Miracle-dz/UniFAR-FTUs huggingface dataset:
Drought Evaluation with CMORPH Satellite Precipitation Data in the Yellow River Basin by Using Gridded Standardized Precipitation Evapotranspiration Index

The traditional station-based drought index is vulnerable because of the inadequate spatial distribution of the station, and also, it does not fully reflect large-scale, dynamic drought information. Thus, large-scale drought monitoring has been widely implemented by using remote sensing precipitation products. Compared with station data, remote sensing precipitation products have the advantages of wide coverage and dynamic, continuous data, which can effectively compensate for the deficiency in the spatial distribution of the ground stations and provide a new data source for the calculation of a drought index. In this study, the Gridded Standardized Precipitation Evapotranspiration Index (GSPEI) was proposed based on a remote sensing dataset produced by the Climate Prediction Center morphing technique (CMORPH), in order to evaluate the gridded drought characteristics in the Yellow River basin (YRB) from 1998 to 2016. The optimal Ordinary Kriging interpolation method was selected to interpolate meteorological station data to the same spatial resolution as CMORPH data (8 km), in order to compare the ground-based meteorological parameters to remote sensing-based data. Additionally, the gridded drought trends were identified based on the Modified Mann–Kendall (MMK) trend test method. The results indicated that: (1) the GSPEI was suitable for drought evaluation in the YRB using CMORPH precipitation data, which were consistent with ground-based meteorological data; (2) the positive correlation between GSPEI and SPEI was high, and all the correlation coefficients (CCs) passed the significance test of α = 0.05, which indicated that the GSPEI could better reflect the gridded drought characteristics of the YRB; (3) the drought severity in each season of the YRB was highest in summer, followed by spring, autumn, and winter, with an average GSPEI of −1.51, −0.09, 0.30, and 1.33, respectively; and (4) the drought showed an increasing trend on the monthly scale in March, May, August, and October, and a decreasing trend on the seasonal and annual scale.
Vocal imitation for query by vocalisation

The human voice presents a rich and powerful medium for expressing sonic ideas such as musical sounds. This capability extends beyond the sounds used in speech, evidenced for example in the art form of beatboxing, and recent studies highlighting the utility of vocal imitation for communicating sonic concepts. Meanwhile, the advance of digital audio has resulted in huge libraries of sounds at the disposal of music producers and sound designers. This presents a compelling search problem: with larger search spaces, the task of navigating sound libraries has become increasingly difficult. The versatility and expressive nature of the voice provides a seemingly ideal medium for querying sound libraries, raising the question of how well humans are able to vocally imitate musical sounds, and how we might use the voice as a tool for search. In this thesis we address these questions by investigating the ability of musicians to vocalise synthesised and percussive sounds, and evaluate the suitability of different audio features for predicting the perceptual similarity between vocal imitations and imitated sounds. In the first experiment, musicians were tasked with imitating synthesised sounds with one or two time–varying feature envelopes applied. The results show that participants were able to imitate pitch, loudness, and spectral centroid features accurately, and that imitation accuracy was generally preserved when the imitated stimuli combined two, non-necessarily congruent features. This demonstrates the viability of using the voice as a natural means of expressing time series of two features simultaneously. The second experiment consisted of two parts. In a vocal production task, musicians were asked to imitate drum sounds. Listeners were then asked to rate the similarity between the imitations and sounds from the same category (e.g. kick, snare etc.). The results show that drum sounds received the highest similarity ratings when rated against their imitations (as opposed to imitations of another sound), and overall more than half the imitated sounds were correctly identified with above chance accuracy from the imitations, although this varied considerably between drum categories. The findings from the vocal imitation experiments highlight the capacity of musicians to vocally imitate musical sounds, and some limitations of non– verbal vocal expression. Finally, we investigated the performance of different
Evaluation of the community response of ecological networks using complexity science

This thesis investigates network properties of natural food webs. In particular, it focuses on the effect that external disturbances have on their substructures and robustness. The importance of a network-level methodology lies in its capacity to capture entangling species interactions and identify inter-connecting properties in heterogeneous food webs. The research first analysed the responses of freshwater food webs under the stress of drought. A core/periphery structure was detected and its relative size was found to be unchanged after drought despite a significant biodiversity loss. Species extinction triggered extensive link rewiring and movement of species from the core to the periphery. These results showed that the robustness was maintained indicating that the redundancy in the core can effectively mitigate species level perturbations. Secondly, the research further examined the effects of Genetically Modified Herbicide Tolerant (GMHT) management on food web properties and robustness. Network analysis showed that such change in farming practice has no significant impact on the agro-ecosystems. However, crop switching, a common practice in agriculture, was found to pose much more significant changes on network properties and robustness when compared to GMHT crops. Thirdly, the research examined over 50 empirical food webs and demonstrated that the relative core size is a much more effective indicator of food web robustness than the classical ecological measure connectance, as the latter was found to be insensitive to changes in the interaction patterns. Lastly, the research established the relationships between centrality measures and species ecological and/or functional role in food webs, and how they impact on network robustness.
A Global, 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data

Solar-induced chlorophyll fluorescence (SIF) brings major advancements in measuring terrestrial photosynthesis. Several recent studies have evaluated the potential of SIF retrievals from the Orbiting Carbon Observatory-2 (OCO-2) in estimating gross primary productivity (GPP) based on GPP data from eddy covariance (EC) flux towers. However, the spatially and temporally sparse nature of OCO-2 data makes it challenging to use these data for many applications from the ecosystem to the global scale. Here, we developed a new global ‘OCO-2’ SIF data set (GOSIF) with high spatial and temporal resolutions (i.e., 0.05°, 8-day) over the period 2000–2017 based on a data-driven approach. The predictive SIF model was developed based on discrete OCO-2 SIF soundings, remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS), and meteorological reanalysis data. Our model performed well in estimating SIF (R2 = 0.79, root mean squared error (RMSE) = 0.07 W m−2 μm−1 sr−1). The model was then used to estimate SIF for each 0.05° × 0.05° grid cell and each 8-day interval for the study period. The resulting GOSIF product has reasonable seasonal cycles, and captures the similar seasonality as both the coarse-resolution OCO-2 SIF (1°), directly aggregated from the discrete OCO-2 soundings, and tower-based GPP. Our SIF estimates are highly correlated with GPP from 91 EC flux sites (R2 = 0.73, p < 0.001). They capture the expected spatial and temporal patterns and also have remarkable ability to highlight the crop areas with the highest daily productivity across the globe. Our product also allows us to examine the long-term trends in SIF globally. Compared with the coarse-resolution SIF that was directly aggregated from OCO-2 soundings, GOSIF has finer spatial resolution, globally continuous coverage, and a much longer record. Our GOSIF product is valuable for assessing terrestrial photosynthesis and ecosystem function, and benchmarking terrestrial biosphere and Earth system models.
Mapping and Monitoring of Biomass and Grazing in Pasture with an Unmanned Aerial System

The tools available to farmers to manage grazed pastures and adjust forage demand to grass growth are generally rather static. Unmanned aerial systems (UASs) are interesting versatile tools that can provide relevant 3D information, such as sward height (3D structure), or even describe the physical condition of pastures through the use of spectral information. This study aimed to evaluate the potential of UAS to characterize a pasture’s sward height and above-ground biomass at a very fine spatial scale. The pasture height provided by UAS products showed good agreement (R2 = 0.62) with a reference terrestrial light detection and ranging (LiDAR) dataset. We tested the ability of UAS imagery to model pasture biomass based on three different combinations: UAS sward height, UAS sward multispectral reflectance/vegetation indices, and a combination of both UAS data types. The mixed approach combining the UAS sward height and spectral data performed the best (adj. R2 = 0.49). This approach reached a quality comparable to that of more conventional non-destructive on-field pasture biomass monitoring tools. As all of the UAS variables used in the model fitting process were extracted from spatial information (raster data), a high spatial resolution map of pasture biomass was derived based on the best fitted model. A sward height differences map was also derived from UAS-based sward height maps before and after grazing. Our results demonstrate the potential of UAS imagery as a tool for precision grazing study applications. The UAS approach to height and biomass monitoring was revealed to be a potential alternative to the widely used but time-consuming field approaches. While reaching a similar level of accuracy to the conventional field sampling approach, the UAS approach provides wall-to-wall pasture characterization through very high spatial resolution maps, opening up a new area of research for precision grazing.
Large Anomalies in the Tropical Upper Troposphere Lower Stratosphere (UTLS) Trace Gases Observed during the Extreme 2015-16 El Niño Event by Using Satellite Measurements

It is well reported that the 2015–16 El Niño event is one of the most intense and long lasting events in the 21st century. The quantified changes in the trace gases (Ozone (O3), Carbon Monoxide (CO) and Water Vapour (WV)) in the tropical upper troposphere and lower stratosphere (UTLS) region are delineated using Aura Microwave Limb Sounder (MLS) and Atmosphere Infrared Radio Sounder (AIRS) satellite observations from June to December 2015. Prior to reaching its peak intensity of El Niño 2015–16, large anomalies in the trace gases (O3 and CO) were detected in the tropical UTLS region, which is a record high in the 21st century. A strong decrease in the UTLS (at 100 and 82 hPa) ozone (~200 ppbv) in July-August 2015 was noticed over the entire equatorial region followed by large enhancement in the CO (150 ppbv) from September to November 2015. The enhancement in the CO is more prevalent over the South East Asia (SEA) and Western Pacific (WP) regions where large anomalies of WV in the lower stratosphere are observed in December 2015. Dominant positive cold point tropopause temperature (CPT-T) anomalies (~5 K) are also noticed over the SEA and WP regions from the high-resolution Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) Global Position System (GPS) Radio Occultation (RO) temperature profiles. These observed anomalies are explained in the light of dynamics and circulation changes during El Niño.
Privacy-Preserving Secure Computation of Skyline Query in Distributed Multi-Party Databases

Selecting representative objects from a large-scale database is an essential task to understand the database. A skyline query is one of the popular methods for selecting representative objects. It retrieves a set of non-dominated objects. In this paper, we consider a distributed algorithm for computing skyline, which is efficient enough to handle “big data”. We have noticed the importance of “big data” and want to use it. On the other hand, we must take care of its privacy. In conventional distributed algorithms for computing a skyline query, we must disclose the sensitive values of each object of a private database to another for comparison. Therefore, the privacy of the objects is not preserved. However, such disclosures of sensitive information in conventional distributed database systems are not allowed in the modern privacy-aware computing environment. Recently several privacy-preserving skyline computation frameworks have been introduced. However, most of them use computationally expensive secure comparison protocol for comparing homomorphically encrypted data. In this work, we propose a novel and efficient approach for computing the skyline in a secure multi-party computing environment without disclosing the individual attributes’ value of the objects. We use a secure multi-party sorting protocol that uses the homomorphic encryption in the semi-honest adversary model for transforming each attribute value of the objects without changing their order on each attribute. To compute skyline we use the order of the objects on each attribute for comparing the dominance relationship among the objects. The security analysis confirms that the proposed framework can achieve multi-party skyline computation without leaking the sensitive attribute value to others. Besides that, our experimental results also validate the effectiveness and scalability of the proposed privacy-preserving skyline computation framework.
Modelling of Wine Production Using Land Surface Temperature and FAPAR - The Case of the Douro Wine Region

The vegetative development of grapevines is orchestrated by very specific meteorological conditions. In the wine industry vineyards demand diligent monitoring, since quality and productivity are the backbone of the economic potential. Regional climate indicators and meteorological information are essential to winemakers to assure proper vineyard management. Satellite data are very useful in this process since they imply low costs and are easily accessible. This work proposes a statistical modelling approach based on parameters obtained exclusively from satellite data to simulate annual wine production. The study has been developed for the Douro Demarcated Region (DDR) due to its relevance in the winemaking industry. It is the oldest demarcated and controlled winemaking region of the world and listed as one of UNESCO’s World Heritage regions. Monthly variables associated with Land Surface Temperatures (LST) and Fraction of Absorbed Photosynthetic Active Radiation (FAPAR), which is representative of vegetation canopy health, were analysed for a 15-year period (2004 to 2018), to assess their relation to wine production. Results showed that high wine production years are associated with higher than normal FAPAR values during approximately the entire growing season and higher than normal values of surface temperature from April to August. A robust linear model was obtained using the most significant predictors, that includes FAPAR in December and maximum and mean LST values in March and July, respectively. The model explains 90% of the total variance of wine production and presents a correlation coefficient of 0.90 (after cross validation). The retained predictors’ anomalies for the investigated vegetative year (October to July) from 2017/2018 satellite data indicate that the ensuing wine production for the DDR is likely to be below normal, i.e., to be lower than what is considered a high-production year. This work highlights that is possible to estimate wine production at regional scale based solely on low-resolution remotely sensed observations that are easily accessible, free and available for numerous grapevines regions worldwide, providing a useful and easy tool to estimate wine production and agricultural monitoring.
An Accurate TLS and UAV Image Point Clouds Registration Method for Deformation Detection of Chaotic Hillside Areas

Deformation detection determines the quantified change of a scene’s geometric state, which is of great importance for the mitigation of hazards and property loss from earth observation. Terrestrial laser scanning (TLS) provides an efficient and flexible solution to rapidly capture high precision three-dimensional (3D) point clouds of hillside areas. Most existing methods apply multi-temporal TLS surveys to detect deformations depending on a variety of ground control points (GCPs). However, on the one hand, the deployment of various GCPs is time-consuming and labor-intensive, particularly for difficult terrain areas. On the other hand, in most cases, TLS stations do not form a closed loop, such that cumulative errors cannot be corrected effectively by the existing methods. To overcome these drawbacks, this paper proposes a deformation detection method with limited GCPs based on a novel registration algorithm that accurately registers TLS stations to the UAV (Unmanned Aerial Vehicle) dense image points. First, the proposed method extracts patch primitives from smoothed hillside points, and adjacent TLS scans are pairwise registered by comparing the geometric and topological information of or between patches. Second, a new multi-station adjustment algorithm is proposed, which makes full use of locally closed loops to reach the global optimal registration. Finally, digital elevation models (DEMs, a DEM is a numerical representation of the terrain surface, formed by height points to represent the topography), slope and aspect maps, and vertical sections are generated from multi-temporal TLS surveys to detect and analyze the deformations. Comprehensive experiments demonstrate that the proposed deformation detection method obtains good performance for the hillside areas with limited (few) GCPs.
Hyperspectral and LiDAR Data Fusion Classification Using Superpixel Segmentation-Based Local Pixel Neighborhood Preserving Embedding

A new method of superpixel segmentation-based local pixel neighborhood preserving embedding (SSLPNPE) is proposed for the fusion of hyperspectral and light detection and ranging (LiDAR) data based on the extinction profiles (EPs), superpixel segmentation and local pixel neighborhood preserving embedding (LPNPE). A new workflow is proposed to calibrate the Goddard’s LiDAR, hyperspectral and thermal (G-LiHT) data, which allows our method to be applied to actual data. Specifically, EP features are extracted from both sources. Then, the derived features of each source are fused by the SSLPNPE. Using the labeled samples, the final label assignment is produced by a classifier. For the open standard experimental data and the actual data, experimental results prove that the proposed method is fast and effective in hyperspectral and LiDAR data fusion.
Comprehensive In Situ Validation of Five Satellite Land Surface Temperature Data Sets over Multiple Stations and Years

Global land surface temperature (LST) data derived from satellite-based infrared radiance measurements are highly valuable for various applications in climate research. While in situ validation of satellite LST data sets is a challenging task, it is needed to obtain quantitative information on their accuracy. In the standardised approach to multi-sensor validation presented here for the first time, LST data sets obtained with state-of-the-art retrieval algorithms from several sensors (AATSR, GOES, MODIS, and SEVIRI) are matched spatially and temporally with multiple years of in situ data from globally distributed stations representing various land cover types in a consistent manner. Commonality of treatment is essential for the approach: all satellite data sets are projected to the same spatial grid, and transformed into a common harmonized format, thereby allowing comparison with in situ data to be undertaken with the same methodology and data processing. The large data base of standardised satellite LST provided by the European Space Agency’s GlobTemperature project makes previously difficult to perform LST studies and applications more feasible and easier to implement. The satellite data sets are validated over either three or ten years, depending on data availability. Average accuracies over the whole time span are generally within ±2.0 K during night, and within ± 4.0 K during day. Time series analyses over individual stations reveal seasonal cycles. They stem, depending on the station, from surface anisotropy, topography, or heterogeneous land cover. The results demonstrate the maturity of the LST products, but also highlight the need to carefully consider their temporal and spatial properties when using them for scientific purposes.
Update in Chronic Obstructive Pulmonary Disease 2018.

Update in Chronic Obstructive Pulmonary Disease 2018 Wassim W. Labaki, Lucas M. Kimmig, Gökhan M. Mutlu, MeiLan K. Han*, and Surya P. Bhatt Division of Pulmonary and Critical Care Medicine, University of Michigan, Ann Arbor, Michigan; Section of Pulmonary and Critical Care Medicine, The University of Chicago, Chicago, Illinois; and Division of Pulmonary, Allergy, and Critical Care Medicine, The University of Alabama at Birmingham, Birmingham, Alabama
Optimizing the production and affinity purification of HIV-1 envelope glycoprotein SOSIP trimers from transiently transfected CHO cells

We describe methods to improve the efficiency with which HIV-1 Envelope glycoprotein SOSIP trimer immunogens can be produced by transient transfection of ExpiCHO-S cells and then affinity purified using the trimer-specific human monoclonal antibody PGT145. The specificity of PGT145 for properly folded trimers allows for the facile, one-step, isolation of these immunogens in research laboratories. PGT145 columns are also valuable as a component of more complex purification processes in current Good Manufacturing Practice programs. However, we found that PGT145 purification was highly variable and markedly inefficient when used to process supernatants from transiently transfected ExpiCHO-S cells expressing the BG505 SOSIP.664 and other trimeric Env proteins. In contrast, no such problems arose when the same Env proteins derived from a stable CHO cell line were processed on the same PGT145 columns, or with transient transfection supernatants from 293F cells. An investigation of the ExpiCHO-S transfection system identified the presence of polyanions, including but perhaps not limited to dextran sulfate, in the Enhancer component of the transfection system. We hypothesized that these polyanions bound to the cationic PGT145 epitope on the trimers and impeded their ability to bind to the PGT145 affinity column. We found that replacing the Enhancer component with alternative culture medium supplements substantially increased the yield of PGT145-purifiable trimers, and we also confirmed that both dextran sulfate and the Enhancer component were indeed inhibitors of PGT145 binding to BG505 SOSIP.664 trimers in immunoassays. The presence of polyanions, including but not limited to nucleic acids, should be considered in other circumstances where PGT145 columns are less efficient than expected at purifying native-like trimers.