An Updated Recurrence Model for Chilean Subduction Seismicity and Statistical Validation of Its Poisson Nature

2018 ◽  
Vol 109 (1) ◽  
pp. 66-74 ◽  
Author(s):  
Alan Poulos ◽  
Mauricio Monsalve ◽  
Natalia Zamora ◽  
Juan Carlos de la Llera
Keyword(s):  
Statistical Validation ◽  
Recurrence Model

The Evaluation of the Role of the Cytokines TNF- alfa and IL 6 in the Production of Hypoalbuminemia in Patients Undergoing Major Surgical Interventions

2018 ◽  
Vol 69 (7) ◽  
pp. 1830-1837
Author(s):  
Cristian Nicolescu ◽  
Alaxendru Pop ◽  
Alin Mihu ◽  
Luminita Pilat ◽  
Ovidiu Bedreag ◽  
...  
Keyword(s):  
Surgical Procedure ◽  
Tnf Alpha ◽  
Phase Reaction ◽  
Statistical Validation ◽  
Necrosis Factor Alpha ◽  
Surgical Interventions ◽  
Plasmatic Level ◽  
Factor Alpha ◽  
Validation Tests

This article presents an observational randomized prospective study done on 65 patients, who underwent major surgical interventions in the field of orthopedic surgery-total hip replacement or general surgery � total colectomy. The level of albuminemia in these cases were determined before the surgical intervention, after 6 hours of the intervention and after 24 h of the intervention. The measurements of the plasmatic concentration of the pro-inflammatory cytokines Tumor Necrosis factor -alpha (TNF-alpha) and interleukin 6 (IL6) were simultaneously done with the determination of the plasmatic levels of albumin. Values of hemoglobin and hematocrit were determined 24 h after the surgical procedure in order to exclude hemodilution, which could lead to a possible drop in the levels of plasmatic albumin. After the collection of the data, the statistical work was done and it consisted of descriptive statistics, correlation and comparison tests as well as statistical validation tests. Obtained results indicate that IL-6 plays a major role comparatively with that of TNF-alfa, regarding the decrease of the plasmatic level of albumin, and due to this, the primordial cause for hypoalbuminemia is an acute hepatic phase reaction. Supplemental permeability of the capillary wall under the action of TNF alpha has a secondary role, but could lead to a faster decrease in plasmatic albumin in the first hours after the surgical procedure.

scholarly journals Evaluation of Mixed Deep Neural Networks for Reverberant Speech Enhancement

Biomimetics ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 1 ◽  
Author(s):  
Michelle Gutiérrez-Muñoz ◽  
Astryd González-Salazar ◽  
Marvin Coto-Jiménez
Keyword(s):  
Neural Networks ◽  
Short Term Memory ◽  
Computational Cost ◽  
Real Life ◽  
Fixed Number ◽  
Training Procedure ◽  
Statistical Validation ◽  
Significant Drop ◽  
Training Time ◽  
Important Solution

Speech signals are degraded in real-life environments, as a product of background noise or other factors. The processing of such signals for voice recognition and voice analysis systems presents important challenges. One of the conditions that make adverse quality difficult to handle in those systems is reverberation, produced by sound wave reflections that travel from the source to the microphone in multiple directions. To enhance signals in such adverse conditions, several deep learning-based methods have been proposed and proven to be effective. Recently, recurrent neural networks, especially those with long short-term memory (LSTM), have presented surprising results in tasks related to time-dependent processing of signals, such as speech. One of the most challenging aspects of LSTM networks is the high computational cost of the training procedure, which has limited extended experimentation in several cases. In this work, we present a proposal to evaluate the hybrid models of neural networks to learn different reverberation conditions without any previous information. The results show that some combinations of LSTM and perceptron layers produce good results in comparison to those from pure LSTM networks, given a fixed number of layers. The evaluation was made based on quality measurements of the signal’s spectrum, the training time of the networks, and statistical validation of results. In total, 120 artificial neural networks of eight different types were trained and compared. The results help to affirm the fact that hybrid networks represent an important solution for speech signal enhancement, given that reduction in training time is on the order of 30%, in processes that can normally take several days or weeks, depending on the amount of data. The results also present advantages in efficiency, but without a significant drop in quality.

scholarly journals Partition Quantitative Assessment (PQA): A Quantitative Methodology to Assess the Embedded Noise in Clustered Omics and Systems Biology Data

2021 ◽  
Vol 11 (13) ◽  
pp. 5999
Author(s):  
Diego A. Camacho-Hernández ◽  
Victor E. Nieto-Caballero ◽  
José E. León-Burguete ◽  
Julio A. Freyre-González
Keyword(s):  
Systems Biology ◽  
Clustering Analysis ◽  
Quantitative Assessment ◽  
Clustering Algorithm ◽  
Statistical Evaluation ◽  
Quantitative Methodology ◽  
Typical Problem ◽  
Statistical Validation ◽  
Common Features ◽  
Biology Research

Identifying groups that share common features among datasets through clustering analysis is a typical problem in many fields of science, particularly in post-omics and systems biology research. In respect of this, quantifying how a measure can cluster or organize intrinsic groups is important since currently there is no statistical evaluation of how ordered is, or how much noise is embedded in the resulting clustered vector. Much of the literature focuses on how well the clustering algorithm orders the data, with several measures regarding external and internal statistical validation; but no score has been developed to quantify statistically the noise in an arranged vector posterior to a clustering algorithm, i.e., how much of the clustering is due to randomness. Here, we present a quantitative methodology, based on autocorrelation, in order to assess this problem.

scholarly journals An Analysis of Past Florence Nightingale Medal Recipients: Insights Into Exceptional Nurses and the Evolution of Nursing

2021 ◽  
Vol 7 ◽  
pp. 237796082098839
Author(s):  
Qian Wang ◽  
Ruifang Zhu ◽  
Zhiguang Duan
Keyword(s):  
Nursing Education ◽  
Red Cross ◽  
Statistical Validation ◽  
Award Recipient ◽  
Culturally Sensitive Care ◽  
Descriptive Research ◽  
Florence Nightingale ◽  
Specialist Nurses ◽  
High Level ◽  
Disaster Aid

Aim To examine past Florence Nightingale Medal recipients’ parallels with the evolving nature of the nursing field as a whole. Design Descriptive research. Method The professional and demographic characteristics of 1,449 Florence Nightingale Medal recipients between 1920 and 2015 were analyzed to develop a high-level overview of the award recipient characteristics. Result Medal recipients were primarily female (98.07%), with 36% being Specialist nurses. Awards were mainly conferred for aid work (30.4%) in the context of war or armed conflict followed by Nursing education (17.2%) and disaster aid (14.9%). The majority of recipients were affiliated with the Red Cross and the majority of recipients were those conducting Red Cross duties. Conclusion Our results offer statistical validation for the dedication of these exceptional individuals, while also highlighting overall parallels with the ongoing development of the nursing field as it expands to better deliver culturally-sensitive care and to overcome outdated stereotypes that would otherwise constrain innovation.

scholarly journals Validity of Surrogate Endpoints and Their Impact on Coverage Recommendations: A Retrospective Analysis across International Health Technology Assessment Agencies

2021 ◽  
pp. 0272989X2199455
Author(s):  
Oriana Ciani ◽  
Bogdan Grigore ◽  
Hedwig Blommestein ◽  
Saskia de Groot ◽  
Meilin Möllenkamp ◽  
...  
Keyword(s):  
Health Technology Assessment ◽  
Technology Assessment ◽  
International Health ◽  
Surrogate Endpoint ◽  
Health Technology ◽  
Surrogate Endpoints ◽  
Patient Centered ◽  
Statistical Validation ◽  
Level Of Evidence ◽  
The Impact

Background Surrogate endpoints (i.e., intermediate endpoints intended to predict for patient-centered outcomes) are increasingly common. However, little is known about how surrogate evidence is handled in the context of health technology assessment (HTA). Objectives 1) To map methodologies for the validation of surrogate endpoints and 2) to determine their impact on acceptability of surrogates and coverage decisions made by HTA agencies. Methods We sought HTA reports where evaluation relied on a surrogate from 8 HTA agencies. We extracted data on the methods applied for surrogate validation. We assessed the level of agreement between agencies and fitted mixed-effects logistic regression models to test the impact of validation approaches on the agency’s acceptability of the surrogate endpoint and their coverage recommendation. Results Of the 124 included reports, 61 (49%) discussed the level of evidence to support the relationship between the surrogate and the patient-centered endpoint, 27 (22%) reported a correlation coefficient/association measure, and 40 (32%) quantified the expected effect on the patient-centered outcome. Overall, the surrogate endpoint was deemed acceptable in 49 (40%) reports ( k-coefficient 0.10, P = 0.004). Any consideration of the level of evidence was associated with accepting the surrogate endpoint as valid (odds ratio [OR], 4.60; 95% confidence interval [CI], 1.60–13.18, P = 0.005). However, we did not find strong evidence of an association between accepting the surrogate endpoint and agency coverage recommendation (OR, 0.71; 95% CI, 0.23–2.20; P = 0.55). Conclusions Handling of surrogate endpoint evidence in reports varied greatly across HTA agencies, with inconsistent consideration of the level of evidence and statistical validation. Our findings call for careful reconsideration of the issue of surrogacy and the need for harmonization of practices across international HTA agencies.

scholarly journals Is the Travel Bubble under COVID-19 a Feasible Idea or Not?

2021 ◽  
Vol 18 (11) ◽  
pp. 5717
Author(s):  
Jo-Hung Yu ◽  
Hsiao-Hsien Lin ◽  
Yu-Chih Lo ◽  
Kuan-Chieh Tseng ◽  
Chin-Hsien Hsu
Keyword(s):  
Health Beliefs ◽  
Preventive Measures ◽  
Health Assessment ◽  
Willingness To Participate ◽  
Physical And Mental Health ◽  
Statistical Validation ◽  
Mental Health Assessment ◽  
Expert Opinions ◽  
Wait And See ◽  
Travel Decisions

The present study aimed to understand Taiwanese people’s willingness to participate in the travel bubble policy. A mixed research method was used to collect 560 questionnaires, and SPSS 22.0 software was used for the statistical validation and Pearson’s performance correlation analysis. Expert opinions were collected and the results were validated using multivariate analysis. Findings: People were aware of the seriousness of the virus and the preventive measures but were not afraid of the threat of infection. They looked forward to traveling to heighten their enthusiasm, relieve stress, and soothe their emotions. However, the infection and death rates have been high, there have been various routes of infection, and it has been difficult to identify the symptoms. The complex backgrounds of people coming in and out of airports, hotels and restaurants may create pressure on the participants of events. In addition, the flawed policies and high prices resulted in a loss of confidence in the policies and a wait-and-see attitude toward tourism activities. Thus, travel decisions (0.634), physical and mental health assessment (0.716), and environmental risk (−0.130) were significantly (p < 0.05) related to travel intentions, and different issues were affected to different degrees, while health beliefs had no significant effect (p > 0.05).

scholarly journals HF Radars for Wave Energy Resource Assessment Offshore NW Spain

2021 ◽  
Vol 13 (11) ◽  
pp. 2070
Author(s):  
Ana Basañez ◽  
Vicente Pérez-Muñuzuri
Keyword(s):  
Wave Energy ◽  
Numerical Models ◽  
Wave Spectrum ◽  
Energy Resource ◽  
Electrical Energy ◽  
Resource Assessment ◽  
Electricity Production ◽  
Statistical Validation ◽  
Wave Energy Converters ◽  
Energy Converters

Wave energy resource assessment is crucial for the development of the marine renewable industry. High-frequency radars (HF radars) have been demonstrated to be a useful wave measuring tool. Therefore, in this work, we evaluated the accuracy of two CODAR Seasonde HF radars for describing the wave energy resource of two offshore areas in the west Galician coast, Spain (Vilán and Silleiro capes). The resulting wave characterization was used to estimate the electricity production of two wave energy converters. Results were validated against wave data from two buoys and two numerical models (SIMAR, (Marine Simulation) and WaveWatch III). The statistical validation revealed that the radar of Silleiro cape significantly overestimates the wave power, mainly due to a large overestimation of the wave energy period. The effect of the radars’ data loss during low wave energy periods on the mean wave energy is partially compensated with the overestimation of wave height and energy period. The theoretical electrical energy production of the wave energy converters was also affected by these differences. Energy period estimation was found to be highly conditioned to the unimodal interpretation of the wave spectrum, and it is expected that new releases of the radar software will be able to characterize different sea states independently.

Measuring Intent to Aid of Lay Responders: Survey Development and Validation

2017 ◽  
Vol 45 (5) ◽  
pp. 730-740 ◽  
Author(s):  
Brian Miller ◽  
Jeffrey L. Pellegrino
Keyword(s):  
Sudden Cardiac Arrest ◽  
Affective Domain ◽  
Red Cross ◽  
American Red Cross ◽  
Statistical Validation ◽  
Expert Review ◽  
The Social ◽  
Two Samples ◽  
Intent To Use ◽  
Development And Validation

Background. Increasing lay responder cardiopulmonary resuscitation and automated external defibrillator use during sudden cardiac arrest depends on an individual’s choice. Investigators designed and piloted an instrument to measure the affective domain of helping behaviors by applying the theory of planned behavior (TPB) to better understand lay responders’ intent to use lifesaving skills. Method. Questionnaire items were compiled into 10 behavioral domains informed by the TPB constructs followed by refinement via piloting and expert review. Two samples from an American Red Cross–trained lay-responder population ( N = 4,979) provided data for an exploratory (EFA, n = 235) and confirmatory (CFA, n = 198) factor analyses. EFA derived interitem relationships into factors and affective subscales. CFA yielded statistical validation of factors and subscales. Results. The EFA identified four factors, aligned with the TPB constructs of attitudes, norms, confidence, and intention to act to explain 57% of interitem variance. The internal consistency of factor-derived subscales ranged between 0.71 and 0.91. Reduction of instrument items went from 47 to 32 (32%). The CFA yielded good model fit with the switching of the legal ramification item from the social norm to intention construct. Conclusion. The Intent to Aid (I2A) survey derived from this investigation aligned with the constructs of the TPB yielding four subscales. The I2A allows health education researchers to differentiate modalities and content impact on learner intention to act in a first aid (FA) emergency. I2A compliments cognitive and psychomotor measurements of learning outcomes. The experimental instrument aims to allow curricula developers and program evaluators a means of assessing the affective domain of human learning regarding intention-to-act in an FA emergency. In combination of with assessment of functional knowledge and essential skills, this instrument may provide curricula developers and health educators an avenue to better describe intention to act in an FA emergency.

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