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30532. Předběžný geoarcheologický průzkum jeskyně ve Vrbici /
- Creator:
- Kos, Petr,
- Subject:
- jeskyně, geoarcheologie, and geoarcheologie, archeogeofyzika, petroarcheologie
- Language:
- Czech and English
- Description:
- Preliminary geoarchaeological survey of a cave in Vrbici, Břeclav district.
- Rights:
- unknown
30533. Predemence a úloha dementologie v prevenci, časné diagnóze a terapii demencí
- Creator:
- Reban, Jan
- Type:
- model:article, article, Text, přehledy, and TEXT
- Subject:
- demence--diagnóza--prevence a kontrola, ošetřovatelské služby--ekonomika, kognitivní poruchy--diagnóza--prevence a kontrola, stárnutí--patologie, lidé, and staří
- Language:
- Czech and English
- Description:
- Prevalence kognitivních poruch stoupá s věkem. Je proto nesmírně důležité odlišovat mezi závažnými poruchami, které mohou v budoucnu vést k demencím, od těch, které jsou svou podstatou benigní a jejichž incidence přechodu do demence je stejná jako u normálních jedinců bez kognitivních problémů. Rozpoznání stavů, které nazýváme predemencemi, je tedy velmi prospěšně jak pro postiženě, pro jejich rodiny i pro společnost. Toto sdělení je pokusem o klasifikaci predementních stavů a zároveň poukazem na závažnost možného vývoje demencí. Definice nového medicínského oboru dementologie by mohla vyřešit problém, kdo má pečovat o jedince s kognitivními poruchami i o efektivní poskytování služeb dementním pacientům., With advancing age complaints about cognitive decline are more and more common. It is therefore essential to distinguish between significant problems leading towards emergence of dementias and those which are benign in its origine and normally accompany ageing process. Effective identification of so called pre-dementias and following interventions will have substantial benefit to affected persons, their families and society as well. Presented is an attempt to classify different stages of cognitive decline with reference to their importance for future development into dementias. The emergence of the specialty of Dementology will help to resolve long running disputes over which medical specialty should take charge of pre-dementias and dementia patients as well., Jan Reban, and Lit. 5
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
30534. Predicting Blood Flow Responses to Rhythmic Handgrip Exercise From One Second Isometric Contractions
- Creator:
- Cook, M., Smart, N.A., and Van der Touw, T.
- Type:
- article, model:article, and TEXT
- Subject:
- Brachial blood flow, Isometric exercise, Hyperaemia, and Blood flow control
- Language:
- English
- Description:
- The aim of this work was to predict blood flow responses to rhythmic handgrip exercise from one second isometric contractions. Seven healthy men were studied. Each subject performed a single 1s handgrip contraction at 10%, 20% and 40 % of the maximum handgrip strength. We then repeatedly summed hyperaemic responses from single contractions to predict hyperaemic response to a prolonged bout of rhythmic exercise. There was similarity between steady state brachial blood flow velocity (BBV) extrapolated from single handgrip contractions and during 2 min of rhythmic exercise for 20% (10.0±3.8 cm/s vs. 10.2±2.6 cm/s, r=0.93, p=0.003) and 40% of maximum contractions (14.2±5.5 cm/s vs. 15.6±3.4 cm/s, r=0.88, p=0.009), but not for 10 % (7.5±4.1 cm/s vs. 5.7± 3.3 cm/s, r=0.94, p=0.018). BBV progressively rose substantially higher during rhythmic contractions than peak BBV observed during single contractions at matched intensity. Respective peak BBV during single contractions and steady state BBV rhythmic contractions were 4.4 ±2.1 and 5.7±3.3 cm.s −1at 10 % forearm strength (p=0.14), 5.6±2.4 and 10.2±2.8 cm.s−1 at 20 % (p=0.002), and 7.0±2.5 and 15.6 ±3.6 cm.s−1at 40 % (p=0.003). In conclusion, there is similarity between the summated blood flow velocity calculated from a single 1 s muscle contraction and the steady state blood flow velocity response of rhythmic exercise.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
30535. Predicting database intrusion with an ensemble of neural networks: A Time Series Approach
- Creator:
- Ramasubramanian , P. and Kannan, A.
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Database security, database anomaly intrusion prediction, Quickprop prediction technique, intrusion prevention, artificial neural networks, and uncertainty
- Language:
- English
- Description:
- This paper describes a framework for a statistical anomaly prediction system using Quickprop neural network ensemble forecasting model, which predicts unauthorized invasions of users based on previous observations and takes further action before intrusion occurs. This paper investigates a NN ensemble approach to the problem of intrusion prediction and the various architectures are investigated using Quickprop algorithm. This paper focuses on intrusion prediction techniques for preventing intrusions that manifest through anomalous changes in intensity of transactions in a relational database systems at the application level. We present a novel approach to prevent misuse within an information system by gathering and maintaining knowledge of the behavior of the user rather than anticipating attacks by unknown assailants. The experimental study is performed using real data provided by a major Corporate Bank. A comparative evaluation of the two ensemble networks over the individual networks was carried out using a mean absolute percentage error on a prediction data set and a better prediction accuracy has been observed. Furthermore, the performance analysis shows that the model captures well the volatility of the user behavior and has a good forecasting ability.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
30536. Predicting psychological distress after primary oncological treatment in elderly breast cancer survivors: Retrospective study
- Creator:
- Skřivanová, Kateřina, Svěrák, Tomáš, Brančíková, Dagmar, Jarkovský, Jiří, Benešová, Klára, Anderková, Ľubomíra, Elfmarková, Nela, Peterková, Hana, Bendová, Marie, Minář, Luboš, Holoubková, Eva, Nedvěd, Jan, Protivánková, Markéta, and Dušek, Ladislav
- Format:
- print, text, and regular print
- Type:
- model:article, article, Text, práce podpořená grantem, and TEXT
- Subject:
- dospělí, lidé, lidé středního věku, staří, staří nad 80 let, ženské pohlaví, retrospektivní studie, nádory prsu--psychologie, psychický stres--diagnóza, adaptace psychologická, stupeň závažnosti nemoci, prognóza, lineární modely, multivariační analýza, C-reaktivní protein, úzkost--diagnóza, sebezhodnocení (psychologie), zpráva o sobě, osobnostní dotazník, škála projevu úzkosti--statistika a číselné údaje, and interview psychologický
- Language:
- Czech and English
- Description:
- Cílem této studie bylo stanovit prognózu psychologického distresu u pacientek s karcinomem prsu po primární onkologické léčbě pomocí biologických a psychologických proměnných. Skupina hodnocených pacientek zahrnovala 98 starších pacientek po léčbě karcinomu prsu (ve středním věku 65 let), které vyplnily dotazník SVF 78 (detekce způsobů zvládání stresu), dotazník NEO-FFI (detekce osobnostních rysů), dotazník SCL-90 (zjišťování psychopatologie) po dokončení léčby, a další retrospektivně po diagnóze. Stupnice SAS (měření stupně úzkosti) byla vyplněna menším počtem pacientek. Údaje o faktorech souvisejících s karcinomem a léčbou byly získány z lékařských záznamů. V rámci této studie byl psychologický distres měřen metodou SCL-90 pomocí indexu celkové závažnosti Global Severity Index (GSI) a indexu zátěže pozitivních příznaků Positive Symptom Distress Index (PSDI). Kvantifikace vztahu mezi biologickými a psychologickými prediktory a GSI a PSDI jakožto závislými proměnnými byla vyhodnocena pomocí lineárních regresních modelů s jednou proměnnou i více proměnnými. Po diagnóze a po jednom roce primární léčby byly sledovány hladiny C-reaktivního proteinu. Nejlepší model predikce GSI po léčbě byl zjištěn lineární regresí s více proměnnými jakožto kombinace prediktorů GSI, hladiny CRP a přívětivosti (podškála NEO-FFI) v době diagnózy, v níž R2 = 76,6 %. Nejlepší model predikce PSDI po léčení sestával z hodnoty PSDI, prvku sebeobviňování SVF 78 a stadia nemoci (IV. oproti nižším) v době diagnózy při R2 = 53,9 %. Začlenění celkového hrubého skóre metody SAS do modelů s více proměnnými pro predikci GSI a PSDI způsobilo nárůst hodnoty R2 (ze 71,5 % na 85,0 % a 46,0 % na 65,1 %). Biologické i psychologické prediktory se prokázaly jako významné a vhodné pro predikci psychologického distresu u starších pacientek s karcinomem prsu po dlouhodobém onkologickém léčení. Klíčová slova: C- reaktivní protein – starší přežívající s karcinomem prsu – multivariantní lineární regrese – psychoneuroimunologie – predikce psychologického distresu– retrospektivní studie, The aim of this study was to determine the prognosis of psychological distress in breast cancer survivors after primary oncological treatment using biological and psychological variables. The test group consisted of 98 elderly breast cancer survivors (median age was 65 years) who completed the SVF 78 questionnaire (coping styles measures), NEO-FFI questionnaire (personality traits measures), SCL-90 questionnaire (psychopathology measures) completing treatment and another retrospectively at diagnosis. The SAS scale (anxiety measures) was completed by a lower number of patients. Data on tumour-related factors and treatment were obtained from medical records. Within the scope of this study, psychological distress was measured via the SCL-90 method using Global Severity Index (GSI) and Positive Symptom Distress Index (PSDI). Quantification of the relationship between biological and psychological predictors and GSI and PSDI as dependent variables was estimated using both univariate and multivariate linear regression models. C-reactive protein levels were monitored at diagnosis and one year after primary treatment. The best model for the prediction of GSI after treatment was identified by multivariate linear regression as the combination of GSI, CRP level and agreeableness (NEO-FFI subscale) predictors at the time of diagnosis in which R2 = 76.6 %. The best model for predicting PSDI after treatment consisted of PSDI, the self-accusation component of SVF 78 and the stage of the disease (IV vs lower) at the time of diagnosis with R2 = 53.9 %. Incorporating the total raw score of the SAS questionnaire into the multivariate models for prediction of GSI and PSDI caused an increase in R2 (71.5 % to 85.0 % and 46.0 % to 65.1 %), respectively. Both biological and psychological predictors proved significant and suitable for psychological distress prediction in elderly breast cancer survivors after long-term oncological treatment. Key words: C-reactive protein – elderly breast cancer survivors – multivariate linear regression – psychoneuroimmunology – psychological distress prediction – retrospective study, and Kateřina Skřivanová, Tomáš Svěrák, Dagmar Brančíková, Jiří Jarkovský, Klára Benešová, Ľubomíra Anderková, Nela Elfmarková, Hana Peterková, Marcela Bendová, Luboš Minář, Eva Holoubková, Jan Nedvěd, Markéta Protivánková, Ladislav Dušek
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
30537. Predicting successful reproduction and establishment of non-native freshwater fish in peninsular Florida using life history traits
- Creator:
- Lawson, Katelyn M. and Hill, Jeffrey E.
- Format:
- počítač and online zdroj
- Type:
- model:article and TEXT
- Subject:
- life history, invasive species, and quantitative risk assessment
- Language:
- English
- Description:
- Identification of factors that facilitate successful completion of invasion process stages by nonnative species is a major priority among invasion biologists. Stage-based analyses of non-native fish species traits have been conducted for several regions, but not for a subtropical non-native species hotspot like peninsular Florida. Typically, establishment is the first stage of analysis but Florida is home to many nonnative fish species that have successfully reproduced, yet failed to establish. Therefore, we used life history traits and three model types (categorical and regression trees, logistic regression, and discriminant analysis) to predict successful reproduction and establishment by non-native fishes in peninsular Florida. Statistical models for predicting both successful reproduction and establishment suggested parental care was the most important variable, but other traits included in the best models differ between the two stages. The high level of parental care in successful non-native fishes of Florida is unique among non-native freshwater fish faunas across the United States. Other studies also found that suites of traits used to predict various stages of the invasion process differ, suggesting that stage-based analyses provide a good foundation for better understanding invasion processes. Our results may be applied to stage-based risk screening tools for nonnative fishes in Florida.
- Rights:
- http://creativecommons.org/licenses/by-nc-sa/4.0/ and policy:public
30538. Predicting the daily traffc volume from hourly traffc data using artificial neural network
- Creator:
- Siddiquee, M. S. A. and Hoque, S.
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- artificial neural network, traffic data prediction, and estimation
- Language:
- English
- Description:
- The prediction of traffic volume over time is very important to control the flow of traffic on a road network. Traffic count is usually averaged over time to predict for the larger time domain. This paper aims at finding the detail variation of a systematic survey of hourly traffic volume data over a time of four years along the North Bengal corridor of Bangladesh (at Jamuna toll collection point) and its equivalent numerical model by using a Artificial Neural Network. The Neural Network is trained with the intermittent data of 13 weeks over four years and the missing data is interpreted with quite reasonable accuracy (12.67% MAE) with this ANN model. The ANN model captured the variety of trends of the traffic data very accurately as has been depicted in the paper
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
30539. Predicting the performance measures of a 2-dimensional message passing multiprocessor architecture by using machine learning methods
- Creator:
- Akay, M. F., Aci, C. İ., and Abut, F.
- Format:
- bez média and svazek
- Type:
- model:article and TEXT
- Subject:
- Support vector regresion, neural networks, multiprocessors, and message passing
- Language:
- English
- Description:
- 2-dimensional Simultaneous Optical Multiprocessor Exchange Bus (2D SOME-Bus) is a reliable, robust implementation of petaflops-performance computer architecture. In this paper, we develop models to predict the performance measures (i.e. average channel utilization, average channel waiting time, average network latency, average processor utilization and average input waiting time) of a message passing architecture interconnected by the 2D SOME-Bus by using Multi- layer Feed-forward Artificial Neural Network (MFANN), Support Vector Regression (SVR) and Multiple Linear Regression (MLR). OPNET Modeler is used to simulate the message passing 2D SOME-Bus multiprocessor architecture and to create the training and testing datasets. Using 10-fold cross validation, the performance of the prediction models have been evaluated using several performance metrics. The results show that the SVR model using the radial basis function kernel (SVR-RBF) yields the lowest prediction error among all models.
- Rights:
- http://creativecommons.org/publicdomain/mark/1.0/ and policy:public
30540. Predicting the spring abundance distribution of red-legged partridge populations in agricultural regions using environmental models and an application for game management
- Creator:
- Peiro, Victoriano and Blanc, Charles P.
- Type:
- article and TEXT
- Subject:
- Alectoris rufa, modelling distribution, Kilometric Abundance Index, GIS, and southern France
- Language:
- English
- Description:
- Several series of available environmental (land use/land cover, agriculture, soil, climate) variables are used in exploratory models to test their use for successful prediction of red-legged partridge (Alectoris rufa L.) abundance in spring. A Geographic Information System and stepwise multiple regression analysis are used to show and predict distribution of this population parameter in an agricultural region of southern France. High spring abundance was observed to be distributed mainly in the central and north-western part of the study area. Two partial models, land use/land cover and agriculture, and a complete model with land use and temperature variables are the most significant and more accurate than any others. The complete model is the best model (lowest Akaike Information Criterion and highest Akaike weight). The potential abundance obtained from this best model shows communes with high Kilometric Abundance Indices (KAI), mainly located in the northwestern part of the region. Partridge abundance was unevenly or irregularly distributed across the study area, which is typical of wildlife species inhabiting complex and changing landscapes limited by various sources of human pressure, such as agriculture, urbanization and game management. A game tool is provided using potential spring abundance to plan the harvest quotas two months before opening the hunting season.
- Rights:
- http://creativecommons.org/licenses/by-nc-sa/4.0/