Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS) for Non-smooth Data Field

Compressive Sensing (CS) has been applied successfully in a wide variety of applications in recent years, including photography, shortwave infrared cameras, optical system research, facial recognition, MRI, etc. In wireless sensor networks (WSNs), significant research work has been pursued to investigate the use of CS to reduce the amount of data communicated, particularly in data aggregation applications and thereby improving energy efficiency. However, most of the previous work in WSN has used CS under the assumption that data field is smooth with negligible white Gaussian noise. In these schemes signal sparsity is estimated globally based on the entire data field, which is then used to determine the CS parameters. In more realistic scenarios, where data field may have regional fluctuations or it is piecewise smooth, existing CS based data aggregation schemes yield poor compression efficiency. In order to take full advantage of CS in WSNs, we propose an Adaptive Hierarchical Data Aggregation using Compressive Sensing (A-HDACS) scheme. The proposed schemes dynamically chooses sparsity values based on signal variations in local regions. We prove that A-HDACS enables more sensor nodes to employ CS compared to the schemes that do not adapt to the changing field. The simulation results also demonstrate the improvement in energy efficiency as well as accurate signal recovery

Numerical simulation of combined mixing and separating flow in channel filled with porous media

Various flow bifurcations are investigated for two dimensional combined mixing and separating geometry. These consist of two reversed channel flows interacting through a gap in the common separating wall filled with porous media of Newtonian fluids and other with unidirectional fluid flows. The Steady solutions are obtained through an unsteady finite element approach that employs a Taylor-Galerkin/pressure-correction scheme. The influence of increasing inertia on flow rates are all studied. Close agreement is attained with numerical data in the porous channels for Newtonian fluids.Peer reviewedSubmitted Versio

Our experience of COVID-19 at a large District General Hospital in the North West of England

Objective: To determine the mortality rate, discharge rate, current admissions, and comorbid conditions in our patients along with a view to further investigate high mortality rates observed at our hospital. Methodology: This retrospective epidemiological study aims to review patients presenting with COVID-19 at a District General Hospital in the north west of England. A total of 514 patients were admitted with a positive COVID-19 swab from March 17, 2020 to midnight May 20, 2020 have been included in this study. All patients admitted with COVID-19 positive swabs were included in the study. Patients discharged from the Emergency Department were excluded. The data was assessed daily by the Clinical Audit and Effectiveness Team and cross referenced across multiple sources to ensure accuracy.  Results: From March 17, 2020 to May 20, 2020 a total of 514 patients were admitted with a positive COVID-19 swab. Out of the 514 patients, 284 (55%) were male while 230 (45%) were female (Figure 1). Among the 514 patients admitted, 236 (45.9%) died, 263 (51.2%) were discharged, 1 (0.2%) was discharged and then readmitted, 1 (0.2%) was transferred while 13 (2.5%) are still admitted at the hospital. Out of the 236 patients who died, 144 (61%) were male and 92 (39%) were female. 130 (49%) of the 263 patients discharged were male and 133 (51%) were female. One female patient was discharged but then readmitted and one male patient was transferred. Out of the 13 patients still admitted at the hospital, 9 (69%) are male and 4 (31%) are female (Fig 2). Upon review of the pre-existing comorbid conditions of the patients, it was noted that 101 (20%) patients had no comorbid conditions, 59 (11%) had one comorbid condition, 93 (18%) had two comorbid conditions, 106 (21%) had three and 155 (30%) had four or more comorbid conditions. Conclusion: Patients with comorbid conditions are more prone to COVID-19 in terms of severity. Due to high mortality rates observed in our study, we propose further research to review the high susceptibility to severe COVID-19 infection in the population of North West, England

Hydrophobic cis-platinum complexes efficiently incorporated into liposomes

The present invention involves the synthesis and use of new platinum compounds. These new platinum compounds are easy to encapsulate in liposomes at high efficiencies. They are further characterized as platinum (II) four coordinate complex having the formula: ##STR1## wherein R.sub.1 and R.sub.2 are carboxylato monoanions bearing a hydrophobic radical function or a single carboxylato dianion bearing a hydrophobic radical function and R.sub.3 is a vicinal diaminoalkane or vicinal diaminocycloalkane. The complex is substantially soluble in methanol or chloroform and substantially insoluble in water. Said complex may be incorporated into phospholipid liposomes. Such platinum complexes encapsulated in phospholipid liposomes are useful for chemotherapy of platinum complex-sensitive tumors.Board of Regents, University of Texas Syste

Automatic classification of power quality disturbances using optimal feature selection based algorithm

The development of renewable energy sources and power electronic converters in conventional power systems leads to Power Quality (PQ) disturbances. This research aims at automatic detection and classification of single and multiple PQ disturbances using a novel optimal feature selection based on Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN). DWT is used for the extraction of useful features, which are used to distinguish among different PQ disturbances by an ANN classifier. The performance of the classifier solely depends on the feature vector used for the training. Therefore, this research is required for the constructive feature selection based classification system. In this study, an Artificial Bee Colony based Probabilistic Neural Network (ABCPNN) algorithm has been proposed for optimal feature selection. The most common types of single PQ disturbances include sag, swell, interruption, harmonics, oscillatory and impulsive transients, flicker, notch and spikes. Moreover, multiple disturbances consisting of combination of two disturbances are also considered. The DWT with multi-resolution analysis has been applied to decompose the PQ disturbance waveforms into detail and approximation coefficients at level eight using Daubechies wavelet family. Various types of statistical parameters of all the detail and approximation coefficients have been analysed for feature extraction, out of which the optimal features have been selected using ABC algorithm. The performance of the proposed algorithm has been analysed with different architectures of ANN such as multilayer perceptron and radial basis function neural network. The PNN has been found to be the most suitable classifier. The proposed algorithm is tested for both PQ disturbances obtained from the parametric equations and typical power distribution system models using MATLAB/Simulink and PSCAD/EMTDC. The PQ disturbances with uniformly distributed noise ranging from 20 to 50 dB have also been analysed. The experimental results show that the proposed ABC-PNN based approach is capable of efficiently eliminating unnecessary features to improve the accuracy and performance of the classifier