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The purpose of this paper is to provide an integrated analysis base to facilitate computational neuroscience experiments, following a user-led approach to provide access to the integrated neuroscience data and to enable the analyses demanded by the biomedical research community.

The design and development of the N4U analysis base and related information services addresses the existing research and practical challenges by offering an integrated medical data analysis environment with the necessary building blocks for neuroscientists to optimally exploit neuroscience workflows, large image data sets and algorithms to conduct analyses.

The provision of an integrated e-science environment of computational neuroimaging can enhance the prospects, speed and utility of the data analysis process for neurodegenerative diseases.

The N4U analysis base enables conducting biomedical data analyses by indexing and interlinking the neuroimaging and clinical study data sets stored on the grid infrastructure, algorithms and scientific workflow definitions along with their associated provenance information.

Neuroeconomics and neurofinance are emerging as intriguing fields of research, despite sharing ambiguity with the concepts of neuroscience. The relationship among the concepts of economics, finance and neuroscience is not explicitly defined in the past literature, which distorts the use of neuroeconomics and neurofinance approaches in real-world practice for financial decision-making. The purpose of this paper is to consolidate the literature in the field of neuroeconomics and neurofinance to set up the research agenda for the upcoming scholarship in the field.

The purpose of this paper is to consolidates the extant literature in the fields of neuroeconomics and neurofinance by conducting an extensive systematic literature review to investigate the current state and define the relationship between economics, finance and neuroscience.

This paper identifies and explains the explicit relationship between different sub-fields of neuroscience with neuroeconomics and neurofinance and providing instances for future research studies.

The exclusive and extensive literature survey in the form of systematic literature review is undertaken for understanding the fields of neuroeconomics and neurofinance and is the key highlight of this paper. Another, interesting fact lies with matching the literature in neuroeconomics and neurofinance with further sub-fields of neuroscience such as neurophysiology, neuroanatomy, molecular neuroscience and cognitive neuroscience.

This chapter explores the phenomenon of managerial overoptimism, focusing on the cognitive underpinnings of the mechanisms that generate this bias. It develops a formal model of probability estimation that is inspired by the biological (cognitive neuroscience) evidence on associative information processing in the brain. The model is able to make novel, testable predictions about managerial overoptimism. It is able to parse out three mechanisms that could lead to overoptimism, as well as predict boundary conditions on when these effects should be observed and when the opposite (a pessimistic bias) should be observed instead. Furthermore, it predicts that under certain conditions, attempts by managers to “debias” their estimates might exacerbate the overoptimistic bias.

The purpose of this paper is to examine the impact of affiliate marketing strategies as a tool for increasing customers' engagement and vulnerability over financial services. This is attempted by examining the connection between affiliate marketing factors and customers' brand engagement and vulnerability metrics.

The authors developed a three-staged methodological context, based on the 7 most known centralized payment network (CPN) firms' website analytical data, which begins with linear regression analysis, followed by hybrid modeling (agent-based and dynamic models), so as to simulate brand engagement and vulnerability factors' variation in a 180-day period. The deployed context ends by applying the cognitive modeling method of producing heatmaps and facial analysis of CPN websites to the selected 47 vulnerable website customers, for gathering more insights into their brand engagement.

Throughout the simulation results of the study, it becomes clear that a higher number of backlinks and referral domains tend to increase CPN firms' brand-engaged and vulnerable customers.

From the simulation modeling process, the implication for backlinks and referral domains as factors that enhance website customers' brand engagement and vulnerability has been highlighted. A higher number of brand-engaged website customers could mean that vulnerable categories of customers would be impacted by CPNs' affiliate marketing. Improving those customers' knowledge of the financial services utility is of utmost importance.

The outcomes of the research indicate that online banking service providers can increase their customers' engagement with their brands by adopting affiliate marketing techniques. To avoid the increase in customers' vulnerability, marketers should aim to apply affiliate marketing strategies to domains relevant to the provided financial services.

The paper's outcomes provide a new approach to the literature, where the website customer's brand engagement comes out as a valuable metric for estimating online banking sector customers' vulnerability.

This paper aims to analyse a set of converging trends underpinning a larger phenomenon called science 2.0 and to assess what are the most important implications for scientific method and research institutions.

It is based on a triangulation of exploratory methods which include a wide-ranging literature review, Web-based mapping and in-depth interviews with stakeholders.

The main implications of science 2.0 are enhanced efficiency, transparency and reliability; raise of data-driven science; microcontributions on a macroscale; multidimensional, immediate and multiform evaluation of science; disaggregation of the value chain of service providers for scientists; influx of multiple actors and the democratisation of science.

The paper rejects the notion of science 2.0 as the mere adoption of Web 2.0 technologies in science and puts forward an original integrated definition covering three trends that have not yet been analysed together: open science, citizens science and data-intensive science. It argues that these trends are mutually reinforcing and puts forward their main implications. It concludes with the identification of three enablers of science 2.0 – policy measures, individual practice of scientists and new infrastructure and services and sees the main bottleneck in lack of incentives on the individual level.

The purpose of this paper is to develop and apply a high-order numerical method based on finite volume approximation for quadratic integrate-and-fire (QIF) neuron model with the help of population density approach.

The authors apply the population density approach for the QIF neuron model to derive the governing equation. The resulting mathematical model cannot be solved with existing analytical or numerical techniques owing to the presence of delay and advance. The numerical scheme is based along the lines of approximation: spatial discretization is performed by weighted essentially non-oscillatory (WENO) finite volume approximation (FVM) and temporal discretization are performed by strong stability-preserving explicit Runge–Kutta (SSPERK) method. Compared with existing schemes of orders 2 and 3 from the literature, the proposed scheme is found to be more efficient and it produces accurate solutions with few grid cells. In addition to this, discontinuity is added in the application of the model equation to illustrate the high performance of the proposed scheme.

The developed scheme works nicely for the simulation of the resulting model equation. The authors discussed the role of inhibitory and excitatory parts in variation of neuronal firing. The validation of the designed scheme is measured by its comparison with existing schemes in the literature. The efficiency of the designed scheme is demonstrated via numerical simulations.

It is expected that the present study will be a useful tool to tackle the complex neuron model and related studies.

The novel aspect of this paper is the application of the numerical methods to study the modified version of leaky integrate-and-fire neuron based on a QIF neuron. The model of the current study is inspired from the base model given in Stein (1965) and modified version in Kadalbajoo and Sharma (2005) and Wang and Zhang (2014). The applicability was confirmed by taking some numerical examples.