6,721 research outputs found
An Exploratory Study of Patient Falls
Debate continues between the contribution of education level and clinical expertise in the nursing practice environment. Research suggests a link between Baccalaureate of Science in Nursing (BSN) nurses and positive patient outcomes such as lower mortality, decreased falls, and fewer medication errors. Purpose: To examine if there a negative correlation between patient falls and the level of nurse education at an urban hospital located in Midwest Illinois during the years 2010-2014? Methods: A retrospective crosssectional cohort analysis was conducted using data from the National Database of Nursing Quality Indicators (NDNQI) from the years 2010-2014. Sample: Inpatients aged ≥ 18 years who experienced a unintentional sudden descent, with or without injury that resulted in the patient striking the floor or object and occurred on inpatient nursing units. Results: The regression model was constructed with annual patient falls as the dependent variable and formal education and a log transformed variable for percentage of certified nurses as the independent variables. The model overall is a good fit, F (2,22) = 9.014, p = .001, adj. R2 = .40. Conclusion: Annual patient falls will decrease by increasing the number of nurses with baccalaureate degrees and/or certifications from a professional nursing board-governing body
A Comparative Assessment of Computer Security Incidence Handling
Incidence response and handling has become quite a crucial, indispensible constituent of information technology security management, as it provides an organised way of handling the aftermaths of a security breach. It presents an organisation’s reaction to illegitimate and unacceptable exploits on its assets or infrastructure. The goal must be to successfully neutralise the incident, such that damages are significantly reduced with attendant reduction in recovery time and costs. To achieve this, several approaches and methodologies proposed have been reviewed with a view to identifying essential processes. What is needed is referred to as incident capability mingled with collaborations. This defines a shift from response to management of computer security incidents in anointer relationship manner that foster collaboration through the exchange and sharing of incidence management details among several distinct organizations. Key step-up aspects centre on issues of enforcing and assuring trust and privacy. A viable collaborative incident response approach must be able to proffer both proactive and reactive mechanisms that are management-oriented and incorporating all required techniques and procedures
A Graph Theoretic Clustering Algorithm based on the Regularity Lemma and Strategies to Exploit Clustering for Prediction
The fact that clustering is perhaps the most used technique for exploratory data analysis is only a semaphore that underlines its fundamental importance. The general problem statement that broadly describes clustering as the identification and classification of patterns into coherent groups also implicitly indicates it\u27s utility in other tasks such as supervised learning. In the past decade and a half there have been two developments that have altered the landscape of research in clustering: One is improved results by the increased use of graph theoretic techniques such as spectral clustering and the other is the study of clustering with respect to its relevance in semi-supervised learning i.e. using unlabeled data for improving prediction accuracies. In this work an attempt is made to make contributions to both these aspects. Thus our contributions are two-fold: First, we identify some general issues with the spectral clustering framework and while working towards a solution, we introduce a new algorithm which we call Regularity Clustering which makes an attempt to harness the power of the Szemeredi Regularity Lemma, a remarkable result from extremal graph theory for the task of clustering. Secondly, we investigate some practical and useful strategies for using clustering unlabeled data in boosting prediction accuracy. For all of these contributions we evaluate our methods against existing ones and also apply these ideas in a number of settings
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Dynamic simulation and exergetic optimization of a Concentrating Photovoltaic/ Thermal (CPVT) system
The development of a dynamic, theoretical model suitable for the prediction of the long-term performance of a parabolic-trough Concentrating Photovoltaic/Thermal CPVT system is discussed in the present study. The formulation of the mathematical model and the considered geometrical and operational parameters of the system, such as the characteristics of the employed PV modules and active cooling system are described in detail. The effect of heat capacity is taken into consideration in the thermal balances and thus the model is able to capture the transient behavior of the system. Besides, the model is validated using available experimental data of a manufactured prototype CPVT system. The daily performance of system is predicted for different values of the cooling fluid flow rate and temperature under various environmental conditions. At a second stage, an exergy analysis is conducted in order to point out the effect of the characteristics of the main system sub-components on the exergetic efficiency and exergy output of the CPVT system. It was established that the system exergetic performance is primarily influenced by the optical quality of the parabolic trough and the electrical efficiency of the PV module. Increasing these two factors to achievable values, e.g. ηopt = 0.75 and ηel = 0.25, can yield an increase of the system exergetic efficiency from 12% to 24%
WLIMES, The Wandering LIMES: Towards a Theoretical Framework for Wandering Logic Intelligence Memory Evolutive Systems
This paper compares two complementary theories, Simeonov’s Wandering
Logic Intelligence and Ehresmann’s & Vanbremeersch’s Memory Evolutive
Systems, in view of developing a common framework for the study of multiscale
complex systems such as living systems. It begins by a brief summary
of WLI and MES, then analyzes their resemblances and differences. Finally,
the article provides an outlook for a future research
Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19
The primary objective of this research is to identify commonly used data-driven decision-making techniques for contact tracing with regards to Covid-19. The virus spread quickly at an alarming level that caused the global health community to rely on multiple methods for tracking the transmission and spread of the disease through systematic contact tracing. Predictive analytics and data-driven decision-making were critical in determining its prevalence and incidence. Articles were accessed from primarily four sources, i.e., Web of Science, Scopus, Emerald, and the Institute of Electrical and Electronics Engineers (IEEE). Retrieved articles were then analyzed in a stepwise manner by applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISM) that guided the authors on eligibility for inclusion. PRISM results were then evaluated and summarized for a total of 845 articles, but only 38 of them were selected as eligible. Logistic regression and SIR models ranked first (11.36%) for supervised learning. 90% of the articles indicated supervised learning methods that were useful for prediction. The most common specialty in healthcare specialties was infectious illness (36%). This was followed closely by epidemiology (35%). Tools such as Python and SPSS (Statistical Package for Social Sciences) were also popular, resulting in 25% and 16.67%, respectively. Doi: 10.28991/ESJ-2023-SPER-02 Full Text: PD
Some resonances between Eastern thought and Integral Biomathics in the framework of the WLIMES formalism for modelling living systems
Forty-two years ago, Capra published “The Tao of Physics” (Capra, 1975). In this book (page 17) he writes: “The exploration of the atomic and subatomic world in the twentieth century has …. necessitated a radical revision of many of our basic concepts” and that, unlike ‘classical’ physics, the sub-atomic and quantum “modern physics” shows resonances with Eastern thoughts and “leads us to a view of the world which is very similar to the views held by mystics of all ages and traditions.“ This article stresses an analogous situation in biology with respect to a new theoretical approach for studying living systems, Integral Biomathics (IB), which also exhibits some resonances with Eastern thought. Stepping on earlier research in cybernetics1 and theoretical biology,2 IB has been developed since 2011 by over 100 scientists from a number of disciplines who have been exploring a substantial set of theoretical frameworks. From that effort, the need for a robust core model utilizing advanced mathematics and computation adequate for understanding the behavior of organisms as dynamic wholes was identified. At this end, the authors of this article have proposed WLIMES (Ehresmann and Simeonov, 2012), a formal theory for modeling living systems integrating both the Memory Evolutive Systems (Ehresmann and Vanbremeersch, 2007) and the Wandering Logic Intelligence (Simeonov, 2002b). Its principles will be recalled here with respect to their
resonances to Eastern thought
Disease spread through animal movements: a static and temporal network analysis of pig trade in Germany
Background: Animal trade plays an important role for the spread of infectious
diseases in livestock populations. As a case study, we consider pig trade in
Germany, where trade actors (agricultural premises) form a complex network. The
central question is how infectious diseases can potentially spread within the
system of trade contacts. We address this question by analyzing the underlying
network of animal movements.
Methodology/Findings: The considered pig trade dataset spans several years
and is analyzed with respect to its potential to spread infectious diseases.
Focusing on measurements of network-topological properties, we avoid the usage
of external parameters, since these properties are independent of specific
pathogens. They are on the contrary of great importance for understanding any
general spreading process on this particular network. We analyze the system
using different network models, which include varying amounts of information:
(i) static network, (ii) network as a time series of uncorrelated snapshots,
(iii) temporal network, where causality is explicitly taken into account.
Findings: Our approach provides a general framework for a
topological-temporal characterization of livestock trade networks. We find that
a static network view captures many relevant aspects of the trade system, and
premises can be classified into two clearly defined risk classes. Moreover, our
results allow for an efficient allocation strategy for intervention measures
using centrality measures. Data on trade volume does barely alter the results
and is therefore of secondary importance. Although a static network description
yields useful results, the temporal resolution of data plays an outstanding
role for an in-depth understanding of spreading processes. This applies in
particular for an accurate calculation of the maximum outbreak size.Comment: main text 33 pages, 17 figures, supporting information 7 pages, 7
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