Principal Component Analysis Regression Research Articles

Predicting High-frequency Stock Price Using Machine Learning Technique

This paper addresses the problem of predicting the stock price using the high frequency data based on a machine learning approach. We study two things in this paper (1) comparison of the prediction performance among selected function classes with given look-back parameter in terms of the proposed evaluation measures in the process of finding the best in-sample empirical loss minimizer (2) the comparison of those results by changing the sampled frequency of financial time series data after obtaining an introduced set of high frequency data features extracted from the Trades and Quotes (TAQ) data. For the analysis of TAQ data, feature engineering involves the computation of 56 number of related features including market microstructure, statistical, and technical indicator features. Re-estimation was done to improve the prediction accuracy for data models to obtain the predicted value every moving window. On the other hand, algorithmic models are used without re-estimation for the practical matter in that the time spent to train the model is often larger than the sampled frequency of the data. Moreover, the look-back parameter is introduced to cut off the irrelevant long past historical data. Among the selected function class in the experiment, the results show that the PCA regression performs the best in terms of the mean directional accuracy and simple back testing for both NASDAQ100 index and TAQ data with given sampled frequencies (i.e., 3min, 5min, etc). Compared to previous studies using NASDAQ100, the results demonstrate that re-estimation and properly chosen look-back parameter improve the prediction performance in terms of proposed evaluation measures. When it comes to maximum draw down, which is a measure critical for risk management, DA-RNN rendered the smallest value and, thereby, was the best performing model for TAQ data for all time frequencies. We also provide DM statistics whose null hypothesis is that the accuracies of prediction values of any two given models will not be different. In case of TAQ data for all sampled frequencies, there is evidence that we cannot reject the null hypothesis when comparing between PCA regression and DA-RNN model. Extensive experiments provide insights into properly evaluating the prediction performance of best in-sample empirical loss minimizer using the high frequency time series data.

Analysis of the effects of climate, host resistance, maturity and sowing date on wheat stem rust epidemics

Under appropriate environmental conditions, stem rust (caused by Puccinia graminis Pers.:Pers. f. sp. tritici) is a destructive disease of wheat crops worldwide. Although fungicide application and host genotype resistance are the most commonly used methods to control wheat stem rust, efficient agronomic methods are needed to lower disease management expenses and improve sustainability of wheat production. However, an understanding of highly effective agronomic practices to control stem rust is still lacking. From 2013 to 2017, 282 wheat-stem-rust progress curves were studied using three agronomic, three climatic, and two disease variables at plot scale in Kermanshah province, Iran. Cultivar, sowing date, and disease-assessment time significantly affected stem rust severity. From principal component analysis (PCA), three principal components explained 79% of data variance. According to PCA and multiple regression model, AUDPC corresponded with disease-onset and maturity date, mean minimum temperature and number of rainy days over designated spring months, number of days with minimum temperature within 5–20 °C and maximum relative humidity above 60%, resistance index, and sowing date. These new findings suggested improvement of current predicting models by involving dates of maturation and sowing, and wheat resistance besides rainfall-temperature-wetness variables for more effective, economic and sustainable management of stem rust epidemics.

Understanding client satisfaction in elderly care: new insights from social resource theory

Social resource theory suggests that social interaction can be conceived as resource transaction or exchange with behaviours falling within six fundamental resource categories (i.e. love, status, information, money, goods, and services) organised along two underlying dimensions: particularism–universalism and concreteness–abstractness. With the purpose of extending knowledge about quality of care, this study adopts a novel approach in that it describes and categorises care behaviours using social resource theory instead of using single instances of care behaviour. The categorisation is further used to predict client satisfaction in care services targeting older people. Daily interactions between care staff and older persons were observed in two different residential care facilities using a structured non-participant observation design. The data were analysed using principal component analysis, correlation, and regression analysis. The results confirmed the hypothesis that satisfaction with care services is predicted by resource transactions that are high on the underlying dimensions of particularism and abstractness. Thus, the resource categories of love and status (resource categories high on particularism and abstractness) were shown to be strong predictors of client satisfaction. The use of social resource theory is a novel and appropriate approach to examine person-centred care and satisfaction with care. Also, in addition to addressing potential problems in previous self-report studies on care staff behaviour, the observational technique was highly practical to this service area where dealing with clients not always able to provide feedback directly.

Determination of protein and glucose of tuber and root flours using NIR and MIR spectroscopy

Infrared spectroscopy associated with chemometrics analysis have been used for protein and glucose determination but researches which compare near-infrared (NIR) and mid-infrared (MIR) spectroscopy especially the ones which study root and tuber crops are rare. In this research, several tuber and root flours, i.e. arrowroot, canna, modified cassava, taro, and sweet potato (purple, yellow, and white) were used. The objective of this study aimed at determining protein and glucose contents of multiple crop flours using a single model for each chemical compound. NIR (10,000–4000 cm−1) and MIR (4000–600 cm−1) spectra as well as chemometrics (principal component analysis, PCA, and partial least-squares regression, PLSR) were used for developing model and analysis. For protein, the best NIR model was developed using original spectra showing Rc2, Rcv2, and Rp2 of 0.97, 0.96, and 0.93, while the best MIR model was developed using SNV method with Rc2, Rcv2, and Rp2 of 0.97, 0.96, and 0.96. Moreover, the best models for glucose were established using MSC pre-processed spectra which have Rc2, Rcv2, and Rp2 of 0.85, 0.78, and 0.86, as well as Rc2, Rcv2, and Rp2 of 0.89, 0.84, and 0.77 for NIR and MIR, respectively. This study showed the potential of NIR and MIR spectroscopy for determining protein and glucose contents of root and tuber flours.

Nutritional Status, Health Status, and Work Productivity of Cocoa Farmers in Polewali Mandar, Indonesia

The objective of this study was to analyze the associations between nutritional status, health status, other determining factors with work productivity of cocoa farmers. This cross-sectional study involved 58 male cocoa farmers aged 18–65 years old in two districts of Polewali Mandar. Data on food consumption and habit as well as health status were collected via structured interviews. While, blood pressure and anthropometric parameters were measured using standard equipment of blood pressure meter, weight scale, and height scale. Spearman’s test and Principal Components Regression (PCR) analysis were implemented to explore factors associated with farmers’ work productivity. There was no relationship between Body Mass Index (BMI) and work productivity. However, there was a significant positive association between central obesity with days of absent (r=0.275; p=0.037). We also found negativeassociation between blood pressure and the amount of cocoa picked daily (r=-0.366; p=0.005). PCR statistical analysis results showed that the amount of cocoa picked per day were significantly associated with smaller waist circumference, coffee consumption, and good exercise habit. While, decrease in consumption of fruits and vegetables was significantly associated with the number of absence days due to health problems. Thus, it can be concluded that work productivity of male cocoa farmers was associated with food consumption, physical activity, and lifestyle factors.

Exposure to Polychlorinated Biphenyls and Organochlorine Pesticides and Risk of Thyroid Cancer in Women

Background: Both polychlorinated biphenyls (PCB) and organochlorine pesticides (OCP) have been associated with thyroid hormone disruption, but their relationship to thyroid cancer is not known. Methods: We investigated the relationship between serum PCB and OCP concentrations and papillary thyroid cancer (PTC) in 250 incident female PTC cases and 250 female controls frequency-matched on age, all residing in Connecticut. Interviews and serum samples were collected from 2010-2013. Samples were analyzed for 32 different chemicals using gas chromatography with isotope dilution high resolution mass spectrometry. We calculated odds ratios (OR) and 95% confidence intervals (CI) using single pollutant logistic regression models for concentrations (per interquartile range) of individual PCB/OCP and summed groups of structurally or biologically similar PCB/OCP, adjusted for education, family history of cancer, alcohol consumption, and body mass index. Sub-analyses included stratification by tumor size (<=1 cm and >1 cm) and birth cohort (born before or in 1960 and born after 1960). We also applied three multi-pollutant approaches (standard multi-pollutant regression, hierarchical Bayesian modeling, principal components regression analysis) to investigate associations with exposures to multiple PCB/OCPs. Results: No PCB/OCPs were positively associated with PTC risk in primary analyses. Statistically significant associations were observed for 9 of the 32 chemicals and 3 summed groups of similar chemicals in the younger birth cohort based on single-pollutant models. Multi-pollutant analyses suggested null associations overall. Conclusions: Our results using single and multi-pollutant modeling do not generally support an association between PCB or OCP exposure and PTC risk, but some associations in the younger birth cohort suggest that additional investigation into early-life exposures and subsequent thyroid cancer risk may be warranted.

Weather-Based Neural Network, Stepwise Linear and Sparse Regression Approach for Rabi Sorghum Yield Forecasting of Karnataka, India

Sorghum is an important dual-purpose crop of India grown for food and fodder. Prevailing weather conditions during the crop growth period determine the yield of sorghum. Hence, the crop yield forecasting models based on weather parameters will be an appropriate option for policymakers and researchers to develop sustainable cropping strategies. In the present study, six multivariate weather-based models viz., least absolute shrinkage and selection operator (LASSO), elastic net (ENET), principal component analysis (PCA) in combination with stepwise multiple linear regression (SMLR), artificial neural network (ANN) alone and in combination with PCA and ridge regression model are examined by fixing 90% of the data for calibration and remaining dataset for validation to forecast rabi sorghum yield for different districts of Karnataka. The R2 and root mean square error (RMSE) during calibration ranged between 0.42 to 0.98 and 30.48 to 304.17 kg ha−1, respectively, without actual evapotranspiration (AET) whereas, these evaluation parameters varied from 0.38 to 0.99 and 19.84 to 308.79 kg ha−1, respectively with AET inclusion. During validation, the RMSE and nRMSE (normalized root mean square error) varied between 88.99 to 1265.03 kg ha−1 and 4.49 to 96.84%, respectively without AET and including AET as one of the weather variable RMSE and nRMSE were 63.48 to 1172.01 kg ha−1 and 4.16 to 92.56%, respectively. The performance of six multivariate models revealed that LASSO was the best model followed by ENET compared to PCA_SMLR, ANN, PCA_ANN and ridge regression models because of reduced overfitting through penalisation of regression coefficient. Thus, it can be concluded that LASSO and ENET weather-based models can be effectively utilized for the district level forecast of sorghum yield.

Relationship between the Shear Strength and Microscopic Pore Parameters of Saline Soil with Different Freeze-Thaw Cycles and Salinities

Saline soil is a widely distributed special soil with poor engineering properties. In seasonally frozen regions, the poor properties of saline soil will cause many types of engineering damage such as road boiling, melt sinking, and subgrade instability. These engineering failures are closely related to the shear strength of saline soil. However, there are relatively few studies on saline soil in cold regions. The strength of the soil is always determined by its microstructure; therefore, the study aims to investigate the relationship between the shear strength and microscopic pore structure of saline soil with different freeze–thaw cycles and salinities. The shear strength characteristics of saline soil with different salinities subjected to different freeze–thaw cycles were obtained by triaxial tests. In addition, the microstructure of the soil samples was investigated by scanning electron microscopy (SEM) tests, and the microscopic pore parameters of the soil samples, including porosity (N), average pore diameter (D¯), average shape coefficient (K), surface fluctuation fractal dimension (F), and orienting probability entropy (Hm), were obtained by image processing software quantitatively. Based on the experimental results, the influence of freeze–thaw cycles and salinity on the shear strength characteristics and microstructure of the soil samples were analyzed. Besides that, in order to effectively eliminate the collinearity between independent variables and obtain a stable and reasonable regression model, principal component regression (PCR) analysis was adopted to establish the relationship between the microscopic pore parameters and the failure strength of the soil samples. The fitting results demonstrated that the failure strength of saline soil is mainly related to the size and direction of the pores in the soil, and it has little correlation with pore shape. The failure strength of the soil was negatively correlated with the average pore diameter (D¯) and porosity (N), and it was positively correlated with the orienting probability entropy of the pores (Hm). This study may provide a quantitative basis for explaining the variation mechanism of the mechanical properties of saline soil from a microscopic perspective and provide references for the symmetry between the changes of the macroscopic properties and microscopic pore structure of the saline soil in cold regions.

Exposure to polychlorinated biphenyls and organochlorine pesticides and thyroid cancer in connecticut women

BackgroundThyroid cancer incidence has increased substantially over the past decades, and environmental risk factors have been suggested to play a role. Polychlorinated biphenyls (PCB) and organochlorine pesticides (OCP) are established thyroid hormone disruptors, but their relationship to thyroid cancer is not known. MethodsWe investigated the relationship between serum PCB and OCP concentrations and papillary thyroid cancer (PTC) in 250 incident female PTC cases and 250 female controls frequency-matched on age, all residing in Connecticut. Interviews and serum samples were collected from 2010 to 2013. Samples were analyzed for 32 different chemicals using gas chromatography with isotope dilution high resolution mass spectrometry. We calculated odds ratios (OR) and 95% confidence intervals (CI) using single pollutant logistic regression models for concentrations (per interquartile range) of individual PCB/OCP and summed groups of structurally or biologically similar PCB/OCP, adjusted for education, family history of cancer, alcohol consumption, age, and body mass index. Sub-analyses included stratification by tumor size (≤ and >1 cm) and birth before or during peak PCB production (born in 1960 or earlier and born after 1960), as exposures during early life may be important. We also applied three multi-pollutant approaches (standard multi-pollutant regression, hierarchical Bayesian modeling, principal components regression analysis) to investigate associations with co-exposures to multiple PCB/OCPs. ResultsNo PCB/OCPs were positively associated with PTC in primary analyses. Statistically significant associations were observed for 9 of the 32 chemicals and 3 summed groups of similar chemicals in the those born during peak production based on single-pollutant models. Multi-pollutant analyses suggested null associations overall. ConclusionsOur results using single and multi-pollutant modeling do not generally support an association between PCB or OCP exposure and PTC, but some associations in those born during peak production suggest that additional investigation into early-life exposures and subsequent thyroid cancer risk may be warranted.

LDL-C/HDL-C is associated with ischaemic stroke in patients with non-valvular atrial fibrillation: a case-control study

BackgroundThis study explored the relationships between the low−/high-density lipoprotein cholesterol ratio (LDL-C/HDL-C) and other clinical indicators and ischaemic stroke (IS) in patients with non-valvular atrial fibrillation (NVAF) in Xinjiang. The findings could provide a theoretical and therapeutic basis for NVAF patients.MethodsNVAF patients who were admitted to 10 medical centres across Xinjiang were divided into stroke (798 patients) and control (2671 patients) groups according to the occurrence of first acute IS. Univariate and multivariate logistic regression analysis were used to examine the independent risk factors for IS in NVAF patients. Factor analysis and principal component regression analysis were used to analyse the main factors influencing IS. Receiver operating characteristic (ROC) curve analysis was used to evaluate the discriminatory ability of LDL-C/HDL-C for predicting the occurrence of IS.ResultsThe stroke group had an average age of 71.64 ± 9.96 years and included 305 females (38.22%). The control group had a mean age of 67.30 ± 12.01 years and included 825 females (30.89%). Multivariate logistic regression showed that the risk of IS in the highest LDL-C/HDL-C quartile (≥2.73) was 16.23-fold that of the lowest quartile (< 1.22); IS risk was 2.27-fold higher in obese patients than in normal-weight subjects; IS risk was 3.15-fold higher in smoking patients than in non-smoking patients. The area under the ROC curve of LDL-C/HDL-C was 0.76, the optimal critical value was 2.33, the sensitivity was 63.53%, and the specificity was 76.34%. Principal component regression analysis showed that LDL-C/HDL-C, age, smoking, drinking, LDL-C and hypertension were risk factors for IS in NVAF patients.ConclusionsLDL-C/HDL-C > 1.22, smoking, BMI ≥24 kg/m2 and CHA2DS2-VASc score were independent risk factors for IS in NVAF patients; LDL-C/HDL-C was the main risk factor.

Rainfall Model Using Principal Component Regression Analysis with R Software in Sulawesi

Indonesia is a tropical country that has two seasons, rainy and dry. Nowadays, the earth is experiencing the climate change phenomenon which causes erratic rainfall. The rainfall is influenced by several factors, one of which is the local scale factor. This research was aimed to build a rainfall model in Sulawesi to find out how the rainfall relationship with local scale factor in Sulawesi. In this research, the data used were secondary data which consisted of 15 samples with 6 variables from Badan Pusat Statistik (BPS). The limitation of the sample size in this study was due to the limited secondary data available in the field. The data was processed using Principal Component Regression Analysis. The first step was reducing local scale factor variables so that the principal component variable could be obtained that can explain variability from the original data which then that variable was analyzed using principal regression analysis. The data were analyzed by utilizing R Studio software. The results show that two principal component variables can explain 75.2% of the variability of original data and only one principal component variable that was significant to the rainfall variable. The regression model explained that the relationship between rainfall, humidity, air temperature, air pressure, and solar radiation was in the same direction while the relationship between rainfall and wind velocity was not in the same direction. Overall, the results of the study provided an overview of the application of the Principal Component Regression analysis to model the rainfall phenomenon in the Sulawesi region using the R program.

Sensitivity of vegetation dynamics to climate variability in a forest-steppe transition ecozone, north-eastern Inner Mongolia, China

Climate change and land use management were competing explanations for vegetation dynamics in cold and semi-arid region of north-eastern Inner Mongolia, China. In order to reveal the role of human disturbance and clarify the regional climate-vegetation relationship, long-term (1982–2013) datasets of climate variables and vegetation dynamics in a forest-steppe transition zone of north-eastern Inner Mongolia, China were collected. Partial correlation analyses, principal components regression (PCR), and residual analyses were conducted to reveal the vegetation sensitivities to different climate variables and the impact of anthropogenic activities on climate-vegetation relationship. The results showed that.(1) Annual mean air temperature (TMP) significantly increased at a linear slope of 0.08 °C per decade, annual precipitation (PRE) had an insignificantly linear slope of −16.42 mm per decade (p = 0.15). The average Normalized Difference Vegetation Index (NDVI) had a significantly negative trend over the past decades. A change point around the year 1998, coincided with the occurrence of an intense global El Niño event was also identified.(2) Regional climate change can be represented by changes in temperature, humidity and radiation. NDVI in the steppes display high sensitivity to moisture availability. Whereas, forests was influenced by the warmth index (WMI), accumulation of monthly temperature above a threshold of 5 °C. Partial correlation analyses showed that pixels of positive correlation with PRE (controlling TMP) overlap with the pixels of high partial correlation with minimum temperature (controlling maximum temperature), which suggests a hidden link between minimum temperature and PRE in this region.(3) The spatial distribution of significantly decreased NDVI overlap with cropland expansion, as well as the low residual square (R2) from PCR analysis. The NDVI decline in these expanded croplands suggests human disturbance on vegetation dynamics. Following climate warming, NDVI of forested land displayed positive trend. Whereas, most of steppe displayed negative trend, possibly resulting from combined effects of climate drying and human disturbance.We conclude that the regional climate change can be characterized as warming and drying. Steppe areas were sensitive to humidity changes while forested land was mostly influenced by growing season warmth. Overall, the regional NDVI displayed significantly negative trend over the past decades. Beyond climate drying, cropland expansion in the transition area between grassland and forested land is also an important driver for decreased NDVI. Further studies on the ecological and hydrological consequences of crop land expansion is necessary.

A three-gene signature and clinical outcome in pediatric acute myeloid leukemia.

Although the 5-year survival rates in pediatric acute myeloid leukemia (AML) have improved over the last decades, there is a high relapse rate for Pediatric AML patients. In the present study, we mainly combine PCA with the LASSO technique to identify prognostic markers for Pediatric AML patients coming from the NCI TARGET database. Three key genes (EEF1A1, RPLP2, RPL19) associated with poor prognosis of pediatric AML has been screened by both PCA and LASSO Cox regression analysis. Simultaneously, we developed a risk score model to predict the prognosis of pediatric AML, according to risk scores, the patients were divided into high- and low-risk groups based on the median risk score. Kaplan-Meier survival analysis indicated that Pediatric AML patients with the high-risk group have a poorer survival rate than those with a low-risk group (p < 0.000). The receiver operating characteristic (ROC) analysis showed that the risk model has a good performance (AUC:0.669). Moreover, the clinicopathologic correlation showed that the expression levels of three genes were related to the central nervous system (CNS) disease and chloroma. GSEA identified that those pathways including oxidative phosphorylation, apoptosis and TGFB signaling pathway were differentially enriched. Taken together, those studies suggested that a gene panel that consists of three genes (EEF1A1, RPLP2, RPL19) may act as a potential prognostic marker.

Influences on interactions between physicians in the public and private sectors and medical representatives in Yemen

AbstractObjectiveThis study aims to assess the relative importance of attitude and subjective norms as well as physicians' characteristics and practice-setting factors in predicting the outcome of physicians' interactions with MRs.MethodsA cross-sectional survey was conducted among a convenience sample of 602 physicians in Sana'a, Yemen. The data were analysed using descriptive and inferential analyses. The t-test/Mann–Whitney test and ANOVA/Kruskal–Wallis test with post hoc analysis, principal component analysis, correlation analysis and regression analysis were applied at an a priori significance level of 0.05.Key findingsThe response rate was 76.5%. Results showed that physicians who see a greater number of MRs per week or have academic affiliations were significantly more likely to have received high/low-value promotional items than were those who saw fewer MRs or have no academic affiliations (P values &amp;lt; 0.001 and 0.021, respectively). Also male physicians and physicians who have private clinics were significantly more likely to have received high-value promotional items (P value &amp;lt; 0.001). Three out of five hypotheses were supported (physicians' belief in the appropriateness of accepting high/low-value pharmaceutical companies' promotional techniques relate positively and significantly to their behaviour of interactions with MRs, and physicians' attitudes towards pharmaceutical companies relates positively and significantly to their interactions with MRs).ConclusionPhysicians in Yemen consider most of the promotional techniques as normal practice. The article provides empirical evidence for policymakers in developing countries in general, and Yemen specifically, to develop suitable policies and regulations for drug promotion.

Physical and biochemical properties of 10 wild almond (Amygdalus scoparia) accessions naturally grown in Iran

The physical and biochemical traits are responsible for the quality of almonds. Therefore, both should be considered in breeding programs aiming to search for almonds of high quality for sustainable production. The aim of this work was to analyze several important physical and biochemical traits in the kernels of 10 native Iranian wild almond accessions grown naturally in different locations in Iran to identify their potential in breeding programs. The studied parameters included nut and kernel dry weights, kernel percentage, shell cracking strength of nuts, contents of microminerals (iron, zinc, manganese and copper) and macrominerals (potassium, phosphorus, magnesium, calcium and sodium), and levels of fatty acids (palmitic, stearic, oleic and linoleic), total phenolics and α-tocopherol in kernels. Principal component and multilinear regression analyses were done to observe the variations of almonds based on physical and biochemical traits. Principal component analysis showed 4 clustering groups of studied almonds based on physical and biochemical traits, explaining 67.2% of the total variance. Results showed differences between the accessions in terms of physical properties of nuts and kernels, as well as in the evaluated biochemical traits. East Azerbaijan and Fars accessions had the highest linoleic, palmitic and stearic acid contents and the lowest oleic acid content, while Qom accession had the highest oleic acid and the lowest palmitic, stearic and linoleic acid contents in kernels among all studied accessions. Results suggested that the naturally diverse accessions and/or environmental effects might be the reason(s) for the observed diversity in physical and biochemical traits.

Reasons to attend college, academic success, and post-college plans

ABSTRACT More than one third of college students in the US do not complete their college education. Through panel data methods and principal component regression analysis, this study examines the effect of different reasons for college enrollment on academic performance, educational outcomes and future planning. The findings suggest that students who attend college for individual growth are more likely to have higher grades and to pursue a graduate degree. Given that the reasons for going to college predict factors which influence withdrawal, understanding the reasons for enrollment can help identify and assist students at risk of not completing their college education.

Simple and Rapid Evaluation of the Unique Manuka Factor in Manuka Honey Using Fluorescence Fingerprints and Principal Component Analysis.

The excellent antibacterial activity of manuka honey has been well-documented and is often evaluated according to the unique manuka factor (UMF) index. UMF is determined by an assay based on a bacterial culture, which is time-consuming and does not allow for quantitative analysis. This study developed a simple and rapid method for UMF evaluation using fluorescence fingerprints, principal component analysis (PCA), and partial least squares (PLS) regression. Manuka honey samples were diluted four times with water and fluorescence was observed at three wavelength combinations, namely 260-300 (excitation; ex) to 370 (emission; em) nm, 340 (ex) to 480 nm (em), and 440 (ex) to 520 nm (em), that are mainly attributed to lepteridine, leptosperin, 2-methoxybenzoic acid, and N-methyl phenazinium. Analyzing fluorescence fingerprints using PCA and PLS regression provided a reliable evaluation of the UMF in manuka honey and could be used to differentiate between manufacturers.

Political Consumerism: An Application on Turkish Consumers

Sosyal, siyasal ve etik gibi bir dizi kıstaslara dayalı olarak üreticiler ve ürünler arasında tercih yapma eylemi olan siyasal tüketicilik bu çalışmanın temel kavramını oluşturmaktadır. Bu doğrultuda çalışmanın temel amacı; Türk tüketicisinin ne derecede siyasal tüketici olduğu ve buna etki eden faktörlerin neler olduğunu belirlemektir. Araştırma çerçevesinde oluşturulan hipotezleri test etmek amacıyla TR düzey 1’de yer alan 12 istatistik bölgesinde bir anket çalışması yürütülmüştür. Anket uygulaması ile elde edilen 672 adet anketin analizlerinde temel bileşenler analizinden ve regresyon analizinden faydalanılmıştır. Bu analizleri gerçekleştirebilmek için SPSS ve STATA programları kullanılmıştır. Elde edilen sonuçlara bakıldığında; siyasal tüketicilik ile gelir, eğitim, geleneksel olmayan katılım, kurumsal güven, genelleştirilmiş güven, postmateryalist değerler değişkenleri ile arasında pozitif yönlü bir ilişki, sosyalleşme değişkeni ile de negatif bir ilişki olduğu tespit edilmiştir. Diğer yandan cinsiyet, yaş, siyasi görüş ve kurumsal katılım değişkenleri ile siyasal tüketicilik arasında istatistikî olarak anlamlı bir ilişki tespit edilememiştir.

Correlation between color and content of eight components of Gardeniae Fructus at different harvest time

This research is to establish an HPLC method for determination of geniposidic acid, genipin-1-β-D-gentiobioside, geniposide, p-trans-coumaroylgenipin gentiobioside, chlorogenic acid, crocin-Ⅰ, crocin-Ⅱ and crocin-Ⅲ in Gardeniae Fructus at different harvest time. The detection wavelength was 238, 320 and 440 nm. Principal component analysis(PCA), correlation analysis, regression analysis and partial least squares(PLS) analysis were used to explore the relationship of color and content of eight components in Gardeniae Fructus. The result showed that the trend of the eight components in Gardeniae Fructus at harvest time in different three years was varied similarly. According to the variation of eight components at different harvest time, the mature and immaturate Gardeniae Fructus were discriminated. The content of crocin-Ⅰwas correlated positively with a~* of color significance. The redder color of Gardeniae Fructus showed the higher value of a~* and content of crocin-Ⅰ, indicating the better quality of Gardeniae Fructus. This method provided reference for justifying the color and quality of Gardeniae Fructus and scientific evidence for &quot;assessing quality by distinguishing color&quot;.

Distribution, abundance, and habitat characteristics of the congregating firefly, Pteroptyx Olivier (Coleoptera: Lampyridae) in Thailand

Pteroptyx fireflies represent an important economic insect for ecotourism. Two methods, visual counting and sweep net collection, were used to survey Pteroptyx species at 23 study sites along the coast of Thailand. P. malaccae and P. valida were the most common species across all sites with significant differences in abundance among study sites. In contrast, P. asymmetria was found only in the southern region, and P. tener was found only at one site in Surat Thani province. Pteroptyx fireflies displayed in trees whose canopies were opened to view as opposed to those blocked by other vegetation. Fireflies most often perched in the top section. Pteroptyx horizontal distribution was affected by ambient wind direction and speed. In addition, most display trees were located at prominent spots on either the inside bends or the outside bends of mangrove rivers. The principal component regression analysis was performed to determine which environmental factors might influence Pteroptyx species abundances and revealed that three Pteroptyx species appear to be influenced by different combinations of environmental variables.