Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of stroke recognition and stroke severity scores for stroke detection in a single cohort

Objective First, to determine the sensitivity and specificity of six stroke recognition scores in a single cohort to improve interscore comparability. Second, to test four stroke severity scores repurposed to recognise stroke in parallel. Methods Of 9154 emergency runs, 689 consecutive cases of preclinically ‘suspected central nervous system disorder’ admitted to the emergency room (ER) of the Heidelberg University Hospital were included in the validation cohort. Using data abstracted from the neurological ER medical reports, retrospective assessment of stroke recognition scores became possible for the Cincinnati Prehospital Stroke Scale (CPSS), Face Arm Speech Test (FAST), Los Angeles Prehospital Stroke Screen (LAPSS), Melbourne Ambulance Stroke Screen (MASS), Medic Prehospital Assessment for Code Stroke (Med PACS) and Recognition of Stroke in the Emergency Room score (ROSIER), and that of stroke severity scores became possible for the Kurashiki Prehospital Stroke Scale (KPSS), Los Angeles Motor Scale (LAMS) and shortened National Institutes of Health Stroke Scale (sNIHSS)-8/sNIHSS-5. Test characteristics were calculated using the hospital discharge diagnosis as the reference standard. Results The CPSS and FAST had a sensitivity of 83% (95% CI 76 to 88) and 85% (78% to 90%) and a specificity of 69% (64% to 73%) and 68% (63% to 72%), respectively. The more complex LAPSS, MASS and Med PACS had a high specificity (92% to 98%) but low sensitivity (44% to 71%). In the ROSIER, sensitivity (80%, 73 to 85) and specificity (79%, 75 to 83) were similar. Test characteristics for KPSS, sNIHSS-8 and sNIHSS-5 were similar to the simple recognition scores (sensitivity 83% to 86%, specificity 60% to 69%). The LAMS offered only low sensitivity. Conclusions The simple CPSS and FAST scores provide good sensitivity for stroke recognition. More complex scores do not result in better diagnostic performance. Stroke severity scores can be repurposed to recognise stroke at the same time because test characteristics are comparable with pure stroke recognition scores. Particular shortcomings of the individual scores are discussed.
Access to financial intermediaries and external capital acquisition

We examine whether low access to financial intermediaries works as an obstacle acquiring financial capital for Swedish firms by using information from the Community Innovation Survey indicating whether firms perceive the acquisition of external capital to be difficult. This perception is explained by the distance to the firms’ nearest financial intermediaries and their total local supply. The results indicate that the distance to banks is related to a larger problem of obtaining external financial capital in rural areas.
Isolated bulbar palsy with anti-GM3 and GT1b antibodies

Isolated acute bulbar palsy has been described as one of the more rare variants of Guillain-Barre syndrome. IgG anti-ganglioside antibodies are associated with axonal subtypes of Guillain-Barre syndrome as well as Fisher syndrome. However, IgG against GM3 and GT1b in relation to bulbar palsy is uncommon. In this case report, we describe a 64 year-old male patient presenting with isolated bulbar weakness and generalized hyporeflexia without limb weakness. Serological testing for antiganglioside antibodies was positive for IgG anti-GM3 and -GT1b, suggesting the association of these antibodies with isolated bulbar palsy.
Mean Field Methods for a Special Class of Belief Networks

The chief aim of this paper is to propose mean-field approximations for a broad class of Belief networks, of which sigmoid and noisy-or networks can be seen as special cases. The approximations are based on a powerful mean-field theory suggested by Plefka. We show that Saul, Jaakkola, and Jordan's approach is the first order approximation in Plefka's approach, via a variational derivation. The application of Plefka's theory to belief networks is not computationally tractable. To tackle this problem we propose new approximations based on Taylor series. Small scale experiments show that the proposed schemes are attractive.
An algorithmic approach to stability verification of polyhedral switched systems

We present an algorithmic approach for analyzing Lyapunov and asymptotic stability of polyhedral switched systems. A polyhedral switched system is a hybrid system in which the continuous dynamics is specified by polyhedral differential inclusions, the invariants and guards are specified by polyhedral sets and the switching between the modes do not involve reset of variables. The analysis consists of first constructing a finite weighted graph from the switched system and a finite partition of the state space, which represents a conservative approximation of the switched system. Then, the weighted graph is analyzed for certain structural properties, satisfaction of which implies stability. However, in the event that the weighted graph does not satisfy the properties, one cannot, in general, conclude that the system is not stable due to the conservativeness of the graph. Nevertheless, when the structural properties do not hold in the graph, a counterexample indicating a potential reason for the failure is returned. Further, a more precise approximation of the switched system can be constructed by considering a finer partition of the state-space in the construction of the finite weighted graph. We present experimental results on analyzing stability of switched systems using the above method.
Stability analysis of periodic solutions for stochastic reaction-diffusion high-order Cohen-Grossberg-type BAM neural networks with delays

In this paper, the mean square exponential stability of the periodic solution for stochastic reaction-diffusion high-order Cohen-Grossberg-Type BAM neural networks with time delays is investigated. By constructing suitable Lyapunov function, applying Ito formula and Poincare mapping, we give some sufficient conditions to guarantee the mean square exponential stability of the periodic solution. An illustrative example are also given in the end to show the effectiveness of our results.
The PUC-CAM-Q: a new questionnaire for delving into the use of complementary and alternative medicines.

OBJECTIVE This paper reports on the design and testing of a new questionnaire, "Perspectives on the Use in Communities of CAM" (the PUC-CAM-Q [questionnaire]). DESIGN The questionnaire consisted of scales and questions for 27 concepts considered to affect complementary and alternative medicine (CAM) usage. Scales encompassed 13 beliefs about nature, scientific medicine, and the environment, as well as personal characteristics, such as stoicism and resilience. A matrix provided space for respondents to indicate their use, or likelihood of use, of 23 of the most commonly available CAM modalities. Also included were questions about the reasons for CAM use and sources of health information. LOCATION The questionnaire was mailed to a randomly selected sample of people in a pilot study of two metropolitan and five rural localities in Victoria, Australia. RESULTS The response rate was 40% (n = 459). The majority of the questionnaires were completed consistently, and the reliability and validity and questions were satisfactory. Seven (7) of the 13 scales that explored the beliefs and concerns about CAM use and the characteristics of the respondents had Cronbach alphas of above 0.7. Refinement of the other six scales resulted in alphas of between 0.6 and 0.7, with good corrected item-total correlations for included questions. Responses to the matrix question on the use, or likelihood of use, of individual CAM modalities were also good. However, some adjustments to the layout would provide more comprehensive information for future use of the PUC-CAM-Q. CONCLUSIONS This questionnaire provided good data that were appropriate for the exploratory nature of this PUC-CAM study. After more attention to the scales, as well as some refinement of some nonscale questions, the PUC-CAM-Q would be a practical instrument for further studies on CAM use.
Oil spills detection: Challenges addressed in the scope of the SEAGULL project

The development of intelligent systems to support maritime situation awareness is the main goal of the SEAGULL project. By equipping unmanned aerial vehicles (UAVs) with different types of optical sensors we target an intelligent maritime surveillance system. In this paper we present experiments performed with an hyperspectral camera to detect oil spills. We have developed a detection algorithm that automatically informs the base station of an oil spill in case a spectral signature recognized as oil is found on open sea. This contributes significantly to the generation of situational awareness of maritime events such as detection and georeferencing of oil spills or hazardous and noxious substances. After a brief overview of the SEAGULL platform and architecture, we focus on presenting the algorithms for automatic detection of oil spills with a hyperspectral camera on board an UAV.
The non-algorithmic side of the mind

The existence of a non-algorithmic side of the mind, conjectured by Penrose on the basis of G\"odel's first incompleteness theorem, is investigated here in terms of a quantum metalanguage. We suggest that, besides human ordinary thought, which can be formalized in a computable, logical language, there is another important kind of human thought, which is Turing-non-computable. This is methatought, the process of thinking about ordinary thought. Metathought can be formalized as a metalanguage, which speaks about and controls the logical language of ordinary thought. Ordinary thought has two computational modes, the quantum mode and the classical mode, the latter deriving from decoherence of the former. In order to control the logical language of the quantum mode, one needs to introduce a quantum metalanguage, which in turn requires a quantum version of Tarski Convention T.
Spectroscopic Analysis of Copper Oxide by Laser-Induced Breakdown Spectroscopyin the Presence and Absence of a Magnetic Field

Laser-induced breakdown spectroscopy of CuO plasma in the presence and absence of magnetic field has been investigated. The plasma was generated by employing Nd: YAG laser (532 nm, 9 ns) at various pulse laser energy ranging from 100 to 400 mj in a vacuum. The increasing of pulse laser energy in the presence and absence of the magnetic field had shown increasing in electron density while the electron temperature was decreases. The presence of the magnetic field cause to a rising the electron density but it reduces the electron temperature comparison with the case of absence of the magnetic field. According to theβ value distribution along the target, the results shows in the central region of the target the plasma plump that produced by laser is in a good confinement in this region in contrasted with edge region of the target region.
A Systems Engineering Methodology for Structuring and Calibrating Lake Ecosystem Models

A methodology is presented which makes use of both theory-based and data-based information to structure and calibrate low-order dynamic models for lake ecosystem analysis. A brief historical review explores the inductive and deductive aspects of past modeling efforts and describes the motivation for this research. A methodology, summarized in a flowchart, is discussed in terms of data analysis, conceptualization, development of a mathematical model, calibration, and three feedback stages for validity checks. This approach has been applied to the study of nutrient cycling in the south basin of Lake George, NY. The results of several iterations of the methodology are given and discussed in terms of statistical validity and agreement with biological theory. The final model is a set of difference equations containing two state variables and three external variables, and shows a good fit to the existing data. This is one of the first lake ecosystem studies to make extensive use of data in order to develop model relationships and estimate parameters.
Determination of neonatal serum immunoglobulin G concentrations associated with mortality during the first 4 months of life in dairy heifer calves

Colostral administration practices on dairy farms have significantly improved over the last 15–20 years resulting in prevalence of calves ingesting insufficient colostrum decreasing from 35–40% to 19%. Despite these improvements, the serum immunoglobulin G (IgG) concentration of ≥1000 g/dl and serum total protein (TP) concentrations of ≥5·2 g/dl are considered indicative of adequate transfer of immunity. We hypothesised that the current serum IgG concentrations of ≥1000 mg/dl is too low to indicate adequate transfer of colostral immunity on modern dairies. The objective of this study was to determine the serum IgG and TP concentrations indicating adequate transfer of passive immunity in dairy heifer calves. A cohort study of 1290 heifers from a calf raising facility for 48 dairy farms was performed. Heifers were assigned into strata based on serum IgG and TP concentrations. Mortality events were recorded for the heifers for 4 months. Interval likelihood ratios for mortality were calculated for heifers in each stratum of serum IgG or TP concentrations. Logistic regression to predict probability of mortality events was performed. Estimates of probability of survival were evaluated using survival analysis. Serum strata of ≤1500, 1501–2000 or >2500 were not significant predictors of mortality during the 120 d of rearing. Serum IgG concentration was not a significant predictor of hazard for mortality. In contrast to previous studies, serum IgG and TP concentrations of 2001–2500 mg/dl and 5·8–6·3 g/dl respectively, were considered optimum for indicating adequate passive transfer of colostral immunity in dairy calves based on the likelihood ratios. On dairies with optimum colostral feeding practices, serum IgG and TP concentrations of 2001–2500 mg/dl and 5·8–6·3 g/dl are recommended as endpoints to indicate adequate passive immunity in dairy calves.
Pericardial Adaptation in Severe Chronic Pulmonary Hypertension: An Intraoperative Transesophageal Echocardiographic Study

Background The pericardium both limits cardiac distension and accentuates ventricular interdependence. Although this effect appears minimal under normal circumstances, the pericardium markedly restricts acute cardiac enlargement. Animal studies have demonstrated gradual pericardial adaptation and expansion in chronic volume overload and cardiomegaly, but the pericardial response in humans with cardiac hypertrophy and enlargement has not been examined fully. To investigate this further, 14 patients with right ventricular hypertrophy and cardiomegaly secondary to chronic pulmonary thromboembolic disease and severe pulmonary hypertension were studied during pulmonary thromboendarterectomy. Methods and Results Simultaneous intraoperative transesophageal Doppler echocardiography and direct biventricular hemodynamic measurements were performed at steady state immediately before and after pericardiotomy. All hemodynamic variables showed no significant change before and after pericardiotomy, including heart rate (76±16 versus 75±15 beats per minute), mean pulmonary arterial pressure (46.3±11.1 versus 45.5±11.7 mm Hg), cardiac index (1.8±0.5 versus 2.0±0.6 I/min/m2), left ventricular end-diastolic pressure (5.9+5.7 versus 7.1±5.0 mm Hg), and right ventricular end-diastolic pressure (7.9±6.6 versus 8.0±6.7 mm Hg). Similarly, there were no significant changes in all Doppler echocardiographic parameters, including right ventricular end-diastolic area (23.2±5.7 versus 22.6±5.4 cm2), left ventricular end-diastolic area (15.3±5.9 versus 15.5±4.4 cm2), the position of the interventricular septum, and the Doppler-derived mitral inflow measures of diastolic function. Conclusions The pericardium appears to have little influence on the marked cardiac and septal deformations seen in patients with chronic, severe right ventricular pressure overload and cardiomegaly. This study confirms that the human pericardium is capable of adapting over time to changes in cardiac size and geometry.
Impulsive consensus seeking in directed networks of multi-agent systems with communication time delays

In this article, we consider average consensus problem in directed delayed networked multi-agent systems having impulsive effects with fixed topology and stochastic switching topology. A simple impulsive consensus protocol for such networks is proposed, and some generic criteria for solving the average consensus problem are analytically derived. It is shown that a directed delayed networked multi-agent system can achieve average consensus globally exponentially with suitable impulsive gain and impulsive interval. Subsequently, two typical illustrative examples, along with computer simulation results, are provided to visualise the effectiveness and feasibility of our theoretical results.
The Proper Balance: Overview of Microstrip Wideband Balance Circuits with Wideband Common Mode Suppression

With the development of wireless communication technology, radiofrequency (RF) circuits and integrated circuits are becoming more complex and packing more functionality and signals into an ever closer space, with a high level of electromagnetic interaction between circuit nodes and interference/crosstalk from substrate coupling and free space [1]. Balanced/differential circuit technology has become more important in modern communication systems because of good common-mode rejection that leads to relatively high immunity to environmental noise when compared with the single-ended technology [1]-[3], as shown in Figure 1(a) and (b). For balanced circuits, the differential mode should have excellent out-of-band rejection and high selectivity for the desired frequency band, while the common mode should be suppressed over a wider frequency band. In the past few years, many microstrip balanced filters for single band and dual band with common-mode suppression have been realized [4]-[10], with different balanced networks, balanced driven antennas, balanced amplifiers, and mixers [11]-[18].
Optimal wire sizing and buffer insertion for low power and a generalized delay model

We present efficient, optimal algorithms for timing optimization by discrete wire sizing and buffer insertion. Our algorithms are able to minimize dynamic power dissipation subject to given timing constraints. In addition, we compute the complete power-delay tradeoff curve for added flexibility. We extend our algorithm to take into account the effect of signal slew on buffer delay which can contribute substantially to overall delay. The effectiveness of these methods is demonstrated experimentally.
Evaluation of genetic diversity in different genotypes of ( Medicago Sativa L . ) using ISSR markers

Alfalfa (Medicago sativa L.) is a widely grown legume and one of the most important forage species throughout the world. It yields forage of high nutritional quality and has positive influences on the environment. Genetic diversity based on different markers, plays a key role in breeding programs and one of the most important criteria for selection of Parents. In this study the genetic diversity among 19 genotypes of Medicago sativa was investigated using 8 ISSR primers. These primers could identify 37 loci. Primer IS5 showed the highest number of bonds(7) and primers IS1, IS9 and IS16 showed the lowest number of bonds(3). The Average percentage of polymorphism was 86% for primers used, that the lowest percentage of polymorphism was for IS14 and IS15 (50%) and IS5 (57.14%). Also it was 100% for other primers. The average of PIC for studied primers was 0.32 that highest rate of PIC belong to IS9 and IS13 and the primers IS5 and IS16 had the lowest. Also, cluster analysis and principal coordinate analysis using Dice similarity coefficients were calculated and dendrogram was drawn using the UPGMA for 19 genotypes. The coordinate analysis was performed using of the similarity matrix and confirmed the results of cluster analysis.
Automatic Text Summarization based on Rhetorical Structure Theory

In this paper, we present an automatic text summarization method based on Rhetorical Structure Theory (RST). The first part gives a general introduction to the Rhetorical Structure Theory. Rhetorical Structure Theory (RST) is an analytic framework designed to account for text structure in text at the clause level. The system extracts the rhetorical structure of text and the compound of the rhetorical relations between sentences, and then cuts out less important parts from the extracted structure. Finally it uses the natural language generation method based on model to produce an exact and fluent summarization. The summarization generated in this way shows its effectiveness and high quality.
Stability of multiscalar black holes

We generalize a class of magnetically charged black holes holes non-minimally coupled to two scalar fields previously found by one of us [gr-qc/9910041] to the case of multiple scalar fields. The black holes possess a novel type of primary scalar hair, which we call a contingent primary hair: although the solutions possess degrees of freedom which are not completely determined by the other charges of the theory, the charges necessarily vanish in the absence of the magnetic monopole. Only one constraint relates the black hole mass to the magnetic charge and scalar charges of the theory. We obtain a Smarr-type thermodynamic relation, and the first law of black hole thermodynamics for the system. We further explicitly show in the two-scalar-field case that, contrary to the case of many other hairy black holes, the black hole solutions are stable to radial perturbations.
Spatial Filter Optimization Using Gaussian Kernel for Single Electro- Encephalo Gram (EEG) Trial Classification

Brain Computer Interfaces (BCI) enable people suffering from Amyotrophic Lateral Sclerosis (ALS) or severe paralysis to communicate and control using only brain signals. These signals are captured from EEG, that is relatively cost effective and non invasive. Raw EEG signals are subject to processing and feature extraction for automating the detection of changes in the amplitude of the brain signals due to imagining of a limb motion and to enable classification. The processed signal is analysed using machine learning tool and translated into interpretable form. In this manuscript, we propose an advanced feature extraction method utilizing spatial characteristics (channel information) of an EEG signal. The method maximizes inter class variance and minimizes intra class variance among the optimized spatial features to facilitate classification. Kernel based Fisher’s criterion is used for Spatial filtering which transforms the data into a suitable feature space for classification. This approach overcomes the limitation of the existing Fisher based Common Spatial Patterns and performs better in general. Experimental results on BCI Competition datasets demonstrate the effectiveness of this methodology in terms of accurate classification, in comparison to the other method.