Clinical Presentation and Microbial Analyses of Contact Lens Keratitis; an Epidemiologic Study

Introduction: Microbial keratitis is an infective process of the cornea with a potentially and serious visual impairments. Contact lenses are a major cause of microbial keratitis in the developed countries especially among young people. Therefore, the purpose of the present study was to evaluate the frequency and microbiological characteristic of CLK in patients referred to the emergency department (ED) of teaching hospitals, Babol, Iran. Methods: This is a cross-sectional study of all patients with contact lens induced corneal ulcers admitted to the teaching hospitals of Babol, Iran, from 2011- 2013. An ophthalmologist examined patients with the slit-lamp and clinical features of them were noted (including pain, redness, foreign body sensation, chemosis, epiphora, blurred vision, discomfort, photophobia, discharge, ocular redness and swelling). All suspected infectious corneal ulcers were scraped for microbial culture and two slides were prepared. Data were analyzed using SPSS software, version 18.0. Results: A total of 14 patients (17 eyes) were recruited into the study (100% female). The patients’ age ranged from 16-37 years old (mean age 21.58±7.23 years). The most prevalent observed clinical signs were pain and redness. Three samples reported as sterile. The most common isolated causative organism was pseudomonas aeroginosa (78.6%), Staphylococcus aureus 14.3%, and enterobacter 7.1%, respectively. Treatment outcome was excellent in 23.5%, good in 47.1%, and poor in 29.4% of cases. Conclusion: Improper lens wear and care as well as the lack of awareness about the importance of aftercare visits have been identified as potential risk factors for the corneal ulcer among contact lens wearers. Training and increasing the awareness of adequate lens care and disinfection practices, consulting with an ophthalmologist, and frequent replacement of contact lens storage cases would greatly help reducing the risk of microbial keratitis

Business and Information Technology Alignment Measurement -- a recent Literature Review

Since technology has been involved in the business context, Business and Information Technology Alignment (BITA) has been one of the main concerns of IT and Business executives and directors due to its importance to overall company performance, especially today in the age of digital transformation. Several models and frameworks have been developed for BITA implementation and for measuring their level of success, each one with a different approach to this desired state. The BITA measurement is one of the main decision-making tools in the strategic domain of companies. In general, the classical-internal alignment is the most measured domain and the external environment evolution alignment is the least measured. This literature review aims to characterize and analyze current research on BITA measurement with a comprehensive view of the works published over the last 15 years to identify potential gaps and future areas of research in the field.Comment: 12 pages, Preprint version, BIS 2018 International Workshops, Berlin, Germany, July 18 to 20, 2018, Revised Paper

An evaluation of hospital admission respiratory disease attributed to sulfur dioxide ambient concentration in Ahvaz from 2011 through 2013

There is no doubt that air pollutants have adverse impacts on human health. The main objective of this study was to evaluate hospital admission respiratory disease (HARD) attributed to sulfur dioxide levels in Ahvaz during three successive years. Data was taken from Iranian Environmental Protection Agency (EPA). The AirQ2,2,3 model is used to quantify the impact of SO2 on inhabitants of Ahvaz and in terms of hospital admission respiratory diseases. This is a kind of statistical model which is based on some epidemiological indices such as relative risk, baseline incidence, and attributable proportion. Sampling was already performed for 24 h in four stations during 2011–2013. Four stations are good representative for residential, high traffic, industry, and background sites which cover the whole area of the Ahvaz city. Regarding to gravimetric scale, raw data of sulfur dioxide was processed using Excel software. Encoding, filtering, and processing were conducted to prepare input file for the Air Q2,2,3 model. After running model,1 Air Pollution and Respiratory Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 2 Environmental Technologies Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 3 Islamic Azad University, Tehran Medical Sciences Branch, Tehran, Iran 4 Razi Teaching Hospital, Clinical Research Development Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 5 Department of Internal Medicine, Division of Pulmonology, Imam Khomeini Hospital, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 6 Nutrition&Metabolic Diseases Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 7 Social Determinants of Health Research Center, Yasuj University of Medical Sciences, Yasuj, Iran 8 Research Center for Environmental Pollutants, Qom University of Medical Sciences, Qom, Iran 9 Department of Environmental Health Engineering, Faculty of Health, Bushehr University of Medical Sciences, Bushehr, Iran 10 The Persian Gulf Marine Biotechnology Research Center, The Persian Gulf Research Center, Bushehr University of Medical Sciences, Bushehr, Iran 11 Hyperlipidemia Research Center, Department of Laboratory Sciences School of Paramedical Sciences, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, IR, Iran 12 Environmental Research Institute, Academic Center for Education, Culture and Research (ACECR), Rasht, Iran 13 Department of Anaesthesiology, Faculty of Medicine, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran 14 Occupational Hazards Control Research Center and Department of Environmental Health Engineering, School of Public Health Environmental, Shahid Beheshti University of Medical Sciences, Tehran, Iran 15 Faculty of Food Science & Technology, Tehran University, Tehran, Iran 16 Student Research Committee, Department of Environmental Health Engineering, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran Environ Sci Pollut Res DOI 10.1007/s11356-016-7447-xoutputs presented in term of hospital admissions respiratory cases. Based on our result, the highest mean and maximum of seasonal and annual levels for sulfur dioxide were observed in 2013. We concluded that obnoxious quality of fuel and some deficiencies in maintenance and operation of industries lead to worse quality of ambient air especially in 2013. Cumulative cases of HARD attributed to sulfur dioxide level at central of relative risk (RR) were estimated 24, 25, and 30 persons for 2011, 2012, and 2013, respectively. The finding of this study showed that total mean of sulfur dioxide was higher than standard concentration. We also noticed that wintertime concentrations of sulfur dioxide during three successive years were higher than of those levels in summer

Classification of Credit Applicants of Banking Systems Using Data Mining and Fuzzy Logic

This research study aims at using Data Mining and Fuzzy Logic approaches to classify the credit scoring of banking system applicants as to cover uncertainties and ambiguity connected with applicant classes and also variables that affect their behavior. The methodology, according to a standard Data Mining process, is to collect and refine the client data, then those variables which are in linguistic forms are converted to fuzzy variables under the supervision of banking experts and final data are modeled using Fuzzy Decision Tree, subsequently. The unfuzzy data are also modeled using the other algorithms. The results of the study suggest that as far as client distinction accuracy is concerned Fuzzy Decision Tree produces better results compared to Traditional Trees, Neural Networks, and statistical procedures such as Logistic Regression and Bayesian Network. However, it is not as accurate as Support Vector Machine and Genetic Tree. On the other hand, Fuzzy Decision Tree technique has gained better prediction than prediction performance of bank credit scoring experts

A conceptual framework of risk identification for scale up companies in transition period

Within an Entrepreneurship ecosystem companies are divided in three phases of development: startup, scale up, and unicorn. This paper addresses the scale up phase and focuses in a bottleneck of transition stage from startup to scale up and consider various challenges from point of view of risk management. This research presents a risk identification framework based on some best practices. The method proposed is supported by established risk management concepts that can be applied to help scale up companies to gain awareness of the risks especially during transition period. This paper contributes for research on entrepreneurship's risks by applying risk management for transition performance of scaling up companies by identifying the relevant risk factors that should be considered. Subsequently, these risks should be evaluated in order to executing proper actions. In addition, it gives to entrepreneur's insights on how the adoption of the growth will affect the enterprise scaling, and how it can increase the assurance of transition stage. The paper concludes with a summary of key ideas and promising directions for future work.publishersversionpublishe

Accurate model based on artificial intelligence for prediction of carbon dioxide solubility in aqueous tetra-n-butylammonium bromide solutions

This study highlights the application of radial basis function (RBF) neural networks, adaptive neuro-fuzzy inference systems (ANFIS), and gene expression programming (GEP) in the estimation of solubility of CO2 in aqueous solutions of tetra-n-butylammonium bromide (TBAB). The experimental data were gathered from a published work in literature. The proposed RBF network was coupled with genetic algorithm (GA) to access a better prediction performance of model. The structure of ANFIS model was trained by using hybrid method. The input parameters of the model were temperature, pressure, mass fraction of TBAB in feed aqueous solution (wTBAB), and mole fraction of TBAB in aqueous phase (xTBAB). The solubility of CO2 (xCO2) was the output parameter. Statistical and graphical analyses of the results showed that the proposed GA-RBF, Hybrid-ANFIS, and GEP models are robust and precise in the estimation of literature solubility data