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1
Alazman, Ibtehal, Badr Saad T. Alkahtani та Shahid Ahmad Wani. "Certain Properties of Δh Multi-Variate Hermite Polynomials". Symmetry 15, № 4 (2023): 839. http://dx.doi.org/10.3390/sym15040839.
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The research described in this paper follows the hypothesis that the monomiality principle leads to novel results that are consistent with past knowledge. Thus, in line with prior facts, our aim is to introduce the Δh multi-variate Hermite polynomials ΔhHm(q1,q2,⋯,qr;h). We obtain their recurrence relations by using difference operators. Furthermore, symmetric identities satisfied by these polynomials are established. The operational rules are helpful in demonstrating the novel characteristics of the polynomial families, and thus the operational principles satisfied by these polynomials are derived and will prove beneficial for future observations.
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FRITZNER, SINDRE M., RUNE G. GRAVERSEN, KEGUANG WANG, and KAI H. CHRISTENSEN. "Comparison between a multi-variate nudging method and the ensemble Kalman filter for sea-ice data assimilation." Journal of Glaciology 64, no. 245 (2018): 387–96. http://dx.doi.org/10.1017/jog.2018.33.
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ABSTRACTIncreasing ship traffic and human activity in the Arctic has led to a growing demand for accurate Arctic weather forecast. High-quality forecasts obtained by models are dependent on accurate initial states achieved by assimilation of observations. In this study, a multi-variate nudging (MVN) method for assimilation of sea-ice variables is introduced. The MVN assimilation method includes procedures for multivariate update of sea-ice volume and concentration, and for extrapolation of observational information spatially. The MVN assimilation scheme is compared with the Ensemble Kalman Filter (EnKF) using the Los Alamos Sea Ice Model. Two multi-variate experiments are conducted: in the first experiment, sea-ice thickness from the European Space Agency's Soil Moisture and Ocean Salinity mission is assimilated, and in the second experiment, sea-ice concentration from the ocean and Sea Ice Satellite Application Facility is assimilated. The multivariate effects are cross-validated by comparing the model with non-assimilated observations. It is found that the simple and computationally cheap MVN method shows comparable skills to the more complicated and expensive EnKF method for multivariate update. In addition, we show that when few observations are available, the MVN method is a significant model improvement compared to the version based on one-dimensional sea-ice concentration assimilation.
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Rannik, Ü., N. Altimir, I. Mammarella, et al. "Ozone deposition into a boreal forest over a decade of observations: evaluating deposition partitioning and driving variables." Atmospheric Chemistry and Physics Discussions 12, no. 5 (2012): 12715–58. http://dx.doi.org/10.5194/acpd-12-12715-2012.
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Abstract. This study scrutinizes a decade-long series of ozone deposition measurements in a boreal forest in search for the signature and relevance of the different deposition processes. Canopy-level ozone flux measurements were analysed for deposition characteristics and partitioning into stomatal and non-stomatal fractions, focusing on growing season day-time data. Ten years of measurements enabled the analysis of ozone deposition variation at different time- scales, including daily to inter-annual variation as well as the dependence on environmental variables and concentration of biogenic volatile organic compounds (BVOC-s). Stomatal deposition was estimated by using multi-layer canopy dispersion and optimal stomatal control modelling from simultaneous carbon dioxide and water vapour flux measurements, non-stomatal was inferred as residual. Also, utilising big-leaf assumption stomatal conductance was inferred from water vapour fluxes for dry canopy conditions. The total ozone deposition was highest during the peak growing season (4 mm s−1) and lowest during winter dormancy (1 mm s−1). During the course of the growing season the fraction of the non-stomatal deposition of ozone was determined to vary from 26 to 44% during day time, increasing from the start of the season until the end of the growing season. By using multi-variate analysis it was determined that day-time total ozone deposition was mainly driven by photosynthetic capacity of the canopy, vapour pressure deficit (VPD), photosynthetically active radiation and monoterpene concentration. The multi-variate linear model explained high portion of ozone deposition variance on daily average level (R2 = 0.79). The explanatory power of the multi-variate model for ozone non-stomatal deposition was much lower (R2 = 0.38). Model calculation was performed to evaluate the potential sink strength of the chemical reactions of ozone with sesquiterpenes in the canopy air space, which revealed that sesquiterpenes in typical amounts at the site were unlikely to cause significant ozone loss in canopy air space. This was also confirmed by the statistical analysis that did not link measured sesquiterpene concentration with ozone deposition. It was concluded that chemical reactions with monoterpenes, or other removal mechanisms such as surface reactions, play a role as ozone non-stomatal sink inside canopy.
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Rannik, Ü., N. Altimir, I. Mammarella, et al. "Ozone deposition into a boreal forest over a decade of observations: evaluating deposition partitioning and driving variables." Atmospheric Chemistry and Physics 12, no. 24 (2012): 12165–82. http://dx.doi.org/10.5194/acp-12-12165-2012.
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Abstract. This study scrutinizes a decade-long series of ozone deposition measurements in a boreal forest in search for the signature and relevance of the different deposition processes. The canopy-level ozone flux measurements were analysed for deposition characteristics and partitioning into stomatal and non-stomatal fractions, with the main focus on growing season day-time data. Ten years of measurements enabled the analysis of ozone deposition variation at different time-scales, including daily to inter-annual variation as well as the dependence on environmental variables and concentration of biogenic volatile organic compounds (BVOC-s). Stomatal deposition was estimated by using multi-layer canopy dispersion and optimal stomatal control modelling from simultaneous carbon dioxide and water vapour flux measurements, non-stomatal was inferred as residual. Also, utilising the big-leaf assumption stomatal conductance was inferred from water vapour fluxes for dry canopy conditions. The total ozone deposition was highest during the peak growing season (4 mm s−1) and lowest during winter dormancy (1 mm s−1). During the course of the growing season the fraction of the non-stomatal deposition of ozone was determined to vary from 26 to 44% during day time, increasing from the start of the season until the end of the growing season. By using multi-variate analysis it was determined that day-time total ozone deposition was mainly driven by photosynthetic capacity of the canopy, vapour pressure deficit (VPD), photosynthetically active radiation and monoterpene concentration. The multi-variate linear model explained the high portion of ozone deposition variance on daily average level (R2 = 0.79). The explanatory power of the multi-variate model for ozone non-stomatal deposition was much lower (R2 = 0.38). The set of common environmental variables and terpene concentrations used in multivariate analysis were able to predict the observed average seasonal variation in total and non-stomatal deposition but failed to explain the inter-annual differences, suggesting that some still unknown mechanisms might be involved in determining the inter-annual variability. Model calculation was performed to evaluate the potential sink strength of the chemical reactions of ozone with sesquiterpenes in the canopy air space, which revealed that sesquiterpenes in typical amounts at the site were unlikely to cause significant ozone loss in canopy air space. The results clearly showed the importance of several non-stomatal removal mechanisms. Unknown chemical compounds or processes correlating with monoterpene concentrations, including potentially reactions at the surfaces, contribute to non-stomatal sink term.
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Wani, Shahid Ahmad, Ibtehal Alazman та Badr Saad T. Alkahtani. "Certain Properties and Applications of Convoluted Δh Multi-Variate Hermite and Appell Sequences". Symmetry 15, № 4 (2023): 828. http://dx.doi.org/10.3390/sym15040828.
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This study follows the line of research that by employing the monomiality principle, new outcomes are produced. Thus, in line with prior facts, our aim is to introduce the Δh multi-variate Hermite Appell polynomials ΔhHAm[r](q1,q2,⋯,qr;h). Further, we obtain their recurrence sort of relations by using difference operators. Furthermore, symmetric identities satisfied by these polynomials are established. The operational rules are helpful in demonstrating the novel characteristics of the polynomial families and thus operational principle satisfied by these polynomials is derived and will prove beneficial for future observations. Further, a few members of the Δh Appell polynomial family are considered and their corresponding results are derived accordingly.
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Brassington, Gary B., and Prasanth Divakaran. "The theoretical impact of remotely sensed sea surface salinity observations in a multi-variate assimilation system." Ocean Modelling 27, no. 1-2 (2009): 70–81. http://dx.doi.org/10.1016/j.ocemod.2008.12.005.
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Ciani, Daniele, Sarah Asdar, and Bruno Buongiorno Nardelli. "Improved Surface Currents from Altimeter-Derived and Sea Surface Temperature Observations: Application to the North Atlantic Ocean." Remote Sensing 16, no. 4 (2024): 640. http://dx.doi.org/10.3390/rs16040640.
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We present a study on the ocean surface currents reconstruction by merging Level-4 (L4, gap-free) altimeter-derived geostrophic currents and satellite sea surface temperature. Building upon past studies on the multi-variate reconstruction of geostrophic currents from satellite observations, we regionalized and optimized an algorithm to improve the altimeter-derived surface circulation estimates in the North Atlantic Ocean. A ten-year-long time series (2010–2019) is presented and validated by means of in situ observations. The newly optimized algorithm allowed us to improve the currents estimate along the main axis of the Gulf Stream and in correspondence of well-known upwelling areas in the North Eastern Atlantic, with percentage improvements of around 15% compared to standard operational altimetry products.
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Mereu, Luigi, Simona Scollo, Antonella Boselli, et al. "Dual-Wavelength Polarimetric Lidar Observations of the Volcanic Ash Cloud Produced during the 2016 Etna Eruption." Remote Sensing 13, no. 9 (2021): 1728. http://dx.doi.org/10.3390/rs13091728.
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Lidar observations are very useful to analyse dispersed volcanic clouds in the troposphere mainly because of their high range resolution, providing morphological as well as microphysical (size and mass) properties. In this work, we analyse the volcanic cloud of 18 May 2016 at Mt. Etna, in Italy, retrieved by polarimetric dual-wavelength Lidar measurements. We use the AMPLE (Aerosol Multi-Wavelength Polarization Lidar Experiment) system, located in Catania, about 25 km from the Etna summit craters, pointing at a thin volcanic cloud layer, clearly visible and dispersed from the summit craters at the altitude between 2 and 4 km and 6 and 7 km above the sea level. Both the backscattering and linear depolarization profiles at 355 nm (UV, ultraviolet) and 532 nm (VIS, visible) wavelengths, respectively, were obtained using different angles at 20°, 30°, 40° and 90°. The proposed approach inverts the Lidar measurements with a physically based inversion methodology named Volcanic Ash Lidar Retrieval (VALR), based on Maximum-Likelihood (ML). VALRML can provide estimates of volcanic ash mean size and mass concentration at a resolution of few tens of meters. We also compared those results with two methods: Single-variate Regression (SR) and Multi-variate Regression (MR). SR uses the backscattering coefficient or backscattering and depolarization coefficients of one wavelength (UV or VIS in our cases). The MR method uses the backscattering coefficient of both wavelengths (UV and VIS). In absence of in situ airborne validation data, the discrepancy among the different retrieval techniques is estimated with respect to the VALR ML algorithm. The VALR ML analysis provides ash concentrations between about 0.1 μg/m3 and 1 mg/m3 and particle mean sizes of 0.1 μm and 6 μm, respectively. Results show that, for the SR method differences are less than <10%, using the backscattering coefficient only and backscattering and depolarization coefficients. Moreover, we find differences of 20–30% respect to VALR ML, considering well-known parametric retrieval methods. VALR algorithms show how a physics-based inversion approaches can effectively exploit the spectral-polarimetric Lidar AMPLE capability.
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Grinsted, A. "An estimate of global glacier volume." Cryosphere Discussions 6, no. 5 (2012): 3647–66. http://dx.doi.org/10.5194/tcd-6-3647-2012.
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Abstract. I asses the feasibility of multi-variate scaling relationships to estimate glacier volume from glacier inventory data. I calibrate scaling laws against volume observations of optimized towards the purpose of estimating the total global ice volume. This is applied individually to each record in the Randolph Glacier Inventory which is the first globally complete inventory of glaciers and ice caps. I estimate that the total volume of all glaciers in the world is 0.35 ± 0.07 m sea level equivalent. This is substantially less than a recent state-of-the-art estimate. Area volume scaling bias issues for large ice masses, and incomplete inventory data are offered as explanations for the difference.
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Pujol, Léo, Pierre-André Garambois, and Jérôme Monnier. "Multi-dimensional hydrological–hydraulic model with variational data assimilation for river networks and floodplains." Geoscientific Model Development 15, no. 15 (2022): 6085–113. http://dx.doi.org/10.5194/gmd-15-6085-2022.
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Abstract. This contribution presents a novel multi-dimensional (multi-D) hydraulic–hydrological numerical model with variational data assimilation capabilities. It allows multi-scale modeling over large domains, combining in situ observations with high-resolution hydrometeorology and satellite data. The multi-D hydraulic model relies on the 2D shallow-water equations solved with a 1D–2D adapted single finite-volume solver. One-dimensional-like reaches are built through meshing methods that cause the 2D solver to degenerate into 1D. They are connected to 2D portions that act as local zooms, for modeling complex flow zones such as floodplains and confluences, via 1D-like–2D interfaces. An existing parsimonious hydrological model, GR4H, is implemented and coupled to the hydraulic model. The forward-inverse multi-D computational model is successfully validated on virtual and real cases of increasing complexity, including using the second-order scheme version. Assimilating multiple observations of flow signatures leads to accurate inferences of multi-variate and spatially distributed parameters among bathymetry friction, upstream and lateral hydrographs and hydrological model parameters. This notably demonstrates the possibility for information feedback towards upstream hydrological catchments, that is, backward hydrology. A 1D-like model of part of the Garonne River is built and accurately reproduces flow lines and propagations of a 2D reference model. A multi-D model of the complex Adour basin network, with inflow from the semi-distributed hydrological model, is built. High-resolution flow simulations are obtained on a large domain, including fine zooms on floodplains, with a relatively low computational cost since the network contains mostly 1D-like reaches. The current work constitutes an upgrade of the DassFlow computational platform. The adjoint of the whole tool chain is obtained by automatic code differentiation.
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Ray, J., J. Lee, V. Yadav, S. Lefantzi, A. M. Michalak, and B. van Bloemen Waanders. "A sparse reconstruction method for the estimation of multi-resolution emission fields via atmospheric inversion." Geoscientific Model Development 8, no. 4 (2015): 1259–73. http://dx.doi.org/10.5194/gmd-8-1259-2015.
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Abstract. Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) and fitting. Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO2 (ffCO2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO2 emissions and synthetic observations of ffCO2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of 2. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.
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Kaushik, Pankaj R., Christopher E. Ndehedehe, Ryan M. Burrows, Mark R. Noll, and Mark J. Kennard. "Assessing Changes in Terrestrial Water Storage Components over the Great Artesian Basin Using Satellite Observations." Remote Sensing 13, no. 21 (2021): 4458. http://dx.doi.org/10.3390/rs13214458.
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The influence of climate change and anthropogenic activities (e.g., water withdrawals) on groundwater basins has gained attention recently across the globe. However, the understanding of hydrological stores (e.g., groundwater storage) in one of the largest and deepest artesian basins, the Great Artesian Basin (GAB) is limited due to the poor distribution of groundwater monitoring bores. In this study, Gravity Recovery and Climate Experiment (GRACE) satellite and ancillary data from observations and models (soil moisture, rainfall, and evapotranspiration (ET)) were used to assess changes in terrestrial water storage and groundwater storage (GWS) variations across the GAB and its sub-basins (Carpentaria, Surat, Western Eromanga, and Central Eromanga). Results show that there is strong relationship of GWS variation with rainfall (r = 0.9) and ET (r = 0.9 to 1) in the Surat and some parts of the Carpentaria sub-basin in the GAB (2002–2017). Using multi-variate methods, we found that variation in GWS is primarily driven by rainfall in the Carpentaria sub-basin. While changes in rainfall account for much of the observed spatio-temporal distribution of water storage changes in Carpentaria and some parts of the Surat sub-basin (r = 0.90 at 0–2 months lag), the relationship of GWS with rainfall and ET in Central Eromanga sub-basin (r = 0.10–0.30 at more than 12 months lag) suggest the effects of human water extraction in the GAB.
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Shattuck, Thomas W., James R. Anderson, Neil W. Tindale, and Peter R. Buseck. "Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific." Proceedings, annual meeting, Electron Microscopy Society of America 50, no. 2 (1992): 1488–89. http://dx.doi.org/10.1017/s0424820100132078.
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Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.
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Liu, Guohua, Mirco Migliavacca, Christian Reimers, et al. "DeepPhenoMem V1.0: deep learning modelling of canopy greenness dynamics accounting for multi-variate meteorological memory effects on vegetation phenology." Geoscientific Model Development 17, no. 17 (2024): 6683–701. http://dx.doi.org/10.5194/gmd-17-6683-2024.
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Abstract. Vegetation phenology plays a key role in controlling the seasonality of ecosystem processes that modulate carbon, water and energy fluxes between the biosphere and atmosphere. Accurate modelling of vegetation phenology in the interplay of Earth's surface and the atmosphere is thus crucial to understand how the coupled system will respond to and shape climatic changes. Phenology is controlled by meteorological conditions at different timescales: on the one hand, changes in key meteorological variables (temperature, water, radiation) can have immediate effects on the vegetation development; on the other hand, phenological changes can be driven by past environmental conditions, known as memory effects. However, the processes governing meteorological memory effects on phenology are not completely understood, resulting in their limited performance of vegetation phenology represented in land surface models. A deep learning model, specifically a long short-term memory network (LSTM), has the potential to capture and model the meteorological memory effects on vegetation phenology. Here, we apply the LSTM to model the vegetation phenology using meteorological drivers and high-temporal-resolution canopy greenness observations through digital repeat photography by the PhenoCam network. We compare a multiple linear regression model, a no-memory-effect LSTM model and a full-memory-effect LSTM model to predict the whole seasonal greenness trajectory and the corresponding phenological transition dates across 50 sites and 317 site years during 2009–2018, covering deciduous broadleaf forests, evergreen needleleaf forests and grasslands. Results show that the deep learning model outperforms the multiple linear regression model, and the full-memory-effect LSTM model performs better than the no-memory-effect model for all three plant function types (median R2 of 0.878, 0.957 and 0.955 for broadleaf forests, evergreen needleleaf forests and grasslands). We also find that the full-memory-effect LSTM model is capable of predicting the seasonal dynamic variations of canopy greenness and reproducing trends in shifting phenological transition dates. We also performed a sensitivity analysis of the full-memory-effect LSTM model to assess its plausibility, revealing its coherence with established knowledge of vegetation phenology sensitivity to meteorological conditions, particularly changes in temperature. Our study highlights that (1) multi-variate meteorological memory effects play a crucial role in vegetation phenology, and (2) deep learning opens up new avenues for improving the representation of vegetation phenological processes in land surface models via a hybrid modelling approach.
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Shrestha, Prabhakar, Silke Trömel, Raquel Evaristo, and Clemens Simmer. "Evaluation of modelled summertime convective storms using polarimetric radar observations." Atmospheric Chemistry and Physics 22, no. 11 (2022): 7593–618. http://dx.doi.org/10.5194/acp-22-7593-2022.
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Abstract. Ensemble simulations with the Terrestrial Systems Modelling Platform (TSMP) covering northwestern Germany are evaluated for three summertime convective storms using polarimetric X-band radar measurements. Using a forward operator, the simulated microphysical processes have been evaluated in radar observation space. Observed differential reflectivity (ZDR) columns, which are proxies for updrafts, and multi-variate fingerprints for size sorting and aggregation processes are captured by the model, but co-located specific differential phase (KDP) columns in observations are not reproduced in the simulations. Also, the simulated ZDR columns, generated by only small-sized supercooled drops, show smaller absolute ZDR values and a reduced width compared to their observational counterparts, which points to deficiencies in the cloud microphysics scheme as well as the forward operator, which does not have explicit information of water content of ice hydrometeors. Above the melting layer, the simulated polarimetric variables also show weak variability, which can be at least partly explained by the reduced particle diversity in the model and the inability of the T-matrix method to reproduce the polarimetric signatures of snow and graupel; i.e. current forward operators need to be further developed to fully exploit radar data for model evaluation and improvement. Below the melting level, the model captures the observed increase in reflectivity, ZDR and specific differential phase (KDP) towards the ground. The contoured frequency altitude diagrams (CFADs) of the synthetic and observed polarimetric variables were also used to evaluate the model microphysical processes statistically. In general, CFADs of the cross-correlation coefficient (ρhv) were poorly simulated. CFADs of ZDR and KDP were similar but the model exhibits a relatively narrow distribution above the melting layer for both, and a bimodal distribution for ZDR below the melting layer, indicating either differences in the mechanism of precipitation formation or errors in forward operator which uses a functional form of drop size distribution. In general, the model was found to underestimate the convective area fraction, high reflectivities, and the width/magnitude of ZDR columns, all leading to an underestimation of the frequency distribution for high precipitation values.
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Berg, Peter, Thomas Bosshard, Wei Yang, and Klaus Zimmermann. "MIdASv0.2.1 – MultI-scale bias AdjuStment." Geoscientific Model Development 15, no. 15 (2022): 6165–80. http://dx.doi.org/10.5194/gmd-15-6165-2022.
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Abstract. Bias adjustment is the practice of statistically transforming climate model data in order to reduce systematic deviations from a reference data set, typically some sort of observations. There are numerous proposed methodologies to perform the adjustments – ranging from simple scaling approaches to advanced multi-variate distribution-based mapping. In practice, the actual bias adjustment method is a small step in the application, and most of the processing handles reading, writing, and linking different data sets. These practical processing steps become especially heavy with increasing model domain size and resolution in both time and space. Here, we present a new implementation platform for bias adjustment, which we call MIdAS (MultI-scale bias AdjuStment). MIdAS is a modern code implementation that supports features such as modern Python libraries that allow efficient processing of large data sets at computing clusters, state-of-the-art bias adjustment methods based on quantile mapping, and “day-of-year-based” adjustments to avoid artificial discontinuities, and it also introduces cascade adjustment in time and space. The MIdAS platform has been set up such that it will continually support development of methods aimed towards higher-resolution climate model data, explicitly targeting cases where there is a scale mismatch between data sets. The paper presents a comparison of different quantile-mapping-based bias adjustment methods and the subsequently chosen code implementation for MIdAS. A current recommended setup of the MIdAS bias adjustment is presented and evaluated in a pseudo-reference setup for regions around the world. Special focus is put on preservation of trends in future climate projections, and it is shown that the cascade adjustments perform better than the standard quantile mapping implementations and are often similar to methods that explicitly preserve trends.
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朱心宇, 朱心宇, та 張惠玲 Hsin-Yu, Chu. "以多變量貝氏系集處理器配合球面調和函數波段拆解進行展期極端低溫預報". 大氣科學 50, № 3 (2022): 214–38. http://dx.doi.org/10.53106/025400022022125003002.
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<p>透過統計後處理(statistical post-processing, SPP)方法將原始模式進行偏差修正，並降尺度到高解析格點或特定點位，可提供使用者更具參考價值的預報資訊；但偏差修正往往需要大量的訓練樣本以估算預報偏差。由於展期系集重預報/後預報(reforecast/hindcast)需要大量計算資源，剛發展成熟之系集預報系統可能缺乏足夠之重(後)預報資料以進行偏差校正。本研究擬發展結合多變量貝氏系集處理器(Multi-Variate Bayesian Processor of Ensemble, BPE)與球面調和函數波段拆解(spherical harmonics decomposition, SHD)之先進SPP技術，目的在於探討以單一預報取代系集預報進行SPP之可行性，以解決上述系集預報系統缺乏大量訓練樣本，無法進行偏差校正之困境。</p> <p>本研究將原始系集模式的控制模擬(Control Run)進行SHD，以拆解後的不同波段作為BPE預報因子，進行SPP以得到臺灣測站點上之展期極端低溫機率預報。BPE演算法利用預報因子及觀測值關聯結構產生的邊緣分布作為概似函數(likelihood)，並以觀測值的氣候分布作為先驗函數(prior)，再根據最新的預報資料予以結合成後驗函數(posterior, 即為預報機率函數)；因其完全貝氏的架構，BPE在重預報資料(Reforecast)較少的狀況下，依然可以充分利用觀測氣候資料建立先驗函數，得到可信的後驗分布函數。</p> <p>本研究以美國國家環境預測中心第12版全球系集預報(NCEP GEFS v12)之 (1)2公尺最低溫系集平均作為單變量BPE預報因子 (實驗SP_BPE)、(2)控制模擬(Control Run)之標準化2公尺最低溫拆解後的波段作為多變量BPE預報因子(實驗MP_BPE_SH)，進行8至14天日極端低溫預報以及第三、四週之週極端溫度預報校正。多項指標顯示：SP_BPE與MP_BPE_SH兩種校正方式皆顯著提升原始模式的預報品質，但SP_BPE校正效果略優於MP_BPE_SH，特別是在第三、四週之週極端低溫預報。上述結果應與預報因子的可預報度有關，因此未來擬利用可預報度較高之大尺度指標做為多變量BPE的預報因子，以提升第三、四週預報之校正成效。</p> <p>&nbsp;</p><p>To produce elaborate, location specific extended-range probabilistic forecasts, it is required to bias-correct and downscale raw model. If statistical methods are used, then the two processes can be collectively phrased as &ldquo;statistical post-processing (SPP)&rdquo;. To infer a representative estimate of the raw model&rsquo;s systematic bias, large hindcast sets have to be produced. However, creating hindcast for a recently operational, state-of the art ensemble that prolongs to extended-range is very computationally extensive. In practical operations, the size of hindcast are often limited, and far smaller than observations. In this research, we aim to evaluate if the forecast performance of filtering non-linear noise using ensemble mean, is achievable by wave decomposition of a single model with the best initial condition and post-processing the wavebands using state-of-art SPP method. We hope this will reduce the reliance on both ensemble and large hindcast in SPP, providing a solution to aforementioned issues. </p> <p>The control run from raw ensemble model are waveband decomposed using spherical harmonics, and the wavebands are used as predictors. These predictors are then feed into a state-of-the-art, multi-variate Bayesian Processor of Ensemble (BPE), to produce post-processed extreme minimum temperature probabilistic forecasts located on specific meteorological stations in Taiwan. As a full Bayesian Method, BPE utilizes the copula of the predictors-predictand pair to generate a marginal distribution as the likelihood, and the climatological distribution constructed by the observational data as prior. The predictive distribution, or the posterior, are generated using fusion of likelihood and prior once receiving the latest run from the raw model. Due to its Bayesian structure, the advantage of BPE is the capability to derive well-calibrated posterior under limited reforecast data, from using a larger observation dataset to construct an informative prior.</p> <p>This research uses (1) The ensemble mean of 2-meter minimum temperature (Experiment SP_BPE), and (2) The wavebands decomposed from the standardized 2-meter minimum temperature of the control run (Experiment MP_BPE) of NCEP GEFSv12 as the predictor(s) to calibrate daily minimum temperature for lead times of 8-14 days and weekly minimum temperature for lead times of week 3 and 4. We use various performance benchmarks to compare the different aspects of probabilistic forecast and found that both SP_BPE and MP_BPE could improve the quality of probability forecasts over raw ensemble model. It is worth noting the performance of SP_BPE is better than MP_BPE_SH, especially in the lead times of week 3 to week 4, but we think that the cause should be related to the selection of predictors, instead of a deficiency in the methodology. In the future, alternative variables can be decomposed, or use large scale indices as predictors to improve the calibration of week 3 and 4.</p> <p>&nbsp;</p>
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Warikar, Evie L., Euniche R. P. F. Ramandey, and Hendra K. Maury. "Analisis Dimorfisme Kupu-Kupu Sayap Burung (Ornithoptera sp.) Endemik Papua." JURNAL BIOLOGI PAPUA 11, no. 1 (2019): 1–7. http://dx.doi.org/10.31957/jbp.634.
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Ornitophera sp. is one of the groups of butterflies endemic in Papua and West Papua provinces. The significant morphological differences between male and female butterflies (dimorphism) are in the shape, size and color of the wings. Morphological characteristics are important sources of information. Based on the results of specimen collections at the Papua Insect Collection Laboratory (KSP) Jayapura, there are several species from various locations in Papua. The variation in dimorphism is likely to occur in this species which gives rise to new sub-species. The purpose of this study was to record the dimorphism of the endemic Bird Wing of Papua which had been stored in the Papua Insect Collection Laboratory (KSP) Jayapura. The method used is a direct measurement of the morphology of bird wing butterfly specimens stored in the KSP Laboratory, Cenderawasih University. Morphometric data were analyzed using the SPSS 20 and Multi Variate Statistical Package (MVSP 3.1) programs. The results obtained were 7 Ornithoptera species stored in the KSP Jayapura Laboratory, namely O. chiamera, O. goliath, O. meridionalis, O. paradisea, O. priamus, O. thitonus and O. rothschildi. Based on observations there are striking differences in color, shape and size between male and female in the same species. Key words: birdwing butterfly, Ornithoptera sp., KSP Jayapura, dimorphisme.
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Keshavarzi, Maryam, Zahra Nazem Bokaee, and Neda Derakhshan. "The culm and glume anatomical study on Lolium (Poaceae) species in Iran." Acta Biologica Szegediensis 66, no. 1 (2022): 63–68. http://dx.doi.org/10.14232/abs.2022.1.63-68.
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Lolium (Poaceae) is composed of five species in Iran that are of forage and weed importance. Culm and glume anatomy of grasses is inadequately explored. In the present study culm and glume anatomical structures of 17 population of four Lolium species is considered by use of 16 qualitative and quantitative features to distinguish Lolium species in Iran and to determine their diagnostic value. The culm cross-section showed multilayered hypodermis, ribbed or un-ribbed culm cross-sections, cuticles with different thicknesses, and the cortex with different tissues. Vascular bundles are arranged in two to more rings in the culm anatomy. Glume anatomical observations showed differences in girder attachment to vascular bundles, the outline of the cross-section, and the cortex tissue composition. The multi-variate statistical method based on culm and glume anatomical features showed that L. perenne and L. multiflorum are grouped near each other but there is more mixture between L. persicum and L. rigidum. Our findings are in agreement with the grouping pattern achieved by previous researches on leaf anatomy and lemma and palea micro-morphology of the same species. The molecular study by ISSR also provides support for the present study. The culm and glume anatomy of species studied provides valuable data for taxonomic purposes.
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Vichi, M., and S. Masina. "Skill assessment of the PELAGOS global ocean biogeochemistry model over the period 1980–2000." Biogeosciences 6, no. 11 (2009): 2333–53. http://dx.doi.org/10.5194/bg-6-2333-2009.
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Abstract. Global Ocean Biogeochemistry General Circulation Models are useful tools to study biogeochemical processes at global and large scales under current climate and future scenario conditions. The credibility of future estimates is however dependent on the model skill in capturing the observed multi-annual variability of firstly the mean bulk biogeochemical properties, and secondly the rates at which organic matter is processed within the food web. For this double purpose, the results of a multi-annual simulation of the global ocean biogeochemical model PELAGOS have been objectively compared with multi-variate observations from the last 20 years of the 20th century, both considering bulk variables and carbon production/consumption rates. Simulated net primary production (NPP) is comparable with satellite-derived estimates at the global scale and when compared with an independent data-set of in situ observations in the equatorial Pacific. The usage of objective skill indicators allowed us to demonstrate the importance of comparing like with like when considering carbon transformation processes. NPP scores improve substantially when in situ data are compared with modeled NPP which takes into account the excretion of freshly-produced dissolved organic carbon (DOC). It is thus recommended that DOC measurements be performed during in situ NPP measurements to quantify the actual production of organic carbon in the surface ocean. The chlorophyll bias in the Southern Ocean that affects this model as well as several others is linked to the inadequate representation of the mixed layer seasonal cycle in the region. A sensitivity experiment confirms that the artificial increase of mixed layer depths towards the observed values substantially reduces the bias. Our assessment results qualify the model for studies of carbon transformation in the surface ocean and metabolic balances. Within the limits of the model assumption and known biases, PELAGOS indicates a net heterotrophic balance especially in the more oligotrophic regions of the Atlantic during the boreal winter period. However, at the annual time scale and over the global ocean, the model suggests that the surface ocean is close to a weakly positive autotrophic balance in accordance with recent experimental findings and geochemical considerations.
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Passera, Roberto, Sofia Zompi, Jessica Gill, and Alessandro Busca. "Explainable Machine Learning (XAI) for Survival in Bone Marrow Transplantation Trials: A Technical Report." BioMedInformatics 3, no. 3 (2023): 752–68. http://dx.doi.org/10.3390/biomedinformatics3030048.
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Artificial intelligence is gaining interest among clinicians, but its results are difficult to be interpreted, especially when dealing with survival outcomes and censored observations. Explainable machine learning (XAI) has been recently extended to this context to improve explainability, interpretability and transparency for modeling results. A cohort of 231 patients undergoing an allogeneic bone marrow transplantation was analyzed by XAI for survival by two different uni- and multi-variate survival models, proportional hazard regression and random survival forest, having as the main outcome the overall survival (OS) and its main determinants, using the survex package for R. Both models’ performances were investigated using the integrated Brier score, the integrated Cumulative/Dynamic AUC and the concordance C-index. Global explanation for the whole cohort was performed using the time-dependent variable importance and the partial dependence survival plot. The local explanation for each single patient was obtained via the SurvSHAP(t) and SurvLIME plots and the ceteris paribus survival profile. The survex package common interface ensured a good feasibility of XAI for survival, and the advanced graphical options allowed us to easily explore, explain and compare OS results coming from the two survival models. Before the modeling results to be suitable for clinical use, understandability, clinical relevance and computational efficiency were the most important criteria ensured by this XAI for survival approach, in adherence to clinical XAI guidelines.
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Nadal-Caraballo, Norberto C., Madison C. Yawn, Luke A. Aucoin, et al. "JOINT PROBABILITY METHOD AIDED BY METAMODEL PREDICTION FOR HURRICANE HAZARDS." Coastal Engineering Proceedings, no. 38 (May 29, 2025): 173. https://doi.org/10.9753/icce.v38.management.173.
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The quantification of coastal hazards caused by tropical cyclones (TCs) faces challenges due to insufficient historical observations, particularly for intense hurricanes, and other data limitations. In the wake of Hurricane Katrina in 2005, a hybrid probabilistic/process-based modeling approach known as the Joint Probability Method (JPM) went through in-depth scruntinity and significant improvements, spurred by the Interagency Performance Evaluation Taskforce (IPET 2009). As a result of this forensic study, IPET developed the JPM with Optimal Sampling (JPM-OS) technique for the analysis of extreme coastal flooding resulting from hurricanes. This collaborative effort involved several U.S. federal agencies, the private sector, and academic researchers. The IPET’s JPM-OS became a standard for U.S. federal agencies, including the U.S. Army Corps of Engineers (USACE) and the Federal Emergency Management Agency (FEMA), for assessing coastal storm risks in areas susceptible to hurricanes. However, this led to the development of two distinct JPM-OS variants, each with different advantantages and limitations. Since then, JPM has informed numerous storm surge and hurricane hazard studies led by the federal government, academia, and private sector. During the last decade, considerable effort was made to address limitations of existing JPM-OS approaches. Advancements in multi-variate statistics and machine learning (ML) led to the development of a more robust version of the JPM called JPM Aided by Metamodel Prediction (JPM-AMP).
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Guo, Danlu, Arash Parehkar, Dongryeol Ryu, Quan J. Wang, and Andrew W. Western. "Parsimonious Gap-Filling Models for Sub-Daily Actual Evapotranspiration Observations from Eddy-Covariance Systems." Remote Sensing 14, no. 5 (2022): 1286. http://dx.doi.org/10.3390/rs14051286.
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Missing data and low data quality are common issues in field observations of actual evapotranspiration (ETa) from eddy-covariance systems, which necessitates the need for gap-filling techniques to improve data quality and utility for further analyses. A number of models have been proposed to fill temporal gaps in ETa or latent heat flux observations. However, existing gap-filling approaches often use multi-variate models that rely on relationships between ETa and other meteorological and flux variables, highlighting a critical lack of parsimonious gap-filling models. This study aims to develop and evaluate parsimonious approaches to fill gaps in ETa observations. We adapted three gap-filling models previously used for other meteorological variables but never applied to infill sub-daily ETa or flux observations from eddy-covariance systems before. All three models are solely based on the observed diurnal patterns in the ETa data, which infill gaps in sub-daily data with sinusoidal functions (Sinusoidal), smoothing functions (Smoothing) and pattern matching (MaxCor) approaches, respectively. We presented a systematic approach for model evaluation, considering multiple patterns of data gaps during different times of the day. The three gap-filling models were evaluated together with another benchmarking gap-filling model, mean diurnal variation (MDV) that has been commonly used and has similar data requirement. We used a case study with field measurements from an EC system over summer 2020–2021, at a maize field in southeastern Australia. We identified the MaxCor model as the best gap-filling model, which informs the diurnal pattern of the day to infill by using another day with similar temporal patterns and complete data. Following the MaxCor model, the MDV and the Sinusoidal models show comparable performances. We further discussed the infilling models in terms of their dependence on data availability and their suitability for different practical situations. The MaxCor model relies on high data availability for both days with complete data and the available records within each day to infill. The Sinusoidal model does not rely on any day with complete data, which makes it the ideal choice in situations where days with complete records are limited.
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Nair, P. J., S. Godin-Beekmann, J. Kuttippurath, et al. "Ozone trends derived from the total column and vertical profiles at a northern mid-latitude station." Atmospheric Chemistry and Physics Discussions 13, no. 3 (2013): 7081–112. http://dx.doi.org/10.5194/acpd-13-7081-2013.
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Abstract. The trends and variability of ozone are assessed over a northern mid-latitude station, Haute-Provence Observatory (OHP – 43.93° N, 5.71° E), using total column ozone observations from the Dobson and Système d'Analyse par Observation Zénithale spectrometers, and stratospheric ozone profile measurements from Light detection and ranging, ozonesondes, Stratospheric Aerosol and Gas Experiment II, Halogen Occultation Experiment and Aura Microwave Limb Sounder. A multi-variate regression model with quasi biennial oscillation (QBO), solar flux, aerosol optical thickness, heat flux, North Atlantic oscillation (NAO) and piecewise linear trend (PWLT) or Equivalent Effective Stratospheric Chlorine (EESC) functions is applied to the ozone anomalies. The maximum variability of ozone in winter/spring is explained by QBO and heat flux in 15–45 km and in 15–24 km, respectively. The NAO shows maximum influence in the lower stratosphere during winter while the solar flux influence is largest in the lower and middle stratosphere in summer. The total column ozone trends estimated from the PWLT and EESC functions are of −1.39±0.26 and −1.40±0.25 DU yr−1, respectively over 1984–1996 and about 0.65±0.32 and 0.42±0.08 DU yr−1, respectively over 1997–2010. The ozone profiles yield similar and significant EESC-based and PWLT trends in 1984–1996 and are about −0.5 and −0.8 % yr−1 in the lower and upper stratosphere, respectively. In 1997–2010, the EESC-based and PWLT trends are significant and of order 0.3 and 0.1 % yr−1, respectively in the 18–28 km range, and at 40–45 km, EESC provides significant ozone trends larger than the insignificant PWLT results. Therefore, this analysis unveils ozone recovery signals from total column ozone and profile measurements at OHP, and hence in the mid-latitudes.
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Chang, Erick Paulo Cesar, and Magdy Noguera. "The governance mechanisms of family-controlled REITs." Journal of Family Business Management 6, no. 2 (2016): 122–42. http://dx.doi.org/10.1108/jfbm-02-2015-0012.
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Purpose – The purpose of this paper is to analyze how founders of family-controlled Real Estate Investment Trusts (REITs) under bounded rationality implement internal governance mechanisms that may affect the long-term performance once the founder retires. These actions create a hurdle for successors to follow the founder’s success. Design/methodology/approach – The authors collected data on secondary sources of 36 family and 22 professionally managed REITs from 1999 to 2012 that resulted in an unbalanced panel data of 726 REIT-year observations. The authors use a series of multi-variate analyses to test the hypotheses. Findings – The findings confirm that founders of family-controlled REITs focus more on developing internal governance mechanisms to satisfy their personal goals. Long-term performance is negatively affected once the successor takes over especially when the successor is a family member. Research limitations/implications – The authors have data limitations about family involvement. The authors suggest future avenues of investigation such as combining perceptual with archival data. Practical implications – The authors expect that REIT managers and families can use the findings to develop viable and sustainable governance practices. Especially, being a publicly traded REIT implies to conform to the market expectations so there is a need to balance socio emotional wealth preservation with financial goals. Originality/value – The authors frame the paper on transaction cost economics and contribute to the literature by stating that the dominance of founders of family-controlled REITs are more aligned to keep the business under family control once the founder retires.
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Tariq, Hasan, Farid Touati, Damiano Crescini, and Adel Ben Mnaouer. "IoT-Based Bi-Cluster Forecasting Using Automated ML-Model Optimization for COVID-19." Atmosphere 14, no. 3 (2023): 534. http://dx.doi.org/10.3390/atmos14030534.
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The current COVID-19 pandemic has raised huge concerns about outdoor air quality due to the expected lung deterioration. These concerns include the challenges associated with an increase of harmful gases like carbon dioxide, the iterative/repetitive inhalation due to mask usage, and harsh environmental temperatures. Even in the presence of air quality sensing devices, these challenges can hinder the prevention and treatment of respiratory diseases, epidemics, and pandemics in severe cases. In this research, a dual time series with a bi-cluster sensor data-stream-based novel optimized regression algorithm was proposed with optimization predictors and responses that use an automated iterative optimization of the model based on the similarity coefficient index. The algorithm was implemented over SeReNoV2 sensor nodes data, i.e., a multi-variate dual time-series sensor, of the environmental and US Environmental Protection Agency standard, which measures variables for the air quality index using air quality sensors with geospatial profiling. The SeReNoV2 systems were placed at four locations that were 3 km apart to monitor the air quality and their data was collected at Ubidots IoT platform over GSM. The results have shown that the proposed technique achieved a root mean square error (RMSE) of 1.0042 with a training time of 469.28 s for the control and an RMSE of 1.646 in a training time of 28.53 s when optimized. The estimated R-Squared error was 0.03, with the Mean-Square Error for temperature being 1.0084 °C, and 293.98 ppm for CO2. Furthermore, the Mean-Absolute Error (MAE) for temperature was 0.66226 °C and 10.252 ppm for the correlated-CO2 at a predicted speed of ~5100 observations/s. In the sample cluster for temperature, 45,000 observations/s for CO2 was achieved due to the iterative optimization of the training time (469.28 s). The correlated temperature and a time of 28.53 s for CO2 were very promising in forecasting COVID-19 countermeasures before time.
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Chowdhury, Ranak Roy, Jiacheng Li, Xiyuan Zhang, Dezhi Hong, Rajesh K. Gupta, and Jingbo Shang. "PrimeNet: Pre-training for Irregular Multivariate Time Series." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7184–92. http://dx.doi.org/10.1609/aaai.v37i6.25876.
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Real-world applications often involve irregular time series, for which the time intervals between successive observations are non-uniform. Irregularity across multiple features in a multi-variate time series further results in a different subset of features at any given time (i.e., asynchronicity). Existing pre-training schemes for time-series, however, often assume regularity of time series and make no special treatment of irregularity. We argue that such irregularity offers insight about domain property of the data—for example, frequency of hospital visits may signal patient health condition—that can guide representation learning. In this work, we propose PrimeNet to learn a self-supervised representation for irregular multivariate time-series. Specifically, we design a time sensitive contrastive learning and data reconstruction task to pre-train a model. Irregular time-series exhibits considerable variations in sampling density over time. Hence, our triplet generation strategy follows the density of the original data points, preserving its native irregularity. Moreover, the sampling density variation over time makes data reconstruction difficult for different regions. Therefore, we design a data masking technique that always masks a constant time duration to accommodate reconstruction for regions of different sampling density. We learn with these tasks using unlabeled data to build a pre-trained model and fine-tune on a downstream task with limited labeled data, in contrast with existing fully supervised approach for irregular time-series, requiring large amounts of labeled data. Experiment results show that PrimeNet significantly outperforms state-of-the-art methods on naturally irregular and asynchronous data from Healthcare and IoT applications for several downstream tasks, including classification, interpolation, and regression.
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Vichi, M., and S. Masina. "Skill assessment of the PELAGOS global ocean biogeochemistry model over the period 1980–2000." Biogeosciences Discussions 6, no. 2 (2009): 3511–62. http://dx.doi.org/10.5194/bgd-6-3511-2009.
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Abstract. Global Ocean Biogeochemistry General Circulation models are useful tools to study biogeochemical processes at gobal and large scales under current climate and future scenario conditions. The accuracy of the future estimate is however dependent on the adequate representation of the current ocean biogeochemical features. To this purpose, the results of an interannual simulation of the global ocean biogeochemical model PELAGOS have been objectively compared with multi-variate observations from the last 20 years of the XX century. The model was assessed in terms of spatial and temporal variability of chlorophyll and primary production derived from satellite sensors, with a specific focus on the simulation of carbon production/consumption rates observed in the equatorial Pacific ocean and at the long-term JGOFS stations. The predicted chlorophyll is acceptable in the northern mid-latitude regions and equatorial Pacific, but is underestimated in the upwelling regions of the Atlantic and Indian Oceans and markedly overestimated in the Southern Ocean. This latter bias is linked to the inadequate representation of the mixed layer seasonal cycle in the region, which favours primary production during austral spring. Simulated primary production is comparable with satellite estimates both at the global scale and when compared with an independent data-set in the equatorial Pacific. A comparison with other models showed that PELAGOS results are as good as the estimates from state-of-the-art diagnostic models based on satellite data. The skill in reproducing the interannual varibility was assessed in the equatorial Pacific and against the decadal JGOFS timeseries BATS and HOT. In the tropical Pacific our analysis suggests that interannual variability of primary production is related to the climate variability both in the observations and in the model. At the JGOFS stations PELAGOS has skill to simulate the observed bacterial biomass and shows realistic means of primary and bacterial production at BATS. These results have been further strenghtened with an analysis of spatial variability of microbial carbon production/consumption and comparison with observations along a transect in the Atlantic ocean. Within the limits of the model assumption and known biases, PELAGOS predicts that the system is net heterotrophic if the boreal winter period only is considered and especially in the more oligotrophic regions. However, at the annual time scale and over the global ocean, the model suggests that surface ocean is close to a slightly positive autotrophic balance in accordance with recent experimental findings and geochemical evidences.
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Xiaoru Yuan, He Xiao, Hanqi Guo, et al. "Scalable Multi-variate Analytics of Seismic and Satellite-based Observational Data." IEEE Transactions on Visualization and Computer Graphics 16, no. 6 (2010): 1413–20. http://dx.doi.org/10.1109/tvcg.2010.192.
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Hlaing Than, Win, Opass Putcharoen, Anchalee Avihingsanon, and Stephen Kerr. "348. Kidney Function Decline Among HIV-infected Thai Adults: Is Low Vitamin D One of the Factors?" Open Forum Infectious Diseases 6, Supplement_2 (2019): S184—S185. http://dx.doi.org/10.1093/ofid/ofz360.421.
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Abstract Background The prevalence of both hypovitaminosis D and Chronic Kidney disease (CKD) are high among Thai HIV-infected adults. Therefore, we examined the association of hypovitaminosis D and kidney function decline among HIV-infected Thai adults. Methods Using data prospectively collected from the HIV-NAT long-term cohort, we selected patients who were on ART, and virologically suppressed for ≥6 months. Baseline was defined as when the patient had a serum 25 OHD measured, with estimated Glomerular filtration rate (eGFR) above 60 mL/minute. Participants with eGFR measured at least twice a year were analyzed in the study. The primary outcome was kidney function impairment assessed as eGFR decline. Generalised estimating equations (GEE) were used to assess associations between the outcome and patient comorbidities and disease-related characteristics, including age, sex, body mass index (BMI) hypertension, gout, diabetes mellitus, co-infections with Hepatitis B or C viruses HIV-viral load and co-variate interactions with vitamin D status defined as normal, insufficient or deficient. Results A total of 435 participants were observed longitudinally through observations over the median follow-up of 24 (IOR 12–48) months. The median age of the participants was 46.6 (IOR 38.06–54.29) years. Median serum 25 OHD was 23.4 (IQR 18.5–29) ng/mL, and 209 (48%) and 126(29%) had insufficient and deficient 25 OH levels, respectively. Median baseline eGFR was 95 (IQR 82.70–104.93) mL/minute/l.73 m2. We found a significant interaction between BMI and vitamin D concentration (P = 0.02). In our multivariate model, the adjusted mean predictions of eGFR change at 24 months for patients with BMI ≥25 kg/m2 and deficient, insufficient and sufficient vitamin D were 89.8 (88.3–91.4), 91.2 (90.1–92.4) and 92.8 (91.3–94.4), respectively. In those with BMI <25 kg/m2 and deficient, insufficient and sufficient Vitamin D the adjusted mean predictions in eGFR change were 92.0 (91.1–93.0), 91.6 (90.9–92.3) and 92.3 (91.3–93.3), respectively. Conclusion HIV-infected Thai adults with high BMI (25 and above) but who are vitamin D deficient had a statistically significant eGFR decline. Further studies in larger populations with multi-ethnic groups are warranted. Disclosures All authors: No reported disclosures.
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Aravamudan, Akshay, Xi Zhang, and Georgios C. Anagnostopoulos. "Anytime User Engagement Prediction in Information Cascades for Arbitrary Observation Periods." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4999–5009. http://dx.doi.org/10.1609/aaai.v37i4.25627.
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Predicting user engagement -- whether a user will engage in a given information cascade -- is an important problem in the context of social media, as it is useful to online marketing and misinformation mitigation just to name a couple major applications. Based on split population multi-variate survival processes, we develop a discriminative approach that, unlike prior works, leads to a single model for predicting whether individual users of an information network will engage a given cascade for arbitrary forecast horizons and observation periods. Being probabilistic in nature, this model retains the interpretability of its generative counterpart and renders count prediction intervals in a disciplined manner. Our results indicate that our model is highly competitive, if not superior, to current approaches, when compared over varying observed cascade histories and forecast horizons.
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Suryati Lindung, Maria Eflinda. "MODEL PEMBELAJARAN KOOPERATIF TIPE STAD DAN TIPE NHT TERHADAP KEAKTIFAN DAN HASIL BELAJAR SISWA." Jurnal Riset Pembelajaran Matematika 3, no. 1 (2021): 1–8. http://dx.doi.org/10.55719/jrpm.v3i1.163.
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The purpose of this study is to determine whether there is a difference in the application of the cooperative learning model type STAD and type NHT on student activity and learning outcomes. This study uses a quantitative approach and the method used is an experimental method with data collection techniques using test and observation methods. The statistical test used was the multi-variate analysis of variance test. The result of this research is that there is a significant difference between the STAD and NHT cooperative learning models on student learning outcomes and activities.
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Mengistu, Merihun, Mesfin Tebeje, Almaz Balta, Debalke Debala, and Belete Limani. "Exploring Potential Adaptation Strategies for Smallholder Farmers to Climate Change: A Case Study at Abala Abaya, Wolaita Zone, South Ethiopia." Trends in Ecological and Indoor Environmental Engineering 2, no. 3 (2024): 26–34. http://dx.doi.org/10.62622/teiee.024.2.3.26-34.
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Background: The frequency and intensity of extreme weather events are expected to increase as a result of climate change, which will contribute to changes in precipitation and temperature, and therefore will affect food security. Research issues related to increasing the sustainability of the agricultural sector, eliminating climate risks in agriculture are coming to the fore. This is especially relevant in arid regions. Objectives: This study aimed to assess the adaptation strategies of smallholder farmers to climate change in Abela Abaya, Wolaita Zone, Southern Ethiopia. Finding optimal practices adapted to random extreme events is fundamental for sustainable food production. In particular, the study attempted to understand the preferences of smallholder farmers in adaptation strategies to climate change in the study area; the main influencing factors determining the choice of adaptation strategies to climate change by smallholder farmers in the study area. Methods: To investigate this issue, qualitative and quantitative data were used within a descriptive research approach to assess potential adaptation strategies of smallholder farmers to climate change. Data on demographic, socio-economic, institutional, physical and psychological factors for adaptation strategies of smallholder farmers were collected through specially designed and pre-tested questionnaires, interviews and focus group discussions (FGDs). Rural counties for the study were selected using simple random sampling due to the same agroecology. The multivariate probit (MVP) model was used as it is a type of correlated binary response regression model that allows for the simultaneous identification and assessment of the impact of a set of independent factors on each of the possible approaches. Five adaptation methods are identified as dependent variables for the Multi-Variate Probit. These include Soil and Water Conservation (SWC) practices, use of drought-tolerant varieties, adjustment of planting dates, use of agroforestry, and implementation of water harvesting. The following were selected as independent variables for the current study. Results: Most farmers (96.7%) acknowledge the ongoing climate change and are concerned that their agriculture will not suffer under the new conditions. This is justified by the fact that farmers confirm their observations regarding the increase in air temperature, the increase in the frequency of plant diseases and the decrease in precipitation. At the same time, only 36.2% of the surveyed farmers reported that society is aware of the possible risks associated with global warming and is trying to adapt agricultural activities to new climatic conditions. In particular, the current study revealed for the first time that smallholder farmers in Abela Abaya primarily prefer approaches such as soil and water conservation (71.6%), adjustment of planting dates (59.4%), and agroforestry (44.5%). Conclusion: The study identified the most influential factors on the adoption of adapted agricultural practices in a specific region of Abala Abaya (Wolaita Zone, Southern Ethiopia), namely, household heads’ access to education, frequency of extension visits, access to climate information and land slope. Thus, decision makers can design and adopt appropriate programs based on the current results to preserve smallholder farming and maintain food security at the national level.
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Qi, Gang, Ming Fan, and Jian Yu Li. "Statistical Ensemble Interactions in Microstructures where Damage Events Present." Key Engineering Materials 577-578 (September 2013): 65–68. http://dx.doi.org/10.4028/www.scientific.net/kem.577-578.65.
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This work reports a study of the ensemble interactions in hierarchical structures where damage events present, a significant issue in the analysis of material failure. Our approach was data-enabled in which we constructed a multi-variate composing of the amplitude spectrum matrix of responses of the evolving hierarchical microstructure to the applied stress. The columns and rows of this matrix are vectors of the amplitude scale and the observation vectors of multiscale random damage events, respectively. The results showed that damage mechanisms occurred in ensemble clusters, and the multiscale random damage was strongly correlated in spacetime.
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Loslever, P. "Error and Data Coding in the Multi-Dimensional Analysis of Human Movement Signals." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 207, no. 2 (1993): 103–10. http://dx.doi.org/10.1243/pime_proc_1993_207_277_02.
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This paper discusses two main problems of human motion data: their uncertainty and analysis. Considering the first point, a simulating method is proposed to assess the error. This approach is applied to a joint angle, computed from the positions of points obtained through a three-dimensional video-computer system. Considering the second point, a multi-variate methodology based on appropriate data coding and the correspondence factor analysis method is proposed. The outcomes of this allow the relations within the time windows of the variable set, the distances within the observation set and the correspondences between these two sets to be shown graphically. To illustrate this approach, two examples are considered: the analysis of the low back-pelvis angle in an ergonomical study about the sitting posture and the analysis of joint angles in the gait.
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Koehler, Jonas, André Bauer, Andreas J. Dietz, and Claudia Kuenzer. "Towards Forecasting Future Snow Cover Dynamics in the European Alps—The Potential of Long Optical Remote-Sensing Time Series." Remote Sensing 14, no. 18 (2022): 4461. http://dx.doi.org/10.3390/rs14184461.
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Snow is a vital environmental parameter and dynamically responsive to climate change, particularly in mountainous regions. Snow cover can be monitored at variable spatial scales using Earth Observation (EO) data. Long-lasting remote sensing missions enable the generation of multi-decadal time series and thus the detection of long-term trends. However, there have been few attempts to use these to model future snow cover dynamics. In this study, we, therefore, explore the potential of such time series to forecast the Snow Line Elevation (SLE) in the European Alps. We generate monthly SLE time series from the entire Landsat archive (1985–2021) in 43 Alpine catchments. Positive long-term SLE change rates are detected, with the highest rates (5–8 m/y) in the Western and Central Alps. We utilize this SLE dataset to implement and evaluate seven uni-variate time series modeling and forecasting approaches. The best results were achieved by Random Forests, with a Nash–Sutcliffe efficiency (NSE) of 0.79 and a Mean Absolute Error (MAE) of 258 m, Telescope (0.76, 268 m), and seasonal ARIMA (0.75, 270 m). Since the model performance varies strongly with the input data, we developed a combined forecast based on the best-performing methods in each catchment. This approach was then used to forecast the SLE for the years 2022–2029. In the majority of the catchments, the shift of the forecast median SLE level retained the sign of the long-term trend. In cases where a deviating SLE dynamic is forecast, a discussion based on the unique properties of the catchment and past SLE dynamics is required. In the future, we expect major improvements in our SLE forecasting efforts by including external predictor variables in a multi-variate modeling approach.
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HEIDARIAN, MEHDI, SEYED MOHAMMAD MAHDI HAMDI, MOHAMMAD MEHDI DEHSHIRI, TAHER NEJADSATTARI, and SEYED MOHAMMAD MASOUMI. "Pollen grain micromorphology in some genera of Ornithogaleae (Asparagaceae, Scilloideae) and its relative significance." Phytotaxa 458, no. 4 (2020): 242–56. http://dx.doi.org/10.11646/phytotaxa.458.4.2.
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In this research, the pollen micromorphology of nine species belonging to Honorius, Loncomelos, Ornithogalum and Stellarioides are observed by LM (Light Microscopy), SEM (Scanning Electron Microscopy) and two species by TEM (Transmission Electron Microscopy). Lately, the phylogenetic studies have determined nineteen monophyletic genera in subfamily Ornithogaloideae (=tribe Ornithogaleae). Our results show that the pollen grains characters such as shape, size, exine ornamentation on equatorial surface, sulcus margin exine ornamentation, sulcus membrane ornamentation and lumina number on exine surface are varied among all studied genera. The dendrogram of palynological characters analyzed on LM and SEM observation by MVSP (Multi Variate Statistical Package) software partly supports the monophyletic genera. According to the obtained palynological dendrogram, Loncomelos arcuatum and Ornithogalum sintenisii are placed in one subtype. Based on the sporoderm structure (exine thickness and the endexine type), L. arcuatum differs from O. sintenisii. Finally, the palynological characters can be useful for clarifying the taxonomic relationship in this subfamily.
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K. C., Ashish, Md Moinuddin, Mary Kinney, et al. "Mistreatment of newborns after childbirth in health facilities in Nepal: Results from a prospective cohort observational study." PLOS ONE 16, no. 2 (2021): e0246352. http://dx.doi.org/10.1371/journal.pone.0246352.
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Background Patient experience of care reflects the quality of health care in health facilities. While there are multiple studies documenting abuse and disrespect to women during childbirth, there is limited evidence on the mistreatment of newborns immediately after childbirth. This paper addresses the evidence gap by assessing the prevalence and risk factors associated with mistreatment of newborns after childbirth in Nepal, based on a large-scale observational study. Methods and findings This is a prospective observational cohort study conducted over a period of 18 months in 4 public referral hospitals in Nepal. All newborns born at the facilities during the study period, who breathed spontaneously and were observed, were included. A set of indicators to measure mistreatment for newborns was analysed. Principal component analysis was used to construct a single newborn mistreatment index. Uni-variate, multi-variate, and multi-level analysis was done to measure the association between the newborn mistreatment index and demographic, obstetric, and neonatal characteristics. A total of 31,804 births of newborns who spontaneously breathed were included. Among the included newborns, 63.0% (95% CI, 62.5–63.5) received medical interventions without taking consent from the parents, 25.0% (95% CI, 24.5–25.5) were not treated with kindness and respect (roughly handled), and 21.4% (95% CI, 20.9–21.8) of them were suctioned with no medical need. Among the newborns, 71.7% (95% CI, 71.2–72.3) had the cord clamped within 1 minute and 77.6% (95% CI, 77.1–78.1) were not breast fed within 1 hour of birth. Only 3.5% (95% CI, 3.2–3.8) were kept in skin to skin contact in the delivery room after birth. The mistreatment index showed maximum variation in mistreatment among those infants born to women of relatively disadvantaged ethnic groups and infants born to women with 2 or previous births. After adjusting for hospital heterogeneity, infants born to women aged 30–34 years (β, -0.041; p value, 0.01) and infants born to women aged 35 years or more (β, -0.064; p value, 0.029) were less mistreated in reference to infants born to women aged 18 years or less. Infants born to women from the relatively disadvantaged (chhetri) ethnic groups (β, 0.077; p value, 0.000) were more likely to be mistreated than the infants born to relatively advantaged (brahmin) ethnic groups. Female newborns (β, 0.016; p value, 0.015) were more likely to be mistreated than male newborns. Conclusions The mistreatment of spontaneously breathing newborns is high in public hospitals in Nepal. Mistreatment varied by hospital, maternal ethnicity, maternal age, and sex of the newborn. Reducing mistreatment of newborns will require interventions at policy, health system, and individual level. Further, implementation studies will be required to identify effective interventions to reduce inequity and mistreatment of newborns at birth.
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Elsayed, Marwa, Samah Mohamed Mahmoud, Lamia Hamed Mohamed, Walid Omar, and Kamel Abdelaziz. "Prediction of Outcome in Anemic Critically ill Patients in Intensive Care Unit: A Retrospective Observational Study." Open Access Macedonian Journal of Medical Sciences 10, B (2022): 584–88. http://dx.doi.org/10.3889/oamjms.2022.7659.
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Objective: Anemia is a universal finding in critically-ill patients which add to the risk of poor outcomes. We aimed to evaluate the prevalence of anemic critically-ill patients and to detect predictors of outcome.
 
 Methods: A retrospective study from 5-year data in the electronic medical record of the critical care department at Cairo University. All medical ICU patients that were admitted with or developed anemia during their ICU stay were included. All data of admission, ICU stay, and APACHE II were recorded and their association with the final outcome was determined.
 
 Results: The study included 301 patients. Anemia was detected in 84.6% of patients. Blood transfusion was given to 61.8% of patients while not given to 38.2% and was not significantly associated with outcome. Non-survivors were 37.5%. Only DM, IHD and CKD were associated with poor outcome (P values 0.045, 0.026, 0.001 respectively). Need for MV or vasopressors was associated with poor outcome (P values 0.0001 for both). WBC, platelets at admission, and Hb at day 14 (P values 0.014, 0.002, 0.0001) respectively. The Multi-variate analysis detected these variables (need for MV, vasopressors, and Hb at the 14 th day ≤7.3 g/dl) could correctly detect outcome with sensitivity 91.6%, specificity 73.6%, PPV 76.7%, and NPV 90.3% and total accuracy 82.4%.
 
 Conclusions: Anemia in critically-ill patients is common and associated with poor outcomes. Blood transfusion was not associated with a better outcome. Combination of MV need and vasopressors with persistent anemia at day 14 were the best predictors of poor outcomes.
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Rahardja, Viryanando Evan, Made Pharmawati, and Ni Made Gari. "ANALISIS KERAGAMAN GENETIK VARIETAS JAGUNG (Zea mays L.) HIBRIDA MENGGUNAKAN MARKA RAPD (RANDOM AMPLIFIED POLYMORPHIC DNA)." SIMBIOSIS 10, no. 2 (2022): 223. http://dx.doi.org/10.24843/jsimbiosis.2022.v10.i02.p09.
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Analysis of genetic diversity is essential in developing hybrid maize (Zea mays L) varieties. The purpose of this study was to analyze the genetic diversity of six maize varieties using the PCR-RAPD (Polymerase Chain Reaction-Random Amplified Polymorphic DNA) technique. Hybrid maize varieties used in this study were Srikandi, Arumba, Mira, Magenta, and Simba. A local maize variety originating from Rote Island, East Nusa Tenggara Province was used as a comparison. Seeds were planted for the observation of morphological characteristics such as roots, leaves, and stems, as well as DNA extraction samples. DNA was extracted using the CTAB method and PCR-RAPD was performed using 3 primers. The size of the PCR product was determined using semilog paper. Genetic diversity between varieties were analyzed using the MVSP (Multi-Variate Statistical Package) program with the UPGMA (Unweighted Pair Group Method with Arithmetic Mean) method. The results of PCR-RAPD gave 28 DNA bands with sizes ranging from 310 bp to 1550 bp and produced 23 polymorphic bands. Based on the dendrogram resulting from PCR-RAPD data, there was one cluster consisting of Rote, Srikandi, Arumba, Mira, and Magenta varieties, as well as another cluster consisting of only the Simba variety. The local variety of Rote and the hybrid variety of Srikandi have the closest genetic distance, while the Simba variety has the farthest genetic distance from the other five varieties.
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Artawan, Gede. "PENGARUH PENDEKATAN KONSTRUKTIVISME TERHADAP AKTIVITAS DAN HASIL BELAJAR SISWA DALAM PEMBELAJARAN TEKS BIOGRAFI." Journal of Education Research and Evaluation 1, no. 4 (2017): 217. http://dx.doi.org/10.23887/jere.v1i4.12151.
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The purpose of this peneitian is to know the influence of constructivism approach to learning in the learning activity biographical text on eighth grade students at SMP Harapan Nusantara. To determine the effect of using constructivism approach to the learning outcomes in learning biography text in class VIII in SMP Harapan Nusantara. To determine the effect of using constructivism approach to the activities and achievement in learning biography text in class VIII in SMP Harapan Nusantara. Collecting data in this study conducted by the method of observation and testing methods / giving tasks to create a text biography. Observation methods used to assess student learning activities and testing methods used to assess student learning outcomes. In the test methods exist in the form of the initial test or pre-test, in the form of tests made text biography and post-test method is given at the end of the study. Final test or post-test given immediately after the students are given learning biography text using constructivism approach the experimental class and the control class conventional model. Based on test results obtained multivariate statistical values Pillai, Strace Wilk's Lambada Hotelling's Trace, and Roy'sLargest Root each with Fhitung = 50, 720 with significance = 0.05 dba = 2 and obtained Ftabel dbd = 80 = 3, 232 at the 5% significance level. From the data obtained by analysis of F larger than F table, the null hypothesis (H0) is rejected and the alternative hypothesis (Ha) is accepted. From the analysis of the data to test the hypothesis using multi-variate statistical variance of the activity and student learning outcomes can be concluded using kosntruktivisme approach has a significant effect on activity and learning outcomes in learning biography text in class VIII in SMP Harapan Nusantara.
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Nagarajan, Karikalan, Malaisamy Muniyandi, Senthil Sellappan, et al. "A study on tuberculosis disease disclosure patterns and its associated factors: Findings from a prospective observational study in Chennai." PLOS ONE 18, no. 1 (2023): e0280812. http://dx.doi.org/10.1371/journal.pone.0280812.
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Background Disclosure of tuberculosis (TB) status by patients is a critical step in their treatment cascade of care. There is a lack of systematic assessment of TB disclosure patterns and its positive outcomes which happens dynamically over the disease period of individual patients with their family and wider social network relations. Methods This prospective observational study was conducted in Chennai Corporation treatment units during 2019–2021. TB patients were recruited and followed-up from treatment initiation to completion. Information on disease disclosures made to different social members at different time points, and outcomes were collected and compared. Bivariate and multi variate analysis were used to identify the patients and contact characteristics predictive of TB disclosure status. Results A total of 466 TB patients were followed-up, who listed a total of 4039 family, extra familial and social network contacts of them. Maximum disclosures were made with family members (93%) and half of the relatives, occupational contacts and friendship contacts (44–58%) were disclosed within 15 days of treatment initiation. Incremental disclosures made during the 150–180 days of treatment were highest among neighbourhood contacts (12%), and was significantly different between treatment initiation and completion period. Middle aged TB patients (31 years and 46–55 years) were found less likely to disclose (AOR 0.56 and 0.46 respectively; p<0.05) and illiterates were found more likely to disclose their TB status (AOR 3.91; p<0.05). Post the disclosure, family contacts have mostly provided resource support (44.90%) and two third of all disclosed contacts have provided emotional support for TB patients (>71%). Conclusion Findings explain that family level disclosures were predominant and disclosures made to extra familial network contacts significantly increased during the latter part of treatment. Emotional support was predominantly received by TB patients from all their contacts post disclosure. Findings could inform in developing interventions to facilitate disclosure of disease status in a beneficial way for TB patients.
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Schmidt, Luisa, Matthias Forkel, Ruxandra-Maria Zotta, et al. "Assessing the sensitivity of multi-frequency passive microwave vegetation optical depth to vegetation properties." Biogeosciences 20, no. 5 (2023): 1027–46. http://dx.doi.org/10.5194/bg-20-1027-2023.
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Abstract. Vegetation attenuates the microwave emission from the land surface. The strength of this attenuation is quantified in models in terms of the parameter vegetation optical depth (VOD) and is influenced by the vegetation mass, structure, water content, and observation wavelength. Earth observation satellite sensors operating in the microwave frequencies are used for global VOD retrievals, enabling the monitoring of vegetation at large scales. VOD has been used to determine above-ground biomass, monitor phenology, or estimate vegetation water status. VOD can be also used for constraining land surface models or modelling wildfires at large scales. Several VOD products exist, differing by frequency/wavelength, sensor, and retrieval algorithm. Numerous studies present correlations or empirical functions between different VOD datasets and vegetation variables such as the normalized difference vegetation index, leaf area index, gross primary production, biomass, vegetation height, or vegetation water content. However, an assessment of the joint impact of land cover, vegetation biomass, leaf area, and moisture status on the VOD signal is challenging and has not yet been done. This study aims to interpret the VOD signal as a multi-variate function of several descriptive vegetation variables. The results will help to select VOD at the most suitable wavelength for specific applications and can guide the development of appropriate observation operators to integrate VOD with large-scale land surface models. Here we use VOD from the Land Parameter Retrieval Model (LPRM) in the Ku, X, and C bands from the harmonized Vegetation Optical Depth Climate Archive (VODCA) dataset and L-band VOD derived from Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) sensors. The leaf area index, live-fuel moisture content, above-ground biomass, and land cover are able to explain up to 93 % and 95 % of the variance (Nash–Sutcliffe model efficiency coefficient) in 8-daily and monthly VOD within a multi-variable random forest regression. Thereby, the regression reproduces spatial patterns of L-band VOD and spatial and temporal patterns of Ku-, X-, and C-band VOD. Analyses of accumulated local effects demonstrate that Ku-, X-, and C-band VOD are mostly sensitive to the leaf area index, and L-band VOD is most sensitive to above-ground biomass. However, for all VODs the global relationships with vegetation properties are non-monotonic and complex and differ with land cover type. This indicates that the use of simple global regressions to estimate single vegetation properties (e.g. above-ground biomass) from VOD is over-simplistic.
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Eneojo, Adah Patrick, Dr Emmanuel Bola Jonah K, Dr Onoja-Alexander Mary Ojonema, et al. "Risk Factor Determinants for Bemonc Services Uptake at Basic Health Care Provision Fund (BHCPF) Ward Focal HCFS in Kogi State." International Journal of Research and Scientific Innovation XII, no. XV (2025): 169–86. https://doi.org/10.51244/ijrsi.2025.121500015p.
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This descriptive cross-sectional (non-exposure assigning), survey on Basic Health Care Provision Fund (BHCPF) Basic Emergency Obstetric and Newborn Care (BEmONC) Quality of Care (QoC) in Kogi State of Nigeria was implemented as evidence base for informed decision making towards action on and leverage for the strengthening of the essential public health functions during a five-month period between April to August, 2024. The epidemiologic applied approach provided the data in a process that identified, characterized and quantified proximate socio-ecological risk factors that are determinants (moderators and mediators) of the uptake of BEmONC services measured by Antenatal care, (ANC), Delivery, Postnatal care (PNC) indicators at n=206 Primary Health Care centres (PHCs) and outcome indicators not mutually exclusive of the optimized Maternal, Newborn and Child Health Week (oMNCHW) which was implemented between 29th of July and the 2nd of August, 2024. The datasets were obtained from on-line real-time responses from eligible population on the pre-tested checklist processed through an open data kit applying unique artificial intelligence and geo-location. The research was based on a sample size for the cross-sectional multi-stage sampling survey derived through applying the Cochran method to arrive at the n=206 eligible derived sample size. The eligibility criteria for the ward focal BHCPF PHC was the accreditation from received in 2020 form National Primary Health Care Development Agency (NPHCDA) and National Health Insurance Agency (NHIA) Gateways towards supply-side provision of equitable, qualitative and standardized BEmONC services or complementary referral to Comprehensive Emergency Obstetric and Newborn Care (CEmONC). The Nigerian 2014 National Health Act and the 2016 National Health Policy effort of the Federal Ministry of Health and Social Welfare (FMoHSW) and relevant multidisciplinary stakeholders has precipitated into improved defragmentation in the scale-up of Basic Minimum Package of Health Services (BMPHS) in an enhanced capacity to enroll clients on the demand side, that vulnerable and hard-to-reach population can access where and when they need them; within financial risk protection so that no one is left behind. Only 86.85% (or n=206) of the checklist via an open data kit (ODK) link was captured by eligible population on average. There are eight indicators categorized under four Health Care Facility (HCF) Quality of Care (QoC) productivity/efficiency thematic areas of focus and three skilled birth attendant (SBA) indicators categorized under performance management. The means of all eleven indicators were analyzed through a Pearson bi-variate correlation towards establishing the decision-making mediators and moderators relative to BEmONC QoC risk factors that determine uptake by vulnerable and hard-to-reach population at ward focal BHCPF HCF (n=206) as encapsulated in the health security and emergency preparedness strategy of the Health Sector Strategic Blueprint (HSSB) 2025 of the Federal Ministry of Health and Social Welfare (FMoHSW) through sector wide approach (SWAp) . Microsoft Excel and SPSS 25 were used to analyze the data obtained during the five-month survey for decisions-making regarding the prioritization of action on improvement of the QoC associative of the risk factors that determine BEmONC services uptake by eligible population. The parametric bivariate Pearson correlation (95% CI) was used to derive the following decisions regarding optimization. Firstly, the signal function relevant to the number of pregnant women who gave birth in the HCF who received prophylactic uteronic (oxytocin) after birth (n=206) was correlated with and positively associative of four key performance indicators which are inclusive of number of babies breastfed within one hour of birth (n=206), the Skilled Birth Attendant (SBA) assisted normal delivery (n=79), the number of children 0-59 months who were administered with Vitamin A (n=206), and the DPT immunization drop-out rate observed from April to August, 2024 where p (0.52, 0.71, 0.10, 0.82) and r (0.875, 0.845, 0.80, 0.13) values respectively, indicating statistically significant relative strength of association except the very weak relationship in the DPT immunization drop-out rate, however, there is an observed p(0.12, 0.15, 0.20, 0.87) level of risk that determine PNC services uptake by under-served population except for the 87% uptake of immunization by under-five population. Secondly, the risks that determine the BEmONC signal functions linked to livebirths at ward focal BHCPF HCF were observed to be laboratory tests, ANC services and uptake of immunization by neonate or infant 0-59 months where p (0.33, 0.13, 0.47) and r (-0.54, -0.76, -0.42) respectively. The tracking of the QoC in the signal function through a test of association in the key performance indicators for BEmONC services uptake at (n=239) ward focal BHCPF HCF in Kogi State provided the observations from the correlation of the risk factors that determine the defragmentation of BEmONC services for taking proactive measure to strengthen the optimization of the uptake of basic minimum package of health services (BMPHS) by vulnerable and hard-to-reach population, so that no one is left behind by 2030 in line with the SDGs 3.8 goal for attaining Universal Health Coverage (UCH) in Kogi State and globally.
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Lee, Joongyo, Seo Hee Choi, Hong In Yoon, et al. "RTHP-09. SINGLE INSTITUTION’S EXPERIENCE FOR PEDIATRIC INTRACRANIAL EPENDYMOMA TREATED WITH ADJUVANT RADIOTHERAPY." Neuro-Oncology 21, Supplement_6 (2019): vi211—vi212. http://dx.doi.org/10.1093/neuonc/noz175.882.
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Abstract PURPOSE To investigate long term outcomes of pediatric intracranial ependymoma treated with maximal safe resection followed by radiotherapy in single institution. METHODS Between 1983 and 2016, 42 pediatric patients received adjuvant radiotherapy after surgery. There were 19 and 23 grade II and III ependymomas, and total resection could be performed in 50% of patients. There were 4 patients with initial leptomeningeal seeding, and craniospinal irradiation (CSI) was the component of adjuvant treatment in 5 patients including them. Local radiotherapy (median 54 Gy (range, 45–60 Gy) was performed in all the rest patients excluding three with whole-brain radiotherapy. RESULTS With a median follow-up of 89 months, 14 patients (33%) experienced recurrences after radiotherapy (m/c: intracranial, 79%, all within tumor bed). The 5- and 10-year progression-free survival (PFS) rates were 70% and 63%, and overall survival (OS) rates were 85% and 78%, respectively. The recurrence rates were significantly higher in patients younger than 3 years (64% vs. 23%, p = 0.024) or with subtotal resection (STR) (52% vs. 15%, p = 0.009). In multivariate analyses, age and STR were significant factors for PFS (p = 0.007 and 0.028, respectively). There was no independent prognostic factor for OS on multi-variate analysis. Fourteen patients experienced recurrence. Among them, all of 6 grade II cases showed local recurrence. Four grade II cases (67%) underwent aggressively salvage treatments such as resection, stereotactic radiosurgery were still under observation. However, 75% of 8 grade III recurrent cases were expired, despite aggressive salvage treatments. All 3 patients with seeding recurrent were grade III and died from disease. CONCLUSION Local adjuvant radiotherapy showed favorable survival in pediatric ependymoma. Even if recurrence occurs, aggressive salvage treatment would be needed for grade II ependymoma.
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Gamucci, T., F. Narducci, I. Sperduti, et al. "Evaluation of the Edmonton Symptoms Assesment Scale (ESAS) symptoms improvement (SI) assessment as a prognostic factor for survival in advanced cancer patients (pts) undergoing palliative care (PC): An observational prospective study." Journal of Clinical Oncology 27, no. 15_suppl (2009): 9591. http://dx.doi.org/10.1200/jco.2009.27.15_suppl.9591.
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9591 Background: ESAS is a validated tool for physical symptoms assessment in PC practice which evaluates symptoms through a numeric scale ( 0–10). The use of SI as a prognostic factor is controversial. To this purpose, an observational prospective study in advanced cancer pts previously treated with anti-cancer treatments and now undergoing only PC was conducted. Methods: Pts were considered eligible if no longer able to receive any anticancer treatment; they were scheduled to undergo ESAS assessment: 1) at the hospitalization time-point (TH); 2) at the hospital discharge time-point (TD). Symptoms’ scores were divided into 3 severity-classes (SC): mild (0–3, MI), moderate (4–6, MO) and severe (≥7, S). Differences across symptoms’ classes between TH and TD was analyzed with the paired-data McNemar-test, according to tumor types. KM method was used for survival calculation, according to ESAS score classes, and logrank test for curves comparison. Uni/multi-variate survival analysis including age, sex, tumour, symptoms number and score class, PaP (Palliative Prognostic)-score, KPS, were carried out using the Cox regression model. Results: ESAS was administered to 68 pts, gastrointesinal (GI)/lung (NSCLC): 39/29, median age: 69-yrs, KPS ≤50/>50: 27 (39.7 %)/ 41 (60.3%), PaP-score A/B/C: 26 (38.2%)/37 (54.4%)/C (7.4%). A statistically significant reduction of S-SC rates was observed, as shown in the Table . SI correlates with survival improvement: PaP score (HR 2.95, 95% CI 1.35–6.41, p=0.006) and anorexia (HR 3.21, 95% CI 1.33–7.72, p=0.009) appear to be prognostic factors for survival at the multivariate analaysis for GI pts; asthenia is the only significant variable (HR 5.11 CI 95% 1.86–14.03, p=.0.002) for NSCLC pts. Conclusions: SI according to ESAS after PC treatment represents an important prognostic factor for survival in pts no longer suitable to receive any anticancer active therapies. [Table: see text] No significant financial relationships to disclose.
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Abou-Ismail, Mouhamed Yazan, Yasmin Arafah, Pingfu Fu, Shufen Cao, Alvin H. Schmaier, and Lalitha V. Nayak. "Outcomes after Treatment with Upfront Cyclophosphamide or Rituximab for Initial Acquired Thrombotic Thrombocytopenic Purpura." Blood 134, Supplement_1 (2019): 1084. http://dx.doi.org/10.1182/blood-2019-123537.
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Background: Acquired thrombotic thrombocytopenic purpura (aTTP) is a rare, life-threatening emergency caused by auto-antibodies against ADAMTS13. Current standard therapy utilizes plasma exchange (PLEX) and corticosteroids. Although this induces remission, disease relapse remains a common problem. Since more than 90% of aTTP is caused by an antibody to ADAMTS13, additional immunosuppressive therapy with rituximab (RTX) given upfront has been shown to lower relapse rates. An alternative therapy, Cyclophosphamide (CTX) has been used in relapsed disease, but data on its use in upfront management is limited to case reports only. Our study aims to compare outcomes at our institution between upfront CTX, RTX, and standard therapy alone. Our secondary objective is to identify initial presenting features that are predictive of higher relapse risk. Methods: In a retrospective cohort study, we identified all patients at our institution with a diagnosis code of TTP (ICD 9, 10: 446.6, M31.1) between 2009 and 2019. We only included patients with a confirmed diagnosis of aTTP, defined as having an ADAMTS13 level <10%, and either a positive ADAMTS13 inhibitor or antibody. We excluded patients with follow-up of less than 30 days, or thrombotic microangiopathy (TMA) due to other causes. Relapse was defined as recurrence of TMA in addition to an ADAMTS13 level <10%, or "laboratory relapse" evidenced as an acute drop of ADATMS13 level with a positive inhibitor with/without TMA. Time to first relapse was measured from the onset of remission, defined as the time of normalization of both platelets and lactate dehydrogenase (LDH) for at least 48 hours, until the time of relapse. The cumulative incidence of relapse (CIR) was estimated using Kaplan-Meier method, and its difference among treatment groups was examined by log-rank test. The effect of continuous and categorical covariates on relapse rate was estimated by univariate Cox model. The association of categorical variables was examined using chi-square test and the difference of continuous measurements among groups was tested using T-test or ANOVA. Results: A total of 42 patients with a diagnosis of aTTP were identified in our cohort. The average age at diagnosis was 43.7 years. All patients received standard therapy with PLEX and steroids upon initial diagnosis. The prophylaxis group (n=29) received additional upfront therapy with either RTX (n=22) or CTX (n=6), and the control group (n=12) was treated with PLEX and steroids alone. There were no significant differences in the baseline characteristics between both groups. The median time to first relapse was 34.9 months in the control group, and 108.3 months in the prophylaxis group. Treatment with standard therapy alone was associated with a multi-variate hazard risk of 6.38 (95% CI: 1.1-37.7, p<0.05) for time to initial relapse. CIR at 2, 4, and 8 years was higher in the control group (41.7%, 53.3%, and 68.8%) as compared to the prophylaxis group (31.3%, 43.8%, and 43.8%, respectively). No difference was noted in CIR with CTX versus RTX, which was 40% at 4 years with CTX compared to 47% with RTX, p = 0.97 (Figure 1). Similarly, no statistically significant difference was noted in the time to first remission between CTX and RTX treatment (18 days in with CTX versus 14.9 days with RTX, p=0.583). Inhibitor titer at diagnosis correlated inversely with duration of remission (HR: 1.29, 95% CI: 1.03-1.61, p<0.05). Initial presenting platelet count, peak LDH value or co-existing autoimmune disease were not predictive of the duration of remission. Conclusion: Our study is the first to examine outcomes in a subset of patients treated with upfront CTX therapy for initial aTTP, and suggests similar time to first remission and 4-year CIR as compared to RTX. Our institutional experience suggests that CTX may be considered as an alternative therapy in patients intolerant to RTX. However, given the small group of patients treated with CTX, larger, prospective studies are warranted to confirm this observation. Consistent with currently published literature, PLEX and steroid therapy alone was associated with a significantly shorter duration of remission compared to additional immunosuppressive therapy. Furthermore, higher inhibitor titers were predictive of a shorter duration of remission, suggesting a role for aggressive upfront immunosuppressive treatment in these patients. Disclosures No relevant conflicts of interest to declare.
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Tobin, Joshua W. D., Gabrielle Rule, Katherine Colvin, et al. "Outcomes of Stage I and II Follicular Lymphoma in the Era of 18F-FDG PET-CT Staging: An International Collaborative Study from the Australian Lymphoma Alliance." Blood 132, Supplement 1 (2018): 4148. http://dx.doi.org/10.1182/blood-2018-99-114097.
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Abstract Background Stage I/II or early-stage follicular lymphoma (ESFL) is considered potentially curable with radiotherapy (XRT). While XRT does achieve local disease control in >90% of cases, more than half the patients (pts) relapse by 10 years (yr), generally outside of the radiation field. A recent randomized controlled trial (TROG 99.03) demonstrated that combined modality therapy (CMT), with sequential XRT and systemic therapy, significantly improved PFS but not overall survival (OS) compared to XRT alone in ESFL. However, only half the pts were staged with 18F‐FDG positron emission tomography and computed tomography (PET) and 58% of CMT pts did not receive rituximab.Compared with CT staging, 20-60% of cases are upstaged by PET. Consequentially, there are limitations in applying this trial to modern populations. Despite the support of current guidelines, only one third of pts in clinical practice are treated with XRT. This suggests a need to better understand the role of other treatments, including watchful waiting (WW), in the PETera. Our aim was to compare outcomes with real-world treatment approaches in rigorously staged ESFL patients. Methods We conducted an international, multicenterretrospective study of stage I and II FL pts rigorously staged with bone marrow biopsy and PET. Eligible pts were >18yr with newly-diagnosed grade 1-3A FL and ≥3 months follow up. Primary outcome measures were overall response rate (ORR), progression free survival (PFS), OS and risk of transformation. Survival curves were estimated with the Kaplan-Meier method and uni- and multi-variate analysis was performed using Cox regression model. Results A total of 387 pts treated at 13 Australian and 3 Canadian centres between 2005-2017 were studied. Median follow-up was 45 months (range 3.1 - 164.0).5-yrPFS and OS rates were 73.5% (95% CI 66.0-78.5) and 94.4% (95% CI 89.4-93.6) respectively. 22 patients had stage IE duodenal FL with 5-yr PFS and OS rates of 100% and 100% respectively. Considering the unique biology and favorable prognosis of duodenal FL, these cases were excluded from subsequent analyses. Treatment approaches 365 pts included WW (defined as absence of treatment within 6 months from diagnosis) (23.2%), XRT (46.8%), immunochemotherapy (17.2%) and CMT (12.6%). Treatment regimens were: R-CHOP (48.1%), R-CVP (24.4%), BR (9.9%), other (17.6%). First-line therapies for actively treated pts yielded comparable ORRs of 95.6%, 96.7% and 95.9% for XRT, immunochemotherapy and CMT, respectively (P=0.94). Overall, 18.2% of pts relapsed at distant sites, 88.2% of all relapses. Treatment cohorts differed in baseline clinical characteristics. WW pts were significantly older (P=0.007) but otherwise comparable to those treated actively. Compared to chemotherapy or CMT pts, those treated with XRT had more favorable features including fewer B symptoms (4.2% vs 11.2% p=0.029), bulk (≥7cm) (6.8% vs 25.3%, p<0.001), nodal sites (≥3) (1.9% vs 9.5% p=0.005) and a higher frequency of stage I FL (73.1% vs 42.1% p<0.001). Outcomes differed among treatment approaches. Active treatment was associated with superior PFS compared with WW pts (HR 0.54 p=0.004) however, 49.4% of WW pts remained untreated at 5-yrs (Fig 1a). Considering actively treated pts, systemic therapy (immunochemotherapy or CMT) was associated with superior PFS compared to XRT by univariate analysis (HR 0.49, p=0.009) (Fig 1b). This association remained after multivariate adjustment for bulk, B symptoms, nodal sites and stage (HR 0.41 p=0.002). Treatment with immunochemotherapy and CMT demonstrated a comparable PFS (p=0.2). Maintenance rituximab (n=45) was associated with superior PFS compared with observation after systemic therapy (HR 0.24, p=0.017). There were no differences in OS among treatment approaches (P=0.734). There was a higher incidence of transformation in XRT pts compared to systemic therapy pts (6.4% vs 1.6% p=0.046). Conclusion In the largest assessment of rigorously-staged ESFL pts in the PETera, pts treated with systemic therapy (chemotherapy or CMT) had a superior PFS and a lower rate of transformation compared to pts treated with XRT, although treatments were not randomized. These findings are similar to the TROG 99.03 trial and challenge the paradigm that ESFL should be uniformly treated with XRT alone. Half the pts observed from diagnosis remained treatment-free at 5-yrs, suggesting that WW may be appropriate in selected pts. Disclosures Tobin: Celgene: Research Funding; Amgen: Other: Educational Travel. Tam:Roche: Honoraria; Roche: Honoraria; Pharmacyclics: Honoraria, Travel funding; Pharmacyclics: Honoraria; Beigene: Honoraria, Other: Travel funding; Beigene: Honoraria, Other: Travel funding; AbbVie: Honoraria, Research Funding; Janssen: Honoraria, Research Funding; Gilead: Honoraria; Gilead: Honoraria; AbbVie: Honoraria, Research Funding. Abro:Amgen: Other: education support congress attendance; Celgene: Other: education support congress attendance; Bristol-Myers Squibb: Speakers Bureau; Janssen: Other: education support congress attendance; Novartis: Consultancy. Hawkes:Bristol Myers Squibb: Other: Speaker fee, Research Funding; Takeda: Other: Speaker fee; Astra Zeneca: Research Funding; Merck Sharpe Dohme: Research Funding; Merck KGA: Research Funding; Celgene: Other: Advisory board, Research Funding; Merck: Other: Advisory board; Roche: Other: Speaker fee; advisory board. Talaulikar:Amgen: Consultancy, Honoraria; Roche: Honoraria, Speakers Bureau; Janssen: Honoraria, Speakers Bureau; Takeda: Research Funding; Novartis: Honoraria, Speakers Bureau. Gandhi:Celgene: Membership on an entity's Board of Directors or advisory committees, Research Funding; Janssen: Honoraria, Membership on an entity's Board of Directors or advisory committees; Merck: Honoraria, Membership on an entity's Board of Directors or advisory committees; Amgen: Honoraria; Takeda: Honoraria; Gilead: Honoraria, Membership on an entity's Board of Directors or advisory committees, Research Funding; BMS: Membership on an entity's Board of Directors or advisory committees, Research Funding.
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Liu, Sicong, Silvestro Roberto Poccia, K. Selcuk Candan, Maria Luisa Sapino, and Xiaolan Wang. "Robust Multi-Variate Temporal Features of Multi-Variate Time Series." January 1, 2018. https://doi.org/10.1145/3152123.
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Many applications generate and/or consume multi-variate temporal data, and experts often lack the means to adequately and systematically search for and interpret multi-variate observations. In this article, we first observe that multi-variate time series often carry localized multi-variate temporal features that are robust against noise. We then argue that these multi-variate temporal features can be extracted by simultaneously considering, at multiple scales, temporal characteristics of the time series <em>along with external knowledge</em>, including variate relationships that are known a priori. Relying on these observations, we develop data models and algorithms to detect <em>robust multi-variate temporal</em> (RMT) features that can be indexed for efficient and accurate retrieval and can be used for supporting data exploration and analysis tasks. Experiments confirm that the proposed RMT algorithm is highly effective and efficient in identifying <em>robust</em> multi-scale temporal features of multi-variate time series.Multivariate
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Archambault, Théo, Arthur Filoche, Anastase Charantonis, Dominique Béréziat, and Sylvie Thiria. "Learning Sea Surface Height Interpolation From Multi‐Variate Simulated Satellite Observations." Journal of Advances in Modeling Earth Systems 16, no. 6 (2024). http://dx.doi.org/10.1029/2023ms004047.
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AbstractSatellite‐based remote sensing missions have revolutionized our understanding of the Ocean state and dynamics. Among them, space‐borne altimetry provides valuable Sea Surface Height (SSH) measurements, used to estimate surface geostrophic currents. Due to the sensor technology employed, important gaps occur in SSH observations. Complete SSH maps are produced using linear Optimal Interpolations (OI) such as the widely used Data Unification and Altimeter Combination System (duacs). On the other hand, Sea Surface Temperature (SST) products have much higher data coverage and SST is physically linked to geostrophic currents through advection. We propose a new multi‐variate Observing System Simulation Experiment (OSSE) emulating 20 years of SSH and SST satellite observations. We train an Attention‐Based Encoder‐Decoder deep learning network (abed) on this data, comparing two settings: one with access to ground truth during training and one without. On our OSSE, we compare abed reconstructions when trained using either supervised or unsupervised loss functions, with or without SST information. We evaluate the SSH interpolations in terms of eddy detection. We also introduce a new way to transfer the learning from simulation to observations: supervised pre‐training on our OSSE followed by unsupervised fine‐tuning on satellite data. Based on real SSH observations from the Ocean Data Challenge 2021, we find that this learning strategy, combined with the use of SST, decreases the root mean squared error by 24% compared to OI.