Miracle-dz/UniFAR-FTUs huggingface dataset:
Modelagem tridimensional da estrutura trófica em ecossistemas aquáticos continentais rasos

This thesis identified several important features of the trophic structure and dynamics of nutrients in ecosystems subjected to external disturbances such as changes in the water level, nutrient and organic matter loading, fish communities and climate. To describe such effects on trophic structure, a complex ecological model, based on physical, chemical and biological processes has been developed and applied in tropical, subtropical and temperate aquatic ecosystems. In such applications, an integrated analysis was employed considering the transfer of impacts from the watershed to the ecosystem, in order to bring closer the understading of the ecosystem dynamics. Simulations revealed important features about system structure and functionality in front of disturbances. For instance, lake Mangueira a subtropical lake in Southern Brazil can oscilate between two alternative steady stables (clear or turbid water), depending orthophosphate concentration and water transparency. It was also suggested that can occurs profound changes in phytoplankton composition from increase of orthophosphate concentration. Horizontal and vertical gradients in the trophic structure are notable in lake Mangueira, indicating the importance to consider spatial heterogeneity in large lakes to improve understanding of ecological processes and patterns. Simulation scenarios indicated that increased nutrient loading negatively affects water transparency, differently from climate changes. Slight changes in trophic structure caused by those factors indicate a strong resistence of the lake Mangueira. The ecological model also was useful to describe the ecosystem dynamics before and after biomanipulation of lake Engelsholm located in Denmark. In that case, the model could reasonably well predict a shift to a clear water state, but the changed composition of phytoplankton functional groups was much more hard to represent. Furthermore, simpler applications also provided support for decision making and planning in the ecosystem. Ecological modeling applied in aquatic ecosystems showed be a promising alternative towards the integrated management of natural resources.
Evidence for ascending upper mantle‐derived melt beneath the Cheb basin, central Europe

We present new 3He/4He data of CO2‐rich gas exhalations of three presently nonvolcanic areas in Europe. The 3He/4He ratios from the Cheb basin (Czech Republic) are clearly higher than data obtained ten years ago, whereas the 3He/4He ratios from the other areas (Laacher See/Germany and Mariánské Lázně/CR) have remained nearly constant. No other locality showing such high 3He/4He ratios in free gases supplied by continental mantle degassing is known in the European Cenozoic rift system. At the northeastern edge of the Cheb basin swarm earthquakes repeatedly occur. In the context of contemporaneous periods of seismicity the increased 3He/4He ratios are interpreted as the first geochemical evidence for ascending mantle‐derived melt beneath the Cheb basin, which is related to the triggering of earthquake swarms.
Exploiting Contextual Word Embedding for Identification of Important Citations: Incorporating Section-Wise Citation Counts and Metadata Features

Finding relevant research papers can be a challenging task due to the enormous number of scientific publications released each year. Recently, the scientific community has been diving deep into citation analysis, specifically examining the content of papers to identify more crucial documents. Citations serve as potential parameters for establishing connections between research articles. They have been widely utilized for various academic purposes, including calculating journal impact factors, determining researchers’ h-index, allocating research grants, and pinpointing the latest research trends. However, researchers have argued that not all citations carry equal weight in terms of influence. Consequently, alternative techniques have been proposed to identify significant citations based on content, metadata, and bibliographic information. Nonetheless, the current state-of-the-art approaches still require further refinement. Additionally, the application of deep learning models and word embedding techniques in this context has not been extensively studied.In this research work, we present an approach consisting of two primary modules: 1) Section-wise citation count, and 2) metadata-based analysis of citation intent. Our study involves conducting several experiments using deep learning models in conjunction with FastText, word2vec, and BERT-based word embeddings to perform citation analysis. These experiments were carried out using two benchmark datasets, and the results were compared with a contemporary study that employed a rich set of content-based features for classification. Our findings reveal that the deep learning CNN model, coupled with FastText word embeddings, achieves the best results in terms of accuracy, precision, and recall. It outperforms the existing state-of-the-art model, achieving a precision score of 0.97
A robust method for estimating motorbike count based on visual information learning

Estimating the number of vehicles in traffic videos is an important and challenging task in traffic surveillance, especially with a high level of occlusions between vehicles, e.g.,in crowded urban area with people and/or motorbikes. In such the condition, the problem of separating individual vehicles from foreground silhouettes often requires complicated computation [1][2][3]. Thus, the counting problem is gradually shifted into drawing statistical inferences of target objects density from their shape [4], local features [5], etc. Those researches indicate a correlation between local features and the number of target objects. However, they are inadequate to construct an accurate model for vehicles density estimation. In this paper, we present a reliable method that is robust to illumination changes and partial affine transformations. It can achieve high accuracy in case of occlusions. Firstly, local features are extracted from images of the scene using Speed-Up Robust Features (SURF) method. For each image, a global feature vector is computed using a Bag-of-Words model which is constructed from the local features above. Finally, a mapping between the extracted global feature vectors and their labels (the number of motorbikes) is learned. That mapping provides us a strong prediction model for estimating the number of motorbikes in new images. The experimental results show that our proposed method can achieve a better accuracy in comparison to others.
Spectral reflectance studies of the Grimaldi Region of the Moon

Near-infrared reflectance spectra were used to investigate the composition and origin of the various geologic units in the Grimaldi region as well as the stratigraphy of the Grimaldi pre-impact target site. The results of our spectral analysis indicate that the portions of the Hevelius Formation that occur in the Grimaldi region are composed of noritic anorthosite and anorthositic norite. Gabbroic material was excavated from beneath Orientale-related units by small impact craters in three areas in the Grimaldi region. The primary ejecta deposits of the Grimaldi basin as well as the pre-Orientale floor unit are dominated by noritic anorthosite and anorthositic norite. The peak ring of Grimaldi is composed, at least in part, of pure anorthosite. The anorthosites on the inner ring and elsewhere within Grimaldi were derived from a layer of pure anorthosite that exists at depth beneath a more pyroxene-rich unit.
Investigation and evaluation of soil heavy metals pollution supported by DTM and 3D visualization

In this paper, a method that integrates 3D GIS visualization, spatial statistical analysis, spatial database, environmental management & evaluation model for evaluating soil heavy metals pollution is presented. Based on the above method and strategies, taking the nearby farmland in the LongJiao hill mine district of Daye City in the Hubei province as an example, we found that the results using integrative method is superior to traditional method. With the technology of DTM and 3D visualization, the quantity of two heavy metals (Cu,Cd) in investigated six kinds of heavy metals (As, Cr, Pb, Cu, Zn, Cd) was easily found that exceeds the soil environment quality standard GB15618-1995. The research discovers the new idea and method can improve the efficiency and precision comparing to traditional heavy metals investigation and evaluation method. Different with traditional 2D representation mode, the new method integrated 3D visualization can visually represent and adequately evaluate soil heavy metals pollution distribution situation in the mineral area.
12/15-lipoxygenase inhibition attenuates neuroinflammation by suppressing inflammasomes

Introduction Lipoxygenases (LOXs) have essential roles in stroke, atherosclerosis, diabetes, and hypertension. 12/15-LOX inhibition was shown to reduce infarct size and brain edema in the acute phase of experimental stroke. However, the significance of 12/15-LOX on neuroinflammation, which has an essential role in the pathophysiology of stroke, has not been clarified yet. Methods In this study, ischemia/recanalization (I/R) was performed by occluding the proximal middle cerebral artery (pMCAo) in mice. Either the 12/15-LOX inhibitor (ML351, 50 mg/kg) or its solvent (DMSO) was injected i.p. at recanalization after 1 h of occlusion. Mice were sacrificed at 6, 24, and 72-h after ischemia induction. Infarct volumes were calculated on Nissl-stained sections. Neurological deficit scoring was used for functional analysis. Lipid peroxidation was determined by the MDA assay, and the inflammatory cytokines IL-6, TNF-alpha, IL-1beta, IL-10, and TGF-beta were quantified by ELISA. The inflammasome proteins NLRP1 and NLRP3, 12/15-LOX, and caspase-1 were detected with immunofluorescence staining. Results Infarct volumes, neurological deficit scores, and lipid peroxidation were significantly attenuated in ML351-treated groups at 6, 24, and 72-h. ELISA results revealed that the pro-inflammatory cytokines IL-1beta, IL-6, and TNF-alpha were significantly decreased at 6-h and/or 24-h of I/R, while the anti-inflammatory cytokines IL-10 and TNF-alpha were increased at 24-h or 72-h of ML351 treatment. NLRP1 and NLRP3 immunosignaling were enhanced at three time points after I/R, which were significantly diminished by the ML351 application. Interestingly, NLRP3 immunoreactivity was more pronounced than NLRP1. Hence, we proceeded to study the co-localization of NLRP3 immunoreactivity with 12/15-LOX and caspase-1, which indicated that NLRP3 was co-localized with 12/15-LOX and caspase-1 signaling. Additionally, NLRP3 was found in neurons at all time points but in non-neuronal cells 72 h after I/R. Discussion These results suggest that 12/15-LOX inhibition suppresses ischemia-induced inflammation in the acute and subacute phases of stroke via suppressing inflammasome activation. Understanding the mechanisms underlying lipid peroxidation and its associated pathways, like inflammasome activation, may have broader implications for the treatment of stroke and other neurological diseases characterized by neuroinflammation.
A Transfer Learning-Based Approach to Estimating Missing Pairs of On/Off Ramp Flows

Each freeway stretch’s traffic states are indispensable in freeway traffic modeling, surveillance, and control. However, the unmeasured ramp pairs always exist in real-world freeway systems, and how to estimate the flows of those ramps is a longstanding and tricky issue. Set the stretch with intact traffic states as Source Stretch while the stretch with the unmeasured ramp pair as Target Stretch; existing work tries to train the non-transfer machine learning model like Random Forest by Source Stretch and act on Target Stretch. However, the estimation accuracy of non-transfer machine learning models could not be guaranteed because the mainstream traffic state distributions of the above two stretches are not the same, and the model structure is too simple to capture traffic flow’s temporal dependencies. Note the great success of transfer learning in distribution-changed situations; this paper addresses this issue via transfer learning and deep learning. First, the Gated Recurrent Unit-Based Ramp Flow Estimator is designed to establish the relationship between the mainstream traffic states and ramp flows in Source Stretch. Then, taking the trained estimator as the backbone, we propose the Deep Domain Adaptation to match the marginal distribution difference between Source Stretch and Target Stretch; design the Model Transfer to reduce the conditional distribution difference (i.e., estimator difference) between Source Stretch and Target Stretch. The two approaches both improve the performance of the Source Stretch’s estimator in Target Stretch. Finally, we evaluate the processed approach in two real-world freeway traffic datasets and observe satisfactory results.
The benefits of using remotely sensed soil moisture in parameter identification of large‐scale hydrological models

Large‐scale hydrological models are nowadays mostly calibrated using observed discharge. As a result, a large part of the hydrological system, in particular the unsaturated zone, remains uncalibrated. Soil moisture observations from satellites have the potential to fill this gap. Here we evaluate the added value of remotely sensed soil moisture in calibration of large‐scale hydrological models by addressing two research questions: (1) Which parameters of hydrological models can be identified by calibration with remotely sensed soil moisture? (2) Does calibration with remotely sensed soil moisture lead to an improved calibration of hydrological models compared to calibration based only on discharge observations, such that this leads to improved simulations of soil moisture content and discharge? A dual state and parameter Ensemble Kalman Filter is used to calibrate the hydrological model LISFLOOD for the Upper Danube. Calibration is done using discharge and remotely sensed soil moisture acquired by AMSR‐E, SMOS, and ASCAT. Calibration with discharge data improves the estimation of groundwater and routing parameters. Calibration with only remotely sensed soil moisture results in an accurate identification of parameters related to land‐surface processes. For the Upper Danube upstream area up to 40,000 km2, calibration on both discharge and soil moisture results in a reduction by 10–30% in the RMSE for discharge simulations, compared to calibration on discharge alone. The conclusion is that remotely sensed soil moisture holds potential for calibration of hydrological models, leading to a better simulation of soil moisture content throughout the catchment and a better simulation of discharge in upstream areas.
Calibration of the Integral Equation Model for SAR data in C‐band and HH and VV polarizations

The inversion of radar images to estimate surface soil parameters requires the use of well‐calibrated backscattering models. The first objective of this paper is to confirm the empirical calibration proposed by Baghdadi et al. (2004) for the backscattering Integral Equation Model (IEM). That calibration was a partial one, because it covered only a few radar configurations. The second objective is to extend this calibration to include Synthetic Aperture Radar (SAR) data in C‐band, for HH and VV polarizations, at any radar incidence between 20° and 50°. Advanced Synthetic Aperture Radar (ASAR) data were used to supplement the database employed by Baghdadi et al. (2004). The results show good agreement between the backscattering coefficients given by the SAR sensors and those estimated from the calibrated IEM. This study also confirms the robustness of this calibration.
Enhancing the mineralizable nitrogen pool through substrate diversity in long-term cropping systems

The development of sustainable N management systems requires a better understanding of the contribution of on-farm resources to the active N pool size and its mineralization. This study explores the effect of substrate diversity on improving N supply through mineralization. A diverse system, consisting of a corn (Zea mays L.)-corn-soybean (Glycine max L.)-wheat (Triticum aestivum L.) rotation with cover crops and fertilized with composted manure was compared with a corn monoculture with conventional fertilizers. Nitrogen mineralization was measured in situ and in laboratory incubations as was the ability of each soil to mineralize added compost and red clover (Trifolium pratense) residue in the 6th and 7th yr of rotation. Net mineralized N in the diverse system was 90 and 40% higher than that in the monoculture at 70 and 150 d of laboratory incubations respectively. Comparable response was found in situ where a 70% higher net mineralization was observed in the diverse system at 70 d. The 70-and 150-d mineralizable N pools increased over time, but the ability of soil organisms to break down additional substrate did not change as a result of system diversity. At 150 d of laboratory incubation, a synergistic effect was observed when 5 Mg ha -1 of compost plus 5 Mg ha 1 of clover was added to either soil. The combination of the two organic materials mineralized more N than the sum of their individual mineralization results. A more diverse cropping system may increase the soil's capacity to supply N to a growing crop while maintaining desirable levels of soil organic matter. This is essential for the overall long-term productivity and sustainability of agricultural systems.
ALGORITHM AND CODE TO CALCULATE SPECULAR REFLECTION OF LIGHT FROM A WAVY WATER SURFACE

In studying light and image transfer in sea water the influence of Fresnel surface reflection is as significant as scattering and absorption phenomena. In these cases a knowledge of the reflective properties of sea surface at different wind speeds is very important. At present, little is published about these properties. We present here results of numerical modeling of Fresnel light reflection coefficient of sea water as a function of solar zenith angle and wind speed. The ray-tracing computer model was developed to generate wave slopes and elevations. In order to generate a realistic sea surface the model used Paul Hwang wave height spectrums. The final result of this paper is a simple equation and very fast FORTRAN code to calculate Fresnel reflection coefficient of wavy water surface and specular part of remote sensing reflectance.
Testing different cross-section spacing in 1D hydraulic modelling: a case study on Johor River, Malaysia

Abstract Cross-section data are essential to describe the topography of rivers and floodplain areas and support flood propagation and inundation modelling. To date, there are only a few guidelines available to assist hydraulic modellers in determining the required quantity and location of cross-sections. The aim of this paper is to investigate the impact of cross-section spacing on the results of hydraulic models. To this end, four hydraulic models were built using the HEC-RAS model code and four different numbers of cross-sections. The geometric data were extracted from a high-resolution digital elevation model (LiDAR DEM) of a 30-km reach of the Johor River in Malaysia. These hydraulic models were then compared using flood water levels recorded at two stream gauging stations during two recent flood events. The independent calibration and validation of the models allowed the evaluation of a set of equations proposed in the literature for determining the distance between cross-sections for hydraulic modelling. Editor D. Koutsoyiannis; Associate editor I. Nalbantis
Effects of temperature and nutrients on phytoplankton biomass during bloom seasons in Taihu Lake

Abstract Long-term variations of phytoplankton chlorophyll-a (Chl-a), nutrients, and suspended solids (SS) in Taihu Lake, a large shallow freshwater lake in China, during algal bloom seasons from May to August were analyzed using the monthly investigated data from 1999 to 2007. The effective accumulated water temperature (EAWT) in months from March to June was calculated with daily monitoring data from the Taihu Laboratory for Lake Ecosystem Research (TLLER). The concentrations of Chl-a and nutrients significantly decreased from Meiliang Bay to Central Lake. Annual averages of the total nitrogen (TN), total phosphorus (TP), and Chl-a concentrations, and EAWT generally increased in the nine years. In Meiliang Bay, the concentration of Chl-a was significantly correlated with EAWT, ammonia nitrogen (NH4+-N), TN, the soluble reactive phosphorus (SRP), TP, and SS. In Central Lake, however, the concentration of Chl-a was only correlated with EAWT, TP, and SS. Multiple stepwise linear regression revealed that EAWT, dissolved total phosphorus (DTP), and TP explained 99.2% of the variation of Chl-a in Meiliang Bay, and that EAWT, NH4+-N , and TP explained 98.7% of the variation of Chl-a in Central Lake. Thus EAWT is an important factor influencing the annual change of phytoplankton biomass. Extreme climate change, such as extremely hot springs or cold springs, could cause very different bloom intensities in different years. It is also suggested that both nutrients and EAWT played important roles in the growth of phytoplankton in Taihu Lake. The climate factors and nutrients dually controlled the risk of harmful algal blooms in Taihu Lake. Cutting down phosphorus and nitrogen loadings from catchments should be a fundamental strategy to reduce the risk of blooms in Taihu Lake.
Preparatory analyses and development of algorithms for agricultural applications in the context of the EnMAP hyperspectral mission

With the launch of the German hyperspectral satellite mission 'Environmental Mapping and Analysis Program' (EnMAP), anticipated in 2014, unprecedented opportunities will open up for a wide range of applications. Along with different areas of application, the agricultural sector will particularly benefit from the availability of such observation capability. Information about state and dynamics of the (non-)vegetated land surface, expressed by biophysical variables, is required for instance in irrigation water determination, stress detection or in advanced crop production modeling. In the context of the mission, a toolbox will be provided to determine these variables from hyperspectral imagery. Algorithms to be implemented will range from empirical methods, such as hyperspectral vegetation indices, to physically based approaches, involving the inversion of canopy reflectance models. In this study, potential techniques for the EnMAP toolbox are selected and tested using data from two field campaigns conducted in two different geographic regions. One of the campaigns was carried out in summer 2009 at the German agricultural 'Landau test site' as a first step towards the scientific preparation of the EnMAP mission. During the campaign, data of the airborne hyperspectral scanner HyMap were acquired concurrently with ground measurements of canopy water content and other variables. The second campaign was conducted in the Cuga river basin in Sardinia (Italy) during summer 2007. First results of data analyses will be presented and discussed, emphasizing in particular the benefits of multi-temporal and multi-seasonal hyperspectral data availability over current operational systems.