Multivariate Statistics in Single Observation Space

The observation data collected from continuous industrial processes usually have two main categories: process data and quality data, and the corresponding industrial data analysis is mainly for the two types of data based on the multivariate statistical techniques.

Tackling Data Quality Challenges in the Finnish Biodiversity Information Facility (FinBIF)

The Finnish Biodiversity Information Facility (FinBIF) Research Infrastructure (Schulman et al. 2021) is a national service with a broad coverage of the components of biodiversity informatics (Bingham et al. 2017). Data flows are managed under a single information technology (IT) architecture. Services are available in a single, branded on-line portal. Data are collated from all relevant sources e.g., research institutes, scientific collections, public authorities and citizen science projects, whose data represent a major contribution. The challenge is to analyse, classify and share good quality data in a way that the user understands its utility. Need for quality data The philosophy of FinBIF is that all observation records are important, and that all data are assessed for quality and able to be annotated. The challenge is that, in practice, many users desire data with 100% reliability. In our experience, most user concerns about data quality are related to citizen science data. Researchers are usually able to manage raw data to serve their purposes. However, decision-making authorities often have less capacity to analyse the data and thus require data that can be used instantly. Therefore, we need tools to provide users the data that are the most relevant and reliable for their specific use. For all users, standardized metadata (information about datasets) are key, when the user has doubts about the fitness-for-use of a particular dataset. There is also a need to provide data in different formats to serve various users. Finally, the service has to be machine-actionable (using an application programming interface (API) and R-package) as well as human-accessible for viewing and downloading data. Quality assignment FinBIF data accuracy varies significantly within and between datasets, and observers. Two quality-based classifications suitable for filtering are therefore applied. The dataset origin filter is based on the quality of a whole dataset (e.g. citizen science project) and includes three broad classes assigned with an appropriate quality label: Datasets by Professionals, by Specialists and by Citizen Scientists. The observation reliability filter is based on a single observation and on annotations by FinBIF users. This classification includes Expert verified, Community verified, Unassessed (default for all records), Uncertain, and Erroneous. The dataset origin does not necessarily determine the quality of the individual records in it. Observations made by citizen scientists are often accurate, while there may be errors in the professionally collected data. Records are frequently subject to annotation, which raises their quality over time (e.g., iNaturalist). Naturally, evidence (e.g., media, detailed descriptions, specimens) is needed for reliable identification. Annotating data When observations are compiled at FinBIF’s portal (Laji.fi), they are initially “Unassessed” (unless they have otherwise been assessed at the original source). When annotating occurrences, volunteers can make various entries using the tools provided. The aim of the commentary is to improve the quality of the observation data. Annotators are divided into two categories with two different roles: As a basic user, anyone who has logged in at Laji.fi can make comments or tag observations for review by experts. Users defined as experts have wider rights than basic users and their comments carry more weight. The most desired actions of expert users are to classify observations into confidence levels or to give them new or refined identifications. As a basic user, anyone who has logged in at Laji.fi can make comments or tag observations for review by experts. Users defined as experts have wider rights than basic users and their comments carry more weight. The most desired actions of expert users are to classify observations into confidence levels or to give them new or refined identifications. Information about new comments passes to the observer if the observation is recorded by using the FinBIF Observation Management System “Notebook”. However, comments cannot yet be automatically forwarded e.g., to the primary data management systems at the original source. Annotations add extra indications of quality. They do not replace or delete the original information. Nevertheless, annotations can change a record’s taxonomic identification, and by default, a record will be handled based on its latest identification. R-package for researchers and Public Authority Portal (PAP) for decision makers FinBIF has produced an R programming language interface to its API, which makes the publicly available data in FinBIF accessible from within R. For authorities, the PAP offers direct access to all available species information to authorised users, including sensitive and restricted-use data.

Simultaneous Retrieval of Soil, Leaf, and Canopy Parameters from Sentinel-3 OLCI and SLSTR Multi-spectral Top-of-Canopy Reflectances

Multi- and hyper-spectral, multi-angular top-of-canopy reflectance data call for an efficient retrieval system which can improve the retrieval of standard canopy parameters (as albedo, LAI, fAPAR), and exploit the information to retrieve additional parameters (e.g. leaf pigments). Furthermore consistency between the retrieved parameters and quantification of uncertainties are required for many applications. % (2) methods We present a retrieval system for canopy and sub-canopy parameters (OptiSAIL), which is based on a model comprising SAIL, PROSPECT-D (leaf properties), TARTES (snow properties), a soil model (BRDF, moisture), and a cloud contamination model. The inversion is gradient based and uses codes % created by Automatic Differentiation. The full per pixel covariance-matrix of the retrieved parameters is computed. For this demonstration, single observation data from the Sentinel-3 SY_2_SYN (synergy) product is used. The results are compared with the MODIS 4-day LAI/fPAR product and PhenoCam site photography. OptiSAIL produces generally consistent and credible results, at least matching the quality of the technically quite different MODIS product. For most of the sites, the PhenoCam images support the OptiSAIL retrievals. The system is computationally efficient with a rate of 150 pixel per second (7 millisecond per pixel) for a single thread on a current desktop CPU using observations on 26 bands. Not all of the model parameters are well determined in all situations. Significant correlations between the parameters are found, which can change sign and magnitude over time. OptiSAIL appears to meet the design goals, puts real-time processing with this kind of system into reach, seamlessly extends to hyper-spectral and multi-sensor retrievals, and promises to be a good platform for sensitivity studies. The incorporated cloud and snow detection adds to the robustness of the system.

A Test Can Have Multiple Reliabilities

It is argued that the generalizability theory interpretation of coefficient alpha is important. In this interpretation, alpha is a slightly biased but consistent estimate for the coefficient of generalizability in a subjects x items design where both subjects and items are randomly sampled. This interpretation is based on the “domain sampling” true scores. It is argued that these true scores have a more solid empirical basis than the true scores of Lord and Novick (1968), which are based on “stochastic subjects” (Holland, 1990), while only a single observation is available for each within-subject distribution. Therefore, the generalizability interpretation of coefficient alpha is to be preferred, unless the true scores can be defined by a latent variable model that has undisputed empirical validity for the test and that is sufficiently restrictive to entail a consistent estimate of the reliability—as, for example, McDonald’s omega. If this model implies that the items are essentially tau-equivalent, both the generalizability and the reliability interpretation of alpha can be defensible.

Ischemic Nephropaty: The Role of the Renal Artery Stenosis Revascularization on Renal Stem Cells

We report the case of a 65-year-old man with acute GFR decline to 37 mL/min and uncontrolled high blood pressure. He was suspected for renovascular hypertension and underwent a renal color Doppler ultrasound scan that detected a bilateral atherosclerotic renal artery stenosis. A digital selective angiography by percutaneous transluminal angioplasty and stenting (PTRAs) was successfully performed. Blood pressure rapidly normalized, GFR increased within a few days, and proteinuria disappeared thereafter. These clinical goals were accompanied by a significant increase of circulating renal stem cells (RSC) and a slight increase of resistive index (RI) in both kidneys. This single observation suggests the need for extensive studies aimed at evaluating the predictive power of RI and RSC in detecting post-ischemic renal repair mechanisms.

Fault Displacement of the 2011 Mw 6.6 Fukushima-ken Hamadori Earthquake Based on a 3D Crustal Deformation Model Constructed Using Differential InSAR–Lidar

ABSTRACT 3D coseismic deformation detected by remote sensing yields essential information for estimating the geometry and slip distribution of the causative fault. However, it is often difficult to be obtained by a single observation method due to data acquisition constraints. This study constructs a 3D coseismic deformation model of the 2011 Fukushima-ken Hamadori earthquake by integrating Differential Interferometric Synthetic Aperture Radar (DInSAR), and differential light detection and ranging (Dlidar) analyses. Both horizontal and vertical movements observed are almost consistent with those of the theoretical dislocation model of normal faulting. The fault displacements measured within ±45 m of the rupture based on the 3D deformation model is also in good agreement with the possible maximum field displacements. Fault dips and lateral displacement components are also harmonious with the field survey measurements. Dlidar detects full 3D motion, whereas the DInSAR detects deformations too small for the light detection and ranging (lidar). Combining the two products is helpful to produce a more robust 3D displacement field than possible from the lidar alone.

Association between ocular axial length and anthropometrics of Asian adults

Objective The purpose of this study is to analyze axial length, body height, hand length, and foot length to find new factors that predict myopia and to identify gender differences as one of the factors of high myopia. A cross-sectional study was conducted as a single observation. Body height, hand length, and foot length were measured according to standard anthropometric methods. Axial length, retinal thickness, and choroidal thickness were measured using the IOL Master 700 and the Heidelberg Spectralis-OCT. To account for body height differences among participants, foot length/body height and hand length/body height were analyzed using a mixed-effects model. Results A total of 80 eyes (men, n = 20, 40 eyes; women, n = 20, 40 eyes) were analyzed. The mean age was 33.5 years (range 21–59 years, SD: 9.6). For choroidal thickness, there was a significant association with axial length in men (p < 0.001) and a trend toward an association in women (p = 0.072). There was also a significant association between foot length/body height and axial length in men (p = 0.015), but not in women (p = 0.58). These results suggest that factors that determine body height and foot length may be related to axial length, although they vary by gender.

Data reduction for inverse modeling: an adaptive approach v1.0

The number of greenhouse gas (GHG) observing satellites has greatly expanded in recent years, and these new datasets provide an unprecedented constraint on global GHG sources and sinks. However, a continuing challenge for inverse models that are used to estimate these sources and sinks is the sheer number of satellite observations, sometimes in the millions per day. These massive datasets often make it prohibitive to implement inverse modeling calculations and/or assimilate the observations using many types of atmospheric models. Although these satellite datasets are very large, the information content of any single observation is often modest and non-exclusive due to redundancy with neighboring observations and due to measurement noise. In this study, we develop an adaptive approach to reduce the size of satellite datasets using geostatistics. A guiding principle is to reduce the data more in regions with little variability in the observations and less in regions with high variability. We subsequently tune and evaluate the approach using synthetic and real data case studies for North America from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite. The proposed approach to data reduction yields more accurate CO2 flux estimates than the commonly used method of binning and averaging the satellite data. We further develop a metric for choosing a level of data reduction; we can reduce the satellite dataset to an average of one observation per ∼ 80–140 km for the specific case studies here without substantially compromising the flux estimate, but we find that reducing the data further quickly degrades the accuracy of the estimated fluxes. Overall, the approach developed here could be applied to a range of inverse problems that use very large trace gas datasets.

Cloud-dependent piecewise assimilation based on a hydrometeor-included background error covariance and its impact on regional Numerical Weather Prediction

The background error covariance (B) behaves differently and needs to be carefully defined in cloudy areas due to larger uncertainties caused by models’ inability to correctly represent complex physical processes. This study proposes a new cloud-dependent B strategy by adaptively adjusting the hydrometeor-included B in the cloudy areas according to the cloud index (CI) derived from the satellite-based cloud products. The adjustment coefficient is determined by comparing the error statistics of B for the clear and cloudy areas based on the two-dimensional geographical masks. The comparison highlights the larger forecast errors and manifests the necessity of using appropriate B in cloudy areas. The cloud-dependent B is then evaluated by a series of single observation tests and three-week cycling assimilation and forecasting experiments. The single observation experiments confirm that the cloud-dependent B allows cloud dependency for the multivariate analysis increments and alleviates the discontinuities at the cloud mask borders by treating the CI as an exponent. The impact study on regional numerical weather prediction (NWP) demonstrates that the application of the cloud-dependent B reduces analyses and forecasts bias and increases precipitation forecast skills. Diagnostics of a heavy rainfall case indicate that the application of the cloud-dependent B enhances the moisture, wind, and hydrometeors analyses and forecasts, resulting in more accurate forecasts of accumulated precipitation. The cloud-dependent piecewise analysis scheme proposed herein is extensible, and a more precise definition of CI can improve the analysis, which deserves future investigation.