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

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

An Investigation of the Policies and Crucial Sectors of Smart Cities Based on IoT Application

As smart cities (SCs) emerge, the Internet of Things (IoT) is able to simplify more sophisticated and ubiquitous applications employed within these cities. In this regard, we investigate seven predominant sectors including the environment, public transport, utilities, street lighting, waste management, public safety, and smart parking that have a great effect on SC development. Our findings show that for the environment sector, cleaner air and water systems connected to IoT-driven sensors are used to detect the amount of CO2, sulfur oxides, and nitrogen to monitor air quality and to detect water leakage and pH levels. For public transport, IoT systems help traffic management and prevent train delays, for the utilities sector IoT systems are used for reducing overall bills and related costs as well as electricity consumption management. For the street-lighting sector, IoT systems are used for better control of streetlamps and saving energy associated with urban street lighting. For waste management, IoT systems for waste collection and gathering of data regarding the level of waste in the container are effective. In addition, for public safety these systems are important in order to prevent vehicle theft and smartphone loss and to enhance public safety. Finally, IoT systems are effective in reducing congestion in cities and helping drivers to find vacant parking spots using intelligent smart parking

Seismic failure probability and vulnerability assessment of steel-concrete composite structures

Building collapse in earthquakes caused huge losses, both in human and economic terms. To assess the risk posed by using the composite members, this paper investigates seismic failure probability and vulnerability assessment of steel-concrete composite structures constituted by rectangular concrete filled steel tube (RCFT) columns and steel beams. To enable numerical simulation of RCFT-structure, the details of components modeling are developed using OpenSEES finite element analysis package and the validation of proposed procedure is investigated through comparisons with available experimental results. The seismic fragility and vulnerability curves of RCFT-structures are created through nonlinear dynamic analysis using an appropriate suite of ground motions for seismic loss assessment. These curves developed for three-, six- and nine-story prototypes of RCFT-structure. Fragility curves are an appropriate tool for representing the seismic failure probabilities and vulnerability curves demonstrate a probability of exceeding loss to a measure of ground motion intensity

Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics

The most successful Machine Learning (ML) systems remain complex black boxes to end-users, and even experts are often unable to understand the rationale behind their decisions. The lack of transparency of such systems can have severe consequences or poor uses of limited valuable resources in medical diagnosis, financial decision-making, and in other high-stake domains. Therefore, the issue of ML explanation has experienced a surge in interest from the research community to application domains. While numerous explanation methods have been explored, there is a need for evaluations to quantify the quality of explanation methods to determine whether and to what extent the offered explainability achieves the defined objective, and compare available explanation methods and suggest the best explanation from the comparison for a specific task. This survey paper presents a comprehensive overview of methods proposed in the current literature for the evaluation of ML explanations. We identify properties of explainability from the review of definitions of explainability. The identified properties of explainability are used as objectives that evaluation metrics should achieve. The survey found that the quantitative metrics for both model-based and example-based explanations are primarily used to evaluate the parsimony/simplicity of interpretability, while the quantitative metrics for attribution-based explanations are primarily used to evaluate the soundness of fidelity of explainability. The survey also demonstrated that subjective measures, such as trust and confidence, have been embraced as the focal point for the human-centered evaluation of explainable systems. The paper concludes that the evaluation of ML explanations is a multidisciplinary research topic. It is also not possible to define an implementation of evaluation metrics, which can be applied to all explanation methods.</jats:p

Distribution and density of juvenile fish in Khouzestan coastal waters

During one year survey (January to December 2007), Khouzestan coastal waters in the north west of Persian Gulf were examined to investigate juvenile fishes distribution, density and biomass. Monthly juvenile fish samples were caught using a 360 hp research vessel towing a 24 mm mesh size bottom trawl. Trawling was carried out at 10 randomly selected stations. Distribution map for dominant species was prepared and biomass and CPUA was estimated in the study area. Highest and lowest CPUA was observed in October and December, repectively.Biomass fluctuations showed increasing trend during warm seasons.Significant correlation was recorded between environmental parameters (water temperature and salinity) with biomass and number of species per month in the study area

The Arithmetic Optimization Algorithm

Abstract This work proposes a new meta-heuristic method called Arithmetic Optimization Algorithm (AOA) that utilizes the distribution behavior of the main arithmetic operators in mathematics including (Multiplication ( ), Division (), Subtraction (), and Addition ()). AOA is mathematically modeled and implemented to perform the optimization processes in a wide range of search spaces. The performance of AOA is checked on twenty-nine benchmark functions and several real-world engineering design problems to showcase its applicability. The analysis of performance, convergence behaviors, and the computational complexity of the proposed AOA have been evaluated by different scenarios. Experimental results show that the AOA provides very promising results in solving challenging optimization problems compared with eleven other well-known optimization algorithms. Source codes of AOA are publicly available at and