Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Model of Tracheostomy Care: Closing the Research–Practice Gap

Performance improvements have brought about fundamental changes in the past year at Walter Reed Army Medical Center (WRAMC), where a concerted effort is underway to put current research into clinical practice. Tracheostomy care and suctioning became the pilot procedure for these changes in November 2002. What began as a unit-level initiative quickly developed into a Department of Nursing project. The focus of the project is adapting the hospital's existing performance improvement model to better facilitate evidence-based practice. Initial surveys on tracheostomy care conducted throughout the hospital showed an inconsistent level of knowledge and a variation in clinical practice. These inconsistencies represented a patient safety threat in the form of nosocomial infections, prolonged hospitalizations, airway complications, and even death. To address these issues, the Nursing Performance Improvement and Nursing Research Departments entered into a research collaboration. Representatives from these departments worked with clinical experts to develop a plan and timeline for conducting a tracheostomy care project with the goal of implementing evidence into practice and thereby improving patient safety at the hospital. The group completed initial data collection in April 2003 and then began work on the evidence-based procedure. A literature review was completed using online search engines such as MEDLINE, the Cumulative Index to Nursing and Allied Health Literature (CINAHL), the Cochrane Collaboration, Medscape, the American Association of Critical-Care Nurses (AACN) practice guidelines, and the Joanna Briggs Institute. Pertinent articles were identified and evaluated by two independent reviewers. The Agency for Healthcare Research and Quality (AHRQ) levels of evidence were used to grade more than 30 articles. The results of this search were used to develop a WRAMC Department of Nursing Procedure for tracheostomy care, which was completed in September 2003. This new model of evidence-based nursing performance improvement has been in use at WRAMC for 2 years at the time of this writing, and it continues to close the research-practice gap. The WRAMC Department of Nursing is presently developing similar evidence-based procedures for pressure ulcer prevention, deep vein thrombosis prophylaxis, and for enteral feeding.
CoSMoMVPA: Multi-Modal Multivariate Pattern Analysis of Neuroimaging Data in Matlab/GNU Octave

Recent years have seen an increase in the popularity of multivariate pattern (MVP) analysis of functional magnetic resonance (fMRI) data, and, to a much lesser extent, magneto-and electro-encephalography (M/EEG) data. We present CoSMoMVPA, a lightweight MVPA (MVP analysis) toolbox implemented in the intersection of the Matlab and GNU Octave languages, that treats both fMRI and M/EEG data as first-class citizens. CoSMoMVPA supports all state-of-the-art MVP analysis techniques, including searchlight analyses, classification, correlations, representational similarity analysis, and the time generalization method. These can be used to address both data-driven and hypothesis-driven questions about neural organization and representations, both within and across: space, time, frequency bands, neuroimaging modalities, individuals, and species. It uses a uniform data representation of fMRI data in the volume or on the surface, and of M/EEG data at the sensor and source level. Through various external toolboxes, it directly supports reading and writing a variety of fMRI and M/EEG neuroimaging formats, and, where applicable, can convert between them. As a result, it can be integrated readily in existing pipelines and used with existing preprocessed datasets. CoSMoMVPA overloads the traditional volumetric searchlight concept to support neighborhoods for M/EEG and surface-based fMRI data, which supports localization of multivariate effects of interest across space, time, and frequency dimensions. CoSMoMVPA also provides a generalized approach to multiple comparison correction across these dimensions using Threshold-Free Cluster Enhancement with state-of-the-art clustering and permutation techniques. CoSMoMVPA is highly modular and uses abstractions to provide a uniform interface for a variety of MVP measures. Typical analyses require a few lines of code, making it accessible to beginner users. At the same time, expert programmers can easily extend its functionality. CoSMoMVPA comes with extensive documentation, including a variety of runnable demonstration scripts and analysis exercises (with example data and solutions). It uses best software engineering practices including version control, distributed development, an automated test suite, and continuous integration testing. It can be used with the proprietary Matlab and the free GNU Octave software, and it complies with open source distribution platforms such as NeuroDebian. CoSMoMVPA is Free/Open Source Software under the permissive MIT license. Website: https://cosmomvpa.org Source code: https://github.com/CoSMoMVPA/CoSMoMVPA
NEURAL NETWORK BASED ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORKS : A SURVEY

The main concern in Wireless Sensor Networks is how to handle with their limited energy resources. The performance of Wireless Sensor Networks strongly depends on their lifetime. As a result, Dynamic Power Management approaches with the purpose of reduction of energy consumption in sensor nodes, after deployment and designing of the network, have drawn attentions of many research studies. Recently, there have been a strong interest to use intelligent tools especially Neural Networks in energy efficient approaches of Wireless Sensor Networks, due to their simple parallel distributed computation, distributed storage, data robustness, auto-classification of sensor nodes and sensor reading. Dimensionality reduction and prediction of sensor data obtained simply from the outputs of the neural-networks algorithms can lead to lower communication costs and energy conservation. All these characteristics show great analogy and compatibility between wireless sensor networks and neural networks. This paper aims to present the most important possible application of neural networks in reduction of energy consumption according to research studies have been done in the literature so far through a general classification of them.
A Range Query Technology for Structured P2P Network

Accurate searching in current structured P2P network is popular, but range query is very difficult and complex. In this paper, based on the Pastry, we propose a resources allocating mechanism for searching the objects among certain ranges in P2P network, and give the searching algorithm in detail. This mechanism does not need change the original P2P infrastructure, the algorithm is simple and easy to implement, and the simulation results show that the efficiency of searching is very good.
A Case for Asynchronous Microengines for Network Processing

We present a network processor architecture which is based on asynchronous microcoded controller hardware (a.k.a asynchronous microengine). The focus of this work is not on the processor architecture, but rather on the asynchronous microcoded style used to build such an architecture. This circuit style tries to fill the performance gap between a specialized ASIC and a more general network processor implementation. It does this by providing a microcoded framework which is close in performance to ASICs and is also programmable at the finer granularity of microcode. Our approach exploits the inherent advantages of asynchronous design techniques to exhibit modularity, lower power consumption and low EMI. We have evaluated our circuit style by demonstrating fast-path IP routing as the packet processing application. For shorter design cycle time, we have implemented our design using Xilinx SpartanII FPGA board. However, we are extrapolating our results for a best-guess ASIC implementation.
State estimation of the nonlinear suspension system based on nonlinear Kalman filter

In reality, a system is almost nonlinear. To estimate the parameter or state of this system, nonlinear approach is needed. The Extended Kalman Filter(EKF) and the Unscented Kalman Filter(UKF) are used to estimate this nonlinear problem. EKF uses first order Taylor expansion to approximate the nonlinear system, while UKF performs a stochastic linearization by using a weighted statistical linear regression process. The purpose of this paper is to estimate the state of the nonlinear suspension system based on the Extended Kalman Filter and the Unscented Kalman Filter. The simulation deals with state estimation of nonlinear suspension system by using these filters and is compared with the true state. Also LQR controller and output feedback PD controller will be designed by aid of UKF and EKF estimation. Simulation results show that two nonlinear Kalman filters are effective in estimating the state of a nonlinear suspension system.
In-depth Analysis of 4T SRAM Cells in Double-Gate CMOS

This paper presents a whole study of sub-32 nm CMOS 4T SRAM cells in fully depleted (FD) double-gate (DG) silicon-on-insulator (SOI) technology with planar independent self-aligned gates. Both independent-and connected-gate operation is analyzed either with symmetrical or asymmetrical transistors which have been adjusted according to the current process possibilities. An improved 4T driverless (DL) SRAM cell is proposed and compared with a 4T loadless (LL). Both cells take advantage of the back gate to improve stability in read and retention mode by using a feedback between access transistor and opposite storage node. A set of criteria have been analyzed for an efficient characterization of read-, retention-and write margins, power and access time. Typical and worst cases have been computed to assure operating margins in presence of accurate process variation.
The K Shortest Paths Problem with Application to Routing

While much work has proposed novel solutions to finding the $k$ shortest simple paths between two nodes, very little research has addressed the challenges in designing efficient solutions for real world networks. More specifically, the computational complexity for solutions to the $k$ shortest simple paths problem on positively weighted graphs typically include an $O(km)$ term, where $m$ is the number of edges in the graph. And even though an almost shortest simple path in an arbitrary graph could consist of $O(m)$ edges, in real world networks, such a solution appears asymptotically inefficient as such paths often scale logarithmically with the size of the network. To address this gap, we provide both theoretical results and simulations illustrating that the ratio between simple paths and weakly simple paths of the same length is well behaved for a range of parameters in the Chung-Lu random graph model. Since the Chung-Lu random graph model captures many properties of real world networks, these results strongly suggest that in application, solutions to the $k$ shortest simple path problem should first identify a slightly larger collection of almost shortest paths and then remove the paths that contain loops. Furthermore, we present a simple algorithm for computing all (weakly simple) paths between two nodes bounded by length $D$ in $O(m\log m+kL)$ time for graphs with positive edge weights, where $L$ is an upperbound for the number of nodes in any returned path and $k$ is the number paths computed. We then consider an application to the almost shortest paths algorithm to measure path diversity for internet routing in a snapshot of the Autonomous System graph subject to an edge deletion process.
Standardizing appraisals for PV installations

As PV installations increase across the U.S., there will be a point when an appraiser will have the opportunity to value the PV system as part of a property sale or re-finance. Proper valuation techniques as applied to solar PV are necessary to reflect the increase in market demand for solar PV systems. Appraisers must follow the Uniform Standards of Professional Appraisal Practices (USPAP) when valuing solar PV systems, which means that appraisers must gain competency to 1) accurately recognize the value proposition of a PV system, and 2) develop the PV system's market value as it contributes to the property. The challenges currently faced by property owners with installed PV are whether the PV system adds market value to the property, and finding an appraiser with competency. Not all markets are the same, and PV market values will vary considerably based on many factors that include, but are not limited to the adoption rate in the particular market, the utility rate paid by the customer, the PV system's condition, aesthetics, and obsolescence. This paper will discuss how past challenges with respect to proper PV system valuation are being addressed in a standard fashion, along with the far-reaching benefits that may be available to future PV adopters as valuation concepts are ultimately recognized and adopted by valuation professionals, real estate agents, mortgage lenders and underwriters.
Eficiencia de las sociedades musicales de la Comunidad Valenciana

Las aplicaciones empíricas basadas en los análisis de eficiencia resultan todavía muy escasas dentro del campo de las organizaciones culturales. En este trabajo se analiza la eficiencia mediante un modelo DEA output orientado no radial que se aplica a una muestra de 292 sociedades musicales de la Comunidad Valenciana. El interés en este tipo de asociación cultural radica tanto en su relevancia socio-económica como en su grado de territorialidad. Los resultados obtenidos indican que la eficiencia media de las sociedades musicales de la Comunidad Valenciana se sitúa en el 72,66% y apuntan hacia la existencia de determinadas especificidades en la oferta y la demanda de los mercados provinciales en que operan.
Elimination and Combination of Classifiers in Multiple Classifier Systems

Traditional character recognition systems use a single classifier to determine the class of a given character. However, by using different types of classifiers simultaneously, the accuracy of classification could be improved. In this paper, we propose a classifier elimination approach based on correlation analysis and the derived heuristic rules to eliminate the redundant classifiers such that the succeeding decision combination process can be conducted in a more efficient and effective manner. Experimental results show our approach works well in the multiple classifier system.
A Predictive Model to Evaluate Student Performance

In this paper we propose a new approach based on text mining techniques for predicting student performance using LSA (latent semantic analysis) and K-means clustering methods. The present study uses free-style comments written by students after each lesson. Since the potentials of these comments can reflect student learning attitudes, understanding of subjects and difficulties of the lessons, they enable teachers to grasp the tendencies of student learning activities. To improve our basic approach using LSA and k-means, overlap and similarity measuring methods are proposed. We conducted experiments to validate our proposed methods. The experimental results reported a model of student academic performance predictors by analyzing their comments data as variables of predictors. Our proposed methods achieved an average 66.4% prediction accuracy after applying the k-means clustering method and those were 73.6% and 78.5% by adding the overlap method and the similarity measuring method, respectively.
Using Video to Automatically Detect Learner Affect in Computer-Enabled Classrooms

Affect detection is a key component in intelligent educational interfaces that respond to students’ affective states. We use computer vision and machine-learning techniques to detect students’ affect from facial expressions (primary channel) and gross body movements (secondary channel) during interactions with an educational physics game. We collected data in the real-world environment of a school computer lab with up to 30 students simultaneously playing the game while moving around, gesturing, and talking to each other. The results were cross-validated at the student level to ensure generalization to new students. Classification accuracies, quantified as area under the receiver operating characteristic curve (AUC), were above chance (AUC of 0.5) for all the affective states observed, namely, boredom (AUC = .610), confusion (AUC = .649), delight (AUC = .867), engagement (AUC = .679), frustration (AUC = .631), and for off-task behavior (AUC = .816). Furthermore, the detectors showed temporal generalizability in that there was less than a 2% decrease in accuracy when tested on data collected from different times of the day and from different days. There was also some evidence of generalizability across ethnicity (as perceived by human coders) and gender, although with a higher degree of variability attributable to differences in affect base rates across subpopulations. In summary, our results demonstrate the feasibility of generalizable video-based detectors of naturalistic affect in a real-world setting, suggesting that the time is ripe for affect-sensitive interventions in educational games and other intelligent interfaces.
Nuclear and mitochondrial genetic structure in the Eurasian beaver (Castor fiber) – implications for future reintroductions

Many reintroduction projects for conservation fail, and there are a large number of factors that may contribute to failure. Genetic analysis can be used to help stack the odds of a reintroduction in favour of success, by conducting assessment of source populations to evaluate the possibility of inbreeding and outbreeding depression and by conducting postrelease monitoring. In this study, we use a panel of 306 SNP (single nucleotide polymorphism) markers and 487–489 base pairs of mitochondrial DNA control region sequence data to examine 321 individuals from possible source populations of the Eurasian beaver for a reintroduction to Scotland. We use this information to reassess the phylogenetic history of the Eurasian beavers, to examine the genetic legacy of past reintroductions on the Eurasian landmass and to assess the future power of the genetic markers to conduct ongoing monitoring via parentage analysis and individual identification. We demonstrate the capacity of medium density genetic data (hundreds of SNPs) to provide information suitable for applied conservation and discuss the difficulty of balancing the need for high genetic diversity against phylogenetic best fit when choosing source population(s) for reintroduction.
Twin experiments with the equivalent weights particle filter and HadCM3

This article investigates the use of a particle filter for data assimilation with a full‐scale coupled ocean–atmosphere general circulation model. Synthetic twin experiments are performed to assess the performance of the equivalent weights filter in such a high‐dimensional system. Artificial two‐dimensional sea surface temperature fields are used as observational data every day. Results are presented for different values of the free parameters in the method. Measures of the performance of the filter are root mean square errors, trajectories of individual variables in the model and rank histograms. Filter degeneracy is not observed and the performance of the filter is shown to depend on the ability to keep maximum spread in the ensemble.
IFN- g Potentiates Atherosclerosis in ApoE Knock-out Mice

The early colocalization of T cells and the potent immunostimulatory cytokine IFNg to atherosclerotic lesions suggest that the immune system contributes to atherogenesis. Since mice with a targeted disruption of the apoE gene (apoE 0 mice) develop profound atherosclerosis, we examined the role of IFNg in this process. First, the presence of CD4 1 and CD8 1 cells, which secrete lesional IFNg , was documented in apoE 0 atheromata. Then, the apoE 0 mice were crossed with IFNg receptor (IFN g R) 0 mice to generate apoE 0/IFN g R 0 mice. Compared to the apoE 0 mice, the compound knock-out mice exhibited a substantial reduction in atherosclerotic lesion size, a 60% reduction in lesion lipid accumulation, a decrease in lesion cellularity, but a marked increase in lesion collagen content. Evaluation of the plasma lipoproteins showed that the compound knockout mice had a marked increase in potentially atheroprotective phospholipid/apoA-IV rich particles as well. This correlated with an induction of hepatic apoA-IV transcripts. These observations suggest that IFNg promotes and modifies atherosclerosis through both local effects in the arterial wall as well as a systemic effect on plasma lipoproteins. Therefore, therapeutic inhibition of IFNg signaling may lead to the formation of more lipid-poor and stable atheromata. ( J. Clin. Invest. 1997. 99:2752–2761.)
Fas/CD95 Regulatory Protein Faim2 Is Neuroprotective after Transient Brain Ischemia

Death receptor (DR) signaling has a major impact on the outcome of numerous neurological diseases, including ischemic stroke. DRs mediate not only cell death signals, but also proinflammatory responses and cell proliferation. Identification of regulatory proteins that control the switch between apoptotic and alternative DR signaling opens new therapeutic opportunities. Fas apoptotic inhibitory molecule 2 (Faim2) is an evolutionary conserved, neuron-specific inhibitor of Fas/CD95-mediated apoptosis. To investigate its role during development and in disease models, we generated Faim2-deficient mice. The ubiquitous null mutation displayed a viable and fertile phenotype without overt deficiencies. However, lack of Faim2 caused an increase in susceptibility to combined oxygen–glucose deprivation in primary neurons in vitro as well as in caspase-associated cell death, stroke volume, and neurological impairment after cerebral ischemia in vivo. These processes were rescued by lentiviral Faim2 gene transfer. In summary, we provide evidence that Faim2 is a novel neuroprotective molecule in the context of cerebral ischemia.