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This chapter reassesses the economics of interest rate risk management in light of the global financial crisis by developing a derivative-based integrated treatment of interest rate and credit risk interrelation. The decade-long historical data on credit default swap spreads and interest rate swap rates are used as proxy measures for credit risk and interest rate risk, respectively. An elasticity of interest rate risk and credit risk, considered a function of the business cycle phases, maturity of instruments, economic sector, creditworthiness, and other macroeconomic parameters, is investigated for optimizing economic capital. This chapter sheds light on how financial institutions may address hedge strategies against downside risks implementing the proposed derivative-based integrated treatment of interest rate and credit risk assessment allowing for optimization of interest rate swap contracts. The developed framework of integrated interest rate and credit risk management is of special importance for emerging markets heavily dependent on foreign capital as it potentially allows emerging market banks to improve risk management practices in terms of capital adequacy and Basel III rules. Analyzing diversification versus compounding effects, it allows enhancing financial stability through jointly optimizing Pillar 1 and Pillar 2 economic capital.

This study aims to apply a systematic statistical approach, including several plot indexes, to diagnose the goodness of fit of a logistic regression model, and then to detect the outliers and influential observations of the data from experimental data.

The proposed statistical approach is applied to analyze some experimental data on internal solitary wave propagation.

A suitable logistic regression model in which the relationship between the response variable and the explanatory variables is found. The problem of multicollinearity is tested. It was found that certain observations would not have the problem of multicollinearity. The P‐values for both the Pearson and deviance χ² tests are greater than 0.05. However, the Pearson χ² value is larger than the degrees of freedom. This finding indicates that although this model fits the data, it has a slight overdispersion. After three outliers and influential observations (cases 11, 27, and 49) are removed from the data, and the remaining observations are refitted the goodness‐of‐fit of the revised model to the data is improved.

A comparison of the four predictive powers: R², max‐rescaled R², the Somers' D, and the concordance index c, shows that the revised model has better predictive abilities than the original model.

The goodness‐of‐fit and prediction ability of the revised logistic regression model are more appropriate than those of the original model.

The growing rate of desertification in Northwestern China and Mongolia that is occurring as a result of the conflict between economic development and natural conservation has been demonstrated in many studies. There have, for example, been some large studies using variations in bi‐weekly normalized difference vegetation index (NDVI) satellite images as a parameter for evaluating the vegetation dynamics in these areas. The purpose of this paper is to identify multi‐temporal variation in vegetated and non‐vegetated areas in remotely sensed satellite images to assess the status of desertification in East Asia.

Spatial data derived from these satellite images are applied to evaluate vegetation dynamics on a regional level, to identify the areas most vulnerable to desertification.

Analytical results indicate that the desert areas in East Asia are primarily distributed over Southern Mongolia, Central and Western Inner Mongolia, and Western China (the Taklimakan Desert). These desert areas expanded from 2000 to 2002, shrunk in 2003, then expanded again from 2003 to 2005. The areas most at risk for desertification are principally distributed in Southeastern Mongolia, and Eastern Inner Mongolia.

Simulation results based on data for deserts distributed throughout Northwestern China and Mongolia indicate that the proposed fuzzy model‐based method would be helpful for assessing and monitoring desertification. These analytical results will help administrators refine planning processes, define the boundaries of protected areas, and facilitate decisions for prioritizing areas for desertification protection.

A tension leg platform (TLP) is a vertically moored, floating structure which is normally used for offshore oil/gas production. However, these types of structures can be damaged by vibration responses that are too large. The purpose of this paper is to consider the influence of the external waves on oceanic structures.

A mathematical model of an ocean environment was constructed, in which wave‐induced flow fields cause structural surge motion, then solutions to the mathematical model were analytically derived.

The Takagi‐Sugeno (T‐S) fuzzy model is employed in the approximation of the oceanic structure. The stability analysis of the TLP system is carried out using the Lyapunov direct method.

The dependence of the wave‐induced flow field and its resonant frequency on the wave characteristics and the structural properties of the platform, which include width, thickness and mass, can be drawn using a parametric approach.

Mathematical modeling is applied to find the wave‐induced displacement due to the surge motion. The vibration of the mechanical motion of the platform structure caused by wave force is also discussed.

This study seeks to develop a systematic means of identifying regression models using a complex regression model with a statistical method.

As a widely adopted statistical scheme for analyzing multifactor data, regression analysis provides a conceptually simple algorithm for examining functional relationships among variables. This investigation assesses the proposed relationship using a sample of data in regression analysis and then estimates the fit using statistics. Furthermore, several algorithms and added variable plots are presented to obtain an appropriate regression model and the relationship between response variables y, p and explanatory variables x₀,x₁,x₂, … ,x_p.

The proposed statistical scheme is demonstrated by the analysis of experimental data on internal waves, in which the results can well illustrate what has been investigated in laboratory experiment and may be applicable to the naturally occurring reflection of internal waves from sloping bottoms.

In previous studies, field observations of internal waves were carried out. Owing to the limit of stationary measurement in situ, sufficient experiment data were not easily obtained. On the other hand, data collected by laboratory experiments express more information on wave mechanism, such as energy dissipation, mixing efficiency, and stratification thickness in a stratified layer fluid system. In present study, the authors supply valuable experiment data and analytic method, which will be a great contribution to the geophysical fluid dynamics. The results illustrate well what has been investigated in laboratory experiment and may be applicable to the naturally occurring reflection of internal waves from sloping bottoms.

More recently, it has been proposed that internal wave mixing may contribute significantly to internal mixing in the ocean and hence has an important influence on world climatic changes. Based on the statistical algorithm and regression model, the reduction in internal wave energy can be predicted on a sloping bottom due to frictional effect. Since, interaction between internal waves and uniform slopes has occurred in an estuary, a lake or in the ocean, the results available in this paper would benefit future study on internal wave hydrodynamics.

Fuzzy theory provides a rigorous, flexible approach to the problem of defining and computing. Therefore, to facilitate decision making in a geographic information system (GIS), the graph layer indicator and the Takagi‐Sugeno (T‐S) fuzzy model must be integrated. This study aims to explain several versions of the T‐S fuzzy model based on fuzzy theory and fuzzy operation.

An inference model is constructed for GIS using the T‐S fuzzy model to formulate an integrated T‐S decision‐making (TSDMK) system.

The TSDMK system accommodates inexact, linguistic, vague and uncertain GIS data. The operator assigns most graph layer indicators by intuition.

Simulation results for the Hualien main station show that the proposed TSDMK system is an effective approach for GIS decision making.

This investigation assesses applications of fuzzy logic for decision making in a GIS based on TSDMK graphs focusing on model‐based systems.

This study aims to investigate the relationship between structural damage and sensitivity indices using the Hilbert‐Huang transform (HHT) method.

The relationship between structural damage and the sensitivity indices is obtained by using the HHT method. Three sensitivity indices are proposed: the ratio of rotation (RR), the ratio of shifting value (SV) and the ratio of bandwidth (RB). The nonlinear single degree of freedom and multiple degree of freedom models with various predominant frequencies are constructed using the SAP2000 program. Adjusted PGA El Centro and Chi‐Chi (TCU068) earthquake data are used as the excitations. Next, the sensitivity indices obtained using the HHT and the fast Fourier transform (FFT) methods are evaluated separately based on the acceleration responses of the roof structures to earthquakes.

Simulation results indicate that, when RR < 1, the structural response is in the elastic region, and neither the RB nor SV in the HHT and FFT spectra change. When the structural response is nonlinear, i.e. RR1, a positive trend of change occurs in RB and RR, while in the HHT spectra, SV increases with an increasing RR. Moreover, the FFT spectra reveal that SV changes only when the RR is sufficiently large. No steady relationship between the RB and the RR can be found.

The paper demonstrates the effectiveness of the HHT method.

The purpose of this paper is to better understand landscape dynamics in arid and semi‐arid environments. Land degradation has recently become an important issue for land management in western China. The oasis ecosystem is especially sensitive to environmental disturbances, such as abnormal/extreme precipitation events, variations in the water supply from the upper watersheds, fluctuations in temperature, etc. Satellite remote sensing of terrestrial ecosystems can provide us with the temporal dynamics and spatial distributions of green cover over large areas of landscape. Seasonal green cover data are especially important in assessing landscape health (e.g. desertification, rate of urban sprawl, natural disturbances) in arid and semi‐arid regions. In this study, green cover data are derived from vegetation indices retrieved from moderate resolution imaging spectroradiometer (MODIS) sensors onboard the satellite Terra.

Satellite images recorded during the period from April 2000 to December 2005 are analyzed and the spatial distribution and temporal changes of the Ejin Oasis quantified.

This study shows that it is possible to derive important parameters linked to landscape sensitivity from MODIS and the derived imagery, such as normalized difference vegetation index (NDVI) time‐series data. Such a MODIS‐based time‐series monitoring system is particularly useful in arid and semi‐arid environments. The results of landscape sensitivity analysis prove the effectiveness of the method in assessing landscape sensitivity from the years 2001‐2005.

The novel strategy used in this investigation is based on the T‐S fuzzy model, which is in turn based on fuzzy theory and fuzzy operations.

Simulation results based on fuzzy models will help to improve the monitoring techniques used to evaluate land degradation and to estimate the newest tendency in landscape green cover dynamics in the Ejin Oasis.

Several brands are striving in their endeavors to find new ways to improve their consumer engagement through social media. They are using different tools including influencer marketing through social networking sites (SNSs). Therefore, this chapter addresses a gap in the academic literature as it distinguishes between two types of online influencers. The researchers investigate the effects of celebrity endorsers’ and of micro influencers’ posts on a brand’s page on Instagram. They examine the number of followers, clicks, comments and likes that are generated following their influencer marketing. The results suggest that both influencers have generated varying levels and types of interactions. In sum, this exploratory study revealed that the chosen celebrity’s posts attracted more followers to the brand’s Instagram page, when compared to the micro influencer’s publications. However, the latter has garnered more clicks, comments and likes, thereby increasing the consumer-brand engagement through social media. In conclusion, this contribution identifies future research avenues relating to influencer marketing.

This paper aims to explore the impact of metaverse technology on the hospitality and tourism industry. The introduction of metaverse technology has revolutionised the way the hospitality and tourism industry works. In the present study, the authors have investigated the role of social media marketing in the adoption of metaverse technology in hotel booking in India.

An extended technology acceptance model was proposed for an empirical investigation in the Indian context. Sample of 344 respondents was collected across India using a purposive sampling technique for the purpose of data analysis. The structural model analysis is used to analyse the data collected from the respondents using the SmartPLS software to check the structural and the measurement fit of the model.

The adoption intentions were largely influenced by the utility, attitude (ATT) and ease of use of the technology, and social media marketing plays a major role in influencing the perceived usefulness (PU) and ease of use (PEU). The study finds positive ATTs of the customers for using metaverse technology for booking their hotels. PU and PEU significantly influence the ATT of the consumer indicating the traveller’s perception of the usefulness and ease of metaverse technology influence their ATTs towards adoption.

Influence of metaverse technology is at a nascent stage in India specifically for hotel booking and tourism. The authors have used discriminant validity by using the criteria for both the square root of the average variance extracted and heterotrait–monotrait ratio tests, and the results suggest that the constructs in the research are distinct from other.