Introduction: To save lives and reduce the disability and death of the patients, emergency medical technicians (EMTs) must make timely decisions in complex and, sometimes, life-threatening situations. Since the pre-hospital decision-making is a continuous and important process. Objective: The present study aims to identify the factors affecting EMTs’ on-scene decision-making in emergency situations. Methods: A qualitative approach using in-depth semi-structured interviews and field observations was employed to explore the factors influencing EMTs’ on-scene decision-making in emergency situations. Purposeful sampling was performed with 19 participants including 12 EMTs, 3 dispatchers, 3 medical directions physicians and one EMS manager as a policy maker. Interviews were conducted from October 2018 to March 2018 and the data were analyzed using Graneheim and Lundman’ s content analysis approach. Results: Eight categories and 18 sub-categories emerged to describe the factors effective in emergency medical technicians’ on-scene decision-making. they were cultural context (community’ s culture and organizational culture), interactions (malingering, threat and violence and considerations), competencies (acquisitive and intrinsic); personal feeling (positive feeling and negative feeling), authority (structural and in processing), education (public and professional), special conditions (patient’ s clinical situation, weather conditions, mission’ s time and mission’ s location), and organizational resource (facility and equipment, and human resources). Conclusions: To facilitate EMTs’ on-scene decision-making, it is recommended to clarify the EMTs’ responsibilities, promote the community’ s culture, modify people’ s expectations, police monitoring and control and value the star of logo on EMTs' uniform. The EMTs’ on-scene decision-making process should also be explored.

Necroptosis, as a novel concept, has been recently introduced in scientific literature. Much of our knowledge about necroptosis comes from ligation of tumor necrosis factor-α to its receptor, TNF receptor 1. Receptor-interacting protein kinase 1, receptor-interacting protein kinase 3 and its substrate, the pseudo kinase mixed lineage kinase domain-like protein, have been comprehensively studied as influential components of this process. Emerging pioneering evidence suggests that many molecules, organelles and mechanisms are involved in necroptosis pathway.The aim of this review is presentation of molecular mechanisms of necroptosis in three phases including initiation, regulation and execution of necroptosis. Moreover, this review will summarize unprecedented insights into the contribution of various organelles and cell compartments such as mitochondria, endoplasmic reticulum, nucleus, lysosomes and Golgi apparatus in necroptosis pathway.

Video compression has become a source of different research studies. It is necessary in order to address channel bandwidth limitations and growing video demand, including digital libraries and streaming media delivery via the Internet. A video is a number of frames captured by a camera while a scene is a series of consecutive frames captured from a specific narrative viewpoint. To compress a video, firstly, the intra frames are separated from inter frames using scene change detection methods. Then, block-based motion estimation algorithms are used to eliminate the temporal redundancy between successive frames. This paper describes some scene change detection methods for use on the uncompressed video to detect scene types such as cut, dissolve, wipe, etc. Absolute Frame Difference (AFD), Mean Absolute Frame Differences (MAFD), Mean Histogram Absolute Frame Difference (MHAFD), and Maximum Gradient Value (MGV) techniques are adaptively tested on different video types to identify accurate scene change in both low and high object motion scenes. Test results show that the proposed approach (MHAFD ) obtains a better accuracy, F1-scor measure of 100%, especially for cuts and gradual transitions (wipe) video types. Dissolve scene change is detected with a high precision of 100% (i. e., no false detection) with the (MAFD) detector. Besides, in terms of time complexity for analyzing all the video samples, the proposed method (MHAFD) provides the best result compared to the selected detectors.