ALT-I pump limiter experiments

International Nuclear Information System (INIS)

Goebel, D.M.; Conn, R.W.; Campbell, G.A.

1987-09-01

Results from the ALT-I pump limiter experiments in TEXTOR are presented. ALT-I has demonstrated control of the plasma density in a high recycling tokamak by pumping up to 15% of the core efflux. The closed pump limiter designs with restricted entrance geometries to reduce the backflow of neutral gas to the plasma remove over 50% of the ion flux incident on the collection slot. Up to 80% of the entrance ion flux is removed when the edge electron temperature is less than 10 eV and plasma-neutral gas interactions are minimized inside the limiter. Results from a 3-D Monte Carlo neutral gas transport code agree closely with these experimental results. The compound curvature of the head is found to distribute the heat over the surface as predicted in the original designs. Impurity removal experiments demonstrate that significant helium exhaust can be achieved with a pump limiter. During ohmic heating in TEXTOR, the energy and particle confinement times are proportional to the line averaged core density. With ICRH auxiliary heating, tau/sub E/ follow L-mode scaling independent of particle removal by the pump limiter. Pump limiter operation does not directly modify the SOL plasma density and electron temperature, but controls the core plasma density by changing the global recycling at the boundary. The global particle confinement, the particle flux to the limiter, and the edge electron temperature follow the changes in the core density and auxiliary heating power. 25 refs

21 CFR 862.1030 - Alanine amino transferase (ALT/SGPT) test system.

Science.gov (United States)

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Alanine amino transferase (ALT/SGPT) test system... Test Systems § 862.1030 Alanine amino transferase (ALT/SGPT) test system. (a) Identification. An alanine amino transferase (ALT/SGPT) test system is a device intended to measure the activity of the...

ELEVATED ALT AND AST IN AN ASYMPTOMATIC PERSON

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KEW ST

2009-01-01

Full Text Available -Abnormal liver function test with raised alanine aminotransferase (ALT and raised aspartate aminotransferase (AST are commonly seen in primary care setting. -Chronic alcohol consumption, drugs, non-alcoholic steatohepatitis (NASH and chronic viral hepatitis are common causes associated with raised ALT and AST. -In chronic viral hepatitis, the elevation of liver enzyme may not correlate well with the degree of liver damage. -Non-hepatic causes of raised ALT and AST include polymyositis, acute muscles injury, acute myocardial infarction and hypothyroidism. -In the primary care setting, the doctor should obtain a complete history regarding the risk factors for viral hepatitis, substance abuse and request investigations accordingly. -Suspected chronic viral hepatitis and liver cirrhosis are best referred to hepatologist for further management.

Liver stiffness becomes stable in patients with chronic hepatitis C three months after ALT normalization due to antiviral therapy

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CHEN Feikai

2013-10-01

Full Text Available ObjectiveTo investigate the time for liver stiffness measurement (LSM to become stable in chronic hepatitis C (CHC patients with elevated alanine aminotransferase (ALT levels after ALT normalization due to antiviral therapy. MethodsCHC patients who sought initial treatment at Peking University People′s Hospital were screened for elevated ALT levels from May 2011. Liver stiffness was determined by FibroScan. A total of 29 patients had been included in the study by September 2012, who were followed up regularly after antiviral treatment. ALT tests were repeated every four weeks and LSM every eight weeks until their medians did not change significantly. Comparisons of matched data at two adjacent time points were made with the non-parametric Wilcoxon test, while multiple comparisons of repeated measurements were performed using Bonferroni correction. Correlation between two variables was analyzed with the Spearman rank test. ResultsPatients were followed up until 24 weeks after antiviral treatment, and 24 patients were included in analysis. The median ALT levels were 64, 26, 21, 20, and 22 U/L at baseline and 4, 8, 12, and 24 weeks, respectively (P= 0.000, 0.006, 0.337, and 0.109 for comparisons between two adjacent values. ALT decreased significantly below 1 ULN at 4 weeks after antiviral therapy and stabilized at 8 weeks. The median LSM values were 8.7, 7.8, 6.8, and 6.7 kPa at baseline and 8, 16, and 24 weeks, respectively (P= 0.009, 0.001, and 0188 for comparisons between two adjacent values. LSM decreased significantly within 16 weeks after antiviral therapy and stabilized afterwards. LSM stabilized 12 weeks after ALT normalization. ConclusionLSM becomes stable in CHC patients with elevated ALT levels three months after ALT normalization due to antiviral therapy.

Evidence for alternative lengthening of telomeres in liposarcomas in the absence of ALT-associated PML bodies.

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Jeyapalan, Jennie N; Mendez-Bermudez, Aaron; Zaffaroni, Nadia; Dubrova, Yuri E; Royle, Nicola J

2008-06-01

Immortalized and cancer cells maintain their telomeres by activation of a telomere maintenance mechanism (TMM). In approximately 85% of cancers telomerase is activated (TA) but in some tumours, in particular sarcomas, an alternative lengthening of telomeres (ALT) pathway is used. Liposarcomas are the most common soft-tissue sarcoma in adults and they activate ALT or telomerase with equal frequency, however no TMM has been identified in approximately 50% of liposarcomas. In our study, we have shown that instability at the minisatellite MS32, usually associated with ALT activation, aids the identification of liposarcomas that have recombination-like activity at telomeres in absence of ALT associated PML-bodies (APBs). Furthermore, using single molecule telomere analysis, we have detected complex telomere mutations directly in ALT positive liposarcomas and interestingly in some liposarcomas with an unknown TMM but high MS32 instability. We have shown by sequence analysis that some of these complex telomere mutations must arise by an inter-molecular recombination-like process rather than by deletion caused by t-loop excision or by unequal telomere-sister-chromatid-exchange (T-SCE), which is known to be elevated in ALT cell lines. Preliminary evidence also suggests that inter-molecular recombination events may be processed differently in liposarcomas with APBs compared to those without. In conclusion, we have shown for the first time, that some telomerase negative liposarcomas without APBs have other features associated with ALT, indicating that the incidence of ALT in these tumours has previously been under-estimated. This has major implications for the use of cancer treatments targeted at TMMs. (c) 2008 Wiley-Liss, Inc.

Editorial: a new direction for ALT-J

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Gráinne Conole

2003-12-01

Full Text Available The Association for Learning Technology celebrated its tenth anniversary this year and it can now be said that learning technology is a recognized research field, with a growing body of researchers and associated conferences and journals. Over the past two years as an editorial team we have undertaken a review of the position of ALT-J in relation to other journals in the area and considered the nature and scope of the papers we publish. We continue to expand our list of referees and have provided them with more detailed review forms to help guide them when considering recommendations on submissions and to encourage detailed, critical (and hopefully! helpful feedback to authors. We would like to take this opportunity to thank all the referees for their involvement and commitment to the journal and for their continued work on refereeing papers. As part of the review process we will be moving to a new publisher, Taylor and Francis, commencing with Volume 12; this is, in part, driven by a desire to increase our international profile and readership, as well as seeking to have a better online presence including electronic access to all papers. We are, after all, a research journal on learning technologies! We are looking forward to taking the journal forward with Taylor and Francis but would also like to take this opportunity to thank our current publisher, University of Wales Press, for all their support and professional work over the years; it has been very good working with them.

Preliminary design analysis of the ALT-II limiter for TEXTOR

International Nuclear Information System (INIS)

Koski, J.A.; Boyd, R.D.; Kempka, S.M.; Romig, A.D. Jr.; Smith, M.F.; Watson, R.D.; Whitley, J.B.; Conn, R.W.; Grotz, S.P.

1983-01-01

Installation of a large toroidal belt pump limiter, Advanced Limiter Test II (ALT-II), on the TEXTOR tokamak at Juelich, FRG is anticipated for early 1986. This paper discusses the preliminary mechanical design and materials considerations undertaken as part of the feasibility study phase for ALT-II

The helicase domain of Polθ counteracts RPA to promote alt-NHEJ.

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Mateos-Gomez, Pedro A; Kent, Tatiana; Deng, Sarah K; McDevitt, Shane; Kashkina, Ekaterina; Hoang, Trung M; Pomerantz, Richard T; Sfeir, Agnel

2017-12-01

Mammalian polymerase theta (Polθ) is a multifunctional enzyme that promotes error-prone DNA repair by alternative nonhomologous end joining (alt-NHEJ). Here we present structure-function analyses that reveal that, in addition to the polymerase domain, Polθ-helicase activity plays a central role during double-strand break (DSB) repair. Our results show that the helicase domain promotes chromosomal translocations by alt-NHEJ in mouse embryonic stem cells and also suppresses CRISPR-Cas9- mediated gene targeting by homologous recombination (HR). In vitro assays demonstrate that Polθ-helicase activity facilitates the removal of RPA from resected DSBs to allow their annealing and subsequent joining by alt-NHEJ. Consistent with an antagonistic role for RPA during alt-NHEJ, inhibition of RPA1 enhances end joining and suppresses recombination. Taken together, our results reveal that the balance between HR and alt-NHEJ is controlled by opposing activities of Polθ and RPA, providing further insight into the regulation of repair-pathway choice in mammalian cells.

Activation of the ALT pathway for telomere maintenance can affect other sequences in the human genome.

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Jeyapalan, Jennie N; Varley, Helen; Foxon, Jenny L; Pollock, Raphael E; Jeffreys, Alec J; Henson, Jeremy D; Reddel, Roger R; Royle, Nicola J

2005-07-01

Immortal human cells maintain telomere length by the expression of telomerase or through the alternative lengthening of telomeres (ALT). The ALT mechanism involves a recombination-like process that allows the rapid elongation of shortened telomeres. However, it is not known whether activation of the ALT pathway affects other sequences in the genome. To address this we have investigated, in ALT-expressing cell lines and tumours, the stability of tandem repeat sequences known to mutate via homologous recombination in the human germline. We have shown extraordinary somatic instability in the human minisatellite MS32 (D1S8) in ALT-expressing (ALT+) but not in normal or telomerase-expressing cell lines. The MS32 mutation frequency varied across 15 ALT+ cell lines and was on average 55-fold greater than in ALT- cell lines. The MS32 minisatellite was also highly unstable in three of eight ALT+ soft tissue sarcomas, indicating that somatic destabilization occurs in vivo. The MS32 mutation rates estimated for two ALT+ cell lines were similar to that seen in the germline. However, the internal structures of ALT and germline mutant alleles are very different, indicating differences in the underlying mutation mechanisms. Five other hypervariable minisatellites did not show elevated instability in ALT-expressing cell lines, indicating that minisatellite destabilization is not universal. The elevation of MS32 instability upon activation of the ALT pathway and telomere length maintenance suggests there is overlap between the underlying processes that may be tractable through analysis of the D1S8 locus.

Production of the Allergenic Protein Alt a 1 by Alternaria Isolates from Working Environments

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Justyna Skóra

2015-02-01

Full Text Available The aim of the study was to evaluate the ability of Alternaria isolates from workplaces to produce Alt a 1 allergenic protein, and to analyze whether technical materials (cellulose, compost, leather present within the working environment stimulate or inhibit Alt a 1 production (ELISA test. Studies included identification of the isolated molds by nucleotide sequences analyzing of the ITS1/ITS2 regions, actin, calmodulin and Alt a 1 genes. It has been shown that Alternaria molds are significant part of microbiocenosis in the archive, museum, library, composting plant and tannery (14%–16% frequency in the air. The presence of the gene encoding the Alt a 1 protein has been detected for the strains: Alternaria alternata, A. lini, A. limoniasperae A. nobilis and A. tenuissima. Environmental strains produced Alt a 1 at higher concentrations (1.103–6.528 ng/mL than a ATCC strain (0.551–0.975 ng/mL. It has been shown that the homogenization of the mycelium and the use of ultrafiltration allow a considerable increase of Alt a 1 concentration. Variations in the production of Alt a 1 protein, depend on the strain and extraction methods. These studies revealed no impact of the technical material from the workplaces on the production of Alt a 1 protein.

PPARα regulates the hepatotoxic biomarker alanine aminotransferase (ALT1) gene expression in human hepatocytes

International Nuclear Information System (INIS)

Thulin, Petra; Rafter, Ingalill; Stockling, Kenneth; Tomkiewicz, Celine; Norjavaara, Ensio; Aggerbeck, Martine; Hellmold, Heike; Ehrenborg, Ewa; Andersson, Ulf; Cotgreave, Ian; Glinghammar, Bjoern

2008-01-01

In this work, we investigated a potential mechanism behind the observation of increased aminotransferase levels in a phase I clinical trial using a lipid-lowering drug, the peroxisome proliferator-activated receptor (PPAR) α agonist, AZD4619. In healthy volunteers treated with AZD4619, serum alanine aminotransferase (ALT) and aspartate aminotransferase (AST) activities were elevated without an increase in other markers for liver injury. These increases in serum aminotransferases have previously been reported in some patients receiving another PPARα agonist, fenofibrate. In subsequent in vitro studies, we observed increased expression of ALT1 protein and mRNA in human hepatocytes after treatment with fenofibric acid. The PPAR effect on ALT1 expression was shown to act through a direct transcriptional mechanism involving at least one PPAR response element (PPRE) in the proximal ALT1 promoter, while no effect of fenofibrate and AZD4619 was observed on the ALT2 promoter. Binding of PPARs to the PPRE located at - 574 bp from the transcriptional start site was confirmed on both synthetic oligonucleotides and DNA in hepatocytes. These data show that intracellular ALT expression is regulated by PPAR agonists and that this mechanism might contribute to increased ALT activity in serum

Rescue excavations on Alt-Laari settlement site, Tartumaa / Anti Lillak, Heiki Valk

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Lillak, Anti

2009-01-01

Alt-Laari linnusasula tekkis nähtavasti rooma rauaajal. Kui Alt-Laari linnus oli kasutused arvatavasti I aastatuhandest II aastatuhandeni pKr. siis asula kestis edasi keskajal ning jäeti maha hiljemalt 14. sajandil

The human CTC1/STN1/TEN1 complex regulates telomere maintenance in ALT cancer cells

Energy Technology Data Exchange (ETDEWEB)

Huang, Chenhui; Jia, Pingping; Chastain, Megan; Shiva, Olga; Chai, Weihang, E-mail: wchai@wsu.edu

2017-06-15

Maintaining functional telomeres is important for long-term proliferation of cells. About 15% of cancer cells are telomerase-negative and activate the alternative-lengthening of telomeres (ALT) pathway to maintain their telomeres. Recent studies have shown that the human CTC1/STN1/TEN1 complex (CST) plays a multi-faceted role in telomere maintenance in telomerase-expressing cancer cells. However, the role of CST in telomere maintenance in ALT cells is unclear. Here, we report that human CST forms a functional complex localizing in the ALT-associated PML bodies (APBs) in ALT cells throughout the cell cycle. Suppression of CST induces telomere instabilities including telomere fragility and elevates telomeric DNA recombination, leading to telomere dysfunction. In addition, CST deficiency significantly diminishes the abundance of extrachromosomal circular telomere DNA known as C-circles and t-circles. Suppression of CST also results in multinucleation in ALT cells and impairs cell proliferation. Our findings imply that the CST complex plays an important role in regulating telomere maintenance in ALT cells. - Highlights: • CST localizes at telomeres and ALT-associated PML bodies in ALT cells throughout the cell cycle. • CST is important for promoting telomeric DNA replication in ALT cells. • CST deficiency decreases ECTR formation and increases T-SCE. • CST deficiency impairs ALT cell proliferation and results in multinucleation.

Mre11 and Blm-Dependent Formation of ALT-Like Telomeres in Ku-Deficient Ustilago maydis.

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Eun Young Yu

2015-10-01

Full Text Available A subset of human cancer cells uses a specialized, aberrant recombination pathway known as ALT to maintain telomeres, which in these cells are characterized by complex aberrations including length heterogeneity, high levels of unpaired C-strand, and accumulation of extra-chromosomal telomere repeats (ECTR. These phenotypes have not been recapitulated in any standard budding or fission yeast mutant. We found that eliminating Ku70 or Ku80 in the yeast-like fungus Ustilago maydis results initially in all the characteristic telomere aberrations of ALT cancer cells, including C-circles, a highly specific marker of ALT. Subsequently the ku mutants experience permanent G2 cell cycle arrest, accompanied by loss of telomere repeats from chromosome ends and even more drastic accumulation of very short ECTRs (vsECTRs. The deletion of atr1 or chk1 rescued the lethality of the ku mutant, and "trapped" the telomere aberrations in the early ALT-like stage. Telomere abnormalities are telomerase-independent, but dramatically suppressed by deletion of mre11 or blm, suggesting major roles for these factors in the induction of the ALT pathway. In contrast, removal of other DNA damage response and repair factors such as Rad51 has disparate effects on the ALT phenotypes, suggesting that these factors process ALT intermediates or products. Notably, the antagonism of Ku and Mre11 in the induction of ALT is reminiscent of their roles in DSB resection, in which Blm is also known to play a key role. We suggest that an aberrant resection reaction may constitute an early trigger for ALT telomeres, and that the outcomes of ALT are distinct from DSB because of the unique telomere nucleoprotein structure.

Pattern of AST and ALT changes in Relation to Hemolysis in sickle cell Disease

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K. Nsiah

2011-01-01

Full Text Available Background Elevated aminotransferase levels are commonly associated with compromised hepatic integrity from various insults. In sickle cell disease, aspartate transaminase (AST is also released via intravascular hemolysis. This study was done to determine the pattern of changes in AST and alanine transaminase (ALT, in particular the AST:ALT ratio, and to relate these to the hemolytic state, which we consider to be more important than hepatic and cardiac dysfunction in some individuals with sickle cell disease. Methods Serum aminotransferase levels were measured in 330 subjects with sickle cell disease, as well as hemoglobin, reticulocytes, and lactate dehydrogenase. The AST:ALT ratio was designated as a hemolytic marker, and simple and multivariate regression analyses were carried out between this ratio and other hemolytic markers. Results Mean AST and ALT levels were 48.24 % 27.78 and 26.48 % 22.73 U/L, respectively. However, for 49 subjects without sickle cell disease, mean AST and ALT levels were the same, ie, 23.0 U/L. In the subjects with sickle cell disease, the increases in AST levels were far higher than for ALT, supporting its release via intravascular hemolysis. In 95.8% of the subjects with sickle cell disease, the AST:ALT ratio was > 1, but our results did not suggest overt malfunctioning of the liver and heart in the majority of subjects. Conclusion Regression analyses support the use of the AST:ALT ratio as a hemolytic marker, because it has an inverse association with the hemoglobin level. Whether in steady state or in crisis, provided hepatic and cardiac integrity has not been compromised, subjects with sickle cell disease would have higher AST levels due to the hemolytic nature of the condition. This is the first report highlighting the AST:ALT ratio in sickle cell disease.

ALT-I pump limiter results on TEXTOR

International Nuclear Information System (INIS)

Dippel, K.H.; Finken, K.H.; Guthrie, S.E.; Malinowski, M.E.; Pontau, A.E.; Campbell, G.A.; Goebel, D.M.; Conn, R.W.

1985-01-01

The ALT-I pump limiter is used to control hydrogen fluxes from the TEXTOR tokamak. The performance of two different modules, the open fixed geometry (FG) and the closed variable geometry (VG) is discussed. In unpumped scoop limiter operation, the pressure in the ALT-I chamber increases to 3x10 -4 torr(FG) and 2x10 -3 torr(VG). With pumping, the fraction of particles incident on the neutralizer plate that is removed is 25-50%(FG) and 50-80%(VG). These removed particles are estimated to be 2-4(8)%(FG) and 6-13%(VG) of the total plasma outflux (Nsub(e)/tausub(p)). The collection of helium from the plasma using the FG module is approximately half as effective as hydrogen collection. The higher particle removal efficiency for the VG module is attributed to lower neutral backstreaming. (author)

ALT Blood Test: MedlinePlus Lab Test Information

Science.gov (United States)

... K. Brunner & Suddarth's Handbook of Laboratory and Diagnostic Tests. 2 nd Ed, Kindle. Philadelphia: Wolters Kluwer Health, Lippincott Williams & Wilkins; c2014. Alanine Aminotransferase (ALT); p. 31. Lab ...

The Role of ATRX in the Alternative Lengthening of Telomeres (ALT Phenotype

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João P. Amorim

2016-09-01

Full Text Available Telomeres are responsible for protecting chromosome ends in order to prevent the loss of coding DNA. Their maintenance is required for achieving immortality by neoplastic cells and can occur by upregulation of the telomerase enzyme or through a homologous recombination-associated process, the alternative lengthening of telomeres (ALT. The precise mechanisms that govern the activation of ALT or telomerase in tumor cells are not fully understood, although cellular origin may favor one of the other mechanisms that have been found thus far in mutual exclusivity. Specific mutational events influence ALT activation and maintenance: a unifying frequent feature of tumors that acquire this phenotype are the recurrent mutations of the Alpha Thalassemia/Mental Retardation Syndrome X-Linked (ATRX or Death-Domain Associated Protein (DAXX genes. This review summarizes the established criteria about this phenotype: its prevalence, theoretical molecular mechanisms and relation with ATRX, DAXX and other proteins (directly or indirectly interacting and resulting in the ALT phenotype.

ALT-II toroidal belt limiter biasing experiments on TEXTOR

International Nuclear Information System (INIS)

Doerner, R.; Boedo, J.A.; Gray, D.S.

1991-01-01

Edge electric fields have been related to H-mode-like behaviour. The experiments reported here are an attempt to control the SOL profiles by electrostatic biasing of the full toroidal-belt limiter ALT-II. The specific goals are: influencing the edge particle flows, particle removal, power deposition and the global confinement. The ALT-II pump limiter is a full toroidal belt located at 45 o below the outer midplane and consisting of eight graphite covered blades which can be independently biased. Particle scoops located behind the limiter neutralize and direct the incoming plasma into the pumping ducts. (author) 5 refs., 3 figs

Influence of gender, BMI, and ethnicity on serum ALT levels of healthy students of a medical school

International Nuclear Information System (INIS)

Bilal, M.; Tariq, A.; Khan, S.; Quratulain, A.; Tariq, A.; Shahid, M.F.; Khan, M.W.; Naveed, A.K.

2011-01-01

Background: Alanine Aminotransferase (ALT) is an enzyme found in Liver and indicates injury to Hepatocytes. It is influenced by various factors. The objectives of this study were to identify the correlates of ALT activity among healthy medical students of Army Medical College, National University of Sciences and Technology, aged 18-22 years. This was to establish the mean ALT levels of the students and compare them with those in various parts of the world and observe various correlations that exist and factors that may influence ALT levels. Methods: This population included 143 volunteer students (93 men and 50 women) selected on the basis of negative answers to a detailed medical questionnaire including past medical history, drug and alcohol consumption, on the absence of clinical signs of liver disease, on the negativity of serological testing for Hepatitis B and C virus. Results: The mean ALT level of the entire population was 28.7 IU/L. A major sex-difference in ALT value was observed, the mean ALT value being higher in men than in women (32.1+- 21.7 vs. 22.6 +- 9.7 IU/L, p<0.004). According to WHO criteria for Asians, normal BMI was taken from 18.5-23.0 Kg/m/sup 2/. There was a positive significant correlation between serum ALT level and BMI (p<0.002). ALT level strongly correlates with body mass index and gender. There was no significant variation in ALT levels among Punjabis and Sindhis, Balochis, Pathans, and Kashmiris. Conclusion: We suggest the need of taking into account these parameters in a clinical interpretation of ALT level. (author)

The major Alternaria alternata allergen, Alt a 1: A reliable and specific marker of fungal contamination in citrus fruits.

Science.gov (United States)

Gabriel, M F; Uriel, N; Teifoori, F; Postigo, I; Suñén, E; Martínez, J

2017-09-18

The ubiquitously present spores of Alternaria alternata can spoil a wide variety of foodstuffs, including a variety of fruits belonging to the Citrus genus. The major allergenic protein of A. alternata, Alt a 1, is a species-specific molecular marker that has been strongly associated with allergenicity and phytopathogenicity of this fungal species. This study aimed to evaluate the potential of the detection of Alt a 1 as a reliable indicator of A. alternata contamination in citrus fruits. To accomplish this aim, sixty oranges were artificially infected with a spore suspension of A. alternata. Internal fruit material was collected at different incubation times (one, two and three weeks after the fungal inoculation) and used for both total RNA extraction and protein extraction. Alt a 1 detection was then performed by polymerase chain reaction (PCR) amplification using Alt a 1 specific primers and by enzyme-linked immunosorbent assay (ELISA). The experimental model presented in this work was effective to simulate the typical Alternaria black rot phenotype and its progression. Although both PCR and ELISA techniques have been successfully carried out for detecting Alt a 1 allergen in A. alternata infected oranges, the PCR method was found to be more sensitive than ELISA. Nevertheless, ELISA results were highly valuable to demonstrate that considerable amounts of Alt a 1 are produced during A. alternata fruit infection process, corroborating the recently proposed hypothesis that this protein plays a role in the pathogenicity and virulence of Alternaria species. Such evidence suggests that the detection of Alt a 1 by PCR-based assay may be used as a specific indicator of the presence of pathogenic and allergenic fungal species, A. alternata, in fruits. This knowledge can be employed to control the fungal infection and mitigate agricultural losses as well as human exposure to A. alternata allergens and toxins. Copyright © 2017 Elsevier B.V. All rights reserved.

Kui kapital veab alt, siis turvaliselt / Aleksander Tsapov

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Tsapov, Aleksander

2006-01-01

Kuraatorinäitus "Kapital (see veab meid alt)" Tallinna Kunstihoones. Kuraator Simon Sheikh. Fia-Stina Sandlundi, Katya Sanderi, Oliver Ressleri, Ashley Hunti ja Susan Kelly&Stephen Mortoni töödest näitusel

Alternagin-C (ALT-C), a disintegrin-like protein from Rhinocerophis alternatus snake venom promotes positive inotropism and chronotropism in fish heart.

Science.gov (United States)

Monteiro, D A; Kalinin, A L; Selistre-de-Araujo, H S; Vasconcelos, E S; Rantin, F T

2016-02-01

Alternagin-C (ALT-C) is a disintegrin-like protein purified from the venom of the snake, Rhinocerophis alternatus. Recent studies showed that ALT-C is able to induce vascular endothelial growth factor (VEGF) expression, endothelial cell proliferation and migration, angiogenesis and to increase myoblast viability. This peptide, therefore, can play a crucial role in tissue regeneration mechanisms. The aim of this study was to evaluate the effects of a single dose of alternagin-C (0.5 mg kg(-1), via intra-arterial) on in vitro cardiac function of the freshwater fish traíra, Hoplias malabaricus, after 7 days. ALT-C treatment increased the cardiac performance promoting: 1) significant increases in the contraction force and in the rates of contraction and relaxation with concomitant decreases in the values of time to the peak tension and time to half- and 90% relaxation; 2) improvement in the cardiac pumping capacity and maximal electrical stimulation frequency, shifting the optimum frequency curve upward and to the right; 3) increases in myocardial VEGF levels and expression of key Ca(2+)-cycling proteins such as SERCA (sarcoplasmic reticulum Ca(2+)-ATPase), PLB (phospholamban), and NCX (Na(+)/Ca(2+) exchanger); 4) abolishment of the typical negative force-frequency relationship of fish myocardium. In conclusion, this study indicates that ALT-C improves cardiac function, by increasing Ca(2+) handling efficiency leading to a positive inotropism and chronotropism. The results suggest that ALT-C may lead to better cardiac output regulation indicating its potential application in therapies for cardiac contractile dysfunction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Performance of an Optimized Paper-Based Test for Rapid Visual Measurement of Alanine Aminotransferase (ALT in Fingerstick and Venipuncture Samples.

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Sidhartha Jain

Full Text Available A paper-based, multiplexed, microfluidic assay has been developed to visually measure alanine aminotransferase (ALT in a fingerstick sample, generating rapid, semi-quantitative results. Prior studies indicated a need for improved accuracy; the device was subsequently optimized using an FDA-approved automated platform (Abaxis Piccolo Xpress as a comparator. Here, we evaluated the performance of the optimized paper test for measurement of ALT in fingerstick blood and serum, as compared to Abaxis and Roche/Hitachi platforms. To evaluate feasibility of remote results interpretation, we also compared reading cell phone camera images of completed tests to reading the device in real time.96 ambulatory patients with varied baseline ALT concentration underwent fingerstick testing using the paper device; cell phone images of completed devices were taken and texted to a blinded off-site reader. Venipuncture serum was obtained from 93/96 participants for routine clinical testing (Roche/Hitachi; subsequently, 88/93 serum samples were captured and applied to paper and Abaxis platforms. Paper test and reference standard results were compared by Bland-Altman analysis.For serum, there was excellent agreement between paper test and Abaxis results, with negligible bias (+4.5 U/L. Abaxis results were systematically 8.6% lower than Roche/Hitachi results. ALT values in fingerstick samples tested on paper were systematically lower than values in paired serum tested on paper (bias -23.6 U/L or Abaxis (bias -18.4 U/L; a correction factor was developed for the paper device to match fingerstick blood to serum. Visual reads of cell phone images closely matched reads made in real time (bias +5.5 U/L.The paper ALT test is highly accurate for serum testing, matching the reference method against which it was optimized better than the reference methods matched each other. A systematic difference exists between ALT values in fingerstick and paired serum samples, and can be

The Education and Training of Learning Technologists: A Competences Approach (Report to IEEE Technical Committee on Learning Technologies)

Science.gov (United States)

Hartley, Roger; Kinshuk; Koper, Rob; Okamoto, Toshio; Spector, J. Michael

2010-01-01

The educational and training requirements of Advanced Learning Technology (ALT) need to engage with curricula that reflect the varied requirements of the workplace and of society. Students have a range of interests and ambitions in ALT which the instructional process has to accommodate and support. With these considerations in mind the IEEE…

The Advanced Limiter Test-I (ALT-I) variable-geometry pump limiter module

International Nuclear Information System (INIS)

Pontau, A.E.; Malinowski, M.E.; Ver Berkmoes, A.A.; Guthrie, S.E.; Watson, R.D.; Goebel, D.M.; Campbell, G.A.

1984-01-01

The ALT-I variable geometry module has been designed to address many of the issues not previously settled by earlier experiments. The goal is to study the basic processes involved in pump limiter operation as well as demonstrate its utility and effect on the plasma. The flexibility and extensive instrumentation of ALT-I will offer a unique opportunity to parameterize operation and facilitate the engineering design of future pump limiters. (orig.)

The LTDP ALTS Project: Contributing to the Continued Understanding and Exploitation of the ATSR Time Series

Science.gov (United States)

Clarke, Hannah; Done, Fay; Casadio, Stefano; Mackin, Stephen; Dinelli, Bianca Maria; Castelli, Elisa

2016-08-01

The long time-series of observations made by the Along Track Scanning Radiometers (ATSR) missions represents a valuable resource for a wide range of research and EO applications.With the advent of ESA's Long-TermData Preservation (LTDP) programme, thought has turned to the preservation and improved understanding of such long time-series, to support their continued exploitation in both existing and new areas of research, bringing the possibility of improving the existing data set and to inform and contribute towards future missions. For this reason, the 'Long Term Stability of the ATSR Instrument Series: SWIR Calibration, Cloud Masking and SAA' project, commonly known as the ATSR Long Term Stability (or ALTS) project, is designed to explore the key characteristics of the data set and new and innovative ways of enhancing and exploiting it.Work has focussed on: A new approach to the assessment of Short Wave Infra-Red (SWIR) channel calibration.; Developmentof a new method for Total Column Water Vapour (TCWV) retrieval.; Study of the South Atlantic Anomaly (SAA).; Radiative Transfer (RT) modelling for ATSR.; Providing AATSR observations with their location in the original instrument grid.; Strategies for the retrieval and archiving of historical ATSR documentation.; Study of TCWV retrieval over land; Development of new methods for cloud masking This paper provides an overview of these activities and illustrates the importance of preserving and understanding 'old' data for continued use in the future.

Olfactory learning without the mushroom bodies: Spiking neural network models of the honeybee lateral antennal lobe tract reveal its capacities in odour memory tasks of varied complexities.

Science.gov (United States)

MaBouDi, HaDi; Shimazaki, Hideaki; Giurfa, Martin; Chittka, Lars

2017-06-01

The honeybee olfactory system is a well-established model for understanding functional mechanisms of learning and memory. Olfactory stimuli are first processed in the antennal lobe, and then transferred to the mushroom body and lateral horn through dual pathways termed medial and lateral antennal lobe tracts (m-ALT and l-ALT). Recent studies reported that honeybees can perform elemental learning by associating an odour with a reward signal even after lesions in m-ALT or blocking the mushroom bodies. To test the hypothesis that the lateral pathway (l-ALT) is sufficient for elemental learning, we modelled local computation within glomeruli in antennal lobes with axons of projection neurons connecting to a decision neuron (LHN) in the lateral horn. We show that inhibitory spike-timing dependent plasticity (modelling non-associative plasticity by exposure to different stimuli) in the synapses from local neurons to projection neurons decorrelates the projection neurons' outputs. The strength of the decorrelations is regulated by global inhibitory feedback within antennal lobes to the projection neurons. By additionally modelling octopaminergic modification of synaptic plasticity among local neurons in the antennal lobes and projection neurons to LHN connections, the model can discriminate and generalize olfactory stimuli. Although positive patterning can be accounted for by the l-ALT model, negative patterning requires further processing and mushroom body circuits. Thus, our model explains several-but not all-types of associative olfactory learning and generalization by a few neural layers of odour processing in the l-ALT. As an outcome of the combination between non-associative and associative learning, the modelling approach allows us to link changes in structural organization of honeybees' antennal lobes with their behavioural performances over the course of their life.

The 'donations for decreased ALT (D4D)' prosocial behavior incentive scheme for NAFLD patients.

Science.gov (United States)

Sumida, Yoshio; Yoshikawa, Toshikazu; Tanaka, Saiyu; Taketani, Hiroyoshi; Kanemasa, Kazuyuki; Nishimura, Tekeshi; Yamaguchi, Kanji; Mitsuyoshi, Hironori; Yasui, Kohichiroh; Minami, Masahito; Naito, Yuji; Itoh, Yoshito

2014-12-01

Physicians often experience difficulties in motivating patients with non-alcoholic fatty liver disease (NAFLD) to undergo lifestyle changes. The aim of this study is to examine whether 'Donations for Decreased alanine aminotransferase (ALT)' (D4D) prosocial behavior incentive can serve as an effective intrinsic motivational factor in comparison with conventional dietary and exercise intervention alone for NAFLD patients. Twenty-five NAFLD patients with elevated ALT were randomly assigned to a control group that received conventional dietary and exercise intervention alone, or a donation group whereby, as an incentive, we would make a monetary donation to the United Nations World Food Programme (WFP) based on the decrease in their ALT levels achieved over 12 weeks, in addition to receiving control intervention. In a donation group, we would donate US$1 to the WFP for every 1 IU/l of decrease in their ALT levels. There were no differences of pre-treatment clinical characteristics between the two groups. Significant reductions of ALT levels were achieved only in a donation group, although post-treatment ALT levels were not different between the two groups. These patients raised a total of $316 for the WFP. Promoting patients' intrinsic motivation by incorporating 'D4D' prosocial behavior incentive into conventional dietary and exercise intervention may provide a means to improve NAFLD. © The Author 2013. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Die Altäre von Selinunt

OpenAIRE

Voigts, Clemens

2014-01-01

In der Dissertation wurden die Altäre von Selinunt, einer griechischen Kolonie auf Sizilien, mit den Methoden der Bauforschung untersucht. Die neun behandelten Brandopferaltäre sind monumentale Quaderbauten, die bis zu 20 m Länge erreichen. Sie stammen aus der Blütezeit Selinunts, dem 6. und 5. Jh. v. Chr. Die Bauten waren bereits im 19. und 20. Jh. freigelegt worden und wurden nun in Bauaufnahmen dokumentiert, zeichnerisch rekonstruiert und zeitlich eingeordnet. Dabei ließ sich an ihnen eine...

Preparation of a Sepia Melanin and Poly(ethylene-alt-maleic Anhydride Hybrid Material as an Adsorbent for Water Purification

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Guido Panzarasa

2018-01-01

Full Text Available Meeting the increasing demand of clean water requires the development of novel efficient adsorbent materials for the removal of organic pollutants. In this context the use of natural, renewable sources is of special relevance and sepia melanin, thanks to its ability to bind a variety of organic and inorganic species, has already attracted interest for water purification. Here we describe the synthesis of a material obtained by the combination of sepia melanin and poly(ethylene-alt-maleic anhydride (P(E-alt-MA. Compared to sepia melanin, the resulting hybrid displays a high and fast adsorption efficiency towards methylene blue (a common industrial dye for a wide pH range (from pH 2 to 12 and under high ionic strength conditions. It is easily recovered after use and can be reused up to three times. Given the wide availability of sepia melanin and P(E-alt-MA, the synthesis of our hybrid is simple and affordable, making it suitable for industrial water purification purposes.

Incremental Predictive Value of Serum AST-to-ALT Ratio for Incident Metabolic Syndrome: The ARIRANG Study

Science.gov (United States)

Ahn, Song Vogue; Baik, Soon Koo; Cho, Youn zoo; Koh, Sang Baek; Huh, Ji Hye; Chang, Yoosoo; Sung, Ki-Chul; Kim, Jang Young

2016-01-01

Aims The ratio of aspartate aminotransferase (AST) to alanine aminotransferase (ALT) is of great interest as a possible novel marker of metabolic syndrome. However, longitudinal studies emphasizing the incremental predictive value of the AST-to-ALT ratio in diagnosing individuals at higher risk of developing metabolic syndrome are very scarce. Therefore, our study aimed to evaluate the AST-to-ALT ratio as an incremental predictor of new onset metabolic syndrome in a population-based cohort study. Material and Methods The population-based cohort study included 2276 adults (903 men and 1373 women) aged 40–70 years, who participated from 2005–2008 (baseline) without metabolic syndrome and were followed up from 2008–2011. Metabolic syndrome was defined according to the harmonized definition of metabolic syndrome. Serum concentrations of AST and ALT were determined by enzymatic methods. Results During an average follow-up period of 2.6-years, 395 individuals (17.4%) developed metabolic syndrome. In a multivariable adjusted model, the odds ratio (95% confidence interval) for new onset of metabolic syndrome, comparing the fourth quartile to the first quartile of the AST-to-ALT ratio, was 0.598 (0.422–0.853). The AST-to-ALT ratio also improved the area under the receiver operating characteristic curve (AUC) for predicting new cases of metabolic syndrome (0.715 vs. 0.732, P = 0.004). The net reclassification improvement of prediction models including the AST-to-ALT ratio was 0.23 (95% CI: 0.124–0.337, Pmetabolic syndrome and had incremental predictive value for incident metabolic syndrome. PMID:27560931

Association between ALT level and the rate of cardio/cerebrovascular events in HIV-positive individuals

DEFF Research Database (Denmark)

Sabin, Caroline A; Ryom, Lene; Kovari, Helen

2013-01-01

An inverse association between serum alanine aminotransferase (ALT) levels and the risk of myocardial infarction (MI) has been reported in the general population. We investigated associations between ALT levels and the risk of various cardiovascular and cerebrovascular outcomes in a large cohort ...

Measurement and purification of Alanine aminotransferase (ALT enzyme activity in patients with celiac disease

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Taghreed U. Mohammed

2017-09-01

Full Text Available Celiac disease (CD is the most common genetically - based disease in correlation with food intolerance. The aim of this study is to measure the activity of ALT enzyme and purify enzyme from sera women with celiac disease. Alanine aminotransferase (ALT activity has been assayed in (30 women serum samples with celiac disease, age range between (20-40 year and (30 serum of healthy women as control group, age range between (22-38 year. In the present study, the mean value of ALT activity was significantly higher in patients with celiac disease than healthy group (p<0.01. The ALT enzyme was partial purified from sera women with celiac disease by dialysis, gel filtration using Sephadex G- 50 and ion exchange chromatography using DEAE- cellulose A-50 . The results showed a single peak by using gel filtration and the activity reached 31-15 U/L .Two isoenzymes were obtained by using ion exchange chromatography and the purity degree of isoenzymse (I, II were (5.7 and (5.53 fold respectively

Genel Anestezi Altında Çocukta Diş Tedavisi

OpenAIRE

Gülhan, A.; Sandallı, N.

2013-01-01

ÖZETBu makalede çocuklarda genel anestezi altında diş tedavisinin koşulları anlatıldı ve normal tedavi koşulları sağlanamadığı takdirde, genel anesteziye başvurmanın yararlarına ve sakıncalarına değinildi. Genel anestezi indikasyonları sıralanarak, bunlar arasında en sık rastladığımız «Güç çocuklarım" genci anesteziye başvurmadan Önce hangi yollara başvurularak normal tedavinin deneneceğinden bahsedildi.Bir hastane ortamında genel anestezi altında çalışmaya karar verildikten sonra, bir a...

Peek ja Lepik soovisid varade aresti alt vabastamist

Index Scriptorium Estoniae

2005-01-01

LHV endine töötaja Oliver Peek väidab, et tema vastu esitatud süüdistused on alusetud ja tõendamata ning nõuab oma varade aresti alt vabastamist. Kristjan Lepingu kohtuasja arutamine lükati edasi 21. detsembrile. Endiselt käib uurimine, kui palju väidetavalt teabevargusega raha teeniti

Learning Time and Educational Effectiveness.

Science.gov (United States)

Anderson, Lorin W.

1980-01-01

To explore the relationship between time and school learning, this paper defines the three kinds of learning time identified by researchers--allocated time, time-on-task, and academic learning time--and relates them to curriculum development. The author cites evidence that time-on-task is related to student achievement and describes two…

Coupling between Metacognition and Emotions during STEM Learning with Advanced Learning Technologies: A Critical Analysis, Implications for Future Research, and Design of Learning Systems

Science.gov (United States)

Azevedo, Roger; Mudrick, Nicholas; Taub, Michelle; Wortha, Franz

2017-01-01

Metacognition and emotions play a critical role in learners' ability to monitor and regulate their learning about 21st-century skills related to science, technology, engineering, and mathematics (STEM) content while using advanced learning technologies (ALTs; e.g., intelligent tutoring systems, serious games, hypermedia, augmented reality). In…

An assessment of racial differences in the upper limits of normal ALT levels in children and the effect of obesity on elevated values.

Science.gov (United States)

Kliethermes, S; Ma, M; Purtell, C; Balasubramanian, N; Gonzalez, B; Layden, T J; Cotler, S J

2017-10-01

Childhood obesity is a risk factor for non-alcoholic fatty liver disease and poses important public health issues for children. Racial differences in alanine aminotransferase (ALT) levels among children have not been described. This study aimed to identify racial differences in upper limit normal (ULN) ALT levels and evaluate the effect of obesity on elevated levels in children without other metabolic risk factors. National Health and Nutrition Examination Surveys and clinical data from the Loyola University Health System were used to determine ULN ALT by race and gender. Quantile regression was used to evaluate the impact of obesity on elevated ALT and to identify potential risk factors for ALT above the ULN. Upper limit normal (ULN) ALT was approximately 28.0 and 21.0-24.0 U/L for boys and girls, respectively. No significant difference in ULN ALT across race was observed. Obesity was significantly associated with elevated ALT; obese children with elevated ALT had values 10 U/L higher than normal-weight children. Racial differences in ALT levels among adults are not evident in children. Obesity, in the absence of metabolic risk factors and other causes of liver disease, is associated with elevated ALT, providing evidence against the concept of healthy obesity in children. © 2016 World Obesity Federation.

Environmental Stress Induces Trinucleotide Repeat Mutagenesis in Human Cells by Alt-Nonhomologous End Joining Repair.

Science.gov (United States)

Chatterjee, Nimrat; Lin, Yunfu; Yotnda, Patricia; Wilson, John H

2016-07-31

Multiple pathways modulate the dynamic mutability of trinucleotide repeats (TNRs), which are implicated in neurodegenerative disease and evolution. Recently, we reported that environmental stresses induce TNR mutagenesis via stress responses and rereplication, with more than 50% of mutants carrying deletions or insertions-molecular signatures of DNA double-strand break repair. We now show that knockdown of alt-nonhomologous end joining (alt-NHEJ) components-XRCC1, LIG3, and PARP1-suppresses stress-induced TNR mutagenesis, in contrast to the components of homologous recombination and NHEJ, which have no effect. Thus, alt-NHEJ, which contributes to genetic mutability in cancer cells, also plays a novel role in environmental stress-induced TNR mutagenesis. Published by Elsevier Ltd.

The medical laboratory issues about recommendation on uniform cutoff values of “normal” ALT in the ACG guidelines

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YU Qian

2018-01-01

Full Text Available In the recent American clinical guidelines dealing with laboratory tests for evaluation of liver disease, the American College of Gastroenterology (ACG recommends ALT upper reference limits of 33 U/L for males and 25 U/L for females respectively, and that individuals with ALT above these “normal” cutoffs should be further investigated. Considering the differences between laboratory assays measuring ALT in our country, the standardization of methods and the consistency of results can not be completely ensured. The uniform “normal” range of ALT recommended by the ACG guidelines is largely based on findings from foreign studies and may not be suitable to Chinese population. On the other hand, reference upper/lower limits should not simply be equated with clinical decision thresholds. However, due to improper application of the related concepts of the above medical laboratory issues, simply recommending the uniform reference range of the ALT may lead to overdiagnosis and unnecessary follow-up examinations.

Effect of Ramadan fasting on alanine transferase (ALT in nonalcoholic fatty liver disease (NAFLD

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Hojjatolah Rahimi

2017-09-01

Full Text Available Background: The effects of Ramadan fasting on NAFLD are unknown and there are very limited studies have done in this area. Some nutritional and behavioral changes of fasting people in Ramadan can affect NAFLD. These include nutrition with high fat and calories, altering in weight and sleep and low physical activity. We decided to evaluate the effects of these changes on one of the important indicators of deterioration of NAFLD, ALT.Methods: Sixty patients with fatty liver disease performed two consecutive ALT exams before and after Ramadan month of whom finally 34 were fasting and 26 nonfasting. After collecting data they were divided in two groups of fasting and nonfasting and compared using SPSS software.Results: Mean ALT change from before to after Ramadan was higher and positive in fasting (+7.38±8.47 IU/L compared to nonfastng patients that was negative (-0.12±10.15 IU/L (P=0.002 and this change was mainly in patients who had fasted 21-30 days.Conclusion: Ramadan fasting may increase ALT. It is needed to perform more studies in patients with NAFLD during Ramadan fasting with larger sample size and in various conditions especially weight loss with patients’ education for observing dietary regimen.

Serum ALT levels as a surrogate marker for serum HBV DNA levels in HBeAg-negative pregnant women.

Science.gov (United States)

Sangfelt, Per; Von Sydow, Madeleine; Uhnoo, Ingrid; Weiland, Ola; Lindh, Gudrun; Fischler, Björn; Lindgren, Susanne; Reichard, Olle

2004-01-01

In Stockholm, Sweden, the majority of pregnant women positive for hepatitis B surface antigen (HBsAg) are hepatitis Be antigen (HBeAg) negative. Newborns to HBeAg positive mothers receive vaccination and hepatitis B immunoglobulin (HBIg). Newborns to HBeAg negative mothers receive vaccine and HBIg only if the mothers have elevated ALT levels. The aim of this study was to retrospectively evaluate ALT levels as a surrogate marker for HBV DNA levels in HBeAg negative carrier mothers. Altogether 8947 pregnant women were screened for HBV markers from 1999 to 2001 at the Virology Department, Karolinska Hospital. Among mothers screened 192 tested positive for HBsAg (2.2%). 13 of these samples could not be retrieved. Of the remaining 179 sera, 8 (4%) tested positive for HBeAg and 171 (95.5%) were HBeAg negative. Among the HBeAg negative mothers, 9 had HBV DNA levels > 10(5) copies/ml, and of these 7 had normal ALT levels indicating low sensitivity of an elevated ALT level as a surrogate marker for high HBV DNA level. Furthermore, no correlation was found between ALT and HBV DNA levels. Hence, it is concluded that the use of ALT as a surrogate marker for high viral replication in HBeAg negative mothers could be questioned.

Mutational analysis of the activator of late transcription, Alt , in the lactococcal bacteriophage TP901-1

DEFF Research Database (Denmark)

Pedersen, Margit; Hammer, Karin

2007-01-01

An activator protein, Alt, synthesized during the early state of lytic infection is required for transcription of the late operon in the lactococcal phage TP901-1. In order to identify amino acid residues in the Alt protein required for activation of the TP901-1 late promoter, Plate, hydroxylamine...

[Impact analysis of shuxuetong injection on abnormal changes of ALT based on generalized boosted models propensity score weighting].

Science.gov (United States)

Yang, Wei; Yi, Dan-Hui; Xie, Yan-Ming; Yang, Wei; Dai, Yi; Zhi, Ying-Jie; Zhuang, Yan; Yang, Hu

2013-09-01

To estimate treatment effects of Shuxuetong injection on abnormal changes on ALT index, that is, to explore whether the Shuxuetong injection harms liver function in clinical settings and to provide clinical guidance for its safe application. Clinical information of traditional Chinese medicine (TCM) injections is gathered from hospital information system (HIS) of eighteen general hospitals. This is a retrospective cohort study, using abnormal changes in ALT index as an outcome. A large number of confounding biases are taken into account through the generalized boosted models (GBM) and multiple logistic regression model (MLRM) to estimate the treatment effects of Shuxuetong injections on abnormal changes in ALT index and to explore possible influencing factors. The advantages and process of application of GBM has been demonstrated with examples which eliminate the biases from most confounding variables between groups. This serves to modify the estimation of treatment effects of Shuxuetong injection on ALT index making the results more reliable. Based on large scale clinical observational data from HIS database, significant effects of Shuxuetong injection on abnormal changes in ALT have not been found.

ELEVATED ALANINE AMINOTRANSFERASE (ALT IN BLOOD DONORS: AN ASSESSMENT OF THE MAIN ASSOCIATED CONDITIONS AND ITS RELATIONSHIP TO THE DEVELOPMENT OF HEPATITIS C

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Fernando Lopes GONÇALES Jr.

1998-07-01

Full Text Available The determination of aminotranferases levels is very useful in the diagnosis of hepatopathies. In recent years, an elevated serum ALT level in blood donors has been associated with an increased risk of post-transfusion hepatitis (PTH. The purpose of the study was to research the factors associated with elevated ALT levels in a cohort of voluntary blood donors and to evaluate the relationship between increased ALT levels and the development of hepatitis C (HCV infection. 166 volunteer blood donors with elevated ALT at the time of their first donation were studied. All of the donors were questioned about previous hepatopathies, exposure to hepatitis, exposure to chemicals, use of medication or drugs, sexual behaviour, contact with blood or secretions and their intake of alcohol. Every three months, the serum levels of AST, ALT, alkaline phosphatase, gamma glutamyl transpeptidase, cholesterol, triglyceride and glycemia are assessed over a two year follow-up. The serum thyroid hormone levels as well as the presence of auto-antibodies were also measured. Abdominal ultrasound was performed in all patients with persistently elevated ALT or AST levels. A needle biopsy of liver was performed in 9 donors without definite diagnostic after medical investigation. The presence of anti-HCV antibodies in 116 donors were assayed again the first clinical evaluation. At the end of follow-up period (2 years later 71 donors were tested again for the presence of anti-HCV antibodies. None of donors resulted positive for hepatitis B or hepatitis C markers during the follow-up. Of the 116 donors, 101 (87% had persistently elevated ALT serum levels during the follow-up. Obesity and alcoholism were the principal conditions related to elevated ALT serum levels in 91/101 (90.1% donors. Hypertriglyceridemia, hypercholesterolemia, hypothyroidism and diabetes mellitus also were associated with increased ALT levels. Only 1/101 (0.9% had mild chronic active non A-G viral

Ephedra alte (Joint Pine: An Invasive, Problematic Weedy Species in Forestry and Fruit Tree Orchards in Jordan

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Jamal R. Qasem

2012-01-01

Full Text Available A field survey was carried out to record plant species climbed by Ephedra alte in certain parts of Jordan during 2008–2010. Forty species of shrubs, ornamental, fruit, and forest trees belonging to 24 plant families suffered from the climbing habit of E. alte. Growth of host plants was adversely affected by E. alte growth that extended over their vegetation. In addition to its possible competition for water and nutrients, the extensive growth it forms over host species prevents photosynthesis, smothers growth and makes plants die underneath the extensive cover. However, E. alte did not climb all plant species, indicating a host preference range. Damaged fruit trees included Amygdalus communis, Citrus aurantifolia, Ficus carica, Olea europaea, Opuntia ficus-indica, and Punica granatum. Forestry species that were adversely affected included Acacia cyanophylla, Ceratonia siliqua, Crataegus azarolus, Cupressus sempervirens, Pinus halepensis, Pistacia atlantica, Pistacia palaestina, Quercus coccifera, Quercus infectoria, Retama raetam, Rhamnus palaestina, Rhus tripartita, and Zizyphus spina-christi. Woody ornamentals attacked were Ailanthus altissima, Hedera helix, Jasminum fruticans, Jasminum grandiflorum, Nerium oleander, and Pyracantha coccinea. Results indicated that E. alte is a strong competitive for light and can completely smother plants supporting its growth. A. communis, F. carica, R. palaestina, and C. azarolus were most frequently attacked.

Ephedra alte (Joint Pine): An Invasive, Problematic Weedy Species in Forestry and Fruit Tree Orchards in Jordan

Science.gov (United States)

Qasem, Jamal R.

2012-01-01

A field survey was carried out to record plant species climbed by Ephedra alte in certain parts of Jordan during 2008–2010. Forty species of shrubs, ornamental, fruit, and forest trees belonging to 24 plant families suffered from the climbing habit of E. alte. Growth of host plants was adversely affected by E. alte growth that extended over their vegetation. In addition to its possible competition for water and nutrients, the extensive growth it forms over host species prevents photosynthesis, smothers growth and makes plants die underneath the extensive cover. However, E. alte did not climb all plant species, indicating a host preference range. Damaged fruit trees included Amygdalus communis, Citrus aurantifolia, Ficus carica, Olea europaea, Opuntia ficus-indica, and Punica granatum. Forestry species that were adversely affected included Acacia cyanophylla, Ceratonia siliqua, Crataegus azarolus, Cupressus sempervirens, Pinus halepensis, Pistacia atlantica, Pistacia palaestina, Quercus coccifera, Quercus infectoria, Retama raetam, Rhamnus palaestina, Rhus tripartita, and Zizyphus spina-christi. Woody ornamentals attacked were Ailanthus altissima, Hedera helix, Jasminum fruticans, Jasminum grandiflorum, Nerium oleander, and Pyracantha coccinea. Results indicated that E. alte is a strong competitive for light and can completely smother plants supporting its growth. A. communis, F. carica, R. palaestina, and C. azarolus were most frequently attacked. PMID:22645486

Ephedra alte (joint pine): an invasive, problematic weedy species in forestry and fruit tree orchards in Jordan.

Science.gov (United States)

Qasem, Jamal R

2012-01-01

A field survey was carried out to record plant species climbed by Ephedra alte in certain parts of Jordan during 2008-2010. Forty species of shrubs, ornamental, fruit, and forest trees belonging to 24 plant families suffered from the climbing habit of E. alte. Growth of host plants was adversely affected by E. alte growth that extended over their vegetation. In addition to its possible competition for water and nutrients, the extensive growth it forms over host species prevents photosynthesis, smothers growth and makes plants die underneath the extensive cover. However, E. alte did not climb all plant species, indicating a host preference range. Damaged fruit trees included Amygdalus communis, Citrus aurantifolia, Ficus carica, Olea europaea, Opuntia ficus-indica, and Punica granatum. Forestry species that were adversely affected included Acacia cyanophylla, Ceratonia siliqua, Crataegus azarolus, Cupressus sempervirens, Pinus halepensis, Pistacia atlantica, Pistacia palaestina, Quercus coccifera, Quercus infectoria, Retama raetam, Rhamnus palaestina, Rhus tripartita, and Zizyphus spina-christi. Woody ornamentals attacked were Ailanthus altissima, Hedera helix, Jasminum fruticans, Jasminum grandiflorum, Nerium oleander, and Pyracantha coccinea. Results indicated that E. alte is a strong competitive for light and can completely smother plants supporting its growth. A. communis, F. carica, R. palaestina, and C. azarolus were most frequently attacked.

Preliminary design analysis of the ALT-II limiter for TEXTOR

International Nuclear Information System (INIS)

Koski, J.A.; Boyd, R.D.; Kempka, S.M.; Romig, A.D. Jr.; Smith, M.F.; Watson, R.D.; Whitley, J.B.; Conn, R.W.; Grotz, S.P.

1984-01-01

Installation of a large toroidal belt pump limiter, Advanced Limiter Test II (ALT-II), on the TEXTOR tokamak at Juelich, FRG is anticipated for early 1986. This paper discusses the preliminary mechanical design and materials considerations undertaken as part of the feasibility study phase for ALT-II. Since the actively cooled limiter blade is the component in direct contact with the plasma edge, and thus subject to the severe plasma environment, most preliminary design efforts have concentrated on analysis of the blade. The screening process which led to the recommended preliminary design consisting of a dispersion strenghthened copper or OFHC copper cover plate over an austenitic stainless steel base plate is discussed. A 1 to 3 mm thick low atomic number coating consisting of a graded plasma-sprayed Silicon Carbide-Aluminium composite is recommended subject to further experiment and evaluation. Thermal-hydraulic and stress analyses of the limiter blade are also discussed. (orig.)

Defibrillator charging before rhythm analysis significantly reduces hands-off time during resuscitation

DEFF Research Database (Denmark)

Hansen, L. K.; Folkestad, L.; Brabrand, M.

2013-01-01

BACKGROUND: Our objective was to reduce hands-off time during cardiopulmonary resuscitation as increased hands-off time leads to higher mortality. METHODS: The European Resuscitation Council (ERC) 2005 and ERC 2010 guidelines were compared with an alternative sequence (ALT). Pulseless ventricular...... physicians were included. All had prior experience in advanced life support. Chest compressions were shorter interrupted using ALT (mean, 6.7 vs 13.0 seconds). Analyzing data for ventricular tachycardia scenarios only, hands-off time was shorter using ALT (mean, 7.1 vs 18.2 seconds). In ERC 2010 vs ALT, 12...... physicians were included. Two physicians had not prior experience in advanced life support. Hands-off time was reduced using ALT (mean, 3.9 vs 5.6 seconds). Looking solely at ventricular tachycardia scenarios, hands-off time was shortened using ALT (mean, 4.5 vs 7.6 seconds). No significant reduction...

Establishing Time for Professional Learning

Science.gov (United States)

Journal of Staff Development, 2013

2013-01-01

Time for collaborative learning is an essential resource for educators working to implement college- and career-ready standards. The pages in this article include tools from the workbook "Establishing Time for Professional Learning." The tools support a complete process to help educators effectively find and use time. The following…

A Styrene-alt-Maleic Acid Copolymer Is an Effective Inhibitor of R5 and X4 Human Immunodeficiency Virus Type 1 Infection

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Vanessa Pirrone

2010-01-01

Full Text Available An alternating copolymer of styrene and maleic acid (alt-PSMA differs from other polyanionic antiviral agents in that the negative charges of alt-PSMA are provided by carboxylic acid groups instead of sulfate or sulfonate moieties. We hypothesized that alt-PSMA would have activity against human immunodeficiency virus type 1 (HIV-1 comparable to other polyanions, such as the related compound, poly(sodium 4-styrene sulfonate (PSS. In assays using cell lines and primary immune cells, alt-PSMA was characterized by low cytotoxicity and effective inhibition of infection by HIV-1 BaL and IIIB as well as clinical isolates of subtypes A, B, and C. In mechanism of action assays, in which each compound was added to cells and subsequently removed prior to HIV-1 infection (“washout” assay, alt-PSMA caused no enhancement of infection, while PSS washout increased infection 70% above control levels. These studies demonstrate that alt-PSMA is an effective HIV-1 inhibitor with properties that warrant further investigation.

Prevalence of epilepsy and seizure disorders as causes of apparent life- threatening event (ALTE) in children admitted to a tertiary hospital.

Science.gov (United States)

Anjos, Alessandra Marques dos; Nunes, Magda Lahorgue

2009-09-01

To determine the prevalence and describe clinical characteristics of seizure disorders and epilepsy as causes of apparent life- threatening event (ALTE) in children admitted at the emergency and followed in a tertiary hospital. Cross-sectional study with prospective data collection using specific guidelines to determine the etiology of ALTE. During the study, 30 (4.2%) children admitted to the hospital had a diagnosis of ALTE. There was a predominance of males (73%) and term infants (70%). Neonatal neurological disorders and neuropsychomotor development delay were found respectively in 13.4% and 10% of the cases. Etiological investigation revealed that 50% of the cases were idiopathic, and 13.4% were caused by epilepsy or seizure disorders. Although all patients had recurrent ALTE events, epilepsy had not been previously suspected. Epilepsy should be included in the differential diagnosis of ALTE, particularly when events are recurrent.

A Human Long Non-Coding RNA ALT1 Controls the Cell Cycle of Vascular Endothelial Cells Via ACE2 and Cyclin D1 Pathway

Directory of Open Access Journals (Sweden)

Wen Li

2017-10-01

Full Text Available Background/Aims: ALT1 is a novel long non-coding RNA derived from the alternatively spliced transcript of the deleted in lymphocytic leukemia 2 (DLEU2. To date, ALT1 biological roles in human vascular endothelial cells have not been reported. Methods: ALT1 was knocked down by siRNAs. Cell proliferation was analyzed by cck-8. The existence and sequence of human ALT1 were identified by 3’ rapid amplification of cDNA ends. The interaction between lncRNA and proteins was analyzed by RNA-Protein pull down assay, RNA immunoprecipitation, and mass spectrometry analysis. Results: ALT1 was expressed in human umbilical vein endothelial cells (HUVECs. The expression of ALT1 was significantly downregulated in contact-inhibited HUVECs and in hypoxia-induced, growth-arrested HUVECs. Knocking down of ALT1 inhibited the proliferation of HUVECs by G0/G1 cell cycle arrest. We observed that angiotensin converting enzyme Ⅱ(ACE2 was a direct target gene of ALT1. Knocking-down of ALT1 or its target gene ACE2 could efficiently decrease the expression of cyclin D1 via the enhanced ubiquitination and degradation, in which HIF-1α and protein von Hippel-Lindau (pVHL might be involved. Conclusion: The results suggested the human long non-coding RNA ALT1 is a novel regulator for cell cycle of HUVECs via ACE2 and cyclin D1 pathway.

Alte Harfe. Vollständige Sammlung alter estnischer Volkslieder : Vorrede / Jakob Hurt

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Hurt, Jakob, 1839-1907

2005-01-01

Eessõna rmt. : Hurt, Jakob. Vana kannel. 1. kogu : täieline kogu vanu eesti rahvalaulusid = Alte Harfe : vollständige Sammlung alter estnischen Volkslieder. Tartu, 1875-1886. - (Eesti Kirjameeste Seltsi toimetised ; 3)

THE CHIMERIC ALT-VASTUS LATERALIS FREE FLAP IN RECONSTRUCTION OF ADVANCED BRONJ OF THE MAXILLA

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Francesca Toia

2015-04-01

Full Text Available ntroduction Bisphosphonate-related osteonecrosis of the jaw (BRONJ is a dangerous complication of bisphosphonates, a class of pharmaceutical agents used in numerous bone disorders. No gold standard therapy exists, but recent literature suggests that, in advanced stages, the best results are achieved with aggressive debridement. In this paper, we report our experience of treatment of stage 3 BRONJ of the maxilla with extensive surgical debridement and reconstruction with a chimeric ALT-Vastus lateralis flap. Methods Five selected patients with stage 3 BRONJ underwent partial maxillectomy with disease-free margins followed by immediate reconstruction with a chimeric ALT-Vastus lateralis free flap. Results Only two patients experienced minor complications. All other patients healed uneventfully within two weeks and donor site morbidity was minimal. Conclusions Our data suggest that aggressive debridement and reconstruction with a chimeric ALT -Vastus lateralis flap is an effective option for the treatment of stage III BRONJ of the maxilla.

Post-irradiation time effects on the graft of poly(ethylene-alt-tetrafluoroethylene) (ETFE) films for ion exchange membrane application

Energy Technology Data Exchange (ETDEWEB)

Geraldes, Adriana N., E-mail: angeral@ipen.b [Instituto de Pesquisas Energeticas e Nucleares (IPEN), Centro de Quimica e Meio Ambiente (CQMA), Av. Professor Lineu Prestes, 2242, 05508-900, Sao Paulo (Brazil); Zen, Heloisa A.; Ribeiro, Geise; Ferreira, Henrique P.; Souza, Camila P.; Parra, Duclerc F.; Santiago, Elisabete I.; Lugao, Ademar B. [Instituto de Pesquisas Energeticas e Nucleares (IPEN), Centro de Quimica e Meio Ambiente (CQMA), Av. Professor Lineu Prestes, 2242, 05508-900, Sao Paulo (Brazil)

2010-03-15

Grafting of styrene followed by sulfonation onto poly(ethylene-alt-tetrafluoroethylene) (ETFE) was studied for synthesis of ion exchange membranes. Radiation-induced grafting of styrene onto ETFE films was investigated after simultaneous irradiation (in post-irradiation condition) using a {sup 60}Co source. The ETFE films were irradiated at 20 kGy dose at room temperature and chemical changes were monitored after contact with styrene for grafting. The post-irradiation time was established at 14 days when the films were remained in styrene/toluene 1:1 v/v. After this period the grafting degree was evaluated in the samples. The grafted films were sulfonated using chlorosulfonic acid and 1, 2-dichloroethane 20:80 (v/v) at room temperature for 5 h. The membranes were analyzed by infrared spectroscopy (FTIR), differential scanning calorimeter (DSC), thermogravimetric measurements (TG) and degree of grafting (DOG). The ion exchange capacity (IEC) of membranes was determined by acid-base titration and the values for ETFE membranes were achieved higher than Nafion films. Preliminary single cell performance was made using pure H{sub 2} and O{sub 2} as reactants at a cell temperature of 80 deg. C and atmospheric gas pressure. The fuel cell performance of ETFE films was satisfactory when compared to state-of-art Nafion membranes.

Post-irradiation time effects on the graft of poly(ethylene-alt-tetrafluoroethylene) (ETFE) films for ion exchange membrane application

Science.gov (United States)

Geraldes, Adriana N.; Zen, Heloísa A.; Ribeiro, Geise; Ferreira, Henrique P.; Souza, Camila P.; Parra, Duclerc F.; Santiago, Elisabete I.; Lugão, Ademar B.

2010-03-01

Grafting of styrene followed by sulfonation onto poly(ethylene-alt-tetrafluoroethylene) (ETFE) was studied for synthesis of ion exchange membranes. Radiation-induced grafting of styrene onto ETFE films was investigated after simultaneous irradiation (in post-irradiation condition) using a 60Co source. The ETFE films were irradiated at 20 kGy dose at room temperature and chemical changes were monitored after contact with styrene for grafting. The post-irradiation time was established at 14 days when the films were remained in styrene/toluene 1:1 v/v. After this period the grafting degree was evaluated in the samples. The grafted films were sulfonated using chlorosulfonic acid and 1, 2-dichloroethane 20:80 (v/v) at room temperature for 5 h. The membranes were analyzed by infrared spectroscopy (FTIR), differential scanning calorimeter (DSC), thermogravimetric measurements (TG) and degree of grafting (DOG). The ion exchange capacity (IEC) of membranes was determined by acid-base titration and the values for ETFE membranes were achieved higher than Nafion ® films. Preliminary single cell performance was made using pure H 2 and O 2 as reactants at a cell temperature of 80 °C and atmospheric gas pressure. The fuel cell performance of ETFE films was satisfactory when compared to state-of-art Nafion ® membranes.

Quality of E-Learners’ Time and Learning Performance Beyond Quantitative Time-on-Task

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Margarida Romero

2011-06-01

Full Text Available AbstractAlong with the amount of time spent learning (or time-on-task, the quality of learning time has a real influence on learning performance. Quality of time in online learning depends on students’ time availability and their willingness to devote quality cognitive time to learning activities. However, the quantity and quality of the time spent by adult e-learners on learning activities can be reduced by professional, family, and social commitments. Considering that the main time pattern followed by most adult e-learners is a professional one, it may be beneficial for online education programs to offer a certain degree of flexibility in instructional time that might allow adult learners to adjust their learning times to their professional constraints. However, using the time left over once professional and family requirements have been fulfilled could lead to a reduction in quality time for learning. This paper starts by introducing the concept of quality of learning time from an online student-centred perspective. The impact of students’ time-related variables (working hours, time-on-task engagement, time flexibility, time of day, day of week is then analyzed according to individual and collaborative grades achieved during an online master’s degree program. The data show that both students’ time flexibility (r = .98 and especially their availability to learn in the morning are related to better grades in individual (r = .93 and collaborative activities (r = .46.

Biomaterial properties evaluation of poly(vinyl acetate- alt-maleic anhydride)/chitosan nanocapsules

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Raţă, Delia Mihaela; Popa, Marcel; Chailan, Jean-François; Zamfir, Carmen Lăcrămioara; Peptu, Cătălina Anişoara

2014-08-01

Nanocapsules with diameter around 100 nm based on a natural polymer (chitosan) and a synthetic polymer poly(vinyl acetate- alt-maleic anhydride) [poly(MAVA)] by interfacial condensation method were prepared. The present study proposes a new type of biocompatible nanocapsules based on poly(vinyl acetate- alt-maleic anhydride-chitosan) (MCS) able to become a reliable support for inclusion and release of drugs. The spherical shape of the nanocapsules was evidenced by scanning electron microscopy. Nanocapsules presented a good Norfloxacin loading and release capacity. Haemocompatibility tests have demonstrated that the nanocapsules present a low toxicity and a good compatibility with sanguine medium. The biocompatibility properties of the nanocapsules after their intraperitoneal administration in rats were evidenced by histopathological examination of different organs (brain, liver, kidney, and lung). The results are encouraging and the nanocapsules can be used as controlled drug delivery systems.

Timepiece: Extending and Enhancing Learning Time.

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Anderson, Lorin W., Ed.; Walberg, Herbert J., Ed.

This publication offers suggestions for making more productive use of time, a scarce and valued educational resource. The chapter authors, authorities on the use of educational time, write about how to extend and enhance learning time within and outside schools. In "Productive Use of Time," Herbert Walberg describes how learning time can be…

Theory in learning technology

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Laura Czerniewicz

2011-12-01

Full Text Available This special issue is being published at a significant point in time in relation tosimultaneous changes in higher education, in technology and in the field of learningtechnology itself. As the 2011 ALT C conference themes clearly state, learningtechnology needs to learn to thrive in a colder and more challenging climate. In thisdifficult political and economic environment technological trends continue todevelop in terms of mobility, cloud computing, ubiquity and the emergence of whathas been called big data. E-learning has become mainstream and the field of learningtechnology itself is beginning to stabilise as a profession. Profession here isunderstood as a knowledge-based occupation and a form of cultural work where thetasks addressed are human problems amenable to expert advice and distinguishablefrom other kinds of work by the fact that it is underpinned by abstract knowledge(Macdonald, 1995.

Time factor in e-learning and assessment

OpenAIRE

Romero Velasco, Margarida

2010-01-01

Peer-reviewed Peer reviewed Time is probably one of the most polysemous words in education. In e-learning, characterization of the time factor is particularly relevant because of the high level of flexibility in the teaching and learning times, and the resulting responsibility of the e-learners in regulating their learning times.

Mirror symmetric bimanual movement priming can increase corticomotor excitability and enhance motor learning.

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Winston D Byblow

Full Text Available Repetitive mirror symmetric bilateral upper limb may be a suitable priming technique for upper limb rehabilitation after stroke. Here we demonstrate neurophysiological and behavioural after-effects in healthy participants after priming with 20 minutes of repetitive active-passive bimanual wrist flexion and extension in a mirror symmetric pattern with respect to the body midline (MIR compared to an control priming condition with alternating flexion-extension (ALT. Transcranial magnetic stimulation (TMS indicated that corticomotor excitability (CME of the passive hemisphere remained elevated compared to baseline for at least 30 minutes after MIR but not ALT, evidenced by an increase in the size of motor evoked potentials in ECR and FCR. Short and long-latency intracortical inhibition (SICI, LICI, short afferent inhibition (SAI and interhemispheric inhibition (IHI were also examined using pairs of stimuli. LICI differed between patterns, with less LICI after MIR compared with ALT, and an effect of pattern on IHI, with reduced IHI in passive FCR 15 minutes after MIR compared with ALT and baseline. There was no effect of pattern on SAI or FCR H-reflex. Similarly, SICI remained unchanged after 20 minutes of MIR. We then had participants complete a timed manual dexterity motor learning task with the passive hand during, immediately after, and 24 hours after MIR or control priming. The rate of task completion was faster with MIR priming compared to control conditions. Finally, ECR and FCR MEPs were examined within a pre-movement facilitation paradigm of wrist extension before and after MIR. ECR, but not FCR, MEPs were consistently facilitated before and after MIR, demonstrating no degradation of selective muscle activation. In summary, mirror symmetric active-passive bimanual movement increases CME and can enhance motor learning without degradation of muscle selectivity. These findings rationalise the use of mirror symmetric bimanual movement as a

Hard Times for HRD, Lean Times for Learning?: Workplace Participatory Practices as Enablers of Learning

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Warhurst, Russell

2013-01-01

Purpose: This article aims to show how in times of austerity when formal HRD activity is curtailed and yet the need for learning is greatest, non-formal learning methods such as workplace involvement and participation initiated by line managers can compensate by enabling the required learning and change. Design/methodology/approach: A qualitative…

Clinico-biochemical factors to early predict biliary etiology of acute pancreatitis: age, female gender, and ALT.

Science.gov (United States)

Zarnescu, N O; Costea, R; Zarnescu Vasiliu, E C; Neagu, S

2015-01-01

Background/ Aims: Despite the existence of an easy tool to diagnose biliary tract disease as an etiology for acute pancreatitis (AP), the sensitivity of abdominal ultrasound is around 80%, which can be even lower in certain conditions. We have retrospectively reviewed data of 146 patients admitted for acute pancreatitis between 1999 and 2013. Bivariate analysis for clinical and biochemical variables was performed with respect to etiology of AP (biliary versus non-biliary). Multivariate analysis was performed by using binary logistic regression. There were 87 males (59.6%) and 59 females (40.4%), with a median age of 51. The etiology of acute pancreatitis was biliary in 71 patients (48.6%). Bivariate analysis found the following as significant association (p=0.001) with biliary pancreatitis: older age, female gender, and elevated AST, ALT. A binary logistic regression analysis identified as predictor factors for biliary etiology of acute pancreatitis: age OR = 1.031 (95% CI 1.004 - 1.059, p = 0.024), sex (female) OR = 2.34 (95% CI 1.022 - 5.359, p = 0.044) and ALT OR = 1.004 (95% CI 1.001 - 1.007, p =0.004). The two clinical scores included the three variables (A.S.ALT scores) in categorical format were generated and then checked with the ROC curves (areas under curve are 0.768 and 0.778). Age, female gender, and elevated ALT can help identifying cases with biliary etiology of acute pancreatitis.

DSLR Double Star Astrometry Using an Alt-Az Telescope

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Frey, Thomas; Haworth, David

2014-07-01

The goal of this project was to determine if the double star's angular separation and position angle measurements could be successfully measured with a motor driven, alt-azimuth Dobsonian-mounted Newtonian telescope (without a field rotator), and a digital single-lens reflex (DSLR) camera. Additionally, the project was constrained by using as much existing equipment as much as possible, including an Apple MacBook Pro laptop and a Canon T2i camera. This project was additionally challenging because the first author had no experience with astrophotography.

The Transition between Telomerase and ALT Mechanisms in Hodgkin Lymphoma and Its Predictive Value in Clinical Outcomes

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Radhia M’kacher

2018-05-01

Full Text Available Background: We analyzed telomere maintenance mechanisms (TMMs in lymph node samples from HL patients treated with standard therapy. The TMMs correlated with clinical outcomes of patients. Materials and Methods: Lymph node biopsies obtained from 38 HL patients and 24 patients with lymphadenitis were included in this study. Seven HL cell lines were used as in vitro models. Telomerase activity (TA was assessed by TRAP assay and verified through hTERT immunofluorescence expression; alternative telomere lengthening (ALT was also assessed, along with EBV status. Results: Both TA and ALT mechanisms were present in HL lymph nodes. Our findings were reproduced in HL cell lines. The highest levels of TA were expressed in CD30−/CD15− cells. Small cells were identified with ALT and TA. Hodgkin and Reed Sternberg cells contained high levels of PML bodies, but had very low hTERT expression. There was a significant correlation between overall survival (p < 10−3, event-free survival (p < 10−4, and freedom from progression (p < 10−3 and the presence of an ALT profile in lymph nodes of EBV+ patients. Conclusion: The presence of both types of TMMs in HL lymph nodes and in HL cell lines has not previously been reported. TMMs correlate with the treatment outcome of EBV+ HL patients.

Prevalence of epilepsy and seizure disorders as causes of apparent life- threatening event (ALTE in children admitted to a tertiary hospital Prevalência de epilepsia e crises epilépticas como causa de eventos com aparente risco de vida (ALTE em crianças internadas em hospital terciário

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Alessandra Marques dos Anjos

2009-09-01

Full Text Available OBJECTIVE: To determine the prevalence and describe clinical characteristics of seizure disorders and epilepsy as causes of apparent life- threatening event (ALTE in children admitted at the emergency and followed in a tertiary hospital. METHOD: Cross-sectional study with prospective data collection using specific guidelines to determine the etiology of ALTE. RESULTS: During the study, 30 (4.2% children admitted to the hospital had a diagnosis of ALTE. There was a predominance of males (73% and term infants (70%. Neonatal neurological disorders and neuropsychomotor development delay were found respectively in 13.4% and 10% of the cases. Etiological investigation revealed that 50% of the cases were idiopathic, and 13.4% were caused by epilepsy or seizure disorders. Although all patients had recurrent ALTE events, epilepsy had not been previously suspected. CONCLUSION: Epilepsy should be included in the differential diagnosis of ALTE, particularly when events are recurrent.OBJETIVO: Determinar a prevalência e características clínicas de crises epilépticas e epilepsia como causa de eventos com aparente risco de vida (ALTE em crianças atendidas na emergência e acompanhadas em hospital terciário. MÉTODO: Estudo transversal com coleta prospectiva de dados através de protocolo específico para identificação da etiologia de ALTE. RESULTADOS: Foram diagnosticadas 30 crianças com ALTE perfazendo 4.2% das crianças internadas no período do estudo. Houve predominância no sexo masculino (73% e em neonatos a termo (70%. História prévia de doenças neurológicas no período neonatal e atraso no desenvolvimento neuropsicomotor ocorreram respectivamente em 13.4% e 10% dos casos. A investigação etiológica identificou 13.4% dos casos relacionados a epilepsia ou crise convulsivas e 50% idiopáticos. Apesar destes pacientes terem apresentados episódios recorrentes em nenhum caso havia a suspeita prévia de epilepsia. CONCLUSÃO: Ao investigar

Radiologists' preferences for just-in-time learning.

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Kahn, Charles E; Ehlers, Kevin C; Wood, Beverly P

2006-09-01

Effective learning can occur at the point of care, when opportunities arise to acquire information and apply it to a clinical problem. To assess interest in point-of-care learning, we conducted a survey to explore radiologists' attitudes and preferences regarding the use of just-in-time learning (JITL) in radiology. Following Institutional Review Board approval, we invited 104 current radiology residents and 86 radiologists in practice to participate in a 12-item Internet-based survey to assess their attitudes toward just-in-time learning. Voluntary participation in the survey was solicited by e-mail; respondents completed the survey on a web-based form. Seventy-nine physicians completed the questionnaire, including 47 radiology residents and 32 radiologists in practice; the overall response rate was 42%. Respondents generally expressed a strong interest for JITL: 96% indicated a willingness to try such a system, and 38% indicated that they definitely would use a JITL system. They expressed a preference for learning interventions of 5-10 min in length. Current and recent radiology trainees have expressed a strong interest in just-in-time learning. The information from this survey should be useful in pursuing the design of learning interventions and systems for delivering just-in-time learning to radiologists.

Learning During Stressful Times

Science.gov (United States)

Shors, Tracey J.

2012-01-01

Stressful life events can have profound effects on our cognitive and motor abilities, from those that could be construed as adaptive to those not so. In this review, I discuss the general notion that acute stressful experience necessarily impairs our abilities to learn and remember. The effects of stress on operant conditioning, that is, learned helplessness, as well as those on classical conditioning procedures are discussed in the context of performance and adaptation. Studies indicating sex differences in learning during stressful times are discussed, as are those attributing different responses to the existence of multiple memory systems and nonlinear relationships. The intent of this review is to highlight the apparent plasticity of the stress response, how it might have evolved to affect both performance and learning processes, and the potential problems with interpreting stress effects on learning as either good or bad. An appreciation for its plasticity may provide new avenues for investigating its underlying neuronal mechanisms. PMID:15054128

An algorithm for learning real-time automata

NARCIS (Netherlands)

Verwer, S.E.; De Weerdt, M.M.; Witteveen, C.

2007-01-01

We describe an algorithm for learning simple timed automata, known as real-time automata. The transitions of real-time automata can have a temporal constraint on the time of occurrence of the current symbol relative to the previous symbol. The learning algorithm is similar to the redblue fringe

Altın Fiyatlarının Yapay Sinir Ağları ile Tahmini ve Bir Uygulama

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Rıdvan YÜKSEL

2016-03-01

Full Text Available Bu çalışmada altın fiyatlarını yapay sinir ağları ile öngörmek amacıyla, altın fiyatlarını etkileyebileceği düşünülen değişkenler olan Gümüş fiyatları, Brent Petrol fiyatları, ABD doları/ EUR paritesi, EuroNext100 endeksi, Amerika Dow Jones Endeksi, 13 Hafta vadeli ABD bonosu faiz oranı ve ABD TÜFE endeksi kullanılarak modeller kurulmuştur. Yapay sinir ağları ile kurulan modellerden elde edilen tahmin sonuçları, gerçek değerler ile R2, RMSE, MAE ve MAPE (% gibi performans kriterleri hesaplanarak karşılaştırılmıştır. Elde edilen bulgular yapay sinir ağlarının altın fiyatlarının tahmininde başarı ile kullanılabileceğini göstermektedir. Yapılan duyarlılık analizinin sonuçları değerlendirildiğinde altın fiyatlarını etkileyen faktörlerin başında gümüş ve petrol fiyatlarının geldiği tespit edilmiştir.

E-Learning, Time and Unconscious Thinking

Science.gov (United States)

Mathew, David

2014-01-01

This article views the temporal dimensions of e-learning through a psychoanalytic lens, and asks the reader to consider links between online learning and psychoanalysis. It argues that time and its associated philosophical puzzles impinge on both psychoanalytic theory and on e-learning at two specific points. The first is in the distinction…

Overcoming Learning Time And Space Constraints Through Technological Tool

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Nafiseh Zarei

2015-08-01

Full Text Available Today the use of technological tools has become an evolution in language learning and language acquisition. Many instructors and lecturers believe that integrating Web-based learning tools into language courses allows pupils to become active learners during learning process. This study investigate how the Learning Management Blog (LMB overcomes the learning time and space constraints that contribute to students’ language learning and language acquisition processes. The participants were 30 ESL students at National University of Malaysia. A qualitative approach comprising an open-ended questionnaire and a semi-structured interview was used to collect data. The results of the study revealed that the students’ language learning and acquisition processes were enhanced. The students did not face any learning time and space limitations while being engaged in the learning process via the LMB. They learned and acquired knowledge using the language learning materials and forum at anytime and anywhere. Keywords: learning time, learning space, learning management blog

Time will tell: The role of mobile learning analytics in self-regulated learning

NARCIS (Netherlands)

Tabuenca, Bernardo; Kalz, Marco; Drachsler, Hendrik; Specht, Marcus

2015-01-01

This longitudinal study explores the effects of tracking and monitoring time devoted to learn with a mobile tool, on self-regulated learning. Graduate students (n = 36) from three different online courses used their own mobile devices to track how much time they devoted to learn over a period of

The Effects of Trained Peer Tutors on the Physical Education of Children Who Are Visually Impaired

Science.gov (United States)

Wiskochil, Brian; Lieberman, Lauren J.; Houston-Wilson, Cathy; Petersen, Susan

2007-01-01

This study examined the effect of trained peer tutors on the academic learning time-physical education (ALT-PE) scores of children with visual impairments. It found a mean increase of 20.8% for ALT-PE and increases in ALT-PE scores for closed and open skills and that trained peer tutors were more effective than were untrained peer tutors.

"Almha La Vengadora": Protagonista del indigenismo de neovanguardia alteño

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Maria Irina Soto-Mejia

2016-03-01

Este artículo discute la obra de Crispín Portugal (1976-2007, un escritor alteño, en el contexto de la lucha contra la pobreza y el racismo que enfrentan a las ciudades de El Alto y La Paz, y del movimiento en contra del mercado  que comenzó en Argentina produciendo libros con tapas de cartón compradas a los cartoneros. Portugal y otros escritores indígenas iniciaron un proyecto editorial llamado Yerba Mala Cartonera, que cuenta con su propio blog, para diseminar la literatura boliviana que no cuenta con otros medios de difusión. A través de la revisión de un cuento de Portugal (Almha La Vengadora que describe a una chola luchadora de El Alto cuyo nombre en el cuadrilátero es “la vengadora”, este trabajo plantea la configuración de un indigenismo alteño de neovanguardia equiparable al vanguardismo de Gamaliel Churata.  Con este propósito, presta especial atención a El Alto –la ciudad indígena más grande de Latinoamérica- situándolo como escenario clave para el desarrollo de una neovanguardia que se alimenta del neoliberalismo fallido para configurar una propuesta de modernidad que recupera el pasado indígena aymara para inventar otras formas de futuro.

A confrontation with reality - Proceedings of the 19th Association for Learning Technology Conference

NARCIS (Netherlands)

Hawkridge, David; Verjans, Steven; Wilson, Gail

2012-01-01

Hawkridge, D., Verjans, S., & Wilson, G. (Eds.) (2012). A confrontation with reality - Proceedings of the 19th Association for Learning Technology Conference (ALT-C 2012). September, 11-14, 2012, Manchester, UK.

Active learning reduces annotation time for clinical concept extraction.

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Kholghi, Mahnoosh; Sitbon, Laurianne; Zuccon, Guido; Nguyen, Anthony

2017-10-01

To investigate: (1) the annotation time savings by various active learning query strategies compared to supervised learning and a random sampling baseline, and (2) the benefits of active learning-assisted pre-annotations in accelerating the manual annotation process compared to de novo annotation. There are 73 and 120 discharge summary reports provided by Beth Israel institute in the train and test sets of the concept extraction task in the i2b2/VA 2010 challenge, respectively. The 73 reports were used in user study experiments for manual annotation. First, all sequences within the 73 reports were manually annotated from scratch. Next, active learning models were built to generate pre-annotations for the sequences selected by a query strategy. The annotation/reviewing time per sequence was recorded. The 120 test reports were used to measure the effectiveness of the active learning models. When annotating from scratch, active learning reduced the annotation time up to 35% and 28% compared to a fully supervised approach and a random sampling baseline, respectively. Reviewing active learning-assisted pre-annotations resulted in 20% further reduction of the annotation time when compared to de novo annotation. The number of concepts that require manual annotation is a good indicator of the annotation time for various active learning approaches as demonstrated by high correlation between time rate and concept annotation rate. Active learning has a key role in reducing the time required to manually annotate domain concepts from clinical free text, either when annotating from scratch or reviewing active learning-assisted pre-annotations. Copyright © 2017 Elsevier B.V. All rights reserved.

B-Learning Tools in Engineering Education

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Angeles Cancela

2013-04-01

Full Text Available 0 false 21 18 pt 18 pt 0 0 false false false /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;} The New Higher Education System implies the adoption of different education systems than the traditional ones. Therefore, many college teachers today want to use some new mechanisms to teach the matter and to find better ways of engaging students in the learning process. The objective of this work is to evaluate different learning methods from the student’s point of view and share the authors’ experience towards a different mode of teaching. Three different learning techniques have been used on University of Vigo’s fifth-year engineering students and the students have answered a questionnaire in order to check the effectiveness of the different methods from the students’ point of view. The results of the experience show that the use of learning methods (cooperative learning and multimedia resources fosters student motivation and improves their retention, assimilation, understanding, and proper application of course content.

A Model for Learning Over Time: The Big Picture

Science.gov (United States)

Amato, Herbert K.; Konin, Jeff G.; Brader, Holly

2002-01-01

Objective: To present a method of describing the concept of “learning over time” with respect to its implementation into an athletic training education program curriculum. Background: The formal process of learning over time has recently been introduced as a required way for athletic training educational competencies and clinical proficiencies to be delivered and mastered. Learning over time incorporates the documented cognitive, psychomotor, and affective skills associated with the acquisition, progression, and reflection of information. This method of academic preparation represents a move away from a quantitative-based learning module toward a proficiency-based mastery of learning. Little research or documentation can be found demonstrating either the specificity of this concept or suggestions for its application. Description: We present a model for learning over time that encompasses multiple indicators for assessment in a successive format. Based on a continuum approach, cognitive, psychomotor, and affective characteristics are assessed at different levels in classroom and clinical environments. Clinical proficiencies are a common set of entry-level skills that need to be integrated into the athletic training educational domains. Objective documentation is presented, including the skill breakdown of a task and a matrix to identify a timeline of competency and proficiency delivery. Clinical Advantages: The advantages of learning over time pertain to the integration of cognitive knowledge into clinical skill acquisition. Given the fact that learning over time has been implemented as a required concept for athletic training education programs, this model may serve to assist those program faculty who have not yet developed, or are in the process of developing, a method of administering this approach to learning. PMID:12937551

2017-04-28_W88 ALT 370 Program Overview(OUO).

Energy Technology Data Exchange (ETDEWEB)

Daniels, Vonceil

2017-04-01

All major program milestones have been met and the program is executing within budget. The ALT 370 program achieved Phase 6.4 authorization in February of this year. Five component Final Design Reviews (FDRs) have been completed, indicating progress in finalizing the design and development phase of the program. A series of ground-based qualification activities have demonstrated that designs are meeting functional requirements. The first fully functional flight test, FCET-53, demonstrated end-to-end performance in normal flight environments in February. Similarly, groundbased nuclear safety and hostile environments testing indicates that the design meets requirements in these stringent environments. The first in a series of hostile blast tests was successfully conducted in April.

E-learning for Part-Time Medical Studies

Directory of Open Access Journals (Sweden)

Półjanowicz Wiesław

2016-12-01

Full Text Available Distance education undoubtedly has many advantages, such as individualization of the learning process, unified transmission of teaching materials, the opportunity to study at any place and any time, reduction of financial costs for commuting to classes or accommodation of participants, etc. Adequate working conditions on the e-learning portal must also be present, eg. well-prepared, substantive courses and good communication between the participants. Therefore, an important element in the process of conducting e-learning courses is to measure the increase of knowledge and satisfaction of participants with distance learning. It allows for fine-tuning the content of the course and for classes to be properly organized. This paper presents the results of teaching and assessment of satisfaction with e-learning courses in “Problems of multiculturalism in medicine”, “Selected issues of visual rehabilitation” and “Ophthalmology and Ophthalmic Nursing”, which were carried out experimentally at the Faculty of Health Sciences at the Medical University of Bialystok for nursing students for the 2010/2011 academic year. The study group consisted of 72 part-time students who learnt in e-learning mode and the control group of 87 students who learnt in the traditional way. The students’ opinions about the teaching process and final exam scores were analyzed based on a specially prepared survey questionnaire. Organization of e-learning classes was rated positively by 90% of students. The average result on the final exams for all distance learning subjects was at the level of 82%, while for classes taught in the traditional form it was 81%. Based on these results, we conclude that distance learning is as effective as learning according to the traditional form in medical education studies.

Professional Learning in Part-time University Study

DEFF Research Database (Denmark)

Rasmussen, Palle

2007-01-01

The theme of this article is adult students' learning in part-time studies at university level in Denmark. One issue discussed is the interplay of research and teaching in this kind of study programme. Examples are presented from the Master of Learning Processes study programme at Aalborg...

Spaced learning and innovative teaching: school time, pedagogy of attention and learning awareness

Directory of Open Access Journals (Sweden)

Garzia Maeca

2016-06-01

Full Text Available Currently, the ‘time’ variable has taken on the function of instructional and pedagogical innovation catalyst, after representing-over the years-a symbol of democratisation, learning opportunity and instruction quality, able to incorporate themes such as school dropout, personalisation and vocation into learning. Spaced Learning is a teaching methodology useful to quickly seize information in long-term memory based on a particular arrangement of the lesson time that comprises three input sessions and two intervals. Herein we refer to a teachers’ training initiative on Spaced Learning within the programme ‘DocentiInFormAzione’ in the EDOC@WORK3.0 Project in Apulia region in 2015. The training experience aimed at increasing teachers’ competencies in the Spaced Learning method implemented in a context of collaborative reflection and reciprocal enrichment. The intent of the article is to show how a process of rooting of the same culture of innovation, which opens to the discovery (or rediscovery of effective teaching practices sustained by scientific evidences, can be successfully implemented and to understand how or whether this innovation- based on the particular organisation of instructional time-links learning awareness to learning outcomes.

Increasing instruction time in school does increase learning

DEFF Research Database (Denmark)

Andersen, Simon Calmar; Humlum, Maria; Nandrup, Anne Brink

2016-01-01

Increasing instruction time in school is a central element in the attempts of many governments to improve student learning, but prior research—mainly based on observational data—disputes the effect of this approach and points out the potential negative effects on student behavior. Based on a large......-scale, cluster-randomized trial, we find that increasing instruction time increases student learning and that a general increase in instruction time is at least as efficient as an expert-developed, detailed teaching program that increases instruction with the same amount of time. These findings support the value...... of increased instruction time....

Predictive value of serum ALT and T-cell receptor beta variable chain for HBeAg seroconversion in chronic hepatitis B patients during tenofovir treatment.

Science.gov (United States)

Yang, Jiezuan; Yan, Dong; Guo, Renyong; Chen, Jiajia; Li, Yongtao; Fan, Jun; Fu, Xuyan; Yao, Xinsheng; Diao, Hongyan; Li, Lanjuan

2017-03-01

Effective antiviral therapy plays a key role in slowing the progression of chronic hepatitis B (CHB). Identification of serum indices, including hepatitis B e antigen (HBeAg) expression and seroconversion, will facilitate evaluation of the efficacy of antiviral therapy in HBeAg-positive CHB patients. The biochemical, serological, virological parameters, and the frequency of circulating CD4CD25 regulatory T cell (Treg) in 32 patients were measured at baseline and every 12 weeks during 96 weeks of tenofovir disoproxil fumarate (TDF) treatment. The relationship between the hepatitis B virus (HBV) deoxyribonucleic acid (DNA) and Treg and alanine aminotransferase (ALT) levels was analyzed, respectively. The molecular profiles of T-cell receptor beta variable chain (TRBV) were determined using gene melting spectral pattern. For the seroconverted 12 patients, ALT declined to normal levels by week 24 and remained at this level in subsequent treatment; moreover, the predictive cutoff value of ALT for HBeAg seroconversion (SC) was 41.5 U/L at week 24. The positive correlation between HBV DNA and Treg and ALT was significant in SC patients, but not in non-SC patients. Six TRBV families (BV3, BV11, BV12, BV14, BV20, and BV24) were predominantly expressed in SC patients at baseline. The decline of ALT could be used to predict HBeAg seroconversion for CHB patients during TDF treatment. In addition, the profile of Tregs and TRBVs may be associated with HBeAg seroconversion and could also be a potential indicator for predicting HBeAg SC and treatment outcome for CHB patients.

Real-time Color Codes for Assessing Learning Process

OpenAIRE

Dzelzkalēja, L; Kapenieks, J

2016-01-01

Effective assessment is an important way for improving the learning process. There are existing guidelines for assessing the learning process, but they lack holistic digital knowledge society considerations. In this paper the authors propose a method for real-time evaluation of students’ learning process and, consequently, for quality evaluation of teaching materials both in the classroom and in the distance learning environment. The main idea of the proposed Color code method (CCM) is to use...

Kas erivajadustega lapsed saavad õigel ajal abi? / Ene Mägi, Urve Raudsepp-Alt, Ale Sprenk, Peeter Aas

Index Scriptorium Estoniae

2009-01-01

Küsimusele vastavad: Tallinna Ülikooli Kasvatusteaduste Instituudi eri- ja sotsiaalpedagoogika osakonna juhataja Ene Mägi, Tallinna Haridusameti üldhariduse osakonna peaspetsialist Urve Raudsepp-Alt, Krabi põhikooli direktor Ale Sprenk, Põlva Maavalitsuse haridus-, kultuuri- ja sotsiaalosakonna juhataja Peeter Aas

Linear time relational prototype based learning.

Science.gov (United States)

Gisbrecht, Andrej; Mokbel, Bassam; Schleif, Frank-Michael; Zhu, Xibin; Hammer, Barbara

2012-10-01

Prototype based learning offers an intuitive interface to inspect large quantities of electronic data in supervised or unsupervised settings. Recently, many techniques have been extended to data described by general dissimilarities rather than Euclidean vectors, so-called relational data settings. Unlike the Euclidean counterparts, the techniques have quadratic time complexity due to the underlying quadratic dissimilarity matrix. Thus, they are infeasible already for medium sized data sets. The contribution of this article is twofold: On the one hand we propose a novel supervised prototype based classification technique for dissimilarity data based on popular learning vector quantization (LVQ), on the other hand we transfer a linear time approximation technique, the Nyström approximation, to this algorithm and an unsupervised counterpart, the relational generative topographic mapping (GTM). This way, linear time and space methods result. We evaluate the techniques on three examples from the biomedical domain.

PDMS-modified poly(styrene-alt-maleic anhydride)s as water-borne coatings based on surfactant-free latexes

NARCIS (Netherlands)

Gunbas, I.D.; Wouters, M.E.L.; Benthem, R.A.T.M. van; Koning, C.E.; Noordover, B.A.J.

2013-01-01

In this work, two series of PDMS-modified poly(styrene-alt-maleic anhydride)s (PSMA) were prepared by the partial imidization of their anhydride groups with mono-functional, amine-terminated polydimethyl siloxanes (PDMS-NH2) with two different molecular weights. Subsequently, surfactant-free

Learned Interval Time Facilitates Associate Memory Retrieval

Science.gov (United States)

van de Ven, Vincent; Kochs, Sarah; Smulders, Fren; De Weerd, Peter

2017-01-01

The extent to which time is represented in memory remains underinvestigated. We designed a time paired associate task (TPAT) in which participants implicitly learned cue-time-target associations between cue-target pairs and specific cue-target intervals. During subsequent memory testing, participants showed increased accuracy of identifying…

A Lecture Supporting System Based on Real-Time Learning Analytics

Science.gov (United States)

Shimada, Atsushi; Konomi, Shin'ichi

2017-01-01

A new lecture supporting system based on real-time learning analytics is proposed. Our target is on-site classrooms where teachers give their lectures, and a lot of students listen to teachers' explanation, conduct exercises etc. We utilize not only an e-Learning system, but also an e-Book system to collect real-time learning activities during the…

Phase 1 trial of ALT-801, an interleukin-2/T cell receptor fusion protein targeting p53 (aa264-272)/HLA-A*0201 complex, in patients with advanced malignancies

Science.gov (United States)

Fishman, Mayer N.; Thompson, John A.; Pennock, Gregory K.; Gonzalez, Rene; Diez, Luz M.; Daud, Adil I.; Weber, Jeffery S.; Huang, Bee Y.; Tang, Shamay; Rhode, Peter R.; Wong, Hing C.

2014-01-01

Purpose ALT-801 is a bifunctional fusion protein comprising interleukin-2 (IL-2) linked to a soluble, single-chain T cell receptor domain that recognizes a peptide epitope (aa264-272) of the human p53 antigen displayed on cancer cells in the context of HLA-A*0201 (p53+/HLA-A*0201). We evaluated the safety, pharmacokinetics and pharmacodynamics of ALT-801 in p53+/HLA-A*0201 patients with metastatic malignancies. Experimental Design p53+/HLA-A*0201 patients were treated with ALT-801 on a schedule of 4 daily 15-minute intravenous infusions, then 10 days rest and 4 more daily infusions. Cohorts of patients were treated at 0.015, 0.040, and 0.080 mg/kg/dose. Results Four, sixteen, and six patients were treated at the 0.015, 0.04 and 0.08 mg/kg cohorts, respectively. Two dose limiting toxicities (a grade 4 transient thrombocytopenia and a myocardial infarction) in the 0.08 mg/kg cohort established the maximum tolerated dose (MTD) at 0.04 mg/kg. Patients treated at the MTD experienced toxicities similar to those associated with high-dose IL-2 but of lesser severity. The serum half-life of ALT-801 was 4 hours and ALT-801 serum recovery was as expected based on the dose administered. ALT-801 treatment induced an increase of serum interferon-γ but not tumor necrosis factor-α. Response assessment showed 10 subjects with stable disease at at least 11 weeks, and in one who had melanoma metastasis, there is an ongoing complete absence of identifiable disease after resection of radiographically identified lesions. Conclusion This first-in-man study defines an ALT-801 regimen that can be administered safely and is associated with immunological changes of potential antitumor relevance. PMID:21994418

#whitegenocide, the Alt-right and Conspiracy Theory: How Secrecy and Suspicion Contributed to the Mainstreaming of Hate

Directory of Open Access Journals (Sweden)

Andrew F. Wilson,

2018-02-01

Full Text Available This article considers the relationship between “hashtag activism” as it is currently being used by the alt-right and the tendency to draw on conspiracy theory that Richard Hofstadter identified as being prevalent among what he termed “pseudo-conservatives” half a century earlier. Both the alt-right and Hofstadter’s “pseudo-conservatives” can be characterised by a pronounced populist nationalism that understands its aims as protecting a particular way of life whilst drawing on an aggrieved sense of injustice at being conspired against by an unseen enemy. That this “enemy” is typically foreign in actuality or in spirit confirms the cultural dimension on which their politics is played out. It is argued here that this paranoid populist nationalism has been figuratively drawn upon in the rhetoric of Donald Trump and that this apparent openness to the “pseudo-conservative” discourse on nationalism has provided a bridging effect via which far right elements are seeking to normalize extremist viewpoints

Human learning: Power laws or multiple characteristic time scales?

Directory of Open Access Journals (Sweden)

Gottfried Mayer-Kress

2006-09-01

Full Text Available The central proposal of A. Newell and Rosenbloom (1981 was that the power law is the ubiquitous law of learning. This proposition is discussed in the context of the key factors that led to the acceptance of the power law as the function of learning. We then outline the principles of an epigenetic landscape framework for considering the role of the characteristic time scales of learning and an approach to system identification of the processes of performance dynamics. In this view, the change of performance over time is the product of a superposition of characteristic exponential time scales that reflect the influence of different processes. This theoretical approach can reproduce the traditional power law of practice – within the experimental resolution of performance data sets - but we hypothesize that this function may prove to be a special and perhaps idealized case of learning.

An Empirical Investigation of Individual Differences in Time to Learn

Science.gov (United States)

Anderson, Lorin W.

1976-01-01

Results show that student differences in time-on-task to learn to criterion are alterable and can be minimized over a sequence of learning units given appropriate adaptive learning strategies. (Author/DEP)

Hierarchical Meta-Learning in Time Series Forecasting for Improved Interference-Less Machine Learning

Directory of Open Access Journals (Sweden)

David Afolabi

2017-11-01

Full Text Available The importance of an interference-less machine learning scheme in time series prediction is crucial, as an oversight can have a negative cumulative effect, especially when predicting many steps ahead of the currently available data. The on-going research on noise elimination in time series forecasting has led to a successful approach of decomposing the data sequence into component trends to identify noise-inducing information. The empirical mode decomposition method separates the time series/signal into a set of intrinsic mode functions ranging from high to low frequencies, which can be summed up to reconstruct the original data. The usual assumption that random noises are only contained in the high-frequency component has been shown not to be the case, as observed in our previous findings. The results from that experiment reveal that noise can be present in a low frequency component, and this motivates the newly-proposed algorithm. Additionally, to prevent the erosion of periodic trends and patterns within the series, we perform the learning of local and global trends separately in a hierarchical manner which succeeds in detecting and eliminating short/long term noise. The algorithm is tested on four datasets from financial market data and physical science data. The simulation results are compared with the conventional and state-of-the-art approaches for time series machine learning, such as the non-linear autoregressive neural network and the long short-term memory recurrent neural network, respectively. Statistically significant performance gains are recorded when the meta-learning algorithm for noise reduction is used in combination with these artificial neural networks. For time series data which cannot be decomposed into meaningful trends, applying the moving average method to create meta-information for guiding the learning process is still better than the traditional approach. Therefore, this new approach is applicable to the forecasting

Real-time individualized training vectors for experiential learning.

Energy Technology Data Exchange (ETDEWEB)

Willis, Matt; Tucker, Eilish Marie; Raybourn, Elaine Marie; Glickman, Matthew R.; Fabian, Nathan

2011-01-01

Military training utilizing serious games or virtual worlds potentially generate data that can be mined to better understand how trainees learn in experiential exercises. Few data mining approaches for deployed military training games exist. Opportunities exist to collect and analyze these data, as well as to construct a full-history learner model. Outcomes discussed in the present document include results from a quasi-experimental research study on military game-based experiential learning, the deployment of an online game for training evidence collection, and results from a proof-of-concept pilot study on the development of individualized training vectors. This Lab Directed Research & Development (LDRD) project leveraged products within projects, such as Titan (Network Grand Challenge), Real-Time Feedback and Evaluation System, (America's Army Adaptive Thinking and Leadership, DARWARS Ambush! NK), and Dynamic Bayesian Networks to investigate whether machine learning capabilities could perform real-time, in-game similarity vectors of learner performance, toward adaptation of content delivery, and quantitative measurement of experiential learning.

[Value of non-invasive models of liver fibrosis in judgment of treatment timing in chronic hepatitis B patients with ALT < 2×upper limit of normal].

Science.gov (United States)

Zhou, Q Q; Hu, Y B; Zhou, K; Zhang, W W; Li, M H; Dong, P; Di, J G; Hong, L; Du, Q W; Xie, Y; Sun, Q F

2016-09-20

Objective: To investigate the value of non-invasive liver fibrosis models, FIB-4, S index, aspartate aminotransferase to platelet ratio index(APRI), globulin-platelet(GP)model, aspartate aminotransferase/platelet/gamma-glutamyl transpeptidase/alpha-fetoprotein(APGA), and platelet/age/phosphatase/alpha-fetoprotein/aspartate aminotransferase(PAPAS), in the diagnosis of marked liver fibrosis in chronic hepatitis B(CHB)patients with ALT liver biopsy was performed to obtain pathological results, and routine serological tests were performed, including routine blood test, serum biochemical parameters, hepatitis B virus(HBV)markers, and HBV DNA. According to liver pathology, the patients were divided into non-marked liver fibrosis group(S liver fibrosis group(S≥2)with 65 patients. The non-invasive models for predicting liver fibrosis was established with reference to original articles. SPSS 19.0 software was used for statistical analysis, and the receiver operating characteristic(ROC)curve was used to compare the value of different non-invasive models in predicting marked liver fibrosis in this population. Results: All the non-invasive models had a certain diagnostic value for liver fibrosis degree in these patients, and the areas under the ROC curve for APRI, FIB-4, APGA, S index, PAPAS, and GP model were 0.718, 0.691, 0.758, 0.729, 0.673, and 0.691, respectively. APGA had the largest area under the ROC curve(0.758, 95% CI 0.673-0.844), and gamma-glutamyl transpeptidase was significantly positively correlated with liver fibrosis degree. Conclusion: The non-invasive models of liver fibrosis can identify marked liver fibrosis in CHB patients with ALT liver biopsy to the certain degree.

Naised käsu korras firmade juhatusse! Jah või ei? / Kairi Alt, Aveli Kippari, Karl Koort... [jt.

Index Scriptorium Estoniae

2011-01-01

Küsimusele vastavad Columbus IT Partner Eesti AS personalijuht Kairi Alt, Stele ja Riveli omanik Aveli Kippari, AS Panaviatic'i turundusjuht Karl Koort, OÜ Tarkvara Tehnoloogia Arenduskeskuse tegevjuht Indrek Vainu, AS PricewaterhouseCoopers auditiosakonna direktor Eva Jansen, Kalev Chocolate Factory tootmisjuht Hardo Reinike

Single machine total completion time minimization scheduling with a time-dependent learning effect and deteriorating jobs

Science.gov (United States)

Wang, Ji-Bo; Wang, Ming-Zheng; Ji, Ping

2012-05-01

In this article, we consider a single machine scheduling problem with a time-dependent learning effect and deteriorating jobs. By the effects of time-dependent learning and deterioration, we mean that the job processing time is defined by a function of its starting time and total normal processing time of jobs in front of it in the sequence. The objective is to determine an optimal schedule so as to minimize the total completion time. This problem remains open for the case of -1 < a < 0, where a denotes the learning index; we show that an optimal schedule of the problem is V-shaped with respect to job normal processing times. Three heuristic algorithms utilising the V-shaped property are proposed, and computational experiments show that the last heuristic algorithm performs effectively and efficiently in obtaining near-optimal solutions.

Time to rethink the neural mechanisms of learning and memory.

Science.gov (United States)

Gallistel, Charles R; Balsam, Peter D

2014-02-01

Most studies in the neurobiology of learning assume that the underlying learning process is a pairing - dependent change in synaptic strength that requires repeated experience of events presented in close temporal contiguity. However, much learning is rapid and does not depend on temporal contiguity, which has never been precisely defined. These points are well illustrated by studies showing that the temporal relations between events are rapidly learned- even over long delays- and that this knowledge governs the form and timing of behavior. The speed with which anticipatory responses emerge in conditioning paradigms is determined by the information that cues provide about the timing of rewards. The challenge for understanding the neurobiology of learning is to understand the mechanisms in the nervous system that encode information from even a single experience, the nature of the memory mechanisms that can encode quantities such as time, and how the brain can flexibly perform computations based on this information. Copyright © 2013 Elsevier Inc. All rights reserved.

On learning science and pseudoscience from prime-time television programming

Science.gov (United States)

Whittle, Christopher Henry

The purpose of the present dissertation is to determine whether the viewing of two particular prime-time television programs, ER and The X-Files, increases viewer knowledge of science and to identify factors that may influence learning from entertainment television programming. Viewer knowledge of scientific dialogue from two science-based prime-time television programs, ER, a serial drama in a hospital emergency room and The X-Files, a drama about two Federal Bureau of Investigation agents who pursue alleged extraterrestrial life and paranormal activity, is studied. Level of viewing, education level, science education level, experiential factors, level of parasocial interaction, and demographic characteristics are assessed as independent variables affecting learning from entertainment television viewing. The present research involved a nine-month long content analysis of target television program dialogue and data collection from an Internet-based survey questionnaire posted to target program-specific on-line "chat" groups. The present study demonstrated that entertainment television program viewers incidentally learn science from entertainment television program dialogue. The more they watch, the more they learn. Viewing a pseudoscientific fictional television program does necessarily influence viewer beliefs in pseudoscience. Higher levels of formal science study are reflected in more science learning and less learning of pseudoscience from entertainment television program viewing. Pseudoscience learning from entertainment television programming is significantly related to experience with paranormal phenomena, higher levels of viewer parasocial interaction, and specifically, higher levels of cognitive parasocial interaction. In summary, the greater a viewer's understanding of science the more they learn when they watch their favorite science-based prime-time television programs. Viewers of pseudoscience-based prime-time television programming with higher levels

A presentation system for just-in-time learning in radiology.

Science.gov (United States)

Kahn, Charles E; Santos, Amadeu; Thao, Cheng; Rock, Jayson J; Nagy, Paul G; Ehlers, Kevin C

2007-03-01

There is growing interest in bringing medical educational materials to the point of care. We sought to develop a system for just-in-time learning in radiology. A database of 34 learning modules was derived from previously published journal articles. Learning objectives were specified for each module, and multiple-choice test items were created. A web-based system-called TEMPO-was developed to allow radiologists to select and view the learning modules. Web services were used to exchange clinical context information between TEMPO and the simulated radiology work station. Preliminary evaluation was conducted using the System Usability Scale (SUS) questionnaire. TEMPO identified learning modules that were relevant to the age, sex, imaging modality, and body part or organ system of the patient being viewed by the radiologist on the simulated clinical work station. Users expressed a high degree of satisfaction with the system's design and user interface. TEMPO enables just-in-time learning in radiology, and can be extended to create a fully functional learning management system for point-of-care learning in radiology.

Simple synthesis of P(Cbz-alt-TBT) and PCDTBT by combining direct arylation with suzuki polycondensation of heteroaryl chlorides.

Science.gov (United States)

Lombeck, Florian; Matsidik, Rukiya; Komber, Hartmut; Sommer, Michael

2015-01-01

Direct arylation (DA) of 2-chlorothiophene and 2-chloro-3-hexylthiophene with 4,7-dibromo-2,1,3-benzothiadiazole is used to synthesize 4,7-bis(5-chloro-2-thienyl)-2,1,3-benzothiadiazole (TBTCl2) and 4,7-bis(5-chloro-4-hexyl-2-thienyl)-2,1,3-benzothiadiazole (DH-TBTCl2) in one step. Suitable conditions of the Suzuki polycondensations (SPC) of TBTCl2 and DH-TBTCl2 with the carbazole comonomer CbzPBE2 are established, furnishing PCDTBT and P(Cbz-alt-TBT) with high molecular weight and yield. Compared with control samples made from the corresponding dibromides, high-temperature NMR and UV-vis spectroscopy indicate similar properties for PCDTBT but an increased content of Cbz-Cbz homocouplings for P(Cbz-alt-TBT). © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Nuclear power plant monitoring using real-time learning neural network

International Nuclear Information System (INIS)

Nabeshima, Kunihiko; Tuerkcan, E.; Ciftcioglu, O.

1994-01-01

In the present research, artificial neural network (ANN) with real-time adaptive learning is developed for the plant wide monitoring of Borssele Nuclear Power Plant (NPP). Adaptive ANN learning capability is integrated to the monitoring system so that robust and sensitive on-line monitoring is achieved in real-time environment. The major advantages provided by ANN are that system modelling is formed by means of measurement information obtained from a multi-output process system, explicit modelling is not required and the modelling is not restricted to linear systems. Also ANN can respond very fast to anomalous operational conditions. The real-time ANN learning methodology with adaptive real-time monitoring capability is described below for the wide-range and plant-wide data from an operating nuclear power plant. The layered neural network with error backpropagation algorithm for learning has three layers. The network type is auto-associative, inputs and outputs are exactly the same, using 12 plant signals. (author)

Effect of chronotype and student learning time on mathematical ability based on self-regulated learning

Science.gov (United States)

Ratnaningsih, N.; El Akbar, R. R.; Hidayat, E.

2018-05-01

One of ways to improve students' learning ability is conduct a research, with purpose to obtain a method to improve students' ability. Research often carried out on the modification of teaching methods, uses of teaching media, motivation, interests and talents of students. Research related to the internal condition of students becomes very interesting to studied, including research on circadian rhythms. Every person in circadian rhythms has its own Chronotype, which divided into two types namely early type and night late type. Chronotype affects the comfort in activity, for example a person with Chronotype category of early type tends to be more comfort in daytime activities. The purpose of this study is to examine the conditions of students, related Chronotype suitable or appropriate for student learning time. This suitability then studied in relation to the ability of learning mathematics with self- regulated learning approach. This study consists of three stages; (i) student Chronotype measurement, (ii) data retrieval, and (iii) analysis of research results. The results show the relationship between the students' learning ability in mathematics to learning time corresponding to Chronotype.

In real time: exploring nursing students' learning during an international experience.

Science.gov (United States)

Afriyie Asenso, Barbara; Reimer-Kirkham, Sheryl; Astle, Barbara

2013-10-11

Abstract Nursing education has increasingly turned to international learning experiences to educate students who are globally minded and aware of social injustices in local and global communities. To date, research with international learning experiences has focused on the benefits for the students participating, after they have completed the international experience. The purpose of this qualitative study was to explore how nursing students learn during the international experience. The sample consisted of eight nursing students who enrolled in an international learning experience, and data were collected in "real time" in Zambia. The students were observed during learning activities and were interviewed three times. Three major themes emerged from the thematic analysis: expectations shaped students' learning, engagement facilitated learning, and critical reflection enhanced learning. Implications are discussed, related to disrupting media representations of Africa that shape students' expectations, and educational strategies for transformative learning and global citizenship.

Towards Real-Time Speech Emotion Recognition for Affective E-Learning

Science.gov (United States)

Bahreini, Kiavash; Nadolski, Rob; Westera, Wim

2016-01-01

This paper presents the voice emotion recognition part of the FILTWAM framework for real-time emotion recognition in affective e-learning settings. FILTWAM (Framework for Improving Learning Through Webcams And Microphones) intends to offer timely and appropriate online feedback based upon learner's vocal intonations and facial expressions in order…

Time representation in reinforcement learning models of the basal ganglia

Directory of Open Access Journals (Sweden)

Samuel Joseph Gershman

2014-01-01

Full Text Available Reinforcement learning models have been influential in understanding many aspects of basal ganglia function, from reward prediction to action selection. Time plays an important role in these models, but there is still no theoretical consensus about what kind of time representation is used by the basal ganglia. We review several theoretical accounts and their supporting evidence. We then discuss the relationship between reinforcement learning models and the timing mechanisms that have been attributed to the basal ganglia. We hypothesize that a single computational system may underlie both reinforcement learning and interval timing—the perception of duration in the range of seconds to hours. This hypothesis, which extends earlier models by incorporating a time-sensitive action selection mechanism, may have important implications for understanding disorders like Parkinson's disease in which both decision making and timing are impaired.

Understanding the effects of time on collaborative learning processes in problem based learning: a mixed methods study.

Science.gov (United States)

Hommes, J; Van den Bossche, P; de Grave, W; Bos, G; Schuwirth, L; Scherpbier, A

2014-10-01

Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning processes developed within and over three periods in the first 1,5 study years of an undergraduate curriculum. Next, a qualitative study using semi-structured individual interviews focused on detailed development of group processes driving collaborative learning during one period in seven tutorial groups. The hierarchic multilevel analyses of the quantitative data showed that a varying combination of group processes developed within and over the three observed periods. The qualitative study illustrated development in psychological safety, interdependence, potency, group learning behaviour, social and task cohesion. Two new processes emerged: 'transactive memory' and 'convergence in mental models'. The results indicate that groups are dynamic social systems with numerous contextual influences. Future research should thus include time as an important influence on collaborative learning. Practical implications are discussed.

Adaptation and learning: characteristic time scales of performance dynamics.

Science.gov (United States)

Newell, Karl M; Mayer-Kress, Gottfried; Hong, S Lee; Liu, Yeou-Teh

2009-12-01

A multiple time scales landscape model is presented that reveals structures of performance dynamics that were not resolved in the traditional power law analysis of motor learning. It shows the co-existence of separate processes during and between practice sessions that evolve in two independent dimensions characterized by time scales that differ by about an order of magnitude. Performance along the slow persistent dimension of learning improves often as much and sometimes more during rest (memory consolidation and/or insight generation processes) than during a practice session itself. In contrast, the process characterized by the fast, transient dimension of adaptation reverses direction between practice sessions, thereby significantly degrading performance at the beginning of the next practice session (warm-up decrement). The theoretical model fits qualitatively and quantitatively the data from Snoddy's [Snoddy, G. S. (1926). Learning and stability. Journal of Applied Psychology, 10, 1-36] classic learning study of mirror tracing and other averaged and individual data sets, and provides a new account of the processes of change in adaptation and learning. 2009 Elsevier B.V. All rights reserved.

Dissociable effects of practice variability on learning motor and timing skills.

Science.gov (United States)

Caramiaux, Baptiste; Bevilacqua, Frédéric; Wanderley, Marcelo M; Palmer, Caroline

2018-01-01

Motor skill acquisition inherently depends on the way one practices the motor task. The amount of motor task variability during practice has been shown to foster transfer of the learned skill to other similar motor tasks. In addition, variability in a learning schedule, in which a task and its variations are interweaved during practice, has been shown to help the transfer of learning in motor skill acquisition. However, there is little evidence on how motor task variations and variability schedules during practice act on the acquisition of complex motor skills such as music performance, in which a performer learns both the right movements (motor skill) and the right time to perform them (timing skill). This study investigated the impact of rate (tempo) variability and the schedule of tempo change during practice on timing and motor skill acquisition. Complete novices, with no musical training, practiced a simple musical sequence on a piano keyboard at different rates. Each novice was assigned to one of four learning conditions designed to manipulate the amount of tempo variability across trials (large or small tempo set) and the schedule of tempo change (randomized or non-randomized order) during practice. At test, the novices performed the same musical sequence at a familiar tempo and at novel tempi (testing tempo transfer), as well as two novel (but related) sequences at a familiar tempo (testing spatial transfer). We found that practice conditions had little effect on learning and transfer performance of timing skill. Interestingly, practice conditions influenced motor skill learning (reduction of movement variability): lower temporal variability during practice facilitated transfer to new tempi and new sequences; non-randomized learning schedule improved transfer to new tempi and new sequences. Tempo (rate) and the sequence difficulty (spatial manipulation) affected performance variability in both timing and movement. These findings suggest that there is a

Diorganosilacetylene-alt-diorganosilvinylene polymers and a process densifying porous silicon-carbide bodies

Science.gov (United States)

Barton, Thomas J.; Ijadi-Maghsoodi, Sina; Pang, Yi

1994-05-17

The present invention provides linear organosilicon polymers including acetylene and vinylene moieties, and a process for their preparation. These diorganosilacetylene-alt-diorganosilvinylene linear polymers can be represented by the formula: --[--(R.sup.1)(R.sup.2)Si--C.tbd.C--(R.sup.3)(R.sup.4)Si--CH=CH--].sub.n-- , wherein n.gtoreq.2; and each R.sup.1, R.sup.2, R.sup.3, and R.sup.4 is independently selected from the group consisting of hydrogen, halogen, alkyl, alkenyl, aryl, and aralkyl radicals. The polymers are soluble in organic solvents, air stable, and can be pulled into fibers or cast into films. They can be thermally converted into silicon carbide ceramic materials.

Common reference intervals for aspartate aminotransferase (AST), alanine aminotransferase (ALT) and γ-glutamyl transferase (GGT) in serum: results from an IFCC multicenter study.

Science.gov (United States)

Ceriotti, Ferruccio; Henny, Joseph; Queraltó, Josep; Ziyu, Shen; Özarda, Yeşim; Chen, Baorong; Boyd, James C; Panteghini, Mauro

2010-11-01

Aspartate aminotransferase (AST), alanine aminotransferase (ALT) and γ-glutamyl transferase (GGT) measurements are important for the assessment of liver damage. The aim of this study was to define the reference intervals (RIs) for these enzymes in adults, paying attention to standardization of the methods used and careful selection of the reference population. AST, ALT and GGT were measured with commercial analytical systems standardized to the IFCC-recommended reference measurement systems. Three centers (two in Italy and one in China) measured their own freshly collected samples; one of these centers also measured frozen samples from the Nordic Countries RI Project and from a Turkish center. RIs were generated using non-parametric techniques from the results of 765 individuals (411 females and 354 males, 18-85 years old) selected on the basis of the results of other laboratory tests and a specific questionnaire. AST results from the four regions (Milan, Beijing, Bursa and Nordic Countries) were statistically different, but these differences were too small to be clinically relevant. Likewise, differences between the upper reference limits for genders was only 1.7 U/L (0.03 μkat/L), allowing a single RI of 11-34 U/L (0.18-0.57 μkat/L) to be defined. Interregional differences were not statistically significant for ALT, but partitioning was required due to significant gender differences. RIs for ALT were 8-41 U/L (0.13-0.68 μkat/L) for females and 9-59 U/L (0.15-0.99 μkat/L) for males, respectively. The upper reference limits for GGT from the Nordic Country population were higher than those from the other three regions and results from this group were excluded from final calculations. The GGT RIs were 6-40 U/L (0.11-0.66 μkat/L) for females and 12-68 U/L (0.20- 1.13 μkat/L) for males, respectively. For AST and ALT, the implementation of common RIs appears to be possible, because no differences between regions were observed. However, a common RI for GGT that is

A novel time series link prediction method: Learning automata approach

Science.gov (United States)

Moradabadi, Behnaz; Meybodi, Mohammad Reza

2017-09-01

Link prediction is a main social network challenge that uses the network structure to predict future links. The common link prediction approaches to predict hidden links use a static graph representation where a snapshot of the network is analyzed to find hidden or future links. For example, similarity metric based link predictions are a common traditional approach that calculates the similarity metric for each non-connected link and sort the links based on their similarity metrics and label the links with higher similarity scores as the future links. Because people activities in social networks are dynamic and uncertainty, and the structure of the networks changes over time, using deterministic graphs for modeling and analysis of the social network may not be appropriate. In the time-series link prediction problem, the time series link occurrences are used to predict the future links In this paper, we propose a new time series link prediction based on learning automata. In the proposed algorithm for each link that must be predicted there is one learning automaton and each learning automaton tries to predict the existence or non-existence of the corresponding link. To predict the link occurrence in time T, there is a chain consists of stages 1 through T - 1 and the learning automaton passes from these stages to learn the existence or non-existence of the corresponding link. Our preliminary link prediction experiments with co-authorship and email networks have provided satisfactory results when time series link occurrences are considered.

Between-Trial Forgetting Due to Interference and Time in Motor Adaptation.

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Sungshin Kim

Full Text Available Learning a motor task with temporally spaced presentations or with other tasks intermixed between presentations reduces performance during training, but can enhance retention post training. These two effects are known as the spacing and contextual interference effect, respectively. Here, we aimed at testing a unifying hypothesis of the spacing and contextual interference effects in visuomotor adaptation, according to which forgetting between trials due to either spaced presentations or interference by another task will promote between-trial forgetting, which will depress performance during acquisition, but will promote retention. We first performed an experiment with three visuomotor adaptation conditions: a short inter-trial-interval (ITI condition (SHORT-ITI; a long ITI condition (LONG-ITI; and an alternating condition with two alternated opposite tasks (ALT, with the same single-task ITI as in LONG-ITI. In the SHORT-ITI condition, there was fastest increase in performance during training and largest immediate forgetting in the retention tests. In contrast, in the ALT condition, there was slowest increase in performance during training and little immediate forgetting in the retention tests. Compared to these two conditions, in the LONG-ITI, we found intermediate increase in performance during training and intermediate immediate forgetting. To account for these results, we fitted to the data six possible adaptation models with one or two time scales, and with interference in the fast, or in the slow, or in both time scales. Model comparison confirmed that two time scales and some degree of interferences in either time scale are needed to account for our experimental results. In summary, our results suggest that retention following adaptation is modulated by the degree of between-trial forgetting, which is due to time-based decay in single adaptation task and interferences in multiple adaptation tasks.

Learning Styles of Medical Students Change in Relation to Time

Science.gov (United States)

Gurpinar, Erol; Bati, Hilal; Tetik, Cihat

2011-01-01

The aim of the present study was to investigate if any changes exist in the learning styles of medical students over time and in relation to different curriculum models with these learning styles. This prospective cohort study was conducted in three different medical faculties, which implement problem-based learning (PBL), hybrid, and integrated…

Generalization bounds of ERM-based learning processes for continuous-time Markov chains.

Science.gov (United States)

Zhang, Chao; Tao, Dacheng

2012-12-01

Many existing results on statistical learning theory are based on the assumption that samples are independently and identically distributed (i.i.d.). However, the assumption of i.i.d. samples is not suitable for practical application to problems in which samples are time dependent. In this paper, we are mainly concerned with the empirical risk minimization (ERM) based learning process for time-dependent samples drawn from a continuous-time Markov chain. This learning process covers many kinds of practical applications, e.g., the prediction for a time series and the estimation of channel state information. Thus, it is significant to study its theoretical properties including the generalization bound, the asymptotic convergence, and the rate of convergence. It is noteworthy that, since samples are time dependent in this learning process, the concerns of this paper cannot (at least straightforwardly) be addressed by existing methods developed under the sample i.i.d. assumption. We first develop a deviation inequality for a sequence of time-dependent samples drawn from a continuous-time Markov chain and present a symmetrization inequality for such a sequence. By using the resultant deviation inequality and symmetrization inequality, we then obtain the generalization bounds of the ERM-based learning process for time-dependent samples drawn from a continuous-time Markov chain. Finally, based on the resultant generalization bounds, we analyze the asymptotic convergence and the rate of convergence of the learning process.

Alternagin-C (ALT-C), a Disintegrin-Like Cys-Rich Protein Isolated from the Venom of the Snake Rhinocerophis alternatus, Stimulates Angiogenesis and Antioxidant Defenses in the Liver of Freshwater Fish, Hoplias malabaricus.

Science.gov (United States)

Monteiro, Diana Amaral; Selistre-de-Araújo, Heloisa Sobreiro; Tavares, Driele; Fernandes, Marisa Narciso; Kalinin, Ana Lúcia; Rantin, Francisco Tadeu

2017-09-28

Alternagin-C (ALT-C) is a disintegrin-like protein isolated from Rhinocerophis alternatus snake venom, which induces endothelial cell proliferation and angiogenesis. The aim of this study was to evaluate the systemic effects of a single dose of alternagin-C (0.5 mg·kg -1 , via intra-arterial) on oxidative stress biomarkers, histological alterations, vascular endothelial growth factor (VEGF) production, and the degree of vascularization in the liver of the freshwater fish traíra, Hoplias malabaricus , seven days after the initiation of therapy. ALT-C treatment increased VEGF levels and hepatic angiogenesis. ALT-C also enhanced hepatic antioxidant enzymes activities such as superoxide dismutase, catalase, glutathione peroxidase, and glutathione reductase, decreasing the basal oxidative damage to lipids and proteins in the fish liver. These results indicate that ALT-C improved hepatic tissue and may play a crucial role in tissue regeneration mechanisms.

DeepRT: deep learning for peptide retention time prediction in proteomics

OpenAIRE

Ma, Chunwei; Zhu, Zhiyong; Ye, Jun; Yang, Jiarui; Pei, Jianguo; Xu, Shaohang; Zhou, Ruo; Yu, Chang; Mo, Fan; Wen, Bo; Liu, Siqi

2017-01-01

Accurate predictions of peptide retention times (RT) in liquid chromatography have many applications in mass spectrometry-based proteomics. Herein, we present DeepRT, a deep learning based software for peptide retention time prediction. DeepRT automatically learns features directly from the peptide sequences using the deep convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) model, which eliminates the need to use hand-crafted features or rules. After the feature learning, pr...

Solvent effect on post-irradiation grafting of styrene onto poly(ethylene-alt-tetrafluoroethylene) (ETFE) films

Science.gov (United States)

Napoleão Geraldes, Adriana; Augusto Zen, Heloísa; Ribeiro, Geise; Fernandes Parra, Duclerc; Benévolo Lugão, Ademar

2013-03-01

Radiation-induced grafting of styrene onto ETFE films in different solvent was investigated after simultaneous irradiation (in post-irradiation condition) using a 60Co source. Grafting of styrene followed by sulfonation onto poly(ethylene-alt-tetrafluoroethylene) (ETFE) are currently studied for synthesis of ion exchange membranes. The ETFE films were immersed in styrene/toluene, styrene/methanol and styrene/isopropyl alcohol and irradiated at 20 and 100 kGy doses at room temperature. The post-irradiation time was established at 14 day and the grafting degree was evaluated. The grafted films were sulfonated using chlorosulfonic acid and 1,2-dichloroethane 20:80 (v/v) at room temperature for 5 h. The degree of grafting (DOG) was determined gravimetrically and physical or chemical changes were evaluated by differential scanning calorimeter analysis (DSC), thermogravimetric analysis (TGA) and scanning electron microscopy (SEM). The ion exchange capacity (IEC) values showed the best performance of sulfonation for ETFE membranes grafted in toluene solvent. Surface images of the grafted films by SEM technique have presented a strong effect of the solvents on the films morphology.

Supervised Learning Using Spike-Timing-Dependent Plasticity of Memristive Synapses.

Science.gov (United States)

Nishitani, Yu; Kaneko, Yukihiro; Ueda, Michihito

2015-12-01

We propose a supervised learning model that enables error backpropagation for spiking neural network hardware. The method is modeled by modifying an existing model to suit the hardware implementation. An example of a network circuit for the model is also presented. In this circuit, a three-terminal ferroelectric memristor (3T-FeMEM), which is a field-effect transistor with a gate insulator composed of ferroelectric materials, is used as an electric synapse device to store the analog synaptic weight. Our model can be implemented by reflecting the network error to the write voltage of the 3T-FeMEMs and introducing a spike-timing-dependent learning function to the device. An XOR problem was successfully demonstrated as a benchmark learning by numerical simulations using the circuit properties to estimate the learning performance. In principle, the learning time per step of this supervised learning model and the circuit is independent of the number of neurons in each layer, promising a high-speed and low-power calculation in large-scale neural networks.

Understanding the Effects of Time on Collaborative Learning Processes in Problem Based Learning: A Mixed Methods Study

Science.gov (United States)

Hommes, J.; Van den Bossche, P.; de Grave, W.; Bos, G.; Schuwirth, L.; Scherpbier, A.

2014-01-01

Little is known how time influences collaborative learning groups in medical education. Therefore a thorough exploration of the development of learning processes over time was undertaken in an undergraduate PBL curriculum over 18 months. A mixed-methods triangulation design was used. First, the quantitative study measured how various learning…

Dinámica del virus de la hepatitis C con carga viral y ALT y monitoreo del daño hepático libre de biopsias Dinámica del virus de la hepatitis C con carga viral y ALT y monitoreo del daño hepático libre de biopsias

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Justino Alavez Domínguez

2006-12-01

Full Text Available Se compara el modelo estándar reducido de tres poblaciones (hepatocitos sanos y enfermos, y carga viral para el estudio de la dinámica del virus de la hepatitis C (VHC, con el modelo de la dinámica viral con alanina aminotransferasa (ALT. En términos del parámetro umbral que es el mismo para ambos modelos, se determina la existencia y estabilidad del estado de equilibrio endémico, así como la estabilidad global del estado de equilibrio del individuo sano. Por otro lado, se presenta un estudio experimental que muestra que es posible monitorear el daño hepático sin biopsias, mediante la estimación numérica de los parámetros de los modelos, considerando solamente mediciones de la carga viral, de los niveles de ALT y una valoración razonable del daño hepático pretratamiento.aThe standard model of three populations (healthy and unhealthy hepatocytes, and viral load for the study of the dynamics of hepatitis C virus (HCV, is compared with viral dynamic model with alanine aminotransferase (ALT. In terms of the threshold parameter that it’s the same for both models, the existence and local asymptotic stability of the endemically infected equilibrium steady state, and the global stability of the healthy individual equilibrium steady state are established. Furthermore, an experimental study is presented showing that it’s possible to carry out the monitoring of hepatic damage biopsies free by numerical parameter estimation, considering only viral load and ALT levels measurements and a reasonable assessment of pretreatment hepatic damage.

Comparing the characteristics of highly cited titles and highly alted titles

Energy Technology Data Exchange (ETDEWEB)

Didegah, F.; Bowman, T.D.; Bowman, S.; Hartley, J.

2016-07-01

This study examines differences in the types of titles for articles that show high altmetric activity (highly alted articles) versus highly cited articles. This work expands on previous research on document titles in combination with a grounded theory approach to develop a codebook in which articles were manually coded based on 11 characteristics. The results show that there are differences and similarities in titles across many of the examined characteristics; highly cited titles and highly mentioned titles on Wikipedia have some similar characteristics such as they have the the highest percentage of substantive words; in addition, there are no or very few titles referencing outside or with humor/lightness on both platforms. Twitter and Facebook also showed some similarities having the highest percentage of humorous/light titles and lowest percentage of substantive words in their titles. (Author)

Time and Associative Learning.

Science.gov (United States)

Balsam, Peter D; Drew, Michael R; Gallistel, C R

2010-01-01

In a basic associative learning paradigm, learning is said to have occurred when the conditioned stimulus evokes an anticipatory response. This learning is widely believed to depend on the contiguous presentation of conditioned and unconditioned stimulus. However, what it means to be contiguous has not been rigorously defined. Here we examine the empirical bases for these beliefs and suggest an alternative view based on the hypothesis that learning about the temporal relationships between events determines the speed of emergence, vigor and form of conditioned behavior. This temporal learning occurs very rapidly and prior to the appearance of the anticipatory response. The temporal relations are learned even when no anticipatory response is evoked. The speed with which an anticipatory response emerges is proportional to the informativeness of the predictive cue (CS) regarding the rate of occurrence of the predicted event (US). This analysis gives an account of what we mean by "temporal pairing" and is in accord with the data on speed of acquisition and basic findings in the cue competition literature. In this account, learning depends on perceiving and encoding temporal regularities rather than stimulus contiguities.

Potential of Red Spinach Leaves Ethanolic Extract (Amaranthus tricolor L. as a Complementary Therapy For Hiperlipidemia: Study in Vivo of Histopathologic and Activity of Alanin Aminotransferase (ALT

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Dimas Adhi Pradana

2016-12-01

Full Text Available This study was conducted to determine the potential of ethanolic extract of red spinach leaves (Amaranthus tricolor L. with control quality as a complementary treatment of hyperlipidemic based on histopathology and activity of alanin aminotrasferase (ALT. A total of 24 male Wistar rats were randomized in 6 groups: normal group; positive control group were given the drug simvastatin; negative control group; 1st treatment group was given extract at dose of 400mg/kgBW rat, 2nd group was given extract at dose of 400mg/kgBW rat and simvastatin dose 0.18 mg/kgBW rat; 3rd treatment groups were given extract at dose of 800mg/kgBW rat and simvastatin dose 0.18 mg/kgBW rat. Induction hyperlipidemic using high fat diet and poloxamer. The data obtained were tested normality with the Shapiro-Wilk test. Statistical analysis using Oneway ANOVA and Post-Hoc Tukey HSD to determine the significance of differences between groups for ALT parameter. Result show the use of ethanolic extract of red spinach leaves can reduce fatty liver condition based on decreased level of ALT and liver histopathologic. It is concluded that the ethanolic extract of red spinach leaves dose 400mg/kgBW rat combine with simvastatin can reduce activity of ALT until 31.57 U1-1.

Cluster analysis of activity-time series in motor learning

DEFF Research Database (Denmark)

Balslev, Daniela; Nielsen, Finn Årup; Frutiger, Sally A.

2002-01-01

Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel...... practice-related activity in a fronto-parieto-cerebellar network, in agreement with previous studies of motor learning. These voxels were separated from a group of voxels showing an unspecific time-effect and another group of voxels, whose activation was an artifact from smoothing. Hum. Brain Mapping 15...

Trip Travel Time Forecasting Based on Selective Forgetting Extreme Learning Machine

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Zhiming Gui

2014-01-01

Full Text Available Travel time estimation on road networks is a valuable traffic metric. In this paper, we propose a machine learning based method for trip travel time estimation in road networks. The method uses the historical trip information extracted from taxis trace data as the training data. An optimized online sequential extreme machine, selective forgetting extreme learning machine, is adopted to make the prediction. Its selective forgetting learning ability enables the prediction algorithm to adapt to trip conditions changes well. Experimental results using real-life taxis trace data show that the forecasting model provides an effective and practical way for the travel time forecasting.

Synthesis and Properties of the Metallo-Supramolecular Polymer Hydrogel Poly[methyl vinyl ether-alt-mono-sodium maleate]∙AgNO3

KAUST Repository

Al-Dossary, Mona S.

2014-05-01

Gels are a special class of materials which are composed of 3D networks of crosslinked polymer chains that encapsulate liquid/air in the matrix. They can be classified into organogels or hydrogels (organic solvent for organogel and water for hydrogel). For hydrogels that contain metallic elements in the form of ions, the term of metallo-supramolecular polymer hydrogel (MSPHG) is often used. The aim of this project is to develop a kind of new MSPHG and investigate its properties and possible applications. The commercial polymeric anhydride poly(methyl vinyl ether-alt-maleic anhydride) (PVM/MA) is converted by reaction with NaOH to give poly(methyl vinyl ether-alt-monosodium maleate) (PVM/Na-MA). By addition of AgNO3-solution, the formation of the silver(I) supramolecular polymer hydrogel poly[methyl vinyl ether-alt-mono-sodium maleate]∙AgNO3 is obtained. Freeze-dried samples of the hydrogel show a mesoporous network of polycarboxylate ligands that are crosslinked by silver(I) cations. The supercritical CO2 dried silver(I) hydrogel was characterized by FT-IR, SEM-EDAX, TEM, TGA and Physical adsorption (BET) measurements. The intact silver(I) hydrogel was characterized by cryo-SEM. In the intact hydrogel, ion-exchange studies are reported and it is shown that Ag+ ions can be exchanged by copper(II) cations without disintegration of the hydrogel. The silver(I) hydrogel shows effective antibacterial activity and potential application as burn wound dressing.

Time course influences transfer of visual perceptual learning across spatial location.

Science.gov (United States)

Larcombe, S J; Kennard, C; Bridge, H

2017-06-01

Visual perceptual learning describes the improvement of visual perception with repeated practice. Previous research has established that the learning effects of perceptual training may be transferable to untrained stimulus attributes such as spatial location under certain circumstances. However, the mechanisms involved in transfer have not yet been fully elucidated. Here, we investigated the effect of altering training time course on the transferability of learning effects. Participants were trained on a motion direction discrimination task or a sinusoidal grating orientation discrimination task in a single visual hemifield. The 4000 training trials were either condensed into one day, or spread evenly across five training days. When participants were trained over a five-day period, there was transfer of learning to both the untrained visual hemifield and the untrained task. In contrast, when the same amount of training was condensed into a single day, participants did not show any transfer of learning. Thus, learning time course may influence the transferability of perceptual learning effects. Copyright © 2017 Elsevier Ltd. All rights reserved.

A Parametric Learning and Identification Based Robust Iterative Learning Control for Time Varying Delay Systems

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Lun Zhai

2014-01-01

Full Text Available A parametric learning based robust iterative learning control (ILC scheme is applied to the time varying delay multiple-input and multiple-output (MIMO linear systems. The convergence conditions are derived by using the H∞ and linear matrix inequality (LMI approaches, and the convergence speed is analyzed as well. A practical identification strategy is applied to optimize the learning laws and to improve the robustness and performance of the control system. Numerical simulations are illustrated to validate the above concepts.

Altıntop Dilim Konservesi Üretiminde Enzim Kullanımı: I. Kabuk Soyma

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Osman Kola

2015-02-01

Full Text Available Bu çalışmada; altıntop dilim konservesi yapımında kabuk soyma ve dilim zarının uzaklaştırılması amaçlarıyla enzim çözeltisi kullanılmasının etkileri araştırılmıştır. Bunun için: önce, çözeltinin meyve kabuğu içine nüfuz ettirilmesi amacıyla uygulanabilecek uygun işlemler, sonra da, enzim çözeltisi uygulamasının kabuk soyma işlemleri üzerindeki etkileri araştırılmıştır. Kabuk soyma işleminde meyvenin; 95 °C sıcaklıktaki suda 5 dakika bekletildikten sonra kapak açma ve kabuk çizme işlemlerini takiben, su ya da %0.5 düzeyindeki Peelzym II çözeltisi içine konulan meyvelerin 1.76 kg/cm2 (25 psi basınç altında 60 saniye süreyle işleme tabi tutulmasının, hem işlemin etkinliği hem de son ürünün kalitesi açısından uygun olabileceği sonucuna varılmıştır.

Learning of pitch and time structures in an artificial grammar setting.

Science.gov (United States)

Prince, Jon B; Stevens, Catherine J; Jones, Mari Riess; Tillmann, Barbara

2018-04-12

Despite the empirical evidence for the power of the cognitive capacity of implicit learning of structures and regularities in several modalities and materials, it remains controversial whether implicit learning extends to the learning of temporal structures and regularities. We investigated whether (a) an artificial grammar can be learned equally well when expressed in duration sequences as when expressed in pitch sequences, (b) learning of the artificial grammar in either duration or pitch (as the primary dimension) sequences can be influenced by the properties of the secondary dimension (invariant vs. randomized), and (c) learning can be boosted when the artificial grammar is expressed in both pitch and duration. After an exposure phase with grammatical sequences, learning in a subsequent test phase was assessed in a grammaticality judgment task. Participants in both the pitch and duration conditions showed incidental (not fully implicit) learning of the artificial grammar when the secondary dimension was invariant, but randomizing the pitch sequence prevented learning of the artificial grammar in duration sequences. Expressing the artificial grammar in both pitch and duration resulted in disproportionately better performance, suggesting an interaction between the learning of pitch and temporal structure. The findings are relevant to research investigating the learning of temporal structures and the learning of structures presented simultaneously in 2 dimensions (e.g., space and time, space and objects). By investigating learning, the findings provide further insight into the potential specificity of pitch and time processing, and their integrated versus independent processing, as previously debated in music cognition research. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

The Impact of Students' Temporal Perspectives on Time-on-Task and Learning Performance in Game Based Learning

Science.gov (United States)

Romero, Margarida; Usart, Mireia

2013-01-01

The use of games for educational purposes has been considered as a learning methodology that attracts the students' attention and may allow focusing individuals on the learning activity through the [serious games] SG game dynamic. Based on the hypothesis that students' Temporal Perspective has an impact on learning performance and time-on-task,…

CASUÍSTICA DE ALTE NUM HOSPITAL TERCIÁRIO – O QUE MUDOU NOS ÚLTIMOS 5 ANOS?

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Daniel Meireles

2016-07-01

Conclusões: O ALTE cursa frequentemente com evolução favorável. Apesar de não haver recomendação ou consensos na literatura para a investigação etiológica, esta mantém-se uma prática comum, sem aparente modificação na abordagem nos últimos anos, em comparação com estudo prévio realizado em 2009.

CORRELATION OF INTEREST TO LEARN AND USE TIME LEARNING WITH LEARNING ACHIEVEMENT AUTOMOTIVE ELECTRICAL IN CLASS XII LIGHT VEHICLE ENGINEERING SMK PIRI I YOGYAKARTA ACADEMIC YEAR 2013/2014

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Ari Pujiatmoko

2014-06-01

Full Text Available The purpose of this study were: 1 to determine whether there is a correlation between students' interest in learning and the learning achievement of automotive electrical, 2 to determine whether there is a correlation between the use of time studying the learning achievement of automotive electrical, 3 to determine whether there is a correlation between student interest and use the time to learn and the learning achievement of students of class XII automotive electrical TKR SMK PIRI 1 Yogyakarta academic year 2013/2014.  This research was conducted in class XII TKR SMK PIRI 1 Yogyakarta academic year 2013/2014. This study is an ex-post facto. This study used two independent variables and the interest in learning the use of learning time, while the dependent variable is the electrical automotive learning achievement. This study is a population study by the respondent amounted to 100 students. Techniques of data collection using questionnaire techniques and engineering documentation. Research instrument in this study is a questionnaire interest in learning, inquiry learning time management and documentation of student achievement. Trials using the instrument validity and reliability test. The analysis technique used is the prerequisite test for normality, linearity, and multicollinearity. Then test hypotheses using partial correlation analysis techniques and correlation.  The results showed that: 1 students' interest to have a strong positive correlation with school performance automotive electrical ρ value of 0.737; 2 the use of learning time have a low positive correlation with school performance automotive electrical ρ value of 0.275; 3 interest student learning and the use of study time has a very strong positive correlation with learning achievement of students of class XII automotive electrical TKR SMK PIRI I Yogyakarta academic year 2013/2014 as evidenced by the value of R = 0.811.

The Relationship between Motivation, Learning Approaches, Academic Performance and Time Spent

Science.gov (United States)

Everaert, Patricia; Opdecam, Evelien; Maussen, Sophie

2017-01-01

Previous literature calls for further investigation in terms of precedents and consequences of learning approaches (deep learning and surface learning). Motivation as precedent and time spent and academic performance as consequences are addressed in this paper. The study is administered in a first-year undergraduate course. Results show that the…

Frederick W. Alt received the 2015 Szent-Györgi Prize for Progress in Cancer Research.

Science.gov (United States)

Scully, Peter; Zhao, Jie; Ba, Sujuan

2016-02-03

The Szent-Györgyi Prize for Progress in Cancer Research is a prestigious scientific award established by the National Foundation for Cancer Research (NFCR)--a leading cancer research charitable organization in the United States that is committed to supporting scientific research and public education relating to the prevention, early diagnosis, better treatments, and ultimately, a cure for cancer. Each year, the Szent-Györgyi Prize honors an outstanding researcher, nominated by colleagues or peers, who has contributed outstanding, significant research to the fight against cancer, and whose accomplishments have helped improve treatment options for cancer patients. The Prize also promotes public awareness of the importance of basic cancer research and encourages the sustained investment needed to accelerate the translation of these research discoveries into new cancer treatments. This report highlights the pioneering work led by the 2015 Prize winner, Dr. Frederick Alt. Dr. Alt's work in the area of cancer genetics over four decades has helped to shape the very roots of modern cancer research. His work continues to profoundly impact the approaches that doctors around the globe use to diagnose and treat cancer. In particular, his seminal discoveries of gene amplification and his pioneering work on molecular mechanisms of DNA damage repair have helped to usher in the era of genetically targeted therapy and personalized medicine.

Learning of time series through neuron-to-neuron instruction

Energy Technology Data Exchange (ETDEWEB)

Miyazaki, Y [Department of Physics, Kyoto University, Kyoto 606-8502, (Japan); Kinzel, W [Institut fuer Theoretische Physik, Universitaet Wurzburg, 97074 Wurzburg (Germany); Shinomoto, S [Department of Physics, Kyoto University, Kyoto (Japan)

2003-02-07

A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space.

Learning of time series through neuron-to-neuron instruction

International Nuclear Information System (INIS)

Miyazaki, Y; Kinzel, W; Shinomoto, S

2003-01-01

A model neuron with delayline feedback connections can learn a time series generated by another model neuron. It has been known that some student neurons that have completed such learning under the instruction of a teacher's quasi-periodic sequence mimic the teacher's time series over a long interval, even after instruction has ceased. We found that in addition to such faithful students, there are unfaithful students whose time series eventually diverge exponentially from that of the teacher. In order to understand the circumstances that allow for such a variety of students, the orbit dimension was estimated numerically. The quasi-periodic orbits in question were found to be confined in spaces with dimensions significantly smaller than that of the full phase space

Synthesis and properties of the metallo-supramolecular polymer hydrogel poly[methyl vinyl ether-alt-mono-sodium maleate]·AgNO3: Ag+/Cu2+ ion exchange and effective antibacterial activity

KAUST Repository

Xu, Feng

2014-01-01

The commercial polymeric anhydride poly(methyl vinyl ether-alt-maleic anhydride) (PVM/MA) is converted by reaction with NaOH to give poly(methyl vinyl ether-alt-mono-sodium maleate) (PVM/Na-MA). By addition of AgNO 3-solution, the formation of the silver(i) supramolecular polymer hydrogel poly[methyl vinyl ether-alt-mono-sodium maleate]·AgNO 3 is reported. Freeze-dried samples of the hydrogel show a mesoporous network of polycarboxylate ligands that are crosslinked by silver(i) cations. In the intact hydrogel, ion-exchange studies are reported and it is shown that Ag+ ions can be exchanged by copper(ii) cations without disintegration of the hydrogel. The silver(i) hydrogel shows effective antibacterial activity and potential application as burn wound dressing. © the Partner Organisations 2014.

Time for Learning: An Exploratory Analysis of NAEP Data

Science.gov (United States)

Ginsburg, Alan; Chudowsky, Naomi

2012-01-01

This report uses NAEP background data to track time and learning since the mid-1990s in three areas: student absenteeism; classroom instructional time in mathematics, reading, music and the visual arts; and homework time expected by teachers. Key report findings are: (1) Students with higher rates of "monthly absenteeism" score…

Benefits for Voice Learning Caused by Concurrent Faces Develop over Time.

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Zäske, Romi; Mühl, Constanze; Schweinberger, Stefan R

2015-01-01

Recognition of personally familiar voices benefits from the concurrent presentation of the corresponding speakers' faces. This effect of audiovisual integration is most pronounced for voices combined with dynamic articulating faces. However, it is unclear if learning unfamiliar voices also benefits from audiovisual face-voice integration or, alternatively, is hampered by attentional capture of faces, i.e., "face-overshadowing". In six study-test cycles we compared the recognition of newly-learned voices following unimodal voice learning vs. bimodal face-voice learning with either static (Exp. 1) or dynamic articulating faces (Exp. 2). Voice recognition accuracies significantly increased for bimodal learning across study-test cycles while remaining stable for unimodal learning, as reflected in numerical costs of bimodal relative to unimodal voice learning in the first two study-test cycles and benefits in the last two cycles. This was independent of whether faces were static images (Exp. 1) or dynamic videos (Exp. 2). In both experiments, slower reaction times to voices previously studied with faces compared to voices only may result from visual search for faces during memory retrieval. A general decrease of reaction times across study-test cycles suggests facilitated recognition with more speaker repetitions. Overall, our data suggest two simultaneous and opposing mechanisms during bimodal face-voice learning: while attentional capture of faces may initially impede voice learning, audiovisual integration may facilitate it thereafter.

Estimating the implicit component of visuomotor rotation learning by constraining movement preparation time.

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Leow, Li-Ann; Gunn, Reece; Marinovic, Welber; Carroll, Timothy J

2017-08-01

When sensory feedback is perturbed, accurate movement is restored by a combination of implicit processes and deliberate reaiming to strategically compensate for errors. Here, we directly compare two methods used previously to dissociate implicit from explicit learning on a trial-by-trial basis: 1 ) asking participants to report the direction that they aim their movements, and contrasting this with the directions of the target and the movement that they actually produce, and 2 ) manipulating movement preparation time. By instructing participants to reaim without a sensory perturbation, we show that reaiming is possible even with the shortest possible preparation times, particularly when targets are narrowly distributed. Nonetheless, reaiming is effortful and comes at the cost of increased variability, so we tested whether constraining preparation time is sufficient to suppress strategic reaiming during adaptation to visuomotor rotation with a broad target distribution. The rate and extent of error reduction under preparation time constraints were similar to estimates of implicit learning obtained from self-report without time pressure, suggesting that participants chose not to apply a reaiming strategy to correct visual errors under time pressure. Surprisingly, participants who reported aiming directions showed less implicit learning according to an alternative measure, obtained during trials performed without visual feedback. This suggests that the process of reporting can affect the extent or persistence of implicit learning. The data extend existing evidence that restricting preparation time can suppress explicit reaiming and provide an estimate of implicit visuomotor rotation learning that does not require participants to report their aiming directions. NEW & NOTEWORTHY During sensorimotor adaptation, implicit error-driven learning can be isolated from explicit strategy-driven reaiming by subtracting self-reported aiming directions from movement directions, or

Time Spent, Workload, and Student and Faculty Perceptions in a Blended Learning Environment

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Schumacher, Christie; Arif, Sally

2016-01-01

Objective. To evaluate student perception and time spent on asynchronous online lectures in a blended learning environment (BLE) and to assess faculty workload and perception. Methods. Students (n=427) time spent viewing online lectures was measured in three courses. Students and faculty members completed a survey to assess perceptions of a BLE. Faculty members recorded time spent creating BLEs. Results. Total time spent in the BLE was less than the allocated time for two of the three courses by 3-15%. Students preferred online lectures for their flexibility, students’ ability to apply information learned, and congruence with their learning styles. Faculty members reported the BLE facilitated higher levels of learning during class sessions but noted an increase in workload. Conclusion. A BLE increased faculty workload but was well received by students. Time spent viewing online lectures was less than what was allocated in two of the three courses. PMID:27667839

Modeling Time Series Data for Supervised Learning

Science.gov (United States)

Baydogan, Mustafa Gokce

2012-01-01

Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning…

Online Quiz Time Limits and Learning Outcomes in Economics

Science.gov (United States)

Evans, Brent; Culp, Robert

2015-01-01

In an effort to better understand the impact of timing limits, the authors compare the learning outcomes of students who completed timed quizzes with students who took untimed quizzes in economics principles courses. Students were assigned two online quizzes--one timed and one untimed--and re-tested on the material the following class day. Our…

Forecasting air quality time series using deep learning.

Science.gov (United States)

Freeman, Brian S; Taylor, Graham; Gharabaghi, Bahram; Thé, Jesse

2018-04-13

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O 3 ) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O 3 exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours. Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution

The time course of location-avoidance learning in fear of spiders.

Science.gov (United States)

Rinck, Mike; Koene, Marieke; Telli, Sibel; Moerman-van den Brink, Wiltine; Verhoeven, Barbara; Becker, Eni S

2016-01-01

Two experiments were designed to study the time course of avoidance learning in spider fearfuls (SFs) under controlled experimental conditions. To achieve this, we employed an immersive virtual environment (IVE): While walking freely through a virtual art museum to search for specific paintings, the participants were exposed to virtual spiders. Unbeknown to the participants, only two of four museum rooms contained spiders, allowing for avoidance learning. Indeed, the more SF the participants were, the faster they learned to avoid the rooms that contained spiders (Experiment. 1), and within the first six trials, high fearfuls already developed a preference for starting their search task in rooms without spiders (Experiment 2). These results illustrate the time course of avoidance learning in SFs, and they speak to the usefulness of IVEs in fundamental anxiety research.

Influence of learning strategy on response time during complex value-based learning and choice.

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Shiva Farashahi

Full Text Available Measurements of response time (RT have long been used to infer neural processes underlying various cognitive functions such as working memory, attention, and decision making. However, it is currently unknown if RT is also informative about various stages of value-based choice, particularly how reward values are constructed. To investigate these questions, we analyzed the pattern of RT during a set of multi-dimensional learning and decision-making tasks that can prompt subjects to adopt different learning strategies. In our experiments, subjects could use reward feedback to directly learn reward values associated with possible choice options (object-based learning. Alternatively, they could learn reward values of options' features (e.g. color, shape and combine these values to estimate reward values for individual options (feature-based learning. We found that RT was slower when the difference between subjects' estimates of reward probabilities for the two alternative objects on a given trial was smaller. Moreover, RT was overall faster when the preceding trial was rewarded or when the previously selected object was present. These effects, however, were mediated by an interaction between these factors such that subjects were faster when the previously selected object was present rather than absent but only after unrewarded trials. Finally, RT reflected the learning strategy (i.e. object-based or feature-based approach adopted by the subject on a trial-by-trial basis, indicating an overall faster construction of reward value and/or value comparison during object-based learning. Altogether, these results demonstrate that the pattern of RT can be informative about how reward values are learned and constructed during complex value-based learning and decision making.

The Negative Impact of Community Stressors on Learning Time: Examining Inequalities between California High Schools

Science.gov (United States)

Mirra, Nicole; Rogers, John

2015-01-01

Allocated classroom time is not the same as time available for learning--a host of economic and social stressors undermine learning time in schools serving low-income students. When time is limited, it is hard to meet rigorous learning standards. The challenge is compounded in high-poverty schools where community stressors place additional demands…

Learning and Teaching Problems in Part-Time Higher Education.

Science.gov (United States)

Trotman-Dickenson, D. I.

1988-01-01

Results of a British survey of the administrations of six universities and six public colleges, employers, and employees who were part-time students are reported and discussed. The survey assessed the perceptions of those groups concerning problems in the instruction and learning of part-time students. (MSE)

Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

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Nicolas Frémaux

2013-04-01

Full Text Available Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic plasticity. On the other hand, the theory of reinforcement learning provides a framework for reward-based learning. Recent models of reward-modulated spike-timing-dependent plasticity have made first steps towards bridging the gap between the two approaches, but faced two problems. First, reinforcement learning is typically formulated in a discrete framework, ill-adapted to the description of natural situations. Second, biologically plausible models of reward-modulated spike-timing-dependent plasticity require precise calculation of the reward prediction error, yet it remains to be shown how this can be computed by neurons. Here we propose a solution to these problems by extending the continuous temporal difference (TD learning of Doya (2000 to the case of spiking neurons in an actor-critic network operating in continuous time, and with continuous state and action representations. In our model, the critic learns to predict expected future rewards in real time. Its activity, together with actual rewards, conditions the delivery of a neuromodulatory TD signal to itself and to the actor, which is responsible for action choice. In simulations, we show that such an architecture can solve a Morris water-maze-like navigation task, in a number of trials consistent with reported animal performance. We also use our model to solve the acrobot and the cartpole problems, two complex motor control tasks. Our model provides a plausible way of computing reward prediction error in the brain. Moreover, the analytically derived learning rule is consistent with experimental evidence for dopamine-modulated spike-timing-dependent plasticity.

Fixation and escape times in stochastic game learning

International Nuclear Information System (INIS)

Realpe-Gomez, John; Szczesny, Bartosz; Galla, Tobias; Dall’Asta, Luca

2012-01-01

Evolutionary dynamics in finite populations is known to fixate eventually in the absence of mutation. We here show that a similar phenomenon can be found in stochastic game dynamical batch learning, and investigate fixation in learning processes in a simple 2×2 game, for two-player games with cyclic interaction, and in the context of the best-shot network game. The analogues of finite populations in evolution are here finite batches of observations between strategy updates. We study when and how such fixation can occur, and present results on the average time-to-fixation from numerical simulations. Simple cases are also amenable to analytical approaches and we provide estimates of the behaviour of so-called escape times as a function of the batch size. The differences and similarities with escape and fixation in evolutionary dynamics are discussed. (paper)

Students' Pressure, Time Management and Effective Learning

Science.gov (United States)

Sun, Hechuan; Yang, Xiaolin

2009-01-01

Purpose: This paper aims to survey the status quo of the student pressure and the relationship between their daily time management and their learning outcomes in three different types of higher secondary schools at Shenyang, the capital city of Liaoning Province in mainland China. Design/methodology/approach: An investigation was carried out in 14…

Representation Learning from Time Labelled Heterogeneous Data for Mobile Crowdsensing

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Chunmei Ma

2016-01-01

Full Text Available Mobile crowdsensing is a new paradigm that can utilize pervasive smartphones to collect and analyze data to benefit users. However, sensory data gathered by smartphone usually involves different data types because of different granularity and multiple sensor sources. Besides, the data are also time labelled. The heterogeneous and time sequential data raise new challenges for data analyzing. Some existing solutions try to learn each type of data one by one and analyze them separately without considering time information. In addition, the traditional methods also have to determine phone orientation because some sensors equipped in smartphone are orientation related. In this paper, we think that a combination of multiple sensors can represent an invariant feature for a crowdsensing context. Therefore, we propose a new representation learning method of heterogeneous data with time labels to extract typical features using deep learning. We evaluate that our proposed method can adapt data generated by different orientations effectively. Furthermore, we test the performance of the proposed method by recognizing two group mobile activities, walking/cycling and driving/bus with smartphone sensors. It achieves precisions of 98.6% and 93.7% in distinguishing cycling from walking and bus from driving, respectively.

The race to learn: spike timing and STDP can coordinate learning and recall in CA3.

Science.gov (United States)

Nolan, Christopher R; Wyeth, Gordon; Milford, Michael; Wiles, Janet

2011-06-01

The CA3 region of the hippocampus has long been proposed as an autoassociative network performing pattern completion on known inputs. The dentate gyrus (DG) region is often proposed as a network performing the complementary function of pattern separation. Neural models of pattern completion and separation generally designate explicit learning phases to encode new information and assume an ideal fixed threshold at which to stop learning new patterns and begin recalling known patterns. Memory systems are significantly more complex in practice, with the degree of memory recall depending on context-specific goals. Here, we present our spike-timing separation and completion (STSC) model of the entorhinal cortex (EC), DG, and CA3 network, ascribing to each region a role similar to that in existing models but adding a temporal dimension by using a spiking neural network. Simulation results demonstrate that (a) spike-timing dependent plasticity in the EC-CA3 synapses provides a pattern completion ability without recurrent CA3 connections, (b) the race between activation of CA3 cells via EC-CA3 synapses and activation of the same cells via DG-CA3 synapses distinguishes novel from known inputs, and (c) modulation of the EC-CA3 synapses adjusts the learned versus test input similarity required to evoke a direct CA3 response prior to any DG activity, thereby adjusting the pattern completion threshold. These mechanisms suggest that spike timing can arbitrate between learning and recall based on the novelty of each individual input, ensuring control of the learn-recall decision resides in the same subsystem as the learned memories themselves. The proposed modulatory signal does not override this decision but biases the system toward either learning or recall. The model provides an explanation for empirical observations that a reduction in novelty produces a corresponding reduction in the latency of responses in CA3 and CA1. Copyright © 2010 Wiley-Liss, Inc.

Virtueller Medizinischer Campus Graz: eine e-Learning Umgebung wird 5 Jahre alt / Virtual Medical Campus Graz: an e-learning environment has its 5 year-anniversary

Directory of Open Access Journals (Sweden)

Reibnegger, Gilbert

2007-12-01

Full Text Available Parallel with the initiation of an integrated curriculum at the Medical University of Graz a virtual learning environment was implemented, designated as Virtual Medical Campus (VMC. Several financial support projects made the development of the VMC and its enhancements possible. Learning objects are granular and strictly equipped with a set of metadata conforming the SCORM 2004 2nd edition-standard and are therefore reusable and exchangeable with other study courses or e-Learning-systems. Simple usability allows authors the intuitive creation of content, which may be enriched with interactive and tutorial systems using several built in authoring tools like web-based-training or a Virtual Microscope. In 2005 more than 3300 students applied for human medicine at the Medical University of Graz and it was decided to give a virtual term with a selection process at the end of it.More than 1 million accesses to learning objects and 257,000 web-based-trainings were handled without a single breakdown. This unique interim solution of a virtual term demonstrated the capacity of the VMC-system and the organisational possibility to intercept rushes of application using e-Learning. In the meanwhile the VMC Graz provides 13 study courses at four universities in two different European countries and two international postgraduate programs. The technical development aims at Web 3.0 – “Semantic Web” and the further expansion of co-operations is a present and future strategy.

Pointillist, Cyclical, and Overlapping: Multidimensional Facets of Time in Online Learning

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Pekka Ihanainen

2011-11-01

Full Text Available A linear, sequential time conception based on in-person meetings and pedagogical activities is not enough for those who practice and hope to enhance contemporary education, particularly where online interactions are concerned. In this article, we propose a new model for understanding time in pedagogical contexts. Conceptual parts of the model will be employed as a “cultural technology” to help us relate to evolving phenomena, both physical and virtual. We label these constructs as pointillist, cyclical, and overlapping times.Pointillist time and learning takes place in “dots” of actions that consist of small, discrete moments (e.g., tweeting. Producing, receiving, and sharing ideas in this context are separate points in each actor’s timeline. Cyclical time and learning emerges from intensive periods, which are highly visible in online forums. This construct reveals itself through interactions that often exist in multiple online environments. Overlapping time and learning involves various configurations of linear, pointillist, and cyclical layers, which are mainly evident through the simultaneous uses of social communication technologies.Pointillist, cyclical, and overlapping time constructs enable new orientations for conceptualizing time in pedagogy. In this article we also introduce de-, re-, and en- modes of these pedagogies that connect with approaches to meet the needs of learners for individualization, personalization, and cyborgization.

An important improvement in Ferron-timed spectrophotometry

NARCIS (Netherlands)

Zhang, Jing; Chang, Fang; Ren, Yijin; Shi, Qiuyi

Ferron dosage ([Ferron]) is key to ferron-timed spectrophotometry (ferron assay). In order to clarify some important questions, the following studies were conducted: (1) The effect of [Ferron] on the sensitivity of total aluminum (AlT) determination was experimentally investigated and ²⁷Al nuclear

Autonomous learning by simple dynamical systems with a discrete-time formulation

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Bilen, Agustín M.; Kaluza, Pablo

2017-05-01

We present a discrete-time formulation for the autonomous learning conjecture. The main feature of this formulation is the possibility to apply the autonomous learning scheme to systems in which the errors with respect to target functions are not well-defined for all times. This restriction for the evaluation of functionality is a typical feature in systems that need a finite time interval to process a unit piece of information. We illustrate its application on an artificial neural network with feed-forward architecture for classification and a phase oscillator system with synchronization properties. The main characteristics of the discrete-time formulation are shown by constructing these systems with predefined functions.

Prime Time for Learning.

Science.gov (United States)

Leidy, Vivian; And Others

1981-01-01

Five elementary teachers explain how they orient pupils and get learning started on the first day of school--whether or not their supplies or textbooks have arrived--by building learning activities around a common interest like dogs, earthworms, football, or the Statue of Liberty. (Editor/SJL)

Event timing in associative learning: from biochemical reaction dynamics to behavioural observations.

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Ayse Yarali

Full Text Available Associative learning relies on event timing. Fruit flies for example, once trained with an odour that precedes electric shock, subsequently avoid this odour (punishment learning; if, on the other hand the odour follows the shock during training, it is approached later on (relief learning. During training, an odour-induced Ca(++ signal and a shock-induced dopaminergic signal converge in the Kenyon cells, synergistically activating a Ca(++-calmodulin-sensitive adenylate cyclase, which likely leads to the synaptic plasticity underlying the conditioned avoidance of the odour. In Aplysia, the effect of serotonin on the corresponding adenylate cyclase is bi-directionally modulated by Ca(++, depending on the relative timing of the two inputs. Using a computational approach, we quantitatively explore this biochemical property of the adenylate cyclase and show that it can generate the effect of event timing on associative learning. We overcome the shortage of behavioural data in Aplysia and biochemical data in Drosophila by combining findings from both systems.

Optimal critic learning for robot control in time-varying environments.

Science.gov (United States)

Wang, Chen; Li, Yanan; Ge, Shuzhi Sam; Lee, Tong Heng

2015-10-01

In this paper, optimal critic learning is developed for robot control in a time-varying environment. The unknown environment is described as a linear system with time-varying parameters, and impedance control is employed for the interaction control. Desired impedance parameters are obtained in the sense of an optimal realization of the composite of trajectory tracking and force regulation. Q -function-based critic learning is developed to determine the optimal impedance parameters without the knowledge of the system dynamics. The simulation results are presented and compared with existing methods, and the efficacy of the proposed method is verified.

Timing of quizzes during learning: Effects on motivation and retention.

Science.gov (United States)

Healy, Alice F; Jones, Matt; Lalchandani, Lakshmi A; Tack, Lindsay Anderson

2017-06-01

This article investigates how the timing of quizzes given during learning impacts retention of studied material. We investigated the hypothesis that interspersing quizzes among study blocks increases student engagement, thus improving learning. Participants learned 8 artificial facts about each of 8 plant categories, with the categories blocked during learning. Quizzes about 4 of the 8 facts from each category occurred either immediately after studying the facts for that category (standard) or after studying the facts from all 8 categories (postponed). In Experiment 1, participants were given tests shortly after learning and several days later, including both the initially quizzed and unquizzed facts. Test performance was better in the standard than in the postponed condition, especially for categories learned later in the sequence. This result held even for the facts not quizzed during learning, suggesting that the advantage cannot be due to any direct testing effects. Instead the results support the hypothesis that interrupting learning with quiz questions is beneficial because it can enhance learner engagement. Experiment 2 provided further support for this hypothesis, based on participants' retrospective ratings of their task engagement during the learning phase. These findings have practical implications for when to introduce quizzes in the classroom. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Changes of serum levels of prealbumin (PAB), cholinesterase (CHE), total bile acid (TBA) and ALT as related to the severity of inflammatory process and hepatic fibrosis in patients with chronic virus B hepatitis

International Nuclear Information System (INIS)

Chi Xiaoxia; Chen Jianxiong; Xiong Ying

2005-01-01

Objective: To study the correlationship between the serum levels of PAB, CHE, TBA, ALT and the severity of the disease process in patients with chronic virus B hepatitis. Methods: Serum levels of PAB, CHE, ALT (with biochemical methods) and TBA (with RIA) were examined in 93 patients with biopsy proven virus B hepatitis and 46 controls. Results: The 93 patients were of two groups: a less advanced group (n=51) and a more advanced group (n=42). Serum TBA, ALT levels were significnatly higher and serum PAB, CHE levels were significantly lower in the more advanced group than those in the less advanced group (P 0 to s 4 . Changes of levels of ALT were of no regular pattern, but serum levels of TBA regularly increased and levels of PAB, CHE regularly decreased as the fibrosis grading proceeded from s 0 to s 4 and the differences between the levels in s 4 and any other grading were significant (P<0.01). Conclusion: Combined determination of these serum markers might reflect the degree of inflammatory process and hepatic fibrosis in patients with virus B hepatitis, leading to earlier detection of cirrhosis. (authors)

Flexible Learning and Teaching: Looking Beyond the Binary of Full-time/Part-time Provision in South African Higher Education

Directory of Open Access Journals (Sweden)

Barbara M Jones

2015-06-01

Full Text Available This paper engages with literature on flexible learning and teaching in order to explore whether it may be possible, within the South African context, to have flexible learning and teaching provide a third way which goes beyond the current practice of full-time/part-time provision. This binary classification of students is a proxy for day-time/after-hours delivery.  The argument is made that effective, flexible learning and teaching requires a fundamental shift in thinking about learning and teaching in higher education that moves us beyond such binaries. The paper proposes that in order to ensure access and success for students, ‘common knowledge’ (Edwards, 2010 will need to be co-constructed which understands flexible learning and teaching in ways which will meet needs of a diversity of students, including working students. It will require ‘resourceful leadership’ (Edwards, 2014 within the university that recognises, enhances and gives purpose to the capability of colleagues at every level of the systems they lead. Also, it will require the building of ‘common knowledge’ between certain sectors of universities and particular workplaces.

Multifunctional e-spun colloidal nanofiber structures from various dispersed blends of PVA/ODA-MMT with PVP/ODA-MMT, poly(VP-alt-MA and AgNPs incorporated polymer complexes as electro-active platforms

Directory of Open Access Journals (Sweden)

U. Bunyatova

2016-07-01

Full Text Available This work presented a new approach to fabricate polymer nanocomposites films with nanofiber structures from solution blends of poly(vinyl alcohol + octadecyl amine-montmorillonite (ODA-MMT (matrix with poly(N-vinylpyrrolidone + ODA-MMT (partner-1, poly(N-vinylpyrrolidone-alt-maleic anhydride ((poly(VP-alt-MA + (ODA-MMT (partner-2 and their silver (Ag-carrying polymer complexes by electrospinning. Chemical and physical structures, surface morphologies, thermal behaviors, electrical conductivity and thermal resistance parameters of nanofiber structures were investigated. Poly(VP-alt-MA was used both as a crosslinker and a donor of the hydrophilic groups such as ‒COOH and ‒NH–C=O amide from pyrrolidone ring. Reactive poly(VP-alt-MA, in situ generated Ag nanoparticles (AgNPs and original partner polymer had an significant effect on the morphology and diameter distribution of nanofibers. High and excellent conductive behaviors were observed for the homopolymer and copolymer of VP based fiber structures, respectively. Upon successive chemical cross-linking of the nanofiber structures by reactive partner copolymer, the conductivity of nanofiber films as electro-active platforms dramatically increased to 3.90·10–2 S·cm–1 at room temperature. Comparative analysis results also indicated that electrical properties strongly depended on the loaded reactive organoclay and in situ generated AgNPs.

In-Time On-Place Learning

Science.gov (United States)

Bauters, Merja; Purma, Jukka; Leinonen, Teemu

2014-01-01

The aim of this short paper is to look at how mobile video recording devices could support learning related to physical practices or places and situations at work. This paper discusses particular kind of workplace learning, namely learning using short video clips that are related to physical environment and tasks preformed in situ. The paper…

EFFECTS OF COOPERATIVE LEARNING MODEL TYPE STAD JUST-IN TIME BASED ON THE RESULTS OF LEARNING TEACHING PHYSICS COURSE IN PHYSICS SCHOOL IN PHYSICS PROGRAM FACULTY UNIMED

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Teguh Febri Sudarma

2013-06-01

Full Text Available Research was aimed to determine: (1 Students’ learning outcomes that was taught with just in time teaching based STAD cooperative learning method and STAD cooperative learning method (2 Students’ outcomes on Physics subject that had high learning activity compared with low learning activity. The research sample was random by raffling four classes to get two classes. The first class taught with just in time teaching based STAD cooperative learning method, while the second class was taught with STAD cooperative learning method. The instrument used was conceptual understanding that had been validated with 7 essay questions. The average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,47 higher than average gain values of students learning results with STAD cooperative learning method. The high learning activity and low learning activity gave different learning results. In this case the average gain values of students learning results with just in time teaching based STAD cooperative learning method 0,48 higher than average gain values of students learning results with STAD cooperative learning method. There was interaction between learning model and learning activity to the physics learning result test in students

Investigating the Relationship Between Self-Directed Learning Readiness and Time Management Skills in Turkish Undergraduate Nursing Students.

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Ertuğ, Nurcan; Faydali, Saide

The aims of this study were to determine self-directed learning and time management skills of undergraduate nursing students and to investigate the relationship between the concepts. The use of self-directed learning has increased as an educational strategy in recent years. This descriptive and correlational study was conducted with 383 undergraduate nursing students in Turkey. Data were collected using a sociodemographic questionnaire, the Self-Directed Learning Readiness Scale, and Time Management Questionnaire. Mean scores were as follows: self-directed learning readiness, 159.12 (SD = 20.8); time management, 87.75 (SD = 12.1). A moderate positive correlation was found between self-directed learning readiness and time management values. Time management scores were 78.42 when self-directed learning readiness was ≤149 and 90.82 when self-directed learning readiness was ≥ 150, with a statistically significant difference (p = .000). Level of self-directed learning and academic achievement were higher in students who managed their time well.

Children benefit differently from night- and day-time sleep in motor learning.

Science.gov (United States)

Yan, Jin H

2017-08-01

Motor skill acquisition occurs while practicing (on-line) and when asleep or awake (off-line). However, developmental questions still remain about whether children of various ages benefit similarly or differentially from night- and day-time sleeping. The likely circadian effects (time-of-day) and the possible between-test-interference (order effects) associated with children's off-line motor learning are currently unknown. Therefore, this study examines the contributions of over-night sleeping and mid-day napping to procedural skill learning. One hundred and eight children were instructed to practice a finger sequence task using computer keyboards. After an equivalent 11-h interval in one of the three states (sleep, nap, wakefulness), children performed the same sequence in retention tests and a novel sequence in transfer tests. Changes in the movement time and sequence accuracy were evaluated between ages (6-7, 8-9, 10-11years) during practice, and from skill training to retrievals across three states. Results suggest that night-time sleeping and day-time napping improved the tapping speed, especially for the 6-year-olds. The circadian factor did not affect off-line motor learning in children. The interference between the two counter-balanced retrieval tests was not found for the off-line motor learning. This research offers possible evidence about the age-related motor learning characteristics in children and a potential means for enhancing developmental motor skills. The dynamics between age, experience, memory formation, and the theoretical implications of motor skill acquisition are discussed. Copyright © 2017 Elsevier B.V. All rights reserved.

Polysynchronous: Dialogic Construction of Time in Online Learning

Science.gov (United States)

Oztok, Murat; Wilton, Lesley; Zingaro, Daniel; Mackinnon, Kim; Makos, Alexandra; Phirangee, Krystle; Brett, Clare; Hewitt, Jim

2014-01-01

Online learning has been conceptualized for decades as being delivered in one of two modes: synchronous or asynchronous. Technological determinism falls short in describing the role that the individuals' psychological, social and pedagogical factors play in their perception, experience and understanding of time online. This article explores…

Online gaming for learning optimal team strategies in real time

Science.gov (United States)

Hudas, Gregory; Lewis, F. L.; Vamvoudakis, K. G.

2010-04-01

This paper first presents an overall view for dynamical decision-making in teams, both cooperative and competitive. Strategies for team decision problems, including optimal control, zero-sum 2-player games (H-infinity control) and so on are normally solved for off-line by solving associated matrix equations such as the Riccati equation. However, using that approach, players cannot change their objectives online in real time without calling for a completely new off-line solution for the new strategies. Therefore, in this paper we give a method for learning optimal team strategies online in real time as team dynamical play unfolds. In the linear quadratic regulator case, for instance, the method learns the Riccati equation solution online without ever solving the Riccati equation. This allows for truly dynamical team decisions where objective functions can change in real time and the system dynamics can be time-varying.

Influence of the composition of hydroxypropyl cellulose/maleic acid-alt-styrene copolymer blends on their properties as matrix for drug release

Directory of Open Access Journals (Sweden)

2009-05-01

Full Text Available Poly(carboxylic acid-polysaccharide compositions have been found suitable for obtaining drug formulations with controlled release, most formulations being therapeutically efficacious, stable, and non-irritant. The influence of the characteristics of the aqueous solutions from which the polymer matrix is prepared (i.e. the total concentration of polymer in solutions and the mixing ratio between the partners, hydroxypropyl cellulose, HPC and maleic acid-alternating-styrene copolymer, MAc-alt-S on the kinetics of some drugs release in acidic environment (pH = 2 has been followed by ‘in vitro’ dissolution tests. It has been established that the kinetics of procaine hydrochloride release from HPC/MAc-alt-S matrix depends on its composition; the diffusion exponent, n is close to 0.5 for matrices where one of the components is in large excess and n~0.02 for middle composition range. The lower value of diffusion exponent for middle composition range could be caused by the so called ‘burst effect’, therefore the kinetic evaluation is difficult.

Effects of Business School Student's Study Time on the Learning Process

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Tetteh, Godson Ayertei

2016-01-01

Purpose: This paper aims to clarify the relationship between the student's study time and the learning process in the higher education system by adapting the total quality management (TQM) principles-process approach. Contrary to Deming's (1982) constancy of purpose to improve the learning process, some students in higher education postpone their…

Finite time convergent learning law for continuous neural networks.

Science.gov (United States)

Chairez, Isaac

2014-02-01

This paper addresses the design of a discontinuous finite time convergent learning law for neural networks with continuous dynamics. The neural network was used here to obtain a non-parametric model for uncertain systems described by a set of ordinary differential equations. The source of uncertainties was the presence of some external perturbations and poor knowledge of the nonlinear function describing the system dynamics. A new adaptive algorithm based on discontinuous algorithms was used to adjust the weights of the neural network. The adaptive algorithm was derived by means of a non-standard Lyapunov function that is lower semi-continuous and differentiable in almost the whole space. A compensator term was included in the identifier to reject some specific perturbations using a nonlinear robust algorithm. Two numerical examples demonstrated the improvements achieved by the learning algorithm introduced in this paper compared to classical schemes with continuous learning methods. The first one dealt with a benchmark problem used in the paper to explain how the discontinuous learning law works. The second one used the methane production model to show the benefits in engineering applications of the learning law proposed in this paper. Copyright © 2013 Elsevier Ltd. All rights reserved.

Educational resources and tools for robotic learning

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Pablo Gil Vazquez

2012-07-01

Full Text Available Normal.dotm 0 0 1 139 795 Universidad de Salamanca 6 1 976 12.0 0 false 18 pt 18 pt 0 0 false false false /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} This paper discusses different teaching experiences which aims are the learning robotics in the university. These experiences are reflected in the development of several robotics courses and subjects at the University of Alicante.  The authors have created various educational platforms or they have used tools of free distribution and open source for the implementation of these courses. The main objetive of these courses is to teach the design and implementation of robotic solutions to solve various problems not only such as the control, programming and handling of robot but also the assembly, building and programming of educational mini-robots. On the one hand, new teaching tools are used such as simulators and virtual labs which make flexible the learning of robot arms. On the other hand, competitions are used to motivate students because this way, the students put into action the skills learned through building and programming low-cost mini-robots.

Feedback on the Inaugural Meeting of the Cost 347 Accelerated Loading Test (ALT) Action in Europe: June 27th to 29th 2001

CSIR Research Space (South Africa)

De Beer, Morris

2001-11-01

Full Text Available . The author presented two topics, i.e. Accelerated Pavement Testing (APT) in South Africa, and Stress-In-Motion (SIM). This COST 347 action started in October 2001, and needs to deliver recommendations on ALT April 2004. There are 6 Working Groups, chaired...

TEXPLORE temporal difference reinforcement learning for robots and time-constrained domains

CERN Document Server

Hester, Todd

2013-01-01

This book presents and develops new reinforcement learning methods that enable fast and robust learning on robots in real-time. Robots have the potential to solve many problems in society, because of their ability to work in dangerous places doing necessary jobs that no one wants or is able to do. One barrier to their widespread deployment is that they are mainly limited to tasks where it is possible to hand-program behaviors for every situation that may be encountered. For robots to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes and could solve the problems of learning and adaptation on robots. This book identifies four key challenges that must be addressed for an RL algorithm to be practical for robotic control tasks. These RL for Robotics Challenges are: 1) it must learn in very few samples; 2) it must learn in domains with continuou...

Hepatic enzyme decline after pediatric blunt trauma: a tool for timing child abuse?

Science.gov (United States)

Baxter, Amy L; Lindberg, Daniel M; Burke, Bonnie L; Shults, Justine; Holmes, James F

2008-09-01

Previous research in adult patients with blunt hepatic injuries has suggested a pattern of serum hepatic transaminase concentration decline. Evaluating this decline after pediatric blunt hepatic trauma could establish parameters for estimating the time of inflicted injuries. Deviation from a consistent transaminase resolution pattern could indicate a developing complication. Retrospective review of pediatric patients with injuries including blunt liver trauma admitted to one of four urban level 1 trauma centers from 1990 to 2000. Cases were excluded for shock, death within 48 h, complications, or inability to determine injury time. Transaminase concentration decline was modeled by individual patients, by injury grade, and as a ratio with regard to injury time. One hundred and seventy-six patients met inclusion criteria. The rate of aspartate aminotransferase (AST) clearance changed significantly over time. Alanine aminotransferase (ALT) fell more slowly. Of the 118 patients who had multiple measurements of AST, for 112 (95%) the first concentration obtained was the highest. When ALT was greater than AST, the injury was older than 12h (97% specificity (95% CI, 95-99%), sensitivity 42% (95% CI, 33-50%)). Patients with enzymes that rose after 14 h post-injury were more likely to develop complications (RR=24, 95% CI 10-58). Hepatic transaminases rise rapidly after uncomplicated blunt liver injury, then fall predictably. Persistently stable or increasing concentrations may indicate complications. ALT>AST indicates subacute injury.

An Investigation of the Effectiveness of Increasing Academic Learning Time for College Undergraduate Students' Achievement in Kuwait

Science.gov (United States)

Al-Shammari, Zaid; Mohammad, Anwar; Al-Shammari, Bandar

2010-01-01

The study investigated the effectiveness of increasing ALT for college students' achievement in Kuwait. In Phase 1, 37 students participated (22, experimental; 15, control); in Phase 2, 19 students participated (8, sub-experimental; 11, sub-control). Several experimental research methods used in conducting this study, including development of a…

Problem based learning: the effect of real time data on the website to student independence

Science.gov (United States)

Setyowidodo, I.; Pramesti, Y. S.; Handayani, A. D.

2018-05-01

Learning science developed as an integrative science rather than disciplinary education, the reality of the nation character development has not been able to form a more creative and independent Indonesian man. Problem Based Learning based on real time data in the website is a learning method focuses on developing high-level thinking skills in problem-oriented situations by integrating technology in learning. The essence of this study is the presentation of authentic problems in the real time data situation in the website. The purpose of this research is to develop student independence through Problem Based Learning based on real time data in website. The type of this research is development research with implementation using purposive sampling technique. Based on the study there is an increase in student self-reliance, where the students in very high category is 47% and in the high category is 53%. This learning method can be said to be effective in improving students learning independence in problem-oriented situations.

Touchscreen Facilitates Young Children’s Transfer of Learning to Tell Time

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Fuxing Wang

2016-11-01

Full Text Available Young children are devoting increasing time to playing on handheld touchscreen devices (e.g., iPads. Though thousands of touchscreen apps are claimed to be educational, there is a lack of sufficient evidence examining the impact of touchscreens on children’s learning outcomes. In the present study, the two questions we focused on were (a whether using a touchscreen was helpful in teaching children to tell time, and (b to what extent young children could transfer what they had learned on the touchscreen to other media. A pre- and posttest design was adopted. After learning to read the time on the iPad touchscreen for 10 minutes, three groups of 5- to 6-year-old children (N = 65 were respectively tested with an iPad touchscreen, a toy clock or a drawing of a clock on paper. The results revealed that posttest scores in the iPad touchscreen test group were significantly higher than those at pretest, indicating that the touchscreen itself could provide support for young children’s learning. Similarly, regardless of being tested with a toy clock or paper drawing, children’s posttest performance was also better than pretest, suggesting that children could transfer what they had learned on an iPad touchscreen to other media. However, comparison among groups showed that children tested with the paper drawing underperformed those tested with the other two media. The theoretical and practical implications of the results, as well as limitations of the present study, are discussed.

Enrichment in Massachusetts Expanded Learning Time (ELT) Schools. Issue Brief

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Caven, Meghan; Checkoway, Amy; Gamse, Beth; Luck, Rachel; Wu, Sally

2012-01-01

This brief highlights key information about enrichment activities, which represent one of the main components of the Massachusetts Expanded Learning Time (ELT) initiative. Over time, the ELT initiative has supported over two dozen schools across the Commonwealth. A comprehensive evaluation of the ELT initiative found that implementation of the…

Synthesis of electro-optically active polymer composite of poly[2,2'-bis(3,4-ethylenedioxythiophene-alt-fluorene]/hydroxypropyl cellulose showing liquid crystal structure

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N. Eguchi

2017-10-01

Full Text Available Electrochemical preparation of a composite consisting of poly[2,2′-bis(3,4-ethylenedioxythiophene-alt-fluorene] and hydroxypropyl cellulose (PEFE/HPC was carried out. We conducted electrochemical polymerization of poly[2,2′-bis(3,4-ethylenedioxythiophene-alt-fluorene] (EFE as a monomer in a lyotropic liquid crystal of HPC. We used an organic solvent instead of water for lyotropic liquid crystal medium to expand the possibility of the range of monomers, although water is usually employed as a solvent for HPC for showing liquid crystallinity. Here, we employed N,N-dimethylformamide (DMF as a solvent for HPC. Electrochemical polymerization in the polymer liquid crystal was carried out to obtain a polymer film with liquid crystal order. The polymer film thus prepared exhibited optical activity. Fourier transfer infrared (FT-IR absorption spectroscopy reveals that the film is a composite consisting of HPC and polymer. The composite PEFE/HPC thus prepared in HPC/DMF system showed electrochromism.

Sensitivity analysis of machine-learning models of hydrologic time series

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O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

Real-time modeling of primitive environments through wavelet sensors and Hebbian learning

Science.gov (United States)

Vaccaro, James M.; Yaworsky, Paul S.

1999-06-01

Modeling the world through sensory input necessarily provides a unique perspective for the observer. Given a limited perspective, objects and events cannot always be encoded precisely but must involve crude, quick approximations to deal with sensory information in a real- time manner. As an example, when avoiding an oncoming car, a pedestrian needs to identify the fact that a car is approaching before ascertaining the model or color of the vehicle. In our methodology, we use wavelet-based sensors with self-organized learning to encode basic sensory information in real-time. The wavelet-based sensors provide necessary transformations while a rank-based Hebbian learning scheme encodes a self-organized environment through translation, scale and orientation invariant sensors. Such a self-organized environment is made possible by combining wavelet sets which are orthonormal, log-scale with linear orientation and have automatically generated membership functions. In earlier work we used Gabor wavelet filters, rank-based Hebbian learning and an exponential modulation function to encode textural information from images. Many different types of modulation are possible, but based on biological findings the exponential modulation function provided a good approximation of first spike coding of `integrate and fire' neurons. These types of Hebbian encoding schemes (e.g., exponential modulation, etc.) are useful for quick response and learning, provide several advantages over contemporary neural network learning approaches, and have been found to quantize data nonlinearly. By combining wavelets with Hebbian learning we can provide a real-time front-end for modeling an intelligent process, such as the autonomous control of agents in a simulated environment.

SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

International Nuclear Information System (INIS)

Joseph, A; Herrera, D; Hijal, T; Kildea, J; Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M

2016-01-01

Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes

SU-F-P-20: Predicting Waiting Times in Radiation Oncology Using Machine Learning

Energy Technology Data Exchange (ETDEWEB)

Joseph, A; Herrera, D; Hijal, T; Kildea, J [McGill University Health Centre, Montreal, Quebec (Canada); Hendren, L; Leung, A; Wainberg, J; Sawaf, M; Gorshkov, M; Maglieri, R; Keshavarz, M [McGill University, Montreal, Quebec (Canada)

2016-06-15

Purpose: Waiting times remain one of the most vexing patient satisfaction challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick or in pain, to worry about when they will receive the care they need. These waiting periods are often difficult for staff to predict and only rough estimates are typically provided based on personal experience. This level of uncertainty leaves most patients unable to plan their calendar, making the waiting experience uncomfortable, even painful. In the present era of electronic health records (EHRs), waiting times need not be so uncertain. Extensive EHRs provide unprecedented amounts of data that can statistically cluster towards representative values when appropriate patient cohorts are selected. Predictive modelling, such as machine learning, is a powerful approach that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The application of a machine learning algorithm to waiting time data has the potential to produce personalized waiting time predictions such that the uncertainty may be removed from the patient’s waiting experience. Methods: In radiation oncology, patients typically experience several types of waiting (eg waiting at home for treatment planning, waiting in the waiting room for oncologist appointments and daily waiting in the waiting room for radiotherapy treatments). A daily treatment wait time model is discussed in this report. To develop a prediction model using our large dataset (with more than 100k sample points) a variety of machine learning algorithms from the Python package sklearn were tested. Results: We found that the Random Forest Regressor model provides the best predictions for daily radiotherapy treatment waiting times. Using this model, we achieved a median residual (actual value minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes

PARÁMETROS BIOQUÍMICOS ENZIMÁTICOS (ALT, AST, ALP, Γ-GT, LDH EN NIÑOS CON LEUCEMIA LINFOBLÁSTICA AGUDA ANTES DEL TRATAMIENTO ANTINEOPLÁSICO

Directory of Open Access Journals (Sweden)

Jeél Moya S

2015-12-01

Full Text Available Objective: To determine the enzymatic biochemical parameters (glutamic pyruvic transaminase (ALT, glutamic oxaloacetic transaminase (AST, alkaline phosphatase (ALP, gamma glutamyltransferase (γ-GT, and lactate dehydrogenase (LDH in children with acute lymphoblastic leukemia (ALL before cancer treatment. Material and Methods: A prospective experimental, observational, cross-sectional study was conducted in 30 children between 2 and 15 years old, from several Neoplastic Centers in Lima. Blood collection was performed in BD red cap Vacutainer tubes, processed in the semi-automated analyzer BIOTEC® EMP-168, with Wiener Lab Group enzyme reagents under the modified method Szaaz and UV-Optimized by IFCC, SSCC and SFBC. Finally, coding and tabulation was performed. Results: 60% were boys and 46.7% are between the ages of 2-6 years. Serum levels of AST were increased by 33.3% in boys and 50% in girls. Serum ALT values were increased in 33.3% of boys and 41.7% of girls; only 25% of girls showed increased levels of γ-GT values; ALP was increased in 44.4% of boys and 66.7% of girls. Moreover LDH levels were increased in 55.6% of boys and 41.7% of girls. Conclusions: The enzymatic tests LDH, AST, ALT and ALP are increased in children with ALL compared to normal values due to tumor lysis syndrome characterized by electrolyte abnormalities, and as a result of the massive destruction of tumor cells and rapid release of large amounts of intracellular elements.

Incremental Impact of Time on Students' Use of E-Learning via Facebook

Science.gov (United States)

Moghavvemi, Sedigheh; Salarzadeh Janatabadi, Hashem

2018-01-01

The majority of studies utilised the cross-sectional method to measure students' intention to learn and investigate their corresponding learning behaviours. Only a few studies have measured the process of change in students' learning behaviour in the context of time. The main purpose of this study is to determine the effects of using a Facebook…

Learning Bounds of ERM Principle for Sequences of Time-Dependent Samples

Directory of Open Access Journals (Sweden)

Mingchen Yao

2015-01-01

Full Text Available Many generalization results in learning theory are established under the assumption that samples are independent and identically distributed (i.i.d.. However, numerous learning tasks in practical applications involve the time-dependent data. In this paper, we propose a theoretical framework to analyze the generalization performance of the empirical risk minimization (ERM principle for sequences of time-dependent samples (TDS. In particular, we first present the generalization bound of ERM principle for TDS. By introducing some auxiliary quantities, we also give a further analysis of the generalization properties and the asymptotical behaviors of ERM principle for TDS.

Making time for learning-oriented leadership in multidisciplinary hospital management groups.

Science.gov (United States)

Singer, Sara J; Hayes, Jennifer E; Gray, Garry C; Kiang, Mathew V

2015-01-01

Although the clinical requirements of health care delivery imply the need for interdisciplinary management teams to work together to promote frontline learning, such interdisciplinary, learning-oriented leadership is atypical. We designed this study to identify behaviors enabling groups of diverse managers to perform as learning-oriented leadership teams on behalf of quality and safety. We randomly selected 12 of 24 intact groups of hospital managers from one hospital to participate in a Safety Leadership Team Training program. We collected primary data from March 2008 to February 2010 including pre- and post-staff surveys, multiple interviews, observations, and archival data from management groups. We examined the level and trend in frontline perceptions of managers' learning-oriented leadership following the intervention and ability of management groups to achieve objectives on targeted improvement projects. Among the 12 intervention groups, we identified higher- and lower-performing intervention groups and behaviors that enabled higher performers to work together more successfully. Management groups that achieved more of their performance goals and whose staff perceived more and greater improvement in their learning-oriented leadership after participation in Safety Leadership Team Training invested in structures that created learning capacity and conscientiously practiced prescribed learning-oriented management and problem-solving behaviors. They made the time to do these things because they envisioned the benefits of learning, valued the opportunity to learn, and maintained an environment of mutual respect and psychological safety within their group. Learning in management groups requires vision of what learning can accomplish; will to explore, practice, and build learning capacity; and mutual respect that sustains a learning environment.

Active controllers and the time duration to learn a task

Science.gov (United States)

Repperger, D. W.; Goodyear, C.

1986-01-01

An active controller was used to help train naive subjects involved in a compensatory tracking task. The controller is called active in this context because it moves the subject's hand in a direction to improve tracking. It is of interest here to question whether the active controller helps the subject to learn a task more rapidly than the passive controller. Six subjects, inexperienced to compensatory tracking, were run to asymptote root mean square error tracking levels with an active controller or a passive controller. The time required to learn the task was defined several different ways. The results of the different measures of learning were examined across pools of subjects and across controllers using statistical tests. The comparison between the active controller and the passive controller as to their ability to accelerate the learning process as well as reduce levels of asymptotic tracking error is reported here.

Crumpled Molecules and Edible Plastic: Science Learning Activation in Out-of-School Time

Science.gov (United States)

Dorph, Rena; Schunn, Christian D.; Crowley, Kevin

2017-01-01

The Coalition for Science After School highlights the dual nature of outcomes for science learning during out-of- school time (OST): Learning experiences should not only be positive in the moment, but also position youth for future success. Several frameworks speak to the first set of immediate outcomes--what youth learn, think, and feel as the…

Oscillations, Timing, Plasticity, and Learning in the Cerebellum.

Science.gov (United States)

Cheron, G; Márquez-Ruiz, J; Dan, B

2016-04-01

The highly stereotyped, crystal-like architecture of the cerebellum has long served as a basis for hypotheses with regard to the function(s) that it subserves. Historically, most clinical observations and experimental work have focused on the involvement of the cerebellum in motor control, with particular emphasis on coordination and learning. Two main models have been suggested to account for cerebellar functioning. According to Llinás's theory, the cerebellum acts as a control machine that uses the rhythmic activity of the inferior olive to synchronize Purkinje cell populations for fine-tuning of coordination. In contrast, the Ito-Marr-Albus theory views the cerebellum as a motor learning machine that heuristically refines synaptic weights of the Purkinje cell based on error signals coming from the inferior olive. Here, we review the role of timing of neuronal events, oscillatory behavior, and synaptic and non-synaptic influences in functional plasticity that can be recorded in awake animals in various physiological and pathological models in a perspective that also includes non-motor aspects of cerebellar function. We discuss organizational levels from genes through intracellular signaling, synaptic network to system and behavior, as well as processes from signal production and processing to memory, delegation, and actual learning. We suggest an integrative concept for control and learning based on articulated oscillation templates.

Autism: Too eager to learn? Event related potential findings of increased dependency on intentional learning in a serial reaction time task.

Science.gov (United States)

Zwart, Fenny S; Vissers, Constance Th W M; van der Meij, Roemer; Kessels, Roy P C; Maes, Joseph H R

2017-09-01

It has been suggested that people with autism spectrum disorder (ASD) have an increased tendency to use explicit (or intentional) learning strategies. This altered learning may play a role in the development of the social communication difficulties characterizing ASD. In the current study, we investigated incidental and intentional sequence learning using a Serial Reaction Time (SRT) task in an adult ASD population. Response times and event related potentials (ERP) components (N2b and P3) were assessed as indicators of learning and knowledge. Findings showed that behaviorally, sequence learning and ensuing explicit knowledge were similar in ASD and typically developing (TD) controls. However, ERP findings showed that learning in the TD group was characterized by an enhanced N2b, while learning in the ASD group was characterized by an enhanced P3. These findings suggest that learning in the TD group might be more incidental in nature, whereas learning in the ASD group is more intentional or effortful. Increased intentional learning might serve as a strategy for individuals with ASD to control an overwhelming environment. Although this led to similar behavioral performances on the SRT task, it is very plausible that this intentional learning has adverse effects in more complex social situations, and hence contributes to the social impairments found in ASD. Autism Res 2017, 10: 1533-1543. © 2017 International Society for Autism Research, Wiley Periodicals, Inc. © 2017 International Society for Autism Research, Wiley Periodicals, Inc.

Politiken, Alt om Ikast Brande (web), Lemvig Folkeblad (Web), Politiken (web), Dabladet Ringkjøbing Skjern (web)

DEFF Research Database (Denmark)

Lauritsen, Jens

2014-01-01

Politiken 01.01.2014 14:16 Danskerne skød nytåret ind med et brag, men for enkeltes vedkommende gik det galt, da nytårskrudtet blev tændt. Skadestuerne har behandlet 73 personer for fyrværkeriskader mellem klokken 18 i aftes og klokken 06 i morges. Det viser en optælling, som Politiken har...... foretaget på baggrund af tal fra Ulykkes Analyse Gruppen på Odense Universitetshospital. Artiklen er også bragt i: Alt om Ikast Brande (web), Lemvig Folkeblad (web), Politiken (web), Dagbladet Ringkjøbing Skjern (web)....

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

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Christian Albers

Full Text Available Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP. Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious and strong (teacher spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

Science.gov (United States)

Albers, Christian; Westkott, Maren; Pawelzik, Klaus

2016-01-01

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

Cluster analysis of activity-time series in motor learning

DEFF Research Database (Denmark)

Balslev, Daniela; Nielsen, Finn Å; Futiger, Sally A

2002-01-01

Neuroimaging studies of learning focus on brain areas where the activity changes as a function of time. To circumvent the difficult problem of model selection, we used a data-driven analytic tool, cluster analysis, which extracts representative temporal and spatial patterns from the voxel......-time series. The optimal number of clusters was chosen using a cross-validated likelihood method, which highlights the clustering pattern that generalizes best over the subjects. Data were acquired with PET at different time points during practice of a visuomotor task. The results from cluster analysis show...

Learning to stay ahead of time

DEFF Research Database (Denmark)

Staunæs, Dorthe; Raffnsøe, Sverre

2014-01-01

In the context of an ongoing change, management is required to take the form of a leadership that must be reignited over and over again. The article examines a new art of leadership that may be viewed as an attempt to keep up with these challenges and stay ahead of time. It emerges from...... a pilgrimage leadership learning laboratory on the road to Santiago de la Compostela. This moving lab creates situations of extraordinary intensity that border on hyperreality and force the leader to find him/herself anew on the verge of him/herself. Conceived as pilgrimage, leadership moves ahead of time...... as it reaches into and anticipates a future still unknown. In this setting, anticipatory affects and the virtual take up a predominant role. As it emerges here, leadership distinguishes itself not only from leadership in the traditional sense, but also from management and governmentality....

Incontri di Fisica delle Alte Energie Italian Meeting on High Energy Physics Napoli

CERN Document Server

Carlino, Gianpaolo; Merola, Leonardo; Paolucci, Pierluigi; Ricciardi, Giulia; IFAE 2007

2008-01-01

This book collects the Proceedings of the Workshop "Incontri di Fisica delle Alte Energie (IFAE) 2007, Napoli, 11-13 April 2007". This is the sixth edition of a series of meetings on fundamental research in particle physics and was attended by about 160 researchers. Presentations, both theoretical and experimental, addressed the status of Physics of the Standard Model and beyond, Flavour phyisc, Neutrino and Astroparticle physics, new technology in high energy physics. Special emphasis was given to the expectations of the forthcoming Large Hadron Collider, due in operation at the end of 2007. The venue of plenary sessions interleaved with parallel ones allowed for a rich exchange of ideas, presented in these Proceedings, that form a coherent picture of the findings and of the open questions in this extremely challenging cultural field. The venue of plenary sessions interleaved with parallel ones allowed for a rich exchange of ideas, presented in these Proceedings, that form a coherent picture of the findings ...

Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology

OpenAIRE

Richard Chiou; Yongjin (james) Kwon; Tzu-Liang (bill) Tseng; Robin Kizirian; Yueh-Ting Yang

2010-01-01

This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote c...

Impact of learning adaptability and time management disposition on study engagement among Chinese baccalaureate nursing students.

Science.gov (United States)

Liu, Jing-Ying; Liu, Yan-Hui; Yang, Ji-Peng

2014-01-01

The aim of this study was to explore the relationships among study engagement, learning adaptability, and time management disposition in a sample of Chinese baccalaureate nursing students. A convenient sample of 467 baccalaureate nursing students was surveyed in two universities in Tianjin, China. Students completed a questionnaire that included their demographic information, Chinese Utrecht Work Engagement Scale-Student Questionnaire, Learning Adaptability Scale, and Adolescence Time Management Disposition Scale. One-way analysis of variance tests were used to assess the relationship between certain characteristics of baccalaureate nursing students. Pearson correlation was performed to test the correlation among study engagement, learning adaptability, and time management disposition. Hierarchical linear regression analyses were performed to explore the mediating role of time management disposition. The results revealed that study engagement (F = 7.20, P < .01) and learning adaptability (F = 4.41, P < .01) differed across grade groups. Learning adaptability (r = 0.382, P < .01) and time management disposition (r = 0.741, P < .01) were positively related with study engagement. Time management disposition had a partially mediating effect on the relationship between study engagement and learning adaptability. The findings implicate that educators should not only promote interventions to increase engagement of baccalaureate nursing students but also focus on development, investment in adaptability, and time management. Copyright © 2014 Elsevier Inc. All rights reserved.

[A technological device for optimizing the time taken for blind people to learn Braille].

Science.gov (United States)

Hernández, Cesar; Pedraza, Luis F; López, Danilo

2011-10-01

This project was aimed at designing and putting an electronic prototype into practice for improving the initial time taken by visually handicapped people for learning Braille, especially children. This project was mainly based on a prototype digital electronic device which identifies and translates material written by a user in Braille by a voice synthesis system, producing artificial words to determine whether a handicapped person's writing in Braille has been correct. A global system for mobile communications (GSM) module was also incorporated into the device which allowed it to send text messages, thereby involving innovation in the field of articles for aiding visually handicapped people. This project's main result was an easily accessed and understandable prototype device which improved visually handicapped people's initial learning of Braille. The time taken for visually handicapped people to learn Braille became significantly reduced whilst their interest increased, as did their concentration time regarding such learning.

E-learning Platforms and E-learning Students: Building the Bridge to Success

Directory of Open Access Journals (Sweden)

Sérgio GONÇALVES

2013-07-01

Full Text Available Normal 0 21 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} E-learning platforms are becoming more and more common in education and with organisations. They are seen as a complementary tool to support learning or, as in many cases, as the primary tool to do it (possibly the only one. In traditional learning, teachers can easily get an insight into how their students work and learn, and how they interact in the classroom. However, in online learning, it is more difficult for teachers to see how individual students behave. Affective states and learning styles are determinant in students’ performance. Together with stress, these are crucial factor to success. It is believed that the sole use of an E-learning platform can in itself be a cause of stress for students. Estimating, in a non-invasive way, such parameters, and taking measures to deal with them, are then the goal of this paper. We do not consider the use of dedicated sensors (invasive such as special gloves or wrist bracelets since we intend not to be dependent on specific hardware and also because we believe that such specific hardware can induce for itself some alteration in the parameters being analysed. Our work focuses on the development of a new module (Dynamic Recognition Module to incorporate in Moodle E-learning platform, to accommodate individualized support to E-learning students.

E-learning Platforms and E-learning Students: Building the Bridge to Success

Directory of Open Access Journals (Sweden)

Manuel RODRIGUES

2012-09-01

Full Text Available Normal 0 21 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Cambria; mso-ascii-font-family:Cambria; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Cambria; mso-hansi-theme-font:minor-latin; mso-ansi-language:EN-US;} E-learning platforms are becoming more and more common in education and with organisations. They are seen as a complementary tool to support learning or, as in many cases, as the primary tool to do it (possibly the only one. In traditional learning, teachers can easily get an insight into how their students work and learn, and how they interact in the classroom. However, in online learning, it is more difficult for teachers to see how individual students behave. Affective states and learning styles are determinant in students’ performance. Together with stress, these are crucial factor to success. It is believed that the sole use of an E-learning platform can in itself be a cause of stress for students. Estimating, in a non-invasive way, such parameters, and taking measures to deal with them, are then the goal of this paper. We do not consider the use of dedicated sensors (invasive such as special gloves or wrist bracelets since we intend not to be dependent on specific hardware and also because we believe that such specific hardware can induce for itself some alteration in the parameters being analysed. Our work focuses on the development of a new module (Dynamic Recognition Module to incorporate in Moodle E-learning platform, to accommodate individualized support to E-learning students.

Time-sensitive Customer Churn Prediction based on PU Learning

OpenAIRE

Wang, Li; Chen, Chaochao; Zhou, Jun; Li, Xiaolong

2018-01-01

With the fast development of Internet companies throughout the world, customer churn has become a serious concern. To better help the companies retain their customers, it is important to build a customer churn prediction model to identify the customers who are most likely to churn ahead of time. In this paper, we propose a Time-sensitive Customer Churn Prediction (TCCP) framework based on Positive and Unlabeled (PU) learning technique. Specifically, we obtain the recent data by shortening the...

EEG Eye State Identification Using Incremental Attribute Learning with Time-Series Classification

Directory of Open Access Journals (Sweden)

Ting Wang

2014-01-01

Full Text Available Eye state identification is a kind of common time-series classification problem which is also a hot spot in recent research. Electroencephalography (EEG is widely used in eye state classification to detect human's cognition state. Previous research has validated the feasibility of machine learning and statistical approaches for EEG eye state classification. This paper aims to propose a novel approach for EEG eye state identification using incremental attribute learning (IAL based on neural networks. IAL is a novel machine learning strategy which gradually imports and trains features one by one. Previous studies have verified that such an approach is applicable for solving a number of pattern recognition problems. However, in these previous works, little research on IAL focused on its application to time-series problems. Therefore, it is still unknown whether IAL can be employed to cope with time-series problems like EEG eye state classification. Experimental results in this study demonstrates that, with proper feature extraction and feature ordering, IAL can not only efficiently cope with time-series classification problems, but also exhibit better classification performance in terms of classification error rates in comparison with conventional and some other approaches.

Influencing Work-Related Learning: The Role of Job Characteristics and Self-Directed Learning Orientation in Part-Time Vocational Education

Science.gov (United States)

Gijbels, David; Raemdonck, Isabel; Vervecken, Dries

2010-01-01

Based on the Demand-Control-Support (DCS) model, the present paper aims to investigate the influence of job characteristics such as job demands, job control, social support at work and self-directed learning orientation on the work-related learning behaviour of workers. The present study was conducted in a centre for part-time vocational education…

Probability Learning: Changes in Behavior across Time and Development

Science.gov (United States)

Plate, Rista C.; Fulvio, Jacqueline M.; Shutts, Kristin; Green, C. Shawn; Pollak, Seth D.

2018-01-01

Individuals track probabilities, such as associations between events in their environments, but less is known about the degree to which experience--within a learning session and over development--influences people's use of incoming probabilistic information to guide behavior in real time. In two experiments, children (4-11 years) and adults…

Learning of temporal motor patterns: An analysis of continuous vs. reset timing

Directory of Open Access Journals (Sweden)

Rodrigo eLaje

2011-10-01

Full Text Available Our ability to generate well-timed sequences of movements is critical to an array of behaviors, including the ability to play a musical instrument or a video game. Here we address two questions relating to timing with the goal of better understanding the neural mechanisms underlying temporal processing. First, how does accuracy and variance change over the course of learning of complex spatiotemporal patterns? Second, is the timing of sequential responses most consistent with starting and stopping an internal timer at each interval or with continuous timing?To address these questions we used a psychophysical task in which subjects learned to reproduce a sequence of finger taps in the correct order and at the correct times—much like playing a melody at the piano. This task allowed us to calculate the variance of the responses at different time points using data from the same trials. Our results show that while standard Weber’s law is clearly violated, variance does increase as a function of time squared, as expected according to the generalized form of Weber’s law—which separates the source of variance into time-dependent and time-independent components. Over the course of learning, both the time-independent variance and the coefficient of the time-dependent term decrease. Our analyses also suggest that timing of sequential events does not rely on the resetting of an internal timer at each event.We describe and interpret our results in the context of computer simulations that capture some of our psychophysical findings. Specifically, we show that continuous timing, as opposed to reset timing, is expected from population clock models in which timing emerges from the internal dynamics of recurrent neural networks.

A Distance Learning Review--The Communicational Module "Learning on Demand--Anywhere at Any Time"

Science.gov (United States)

Tatkovic, Nevenka; Ruzic, Maja

2004-01-01

The society of knowledge refers to the society marked with the principle which requires that knowledge, information and life-time learning hold a key to success in the world of IT technology. Internet, World Wide Web, Web Based Education and ever so growing speed of IT and communicational technologies have enabled the application of new modes,…

Overlay improvements using a real time machine learning algorithm

Science.gov (United States)

Schmitt-Weaver, Emil; Kubis, Michael; Henke, Wolfgang; Slotboom, Daan; Hoogenboom, Tom; Mulkens, Jan; Coogans, Martyn; ten Berge, Peter; Verkleij, Dick; van de Mast, Frank

2014-04-01

While semiconductor manufacturing is moving towards the 14nm node using immersion lithography, the overlay requirements are tightened to below 5nm. Next to improvements in the immersion scanner platform, enhancements in the overlay optimization and process control are needed to enable these low overlay numbers. Whereas conventional overlay control methods address wafer and lot variation autonomously with wafer pre exposure alignment metrology and post exposure overlay metrology, we see a need to reduce these variations by correlating more of the TWINSCAN system's sensor data directly to the post exposure YieldStar metrology in time. In this paper we will present the results of a study on applying a real time control algorithm based on machine learning technology. Machine learning methods use context and TWINSCAN system sensor data paired with post exposure YieldStar metrology to recognize generic behavior and train the control system to anticipate on this generic behavior. Specific for this study, the data concerns immersion scanner context, sensor data and on-wafer measured overlay data. By making the link between the scanner data and the wafer data we are able to establish a real time relationship. The result is an inline controller that accounts for small changes in scanner hardware performance in time while picking up subtle lot to lot and wafer to wafer deviations introduced by wafer processing.

Incorporating Real-time Earthquake Information into Large Enrollment Natural Disaster Course Learning

Science.gov (United States)

Furlong, K. P.; Benz, H.; Hayes, G. P.; Villasenor, A.

2010-12-01

Although most would agree that the occurrence of natural disaster events such as earthquakes, volcanic eruptions, and floods can provide effective learning opportunities for natural hazards-based courses, implementing compelling materials into the large-enrollment classroom environment can be difficult. These natural hazard events derive much of their learning potential from their real-time nature, and in the modern 24/7 news-cycle where all but the most devastating events are quickly out of the public eye, the shelf life for an event is quite limited. To maximize the learning potential of these events requires that both authoritative information be available and course materials be generated as the event unfolds. Although many events such as hurricanes, flooding, and volcanic eruptions provide some precursory warnings, and thus one can prepare background materials to place the main event into context, earthquakes present a particularly confounding situation of providing no warning, but where context is critical to student learning. Attempting to implement real-time materials into large enrollment classes faces the additional hindrance of limited internet access (for students) in most lecture classrooms. In Earth 101 Natural Disasters: Hollywood vs Reality, taught as a large enrollment (150+ students) general education course at Penn State, we are collaborating with the USGS’s National Earthquake Information Center (NEIC) to develop efficient means to incorporate their real-time products into learning activities in the lecture hall environment. Over time (and numerous events) we have developed a template for presenting USGS-produced real-time information in lecture mode. The event-specific materials can be quickly incorporated and updated, along with key contextual materials, to provide students with up-to-the-minute current information. In addition, we have also developed in-class activities, such as student determination of population exposure to severe ground

Why Hong Kong students favour more face-to-face classroom time in blended learning

Directory of Open Access Journals (Sweden)

James Henri

Full Text Available A three year study in student characteristics, needs and learning styles guided instructors at the University of Hong Kong Faculty of Education to improve teaching and learning in a core module: Information Literacy. A mixed-method approach analyzed data collected from undergraduate, in-service teachers in a BEd program, and helped instructors in the program to gain insight into the Hong Kong teacher working, post-service towards a BEd in Library and Information Science. Part-time students indicated a preference for a combination of online and face-to-face teaching, with more face-to-face class time in that mix. These findings would also be informative for other part-time programs using blended teaching and learning models.

Learning motion concepts using real-time microcomputer-based laboratory tools

Science.gov (United States)

Thornton, Ronald K.; Sokoloff, David R.

1990-09-01

Microcomputer-based laboratory (MBL) tools have been developed which interface to Apple II and Macintosh computers. Students use these tools to collect physical data that are graphed in real time and then can be manipulated and analyzed. The MBL tools have made possible discovery-based laboratory curricula that embody results from educational research. These curricula allow students to take an active role in their learning and encourage them to construct physical knowledge from observation of the physical world. The curricula encourage collaborative learning by taking advantage of the fact that MBL tools present data in an immediately understandable graphical form. This article describes one of the tools—the motion detector (hardware and software)—and the kinematics curriculum. The effectiveness of this curriculum compared to traditional college and university methods for helping students learn basic kinematics concepts has been evaluated by pre- and post-testing and by observation. There is strong evidence for significantly improved learning and retention by students who used the MBL materials, compared to those taught in lecture.

Cerebellar motor learning versus cerebellar motor timing: the climbing fibre story

Science.gov (United States)

Llinás, Rodolfo R

2011-01-01

Abstract Theories concerning the role of the climbing fibre system in motor learning, as opposed to those addressing the olivocerebellar system in the organization of motor timing, are briefly contrasted. The electrophysiological basis for the motor timing hypothesis in relation to the olivocerebellar system is treated in detail. PMID:21486816

EDUCATIONAL LEAPFROGGING IN THE mLEARNING TIME

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Abdel Rahman IBRAHIM SULEIMAN

2014-07-01

Full Text Available In this theoretical study, researcher tries to shed light on the modern strategy of education, Mobile learning is this strategy, which has become a reality exists in the educational institutions and aims researcher of this study. Trying to figure out the reality of Mobil Determining if the mobile learning part of the E-Learning. Trying for identify future of mobile learning. And the researcher collect the information and the data from previous research in addition to what has been published on websites and blogs and has reached the researcher to achieve the successes of Mobile learning at the level of the educational process now , and that this strategy of mobile learning is not part of the e-learning, and generation of generations , but a new way for the development of the educational process educational , researcher is expected to evolve Mobile learning expands even at the all levels of educational.

Time-place learning and memory persist in mice lacking functional Per1 and Per2 clock genes.

Science.gov (United States)

Mulder, C; Van Der Zee, E A; Hut, R A; Gerkema, M P

2013-12-01

With time-place learning, animals link a stimulus with the location and the time of day. This ability may optimize resource localization and predator avoidance in daily changing environments. Time-place learning is a suitable task to study the interaction of the circadian system and memory. Previously, we showed that time-place learning in mice depends on the circadian system and Cry1 and/or Cry2 clock genes. We questioned whether time-place learning is Cry specific or also depends on other core molecular clock genes. Here, we show that Per1/Per2 double mutant mice, despite their arrhythmic phenotype, acquire time-place learning similar to wild-type mice. As well as an established role in circadian rhythms, Per genes have also been implicated in the formation and storage of memory. We found no deficiencies in short-term spatial working memory in Per mutant mice compared to wild-type mice. Moreover, both Per mutant and wild-type mice showed similar long-term memory for contextual features of a paradigm (a mild foot shock), measured in trained mice after a 2-month nontesting interval. In contrast, time-place associations were lost in both wild-type and mutant mice after these 2 months, suggesting a lack of maintained long-term memory storage for this type of information. Taken together, Cry-dependent time-place learning does not require Per genes, and Per mutant mice showed no PER-specific short-term or long-term memory deficiencies. These results limit the functional role of Per clock genes in the circadian regulation of time-place learning and memory.

Blended learning pedagogy: the time is now!

Science.gov (United States)

Pizzi, Michael A

2014-07-01

Pedagogy is rapidly changing. To develop best practice in academia, it is important that we change with the changing needs of students. This article suggests that blended learning is one of the most important pedagogical formats that can enhance student learning, optimize the use of active learning strategies, and potentially improve student learning outcomes.

A Computational Model of the Temporal Dynamics of Plasticity in Procedural Learning: Sensitivity to Feedback Timing

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Vivian V. Valentin

2014-07-01

Full Text Available The evidence is now good that different memory systems mediate the learning of different types of category structures. In particular, declarative memory dominates rule-based (RB category learning and procedural memory dominates information-integration (II category learning. For example, several studies have reported that feedback timing is critical for II category learning, but not for RB category learning – results that have broad support within the memory systems literature. Specifically, II category learning has been shown to be best with feedback delays of 500ms compared to delays of 0 and 1000ms, and highly impaired with delays of 2.5 seconds or longer. In contrast, RB learning is unaffected by any feedback delay up to 10 seconds. We propose a neurobiologically detailed theory of procedural learning that is sensitive to different feedback delays. The theory assumes that procedural learning is mediated by plasticity at cortical-striatal synapses that are modified by dopamine-mediated reinforcement learning. The model captures the time-course of the biochemical events in the striatum that cause synaptic plasticity, and thereby accounts for the empirical effects of various feedback delays on II category learning.

Twist-3 effect from the longitudinally polarized proton for ALT in hadron production from pp collisions

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Yuji Koike

2016-08-01

Full Text Available We compute the contribution from the longitudinally polarized proton to the twist-3 double-spin asymmetry ALT in inclusive (light hadron production from proton–proton collisions, i.e., p↑p→→hX. We show that using the relevant QCD equation-of-motion relation and Lorentz invariance relation allows one to eliminate the twist-3 quark-gluon correlator (associated with the longitudinally polarized proton in favor of one-variable twist-3 quark distributions and the (twist-2 transversity parton density. Including this result with the twist-3 pieces associated with the transversely polarized proton and unpolarized final-state hadron (which have already been calculated in the literature, we now have the complete leading-order cross section for this process.

Plasma influence on throat conductance of the TEXTOR pump limiter ALT-I

International Nuclear Information System (INIS)

Hardtke, A.; Finken, K.H.; Reiter, D.; Dippel, K.H.; Goebel, D.M.; McGrath, R.T.; Sagara, A.

1989-01-01

On the TEXTOR pump limiter ALT-I conductance measurements for the backstreaming of gas from the pump limiter vessel through the pump limiter entrance have been performed. In these experiments neutral gas has been injected into the pump limiter plenum during a short pulse. The influence of the instreaming plasma results in a reduction of the conductance of the outstreaming gas. For helium the conductance is reduced to about 40% of the molecular conductance when a plasma flux of 0.8 A/cm 2 (T e =T i =11 eV) is streaming into the pump limiter throat. The reduction of the conductance for backstreaming hydrogen and deuterium under the same plasma conditions is smaller; about 70% of the molecular conductance is obtained. This reduction can be explained by an increased recycling of ions which have been produced in the throat back to the neutralizer plate. The experimental results can be reproduced by Monte Carlo neutral transport code calculations if the recycling coefficient is about 0.85 for hydrogen and deuterium and about 0.95 for helium ions. Processes causing these high recycling coefficients are discussed and their influence is estimated. (orig.)

KOMPARASI KEMAMPUAN KOMUNIKASI MATEMATIS SISWA DENGAN MODEL LEARNING CYCLE DAN TIME TOKEN

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Arin Ayundhita

2014-11-01

Full Text Available Tujuan penelitian ini untuk mengetahui apakah model pembelajaran Learning Cycle 5E dan model pembelajaran Time Token pada siswa kelas VIII materi keliling dan luas lingkaran dapat mencapai ketuntasan belajar dan untuk mengetahui manakah yang lebih baik antara model pembelajaran Learning Cycle 5E dan model pembelajaran Time Token. Populasi dalam penelitian ini adalah siswa kelas VIII SMP Negeri 1 Sine Kabupaten Ngawi tahun pelajaran 2013/2014. Dengan menggunakan teknik cluster random sampling, terpilih sampel yaitu siswa kelas VIII A sebagai kelas eksperimen 1 dan kelas VIII E sebagai kelas eksperimen 2. Pengumpulan data dilakukan dengan metode dokumentasi, tes, dan observasi. Analisis data menggunakan uji proporsi dan uji perbedaan dua rata-rata. Dari hasil uji ketuntasan belajar diperoleh siswa kelas eksperimen 1 mencapai ketuntasan belajar klasikal sementara kelas eksperimen 2 belum mencapai ketuntasan belajar klasikal. Dari hasil uji perbedaan rata-rata satu pihak diperoleh rata-rata kemampuan komunikasi matematis siswa kelas eksperimen 1 lebih baik daripada rata-rata kemampuan komunikasi matematis siswa kelas eksperimen 2. Simpulan yang diperoleh adalah model pembelajaran Learning Cycle 5E lebih baik dari pembelajaran dengan model Time Token.

Distributing learning over time: the spacing effect in children's acquisition and generalization of science concepts.

Science.gov (United States)

Vlach, Haley A; Sandhofer, Catherine M

2012-01-01

The spacing effect describes the robust finding that long-term learning is promoted when learning events are spaced out in time rather than presented in immediate succession. Studies of the spacing effect have focused on memory processes rather than for other types of learning, such as the acquisition and generalization of new concepts. In this study, early elementary school children (5- to 7-year-olds; N = 36) were presented with science lessons on 1 of 3 schedules: massed, clumped, and spaced. The results revealed that spacing lessons out in time resulted in higher generalization performance for both simple and complex concepts. Spaced learning schedules promote several types of learning, strengthening the implications of the spacing effect for educational practices and curriculum. © 2012 The Authors. Child Development © 2012 Society for Research in Child Development, Inc.

Real time eye tracking using Kalman extended spatio-temporal context learning

Science.gov (United States)

Munir, Farzeen; Minhas, Fayyaz ul Amir Asfar; Jalil, Abdul; Jeon, Moongu

2017-06-01

Real time eye tracking has numerous applications in human computer interaction such as a mouse cursor control in a computer system. It is useful for persons with muscular or motion impairments. However, tracking the movement of the eye is complicated by occlusion due to blinking, head movement, screen glare, rapid eye movements, etc. In this work, we present the algorithmic and construction details of a real time eye tracking system. Our proposed system is an extension of Spatio-Temporal context learning through Kalman Filtering. Spatio-Temporal Context Learning offers state of the art accuracy in general object tracking but its performance suffers due to object occlusion. Addition of the Kalman filter allows the proposed method to model the dynamics of the motion of the eye and provide robust eye tracking in cases of occlusion. We demonstrate the effectiveness of this tracking technique by controlling the computer cursor in real time by eye movements.

Neuromodulated Spike-Timing-Dependent Plasticity and Theory of Three-Factor Learning Rules

Directory of Open Access Journals (Sweden)

Wulfram eGerstner

2016-01-01

Full Text Available Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulatorson synaptic plasticity is useful not just for action learning in classical conditioning, but also to decide 'when' to create new memories in response to a flow of sensory stimuli.In this review, we focus on timing requirements for pre- and postsynaptic activity in conjunction with one or several phasic neuromodulatory signals. While the emphasis of the text is on conceptual models and mathematical theories, we also discusssome experimental evidence for neuromodulation of Spike-Timing-Dependent Plasticity.We highlight the importance of synaptic mechanisms in bridging the temporal gap between sensory stimulation and neuromodulatory signals, and develop a framework for a class of neo-Hebbian three-factor learning rules that depend on presynaptic activity, postsynaptic variables as well as the influence of neuromodulators.

[Lange, Thomas. Zwischen Reformation und Untergang Alt-Livlands. Der Rigaer Erzbischof Wilhelm von Brandenburg im Beziehungsgeflecht der livländischen Konföderation und ihrer Nachbarländer] / Madis Maasing

Index Scriptorium Estoniae

Maasing, Madis, 1984-

2014-01-01

Arvustus: Lange, Thomas. Zwischen Reformation und Untergang Alt-Livlands. Der Rigaer Erzbischof Wilhelm von Brandenburg im Beziehungsgeflecht der livländischen Konföderation und ihrer Nachbarländer

LIMITED ELEVATIONS IN ANTITUBERCULOSIS DRUG-INDUCED SERUM ALANINE AMINOTRANSFERASE (ALT) LEVELS IN A COHORT OF NIGERIANS ON TREATMENT FOR PULMONARY TUBERCULOSIS AND HIV INFECTION IN YENAGOA

NARCIS (Netherlands)

Ikuabe, Peter Ogie; Ebuenyi, Ikenna Desmond; Harry, Tubonye Clement

2015-01-01

BACKGROUND: This study, undertaken in a major tertiary hospital in the Niger Delta region of Nigeria was designed to examine the incidence of elevation in serum alanine aminotransference (ALT) in our patients who were on treatment for HIV/AIDS with some of them on antituberculosis drugs. METHOD:

Karakteristik og vurdering af Time-Driven Activity-Based Costing på baggrund af ABC’s udvikling

DEFF Research Database (Denmark)

Israelsen, Poul; Kristensen, Thomas Borup

2015-01-01

Artiklen giver en beskrivelse af Activity Based Costings udvikling i fire varianter. Disse benyttes som grundlag for at karakterisere og vurdere de ændringer, som foretages i Time-Driven ABC (TDABC). Det konkluderes at TDABC i nogle tilfælde griber tilbage til omkostningskalkulation forud for ABC......’s tilblivelse (f.eks. homogene afdelinger i stedet for aktiviteter), i andre tilfælde til ABC’s oprindelse (f.eks. brug af transaktionsdrivere i stedet for mere sofistikerede drivere), og på andre områder til metoder uden for ABC’s normale anvendelse (f.eks. “standard variable costing”). Alt i alt argumenteres...

Experiments with Online Reinforcement Learning in Real-Time Strategy Games

DEFF Research Database (Denmark)

Toftgaard Andersen, Kresten; Zeng, Yifeng; Dahl Christensen, Dennis

2009-01-01

Real-time strategy (RTS) games provide a challenging platform to implement online reinforcement learning (RL) techniques in a real application. Computer, as one game player, monitors opponents' (human or other computers) strategies and then updates its own policy using RL methods. In this article......, we first examine the suitability of applying the online RL in various computer games. Reinforcement learning application depends on both RL complexity and the game features. We then propose a multi-layer framework for implementing online RL in an RTS game. The framework significantly reduces RL...... the effectiveness of our proposed framework and shed light on relevant issues in using online RL in RTS games....

CAT-PUMA: CME Arrival Time Prediction Using Machine learning Algorithms

Science.gov (United States)

Liu, Jiajia; Ye, Yudong; Shen, Chenglong; Wang, Yuming; Erdélyi, Robert

2018-04-01

CAT-PUMA (CME Arrival Time Prediction Using Machine learning Algorithms) quickly and accurately predicts the arrival of Coronal Mass Ejections (CMEs) of CME arrival time. The software was trained via detailed analysis of CME features and solar wind parameters using 182 previously observed geo-effective partial-/full-halo CMEs and uses algorithms of the Support Vector Machine (SVM) to make its predictions, which can be made within minutes of providing the necessary input parameters of a CME.

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Science.gov (United States)

Lee, Hanbong

2016-01-01

Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

Tensorial dynamic time warping with articulation index representation for efficient audio-template learning.

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Le, Long N; Jones, Douglas L

2018-03-01

Audio classification techniques often depend on the availability of a large labeled training dataset for successful performance. However, in many application domains of audio classification (e.g., wildlife monitoring), obtaining labeled data is still a costly and laborious process. Motivated by this observation, a technique is proposed to efficiently learn a clean template from a few labeled, but likely corrupted (by noise and interferences), data samples. This learning can be done efficiently via tensorial dynamic time warping on the articulation index-based time-frequency representations of audio data. The learned template can then be used in audio classification following the standard template-based approach. Experimental results show that the proposed approach outperforms both (1) the recurrent neural network approach and (2) the state-of-the-art in the template-based approach on a wildlife detection application with few training samples.

Precarious Learning and Labour in Financialized Times

Directory of Open Access Journals (Sweden)

Jamie Magnusson

2013-07-01

Full Text Available Our current globalized economic regimes of financialized capital have systematically altered relations of learning and labour through the dynamics of precarity, debt, and the political economy of new wars. The risks of these regimes are absorbed unevenly across transnational landscapes, creating cartographies of violence and dispossession, particularly among youth, indigenous, working class, and racialized women. Presently there is surprisingly little discussion on the relevance of financialization for adult educators. Transnational resistances organizing against neoliberal restructuring, austerity policies, and debt crises are emerging at the same time that massive investments are being made into homeland security and the carceral state. This paper opens up discussion on the implications of financialized times for educators, and develops an analytic framework for examining how these global realities are best addressed at local sites of adult and higher education.

Learning characteristics of a space-time neural network as a tether skiprope observer

Science.gov (United States)

Lea, Robert N.; Villarreal, James A.; Jani, Yashvant; Copeland, Charles

1993-01-01

The Software Technology Laboratory at the Johnson Space Center is testing a Space Time Neural Network (STNN) for observing tether oscillations present during retrieval of a tethered satellite. Proper identification of tether oscillations, known as 'skiprope' motion, is vital to safe retrieval of the tethered satellite. Our studies indicate that STNN has certain learning characteristics that must be understood properly to utilize this type of neural network for the tethered satellite problem. We present our findings on the learning characteristics including a learning rate versus momentum performance table.

Kinesthetic Astronomy: Significant Upgrades to the Sky Time Lesson that Support Student Learning

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Morrow, C. A.; Zawaski, M.

2004-12-01

This paper will report on a significant upgrade to the first in a series of innovative, experiential lessons we call Kinesthetic Astronomy. The Sky Time lesson reconnects students with the astronomical meaning of the day, year, and seasons. Like all Kinesthetic Astronomy lessons, it teaches basic astronomical concepts through choreographed bodily movements and positions that provide educational sensory experiences. They are intended for sixth graders up through adult learners in both formal and informal educational settings. They emphasize astronomical concepts and phenomenon that people can readily encounter in their "everyday" lives such as time, seasons, and sky motions of the Sun, Moon, stars, and planets. Kinesthetic Astronomy lesson plans are fully aligned with national science education standards, both in content and instructional practice. Our lessons offer a complete learning cycle with written assessment opportunities now embedded throughout the lesson. We have substantially strengthened the written assessment options for the Sky Time lesson to help students translate their kinesthetic and visual learning into the verbal-linguistic and mathematical-logical realms of expression. Field testing with non-science undergraduates, middle school science teachers and students, Junior Girl Scouts, museum education staff, and outdoor educators has been providing evidence that Kinesthetic Astronomy techniques allow learners to achieve a good grasp of concepts that are much more difficult to learn in more conventional ways such as via textbooks or even computer animation. Field testing of the Sky Time lesson has also led us to significant changes from the previous version to support student learning. We will report on the nature of these changes.

A comparison of the stochastic and machine learning approaches in hydrologic time series forecasting

Science.gov (United States)

Kim, T.; Joo, K.; Seo, J.; Heo, J. H.

2016-12-01

Hydrologic time series forecasting is an essential task in water resources management and it becomes more difficult due to the complexity of runoff process. Traditional stochastic models such as ARIMA family has been used as a standard approach in time series modeling and forecasting of hydrological variables. Due to the nonlinearity in hydrologic time series data, machine learning approaches has been studied with the advantage of discovering relevant features in a nonlinear relation among variables. This study aims to compare the predictability between the traditional stochastic model and the machine learning approach. Seasonal ARIMA model was used as the traditional time series model, and Random Forest model which consists of decision tree and ensemble method using multiple predictor approach was applied as the machine learning approach. In the application, monthly inflow data from 1986 to 2015 of Chungju dam in South Korea were used for modeling and forecasting. In order to evaluate the performances of the used models, one step ahead and multi-step ahead forecasting was applied. Root mean squared error and mean absolute error of two models were compared.

Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics.

Science.gov (United States)

Geilleit, R; Hen, Z Q; Chong, C Y; Loh, A P; Pang, N L; Peterson, G M; Ng, K C; Huis, A; de Korne, D F

2018-04-09

Various technologies have been developed to improve hand hygiene (HH) compliance in inpatient settings; however, little is known about the feasibility of machine learning technology for this purpose in outpatient clinics. To assess the effectiveness, user experiences, and costs of implementing a real-time HH notification machine learning system in outpatient clinics. In our mixed methods study, a multi-disciplinary team co-created an infrared guided sensor system to automatically notify clinicians to perform HH just before first patient contact. Notification technology effects were measured by comparing HH compliance at baseline (without notifications) with real-time auditory notifications that continued till HH was performed (intervention I) or notifications lasting 15 s (intervention II). User experiences were collected during daily briefings and semi-structured interviews. Costs of implementation of the system were calculated and compared to the current observational auditing programme. Average baseline HH performance before first patient contact was 53.8%. With real-time auditory notifications that continued till HH was performed, overall HH performance increased to 100% (P machine learning system were estimated to be 46% lower than the observational auditing programme. Machine learning technology that enables real-time HH notification provides a promising cost-effective approach to both improving and monitoring HH, and deserves further development in outpatient settings. Copyright © 2018 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.

Working and Learning in Times of Uncertainty

DEFF Research Database (Denmark)

This book analyses the challenges of globalisation and uncertainty impacting on working and learning at individual, organisational and societal levels. Each of the contributions addresses two overall questions: How is working and learning affected by uncertainty and globalisation? And, in what ways...... do individuals, organisations, political actors and education systems respond to these challenges? Part 1 focuses on the micro level of working and learning for understanding the learning processes from an individual point of view by reflecting on learners’ needs and situations at work and in school......). Finally, Part 3 addresses the macro level of working and learning by analysing how to govern, structure and organise vocational, professional and adult education at the boundaries of work, education and policy making....

Time to experiment…

Directory of Open Access Journals (Sweden)

Gráinne Conole

2002-12-01

Full Text Available In the last edition of ALT-J, the editorial team decided to try an experiment by creating a more interactive and critical debate on a particular article with the journal. Readers were invited to submit comments on an article by Davis and Denning entitled: 'Almost as helpful as good theory: some conceptual possibilities for the online classroom' (Davis and Denning, 2000. Summarized below are some of the key comments raised about the paper. Many thanks to everyone who took the time to send in these comments; individual acknowledgements have not been included for reasons of confidentiality.

Examining the Effect of Time Constraint on the Online Mastery Learning Approach towards Improving Postgraduate Students' Achievement

Science.gov (United States)

Ee, Mong Shan; Yeoh, William; Boo, Yee Ling; Boulter, Terry

2018-01-01

Time control plays a critical role within the online mastery learning (OML) approach. This paper examines the two commonly implemented mastery learning strategies--personalised system of instructions and learning for mastery (LFM)--by focusing on what occurs when there is an instructional time constraint. Using a large data set from a postgraduate…

A Study of Time Spent Working at Learning Centers. Technical Report #17.

Science.gov (United States)

Omori, Sharon; And Others

This study examined the proportion of time children in the Kamehameha Early Education Program schools spend at actual school work in learning centers. Systematic time-sampled observations using multiple observers were conducted in December-January and again in March-April. The subjects, 12 children (6 kindergarteners and 6 first graders) were…

Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum

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Tjeerd V. olde Scheper

2018-01-01

Full Text Available Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized

Information extraction from dynamic PS-InSAR time series using machine learning

Science.gov (United States)

van de Kerkhof, B.; Pankratius, V.; Chang, L.; van Swol, R.; Hanssen, R. F.

2017-12-01

Due to the increasing number of SAR satellites, with shorter repeat intervals and higher resolutions, SAR data volumes are exploding. Time series analyses of SAR data, i.e. Persistent Scatterer (PS) InSAR, enable the deformation monitoring of the built environment at an unprecedented scale, with hundreds of scatterers per km2, updated weekly. Potential hazards, e.g. due to failure of aging infrastructure, can be detected at an early stage. Yet, this requires the operational data processing of billions of measurement points, over hundreds of epochs, updating this data set dynamically as new data come in, and testing whether points (start to) behave in an anomalous way. Moreover, the quality of PS-InSAR measurements is ambiguous and heterogeneous, which will yield false positives and false negatives. Such analyses are numerically challenging. Here we extract relevant information from PS-InSAR time series using machine learning algorithms. We cluster (group together) time series with similar behaviour, even though they may not be spatially close, such that the results can be used for further analysis. First we reduce the dimensionality of the dataset in order to be able to cluster the data, since applying clustering techniques on high dimensional datasets often result in unsatisfying results. Our approach is to apply t-distributed Stochastic Neighbor Embedding (t-SNE), a machine learning algorithm for dimensionality reduction of high-dimensional data to a 2D or 3D map, and cluster this result using Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The results show that we are able to detect and cluster time series with similar behaviour, which is the starting point for more extensive analysis into the underlying driving mechanisms. The results of the methods are compared to conventional hypothesis testing as well as a Self-Organising Map (SOM) approach. Hypothesis testing is robust and takes the stochastic nature of the observations into account

The Role of Age and Occupational Future Time Perspective in Workers' Motivation to Learn

Science.gov (United States)

Kochoian, Nané; Raemdonck, Isabel; Frenay, Mariane; Zacher, Hannes

2017-01-01

The purpose of this paper is to better understand the relationship between employees' chronological age and their motivation to learn, by adopting a lifespan perspective. Based on socioemotional selectivity theory, we suggest that occupational future time perspective mediates the relationship between age and motivation to learn. In accordance with…

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Science.gov (United States)

Lee, Hanbong; Malik, Waqar; Jung, Yoon C.

2016-01-01

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

The Iberian hamlet of L’Alt del Punxó: agricultural production and peasant settlement in central Contestania | La aldea ibérica de L’Alt del Punxó: producción agrícola y asentamiento campesino en el área central de la Contestania

Directory of Open Access Journals (Sweden)

Israel Espí Pérez

2009-12-01

Full Text Available In this paper we describe an Iberian Iron Age village recently excavated as a result of the construction of a new motorway through Muro de l’Alcoi (El Comtat, Alicante province, Spain. The hamlet consists of huts, agricultural buildings and a paved road dating to between the seventh and fourth centuries BC. L’Alt del Punxó has significantly advanced our knowledge of Iberian settlement patterns and the organisation of economic activity in this area. An integrated analysis of the evidence furnished by the fieldwork increases our knowledge of different aspects of the social and economic organization of Iberian Iron Age populations in central Contestania. | En este trabajo damos a conocer un hábitat rural de época ibérica excavado con motivo de recientes obras de construcción de la Autovía del Mediterráneo a su paso por la localidad de Muro de l’Alcoi (El Comtat, Alacant. La aldea está conformada por cabañas, instalaciones de procesamiento agrícola y un camino empedrado que se datan entre los ss. VII y IV aC. L’Alt del Punxó ha permitido un considerable avance en el conocimiento de las formas de ocupación rural y articulación de la actividad económica de este territorio ibérico. La lectura integrada de la documentación permite abordar distintas cuestiones de la organización económica y social de los grupos ibéricos del área central de la Contestania.

The difference of delay time in monitoring system of facial acupressure learning media using bluetooth, wireless and ethernet

Science.gov (United States)

Agustin, Eny Widhia; Hangga, Arimaz; Fahrian, Muhammad Iqbal; Azhari, Anis Fikri

2018-03-01

The implementation of monitoring system in the facial acupressure learning media could increase the students' proficiency. However the common learning media still has not implemented a monitoring system in their learning process. This research was conducted to implement monitoring system in the mannequin head prototype as a learning media of facial acupressure using Bluetooth, wireless and Ethernet. The results of the implementation of monitoring system in the prototype showed that there were differences in the delay time between Bluetooth and wireless or Ethernet. The results data showed no difference in the average delay time between the use of Bluetooth with wireless and the use of Bluetooth with Ethernet in monitoring system of facial acupressure learning media. From all the facial acupressure points, the forehead facial acupressure point has the longest delay time of 11.93 seconds. The average delay time in all 3 class rooms was 1.96 seconds therefore the use of Bluetooth, wireless and Ethernet is highly recommended in the monitoring system of facial acupressure.

Student perspectives about mobile learning initiatives at Open University of Brazil: the mobile phone issue - doi: 10.4025/actascieduc.v32i2.11545

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Otacilio Antunes Santana

2010-12-01

Full Text Available Normal 0 21 false false false MicrosoftInternetExplorer4 /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabela normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:10.0pt; font-family:"Times New Roman"; mso-ansi-language:#0400; mso-fareast-language:#0400; mso-bidi-language:#0400;} The objectives of this study were to verify if students of Open University of Brazil approve of mobile learning (m-learning initiatives, to identify the students' perspectives about m-learning, to develop a model of instructional design for m-learning environments, and to quantify student satisfaction with the presented model. 1,328 students agreed to participate in this study, all students of Open University of Brazil. They were questioned about their perspectives on m-learning at this university and if they agree with this educational model. The students agreed with the possible implementation of m-learning at this university, especially through mobiles phones. Collectively, the main ideas that the students offered to improve the efficiency of knowledge construction were classified into three groups: theory, practice, and interactivity. They also agreed with an instructional design model that was developed and shown to the three groups.  

An investigation of fMRI time series stationarity during motor sequence learning foot tapping tasks.

Science.gov (United States)

Muhei-aldin, Othman; VanSwearingen, Jessie; Karim, Helmet; Huppert, Theodore; Sparto, Patrick J; Erickson, Kirk I; Sejdić, Ervin

2014-04-30

Understanding complex brain networks using functional magnetic resonance imaging (fMRI) is of great interest to clinical and scientific communities. To utilize advanced analysis methods such as graph theory for these investigations, the stationarity of fMRI time series needs to be understood as it has important implications on the choice of appropriate approaches for the analysis of complex brain networks. In this paper, we investigated the stationarity of fMRI time series acquired from twelve healthy participants while they performed a motor (foot tapping sequence) learning task. Since prior studies have documented that learning is associated with systematic changes in brain activation, a sequence learning task is an optimal paradigm to assess the degree of non-stationarity in fMRI time-series in clinically relevant brain areas. We predicted that brain regions involved in a "learning network" would demonstrate non-stationarity and may violate assumptions associated with some advanced analysis approaches. Six blocks of learning, and six control blocks of a foot tapping sequence were performed in a fixed order. The reverse arrangement test was utilized to investigate the time series stationarity. Our analysis showed some non-stationary signals with a time varying first moment as a major source of non-stationarity. We also demonstrated a decreased number of non-stationarities in the third block as a result of priming and repetition. Most of the current literature does not examine stationarity prior to processing. The implication of our findings is that future investigations analyzing complex brain networks should utilize approaches robust to non-stationarities, as graph-theoretical approaches can be sensitive to non-stationarities present in data. Copyright © 2014 Elsevier B.V. All rights reserved.

Activity of Catalase (CAT, ALT and AST in Different Organs of Swiss Albino Mice Treated with Lead Acetate, Vitamin C and Magnesium-L-Threonate

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Ilir Nazmi Mazreku

2017-11-01

Full Text Available Introduction: Lead is a natural element with toxic properties and is widespread in the environment. Lead toxicity is associated with generation of reactive oxygen and nitrogen species and consumption of antioxidants elements (vitamin E and C, glutathione, thioredoxin and lipoic acid, melatonin, carotenoids and natural flavonoids in the cell, and unbalancing oxidantsantioxidants levels. Aim: To evaluate the effects of different chemical combinations (lead acetate, Vitamin C and Magnesium-L-threonate on antioxidant enzyme activity (catalase-CAT of liver, kidney, spleen, pancreas and brain, and serum transaminases [Serum Alanine Transaminase (ALT and Serum Aspartate Transaminase (AST]. Materials and Methods: Experimental animals (49 male Mus musculus-swiss albino mice were separated into five different groups. The first group was used as a control, hence the other four groups were treated with sub-lethal doses (90 mg/kg of lead acetate (group 2, lead acetate (90 mg/kg and Vitamin C dose 40mg/kg (group 3, lead acetate (90 mg/kg and Magnesium-Lthreonate dose 100 mg/kg (group 4 and only with MagnesiumL-threonate dose 100 mg/kg (group 5, during the treatment period (40 days. Blood samples were taken from the facial vein and used for transaminase analysis. Organ tissue was collected after euthanizing anaesthetized animals with neck dislocation technique. Results: The results showed that lead acetate treatment has caused significant elevation in the activity of AST (group 2 and 3 and ALT (group 3. Also, CAT activity was significantly (p<0.05 increased in groups treated with lead acetate (liver, pancreas, kidney and brain but not in spleen. Treatment of lead intoxicated groups with Vitamin C and Magnesium L-threonate increased significantly CAT activity in brain. Conclusion: Lead effects by interacting with different molecular systems and increasing enzyme activity (CAT, ALT and AST. Effects on CAT activity of Magnesium-L-threonate and Vitamin C treatment

Instructional Advice, Time Advice and Learning Questions in Computer Simulations

Science.gov (United States)

Rey, Gunter Daniel

2010-01-01

Undergraduate students (N = 97) used an introductory text and a computer simulation to learn fundamental concepts about statistical analyses (e.g., analysis of variance, regression analysis and General Linear Model). Each learner was randomly assigned to one cell of a 2 (with or without instructional advice) x 2 (with or without time advice) x 2…

The Effect of Inquiry Training Learning Model Based on Just in Time Teaching for Problem Solving Skill

Science.gov (United States)

Turnip, Betty; Wahyuni, Ida; Tanjung, Yul Ifda

2016-01-01

One of the factors that can support successful learning activity is the use of learning models according to the objectives to be achieved. This study aimed to analyze the differences in problem-solving ability Physics student learning model Inquiry Training based on Just In Time Teaching [JITT] and conventional learning taught by cooperative model…

Unsupervised Learning of Digit Recognition Using Spike-Timing-Dependent Plasticity

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Peter U. Diehl

2015-08-01

Full Text Available In order to understand how the mammalian neocortex is performing computations, two things are necessary; we need to have a good understanding of the available neuronal processing units and mechanisms, and we need to gain a better understanding of how those mechanisms are combined to build functioning systems. Therefore, in recent years there is an increasing interest in how spiking neural networks (SNN can be used to perform complex computations or solve pattern recognition tasks. However, it remains a challenging task to design SNNs which use biologically plausible mechanisms (especially for learning new patterns, since most of such SNN architectures rely on training in a rate-based network and subsequent conversion to a SNN. We present a SNN for digit recognition which is based on mechanisms with increased biological plausibility, i.e. conductance-based instead of current-based synapses, spike-timing-dependent plasticity with time-dependent weight change, lateral inhibition, and an adaptive spiking threshold. Unlike most other systems, we do not use a teaching signal and do not present any class labels to the network. Using this unsupervised learning scheme, our architecture achieves 95% accuracy on the MNIST benchmark, which is better than previous SNN implementations without supervision. The fact that we used no domain-specific knowledge points toward the general applicability of our network design. Also, the performance of our network scales well with the number of neurons used and shows similar performance for four different learning rules, indicating robustness of the full combination of mechanisms, which suggests applicability in heterogeneous biological neural networks.

Time to Engage? Texting to Support and Enhance First Year Undergraduate Learning

Directory of Open Access Journals (Sweden)

Geraldine Jones

2009-04-01

Full Text Available In this paper we discuss a case study investigating how the academic and personal development of first year students on an undergraduate sports education degree can be supported and enhanced with mobile SMS communication. SMS-based technologies were introduced in response to students’ particular needs (in transition to Higher Education and characteristics (‘digital natives’. Despite being unaccustomed to using their mobile phones for academic study, students willingly participated in SMS communication with their tutor via a texting management service. Drawing on evidence from two student surveys, focus groups and a tutor’s journal, we illustrate the potential that mobile SMS communication has to link and establish continuity between face to face teaching sessions and online learning activities in the Virtual Learning Environment (VLE. Many students perceived the SMS communication to have had a positive impact on their management of study time. We link our findings with the existing literature and argue that mobile text based communication has the potential to support the development of time management skills, an important component of self regulatory learning, a skill which has been shown to be key in making a successful transition.

The 3 R's of Learning Time: Rethink, Reshape, Reclaim

Science.gov (United States)

Sackey, Shera Carter

2012-01-01

The Learning School Alliance is a network of schools collaborating about professional practice. The network embodies Learning Forward's purpose to advance effective job-embedded professional learning that leads to student outcomes. A key component of Learning Forward's Standards for Professional Learning is a focus on collaborative learning,…

Do learning collaboratives strengthen communication? A comparison of organizational team communication networks over time.

Science.gov (United States)

Bunger, Alicia C; Lengnick-Hall, Rebecca

Collaborative learning models were designed to support quality improvements, such as innovation implementation by promoting communication within organizational teams. Yet the effect of collaborative learning approaches on organizational team communication during implementation is untested. The aim of this study was to explore change in communication patterns within teams from children's mental health organizations during a year-long learning collaborative focused on implementing a new treatment. We adopt a social network perspective to examine intraorganizational communication within each team and assess change in (a) the frequency of communication among team members, (b) communication across organizational hierarchies, and (c) the overall structure of team communication networks. A pretest-posttest design compared communication among 135 participants from 21 organizational teams at the start and end of a learning collaborative. At both time points, participants were asked to list the members of their team and rate the frequency of communication with each along a 7-point Likert scale. Several individual, pair-wise, and team level communication network metrics were calculated and compared over time. At the individual level, participants reported communicating with more team members by the end of the learning collaborative. Cross-hierarchical communication did not change. At the team level, these changes manifested differently depending on team size. In large teams, communication frequency increased, and networks grew denser and slightly less centralized. In small teams, communication frequency declined, growing more sparse and centralized. Results suggest that team communication patterns change minimally but evolve differently depending on size. Learning collaboratives may be more helpful for enhancing communication among larger teams; thus, managers might consider selecting and sending larger staff teams to learning collaboratives. This study highlights key future

Learning about Learning: Action Learning in Times of Organisational Change

Science.gov (United States)

Hill, Robyn

2009-01-01

This paper explores the conduct and outcomes of an action learning activity during a period of intense organisational change in a medium-sized vocational education and training organisation in Victoria, Australia. This organisation was the subject of significant change due to government-driven and statewide amalgamation, downsizing and sector…

Training haptic stiffness discrimination: time course of learning with or without visual information and knowledge of results.

Science.gov (United States)

Teodorescu, Kinneret; Bouchigny, Sylvain; Korman, Maria

2013-08-01

In this study, we explored the time course of haptic stiffness discrimination learning and how it was affected by two experimental factors, the addition of visual information and/or knowledge of results (KR) during training. Stiffness perception may integrate both haptic and visual modalities. However, in many tasks, the visual field is typically occluded, forcing stiffness perception to be dependent exclusively on haptic information. No studies to date addressed the time course of haptic stiffness perceptual learning. Using a virtual environment (VE) haptic interface and a two-alternative forced-choice discrimination task, the haptic stiffness discrimination ability of 48 participants was tested across 2 days. Each day included two haptic test blocks separated by a training block Additional visual information and/or KR were manipulated between participants during training blocks. Practice repetitions alone induced significant improvement in haptic stiffness discrimination. Between days, accuracy was slightly improved, but decision time performance was deteriorated. The addition of visual information and/or KR had only temporary effects on decision time, without affecting the time course of haptic discrimination learning. Learning in haptic stiffness discrimination appears to evolve through at least two distinctive phases: A single training session resulted in both immediate and latent learning. This learning was not affected by the training manipulations inspected. Training skills in VE in spaced sessions can be beneficial for tasks in which haptic perception is critical, such as surgery procedures, when the visual field is occluded. However, training protocols for such tasks should account for low impact of multisensory information and KR.

[Thomas Lange. Zwischen Reformation und Untergang Alt-Livlands : der Rigaer Erzbischof Wilhelm von Brandenburg im Beziehungsgeflecht der livländischen Konföderation und ihrer Nachbarländer] / Juhan Kreem

Index Scriptorium Estoniae

Kreem, Juhan, 1971-

2015-01-01

Arvustus: Lange, Thomas. Zwischen Reformation und Untergang Alt-Livlands : der Rigaer Erzbischof Wilhelm von Brandenburg im Beziehungsgeflecht der livländischen Konföderation und ihrer Nachbarländer (Hamburger Beiträge zur Geschichte des östlichen Europa)

What time is it? Deep learning approaches for circadian rhythms.

Science.gov (United States)

Agostinelli, Forest; Ceglia, Nicholas; Shahbaba, Babak; Sassone-Corsi, Paolo; Baldi, Pierre

2016-06-15

Circadian rhythms date back to the origins of life, are found in virtually every species and every cell, and play fundamental roles in functions ranging from metabolism to cognition. Modern high-throughput technologies allow the measurement of concentrations of transcripts, metabolites and other species along the circadian cycle creating novel computational challenges and opportunities, including the problems of inferring whether a given species oscillate in circadian fashion or not, and inferring the time at which a set of measurements was taken. We first curate several large synthetic and biological time series datasets containing labels for both periodic and aperiodic signals. We then use deep learning methods to develop and train BIO_CYCLE, a system to robustly estimate which signals are periodic in high-throughput circadian experiments, producing estimates of amplitudes, periods, phases, as well as several statistical significance measures. Using the curated data, BIO_CYCLE is compared to other approaches and shown to achieve state-of-the-art performance across multiple metrics. We then use deep learning methods to develop and train BIO_CLOCK to robustly estimate the time at which a particular single-time-point transcriptomic experiment was carried. In most cases, BIO_CLOCK can reliably predict time, within approximately 1 h, using the expression levels of only a small number of core clock genes. BIO_CLOCK is shown to work reasonably well across tissue types, and often with only small degradation across conditions. BIO_CLOCK is used to annotate most mouse experiments found in the GEO database with an inferred time stamp. All data and software are publicly available on the CircadiOmics web portal: circadiomics.igb.uci.edu/ fagostin@uci.edu or pfbaldi@uci.edu Supplementary data are available at Bioinformatics online. © The Author 2016. Published by Oxford University Press.

Upper Extremity Motor Learning among Individuals with Parkinson's Disease: A Meta-Analysis Evaluating Movement Time in Simple Tasks

Directory of Open Access Journals (Sweden)

K. Felix

2012-01-01

Full Text Available Motor learning has been found to occur in the rehabilitation of individuals with Parkinson's disease (PD. Through repetitive structured practice of motor tasks, individuals show improved performance, confirming that motor learning has probably taken place. Although a number of studies have been completed evaluating motor learning in people with PD, the sample sizes were small and the improvements were variable. The purpose of this meta-analysis was to determine the ability of people with PD to learn motor tasks. Studies which measured movement time in upper extremity reaching tasks and met the inclusion criteria were included in the analysis. Results of the meta-analysis indicated that people with PD and neurologically healthy controls both demonstrated motor learning, characterized by a decrease in movement time during upper extremity movements. Movement time improvements were greater in the control group than in individuals with PD. These results support the findings that the practice of upper extremity reaching tasks is beneficial in reducing movement time in persons with PD and has important implications for rehabilitation.

Using Online Lectures to Make Time for Active Learning

Science.gov (United States)

Prunuske, Amy J.; Batzli, Janet; Howell, Evelyn; Miller, Sarah

2012-01-01

To make time in class for group activities devoted to critical thinking, we integrated a series of short online lectures into the homework assignments of a large, introductory biology course at a research university. The majority of students viewed the online lectures before coming to class and reported that the online lectures helped them to complete the in-class activity and did not increase the amount of time they devoted to the course. In addition, students who viewed the online lecture performed better on clicker questions designed to test lower-order cognitive skills. The in-class activities then gave the students practice analyzing the information in groups and provided the instructor with feedback about the students’ understanding of the material. On the basis of the results of this study, we support creating hybrid course models that allow students to learn the fundamental information outside of class time, thereby creating time during the class period to be dedicated toward the conceptual understanding of the material. PMID:22714412

Incoherent dictionary learning for reducing crosstalk noise in least-squares reverse time migration

Science.gov (United States)

Wu, Juan; Bai, Min

2018-05-01

We propose to apply a novel incoherent dictionary learning (IDL) algorithm for regularizing the least-squares inversion in seismic imaging. The IDL is proposed to overcome the drawback of traditional dictionary learning algorithm in losing partial texture information. Firstly, the noisy image is divided into overlapped image patches, and some random patches are extracted for dictionary learning. Then, we apply the IDL technology to minimize the coherency between atoms during dictionary learning. Finally, the sparse representation problem is solved by a sparse coding algorithm, and image is restored by those sparse coefficients. By reducing the correlation among atoms, it is possible to preserve most of the small-scale features in the image while removing much of the long-wavelength noise. The application of the IDL method to regularization of seismic images from least-squares reverse time migration shows successful performance.

Evidence for an alternation strategy in time-place learning.

Science.gov (United States)

Pizzo, Matthew J; Crystal, Jonathon D

2004-11-30

Many different conclusions concerning what type of mechanism rats use to solve a daily time-place task have emerged in the literature. The purpose of this study was to test three competing explanations of time-place discrimination. Rats (n = 10) were tested twice daily in a T-maze, separated by approximately 7 h. Food was available at one location in the morning and another location in the afternoon. After the rats learned to visit each location at the appropriate time, tests were omitted to evaluate whether the rats were utilizing time-of-day (i.e., a circadian oscillator) or an alternation strategy (i.e., visiting a correct location is a cue to visit the next location). Performance on this test was significantly lower than chance, ruling out the use of time-of-day. A phase advance of the light cycle was conducted to test the alternation strategy and timing with respect to the light cycle (i.e., an interval timer). There was no difference between probe and baseline performance. These results suggest that the rats used an alternation strategy to meet the temporal and spatial contingencies in the time-place task.

Giving English Language Learners the Time They Need to Succeed: Profiles of Three Expanded Learning Time Schools

Science.gov (United States)

Farbman, David A.

2015-01-01

With the number of students who are English language learners (ELLs) likely to double in coming years, it is more important than ever for schools across the U.S. to design and implement educational practices and strategies that best meet ELLs' learning needs, says the report, "Giving English Language Learners the Time They Need to…

The right time to learn: mechanisms and optimization of spaced learning

Science.gov (United States)

Smolen, Paul; Zhang, Yili; Byrne, John H.

2016-01-01

For many types of learning, spaced training, which involves repeated long inter-trial intervals, leads to more robust memory formation than does massed training, which involves short or no intervals. Several cognitive theories have been proposed to explain this superiority, but only recently have data begun to delineate the underlying cellular and molecular mechanisms of spaced training, and we review these theories and data here. Computational models of the implicated signalling cascades have predicted that spaced training with irregular inter-trial intervals can enhance learning. This strategy of using models to predict optimal spaced training protocols, combined with pharmacotherapy, suggests novel ways to rescue impaired synaptic plasticity and learning. PMID:26806627

Lessons Learned from Real-Time, Event-Based Internet Science Communications

Science.gov (United States)

Phillips, T.; Myszka, E.; Gallagher, D. L.; Adams, M. L.; Koczor, R. J.; Whitaker, Ann F. (Technical Monitor)

2001-01-01

For the last several years the Science Directorate at Marshall Space Flight Center has carried out a diverse program of Internet-based science communication. The Directorate's Science Roundtable includes active researchers, NASA public relations, educators, and administrators. The Science@NASA award-winning family of Web sites features science, mathematics, and space news. The program includes extended stories about NASA science, a curriculum resource for teachers tied to national education standards, on-line activities for students, and webcasts of real-time events. The focus of sharing science activities in real-time has been to involve and excite students and the public about science. Events have involved meteor showers, solar eclipses, natural very low frequency radio emissions, and amateur balloon flights. In some cases, broadcasts accommodate active feedback and questions from Internet participants. Through these projects a pattern has emerged in the level of interest or popularity with the public. The pattern differentiates projects that include science from those that do not, All real-time, event-based Internet activities have captured public interest at a level not achieved through science stories or educator resource material exclusively. The worst event-based activity attracted more interest than the best written science story. One truly rewarding lesson learned through these projects is that the public recognizes the importance and excitement of being part of scientific discovery. Flying a camera to 100,000 feet altitude isn't as interesting to the public as searching for viable life-forms at these oxygen-poor altitudes. The details of these real-time, event-based projects and lessons learned will be discussed.

Grafting amino drugs to poly(styrene-alt-maleic anhydride) as a potential method for drug release

Energy Technology Data Exchange (ETDEWEB)

Khazaei, Ardeshir; Saednia, Shahnaz; Saien, Javad; Abbasi, Fatemeh, E-mail: Khazaei_1326@yahoo.com, E-mail: ssaednia@gmail.com [Faculty of Chemistry, Bu-Ali Sina University, Hamedan (Iran, Islamic Republic of); Kazem-Rostami, Masoud [Young Researchers Club and Elite, Takestan Branch, Islamic Azad University, Takestan (Iran, Islamic Republic of); Sadeghpour, Mahdieh [Department of Chemistry, Takestan Branch, Islamic Azad University, Takestan (Iran, Islamic Republic of); Borazjani, Maryam Kiani [Faculty of Science, Department of Chemistry, Bushehr Payame Noor University (PNU), Bushehr (Iran, Islamic Republic of)

2013-07-15

Drug delivery systems based on polymer-drug conjugates give an improved treatment with lower toxicity or side effects and be used for the treatment of different diseases. Conjugates of biodegradable poly(styrene-alt-maleic anhydride) (PSMA), with a therapeutic agents such as amantadine hydrochloride, amlodipine, gabapentin, zonisamide and mesalamine, were afforded by the formation of the amide bonds of the amino drugs that reacted with the PSMA anhydride groups. The amounts of covalently conjugated drugs were determined by a {sup 1}H NMR spectroscopic method, and the in vitro release rate in buffer solution (pH 1.3) was studied at body temperature 37 Degree-Sign C. In kinetic studies, different dissolution models were examined to obtain drug release data and the collected data were well-fitted to the Korsmeyer-Peppas equation, revealing a dominant Fickian diffusion mechanism for drug release under the in vitro conditions. (author)

Using Just-in-Time Information to Support Scientific Discovery Learning in a Computer-Based Simulation

Science.gov (United States)

Hulshof, Casper D.; de Jong, Ton

2006-01-01

Students encounter many obstacles during scientific discovery learning with computer-based simulations. It is hypothesized that an effective type of support, that does not interfere with the scientific discovery learning process, should be delivered on a "just-in-time" base. This study explores the effect of facilitating access to…

Learning Management System Calendar Reminders and Effects on Time Management and Academic Performance

Science.gov (United States)

Mei, Jianyang

2016-01-01

This research project uses a large research university in the Midwest as a research site to explore the time management skills of international students and analyzes how using the Course Hack, an online Learning Management System (LMS) calendar tool, improves participants' time management skills and positively impacts their academic performance,…

Time to learn: the outlook for renewal of patient-centred education in the digital age.

Science.gov (United States)

Glick, T H; Moore, G T

2001-05-01

Major forces in society and within health systems are fragmenting patient care and clinical learning. The distancing of physician and trainee from the patient undermines learning about the patient-doctor relationship. The disconnection of care and learning from one successive venue to another impedes the ability of trainees to learn about illness longitudinally. As a conceptual piece, our methods have been those of witnessing the experiences of patients, practitioners, and students over time and observing the impact of fragmented systems and changing expectations on care and learning. We have reflected on the opportunities created by digital information systems and interactive telemedicine to help renew essential relationships. Although there is, as yet, little in the literature on educational or health outcomes of this kind of technological enablement, we anticipate opportunities for a renewed focus on the patient in that patient's own space and time. Multimedia applications can achieve not only real-time connections, but can help construct a "virtual patient" as a platform for supervision and assessment, permitting preceptors to evaluate trainee-patient interactions, utilization of Web-based data and human resources, and on-line professionalism. Just as diverse elements in society are capitalizing upon digital technology to create advantageous relationships, all of the elements in the complex systems of health care and medical training can be better connected, so as to put the patient back in the centre of care and the trainee's ongoing relationship to the patient back in the centre of education.

Learning Constructive Primitives for Real-time Dynamic Difficulty Adjustment in Super Mario Bros

OpenAIRE

Shi, Peizhi; Chen, Ke

2017-01-01

Among the main challenges in procedural content generation (PCG), content quality assurance and dynamic difficulty adjustment (DDA) of game content in real time are two major issues concerned in adaptive content generation. Motivated by the recent learning-based PCG framework, we propose a novel approach to seamlessly address two issues in Super Mario Bros (SMB). To address the quality assurance issue, we exploit the synergy between rule-based and learning-based methods to produce quality gam...

Car-following Behavior Model Learning Using Timed Automata

NARCIS (Netherlands)

Zhang, Yihuan; Lin, Q.; Wang, Jun; Verwer, S.E.; Dochain, D.; Henrion, D.; Peaucelle, D.

Learning driving behavior is fundamental for autonomous vehicles to “understand” traffic situations. This paper proposes a novel method for learning a behavioral model of car-following using automata learning algorithms. The model is interpretable for car-following behavior analysis. Frequent common

Focusing on Student Learning to Guide the Use of Staff Time

Science.gov (United States)

Bates, Imelda; Baume, David; Assinder, Susan

2010-01-01

The paper develops and illustrates a model for designing courses. The model gives explicit attention to educational considerations, principally to the importance of active, goal-directed student learning. It also explores economic considerations, principally how to make the best possible use of the time of the teacher in planning and running the…

Real-time learning of predictive recognition categories that chunk sequences of items stored in working memory

Directory of Open Access Journals (Sweden)

Stephen eGrossberg

2014-10-01

Full Text Available How are sequences of events that are temporarily stored in a cognitive working memory unitized, or chunked, through learning? Such sequential learning is needed by the brain in order to enable language, spatial understanding, and motor skills to develop. In particular, how does the brain learn categories, or list chunks, that become selectively tuned to different temporal sequences of items in lists of variable length as they are stored in working memory, and how does this learning process occur in real time? The present article introduces a neural model that simulates learning of such list chunks. In this model, sequences of items are temporarily stored in an Item-and-Order, or competitive queuing, working memory before learning categorizes them using a categorization network, called a Masking Field, which is a self-similar, multiple-scale, recurrent on-center off-surround network that can weigh the evidence for variable-length sequences of items as they are stored in the working memory through time. A Masking Field hereby activates the learned list chunks that represent the most predictive item groupings at any time, while suppressing less predictive chunks. In a network with a given number of input items, all possible ordered sets of these item sequences, up to a fixed length, can be learned with unsupervised or supervised learning. The self-similar multiple-scale properties of Masking Fields interacting with an Item-and-Order working memory provide a natural explanation of George Miller's Magical Number Seven and Nelson Cowan's Magical Number Four. The article explains why linguistic, spatial, and action event sequences may all be stored by Item-and-Order working memories that obey similar design principles, and thus how the current results may apply across modalities. Item-and-Order properties may readily be extended to Item-Order-Rank working memories in which the same item can be stored in multiple list positions, or ranks, as in the list

Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment

NARCIS (Netherlands)

Noroozi, O.; Busstra, M.C.; Mulder, M.; Biemans, H.J.A.; Tobi, H.; Geelen, A.; Veer, van 't P.; Chizari, M.

2012-01-01

This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs.

Large-scale machine learning and evaluation platform for real-time traffic surveillance

Science.gov (United States)

Eichel, Justin A.; Mishra, Akshaya; Miller, Nicholas; Jankovic, Nicholas; Thomas, Mohan A.; Abbott, Tyler; Swanson, Douglas; Keller, Joel

2016-09-01

In traffic engineering, vehicle detectors are trained on limited datasets, resulting in poor accuracy when deployed in real-world surveillance applications. Annotating large-scale high-quality datasets is challenging. Typically, these datasets have limited diversity; they do not reflect the real-world operating environment. There is a need for a large-scale, cloud-based positive and negative mining process and a large-scale learning and evaluation system for the application of automatic traffic measurements and classification. The proposed positive and negative mining process addresses the quality of crowd sourced ground truth data through machine learning review and human feedback mechanisms. The proposed learning and evaluation system uses a distributed cloud computing framework to handle data-scaling issues associated with large numbers of samples and a high-dimensional feature space. The system is trained using AdaBoost on 1,000,000 Haar-like features extracted from 70,000 annotated video frames. The trained real-time vehicle detector achieves an accuracy of at least 95% for 1/2 and about 78% for 19/20 of the time when tested on ˜7,500,000 video frames. At the end of 2016, the dataset is expected to have over 1 billion annotated video frames.

Preserved GLP-1 and exaggerated GIP secretion in type 2 diabetes and relationships with triglycerides and ALT

DEFF Research Database (Denmark)

Alssema, Marjan; Rijkelijkhuizen, Josina M; Holst, Jens Juul

2013-01-01

OBJECTIVE: To i) compare incretin responses to oral glucose and mixed meal of diabetic patients with the normoglycaemic population and ii) to investigate whether incretin responses are associated with hypertriglyceridaemia and alanine aminotransferase (ALT) as liver fat marker. DESIGN: A population......-based study. METHODS: A total of 163 persons with normal glucose metabolism (NGM), 20 with intermediate hyperglycaemia and 20 with type 2 diabetes aged 40-65 years participated. Participants received a mixed meal and oral glucose load on separate occasions. Glucagon-like peptide 1 (GLP-1), glucose......-dependent insulinotropic polypeptide (GIP) and glucagon profiles were analysed as total area under the curve (tAUC) and incremental area under the curve. RESULTS: In diabetic patients compared with persons with NGM, we found increased GLP-1 secretion (tAUC per hour) following oral glucose (23.2 pmol/l (95% CI 17...

Ensemble Deep Learning for Biomedical Time Series Classification

Directory of Open Access Journals (Sweden)

Lin-peng Jin

2016-01-01

Full Text Available Ensemble learning has been proved to improve the generalization ability effectively in both theory and practice. In this paper, we briefly outline the current status of research on it first. Then, a new deep neural network-based ensemble method that integrates filtering views, local views, distorted views, explicit training, implicit training, subview prediction, and Simple Average is proposed for biomedical time series classification. Finally, we validate its effectiveness on the Chinese Cardiovascular Disease Database containing a large number of electrocardiogram recordings. The experimental results show that the proposed method has certain advantages compared to some well-known ensemble methods, such as Bagging and AdaBoost.

Supervised spike-timing-dependent plasticity: a spatiotemporal neuronal learning rule for function approximation and decisions.

Science.gov (United States)

Franosch, Jan-Moritz P; Urban, Sebastian; van Hemmen, J Leo

2013-12-01

How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as "supervisor." Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

Communication, timing, and common learning

Czech Academy of Sciences Publication Activity Database

Steiner, Jakub; Stewart, C.

2011-01-01

Roč. 146, č. 1 (2011), s. 230-247 ISSN 0022-0531 Institutional research plan: CEZ:AV0Z70850503 Keywords : common knowledge * learning * communication Subject RIV: AH - Economics Impact factor: 1.235, year: 2011

Integrating Passenger and Modulo as Learning Tools for Collaborative Engineering Education

Directory of Open Access Journals (Sweden)

Kalamullah Ramli

2010-10-01

Full Text Available Normal 0 false false false IN X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-fareast-language:EN-US;} The University of Indonesia (UI, Gerhard Mercator University (GMU, the Universiti Kebangsaan Malaysia (UKM have agreed to cooperate as partners to develop a joint bachelor (3+1 and masters (1+1 degree, and collaborate in the research fi elds of multimedia- and internet- based learning and application level active networks. This paper describes the introduction of the Offshore Project and the extending of information technology applications to the partner institutions in this international cooperation which would very much enhance the operations of the new initiative in engineering education, which could provide students in far distance with more fl exible learning environment.

Staged Inference using Conditional Deep Learning for energy efficient real-time smart diagnosis.

Science.gov (United States)

Parsa, Maryam; Panda, Priyadarshini; Sen, Shreyas; Roy, Kaushik

2017-07-01

Recent progress in biosensor technology and wearable devices has created a formidable opportunity for remote healthcare monitoring systems as well as real-time diagnosis and disease prevention. The use of data mining techniques is indispensable for analysis of the large pool of data generated by the wearable devices. Deep learning is among the promising methods for analyzing such data for healthcare applications and disease diagnosis. However, the conventional deep neural networks are computationally intensive and it is impractical to use them in real-time diagnosis with low-powered on-body devices. We propose Staged Inference using Conditional Deep Learning (SICDL), as an energy efficient approach for creating healthcare monitoring systems. For smart diagnostics, we observe that all diagnoses are not equally challenging. The proposed approach thus decomposes the diagnoses into preliminary analysis (such as healthy vs unhealthy) and detailed analysis (such as identifying the specific type of cardio disease). The preliminary diagnosis is conducted real-time with a low complexity neural network realized on the resource-constrained on-body device. The detailed diagnosis requires a larger network that is implemented remotely in cloud and is conditionally activated only for detailed diagnosis (unhealthy individuals). We evaluated the proposed approach using available physiological sensor data from Physionet databases, and achieved 38% energy reduction in comparison to the conventional deep learning approach.

Chitosan: poly(N-vinylpyrrolidone-alt-itaconic anhydride) nanocapsules—a promising alternative for the lung cancer treatment

Energy Technology Data Exchange (ETDEWEB)

Raţă, Delia Mihaela, E-mail: iureadeliamihaela@yahoo.com [„Apollonia” University of Iasi, Faculty of Medical Dentistry, „Academician Ioan Haulică” Research Institute (Romania); Chailan, Jean-François, E-mail: chailan@univ-tln.fr [University of Sud Toulon-Var, « Matériaux-Polymères-Interfaces-Environnement Marin (MAPIEM) Laboratory (France); Peptu, Cătălina Anişoara, E-mail: catipeptu@yahoo.co.uk [“Gheorghe Asachi” Technical University of Iasi, Department of Natural and Synthetic Polymers, Faculty of Chemical Engineering and Environmental Protection (Romania); Costuleanu, Marcel, E-mail: mcostuleanu@yahoo.com [University of Medicine and Pharmacy “Grigore T. Popa”- Iaşi, Department of General Pathology, Faculty of Dental Medicine (Romania); Popa, Marcel, E-mail: marpopa2001@yahoo.fr [“Gheorghe Asachi” Technical University of Iasi, Department of Natural and Synthetic Polymers, Faculty of Chemical Engineering and Environmental Protection (Romania)

2015-07-15

This study reports the preparation of novel polymeric nanocapsules based on a natural polymer, chitosan and a synthetic one, poly(N-vinylpyrrolidone-alt-itaconic anhydride) [(poly(NVPAI)] using an interfacial condensation technique. The infrared spectroscopy studies confirmed the crosslinking through the presence of amide bonds, formed between the two polymers chains. The diameter of nanocapsules was found in the range of 126–214 nm and it was determined by dynamic light scattering method. Morphological characterization demonstrated their nano size, the spherical shape of the nanocapsules and the formation of hollow particles. The nanocapsules presented good swelling capacity in aqueous solutions. 5-Fluorouracil (5-FU) loading and release capacity was studied, the processes being controlled by the drug diffusion through the polymeric membrane. The obtained results were encouraging, showing that 5-FU-loaded nanocapsules had 70 % higher apoptotic effect on A549 tumour cells than the drug in free state or mixed with the nanocapsules.

Energy Level Tuning of Poly(phenylene-alt-dithienobenzothiadiazole)s for Low Photon Energy Loss Solar Cells.

Science.gov (United States)

Heuvel, Ruurd; van Franeker, Jacobus J; Janssen, René A J

2017-03-01

Six poly(phenylene- alt -dithienobenzothiadiazole)-based polymers have been synthesized for application in polymer-fullerene solar cells. Hydrogen, fluorine, or nitrile substitution on benzo-thiadiazole and alkoxy or ester substitution on the phenylene moiety are investigated to reduce the energy loss per converted photon. Power conversion efficiencies (PCEs) up to 6.6% have been obtained. The best performance is found for the polymer-fullerene combination with distinct phase separation and crystalline domains. This improves the maximum external quantum efficiency for charge formation and collection to 66%. The resulting higher photocurrent compensates for the relatively large energy loss per photon ( E loss = 0.97 eV) in achieving a high PCE. By contrast, the poly-mer that provides a reduced energy loss ( E loss = 0.49 eV) gives a lower photocurrent and a reduced PCE of 1.8% because the external quantum efficiency of 17% is limited by a suboptimal morphology and a reduced driving force for charge transfer.

Chitosan: poly( N-vinylpyrrolidone- alt-itaconic anhydride) nanocapsules—a promising alternative for the lung cancer treatment

Science.gov (United States)

Raţă, Delia Mihaela; Chailan, Jean-François; Peptu, Cătălina Anişoara; Costuleanu, Marcel; Popa, Marcel

2015-07-01

This study reports the preparation of novel polymeric nanocapsules based on a natural polymer, chitosan and a synthetic one, poly( N-vinylpyrrolidone- alt-itaconic anhydride) [(poly(NVPAI)] using an interfacial condensation technique. The infrared spectroscopy studies confirmed the crosslinking through the presence of amide bonds, formed between the two polymers chains. The diameter of nanocapsules was found in the range of 126-214 nm and it was determined by dynamic light scattering method. Morphological characterization demonstrated their nano size, the spherical shape of the nanocapsules and the formation of hollow particles. The nanocapsules presented good swelling capacity in aqueous solutions. 5-Fluorouracil (5-FU) loading and release capacity was studied, the processes being controlled by the drug diffusion through the polymeric membrane. The obtained results were encouraging, showing that 5-FU-loaded nanocapsules had 70 % higher apoptotic effect on A549 tumour cells than the drug in free state or mixed with the nanocapsules.

Chitosan: poly(N-vinylpyrrolidone-alt-itaconic anhydride) nanocapsules—a promising alternative for the lung cancer treatment

International Nuclear Information System (INIS)

Raţă, Delia Mihaela; Chailan, Jean-François; Peptu, Cătălina Anişoara; Costuleanu, Marcel; Popa, Marcel

2015-01-01

This study reports the preparation of novel polymeric nanocapsules based on a natural polymer, chitosan and a synthetic one, poly(N-vinylpyrrolidone-alt-itaconic anhydride) [(poly(NVPAI)] using an interfacial condensation technique. The infrared spectroscopy studies confirmed the crosslinking through the presence of amide bonds, formed between the two polymers chains. The diameter of nanocapsules was found in the range of 126–214 nm and it was determined by dynamic light scattering method. Morphological characterization demonstrated their nano size, the spherical shape of the nanocapsules and the formation of hollow particles. The nanocapsules presented good swelling capacity in aqueous solutions. 5-Fluorouracil (5-FU) loading and release capacity was studied, the processes being controlled by the drug diffusion through the polymeric membrane. The obtained results were encouraging, showing that 5-FU-loaded nanocapsules had 70 % higher apoptotic effect on A549 tumour cells than the drug in free state or mixed with the nanocapsules

A real-time standard parts inspection based on deep learning

Science.gov (United States)

Xu, Kuan; Li, XuDong; Jiang, Hongzhi; Zhao, Huijie

2017-10-01

Since standard parts are necessary components in mechanical structure like bogie and connector. These mechanical structures will be shattered or loosen if standard parts are lost. So real-time standard parts inspection systems are essential to guarantee their safety. Researchers would like to take inspection systems based on deep learning because it works well in image with complex backgrounds which is common in standard parts inspection situation. A typical inspection detection system contains two basic components: feature extractors and object classifiers. For the object classifier, Region Proposal Network (RPN) is one of the most essential architectures in most state-of-art object detection systems. However, in the basic RPN architecture, the proposals of Region of Interest (ROI) have fixed sizes (9 anchors for each pixel), they are effective but they waste much computing resources and time. In standard parts detection situations, standard parts have given size, thus we can manually choose sizes of anchors based on the ground-truths through machine learning. The experiments prove that we could use 2 anchors to achieve almost the same accuracy and recall rate. Basically, our standard parts detection system could reach 15fps on NVIDIA GTX1080 (GPU), while achieving detection accuracy 90.01% mAP.

Strategic learning and information and communication technology: a critical review

Directory of Open Access Journals (Sweden)

Carles Monerero Font

2013-08-01

Full Text Available 0 0 1 93 514 USAL 4 1 606 14.0 Normal 0 21 false false false ES JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:ES; mso-fareast-language:EN-US;} This article presents an outline of a learning strategy redefinition, based on recent contributions from learning technologies. This new definition addresses the question of how technological affordances of the software, of the technological tools, or an educational environment, allow a learner or a group of learners develop a new ways to perform a strategic learning action. We review how different characteristics of learning strategy definition have been transformed because of introduction of ICT into education. Finally, the article points toward conceptual challenges and future research questions.

Learning the language of time: Children's acquisition of duration words.

Science.gov (United States)

Tillman, Katharine A; Barner, David

2015-05-01

Children use time words like minute and hour early in development, but take years to acquire their precise meanings. Here we investigate whether children assign meaning to these early usages, and if so, how. To do this, we test their interpretation of seven time words: second, minute, hour, day, week, month, and year. We find that preschoolers infer the orderings of time words (e.g., hour>minute), but have little to no knowledge of the absolute durations they encode. Knowledge of absolute duration is learned much later in development - many years after children first start using time words in speech - and in many children does not emerge until they have acquired formal definitions for the words. We conclude that associating words with the perception of duration does not come naturally to children, and that early intuitive meanings of time words are instead rooted in relative orderings, which children may infer from their use in speech. Copyright © 2015 Elsevier Inc. All rights reserved.

A Virtual Learning Environment for Part-Time MASW Students: An Evaluation of the WebCT

Science.gov (United States)

Chan, Charles C.; Tsui, Ming-sum; Chan, Mandy Y. C.; Hong, Joe H.

2008-01-01

This study aims to evaluate the perception of a cohort of social workers studying for a part-time master's program in social work in using the popular Web-based learning platform--World Wide Web Course Tools (WebCT) as a complimentary method of teaching and learning. It was noted that social work profession began incorporating computer technology…

Language Learning Attitudes: Ingrained Or Shaped In Time?

Directory of Open Access Journals (Sweden)

Gökçe DİŞLEN DAĞGÖL

2017-09-01

Full Text Available Language learning has become an essential need in today’s world. From academic to social settings, humans need to communicate in a different language to survive in their community. However, despite this increasing importance of language, it is difficult to say we have attained successful language learning on a large scale since there are a lot of factors in language learning process. Language attitudes, one of these factors, influence this process both positively and negatively, depending on how we view learning a foreign language. Therefore, this study deals with the issue of language attitudes to uncover learners’ language conceptions and probable effects on their learning. Moreover, this study aims to reveal the potential role of past learning experiences on the development of language beliefs positively or negatively. Thus, 35 university students in their 1st, 2nd, 3rd and 4th years constitute the participants of the study. Based on mixed research design, the study is comprised of both quantitative and qualitative data. Quantitative data were gathered through Attitude Scale towards English Course, and the analyses were performed with Statistical Packages for Social Sciences (SPSS 17.0 version for Windows. The qualitative data were collected from students’ reports of their own autobiographies regarding their previous language learning experiences in elementary, secondary, high school and university years, and were subjected to the content analysis. The study showed language attitudes from behavioural, cognitive and affective perspectives and found out different factors in shaping their learning conceptions.

Time-place learning over a lifetime : Absence of memory loss in trained old mice

NARCIS (Netherlands)

Mulder, Cornelis K; Reckman, Gerlof A R; Gerkema, Menno P; van der Zee, Eddy A

Time-place learning (TPL) offers the possibility to study the functional interaction between cognition and the circadian system with aging. With TPL, animals link biological significant events with the location and the time of day. This what-where-when type of memory provides animals with an

Synthesis and photophysical and electroluminescent properties of poly(1,4-phenylene–ethynylene)-alt-poly(1,4-phenylene–vinylene)s with various dissymmetric substitution of alkoxy side chains

Czech Academy of Sciences Publication Activity Database

Bouguerra, N.; Růžička, Aleš; Ulbricht, C.; Enengl, C.; Enengl, S.; Pokorná, Veronika; Výprachtický, Drahomír; Tordin, E.; Aitout, R.; Cimrová, Věra; Egbe, D. A. M.

2016-01-01

Roč. 49, č. 2 (2016), s. 455-464 ISSN 0024-9297 R&D Projects: GA ČR(CZ) GA13-26542S; GA ČR(CZ) GAP106/12/0827 Institutional support: RVO:61389013 Keywords : poly(1,4-phenylene-ethynylene)-alt-poly(1,4-phenylene-vinylene)s * dissymmetric side chains * synthesis Subject RIV: CD - Macromolecular Chemistry Impact factor: 5.835, year: 2016

Enhancement of Online Robotics Learning Using Real-Time 3D Visualization Technology

Directory of Open Access Journals (Sweden)

Richard Chiou

2010-06-01

Full Text Available This paper discusses a real-time e-Lab Learning system based on the integration of 3D visualization technology with a remote robotic laboratory. With the emergence and development of the Internet field, online learning is proving to play a significant role in the upcoming era. In an effort to enhance Internet-based learning of robotics and keep up with the rapid progression of technology, a 3- Dimensional scheme of viewing the robotic laboratory has been introduced in addition to the remote controlling of the robots. The uniqueness of the project lies in making this process Internet-based, and remote robot operated and visualized in 3D. This 3D system approach provides the students with a more realistic feel of the 3D robotic laboratory even though they are working remotely. As a result, the 3D visualization technology has been tested as part of a laboratory in the MET 205 Robotics and Mechatronics class and has received positive feedback by most of the students. This type of research has introduced a new level of realism and visual communications to online laboratory learning in a remote classroom.

Mapping exciton quenching in photovoltaic-applicable polymer blends using time-resolved scanning near-field optical microscopy

Science.gov (United States)

Cadby, A.; Khalil, G.; Fox, A. M.; Lidzey, D. G.

2008-05-01

We have used time-resolved scanning near-field microscopy to image the fluorescence decay lifetime across a phase-separated blend of the photovoltaic-applicable polymers poly(9,9'-dioctylfluorene-alt-benzothiadiazole) (F8BT) and poly(9,9'-dioctylfluorene-alt-bis- N ,N'-(4-butylphenyl)-bis-N ,N'-phenyl-1,4-phenylenediamine) (PFB). We show that the efficiency of local fluorescence quenching is composition dependent, with excitons on F8BT molecules being more effectively quenched when F8BT is trapped at a low concentration in a PFB-rich phase. Despite such presumed differences in charge-carrier generation efficiency, our results demonstrate that charge extraction from F8BT:PFB devices is the most dominant mechanism limiting their operational efficiency.

Thermal load distribution on the ALT-II limiter of TEXTOR-94 during RI mode operation and during disruptions

International Nuclear Information System (INIS)

Finken, K.H.; Denner, T.; Mank, G.

2000-01-01

Thermographic measurements using an IR scanner have been performed at the pump limiter ALT-II of TEXTOR-94 during RI mode discharges and during disruptions. The measurements on the RI mode discharges were done to complete the TEXTOR database which had shown a structured decay pattern of the deposited power. It was found that the underlying radial heat flux can be described by two exponential decay functions. This structure, which generates an unexpected heat component close to the tangent line, has been observed in all discharge conditions including the RI mode. During disruptions, the heat is released in short pulses with a typical duration of 0.01-0.1 ms. The radial decay length of these pulses has a similar shape to the heat flux during normal discharges: it consists again of a strong component close to the tangent line with a radial decay length of 2-5 mm and probably one with a decay length of the order of 1 cm. The heat is released at the time when the edge electron temperature of the plasma drops, when intense hydrogen and carbon fluxes occur near the walls, and when electrical currents in the limiter blades are excited. In a tentative interpretation, the temporal and spatial structure of the heat pulse is attributed to the presence and growth of a laminar zone at the plasma edge, which is connected with the ergodization of the plasma edge during a disruption. (author)

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

Science.gov (United States)

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Kwan Chan, Pak; Tin, Chung

2018-02-01

Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

Development of the profession and qualifications of adult educators in Lithuania in the context of reforms of adult education

Science.gov (United States)

Gedvilienė, Genutė; Tūtlys, Vidmantas; Lukošūnienė, Vilija; Zuzevičiūtė, Vaiva

2018-01-01

The Baltic countries regained their independence from the Soviet Union in the early 1990s and joined the European Union in 2004. This article seeks to explore institutional development and reforms of adult education and lifelong learning in Lithuania with respect to the processes, the actors and the context of socioeconomic change over the past 20 years. It also looks at the implications of these processes for the professionalisation of adult educators, referred to here as "adult learning teachers" (ALTs). The authors begin with an analysis of the historical-institutional and political-economical aspects of the development of adult education and lifelong learning by providing a retrospective of institutional change in Lithuania. They then move on to analyse the existing institutional and legal arrangements of adult education which shape and institutionalise the profession and qualifications of ALTs. Their empirical research reveals the opinions of Lithuanian ALTs on their current professional occupational profile and its future development.

Machine learning in heart failure: ready for prime time.

Science.gov (United States)

Awan, Saqib Ejaz; Sohel, Ferdous; Sanfilippo, Frank Mario; Bennamoun, Mohammed; Dwivedi, Girish

2018-03-01

The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

Self-regulated learning as a framework for the educational application of virtual communities and personal learning environments

Directory of Open Access Journals (Sweden)

Julio Cabero Almenara

2013-08-01

Full Text Available 0 0 1 187 1033 USAL 8 2 1218 14.0 Normal 0 21 false false false ES JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:ES; mso-fareast-language:EN-US;} “Virtual Communities” (VC and “Personal learning Environments” (PLE, as products of the Web 2.0, of the cloud computing and of the “social media”, are impacting the field of education and are carrying the students to perform a more active role in the process of learning, and to integrate in their training not only the formal contexts, but also the informal and non-formal. However, we must be aware that the students control over the technology does not imply, necessarily, their control over their processes of teaching-learning and over the experience of learning. And for such control, self-regulation of learning by the student in CV and PLE can serve them to pass of their perception of technologies as technological tools to their perception of them as pedagogical tools, and to its use in the learning process in a planned and organized action, and directed toward specific goals. In order to do it, self-regulated learning, ie the application of learning strategies, their self-assessment, and the use of corrective actions in CV and in PLE, will led the student to take a more active, participatory and critical position in them, which will result in the creation of meaning mediated learning environments.

Why Hong Kong students favour more face-to-face classroom time in blended learning

OpenAIRE

Henri,James; Lee,Sandra

2007-01-01

A three year study in student characteristics, needs and learning styles guided instructors at the University of Hong Kong Faculty of Education to improve teaching and learning in a core module: Information Literacy. A mixed-method approach analyzed data collected from undergraduate, in-service teachers in a BEd program, and helped instructors in the program to gain insight into the Hong Kong teacher working, post-service towards a BEd in Library and Information Science. Part-time students in...

Assessment of the quality in e-learning processes: a proposal with new dimensions

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João Paulo Vagarinho

2013-08-01

Full Text Available 0 0 1 74 413 USAL 3 1 486 14.0 Normal 0 21 false false false ES JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:ES; mso-fareast-language:EN-US;} The globalization in education increased competitiveness among institutions of learning especially in higher education. The method for the differentiation of institutions is the quality factor. In e-learning quality we can find interesting discussions about the complexity of adopting a methodology. This article show the state of art quality oriented processes in e-learning and a proposal for new dimensions to be considered in assessing the quality of e-learning-oriented processes.

Procedural learning is impaired in dyslexia: Evidence from a meta-analysis of serial reaction time studies☆

Science.gov (United States)

Lum, Jarrad A.G.; Ullman, Michael T.; Conti-Ramsden, Gina

2013-01-01

A number of studies have investigated procedural learning in dyslexia using serial reaction time (SRT) tasks. Overall, the results have been mixed, with evidence of both impaired and intact learning reported. We undertook a systematic search of studies that examined procedural learning using SRT tasks, and synthesized the data using meta-analysis. A total of 14 studies were identified, representing data from 314 individuals with dyslexia and 317 typically developing control participants. The results indicate that, on average, individuals with dyslexia have worse procedural learning abilities than controls, as indexed by sequence learning on the SRT task. The average weighted standardized mean difference (the effect size) was found to be 0.449 (CI95: .204, .693), and was significant (p dyslexia. PMID:23920029

Scheduling with Learning Effects and/or Time-Dependent Processing Times to Minimize the Weighted Number of Tardy Jobs on a Single Machine

Directory of Open Access Journals (Sweden)

Jianbo Qian

2013-01-01

Full Text Available We consider single machine scheduling problems with learning/deterioration effects and time-dependent processing times, with due date assignment consideration, and our objective is to minimize the weighted number of tardy jobs. By reducing all versions of the problem to an assignment problem, we solve them in O(n4 time. For some important special cases, the time complexity can be improved to be O(n2 using dynamic programming techniques.

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning

Science.gov (United States)

Xu, Tao; Xiao, Na; Zhai, Xiaolong; Chan, Pak Kwan; Tin, Chung

2018-02-01

Objective. Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit have recently emerged as a possible solution to these problems. Here we describe the development of a real-time cerebellar neuroprosthetic system to substitute neural function in cerebellar circuitry for learning delay eyeblink conditioning (DEC). Approach. The system was empowered by a biologically realistic spiking neural network (SNN) model of the cerebellar neural circuit, which considers the neuronal population and anatomical connectivity of the network. The model simulated synaptic plasticity critical for learning DEC. This SNN model was carefully implemented on a field programmable gate array (FPGA) platform for real-time simulation. This hardware system was interfaced in in vivo experiments with anesthetized rats and it used neural spikes recorded online from the animal to learn and trigger conditioned eyeblink in the animal during training. Main results. This rat-FPGA hybrid system was able to process neuronal spikes in real-time with an embedded cerebellum model of ~10 000 neurons and reproduce learning of DEC with different inter-stimulus intervals. Our results validated that the system performance is physiologically relevant at both the neural (firing pattern) and behavioral (eyeblink pattern) levels. Significance. This integrated system provides the sufficient computation power for mimicking the cerebellar circuit in real-time. The system interacts with the biological system naturally at the spike level and can be generalized for including other neural components (neuron types and plasticity) and neural functions for potential neuroprosthetic applications.

From brain synapses to systems for learning and memory: Object recognition, spatial navigation, timed conditioning, and movement control.

Science.gov (United States)

Grossberg, Stephen

2015-09-24

This article provides an overview of neural models of synaptic learning and memory whose expression in adaptive behavior depends critically on the circuits and systems in which the synapses are embedded. It reviews Adaptive Resonance Theory, or ART, models that use excitatory matching and match-based learning to achieve fast category learning and whose learned memories are dynamically stabilized by top-down expectations, attentional focusing, and memory search. ART clarifies mechanistic relationships between consciousness, learning, expectation, attention, resonance, and synchrony. ART models are embedded in ARTSCAN architectures that unify processes of invariant object category learning, recognition, spatial and object attention, predictive remapping, and eye movement search, and that clarify how conscious object vision and recognition may fail during perceptual crowding and parietal neglect. The generality of learned categories depends upon a vigilance process that is regulated by acetylcholine via the nucleus basalis. Vigilance can get stuck at too high or too low values, thereby causing learning problems in autism and medial temporal amnesia. Similar synaptic learning laws support qualitatively different behaviors: Invariant object category learning in the inferotemporal cortex; learning of grid cells and place cells in the entorhinal and hippocampal cortices during spatial navigation; and learning of time cells in the entorhinal-hippocampal system during adaptively timed conditioning, including trace conditioning. Spatial and temporal processes through the medial and lateral entorhinal-hippocampal system seem to be carried out with homologous circuit designs. Variations of a shared laminar neocortical circuit design have modeled 3D vision, speech perception, and cognitive working memory and learning. A complementary kind of inhibitory matching and mismatch learning controls movement. This article is part of a Special Issue entitled SI: Brain and Memory

Real-time Stereoscopic 3D for E-Robotics Learning

Directory of Open Access Journals (Sweden)

Richard Y. Chiou

2011-02-01

Full Text Available Following the design and testing of a successful 3-Dimensional surveillance system, this 3D scheme has been implemented into online robotics learning at Drexel University. A real-time application, utilizing robot controllers, programmable logic controllers and sensors, has been developed in the “MET 205 Robotics and Mechatronics” class to provide the students with a better robotic education. The integration of the 3D system allows the students to precisely program the robot and execute functions remotely. Upon the students’ recommendation, polarization has been chosen to be the main platform behind the 3D robotic system. Stereoscopic calculations are carried out for calibration purposes to display the images with the highest possible comfort-level and 3D effect. The calculations are further validated by comparing the results with students’ evaluations. Due to the Internet-based feature, multiple clients have the opportunity to perform the online automation development. In the future, students, in different universities, will be able to cross-control robotic components of different types around the world. With the development of this 3D ERobotics interface, automation resources and robotic learning can be shared and enriched regardless of location.

An integrated theory of prospective time interval estimation : The role of cognition, attention, and learning

NARCIS (Netherlands)

Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval

Time-Place Learning over a Lifetime: Absence of Memory Loss in Trained Old Mice

Science.gov (United States)

Mulder, Cornelis K.; Reckman, Gerlof A. R.; Gerkema, Menno P.; Van der Zee, Eddy A.

2015-01-01

Time-place learning (TPL) offers the possibility to study the functional interaction between cognition and the circadian system with aging. With TPL, animals link biological significant events with the location and the time of day. This what-where-when type of memory provides animals with an experience-based daily schedule. Mice were tested for…

Aging and Network Properties: Stability Over Time and Links with Learning during Working Memory Training

Directory of Open Access Journals (Sweden)

Alexandru D. Iordan

2018-01-01

Full Text Available Growing evidence suggests that healthy aging affects the configuration of large-scale functional brain networks. This includes reducing network modularity and local efficiency. However, the stability of these effects over time and their potential role in learning remain poorly understood. The goal of the present study was to further clarify previously reported age effects on “resting-state” networks, to test their reliability over time, and to assess their relation to subsequent learning during training. Resting-state fMRI data from 23 young (YA and 20 older adults (OA were acquired in 2 sessions 2 weeks apart. Graph-theoretic analyses identified both consistencies in network structure and differences in module composition between YA and OA, suggesting topological changes and less stability of functional network configuration with aging. Brain-wide, OA showed lower modularity and local efficiency compared to YA, consistent with the idea of age-related functional dedifferentiation, and these effects were replicable over time. At the level of individual networks, OA consistently showed greater participation and lower local efficiency and within-network connectivity in the cingulo-opercular network, as well as lower intra-network connectivity in the default-mode network and greater participation of the somato-sensorimotor network, suggesting age-related differential effects at the level of specialized brain modules. Finally, brain-wide network properties showed associations, albeit limited, with learning rates, as assessed with 10 days of computerized working memory training administered after the resting-state sessions, suggesting that baseline network configuration may influence subsequent learning outcomes. Identification of neural mechanisms associated with learning-induced plasticity is important for further clarifying whether and how such changes predict the magnitude and maintenance of training gains, as well as the extent and limits of

A longitudinal study on time perspectives: relations with academic delay of gratification and learning environment

NARCIS (Netherlands)

Peetsma, T.; Schuitema, J.; van der Veen, I.

2012-01-01

After they start secondary school (at age 12 in the Netherlands), students' time perspectives on school and professional career and self-regulated learning decrease, while their perspectives on leisure increase. We aimed to investigate relations in the developments in time perspectives and delay of

Robotic education, a tool for the theaching-learning of the science and technology

Directory of Open Access Journals (Sweden)

Kathia Pittí Patiño

2012-07-01

Full Text Available Normal.dotm 0 0 1 113 649 Universidad de Salamanca 5 1 797 12.0 0 false 18 pt 18 pt 0 0 false false false /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} This paper presents and analyzes the educational robotics as a tool to support teaching and learning process at the level of pre-media, primarily engaged in complex subjects such as mathematics, physics and computer science, among others. The study was limited to high schools in the province of Chiriqui, Panama, took a sample of six schools in the province and for each school involved both students and teachers. The main objective of the project was to demonstrate how robotics education, facilitates and encourages teaching and learning of science and technology. The results showed that robotics could become an excellent tool to understand abstract concepts and complex subjects in the area of science and technology, as well as allowing developing basic skills such as teamwork.

Changes in recognition memory over time: an ERP investigation into vocabulary learning.

Directory of Open Access Journals (Sweden)

Shekeila D Palmer

Full Text Available Although it seems intuitive to assume that recognition memory fades over time when information is not reinforced, some aspects of word learning may benefit from a period of consolidation. In the present study, event-related potentials (ERP were used to examine changes in recognition memory responses to familiar and newly learned (novel words over time. Native English speakers were taught novel words associated with English translations, and subsequently performed a Recognition Memory task in which they made old/new decisions in response to both words (trained word vs. untrained word, and novel words (trained novel word vs. untrained novel word. The Recognition task was performed 45 minutes after training (Day 1 and then repeated the following day (Day 2 with no additional training session in between. For familiar words, the late parietal old/new effect distinguished old from new items on both Day 1 and Day 2, although response to trained items was significantly weaker on Day 2. For novel words, the LPC again distinguished old from new items on both days, but the effect became significantly larger on Day 2. These data suggest that while recognition memory for familiar items may fade over time, recognition of novel items, conscious recollection in particular may benefit from a period of consolidation.

Data on three-year pesticide monitoring in ditches of the apple orchard region of Altes Land, Germany

Directory of Open Access Journals (Sweden)

Stefan Lorenz

2018-06-01

Full Text Available The data presented in this article are related to the research article 'Chemical and biological monitoring of the load of plant protection products and of zoocoenoses in ditches of the orchard region Altes Land' (Süß et al., 2006 [1], which is only available in the German language. The pesticide data presented here were acquired from four ditches (three ditches were located in apple orchards, and one ditch was located in a grassland region between 2001 and 2003 (Lorenz et al., 2018 [2]. Two different monitoring strategies were applied: event-driven sampling after pesticide applications and weekly integrated sampling using automatic water samplers. A total of 70 active substances were monitored while farmers applied 25 active substances. This article describes the study sites and the analytical methods used to quantify the pesticides in the water samples. The field data set is publicly available at the OpenAgrar repository under https://doi.org/10.5073/20180213-144359 (Lorenz et al., 2018 [2].

A Social Practice Theory of Learning and Becoming across Contexts and Time

Science.gov (United States)

Penuel, William R.; DiGiacomo, Daniela K.; Van Horne, Katie; Kirshner, Ben

2016-01-01

This paper presents a social practice theory of learning and becoming across contexts and time. Our perspective is rooted in the Danish tradition of critical psychology (Dreier, 1997; Mørck & Huniche, 2006; Nissen, 2005), and we use social practice theory to interpret the pathway of one adolescent whom we followed as part of a longitudinal…

Optimizing Earth Data Search Ranking using Deep Learning and Real-time User Behaviour

Science.gov (United States)

Jiang, Y.; Yang, C. P.; Armstrong, E. M.; Huang, T.; Moroni, D. F.; McGibbney, L. J.; Greguska, F. R., III

2017-12-01

Finding Earth science data has been a challenging problem given both the quantity of data available and the heterogeneity of the data across a wide variety of domains. Current search engines in most geospatial data portals tend to induce end users to focus on one single data characteristic dimension (e.g., term frequency-inverse document frequency (TF-IDF) score, popularity, release date, etc.). This approach largely fails to take account of users' multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering the most applicable dataset out of a vast range of available datasets. With users interacting with search engines, sufficient information is already hidden in the log files. Compared with explicit feedback data, information that can be derived/extracted from log files is virtually free and substantially more timely. In this dissertation, I propose an online deep learning framework that can quickly update the learning function based on real-time user clickstream data. The contributions of this framework include 1) a log processor that can ingest, process and create training data from web logs in a real-time manner; 2) a query understanding module to better interpret users' search intent using web log processing results and metadata; 3) a feature extractor that identifies ranking features representing users' multidimensional interests of geospatial data; and 4) a deep learning based ranking algorithm that can be trained incrementally using user behavior data. The search ranking results will be evaluated using precision at K and normalized discounted cumulative gain (NDCG).

Separator Membrane from Crosslinked Poly(Vinyl Alcohol and Poly(Methyl Vinyl Ether-alt-Maleic Anhydride

Directory of Open Access Journals (Sweden)

Charu Vashisth Rohatgi

2015-03-01

Full Text Available In this work, we report separator membranes from crosslinking of two polymers, such as poly vinyl alcohol (PVA with an ionic polymer poly(methyl vinyl ether-alt-maleic anhydride (PMVE-MA. Such interpolymer-networked systems were extensively used for biomedical and desalination applications but they were not examined for their potential use as membranes or separators for batteries. Therefore, the chemical interactions between these two polymers and the influence of such crosslinking on physicochemical properties of the membrane are systematically investigated through rheology and by critical gel point study. The hydrogen bonding and the chemical interaction between PMVE-MA and PVA resulted in highly cross-linked membranes. Effect of the molecular weight of PVA on the membrane properties was also examined. The developed membranes were extensively characterized by studying their physicochemical properties (water uptake, swelling ratio, and conductivity, thermal and electrochemical properties using differential scanning calorimetry (DSC, dynamic mechanical analysis (DMA, thermo-gravimetric analysis (TGA and electrochemical impedance spectroscopy (EIS. The DSC study shows the presence of a single Tg in the membranes indicating compatibility of the two polymers in flexible and transparent films. The membranes show good stability and ion conductivity suitable for separator applications.

Separator Membrane from Crosslinked Poly(Vinyl Alcohol) and Poly(Methyl Vinyl Ether-alt-Maleic Anhydride)

Science.gov (United States)

Rohatgi, Charu Vashisth; Dutta, Naba K.; Choudhury, Namita Roy

2015-01-01

In this work, we report separator membranes from crosslinking of two polymers, such as poly vinyl alcohol (PVA) with an ionic polymer poly(methyl vinyl ether-alt-maleic anhydride) (PMVE-MA). Such interpolymer-networked systems were extensively used for biomedical and desalination applications but they were not examined for their potential use as membranes or separators for batteries. Therefore, the chemical interactions between these two polymers and the influence of such crosslinking on physicochemical properties of the membrane are systematically investigated through rheology and by critical gel point study. The hydrogen bonding and the chemical interaction between PMVE-MA and PVA resulted in highly cross-linked membranes. Effect of the molecular weight of PVA on the membrane properties was also examined. The developed membranes were extensively characterized by studying their physicochemical properties (water uptake, swelling ratio, and conductivity), thermal and electrochemical properties using differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), thermo-gravimetric analysis (TGA) and electrochemical impedance spectroscopy (EIS). The DSC study shows the presence of a single Tg in the membranes indicating compatibility of the two polymers in flexible and transparent films. The membranes show good stability and ion conductivity suitable for separator applications. PMID:28347019

Leading Learning in Our Times

Science.gov (United States)

Trilling, Bernie

2010-01-01

Important tools that schools need to support a 21st century approach to teaching and learning include the usual suspects: the Internet, pen and paper, cell phones, educational games, tests and quizzes, good teachers, caring communities, educational funding, and loving parents. All of these items and more contribute to a 21st century education, but…

A real-time articulatory visual feedback approach with target presentation for second language pronunciation learning.

Science.gov (United States)

Suemitsu, Atsuo; Dang, Jianwu; Ito, Takayuki; Tiede, Mark

2015-10-01

Articulatory information can support learning or remediating pronunciation of a second language (L2). This paper describes an electromagnetic articulometer-based visual-feedback approach using an articulatory target presented in real-time to facilitate L2 pronunciation learning. This approach trains learners to adjust articulatory positions to match targets for a L2 vowel estimated from productions of vowels that overlap in both L1 and L2. Training of Japanese learners for the American English vowel /æ/ that included visual training improved its pronunciation regardless of whether audio training was also included. Articulatory visual feedback is shown to be an effective method for facilitating L2 pronunciation learning.

Treatment of skeletal class III malocclusion using face mask therapy with alternate rapid maxillary expansion and constriction (Alt-RAMEC protocol

Directory of Open Access Journals (Sweden)

Anand Ramchandra Rathi

2015-01-01

Full Text Available Class III malocclusion is very common malocclusion and can be due to maxillary retrusion, mandibular prognathism, or combination. Ellis and McNamara found a combination of maxillary retrusion and mandibular protrusion to be the most common skeletal relationship (30%. The treatment should be carried out as early as possible for permitting normal growth of the skeletal bases. Reverse pull head gear combined with maxillary expansion can effectively correct skeletal Class III malocclusion due to maxillary deficiency in growing patient. An eight-year-old female patient with chief complaint of prognathic mandible and anterior crossbite was successfully treated in duration of 5 months with facemask and expansion therapy based on Alternate Rapid Maxillary Expansion and Constriction (Alt-RAMEC protocol.

An Integrated Theory of Prospective Time Interval Estimation: The Role of Cognition, Attention, and Learning

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Taatgen, Niels A.; van Rijn, Hedderik; Anderson, John

2007-01-01

A theory of prospective time perception is introduced and incorporated as a module in an integrated theory of cognition, thereby extending existing theories and allowing predictions about attention and learning. First, a time perception module is established by fitting existing datasets (interval estimation and bisection and impact of secondary…

A Matter of Time: Faster Percolator Analysis via Efficient SVM Learning for Large-Scale Proteomics.

Science.gov (United States)

Halloran, John T; Rocke, David M

2018-05-04

Percolator is an important tool for greatly improving the results of a database search and subsequent downstream analysis. Using support vector machines (SVMs), Percolator recalibrates peptide-spectrum matches based on the learned decision boundary between targets and decoys. To improve analysis time for large-scale data sets, we update Percolator's SVM learning engine through software and algorithmic optimizations rather than heuristic approaches that necessitate the careful study of their impact on learned parameters across different search settings and data sets. We show that by optimizing Percolator's original learning algorithm, l 2 -SVM-MFN, large-scale SVM learning requires nearly only a third of the original runtime. Furthermore, we show that by employing the widely used Trust Region Newton (TRON) algorithm instead of l 2 -SVM-MFN, large-scale Percolator SVM learning is reduced to nearly only a fifth of the original runtime. Importantly, these speedups only affect the speed at which Percolator converges to a global solution and do not alter recalibration performance. The upgraded versions of both l 2 -SVM-MFN and TRON are optimized within the Percolator codebase for multithreaded and single-thread use and are available under Apache license at bitbucket.org/jthalloran/percolator_upgrade .

A deep learning framework for financial time series using stacked autoencoders and long-short term memory.

Science.gov (United States)

Bao, Wei; Yue, Jun; Rao, Yulei

2017-01-01

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

Robotics as a resource to facilitate the learning and general skills development

Directory of Open Access Journals (Sweden)

Flor Ángela Bravo Sánchez

2012-07-01

Full Text Available Normal.dotm 0 0 1 127 729 Universidad de Salamanca 6 1 895 12.0 0 false 18 pt 18 pt 0 0 false false false /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:12.0pt; font-family:"Times New Roman"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin;} The growing importance of technology in the world today and its continuous development, makes the technology becomes an integral part of the formation process in childhood and youth. For this reason is important to develop proposals that are offered to children and young people to come into contact with new technologies, that is possible through the use of software and hardware tools, such as robotic prototypes and specialized programs for educational purposes This paper shows the importance of the use of robotics as a learning tool and presents the typical stages that must be confronted in implementing educational robotics projects in the classroom. It also presents an educational robotics project called "Mundo Robotica" which seeks to involve robotics in the classroom through practical activities and learning resources, all this is articulated from a virtual platform.

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

Science.gov (United States)

Bao, Wei; Rao, Yulei

2017-01-01

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day’s closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance. PMID:28708865

A deep learning framework for financial time series using stacked autoencoders and long-short term memory.

Directory of Open Access Journals (Sweden)

Wei Bao

Full Text Available The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT, stacked autoencoders (SAEs and long-short term memory (LSTM are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

Adjustment to subtle time constraints and power law learning in rapid serial visual presentation

Directory of Open Access Journals (Sweden)

Jacqueline Chakyung Shin

2015-11-01

Full Text Available We investigated whether attention could be modulated through the implicit learning of temporal information in a rapid serial visual presentation (RSVP task. Participants identified two target letters among numeral distractors. The stimulus-onset asynchrony immediately following the first target (SOA1 varied at three levels (70, 98, and 126 ms randomly between trials or fixed within blocks of trials. Practice over three consecutive days resulted in a continuous improvement in the identification rate for both targets and attenuation of the attentional blink (AB, a decrement in target (T2 identification when presented 200-400 ms after another target (T1. Blocked SOA1s led to a faster rate of improvement in RSVP performance and more target order reversals relative to random SOA1s, suggesting that the implicit learning of SOA1 positively affected performance. The results also reveal power law learning curves for individual target identification as well as the reduction in the AB decrement. These learning curves reflect the spontaneous emergence of skill through subtle attentional modulations rather than general attentional distribution. Together, the results indicate that implicit temporal learning could improve high level and rapid cognitive processing and highlights the sensitivity and adaptability of the attentional system to subtle constraints in stimulus timing.

Predicting the time of conversion to MCI in the elderly: role of verbal expression and learning.

Science.gov (United States)

Oulhaj, Abderrahim; Wilcock, Gordon K; Smith, A David; de Jager, Celeste A

2009-11-03

Increasing awareness that minimal or mild cognitive impairment (MCI) in the elderly may be a precursor of dementia has led to an increase in the number of people attending memory clinics. We aimed to develop a way of predicting the period of time before cognitive impairment occurs in community-dwelling elderly. The method is illustrated by the use of simple tests of different cognitive domains. A cohort of 241 normal elderly volunteers was followed for up to 20 years with regular assessments of cognitive abilities using the Cambridge Cognitive Examination (CAMCOG); 91 participants developed MCI. We used interval-censored survival analysis statistical methods to model which baseline cognitive tests best predicted the time to convert to MCI. Out of several baseline variables, only age and CAMCOG subscores for expression and learning/memory were predictors of the time to conversion. The time to conversion was 14% shorter for each 5 years of age, 17% shorter for each point lower in the expression score, and 15% shorter for each point lower in the learning score. We present in tabular form the probability of converting to MCI over intervals between 2 and 10 years for different combinations of expression and learning scores. In apparently normal elderly people, subtle measurable cognitive deficits that occur within the normal range on standard testing protocols reliably predict the time to clinically relevant cognitive impairment long before clinical symptoms are reported.

Sleep parameters, functional status and time post-stroke are associated with off-line motor skill learning in people with chronic stroke

Directory of Open Access Journals (Sweden)

Catherine eSiengsukon

2015-10-01

Full Text Available Background: Mounting evidence demonstrates that individuals with stroke benefit from sleep to enhance learning of a motor task. While stage NREM2 sleep and REM sleep have been associated with off-line motor skill learning in neurologically-intact individuals, it remains unknown which sleep parameters or specific sleep stages are associated with off-line motor skill learning in individuals with stroke. Methods: Twenty individuals with chronic stroke (> 6 months following stroke and 10 neurologically slept for three consecutive nights in a sleep laboratory with polysomnography. Participants practiced a tracking task the morning before the third night and underwent a retention test the morning following the third night. Off-line learning on the tracking task was assessed. Pearson’s correlations assessed for associations between the magnitude of off-line learning and sleep variables, age, upper extremity motor function, stroke severity, depression and time since stroke occurrence.Results: Individuals with stroke performed with significantly less error on the tracking task following a night of sleep (p=.006 while the control participants did not (p=.816. Increased sleep efficiency (r= -.285, less time spent in stage NREM3 sleep (r=.260, and more time spent in stage REM sleep (r= -.266 was weakly-to-moderately associated with increased magnitude of off-line motor learning. Furthermore, higher upper-extremity motor function (r = -.400, lower stroke severity (r = .360, and less time since stroke occurrence (r=.311 were moderately associated with increased magnitude of off-line motor learning. Conclusion: This study is the first study to provide insight into which sleep stages and individual characteristics may be associated with off-line learning in people with stroke. Future work should continue to understand which factors or combination of factors promote off-line motor learning in people with neurologic injury to best promote motor recovery in

Cerebral activation related to implicit sequence learning in a Double Serial Reaction Time task

NARCIS (Netherlands)

van der Graaf, FHCE; Maguire, RP; Leenders, KL; de Jong, BM

2006-01-01

Using functional magnetic resonance imaging (fMRI), we examined the distribution of cerebral activations related to implicitly learning a series of fixed stimulus-response combinations. In a novel - bimanual - variant of the Serial Reaction Time task (SRT), simultaneous finger movements of the two

PENERAPAN BLENDED LEARNING BERBASIS SCAFFOLDING UNTUK MENINGATKAN KEMAMPUAN BERPIKIR LOGIS DAN HASIL BELAJAR MAHASISWA PADA MATA KULIAH BIOLOGI UMUM

Directory of Open Access Journals (Sweden)

Dewi Murni

2017-10-01

Full Text Available Normal 0 false false false IN X-NONE X-NONE ABSTRACTScaffolding learning approach that is applied through learning blended learning is expected to improve the student logical thinking ability. It allows students to build their own knowledge with the help of lecturers gradually. The purpose of this study was to determine the student logical thinking abilityandlearningoutcomesingeneralbiologycoursesthroughthelearningblendedlearningbased scaffolding. This research was conducted by descriptive method. Stages of research include the preparationofanevaluationinstrument,implementationoftheproductintheformofblendedlearning ingeneralbiologylectures,measurementoflogicalthinkingabilityofstudentsandlearningoutcomes evaluation stage. The results showed that there is an improvement of student logical thinking ability fromtheconcretelevelintoaformallevel.Variablecontrolingarethehighestlogicalthingkingability aspect with a percentage 81,9. The students learning outcomes also increased from lowest criteria into very good criteria with percentage29,6%. /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:none; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi; mso-ansi-language:EN-US; mso-fareast-language:EN-US;} Normal 0 false false false IN X-NONE X-NONE ABSTRACTScaffolding learning approach that is applied through learning blended learning is expected to improve the student logical thinking ability. It allows students to build their own knowledge with the help of lecturers gradually. The purpose of this study was

Morphological control of calcium oxalate particles in the presence of poly-(styrene-alt-maleic acid)

International Nuclear Information System (INIS)

Yu Jiaguo; Tang Hua; Cheng Bei; Zhao Xiujian

2004-01-01

Calcium oxalate (CaOx) particles exhibiting different shapes and phase structures were fabricated by a simple precipitation reaction of sodium oxalate with calcium chloride in the absence and presence of poly-(styrene-alt-maleic acid) (PSMA) as a crystal modifier at room temperature. The as-obtained products were characterized with scanning electron microscopy (SEM) and X-ray diffraction (XRD). The effects of reaction conditions including pH, [Ca 2+ ]/[C 2 O 4 2- ] ratio and concentration of PSMA and CaC 2 O 4 on the crystal forms and morphologies of the as-obtained calcium oxalate were investigated. The results show that various crystal morphologies of calcium oxalate, such as parallelograms, plates, spheres, bipyramids etc. can be obtained depending on the experimental conditions. Higher polymer concentration favors formation of the metastable calcium oxalate dihydrate (COD) crystals. Lower pH is beneficial to the formation of plate-like CaOx crystals. Especially, the monodispersed parallelogram-like CaOx crystals can be produced by PSMA as an additive at pH 2. PSMA may act as a good inhibitor for urolithiasis since it induces the formation of COD and reduces the particle size of CaOx. This research may provide new insight into the morphological control of CaOx particles and the prevention of urolithiasis

Ethnobotany of the Alt Empordà region (Catalonia, Iberian Peninsula): plants used in human traditional medicine.

Science.gov (United States)

Parada, Montse; Carrió, Esperança; Bonet, Maria Angels; Vallès, Joan

2009-07-30

This paper provides significant ethnobotanical information on pharmaceutical plant uses from a tourist and industrialised region, where some degree of acculturation exists, so that there is urgency in recording such data. To collect, analyze and evaluate the ethnobotanical knowledge about medicinal plants in a north-eastern Iberian region (Alt Empordà, 1358 km(2), 129,160 inhabitants). We performed 101 semi-structured interviews with 178 informants (mean age 69; 71% women, 29% men), identified the plant taxa reported and analyzed the results, comparing them with those from other territories. The informants reported data on 518 species. Of these, 335, belonging to 80 botanical families, were claimed as medicinal. This work is focused on human medicinal plant uses, which represent 98% of the pharmaceutical uses (3581 out of 3643 use reports). Around 800 medicinal uses, concerning 200 species, have not, or have very rarely been cited as medicinal; of these, 32 uses of 30 species have been reported by three or more independent informants. The folk knowledge about medicinal plant use is still alive in the studied region, and a number of scarcely reported plant uses has been detected, some of them with promising phytotherapeutical applications.

Distributed Open and Distance Learning: How Does E-Learning Fit? LSDA Reports.

Science.gov (United States)

Fletcher, Mick

The distinctions between types of open and distance learning broadly equate to the concept of learning at a time, place, and pace that best suits the learner. Distance learning refers to geography, whereas open learning refers to time. Flexible learning is a generic term referring either to geography or time. Combining these distinctions allows…

Sci-Fri AM: Quality, Safety, and Professional Issues 04: Predicting waiting times in Radiation Oncology using machine learning

International Nuclear Information System (INIS)

Joseph, Ackeem; Herrera, David; Hijal, Tarek; Hendren, Laurie; Leung, Alvin; Wainberg, Justin; Sawaf, Marya; Maxim, Gorshkov; Maglieri, Robert; Keshavarz, Mehryar; Kildea, John

2016-01-01

We describe a method for predicting waiting times in radiation oncology. Machine learning is a powerful predictive modelling tool that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The patient waiting experience remains one of the most vexing challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick and in pain, to worry about when they will receive the care they need. In radiation oncology, patients typically experience three types of waiting: Waiting at home for their treatment plan to be prepared Waiting in the waiting room for daily radiotherapy Waiting in the waiting room to see a physician in consultation or follow-up These waiting periods are difficult for staff to predict and only rough estimates are typically provided, based on personal experience. In the present era of electronic health records, waiting times need not be so uncertain. At our centre, we have incorporated the electronic treatment records of all previously-treated patients into our machine learning model. We found that the Random Forest Regression model provides the best predictions for daily radiotherapy treatment waiting times (type 2). Using this model, we achieved a median residual (actual minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. The main features that generated the best fit model (from most to least significant) are: Allocated time, median past duration, fraction number and the number of treatment fields.

Sci-Fri AM: Quality, Safety, and Professional Issues 04: Predicting waiting times in Radiation Oncology using machine learning

Energy Technology Data Exchange (ETDEWEB)

Joseph, Ackeem; Herrera, David; Hijal, Tarek; Hendren, Laurie; Leung, Alvin; Wainberg, Justin; Sawaf, Marya; Maxim, Gorshkov; Maglieri, Robert; Keshavarz, Mehryar; Kildea, John [McGill University Health Centre (Canada)

2016-08-15

We describe a method for predicting waiting times in radiation oncology. Machine learning is a powerful predictive modelling tool that benefits from large, potentially complex, datasets. The essence of machine learning is to predict future outcomes by learning from previous experience. The patient waiting experience remains one of the most vexing challenges facing healthcare. Waiting time uncertainty can cause patients, who are already sick and in pain, to worry about when they will receive the care they need. In radiation oncology, patients typically experience three types of waiting: Waiting at home for their treatment plan to be prepared Waiting in the waiting room for daily radiotherapy Waiting in the waiting room to see a physician in consultation or follow-up These waiting periods are difficult for staff to predict and only rough estimates are typically provided, based on personal experience. In the present era of electronic health records, waiting times need not be so uncertain. At our centre, we have incorporated the electronic treatment records of all previously-treated patients into our machine learning model. We found that the Random Forest Regression model provides the best predictions for daily radiotherapy treatment waiting times (type 2). Using this model, we achieved a median residual (actual minus predicted value) of 0.25 minutes and a standard deviation residual of 6.5 minutes. The main features that generated the best fit model (from most to least significant) are: Allocated time, median past duration, fraction number and the number of treatment fields.

A matter of timing: harm reduction in learned helplessness.

Science.gov (United States)

Richter, Sophie Helene; Sartorius, Alexander; Gass, Peter; Vollmayr, Barbara

2014-11-03

Learned helplessness has excellent validity as an animal model for depression, but problems in reproducibility limit its use and the high degree of stress involved in the paradigm raises ethical concerns. We therefore aimed to identify which and how many trials of the learned helplessness paradigm are necessary to distinguish between helpless and non-helpless rats. A trial-by-trial reanalysis of tests from 163 rats with congenital learned helplessness or congenital non-learned helplessness and comparison of 82 rats exposed to inescapable shock with 38 shock-controls revealed that neither the first test trials, when rats showed unspecific hyperlocomotion, nor trials of the last third of the test, when almost all animals responded quickly to the stressor, contributed to sensitivity and specificity of the test. Considering only trials 3-10 improved the classification of helpless and non-helpless rats. The refined analysis allows abbreviation of the test for learned helplessness from 15 trials to 10 trials thereby reducing pain and stress of the experimental animals without losing statistical power.

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

Science.gov (United States)

Wehmeyer, Christoph; Noé, Frank

2018-06-01

Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes—beyond the capabilities of linear dimension reduction techniques.

Novel real-time tumor-contouring method using deep learning to prevent mistracking in X-ray fluoroscopy.

Science.gov (United States)

Terunuma, Toshiyuki; Tokui, Aoi; Sakae, Takeji

2018-03-01

Robustness to obstacles is the most important factor necessary to achieve accurate tumor tracking without fiducial markers. Some high-density structures, such as bone, are enhanced on X-ray fluoroscopic images, which cause tumor mistracking. Tumor tracking should be performed by controlling "importance recognition": the understanding that soft-tissue is an important tracking feature and bone structure is unimportant. We propose a new real-time tumor-contouring method that uses deep learning with importance recognition control. The novelty of the proposed method is the combination of the devised random overlay method and supervised deep learning to induce the recognition of structures in tumor contouring as important or unimportant. This method can be used for tumor contouring because it uses deep learning to perform image segmentation. Our results from a simulated fluoroscopy model showed accurate tracking of a low-visibility tumor with an error of approximately 1 mm, even if enhanced bone structure acted as an obstacle. A high similarity of approximately 0.95 on the Jaccard index was observed between the segmented and ground truth tumor regions. A short processing time of 25 ms was achieved. The results of this simulated fluoroscopy model support the feasibility of robust real-time tumor contouring with fluoroscopy. Further studies using clinical fluoroscopy are highly anticipated.

Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.

Science.gov (United States)

Wei, Qinglai; Li, Benkai; Song, Ruizhuo

2018-04-01

In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.

Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-Time Popularities

Science.gov (United States)

Sadeghi, Alireza; Sheikholeslami, Fatemeh; Giannakis, Georgios B.

2018-02-01

Small basestations (SBs) equipped with caching units have potential to handle the unprecedented demand growth in heterogeneous networks. Through low-rate, backhaul connections with the backbone, SBs can prefetch popular files during off-peak traffic hours, and service them to the edge at peak periods. To intelligently prefetch, each SB must learn what and when to cache, while taking into account SB memory limitations, the massive number of available contents, the unknown popularity profiles, as well as the space-time popularity dynamics of user file requests. In this work, local and global Markov processes model user requests, and a reinforcement learning (RL) framework is put forth for finding the optimal caching policy when the transition probabilities involved are unknown. Joint consideration of global and local popularity demands along with cache-refreshing costs allow for a simple, yet practical asynchronous caching approach. The novel RL-based caching relies on a Q-learning algorithm to implement the optimal policy in an online fashion, thus enabling the cache control unit at the SB to learn, track, and possibly adapt to the underlying dynamics. To endow the algorithm with scalability, a linear function approximation of the proposed Q-learning scheme is introduced, offering faster convergence as well as reduced complexity and memory requirements. Numerical tests corroborate the merits of the proposed approach in various realistic settings.

Real-time, adaptive machine learning for non-stationary, near chaotic gasoline engine combustion time series.

Science.gov (United States)

Vaughan, Adam; Bohac, Stanislav V

2015-10-01

Fuel efficient Homogeneous Charge Compression Ignition (HCCI) engine combustion timing predictions must contend with non-linear chemistry, non-linear physics, period doubling bifurcation(s), turbulent mixing, model parameters that can drift day-to-day, and air-fuel mixture state information that cannot typically be resolved on a cycle-to-cycle basis, especially during transients. In previous work, an abstract cycle-to-cycle mapping function coupled with ϵ-Support Vector Regression was shown to predict experimentally observed cycle-to-cycle combustion timing over a wide range of engine conditions, despite some of the aforementioned difficulties. The main limitation of the previous approach was that a partially acasual randomly sampled training dataset was used to train proof of concept offline predictions. The objective of this paper is to address this limitation by proposing a new online adaptive Extreme Learning Machine (ELM) extension named Weighted Ring-ELM. This extension enables fully causal combustion timing predictions at randomly chosen engine set points, and is shown to achieve results that are as good as or better than the previous offline method. The broader objective of this approach is to enable a new class of real-time model predictive control strategies for high variability HCCI and, ultimately, to bring HCCI's low engine-out NOx and reduced CO2 emissions to production engines. Copyright © 2015 Elsevier Ltd. All rights reserved.

Assessment of Stand-Alone Displays for Time Management in a Creativity-Driven Learning Environment

DEFF Research Database (Denmark)

Frimodt-Møller, Søren

2017-01-01

This paper considers the pros and cons of stand-alone displays, analog (e.g. billboards, blackboards, whiteboards, large pieces of paper etc.) as well as digital (e.g. large shared screens, digital whiteboards or similar), as tools for time management processes in a creativity-driven learning...

Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case

International Nuclear Information System (INIS)

Voyant, Cyril; Notton, Gilles; Darras, Christophe; Fouilloy, Alexis; Motte, Fabrice

2017-01-01

As global solar radiation forecasting is a very important challenge, several methods are devoted to this goal with different levels of accuracy and confidence. In this study we propose to better understand how the uncertainty is propagated in the context of global radiation time series forecasting using machine learning. Indeed we propose to decompose the error considering four kinds of uncertainties: the error due to the measurement, the variability of time series, the machine learning uncertainty and the error related to the horizon. All these components of the error allow to determinate a global uncertainty generating prediction bands related to the prediction efficiency. We also have defined a reliability index which could be very interesting for the grid manager in order to estimate the validity of predictions. We have experimented this method on a multilayer perceptron which is a popular machine learning technique. We have shown that the global error and its components are essential to quantify in order to estimate the reliability of the model outputs. The described method has been successfully applied to four meteorological stations in Mediterranean area. - Highlights: • Solar irradiation predictions require confidence bands. • There are a lot of kinds of uncertainties to take into account in order to propose prediction bands. • the ranking of different kinds of uncertainties is essential to propose an operational tool for the grid managers.

Evento com aparente risco de morte: uma revisão Evento con aparente riesgo de muerte (alte: una revisión Apparent life-threatening event: a review

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Mariana Tresoldi das N. Romaneli

2012-12-01

Full Text Available OBJETIVO: Realizar uma revisão crítica reunindo informações disponíveis a respeito dos eventos com aparente risco de morte. FONTES DE DADOS: Revisão bibliográfica dos artigos (em português, inglês e espanhol obtidos dos bancos de dados eletrônicos Medline, Lilacs e SciELO, utilizando as palavras-chave: eventos com aparente risco de morte, evento com aparente risco de vida infantil, lactente, apneia, monitorização e cianose. SÍNTESE DOS DADOS: Os eventos com aparente risco de mortesão súbitos e caracterizados por uma combinação de apneia, alteração na coloração da pele e tônus muscular, com inúmeras causas subjacentes. Sua incidência verdadeira é desconhecida e a faixa etária mais acometida é de 11 a 12 semanas. Não há correlação entre o evento com aparente risco de morte e a síndrome da morte súbita do lactente, embora já tenham sido consideradas manifestações da mesma doença. Muitas vezes, o lactente tem aparência saudável ao ser avaliado pelo pediatra após apresentar eventos com aparente risco de morte, porém, isso não afasta a possibilidade de existir uma doença grave associada ao evento, que deve ser investigada e tratada. Quando não são encontradas as causas, o evento é idiopático, geralmente com boa evolução. CONCLUSÕES: É necessário investigar os lactentes levados ao pronto-socorro após apresentarem eventos com aparente risco de morte, devido ao risco de sequelas e mortalidade. Não há uma padronização das condutas a serem realizadas diante de um lactente com aparência saudável que tenha evento com aparente risco de morte, mas recomenda-se que o paciente seja internado e a causa do evento, investigada. A observação e o monitoramento em ambiente hospitalar devem ocorrer no mínimo 24 horas após o evento.OBJETIVO: Realizar una revisión crítica, reuniendo las informaciones disponibles respecto a los Eventos con Aparente Riesgo de Muerte (ALTE - Apparent life-threatening event

From Learning Object to Learning Cell: A Resource Organization Model for Ubiquitous Learning

Science.gov (United States)

Yu, Shengquan; Yang, Xianmin; Cheng, Gang; Wang, Minjuan

2015-01-01

This paper presents a new model for organizing learning resources: Learning Cell. This model is open, evolving, cohesive, social, and context-aware. By introducing a time dimension into the organization of learning resources, Learning Cell supports the dynamic evolution of learning resources while they are being used. In addition, by introducing a…

Time-Contrastive Learning Based DNN Bottleneck Features for Text-Dependent Speaker Verification

DEFF Research Database (Denmark)

Sarkar, Achintya Kumar; Tan, Zheng-Hua

2017-01-01

In this paper, we present a time-contrastive learning (TCL) based bottleneck (BN) feature extraction method for speech signals with an application to text-dependent (TD) speaker verification (SV). It is well-known that speech signals exhibit quasi-stationary behavior in and only in a short interval......, and the TCL method aims to exploit this temporal structure. More specifically, it trains deep neural networks (DNNs) to discriminate temporal events obtained by uniformly segmenting speech signals, in contrast to existing DNN based BN feature extraction methods that train DNNs using labeled data...... to discriminate speakers or pass-phrases or phones or a combination of them. In the context of speaker verification, speech data of fixed pass-phrases are used for TCL-BN training, while the pass-phrases used for TCL-BN training are excluded from being used for SV, so that the learned features can be considered...

Evaluation of Online Log Variables That Estimate Learners' Time Management in a Korean Online Learning Context

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Jo, Il-Hyun; Park, Yeonjeong; Yoon, Meehyun; Sung, Hanall

2016-01-01

The purpose of this study was to identify the relationship between the psychological variables and online behavioral patterns of students, collected through a learning management system (LMS). As the psychological variable, time and study environment management (TSEM), one of the sub-constructs of MSLQ, was chosen to verify a set of time-related…

Simple serum markers for significant liver inflammation in chronic hepatitis B patients with an alanine aminotransferase level lower than 2 times upper limit of normal

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LI Qiang

2016-06-01

Full Text Available ObjectiveTo investigate the simple serum markers for significant liver inflammation in chronic hepatitis B (CHB patients with an alanine aminotransferase (ALT level of ＜2 times upper limit of normal (ULN. MethodsThe clinical data of 278 CHB patients with ALT ＜2×ULN (ULN=40 U/L were analyzed retrospectively. Significant liver inflammation was defined as a liver inflammatory activity grade (G ≥2. The t-test was used for comparison of normally distributed continuous data between groups, and the Kruskal-Wallis rank sum test was used for non-normally distributed continuous data; the chi-square test was used for comparison of categorical data between groups. Multivariate logistic regression analysis was used to identify independent predictors for significant liver inflammation in CHB patients with ALT ＜2×ULN. The receiver operating characteristic (ROC curve was used to evaluate the diagnostic value of serum markers in significant liver inflammation. ResultsOf the 278 CHB patients enrolled, 175 (62.9% had no significant liver inflammation (G0-1 group and 103 (37.1% had significant liver inflammation (G2-4 group. There were significant differences in ALT, aspartate aminotransferase, alkaline phosphatase, gamma-glutamyl transpeptidase (GGT, albumin, globulin, prothrombin time (PT, platelet, absolute neutrophil count, hyaluronic acid (HA, glycocholic acid, precollagen Ⅲ, and collagen type Ⅳ(ⅣC between the two groups (all P＜0.05. The multivariate regression analysis showed that GGT, PT, ⅣC, and HA were independent predictors for significant liver inflammation in CHB patients with ALT＜2×ULN (OR=1.015, 1.600, 1.151, and 1.014, P=0.008, 0.021, 0.003, and 0.018. The areas under the ROC curve for GGT, PT, IVC, and HA to diagnose significant liver inflammation were 0.804, 0.722, 0.707, and 0.632, respectively. The cut-off value of 48.5 U/L for GGT to predict significant liver inflammation had a specificity of 90.3% and a negative

Resonance energy transfer from quinolinone modified polystyrene-block-poly(styrene-alt-maleic anhydride) copolymer to terbium(III) metal ions

Energy Technology Data Exchange (ETDEWEB)

Výprachtický, Drahomír, E-mail: vyprachticky@imc.cas.cz [Institute of Macromolecular Chemistry, Academy of Sciences of the Czech Republic, Heyrovský Sq. 2, 162 06 Prague 6 (Czech Republic); Mikeš, František [New York University Polytechnic School of Engineering, Polymer Research Institute, 6 MetroTech Center, Brooklyn, NY 11201 (United States); Lokaj, Jan; Pokorná, Veronika; Cimrová, Věra [Institute of Macromolecular Chemistry, Academy of Sciences of the Czech Republic, Heyrovský Sq. 2, 162 06 Prague 6 (Czech Republic)

2015-04-15

Polystyrene-block-poly(styrene-alt-maleic anhydride) was synthesized by nitroxide mediated radical polymerization and modified with 7-amino-4-methylquinolin-2(1H)-one (I) and methanol. The formed block polymer ligand contained a quinolinone fluorophore (Ω) and carboxyl (III) or sodium carboxylate (IV) binding sites. The ligand-to-metal resonance energy transfer (RET) and ligand binding properties of [III–Tb{sup 3+}] and [IV–Tb{sup 3+}] complexes were investigated by steady-state and time-resolved luminescence spectroscopy in tetrahydrofuran/methanol and/or tetrahydrofuran/deuterated methanol mixtures and compared with those of a low-molecular-weight model ligand, i.e. the sodium salt of N-(4-methyl-2-oxo-1,2-dihydroquinolin-7-yl)succinamic acid (II). The long-lived emission intensities of Tb{sup 3+} at 490, 545, 585, and 620 nm corresponding to the {sup 5}D{sub 4}→{sup 7}F{sub 6}, {sup 5}D{sub 4}→{sup 7}F{sub 5}, {sup 5}D{sub 4}→{sup 7}F{sub 4}, and {sup 5}D{sub 4}→{sup 7}F{sub 3} transitions, respectively, were strongly increased by the addition of ligands in the order [II-Tb{sup 3+}]⪡[III-Tb{sup 3+}]<[IV-Tb{sup 3+}]. The efficiency of energy transfer (E) was evaluated from the emission intensity of the donor (Ω) in the presence or absence of the acceptor (Tb{sup 3+}) depending on the acceptor concentration and ligand neutralization. It was concluded that the macromolecular ligand structural properties (polymer coil and supramolecular structures, e.g. micelles) were responsible for the increase in RET. The time-resolved luminescence measurements revealed that the binding affinity of the ligands II, III, and IV increased in the order II

Els lepidòpters de la vall d’Alinyà (Alt Urgell, NE de la península Ibèrica: algunes consideracions sobre l’altitud i la classificació biogeogràfica

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Vallhonrat, F.

2005-12-01

Full Text Available The Lepidoptera of the Alinyà valley (Alt Urgell, NE Iberian peninsula: some remarks about altitude and biogeographical classification Results from the census on Lepidoptera fauna carried out in Alinyà valley during years 2000 and 2001 (see annex show few significant differences in the altitudinal range between Mediterranean and Eurasiatic species.

Ecologia: The Assumptions, Expectations, and Strategies of Modern Language Students Working in a Self-Access Learning Environment for the First Time.

Science.gov (United States)

Piper, Alison

1994-01-01

This study examined 29 second-year undergraduate students of Spanish using a self-access learning environment for the first time, focusing on their language attitudes and learning strategies. The results show that, even as modern languages majors, the students possessed a model of language and strategies for learning that were significantly…

On the best learning algorithm for web services response time prediction

DEFF Research Database (Denmark)

Madsen, Henrik; Albu, Razvan-Daniel; Popentiu-Vladicescu, Florin

2013-01-01

In this article we will examine the effect of different learning algorithms, while training the MLP (Multilayer Perceptron) with the intention of predicting web services response time. Web services do not necessitate a user interface. This may seem contradictory to most people's concept of what...... an application is. A Web service is better imagined as an application "segment," or better as a program enabler. Performance is an important quality aspect of Web services because of their distributed nature. Predicting the response of web services during their operation is very important....

Prognostic value of pretreatment serum alanine aminotransferase/aspartate aminotransferase (ALT/AST) ratio and gamma glutamyltransferase (GGT) in patients with esophageal squamous cell carcinoma.

Science.gov (United States)

Huang, Hao; Wang, Xue-Ping; Li, Xiao-Hui; Chen, Hao; Zheng, Xin; Lin, Jian-Hua; Kang, Ting; Zhang, Lin; Chen, Pei-Song

2017-08-14

The levels of liver function tests (LFTs) are often used to assess liver injury and non-liver disease-related mortality. In our study, the relationship between pretreatment serum LFTs and overall survival (OS) was evaluated in esophageal squamous cell carcinoma (ESCC) patients. Our purpose was to investigate the prognostic value of the preoperative alanine aminotransferase/aspartate aminotransferase (ALT/AST) ratio and gamma glutamyltransferase (GGT) in ESCC patients. A retrospective study was performed in 447 patients with ESCC, and follow-up period was at least 60 months until death. The prognostic significance of serum LFTs were determined by univariate and multivariate Cox hazard models. LFTs including ALT, AST, LSR, GGT, TBA and LDH were analyzed. Serum LSR (HR: 0.592, 95% CI = 0.457-0.768, p < 0.001 and GGT (HR: 1.507, 95% CI = 1.163-1.953, p = 0.002) levels were indicated as significant predictors of OS. The 5-year OS among patients with higher LSR levels was longer compared with those patients with decreased LSR levels, not only in the whole cohort but also in the subgroups stratified by pathological stage (T1-T2 subgroup, T3-T4 subgroup, N0 subgroup and M0 subgroup). We also found that patients with a higher GGT might predict worse OS than patients with a normal GGT, not only in the whole cohort but also in the subgroups stratified by pathological stage (T3-T4 subgroup and N1-N2 subgroup). Both increased levels of LSR and decreased levels of GGT might predict shorter overall survival in ESCC patients. Our findings suggest that serum LSR and GGT levels could be used as a key predictor of survival in patients with ESCC.

Using machine learning to identify structural breaks in single-group interrupted time series designs.

Science.gov (United States)

Linden, Ariel; Yarnold, Paul R

2016-12-01

Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. In this paper, we introduce a machine-learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter-initiated Proposition 99 to reduce smoking rates. The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single-group ITSA design. Given the widespread use of ITSA for evaluating observational data and the increasing use of machine-learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single-group ITSA design. © 2016 John Wiley & Sons, Ltd.

Part-Time Community College Instructors Teaching in Learning Communities: An Exploratory Multiple Case Study

Science.gov (United States)

Paterson, John W.

2017-01-01

Community colleges have a greater portion of students at-risk for college completion than four-year schools and faculty at these institutions are overwhelmingly and increasingly part-time. Learning communities have been identified as a high-impact practice with numerous benefits documented for community college instructors and students: a primary…

Perceived Learning and Timely Graduation for Business Undergraduates Taking an Online or Hybrid Course

Science.gov (United States)

Blau, Gary; Drennan, Rob B.; Hochner, Arthur; Kapanjie, Darin

2016-01-01

An online survey tested the impact of background, technological, and course-related variables on perceived learning and timely graduation for a complete data sample of 263 business undergraduates taking at least one online or hybrid course in the fall of 2015. Hierarchical regression results showed that course-related variables (instructor…

Distributed Cerebellar Motor Learning; a Spike-Timing-Dependent Plasticity Model

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Niceto Rafael Luque

2016-03-01

Full Text Available Deep cerebellar nuclei neurons receive both inhibitory (GABAergic synaptic currents from Purkinje cells (within the cerebellar cortex and excitatory (glutamatergic synaptic currents from mossy fibres. Those two deep cerebellar nucleus inputs are thought to be also adaptive, embedding interesting properties in the framework of accurate movements. We show that distributed spike-timing-dependent plasticity mechanisms (STDP located at different cerebellar sites (parallel fibres to Purkinje cells, mossy fibres to deep cerebellar nucleus cells, and Purkinje cells to deep cerebellar nucleus cells in close-loop simulations provide an explanation for the complex learning properties of the cerebellum in motor learning. Concretely, we propose a new mechanistic cerebellar spiking model. In this new model, deep cerebellar nuclei embed a dual functionality: deep cerebellar nuclei acting as a gain adaptation mechanism and as a facilitator for the slow memory consolidation at mossy fibres to deep cerebellar nucleus synapses. Equipping the cerebellum with excitatory (e-STDP and inhibitory (i-STDP mechanisms at deep cerebellar nuclei afferents allows the accommodation of synaptic memories that were formed at parallel fibres to Purkinje cells synapses and then transferred to mossy fibres to deep cerebellar nucleus synapses. These adaptive mechanisms also contribute to modulate the deep-cerebellar-nucleus-output firing rate (output gain modulation towards optimising its working range.

Unsupervised learning by spike timing dependent plasticity in phase change memory (PCM synapses

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Stefano eAmbrogio

2016-03-01

Full Text Available We present a novel one-transistor/one-resistor (1T1R synapse for neuromorphic networks, based on phase change memory (PCM technology. The synapse is capable of spike-timing dependent plasticity (STDP, where gradual potentiation relies on set transition, namely crystallization, in the PCM, while depression is achieved via reset or amorphization of a chalcogenide active volume. STDP characteristics are demonstrated by experiments under variable initial conditions and number of pulses. Finally, we support the applicability of the 1T1R synapse for learning and recognition of visual patterns by simulations of fully connected neuromorphic networks with 2 or 3 layers with high recognition efficiency. The proposed scheme provides a feasible low-power solution for on-line unsupervised machine learning in smart reconfigurable sensors.

Statistical Learning and Adaptive Decision-Making Underlie Human Response Time Variability in Inhibitory Control

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Ning eMa

2015-08-01

Full Text Available Response time (RT is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task, in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop, and stop-signal onset time, SSD (stop-signal delay, with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop and SSD. The human behavioral data (n=20 bear out this prediction, showing P(stop and SSD both to be significant, independent predictors of RT, with P(stop being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

Statistical learning and adaptive decision-making underlie human response time variability in inhibitory control.

Science.gov (United States)

Ma, Ning; Yu, Angela J

2015-01-01

Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.

Off-Policy Reinforcement Learning: Optimal Operational Control for Two-Time-Scale Industrial Processes.

Science.gov (United States)

Li, Jinna; Kiumarsi, Bahare; Chai, Tianyou; Lewis, Frank L; Fan, Jialu

2017-12-01

Industrial flow lines are composed of unit processes operating on a fast time scale and performance measurements known as operational indices measured at a slower time scale. This paper presents a model-free optimal solution to a class of two time-scale industrial processes using off-policy reinforcement learning (RL). First, the lower-layer unit process control loop with a fast sampling period and the upper-layer operational index dynamics at a slow time scale are modeled. Second, a general optimal operational control problem is formulated to optimally prescribe the set-points for the unit industrial process. Then, a zero-sum game off-policy RL algorithm is developed to find the optimal set-points by using data measured in real-time. Finally, a simulation experiment is employed for an industrial flotation process to show the effectiveness of the proposed method.

Mental Time Travel, Memory and the Social Learning Strategies Tournament

Science.gov (United States)

Fogarty, L.; Rendell, L.; Laland, K. N.

2012-01-01

The social learning strategies tournament was an open computer-based tournament investigating the best way to learn in a changing environment. Here we present an analysis of the impact of memory on the ability of strategies entered into the social learning strategies tournament (Rendell, Boyd, et al., 2010) to modify their own behavior to suit a…

Theoretical Investigation on the Electronic and Optical Properties of Poly(fluorenevinylene Derivatives as Light-Emitting Materials

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Thanisorn Yakhanthip

2011-01-01

Full Text Available Density functional theory (DFT and time-dependent DFT (TDDFT were employed to study ground-state properties, HOMO-LUMO gaps (ΔH-L, excitation energies (Eg, ionization potentials (IPs, and electron affinities (EA for PFV-alt-PDONV and PFV-alt-PDIH-PPV having different alternating groups. Excited-state properties were investigated using configuration interaction singles (CISs while fluorescence energies were calculated using TDDFT. The results show that PFV-alt-PDONV exhibits blue-shifted energies for both HOMO-LUMO gaps (ΔH-L and excitation energies (Eg compared with PFV-alt-PDIH-PPV. The predicted IP and EA clearly indicate that PFV-alt-PDIH-PPV has both easier hole creation and electron injection than that of PFV-alt-PDONV. The maximal absorption wavelengths of all polymers are strongly assigned to π→π∗ transition. The predicted radiative lifetimes of PFV-alt-PDONV and PFV-alt-PDIH-PPV for B3LYP/6-31G(d are 0.36 and 0.61 ns, respectively, indicating that PFV-alt-PDIH-PPV should have a better performance for long-time emission than that of PFV-alt-PDONV.

Multi-layer network utilizing rewarded spike time dependent plasticity to learn a foraging task.

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Pavel Sanda

2017-09-01

Full Text Available Neural networks with a single plastic layer employing reward modulated spike time dependent plasticity (STDP are capable of learning simple foraging tasks. Here we demonstrate advanced pattern discrimination and continuous learning in a network of spiking neurons with multiple plastic layers. The network utilized both reward modulated and non-reward modulated STDP and implemented multiple mechanisms for homeostatic regulation of synaptic efficacy, including heterosynaptic plasticity, gain control, output balancing, activity normalization of rewarded STDP and hard limits on synaptic strength. We found that addition of a hidden layer of neurons employing non-rewarded STDP created neurons that responded to the specific combinations of inputs and thus performed basic classification of the input patterns. When combined with a following layer of neurons implementing rewarded STDP, the network was able to learn, despite the absence of labeled training data, discrimination between rewarding patterns and the patterns designated as punishing. Synaptic noise allowed for trial-and-error learning that helped to identify the goal-oriented strategies which were effective in task solving. The study predicts a critical set of properties of the spiking neuronal network with STDP that was sufficient to solve a complex foraging task involving pattern classification and decision making.

Age and time effects on implicit and explicit learning

NARCIS (Netherlands)

Verneau, M.; Kamp, J. van der; Savelsbergh, G.J.P.; Looze, M.P. de

2014-01-01

Study Context: It has been proposed that effects of aging are more pronounced for explicit than for implicit motor learning. The authors evaluated this claim by comparing the efficacy of explicit and implicit learning of a movement sequence in young and older adults, and by testing the resilience

Age and Time Effects on Implicit and Explicit Learning

NARCIS (Netherlands)

Verneau, M.M.N.; van der Kamp, J.; Savelsbergh, G.J.P.; de Looze, M.P.

2014-01-01

Study Context: It has been proposed that effects of aging are more pronounced for explicit than for implicit motor learning. The authors evaluated this claim by comparing the efficacy of explicit and implicit learning of a movement sequence in young and older adults, and by testing the resilience

Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

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Ivana Šemanjski

2015-12-01

Full Text Available Travel time forecasting is an interesting topic for many ITS services. Increased availability of data collection sensors increases the availability of the predictor variables but also highlights the high processing issues related to this big data availability. In this paper we aimed to analyse the potential of big data and supervised machine learning techniques in effectively forecasting travel times. For this purpose we used fused data from three data sources (Global Positioning System vehicles tracks, road network infrastructure data and meteorological data and four machine learning techniques (k-nearest neighbours, support vector machines, boosting trees and random forest. To evaluate the forecasting results we compared them in-between different road classes in the context of absolute values, measured in minutes, and the mean squared percentage error. For the road classes with the high average speed and long road segments, machine learning techniques forecasted travel times with small relative error, while for the road classes with the small average speeds and segment lengths this was a more demanding task. All three data sources were proven itself to have a high impact on the travel time forecast accuracy and the best results (taking into account all road classes were achieved for the k-nearest neighbours and random forest techniques.

Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics

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Yu, Tao; Cai, Weiwei; Liu, Yingzheng

2018-04-01

Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

Two Machine Learning Approaches for Short-Term Wind Speed Time-Series Prediction.

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Ak, Ronay; Fink, Olga; Zio, Enrico

2016-08-01

The increasing liberalization of European electricity markets, the growing proportion of intermittent renewable energy being fed into the energy grids, and also new challenges in the patterns of energy consumption (such as electric mobility) require flexible and intelligent power grids capable of providing efficient, reliable, economical, and sustainable energy production and distribution. From the supplier side, particularly, the integration of renewable energy sources (e.g., wind and solar) into the grid imposes an engineering and economic challenge because of the limited ability to control and dispatch these energy sources due to their intermittent characteristics. Time-series prediction of wind speed for wind power production is a particularly important and challenging task, wherein prediction intervals (PIs) are preferable results of the prediction, rather than point estimates, because they provide information on the confidence in the prediction. In this paper, two different machine learning approaches to assess PIs of time-series predictions are considered and compared: 1) multilayer perceptron neural networks trained with a multiobjective genetic algorithm and 2) extreme learning machines combined with the nearest neighbors approach. The proposed approaches are applied for short-term wind speed prediction from a real data set of hourly wind speed measurements for the region of Regina in Saskatchewan, Canada. Both approaches demonstrate good prediction precision and provide complementary advantages with respect to different evaluation criteria.

Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

International Nuclear Information System (INIS)

Veronesi, F; Grassi, S

2016-01-01

Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners. (paper)

Generation and Validation of Spatial Distribution of Hourly Wind Speed Time-Series using Machine Learning

Science.gov (United States)

Veronesi, F.; Grassi, S.

2016-09-01

Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.

Calculation of upper esophageal sphincter restitution time from high resolution manometry data using machine learning.

Science.gov (United States)

Jungheim, Michael; Busche, Andre; Miller, Simone; Schilling, Nicolas; Schmidt-Thieme, Lars; Ptok, Martin

2016-10-15

After swallowing, the upper esophageal sphincter (UES) needs a certain amount of time to return from maximum pressure to the resting condition. Disturbances of sphincter function not only during the swallowing process but also in this phase of pressure restitution may lead to globus sensation or dysphagia. Since UES pressures do not decrease in a linear or asymptotic manner, it is difficult to determine the exact time when the resting pressure is reached, even when using high resolution manometry (HRM). To overcome this problem a Machine Learning model was established to objectively determine the UES restitution time (RT) and moreover to collect physiological data on sphincter function after swallowing. HRM-data of 15 healthy participants performing 10 swallows each were included. After manual annotation of the RT interval by two swallowing experts, data were transferred to the Machine Learning model, which applied a sequence labeling modeling approach based on logistic regression to learn and objectivize the characteristics of all swallows. Individually computed RT values were then compared with the annotated values. Estimates of the RT were generated by the Machine Learning model for all 150 swallows. When annotated by swallowing experts mean RT of 11.16s±5.7 (SD) and 10.04s±5.74 were determined respectively, compared to model-generated values from 8.91s±3.71 to 10.87s±4.68 depending on model selection. The correlation score for the annotated RT of both examiners was 0.76 and 0.63 to 0.68 for comparison of model predicted values. Restitution time represents an important physiologic swallowing parameter not previously considered in HRM-studies of the UES, especially since disturbances of UES restitution may increase the risk of aspiration. The data presented here show that it takes approximately 9 to 11s for the UES to come to rest after swallowing. Based on maximal RT values, we demonstrate that an interval of 25-30s in between swallows is necessary until the

Project Management in Real Time: A Service-Learning Project

Science.gov (United States)

Larson, Erik; Drexler, John A., Jr.

2010-01-01

This article describes a service-learning assignment for a project management course. It is designed to facilitate hands-on student learning of both the technical and the interpersonal aspects of project management, and it involves student engagement with real customers and real stakeholders in the creation of real events with real outcomes. As…

MECAR (Main Ring Excitation Controller and Regulator): A real time learning regulator for the Fermilab Main Ring or the Main Injector synchrotron

International Nuclear Information System (INIS)

Flora, R.; Martin, K.; Moibenko, A.; Pfeffer, H.; Wolff, D.; Prieto, P.; Hays, S.

1995-04-01

The real time computer for controlling and regulating the FNAL Main Ring power supplies has been upgraded with a new learning control system. The learning time of the system has been reduced by an order of magnitude, mostly through the implementation of a 95 tap FIR filter in the learning algorithm. The magnet system consists of three buses, which must track each other during a ramp from 100 to 1700 amps at a 2.4 second repetition rate. This paper will present the system configuration and the tools used during development and testing

Developing Mentors: Adult participation, practices, and learning in an out-of-school time STEM program

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Scipio, Deana Aeolani

This dissertation examines learning within an out-of-school time (OST) Science, Technology, Engineering, and Mathematics (STEM) broadening participation program. The dissertation includes an introduction, three empirical chapters (written as individual articles), and a conclusion. The dissertation context is a chemical oceanography OST program for middle school students called Project COOL---Chemical Oceanography Outside the Lab. The program was a collaboration between middle school OST programming, a learning sciences research laboratory, and a chemical oceanography laboratory. Both labs were located at a research-based university in the Pacific Northwest of the United States. Participants include 34 youth, 12 undergraduates, and five professional scientists. The dissertation data corpus includes six years of ethnographic field notes across three field sites, 400 hours of video and audio recordings, 40 hours of semi-structured interviews, and more than 100 participant generated artifacts. Analysis methods include comparative case analysis, cognitive mapping, semiotic cluster analysis, video interaction analysis, and discourse analysis. The first empirical article focuses on synthesizing productive programmatic features from four years of design-based research.. The second article is a comparative case study of three STEM mentors from non-dominant communities in the 2011 COOL OST Program. The third article is a comparative case study of undergraduates learning to be mentors in the 2014 COOL OST Program. Findings introduce Deep Hanging as a theory of learning in practice. Deep Hanging entails authentic tasks in rich contexts, providing access, capitalizing on opportunity, and building interpersonal relationships. Taken together, these three chapters illuminate the process of designing a rich OST learning environment and the kinds of learning in practice that occurred for adult learners learning to be mentors through their participation in the COOL OST program. In

Editorial

Directory of Open Access Journals (Sweden)

Gráinne Conole

2002-12-01

Full Text Available This issue of ALT-J is dedicated to Professor David Squires, the previous editor of ALT-J, who died tragically in August 2001. A previous editorial of this journal paid tribute to David's work and provided a review of some of his research interests (Conole, 2001. The seven articles in this issue are from friends and colleagues who worked with David over the years. Some worked with David for only a relatively short time; others are lifelong friends. The authors represent internationally recognized experts in their field and come from all corners of the earth. This in itself is a testament to David's significant contribution to learning technology research. The authors have submitted papers which in some way align with David's own research work or report on joint work done by the authors in partnership with him.

Active Learning and Just-in-Time Teaching in a Material and Energy Balances Course

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Liberatore, Matthew W.

2013-01-01

The delivery of a material and energy balances course is enhanced through a series of in-class and out-of-class exercises. An active learning classroom is achieved, even at class sizes over 150 students, using multiple instructors in a single classroom, problem solving in teams, problems based on YouTube videos, and just-in-time teaching. To avoid…

Investigation of the Relationship between Learning Process and Learning Outcomes in E-Learning Environments

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Yurdugül, Halil; Menzi Çetin, Nihal

2015-01-01

Problem Statement: Learners can access and participate in online learning environments regardless of time and geographical barriers. This brings up the umbrella concept of learner autonomy that contains self-directed learning, self-regulated learning and the studying process. Motivation and learning strategies are also part of this umbrella…

Travel time tomography with local image regularization by sparsity constrained dictionary learning

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Bianco, M.; Gerstoft, P.

2017-12-01

We propose a regularization approach for 2D seismic travel time tomography which models small rectangular groups of slowness pixels, within an overall or `global' slowness image, as sparse linear combinations of atoms from a dictionary. The groups of slowness pixels are referred to as patches and a dictionary corresponds to a collection of functions or `atoms' describing the slowness in each patch. These functions could for example be wavelets.The patch regularization is incorporated into the global slowness image. The global image models the broad features, while the local patch images incorporate prior information from the dictionary. Further, high resolution slowness within patches is permitted if the travel times from the global estimates support it. The proposed approach is formulated as an algorithm, which is repeated until convergence is achieved: 1) From travel times, find the global slowness image with a minimum energy constraint on the pixel variance relative to a reference. 2) Find the patch level solutions to fit the global estimate as a sparse linear combination of dictionary atoms.3) Update the reference as the weighted average of the patch level solutions.This approach relies on the redundancy of the patches in the seismic image. Redundancy means that the patches are repetitions of a finite number of patterns, which are described by the dictionary atoms. Redundancy in the earth's structure was demonstrated in previous works in seismics where dictionaries of wavelet functions regularized inversion. We further exploit redundancy of the patches by using dictionary learning algorithms, a form of unsupervised machine learning, to estimate optimal dictionaries from the data in parallel with the inversion. We demonstrate our approach on densely, but irregularly sampled synthetic seismic images.

Time series classification using k-Nearest neighbours, Multilayer Perceptron and Learning Vector Quantization algorithms

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Jiří Fejfar

2012-01-01

Full Text Available We are presenting results comparison of three artificial intelligence algorithms in a classification of time series derived from musical excerpts in this paper. Algorithms were chosen to represent different principles of classification – statistic approach, neural networks and competitive learning. The first algorithm is a classical k-Nearest neighbours algorithm, the second algorithm is Multilayer Perceptron (MPL, an example of artificial neural network and the third one is a Learning Vector Quantization (LVQ algorithm representing supervised counterpart to unsupervised Self Organizing Map (SOM.After our own former experiments with unlabelled data we moved forward to the data labels utilization, which generally led to a better accuracy of classification results. As we need huge data set of labelled time series (a priori knowledge of correct class which each time series instance belongs to, we used, with a good experience in former studies, musical excerpts as a source of real-world time series. We are using standard deviation of the sound signal as a descriptor of a musical excerpts volume level.We are describing principle of each algorithm as well as its implementation briefly, giving links for further research. Classification results of each algorithm are presented in a confusion matrix showing numbers of misclassifications and allowing to evaluate overall accuracy of the algorithm. Results are compared and particular misclassifications are discussed for each algorithm. Finally the best solution is chosen and further research goals are given.

Comparison of haptic guidance and error amplification robotic trainings for the learning of a timing-based motor task by healthy seniors.

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Bouchard, Amy E; Corriveau, Hélène; Milot, Marie-Hélène

2015-01-01

With age, a decline in the temporal aspect of movement is observed such as a longer movement execution time and a decreased timing accuracy. Robotic training can represent an interesting approach to help improve movement timing among the elderly. Two types of robotic training-haptic guidance (HG; demonstrating the correct movement for a better movement planning and improved execution of movement) and error amplification (EA; exaggerating movement errors to have a more rapid and complete learning) have been positively used in young healthy subjects to boost timing accuracy. For healthy seniors, only HG training has been used so far where significant and positive timing gains have been obtained. The goal of the study was to evaluate and compare the impact of both HG and EA robotic trainings on the improvement of seniors' movement timing. Thirty-two healthy seniors (mean age 68 ± 4 years) learned to play a pinball-like game by triggering a one-degree-of-freedom hand robot at the proper time to make a flipper move and direct a falling ball toward a randomly positioned target. During HG and EA robotic trainings, the subjects' timing errors were decreased and increased, respectively, based on the subjects' timing errors in initiating a movement. Results showed that only HG training benefited learning, but the improvement did not generalize to untrained targets. Also, age had no influence on the efficacy of HG robotic training, meaning that the oldest subjects did not benefit more from HG training than the younger senior subjects. Using HG to teach the correct timing of movement seems to be a good strategy to improve motor learning for the elderly as for younger people. However, more studies are needed to assess the long-term impact of HG robotic training on improvement in movement timing.

Testing the scalar expectancy theory (SET) and the learning-to-time model (LeT) in a double bisection task.

Science.gov (United States)

Machado, Armando; Pata, Paulo

2005-02-01

Two theories of timing, scalar expectancy theory (SET) and learning-to-time (LeT), make substantially different assumptions about what animals learn in temporal tasks. In a test of these assumptions, pigeons learned two temporal discriminations. On Type 1 trials, they learned to choose a red key after a 1-sec signal and a green key after a 4-sec signal; on Type 2 trials, they learned to choose a blue key after a 4-sec signal and a yellow key after either an 8-sec signal (Group 8) or a 16-sec signal (Group 16). Then, the birds were exposed to signals 1 sec, 4 sec, and 16 sec in length and given a choice between novel key combinations (red or green vs. blue or yellow). The choice between the green key and the blue key was of particular significance because both keys were associated with the same 4-sec signal. Whereas SET predicted no effect of the test signal duration on choice, LeT predicted that preference for green would increase monotonically with the length of the signal but would do so faster for Group 8 than for Group 16. The results were consistent with LeT, but not with SET.

New learning : three ways to learn in a new balance

NARCIS (Netherlands)

Simons, P.R.J.

2000-01-01

Because people are learning all the time, we need criteria that can help us distinguish between better and worse kinds of learning. Organizations and societies as well as the psychology of learning ask for new learning outcomes, new learning processes and new forms of instruction. New learning

Night-Time Vehicle Detection Algorithm Based on Visual Saliency and Deep Learning

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Yingfeng Cai

2016-01-01

Full Text Available Night vision systems get more and more attention in the field of automotive active safety field. In this area, a number of researchers have proposed far-infrared sensor based night-time vehicle detection algorithm. However, existing algorithms have low performance in some indicators such as the detection rate and processing time. To solve this problem, we propose a far-infrared image vehicle detection algorithm based on visual saliency and deep learning. Firstly, most of the nonvehicle pixels will be removed with visual saliency computation. Then, vehicle candidate will be generated by using prior information such as camera parameters and vehicle size. Finally, classifier trained with deep belief networks will be applied to verify the candidates generated in last step. The proposed algorithm is tested in around 6000 images and achieves detection rate of 92.3% and processing time of 25 Hz which is better than existing methods.

NFC LearnTracker: Seamless support for learning with mobile and sensor technology

NARCIS (Netherlands)

Tabuenca, Bernardo; Kalz, Marco; Specht, Marcus

2014-01-01

Lifelong learning activities are scattered along the day, in different locations and making use of multiple devices. Most of the times adults have to merge learning, work and everyday life making it difficult to have an account on how much time is devoted to learning activities and learning goals.

Integral reinforcement learning for continuous-time input-affine nonlinear systems with simultaneous invariant explorations.

Science.gov (United States)

Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho

2015-05-01

This paper focuses on a class of reinforcement learning (RL) algorithms, named integral RL (I-RL), that solve continuous-time (CT) nonlinear optimal control problems with input-affine system dynamics. First, we extend the concepts of exploration, integral temporal difference, and invariant admissibility to the target CT nonlinear system that is governed by a control policy plus a probing signal called an exploration. Then, we show input-to-state stability (ISS) and invariant admissibility of the closed-loop systems with the policies generated by integral policy iteration (I-PI) or invariantly admissible PI (IA-PI) method. Based on these, three online I-RL algorithms named explorized I-PI and integral Q -learning I, II are proposed, all of which generate the same convergent sequences as I-PI and IA-PI under the required excitation condition on the exploration. All the proposed methods are partially or completely model free, and can simultaneously explore the state space in a stable manner during the online learning processes. ISS, invariant admissibility, and convergence properties of the proposed methods are also investigated, and related with these, we show the design principles of the exploration for safe learning. Neural-network-based implementation methods for the proposed schemes are also presented in this paper. Finally, several numerical simulations are carried out to verify the effectiveness of the proposed methods.

Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment.

Science.gov (United States)

Oblak, Ethan F; Lewis-Peacock, Jarrod A; Sulzer, James S

2017-07-01

Direct manipulation of brain activity can be used to investigate causal brain-behavior relationships. Current noninvasive neural stimulation techniques are too coarse to manipulate behaviors that correlate with fine-grained spatial patterns recorded by fMRI. However, these activity patterns can be manipulated by having people learn to self-regulate their own recorded neural activity. This technique, known as fMRI neurofeedback, faces challenges as many participants are unable to self-regulate. The causes of this non-responder effect are not well understood due to the cost and complexity of such investigation in the MRI scanner. Here, we investigated the temporal dynamics of the hemodynamic response measured by fMRI as a potential cause of the non-responder effect. Learning to self-regulate the hemodynamic response involves a difficult temporal credit-assignment problem because this signal is both delayed and blurred over time. Two factors critical to this problem are the prescribed self-regulation strategy (cognitive or automatic) and feedback timing (continuous or intermittent). Here, we sought to evaluate how these factors interact with the temporal dynamics of fMRI without using the MRI scanner. We first examined the role of cognitive strategies by having participants learn to regulate a simulated neurofeedback signal using a unidimensional strategy: pressing one of two buttons to rotate a visual grating that stimulates a model of visual cortex. Under these conditions, continuous feedback led to faster regulation compared to intermittent feedback. Yet, since many neurofeedback studies prescribe implicit self-regulation strategies, we created a computational model of automatic reward-based learning to examine whether this result held true for automatic processing. When feedback was delayed and blurred based on the hemodynamics of fMRI, this model learned more reliably from intermittent feedback compared to continuous feedback. These results suggest that different

Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment.

Directory of Open Access Journals (Sweden)

Ethan F Oblak

2017-07-01

Full Text Available Direct manipulation of brain activity can be used to investigate causal brain-behavior relationships. Current noninvasive neural stimulation techniques are too coarse to manipulate behaviors that correlate with fine-grained spatial patterns recorded by fMRI. However, these activity patterns can be manipulated by having people learn to self-regulate their own recorded neural activity. This technique, known as fMRI neurofeedback, faces challenges as many participants are unable to self-regulate. The causes of this non-responder effect are not well understood due to the cost and complexity of such investigation in the MRI scanner. Here, we investigated the temporal dynamics of the hemodynamic response measured by fMRI as a potential cause of the non-responder effect. Learning to self-regulate the hemodynamic response involves a difficult temporal credit-assignment problem because this signal is both delayed and blurred over time. Two factors critical to this problem are the prescribed self-regulation strategy (cognitive or automatic and feedback timing (continuous or intermittent. Here, we sought to evaluate how these factors interact with the temporal dynamics of fMRI without using the MRI scanner. We first examined the role of cognitive strategies by having participants learn to regulate a simulated neurofeedback signal using a unidimensional strategy: pressing one of two buttons to rotate a visual grating that stimulates a model of visual cortex. Under these conditions, continuous feedback led to faster regulation compared to intermittent feedback. Yet, since many neurofeedback studies prescribe implicit self-regulation strategies, we created a computational model of automatic reward-based learning to examine whether this result held true for automatic processing. When feedback was delayed and blurred based on the hemodynamics of fMRI, this model learned more reliably from intermittent feedback compared to continuous feedback. These results

Self-regulation strategy, feedback timing and hemodynamic properties modulate learning in a simulated fMRI neurofeedback environment

Science.gov (United States)

Sulzer, James S.

2017-01-01

Direct manipulation of brain activity can be used to investigate causal brain-behavior relationships. Current noninvasive neural stimulation techniques are too coarse to manipulate behaviors that correlate with fine-grained spatial patterns recorded by fMRI. However, these activity patterns can be manipulated by having people learn to self-regulate their own recorded neural activity. This technique, known as fMRI neurofeedback, faces challenges as many participants are unable to self-regulate. The causes of this non-responder effect are not well understood due to the cost and complexity of such investigation in the MRI scanner. Here, we investigated the temporal dynamics of the hemodynamic response measured by fMRI as a potential cause of the non-responder effect. Learning to self-regulate the hemodynamic response involves a difficult temporal credit-assignment problem because this signal is both delayed and blurred over time. Two factors critical to this problem are the prescribed self-regulation strategy (cognitive or automatic) and feedback timing (continuous or intermittent). Here, we sought to evaluate how these factors interact with the temporal dynamics of fMRI without using the MRI scanner. We first examined the role of cognitive strategies by having participants learn to regulate a simulated neurofeedback signal using a unidimensional strategy: pressing one of two buttons to rotate a visual grating that stimulates a model of visual cortex. Under these conditions, continuous feedback led to faster regulation compared to intermittent feedback. Yet, since many neurofeedback studies prescribe implicit self-regulation strategies, we created a computational model of automatic reward-based learning to examine whether this result held true for automatic processing. When feedback was delayed and blurred based on the hemodynamics of fMRI, this model learned more reliably from intermittent feedback compared to continuous feedback. These results suggest that different

Relations between the development of future time perspective in three life domains, investment in learning, and academic achievement

NARCIS (Netherlands)

Peetsma, T.; van der Veen, I.

2011-01-01

Relations between the development of future time perspectives in three life domains (i.e., school and professional career, social relations, and leisure time) and changes in students’ investment in learning and academic achievement were examined in this study. Participants were 584 students in the

Learning during processing Word learning doesn’t wait for word recognition to finish

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Apfelbaum, Keith S.; McMurray, Bob

2017-01-01

Previous research on associative learning has uncovered detailed aspects of the process, including what types of things are learned, how they are learned, and where in the brain such learning occurs. However, perceptual processes, such as stimulus recognition and identification, take time to unfold. Previous studies of learning have not addressed when, during the course of these dynamic recognition processes, learned representations are formed and updated. If learned representations are formed and updated while recognition is ongoing, the result of learning may incorporate spurious, partial information. For example, during word recognition, words take time to be identified, and competing words are often active in parallel. If learning proceeds before this competition resolves, representations may be influenced by the preliminary activations present at the time of learning. In three experiments using word learning as a model domain, we provide evidence that learning reflects the ongoing dynamics of auditory and visual processing during a learning event. These results show that learning can occur before stimulus recognition processes are complete; learning does not wait for ongoing perceptual processing to complete. PMID:27471082

A Learning Object Approach To Evidence based learning

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Zabin Visram

2005-06-01

Full Text Available This paper describes the philosophy, development and framework of the body of elements formulated to provide an approach to evidence-based learning sustained by Learning Objects and web based technology Due to the demands for continuous improvement in the delivery of healthcare and in the continuous endeavour to improve the quality of life, there is a continuous need for practitioner's to update their knowledge by accomplishing accredited courses. The rapid advances in medical science has meant increasingly, there is a desperate need to adopt wireless schemes, whereby bespoke courses can be developed to help practitioners keep up with expanding knowledge base. Evidently, without current best evidence, practice risks becoming rapidly out of date, to the detriment of the patient. There is a need to provide a tactical, operational and effective environment, which allows professional to update their education, and complete specialised training, just-in-time, in their own time and location. Following this demand in the marketplace the information engineering group, in combination with several medical and dental schools, set out to develop and design a conceptual framework which form the basis of pioneering research, which at last, enables practitioner's to adopt a philosophy of life long learning. The body and structure of this framework is subsumed under the term Object oriented approach to Evidence Based learning, Just-in-time, via Internet sustained by Reusable Learning Objects (The OEBJIRLO Progression. The technical pillars which permit this concept of life long learning are pivoted by the foundations of object oriented technology, Learning objects, Just-in-time education, Data Mining, intelligent Agent technology, Flash interconnectivity and remote wireless technology, which allow practitioners to update their professional skills, complete specialised training which leads to accredited qualifications. This paper sets out to develop and

Research on Healthy Anomaly Detection Model Based on Deep Learning from Multiple Time-Series Physiological Signals

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Kai Wang

2016-01-01

Full Text Available Health is vital to every human being. To further improve its already respectable medical technology, the medical community is transitioning towards a proactive approach which anticipates and mitigates risks before getting ill. This approach requires measuring the physiological signals of human and analyzes these data at regular intervals. In this paper, we present a novel approach to apply deep learning in physiological signals analysis that allows doctor to identify latent risks. However, extracting high level information from physiological time-series data is a hard problem faced by the machine learning communities. Therefore, in this approach, we apply model based on convolutional neural network that can automatically learn features from raw physiological signals in an unsupervised manner and then based on the learned features use multivariate Gauss distribution anomaly detection method to detect anomaly data. Our experiment is shown to have a significant performance in physiological signals anomaly detection. So it is a promising tool for doctor to identify early signs of illness even if the criteria are unknown a priori.

Predicting progression of mild cognitive impairment to dementia using neuropsychological data: a supervised learning approach using time windows.

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Pereira, Telma; Lemos, Luís; Cardoso, Sandra; Silva, Dina; Rodrigues, Ana; Santana, Isabel; de Mendonça, Alexandre; Guerreiro, Manuela; Madeira, Sara C

2017-07-19

Predicting progression from a stage of Mild Cognitive Impairment to dementia is a major pursuit in current research. It is broadly accepted that cognition declines with a continuum between MCI and dementia. As such, cohorts of MCI patients are usually heterogeneous, containing patients at different stages of the neurodegenerative process. This hampers the prognostic task. Nevertheless, when learning prognostic models, most studies use the entire cohort of MCI patients regardless of their disease stages. In this paper, we propose a Time Windows approach to predict conversion to dementia, learning with patients stratified using time windows, thus fine-tuning the prognosis regarding the time to conversion. In the proposed Time Windows approach, we grouped patients based on the clinical information of whether they converted (converter MCI) or remained MCI (stable MCI) within a specific time window. We tested time windows of 2, 3, 4 and 5 years. We developed a prognostic model for each time window using clinical and neuropsychological data and compared this approach with the commonly used in the literature, where all patients are used to learn the models, named as First Last approach. This enables to move from the traditional question "Will a MCI patient convert to dementia somewhere in the future" to the question "Will a MCI patient convert to dementia in a specific time window". The proposed Time Windows approach outperformed the First Last approach. The results showed that we can predict conversion to dementia as early as 5 years before the event with an AUC of 0.88 in the cross-validation set and 0.76 in an independent validation set. Prognostic models using time windows have higher performance when predicting progression from MCI to dementia, when compared to the prognostic approach commonly used in the literature. Furthermore, the proposed Time Windows approach is more relevant from a clinical point of view, predicting conversion within a temporal interval

Ways to live history. Or philosophy, culture, and architecture in the origins of the museum Altes in Berlín Formas de habitar la historia. O la filosofía, la cultura y la arquitectura en la fundación del Museo Altes de Berlín Modos de habitar a história ou Filosofia, cultura e arquitetura na fundação do Altes Museum de Berlim

Directory of Open Access Journals (Sweden)

Fabiano Lemos

2011-01-01

Full Text Available From the beginnings of what we now know as our modernity, man has been surprised with his own finitude. The 18^th and 19^th  century bourgeois needed to formulate complex ways of preserving the past and of linking with it. In the philosophes’ circle, time concept starts to double itself in the idea of origin, that, for them, had become opaque. We just need to think of the broad-range process of structuring museums and restructuring natural history collections in cities such as Paris and Berlin, around the turn to the 19^th century, so that we can be convinced that the surprise with that origin that one cannot recognize anymore, that becomes object of popular and scientific interest, leads each and all of the decisions in this process. Museum is just one of the institutions in which man, through a complex series of idealizations of space, show himself the spectacle of a lost time and, thus, of a culture whose educational thrive can only be understood by associating to these institutions. Our task is to investigate – and the case study of the grounding of the Altes Museum in Berlin, between 1822 and 1830, will perform this concretely – which educational policy made the emergence of this new ideological model possible, and , on the opposite way, which conceptual elaborations confirmed or legitimated the new pragmatic topography of time in modernity within the institution that had as aim, precisely, articulate and administrate past and memory. Desde los albores de la modernidad tal como la entendemos hoy en día, el hombre se espanta por su finitud. El burgués de los siglos XVIII y XIX tuvo que crear mecanismos complejos para la preservación del pasado y su relación con él. En el círculo de philosophes, la noción del tiempo comienza a replicarse en la idea de origen, que para ellos se convirtió en opaca. Basta pensar en el amplio

Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets.

Science.gov (United States)

Gangodagamage, Chandana; Rowland, Joel C; Hubbard, Susan S; Brumby, Steven P; Liljedahl, Anna K; Wainwright, Haruko; Wilson, Cathy J; Altmann, Garrett L; Dafflon, Baptiste; Peterson, John; Ulrich, Craig; Tweedie, Craig E; Wullschleger, Stan D

2014-08-01

Landscape attributes that vary with microtopography, such as active layer thickness ( ALT ), are labor intensive and difficult to document effectively through in situ methods at kilometer spatial extents, thus rendering remotely sensed methods desirable. Spatially explicit estimates of ALT can provide critically needed data for parameterization, initialization, and evaluation of Arctic terrestrial models. In this work, we demonstrate a new approach using high-resolution remotely sensed data for estimating centimeter-scale ALT in a 5 km 2 area of ice-wedge polygon terrain in Barrow, Alaska. We use a simple regression-based, machine learning data-fusion algorithm that uses topographic and spectral metrics derived from multisensor data (LiDAR and WorldView-2) to estimate ALT (2 m spatial resolution) across the study area. Comparison of the ALT estimates with ground-based measurements, indicates the accuracy (r 2  = 0.76, RMSE ±4.4 cm) of the approach. While it is generally accepted that broad climatic variability associated with increasing air temperature will govern the regional averages of ALT , consistent with prior studies, our findings using high-resolution LiDAR and WorldView-2 data, show that smaller-scale variability in ALT is controlled by local eco-hydro-geomorphic factors. This work demonstrates a path forward for mapping ALT at high spatial resolution and across sufficiently large regions for improved understanding and predictions of coupled dynamics among permafrost, hydrology, and land-surface processes from readily available remote sensing data.

The Office Software Learning and Examination System Design Based on Fragmented Learning Idea

Directory of Open Access Journals (Sweden)

Xu Ling

2016-01-01

Full Text Available Fragmented learning is that through the segmentation of learning content or learning time, make learners can use the fragmented time for learning fragmentated content, have the characteristics of time flexibility, learning targeted and high learning efficiency. Based on the fragmented learning ideas, combined with the teaching idea of micro class and interactive teaching, comprehensive utilization of flash animation design software, .NET development platform, VSTO technology, multimedia development technology and so on, design and develop a system integrated with learning, practice and examination of the Office software, which is not only conducive to the effective and personalized learning of students, but also conducive to the understanding the students’ situation of teachers, and liberate teachers from the heavy labor of mechanical, focus on promoting the formation of students’ knowledge system.

Adventure Learning: Theory and Implementation of Hybrid Learning

Science.gov (United States)

Doering, A.

2008-12-01

Adventure Learning (AL), a hybrid distance education approach, provides students and teachers with the opportunity to learn about authentic curricular content areas while interacting with adventurers, students, and content experts at various locations throughout the world within an online learning environment (Doering, 2006). An AL curriculum and online environment provides collaborative community spaces where traditional hierarchical classroom roles are blurred and learning is transformed. AL has most recently become popular in K-12 classrooms nationally and internationally with millions of students participating online. However, in the literature, the term "adventure learning" many times gets confused with phrases such as "virtual fieldtrip" and activities where someone "exploring" is posting photos and text. This type of "adventure learning" is not "Adventure Learning" (AL), but merely a slideshow of their activities. The learning environment may not have any curricular and/or social goals, and if it does, the environment design many times does not support these objectives. AL, on the other hand, is designed so that both teachers and students understand that their online and curriculum activities are in synch and supportive of the curricular goals. In AL environments, there are no disparate activities as the design considers the educational, social, and technological affordances (Kirschner, Strijbos, Kreijns, & Beers, 2004); in other words, the artifacts of the learning environment encourage and support the instructional goals, social interactions, collaborative efforts, and ultimately learning. AL is grounded in two major theoretical approaches to learning - experiential and inquiry-based learning. As Kolb (1984) noted, in experiential learning, a learner creates meaning from direct experiences and reflections. Such is the goal of AL within the classroom. Additionally, AL affords learners a real-time authentic online learning experience concurrently as they

Pre-Service Post Graduate Teachers' First Time Experience with Constructivist Learning Environment (CLE) Using MOODLE

Science.gov (United States)

Boopathiraj, C.; Chellamani, K.

2015-01-01

The aim of this study is to enlighten and discuss Post Graduate student teachers' first time experiences and their level of satisfaction with the use of Moodle Learning Management System (LMS) during their "Research Methods in Education" course offered online. This study investigated 30 pre-service Post Graduate student teachers' to…

Learning to trust : network effects through time.

NARCIS (Netherlands)

Barrera, D.; Bunt, G. van de

2009-01-01

This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

Learning to trust: network effects through time

NARCIS (Netherlands)

Barrera, D.; van de Bunt, G

2009-01-01

This article investigates the effects of information originating from social networks on the development of interpersonal trust relations in the context of a dialysis department of a Dutch medium-sized hospital. Hypotheses on learning effects are developed from existing theories and tested using

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.