Why is the Aetiology of Facial Bone Fractures in West of Libya is Diffrent

A maxillofacial fracture is a serious clinical problem because of its functional and aesthetic significance. If these injuries are treated improperly may ultimately result in a patient’s low quality of life. Diagnosis and treatment of these fractures remain a challenge for oral and maxillofacial surgeons, demanding a high level of proficiency. Objective: This study aims to analyze the epidemiology of maxillofacial fractures treated in ministry hospital, Ali Omar Askar Neuro Center in Sbea Tripoli, Libya, to identify the causative factors, and to help in planning programs to control the incident in a population. Study Design: A retrospective review of all patients with maxillofacial fracture presented to Oral & Maxillofacial Surgery Department of Ali Omar Askar Neuro Center Sbea, Tripoli between January 2010 and December 2015 was performed. Result: Total of 437 patients obtained 752 maxillofacial fractures. Male were mostly affected comprising 83%, with the majority occurring in individuals 21-30 year age range. Road traffic accident was the most common cause of maxillofacial fractures with a total of 63.84%. Mandible fracture was mostly affected consisting 59.18% of all maxillofacial fractures. The most fractured anatomical part of the mandible is parasymphysis containing 23% of all mandible fractures. Open reduction and internal fixation was the most common treatment modality. Thirteen percent of patients had associated injuries, and four percent had complications. Conclusion: According to the World Health Organization established in May 2014, Libya is the leading country of traffic deaths per capita. This retrospective study of maxillofacial fracture is congruent to the research that road traffic accident in the country was the most common incident. It is capturing to both government officials to implement legislation and healthcare providers to develop programs to educate the public and reduce such injurie

Uncertainty-wise software anti-patterns detection: A possibilistic evolutionary machine learning approach

Context: Code smells (a.k.a. anti-patterns) are manifestations of poor design solutions that can deteriorate software maintainability and evolution. Research gap: Existing works did not take into account the issue of uncertain class labels, which is an important inherent characteristic of the smells detection problem. More precisely, two human experts may have different degrees of uncertainty about the smelliness of a particular software class not only for the smell detection task but also for the smell type identification one. Unluckily, existing approaches usually reject and/or ignore uncertain data that correspond to software classes (i.e. dataset instances) with uncertain labels. Throwing away and/or disregarding the uncertainty factor could considerably degrade the detection/identification process effectiveness. From a solution approach viewpoint, there is no work in the literature that proposed a method that is able to detect and/or identify code smells while preserving the uncertainty aspect. Objective: The main goal of our research work is to handle the uncertainty factor, issued from human experts, in detecting and/or identifying code smells by proposing an evolutionary approach that is able to deal with anti-patterns classification with uncertain labels. Method: We suggest Bi-ADIPOK, as an effective search-based tool that is capable to tackle the previously mentioned challenge for both detection and identification cases. The proposed method corresponds to an EA (Evolutionary Algorithm) that optimizes a set of detectors encoded as PK-NNs (Possibilistic K-nearest neighbors) based on a bi-level hierarchy, in which the upper level role consists on finding the optimal PK-NNs parameters, while the lower level one is to generate the PK-NNs. A newly fitness function has been proposed fitness function PomAURPC-OVA_dist (Possibilistic modified Area Under Recall Precision Curve One-Versus-All_distance, abbreviated PAURPC_d in this paper). Bi-ADIPOK is able to deal with label uncertainty using some concepts stemming from the Possibility Theory. Furthermore, the PomAURPC-OVA_dist is capable to process the uncertainty issue even with imbalanced data. We notice that Bi-ADIPOK is first built and then validated using a possibilistic base of smell examples that simulates and mimics the subjectivity of software engineers opinions. Results: The statistical analysis of the obtained results on a set of comparative experiments with respect to four relevant state-of-the-art methods shows the merits of our proposal. The obtained detection results demonstrate that, for the uncertain environment, the PomAURPC-OVA_dist of Bi-ADIPOK ranges between 0.902 and 0.932 and its IAC lies between 0.9108 and 0.9407, while for the certain environment, the PomAURPC-OVA_dist lies between 0.928 and 0.955 and the IAC ranges between 0.9477 and 0.9622. Similarly, the identification results, for the uncertain environment, indicate that the PomAURPC-OVA_dist of Bi-ADIPOK varies between 0.8576 and 0.9273 and its IAC is between 0.8693 and 0.9318. For the certain environment, the PomAURPC-OVA_dist lies between 0.8613 and 0.9351 and the IAC values are between 0.8672 and 0.9476. With uncertain data, Bi-ADIPOK can find 35% more code smells than the second best approach (i.e., BLOP). Furthermore, Bi-ADIPOK has succeeded to reduce the number of false alarms (i.e., misclassified smelly instances) by 12%. In addition, our proposed approach can identify 43% more smell types than BLOP and reduces the number of false alarms by 32%. The same results have been obtained for the certain environment, demonstrating Bi-ADIPOK's ability to deal with such environment

Time Series Forecasting of New Cases for COVID-19 Pandemic in Jordan Using Enhanced Hybrid EMD-ARIMA

In this study, the enhanced hybrid empirical mode decomposition with autoregressive integrated moving average (EMD- ARIMA) method is proposed and applied to forecast daily new COVID-19 reported cases in Jordan. The EMD method is applied to decompose the COVID-19 data into a number of IMFs components as a simple time series. Then, the appropriate ARIMA(p,d,q) model is applied to evaluate the forecasting value for the low-frequency components. Then, the forecasting results are collected together. Data for this study are collected from the Jordanian Ministry of Health. Seven forecasting accuracy measures are employed to compare the forecasting results of the proposed technique with the results of seven forecasting methods. The comparison of forecasting results shows that the enhanced EMD-ARIMA method is better than selecting forecasting methodologies in COVID-19 data

Aerodynamic simulation of flapping dragonfly wing kinematics

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Service Oriented Centered E-Health Solution for Monitoring and Preventing Chronic Diseases

The modern and continuously changing lifestyles in almost allparts of the world resulted in an increase in the incidence ofchronic diseases (CDs). To reduce risks associated with chronicdiseases, health professionals are studying various clinicalsolutions. As a result of recent advances in sensing technology,wireless communications, and distributed communication, themonitoring of patients' health condition and the elaboration ofprevention plans are considered the most promising solutions forthe treatment of chronic diseases. In this paper, we propose anovel framework for monitoring chronic diseases and trackingtheir vital signs. The framework relies on the service orientationconcepts and standards to integrate various subsystems.Monitoring of subjects’ health condition, using various sensorsand wireless devices, aims to proactively detect any risk ofchronic diseases. The system will allow generating andcustomizing preventive plans dynamically according to thesubject’s health profile and context while considering manyimpelling parameters. As a proof of concept of our monitoringand tracking schemes, we have considered a case study for whichwe have collected and analyzed preliminary data