Does independent component analysis play a role in unmixing hyperspectral data?

Independent component analysis (ICA) has recently been proposed as a tool to unmix hyperspectral data. ICA is founded on two assumptions: 1) the observed spectrum vector is a linear mixture of the constituent spectra (endmember spectra) weighted by the correspondent abundance fractions (sources); 2)sources are statistically independent. Independent factor analysis (IFA) extends ICA to linear mixtures of independent sources immersed in noise. Concerning hyperspectral data, the first assumption is valid whenever the multiple scattering among the distinct constituent substances (endmembers) is negligible, and the surface is partitioned according to the fractional abundances. The second assumption, however, is violated, since the sum of abundance fractions associated to each pixel is constant due to physical constraints in the data acquisition process. Thus, sources cannot be statistically independent, this compromising the performance of ICA/IFA algorithms in hyperspectral unmixing. This paper studies the impact of hyperspectral source statistical dependence on ICA and IFA performances. We conclude that the accuracy of these methods tends to improve with the increase of the signature variability, of the number of endmembers, and of the signal-to-noise ratio. In any case, there are always endmembers incorrectly unmixed. We arrive to this conclusion by minimizing the mutual information of simulated and real hyperspectral mixtures. The computation of mutual information is based on fitting mixtures of Gaussians to the observed data. A method to sort ICA and IFA estimates in terms of the likelihood of being correctly unmixed is proposed.

Cross-cultural validity of the demand-control questionnaire: Swedish and Brazilian workers

OBJECTIVE To evaluate the cross-cultural validity of the Demand-Control Questionnaire, comparing the original Swedish questionnaire with the Brazilian version. METHODS We compared data from 362 Swedish and 399 Brazilian health workers. Confirmatory and exploratory factor analyses were performed to test structural validity, using the robust weighted least squares mean and variance-adjusted (WLSMV) estimator. Construct validity, using hypotheses testing, was evaluated through the inspection of the mean score distribution of the scale dimensions according to sociodemographic and social support at work variables. RESULTS The confirmatory and exploratory factor analyses supported the instrument in three dimensions (for Swedish and Brazilians): psychological demands, skill discretion and decision authority. The best-fit model was achieved by including an error correlation between work fast and work intensely (psychological demands) and removing the item repetitive work (skill discretion). Hypotheses testing showed that workers with university degree had higher scores on skill discretion and decision authority and those with high levels of Social Support at Work had lower scores on psychological demands and higher scores on decision authority. CONCLUSIONS The results supported the equivalent dimensional structures across the two culturally different work contexts. Skill discretion and decision authority formed two distinct dimensions and the item repetitive work should be removed.

Contextual and individual indicators associated with the presence of teeth in adults

OBJECTIVE The objective of this study was to analyze whether socioeconomic conditions and the period of availability of fluoridated water are associated with the number of teeth present.METHODSThis cross-sectional study analyzed data from 1,720 adults between 20 and 59 years of age who resided in Florianópolis, SC, Southern Brazil, in 2009. The outcome investigated was the self-reported number of teeth present. The individual independent variables included gender, age range, skin color, number of years of schooling, and per capita household income. The duration of residence was used as a control variable. The contextual exposures included the period of availability of fluoridated water to the households and the socioeconomic variable for the census tracts, which was created from factor analysis of the tract’s mean income, education level, and percentage of households with treated water. Multilevel logistic regression was performed and inter-level interactions were tested.RESULTS Residents in intermediate and poorer areas and those with fluoridated water available for less time exhibited the presence of fewer teeth compared with those in better socioeconomic conditions and who had fluoridated water available for a longer period (OR = 1.02; 95%CI 1.01;1.02). There was an association between the period of availability of fluoridated water, per capita household income and number of years of education. The proportion of individuals in the poorer and less-educated stratum, which had fewer teeth present, was higher in regions where fluoridated water had been available for less time.CONCLUSIONS Poor socioeconomic conditions and a shorter period of availability of fluoridated water were associated with the probability of having fewer teeth in adulthood. Public policies aimed at reducing socioeconomic inequalities and increasing access to health services such as fluoridation of the water supply may help to reduce tooth loss in the future.

Unmixing hyperspectral data: independent and dependent component analysis

The development of high spatial resolution airborne and spaceborne sensors has improved the capability of ground-based data collection in the fields of agriculture, geography, geology, mineral identification, detection [2, 3], and classification [4–8]. The signal read by the sensor from a given spatial element of resolution and at a given spectral band is a mixing of components originated by the constituent substances, termed endmembers, located at that element of resolution. This chapter addresses hyperspectral unmixing, which is the decomposition of the pixel spectra into a collection of constituent spectra, or spectral signatures, and their corresponding fractional abundances indicating the proportion of each endmember present in the pixel [9, 10]. Depending on the mixing scales at each pixel, the observed mixture is either linear or nonlinear [11, 12]. The linear mixing model holds when the mixing scale is macroscopic [13]. The nonlinear model holds when the mixing scale is microscopic (i.e., intimate mixtures) [14, 15]. The linear model assumes negligible interaction among distinct endmembers [16, 17]. The nonlinear model assumes that incident solar radiation is scattered by the scene through multiple bounces involving several endmembers [18]. Under the linear mixing model and assuming that the number of endmembers and their spectral signatures are known, hyperspectral unmixing is a linear problem, which can be addressed, for example, under the maximum likelihood setup [19], the constrained least-squares approach [20], the spectral signature matching [21], the spectral angle mapper [22], and the subspace projection methods [20, 23, 24]. Orthogonal subspace projection [23] reduces the data dimensionality, suppresses undesired spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel onto a subspace that is orthogonal to the undesired signatures. As shown in Settle [19], the orthogonal subspace projection technique is equivalent to the maximum likelihood estimator. This projection technique was extended by three unconstrained least-squares approaches [24] (signature space orthogonal projection, oblique subspace projection, target signature space orthogonal projection). Other works using maximum a posteriori probability (MAP) framework [25] and projection pursuit [26, 27] have also been applied to hyperspectral data. In most cases the number of endmembers and their signatures are not known. Independent component analysis (ICA) is an unsupervised source separation process that has been applied with success to blind source separation, to feature extraction, and to unsupervised recognition [28, 29]. ICA consists in finding a linear decomposition of observed data yielding statistically independent components. Given that hyperspectral data are, in given circumstances, linear mixtures, ICA comes to mind as a possible tool to unmix this class of data. In fact, the application of ICA to hyperspectral data has been proposed in reference 30, where endmember signatures are treated as sources and the mixing matrix is composed by the abundance fractions, and in references 9, 25, and 31–38, where sources are the abundance fractions of each endmember. In the first approach, we face two problems: (1) The number of samples are limited to the number of channels and (2) the process of pixel selection, playing the role of mixed sources, is not straightforward. In the second approach, ICA is based on the assumption of mutually independent sources, which is not the case of hyperspectral data, since the sum of the abundance fractions is constant, implying dependence among abundances. This dependence compromises ICA applicability to hyperspectral images. In addition, hyperspectral data are immersed in noise, which degrades the ICA performance. IFA [39] was introduced as a method for recovering independent hidden sources from their observed noisy mixtures. IFA implements two steps. First, source densities and noise covariance are estimated from the observed data by maximum likelihood. Second, sources are reconstructed by an optimal nonlinear estimator. Although IFA is a well-suited technique to unmix independent sources under noisy observations, the dependence among abundance fractions in hyperspectral imagery compromises, as in the ICA case, the IFA performance. Considering the linear mixing model, hyperspectral observations are in a simplex whose vertices correspond to the endmembers. Several approaches [40–43] have exploited this geometric feature of hyperspectral mixtures [42]. Minimum volume transform (MVT) algorithm [43] determines the simplex of minimum volume containing the data. The MVT-type approaches are complex from the computational point of view. Usually, these algorithms first find the convex hull defined by the observed data and then fit a minimum volume simplex to it. Aiming at a lower computational complexity, some algorithms such as the vertex component analysis (VCA) [44], the pixel purity index (PPI) [42], and the N-FINDR [45] still find the minimum volume simplex containing the data cloud, but they assume the presence in the data of at least one pure pixel of each endmember. This is a strong requisite that may not hold in some data sets. In any case, these algorithms find the set of most pure pixels in the data. Hyperspectral sensors collects spatial images over many narrow contiguous bands, yielding large amounts of data. For this reason, very often, the processing of hyperspectral data, included unmixing, is preceded by a dimensionality reduction step to reduce computational complexity and to improve the signal-to-noise ratio (SNR). Principal component analysis (PCA) [46], maximum noise fraction (MNF) [47], and singular value decomposition (SVD) [48] are three well-known projection techniques widely used in remote sensing in general and in unmixing in particular. The newly introduced method [49] exploits the structure of hyperspectral mixtures, namely the fact that spectral vectors are nonnegative. The computational complexity associated with these techniques is an obstacle to real-time implementations. To overcome this problem, band selection [50] and non-statistical [51] algorithms have been introduced. This chapter addresses hyperspectral data source dependence and its impact on ICA and IFA performances. The study consider simulated and real data and is based on mutual information minimization. Hyperspectral observations are described by a generative model. This model takes into account the degradation mechanisms normally found in hyperspectral applications—namely, signature variability [52–54], abundance constraints, topography modulation, and system noise. The computation of mutual information is based on fitting mixtures of Gaussians (MOG) to data. The MOG parameters (number of components, means, covariances, and weights) are inferred using the minimum description length (MDL) based algorithm [55]. We study the behavior of the mutual information as a function of the unmixing matrix. The conclusion is that the unmixing matrix minimizing the mutual information might be very far from the true one. Nevertheless, some abundance fractions might be well separated, mainly in the presence of strong signature variability, a large number of endmembers, and high SNR. We end this chapter by sketching a new methodology to blindly unmix hyperspectral data, where abundance fractions are modeled as a mixture of Dirichlet sources. This model enforces positivity and constant sum sources (full additivity) constraints. The mixing matrix is inferred by an expectation-maximization (EM)-type algorithm. This approach is in the vein of references 39 and 56, replacing independent sources represented by MOG with mixture of Dirichlet sources. Compared with the geometric-based approaches, the advantage of this model is that there is no need to have pure pixels in the observations. The chapter is organized as follows. Section 6.2 presents a spectral radiance model and formulates the spectral unmixing as a linear problem accounting for abundance constraints, signature variability, topography modulation, and system noise. Section 6.3 presents a brief resume of ICA and IFA algorithms. Section 6.4 illustrates the performance of IFA and of some well-known ICA algorithms with experimental data. Section 6.5 studies the ICA and IFA limitations in unmixing hyperspectral data. Section 6.6 presents results of ICA based on real data. Section 6.7 describes the new blind unmixing scheme and some illustrative examples. Section 6.8 concludes with some remarks.

High-Content Analysis of Breast Cancer Using Single-Cell Deep Transfer Learning

High-content analysis has revolutionized cancer drug discovery by identifying substances that alter the phenotype of a cell, which prevents tumor growth and metastasis. The high-resolution biofluorescence images from assays allow precise quantitative measures enabling the distinction of small molecules of a host cell from a tumor. In this work, we are particularly interested in the application of deep neural networks (DNNs), a cutting-edge machine learning method, to the classification of compounds in chemical mechanisms of action (MOAs). Compound classification has been performed using image-based profiling methods sometimes combined with feature reduction methods such as principal component analysis or factor analysis. In this article, we map the input features of each cell to a particular MOA class without using any treatment-level profiles or feature reduction methods. To the best of our knowledge, this is the first application of DNN in this domain, leveraging single-cell information. Furthermore, we use deep transfer learning (DTL) to alleviate the intensive and computational demanding effort of searching the huge parameter's space of a DNN. Results show that using this approach, we obtain a 30% speedup and a 2% accuracy improvement.

Cluster analysis and segmentation of Global M&A transactions

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Finance from the NOVA – School of Business and Economics

Quantitative analysis of image factors in a cultural heritage tourist destination

The tourism consumer’s purchase decision process is, to a great extent, conditioned by the image the tourist has of the different destinations that make up his or her choice set. In a highly competitive international tourist market, those responsible for destinations’ promotion and development policies seek differentiation strategies so that they may position the destinations in the most suitable market segments for their product in order to improve their attractiveness to visitors and increase or consolidate the economic benefits that tourism activity generates in their territory. To this end, the main objective we set ourselves in this paper is the empirical analysis of the factors that determine the image formation of Tarragona city as a cultural heritage destination. Without a doubt, UNESCO’s declaration of Tarragona’s artistic and monumental legacies as World Heritage site in the year 2000 meant important international recognition of the quality of the cultural and patrimonial elements offered by the city to the visitors who choose it as a tourist destination. It also represents a strategic opportunity to boost the city’s promotion of tourism and its consolidation as a unique destination given its cultural and patrimonial characteristics. Our work is based on the use of structured and unstructured techniques to identify the factors that determine Tarragona’s tourist destination image and that have a decisive influence on visitors’ process of choice of destination. In addition to being able to ascertain Tarragona’s global tourist image, we consider that the heterogeneity of its visitors requires a more detailed study that enables us to segment visitor typology. We consider that the information provided by these results may prove of great interest to those responsible for local tourism policy, both when designing products and when promoting the destination.

The Identity Style Inventory (ISI-3) and the Utrecht-Management of Identity Commitments Scale (U-MICS): Factor structure, reliability, and convergent validity in French-speaking college students

The purpose of this study was to evaluate the factor structure and the reliability of the French versions of the Identity Style Inventory (ISI-3) and the Utrecht-Management of Identity Commitments Scale (U-MICS) in a sample of college students (N = 457, 18 to 25 years old). Confirmatory factor analyses confirmed the hypothesized three-factor solution of the ISI-3 identity styles (i.e. informational, normative, and diffuse-avoidant styles), the one-factor solution of the ISI-3 identity commitment, and the three-factor structure of the U-MICS (i.e. commitment, in-depth exploration, and reconsideration of commitment). Additionally, theoretically consistent and meaningful associations among the ISI-3, U-MICS, and Ego Identity Process Questionnaire (EIPQ) confirmed convergent validity. Overall, the results of the present study indicate that the French versions of the ISI-3 and UMICS are useful instruments for assessing identity styles and processes, and provide additional support to the cross-cultural validity of these tools.

Validación de la versión española del cuestionario sobre la práctica basada en la evidencia en enfermería

Background: The lack of adequate instruments prevents the possibility of assessing the competence of health care staff in evidence-based decision making and further, the identification of areas for improvement with tailored strategies. The aim of this study is to report about the validation process in the Spanish context of the Evidence-Based Practice Questionnaire (EBPQ) from Upton y Upton. Methods: A multicentre, cross-sectional, descriptive psychometric validation study was carried out. For cultural adaptation, a bidirectional translation was developed, accordingly to usual standards. The measuring model from the questionnaire was undergone to contrast, reproducing the original structure by Exploratory Factorial Analysis (EFA) and Confirmatory Factorial Analysis (CFA), including the reliability of factors. Results: Both EFA (57.545% of total variance explained) and CFA (chi2=2359,9555; gl=252; p<0.0001; RMSEA=0,1844; SRMR=0,1081), detected problems with items 7, 16, 22, 23 and 24, regarding to the original trifactorial version of EBPQ. After deleting some questions, a reduced version containing 19 items obtained an adequate factorial structure (62.29% of total variance explained), but the CFA did not fit well. Nevertheless, it was significantly better than the original version (chi2=673.1261; gl=149; p<0.0001; RMSEA=0.1196; SRMR=0.0648). Conclusions: The trifactorial model obtained good empiric evidence and could be used in our context, but the results invite to advance with further refinements into the factor “attitude”, testing it in more contexts and with more diverse professional profiles.

Undertreatment of acute pain (oligoanalgesia) and medical practice variation in prehospital analgesia of adult trauma patients: a 10 yr retrospective study.

BACKGROUND: Prehospital oligoanalgesia is prevalent among trauma victims, even when the emergency medical services team includes a physician. We investigated if not only patients' characteristics but physicians' practice variations contributed to prehospital oligoanalgesia. METHODS: Patient records of conscious adult trauma victims transported by our air rescue helicopter service over 10 yr were reviewed retrospectively. Oligoanalgesia was defined as a numeric rating scale (NRS) >3 at hospital admission. Multilevel logistic regression analysis was used to predict oligoanalgesia, accounting first for patient case-mix, and then physician-level clustering. The intraclass correlation was expressed as the median odds ratio (MOR). RESULTS: A total of 1202 patients and 77 physicians were included in the study. NRS at the scene was 6.9 (1.9). The prevalence of oligoanalgesia was 43%. Physicians had a median of 5.7 yr (inter-quartile range: 4.2-7.5) of post-graduate training and 27% were female. In our multilevel analysis, significant predictors of oligoanalgesia were: no analgesia [odds ratio (OR) 8.8], National Advisory Committee for Aeronautics V on site (OR 4.4), NRS on site (OR 1.5 per additional NRS unit >4), female physician (OR 2.0), and years of post-graduate experience [>4.0 to ≤5.0 (OR 1.3), >3.0 to ≤4.0 (OR 1.6), >2.0 to ≤3.0 (OR 2.6), and ≤2.0 yr (OR 16.7)]. The MOR was 2.6, and was statistically significant. CONCLUSIONS: Physicians' practice variations contributed to oligoanalgesia, a factor often overlooked in analyses of prehospital pain management. Further exploration of the sources of these variations may provide innovative targets for quality improvement programmes to achieve consistent pain relief for trauma victims.

Measuring spiritual well-being in older rehabilitation patients using the facit-sp

Objectives: To determine psychometric properties of the Functional Assessment of Chronic Illness Therapy-Spiritual well-being (FACIT-Sp) in a cohort of older patients admitted to rehabilitation. Method: Patients aged 65+, with MMSE score>19, admitted consecutively over 6 months in post-acute rehabilitation were enrolled (N=144, 81.27.2 years, 68.8% women). Data on medical, functional, and mental status were systematically collected upon admission. Spiritual well-being was assessed within 3 days after admission using the FACIT-Sp (12 items, score from 0 to 48, high spiritual well-being defined as a score ≥36) and the single question "Are you at peace?" (score from 0 to 10 on a visual analog scale). Results: FACIT-Sp scores ranged from 7 to 44(mean=29.87.7). Overall, 24.3% of the patients had high spiritual well-being. Internal consistency was optimal for total score (Cronbach's alpha =0.85). Itemto- total correlations were all significant, ranging from 0.28 to 0.73. A confirmatory factorial analysis yielded a 2-factor solution, consistent with Meaning and Faith proposed subscales and accounting for 52% of the variance . FACIT-Sp total score correlated positively with the question «Are you at peace ?» (Spearman's rho 0.49, P<.001) and negatively with depressive symptoms (Spearman's rho -0.37, P<.001), confirming FACIT-Sp validity. Compared to the others, patients with higher spiritual well-being had significantly better function at admission (Barthel's score 74.817.1 vs 68.514.1, P=.014). Conclusions: The FACIT-Sp remains a reliable and valid measure to assess spiritual well-being in older patients undergoing rehabilitation. The relatively low proportion of patients with high spiritual well-being deserves further investigation.

Factor structure of the SOCRATES questionnaire in hospitalized medical patients.

The Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES), a 19-item instrument developed to assess readiness to change alcohol use among individuals presenting for specialized alcohol treatment, has been used in various populations and settings. Its factor structure and concurrent validity has been described for specialized alcohol treatment settings and primary care. The purpose of this study was to determine the factor structure and concurrent validity of the SOCRATES among medical inpatients with unhealthy alcohol use not seeking help for specialized alcohol treatment. The subjects were 337 medical inpatients with unhealthy alcohol use, identified during their hospital stay. Most of them had alcohol dependence (76%). We performed an Alpha Factor Analysis (AFA) and Principal Component Analysis (PCA) of the 19 SOCRATES items, and forced 3 factors and 2 components, in order to replicate findings from Miller and Tonigan (Miller, W. R., & Tonigan, J. S., (1996). Assessing drinkers' motivations for change: The Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES). Psychology of Addictive Behavior, 10, 81-89.) and Maisto et al. (Maisto, S. A., Conigliaro, J., McNeil, M., Kraemer, K., O'Connor, M., & Kelley, M. E., (1999). Factor structure of the SOCRATES in a sample of primary care patients. Addictive Behavior, 24(6), 879-892.). Our analysis supported the view that the 2 component solution proposed by Maisto et al. (Maisto, S.A., Conigliaro, J., McNeil, M., Kraemer, K., O'Connor, M., & Kelley, M.E., (1999). Factor structure of the SOCRATES in a sample of primary care patients. Addictive Behavior, 24(6), 879-892.) is more appropriate for our data than the 3 factor solution proposed by Miller and Tonigan (Miller, W. R., & Tonigan, J. S., (1996). Assessing drinkers' motivations for change: The Stages of Change Readiness and Treatment Eagerness Scale (SOCRATES). Psychology of Addictive Behavior, 10, 81-89.). The first component measured Perception of Problems and was more strongly correlated with severity of alcohol-related consequences, presence of alcohol dependence, and alcohol consumption levels (average number of drinks per day and total number of binge drinking days over the past 30 days) compared to the second component measuring Taking Action. Our findings support the view that the SOCRATES is comprised of two important readiness constructs in general medical patients identified by screening.

Service Quality Analysis in the Five Stars Resort Hotels of Cape Verde

The objective of this study was the identification of the attributes and dimensions of service quality affecting the service performance of the five stars resort hotels located in the Cape Verde Islands. The reason boosting the initiative to do this research was the paramount role of the resort hotels in the development of the travel and tourism sector in Cape Verde, and the impact that today this sector has had in the economy of that country. The research opens with a literature review on the service quality theory in the hotel industry, starting from the middle of the 1980s with the classic model of service quality and SERVQUAL instrument to the analysis of recent models of service quality measurement in the hotel industry, as it is an example the scale of items developed in 2003 in the Lodging Quality Index (LQI). Furthermore, the study elaborates an analysis on the importance of the travel and tourism activities in the Cape Verde Islands, and it evidences the enormous importance of those activities in the performance of the Cape Verdean hotel industry. In sequence the study analyzes in details the hotel industry of Cape Verde and it identifies the market size of the five stars resort hotels and their current operators in that market. Moreover, the research develops with an online questionnaire elaborated and sent through the platforms of travel websites and communities to the guests whom have experienced the service of the five stars resort hotels located in the Cape Verde Islands. The scope of the questionnaire was to assess the attributes and dimensions of service quality in the five stars resort hotels of Cape Verde. The results of the questionnaire were in sequence analyzed through descriptive and applied statistics, using Microsoft Excel and the Statistical Package for Social Science (SPSS). Content validity analysis, factor analysis, and reliability analysis of the factors were made to purify an initial scale of 47 items of service quality. An instrument with three dimensions covering twenty four attributes of service quality assessment in the five stars resort hotels of Cape Verde was finally created. The three dimensions found were: staff competence; food and entertainment; and physical facilities. This study on the service in the five stars resort hotels of Cape Verde ends with brief comments on the status of service quality according to the identified dimensions and their attributes. In the conclusion, the study summarizes the whole work and gives some directions for future research.

Faktorenanalytische Untersuchung und Rehabilitätsbestimmung der statischen und dynamischen Pupillometrie bei Normalpersonen und psychopathologischen Gruppen [Factor analytic study and determination of rehabilitation of static and dynamic pupillometry in normal persons and psychopathologic groups]

Variables measured during static and dynamic pupillometry were factor-analyzed. Following factors were obtained regardless whether investigations were carried out in normals or in psychiatric patients: A static factor, a dynamic factor, a stimulus-specific factor and a restitution-dependent factor. Evaluation of reliability in normals demonstrated a high reliability for the static variables of pupillometry.

Irrigation with domestic wastewater: a multivariate analysis of main soil changes

Irrigation with treated domestic sewage wastewater (TSE) is an agricultural practice to reduce water requirements of agroecossystems and the nutrient load impact on freshwaters, but adverse effects on soil chemical (salinization, sodification, etc.) and soil physical properties (alteration in soil porosity and hydraulic conductivity, etc.) have been reported. This study aimed to define some relationships among these changes in an Oxisol using multivariate analysis. Corn (Zea mays L.) and sunflower (Helianthus annuus L.) were grown for two years, irrigated with TSE. The following soil properties were determined: Ca2+; Mg2+; Na+; K+ and H + Al contents, cationic exchangeable capacity (CEC), sum of bases (SB), base saturation (V), texture (sand, silt and clay), macro-, micro-, and cryptoporosity (V MA, V MI and V CRI), water content at soil saturation (θS) and at field capacity (θFC), residual water content (θR), soil bulk density (d s), water dispersed clay (WDC) and saturated hydraulic conductivity (K SAT). Factor analysis revealed the following six principal factors: Fine Porosity (composed of Na+; K+; WDC, θR, θRFC, and V CRI); Large Porosity (θS, d s, V MA, Vs); Soil CEC (Ca2+; Mg2+; CEC, SB, V); Soil Acidity (H + Al); and Soil Texture (factors 5 and 6). A dual pore structure appears clearly to the factors 1 and 2, with an apparent relationship between fine porosity and the monovalent cations Na+ and K+. The irrigation (with potable sodic tap water or sewage wastewater) only had a significant effect on Fine Porosity and Large Porosity factors, while factors 3 and 4 (Soil CEC and Soil Acidity) were correlated with soil depth. The main conclusion was a shift in pore distribution (large to fine pores) during irrigation with TSE, which induces an increase of water storage and reduces the capacity of drainage of salts.