LLukas22/scidocs huggingface dataset:
Anisotropic Ru3+ 4d5 magnetism in the α-RuCl3 honeycomb system: Susceptibility, specific heat, and zero-field NMR

Hexagonal α-Ru trichloride single crystals exhibit a strong magnetic anisotropy and we show that upon applying fields up to 14 T in the honeycomb plane the successive magnetic order at T1 = 14 K and T2 = 8 K could be completely suppressed whereas in the perpendicular direction the magnetic order is robust. Furthermore the field dependence of χ(T) implies coexisting ferroand antiferromagnetic exchange between in-plane components of Ru-spins, whereas for out-of-plane components a strong antiferromagnetic exchange becomes evident. Ru zero-field nuclear magnetic resonance evidence a complex (probably chiral) long-range magnetic order below 14 K. The large orbital moment on Ru is found in density-functional calculations.
Using neural networks and data mining techniques for the financial distress prediction model

The operating status of an enterprise is disclosed periodically in a financial statement. As a result, investors usually only get information about the financial distress a company may be in after the formal financial statement has been published. If company executives intentionally package financial statements with the purpose of hiding the actual status of the company, then investors will have even less chance of obtaining the real financial information. For example, a company can manipulate its current ratio by up to 200% so that its liquidity deficiency will not show up as a financial distress in the short run. To improve the accuracy of the financial distress prediction model, this paper adopted the operating rules of the Taiwan stock exchange corporation (TSEC) which were violated by those companies that were subsequently stopped and suspended, as the range of the analysis of this research. In addition, this paper also used financial ratios, other non-financial ratios, and factor analysis to extract adaptable variables. Moreover, the artificial neural network (ANN) and data mining (DM) techniques were used to construct the financial distress prediction model. The empirical experiment with a total of 37 ratios and 68 listed companies as the initial samples obtained a satisfactory result, which testifies for the feasibility and validity of our proposed methods for the financial distress prediction of listed companies. This paper makes four critical contributions: (1) The more factor analysis we used, the less accuracy we obtained by the ANN and DM approach. (2) The closer we get to the actual occurrence of financial distress, the higher the accuracy we obtain, with an 82.14% correct percentage for two seasons prior to the occurrence of financial distress. (3) Our empirical results show that factor analysis increases the error of classifying companies that are in a financial crisis as normal companies. (4) By developing a financial distress prediction model, the ANN approach obtains better prediction accuracy than the DM clustering approach. Therefore, this paper proposes that the artificial intelligent (AI) approach could be a more suitable methodology than traditional statistics for predicting the potential financial distress of a company. Crown Copyright 2008 Published by Elsevier Ltd. All rights reserved.
The complex link between severity of asthma and rhinitis in mite allergic patients.

AIM
The aim of the study was to evaluate the link between the severity of upper and lower airways diseases in mite allergic patients with respiratory allergy.


PATIENTS AND METHOD
A multicentre, observational, cross-sectional study was carried out in 556 consecutively enrolled mite allergic patients with rhinitis and asthma comorbidity attending a specialist unit. Severity assessment of rhinitis and asthma was evaluated in accordance with ARIA and GINA guidelines.


RESULTS
Reliable data were available for 518 patients. The distribution of rhinitis severity was: 15.6% mild intermittent rhinitis, 4.4% moderate-severe intermittent rhinitis, 30.3% mild persistent rhinitis and 49.6% moderate persistent rhinitis. The distribution of asthma severity was: 41.3% mild intermittent asthma, 14.3% mild persistent asthma, 19.1% moderate persistent asthma and 25.3% severe persistent asthma. In patients with moderate-severe persistent rhinitis (49.5%) a significant trend (p = 0.005) was found pointing to an increased link with asthma severity.


CONCLUSION
A link between respective severities of rhinitis and asthma was found in only half of mite allergic patients with rhinitis and asthma.
Clinical Utility of a Bronchial Genomic Classifier in Patients With Suspected Lung Cancer.

BACKGROUND
Bronchoscopy is often the initial diagnostic procedure performed in patients with pulmonary lesions suggestive of lung cancer. A bronchial genomic classifier was previously validated to identify patients at low risk for lung cancer after an inconclusive bronchoscopy. In this study, we evaluated the potential of the classifier to reduce invasive procedure utilization in patients with suspected lung cancer.


METHODS
In two multicenter trials of patients undergoing bronchoscopy for suspected lung cancer, the classifier was measured in normal-appearing bronchial epithelial cells from a mainstem bronchus. Among patients with low and intermediate pretest probability of cancer (n = 222), subsequent invasive procedures after an inconclusive bronchoscopy were identified. Estimates of the ability of the classifier to reduce unnecessary procedures were calculated.


RESULTS
Of the 222 patients, 188 (85%) had an inconclusive bronchoscopy and follow-up procedure data available for analysis. Seventy-seven (41%) patients underwent an additional 99 invasive procedures, which included surgical lung biopsy in 40 (52%) patients. Benign and malignant diseases were ultimately diagnosed in 62 (81%) and 15 (19%) patients, respectively. Among those undergoing surgical biopsy, 20 (50%) were performed in patients with benign disease. If the classifier had been used to guide decision making, procedures could have been avoided in 50% (21 of 42) of patients undergoing further invasive testing. Further, among 35 patients with an inconclusive index bronchoscopy who were diagnosed with lung cancer, the sensitivity of the classifier was 89%, with 4 (11%) patients having a false-negative classifier result.


CONCLUSIONS
Invasive procedures after an inconclusive bronchoscopy occur frequently, and most are performed in patients ultimately diagnosed with benign disease. Using the genomic classifier as an adjunct to bronchoscopy may reduce the frequency and associated morbidity of these invasive procedures.


TRIAL REGISTRY
ClinicalTrials.gov; Nos. NCT01309087 and NCT00746759; URL: www.clinicaltrials.gov.
The next-to-shortest path problem on directed graphs with positive edge weights

Given an edge-weighted graph G and two distinct vertices s and t of G, the next-to-shortest path problem asks for a path from s to t of minimum length among all paths from s to t except the shortest ones. In this article, we consider the version where G is directed and all edge weights are positive. Some properties of the requested path are derived when G is an arbitrary digraph. In addition, if G is planar, an O(n3)-time algorithm is proposed, where n is the number of vertices of G. © 2015 Wiley Periodicals, Inc. NETWORKS, Vol. 000(00), 000–00
Prediction of Objective Child Motivation Test Scores from Parents' Reports of Child-Rearing Practices

The mothers and fathers of 310 junior high school children completed a child-rearing questionnaire and the children completed the School Motivation Analysis Test. The mothers' and fathers' data were factor analyzed separately and factor scores computed and employed as predictor variables in separate sets of multiple regression analyses employing the childrens' school motivation scores as criteria. Of the 40 possible full model R2s 29 were significant. The pattern of variable interrelationships is discussed, with some of the most consistent results being a significant positive relationship of the children's integrated and unintegrated assertiveness scores with the fathers' late authoritative discipline and the mothers' high family-adjustment scores. Among numerous other relationships discussed were significant positive relationships of fathers' low use of discipline and mothers' low physical punishment vs high use of reason with the childrens' integrated self-sentiment and superego.
State Complexity of Prefix-Free Regular Languages

We investigate the state complexities of basic operations for prefix-free regular languages. The state complexity of an operation for regular languages is the number of states that are necessary and sufficient in the worst-case for the minimal deterministic finite-state automaton (DFA) that accepts the language obtained from the operation. We know that a regular language is prefix-free if and only if its minimal DFA has only one final state and the final state has no out-transitions whose target state is not a sink state. Based on this observation, we reduce the state complexities for prefix-free regular languages compared with the state complexities for (general) regular languages. For both catenation and Kleene star operations of (general) regular languages, the state complexities are exponential in the size of given minimal DFAs. On the other hand, if both regular languages are prefix-free, then the state complexities are at most linear. We also demonstrate that we can reduce the state complexities of intersection and union operations based on the structural properties of prefix-free minimal DFAs.
Designing Centers of Expertise for Academic Learning Through Video Games

Schools appear to be facing a crisis of engaging secondary students in meaningful learning. Many are recognizing that the learning principles in computer and video games make reflect our best theories of cognition, yet are underutilized as an educational resource. This paper suggests an alternative model for game-based learning outside of schools. Drawing on case studies of youth participating in a year-long program, it describes a n approach to bridging learners’ identities in and out of school through historical simulation computer games situated within a community of practice of game experts. Participants developed both academic skills and productive identities as consumers and producers of information. through these cases, we propose a model of centers of expertise, learning programs that seek to foster and develop new media literacies with pay off in schools and that lead to new identities outside of school as well.
Biomechanical advantages of triple-loaded suture anchors compared with double-row rotator cuff repairs.

PURPOSE
To evaluate the strength and suture-tendon interface security of various suture anchors triply and doubly loaded with ultrahigh-molecular weight polyethylene-containing sutures and to evaluate the relative effectiveness of placing these anchors in a single-row or double-row arrangement by cyclic loading and then destructive testing.


METHODS
The infraspinatus muscle was reattached to the original humeral footprint by use of 1 of 5 different repair patterns in 40 bovine shoulders. Two single-row repairs and three double-row repairs were tested. High-strength sutures were used for all repairs. Five groups were studied: group 1, 2 triple-loaded screw suture anchors in a single row with simple stitches; group 2, 2 triple-loaded screw anchors in a single row with simple stitches over a fourth suture passed perpendicularly ("rip-stop" stitch); group 3, 2 medial and 2 lateral screw anchors with a single vertical mattress stitch passed from the medial anchors and 2 simple stitches passed from the lateral anchors; group 4, 2 medial double-loaded screw anchors tied in 2 mattress stitches and 2 push-in lateral anchors capturing the medial sutures in a "crisscross" spanning stitch; and group 5, 2 medial double-loaded screw anchors tied in 2 mattress stitches and 2 push-in lateral anchors creating a "suture-bridge" stitch. The specimens were cycled between 10 and 180 N at 1.0 Hz for 3,500 cycles or until failure. Endpoints were cyclic loading displacement (5 and 10 mm), total displacement, and ultimate failure load.


RESULTS
A single row of triply loaded anchors was more resistant to stretching to a 5- and 10-mm gap than the double-row repairs with or without the addition of a rip-stop suture (P < .05). The addition of a rip-stop stitch made the repair more resistant to gap formation than a double row repair (P < .05). The crisscross double row created by 2 medial double-loaded suture anchors and 2 lateral push-in anchors stretched more than any other group (P < .05).


CONCLUSIONS
Double-row repairs with either crossing sutures or 4 separate anchor points were more likely to fail (5- or 10-mm gap) than a single-row repair loaded with 3 simple sutures.


CLINICAL RELEVANCE
The triple-loaded anchors with ultrahigh-molecular weight polyethylene-containing sutures placed in a single row were more resistant to stretching than the double-row groups.
Probability Models for Customer-Base Analysis

As more firms begin to collect (and seek value from) richer customer-level datasets, a focus on the emerging concept of customer-base analysis is becoming increasingly common and critical. Such analyses include forward-looking projections ranging from aggregate-level sales trajectories to individual-level conditional expectations (which, in turn, can be used to derive estimates of customer lifetime value). We provide an overview of a class of parsimonious models (called probability models) that are well-suited to meet these rising challenges. We first present a taxonomy that captures some of the key distinctions across different kinds of business settings and customer relationships, and identify some of the unique modeling and measurement issues that arise across them. We then provide deeper coverage of these modeling issues, first for noncontractual settings (i.e., situations in which customer “death” is unobservable), then contractual ones (i.e., situations in which customer “death” can be observed). We review recent literature in these areas, highlighting substantive insights that arise from the research as well as the methods used to capture them. We focus on practical applications that use appropriately chosen data summaries (such as recency and frequency) and rely on commonly available software packages (such as Microsoft Excel). n 2009 Direct Marketing Educational Foundation, Inc. Published by Elsevier B.V. All rights reserved.
Analysis and Implementation of a New Switching Memristor Scroll Hyperchaotic System and Application in Secure Communication

This paper proposed a novel switching scroll hyperchaotic system based on a memristor device and explored its application to secure communication. The new system could be switched between the double-scroll chaotic system and multiscroll one by switch S1 and switch S2. We gave the construction process of the novel system, its numerical simulations, and dynamical properties, firstly. Moreover, the memristive circuit implementation of the new switching system was presented and the results were also in agreement with those of numerical simulation. Finally, the new switching memristive system was applied to secure communication by means of the drive-response synchronization with chaotic masking. When the voice signal is a rising waveform, it is encrypted by the double-scroll memristive system. When the voice signal is a falling waveform, the multiscroll memristive system works. The voice signal is completely submerged in the chaotic signal and could not be distinguished at all. Security analyses show that it is a successful application to secure communication.
Tamper-proofing of electronic and printed text documents via robust hashing and data-hiding

In this paper, we deal with the problem of authentication and tamper-proofing of text documents that can be distributed in electronic or printed forms. We advocate the combination of robust text hashing and text data-hiding technologies as an efficient solution to this problem. First, we consider the problem of text data-hiding in the scope of the Gel'fand-Pinsker data-hiding framework. For illustration, two modern text data-hiding methods, namely color index modulation (CIM) and location index modulation (LIM), are explained. Second, we study two approaches to robust text hashing that are well suited for the considered problem. In particular, both approaches are compatible with CIM and LIM. The first approach makes use of optical character recognition (OCR) and a classical cryptographic message authentication code (MAC). The second approach is new and can be used in some scenarios where OCR does not produce consistent results. The experimental work compares both approaches and shows their robustness against typical intentional/unintentional document distortions including electronic format conversion, printing, scanning, [...] VILLAN SEBASTIAN, Renato Fisher, et al. Tamper-proofing of Electronic and Printed Text Documents via Robust Hashing and Data-Hiding. In: Proceedings of SPIE-IS&T Electronic Imaging 2007, Security, Steganography, and Watermarking of Multimedia
Recognition of Urban Sound Events Using Deep Context-Aware Feature Extractors and Handcrafted Features

This paper proposes a method for recognizing audio events in urban environments that combines handcrafted audio features with a deep learning architectural scheme (Convolutional Neural Networks, CNNs), which has been trained to distinguish between different audio context classes. The core idea is to use the CNNs as a method to extract context-aware deep audio features that can offer supplementary feature representations to any soundscape analysis classification task. Towards this end, the CNN is trained on a database of audio samples which are annotated in terms of their respective "scene" (e.g. train, street, park), and then it is combined with handcrafted audio features in an early fusion approach, in order to recognize the audio event of an unknown audio recording. Detailed experimentation proves that the proposed context-aware deep learning scheme, when combined with the typical handcrafted features, leads to a significant performance boosting in terms of classification accuracy. The main contribution of this work is the demonstration that transferring audio contextual knowledge using CNNs as feature extractors can significantly improve the performance of the audio classifier, without need for CNN training (a rather demanding process that requires huge datasets and complex data augmentation procedures).
A longitudinal study of emotional intelligence in graduate nurse anesthesia students

OBJECTIVE
Emotional intelligence (EI) is an important component not only for success in the nurse anesthesia (NA) profession, but as a NA student as well. Using the ability-based EI model, the purpose of this was to examine the difference in EI between the first semester and last semester of NA training programs.


METHODS
First semester NA students completed the online Mayer-Salovey-Caruso Emotional Intelligence Test V2.0 EI instrument, and then the same students repeated the instrument in their last (7th) semester.


RESULTS
There was a statistically significant correlation between overall EI and long-term overall EI (P = 0.000), reasoning area and long-term reasoning area (P = 0.035), experiencing area (P = 0.000) and long-term experiencing area, perceiving branch and long-term perceiving branch (P = 0.000), using and long-term using branch (P = 0.000), and the managing branch and long-term managing branch (P = 0.026). The correlation between the understanding branch and the long-term understanding branch was not statistically significant (P < 0.157). The paired sample t-test demonstrated no statistically significant change (n = 34) in overall EI, the two areas scores, or the four-branch scores between the first semester and the last semester of a NA training program.


CONCLUSIONS
This longitudinal study shows the lack of EI change in NA students over time. Thus, no change in EI occurs as a result of transitioning through a NA program based on the accrediting body's standardized curriculum, but the results helped the researcher provide useful data to inform future research on the use of EI measures as predictors of NA program success.
Convergent Learning: Do different neural networks learn the same representations?

Recent successes in training large, deep neural networks have prompted active investigation into the representations learned on their intermediate layers. Such research is difficult because it requires making sense of non-linear computations performed by millions of learned parameters, but valuable because it increases our ability to understand current models and training algorithms and thus create improved versions of them. In this paper we investigate the extent to which neural networks exhibit what we call convergent learning, which is when the representations learned by multiple nets converge to a set of features which are either individually similar between networks or where subsets of features span similar lowdimensional spaces. We propose a specific method of probing representations: training multiple networks and then comparing and contrasting their individual, learned representations at the level of neurons or groups of neurons. We begin research into this question by introducing three techniques to approximately align different neural networks on a feature or subspace level: a bipartite matching approach that makes one-to-one assignments between neurons, a sparse prediction and clustering approach that finds one-to-many mappings, and a spectral clustering approach that finds many-to-many mappings. This initial investigation reveals a few interesting, previously unknown properties of neural networks, and we argue that future research into the question of convergent learning will yield many more. The insights described here include (1) that some features are learned reliably in multiple networks, yet other features are not consistently learned; (2) that units learn to span low-dimensional subspaces and, while these subspaces are common to multiple networks, the specific basis vectors learned are not; (3) that the representation codes are a mix between a local (single unit) code and slightly, but not fully, distributed codes across multiple units; (4) that the average activation values of neurons vary considerably within a network, yet the mean activation values across different networks converge to an almost identical distribution. 1
Preparation and characterization of chitosan/gelatin/PVA hydrogel for wound dressings.

Chitosan (CS)/gelatin (Gel)/polyvinyl alcohol (PVA) hydrogels were prepared by the gamma irradiation method for usage in wound dressing applications. Chitosan and gelatin solution was mixed with poly(vinyl alcohol) (PVA) solution at different weight ratios of CS/Gel of 1:3, 1:2, 1:1, 2:1 and 3:1. The hydrogels irradiated at 40kGy. The structure of the hydrogels was characterized by using FT-IR and SEM. The CS/Gel/PVA hydrogels were characterized for physical properties and blood clotting activity. The tensile strength of CS/Gel/PVA hydrogel enhanced than on the basis of the Gel/PVA hydrogel. The highest tensile strength reached the 2.2Mpa. All hydrogels have shown a good coagulation effect. It takes only 5min for the BCI index to reached 0.032 only 5min when the weight ratio of CS/Gel was 1:1. It means that the hemostatic effect of hydrogels were optimal. And the hydrogrls also showed good pH-sensitivity, swelling ability and water evaporation rate. Therefore, this hydrogel showed a promising potential to be applied as wound dressing.