scholarly journals Incorporating method recovery uncertainties in stochastic estimates of raw water protozoan concentrations for QMRA

2007 ◽  
Vol 5 (S1) ◽  
pp. 51-65 ◽  
Author(s):  
Susan R. Petterson ◽  
Ryan S. Signor ◽  
Nicholas J. Ashbolt
Keyword(s):  
Initial Period ◽  
Water Concentration ◽  
Random Variable ◽  
Source Water ◽  
Large Dataset ◽  
Assay Result ◽  
Dataset Size ◽  
Sample Recovery ◽  
The Impact ◽  
Cyst Concentration

The impact of incorporating recovery data on protozoan concentration estimates was investigated for Cryptosporidium and Giardia using a large dataset (n=99) of [oo]cyst assay results with paired recovery estimates. Stochastic [oo]cyst concentration was estimated using three approaches: I – no availability/consideration of recovery, II – limited recovery data, where sample recovery was considered as an independent random variable, and III – every [oo]cyst assay result was adjusted for a concurrently derived recovery estimate. Critically, Approach I underestimated [oo]cyst concentrations by about 100% compared to Approaches II and III, which were similar. The impact of dataset size on statistical uncertainty about the concentration estimate for Approach II was investigated; little improvement in parameter uncertainty was achieved beyond n=20. It is suggested that recovery data be incorporated into source water concentration estimates, especially when used to infer health risks to consumers, so as not to underestimate the risk. Where none is available, conservatively low recoveries should be assumed. When designing monitoring programmes, recovery data should be collected as a pair with [oo]cyst count data for an initial period at least, so that site-specific relationships between those parameters may be ascertained and incorporated into source water concentration estimates.

scholarly journals Minimization of the Line Resistance Impact on Memdiode-Based Simulations of Multilayer Perceptron Arrays Applied to Pattern Recognition

2021 ◽  
Vol 11 (1) ◽  
pp. 9
Author(s):  
Fernando Leonel Aguirre ◽  
Nicolás M. Gomez ◽  
Sebastián Matías Pazos ◽  
Félix Palumbo ◽  
Jordi Suñé ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Ex Situ ◽  
Multilayer Perceptrons ◽  
Spice Simulation ◽  
Large Dataset ◽  
High Line ◽  
Calibration Algorithm ◽  
The Impact ◽  
Line Resistance

In this paper, we extend the application of the Quasi-Static Memdiode model to the realistic SPICE simulation of memristor-based single (SLPs) and multilayer perceptrons (MLPs) intended for large dataset pattern recognition. By considering ex-situ training and the classification of the hand-written characters of the MNIST database, we evaluate the degradation of the inference accuracy due to the interconnection resistances for MLPs involving up to three hidden neural layers. Two approaches to reduce the impact of the line resistance are considered and implemented in our simulations, they are the inclusion of an iterative calibration algorithm and the partitioning of the synaptic layers into smaller blocks. The obtained results indicate that MLPs are more sensitive to the line resistance effect than SLPs and that partitioning is the most effective way to minimize the impact of high line resistance values.

scholarly journals Impact of Dataset Size on Classification Performance: An Empirical Evaluation in the Medical Domain

2021 ◽  
Vol 11 (2) ◽  
pp. 796
Author(s):  
Alhanoof Althnian ◽  
Duaa AlSaeed ◽  
Heyam Al-Baity ◽  
Amani Samha ◽  
Alanoud Bin Dris ◽  
...  
Keyword(s):  
Empirical Evaluation ◽  
Classification Performance ◽  
Support Vector ◽  
Robust Model ◽  
Original Distribution ◽  
C4.5 Decision Tree ◽  
Dataset Size ◽  
Overall Performance ◽  
Medical Domain ◽  
The Impact

Dataset size is considered a major concern in the medical domain, where lack of data is a common occurrence. This study aims to investigate the impact of dataset size on the overall performance of supervised classification models. We examined the performance of six widely-used models in the medical field, including support vector machine (SVM), neural networks (NN), C4.5 decision tree (DT), random forest (RF), adaboost (AB), and naïve Bayes (NB) on eighteen small medical UCI datasets. We further implemented three dataset size reduction scenarios on two large datasets and analyze the performance of the models when trained on each resulting dataset with respect to accuracy, precision, recall, f-score, specificity, and area under the ROC curve (AUC). Our results indicated that the overall performance of classifiers depend on how much a dataset represents the original distribution rather than its size. Moreover, we found that the most robust model for limited medical data is AB and NB, followed by SVM, and then RF and NN, while the least robust model is DT. Furthermore, an interesting observation is that a robust machine learning model to limited dataset does not necessary imply that it provides the best performance compared to other models.

scholarly journals A REVIEW ON THE IMPACT OF THE COVID-19 PANDEMIC ON THE HEALTH CARE SECTOR

2021 ◽  
pp. 1-6
Author(s):  
PRAJITHA BIJU ◽  
PALLAVI K. ◽  
VAGEESH REVADIGAR ◽  
SONAL DSOUZA ◽  
MOHAMMED ASIF IQBAL ◽  
...  
Keyword(s):  
Health Care ◽  
Initial Period ◽  
Health Care Sector ◽  
Healthcare Sector ◽  
Future Challenges ◽  
Artificial Intelligence Technology ◽  
Pharmaceutical Industries ◽  
Impact Problems ◽  
The One ◽  
The Impact

The global crisis of the present era, the COVID-19 pandemic, has changed given new normal ways in many of the sectors. The present review highlights the impact, problems, and challenges faced by major areas of the health care sector due to pandemics and also addresses some of the aspects of upcoming approaches. The healthcare sector is the one sector that is on-demand since this COVID-19 pandemic raised. During the initial period, there was disruption of various services provided by the health care sector due to supply chain management issues and reduction in demand by consumers, quarantine, and lockdown period. The healthcare workers also confronted a huge challenge due to the increased number of cases and shortage of amenities and safety measures. This significantly affected even COVID-19 patients and the general public suffering from other diseases. To fight this issue, research and development (RandD) in pharmaceutical industries with great efforts to explore molecules and save many lives. Gradually innovative ways to strengthen and combat pandemics started emerging. Numeral ways and rules were adopted to prevent, diagnose and cure the disease. Artificial intelligence technology has emerged as one of the boons to address many of the unresolved or time-consuming mysteries. All the divisions of health care sectors have started working more efficiently with adopted new strategies to face future challenges.

scholarly journals The impact of inclusive business on ethical values & internal control quality: an accounting perspective

2016 ◽  
Vol 5 (4) ◽  
pp. 106-113 ◽  
Author(s):  
Tamer El Nashar
Keyword(s):  
Probability Distribution ◽  
Internal Control ◽  
Random Variable ◽  
Ethical Values ◽  
Discrete Probability ◽  
Control Quality ◽  
Inclusive Business ◽  
Potential Impact ◽  
Business Approach ◽  
The Impact

The objective of this paper is to examine the impact of inclusive business on the internal ethical values and the internal control quality while conceiving the accounting perspective. I construct the hypothesis for this paper based on the potential impact on the organizations’ awareness to be directed to the inclusive business approach that will significantly impact the culture of the organizations then the ethical values and the internal control quality. I use the approach of the expected value and variance of random variable test in order to analyze the potential impact of inclusive business. I support the examination by discrete probability distribution and continuous probability distribution. I find a probability of 85.5% to have a significant potential impact of the inclusive business by 100% score on internal ethical values and internal control quality. And to help contribute to sustainability growth, reduce poverty and improve organizational culture and learning.

The Compatibility Assessment between Voltage Sags and Equipment Tolerance Based on Fuzzy-Random Method

2012 ◽  
Vol 588-589 ◽  
pp. 458-462
Author(s):  
Zhi Jian Yuan ◽  
Yan Li
Keyword(s):  
Engineering Application ◽  
Fuzzy Random Variable ◽  
Random Variable ◽  
Assessment Model ◽  
Voltage Sags ◽  
Equipment Failure ◽  
Failure Event ◽  
Cut Set ◽  
The Impact ◽  
Fuzzy Random

The impact of voltage sags on equipment is usually described by equipment failure probability.It is generally difficult to assess and predict the probability because of the uncertainty of both the nature of voltage sags and the VTL (VTL) of equipment. By defining the equipment failure event caused by voltage sags as a fuzzy-random event, a fuzzy-random assessment model incorporating those uncertainty is developed. The model is able to convert the probability problem of a fuzzy-random variable to that of a common random variable by using λ-cut set. It is thus valuable in theoretical analysis and engineering application. The validity of the developed model is verified by Monte Carlo stochastic simulation using personal computers (PCs)as test equipment.

Effect of bodyweight on VerifyNow Aspirin platelet function test: a retrospective review

2020 ◽  
pp. neurintsurg-2020-016842
Author(s):  
Melissa Sandler ◽  
Cuong Hoang ◽  
Hannah Y Mak ◽  
Michael R Levitt
Keyword(s):  
Stent Thrombosis ◽  
Retrospective Review ◽  
Platelet Reactivity ◽  
Thrombotic Event ◽  
Optimal Dose ◽  
Platelet Inhibition ◽  
Platelet Function Test ◽  
Assay Result ◽  
The Mean ◽  
The Impact

BackgroundAntiplatelet therapy is used to prevent stent thrombosis in intracranial stents, but the optimal dose of aspirin is unknown. This study sought to determine whether the degree of platelet inhibition with aspirin is affected by bodyweight as observed through a platelet reactivity assay.MethodsThis is a retrospective review of patients who underwent neurovascular stent placement and had a VerifyNow Aspirin assay result. The primary outcome was the correlation between the VerifyNow Aspirin result, bodyweight, and the initial dose of aspirin. Secondary outcomes included the impact of the VerifyNow P2Y12 result and of weight on the incidence of bleeding or a thrombotic event.ResultsOf the 142 included patients, 62.7% weighed ≥70 kg and 88.7% were initiated on aspirin 300–325 mg daily. 83.8% achieved a therapeutic VerifyNow Aspirin result. There was minimal correlation between the VerifyNow Aspirin result, bodyweight, and aspirin dose (R2=0.02). Between patients who weighed <70 kg versus ≥70 kg, there was no difference in the mean aspirin reaction units (ARU) (449 vs 435, p=0.32) or in the incidence of bleeding (28% vs 17.1%, p=0.14) or a thrombotic event (4% vs 5.3%, p=0.59). No patient experienced stent thrombosis and eight patients experienced in-stent stenosis. In a multivariate analysis, only the VerifyNow P2Y12 result predicted the development of either bleeding or a thrombotic event (p<0.01).ConclusionsBodyweight did not influence the likelihood of obtaining a therapeutic VerifyNow Aspirin result. The clinical utility of obtaining VerifyNow Aspirin assays for this patient population is unknown.

Source water impact model (SWIM) - a watershed guided approach as a new planing tool for indirect potable water reuse

2001 ◽  
Vol 43 (10) ◽  
pp. 267-275 ◽  
Author(s):  
J. E. Drewes ◽  
P. Fox
Keyword(s):  
Water Quality ◽  
Water Reuse ◽  
Reclaimed Water ◽  
Water Sources ◽  
Distribution Data ◽  
Source Water ◽  
Impact Model ◽  
Water Impact ◽  
Potable Reuse ◽  
The Impact

The scope of this study was to develop a model to assess the impact of source water quality on reclaimed water used for indirect potable reuse. The source water impact model (SWIM) considered source water qualities, water supply distribution data, water use and the impact of wastewater treatment to calculate reclaimed water quality. It was applied for sulfate, chloride, and dissolved organic carbon (DOC) at four water reuse sites in Arizona and California. SWIM was able to differentiate between the amount of salts derived by drinking water sources and the amount added by consumers. At all sites, the magnitude of organic residuals in reclaimed water was strongly effected by the concentration of organics in corresponding water sources and effluent-derived organic matter. SWIM can be used as a tool to predict reclaimed water quality in existing or planned water reuse systems.

The Spectre of Americanization: Western Europe in the American Century

2012 ◽  
Author(s):  
Philipp Gassert
Keyword(s):  
World War Ii ◽  
Western Europe ◽  
Initial Period ◽  
Film Music ◽  
The United States ◽  
Colonial Rule ◽  
World War ◽  
Economic Threat ◽  
The Impact ◽  
First Time

By 1945, the spectre of Americanisation had been haunting Europe for half a century. With the United States still struggling to establish colonial rule over the Philippine Islands, European observers began framing the ‘American challenge’ as a cultural and most of all economic threat to national independence. Controversies about the impact of ‘America’ often served as a stand-in for a more fundamental reckoning with processes of modernisation. The initial period of sustained Americanisation was the 1920s, when American film, music, and automobiles were conquering Europe for the first time. A second heyday of Americanisation ‘from below’ started with the ‘American occupation of Britain’ and that of continental Europe during and after World War II. This article focuses on Western Europe and Americanisation, highlighting Americanisation from above and Americanisation from below. It looks at two concepts that often come up within debates about Americanisation: Westernisation and anti-Americanism.

Sign in / Sign up

Export Citation Format

Share Document