The determinants of earnings management by the acquirer: The case of french corporate takeovers.

This paper analyses the determinants of earnings management by the acquirer of 60 takeover-bids that occurred between 1998 and 2008 on the French market. Four main findings are shown in this study. First, we find strong evidence that bidder firms manipulate earnings prior to takeovers suggesting that target managers have incentive to detect earnings manipulation to ensure the interests of the target shareholders. Second, managers manipulate their earnings either downward or upward and regardless of the method of payment. Third, bidder toehold influences negatively and significantly the discretionary accruals. Finally, the relationship between discretionary accruals and managerial ownership is negatively significant. This practice favors a resource transfer to the bidder controlling shareholder at the expense of minority shareholders.Corporate takeover, Earnings management, Information asymmetry, Discretionary accruals, Method of payment

Normalisation techniques in proof theory and category theory

The word problem for the free categories with some structure generated by a category X can be solved using proof-theoretical means. These free categories give a semantics in which derivations of GENTZEN's propositional sequent calculus can be interpreted by means of arrows of those categories. In this thesis we describe, implement and document the cut-elimination and the normalization techniques in proof theory as outlined in SZABO [1978]: we show how these are used in order to solve, mechanically, the word problem for the free categories with structure of : cartesian, bicartesian, distributive bicartesian, cartesian closed, and bicartesian closed. This implementation is extended by a procedure to interpret intuitionistic propositional sequent derivations as arrows of the above categories. Implementation of those techniques has forced us to modify the techniques in various inessential ways. The description and the representation in the syntax of our implementation of the above categories is contained in chapters 1 - 5, where each chapter describes one theory and concludes with examples of the system In use to represent concepts and solve simple word problems from category theory ( of various typos ). Appendix 1 contains some apparent printing errors we have observed in the work done by SZABO. The algorithms used in the proof of the cut-elimination theorems and normalization through chapters 1 - 5 are collected in appendices 2 - 4. Appendices 5 - 8 concern the implementation and its user manual

Design and evaluation of optical laser diodes LD positioning arrangement and multiple input/ multiple output MIMO-OFDM systems

Optical communication system for the next generation of wireless communication systems are an exciting, unparalleled new technology. This paper presents a new visible light positioning algorithm system based on position by utilized neural network, which depending on directly measured received signal strength (RSS) information of 3D coordinates. This algorithm is called light positioning algorithm neural network (LPANN) which used 5 laser diodes LDs, each one consists of 5×5 LD chips. In addition, a novel multi Input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) based VLC systems generalized laser diodes (LD) modulation scheme as second part of this paper that is called Zero Forcing Equalizer Neural network ZFENN algorithm which based on 4 × 4 optical MIMO-VLC. It is accomplished by using LD index modulation and spatial multiplexing. Actual and imaginary parts of the complex time domain OFDM signals are therefore separated first and then, bipolar signals are transmitted through VLC channels by encoding sign-information in LD indexes. In addition, a novel receiver configuration is also suggested for flat frequency or limited channel scenarios. Based on the results of this analysis, the positioning accuracy have been improved, so this is lead to enhance data rate. While, by using the second part of the MIMO-OFDM system that leads to enhancing the SNR and BER more than 10-4, which are introduced to eliminate multi-user interference (MUI)

Total Quality Management as a Philosophy to Improve the Performance of the Academic Organization

Purpose: The purpose of this study is to examine the role of total quality management as a philosophy for improvement in the academic organization, as it represents a necessary trend in developing the activities of many organizations in the light of globalization and the challenges that these organizations face, in order to bring about fundamental developments, and the use of that philosophy as an effective means towards customer satisfaction and meeting his requirements.   Theoretical framework: Total quality management is regarded as one of the contemporary concepts that concentrates on a set of administrative principles; if it has been applied in organization, it will succeed in achieving quality.   Design/Methodology/Approach: To achieve the objectives of the study, a questionnaire of 60-item has been used. The sample comprised 65 academic staff members from various parts of the organization. According to the purpose of the study, two main hypotheses were formulated. A set of statistical method  of spss vr.24.  has been used.     Findings: It is concluded that supporting and adopting the total quality will be fruitful as a successful business philosophy for the continuity by creating appropriate requirements and conditions.   Research/Practical/Social Implications: Establishing the desire towards change by following the best by individuals and adopting stimulus programs that reinforce their ability to realize cognitive new ness.   Originality/Value:  The value of the study is that the organization's interest in the social aspect and its adoption confirms the organization's adaptation to the requirements of society

Total white blood cell count is associated with the presence, severity and extent of coronary atherosclerosis detected by dual-source multislice computed tomographic coronary angiography

Background: Total white blood cell (WBC) count has been consistently shown to be an independent risk factor and predictor for future cardiovascular outcomes, regardless of disease status in coronary artery disease (CAD). The purpose of this study is to evaluate the relationship between total WBC count and the presence, severity and extent of coronary atherosclerosis detected in subjects undergoing multislice computed tomographic (MSCT) coronary angiography for suspected CAD. Methods: A total of 817 patients were enrolled in this cross-sectional study. Non-significant coronary plaque was defined as lesions causing &#163; 50% luminal narrowing, and significant coronary plaque was defined as lesions causing > 50% luminal narrowing. For each segment, coronary atherosclerotic lesions were categorized as none, calcified, non-calcified and mixed. All images were interpreted immediately after scanning by an experienced radiologist. Results: An association between hypertension, diabetes mellitus, age, gender, hyperlipidemia, smoking, total WBC counts and coronary atherosclerosis was found when patients were grouped into two categories according to the presence of coronary atherosclerosis (p < 0.05). Although plaque morphology was not associated with total WBC counts, the extent of coronary atherosclerosis was increased with higher total WBC quartiles (p = 0.006). Patients with critical luminal stenosis had higher levels of total WBC counts when compared to patients with non-critical luminal narrowing (7,982 &#177; 2,287 vs 7,184 &#177; 1,944, p < 0.05). Conclusions: Our study demonstrated that total WBC counts play an important role in inflammation and are associated with the presence, severity and extent of coronary atherosclerosis detected by MSCT. Further studies are needed to assess the true impact of WBC counts on coronary atherosclerosis, and to promote its use in predicting CAD. (Cardiol J 2011; 18, 4: 371&#8211;377

Heart Disease Prediction Using Machine Learning Method

The heart disease is also known as coronary artery disease, many hearts affecting symptoms that are very common nowadays and causes death. It is a challenging task to diagnose heart diseases without any intelligent diagnosing system. Many researchers did research on it and developed a diagnostic system to diagnose heart diseases and worked on it. The prediction of cardiovascular disease, required a brief medical history of patients, including genetic information. The world is in acute need of a system for predicting heart disease and it became crucial. Data mining and machine learning are common techniques used in the field of health care to process large and complex data. This research paper presents reasons for heart disease and a model based on Machine learning algorithms for prediction