Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning from errors at work. Studies on nurses' engagement in error-related learning activities

This study investigated under what conditions nurses aim to learn from errors at work by engaging in joint cause analysis and the development of new action strategies together with colleagues. It was hypothesised that the engagement in such social learning activities depends on the cognitive, emotional, and motivational interpretation of an error situation as well as on the perception of a safe team climate. These research questions were addressed in three studies. (a) Expert interviews served to contextualise a framework of error-related learning activities to the domain of nursing. Furthermore, exemplary error cases were collected. (b) Based on the interviews, two questionnaires for the measurement of the engagement in learning activities after specific error episodes were developed. In the first condition, vignettes of error cases were presented to the participants (Cases Approach). In the second condition, the participants provided error episodes from their own work experience (Critical Incident Technique). Based on the results from a pilot study, a decision for the Cases Approach was made. (c) In a cross-sectional field study, 276 nurses were surveyed for their engagement in social learning activities in the standardised error episodes. The results from a structural equation model indicate that the estimation of an error as chance for learning and the tendency to cover up an error are significant predictors for the engagement in social learning activities. In contrast no significant direct relationship with the amount of emotional strain and with the perception of a safe team climate could be found.
Advances in Lipid Nanoparticles for siRNA Delivery

Technological advances in both siRNA (small interfering RNA) and whole genome sequencing have demonstrated great potential in translating genetic information into siRNA-based drugs to halt the synthesis of most disease-causing proteins. Despite its powerful promises as a drug, siRNA requires a sophisticated delivery vehicle because of its rapid degradation in the circulation, inefficient accumulation in target tissues and inability to cross cell membranes to access the cytoplasm where it functions. Lipid nanoparticle (LNP) containing ionizable amino lipids is the leading delivery technology for siRNA, with five products in clinical trials and more in the pipeline. Here, we focus on the technological advances behind these potent systems for siRNA-mediated gene silencing.
Off Track: When Poor Readers Become ""Learning Disabled""

* What Ails the Field of Learning Disabilities * The History of LD: Variations on a Theme * What Is Reading Disability? The Road to Proficient Reading * Roads to Reading Disability * Issues in the Education of Children with Reading Disability * Educational Practices for Children with Reading Disability * Possible Causes of Reading Disability * Abilities, Disabilities, and Reading Disability: How Are They Related? Early Intervention and Prevention * Toward Another Way of Thinking About Reading Difficulties
Mathematical models of cell factories: moving towards the core of industrial biotechnology

Industrial biotechnology involves the utilization of cell factories for the production of fuels and chemicals. Traditionally, the development of highly productive microbial strains has relied on random mutagenesis and screening. The development of predictive mathematical models provides a new paradigm for the rational design of cell factories. Instead of selecting among a set of strains resulting from random mutagenesis, mathematical models allow the researchers to predict in silico the outcomes of different genetic manipulations and engineer new strains by performing gene deletions or additions leading to a higher productivity of the desired chemicals. In this review we aim to summarize the main modelling approaches of biological processes and illustrate the particular applications that they have found in the field of industrial microbiology.
Leveraging Multiple MT Engines for Paraphrase Generation

This paper proposes a method that leverages multiple machine translation (MT) engines for paraphrase generation (PG). The method includes two stages. Firstly, we use a multi-pivot approach to acquire a set of candidate paraphrases for a source sentence S. Then, we employ two kinds of techniques, namely the selection-based technique and the decoding-based technique, to produce a best paraphrase T for S using the candidates acquired in the first stage. Experimental results show that: (1) The multi-pivot approach is effective for obtaining plenty of valuable candidate paraphrases. (2) Both the selection-based and decoding-based techniques can make good use of the candidates and produce high-quality paraphrases. Moreover, these two techniques are complementary. (3) The proposed method outperforms a state-of-the-art paraphrase generation approach.
Comparison of different driving style analysis approaches based on trip segmentation over GPS information

Over one billion cars interact with each other on the road every day. Each driver has his own driving style, which could impact safety, fuel economy and road congestion. Knowledge about the driving style of the driver could be used to encourage “better” driving behaviour through immediate feedback while driving, or by scaling auto insurance rates based on the aggressiveness of the driving style. In this work we report on our study of driving behaviour profiling based on unsupervised data mining methods. The main goal is to detect the different driving behaviours, and thus to cluster drivers with similar behaviour. This paves the way to new business models related to the driving sector, such as Pay-How-You-Drive insurance policies and car rentals. Driver behavioral characteristics are studied by collecting information from GPS sensors on the cars and by applying three different analysis approaches (DP-means, Hidden Markov Models, and Behavioural Topic Extraction) to the contextual scene detection problems on car trips, in order to detect different behaviour along each trip. Subsequently, drivers are clustered in similar profiles based on that and the results are compared with a human-defined groundtruth on drivers classification. The proposed framework is tested on a real dataset containing sampled car signals. While the different approaches show relevant differences in trip segment classification, the coherence of the final driver clustering results is surprisingly high.
Task discrimination from myoelectric activity: A learning scheme for EMG-based interfaces

A learning scheme based on Random Forests is used to discriminate the task to be executed using only myoelectric activity from the upper limb. Three different task features can be discriminated: subspace to move towards, object to be grasped and task to be executed (with the object). The discrimination between the different reach to grasp movements is accomplished with a random forests classifier, which is able to perform efficient features selection, helping us to reduce the number of EMG channels required for task discrimination. The proposed scheme can take advantage of both a classifier and a regressor that cooperate advantageously to split the task space, providing better estimation accuracy with task-specific EMG-based motion decoding models, as reported in [1] and [2]. The whole learning scheme can be used by a series of EMG-based interfaces, that can be found in rehabilitation cases and neural prostheses.
COMBINED HAEMATOXYLIN, EOSIN AND FLUORESCENT STAIN FOR PARAFFIN SECTIONS

&NA; Combined staining is achieved by adding Auramine 0 to 2 of the steps of the standard H & E staining procedure. Auramine O confers fluorescence to mucin, reticulin, elastin and protein aggregates without altering the H & E colouring pattern. The technique is designed as a substitute for some special stains.
Dynamic SimpleScalar: Simulating Java Virtual Machines

Current user-mode machine simulators typically do not supp ort simulation of dynamic compilation, threads, or garbage col le tion, all of which Java Virtual Machines (JVMs) require. In this pa per, we describe, evaluate, and validate Dynamic SimpleScalar (DSS). DSS is a tool that simulates Java programs running on a JVM, us ing just-in-time compilation, executing on a simulated mul ti-way issue, out-of-order execution superscalar processor with a sophisticated memory system. We describe the implementation of th e minimal support necessary for simulating a JVM in SimpleSca lar, including signals, thread scheduling, synchronization, a nd dynamic code generation, all required by a JVM. We validate our simul ator using IBM Research’s Jikes RVM, a state-of-the-art JVM that runs Submitting to the First Annual IEEE/ACM International Symposium On Code Generation and Optimization. This work is supported by NSF ITR grant CCR-0085792, and DARP grants F30602-98-1-0101 and F33615-01-C-1892, and Internationa l Business Machines Corporation. on a PowerPC architecture, and show that DSS loyally reflects the performance trends of a real JVM system. We then present a set of results using DSS. On the SPECjvm98 benchmarks, we study the best heap size for three different copying garbage collecto rs, and measure total, mutator, and collector memory characterist ics. We compare our results with previous work, pointing out new ins ights, differences, and similarities. For example, we show there i s a trade off between the locality benefits of copying collectors and t he time to collect.
Manipulation of peripheral neural feedback loops alters human corticomuscular coherence

Sensorimotor EEG shows ∼20 Hz coherence with contralateral EMG. This could involve efferent and/or afferent components of the sensorimotor loop. We investigated the pathways responsible for coherence genesis by manipulating nervous conduction delays using cooling. Coherence between left sensorimotor EEG and right EMG from three hand and two forearm muscles was assessed in healthy subjects during the hold phase of a precision grip task. The right arm was then cooled to 10°C for ∼90 min, increasing peripheral motor conduction time (PMCT) by ∼35% (assessed by F‐wave latency). EEG and EMG recordings were repeated, and coherence recalculated. Control recordings revealed a heterogeneous subject population. In 6/15 subjects (Group A), the corticomuscular coherence phase increased linearly with frequency, as expected if oscillations were propagated along efferent pathways from cortex to muscle. The mean corticomuscular conduction delay for intrinsic hand muscles calculated from the phase–frequency regression slope was 10.4 ms; this is smaller than the delay expected for conduction over fast corticospinal pathways. In 8/15 subjects (Group B), the phase showed no dependence with frequency. One subject showed both Group A and Group B patterns over different frequency ranges. Following cooling, averaged corticomuscular coherence was decreased in Group A subjects, but unchanged for Group B, even though both groups showed comparable slowing of nervous conduction. The delay calculated from the slope of the phase–frequency regression was increased following cooling. However, the size of this increase was around twice the rise in PMCT measured using the F‐wave (regression slope 2.33, 95% confidence limits 1.30–3.36). Both afferent and efferent peripheral nerves will be slowed by similar amounts following cooling. The change in delay calculated from the coherence phase therefore better matches the rise in total sensorimotor feedback loop time caused by cooling, rather than just the change in the efferent limb. A model of corticomuscular coherence which assumes that only efferent pathways contribute cannot be reconciled to these results. The data rather suggest that afferent feedback pathways may also play a role in the genesis of corticomuscular coherence.
On the shape of a set of points in the plane

A generalization of the convex hull of a finite set of points in the plane is introduced and analyzed. This generalization leads to a family of straight-line graphs, " \alpha -shapes," which seem to capture the intuitive notions of "fine shape" and "crude shape" of point sets. It is shown that a-shapes are subgraphs of the closest point or furthest point Delaunay triangulation. Relying on this result an optimal O(n \log n) algorithm that constructs \alpha -shapes is developed.
Efficient algorithm for Context Sensitive Aggregation in Natural Language generation

Aggregation is a sub-task of Natural Language Generation (NLG) that improves the conciseness and readability of the text outputted by NLG systems. Till date, approaches towards the aggregation task have been predominantly manual (manual analysis of domain specific corpus and development of rules). In this paper, a new algorithm for aggregation in NLG is proposed, that learns context sensitive aggregation rules from a parallel corpus of multi-sentential texts and their underlying semantic representations. Additionally, the algorithm accepts external constraints and interacts with the surface realizer to generate the best output. Experiments show that the proposed context sensitive probablistic aggregation algorithm performs better than the deterministic hand crafted aggregation rules.
Gate defects in AlGaN/GaN HEMTs revealed by low frequency noise measurements

From the last decade, Nitride-based High Electron Mobility Transistors (HEMTs) have demonstrated excellent electrical and noise performances to address transceivers modules. Within this paper, a discussion on the low frequency noise on the gate access (i.e. gate current spectral density SIG) in the frequency range of 1Hz-100kHz is presented. SIG spectra reveal different signatures according to the configuration of biasing of the transistor, and to the dimensions of the transistor: the noise of the Schottky diode is studied alone (under open drain configuration), and compared with SIG of the transistor biased in the saturated region (at VDs=8V). Two designs of devices are tested: research-level devices featuring single gate finger are compared with commercial devices featuring four gate fingers, where each finger is four times larger (i.e. 16 times larger gate width than research level devices). The two sets of devices followed the same fabrication process. It is found that whatever the sizing and the gate pad configuration of the devices, the LFN spectra observed on each set of transistors feature identical signatures. The leakage carriers are following the same path between the gate and source accesses. LFN can be used as an accurate tool to discriminate between conduction mechanisms of devices, and to help to understand what are the underlying mechanisms leading to the conduction of the Schottky diode (and thus to its degradation). Moreover, it is shown that the SIG measurements under transistor biasing conditions can be correlated to the gate current spectral density in diode mode: the leakage current zone can also be tracked under this biasing operating mode.
Self-constructive high-rate system energy modeling for battery-powered mobile systems

System energy models are important for energy optimization and management in mobile systems. However, existing system energy models are built in a lab setting with the help from a second computer. Not only are they labor-intensive; but also they do not adequately account for the great diversity in the hardware and usage of mobile systems. Moreover, existing system energy models are intended for energy estimation for time intervals of one second or longer; they do not provide the required rate for fine-grain use such as per-application energy accounting. In this work, we study a self-modeling paradigm in which a mobile system automatically generates its energy model without any external assistance. Our solution, Sesame, leverages the possibility of self power measurement through the smart battery interface and employs a suite of novel techniques to achieve accuracy and rate much higher than that of the smart battery interface. We report the implementation and evaluation of Sesame on a laptop and a smartphone. The experiment results show that Sesame is able to generate system energy models of 95% accuracy at one estimation per second and of 88% accuracy at one estimation per 10ms, without any external assistance. Two five-day field studies with four laptop and four smartphone users further demonstrate the effectiveness, efficiency, and non-invasiveness of Sesame.
Exemplar based Image Inpainting with Reduced Search Region

Image inpainting is a technique to recover lost data or manually remove data from an image. This process is carried out in such a way that it seems reasonable to human eyes. The lost part of the image is filled with the remaining available data of the image. Inpainting can be done using various techniques, mainly variation based, exemplar based and wavelet based. In this paper, exemplar based image inpainting techniques is used and an algorithm which improves and extends the previously proposed algorithm is presented. Instead of searching the entire image, the proposed approach has reduced the search region by searching the nearby pixels since most of the relevant information lies in neighbouring pixels which reduces time to complete inpainting. This technique is mainly used for removing objects and cracks from the image. General Terms Image processing, Inpainting, Object removal.
RAVLT and Nonverbal Analog: French Forms and Clinical Findings

Background: Objective clinical evaluation of memory frequently requires serial testing but the issue of whether multiformed tests are equivalent and can be used interchangeably is seldom examined. An added problem in bilingual Canadian settings is the extent to which it is appropriate to measure French speakers’ performance on translations of English tests. The present work used the Rey Auditory Verbal Learning Test (RAVLT) and a nonverbal analog, the Aggie Figures Learning Test (AFLT), to examine whether a) different forms of the same test are equivalent, b) performance on the two tests is comparable, c) two language groups perform similarly, and d) the RAVLT can detect dysfunction in patients with temporal lobe epilepsy (TLE). Methods: We compared three French versions of the RAVLT and three forms of the AFLT in 114 healthy francophone adults. We subsequently compared the performance of the same francophone subjects to a previously obtained sample of anglophones on both tests, and then administered the RAVLT to anglophone or francophone patients with TLE. Results: For both tasks the three forms were equivalent and performance on the RAVLT was comparable to that on the AFLT. Francophone subjects performed slightly worse on the RAVLT compared to anglophones but performance of the two language groups did not differ on the AFLT. Finally, left TLE patients were impaired compared to right on the RAVLT, but no performance differences were observed across the two language groups in the patient sample. Conclusions: The RAVLT and AFLT are useful tools for examination of learning and memory in French and English speaking populations. On the RAVLT, the lesion effect in patients is not affected by differences in performance between language groups.
Monitoring and modelling of the energy consumption in polymer extrusion

Extrusion is one of the fundamental methods in polymer processing and is involved in the production of various commodities in diverse industrial sectors. Being an energy intensive production method, process energy efficiency is one of the major concerns and the selection of the most energy efficient processing conditions is a key to reduce operating expenses. Usually, extruders consume energy through the drive motor, barrel heaters, cooling fans, cooling water pumps, gear pumps, etc. Typically the drive motor is the dominant energy consuming device in a polymer processing extruder while barrel/die heaters are responsible for the second largest energy demand. This study was focused on investigating the total energy demand of an extrusion plant while identifying ways to optimise the energy consumption. Total energy consumption of a single screw extruder was measured over different processing conditions and then modelled as a function of a number of major process variables. The results show that the extruder energy demand is heavily coupled with machine, material and process parameters. Also, the proposed models show excellent agreement with the experimental measurements and hence these should be useful in optimising process energy efficiency.
Effect of Etomidate Versus Combination of Propofol-Ketamine and Thiopental-Ketamine on Hemodynamic Response to Laryngoscopy and Intubation: A Randomized Double Blind Clinical Trial

Background: Laryngoscopy and intubation frequently used for airway management during general anesthesia, is frequently associated with undesirable hemodynamic disturbances. Objectives: The aim of this study was to compare the effects of etomidate, combination of propofol-ketamine and thiopental-ketamine as induction agents on hemodynamic response to laryngoscopy and intubation. Patients and Methods: In a double blind, randomized clinical trial a total of 120 adult patients of both sexes, aged 18 - 45 years, scheduled for elective surgery under general anesthesia were randomly assigned into three equally sized groups. Patients in group A received etomidate (0.3 mg/kg) plus normal saline as placebo. Patients in group B and C received propofol (1.5 mg/kg) plus ketamine (0.5 mg/kg) and thiopental sodium (3 mg/kg) plus ketamine (0.5 mg/kg), respectively for anesthesia induction. Before laryngoscopy and tracheal intubation, immediately after, and also one and three minutes after the procedures, hemodynamic values (SBP, DBP, MAP and HR) were measured. Results: A repeated measurement ANOVA showed significant changes in mean SBP and DBP between the time points (P < 0.05). In addition, the main effect of MAP and HR were statistically significant during the course of study (P < 0.05). Furthermore, after induction of anesthesia, the three study groups had significantly different SBP, DBP and MAP changes overtime (P < 0.05). However, HR changes over time were not statistically significant (P > 0.05). Combination of propofol-ketamine had superior hemodynamic stability compared to other induction agents. Conclusions: Combination of propofol-ketamine may be recommended as an effective and safe induction agent for attenuating hemodynamic responses to laryngoscopy and intubation with better hemodynamic stability. Although, further well-designed randomized clinical trials to confirm the safety and efficacy of this combination, especially in critically ill patients or patients with cardiovascular disease, are warranted.
Merger waves : a model of endogenous mergers

We develop a model of endogenous mergers to study their dynamic process. Firms choose whether, when, and with whom to merge. Two necessary conditions are identified for mergers to occur: firm heterogeneity and negative demand shocks. We show that mergers are strategic complements and therefore tend to occur in waves. Moreover, some mergers occur for strategic reasons in order to precipitate further mergers.