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The graphical representation of the correlation matrix by means of different multivariate statistical methods is reviewed, a comparison of the different procedures is presented with the use of an example data set, and an improved representation with better fit is proposed. Principal component analysis is widely used for making pictures of correlation structure, though as shown a weighted alternating least squares approach that avoids the fitting of the diagonal of the correlation matrix outperforms both principal component analysis and principal factor analysis in approximating a correlation matrix. Weighted alternating least squares is a very strong competitor for principal component analysis, in particular if the correlation matrix is the focus of the study, because it improves the representation of the correlation matrix, often at the expense of only a minor percentage of explained variance for the original data matrix, if the latter is mapped onto the correlation biplot by regression. In this article, we propose to combine weighted alternating least squares with an additive adjustment of the correlation matrix, and this is seen to lead to further improved approximation of the correlation matrix.

Nitrate is one of the pollutants that affects groundwater in most aquifers of the country. In order to investigate the behavior of hydrogeochemical parameters of groundwater in the Ardabil plain aquifer, the results of qualitative data of 139 wells that were measured in both wet and dry seasons during the water year 2012- 2011 were used. Using XLSTAT software, contamination was identified and samples were classified using principal factor analysis (PCA) and hierarchical clustering (HCA). Factor analysis method led to the extraction of five factors affecting groundwater quality with a total variance of 68.09 percent. Also, the results of factor analysis showed that the first, fourth and fifth factors are due to geogenic processes and the second and third factors are due to anthropogenic processes. The results of the hierarchical clustering method divided the samples into three groups, each group having two subsets. The set related to cluster one is spread in the northern, eastern, southern and parts of the center, west by mix(agriculture-garden), medium pasture and dry farming as well as urban areas. The set of the second cluster is often spread in the central, western and urban areas of the aquifer, with mix(agriculture-garden) and residential areas. The set of third cluster has been expanded to southwestern and urban areas. Generally, the existence of dissolution and ion exchange processes on the one hand, as well as leaching of fertilizers and incomplete development of the sewage system in the aquifer on the other hand has provided the groundwater pollution. [ABSTRACT FROM AUTHOR]

Abstract This paper examines the impact of COVID-19 on the mental health of people from different socioeconomic classes. This has been done by creating an anxiety score, based on responses to a set of questions that were asked as part of a two-round telephonic survey done by Young Lives Data for India. Using this index, anxiety levels have been classified as high, medium, and low. As the dependent variable has an ordered nature, an ordered logit model has been used for regression. According to the results, job loss, death of the family’s earning member or mishap in the family, and price increases all contributed to increased anxiety. The analysis indicates that anxiety levels among women were higher than among men because of the increased burden of household chores and childcare responsibilities. Also, anxiety levels were higher among those who perceived themselves as rich or poor as opposed to those who were comfortable in their income group. There was a higher anxiety level among the rich due to income loss and increased household responsibilities. A high percentage of children and elders reported feeling anxious; school closures and a lack of social interaction caused stress. Isolation also plagued the elderly. Furthermore, many students couldn’t avail online learning opportunities due to a lack of resources. The government implemented many policies to mitigate these issues, which included those to mitigate the immediate hunger problem.

Genuine leather is produced from animal skin by chemical tanning using chemical or vegetable agents, while synthetic leather is a combination of fabric and polymer. The replacement of natural leather by synthetic leather is becoming more difficult to identify. In this work, Laser Induced Breakdown Spectroscopy (LIBS) is evaluated to separate between very similar materials: leather, synthetic leather, and polymers. LIBS is now widely employed to provide a specific fingerprint from the different materials. Animal leathers processed with vegetable, chromium, or titanium tanning were analyzed together with polymers and synthetic leather from different origins. The spectra exhibited typical signatures from the tanning agents (Cr, Ti, Al) and the dyes and pigments, but also from polymer characteristic bands. The principal factor analysis allowed to discriminate between four main groups of samples representing the tanning processes and the polymer or synthetic leather character. [ABSTRACT FROM AUTHOR]

Maternal behavior has been related to ewe’s reactivity, as more reactive ewes might have greater risk of presenting worse maternal ability. This study aimed to determine the association between maternal behavior score (MBS) of Corriedale ewes with their physiological and behavioral variables measured at weaning of their offspring within two handling systems. Data from 335 ewes with average (± SE) age of 3.1 ± 1.8 years and 46.5 ± 6.1 kg of body weight were used. Experimental farms were classified into two handling systems: intensive (n = 3) and extensive (n = 2), based on stocking density and nature of human-animal relationships. Maternal behavior was evaluated in the field within 24 h after delivery, using a 6-rated scale (1 = poor to 6 = excellent). This scale takes into account the distance the ewe takes from the lamb when the lamb is restrained by an observer. The temperament of the ewes was evaluated during an arena test, at the weaning of the lambs, and was divided into two phases: social isolation and human presence. Data were subjected to principal component analysis and logistic regression. Logistic regression of principal factor (PF) scores in MBS showed that physical activity during the isolation phase increased in 1.57 (CI: 1.02 to 2.42; P < 0.05) the probability of extensively handling ewes showing high MBS (≥4), while in intensively handling ewes, logistic regression showed that the number of vocalizations in both phases of the arena test increased in 1.64 (CI: 1.12 to 2.4; P < 0.05) the probability of ewes having high MBS. Albeit the handling systems are not comparable, MBS was positively associated with some behavioral variables measured at the arena test. [ABSTRACT FROM AUTHOR]

Genuine leather is produced from animal skin by chemical tanning using chemical or vegetable agents, while synthetic leather is a combination of fabric and polymer. The replacement of natural leather by synthetic leather is becoming more difficult to identify. In this work, Laser Induced Breakdown Spectroscopy (LIBS) is evaluated to separate between very similar materials: leather, synthetic leather, and polymers. LIBS is now widely employed to provide a specific fingerprint from the different materials. Animal leathers processed with vegetable, chromium, or titanium tanning were analyzed together with polymers and synthetic leather from different origins. The spectra exhibited typical signatures from the tanning agents (Cr, Ti, Al) and the dyes and pigments, but also from polymer characteristic bands. The principal factor analysis allowed to discriminate between four main groups of samples representing the tanning processes and the polymer or synthetic leather character.

Objective To analyze the relationship between dietary patterns and metabolic syndrome (MS) in Guangdong Province. Methods A survey was undertook based on the data from chronic disease and nutrition monitoring. Dietary patterns of adults in Guangdong Province were exploring by principal component analysis (PCA). PCA was used to identify dietary patterns among adult in Guangdong Province and unconditional Logistic regression model was used to analysis the effects of different dietary patterns on MS. Results Three evident dietary patterns were derived by PCA including "modern fast food dietary pattern", "high plant-based dietary pattern" and "coastal special dietary pattern". Rice and its products, fruits, milk, instant foods, noodles and their products, eggs were the main foods of "modern fast food dietary pattern"; light-colored vegetables, refined vegetable oil, salt, other livestock meat, starch/sugar, beans were the main foods of "high plant-based dietary pattern"; dark vegetables, light vegetables, fish and shrimps, refined animal oil, refined vegetable oil, and pork were the main foods of "coastal special dietary pattern". After adjusting for confounding factors, the modern fast food dietary pattern was a risk factor for hyperglycemia [odds ratio (OR)=2.161, confidence interval (95%CI)=1.173-3.981], and high plant-based dietary pattern was a protective factor for MS (OR=0.494, 95%CI=0.253-0.963). Conclusion High plant dietary patterns could help reduce the risk of MS or reduce the abnormal components of MS. The dietary structure and eating habits should be adjusted according to local conditions to prevent and control the occurrence of MS.

Background: Previous diet findings in Hispanics/Latinos rarely reflect differences in commonly consumed and culturally relevant foods across heritage groups and by years lived in the United States.Objectives: We aimed to identify and compare a posteriori heritage-specific dietary patterns (DPs) and evaluate their associations with "healthfulness" [using the Alternative Healthy Eating Index-2010 (AHEI-2010)] and years living in the United States.Methods: We used baseline data from a population-based cohort of 14,099 Hispanics/Latinos aged 18-74 y in the Hispanic Community Health Study/Study of Latinos. We performed principal factor analysis using two 24-h recalls to derive DPs, separately, in each heritage group (Cuban, Dominican, Mexican, Puerto Rican, Central American, and South American), and identified overarching DPs based on high-loading foods shared by ≥2 groups. We used multivariable linear regression to test associations of DPs with AHEI-2010 and years living in the United States.Results: We identified 5 overarching DPs (Burgers, Fries, & Soft Drinks; White Rice, Beans, & Red Meats; Fish; Egg & Cheese; and Alcohol). All Burgers, Fries, & Soft Drinks DPs were inversely associated with AHEI-2010, whereas all Fish DPs (except Dominican) were positively associated with this index (all P-trend < 0.001). White Rice, Beans, & Red Meats DPs showed inverse associations in Cuban and Central American groups and positive associations in Mexican-origin individuals (all P-trend < 0.001). Fewer years living in the United States was associated with higher scores for White Rice, Beans, & Red Meats DPs in Cuban and Mexican heritage groups and lower scores on Burgers, Fries, & Soft Drinks DPs in Cuban, Mexican, and Puerto Rican groups (all P-trend < 0.01).Conclusions: Our findings show substantial variation in DPs across Hispanics/Latinos and adherence to DPs by time in the United States, which could inform dietary interventions targeting this diverse US population. This trial was registered at clinicaltrials.gov as NCT02060344. [ABSTRACT FROM AUTHOR]

Abstract Background Birth weight is continuously decreasing in Japan since food satiation has become a problem in recent years. The present study aimed to develop and examine the reliability and validity of a scale for the assessment of risk factors for low birth weight in infants born at term. Methods A self-administered postal questionnaire survey comprising a low birth weight risk assessment scale was conducted on mothers with children of nursery school or kindergarten age. After item analysis (scale), factor structure was confirmed by an exploratory factor analysis using the main factor method promax rotation. The reliability of this scale was confirmed by Cronbach’s α coefficient and Item–Total correlation. The validity was confirmed by known-groups validity and internal validity. Results The responses of 630 mothers (valid response rate, 18.5%) were analyzed. Factor analysis (principal factor analysis and promax rotation) obtained an optimal scale comprising 25 items in the following nine factors: “guidance at each checkup,” “adequate rest,” “support from husband,” “effects on the fetus,” “support from society,” “support from family,” “effects of minor troubles,” “good lifestyle habits,” and “fall risk and lifestyle changes.” The overall Cronbach’s α coefficient for the scale was 0.701. Known-groups validity examination revealed significant differences in scale scores of birth weight, birth history, and maternal smoking status. Conclusion The scale demonstrated internal consistency, construct validity, and known-groups validity, indicating that it can be used as an indicator of low birth weight risk. In the future, this scale may be included in medical questionnaires as part of health guidance for pregnant women at a risk of delivering low birth weight children.

The aesthetic value of 35 plant landscape samples of Jinlong lake wetland park in Xuzhou city of China were evaluated with principal factor analysis and SBE method. The results showed that: The SBE value of 35 plant landscape samples fluctuated greatly and the evaluation value of the principal factor did not change much. The order of SBE mean values of different types was: plant landscape on both sides of the roads > plant landscape beside buildings and surroundings > typical plant community > waterfront plant landscape. The order of value of the principal factor analysis of different types was: plant landscape on both sides of the roads > typical plant community > plant landscape beside buildings and surroundings > waterfront plant landscape. The principal factor evaluation method contained more than 90% information of seven evaluation factors, while the SBE method mainly evaluated from “plant aesthetic factors”. The satisfaction of waterfront plant landscape was low, the transformation of plant landscape should focus on increasing the species of waterfront plants, especially native plants, building natural revetments, creating rich canopy lines.

In this study, the pollution risks related to arsenic, fluorine, iron, physicochemical parameters, and various indices have been investigated in the Bazoft dam site. The proposed dam is a double-curvature arch dam with a height of 211 m in order to produce hydroelectric energy and the supply of agricultural and drinking water in the west of Iran. Chemical analysis of water samples obtained from springs, river, and boreholes in the dam site was conducted. Anions such as HCO3, Cl, SO4, CO3, and NO3, and cations including Ca, Na, Mg, K, F, Fe, and As had the highest amounts, respectively. The concentration of assessed variables was lower than the WHO permissible limit. The results of the principal factor analysis (PCA) showed that four factors control 82% of the water quality variations. The factors such as EC, TDS, Na, K, SO4−, CO3, HCO3, total hardness, and alkalinity with 43.10% were the most important factors in the dam site. The heavy metals of iron and arsenic have no meaningful relationship with any of the ions. The accuracy of the relations was investigated based on different statistical criteria. The main processes in controlling the water quality are weathering and the dissolution of the carbonates caused by the interaction of water and rock. Ionic ratios indicated that the water origin of the springs is the dolomitic aquifers and the main source of magnesium is carbonate dissolution. The water resources of the dam site are corrosive, and all indices indicate the appropriate quality of water for irrigation and drinking purposes. Filed studies and boreholes water level variations showed that karstification is limit in the dam site. [ABSTRACT FROM AUTHOR]

Desertification is one the main problems of arid and semi-arid ecosystems in the world. The rapid growth of human population and excessive exploitation of resources have exacerbated the negative consequences of this phenomenon, such as poverty, diseases and human growth inequalities. So, In order to control and reduce desertification, presenting important strategies based on all the effective factors is necessary. This paper aims to provide a systematic and optimal de-desertification approach in a group decision-making model. For this aim, within the framework of multi-attribute decision-making procedure, a decision matrix was obtained by Delphi method. Then, the approach priority was determined by principal factor analysis. Based on the results, among 40 investigated strategies and 16 criteria, the strategies of development and reclamation of plant cover (A23), change of irrigation patterns (A33), prevention of improper change in land use (A18), and control of using groundwater sources (A31) were selected with the priority rates of 0.6122, 0.5102 and 0.4391 respectively. Thereby, they were introduced as the most important strategies to deal with desertification in the study area. The results and the ranks obtained in this study can be considered in projects of controlling or reducing the effects of desertification and in the plans for rehabilitation of degraded lands.

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Improving safety performance is paramount to the success of oil and gas construction projects. Although considerable attention has been paid to developing and using new types of safety performance indicators, contractor safety has traditionally been measured and managed through lagging indicators. Alternatively, risk mitigation measures can be used to predict safety performance as leading indicators. Several research studies have been performed on safety leading indicators; however, no research has empirically validated the predictive validity of candidate indicators. To address this knowledge gap, empirical data were used to measure both potential safety leading and lagging indicators in an effort to test the hypothesis that variability in candidate safety leading indicators predicts variability in lagging indicators of safety performance. A total of 261 contractors were included in the study, with more than 60,000 data points. Using principal factor analysis, model building, and regression, factors with predictive power were identified. The predicted total recordable incident rate (TRIR) and severity rate (SR) provided strong correlation with the actual TRIR and SR, with coefficients of 0.7251 and 0.5338, respectively. The models of predictive TRIR and SR were validated and suggest a good fit when applied to new contractor safety data set. The models can be used as new safety leading indicators that provide warning signs for weakness in contractor safety performance. Consequently, clients can use the results to implement adaptive risk mitigation, reduce incidents, and continuously improve contractor safety performance. This research contributes to the existing body of knowledge by empirically identifying safety leading indicators, testing their efficacy, and validating the resulting models. This research set a foundation for establishing quantitative safety leading indicators in the construction industry in the future for other forms of project outcomes. [ABSTRACT FROM AUTHOR]

Process performance and operation of wastewater treatment plants (WWTP) are carried out to ensure their compliance with legislative requirements imposed by European Union. Because a high amount of variables are daily measured, a coherent and structured approach of such a system is required to understand its inherent behavior and performance efficiency. In this sense, both principal factor analysis (PFA) and hierarchical cluster analysis (HCA) are multivariate techniques that have been widely applied to extract and structure information for different purposes. In this paper, both statistical tools are applied in an urban WWTP situated in the Southeast of Spain, a zone with special characteristics related to the geochemical background composition of water and an important use of fertilizers. Four main factors were extracted in association with nutrients, the ionic component, the organic load to the WWTP, and the efficiency of the whole process. HCA allowed distinguish between influent and effluent parameters, although a deeper examination resulted in a dendrogram with groupings similar to those previously reported for PFA. [ABSTRACT FROM AUTHOR]

BACKGROUND To investigate corn silage fermentative quality, a principal factor analysis was carried out on a database consisting of 196 corn silages sampled from the core, lateral and apical parts of silo feed-out face and characterised by 36 variables. Eleven principal factor components ( PCs) were retained and interpreted. Two PCs were related to chemical and digestibility variables; four PCs were characterised by end-products associated with clostridia, heterolactic, homolactic or aerobic fermentations; two PCs were associated with mycotoxins produced by Penicillium roqueforti or by Aspergillus fumigatus and Fusarium spp., while three PCs explained ensiling procedures adopted to store corn silages. RESULTS Lower ( P < 0.05) yeast or mould counts and greater ( P < 0.05) aerobic stability were measured in core than in peripheral samples. Excluding PCs related to ensiling procedures, other PCs were able to predict microbiological counts, aerobic stability or biogenic amine content as verified by multiple linear regression analysis. Based on these results, several corn silage quality index calculations were computed by using a summative equation approach in which different PCs as well as diverse relative weights multiplying each PCs were combined. To compute definitive index calculation, only PCs explaining clostridia, heterolactic and homolactic fermentations were used with relative weights of 30%, 50% and 20%. CONCLUSIONS The new proposed fermentative quality index was highly correlated to parameters related to corn silage fermentative quality, such as microbiological counts, aerobic stability or biogenic amines and it properly discriminated well- and poorly preserved forages. © 2015 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

BACKGROUND A survey on 68 dairy farms was carried out to evaluate the ensiling procedures adopted to store corn silage. Samples from core, lateral and apical zones of the feed-out face of silos were analysed. A principal factor analysis ( PFA) was carried out on the entire database (196 silage samples and 36 variables) and 11 principal factor components ( PCs) were retained and interpreted. RESULTS Ensiling procedures influenced the area exposed to risk of air penetration. Cores had higher dry matter, starch and lactic acid content or lower pH, fibre, propionate and butyrate concentrations than peripheral samples ( P < 0.05). The highest ( P < 0.05) mycophenolic acid and roquefortina C concentrations were detected in lateral samples. Chemical and digestibility variables loaded on two PCs; four PCs were characterized by end-products associated with clostridia, heterolactic, homolactic and aerobic fermentations; two PCs were associated with mycotoxins, whereas three PCs explained ensiling procedures. CONCLUSION The main quality traits of corn silages differed throughout the entire silo face. Minimization of the area exposed to risk of air penetration represents the best strategy to preserve the nutritional value and safety of corn silages. PFA allowed a clusterization of original variables into 11 PCs, appearing able to discriminate well and poorly preserved corn silages. © 2015 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Water quality and its effects on human life have become one of the major concerns in aquatic ecosystems. The water quality index (WQI) is defined as a parameter to interpret water-monitoring data and clarify the quality of water. In this study, the gene expression programming (GEP) and artificial neural networks (ANNs) were employed to predict WQI in free surface constructed wetlands. Seventeen points of a selected wetland were monitored twice a month over a period of 14 months, and an extensive data set was collected for 11 water quality variables (WQVs). A principal factor analysis (PFA) indicated that WQI was greatly affected by pH and SS, while temperature no has significant effect on the WQI in tropical areas. A sensitivity analysis was carried out to reduce the number of 11 WQVs in prediction of the WQI. Subsequently, five significant parameters, pH, suspended solid (SS), ammoniacal nitrogen (AN), dissolved oxygen (DO) and chemical oxygen demand were selected to develop a GEP and ANNs. The GEP was able to successfully predict the WQI with high accuracy ( R = 0.983 and MAE = 0.295). The statistical parameters indicate that, although the ANNs with R = 0.988 and MAE = 0.013 produced better results compared with GEP, the GEP-based formula is more useful for practical purposes. The GEP and ANNs are recommended as rapid and powerful WQI evaluation techniques to reduce substantial effort and time by optimizing the calculations. [ABSTRACT FROM AUTHOR]

Relationships between fatty acids (FAs) in plant oils included in goat diets and milk fat C18 isomers were determined by Principal Factor Analysis (PFA). The three first principal factors (PF1, PF2 and PF3) accounted for 64·5% of the total variation in milk FAs contents. Fatty acids with a double bond at carbons 13, 14, 15 or 16 had high (>0·6) and positive loadings for PF1, trans-4 to trans-8 C18:1 for PF2, whereas trans-10 C18:1, trans-11 C18:1 and cis-9 trans-11 C18:2 showed high and positive loadings for PF3. Pearson's correlations supported that PF1, PF2 and PF3 were related to α-linolenic, oleic and linoleic acid intakes, respectively. Our results show that the quantitatively main FAs in plant lipids supplemented to dairy ruminants are often the main cause of the observed changes in milk C18 isomer contents. However, sometimes the observed changes are caused, or at least are influenced, by other FAs present in lower quantities in the plant lipids. Thus, using mixtures of plant oils with differently unsaturated main FAs could be a way of tailoring milk fat composition to a pre-designed pattern. [ABSTRACT FROM AUTHOR]

Biosorption behavior of compound bioflocculant (CBF), produced by a mixed culture ofRhizobium radiobacterF2 andBacillus sphaeicusF6, was investigated for Pb(II) removal under various parameters (pH, initial Pb(II) concentration, temperature, and stirring rate). Biosorption kinetics, isotherm, thermodynamics, and Fourier transform infrared (FTIR) spectra were studied to explore the biosorption mechanism. Additionally, principal factor analysis was also performed with a statistical method to identify the main influencing factor. Optimum pH was determined as 7.0 and biosorption equilibrium was attained within 60 min. The order of effects caused by the four operational parameters was pH > initial Pb(II) concentration ≈ stirring rate > temperature. The experimental data obeyed the pseudo-first-order kinetic model well. Langmuir model fitted the equilibrium data best among Langmuir, Freundlich and Redlich–Peterson models. Negative ΔG° and positive ΔH° indicated that the biosorption process was spontaneous and endothermic in nature. FTIR analysis showed that –OH, –NH, and –C=O groups were involved in the biosorption. These results demonstrate that CBF could be a promising alternative for Pb(II) removal from aqueous solution. [ABSTRACT FROM PUBLISHER]

Free-surface constructed wetlands are known as a low-energy green technique to highly decrease a wide range of pollutants in wastewater and stormwater before discharge into natural water. In this study, two spatial analyses, principal factor analysis and hierarchical cluster analysis (HACA), were employed to interpret the effect of wetland on the water quality variables (WQVs) and to classify the wetland into groups with similar characteristics. Eleven WQVs were collected at the 17 sampling stations twice a month for 13 months. All sampling stations were classified by HACA into three clusters, with high, moderate, and low pollution areas. To improve the water quality, the performance of Cluster-III (micropool) is more significant than Cluster-I and Cluster-II. Implications of this study include potential savings of time and cost for long-term data monitoring purposes in the free-constructed wetland. [ABSTRACT FROM AUTHOR]

China has limitedly encouraged the talent flow, in which the flow of young talents mainly including the university graduates draws many people’s attention. But the recent employment of university graduates is not desirable. The key of this problem lies in the ability of university graduates does not meet the demand of units, so it is necessary now to solve the problem of employability of university vocational education. The eastern region of China is a developed area and has the hardware environment to realize this kind of education. This article made a research on 300 students of 18 majors in 11 universities of Yangtze River Delta Region under this background, established an employability model, and verified the efficiency of this model by applying the principal factor analysis and regression analysis. This article applies the employability model to explore the influence route and cultivation way of employability, expecting to provide theoretical basis for employability cultivation of Chinese university students.

The true principal factors for the diurnal water-demand pattern of urban water are often difficult to identify using traditional rough-set algorithms because the demand pattern is usually affected by many factors that are uncertain and hard to quantify. An improved attribute-reduction algorithm based on the cumulative weighting coefficient was proposed to solve this problem. The weighting coefficient was determined by the result of the variable precision rough-set algorithm. To discuss the consecutive curves with mathematical tools, an improved fuzzy c-mean (FCM) algorithm was proposed to discretize the diurnal water-demand pattern spatially. The proposed algorithms were then used to analyze the principal factors of the diurnal water-demand pattern in the city of Hangzhou, China. The results show that the improved attribute-reduction algorithm is capable of distinguishing the false attribute from the dynamic reduction sets, and the fuzzy c-mean algorithm is an effective and feasible method of solving the cluster problem for the consecutive curves. The principal factors affecting the diurnal water-demand pattern in Hangzhou are maximum air temperature, minimum air temperature, and weekday or weekend. [ABSTRACT FROM AUTHOR]

Canonical correlation analysis (CCA), principal component analysis (PCA), and principal factor analysis (PFA) have been adopted to provide ease of understanding: interpretation of a large complex data set in the Gorganrud River monitoring networks, evaluation of the temporal and spatial variations of water quality, and finally identification of monitoring stations and parameters which are most important in assessing annual variations of water quality in the river. In accomplishing the research, 11 surface water quality data related to both of physical and chemical parameters have been collected from seven monitoring stations from 1996 to 2002. In general, our results from CCA method indicated strong relationship between physical and chemical parameters in the Gorganrud River. In addition, analyzing data through the PCA and PFA techniques revealed that all monitoring stations are important in explaining the annual variation of data set. From the point of view of the degree of importance of parameters contributing to water quality variations, further investigations by running two scenarios (rotated factor correlation coefficient value equal to 0.95 and 0.90 for the first and second scenarios, respectively) showed that the important parameters in one season may not be important for another season. For example, unlike in summer, water temperature, total suspended solids, total phosphorous, and nitrate parameters were important, electrical conductivity, and turbidity parameters had been realized as important parameters in spring through the first scenario. [ABSTRACT FROM AUTHOR]

The Messolonghi lagoon complex in Western Greece receives agricultural and domestic effluents both from point and diffused sources. Surface sediments were analyzed for grain size, organic carbon, total nitrogen, total sulfur, major and minor elements, aiming at the identification of geochemical relationships between all variables. Enrichment factors and the modified degree of contamination methods were applied to assess potential heavy metal enrichment related to human activities. Sediment texture was highly variable, with muddy sediments prevailing. In the central sector of the Messolonghi lagoon, organic carbon contents were high. Principal factor analysis revealed the following main groups of variables with common geochemical behavior: (1) terrigenous aluminosilicates (2) organic matter, (3) biogenic carbonates, (4) mineral quartz-aluminosilicates, and (5) Mn-oxides. Enrichment factors estimated for V, Cr, Mn, Co, Ni, Cu, Zn, and Pb using local pre-industrial sediment showed that all metals exhibit almost natural background levels, except for Pb, which was found to be slightly elevated (legacy of leaded fuel). Estimation of contamination factors concluded in similar results, whereas the overall modified degree of contamination was at the lowest level, therefore suggesting that this transitional water body has not been affected by anthropogenic activities. The data set may be considered as a baseline for future monitoring projects according to EU policy. [ABSTRACT FROM AUTHOR]

Abstract: Background and aims: Dietary habits have been associated with cardiovascular disease (CVD) risk factors. This study aimed at evaluating the association of non-predefined dietary patterns with CVD risk profile and C-reactive protein (CRP). Methods and results: We analyzed 7646 healthy subjects from the Moli-sani project, an on-going cross-sectional cohort study of men and women aged ≥35, randomly recruited from a general Italian population. The Italian EPIC food frequency questionnaire was used. Food patterns were generated using principal factor analysis (PFA) and reduced rank regression (RRR). Three dietary patterns were identified by PFA. The “Olive Oil and Vegetables” pattern, characterized by high intake of olive oil, vegetables, legumes, soups, fruits and fish, was associated with relatively lower values of glucose, lipids, CRP, blood pressure and individual global CVD risk score. The “Pasta and Meat” pattern, characterized by high intake of pasta, tomato sauce, red meat, animal fats and alcohol, was positively associated with glucose, lipids, CRP and CVD risk score. The “Eggs and Sweets” pattern, characterized by positive loadings of eggs, processed meat, margarines, butter, sugar and sweets, was associated with high values of CRP. The first RRR pattern was similar to the “Pasta and Meat” pattern both in composition and association with CVD risk profile. Conclusions: In a large healthy Italian population, non-predefined dietary patterns including foods considered to be rather unhealthy, were associated with higher levels of cardiovascular risk factors, CRP and individual global CVD risk, whereas a “prudent–healthy” pattern was associated with lower levels. [Copyright &y& Elsevier]

The presence of two morphotypes of Arctic charr Salvelinus alpinus was confirmed via morphological variation and otolith strontium (Sr) within three open-lake systems of southern Baffin Island, Nunavut, Canada: Qinngu (LH001), Iqalugaarjuit Lake (PG082) and Qasigiat (PG015). Analysis of otolith Sr indicates that a component of each S. alpinus population within lakes LH001 and PG082 is migratory (large–maturing S. alpinus), whereas another component is lake-resident (small–maturing S. alpinus). Alternatively, small and large maturing S. alpinus may both inhabit tidal habitats during their lifetime in lake PG015. Three morphological characters were identified by principal factor analysis (PFA) as characters that were different between maturity groups for all lakes studied: eye diameter, pectoral fin length and pelvic fin length. As well, upper jaw length (LH001 and PG082) and fork depth (PG015) were identified in PFA as traits that differed between morphs. Univariate tests of morphological characters identified by PFA demonstrated maturity group differences with the exception of eye diameter in Lake PG015 and upper jaw length and pelvic fin length in lake LH001. No difference was found in the MANOVA test of upper and lower gill raker number between small–maturing and undeveloped fish within all lakes studied. Clear morphological variation observed between small–maturing and undeveloped fish in all three lakes of the study suggests ecological niche separation between morphotypes. This is the first documented case of lake-resident S. alpinus use of the tidal habitat in the presence of a migratory large–maturing morphotype. [ABSTRACT FROM AUTHOR]

Pollutant washoff loads from an expressway in Shanghai, China were investigated during a 1-year study program. The median washoff load during an rainfall event for total solids, chemical oxygen demand, total nitrogen and total phosphorus were 4,389.8, 2,123.0, 47.6 and 1.6 mg/m2, respectively. Through principal factor analysis, three factors that represent the influence of pollutant source availability, rainfall volume and rainfall intensity account for 89% variance of the monitoring data. The result of multiple regression analysis reveals that antecedent dry period significantly influences the washoff load, while peak rainfall intensity and runoff volume may have some influence, which correlates well with the principal factor analysis results. [ABSTRACT FROM AUTHOR]

This article reports the results of a study of consumer attitudes to brand loyalty. An empirical investigation is based on the sample of 872 individuals aged from sixteen years and located in the cities with upper and lower ten thousands inhabitants in sports shoes market. First, the creation of brand loyalty is defined, followed by an overview of theoretical works in the field. Finally, the study itself is presented. The study concludes that there is a significant dependence on the degree of loyalty determined by the environment in which consumers live and expenses connected with a specific sport. It also reveals the dimensions of brand selection that is influenced by a reflection of self-image of consumer in spite of the increase of price. [ABSTRACT FROM AUTHOR]

Abstract: Six major (Na, Al, K, Ca, Ti, Fe) and 28 trace (Sc, Cr, V, Mn, Co, Zn, Cu, As, Br, Sr, Rb, Zr, Mo, Sn, Sb, Ba, Cs, La, Ce, Nd, Eu, Sm, Tb, Hf, Ta, W, Th and U) elements were determined by epithermal neutron activation analysis (ENAA) in nine Meridional Carpathian and Macin Mountains samples of igneous and metamorphic rocks. Correlation and principal factor analysis were used to interpret data while natural radionuclides radiometry shows a good correlation with ENAA results. [Copyright &y& Elsevier]

• Methodology to analyze high-resolution spatial and temporal weather data to establish pavement-specific climate regions. • Weather, comprising 24 meteorological variables, is preprocessed, and transformed into county-level climate attributes. • Correlation analysis and principal factor analysis is performed to identify and remove redundancy in climate attributes. • Unsupervised agglomerative hierarchical cluster analysis is performed to identify seven pavement-specific climate regions. • A supervised decision tree classifier is used to separate predictor climate attributes into response climate regions. Incorporating climate impact into pavement distress initiation and propagating modeling and management, suffers from the extent of weather data available, and ability to process into a useable form in a time and computationally inexpensive manner. Analysts have overcome this challenge by partitioning large geographic areas into regions with similarities in climate impact on pavement distress initiation and propagation. Historically, large geographical areas have been empirically carved out into smaller regions with similar climates by experienced geographers and climatologists. The availability of extensive weather data, comprising hundreds of meteorological variables reported with a high spatial and temporal resolution, has made the use of empirical methodologies for establishing climate regions obsolete and untenable. This paper presents a new machine learning methodology for (1) establishing pavement-specific climate regions and (2) accurately separating predictor climate attributes into climate regions for use in the pavement management and high-level analyses. The new methodology comprises stochastic and machine learning algorithms: correlation analysis, principal factor analysis, cluster analysis, and decision tree. It entails preprocessing and transforming of raw climate data into uncorrelated scores that represent the majority of variance in original climate data, performing cluster analysis to partition large geographical areas into regions with similar climatic effects on pavement distress initiation and propagation, and development and extraction of if-then-else rules from a DT classifier capable of separating predictor climate attribute into identified climate regions. The methodology is tested using National Aeronautics and Space Administration (NASA) Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) weather data. The outcome is the identification and prediction of seven distinct pavement-specific climate regions with 88.1 percent accuracy. This methodology assures an evidence-based definition of climate regions based on the similarity of climatic effect on pavement distress initiation and propagation and improves historical empirical partitioning methods. Methodology outcomes inform pavement managers on (1) the positive and negative impact of local climate on pavement distress initiation and propagation and (2) measures required to mitigate adverse impacts of climate. [ABSTRACT FROM AUTHOR]