**Author details**

Diego A. Rojas1 , Sandra Moreira-Ramos2 , Fabiola Urbina2 and Edio Maldonado2 \*

1 Programa de Microbiología y Micología, Instituto de Ciencias Biomedicas, Facultad de Medicina, Universidad de Chile, Independencia, Santiago, Chile

2 Programa de Biología Celular y Molecular, Instituto de Ciencias Biomedicas, Facultad de Medicina, Universidad de Chile, Independencia, Santiago, Chile

\*Address all correspondence to<EMAIL_ADDRESS>
© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**161**

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[11] Lee TI, Young RA. Transcription of eukaryotic protein-coding genes. Annual Review of Genetics. 2000;**34**:

[12] Hahn S. Structure and mechanism of the RNA polymerase II transcription machinery. Nature Structural & Molecular Biology. 2004;**11**:394-403.

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*DOI: http://dx.doi.org/10.5772/intechopen.80602*

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**2.3 Deep belief network (DBN)**

DBN is a probabilistic unsupervised deep learning algorithm. It has many layers of hidden variables. To solve the more complex problems, it needs more hidden layers; each layer is a special statistical relation with the other layer. DBN can learn probabilistically; after learning, BDN needs training under supervisor to perform classification. The DBN is used to recognize clusters and generates images, video sequences, and motion-capture data (**Figure 4**).

## **2.4 Boltzmann machine (BM)**

The BM is a network that is a uniformly attached, neuron-like unit, which is responsible for taking decisions stochastically about whether to be off or on. Computational problems are solved through BM like search, optimization, and learning problem. Many features are uncovered in learning algorithm that shows

very complex behavior in training dataset. Boltzmann machine is used for classification and dimensionality reduction.


Meet the editors

Reza Gharoie Ahangar is a PhD student in Management Science with a minor in Finance, and a Teaching and Research Assistant in G. Brint Ryan College of Business at the University of North Texas, USA. He holds a BSc in Business Management, an MBA in Finance, an MSc in Finance, and an MSc in Business Analytics. He is a member of the American Finance Association (AFA), Decision Sciences Institute (DSI), and the Institute for Opera-

tions Research and the Management Sciences (INFORMS) as well as the Association of Scientists, Developers, and Faculties (ASDF). Mr. Gharoie has authored a series of books and journal and conference publications in finance and business analytics, with more than 30 technical publications.

He serves as an editorial board member for several scientific journals. His current research interests include asset pricing, investment, banking, forecasting, machine learning, artificial intelligence, and optimization.

Dr. Can Öztürk is an associate professor of financial, international and comparative accounting at the Department of Management, Çankaya University, Turkey. He received a bachelor's degree in Banking and Finance (2001) from Bilkent University and a second bachelor's degree in Finance (2003) and a master's in Business Administration (2004) from the University of Massachusetts Dartmouth, USA. He obtained a PhD in Accounting

and Finance in 2011 f rom Başkent University, Turkey. His primary research interests are International Financial Reporting Standards (IFRS), IFRS for small and medium-sized enterprises (SMEs), ISAs, and corporate social responsibility (CSR) reporting. He has published several articles in national and international journals such as *Accounting in Europe*, *Accounting and Management Information Systems*, *Emerging Markets Journal*, and *The World of Accounting Science*. Dr. Öztürk speaks English, French, and Turkish.

Contents

**Section 1**

**Section 2**

**Section 3**

**Section 4**

*by Birsel Sabuncu*

*by Maria-Gabriella Baldarelli*

*by Oliver Neoh and Matthias Nnadi*

Application of Fuzzy Neural Networks

**Preface III**

Introduction **1**

**Chapter 1 3**

Financial and Non-Financial Reporting **7**

**Chapter 2 9**

**Chapter 3 23**

Accounting and Financial Statements **39**

**Chapter 4 41** Nonlinear Effect of Financial Development and Foreign Direct Investment in Integration Economies Among ASEAN-5 Countries Following IFRS Adoption

**Chapter 5 59**

Banking **79**

**Chapter 6 81**

Introductory Chapter: Some Current Issues on Accounting and Finance

CSR Training and Financial Statement "Disclosure": The Case of Italy

*by Elya Nabila Abdul Bahri and Nor Hakimah Haji Mohd Nor*

The Roles of Accounting Valuations and Earnings Management in the Survivorship of Technology Firms during the Global Financial Crisis

Banks Financial State Analysis and Bankruptcy Risk Forecasting with

*by Yuriy Zaychenko, Michael Zgurovsky and Galib Hamidov*

*by Reza Gharoie Ahangar and Can Öztürk*

National Accounting Standards in Turkey


**5. Challenges to entomological surveillance**

Entomological surveillance has been employed to (1) determine changes in the geographical distribution of *Ae. aegypti*, (2) obtain relative measurements of *Ae. aegypti* populations through time and identify areas of "high" infestation or periods of growth in vector populations and (3) evaluate the impact of anti-vector interventions. These indicators cannot be used straightforwardly to estimate the risk of virus transmission in the population at a certain time or location.

The limitations of these methods for measuring mosquito populations are the absence of a "gold standard," the fact that all measurements have a range of error (they are not precise) and that only a proportion of the total mosquito population (eggs, larvae or adults) is measured. Furthermore, it must be understood that the risk of transmission can occur in various locations and not necessarily where the measurement and/or intervention is performed and that in the selection of methods of measurement and entomological monitoring, precision is always sacrificed. This is to say that, despite being less precise, easier and cheaper methods are chosen over those (e.g., adult surveys) that require more resources and thus are more

Challenges for the Introduction and Evaluation of the Impact of Innovative *Aedes aegypti*…

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101

An additional challenge is the combination of strategies (not yet their integration) and the differentiated evaluation of their impact, since while one intervention can modify the physical availability of breeding sites, it does not necessarily result in a decrease of vector density nor control the most stable and productive breeding sites. On the other hand, there is insufficient evidence to support the idea that achieving a lower egg or larval density through a variety of available interventions has an impact on the rate of disease transmission. Nevertheless, the combined use of old strategies and/or the incorporation of new vector control tools imposes various challenges: (1) the use of indicators that measure more specifically the density of mosquitoes in all stages of development in order to more concretely evaluate all available modes of intervention, (2) the definition of risk thresholds and (3) that the programs demonstrate their capacity (in terms of human resources, equipment and finances) to be executed with the

The evaluation of interventions to control *Ae. aegypti* faces diverse challenges regarding the potential impact they may have on the risk of transmission not only of dengue but also of other arboviruses recently associated with the region's epidemiological profile: Zika, chikungunya and yellow fever. The first challenge is estimating the impact derived from the disease that may be affected and the second in measuring the direct impact of the interventions on the vector populations in all of their stages and their relation to transmission risk (vectorial

Systems of epidemiological surveillance now have the task of measuring, in the most precise manner possible, three infections transmitted by *Ae. aegypti*. Now things are complicated because the syndrome of fever and exanthema may be indicative of dengue, Zika and chikungunya. The diseases are also associated with other signs, distinctive symptoms and highly specific clinical complications (hemorrhages with severe dengue, chronic arthralgia with the chikungunya virus and congenital syndromes and neurological complications with Zika).

The estimate of the actual number of dengue cases, and now of Zika and chikungunya, is very difficult to calculate due to *biological problems* inherent to the infection, such as the number asymptomatic infections, or of unspecified fevers, which hinders the correct quantification of the impact of each of these illnesses. Clinical confusion regarding symptomatic

coverage and frequency necessary to make them valid [1, 2].

**6. Challenges to epidemiological surveillance**

competence and capacity).

expensive [32].

*Entomological indexes*: There are various indicators (indexes) and methods to detect or monitor *Aedes* populations (egg, larval, pupal and adult stages) in relation to their location (containers, home or geographical area). The indicators were initially qualitative (negative/positive breeding sites or houses) and evolved toward being quantitative in order to identify the number of mosquitoes, though without specifying density, productivity or breeding site relevance (cryptic). The indices are not sufficiently exact to identify the risk of transmission [14].

One element of the evolution of control programs has been the slow innovation of entomological monitoring indicators, an area dominated by the traditional *Stegomyia* indexes used in the campaign to eliminate *Ae. aegypti* in the fight against yellow fever: house (HI), container (CI) and Breteau (BI) indexes. These indices were useful in the extent to which they indicated the (qualitative) presence and absence of the vector in a campaign that sought its elimination and attempted to evaluate the endeavors toward physical elimination of breeding sites (positive breeding sites or houses). The focus now turned toward the reduction in density (rather than the elimination) of the vector, and these indicators have lost their usefulness [15, 16].

The need for better indicators led to indices of pupae and oviposition, closer life stages to the ideal measure of adult (female) mosquito populations, which would allow for a better approximation of the estimated risk of transmitting dengue [17, 18]. These indicators of entomological risk did not reduce or eliminate the challenges to evaluate the interventions because the need to relate density and/or the threshold of the different vector stages to risk of transmission still persists [19–21].

The use of "nonentomological" (though associated with infestation and facilitators of vectorhuman contact and epidemiological risk) indicators has also been proposed and ought to be considered in order to better understand the dynamics of dengue transmission—for example, density and distribution of human populations, socioeconomic conditions, living and public services, climate, etc. [22–27].

The selection of indicators and surveillance methods depends on the objective of surveillance (density reduction, risk detection and outbreak prevention), the levels of infestation and the capacity for implementation. Nevertheless, there is little evidence showing that the control programs employ systematic monitoring of vector populations—in particular, monitoring of adult females—in order to measure infestation and risk of dengue transmission [18, 28, 29]. In the best of cases, programs still employ indices of infested sites/breeding sites [29, 30] in order to establish "areas" of transmission risk without demonstrating the predictive capacity of these indices as indicators of dengue transmission risk in the last 50 years [31].

The limitations of these methods for measuring mosquito populations are the absence of a "gold standard," the fact that all measurements have a range of error (they are not precise) and that only a proportion of the total mosquito population (eggs, larvae or adults) is measured. Furthermore, it must be understood that the risk of transmission can occur in various locations and not necessarily where the measurement and/or intervention is performed and that in the selection of methods of measurement and entomological monitoring, precision is always sacrificed. This is to say that, despite being less precise, easier and cheaper methods are chosen over those (e.g., adult surveys) that require more resources and thus are more expensive [32].

An additional challenge is the combination of strategies (not yet their integration) and the differentiated evaluation of their impact, since while one intervention can modify the physical availability of breeding sites, it does not necessarily result in a decrease of vector density nor control the most stable and productive breeding sites. On the other hand, there is insufficient evidence to support the idea that achieving a lower egg or larval density through a variety of available interventions has an impact on the rate of disease transmission. Nevertheless, the combined use of old strategies and/or the incorporation of new vector control tools imposes various challenges: (1) the use of indicators that measure more specifically the density of mosquitoes in all stages of development in order to more concretely evaluate all available modes of intervention, (2) the definition of risk thresholds and (3) that the programs demonstrate their capacity (in terms of human resources, equipment and finances) to be executed with the coverage and frequency necessary to make them valid [1, 2].