Adenoid cystic carcinoma of the external auditory canal

This is a rare case of a young male with biopsy proven adenoid cystic carcinoma of the external auditory canal who underwent excision of the lesion with superficial parotidectomy sparing the facial nerve. Histopathology showed perineural invasion, which is a diagnostic hallmark of adenoid cystic carcinoma. Clinical examination, chest X-ray and CT scan showed no signs of recurrence or metastasis 2 years postoperatively

Analyzing and evaluating the energy efficiency based on multi-5G small cells with a mm-waves in the next generation cellular networks

This paper evaluates the impact of multi-5G small cell systems on the energy efficiency (EE) in a Fifth Generation (5G) of cellular networks. Both the proposed model and the analysis of the EE in this study take into account (i) the path losses, fading, and shadowing that affect the received signal at the user equipment (UE) within the same cell, and (ii) the interference effects of adjacent cells. In addition, the concepts of new technologies such as large MIMO in millimeter range communication have also been considered. The simulation results show that the interference from adjacent cells can degrade the EE of a multi-cell cellular network. With the high interference the number of bits that will be transferred per joule of energy is 1.29 Mb/J with a 0.25 GHz bandwidth and 16 transmit antennas. While, with a 1 GHz bandwidth the transfer rate increases to 5.17 Mb/J. Whereas, with 64 transmit antennas the EE improved to 5.17 Mb/J with a 0.25 GHz BW and 20.70 Mb/J with a 1 GHz BW. These results provide insight into the impact of the number of antennas in millimeter range communication and the interference from adjacent cells on achieving real gains in the EE of multi-5G small cells cellular network

Robust Framework to Combine Diverse Classifiers Assigning Distributed Confidence to Individual Classifiers at Class Level

We have presented a classification framework that combines multiple heterogeneous classifiers in the presence of class label noise. An extension of m-Mediods based modeling is presented that generates model of various classes whilst identifying and filtering noisy training data. This noise free data is further used to learn model for other classifiers such as GMM and SVM. A weight learning method is then introduced to learn weights on each class for different classifiers to construct an ensemble. For this purpose, we applied genetic algorithm to search for an optimal weight vector on which classifier ensemble is expected to give the best accuracy. The proposed approach is evaluated on variety of real life datasets. It is also compared with existing standard ensemble techniques such as Adaboost, Bagging, and Random Subspace Methods. Experimental results show the superiority of proposed ensemble method as compared to its competitors, especially in the presence of class label noise and imbalance classes

Determination of System Dimensionality from Observing Near-Normal Distributions

This paper identifies a previously undiscovered behavior of uniformly distributed data points or vectors in high dimensional ellipsoidal models. Such models give near normal distributions for each of its dimensions. Converse of this may also be true; that is, for a normal-like distribution of an observed variable, it is possible that the distribution is a result of uniform distribution of data points in a high dimensional ellipsoidal model, to which the observed variable belongs. Given the currently held notion of normal distributions, this new behavior raises many interesting questions. This paper also attempts to answer some of those questions. We cover both volume based (filled) and surface based (shell) ellipsoidal models. The phenomenon is demonstrated using statistical as well as mathematical approaches. We also show that the dimensionality of the latent model, that is, the number of hidden variables in a system, can be calculated from the observed distribution. We call the new distribution “Tanazur” and show through experiments that it is at least observed in one real world scenario, that of the motion of particles in an ideal gas. We show that the Maxwell-Boltzmann distribution of particle speeds can be explained on the basis of Tanazur distributions

Internet Use and Addiction Among Medical Students in Qassim University, Saudi Arabia

ABSTRACT: Objectives: This study aimed to measure the prevalence of Internet use and addiction and determine its association with gender, academic performance and health among medical students. Methods: This cross-sectional study was conducted between December 2017 and April 2018 at the College of Medicine, Qassim University, Buraydah, Saudi Arabia. The validated Internet Addiction Test questionnaire was distributed by simple random methods to medical students (N = 216) in the pre-clinical phase (first-, second- and third-years). A chi-square test was used to determine significant relationships between Internet use and addiction and gender, academic performance and health. Results: A total of 209 student completed the questionnaire (response rate: 96.8%) and the majority (57.9%) were male. In total, 12.4% were addicted to the Internet and 57.9 had the potential to become addicted. Females were more frequent Internet users than males (P = 0.006). Academic performance was affected in 63.1% of students and 71.8% lost sleep due to late-night Internet use, which affected their attendance to morning activities. The majority (59.7%) expressed feeling depressed, moody or nervous when they were offline. Conclusion: Internet addiction among medical students at Qassim University was very high, with addiction affecting academic performance and psychological well-being. Suitable interventional and preventive measures are needed for proper Internet use to protect students’ mental and physical health.Keywords: Internet; Addictive Behavior; Medical Students; Universities; Academic Performance; Saudi Arabia

Machine Learning Algorithms for Smart Data Analysis in Internet of Things Environment: Taxonomies and Research Trends

Machine learning techniques will contribution towards making Internet of Things (IoT) symmetric applications among the most significant sources of new data in the future. In this context, network systems are endowed with the capacity to access varieties of experimental symmetric data across a plethora of network devices, study the data information, obtain knowledge, and make informed decisions based on the dataset at its disposal. This study is limited to supervised and unsupervised machine learning (ML) techniques, regarded as the bedrock of the IoT smart data analysis. This study includes reviews and discussions of substantial issues related to supervised and unsupervised machine learning techniques, highlighting the advantages and limitations of each algorithm, and discusses the research trends and recommendations for further study

Effectiveness of Vitamin D in Prevention of Dengue Haemorrhagic Fever and Dengue Shock Syndrome

Background: To compare the risk and severity of development of Dengue Hemorrhagic Fever (DHF) and Dengue Shock Syndrome(DSS) in patients receiving Vitamin D supplement compared to those not receiving it.Methods: Diagnosed patients of DF (n=124) were enrolled in this comparative study. Patients were randomized into two groups having 62 participants in each group. Group A received single dose of 200,000 IU Vitamin D and Group B received no intervention. Both groups were followed for development of DHF or DSS. Chi square was applied to compare the groups.Results: One patient (1.6%) in Group A developed DHF. Seventeen (27%) patients in Group B progressed to DHF. The relationship between Vitamin D and progression to DHF was significant, X2 (2, N=170) =16.43, p= 0.000). The calculated relative risk was 0.0588 (95% confidence interval, .0081 to .4285; p for trend = 0.0588).Conclusion: Vitamin D decreases the risk of DHF and may have a role in management of dengue fever

A Comparison of Standard Setting Methods for Setting Cut-Scores for Assessments with Constructed Response Questions

Standard setting provides a way to define minimal competency for various professional assessments. In the literature, a number of methods are proposed but there are implications for examinees because they can produce varied passing scores. Angoff is a widely applied method in context of educational assessments to define the borderline student that required extensive training of judges and skills to conceptualize minimum proficiency. The Cohen has defined an alternative procedure to overcome the limitations of Angoff. Additionally, we explored the relative method by computing average of score distribution as a point below that mean as the passing mark. Objective of the study was to investigate performance of Angoff with other standard setting procedures to inform future standard setting practices. These methods were applied to various exams having small, medium and large number of students. We found Angoff method produced credible and reliable pass scores and close to the relative method but Cohen and Modified Cohen gave divergent results. We recommend studied standard setting procedures explored further with different formats of assessments having varied sample sizes