Miracle-dz/UniFAR-FTUs huggingface dataset:
Examining the Support for Evidence-based Nursing Practice

The force behind the call for evidence-based management is often a belief that managers can be more effective if they are informed by logic and evidence and are relentless in their search for knowledge from internal and external sources. This force is shadowed by a concern that healthcare managers make strategic decisions based on low levels of evidence. The awareness that evidence-based medicine results in better care drives the education of healthcare administrators. The benefits of evidencebased practice (EBP), however, not only applies to medicine but to nursing as well. The link between nursing care and quality outcomes is unmistakable, but nursing’s future in quality movement is dependent on the commitment of nurse leaders to incorporate evidence into all areas of nursing practice. Enabling EBP is a goal of many nurse executives within healthcare organizations. A number of standards drive this aspiration and include evidence-based nursing practice as an essential component of a quality environment. Nurses need information to make appropriate practice decisions, yet they do not perceive that they are prepared or given the adequate resources to do so. Nurse executives allocate these resources to the work environment, providing the essential infrastructure for EBP and establishing the appropriate level of human and material resources (such as mentors, processes, and evidence sources). This requires a strategic approach to planning and budgeting. A strategic approach will require an understanding of the benefits of creating an infrastructure before the examination of associated cost. This article examines the evidence on the effect of evidencebased nursing practice on patient and nurse outcomes in healthcare organizations.
Anthelmintic resistance in human helminths: a review

We briefly review reports on drug resistance in human helminths and compare the factors which contribute to the development of anthelmintic resistance in livestock and man, i.e. high treatment frequency, single‐drug regimens, targeting and timing of mass treatments and underdosing. Conclusions are drawn from the mistakes in the treatment and control of livestock helminths. The advantages and inconveniences of current methods for the detection of drug resistance in helminths of livestock are discussed and some suggestions are put forward to standardize the tests for the detection of resistance in human helminths. Finally, based on veterinary experience, some recommendations are made to reduce the risks of development of drug resistance in human helminths. The dramatic and rapid spread of resistance to all major classes of veterinary anthelmintics should be a warning against too strong a reliance on drugs in helminth control programmes.
Longitudinal, Expert, All Data Procedure for Psychiatric Diagnosis in Patients with Psychoactive Substance Use Disorders

The Longitudinal, Expert, All Data (LEAD) procedure has been proposed as a criterion for the assessment of the procedural validity of diagnostic instruments. The authors evaluated the procedure's test-retest reliability and whether it enhanced diagnosis based on a single interview. Data were collected using interviews and questionnaires to assess current and lifetime substance use disorders and common comorbid disorders in 100 patients recruited from a substance abuse treatment program. An initial diagnostic interview was conducted by a primary expert who, at the end of treatment, formulated LEAD diagnoses for each patient, based on the results of the research assessment and all available clinical records. Secondary experts used a similar procedure in a subsample of 40 patients to provide a measure of test-retest reliability. Overall reliability of LEAD diagnoses was good, though it varied from excellent for substance use disorders to poor for some comorbid psychiatric disorders. As a consequence of the LEAD procedure, the total number of substance use diagnoses increased significantly, with no effect on diagnostic reliability. Based on these findings, we conclude that, while the overall reliability of the LEAD procedure is comparable to other diagnostic methods, there was considerable variability among groups of diagnoses. The additional cost of the procedure would appear to be justified only when diagnostic sensitivity is at a premium and principally for the diagnosis of psychoactive substance use disorders. Before it is widely used as a diagnostic criterion measure, the utility of the LEAD procedure should be evaluated in a variety of patient samples and under varying circumstances.
Energy Consumption Analysis of Practical Programming Languages

Energy consumption of software has received significantly more attention in the past decade due to the advent of increasingly small, mobile technology; embedded systems or smart-phones are common enough to make energy and battery consumption a real issue, even aside from the general power consumption of server farms and other such large-scale hardware. Care is being given to optimizing energy usage on the level of the software running on the hardware, rather than just hardware optimizations. Two avenues for measuring and reasoning about software energy consumption exist: static and dynamic analysis. Most work focuses on dynamic analysis by executing software on specific hardware and measuring the resulting power draw, but the practice of static analysis draws attention by virtue of the lack of requirement for measuring equipment and its relative ease. Based on a set of energy analysis rules[1], one tool performing such static analysis on a small, While-like[2] language is EcaLogic[3]. The analysis uses hardware models coupled with user-defined bounds to analyse the energy consumption of a program. A limited static energy analysis is also limited in its application. This thesis focuses on extending the energy analysis and its implementation in EcaLogic to include elements from real, practical programming languages: by adding additional data structures, support for recursion and an overall extension of the grammar, usability of the analysis can be significantly increased. The end result is an extension of the static energy analysis method, culminating into an implementation applying it to the C programming language, chosen due to its prevalence mainly in embedded system applications; the extended tool is referred to as EcaLogic-C.
Generalized disjunction-free representation of frequent patterns with negation

The discovery of frequent patterns is one of the main data mining problems. Frequent patterns are used for discovering association rules, episode rules, sequential patterns and clusters. The number of frequent patterns is usually huge. In order to alleviate this problem, a number of lossless representations of frequent patterns have been proposed recently. Theoretical works prove that frequent closed itemsets and models based on generalized disjunction-free sets or non-derivable itemsets are minimal representations of frequent patterns. Experiments prove further that the latter representations are by a few orders of magnitude more concise than the former one. The offered models were intended to represent only frequent patterns without negation. In this article, we offer a lossless representation based on generalized disjunction-free sets that represents frequent patterns both with and without negation. An algorithm for discovering this representation is also provided.
Cefoxitin mannitol salt agar for selective isolation of methicilin resistant Staphylococcus aureus

Methicillin-resistant Staphylococcus aureus (MRSA) is a major nosocomial infection that causes problems in screening testing. Cefoxitin is potent selective antibiotic agent widely recommended for the detection of MRSA. The aim is to evaluate the optimum concentration of cefoxitin incorporated into mannitol salt agar (MSA) needed to select and detect MRSA. A total of 99 MRSA isolates were tested against 4 different concentrations of cefoxitin mannitol salt agar (MSC) of: 2 μg/ml (MSC-2), 4 μg/ml (MSC-4), 6 μg/ ml (MSC-6) and 8 μg/ml (MSC-8). The susceptibility of MSCs were respectively as follows: 53.5%, 53.5%, 74% and 78%. Accurate and effective laboratory screening of MRSA is an important measure control of MRSA.
Multilevel models for data processing

The paper suggests multilevel models for data processing and demonstrates advantages of such models on examples of data sorting. Three different techniques are discussed, namely graph walk, using tree-like structures and sorting networks. The relevant implementations were done on the basis of hierarchical finite state machines and verified in commercially available FPGAs. Experiments and comparisons demonstrate that the results enable the performance of processing for different types of data to be increased compared to known implementations.
Probabilistically Tracking System Calls

Monitoring system calls made by an application is useful for debugging, for diagnostics as well as for security applications. Existing tools for monitoring system calls either suffer from a large runtime overhead, or require root permission to change the kernel of the operating system. We propose an approach that tackles both of these issues. We implemented a tool that runs completely in user space and requires no change to the operating system. It works by probabilistically sampling a few of the system calls made a process, thus lowering its overhead. We show that by using proper sampling techniques, our tool is able to figure out the same information as a tool that tracks all system calls.
A note on empiricism and structure-dependence

In several publications, Chomsky has expressed his opinion that rationalist theories of language acquisition are more adequate than empiricist ones (e.g. 1965, 1968, 1975). His main reason is that the innate capacities which empiricists attribute to children are insufficient to account for the capacity which underlies the linguistic competence that children acquire. He argues, for example, that by attributing to children only general learning capacities, the empiricist cannot explain their tendency to use structure-dependent rules when analysing the language to which they are exposed (1975: 153—5). In earlier publications, I have defended a different view (Stemmer 1971,1973 a, 19736, 1978). I have maintained that empiricist theories of language indeed are adequate. In particular, I have pointed out that the capacity to generalize according to functional similarity which is a general learning capacity accounts for the creative aspect of language use. However, I have not dealt directly with structure-dependence. In this note, I shall therefore indicate how empiricists explain the children's tendency to use structure-dependent rules.
Fast Face Recognition Based on 2 D Fractional Fourier Transform

In order to overcome the deficiency of computational complexity and low accuracy of traditional face recognition algorithm under complex surroundings, this paper proposes a novel face recognition method based on 2D-FrFT. Firstly, we construct Gaussian skin color model to segment potential face region; then adaptive template matching and secondary matching algorithm are adopted to eliminate the impact of background and reduce computational cost in the process of matching.Meanwhile, LBP operator encodes the amplitude and phase information of 2D-FrFT as texture feature. Eventually, a nearest neighbor classifier is employed for classification. Our proposed approach is examined on the public available database. The experimental results demonstrate that this method is not only simple and robust, but efficient in recognition speed and rate.
Bounds on Rates of LDPC Codes for BEC with Varying Erasure Rate

A binary erasure channel with erasure probability which can take one of two values is considered. Transmission is done by using a low density parity-check code under the requirement that completely successful decoding is possible when the channel is in its better state, while tolerating some predetermined residual erasure fraction when the channel is in its worse state. Upper bounds on the achievable design rate under iterative decoding are derived for this setting. These bounds are compared to rates obtained by practical code profiles. It is also observed that when exceeding the capacity of the erasure channel, the performance of such codes exhibits graceful degradation as measured by the residual erasure fraction.
Social ant colony-inspired modelling approach for rapid response design

Some unexpected natural or man-made disasters would cause damage to human life and key infrastructural facilities. In this context, we define rapid response design RRD as a design methodology for rapidly designing equipment in a changing environment to minimise loss in the disaster. To adapt to the unpredictable requirements, RRD has to incorporate the ability to respond rapidly, be flexible, robust and adaptable. To reach these goals, we present a social ant colony-inspired modelling approach for RRD. This study firstly makes a survey on the social ant colony to find its inherent mechanism used in RRD modelling process. Then, this study focuses on how to make a rapid response design system incorporating the ability to be insensitive to disasters, and to be resilient or reconfigured after breakdown. Finally, we illustrate a rapid response design framework based on multi-agent system that simulates the operation environment of the social ant colony to cope with changing fluctuant conditions. The proposed modelling approach would be expanded to the rapid response processing in the management field.
Postnatal developmental changes in activation profiles of the respiratory neuronal network in the rat ventral medulla

Two putative respiratory rhythm generators (RRGs), the para‐facial respiratory group (pFRG) and the pre‐Bötzinger complex (preBötC), have been identified in the neonatal rodent brainstem. To elucidate their functional roles during the neonatal period, we evaluated developmental changes of these RRGs by optical imaging using a voltage‐sensitive dye. Optical signals, recorded from the ventral medulla of brainstem–spinal cord preparations of neonatal (P0–P4) rats (n= 44), were analysed by a cross correlation method. With development during the first few postnatal days, the respiratory‐related activity in the pFRG reduced and shifted from a preinspiratory (P0–P1) to an inspiratory (P2–P4) pattern, whereas preBötC activity remained unchanged. The μ‐opioid agonist [d‐Ala(2),N‐Me‐Phe(4),Gly(5)‐ol]‐enkephalin (DAMGO) augmented preinspiratory activity in the pFRG, while the μ‐opioid antagonist naloxone induced changes in spatiotemporal activation profiles that closely mimicked the developmental changes. These results are consistent with the recently proposed hypothesis by Janczewski and Feldman that the pFRG is activated to compensate for the depression of the preBötC by perinatal opiate surge. We conclude that significant reorganization of the respiratory neuronal network, characterized by a reduction of preinspiratory activity in the pFRG, occurs at P1–P2 in rats. The changes in spatiotemporal activation profiles of the pFRG neurones may reflect changes in the mode of coupling of the two respiratory rhythm generators.
Adenovirus-vectored vaccines

Background: Engineered adenoviruses are being increasingly explored as immunoprophylactic or immunotherapeutic vaccine vectors. Encouraging data from preclinical studies using human adenovirus vectors carrying different antigen genes have resulted in many currently ongoing clinical trials. Objective: The article seeks to review the current status of the use of adenoviruses as vaccine vectors. Methods: This review is based on the patent literature since 2000 pertaining to the development of adenovirus vaccine vectors for infectious and non-infectious diseases. Conclusion: Human adenovirus-vectored vaccines have important limitations that stem from their immunogenicity and restrict their utility. This has spurred intensive efforts to find alternative adenovirus vectors and strategies, each with its own advantages and shortcomings.
Electrowetting on Dielectric (EWOD)-Based Thermo-Responsive Microvalve for Interfacing Droplet Flow With Continuous Flow

This paper reports on a hybrid electrowetting-thermal passive microvalve for a flow control mechanism to interface droplet flow with continuous flow. When heated, a thermo-responsive polymer, in the form of a dilute aqueous droplet, forms a gel, and upon cooling, it liquefies. The gel blocks and its subsequent liquefaction unblocks the pressurized flow. Electrowetting on dielectric (EWOD) actuation was used to control and position a polymer droplet at a desired valving location. The valve interfaced a pressurized flow to a planar EWOD-based droplet flow in a single device. It was fabricated using a standard planar microfluidic glass platform. It has a three-layer architecture bonded by oxygen plasma bonding. The valve demonstrated zero leakage and no cross-mixing across the interface.
Using the Implicit Surface Paradigm for Smooth Animation of Triangle Meshes

We consider the problem of real-time animation of a cartoon character which is represented by a skeleton implicit surface. If the character's motions permit a constant topology for the skin, it is possible to reduce the problem of computing an approximation of the implicit surface to solve only one scalar equation per surface point for a fixed number of surface points; these surface points define the shape of a triangle mesh with constant topology. We present the algorithm, and demonstrate its working in a real-time model of walking cartoon legs.
Tumor Progression Locus 2 (Tpl2) Activates the Mammalian Target of Rapamycin (mTOR) Pathway, Inhibits Forkhead Box P3 (FoxP3) Expression, and Limits Regulatory T Cell (Treg) Immunosuppressive Functions*

The serine/threonine kinase tumor progression locus 2 (Tpl2, also known as Map3k8/Cot) is a potent inflammatory mediator that drives the production of TNFα, IL-1β, and IFNγ. We previously demonstrated that Tpl2 regulates T cell receptor (TCR) signaling and modulates T helper cell differentiation. However, very little is known about how Tpl2 modulates the development of regulatory T cells (Tregs). Tregs are a specialized subset of T cells that express FoxP3 and possess immunosuppressive properties to limit excess inflammation. Because of the documented role of Tpl2 in promoting inflammation, we hypothesized that Tpl2 antagonizes Treg development and immunosuppressive function. Here we demonstrate that Tpl2 constrains the development of inducible Tregs. Tpl2−/− naïve CD4+ T cells preferentially develop into FoxP3+ inducible Tregs in vitro as well as in vivo in a murine model of ovalbumin (OVA)-induced systemic tolerance. Treg biasing of Tpl2−/− T cells depended on TCR signal strength and corresponded with reduced activation of the mammalian target of rapamycin (mTOR) pathway. Importantly, Tpl2−/− Tregs have basally increased expression of FoxP3 and immunosuppressive molecules, IL-10 and cytotoxic T lymphocyte-associated protein 4 (CTLA-4). Furthermore, they were more immunosuppressive in vivo in a T cell transfer model of colitis, as evidenced by reduced effector T cell accumulation, systemic production of inflammatory cytokines, and colonic inflammation. These results demonstrate that Tpl2 promotes inflammation in part by constraining FoxP3 expression and Treg immunosuppressive functions. Overall, these findings suggest that Tpl2 inhibition could be used to preferentially drive Treg induction and thereby limit inflammation in a variety of autoimmune diseases.
Nonparametric estimation of search query patterns

In this poster, we adopted nonparametric regression as a method to identify the unique distribution of query log data collected from the Excite search service in May 2001. In Informetrics, parametric modeling has been widely used in tracing term frequency data, such as Zipf’s law, Lotka’s law, or Bradford’s law. However, these traditional parametric methods have had limited application when detecting distributions for large datasets with a nonlinear pattern and a long tail. This study tested kernel regression as an alternative tool to model nonlinearity of term frequency patterns. The results indicated that the kernel regression produced an improved model fit compared to previous parametric approaches in modeling query patterns.
Folksonomies: Tidying Up Tags?

A folksonomy is a type of distributed classification system. It is usually created by a group of individuals, typically the resource users. Users add tags to online items, such as images, videos, bookmarks and text. These tags are then shared and sometimes refined. In this article we look at what makes folksonomies work. We agree with the premise that tags are no replacement for formal systems, but we see this as being the core quality that makes folksonomy tagging so useful
Uhr Institute for Software & Systems Engineering Universitätsstraße 6 a D-86135 Augsburg Brain-inspired Recurrent Neural Algorithms for Advanced Object Recognition

Deep learning has enabled breakthroughs in machine learning, resulting in performance levels seeming on par with humans. This is particularly the case in computer vision where models can learn to classify objects in the images of a dataset. These datasets however often feature perfect information, whereas objects in the real world are frequently cluttered with other objects and thereby occluded. In this thesis, we argue for the usefulness of recurrency for solving these questions of partial information and visual context. First, we show that humans robustly recognize partial objects even at low visibilities while today’s feed-forward models in computer vision are not robust to occlusion with classification performance lacking far behind human baseline. We argue that recurrent computations in the visual cortex are the crucial piece, evident through performance deficits with backward masking and neurophysiological delays for partial images. By extending the neural network Alexnet with recurrent connections at the last feature layer, we are able to outperform feed-forward models and even human subjects. These recurrent models also correlate with human behavior and capture the effects of backward masking. Second, we empirically demonstrate that human subjects benefit from visual context in recognizing difficult images. Building on top of feed-forward Alexnet, we add scene information and recurrent connections to the object prediction layer to define a simple model capable of context integration. Through the use of semantic relationships between objects and scenes derived from a lexical corpus, we can define the recurrent weights without training on large image datasets. The results of this work suggest that recurrent connections are a powerful tool for integrating spatiotemporal information, allowing for the robust recognition of even complex images.