A novel method to predict Non-uniform illumination pattern on a large scale rooftop SPV array

2016 ◽  
Author(s):  
Kuntal Ghosh ◽  
P. Phalguna ◽  
Siddhartha P. Duttagupta ◽  
P.K. Gupta
Keyword(s):  
Large Scale ◽  
Uniform Illumination ◽  
Novel Method

Enzymatic Synthesis of 15N-L-aspartic Acid Using Recombinant Aspartase from Escherichia Coli K12

2008 ◽  
Vol 59 (11) ◽  
Author(s):  
Iulia Lupan ◽  
Sergiu Chira ◽  
Maria Chiriac ◽  
Nicolae Palibroda ◽  
Octavian Popescu
Keyword(s):  
Amino Acids ◽  
Aspartic Acid ◽  
Enzymatic Synthesis ◽  
Large Scale ◽  
Recombinant Dna ◽  
Industrial Enzymes ◽  
Recombinant Dna Technology ◽  
Bacterial Fermentation ◽  
Natural Protein ◽  
Novel Method

Amino acids are obtained by bacterial fermentation, extraction from natural protein or enzymatic synthesis from specific substrates. With the introduction of recombinant DNA technology, it has become possible to apply more rational approaches to enzymatic synthesis of amino acids. Aspartase (L-aspartate ammonia-lyase) catalyzes the reversible deamination of L-aspartic acid to yield fumaric acid and ammonia. It is one of the most important industrial enzymes used to produce L-aspartic acid on a large scale. Here we described a novel method for [15N] L-aspartic synthesis from fumarate and ammonia (15NH4Cl) using a recombinant aspartase.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

scholarly journals A Novel Method Facilitating the Simple and Low-Cost Preparation of Human Osteochondral Slice Explants for Large-Scale Native Tissue Analysis

2021 ◽  
Vol 22 (12) ◽  
pp. 6394
Author(s):  
Jacob Spinnen ◽  
Lennard K. Shopperly ◽  
Carsten Rendenbach ◽  
Anja A. Kühl ◽  
Ufuk Sentürk ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Viability ◽  
Large Scale ◽  
Marker Gene ◽  
Cell Swelling ◽  
Tissue Cell ◽  
Joint Research ◽  
Sample Number ◽  
Tnf Α ◽  
Novel Method

For in vitro modeling of human joints, osteochondral explants represent an acceptable compromise between conventional cell culture and animal models. However, the scarcity of native human joint tissue poses a challenge for experiments requiring high numbers of samples and makes the method rather unsuitable for toxicity analyses and dosing studies. To scale their application, we developed a novel method that allows the preparation of up to 100 explant cultures from a single human sample with a simple setup. Explants were cultured for 21 days, stimulated with TNF-α or TGF-β3, and analyzed for cell viability, gene expression and histological changes. Tissue cell viability remained stable at >90% for three weeks. Proteoglycan levels and gene expression of COL2A1, ACAN and COMP were maintained for 14 days before decreasing. TNF-α and TGF-β3 caused dose-dependent changes in cartilage marker gene expression as early as 7 days. Histologically, cultures under TNF-α stimulation showed a 32% reduction in proteoglycans, detachment of collagen fibers and cell swelling after 7 days. In conclusion, thin osteochondral slice cultures behaved analogously to conventional punch explants despite cell stress exerted during fabrication. In pharmacological testing, both the shorter diffusion distance and the lack of need for serum in the culture suggest a positive effect on sensitivity. The ease of fabrication and the scalability of the sample number make this manufacturing method a promising platform for large-scale preclinical testing in joint research.

scholarly journals A Novel Method for the Preparation of Fibrous CeO2–ZrO2–Y2O3 Compacts for Thermochemical Cycles

Crystals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 885
Author(s):  
Nicole Knoblauch ◽  
Peter Mechnich
Keyword(s):  
Large Scale ◽  
Preparation Method ◽  
Cost Effective ◽  
High Porosity ◽  
Thermochemical Cycles ◽  
Redox Cycles ◽  
Redox Kinetics ◽  
Direct Infiltration ◽  
Novel Method ◽  
Functional Components

Zirconium-Yttrium-co-doped ceria (Ce0.85Zr0.13Y0.02O1.99) compacts consisting of fibers with diameters in the range of 8–10 µm have been successfully prepared by direct infiltration of commercial YSZ fibers with a cerium oxide matrix and subsequent sintering. The resulting chemically homogeneous fiber-compacts are sinter-resistant up to 1923 K and retain a high porosity of around 58 vol% and a permeability of 1.6–3.3 × 10−10 m² at a pressure gradient of 100–500 kPa. The fiber-compacts show a high potential for the application in thermochemical redox cycling due its fast redox kinetics. The first evaluation of redox kinetics shows that the relaxation time of oxidation is five times faster than that of dense samples of the same composition. The improved gas exchange due to the high porosity also allows higher reduction rates, which enable higher hydrogen yields in thermochemical water-splitting redox cycles. The presented cost-effective fiber-compact preparation method is considered very promising for manufacturing large-scale functional components for solar-thermal high-temperature reactors.

scholarly journals Using Sentinel-2 Images to Estimate Topography, Tidal-Stage Lags and Exposure Periods over Large Intertidal Areas

2021 ◽  
Vol 13 (2) ◽  
pp. 320
Author(s):  
José P. Granadeiro ◽  
João Belo ◽  
Mohamed Henriques ◽  
João Catalão ◽  
Teresa Catry
Keyword(s):  
Large Scale ◽  
Exposure Period ◽  
Intertidal Area ◽  
Digital Elevation ◽  
Geographical Bias ◽  
Novel Method ◽  
Tidal Stage ◽  
Sentinel 2

Intertidal areas provide key ecosystem services but are declining worldwide. Digital elevation models (DEMs) are important tools to monitor the evolution of such areas. In this study, we aim at (i) estimating the intertidal topography based on an established pixel-wise algorithm, from Sentinel-2 MultiSpectral Instrument scenes, (ii) implementing a set of procedures to improve the quality of such estimation, and (iii) estimating the exposure period of the intertidal area of the Bijagós Archipelago, Guinea-Bissau. We first propose a four-parameter logistic regression to estimate intertidal topography. Afterwards, we develop a novel method to estimate tide-stage lags in the area covered by a Sentinel-2 scene to correct for geographical bias in topographic estimation resulting from differences in water height within each image. Our method searches for the minimum differences in height estimates obtained from rising and ebbing tides separately, enabling the estimation of cotidal lines. Tidal-stage differences estimated closely matched those published by official authorities. We re-estimated pixel heights from which we produced a model of intertidal exposure period. We obtained a high correlation between predicted and in-situ measurements of exposure period. We highlight the importance of remote sensing to deliver large-scale intertidal DEM and tide-stage data, with relevance for coastal safety, ecology and biodiversity conservation.

scholarly journals Topology assisted self-organization of colloidal nanoparticles: application to 2D large-scale nanomastering

2014 ◽  
Vol 5 ◽  
pp. 1203-1209 ◽  
Author(s):  
Hind Kadiri ◽  
Serguei Kostcheev ◽  
Daniel Turover ◽  
Rafael Salas-Montiel ◽  
Komla Nomenyo ◽  
...  
Keyword(s):  
Large Scale ◽  
The Self ◽  
Surface Topology ◽  
Self Organization ◽  
Hydrogen Silsesquioxane ◽  
Polystyrene Beads ◽  
Structure Topology ◽  
Self Organized ◽  
Novel Method ◽  
Long Range Ordering

Our aim was to elaborate a novel method for fully controllable large-scale nanopatterning. We investigated the influence of the surface topology, i.e., a pre-pattern of hydrogen silsesquioxane (HSQ) posts, on the self-organization of polystyrene beads (PS) dispersed over a large surface. Depending on the post size and spacing, long-range ordering of self-organized polystyrene beads is observed wherein guide posts were used leading to single crystal structure. Topology assisted self-organization has proved to be one of the solutions to obtain large-scale ordering. Besides post size and spacing, the colloidal concentration and the nature of solvent were found to have a significant effect on the self-organization of the PS beads. Scanning electron microscope and associated Fourier transform analysis were used to characterize the morphology of the ordered surfaces. Finally, the production of silicon molds is demonstrated by using the beads as a template for dry etching.

scholarly journals Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction

2018 ◽  
Vol 6 ◽  
pp. 451-465 ◽  
Author(s):  
Daniela Gerz ◽  
Ivan Vulić ◽  
Edoardo Ponti ◽  
Jason Naradowsky ◽  
Roi Reichart ◽  
...  
Keyword(s):  
Large Scale ◽  
Language Modeling ◽  
Language Models ◽  
Data Sets ◽  
High Type ◽  
Word Level ◽  
Level Information ◽  
Character Sequences ◽  
Novel Method ◽  
Morphologically Rich Languages

Neural architectures are prominent in the construction of language models (LMs). However, word-level prediction is typically agnostic of subword-level information (characters and character sequences) and operates over a closed vocabulary, consisting of a limited word set. Indeed, while subword-aware models boost performance across a variety of NLP tasks, previous work did not evaluate the ability of these models to assist next-word prediction in language modeling tasks. Such subword-level informed models should be particularly effective for morphologically-rich languages (MRLs) that exhibit high type-to-token ratios. In this work, we present a large-scale LM study on 50 typologically diverse languages covering a wide variety of morphological systems, and offer new LM benchmarks to the community, while considering subword-level information. The main technical contribution of our work is a novel method for injecting subword-level information into semantic word vectors, integrated into the neural language modeling training, to facilitate word-level prediction. We conduct experiments in the LM setting where the number of infrequent words is large, and demonstrate strong perplexity gains across our 50 languages, especially for morphologically-rich languages. Our code and data sets are publicly available.

scholarly journals Temporal Activity Path Based Character Correction in Heterogeneous Social Networks via Multimedia Sources

2018 ◽  
Vol 2018 ◽  
pp. 1-16
Author(s):  
Jun Long ◽  
Lei Zhu ◽  
Zhan Yang ◽  
Chengyuan Zhang ◽  
Xinpan Yuan
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Large Scale ◽  
Multimedia Data ◽  
Attribute Information ◽  
Novel Method ◽  
Relationship Network ◽  
Heterogeneous Social Networks ◽  
The Relationship ◽  
Over Time

Vast amount of multimedia data contains massive and multifarious social information which is used to construct large-scale social networks. In a complex social network, a character should be ideally denoted by one and only one vertex. However, it is pervasive that a character is denoted by two or more vertices with different names; thus it is usually considered as multiple, different characters. This problem causes incorrectness of results in network analysis and mining. The factual challenge is that character uniqueness is hard to correctly confirm due to lots of complicated factors, for example, name changing and anonymization, leading to character duplication. Early, limited research has shown that previous methods depended overly upon supplementary attribute information from databases. In this paper, we propose a novel method to merge the character vertices which refer to the same entity but are denoted with different names. With this method, we firstly build the relationship network among characters based on records of social activities participating, which are extracted from multimedia sources. Then we define temporal activity paths (TAPs) for each character over time. After that, we measure similarity of the TAPs for any two characters. If the similarity is high enough, the two vertices should be considered as the same character. Based on TAPs, we can determine whether to merge the two character vertices. Our experiments showed that this solution can accurately confirm character uniqueness in large-scale social network.

scholarly journals Artificial Intelligence Predicts and Explains West Nile Virus Risks Across Europe: Extraordinary Outbreaks Determined by Climate and Local Factors

2020 ◽  
Author(s):  
Albert A Gayle
Keyword(s):  
West Nile Virus ◽  
Large Scale ◽  
Predictive Accuracy ◽  
Local Effect ◽  
Research Literature ◽  
West Nile ◽  
Priority Direction ◽  
Minimal Data ◽  
Novel Method ◽  
Using Data

Year-to-year emergence of West Nile virus has been sporadic and notoriously hard to predict. In Europe, 2018 saw a dramatic increase in the number of cases and locations affected. In this work, we demonstrate a novel method for predicting outbreaks and understanding what drives them. This method creates a simple model for each region that directly explains how each variable affects risk. Behind the scenes, each local explanation model is produced by a state-of-the-art AI engine. This engine unpacks and restructures output from an XGBoost machine learning ensemble. XGBoost, well-known for its predictive accuracy, has always been considered a "black box" system. Not any more. With only minimal data curation and no "tuning", our model predicted where the 2018 outbreak would occur with an AUC of 97%. This model was trained using data from 2010-2016 that reflected many domains of knowledge. Climate, sociodemographic, economic, and biodiversity data were all included. Our model furthermore explained the specific drivers of the 2018 outbreak for each affected region. These effect predictions were found to be consistent with the research literature in terms of priority, direction, magnitude, and size of effect. Aggregation and statistical analysis of local effects revealed strong cross-scale interactions. From this, we concluded that the 2018 outbreak was driven by large-scale climatic anomalies enhancing the local effect of mosquito vectors. We also identified substantial areas across Europe at risk for sudden outbreak, similar to that experienced in 2018. Taken as a whole, these findings highlight the role of climate in the emergence and transmission of West Nile virus. Furthermore, they demonstrate the crucial role that the emerging "eXplainable AI" (XAI) paradigm will have in predicting and controlling disease.

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