LLukas22/scidocs huggingface dataset:
Regionalisation of catchment model parameters

We simulate the water balance dynamics of 308 catchments in Austria using a lumped conceptual model involving 11 calibration parameters. We calibrate and verify the model for two non-overlapping 11-year periods of daily runoff data. A comparison of the calibrated parameter values of the two periods suggests that all parameters are associated with some uncertainty although the degree of uncertainty differs between the parameters. The regional patterns of the calibrated parameters can be interpreted based on hydrological process reasoning indicating that they are able to represent the regional or large-scale differences in the hydrological conditions. Catchment attributes explain some of the spatial parameter variability with coefficients of determination of up to R 1⁄4 0:27; but usually the R values are lower. Parameter uncertainty does not seem to cloud the relationship between calibrated parameters and catchment attributes to a significant extent as suggested by an optimised correlation analysis. The median Nash–Sutcliffe efficiencies of simulating streamflow decrease from 0.67 to 0.63 when moving from the calibration to the verification period. This is a small decrease, which suggests that problems with overparameterisation of the model are unlikely. We then compare regionalisation methods for estimating the model parameters in ungauged catchments, in terms of the model performance. The best regionalisation methods are the use of the average parameters of immediate upstream and downstream (nested) neighbours and regionalisation by kriging. For the calibration period, the average decrease in the Nash–Sutcliffe model efficiency, as a result of the regionalisation, is 0.10 which is about twice the decrease of moving from the calibration to the verification period. The methods based on multiple regressions with catchment attributes perform significantly poorer. Apparently, spatial proximity is a better surrogate of unknown controls on runoff dynamics than catchment attributes. q 2004 Elsevier B.V. All rights reserved.
Assessing the wind energy potential locations in province of Semnan in Iran

In this study, the 3-h period measured wind speed data for years 2003–2007 at 10 m, 30 m and 40 m heights for one of the provinces of Iran. Semnan have been statistically analyzed to determine the potential of wind power generation. This paper presents the wind energy potential at five towns in the province – Biarjmand, Damghan, Garmsar, Semnan, and Shahrood. Extrapolation of the 10 m data, using the Power Law, has been used to determine the wind data at heights of 30 m and 40 m. From the primary evaluation and determining mean wind speed and also weibull distribution, it is found that Damghan has better potential for using wind energy in the province. Thus concentrated on Damghan town and its sites – Moalleman, Haddadeh and also Kahak of Garmsar (only had Meteorological stop) using a 10-min time step wind speed and wind direction data for three measured heights. Between these sites, Moalleman is selected for a more accurate and spacious analysis. The objective is to evaluate the most important characteristic of wind energy in the studied site. The statistical attitudes permit us to estimate the mean wind speed, the wind speed distribution function, the mean wind power density and the wind rose in the site at the height of 10 m, 30 m and 40 m. Some local phenomena are also considered in the characterization of the site. Crown Copyright 2010 Published by Elsevier Ltd. All rights reserved.
An Exploration of Emotional Intelligence of the Students of IIUI in Relation to Gender, Age and Academic Achievement

This correlational study was intended to examine the relationship of emotional intelligence (EI) with gender, age and academic achievement of students of International Islamic University Islamabad (IIUI). In this study the predictor variable was emotional intelligence and criterion variable was academic achievement as measured by students’ Cumulative Grade Point Average (CGPA). Emotional intelligence was measured with the help of BarOn Emotional Quotient Inventory (EQi). The validity and reliability of EQi was measured and the instrument was found to be valid and highly internally consistent. Correlation analysis, regression analysis and t-test were performed to test the hypotheses. Results indicated a significant correlation between emotional intelligence and academic achievement. Emotional intelligence was found a significant predictor of academic achievement. No significant correlation was found between age and emotional intelligence. There was no difference in the mean EQi scores of male and female students except on stress management scale where male students scored higher than female students.
Multicenter clinical assessment of the Raumedic Neurovent-P intracranial pressure sensor: a report by the BrainIT group.

OBJECTIVE
The aim of this study was to evaluate the robustness and zero-drift of an intracranial pressure sensor, Neurovent-P (Raumedic AG, Münchberg, Germany), when used in the clinical environment.


METHODS
A prospective multicenter trial, conforming to the International Organization for Standardization 14155 Standard, was conducted in 6 European BrainIT centers between July 2005 and December 2006. Ninety-nine catheters were used. The study was observational, followed by a centralized sensor bench test after catheter removal.


RESULTS
The mean recorded value before probe insertion was 0.17 +/- 1.1 mm Hg. Readings outside the range +/-1 mm Hg were recorded in only 3 centers on a total of 15 catheters. Complications were minimal and mainly related to the insertion bolt. The mean recorded pressure value at removal was 0.8 +/- 2.2 mm Hg. No relationship was identified between postremoval reading and length of monitoring. The postremoval bench test indicated the probability of a system failure, defined as a drift of more than 3 mm Hg, at a range between 12 and 17%.


CONCLUSION
The Neurovent-P catheter performed well in clinical use in terms of robustness. The majority of technical complications were associated with the bolt fixation technology. Adverse events were rare and clinically nonsignificant. Despite the earlier reported excellent bench test zero-drift rates, under the more demanding clinical conditions, zero-drift rate remains a concern with catheter tip strain gauge technology. This performance is similar, and not superior, to other intracranial pressure devices.
Marine pharmacology in 2000: antitumor and cytotoxic compounds.

During 2000, marine antitumor pharmacology research aimed at the discovery of novel antitumor agents was published in 85 peer-reviewed articles. The purpose of this article is to present a structured review of the antitumor and cytotoxic properties of 143 marine natural products, many of them novel compounds that belong to diverse structural classes, including polyketides, terpenes, steroids and peptides. The organisms yielding these bioactive compounds comprised a taxonomically diverse group of marine invertebrate animals, algae, fungi and bacteria. Antitumor pharmacological studies were conducted with 19 marine natural products in a number of experimental and clinical models that defined or further characterized their mechanisms of action. Potentially promising in vitro cytotoxicity data generated with murine and human tumor cell lines were reported for 124 novel marine chemicals with as yet undetermined mechanisms of action. Noteworthy is the fact that marine anticancer research clearly remains a multinational effort, involving researchers from Austria, Australia, Brazil, Canada, England, France, Germany, Greece, Indonesia, Italy, Japan, New Zealand, Russia, Spain, South Korea, Switzerland, Taiwan, the Netherlands and the United States. Finally, this 2000 overview of the marine pharmacology literature highlights the fact that the discovery of novel marine antitumor agents continued at the same high level of research activity as during 1998 and 1999.
Is preprocessing of text really worth your time for online comment classification?

A large proportion of online comments present on public domains are usually constructive, however a significant proportion are toxic in nature. The comments contain lot of typos which increases the number of features manifold, making the ML model difficult to train. Considering the fact that the data scientists spend approximately 80% of their time in collecting, cleaning and organizing their data [1], we explored how much effort should we invest in the preprocessing (transformation) of raw comments before feeding it to the state-of-the-art classification models. With the help of four models on Jigsaw toxic comment classification data, we demonstrated that the training of model without any transformation produce relatively decent model. Applying even basic transformations, in some cases, lead to worse performance and should be applied with caution.
A biomedical system based on hidden Markov model for diagnosis of the heart valve diseases

In this study, a biomedical diagnosis system for pattern recognition with normal and abnormal classes has been developed. First, feature extraction processing was made by using the Doppler Ultrasound. During feature extraction stage, Wavelet transforms and shorttime Fourier transform were used. As next step, wavelet entropy were applied to these features. In the classification stage, hidden Markov model (HMM) was used. To compute the correct classification rate of proposed HMM classifier, it was compared to ANN by using a data set containing 215 samples. In our experiments, specificity rate and sensitivity rates of proposed HMM classifier system with fuzzy C means (FCM)/K-means algorithms were found as 92% and 97.26% respectively. The present study shows that proper selection of the HMMs initial parameter values according to FCM/K-means algorithms improves the recognition rate of the proposed system which was also compared to our previous study named ANN. 2006 Elsevier B.V. All rights reserved.
Forecasting Economic Time Series Using Targeted Predictors

This paper studies two refinements to the method of factor forecasting. First, we consider the method of quadratic principal components that allows the link function between the predictors and the factors to be non-linear. Second, the factors used in the forecasting equation are estimated in a way to take into account that the goal is to forecast a specific series. This is accomplished by applying the method of principal components to ‘targeted predictors’ selected using hard and soft thresholding rules. Our three main findings can be summarized as follows. First, we find improvements at all forecast horizons over the current diffusion index forecasts by estimating the factors using fewer but informative predictors. Allowing for non-linearity often leads to additional gains. Second, forecasting the volatile one month ahead inflation warrants a high degree of targeting to screen out the noisy predictors. A handful of variables, notably relating to housing starts and interest rates, are found to have systematic predictive power for inflation at all horizons. Third, the variables chosen as targeted predictors selected by both soft and hard thresholding changes with the forecast horizon and the sample period. Holding the set of predictors fixed as is the current practice of factor forecasting is unnecessarily restrictive. ∗Department of Economics, NYU, 269 Mercer St, New York, NY 10003 Email: Jushan.Bai@nyu.edu. †Department of Economics, University of Michigan, Ann Arbor, MI 48109 Email: Serena.Ng@umich.edu We would like to thank Jeremy Piger (discussant) and conference participants for helpful comments. We also acknowledge financial support from the NSF (grants SES-0137084, SES-0136923, SES-0549978)
An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an a ppropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from t he sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while sti l providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility F unction (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is de termined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where mo secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as wel l as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encrypti on scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (G A) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret imag e so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-imag e. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achi eve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and i m ge processing attacks.
Rook Jumping Maze Generation for AI Education

Rook Jumping Maze design provides a number of good opportunities for experiential learning of AI concepts, including uninformed search, stochastic local search, machine learning, and objective/utility function design. In this paper we will define the maze and present a collection of exercises that allow exploration of several AI topics in the context of an engaging, fun, and unifying task.
Associations between housing instability and food insecurity with health care access in low-income children.

OBJECTIVE
Homelessness and hunger are associated with poor health care access among children. Housing instability and food insecurity represent milder and more prevalent forms of homelessness and hunger. The aim of this study was to determine the association between housing instability and food insecurity with children's health care access and acute health care utilization.


METHODS
We conducted a cross-sectional analysis of 12,746 children from low-income households included in the 2002 National Survey of America's Families (NSAF). In multivariate models controlling for important covariates, we measured the association between housing instability and food insecurity with 3 health care access measures: 1) no usual source of care, 2) postponed medical care, and 3) postponed medications. We also measured 3 health care utilization measures: 1) not receiving the recommended number of well-child care visits, 2) increased emergency department visits, and 3) hospitalizations.


RESULTS
Our analysis showed that 29.5% of low-income children lived in households with housing instability and 39.0% with food insecurity. In multivariate logistic regression models, housing instability was independently associated with postponed medical care, postponed medications, and increased emergency department visits. Food insecurity was independently associated with no usual source of care, postponed medical care, postponed medications, and not receiving the recommended well-child care visits.


CONCLUSION
Families that experience housing instability and food insecurity, without necessarily experiencing homelessness or hunger, have compromised ability to receive adequate health care for their children. Policy makers should consider improving programs that decrease housing instability and food insecurity, and clinicians should consider screening for housing instability and food insecurity so as to provide comprehensive care.
Smart parking systems: A survey

Not finding a parking space for you sometimes is indeed a critical issue. The number of vehicles is also increasing daily adding to the parking vows at public places. Cities noticed that their drivers had real problems to find a parking space easily especially during peak hours, the difficulty roots from not knowing where the parking spaces are available at the given time. Even if this is known, many vehicles may pursue a small number of parking spaces which in turn leads to traffic congestion. The traffic on roads and parking space has been an area of concern in majority of cities. So, parking monitoring is an important solution. To avoid these problems, recently many new technologies have been developed that help in solving the parking problems to a great extent. Firstly, this paper gives an overview about the concept of smart parking system, their categories and different functionalities. Then we present the latest developments in parking infrastructures. We describe the technologies around parking availability monitoring, parking reservation and dynamic pricing and see how they are utilized in different settings. In addition, a theoretical comparison is presented to show advantages and drawbacks of each different smart parking system to discuss results and open directions for future research.
Bag-of-Visual-Words Based on Clonal Selection Algorithm for SAR Image Classification

Synthetic aperture radar (SAR) image classification involves two crucial issues: suitable feature representation technique and effective pattern classification methodology. Here, we concentrate on the first issue. By exploiting a famous image feature processing strategy, Bag-of-Visual-Words (BOV) in image semantic analysis and the artificial immune systems (AIS)'s abilities of learning and adaptability to solve complicated problems, we present a novel and effective image representation method for SAR image classification. In BOV, an effective fused feature sets for local feature representation are first formulated, which are viewed as the low-level features in it. After that, clonal selection algorithm (CSA) in AIS is introduced to optimize the prediction error of k-fold cross-validation for getting more suitable visual words from the low-level features. Finally, the BOV features are represented by the learned visual words for subsequent pattern classification. Compared with the other four algorithms, the proposed algorithm obtains more satisfactory and cogent classification experimental results.
Maximum Correntropy Unscented Kalman Filter for Spacecraft Relative State Estimation

A new algorithm called maximum correntropy unscented Kalman filter (MCUKF) is proposed and applied to relative state estimation in space communication networks. As is well known, the unscented Kalman filter (UKF) provides an efficient tool to solve the non-linear state estimate problem. However, the UKF usually plays well in Gaussian noises. Its performance may deteriorate substantially in the presence of non-Gaussian noises, especially when the measurements are disturbed by some heavy-tailed impulsive noises. By making use of the maximum correntropy criterion (MCC), the proposed algorithm can enhance the robustness of UKF against impulsive noises. In the MCUKF, the unscented transformation (UT) is applied to obtain a predicted state estimation and covariance matrix, and a nonlinear regression method with the MCC cost is then used to reformulate the measurement information. Finally, the UT is adopted to the measurement equation to obtain the filter state and covariance matrix. Illustrative examples demonstrate the superior performance of the new algorithm.
Reply to comment Reply to criticism of the ‘ Orch OR qubit ’ – ‘ Orchestrated objective reduction ’ is scientifically justified

The critical commentary by Reimers et al. [1] regarding the Penrose–Hameroff theory of ‘orchestrated objective reduction’ (‘Orch OR’) is largely uninformed and basically incorrect, as they solely criticize non-existent features of Orch OR, and ignore (1) actual Orch OR features, (2) supportive evidence, and (3) previous answers to their objections (Section 5.6 in our review [2]). Here we respond point-by-point to the issues they raise.
Diagnosing Aortic Valve Stenosis by Parameter Extraction of Heart Sound Signals

The objective of this study was to develop an automatic signal analysis system for heart sound diagnosis. This should support the general practitioner in discovering aortic valve stenoses at an early stage to avoid or decrease the number of surgical interventions. The applied analysis method is based on classification of heart sound signals utilising parameter extraction. From the wavelet decomposition of a representative heart cycle as well as from the Short Time Fourier Transform (STFT) and the Wavelet Transform (WT) spectra new time series were derived. In several segments, parameters were extracted and analysed. In addition, features of the Fast Fourier Transform (FFT) of the raw signal were examined. In this study, 206 patients were enrolled, 159 with no heart valve disease or any other heart valve disease but aortic valve stenosis and 47 suffering from aortic valve stenosis in a mild, moderate or severe stage. To separate the groups, a linear discriminant function analysis was applied leading to a reduced parameter set. The introduced two classification stage (CS) system for automatic detection of aortic valve stenoses achieves a high sensitivity of 100% for moderate and severe aortic valve stenosis and a sensitivity of 75% for mild aortic valve stenosis. A specificity of 93.7% for patients without aortic valve stenosis is provided. The developed method is robust, cost effective and easy to use, and could, therefore, be a suitable method to diagnose aortic valve stenosis by general practitioners.
Drug drug interaction extraction from biomedical literature using syntax convolutional neural network

MOTIVATION
Detecting drug-drug interaction (DDI) has become a vital part of public health safety. Therefore, using text mining techniques to extract DDIs from biomedical literature has received great attentions. However, this research is still at an early stage and its performance has much room to improve.


RESULTS
In this article, we present a syntax convolutional neural network (SCNN) based DDI extraction method. In this method, a novel word embedding, syntax word embedding, is proposed to employ the syntactic information of a sentence. Then the position and part of speech features are introduced to extend the embedding of each word. Later, auto-encoder is introduced to encode the traditional bag-of-words feature (sparse 0-1 vector) as the dense real value vector. Finally, a combination of embedding-based convolutional features and traditional features are fed to the softmax classifier to extract DDIs from biomedical literature. Experimental results on the DDIExtraction 2013 corpus show that SCNN obtains a better performance (an F-score of 0.686) than other state-of-the-art methods.


AVAILABILITY AND IMPLEMENTATION
The source code is available for academic use at http://202.118.75.18:8080/DDI/SCNN-DDI.zip CONTACT: yangzh@dlut.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.