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Biometric systems are widely used for face recognition. They have rapidly developed in recent years. Compared with other approaches, such as fingerprint recognition, handwriting verification and retinal and iris scanning, face recognition is more straightforward, user friendly and extensively used. The aforementioned approaches, including face recognition, are vulnerable to malicious attacks by impostors; in such cases, face liveness detection comes in handy to ensure both accuracy and robustness. Liveness is an important feature that reflects physiological signs and differentiates artificial from real biometric traits. This paper aims to provide a simple path for the future development of more robust and accurate liveness detection approaches.

This paper discusses about introduction to the face biometric system, liveness detection in face recognition system and comparisons between the different discussed works of existing measures.

This paper presents an overview, comparison and discussion of proposed face liveness detection methods to provide a reference for the future development of more robust and accurate liveness detection approaches.

This paper aims to effectively achieve endmembers and relative abundances simultaneously in hyperspectral image unmixing yield. Hyperspectral unmixing, which is an important step before image classification and recognition, is a challenging issue because of the limited resolution of image sensors and the complex diversity of nature. Unmixing can be performed using different methods, such as blind source separation and semi-supervised spectral unmixing. However, these methods have disadvantages such as inaccurate results or the need for the spectral library to be known a priori.

This paper proposes a novel method for hyperspectral unmixing called fuzzy c-means unmixing, which achieves endmembers and relative abundance through repeated iteration analysis at the same time.

Experimental results demonstrate that the proposed method can effectively implement hyperspectral unmixing with high accuracy.

The proposed method present an effective framework for the challenging field of hyperspectral image unmixing.

As measurement results of optical gas sensors are constantly affected by the pressure of a target gas, approaches must be taken to modify the results. The purpose of this paper is to compare the traditional measurement method with the new measurement system. At the same time, measurement results of the two systems under different pressures are presented for comparison of the detection performance.

A theoretical model of line shape function and its impact on the measured results is presented, which is based on direct absorption and wavelength modulation spectroscopy (WMS) for gas concentration detection under different pressures. Methods of linear fitting and result modification have been illustrated and compared. A new testing system with the result modification method used for water vapor concentration detection under different pressures between 1 and 7 atm is applied. With an additional pressure sensor placed in the gas cell and calibration performed, relationships between the amplitude of the second harmonic and concentration of the target gas under different pressures can be obtained.

Amplitude detection can be used for concentration detection regardless of the change in pressure for the method of direct absorption. Although, WMS with second harmonic detection is not enough. Two methods are presented and compared with WMS under different pressures. Moreover, the result modification method shows better performance as computation is significantly reduced.

The comparison between linear fitting and the result modification method with WMS under different pressures is firstly presented. At the same time, a new testing system with better performance for water vapor concentration detection under different pressures is presented as well.

The purpose of this study is to investigate the effect of aging temperature on the barrel-type solder joint lifetime of electronic devices and to include these effects in the modified prediction model.

Several accelerated shear stress tests under different stress amplitudes and aging temperatures were performed.

It was found that by aging temperature increasing, the lifetime decreases. Morrow energy model was also found as the best prediction model when the aging temperature is taken into consideration.

It is confirmed.

With the increase in population, the energy needs of countries are also increasing. These countries have difficulties in meeting these increasing needs. Countries that cannot meet this need have to import energy from abroad. This situation adversely affects the current account balance of countries. Nuclear energy investments allow countries to obtain their own energy, although there are some criticisms. In this framework, while some countries in the world increase their nuclear energy investments, some countries do not have any nuclear power plants (NPP). There are 32 such countries where nuclear energy projects are running till date. Therefore, it is very important to determine the socio-economic variables of countries that have nuclear energy investments. In this context, a detailed literature analysis will be made first to determine socio-economic criteria. Then, the importance weights of these factors will be calculated using the Decision Making Trial and Evaluation Laboratory (DEMATEL) method. The profiles of the countries that make nuclear energy investments demonstrate that education level is the most essential socio-economic factor for the improvement of nuclear energy investments. Also, income inequality is another important variable in this regard. However, consumption behaviour and saving behaviour have the lowest weights.

The aim of this study is to examine the significant factors to improve the green nuclear energy investments in the emerging economies. For this purpose, balanced scorecard-based criteria are weighted with DEMATEL methodology. The findings demonstrate that technological improvement and financial issues are the most important issues for the improvement of the green nuclear energy investments in these countries. Nuclear energy working with thorium can also be obtained with proton accelerator technology that is currently quite expensive. Because of this problem, the investors are not willing to make investments for green nuclear energy projects. Hence, emerging countries should make the necessary technological investments to have proton accelerator technologies. With the help of this condition, it will be possible to reduce the cost of green nuclear energy projects which attracts the attention of the investors. This situation has a powerful contribution for the sustainable economic development of these countries.

This paper discusses theoretical extensions of the implied volatility method of Dupire (1994) when the stock prices follow the Geometric Levy process. For the extensions of Kolmogorov forward equation for Levy process, this paper uses adjoint operator in L² spaces. This paper obtains similar results of Dupire (1994) and Andersen and Andreasan (2001). However, our results can be applied to more general semi-martingale processes such as well-known VG (Variance Gamma) model and NIG (Normal Inverse Gaussian) model with diffusion processes. This paper also applies the approach to the case of stochastic time changed Levy process, which generates the stochastic volatility models.

The purpose of this paper is to establish a combined forecasting model to predict regional logistics demand, which is an important procedure on decision making of regional logistics planning.

There are several kinds of mathematical models often used in forecasting regional logistics demand. Trend extrapolation method extrapolates the future development trends bases on the hypothesis that the regional logistics will develop steadily. Grey system method predicts the change of logistics demand by the generation and development of original data sequence and excavation of inherent rules of the original data. Regression method obtains the change rules through the analysis between explained variable and explanatory variables. Each method has unique characteristics. In order to improve the accuracy of the prediction, combined methods are established. Genetic algorithm is used to determine the weights of different single models.

The results show that the combined forecasting model optimised by genetic algorithm can improve the accuracy.

Combined forecasting model can integrate the advantages of different single forecasting models. The key of improving the accuracy is to determine the weights of single forecasting models. Genetic algorithm can do well in finding suitable weights of each single forecasting model.

The paper succeeds in providing a combined forecasting model using genetic algorithm to determine the weights of each single prediction model, which helps to the decision making of regional logistics demand.

The goal of this paper is to investigate the relationships between consumers’ value perceptions, satisfaction and involvement, and, ultimately, their effects on behavioral intentions in the contexts of alternative golf (AG) and traditional golf (TG).

Data were collected from potential golfers who had visited AG facilities in the past 12 months. Data were analyzed using confirmatory factor analysis and structural equation modeling.

The findings of this study indicated that perceived value is a key element of developing satisfaction and promoting involvement, which resulted in visitors’ behavioral intentions toward AG and TG. In addition, satisfaction and involvement were found to sequentially mediate these relationships, and gender had a moderating effect on the relationship between AG and TG behavioral intentions.

This study theoretically contributes to the literature by proposing an extensive research model that attempted to capture the connection between AG and TG intentions and the sequential mediating effects of satisfaction and involvement. The strong connection between AG and TG found in this study suggest practical implications for managers, marketers and sales personnel for both AG and TG.

AG is defined as a non-traditional way to play golf that focuses more on entertainment and leisure activities. AG facilities are highly experiential spaces that include both golf and hospitality elements. The popularity of AG has increased in recent years with mostly anecdotal evidence of its influence on TG. This study empirically tested the role of AG in increasing the TG population.

The purpose of this study is to delineate the factors influencing the use of financial derivatives by non-financial firms in managing their exchange rate exposure. In total, 219 non-financial firms are surveyed in regard to their financial hedging decision.

This study is conducted via a survey and the questionnaires were sent to the treasurers and financial controller of the firms. Descriptive analysis is employed to assess the profiles of the respondents. Then, factor analysis is carried out to determine the factors influencing the use of financial derivatives in Malaysia.

The results indicate that the hedging decision of non-financial firms is influenced by their assertive level toward the market and regulators and also how flexible they are for derivative instruments. The intellectual capability that firms acquire to perform hedging strategies is also vital in influencing them to make hedging decision.

The insights of this survey would assist and prepare firms to hedge their exchange rate risk by employing financial derivatives. Knowing the influences of firms' adoption of currency derivatives would allow policy makers to formulate their policies in boosting the liquidity of Malaysian derivative market.

This study presents findings on the factors influencing the execution of financial hedging by non-financial firms in Malaysia. Survey data are used to seek for the feedback from the market players in order to provide empirical evidence on the corporate use of financial hedging.