Executive Compensation And Ownership Structure

The objective of this paper is to highlight the impact of ownership discrepancy and type (managers, families, institutions) on executive compensation. Based on a sample of French listed firms and using panel data regressions, the results show that capital concentration (Jensen 1986) negatively affects both the level of total executive compensation and the probability of use of stock option incentive plans. This confirms our theoretical alignment hypothesis. Moreover, the results show no evidence of the existence of a significant effect of ownership discrepancy on managerial compensation. Institutional shareholders are likely to encourage the use of stock option incentive plans and managerial ownership positively and significantly influences the level of total and fixed compensation. Family shareholding negatively affects executive compensation variables

Effect of Curing Temperature in the Alkali-Activated Brick Waste and Glass Powder mortar and Their Influence of Mechanical resistances

In this study, compressive strength values were measured at different curing times(7,14 and 28 days).The alkali-activation of the brick and glass powder body with potassium water glass having a silicate modulus of 3. Compressive strengths, flexural strength and specific fracture energy of the specimens stored at 40° C and 60° C are evaluated at 28-days. The study demonstrates that the storage temperature of specimens and the content of the alkaline solution have a significant influence on all mechanical properties of the studied materials. Keywords: brick waste, glass powder, curing temperature, alkali-activated

Intelligent and Improved Self-Adaptive Anomaly based Intrusion Detection System for Networks

With the advent of digital technology, computer networks have developed rapidly at an unprecedented pace contributing tremendously to social and economic development. They have become the backbone for all critical sectors and all the top Multi-National companies. Unfortunately, security threats for computer networks have increased dramatically over the last decade being much brazen and bolder. Intrusions or attacks on computers and networks are activities or attempts to jeopardize main system security objectives, which called as confidentiality, integrity and availability. They lead mostly in great financial losses, massive sensitive data leaks, thereby decreasing efficiency and the quality of productivity of an organization. There is a great need for an effective Network Intrusion Detection System (NIDS), which are security tools designed to interpret the intrusion attempts in incoming network traffic, thereby achieving a solid line of protection against inside and outside intruders. In this work, we propose to optimize a very popular soft computing tool prevalently used for intrusion detection namely Back Propagation Neural Network (BPNN) using a novel machine learning framework called “ISAGASAA”, based on Improved Self-Adaptive Genetic Algorithm (ISAGA) and Simulated Annealing Algorithm (SAA). ISAGA is our variant of standard Genetic Algorithm (GA), which is developed based on GA improved through an Adaptive Mutation Algorithm (AMA) and optimization strategies. The optimization strategies carried out are Parallel Processing (PP) and Fitness Value Hashing (FVH) that reduce execution time, convergence time and save processing power. While, SAA was incorporated to ISAGA in order to optimize its heuristic search. Experimental results based on Kyoto University benchmark dataset version 2015 demonstrate that our optimized NIDS based BPNN called “ANID BPNN-ISAGASAA” outperforms several state-of-art approaches in terms of detection rate and false positive rate. Moreover, improvement of GA through FVH and PP saves processing power and execution time. Thus, our model is very much convenient for network anomaly detection.

A Cooperative and Hybrid Network Intrusion Detection Framework in Cloud Computing Based on Snort and Optimized Back Propagation Neural Network

AbstractCloud computing provides a framework for supporting end users easily attaching powerful services and applications through Internet. To give secure and reliable services in cloud computing environment is an important issue. Providing security requires more than user authentication with passwords or digital certificates and confidentiality in data transmission, because it is vulnerable and prone to network intrusions that affect confidentiality, availability and integrity of Cloud resources and offered services. To detect DoS attack and other network level malicious activities in Cloud, use of only traditional firewall is not an efficient solution. In this paper, we propose a cooperative and hybrid network intrusion detection system (CH-NIDS) to detect network attacks in the Cloud environment by monitoring network traffic, while maintaining performance and service quality. In our NIDS framework, we use Snort as a signature based detection to detect known attacks, while for detecting network anomaly, we use Back-Propagation Neural network (BPN). By applying snort prior to the BPN classifier, BPN has to detect only unknown attacks. So, detection time is reduced. To solve the problem of slow convergence of BPN and being easy to fall into local optimum, we propose to optimize the parameters of it by using an optimization algorithm in order to ensure high detection rate, high accuracy, low false positives and low false negatives with affordable computational cost. In addition, in this framework, the IDSs operate in cooperative way to oppose the DoS and DDoS attacks by sharing alerts stored in central log. In this way, unknown attacks that were detected by any IDS can easily be detected by others IDSs. This also helps to reduce computational cost for detecting intrusions at others IDS, and improve detection rate in overall the Cloud environment

Serum vitamin D and vitamin D receptor gene polymorphism in Moroccan patients with systemic lupus erythematosus

Background: Vitamin D plays an important role in the immunomodulation and could be involved in the development of autoimmune diseases such as systemic lupus erythematous (SLE). The study of the polymorphism of the Vitamin D Receptor (VDR) gene may be of interest in explaining the pathophysiology of SLE.Methods: In this study, we aimed to examine the characteristics of VDR gene BsmI polymorphism for the first time in Moroccan patients with SLE and their relationship with clinical manifestations of the disease. We also measured the serum level of 25-hyroxyvitamin D3 to assess its relation to such polymorphism.Results: The study included 66 SLE patients and 91 healthy controls. Our results showed that there were no differences observed in VDR genotypes and allelic distribution within the two groups. Both groups were in Hardy-Weinberg equilibrium, with no significant P values for the observed and expected genotype frequencies. 25-hyroxyvitamin D3 serum levels were the same in the two groups.Conclusions: Based on the results of the present study. We cannot verify any association between VDR gene BsmI polymorphism and SLE. This polymorphism could not be regarded as a genetic marker of the SLE. A larger study examining BsmI and other VDR gene polymorphisms is needed

Magnetic properties in amorphous Co95­xDyxZr5 thin films

Amorphous Co95-xDyxZr5 thin films were prepared by RF sputtering and their magnetic properties were studied as a function of temperature and for the composition range 0<x<30. The mean field theory has been used to explain the temperature dependence of the magnetization. The exchange interactions between Co-Co and Dy-Co atom pairs have been evaluated. The magnetic phase diagrams are presented.Amorphous Co95-xDyxZr5 thin films were prepared by RF sputtering and their magnetic properties were studied as a function of temperature and for the composition range 0<x<30. The mean field theory has been used to explain the temperature dependence of the magnetization. The exchange interactions between Co-Co and Dy-Co atom pairs have been evaluated. The magnetic phase diagrams are presented

Carbimazol and acenocoumarol, where is the problem?

Acenocoumarol and carbimazole are two drugs widely prescribed, they can sometimes be used in the same time. There is no known drug interaction between the two drugs but we report a case of a serious hemorrhagic complication following the concomitant use of the acenocoumarol and carbimazole. A 70-year old man treated by acenocoumarol for an aortic and mitral valve replacement. For a clinical and biological hyperthyroidism, he began treatment with carbimazole, ten days before admission. Three days later, he developed a mucocutaneous icterus with major hemorrhagic syndrome. The outcome was favourable after stopping medication and the use of vitamin K

Cellular membrane affinity chromatography (CMAC) in drug discovery from complex natural matrices

Secondary plant metabolites are evolutionary-designed molecules that interact with multiple biological targets in human organisms. Identification of pharmacologically active phytochemicals is usually a time consuming and costly process. Cellular membrane affinity chromatography (CMAC) allows the detection of secondary metabolites present in complex natural matrices, e.g. plant extracts and their interactions with the immobilized fully-functional transmembrane proteins. After the isolation process of the binding compounds, CMAC columns can be used to study the binding process between the potential new ligands and the immobilized transmembrane protein target. The following parameters can be determined using CMAC columns: binding affinity (Kd), association rate constant (kon), dissociation rate constant (koff) and the equilibrium constant for complex formation (K). This review summarizes the preparation steps and the use of CMAC columns in the drug discovery process of new potential drug leads present in complex natural matrices