BackgroundHIV programs are increasingly confronted with failing antiretroviral therapy (ART), including second-line regimens. WHO has provided guidelines on switching to third-line ART. In a Médecins Sans Frontières clinic in Mumbai, India, receiving referred presumptive second-line ART failure cases, an evidence-based protocol consisting of viral load (VL) testing, enhanced adherence counselling (EAC) and genotype for switching was implemented.ObjectiveTo document the outcome and genotype of presumptive second-line ART failure cases switched to third-line or maintained on second-line ART.DesignRetrospective cohort study of patients referred between January 2011 and September 2017.ResultsThe cases (n = 120) were complex with median 9.2 years of ART exposure, poor adherence at baseline, and exposure to multiple ART regimens other than recommended by WHO. Out of 90 evaluated cases, 39(43%) were maintained on second-line ART. Forty-nine (54%) were ever switched to third-line ART. Twelve months virological suppression was 72% in the second-line and 93% in the third-line ART cohort, while retention in care was 80% and 94% respectively. Genotyping showed 62% resistance for PIs, and 52% triple class resistance to NRTIs, NNRTIs and PIs. Resistance was noted for the new class of integrase inhibitors, and for different drugs without any documented previous exposure to the same drug.ConclusionAdopting WHO guidelines on switching ART regimens and provision of EAC can prevent unnecessary switching/exposure to third-line ART regimens. Genotyping is urgently required in national HIV programs, which currently use only the exposure history of patients for switching to third-line ART regimens.
Cabin pressure control system of an aircraft maintains cabin pressure in all flight modes as per the aircraft cabin pressurization characteristics by controlling the air flow from the cabin through the outflow valve of the cabin pressure control valve. The movement of outflow valve in turn depends on the air flow from the control chamber of cabin pressure control valve, which is controlled by the clapper and the poppet valves. These valves are actuated by absolute pressure and the differential pressure capsules, respectively depending upon the operating flight conditions. Mathematical models have been developed to simulate the air outflow rates from the cabin and the control chamber of cabin pressure control valve during steady-state and transient flight conditions. These mathematical models have then been translated into a MATLAB program to obtain plots of cabin pressures as a function of aircraft altitudes. The mathematical models are validated for standard cabin pressurization characteristics of a multirole light fighter/trainer aircraft. The model developed, thus can be used to produce a number of variants of cabin pressure control valve to suit different cabin pressurization characteristics.
Gas metal arc welding process has the capability of producing high quality, all position welding, and is easily adaptable for automated welding applications. Repair welding of random cracks on existing assembly/structure through automatic welding would need real time crack/gap identification and weld path generation. In this work, an image processing-based system is presented for identifying the crack geometry. Graphical user interfaces are also developed to take necessary user inputs required at different stages for crack identification, predicting weld bead dimension, and weld path generation. Based on the identified crack geometry and predicted weld bead feature, linear and curved weld path planning methodology is proposed. The proposed modules are validated for a case study by successfully generating the desired weld paths. Different natures of velocity profiles are considered to appraise the role on motion behaviour and a suitable profile is selected for reducing the jerks at sharp corners/via points on the weld path and maintaining uniform bead geometry.
<div class="section abstract"><div class="htmlview paragraph">The transmission is an integral part of the driveline in an automotive vehicle. Global vehicle pass-by noise regulations are becoming more stringent and transmissions are expected to be very quiet. Typically for an automotive system, engine is the most dominant noise source and transmissions have been considered a secondary noise source but as the trend is shifting towards more electric vehicles where engine noise is absent and overall vehicle is becoming quieter, the transmission can be more of a significant noise contributor. Gear whine is the major concern for sound radiation from the transmission. The gear whine simulation and acoustic radiation analysis of the transmission using traditional methods (FEM and BEM) is a crucial but very time-consuming part of the product development cycle. On top of that, electric vehicle transmissions operate at higher RPM which in turn increases the excitation frequency arising from the gear whine phenomenon. Hence present work focuses on the development of system level reduced order model using Statistical Energy Analysis (SEA) which could take fraction of computational time compared to FEM and BEM and can provide quick design solutions such as changes in ribbing pattern, enclosure thickness etc and hence making entire transmission product development process leaner and more efficient. The entire geometry of the enclosure is divided into SEA subsystems, such as flat plates, curved plates and beams. The gear whine force is provided as excitation to the SEA model. This work includes the sensitivity analysis of all the parameters influencing the SPL. The results from the SEA method are compared with actual test data for final validation. The obtained results are within the limits of +/- 3 dB with respect to test data. On top of that, computational time taken by SEA is 1500 times lesser than deterministic methods (BEM).</div></div>
From the past few years, Intrusion Detection Systems (IDS) are employed as a second line of defence and have shown to be a useful tool for enhancing security by detecting suspicious activity. Anomaly based intrusion detection is a type of intrusion detection system that identifies anomalies. Conventional IDS are less accurate in detecting anomalies because of the decision taking based on rules. The IDS with machine learning method improves the detection accuracy of the security attacks. To this end, this paper studies the classification analysis of intrusion detection using various supervised learning algorithms such as SVM, Naive Bayes, KNN, Random Forest, Logistic Regression and Decision tree on the NSL-KDD dataset. The findings reveal which method performed better in terms of accuracy and running time.
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