Revisiting Capital Structure and Firm Value: Moderating Role of Corporate Governance: Evidence from Pakistan

The aim of the study was to examine the most discussed relationship between capital structure and firm value by investigating the intervening impact of various corporate governance measures. chief idea of the study was to observe the moderating impact of chosen governance attributes (board size, board independence, CEO role duality, managerial ownership and ownership concentration) on the relationship between capital structure (leverage) and firm value (Tobin’ Q). The study used the 775 firm year observations of 155 non-financial companies listed at Karachi Stock Exchange for financial years containing 2008 to 2012. Keeping in view the nature of data (balanced panel), fixed effects regression method was employed to estimate the formulated relationship. In finding moderation, this study found significant positive moderation for board independence and ownership concentration. However for managerial ownership this study found significant negative moderating effect between leverage and firm value. Keywords: Moderation, Capital structure, Firm value, Corporate Governance

Quasi Variational Inclusions Involving Three Operators

In this paper, we consider some new classes of the quasi-variational inclusions involving three monotone operators. Some interesting problems such as variational inclusions involving sum of two monotone operators, difference of two monotone operators, system of absolute value equations, hemivariational inequalities and variational inequalities are the special cases of quasi variational inequalities. It is shown that quasi-variational inclusions are equivalent to the implicit fixed point problems. Some new iterative methods for solving quasi-variational inclusions and related optimization problems are suggested by using resolvent methods, resolvent equations and dynamical systems coupled with finite difference technique. Convergence analysis of these methods is investigated under monotonicity. Some special cases are discussed as applications of the main results

Quasi Variational Inclusions Involving Three Operators

In this paper, we consider some new classes of the quasi-variational inclusions involving three monotone operators. Some interesting problems such as variational inclusions involving sum of two monotone operators, difference of two monotone operators, system of absolute value equations, hemivariational inequalities and variational inequalities are the special cases of quasi variational inequalities. It is shown that quasi-variational inclusions are equivalent to the implicit fixed point problems. Some new iterative methods for solving quasi-variational inclusions and related optimization problems are suggested by using resolvent methods, resolvent equations and dynamical systems coupled with finite difference technique. Convergence analysis of these methods is investigated under monotonicity. Some special cases are discussed as applications of the main results

assessment of isometric pulls strength of industrial cart pullers an electromyography study from an apparel manufacturing industry

n/

Assessing Nitrate Contamination Risks in Groundwater: A Machine Learning Approach

Groundwater is one of the primary sources for the daily water requirements of the masses, but it is subjected to contamination due to the pollutants, such as nitrate, percolating through the soil with water. Especially in built-up areas, groundwater vulnerability and contamination are of major concern, and require appropriate consideration. The present study develops a novel framework for assessing groundwater nitrate contamination risk for the area along the Karakoram Highway, which is a part of the China Pakistan Economic Corridor (CPEC) route in northern Pakistan. A groundwater vulnerability map was prepared using the DRASTIC model. The nitrate concentration data from a previous study were used to formulate the nitrate contamination map. Three machine learning (ML) models, i.e., Support Vector Machine (SVM), Multivariate Discriminant Analysis (MDA), and Boosted Regression Trees (BRT), were used to analyze the probability of groundwater contamination incidence. Furthermore, groundwater contamination probability maps were obtained utilizing the ensemble modeling approach. The models were calibrated and validated through calibration trials, using the area under the receiver operating characteristic curve method (AUC), where a minimum AUC threshold value of 80% was achieved. Results indicated the accuracy of the models to be in the range of 0.82–0.87. The final groundwater contamination risk map highlights that 34% of the area is moderately vulnerable to groundwater contamination, and 13% of the area is exposed to high groundwater contamination risk. The findings of this study can facilitate decision-making regarding the location of future built-up areas properly in order to mitigate the nitrate contamination that can further reduce the associated health risks

Harmonic m-Preinvex Functions and Inequalities

In this paper, we introduce a new class of harmonic functions, which is called harmonic mpreinvex functions for a fixed m. Some Hermite-Hadamard inequality for harmonic m-preinvex functions are derived. Several special cases are discussed as applications of the main results. The ideas and techniques of this paper may be starting point for further research

Water Induced Ferroelectric Switching: The Crucial Role of Collective Dynamics

The interaction mechanisms of water with nanoscale geometries remain poorly understood. This study focuses on behaviour of water clusters under varying external electric fields with a particular focus on molecular ferroelectric devices. We employ a two-fold approach, combining experiments with large-scale molecular dynamics simulations on graphene nanoribbon field effect transistors. We show that bilayer graphene nanoribbons provide stable anchoring of water clusters on the oxygenated edges, resulting in a ferroelectric effect. A molecular dynamics model is then used to investigate water cluster behaviour under varying external electric fields. Finally, we show that these nanoribbons exhibit significant and persistent remanent fields that can be employed in ferroelectric heterostructures and neuromorphic circuits

Establishing effective communications in disaster affected areas and artificial intelligence based detection using social media platform

Floods, earthquakes, storm surges and other natural disasters severely affect the communication infrastructure and thus compromise the effectiveness of communications dependent rescue and warning services. In this paper, a user centric approach is proposed to establish communications in disaster affected and communication outage areas. The proposed scheme forms ad hoc clusters to facilitate emergency communications and connect end-users/ User Equipment (UE) to the core network. A novel cluster formation with single and multi-hop communication framework is proposed. The overall throughput in the formed clusters is maximized using convex optimization. In addition, an intelligent system is designed to label different clusters and their localities into affected and non-affected areas. As a proof of concept, the labeling is achieved on flooding dataset where region specific social media information is used in proposed machine learning techniques to classify the disaster-prone areas as flooded or unflooded. The suitable results of the proposed machine learning schemes suggest its use along with proposed clustering techniques to revive communications in disaster affected areas and to classify the impact of disaster for different locations in disaster-prone areas