Level of Knowledge about Human Papillomavirus Infection among Women of Kashan City, Iran

Abstract Aims: A few studies concentrate on the level of knowledge of HPV. This study was conducted to evaluate the level of knowledge about HPV, its risk factors, and its relation with cervical cancer in women of Kashan City, Iran. Instrument & Methods: This descriptive cross-sectional study was conducted in January 2015 in the population of the women of Kashan City, Iran, and 200 persons were selected by simple sampling method. The level of knowledge about HPV and cervical cancer were measured using a questionnaire with 10 questions about knowledge. The data was analyzed in SPSS 16 software by Chi-square, Exact Fisher and Kruskal-Wallis tests. Findings: Most of the participants (152 persons; 76) had “weak, 26 participants (13) had “moderate” and only 22 participants (11) had “strong” level of knowledge about HPV. There were significant differences between the level of knowledge according to educational level (p=0.014) and professional status (p<0.001) but there were no differences according to marital status (p=0.9) and age (p>0.05). In all the questions, the most frequent answer was “don’t know”. The participants had some knowledge about “HPV causing cervical cancer” (34.5), “HPV causing genital warts” (38), “sexually transmission of HPV” (37.5) and “increased risk of getting HPV by extramarital sexual affairs” (43.5) Conclusion: The level of knowledge about HPV, genital warts, and ways of infection transmission and its preventions in women of Kashan City, Iran, is insufficient

SPAM detection: Naïve bayesian classification and RPN expression-based LGP approaches compared

An investigation is performed of a machine learning algorithm and the Bayesian classifier in the spam-filtering context. The paper shows the advantage of the use of Reverse Polish Notation (RPN) expressions with feature extraction compared to the traditional Naïve Bayesian classifier used for spam detection assuming the same features. The performance of the two is investigated using a public corpus and a recent private spam collection, concluding that the system based on RPN LGP (Linear Genetic Programming) gave better results compared to two popularly used open source Bayesian spam filters. © Springer International Publishing Switzerland 2016

Do evolutionary algorithms indeed require random numbers? Extended study

An inherent part of evolutionary algorithms, that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes. In this participation, we discuss whether are random processes really needed in evolutionary algorithms. We use n periodic deterministic processes instead of random number generators and compare performance of evolutionary algorithms powered by those processes and by pseudo-random number generators. Deterministic processes used in this participation are based on deterministic chaos and are used to generate periodical series with different length. Results presented here are numerical demonstration rather than mathematical proofs. We propose that a certain class of deterministic processes can be used instead of random number generators without lowering of evolutionary algorithms performance. © Springer International Publishing Switzerland 2013

High Performance Multicell Series Inverter-Fed Induction Motor Drive

This document is the Accepted Manuscript version of the following article: M. Khodja, D. Rahiel, M. B. Benabdallah, H. Merabet Boulouiha, A. Allali, A. Chaker, and M. Denai, ‘High-performance multicell series inverter-fed induction motor drive’, Electrical Engineering, Vol. 99 (3): 1121-1137, September 2017. The final publication is available at Springer via DOI: https://doi.org/10.1007/s00202-016-0472-4.The multilevel voltage-source inverter (VSI) topology of the series multicell converter developed in recent years has led to improved converter performance in terms of power density and efficiency. This converter reduces the voltage constraints between all cells, which results in a lower transmission losses, high switching frequencies and the improvement of the output voltage waveforms. This paper proposes an improved topology of the series multicell inverter which minimizes harmonics, reduces torque ripples and losses in a variable-speed induction motor drive. The flying capacitor multilevel inverter topology based on the classical and modified phase shift pulse width modulation (PSPWM, MPSPWM) techniques are applied in this paper to minimize harmonic distortion at the inverter output. Simulation results are presented for a 2-kW induction motor drive and the results obtained demonstrate reduced harmonics, improved transient responses and reference tracking performance of the voltage in the induction motor and consequently reduced torque ripplesPeer reviewe

Attraction and diffusion in nature-inspired optimization algorithms

Nature-inspired algorithms usually use some form of attraction and diffusion as a mechanism for exploitation and exploration. In this paper, we investigate the role of attraction and diffusion in algorithms and their ways in controlling the behaviour and performance of nature-inspired algorithms. We highlight different ways of the implementations of attraction in algorithms such as the firefly algorithm, charged system search, and the gravitational search algorithm. We also analyze diffusion mechanisms such as random walks for exploration in algorithms. It is clear that attraction can be an effective way for enhancing exploitation, while diffusion is a common way for exploration. Furthermore, we also discuss the role of parameter tuning and parameter control in modern metaheuristic algorithms, and then point out some key topics for further research

Multi-stage optimization of a deep model: A case study on ground motion modeling.

In this study, a multi-stage optimization procedure is proposed to develop deep neural network models which results in a powerful deep learning pipeline called intelligent deep learning (iDeepLe). The proposed pipeline is then evaluated by a challenging real-world problem, the modeling of the spectral acceleration experienced by a particle during earthquakes. This approach has three main stages to optimize the deep model topology, the hyper-parameters, and its performance, respectively. This pipeline optimizes the deep model via adaptive learning rate optimization algorithms for both accuracy and complexity in multiple stages, while simultaneously solving the unknown parameters of the regression model. Among the seven adaptive learning rate optimization algorithms, Nadam optimization algorithm has shown the best performance results in the current study. The proposed approach is shown to be a suitable tool to generate solid models for this complex real-world system. The results also show that the parallel pipeline of iDeepLe has the capacity to handle big data problems as well

Novel Missense Mutation A789V in IQSEC2 underlies X-Linked intellectual disability in the MRX78 family

Disease gene discovery in neurodevelopmental disorders, including X-linked intellectual disability (XLID) has recently been accelerated by next-generation DNA sequencing approaches. To date, more than 100 human X chromosome genes involved in neuronal signaling pathways and networks implicated in cognitive function have been identified. Despite these advances, the mutations underlying disease in a large number of XLID families remained unresolved. We report the resolution of MRX78, a large family with six affected males and seven affected females, showing X-linked inheritance. Although a previous linkage study had mapped the locus to the short arm of chromosome X (Xp11.4-p11.23), this region contained too many candidate genes to be analyzed using conventional approaches. However, our X-chromosome exome resequencing, bioinformatics analysis and inheritance testing revealed a missense mutation (c.C2366T, p.A789V) in IQSEC2, encoding a neuronal GDP-GTP exchange factor for Arf family GTPases (ArfGEF) previously implicated in XLID. Molecular modeling of IQSEC2 revealed that the A789V substitution results in the insertion of a larger side-chain into a hydrophobic pocket in the catalytic Sec7 domain of IQSEC2. The A789V change is predicted to result in numerous clashes with adjacent amino acids and disruption of local folding of the Sec7 domain. Consistent with this finding, functional assays revealed that recombinant IQSEC2A789V was not able to catalyze GDP-GTP exchange on Arf6 as efficiently as wild-type IQSEC2. Taken together, these results strongly suggest that the A789V mutation in IQSEC2 is the underlying cause of XLID in the MRX78 family

Hateful Sentiment Detection in Real-Time Tweets: An LSTM-Based Comparative Approach

It is undeniable that social media has improved our lives in many ways, like allowing interactions with others all over the world and network expansion for businesses. However, there are detrimental effects of such accessibility, including the rapid spread of hate through offensive messages typically directed toward gender, religion, race, and disability, which can cause psychological harm. To address this problem of social media, many researchers have recently proposed various algorithms powered by machine learning (ML) and deep learning for the detection of hate speech. This work proposes a hate speech detection model based on long-short term memory (LSTM), using term frequency inverse document frequency (TF-IDF) vectorization, and makes comparisons with support vector machine (SVM), Naïve Bayes (NB), logistic regression (LR), XGBoost (XGB), random forest (RF), $K$ -nearest neighbor ( $k$ -NN), artificial neural network (ANN), and bidirectional encoder representations from transformers (BERT) models. To validate and authenticate our proposed work, we obtained and classified a real-time Twitter data stream of a trending topic using Twitter API into two classes: hate speech and nonhate speech. The precision, recall, and $F$ 1 score achieved by LSTM are 0.98, 0.99, and 0.98, respectively. The accuracy of LSTM for detecting hateful sentiment was found to be 97%, surpassing the accuracy of other models