Increasing polarization: enumerating the consequences of increasing inequality

“Remember your humanity. Forget the rest”. (Bertrand Russell in Russell-Einstein Manifesto) In a nutshell, this review is not trying to propagate rocket science or eureka moment that scientifically finds the cure for all the ills of economic inequality like penicillin does for infections. This review is a basic but effective exploration into the true nature of social realities. This review holistically elaborates how economic inequality is leading to increasing polarization in our societies. Two important drivers of increasing inequality are highlighted here as finance and technology and their contributions to higher polarization is detailed. A case study of polarization is followed along with the conclusion

Philosophical & Sociological Inquires in Material Aspects of the Human Life namely Risk, Finance and Insurance

Given how much importance there is of economics and finance in our lives as humans (materialist side is foremost as per Marx), it should be given more importance by Philosophy and Sociology. This brief report is meant to highlight few research paradigms available in Philosophy and Sociology to give its proper social context and provide deep underlying of Risk, Insurance and Finance

ANTIHYPERGLYCAEMIC EFFECT OF FICUS DALHOUSIAE MIQ LEAF ETHANOLIC EXTRACT IN ALLOXAN-INDUCED DIABETIC RATS

Objective: Ficus dalhousiae Miq. Has been documented for a wide range of uses in Ayurvedic and Unani medicine. The aim of this study was to evaluate the anti-hyperglycaemic effect of FDLEE in alloxan induced diabetic rats. Methods: Plant material was collected from Tirupati A. P, during the month of March 2013 the leaves were wiped carefully to make them free from dust and foreign material and dried under shade at room temperature. After seven days, the leaves were powdered and passed through a sieve. The powder was weighed (500 gm) and was extracted by maceration process and the solvent was evaporated in a rotavapor at 40º- 50º C under reduced pressure. The total yield of the extract was 16.5%. Phytochemical screening was carried out for the detection of alkaloids, flavanoids, glycosides, saponins, sterols and tannins by simple qualitative methods. Diabetes mellitus was induced by single i. p injection of freshly prepared solution of Alloxan monohydrate at a dose of 150mg/kg b. w. The animals were kept under observation for 48hr; Blood glucose was measured by glucometer. The rats with blood glucose levels above 250 mg/dl were selected for the experimental studies. FDLEE (100, 200 & 400mg/kg, b. w) was administered orally once a day for a period of 10 days. Body weight and blood glucose levels were determined on different experimental days. Results: Significant decrease in body weight and increase in blood glucose and lipid profile were observed in diabetic rats. The administration of FDLEE and glibenclamide daily for 10 days reversed body weights and blood glucose significantly. Conclusion: FDLEE exhibited anti-hyperglycaemic and anti-hyperlipidaemic effects in diabetic rats which supports its use as an adjunct in treatment of diabetes

The relation of ABO blood group to the severity of coronavirus disease: A cross-sectional study from a tertiary care hospital in Karachi

Background: Blood groups are considered to have an impact on the occurrence and severity of coronavirus disease. While among Chinese and Caucasian, blood group O individuals were less and group A were more likely to have severe disease and mortality, data on South Asians aren’t available. Objective: This study aimed to find out the association of disease severity with blood group among coronavirus disease 2019 (COVID-19) patients.Materials and methodology: Data were collected on a predesigned questionnaire containing details of patient demographics, medical comorbidities, clinical presentation, and laboratory parameters. Multiple logistic regression was used to determine the association of the blood group with the severity of coronavirus disease.Result: Among the study participants, blood group B has the highest distribution (39.8%), followed by O (30.0), A (21.9%), and AB (8.1%). About three-fourths (69.9%) had mild to moderate disease while 30.0% had severe disease. Age, gender, hypertension, diabetes mellitus, and hemoglobin level were all associated with disease severity among COVID-19 patients in univariate analysis on P-value for selection (Conclusion: Blood groups don’t have any role in forecasting the severity of coronavirus disease. However, the male gender and diabetics are prone to have severe disease

Milligan Morgan Haemorrhoidectomy vs LigaSure Haemorrhoidectomy : Comparative Postoperative Outcomes

Objective: To compare the traditional Milligan Morgan haemorrhoidectomy with haemorrhoidectomy using LigaSure in terms of postoperative complications, patient satisfaction and hospital stay. Methodology: This is a randomized controlled trial carried out at the Department of Surgery Liaquat university hospital Jamshoro from July 2017 to June 2019. A total of 88 patients were admitted with the diagnoses of 3rd and 4th degree haemorrhoid were included in the study. Patients were randomly divided into two groups by lottery method. Group A underwent Milligan Morgan Haemorrhoidectomy and group B underwent Haemorrhoidectomy by Ligasure after the informed consent. Outcomes of both procedures were also compared by complications, patient satisfaction and hospital stay. Results: Out of 88 patients 35 were male (39.78%) and 53 were female (60.22%). The most common group of age involved was between 35–55 years. Third degree Haemorrhoids were present in 40 (45.45%) of patients while the remaining 48 (54.55%) had fourth degree Haemorrhoids. Group A included 44(50%) cases while Group B included 44 (50%) cases. The mean operating time of Group A was 50.5 (minutes) with a standard deviation of 11.5 while it was 35.5 ± 9.4 in B group. The mean blood loss in group A was 65.30 ml with a standard deviation of 14.58 while it was 45.45 ml ± 20.49 in group B. Conclusion: The Haemorrhoidectomy done by Ligasure is comparatively better than the Milligan Morgan Heamorrhoidectomy, in terms of operative time, less bleeding, less pain, less hospital stays and early return to work

Image forgery detection using deeplearning by recompressing the images

Capturing images has been increasingly popular in recent years, owing to the widespread availability of cameras. Images are essential in our daily lives because they contain a wealth of information, and it is often required to enhance images to obtain additional information. A variety of tools are available to improve image quality; nevertheless, they are also frequently used to falsify images, resulting in the spread of misinformation. This increases the severity and frequency of image forgeries, which is now a major source of concern. Numerous traditional techniques have been developed over time to detect image forgeries. In recent years, convolutional neural networks (CNNs) have received much attention, and CNN has also influenced the field of image forgery detection. However, most image forgery techniques based on CNN that exist in the literature are limited to detecting a specific type of forgery (either image splicing or copy-move). As a result, a technique capable of efficiently and accurately detecting the presence of unseen forgeries in an image is required. In this paper, we introduce a robust deep learning based system for identifying image forgeries in the context of double image compression. The difference between an image’s original and recompressed versions is used to train our model. The proposed model is lightweight, and its performance demonstrates that it is faster than state-of-the-art approaches. The experiment results are encouraging, with an overall validation accuracy of 92.23%

FIBER QUALITY PREDICTION USING NIR SPECTRAL DATA: TREE-BASED ENSEMBLE LEARNING VS DEEP NEURAL NETWORKS

The growing applications of near infrared (NIR) spectroscopy in wood quality control and monitoring necessitates focusing on data-driven methods to develop predictive models. Despite the advancements in analyzing NIR spectral data, literature on wood science and engineering has mainly uti- lizedthe classic model development methods, such as principal component analysis (PCA) regression or partial least squares (PLS) regression, with relatively limited studies conducted on evaluating machine learning (ML) models, and specifically, artificial neural networks (ANNs). This couldpotentially limit the performance of predictive models, specifically for some wood properties, such as tracheid width that are both time-consuming tomeasure and challenging to predict using spectral data. This study aims to enhance the prediction accuracy for tracheid width using deep neural networks and tree-based ensemble learning algorithms on a dataset consisting of 2018 samples and 692 features (NIR spectra wavelengths). Accord- ingly, NIR spectra were fed into multilayer perceptron (MLP), 1 dimensional-convolutional neural net- works (1D-CNNs), random forest, TreeNet gradient-boosting, extreme gradient-boosting (XGBoost), and light gradient-boosting machine (LGBM). It was of interest to study the performance of the models with and without applying PCA to assess how effective they would perform when analyzing NIR spectra with- out employing dimensionality reduction on data. It was shown that gradient-boosting machines outper- formed the ANNs regardless of the number of features (data dimension). Allthe models performed better without PCA. It is concluded that tree-based gradient-boosting machines could be effectively used for wood characterization utilizing a medium-sized NIR spectral dataset

Analysis of deep convolutional neural network models for the fine-grained classification of vehicles

Intelligent transportation systems (ITS) is a broad area that encompasses vehicle identification, classification, monitoring, surveillance, prediction, management, reduction of traffic jams, license plate recognition, etc. Machine learning has practical and significant applications in ITS. Intelligent transportation systems rely heavily on vehicle classification for traffic management and monitoring. This research uses convolutional neural networks to classify cars at fine-grained classifications (make and model). Numerous obstacles must be overcome in order to complete the task, the greatest of which are intra- and inter-class similarities between the manufacturer and model of vehicles, different lighting effects, the shape and size of the vehicle, shadows, camera view angle, background, vehicle speed, colour occlusion and environmental conditions. This paper studies various machine learning algorithms used for the fine-grained classification of vehicles and presents a comparative analysis in terms of accuracy and the size of the implemented deep convolutional neural network (DCNN). Specifically, four DCNN models, mobilenet-v2, inception-v3, vgg-19 and resnet-50, are evaluated with three datasets, BMW-10, Stanford Cars and PAKCars. The evaluation results show that mobileNet-v2 is the smallest model as it is not computationally intensive due to depthwise separable convolution. However, resnet-50 and vgg-19 outperform inception-v3 and mobilenet-v2 in terms of accuracy due to their complex structure