Modeling batch annealing process using data mining techniques for cold rolled steel sheets

The annealing process is one of the important operations in production of cold rolled steel sheets, which significantly influences the final product quality of cold rolling mills. In this process, cold rolled coils are heated slowly to a desired temperature and then cooled. Modelling of annealing process (prediction of heating and cooling time and trend prediction of coil core temperature) is a very sophisticated and expensive work. Modelling of annealing process can be done by using of thermal models. In this paper, Modelling of steel annealing process is proposed by using data mining techniques. The main advantages of modelling with data mining techniques are: high speed in data processing, acceptable accuracy in obtained results and simplicity in using of this method. In this paper, after comparison of results of some data mining techniques, feed forward back propagation neural network is applied for annealing process modelling. A good correlation between results of this method and results of thermal models has been obtained

Latent dirichlet markov allocation for sentiment analysis

In recent years probabilistic topic models have gained tremendous attention in data mining and natural language processing research areas. In the field of information retrieval for text mining, a variety of probabilistic topic models have been used to analyse content of documents. A topic model is a generative model for documents, it specifies a probabilistic procedure by which documents can be generated. All topic models share the idea that documents are mixture of topics, where a topic is a probability distribution over words. In this paper we describe Latent Dirichlet Markov Allocation Model (LDMA), a new generative probabilistic topic model, based on Latent Dirichlet Allocation (LDA) and Hidden Markov Model (HMM), which emphasizes on extracting multi-word topics from text data. LDMA is a four-level hierarchical Bayesian model where topics are associated with documents, words are associated with topics and topics in the model can be presented with single- or multi-word terms. To evaluate performance of LDMA, we report results in the field of aspect detection in sentiment analysis, comparing to the basic LDA model

Interacting agegraphic quintessence dark energy in non-flat universe

We suggest a correspondence between interacting agegraphic dark energy models and the quintessence scalar field in a non-flat universe. We demonstrate that the agegraphic evolution of the universe can be described completely by a single quintessence scalar field. Then, we reconstruct the potential of the interacting agegraphic quintessence dark energy as well as the dynamics of the scalar field according to the evolution of the agegraphic dark energy.Comment: 12 pages, 12 figure

FARS: Fuzzy Ant based Recommender System for Web Users

Recommender systems are useful tools which provide an adaptive web environment for web users. Nowadays, having a user friendly website is a big challenge in e-commerce technology. In this paper, applying the benefits of both collaborative and content based filtering techniques is proposed by presenting a fuzzy recommender system based on collaborative behavior of ants (FARS). FARS works in two phases: modeling and recommendation. First, user’s behaviors are modeled offline and the results are used in second phase for online recommendation. Fuzzy techniques provide the possibility of capturing uncertainty among user interests and ant based algorithms provides us with optimal solutions. The performance of FARS is evaluated using log files of “Information and Communication Technology Center” of Isfahan municipality in Iran and compared with ant based recommender system (ARS). The results shown are promising and proved that integrating fuzzy Ant approach provides us with more functional and robust recommendations

Feature selection methods in Persian sentiment analysis

With the enormous growth of digital content in internet, various types of online reviews such as product and movie reviews present a wealth of subjective information that can be very helpful for potential users. Sentiment analysis aims to use automated tools to detect subjective information from reviews. Up to now as there are few researches conducted on feature selection in sentiment analysis, there are very rare works for Persian sentiment analysis. This paper considers the problem of sentiment classification using different feature selection methods for online customer reviews in Persian language. Three of the challenges of Persian text are using of a wide variety of declensional suffixes, different word spacing and many informal or colloquial words. In this paper we study these challenges by proposing a model for sentiment classification of Persian review documents. The proposed model is based on stemming and feature selection and is employed Naive Bayes algorithm for classification. We evaluate the performance of the model on a collection of cellphone reviews, where the results show the effectiveness of the proposed approache

Phase Transition and Thermal Order-by-Disorder in the Pyrochlore Quantum Antiferromagnet Er2Ti2O7: a High-Temperature Series Expansion Study

Several rare earth magnetic pyrochlore materials are well modeled by a spin-1/2 quantum Hamiltonian with anisotropic exchange parameters Js. For the Er2Ti2O7 material, the Js were recently determined from high-field inelastic neutron scattering measurements. Here, we perform high-temperature (T) series expansions to compute the thermodynamic properties of this material using these Js. Comparison with experimental data show that the model describes the material very well including the finite temperature phase transition to an ordered phase at Tc~1.2 K. We show that high temperature expansions give identical results for different q=0 xy order parameter susceptibilities up to 8th order in \beta=1/T (presumably to all orders in \beta). Conversely, a non-linear susceptibility related to the 6th power of the order parameter reveals a thermal order-by-disorder selection of the same non-colinear \psi_2 state as found in Er2Ti2O7.Comment: 12 pages, 4 figure

Induction of tetraploidy in rainbow trout (Oncorhynchus mykiss) by heat shock

This study has been conducted for determination of optimum time and duration of thermal shock on tetraploidy induction in Rainbow trout. This study was carried out by induction of 28°C thermal shock in different times after fertilization (49.5, 54.0, 58.5, 63.0, 67.5, 72.0, 76.5 and 81,0°C/hour and different duration (8, 10 and 12 min). Nuclear erythrocytes, cellular area and volume measurement were determined among blood smears of fingerling and statistic analysis showed that, induction of tetraploidy was conducted between 0-75% on various treatment, but highest tetraploidy yield (8.4%) was achieved by thermal shock on 28°C within 12 minutes, 74 degree-hour after fertilization. Moreover, tetra ploidy diagnostic was also approved by determination of nucleolus number in fishes. In this respect tetraploid and diploid fishes have 3-4 and 1-2 nucleolus, respectively

Iris segmentation using a non-decimated wavelet transform

This paper presents an iris segmentation algorithm. The proposed technique applies a histogram based method on the input eye image extracting a point within the pupil. The image is then intensity sampled over M equiangular radial scan line, generating M 1-dimensional signals. A Fuzzy multi-scale edge detection algorithm is then applied to each of the resulting radii signals, to accurately detect and locate one positive edge point from the signal. A uniform cubic B-spline approximation method is further applied to the detected edges determining the iris outer boundary. The histogram of the area within the extracted outer iris bondary of the eye image is finaly used to extract the pupil outer bondary. Experimental results on a number of eye test images taken under visible wavelenght from UBIRISv.1 and UBIRISv.2 databases show that the proposed segmentation method accurately extracts the iris boundaries

The impact of hyperglycemia or hypoalbuminemia on duration and need to mechanical ventilation in critically ill patients

چکیده: زمینه و هدف: چندین فاکتور فیزیولوژیکی علاوه بر معیارهای مکانیکی ریوی، بر روی جدا سازی بیماران بستری در واحد مراقبت های ویژه از دستگاه ونتیلاتور موثراست. هیپرگلیسمی باعث افزایش بیشتری در نیاز به ونتیلاتور می شود و به طور بارزی روزهای اتصال به دستگاه را افزایش می دهد. هدف از این مطالعه بررسی تاثیر هیپوآلبومینمی یا هیپرگلیسمی در پیش بینی نیاز به ونتیلاتور و جدا سازی از ونتیلاتور در واحد مراقبت های ویژه بوده است. روش بررسی: این مطالعه توصیفی – تحلیلی بر روی 600 بیمار بالای 16 سال در افرادی که بمدت بیش از 3 روز در واحد مراقبت های ویژه مرکز پزشکی الزهرا(س) اصفهان بستری شده بودند انجام شد. پارامترهای آلبومین سرمی، قند خون، نیاز به ونتیلاتور، مدت اتصال به ونتیلاتور بالانس مایعات و ضریب اپاچی سه (APACHE-3= Acute –physiology and chronic health evaluation) بیماران ثبت و داده ها با استفاده از آزمونهای آماری تی استیودنت، کای دو، رگرسیون کاکس، رگرسیون لوجستیک یک طرفه، آنالیز رگرسیون Multilinear و آنالیز رگرسیون لوجستیکی مولتیپل تجزیه و تحلیل شد. یافته ها: در بدو ورود میانگین غلظت قند سرمی mmol/L 2/0±3/9 و آلبومین سرمی g/l 02/0±2/30 بود. در بیماران فوت شده غلظت آلبومین سرمی بطور معنی دار کمتر و غلظت قند سرمی بطور معنی دار نسبت به افراد زنده بالاتر بود (001/0P) و قند خون (05/0r=، 05/0P>) حین بستری در واحد مراقبت های ویژه نتوانست پیش بینی کننده مدت زمان اتصال به ونتیلاتور باشد. بیماران با غلظت آلبومین سرمی کمتر از 3/30 نسبت به افراد با آلبومین سرمی نرمال با افزایش نیاز به ونتیلاتور در حد 2/0-1/0 برابر روبرو شدند. (31/1-06/1=95CI و 2/1=OR) خطر نیاز به ونتیلاتور با قند خون بالای mg/dl 200 افزایش نیافت. نتیجه گیری: بر اساس یافته های این مطالعه آلبومین و قند خون می تواند اندکس هایی از وضعیت متابولیکی بیماران باشد که در تعیین نیاز به ونتیلاتور و جداسازی از آن در دوره های طولانی مدت تنفس مکانیکی اهمیت دارد