Automatic summarisation of Instagram social network posts Combining semantic and statistical approaches

The proliferation of data and text documents such as articles, web pages, books, social network posts, etc. on the Internet has created a fundamental challenge in various fields of text processing under the title of "automatic text summarisation". Manual processing and summarisation of large volumes of textual data is a very difficult, expensive, time-consuming and impossible process for human users. Text summarisation systems are divided into extractive and abstract categories. In the extractive summarisation method, the final summary of a text document is extracted from the important sentences of the same document without any modification. In this method, it is possible to repeat a series of sentences and to interfere with pronouns. However, in the abstract summarisation method, the final summary of a textual document is extracted from the meaning and significance of the sentences and words of the same document or other documents. Many of the works carried out have used extraction methods or abstracts to summarise the collection of web documents, each of which has advantages and disadvantages in the results obtained in terms of similarity or size. In this work, a crawler has been developed to extract popular text posts from the Instagram social network with appropriate preprocessing, and a set of extraction and abstraction algorithms have been combined to show how each of the abstraction algorithms can be used. Observations made on 820 popular text posts on the social network Instagram show the accuracy (80%) of the proposed system

A new approach for harmonic detection based on eliminating oscillatory coupling effects in microgrids

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs LicenseThe primary goal of grid-connected microgrids is to control the active and reactive power, which is reachable by the inner current control loop in the control structure of power converters. However, when facing unbalanced conditions, the inner current control loop implemented in the dq frame does not function properly. In such conditions, the popular current control loop malfunctions since there is an oscillatory coupling between harmonic components. Therefore, in this study, a new harmonic detector based on decoupled double synchronous reference frame within the current control loop is proposed in which the oscillatory coupling between harmonic components is eliminated, and the overall performance of the power converter control system is significantly improved. The performance of the precisely developed mathematical models is verified by Matlab simulations, and the simulation results confirm the accuracy and proper operation of the proposed strategy

A New Optimal Sensor Location Method for Double-curvature Arch Dams: A Comparison with the Modal Assurance Criterion (MAC)

Determining the optimal location of sensors in order to identify modal parameters in large structures such as dams is one of the most important and widely used topics in damage detection and health monitoring of structures. In this research, the modal parameters including the natural frequency and mode shape of two arched concrete dams have been calculated using the finite element method for healthy and damaged dams. The reduction of the elastic modulus of concrete in different parts and percentages has been used as the degree of damage. Then, using the modal confidence criterion (MAC) method, the optimal location of the sensors is determined, then the results of this method are compared with the new method. The results show that in both dams, the new method matches the MAC method with 90% accuracy. This new method is a fast and suitable measure to determine the optimal location of sensors in arched concrete dams

A phenomenological model for electrical transport characteristics of MSM contacts based on GNS

Graphene nanoscroll, because of attractive electronic, mechanical, thermoelectric and optoelectronics properties, is a suitable candidate for transistor and sensor applications. In this research, the electrical transport characteristics of high-performance field effect transistors based on graphene nanoscroll are studied in the framework of analytical modeling. To this end, the characterization of the proposed device is investigated by applying the analytical models of carrier concentration, quantum capacitance, surface potential, threshold voltage, subthreshold slope and drain induced barrier lowering. The analytical modeling starts with deriving carrier concentration and surface potential is modeled by adopting the model of quantum capacitance. The effects of quantum capacitance, oxide thickness, channel length, doping concentration, temperature and voltage are also taken into account in the proposed analytical models. To investigate the performance of the device, the current-voltage characteristics are also determined with respect to the carrier density and its kinetic energy. According to the obtained results, the surface potential value of front gate is higher than that of back side. It is noteworthy that channel length affects the position of minimum surface potential. The surface potential increases by increasing the drain-source voltage. The minimum potential increases as the value of quantum capacitance increases. Additionally, the minimum potential is symmetric for the symmetric structure (Vfg = Vbg). In addition, the threshold voltage increases by increasing the carrier concentration, temperature and oxide thickness. It is observable that the subthreshold slope gets closer to the ideal value of 60 mV/dec as the channel length increases. As oxide thickness increases the subthreshold slope also increases. For thinner gate oxide, the gate capacitance is larger while the gate has better control over the channel. The analytical results demonstrate a rational agreement with existing data in terms of trends and values

Malingering and PTSD: Detecting malingering and war related PTSD by Miller Forensic Assessment of Symptoms Test (M-FAST)

BACKGROUND: Malingering is prevalent in PTSD, especially in delayed-onset PTSD. Despite the attempts to detect it, indicators, tools and methods to accurately detect malingering need extensive scientific and clinical research. Therefore, this study was designed to validate a tool that can detect malingering of war-related PTSD by Miller Forensic Assessment of Symptoms Test (M-FAST). METHODS: In this blind clinical diagnosis study, one hundred and twenty veterans referred to War Related PTSD Diagnosis Committee in Iran in 2011 were enrolled. In the first step, the clients received Psychiatry diagnosis and were divided into two groups based on the DSM-IV-TR, and in the second step, the participants completed M-FAST. RESULTS: The t-test score within two groups by M-FAST Scale showed a significant difference (t = 14.058, P < 0.0001), and 92% of malingering war-related PTSD participants scored more than 6 and %87 of PTSD group scored less than 6 in M-FAST Scale. CONCLUSIONS: M-FAST showed a significant difference between war-related PTSD and malingering participants. The ≥6 score cutoff was suggested by M-FAST to detect malingering of war-related PTSD

Neck and Back Pain Prevalence in Workers of Iranian Steel industries at 2015

Work related musculoskeletal disorders (WMSDs) are considered as the main cause of occupational complications and disability in developing countries. In Iranian steel companies, workers commonly are directly involved in the production process and physical activities such as manual material handling and awkward postures. Present study was performed for assessment of neck and back pain prevalence among workers of four Iranian steel industries. Study participants in our cross sectional study, were randomly selected from workers of four Iranian steel industries. Data of neck and back pain were gathered by Nordic questionnaire. Logistic regression was used for controlling confounding variables and determining independent predictors of neck and back pain among study workers. Among study workers, prevalence of neck and back pain in a recent year were 18.40% and 13.90% respectively. Age (p≤0.02) and job experience (p≤0.00) had significant association with neck pain. Age, sex, BMI, and job duration were not known as an independent predictor of neck or back pain. Neck and back pain prevalence in steel industries were happened whit higher rate compared to most of other countries. Next studies will suggest for determining work related risk factors of WMSDs in workers and designing preventive strategies