C±10C for a period of 24 hours. The lowest load of force was registered as the reference to which the sample could be broken by(according to the criteria No: 30 of ANSIIADA). The value of Compressive strength was then calculated using the following formula (K =4F/πD2 ). Results: The mean Compressive strength of five samples was measuredas: 14.33 Mpa for Zoliran cement and 31.83 Mpa for Zonalin cement. The mean Compressive strength of Zonalincement was significantly higher than the mean suggested in ANSl/ADA Specification No.30. The mean Compressivestrength of Zoliran cement was also lower than the mean value registered in ANSI/ADA SpecificationNo.30. Conclusion: Compressive strength of Zoliran cement was significantlylower than that of Zonalin cement. Further tests are required to compare the other physical properties of this material before it can be clinically recommended.

One of the main challenges of wireless sensor networks (WSNs), is unequal energy consumption of the nodes and early dead of the forwarder nodes around the base station because of the high load on these nodes. This matter causes a hole around the base station, thus, the communications between the alive nodes of the network and the base station are disrupted. In this paper, balancing the load and then energy consumption of the network nodes are followed. The aim is prolongation of the lifetime of all the nodes, particularly, the nodes around the base station to keep the communications between the network nodes and the base station until the end of lifetime of the nodes without any hole. To this purpose, a method for zoning the area of sensor networks is proposed. The distance of data transfer hop of each forwarder node is adjusted based on the amount of data to be transmitted by the forwarder. Hence, energy consumption of forwarders and thus their lifetimes are balanced. The proposed approach presents a solution for the challenge of short life of forwarders around the base station. Moreover, the dimensions of zones are calculated in such way that the communications between the sensors and the forwarder in each zone are performed in single hop manner. The approach balances the density of sensors of the created zones to uniform the coverage ratio in all the network area. The performance evaluations of the proposed scheme indicate that the scheme prolongs the lifetime of both forwarders and sensor nodes compared with the related works.