Retrieval of grassland biophysical parameters using multitemporal optical and radar satellite data

The amount and quality of available biomass is a key factor for the sustainable livestock industry and agricultural management related decision making. Globally 31.5% of land cover is grassland while 80% of Ireland’s agricultural land is grassland. In Ireland, grasslands are intensively managed and provide the cheapest feed source for animals. This dissertation presents a detailed state of the art review of satellite remote sensing of grasslands, and the potential application of optical (Moderate–resolution Imaging Spectroradiometer (MODIS)) and radar (TerraSAR-X) time series imagery to estimate the grassland biomass at two study sites (Moorepark and Grange) in the Republic of Ireland using both statistical and state of the art machine learning algorithms. High quality weather data available from the on-site weather station was also used to calculate the Growing Degree Days (GDD) for Grange to determine the impact of ancillary data on biomass estimation. In situ and satellite data covering 12 years for the Moorepark and 6 years for the Grange study sites were used to predict grassland biomass using multiple linear regression, Neuro Fuzzy Inference Systems (ANFIS) models. The results demonstrate that a dense (8-day composite) MODIS image time series, along with high quality in situ data, can be used to retrieve grassland biomass with high performance (R2 = 0:86; p < 0:05, RMSE = 11.07 for Moorepark). The model for Grange was modified to evaluate the synergistic use of vegetation indices derived from remote sensing time series and accumulated GDD information. As GDD is strongly linked to the plant development, or phonological stage, an improvement in biomass estimation would be expected. It was observed that using the ANFIS model the biomass estimation accuracy increased from R2 = 0:76 (p < 0:05) to R2 = 0:81 (p < 0:05) and the root mean square error was reduced by 2.72%. The work on the application of optical remote sensing was further developed using a TerraSAR-X Staring Spotlight mode time series over the Moorepark study site to explore the extent to which very high resolution Synthetic Aperture Radar (SAR) data of interferometrically coherent paddocks can be exploited to retrieve grassland biophysical parameters. After filtering out the non-coherent plots it is demonstrated that interferometric coherence can be used to retrieve grassland biophysical parameters (i. e., height, biomass), and that it is possible to detect changes due to the grass growth, and grazing and mowing events, when the temporal baseline is short (11 days). However, it not possible to automatically uniquely identify the cause of these changes based only on the SAR backscatter and coherence, due to the ambiguity caused by tall grass laid down due to the wind. Overall, the work presented in this dissertation has demonstrated the potential of dense remote sensing and weather data time series to predict grassland biomass using machine-learning algorithms, where high quality ground data were used for training. At present a major limitation for national scale biomass retrieval is the lack of spatial and temporal ground samples, which can be partially resolved by minor modifications in the existing PastureBaseIreland database by adding the location and extent ofeach grassland paddock in the database. As far as remote sensing data requirements are concerned, MODIS is useful for large scale evaluation but due to its coarse resolution it is not possible to detect the variations within the fields and between the fields at the farm scale. However, this issue will be resolved in terms of spatial resolution by the Sentinel-2 mission, and when both satellites (Sentinel-2A and Sentinel-2B) are operational the revisit time will reduce to 5 days, which together with Landsat-8, should enable sufficient cloud-free data for operational biomass estimation at a national scale. The Synthetic Aperture Radar Interferometry (InSAR) approach is feasible if there are enough coherent interferometric pairs available, however this is difficult to achieve due to the temporal decorrelation of the signal. For repeat-pass InSAR over a vegetated area even an 11 days temporal baseline is too large. In order to achieve better coherence a very high resolution is required at the cost of spatial coverage, which limits its scope for use in an operational context at a national scale. Future InSAR missions with pair acquisition in Tandem mode will minimize the temporal decorrelation over vegetation areas for more focused studies. The proposed approach complements the current paradigm of Big Data in Earth Observation, and illustrates the feasibility of integrating data from multiple sources. In future, this framework can be used to build an operational decision support system for retrieval of grassland biophysical parameters based on data from long term planned optical missions (e. g., Landsat, Sentinel) that will ensure the continuity of data acquisition. Similarly, Spanish X-band PAZ and TerraSAR-X2 missions will ensure the continuity of TerraSAR-X and COSMO-SkyMed.

FTIR-inferred biogeochemical properties for the past 3.6 Myr in the sediment record of core ICDP5011-1 in Lake Elgygytgyn, NE Russia

A number of studies have shown that Fourier transform infrared spectroscopy (FTIR) can be applied to quantitatively assess lacustrine sediment constituents. In this study, we developed calibration models based on FTIR for the quantitative determination of biogenic silica (BSi; n = 420; gradient: 0.9-56.5%), total organic carbon (TOC; n = 309; gradient: 0-2.9%), and total inorganic carbon (TIC; n= 152; gradient: 0-0.4%) in a 318 m-long sediment record with a basal age of 3.6 million years from Lake El'gygytgyn, Far East Russian Arctic. The developed partial least squares (PLS) regression models yield high cross-validated (CV) R2CV = 0.86-0.91 and low root mean square error of cross-validation (RMSECV) (3.1-7.0% of the gradient for the different properties). By applying these models to 6771 samples from the entire sediment record, we obtained detailed insight into bioproductivity variations in Lake El'gygytgyn throughout the middle to late Pliocene and Quaternary. High accumulation rates of BSi indicate a productivity maximum during the middle Pliocene (3.6-3.3 Ma), followed by gradually decreasing rates during the late Pliocene and Quaternary. The average BSi accumulation during the middle Pliocene was ~3 times higher than maximum accumulation rates during the past 1.5 million years. The indicated progressive deterioration of environmental and climatic conditions in the Siberian Arctic starting at ca. 3.3 Ma is consistent with the first occurrence of glacial periods and the finally complete establishment of glacial-interglacial cycles during the Quaternary.

Reconstructions of eolian dust accumulation in northwest African margin sediments

Reconstructions of eolian dust accumulation in northwest African margin sediments provide important continuous records of past changes in atmospheric circulation and aridity in the region. Existing records indicate dramatic changes in North African dust emissions over the last 20 ka, but the limited spatial extent of these records and the lack of high-resolution flux data do not allow us to determine whether changes in dust deposition occurred with similar timing, magnitude and abruptness throughout northwest Africa. Here we present new records from a meridional transect of cores stretching from 31°N to 19°N along the northwest African margin. By combining grain size endmember modeling with 230Th-normalized fluxes for the first time, we are able to document spatial and temporal changes in dust deposition under the North African dust plume throughout the last 20 ka. Our results provide quantitative estimates of the magnitude of dust flux changes associated with Heinrich Stadial 1, the Younger Dryas, and the African Humid Period (AHP; ~11.7-5 ka), offering robust targets for model-based estimates of the climatic and biogeochemical impacts of past changes in North African dust emissions. Our data suggest that dust fluxes between 8 and 6 ka were a factor of ~5 lower than average fluxes during the last 2 ka. Using a simple model to estimate the effects of bioturbation on dust input signals, we find that our data are consistent with abrupt, synchronous changes in dust fluxes in all cores at the beginning and end of the AHP. The mean ages of these transitions are 11.8±0.2 ka (1Sigma) and 4.9±0.2 ka, respectively.

Planktic foraminifera abundances and winter SST reconstruction for sediment core SO90-39KG

The Indian winter monsoon (IWM) is a key component of the seasonally changing monsoon system that affects the densely populated regions of South Asia. Cold winds originating in high northern latitudes provide a link of continental-scale Northern Hemisphere climate to the tropics. Western Disturbances (WD) associated with the IWM play a critical role for the climate and hydrology in northern India and the western Himalaya region. It is vital to understand the mechanisms and teleconnections that influence IWM variability to better predict changes in future climate. Here we present a study of regionally calibrated winter (January) temperatures and according IWM intensities, based on a planktic foraminiferal record with biennial (2.55 years) resolution. Over the last ~250 years, IWM intensities gradually weakened, based on the long-term trend of reconstructed January temperatures. Furthermore, the results indicate that IWM is connected on interannual- to decadal time scales to climate variability of the tropical and extratropical Pacific, via El Niño Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). However, our findings suggest that this relationship appeared to begin to decouple since the beginning of the 20th century. Cross-spectral analysis revealed that several distinct decadal-scale phases of colder climate and accordingly more intense winter monsoon centered at the years ~1800, ~1890 and ~1930 can be linked to changes of the North Atlantic Oscillation (NAO).

Variable 360° Vector-Sum Phase Shifter With Coarse and Fine Vector Scaling

A CMOS vector-sum phase shifter covering the full 360° range is presented in this paper. Broadband operational transconductance amplifiers with variable transconductance provide coarse scaling of the quadrature vector amplitudes. Fine scaling of the amplitudes is accomplished using a passive resistive network. Expressions are derived to predict the maximum bit resolution of the phase shifter from the scaling factor of the coarse and fine vector-scaling stages. The phase shifter was designed and fabricated using the standard 130-nm CMOS process and was tested on-wafer over the frequency range of 4.9–5.9 GHz. The phase shifter delivers root mean square (rms) phase and amplitude errors of 1.25° and 0.7 dB, respectively, at the midband frequency of 5.4 GHz. The input and output return losses are both below 17 dB over the band, and the insertion loss is better than 4 dB over the band. The circuit uses an area of 0.303 mm2 excluding bonding pads and draws 28 mW from a 1.2 V supply.

Underway spectrophotometry along the Atlantic Meridional Transect reveals high performance in satellite chlorophyll retrievals

To evaluate the performance of ocean-colour retrievals of total chlorophyll-a concentration requires direct comparison with concomitant and co-located in situ data. For global comparisons, these in situ match-ups should be ideally representative of the distribution of total chlorophyll-a concentration in the global ocean. The oligotrophic gyres constitute the majority of oceanic water, yet are under-sampled due to their inaccessibility and under-represented in global in situ databases. The Atlantic Meridional Transect (AMT) is one of only a few programmes that consistently sample oligotrophic waters. In this paper, we used a spectrophotometer on two AMT cruises (AMT19 and AMT22) to continuously measure absorption by particles in the water of the ship's flow-through system. From these optical data continuous total chlorophyll-a concentrations were estimated with high precision and accuracy along each cruise and used to evaluate the performance of ocean-colour algorithms. We conducted the evaluation using level 3 binned ocean-colour products, and used the high spatial and temporal resolution of the underway system to maximise the number of match-ups on each cruise. Statistical comparisons show a significant improvement in the performance of satellite chlorophyll algorithms over previous studies, with root mean square errors on average less than half (~ 0.16 in log10 space) that reported previously using global datasets (~ 0.34 in log10 space). This improved performance is likely due to the use of continuous absorption-based chlorophyll estimates, that are highly accurate, sample spatial scales more comparable with satellite pixels, and minimise human errors. Previous comparisons might have reported higher errors due to regional biases in datasets and methodological inconsistencies between investigators. Furthermore, our comparison showed an underestimate in satellite chlorophyll at low concentrations in 2012 (AMT22), likely due to a small bias in satellite remote-sensing reflectance data. Our results highlight the benefits of using underway spectrophotometric systems for evaluating satellite ocean-colour data and underline the importance of maintaining in situ observatories that sample the oligotrophic gyres.

Underway spectrophotometry along the Atlantic Meridional Transect reveals high performance in satellite chlorophyll retrievals

To evaluate the performance of ocean-colour retrievals of total chlorophyll-a concentration requires direct comparison with concomitant and co-located in situ data. For global comparisons, these in situ match-ups should be ideally representative of the distribution of total chlorophyll-a concentration in the global ocean. The oligotrophic gyres constitute the majority of oceanic water, yet are under-sampled due to their inaccessibility and under-represented in global in situ databases. The Atlantic Meridional Transect (AMT) is one of only a few programmes that consistently sample oligotrophic waters. In this paper, we used a spectrophotometer on two AMT cruises (AMT19 and AMT22) to continuously measure absorption by particles in the water of the ship's flow-through system. From these optical data continuous total chlorophyll-a concentrations were estimated with high precision and accuracy along each cruise and used to evaluate the performance of ocean-colour algorithms. We conducted the evaluation using level 3 binned ocean-colour products, and used the high spatial and temporal resolution of the underway system to maximise the number of match-ups on each cruise. Statistical comparisons show a significant improvement in the performance of satellite chlorophyll algorithms over previous studies, with root mean square errors on average less than half (~ 0.16 in log10 space) that reported previously using global datasets (~ 0.34 in log10 space). This improved performance is likely due to the use of continuous absorption-based chlorophyll estimates, that are highly accurate, sample spatial scales more comparable with satellite pixels, and minimise human errors. Previous comparisons might have reported higher errors due to regional biases in datasets and methodological inconsistencies between investigators. Furthermore, our comparison showed an underestimate in satellite chlorophyll at low concentrations in 2012 (AMT22), likely due to a small bias in satellite remote-sensing reflectance data. Our results highlight the benefits of using underway spectrophotometric systems for evaluating satellite ocean-colour data and underline the importance of maintaining in situ observatories that sample the oligotrophic gyres.

Conversor de energia das ondas em energia elétrica com dispositivo de coluna de água oscilante: simulação numérica e estudo geométrico

Os oceanos representam um dos maiores recursos naturais, possuindo expressivo potencial energético, podendo suprir parte da demanda energética mundial. Nas últimas décadas, alguns dispositivos destinados à conversão da energia das ondas dos oceanos em energia elétrica têm sido estudados. No presente trabalho, o princípio de funcionamento do conversor do tipo Coluna de Água Oscilante, do inglês Oscillating Water Colum, (OWC) foi analisado numericamente. As ondas incidentes na câmara hidro-pneumática da OWC, causam um movimento alternado da coluna de água no interior da câmara, o qual produz um fluxo alternado de ar que passa pela chaminé. O ar passa e aciona uma turbina a qual transmite energia para um gerador elétrico. O objetivo do presente estudo foi investigar a influência de diferentes formas geométricas da câmara sobre o fluxo resultante de ar que passa pela turbina, que influencia no desempenho do dispositivo. Para isso, geometrias diferentes para o conversor foram analisadas empregando modelos computacionais 2D e 3D. Um modelo computacional desenvolvido nos softwares GAMBIT e FLUENT foi utilizado, em que o conversor OWC foi acoplado a um tanque de ondas. O método Volume of Fluid (VOF) e a teoria de 2ª ordem Stokes foram utilizados para gerar ondas regulares, permitindo uma interação mais realista entre o conversor, água, ar e OWC. O Método dos Volumes Finitos (MVF) foi utilizado para a discretização das equações governantes. Neste trabalho o Contructal Design (baseado na Teoria Constructal) foi aplicado pela primeira vez em estudos numéricos tridimensionais de OWC para fim de encontrar uma geometria que mais favorece o desempenho do dispositivo. A função objetivo foi a maximização da vazão mássica de ar que passa através da chaminé do dispositivo OWC, analisado através do método mínimos quadrados, do inglês Root Mean Square (RMS). Os resultados indicaram que a forma geométrica da câmara influencia na transformação da energia das ondas em energia elétrica. As geometrias das câmaras analisadas que apresentaram maior área da face de incidência das ondas (sendo altura constante), apresentaram também maior desempenho do conversor OWC. A melhor geometria, entre os casos desse estudo, ofereceu um ganho no desempenho do dispositivo em torno de 30% maior.

A Multidimensional Approach to Social Support: the Questionnaire on the Frequency of and Satisfaction with Social Support (QFSSS)

The Questionnaire on the Frequency of and Satisfaction with Social Support (QFSSS) was designed to assess the frequency of and the degree of satisfaction with perceived social support received from different sources in relation to three types of support: emotional, informational, and instrumental. This study tested the reliability of the questionnaire scores and its criterion and structural validity. The data were drawn from survey interviews of 2042 Spanish people. The results show high internal consistency (values of Cronbach's alpha ranged from .763 to .952). The correlational analysis showed significant positive associations between QFSSS scores and measures of subjective well-being and perceived social support, as well as significant negative associations with measures of loneliness (values of Pearson's r correlation ranged from .11 to .97). Confirmatory factor analysis using structural equation modelling verified an internal 4-factor structure that corresponds to the sources of support analysed: partner, family, friends, and community (values ranged from .93 to .95 for the Goodness of Fit Index (GFI); from .95 to .98 for the Comparative Fit Index (CFI); and from .10 to .07 for the Root Mean Square Error of Approximation (RMSEA)). These results confirm the validity of the QFSSS as a versatile tool which is suitable for the multidimensional assessment of social support.

How essential are Argo observations to constrain a global ocean data assimilation system?

Observing system experiments (OSEs) are carried out over a 1-year period to quantify the impact of Argo observations on the Mercator Ocean 0.25° global ocean analysis and forecasting system. The reference simulation assimilates sea surface temperature (SST), SSALTO/DUACS (Segment Sol multi-missions dALTimetrie, d'orbitographie et de localisation précise/Data unification and Altimeter combination system) altimeter data and Argo and other in situ observations from the Coriolis data center. Two other simulations are carried out where all Argo and half of the Argo data are withheld. Assimilating Argo observations has a significant impact on analyzed and forecast temperature and salinity fields at different depths. Without Argo data assimilation, large errors occur in analyzed fields as estimated from the differences when compared with in situ observations. For example, in the 0–300 m layer RMS (root mean square) differences between analyzed fields and observations reach 0.25 psu and 1.25 °C in the western boundary currents and 0.1 psu and 0.75 °C in the open ocean. The impact of the Argo data in reducing observation–model forecast differences is also significant from the surface down to a depth of 2000 m. Differences between in situ observations and forecast fields are thus reduced by 20 % in the upper layers and by up to 40 % at a depth of 2000 m when Argo data are assimilated. At depth, the most impacted regions in the global ocean are the Mediterranean outflow, the Gulf Stream region and the Labrador Sea. A significant degradation can be observed when only half of the data are assimilated. Therefore, Argo observations matter to constrain the model solution, even for an eddy-permitting model configuration. The impact of the Argo floats' data assimilation on other model variables is briefly assessed: the improvement of the fit to Argo profiles do not lead globally to unphysical corrections on the sea surface temperature and sea surface height. The main conclusion is that the performance of the Mercator Ocean 0.25° global data assimilation system is heavily dependent on the availability of Argo data.

Investigation and prediction of sea level variations in the northern coasts of Persian Gulf using artificial neural network

Sea- level variations have a significant impact on coastal areas. Prediction of sea level variations expected from the pre most critical information needs associated with the sea environment. For this, various methods exist. In this study, on the northern coast of the Persian Gulf have been studied relation to the effectiveness of parameters such as pressure, temperature and wind speed on sea leve and associated with global parameters such as the North Atlantic Oscillation index and NAO index and present statistic models for prediction of sea level. In the next step by using artificial neural network predict sea level for first in this region. Then compared results of the models. Prediction using statistical models estimated in terms correlation coefficient R = 0.84 and root mean square error (RMS) 21.9 cm for the Bushehr station, and R = 0.85 and root mean square error (RMS) 48.4 cm for Rajai station, While neural network used to have 4 layers and each middle layer six neurons is best for prediction and produces the results reliably in terms of correlation coefficient with R = 0.90126 and the root mean square error (RMS) 13.7 cm for the Bushehr station, and R = 0.93916 and the root mean square error (RMS) 22.6 cm for Rajai station. Therefore, the proposed methodology could be successfully used in the study area.

Modelling of an efficient dynamic smart solar photovoltaic power grid system

A smart solar photovoltaic grid system is an advent of innovation coherence of information and communications technology (ICT) with power systems control engineering via the internet [1]. This thesis designs and demonstrates a smart solar photovoltaic grid system that is selfhealing, environmental and consumer friendly, but also with the ability to accommodate other renewable sources of energy generation seamlessly, creating a healthy competitive energy industry and optimising energy assets efficiency. This thesis also presents the modelling of an efficient dynamic smart solar photovoltaic power grid system by exploring the maximum power point tracking efficiency, optimisation of the smart solar photovoltaic array through modelling and simulation to improve the quality of design for the solar photovoltaic module. In contrast, over the past decade quite promising results have been published in literature, most of which have not addressed the basis of the research questions in this thesis. The Levenberg-Marquardt and sparse based algorithms have proven to be very effective tools in helping to improve the quality of design for solar photovoltaic modules, minimising the possible relative errors in this thesis. Guided by theoretical and analytical reviews in literature, this research has carefully chosen the MatLab/Simulink software toolbox for modelling and simulation experiments performed on the static smart solar grid system. The auto-correlation coefficient results obtained from the modelling experiments give an accuracy of 99% with negligible mean square error (MSE), root mean square error (RMSE) and standard deviation. This thesis further explores the design and implementation of a robust real-time online solar photovoltaic monitoring system, establishing a comparative study of two solar photovoltaic tracking systems which provide remote access to the harvested energy data. This research made a landmark innovation in designing and implementing a unique approach for online remote access solar photovoltaic monitoring systems providing updated information of the energy produced by the solar photovoltaic module at the site location. In addressing the challenge of online solar photovoltaic monitoring systems, Darfon online data logger device has been systematically integrated into the design for a comparative study of the two solar photovoltaic tracking systems examined in this thesis. The site location for the comparative study of the solar photovoltaic tracking systems is at the National Kaohsiung University of Applied Sciences, Taiwan, R.O.C. The overall comparative energy output efficiency of the azimuthal-altitude dual-axis over the 450 stationary solar photovoltaic monitoring system as observed at the research location site is about 72% based on the total energy produced, estimated money saved and the amount of CO2 reduction achieved. Similarly, in comparing the total amount of energy produced by the two solar photovoltaic tracking systems, the overall daily generated energy for the month of July shows the effectiveness of the azimuthal-altitude tracking systems over the 450 stationary solar photovoltaic system. It was found that the azimuthal-altitude dual-axis tracking systems were about 68.43% efficient compared to the 450 stationary solar photovoltaic systems. Lastly, the overall comparative hourly energy efficiency of the azimuthal-altitude dual-axis over the 450 stationary solar photovoltaic energy system was found to be 74.2% efficient. Results from this research are quite promising and significant in satisfying the purpose of the research objectives and questions posed in the thesis. The new algorithms introduced in this research and the statistical measures applied to the modelling and simulation of a smart static solar photovoltaic grid system performance outperformed other previous works in reviewed literature. Based on this new implementation design of the online data logging systems for solar photovoltaic monitoring, it is possible for the first time to have online on-site information of the energy produced remotely, fault identification and rectification, maintenance and recovery time deployed as fast as possible. The results presented in this research as Internet of things (IoT) on smart solar grid systems are likely to offer real-life experiences especially both to the existing body of knowledge and the future solar photovoltaic energy industry irrespective of the study site location for the comparative solar photovoltaic tracking systems. While the thesis has contributed to the smart solar photovoltaic grid system, it has also highlighted areas of further research and the need to investigate more on improving the choice and quality design for solar photovoltaic modules. Finally, it has also made recommendations for further research in the minimization of the absolute or relative errors in the quality and design of the smart static solar photovoltaic module.

Cancer appetite and symptom questionnaire (CASQ) for brazilian patients: Cross-cultural adaptation and validation study

Background: Appetite and symptoms, conditions generally reported by the patients with cancer, are somewhat challenging for professionals to measure directly in clinical routine (latent conditions). Therefore, specific instruments are required for this purpose. This study aimed to perform a cultural adaptation of the Cancer Appetite and Symptom Questionnaire (CASQ), into Portuguese and evaluate its psychometric properties on a sample of Brazilian cancer patients. Methods: This is a validation study with Brazilian cancer patients. The face, content, and construct (factorial and convergent) validities of the Cancer Appetite and Symptom Questionnaire, the study tool, were estimated. Further, a confirmatory factor analysis (CFA) was conducted. The ratio of chi-square and degrees of freedom (χ2/df), comparative fit index (CFI), goodness of fit index (GFI) and root mean square error of approximation (RMSEA) were used for fit model assessment. In addition, the reliability of the instrument was estimated using the composite reliability (CR) and Cronbach’s alpha coefficient (α), and the invariance of the model in independent samples was estimated by a multigroup analysis (Δχ2). Results: Participants included 1,140 cancer patients with a mean age of 53.95 (SD = 13.25) years; 61.3% were women. After the CFA of the original CASQ structure, 2 items with inadequate factor weights were removed. Four correlations between errors were included to provide adequate fit to the sample (χ2/df = 8.532, CFI = .94, GFI = .95, and RMSEA = .08). The model exhibited a low convergent validity (AVE = .32). The reliability was adequate (CR = .82 α = .82). The refined model showed strong invariance in two independent samples (Δχ2: λ: p = .855; i: p = .824; Res: p = .390). A weak stability was obtained between patients undergoing chemotherapy and radiotherapy (Δχ2: λ: p = .155; i: p < .001; Res: p < .001), and between patients undergoing chemotherapy combined with radiotherapy and palliative care (Δχ2: λ: p = .058; i: p < .001; Res: p < .001). Conclusion: The Portuguese version of the CASQ had good face and construct validity and reliability. However, the CASQ still presented invariance in independent samples of Brazilian patients with cancer. However, the tool has low convergent validity and weak invariance in samples with different treatments.

Psychometric properties of the oral health impact profile and new methodological approach

Objective: Evaluate the validity, reliability, and factorial invariance of the complete Portuguese version of the Oral Health Impact Profile (OHIP) and its short version (OHIP-14). Methods: A total of 1,162 adults enrolled in the Faculty of Dentistry of Araraquara/UNESP participated in the study; 73.1% were women; and the mean age was 40.7 ± 16.3 yr. We conducted a confirmatory factor analysis, where χ2/df, comparative fit index, goodness of fit index, and root mean square error of approximation were used as indices of goodness of fit. The convergent validity was judged from the average variance extracted and the composite reliability, and the internal consistency was estimated by Cronbach standardized alpha. The stability of the models was evaluated by multigroup analysis in independent samples (test and validation) and between users and nonusers of dental prosthesis. Results: We found best-fitting models for the OHIP-14 and among dental prosthesis users. The convergent validity was below adequate values for the factors “functional limitation” and “physical pain” for the complete version and for the factors “functional limitation” and “psychological discomfort” for the OHIP-14. Values of composite reliability and internal consistency were below adequate in the OHIP-14 for the factors “functional limitation” and “psychological discomfort.” We detected strong invariance between test and validation samples of the full version and weak invariance for OHIP-14. The models for users and nonusers of dental prosthesis were not invariant for both versions. Conclusion: The reduced version of the OHIP was parsimonious, reliable, and valid to capture the construct “impact of oral health on quality of life,” which was more pronounced in prosthesis users.