Forecasting models for Taiwanese tourism demand after allowance for Mainland China tourists visiting Taiwan

The tourism industry is an increasingly important national industry for Taiwan. Government policymakers and business managers pay close attention to the development of the tourism industry. In a rapidly changing environment that is influenced by numerous socioeconomic factors, the tourism industry must have an accurate method to forecast future tourism demand such that decision makers will be able to meet future challenges more effectively. Based on these concerns, this study proposes the SARIMA–GARCH model to analyze and forecast the tourism demand in Taiwan and compare the predictive power of this model and other forecasting models. The results provide a valuable reference for decision-makers in the tourism industry of Taiwan.     

Colour shift and mechanism investigation on the PMMA diffuser used in LED-based luminaires

PMMA material is widely used in LED-based luminaires due to several advantages such as excellent optical transparency, durability against radiation, surface hardness (scratch free), rigidity and strength and can be completely recycled. However, few studies have been reported on the colour shift and failure mechanisms caused by this type of material. This paper experimentally investigated PMMA materials with different aging conditions. The following conclusions could be drawn. (1) Discolouration was not observed for any sample subjected to aging of 85 °C for 5000 h, or with additional blue light irradiation for 5000 h, or with additional humidity of 85%RH for 5000 h, or even with aging of 100 °C for 3000 h. (2) The specimen subjected to aging of 150 °C for 360 h has a surface discoloration and has a significant wavelength dependent degradation in the transmission spectrum caused by oxidation. The specimen with aging of 100 °C for 3000 h has a less oxidation, although no significant transmission spectrum reduction was observed. (3) Using such aged specimen as a diffuser mounted on a LED-based luminaire, the radiant flux peak intensity in the blue light area has a more severe reduction than that in the yellow light area, which results in a reduction of the radiant flux intensity ratio of blue light to yellow light and hence induces the colour shift to yellow. The colour shift investigated is 0.005, very close to the general failure criterion of 0.007, while the lumen decay is 10.2%, far less than the failure criterion of 30%.     

客家传统服饰刺绣图案

 从客家服饰刺绣图案的分类、刺绣技法、图案构成、色彩运用角度入手，总结出客家传统服饰刺绣图案按其表现题材可分为动植物、几何图纹、人物、文字、生活器物等五大类；刺绣技法分为七大系列二十七种针法；图案构成以边缘式、主题式与对称式为主；服饰图案色彩对比强烈、具有尚“俗”审美情趣且倾向装饰功利性。得出客家传统服饰图案在构图、表现题材、技法、用色等方面呈现地域性特征，并且具有深沉的中原汉民族审美情感倾向。     

Extraction and analysis of multiple time window features associated with muscle fatigue conditions using sEMG signals

In this work, an attempt has been made to differentiate surface electromyography (sEMG) signals under muscle fatigue and non-fatigue conditions with multiple time window (MTW) features. sEMG signals are recorded from biceps brachii muscles of 50 volunteers. Eleven MTW features are extracted from the acquired signals using four window functions, namely rectangular windows, Hamming windows, trapezoidal windows, and Slepian windows. Prominent features are selected using genetic algorithm and information gain based ranking. Four different classification algorithms, namely naïve Bayes, support vector machines, k-nearest neighbour, and linear discriminant analysis, are used for the study. Classifier performances with the MTW features are compared with the currently used time- and frequency-domain features. The results show a reduction in mean and median frequencies of the signals under fatigue. Mean and variance of the features differ by an order of magnitude between the two cases considered. The number of features is reduced by 45% with the genetic algorithm and 36% with information gain based ranking. The k-nearest neighbour algorithm is found to be the most accurate in classifying the features, with a maximum accuracy of 93% with the features selected using information gain ranking.     

Stress relaxation behavior of an Al–Zn–Mg–Cu alloy in simulated age-forming process

The stress relaxation behavior of age-forming for an Al–Zn–Mg–Cu alloy was studied using a designed device that can simulate the age forming process. The mechanism of stress relaxation was also revealed through calculating thermal activation parameters and analyzing the microstructures. The results suggested that the stress relaxation behavior of the Al–Zn–Mg–Cu alloy in the simulated age-forming process can be divided into three stages according to the stress level. The three stages of stress relaxation are: (i) the initial high stress stage, (ii) the subsequent middle stress transition stage and (iii) the last low stress equilibrium stage, respectively. The deformation activation energies are 132 kJ/mol in the initial high stress stage, 119 kJ/mol in the subsequent middle stress transition stage and 91 kJ/mol in the last low stress equilibrium stage, respectively. The analysis of the thermal activation parameters and microstructures revealed that dislocation creep was the dominant deformation mechanism in the initial and subsequent stages of the stress relaxation; whereas diffusion creep is the mechanism in the last stage of the stress relaxation. Additionally, a special threshold stress phenomena was present in the stress relaxation of the age-forming process, which was scribed to the interaction between precipitation and dislocation in the Al–Zn–Mg–Cu alloy     

Data classification with binary response through the Boosting algorithm and logistic regression

The task of classifying is natural to humans, but there are situations in which a person is not best suited to perform this function, which creates the need for automatic methods of classification. Traditional methods, such as logistic regression, are commonly used in this type of situation, but they lack robustness and accuracy. These methods do not not work very well when the data or when there is noise in the data, situations that are common in expert and intelligent systems. Due to the importance and the increasing complexity of problems of this type, there is a need for methods that provide greater accuracy and interpretability of the results. Among these methods, is Boosting, which operates sequentially by applying a classification algorithm to reweighted versions of the training data set. It was recently shown that Boosting may also be viewed as a method for functional estimation. The purpose of the present study was to compare the logistic regressions estimated by the maximum likelihood model (LRMML) and the logistic regression model estimated using the Boosting algorithm, specifically the Binomial Boosting algorithm (LRMBB), and to select the model with the better fit and discrimination capacity in the situation of presence(absence) of a given property (in this case, binary classification). To illustrate this situation, the example used was to classify the presence (absence) of coronary heart disease (CHD) as a function of various biological variables collected from patients. It is shown in the simulations results based on the strength of the indications that the LRMBB model is more appropriate than the LRMML model for the adjustment of data sets with several covariables and noisy data. The following sections report lower values of the information criteria AIC and BIC for the LRMBB model and that the Hosmer–Lemeshow test exhibits no evidence of a bad fit for the LRMBB model. The LRMBB model also presented a higher AUC, sensitivity, specificity and accuracy and lower values of false positives rates and false negatives rates, making it a model with better discrimination power compared to the LRMML model. Based on these results, the logistic model adjusted via the Binomial Boosting algorithm (LRMBB model) is better suited to describe the problem of binary response, because it provides more accurate information regarding the problem considered.     

Changes in flavour characteristics and bacterial diversity during the traditional fermentation of Chinese rice wines from Shaoxing region

As a traditional alcoholic beverage, Chinese rice wine is quite popular for its unique flavor and high nutritional value. In this study, we investigated the changes of flavor compounds in varying stages of fermentation by HS-SPME-GC-MS and HPLC. In addition, 16s rDNA-PCR was utilized to analyze the changes of bacterial communities during fermentation of Chinese rice wine from the Shaoxing region. It was noted that the content of flavor compounds including esters, higher alcohols, amino acids and organic acids was different in varying stages of fermentation. In general, the flavor compounds of Chinese rice wine were mainly produced after pre-fermentation. The results also showed that the bacterial community structures and diversity of Chinese rice wine varied significantly during different fermentation stages, and more than 10 genera of bacteria was detected in Chinese rice wine fermentation broth. Among these specific bacteria identified in the study, Lactobacillus (the predominant genus) and Bacillus might take an active part in flavor development in Chinese rice wine. This implied that these bacteria might play significant contributions on the flavor of Chinese rice wine.