Post Thrombolytic St-Segment Resolution Outcome in Acute Myocardial Infarction Patients

OBJECTIVES: The main objective of this study was to assess post thrombolytic resolution of ST-segment and its outcome in patients with acute myocardial infarction. METHODOLOGY: This Prospective Comparative Study was carried out at the Cardiology Unit of Ayub Teaching Hospital, Abbottabad. All patients irrespective of gender and age with ST-Segment elevation myocardial infarction (STEMI), having no immediate access to angioplasty and thrombolysed with streptokinase, were included in this study. ECG was taken at the beginning and 90 minutes after the administration of streptokinase. Based on ST-segment resolution on ECG taken at 90 minutes these patients were classified into group A and B. Group A included patients with ST-segment resolution while group B showed no resolution of ST-segment after streptokinase administration. These patients were followed during their hospital stay for complications such as arrhythmias, cardiogenic shock, acquired ventricular septal defects (VSD) aneurysm and death. RESULTS: Among 115 patients, 94 were male and 21 female. Group A included 102 (89%) patients and group B included 13 (11%). In group A, only 1 (0.98 %) patient developed complications and in group B, 13 patients (100%) developed complications. Arrhythmias were the most common complication among MI patients in group A while cardiogenic shock was the commonest complication in group B. CONCLUSION: ST-segment resolution is a practical and applicable indicator of successful thrombolysis and has a significant correlation with clinical outcome in acute myocardial patients after thrombolysis with streptokinase

Performance of DOA estimation algorithms for acoustic localization of indoor flying drones using artificial sound source

Flying unmanned aerial vehicles (UAVs) in swarms can have numerous advantages. However, to maintain a safe distance between them during flight is very challenging. To achieve this, each UAV in the swarms needs to know its relative location with respect to one another. This work proposes a method for relative localization using the chirping sound emitted from UAVs flying together indoors. The strategy is simulated to assess localization performance of three different types of chirping sounds indoors using six microphone arrays. The estimated direction of arrival (DOA) of the chirping sound is calculated using several published algorithms that include MUSIC, CSSM, SRP-PHAT, TOPS and WAVES. The sound is produced in a simulated flying indoor environment with several different settings of sound-to-noise ratio (SNR) and reverberation time (RT). Based on the results, it has been found that chirping sound with a wider frequency band produced better results in terms of mean values of DOA estimation error. The chirping sound performance is also tested with the actual UAVs operating under different rotor speeds. Similarly, it is observed that the chirping sound with wider band also produced better results in three of the algorithms, which is reflected in their absolute mean error. Nevertheless, further work has to be done to filter out the UAVs’ rotor noise and also the indoor reverberation effects for better performance

Soft biometrics: gender recognition from unconstrained face images using local feature descriptor

Gender recognition from unconstrained face images is a challenging task due to the high degree of misalignment, pose, expression, and illumination variation. In previous works, the recognition of gender from unconstrained face images is approached by utilizing image alignment, exploiting multiple samples per individual to improve the learning ability of the classifier, or learning gender based on prior knowledge about pose and demographic distributions of the dataset. However, image alignment increases the complexity and time of computation, while the use of multiple samples or having prior knowledge about data distribution is unrealistic in practical applications. This paper presents an approach for gender recognition from unconstrained face images. Our technique exploits the robustness of local feature descriptor to photometric variations to extract the shape description of the 2D face image using a single sample image per individual. The results obtained from experiments on Labeled Faces in the Wild (LFW) dataset describe the effectiveness of the proposed method. The essence of this study is to investigate the most suitable functions and parameter settings for recognizing gender from unconstrained face images

Introducing adaptive machine learning technique for solving short-term hydrothermal scheduling with prohibited discharge zones

The short-term hydrothermal scheduling (STHTS) problem has paramount importance in an interconnected power system. Owing to an operational research problem, it has been a basic concern of power companies to minimize fuel costs. To solve STHTS, a cascaded topology of four hydel generators with one equivalent thermal generator is considered. The problem is complex and non-linear and has equality and inequality constraints, including water discharge rate constraint, power generation constraint of hydel and thermal power generators, power balance constraint, reservoir storage constraint, initial and end volume constraint of water reservoirs, and hydraulic continuity constraint. The time delays in the transport of water from one reservoir to the other are also considered. A supervised machine learning (ML) model is developed that takes the solution of the STHTS problem without PDZ, by any metaheuristic technique, as input and outputs an optimized solution to STHTS with PDZ and valve point loading (VPL) effect. The results are quite promising and better compared to the literature. The versatility and effectiveness of the proposed approach are tested by applying it to the previous works and comparing the cost of power generation given by this model with those in the literature. A comparison of results and the monetary savings that could be achieved by using this approach instead of using only metaheuristic algorithms for PDZ and VPL are also given. The slipups in the VPL case in the literature are also addressed

An effective approach for managing power consumption in cloud computing infrastructure

Cloud computing offers a dynamic provisioning of server capabilities as a scalable virtualized service. Big datacenters which deliver cloud computing services consume a lot of power. This results in high operational cost and large carbon emission. One way to lower power consumption without affecting the cloud services quality is to consolidate resources for reducing power. In this paper, we introduce a DNA-based Fuzzy Genetic Algorithm (DFGA) that employs DNA-based scheduling strategies to reduce power consumption in cloud datacenters. It is a power-aware architecture for managing power consumption in the cloud computing infrastructure. We also identify the performances metrics that are needed to evaluate the proposed work performance. The experimental results show that DFGA reduced power consumption when comparing with other algorithms. Our proposed work deals with real time task which is not static, and concentrates on the dynamic users since they are involved in cloud

Dragonfly algorithm-based optimization for selective harmonics elimination in cascaded H-bridge multilevel inverters with statistical comparison

Harmonics worsen the quality of electrical signals, hence, there is a need to eliminate them. The test objects under discussion are single-phase versions of cascaded H-bridge (CHB) multilevel inverters (MLIs) whose switching angles are optimized to eliminate specific harmonics. The Dragonfly Algorithm (DA) is used to eradicate low-order harmonics, and its statistical performance is compared to that of many other optimization techniques, including Particle Swarm Optimization (PSO), Accelerated Particle Swarm Optimization (APSO), Differential Evolution (DE), and Grey Wolf Optimization (GWO). Various scenarios of the algorithms’ search agent population for inverters with seven, nine, and eleven levels of output voltages are comprehensively addressed in this research. No algorithm shows total dominance in every scenario. The DA is least impacted by the change in dimensions of the narrated problem

Online signature verification using neural network and Pearson correlation features

In this paper, we proposed a method for feature extraction in online signature verification. We first used signature coordinate points and pen pressure of all signatures, which are available in the SIGMA database. Then, Pearson correlation coefficients were selected for feature extraction. The obtained features were used in back-propagation neural network for verification. The results indicate an accuracy of 82.42%

Harvesting Electrical Energy from Rooftop Ventilator

Development of the renewable energy is one of the most challenging issue in Malaysia since the fossil fuels and natural gas is foresee to be depleted in one day. Renewable energy is able to provide sustainable yet clean energy to the country in the near future. Other than Solar, wind energy is only of the renewable energy that has the potential to provide green energy to not only domestic but industrial users. This paper proposes a development of similar concept of wind energy by harvesting electrical energy from rooftop ventilator. Modification is made on a commercial rooftop ventilator with an integration of a 120V DC motor and conversion gear (gear ratio 1:2) to the turbine shaft. The continuous rotation of the rooftop ventilator due to temperature difference indoor and outdoor rotated the rotor of the DC generator. The DC energy is stored in the battery via a charge controller before distributing to the load. An average of 60 rpm turning speed of the ventilator is simulated mimicking the general turning speed of the ventilator installed on the roof. The generated energy characteristics study is carried out by using standard fan to produce steady rotational of the ventilator. As the spin rate is very low to reach the optimum spin rate, the generated energy is small and only for a small DC load

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

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