Knowledge-based systems that determine the appropriate students major: In the faculty of engineering and information technology

In this paper a Knowledge-Based System (KBS) for determining the appropriate students major according to his/her preferences for sophomore student enrolled in the Faculty of Engineering and Information Technology in Al-Azhar University of Gaza was developed and tested. A set of predefined criterions that is taken into consideration before a sophomore student can select a major is outlined. Such criterion as high school score, score of subject such as Math I, Math II, Electrical Circuit I, and Electronics I taken during the student freshman year, number of credits passed, student cumulative grade point average of freshman year, among others, were then used as input data to KBS. KBS was designed and developed using Simpler Level Five (SL5) Object expert system language. KBS was tested on three generation of sophomore students from the Faculty of Engineering and Information Technology of the Al-Azhar University, Gaza. The results of the evaluation show that the KBS is able to correctly determine the appropriate students major without errors

Antimicrobial resistance among migrants in Europe: a systematic review and meta-analysis

BACKGROUND: Rates of antimicrobial resistance (AMR) are rising globally and there is concern that increased migration is contributing to the burden of antibiotic resistance in Europe. However, the effect of migration on the burden of AMR in Europe has not yet been comprehensively examined. Therefore, we did a systematic review and meta-analysis to identify and synthesise data for AMR carriage or infection in migrants to Europe to examine differences in patterns of AMR across migrant groups and in different settings. METHODS: For this systematic review and meta-analysis, we searched MEDLINE, Embase, PubMed, and Scopus with no language restrictions from Jan 1, 2000, to Jan 18, 2017, for primary data from observational studies reporting antibacterial resistance in common bacterial pathogens among migrants to 21 European Union-15 and European Economic Area countries. To be eligible for inclusion, studies had to report data on carriage or infection with laboratory-confirmed antibiotic-resistant organisms in migrant populations. We extracted data from eligible studies and assessed quality using piloted, standardised forms. We did not examine drug resistance in tuberculosis and excluded articles solely reporting on this parameter. We also excluded articles in which migrant status was determined by ethnicity, country of birth of participants' parents, or was not defined, and articles in which data were not disaggregated by migrant status. Outcomes were carriage of or infection with antibiotic-resistant organisms. We used random-effects models to calculate the pooled prevalence of each outcome. The study protocol is registered with PROSPERO, number CRD42016043681. FINDINGS: We identified 2274 articles, of which 23 observational studies reporting on antibiotic resistance in 2319 migrants were included. The pooled prevalence of any AMR carriage or AMR infection in migrants was 25·4% (95% CI 19·1-31·8; I2 =98%), including meticillin-resistant Staphylococcus aureus (7·8%, 4·8-10·7; I2 =92%) and antibiotic-resistant Gram-negative bacteria (27·2%, 17·6-36·8; I2 =94%). The pooled prevalence of any AMR carriage or infection was higher in refugees and asylum seekers (33·0%, 18·3-47·6; I2 =98%) than in other migrant groups (6·6%, 1·8-11·3; I2 =92%). The pooled prevalence of antibiotic-resistant organisms was slightly higher in high-migrant community settings (33·1%, 11·1-55·1; I2 =96%) than in migrants in hospitals (24·3%, 16·1-32·6; I2 =98%). We did not find evidence of high rates of transmission of AMR from migrant to host populations. INTERPRETATION: Migrants are exposed to conditions favouring the emergence of drug resistance during transit and in host countries in Europe. Increased antibiotic resistance among refugees and asylum seekers and in high-migrant community settings (such as refugee camps and detention facilities) highlights the need for improved living conditions, access to health care, and initiatives to facilitate detection of and appropriate high-quality treatment for antibiotic-resistant infections during transit and in host countries. Protocols for the prevention and control of infection and for antibiotic surveillance need to be integrated in all aspects of health care, which should be accessible for all migrant groups, and should target determinants of AMR before, during, and after migration. FUNDING: UK National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare Charity, the Wellcome Trust, and UK National Institute for Health Research Health Protection Research Unit in Healthcare-associated Infections and Antimictobial Resistance at Imperial College London

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

Development and Evaluation of Neural Network Models for Cost Reduction in Unmanned Air Vehicles

With a growing demand for cost reduction in unmanned air vehicles (UAVs), there has been considerable interest in exploiting existing aircraft technologies. This thesis focuses on two technologies: model-based sensor fault detection, isolation and accommodation (SFDIA) schemes and flush air data sensing (FADS) systems. In the aerospace industry, SFDIA is traditionally based on physical (sensor) redundancy. Unfortunately this approach can be inadequate in UAVs due to cost, weight and space implications. Consequently researchers have found the concept of ‘virtual’ sensor redundancy (i.e. model-based methods) an invaluable alternative to physical redundancy. Current model-based SFDIA schemes rely on linear time-invariant (LTI) models. In nonlinear, time-varying systems (such as aircraft), LTI-based methods can sometimes fail to give satisfactory results. New approaches make use of neural network (NN) models due to their nonlinear and adaptive structure. In this thesis, a NN-based SFDIA scheme is designed to detect single and multiple sensor faults in a nonlinear UAV model. The proposed scheme has been shown to be robust to system and measurement noise and sensitive to a wide range of fault types. In the second part of this thesis, a FADS system is designed and tested on a mini air vehicle (MAV). With the primary goal of most air vehicle manufacturers being the reduction of costs, researchers found the concept of air data measurements using a matrix of pressure orifices to be a cheaper alternative to the standard air data boom. The concept of FADS systems is not new and has been quite popular in several NASA projects. However few applications consider MAVs where weight and cost implications can restrict the use of air data booms. The FADS system designed in this thesis has been shown to produce accurate air data estimations but more importantly has reduced instrumentation weight and cost by almost 80% and 97% respectively in comparison to a standard air data boom. The conclusions drawn from this thesis are clearly outlined at the end of each chapter and future work is also brought together in the final chapter

Detection of additive sensor faults in an Unmanned Air Vehicle (UAV) model using neural networks

Abstract: Sensor fault detection and isolation (SFDI) is an already well-established field where popular methods, such as unknown input observers, are mostly based on linear time-invariant system equations. New approaches consider neural networks (NNs) as nonlinear system approximators and make use of their online learning capabilities to adapt to time-varying systems. This paper aims to contribute to this field with the application of NNs to a nonlinear unmanned air vehicle (UAV) model. A Radial-Basis Function (RBF) NN trained online with Extended Minimum Resource Allocating Network (EMRAN) algorithms is chosen for modelling purposes due to its good estimation capabilities and compact size. To improve SFDI robustness to unknown inputs we also propose a novel approach which is referred to as residual padding. False alarms and missed faults are found to be avoided with a slight increase in fault detection time in comparison to a conventional residual generator

A comparative study of NN- and EKF-based SFDA schemes with application to a nonlinear UAV model

In this article, we propose two schemes for sensor fault detection and accommodation (SFDA): one based on a neural network (NN) and the other on an extended Kalman filter (EKF). The objective of this article is to compare both approaches in terms of execution time, robustness to poorly modelled dynamics and sensitivity to different fault types. The schemes are tested on an unmanned air vehicle (UAV) application where traditional sensor redundancy methods can be too heavy and/or costly. In an attempt to reduce the false alarm rates and the number of undetected faults, a modified residual generator, originally proposed in Samy, Postlethwaite, and Gu in 2008 (Samy, I., Postlethwaite, I., and Gu, D.-W. (2008a). Neural Network Sensor Validation Scheme Demonstrated on a UAV Model, in IEEE Proceedings of CDC, Cancun, Mexico, pp. 1237–1242) is implemented. Simulation work is presented for use on a UAV demonstrator under construction with support from BAE systems and EPSRC. Results have shown that the NN-SFDA scheme outperforms the EKF-SFDA scheme with only one missed fault, zero false alarms and an average estimation error of 0.31_/s for 112 different test conditions

Subsonic tests of a flush air data sensing system applied to a fixed-wing micro air vehicle

Flush air data sensing (FADS) systems have been successfully tested on the nose tip of large manned/unmanned air vehicles. In this paper we investigate the application of a FADS system on the wing leading edge of a micro (unmanned) air vehicle (MAV) flown at speed as low as Mach 0.07. The motivation behind this project is driven by the need to find alternative solutions to air data booms which are physically impractical for MAVs. Overall an 80% and 97% decrease in instrumentation weight and cost respectively were achieved. Air data modelling is implemented via a radial basis function (RBF) neural network (NN) trained with the extended minimum resource allocating network (EMRAN) algorithm. Wind tunnel data were used to train and test the NN, where estimation accuracies of 0.51°, 0.44 lb/ft2 and 0.62 m/s were achieved for angle of attack, static pressure and wind speed respectively. Sensor faults were investigated and it was found that the use of an autoassociative NN to reproduce input data improved the NN robustness to single and multiple sensor faults. Additionally a simple NN domain of validity test demonstrated how the careful selection of the NN training data set is crucial for accurate estimations

Neural network based sensor validation scheme demonstrated on an unmanned air vehicle (UAV) model

Nowadays model-based fault detection and isolation (FDI) systems have become a crucial step towards autonomy in aerospace engineering. However few publications consider FDI applications to unmanned air vehicles (UAV) where full-autonomy is obligatory. In this paper we demonstrate a sensor fault detection and accommodation (SFDA) system, which makes use of analytical redundancy between flight parameters, on a UAV model. A Radial-Basis Function (RBF) neural network (NN) trained online with Extended Minimum Resource Allocating Network (EMRAN) algorithms is chosen for modelling purposes due to its ability to adapt well to nonlinear environments while maintaining high computational speeds. Furthermore, in an attempt to reduce false alarms (FA) and missed faults (MF) in current SFDA systems, we introduce a novel residual generator. After 47 minutes (CPU running time) of NN offline training, the SFDA scheme is able to detect additive and constant bias sensor faults with zero FA and MF. It also shows good global approximation capabilities, essential for fault accommodation, with an average pitch gyro estimation error of 0.0075 rad/s