nils-herrmann/clean_scirepeval huggingface dataset:
Transit times the link between hydrology and water quality at the catchment scale
Year: 2016

In spite of trying to understand processes in the same spatial domain, the catchment hydrology and water quality scientific communities are relatively disconnected and so are their respective models. This is emphasized by an inadequate representation of transport processes, in both catchment scale hydrological and water quality models. While many hydrological models at the catchment scale only account for pressure propagation and not for mass transfer, catchment scale water quality models are typically limited by overly simplistic representations of flow processes. With the objective of raising awareness for this issue and outlining potential ways forward we provide a nontechnical overview of (1) the importance of hydrology controlled transport through catchment systems as the link between hydrology and water quality; (2) the limitations of current generation catchment scale hydrological and water quality models; (3) the concept of transit times as tools to quantify transport; and (4) the benefits of transit time based formulations of solute transport for catchment scale hydrological and water quality models. There is emerging evidence that an explicit formulation of transport processes, based on the concept of transit times has the potential to improve the understanding of the integrated system dynamics of catchments and to provide a stronger link between catchment scale hydrological and water quality models. WIREs Water 2016, 3:629 657. doi: 10.1002/wat2.1155 For further resources related to this article, please visit the
The impact of green economy measures on rural employment: Green jobs in farms
Year: 2019

In the circular economy concept, besides protecting the environment, green policy measures provide essential economic benefits through resource security, economic stability, and the creation of green jobs. This study centres its attention on the labour use aspect of the circular economy and aims to examine the potential for green economy measures to create green jobs in the agriculture sector. As a methodological approach, we combine "top down" and "bottom up" analyses of the green economy experience of Slovenia, where agri environmental measures (AEM) play an essential role in the Slovenian rural development programme (2007 2014) with the highest amount of subsidies per unit of output among the 10 new European Union Member States. The results show that an AEM adoption of green policy measures by very large dairy and field crop farms significantly increases total labour use: while for field crop farms this increase is in hired labour, for dairy farms this increase is in family labour. While hired and family labour perform as substitutes for very large dairy farms, they perform as complements for very large field crop farms. The present study suggests further steps towards identifying the green economy measures that are needed to create green jobs in the agriculture sector for rural youth.
Ownership structure and corporate tax avoidance: Evidence from publicly listed private firms in China
Year: 2016

This study examines the association between ownership structure and corporate tax avoidance in publicly listed private firms in China. We find a significant non linear association between ownership concentration and tax avoidance that exhibits an inverted U shaped pattern. At a lower level, increased ownership concentration is positively associated with tax avoidance due to the entrenchment effect. However, beyond the minimum level necessary for effective control, concentrated ownership through voting rights is negatively associated with tax avoidance because of the alignment effect. We also find a significantly positive association between pyramidal ownership structure and tax avoidance due to the entrenchment effect. When voting rights and cash flow rights diverge, a lower level of cash flow rights fails to offer the controlling owner incentive alignment sufficient to reduce the entrenchment effect and tax avoidance.
Comparison of Assesment of Imaging Response with Magnetic Resonance (MR) and 18fdg PET/TC in Multiple Myeloma (MM) Single Centre Experience
Year: 2014

Conventional radiography remains the "gold standard" technique for bone involvement assessment in patients with multiple myeloma (MM) Newer imaging modalities such as whole body Magnetic Resonance (MR) and 18FDG PET/CT have emerged as more sensitive techniques than routine skeletal survey in the detection of bone involvement in the diagnostic and follow up of patients with MM. The advantages and disadvantages of MR and 18FDG PET/CT are discussed.  We have retrospectively analyzed 12 patients since 2012 to 2014 with multiple myeloma in our institution whose bone involvement was evaluated with MR and 18FDG PET/CT. Age range: 36 70. Seven patients were female and five were male. Eight cases were treated with an induction regimen containing bortezomib, three cases with chemotherapy with alternating VBCMP/VBAD and one of them with VAD. After induction, ten of them received autologous stem cell transplantation (ASCT) one patient allogeneic stem cell transplantation and one patient no transplantation.  All patients presented bone lesions on MR and all of them were also positive at PET/CT. One showed leptomeningeal involvement on RM and PET. Regarding extramedullar disease two patients presented soft tissue masses and in other two cases there was ganglionar involvement, all of them positives by both techniques. Of nine evaluable patients after complete treatment, six of them have a negative PET and three have a low positive SUV value, however eight of them still had persistent residual lesions on MR, what could indicate not stringent complete response. The patient with leptomeningeal involvement had both MR and PET negative result after treatment.  Our data suggest that whole body MR and 18FDG PET/CT provide valuable complementary information, MR could be superior to assess extent of lesions and PET to monitor disease activity and to detect asymptomatic relapse. The optimal imaging technique for the management of patients with MM is not well defined and our proposal is a multimodality imaging approach according to individualized criteria.
Assessment of SARS CoV 2 serological tests for the diagnosis of COVID 19 through the evaluation of three immunoassays: Two automated immunoassays (Euroimmun and Abbott) and one rapid lateral flow immunoassay (NG Biotech)
Venue: Journal of Clinical Virology
Year: 2020

The emergence of new SARS CoV 2 has promoted the development of new serological tests that could be complementary to RT PCR. Nevertheless, the assessment of clinical performances of available tests is urgently required as their use has just been initiated for diagnose.  The aim of this study was to assess the performance of three immunoassays for the detection of SARS CoV 2 antibodies.  Two automated immunoassays (Abbott SARS CoV 2 CLIA IgG and Euroimmun Anti SARS CoV 2 ELISA IgG/IgA assays) and one lateral flow immunoassay (LFIA NG Test(r) IgG IgM COVID 19) were tested. 293 specimens were analyzed from patients with a positive RT PCR response, from patients with symptoms consistent with COVID 19 but exhibiting a negative response to the RT PCR detection test, and from control group specimens. Days since symptoms onset were collected from clinical information sheet associated with respiratory tract samples.  Overall sensitivity for IgG was equivalent (around 80% for CLIA, ELISA and LFIA. Sensitivity for IgG detection, >14 days after onset of symptoms, was 100.0% for all assays. Overall specificity for IgG was greater for CLIA and LFIA (more than 98% compared to ELISA (95.8% Specificity was significantly different between IgA ELISA (78.9% and IgM LFIA (95.8% (p 0.05) The best agreement was observed between CLIA and LFIA assays (97% k 0.936)  Excellent sensitivity for IgG detection was obtained >14 days after onset of symptoms for all immunoassays. Specificity was also excellent for IgG CLIA and IgG LFIA. Our study shows that NG Test(r) is reliable and accurate for routine use in clinical laboratories.
Is Re ranking Useful for Open set Person Re identification?
Venue: 2018 IEEE International Conference on Big Data (Big Data)
Year: 2018

Re ranking algorithms can often boost the performance of close set person re identification. However, limited efforts have been devoted to answering whether a similar conclusion could be derived on open set person re identification. Considering that open set scenario is more practical in real applications, in this paper, we try to answer this question and do a benchmark study of re ranking on open set person re identification. Specifically, we evaluate three feature descriptors, namely MB LBP, LOMO, and IDE, and four distance metrics, namely Euclidean, Cosine, RRDA, and XQDA, with their combinations as baseline algorithms. Then, we evaluate four popular re ranking algorithms, including k reciprocal Encoding, ECN 3, ECN 4, and DaF. Through extensive benchmark studies on the OPeRIDv1.0 dataset, the results show that re ranking algorithms, though useful for closed set person re identification, are not generally effective for the open set person re identification problem. We argue that this is because re ranking algorithms change the score distributions per query, and hence disrupt the FAR estimation across all queries. Accordingly, we propose to align the re ranking scores to the original score via the min max normalization, which verifies our hypothesis above.
Adaptive Bayesian multivariate density estimation with Dirichlet mixtures BY WEINING SHEN
Year: 2013

We show that rate adaptive multivariate density estimation can be performed using Bayesian methods based on Dirichlet mixtures of normal kernels with a prior distribution on the kernel's covariance matrix parameter. We derive sufficient conditions on the prior specification that guarantee convergence to a true density at a rate that is minimax optimal for the smoothness class to which the true density belongs. No prior knowledge of smoothness is assumed. The sufficient conditions are shown to hold for the Dirichlet location mixture of normals prior with a Gaussian base measure and an inverse Wishart prior on the covariance matrix parameter. Locally Holder smoothness classes and their anisotropic extensions are considered. Our study involves several technical novelties, including sharp approximation of finitely differentiable multivariate densities by normal mixtures and a new sieve on the space of such densities.
Method for preparing firecracker lead paper making felt
Year: 2015

The invention discloses a method for preparing a firecracker lead paper making felt. The firecracker lead paper making felt comprises a front side fiber layer, a basic fabric layer and a back side fiber layer from a working surface to the bottom. The preparation method comprises the following steps: weaving a basic fabric blank layer, paving fiber layers on the front side and the back side of the basic fabric layer, performing empty puncture operation on the fiber layers on the front side and the back side of the basic fabric layer, performing heat setting treatment on the back side fiber layer on the basic fabric layer, and fluzzing the front side fiber layer on the basic fabric layer. By the adoption of the method for preparing the paper making felt for the firecracker lead paper, the obtained firecracker lead paper making felt is long in service life, good in stability, free of felt mark and not easy to emboss, the paper produced by using the firecracker lead paper making felt is good in comprehensive quality, the production process is a simple endless junction free production process, firecracker lead paper is only fixed and joined on the back side, the natural scale structure on the surface of wool on the front side can be maintained, a single side can be fuzzed, the method is simple in process and convenient to operate and has a very large application space, and energy can be saved and consumption can be reduced.
Driving factors of CO2 emissions and inequality characteristics in China: A combined decomposition approach
Venue: Energy Economics
Year: 2019

Decomposition analysis has become a popular tool to study CO2 emissions and, in this study, we developed a combined decomposition approach to emissions analysis by integrating the logarithmic mean Divisia index and production theoretical decomposition analysis. Based on this novel approach, we investigated the driving factors of CO2 emissions in China over the latest Five Year Plan period (2011 2015) and analyzed the inequality characteristics of such emissions. The results showed that 1) the peak value of CO2 emissions in China declined over the period; 2) the overall inequality presented a decreasing trend, whereas intragroup inequality presented a slightly increasing trend over the period; and 3) generally, the potential energy intensity effect contributed to the decrease in CO2 emissions in developed provinces, whereas the potential carbon factor effect accounted for the decrease in CO2 emissions in less developed provinces. Based on our empirical results, we recommend that policy makers consider several factors when implementing CO2 policies.
Experimental investigation of machined hole and optimization of machining parameters using electrochemical machining
Venue: Journal of Materials Research and Technology
Year: 2019

The electrochemical micromachining in non conventional machining which suffers from process control in micro level. In addition, the work material i.e. stainless steel provides high strength, high toughness and adherence to tool material, hence machining of such material is arduous. However, the use of stainless steel offers its availability in the field of aerospace fuel injection and orthodontic application, since it possess superior qualities. In this paper, the optimization of micro drilling process is carried out in stainless steel by considering certain metrics like feed rate, voltage and duty ratio. The geometric characteristics to drill the tool depends on response parameters like overcut, removal rate of material and conicity. Such parameter determines both the geometric and machining characteristics of the drill bit. The study is evaluated to observe the effect of response and duty ratio parameters such as material removal rate (MRR) machining time and overcut. Further, the conicity is analyzed using VMS images. Finally, the proposed work establishes duty cycle in pulsed electrochemical micromachining domain of hard materials and tests its performance.
A novel approach for license plate location in natural images
Venue: 2009 Second International Conference on the Applications of Digital Information and Web Technologies
Year: 2009

Extraction of the license plate region is challenging first step in the license plate recognition system. The paper proposed approach is not only simple but also more effective than some of the existing method earlier. A new dynamic RGB threshold formula by limiting the hue change range is educed. The license image is made grids. Then we can plot and mark them by the formula and operate them with mathematical morphology because of precision cutting the license knowledge. More than five hundred images are tested, none of the results manifested missed. Only 1 candidate absented plate had been got. Plates were extracted and a 99% rate was achieved.
Multimode optical fiber transmission with a deep learning network
Venue: Light, science applications
Year: 2018

Multimode fibers (MMFs) are an example of a highly scattering medium, which scramble the coherent light propagating within them to produce seemingly random patterns. Thus, for applications such as imaging and image projection through an MMF, careful measurements of the relationship between the inputs and outputs of the fiber are required. We show, as a proof of concept, that a deep neural network can learn the input output relationship in a 0.75 m long MMF. Specifically, we demonstrate that a deep convolutional neural network (CNN) can learn the nonlinear relationships between the amplitude of the speckle pattern (phase information lost) obtained at the output of the fiber and the phase or the amplitude at the input of the fiber. Effectively, the network performs a nonlinear inversion task. We obtained image fidelities (correlations) as high as ~98% for reconstruction and ~94% for image projection in the MMF compared with the image recovered using the full knowledge of the system transmission characterized with the complex measured matrix. We further show that the network can be trained for transfer learning, i.e. it can transmit images through the MMF, which belongs to another class not used for training/testing.Multimode fibres: Neural network descramblingA convolutional neural network (CNN) can successfully learn the nonlinear transmission characteristics of a multimode fibre thus allowing accurate image transmission and reconstruction. Propagation along a multimode fibre usually scrambles an input image, resulting in a seemingly random speckle pattern at the output. Babak Rahmani and coworkers from the Ecole Polytechnique Federale de Lausanne in Switzerland have now shown that a deep neural network(either a 22 layer CNN based on VGG net technology or a 20 layer CNN based on Res net technology) can learn the input output relationship in a 0.75 m long piece of multimode fibre) and thus undo this scrambling. Experiments showed that both neural networks could perform highly accurate image reconstruction with an image fidelity as high as ~98% for image reconstruction and ~94% for image projection in the best case.
Shared evolutionary origin of vertebrate neural crest and cranial placodes
Venue: Nature
Year: 2018

Placodes and neural crests represent defining features of vertebrates, yet their relationship remains unclear despite extensive investigation1 3. Here we use a combination of lineage tracing, gene disruption and single cell RNA sequencing assays to explore the properties of the lateral plate ectoderm of the proto vertebrate, Ciona intestinalis. There are notable parallels between the patterning of the lateral plate in Ciona and the compartmentalization of the neural plate ectoderm in vertebrates4. Both systems exhibit sequential patterns of Six1/2, Pax3/7 and Msxb expression that depend on a network of interlocking regulatory interactions4. In Ciona, this compartmentalization network produces distinct but related types of sensory cells that share similarities with derivatives of both cranial placodes and the neural crest in vertebrates. Simple genetic disruptions result in the conversion of one sensory cell type into another. We focused on bipolar tail neurons, because they arise from the tail regions of the lateral plate and possess properties of the dorsal root ganglia, a derivative of the neural crest in vertebrates5. Notably, bipolar tail neurons were readily transformed into palp sensory cells, a proto placodal sensory cell type that arises from the anterior most regions of the lateral plate in the Ciona tadpole6. Proof of transformation was confirmed by whole embryo single cell RNA sequencing assays. These findings suggest that compartmentalization of the lateral plate ectoderm preceded the advent of vertebrates, and served as a common source for the evolution of both cranial placodes and neural crest3,4.Similarities between the patterning of the lateral plate in Ciona and of the neural plate ectoderm in vertebrates indicate that compartmentalization of the lateral plate ectoderm preceded the advent of vertebrates.
Attenuation correction using 3D deep convolutional neural network for brain 18F FDG PET/MR: Comparison with Atlas, ZTE and CT based attenuation correction
Venue: PloS one
Year: 2019

One of the main technical challenges of PET/MRI is to achieve an accurate PET attenuation correction (AC) estimation. In current systems, AC is accomplished by generating an MRI based surrogate computed tomography (CT) from which AC maps are derived. Nevertheless, all techniques currently implemented in clinical routine suffer from bias. We present here a convolutional neural network (CNN) that generated AC maps from Zero Echo Time (ZTE) MR images. Seventy patients referred to our institution for 18FDG PET/MR exam (SIGNA PET/MR, GE Healthcare) as part of the investigation of suspected dementia, were included. 23 patients were added to the training set of the manufacturer and 47 were used for validation. Brain computed tomography (CT) scan, two point LAVA flex MRI (for atlas based AC) and ZTE MRI were available in all patients. Three AC methods were evaluated and compared to CT based AC (CTAC) one based on a single head atlas, one based on ZTE segmentation and one CNN with a 3D U net architecture to generate AC maps from ZTE MR images. Impact on brain metabolism was evaluated combining voxel and regions of interest based analyses with CTAC set as reference. The U net AC method yielded the lowest bias, the lowest inter individual and inter regional variability compared to PET images reconstructed with ZTE and Atlas methods. The impact on brain metabolism was negligible with average errors of 0.2% in most cortical regions. These results suggest that the U net AC is more reliable for correcting photon attenuation in brain FDG PET/MR than atlas AC and ZTE AC methods.
Use of satellite telemetry to determine ecology and management of loggerhead turtle (Caretta caretta) during the nesting season in Georgia
Year: 2006

by JASON A. SCOTT (Under the Direction of Steven B. Castleberry) ABSTRACT Loggerhead turtle (Caretta caretta) populations are threatened across the distribution, and susceptible to high adult mortality in neritic zone habitats. Commercial fisheries, especially shrimp trawling, are believed to exact heavy losses on the adult loggerhead population within these habitats. Yet, little is known about how commercial shrimp trawler fleets interact with adult loggerhead populations cross temporal and spatial scales. Observations of distribution and movement patterns for adult female loggerhead turtle and the commercial shrimp trawl fishery were made during the 2004 and 2005 nesting seasons in Georgia. Comparisons between the distributions were used to produce potential management scenarios (closures and fleet reductions) designed to reduce the likelihood of interactions. Through application of predictive modeling, spatial closures were identified to have only limited merits as management options, whereas fleet reductions were most likely to produce actual decreases in interactions between turtles and shrimp trawlers during nesting seasons.