Corporate finance and option theory: an extension model of rao and stevens (2007)

El objetivo planteado es contribuir a la teoría y práctica de la valoración financiera de la empresa estudiando la influencia que sobre dicha valoración ejercen integradamente gobierno, accionistas, acreedores, empleados y clientes. La necesaria flexibilidad de la vida empresarial se modeliza usando la metodología de las opciones, tanto ordinarias como reales. Las principales novedades se hallan en los dos ámbitos siguientes: 1) de carácter instrumental, que consiste en la forma de utilizar las opciones; 2) en la introducción y cuantificación del papel que empleados y clientes desempeñan en la creación de valor por parte de la firma. Este planteamiento pretende dar un paso adelante en la línea de evaluación de la remuneración de los empleados (lo que podría permitir la mejora de los esquemas de remuneración actualmente utilizados) así como en la de clasificar la clientela de acuerdo al valor añadido que estos dos grupos de personas (stakeholders) hayan aportado durante el ejercicio económico La investigación realizada abre importantes líneas de avance para el futuro, muchas de ellas reseñadas en la Tesis, que indudablemente servirán como hoja de ruta para ir completando el marco de trabajo aquí establecido. El actual estado de desarrollo de nuestros sistemas económicos necesitan nuevos enfoques, siendo muy prometedor el aquí planteado y utilizado.This thesis focuses on the field of market valuation of relatively large firms and it refers markets with “normal” behavior, where classical assumptions apply, such as rationality and dynamical stability, in an attempt to investigate the firm's value creation so as to reveal the contribution of all possible stakeholders that might be involved in the formation of the market value of a firm. The literature review related to valuation models, especially the DCF model, has shown that the conceptual frame of Modigliani and Miller, to determine the market value of a firm as it is understood today, is too restricted because only three types of stakeholders (shareholders, debtholders and government) are considered. This work contributes to build an extending valuation model which incorporates some other stakeholders (different from shareholders, debtholders and government), such as employees and clients so as to reflect their influence on the firm's market value. Based upon the work of Rao and Stevens (2007) which reflects the role of the three types of stakeholders with a special emphasis on the role of government, real options theory is applied here to quantify the value created through a major degree of loyalty and capture policies for both employees and clients. One fundamental option is proposed when building the model and it is related to the employees' and clients' portfolio of a firm, which has options to improve returns by driving up the motivations of employees, the fidelity of clients, the capture of talents, the information campaigns to clients and/or investors. Through applying real options theory, this thesis finds an appropriate way for treating the uncertainty and integrating the risks associated with it in valuation models, along with considering the flexibility as an ingredient of value in managerial decisions, increasing the capability to give alternative actions. The approach in this thesis is almost theoretical with a possible scheme for empirical experiments suggested for future research. The results achieved attempt to suggest that there exists communication vehicles for the information about an increase of the satisfaction degree of employees and customers in such way to be truly transmitted to investors and thus to be converted into an increase of the firm's market value

An overview of bankruptcy prediction models for corporate firms: a systematic literature review

Purpose: The aim of this paper is to conduct a literature review of corporate bankruptcy prediction models, on the basis of the existing international academic literature in the corresponding area. It primarily attempts to provide a comprehensive overview of literature related to corporate bankruptcy prediction, to investigate and address the link between the different authors (co-authorship), and to address the primary models and methods that are used and studied by authors of this area in the past five decades. Design/methodology: A systematic literature review (SLR) has been conducted, using the Scopus database for identifying core international academic papers related to the established research topic from the year 1968 to 2017. Findings: It has been verified, firstly, that bankruptcy prediction in the corporate world is a field of growing interest, as the number of papers has increased significantly, especially after 2008 global financial crisis, which demonstrates the importance of this topic for corporate firms. Secondly, it should be mentioned that there is little co-authorship in this researching area, as researchers with great influence were barely working together during the last five decades. Thirdly, it has been identified that the two most frequently used and studied models in bankruptcy prediction area are Logistic Regression (Logit) and Neural Network. However, there are many other innovative methods as machine learning models applied in this field lately due to the emerging technology of computer science and artificial intelligence. Originality/value: We used an approach that allows a better view of the academic contribution related to the corporate bankruptcy prediction; this serves as the link among the different elements of the concept studied, and it demonstrates the growing interest in this area.Peer Reviewe

CELLCOUNTER: Novel Open-Source Software for Counting Cell Migration and Invasion In Vitro

Transwell Boyden chamber based migration/invasion assay is a simple and extensively used approach for the characterization of cell motility in vitro. Cell motility is quantified by counting the number of cells that pass through the filter membrane. The counting is usually performed manually, which is laborious and error prone. We have therefore developed CELLCOUNTER, an application that is capable of recognizing and counting the total number of cells through an intuitive graphical user interface. The counting can be performed in batch, and the counting results can be visualized and further curated manually. CELLCOUNTER will be helpful in streamlining the experimental process and improving the reliability of the data acquisition

Resilience to Plasma and Cerebrospinal Fluid Amyloid-β in Cognitively Normal Individuals: Findings From Two Cohort Studies.

Objective: To define resilience metrics for cognitive decline based on plasma and cerebrospinal fluid (CSF) amyloid-β (Aβ) and examine the demographic, genetic, and neuroimaging factors associated with interindividual differences among metrics of resilience and to demonstrate the ability of such metrics to predict the diagnostic conversion to mild cognitive impairment (MCI). Methods: In this study, cognitively normal (CN) participants with Aβ-positive were included from the Sino Longitudinal Study on Cognitive Decline (SILCODE, n = 100) and Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 144). Using a latent variable model of data, metrics of resilience [brain resilience (BR), cognitive resilience (CR), and global resilience (GR)] were defined based on the plasma Aβ and CSF Aβ. Linear regression analyses were applied to investigate the association between characteristics of individuals (age, sex, educational level, genetic, and neuroimaging factors) and their resilience. The plausibility of these metrics was tested using linear mixed-effects models and Cox regression models in longitudinal analyses. We also compared the effectiveness of these metrics with conventional metrics in predicting the clinical progression. Results: Although individuals in the ADNI cohort were older (74.68 [5.65] vs. 65.38 [4.66], p < 0.001) and had higher educational levels (16.3 [2.6] vs. 12.6 [2.8], p < 0.001) than those in the SILCODE cohort, similar loadings between resilience and its indicators were found within both models. BR and GR were mainly associated with age, women, and brain volume in both cohorts. Prediction models showed that higher CR and GR were related to better cognitive performance, and specifically, all types of resilience to CSF Aβ could predict longitudinal cognitive decline. Conclusion: Different phenotypes of resilience depending on cognition and brain volumes were associated with different factors. Such comprehensive resilience provided insight into the mechanisms of susceptibility for Alzheimer's disease (AD) at the individual level, and interindividual differences in resilience had the potential to predict the disease progression in CN people

An efficient optimized independent component analysis method based on genetic algorithm

Three simulation experiments are designed to evaluate and compare the performance of three common independent component analysis implementation algorithms – FastICA, JADE, and extended-Infomax. Experiment results show that the above three algorithms can’t separate the mixtures of super-Gaussian and sub-Gaussian precisely, and FastICA fails in recovering weak source signals from mixed signals. In this case an independent component analysis algorithm, which applies genetic algorithm to minimize the difference between joint probability and product of marginal probabilities of separated signals, is proposed. The computation procedure, especially the fitness evaluation when signals are in discrete form, is discussed in detail. The validity of the proposed algorithm is proved by simulation tests. Moreover, the results indicate that the proposed algorithm outperforms the above three common algorithms significantly. Finally the proposed algorithm is applied to separate the mixture of rolling bearing sound signal and electromotor signal, and the results are satisfied

Characteristics, current exploration practices, and prospects of continental shale oil in China

Oil generation in the continental shale has laid the resource foundation for the originality and development of China’s petroleum industry; continental shale oil production is blazing a new trail in this field. In this paper, based on the geological conditions of continental shale oil in China, it is found that the main types of shale oil generally have four basic geological characteristics, which are large-scale continuous distribution, the domination of inorganic pores, the enrichment of “sweet areas”, and initial production that is controlled by relatively high organic maturity and high yield that is governed by relatively high formation pressure. Then, as examples for the geological characteristics and development practice of continental shale oil, four key areas of Longdong, Gulong, Jimsar, and Jiyang are systematically summarized. Finally, the future prospects of continental shale oil in China are put forward. Middle-high maturity shale oil is currently the main force of development, and middle-low maturity shale oil also has a considerable development prospect after technological improvement. Meanwhile, “sweet area/spot sections” assessment and technological innovation are still research areas to be improved.Cited as: Wang, X., Li, J., Jiang, W., Zhang, H., Feng Y., Yang Z. Characteristics, current exploration practices, and prospects of continental shale oil in China. Advances in Geo-Energy Research, 2022, 6(6): 454-459. https://doi.org/10.46690/ager.2022.06.0

Geological characteristics and main challenges of onshore deep oil and gas development in China

More than 30 years of continuous development has made onshore deep and ultra-deep conventional and unconventional oil and gas become an integral part of increasing the energy reserves and output by China’s petroleum industry. Based on the deep oil and gas geological conditions in the country, the present study finds that paleo stratum and deep burial are the two basic geological characteristics of deep oil and gas. Furthermore, we put forward the notion that it is necessary to strengthen the fundamental research of theories in four aspects and the core technology in five aspects of deep oil and gas. It is suggested that it is of special importance to promote the scientific and technological research of deep oil and gas through the scientific exploration of “myriameter deep” wells as the starting point, so as to boost the development of deep oil and gas field in China.Cited as: Yang, Z., Zou, C., Gu, Z., Yang, F., Li, J., Wang, X. Geological characteristics and main challenges of onshore deep oil and gas development in China. Advances in Geo-Energy Research, 2022, 6(3): 264-266. https://doi.org/10.46690/ager.2022.03.0

RNA Editing, ADAR1, and the Innate Immune Response

RNA editing, particularly A-to-I RNA editing, has been shown to play an essential role in mammalian embryonic development and tissue homeostasis, and is implicated in the pathogenesis of many diseases including skin pigmentation disorder, autoimmune and inflammatory tissue injury, neuron degeneration, and various malignancies. A-to-I RNA editing is carried out by a small group of enzymes, the adenosine deaminase acting on RNAs (ADARs). Only three members of this protein family, ADAR1–3, exist in mammalian cells. ADAR3 is a catalytically null enzyme and the most significant function of ADAR2 was found to be in editing on the neuron receptor GluR-B mRNA. ADAR1, however, has been shown to play more significant roles in biological and pathological conditions. Although there remains much that is not known about how ADAR1 regulates cellular function, recent findings point to regulation of the innate immune response as an important function of ADAR1. Without appropriate RNA editing by ADAR1, endogenous RNA transcripts stimulate cytosolic RNA sensing receptors and therefore activate the IFN-inducing signaling pathways. Overactivation of innate immune pathways can lead to tissue injury and dysfunction. However, obvious gaps in our knowledge persist as to how ADAR1 regulates innate immune responses through RNA editing. Here, we review critical findings from ADAR1 mechanistic studies focusing on its regulatory function in innate immune responses and identify some of the important unanswered questions in the field

Genetic algorithm for Lagrangian support vector machine optimization and its application in diagnostic practice

In this article a genetic algorithm optimized Lagrangian support vector machine algorithm and its application in rolling bearing fault diagnosis is introduced. As an effective global optimization method, genetic algorithm is applied to find the optimum multiplier of Lagrangian support vector machine. Synthetic numerical experiments revealed the effectiveness of this genetic algorithm optimized Lagrangian support vector machine as a classifier. Then this classifier is applied to recognize faulty bearings from normal ones. Its performance is compared with that of backpropagation neural network and standard Lagrangian support vector machine. Experimental results show that the classification ability of our classifier is higher and the computing time required to find the separating plane is relative shorter