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2021 ◽  
Vol 58 (1) ◽  
pp. 101-106
R. K. GIRI ◽  
L. R. MEENA ◽  

Water vapour is highly variable in space and time, and plays a large role in atmospheric processes that act over a wide range of temporal and spatial scales on global climate to micrometeorology. This paper deals with a new approach to remotely sense the water vapour based on the Global Position System (GPS). The signal propagating from GPS satellites to ground based receivers is delayed by atmospheric water vapour. The delay is parameterized in terms of time varying Zenith-Wet Delay (ZWD), which is retrieved by stochastic filtering of GPS data. With the help of surface pressure and temperature readings at the GPS receiver, the retrieved ZWD can be transformed into Integrated Water Vapour (IWV) overlying at the receiver with little additional uncertainties. In this study the Zenith Total time Delay (ZTD) data without met package is retrieved using the GAMIT (King and Bock, 1997) GPS data processing software developed by Massachusetts Institute of Technology (MIT) for the period of January 2003 to February 2003 for two stations New Delhi and Bangalore .The IWV retrieved from GPS and its comparison with Limited Area Model (LAM) retrieved IWV shows fairly good agreement.

2021 ◽  
Vol 2 (2) ◽  
pp. 77-88
Bhebeind Aryanto Phubenast ◽  
Risnaini Masdalipa

Management of Primary School (SD) Exam Participant Registration (DPU) at the Pagar Alam City Education and Culture Office is currently still done manually, namely by recording it in a ledger then the operator manages the data using Microsoft word and Microsoft Excel so it takes a long time to get good results. With the new system that can later create data processing effectively and efficiently, for this success a better system of data processing software is needed as a tool to reduce the error rate in the data search process for Primary School Exam Participant Registration (DPU). (SD). The method used in this research is the waterfall method which has the following sequence of communication, planning, modeling, construction and distribution. Based on the summary above, the authors are interested in raising the Software for Assisting Exam Participant Registration (DPU) in Elementary Schools (SD). At the Department of Education and Culture of the City of Pagar Alam.

2021 ◽  
Andrew L Walker ◽  
Cheri Watson ◽  
Ryan Butcher ◽  
Ryan Butcher ◽  
Mark Yandell ◽  

Background: Real-world evidence derived from the electronic medical record (EMR) is increasingly prevalent. How best to ascertain cardiovascular outcomes from EMRs is unknown. We sought to validate a commercially available natural language processing (NLP) software to extract bleeding events. Methods: We included patients with atrial fibrillation and cancer seen at our cancer center from 1/1/2016 to 12/31/2019. A query set based on SNOMED CT expressions was created to represent bleeding from 11 different organ systems. We ran the query against the clinical notes and randomly selected a sample of notes for physician validation. The primary outcome was the positive predictive value (PPV) of the software to identify bleeding events stratified by organ system. Results: We included 1370 patients with mean age 72 years old (SD 1.5) and 35% female. We processed 66,130 notes; the NLP software identified 6522 notes including 654 unique patients with possible bleeding events. Among 1269 randomly selected notes, the PPV of the software ranged from 0.921 for neurologic bleeds to 0.571 for OB/GYN bleeds. Patterns related to false positive bleeding events identified by the software included historic bleeds, hypothetical bleeds, missed negatives, and word errors. Conclusions: NLP may provide an alternative for population-level screening for bleeding outcomes in cardiovascular studies. Human validation is still needed, but an NLP-driven screening approach may improve efficiency. 

2021 ◽  
Vol 31 (1) ◽  
pp. 41-61
Jean Écalle ◽  
Monique Sanchez ◽  
Annie Magnan

The aim of this research was to provide to eight children with Down Syndrome a syllable-processing software program that drew their attention to phonological and orthographic syllables. The children participated in a 10-hour training course (spread over 5 weeks) that used an experimental design with four assessment sessions, the first two of which were used to obtain a baseline in literacy skills. The effect of training was assessed just after training and two months later. A significant effect on decoding was observed at medium term after training. All children progressed in at least one domain, either in phonological skills, in decoding, or in word reading. Four children progressed in decoding and word reading. This study confirms the appropriateness of using phonetic approaches to reading instruction in order to stimulate learning to read in children with Down Syndrome. The syllable-based training facilitates the construction of associations between letters and syllables—the “syllabic bridge”—and could be a faster and easier way to learn letter-sound correspondences in French.

2021 ◽  
Vol 10 (11) ◽  
pp. 770
Guiye Lin ◽  
Andrea Giordano ◽  
Kun Sang ◽  
Luigi Stendardo ◽  
Xiaochun Yang

Historical villages bear historical, cultural, architectural, aesthetic, and landscape values, but they are facing a series of dangers and problems during the process of urbanization. Digital survey for traditional villages plays a crucial role in the preservation, planning, and development of this kind of heritage. The introduction of the terrestrial laser scanning technique is essential for heritage surveying, mapping, and modeling due to its advantages of noncontact measurement, accurate sensing of complex objects, and efficient operation. In recent years, TLS and related processing software (“SCENE”) have been widely presented as effective techniques for dealing with the management and protection of historical buildings in Fenghuang village. Thus, this paper highlights the process of using laser scanning to obtain architectural data, process point clouds, and compare the characteristics of historical buildings in Fenghuang village. The cloud-to-cloud registration technique is applied to build point clouds. As a result of model construction, some architectural patterns are summarized in this village, such as the spatial sequence of ancestral halls, the dominant position of memorial halls, and the character of building decorations and roof slopes. Furthermore, a BIM model is also explained to fulfill the statistical function for architectural components. In the future, more research can be fulfilled based on the built point cloud model, which will be beneficial for the development of the whole village.

2021 ◽  
Vol 11 (22) ◽  
pp. 10610
Badr-Eddine Boudriki Semlali ◽  
Felix Freitag

Nowadays, several environmental applications take advantage of remote sensing techniques. A considerable volume of this remote sensing data occurs in near real-time. Such data are diverse and are provided with high velocity and variety, their pre-processing requires large computing capacities, and a fast execution time is critical. This paper proposes a new distributed software for remote sensing data pre-processing and ingestion using cloud computing technology, specifically OpenStack. The developed software discarded 86% of the unneeded daily files and removed around 20% of the erroneous and inaccurate datasets. The parallel processing optimized the total execution time by 90%. Finally, the software efficiently processed and integrated data into the Hadoop storage system, notably the HDFS, HBase, and Hive.

2021 ◽  
Stella Kim ◽  
Cass Dykeman

Obsessive-compulsive disorder (OCD) is typically thought of as a single mental health disorder. More recently, internally-focused (autogenous) and externally-focused (reactive) subtypes have been proposed. This study examined the language used in describing these subtypes using anonymous posts from Reddit. The study used natural language processing software to look at differences in the use of the linguistic variables of first-person singular, first-person plural, third-person singular, and third-person plural as well as use of the psychological variables negative emotion, anger, anxiety, insight, causation, certainty, risk, religion, swear words, body, health, sexual, discrepancy, future, and death. A log likelihood ratio (G2) was calculated to determine the differences of usage rates in the two corpuses. The results showed a large effect size of the use of first-person singular pronouns in both the autogenous and reactive corpuses. The psychological variables of “insight” and “sexual” had the largest effect sizes in the autogenous corpus, while “health” and “body” had the largest effect sizes in the reactive corpus. The differences in the usage rates of the linguistic and psychological variables in the corpuses show the heterogeneity of OCD and the importance of understanding lesser known forms of obsessions such as those with repugnant themes. Clinical implications and future research recommendations are discussed.Keywords: reactive OCD, autogenous OCD, obsessive-compulsive disorder, LIWC, corpus linguistics

2021 ◽  
Vol 21 (1) ◽  
Da-wei Zhao ◽  
Wen-jun Fan ◽  
Ling-ling Meng ◽  
Yan-rong Luo ◽  
Jian Wei ◽  

Abstract Background Functional MRI (fMRI) parameters analysis has been proven to be a promising tool of predicting therapeutic response to induction chemotherapy (IC) in nasopharyngeal carcinoma (NPC). The study was designed to identify and compare the value of fMRI parameters in predicting early response to IC in patients with NPC. Methods This prospective study enrolled fifty-six consecutively NPC patients treated with IC from January 2021 to May 2021. Conventional diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) protocols were performed before and after IC. Parameters maps (ADC, MD, MK, Dslow, Dfast, PF, Ktrans, Ve and Kep) of the primary tumor were calculated by the Functool post-processing software. The participants were classified as responding group (RG) and non-responding group (NRG) according to Response Evaluation Criteria in Solid Tumors 1.1. The fMRI parameters were compared before and after IC and between RG with NRG. Logistic regression analysis and ROC were performed to further identify and compare the efficacy of the parameters. Results After IC, the mean values of ADC(p < 0.001), MD(p < 0.001), Dslow(p = 0.001), PF(p = 0.030) and Ve(p = 0.003) significantly increased, while MK(p < 0.001), Dfast(p = 0.009) and Kep(p = 0.003) values decreased dramatically, while no significant difference was detected in Ktrans(p = 0.130). Compared with NRG, ADC-pre(p < 0.001), MD-pre(p < 0.001) and Dslow-pre(p = 0.002) values in RG were lower, while MK-pre(p = 0.017) values were higher. The areas under the ROC curves for the ADC-pre, MD-pre, MK-pre, Dslow-pre and PRE were 0.885, 0.855, 0.809, 0.742 and 0.912, with the optimal cutoff value of 1210 × 10− 6 mm2/s, 1010 × 10− 6 mm2/s, 832 × 10− 6, 835 × 10− 6 mm2/s and 0.799 respectively. Conclusions The pretreatment conventional DWI (ADC), DKI (MD and MK), and IVIM (Dslow) values derived from fMRI showed a promising potential in predicting the response of the primary tumor to IC in NPC patients. Trial registration This study was approved by ethics board of the Chinese PLA General Hospital, and registered on January 30, 2021, in Chinese Clinical Trial Registry (ChiCTR2100042863).

Kaori Itto-Nakama ◽  
Shun Watanabe ◽  
Naoko Kondo ◽  
Shinsuke Ohnuki ◽  
Ryota Kikuchi ◽  

Abstract Several industries require getting information of products as soon as possible during fermentation. However, the trade-off between sensing speed and data quantity presents challenges for forecasting fermentation product yields. In this study, we tried to develop AI models to forecast ethanol yields in yeast fermentation cultures, using cell morphological data. Our platform involves the quick acquisition of yeast morphological images using a non-staining protocol, extraction of high-dimensional morphological data using image processing software, and forecasting of ethanol yields via supervised machine learning. We found that the neural network algorithm produced the best performance, which had a coefficient of determination of &gt; 0.9 even at 30 and 60 min in the future. The model was validated using test data collected using the CalMorph-PC(10) system, which enables rapid image acquisition within 10 min. AI-based forecasting of product yields based on cell morphology will facilitate the management and stable production of desired biocommodities.

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