An unusual presentation of gbs: case report and literature review

Guillain-Barre syndrome (GBS), also known as Landry paralysis is an acute idiopathic polyneuritis, believed to be immunologically mediated. It usually presents as a demyelinating neuropathy with ascending weakness, however, many clinical variants have been well documented in the medical literature, and variants involving the cranial nerves or pure motor involvement with axonal injury have also been described. We report a case of a 50 year old patient who initially presented to the ER with hemiparesis and cranial nerve palsies simulating a cerebrovascular event. Based on neurological examination, CSF analysis and needle EMG finding a diagnosis of GBS was made

Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments

In this paper, we considered the problem of the speech enhancement similar to the real-world environments where several complex noise sources simultaneously degrade the quality and intelligibility of a target speech. The existing literature on the speech enhancement principally focuses on the presence of one noise source in mixture signals. However, in real-world situations, we generally face and attempt to improve the quality and intelligibility of speech where various complex stationary and nonstationary noise sources are simultaneously mixed with the target speech. Here, we have used deep learning for speech enhancement in complex-noisy environments and used ideal binary mask (IBM) as a binary classification function by using deep neural networks (DNNs). IBM is used as a target function during training and the trained DNNs are used to estimate IBM during enhancement stage. The estimated target function is then applied to the complex-noisy mixtures to obtain the target speech. The mean square error (MSE) is used as an objective cost function at various epochs. The experimental results at different input signal-to-noise ratio (SNR) showed that DNN-based complex-noisy speech enhancement outperformed the competing methods in terms of speech quality by using perceptual evaluation of speech quality (PESQ), segmental signal-to-noise ratio (SNRSeg), log-likelihood ratio (LLR), weighted spectral slope (WSS). Moreover, short-time objective intelligibility (STOI) reinforced the better speech intelligibility

Broadside Pattern Correction Techniques for Conformal Antenna Arrays

Phase compensation techniques based on projection method and convex optimization (phase correction only) for comparing the maximum gain of a phase-compensated conformal antenna array have been discussed. In particular, these techniques are validated with conformal phased array antenna attached to a cylindrical-shaped surface with various radii of curvatures. These phase compensation techniques are used to correct the broadside radiation pattern. It is shown that the maximum broadside gain compensated is still less than the gain of a linear flat array for any surface deformation. This fundamental maximum compensated gain limitations of the phase compensation techniques can be used by a designer to predict the maximum broadside obtainable theoretical gain on a conformal antenna array for a particular deformed surface

Prevalence of non-communicable diseases and their risk factors at a semi-urban community, Pakistan

Introduction: Pakistan is currently facing the double burden of communicable (38%) and non- communicable diseases (49%) according to WHO NCD Country Profiles 2014. About 50% of all deaths are attributed to NCD's. The objective of this study was to determine the burden of noncommunicable diseases in semi urban community of Islamabad. Methods: We carried a cross sectional study to estimate the burden of noncommunicable diseases in an urban setting, a community based cross sectional survey covering 1210 households was carried out over a period of three months. Households were selected through consecutive non-probability sampling, among which adult females and males who were permanent resident of the community were interviewed through a structured questionnaire in urdu language. SPSS version 21 was used to analyze the data. Descriptive statistics were calculated. Results: About 38.7% individuals had High BP / IHD, 34.4% had oro-dental health problems, 24.3% were physically disabled and 14.6% had diabetes. Among the risk factors, 48.2% were tobacco user, 13.60% were drug abuser and 1.8% alcoholics. Conclusion: We conclude that the prevalence of non-communicable diseases is quite high in the above setting as compared to the National indicators, which demands timely intervention to curtail the existing burden of NCD.Pan African Medical Journal 2016; 2

Six Sigma in Synergy with Risk Management

Because of globalization, stiff competition, Rapid market change?higher environmental uncertainty and lower technology cycle time, it is inevitable to include risk management in the six sigma methodology no matter whether the organization is manufacturing concern or service concern. Risk management is to play a basic role in Define, Measure, Analyze, Improve and Control phase (DMAIC) and Define, Measure, Analyze, Design and Verify  phase (DMADV) in the supply chain. In this paper a need is established using exiting literature to include risk management into six sigma methodology and its potential benefits are described. Keywords: Six Sigma, Risk Management, Supply Chain Managemen

A novel hybrid deep learning model for human activity recognition based on transitional activities

In recent years, a plethora of algorithms have been devised for efficient human activity recognition. Most of these algorithms consider basic human activities and neglect postural transitions because of their subsidiary occurrence and short duration. However, postural transitions assume a significant part in the enforcement of an activity recognition framework and cannot be neglected. This work proposes a hybrid multi-model activity recognition approach that employs basic and transition activities by utilizing multiple deep learning models simultaneously. For final classification, a dynamic decision fusion module is introduced. The experiments are performed on the publicly available datasets. The proposed approach achieved a classification accuracy of 96.11% and 98.38% for the transition and basic activities, respectively. The outcomes show that the proposed method is superior to the state-of-the-art methods in terms of accuracy and precision

Diet Composition and Seasonal Fluctuations in the Feeding Habit of Snow Barbel (Schizothorax plagiostomus) in River Indus, Pakistan

Background:Schizothorax plagiostomus is widely distributed in river Indus and is most important food fish in Pakistan. The feeding habit of fish is directly related to the size of fish, its metabolic rate and environmental temperature. The accurate description of fish diet and feeding habit is a very important aspect in fisheries management for the purpose of species conservation, breeding and culture. The present work was aimed to investigate the specie abundance, the diet composition and seasonal variations in the feeding habit of Snow barbell Schizothorax plagiostomus.Materials, Methods & Results: A total of 1799 fish specimens were caught at the confluence of six tributaries along river Indus at Indus Kohistan, northeastern Pakistan. The fish were collected by 5-panels of gill net during first week of each month. The site specific Catch Per Unit Effort (CPUE) and season specific CPUE of fish fauna were assessed. For the gut content analysis 240 samples (99 male and 141 females) of S. plagiostomus were selected on monthly basis. Frequency of occurrence method and volumetric method were applied to record the different food items in the gut of S. plagiostomus. The physico-chemical parameters, NO3 concentration and dissolved Co2 of water from different localities of river Indus were recorded month wise by Hach sensION 156 meter, Horiba LAQUA Nitrate Meter and EA80 meter respectively. Significant difference was observed in water temperature during the four seasons. Except alkalinity no other water parameter showed significant variation across different localities. The results showed that highest Mean CPUE was observed for Darel Stream (0.55) and lowest for Jalkot stream (0.26). Peak abundance of fish was recorded in the month of November with a mean catch of 44.50, mean CPUE of 0.74 and mean Kruskal-Wallis rank value of 63.25. Spirogyra and Ulothrix occurred as maximum food items in the gut of S. plagiostomus during summer while their minimum amount occurred during autumn. According to the ranking index spirogyra and ulothrix ranked higher with significant difference in comparison to other food items. The results showed that S. plagiostomus is phytophagous in its feeding habit, which consumed mainly algae attached to stones and pebbles during the whole year. However, the presence of some secondary items such as animal matter, detritus, sand and mud might be due to the distinct availabilities of food along the seasons. The highest feeding activity of S. plagiostomus was recorded during summer while the lowest one occurred during autumn, spring and winter. Discussion: Catch per unit effort (CPUE) is an indirect measure of the abundance of a target species. It is used as an index of stock abundance in fisheries and conservation biology. During the study low fish fauna was found in River Indus as reported previously. Majority of the fish occurred in snow fed river tributaries in the study area as these tributaries are comparatively less turbulent. Previous studies have also recorded that Schizothoracine generally prefer clean waters. The present findings of gut contents analysis showed clearly that S. plagiostomus is a phytophagous fish which scrap and consumed spirogyra and ulothrix attached to stones and pebbles. Earlier it was reported that mouth of S. plagiostomusis is inferior, wide, with deep lower jaw having keratinized cutting edge and the lower lip is folded and expanded with numerous papillae making it best suited for scrapping algae attached to stones and pebbles. The highest feeding activity was observed during warmer months as compared to cold months. S. plagiostomus spawn twice in a year in autumn and in spring. The highest feeding activity of S. plagiostomus seems to be link with a reflex of recovery strategy due to physiological process of gonadal development

Automated Detection of COVID-19 using Chest X-Ray Images and CT Scans through Multilayer- Spatial Convolutional Neural Networks

The novel coronavirus-2019 (Covid-19), a contagious disease became a pandemic and has caused overwhelming effects on the human lives and world economy. The detection of the contagious disease is vital to avert further spread and to promptly treat the infected people. The need of automated scientific assisting diagnostic methods to identify Covid-19 in the infected people has increased since less accurate automated diagnostic methods are available. Recent studies based on the radiology imaging suggested that the imaging patterns on X-ray images and Computed Tomography (CT) scans contain leading information about Covid-19 and is considered as a potential automated diagnosis method. Machine learning and deep learning techniques combined with radiology imaging can be helpful for accurate detection of the disease. A deep learning approach based on the multilayer-Spatial Convolutional Neural Network for automatic detection of Covid-19 using chest X-ray images and CT scans is proposed in this paper. The proposed model, named as the Multilayer Spatial Covid Convolutional Neural Network (MSCovCNN), provides an automated accurate diagnostics for Covid-19 detection. The proposed model showed 93.63% detection accuracy and 97.88% AUC (Area Under Curve) for chest x-ray images and 91.44% detection accuracy and 95.92% AUC for chest CT scans, respectively. We have used 5-tiered 2D-CNN frameworks followed by the Artificial Neural Network (ANN) and softmax classifier. In the CNN each convolution layer is followed by an activation function and a Maxpooling layer. The proposed model can be used to assist the radiologists in detecting the Covid-19 and confirming their initial screening

ERBM-SE: Extended Restricted Boltzmann Machine for Multi-Objective Single-Channel Speech Enhancement

Machine learning-based supervised single-channel speech enhancement has achieved considerable research interest over conventional approaches. In this paper, an extended Restricted Boltzmann Machine (RBM) is proposed for the spectral masking-based noisy speech enhancement. In conventional RBM, the acoustic features for the speech enhancement task are layerwise extracted and the feature compression may result in loss of vital information during the network training. In order to exploit the important information in the raw data, an extended RBM is proposed for the acoustic feature representation and speech enhancement. In the proposed RBM, the acoustic features are progressively extracted by multiple-stacked RBMs during the pre-training phase. The hidden acoustic features from the previous RBM are combined with the raw input data that serve as the new inputs to the present RBM. By adding the raw data to RBMs, the layer-wise features related to the raw data are progressively extracted, that is helpful to mine valuable information in the raw data. The results using the TIMIT database showed that the proposed method successfully attenuated the noise and gained improvements in the speech quality and intelligibility. The STOI, PESQ and SDR are improved by 16.86%, 25.01% and 3.84dB over the unprocessed noisy speech

Triboelectric Nanogenerator Based Self-Powered Tilt Sensor

This work focuses on the fabrication and evaluation of triboelectric nanogenerator (TENG) based self-powered tilt sensor. The proposed fabricated structure is composed of polydimethylsiloxane (PDMS), steel ball, gold (Au) as electrode and circular ring housing. A specific configuration of electrodes was used to measure the tilt at different angles. FEM simulations were used to verify the electric potential at the electrodes at different angles. The outputs of the fabricated sensor were measured at different angles from 0 to 360°. A sensitivity of 254 mV/rad is obtained in single axis. The TENG based tilt sensor generates an open circuit voltage of 450 mV at 10 MΩ