Corporate governance in emerging economies will have to change

Mohammad Nurunnabi explores how COVID-19 exposes the challenges to corporate governance practices in emerging economies, in particular AGMs, executive pay, audit committee, and disclosure. Despite the enormous challenges COVID-19 poses, he argues it should be seen as an opportunity for reform of their corporate governance structure

العادة الدوطة في شبه القارة الهندية وحلولها على ضوء مقاصد الشريعة

The custom of dowry which has been widespread in the Indian sub-continent is one of the major reasons for domestic violence against women. Moreover, due to dowry system and gender-based inequality, this region kills thousands of women each year according to the international human rights organization. The anti-dowry law was added to the Indian criminal act in 1961, Bangladesh in 1980, and Pakistan in 1976. Nevertheless the dowry custom is still wide spreading extensively. This study aims to propose the solutions for dowry custom in Indian sub-continent based on maqasid al-shariah

PRODUCTION OF BIODIESEL FROM THE SOLID SLUDGE OF WASTEWATER TREATMENT PLANT

Biodiesel, a promising renewable fuel, is produced mainly via catalytic transesterification and/or esterification reaction from various lipid sources. Primary and secondary sludges of municipal wastewater treatment facilities are potential lipid sources. In this study, factorial experimental analyses were used to study the influence of different variables on the lipid extraction and biodiesel production from primary and secondary sludges (Adelaide Pollution Control Plant, London, Canada). The maximum extracted lipids from the primary and secondary sludge sources were 14.46 (wt/wt) % and 10.04 (wt/wt) % (on the basis of dry sludge), respectively. The maximum biodiesel yield from extracted lipid by using homogeneous catalyst was 57.12 (wt/wt) % (on the basis of lipid) at 60°C for 14h of reaction. The biodiesel yield from the lipid of wastewater sludge by using mesoporous heterogeneous catalyst, SBA-15 impregnated with heteropolyacid (15% PW12), was 30.14 (wt/wt) % (on the basis of lipid) at 135°C and 135psi for 3h of reaction

Robust statistical approaches for feature extraction in laser scanning 3D point cloud data

Three dimensional point cloud data acquired from mobile laser scanning system commonly contain outliers and/or noise. The presence of outliers and noise means most of the frequently used methods for feature extraction produce inaccurate and non-robust results. We investigate the problems of outliers and how to accommodate them for automatic robust feature extraction. This thesis develops algorithms for outlier detection, point cloud denoising, robust feature extraction, segmentation and ground surface extraction

Social distancing and reopening universities after the COVID-19 pandemic: policy complexity in G20 countries

Background: The coronavirus disease (COVID-19) has affected the entire globe, and various mythologies argue about its diagnosis, cure, and prevention. Globally, as of September 18th, 2020, there have been 30.055 million confirmed cases, including 0.943 million deaths. The nationwide closures are impacting over 60% of the world’s student population. This study aimed to explore the social distancing policies and universities' reopening after COVID-19 in G20 countries (19 member countries and the European Union).Design and Methods: The study is based on documentary analysis. G20 members represent around 80% of the world’s economic output, two-thirds of the global population (including more than half of the world's poor), and 75% of international trade. Based on documentary analysis, the study revealed that there is a policy dilemma among G20 countries regarding school reopening and a variety of conflicting policies within each country.Results: Based on a sample of 838 universities in the USA, 66% of universities (552 of 838) plan for in-person instruction, while only 7% are planning for a completely online teaching mode in the fall 2020 semester. Conclusions: Interestingly, none of the private universities in this study are planning to implement an online teaching mode. Policymakers need an integrated set of policy guidelines for school reopening, considering the evaluation of current COVID-19 pandemic circumstances and social distancing capacity

Outlier Detection in Logistic Regression: A Quest for Reliable Knowledge from Predictive Modeling and Classification

Logistic regression is well known to the data mining research community as a tool for modeling and classification. The presence of outliers is an unavoidable phenomenon in data analysis. Detection of outliers is important to increase the accuracy of the required estimates and for reliable knowledge discovery from the underlying databases. Most of the existing outlier detection methods in regression analysis are based on the single case deletion approach that is inefficient in the presence of multiple outliers because of the well known masking and swamping effects. To avoid these effects the multiple case deletion approach has been introduced. We propose a group deletion approach based diagnostic measure for identifying multiple influential observations in logistic regression. At the same time we introduce a plotting technique that can classify data into outliers, high leverage points, as well as influential and regular observations. This paper has two objectives. First, it investigates the problems of outlier detection in logistic regression, proposes a new method that can find multiple influential observations, and classifies the types of outlier. Secondly, it shows the necessity for proper identification of outliers and influential observations as a prelude for reliable knowledge discovery from modeling and classification via logistic regression. We demonstrate the efficiency of our method, compare the performance with the existing popular diagnostic methods, and explore the necessity of outlier detection for reliability and robustness in modeling and classification by using real datasets

Study on Comparative Analysis of Basic Woven Fabrics Produced in Air-Jet Loom and Determining Structure for Optimum Mechanical Properties and Production

This analysis was directed at dissecting the impact of the structure of the fabric on different properties of the fabric, for example tear strength, tensile strength, shrinkage, elongation, skewness, and so on. The work demonstrated how various structures of the fabric influence these properties. Fabrics with a fundamental woven structure, namely plain, twill, satin and a couple of their subsidiaries, were produced to explore the influence of the structure on different properties of the fabric. The examination built up an approach to gauge the mechanical conduct of the fabric dependent on its structure. The exploration accentuated the structure and detail of the fabric to decide the underlying driver of the change in the mechanical conduct. The properties of the fabric, such as tear strength, tensile strength, elongation, shrinkage and skewness, were extraordinarily affected by the structure of the fabric. It likewise demonstrated to having more noteworthy mechanical properties for firmly interwoven structures, such as plain and twill. The analysis led to the conclusion that the plain structure has the best mechanical properties among different structure

Robust segmentation in laser scanning 3D point cloud data

Segmentation is a most important intermediate step in point cloud data processing and understanding. Covariance statistics based local saliency features from Principal Component Analysis (PCA) are frequently used for point cloud segmentation. However it is well known that PCA is sensitive to outliers. Hence segmentation results can be erroneous and unreliable. The problems of surface segmentation in laser scanning point cloud data are investigated in this paper. We propose a region growing based statistically robust segmentation algorithm that uses a recently introduced fast Minimum Covariance Determinant (MCD) based robust PCA approach. Experiments for several real laser scanning datasets show that PCA gives unreliable and non-robust results whereas the proposed robust PCA based method has intrinsic ability to deal with noisy data and gives more accurate and robust results for planar and non planar smooth surface segmentation

Robust statistical approaches for local planar surface fitting in 3D laser scanning data

This paper proposes robust methods for local planar surface fitting in 3D laser scanning data. Searching through the literature revealed that many authors frequently used Least Squares (LS) and Principal Component Analysis (PCA) for point cloud processing without any treatment of outliers. It is known that LS and PCA are sensitive to outliers and can give inconsistent and misleading estimates. RANdom SAmple Consensus (RANSAC) is one of the most well-known robust methods used for model fitting when noise and/or outliers are present. We concentrate on the recently introduced Deterministic Minimum Covariance Determinant estimator and robust PCA, and propose two variants of statistically robust algorithms for fitting planar surfaces to 3D laser scanning point cloud data. The performance of the proposed robust methods is demonstrated by qualitative and quantitative analysis through several synthetic and mobile laser scanning 3D data sets for different applications. Using simulated data, and comparisons with LS, PCA, RANSAC, variants of RANSAC and other robust statistical methods, we demonstrate that the new algorithms are significantly more efficient, faster, and produce more accurate fits and robust local statistics (e.g. surface normals), necessary for many point cloud processing tasks.Consider one example data set used consisting of 100 points with 20% outliers representing a plane. The proposed methods called DetRD-PCA and DetRPCA, produce bias angles (angle between the fitted planes with and without outliers) of 0.20° and 0.24° respectively, whereas LS, PCA and RANSAC produce worse bias angles of 52.49°, 39.55° and 0.79° respectively. In terms of speed, DetRD-PCA takes 0.033 s on average for fitting a plane, which is approximately 6.5, 25.4 and 25.8 times faster than RANSAC, and two other robust statistical methods, respectively. The estimated robust surface normals and curvatures from the new methods have been used for plane fitting, sharp feature preservation and segmentation in 3D point clouds obtained from laser scanners. The results are significantly better and more efficiently computed than those obtained by existing methods

Outlier detection and robust normal-curvature estimation in mobile laser scanning 3D point cloud data

This paper proposes two robust statistical techniques for outlier detection and robust saliency features, such as surface normal and curvature, estimation in laser scanning 3D point cloud data. One is based on a robust z-score and the other uses a Mahalanobis type robust distance. The methods couple the ideas of point to plane orthogonal distance and local surface point consistency to get Maximum Consistency with Minimum Distance (MCMD). The methods estimate the best-fit-plane based on most probable outlier free, and most consistent, points set in a local neighbourhood. Then the normal and curvature from the best-fit-plane will be highly robust to noise and outliers. Experiments are performed to show the performance of the algorithms compared to several existing well-known methods (from computer vision, data mining, machine learning and statistics) using synthetic and real laser scanning datasets of complex (planar and non-planar) objects. Results for plane fitting, denoising, sharp feature preserving and segmentation are significantly improved. The algorithms are demonstrated to be significantly faster, more accurate and robust. Quantitatively, for a sample size of 50 with 20% outliers the proposed MCMD_Z is approximately 5, 15 and 98 times faster than the existing methods: uLSIF, RANSAC and RPCA, respectively. The proposed MCMD_MD method can tolerate 75% clustered outliers, whereas, RPCA and RANSAC can only tolerate 47% and 64% outliers, respectively. In terms of outlier detection, for the same dataset, MCMD_Z has an accuracy of 99.72%, 0.4% false positive rate and 0% false negative rate; for RPCA, RANSAC and uLSIF, the accuracies are 97.05%, 47.06% and 94.54%, respectively, and they have misclassification rates higher than the proposed methods. The new methods have potential for local surface reconstruction, fitting, and other point cloud processing tasks