Study of nonlinear optical diffraction patterns using machine learning models based on ResNet 152 architecture

As the advancements in the field of artificial intelligence and nonlinear optics continues new methods can be used to better describe and determine nonlinear optical phenomena. In this research we aimed to analyze the diffraction patterns of an organic material and determine the nonlinear refraction index of the material in question by utilizing ResNet 152 convolutional neural network architecture in the regions of laser power that the diffraction rings are not clearly distinguishable. This approach can open new sights for optical material characterization in situations where the conventional methods do not apply

A framework to model thermomechanical coupled of fracture and martensite transformation in austenitic microstructures

A fully thermomechanical coupled phase-field (PF) model is presented to investigate the mechanism of austenite-to-martensite phase transformation (MPT) and crack initiation as well as its propagation in pure austenitic microstructures. The latent heat release and absorption involved in the MPT are explicitly taken into account by coupling the PF model with transient latent heat transfer. In order to consider temperature dependency in the PF model for MPT, a temperature-dependent Landau polynomial function, whose parameters are identified using molecular dynamics (MD) simulations, is proposed. Furthermore, the fracture surface energy is approximated based on the second-order PF model and then, the temporal evolution of the damage variable is given by the variational derivative of the total potential free energy of the system with respect to the damage variable. The achieved numerical results demonstrate that the model can be employed to predict the fracture mechanism of austenitic microstructures under a thermomechanical field in a multiphysics environment. The results reveal that the temperature has a tremendous impact on the growth rate of both martensitic variants and consequently on the crack growth path. The key contributions of this work are to shed light on the impact of thermal boundary conditions on the coupled process of MPT, crack initiation and growth

An automated pipeline for the discovery of conspiracy and conspiracy theory narrative frameworks: Bridgegate, Pizzagate and storytelling on the web

Although a great deal of attention has been paid to how conspiracy theories circulate on social media and their factual counterpart conspiracies, there has been little computational work done on describing their narrative structures. We present an automated pipeline for the discovery and description of the generative narrative frameworks of conspiracy theories on social media, and actual conspiracies reported in the news media. We base this work on two separate repositories of posts and news articles describing the well-known conspiracy theory Pizzagate from 2016, and the New Jersey conspiracy Bridgegate from 2013. We formulate a graphical generative machine learning model where nodes represent actors/actants, and multi-edges and self-loops among nodes capture context-specific relationships. Posts and news items are viewed as samples of subgraphs of the hidden narrative network. The problem of reconstructing the underlying structure is posed as a latent model estimation problem. We automatically extract and aggregate the actants and their relationships from the posts and articles. We capture context specific actants and interactant relationships by developing a system of supernodes and subnodes. We use these to construct a network, which constitutes the underlying narrative framework. We show how the Pizzagate framework relies on the conspiracy theorists' interpretation of "hidden knowledge" to link otherwise unlinked domains of human interaction, and hypothesize that this multi-domain focus is an important feature of conspiracy theories. While Pizzagate relies on the alignment of multiple domains, Bridgegate remains firmly rooted in the single domain of New Jersey politics. We hypothesize that the narrative framework of a conspiracy theory might stabilize quickly in contrast to the narrative framework of an actual one, which may develop more slowly as revelations come to light.Comment: conspiracy theory, narrative structur

The Investigation Relationship between Mental Workload and Occupational Fatigue in the Administrative Staffs of a Communications Service Company

Mental workload reflects the level of attention resources required to meet both objective and subjective performance criteria, which may be affected by task demand, external support and past experience. Mental workload and occupational fatigue have been commonly cited as a major cause of workplace accidents. The aim of this study was to investigate the relationship between workload and occupational fatigue in the administrative staffs of a communications service company in Tehran. In this study, 94 employees of the administrative service (69 female and 25 male) were provided with a demographic characteristics questionnaire including age, body mass index (BMI), level of education and work experience. Then the Swedish occupational fatigue inventory questionnaire was used to determine the job fatigue. The NASA-TLX mental workload questionnaire used for assessing mental workload. Finally, Data were analyzed by SPSS Version 20, descriptive statistics, Pearson correlation test and ANOVA test. Results showed that NASA-TLX mental workload in female (59.14) is more than from male (54.56). Also result showed Swedish Occupational Fatigue Inventory (SOFI) in female (30.12) is more than from Male (28.12). Also, the Pearson correlation test showed that there is a significant correlation between NASA-TLX and SOFI (r = 0.76,

The Investigation Relationship between Mental Workload and Occupational Fatigue in the Administrative Staffs of a Communications Service Company

Mental workload reflects the level of attention resources required to meet both objective and subjective performance criteria, which may be affected by task demand, external support and past experience. Mental workload and occupational fatigue have been commonly cited as a major cause of workplace accidents. The aim of this study was to investigate the relationship between workload and occupational fatigue in the administrative staffs of a communications service company in Tehran. In this study, 94 employees of the administrative service (69 female and 25 male) were provided with a demographic characteristics questionnaire including age, body mass index (BMI), level of education and work experience. Then the Swedish occupational fatigue inventory questionnaire was used to determine the job fatigue. The NASA-TLX mental workload questionnaire used for assessing mental workload. Finally, Data were analyzed by SPSS Version 20, descriptive statistics, Pearson correlation test and ANOVA test. Results showed that NASA-TLX mental workload in female (59.14) is more than from male (54.56). Also result showed Swedish Occupational Fatigue Inventory (SOFI) in female (30.12) is more than from Male (28.12). Also, the Pearson correlation test showed that there is a significant correlation between NASA-TLX and SOFI (r = 0.76,

An Automated Pipeline for Character and Relationship Extraction from Readers' Literary Book Reviews on Goodreads.com

Reader reviews of literary fiction on social media, especially those in persistent, dedicated forums, create and are in turn driven by underlying narrative frameworks. In their comments about a novel, readers generally include only a subset of characters and their relationships, thus offering a limited perspective on that work. Yet in aggregate, these reviews capture an underlying narrative framework comprised of different actants (people, places, things), their roles, and interactions that we label the "consensus narrative framework". We represent this framework in the form of an actant-relationship story graph. Extracting this graph is a challenging computational problem, which we pose as a latent graphical model estimation problem. Posts and reviews are viewed as samples of sub graphs/networks of the hidden narrative framework. Inspired by the qualitative narrative theory of Greimas, we formulate a graphical generative Machine Learning (ML) model where nodes represent actants, and multi-edges and self-loops among nodes capture context-specific relationships. We develop a pipeline of interlocking automated methods to extract key actants and their relationships, and apply it to thousands of reviews and comments posted on Goodreads.com. We manually derive the ground truth narrative framework from SparkNotes, and then use word embedding tools to compare relationships in ground truth networks with our extracted networks. We find that our automated methodology generates highly accurate consensus narrative frameworks: for our four target novels, with approximately 2900 reviews per novel, we report average coverage/recall of important relationships of > 80% and an average edge detection rate of >89\%. These extracted narrative frameworks can generate insight into how people (or classes of people) read and how they recount what they have read to others

A Semi-Physiological Three-Compartment Model Describes Brain Uptake Clearance and Efflux of Sucrose and Mannitol after IV Injection in Awake Mice

Purpose To evaluate a three-compartmental semi-physiological model for analysis of uptake clearance and efflux from brain tissue of the hydrophilic markers sucrose and mannitol, compared to non-compartmental techniques presuming unidirectional uptake. Methods Stable isotope-labeled [13C]sucrose and [13C]mannitol (10 mg/kg each) were injected as IV bolus into the tail vein of awake young adult mice. Blood and brain samples were taken after different time intervals up to 8 h. Plasma and brain concentrations were quantified by UPLC-MS/MS. Brain uptake clearance (Kin) was analyzed using either the single-time point analysis, the multiple time point graphical method, or by fitting the parameters of a three-compartmental model that allows for symmetrical exchange across the blood-brain barrier and an additional brain efflux clearance. Results The three-compartment model was able to describe the experimental data well, yielding estimates for Kin of sucrose and mannitol of 0.068 ± 0.005 and 0.146 ± 0.020 μl.min−1.g−1, respectively, which were significantly different (p \u3c 0.01). The separate brain efflux clearance had values of 0.693 ± 0.106 (sucrose) and 0.881 ± 0.20 (mannitol) μl.min−1.g−1, which were not statistically different. Kin values obtained by single time point and multiple time point analyses were dependent on the terminal sampling time and showed declining values for later time points. Conclusions Using the three-compartment model allows determination of Kin for small molecule hydrophilic markers with low blood-brain barrier permeability. It also provides, for the first time, an estimate of brain efflux after systemic administration of a marker, which likely represents bulk flow clearance from brain tissue