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2022 ◽  
Vol 191 ◽  
pp. 116239
Author(s):  
Carlo Marcelo Revoredo da Silva ◽  
Bruno José Torres Fernandes ◽  
Eduardo Luzeiro Feitosa ◽  
Vinicius Cardoso Garcia
Keyword(s):  

2022 ◽  
pp. 17-26
Author(s):  
Zhen-Zhen Chen ◽  
Rong-Jie Li ◽  
Xin-Yi He ◽  
Zhen-Xin Lian ◽  
Zne-Jung Lee

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, the pandemic situation has begun to undergo positive changes with the joint efforts of various countries and world organizations. However, pressures such as the COVID-19 mutations and the sharp rise in confirmed cases have brought uncertainties to the prevention and control of the pandemic. The overall situation is still severe and complex. Based on the multi-dimensional spatial-temporal COVID-19 data collected by the open-source NetEase News (NEN) website and a real-time dynamic website, it is to explore the characteristics of the pandemic data, visualize the development trend, and analyze the spread of the pandemic in this paper. Moreover, it is to provide a rule basis for the prevention and control of the COVID-19 pandemic by constructing the decision tree model. From the results, some suggestions are provided for decision-makers.


2022 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Manisha Bhardwaj ◽  
Rajat Agrawal

PurposeThe purpose of this paper is to facilitate perishable product supply chain (PPSC) managers and practitioners to assess PPSC failure events. The paper proposed fault tree methodology for assessing failures associated with PPSC for evaluating the performance in terms of effective PPSC management adoption.Design/methodology/approachInitially, different failure events were identified from literature and semi-structured interviews from experts. Fault tree model was developed from the identified failure events. Probability of failure events was calculated using Poisson distribution based on the annual reports and interviews conducted from experts. Further, qualitative analysis – minimum cut sets (MCSs), structural importance coefficient (SIC) – and quantitative analysis – Birnbaum importance measure (BIM), criticality importance factor (CIF) and diagnosis importance factor (DIF) – were performed for ranking of failure events. In this study, fault tree development and analysis were conducted on apple supply chain to present the authenticity of this method for failure analysis.FindingsThe findings indicate that the failure events, given as failure at production and procurement (A2), that is, involvement of middleman (BE3), handling and packaging failure (BE4) and transportation failure (A3), hold the highest-ranking scores in analysis of PPSC using fault tree approach.Originality/valueThis research uses the modularization approach for evaluation of failure events of PPSC. This paper explores failures related to PPSC for efficient management initiatives in apple supply chain context. The paper also provides suggestion from managerial perspective with respect to each failure event.


2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Rajkumar Gangappa Nadakinamani ◽  
A. Reyana ◽  
Sandeep Kautish ◽  
A. S. Vibith ◽  
Yogita Gupta ◽  
...  

Cardiovascular disease is difficult to detect due to several risk factors, including high blood pressure, cholesterol, and an abnormal pulse rate. Accurate decision-making and optimal treatment are required to address cardiac risk. As machine learning technology advances, the healthcare industry’s clinical practice is likely to change. As a result, researchers and clinicians must recognize the importance of machine learning techniques. The main objective of this research is to recommend a machine learning-based cardiovascular disease prediction system that is highly accurate. In contrast, modern machine learning algorithms such as REP Tree, M5P Tree, Random Tree, Linear Regression, Naive Bayes, J48, and JRIP are used to classify popular cardiovascular datasets. The proposed CDPS’s performance was evaluated using a variety of metrics to identify the best suitable machine learning model. When it came to predicting cardiovascular disease patients, the Random Tree model performed admirably, with the highest accuracy of 100%, the lowest MAE of 0.0011, the lowest RMSE of 0.0231, and the fastest prediction time of 0.01 seconds.


2022 ◽  
Author(s):  
Liliana Dell’Osso ◽  
Ivan Mirko Cremone ◽  
Ilaria Chiarantini ◽  
Alessandro Arone ◽  
Danila Casagrande ◽  
...  

Abstract Purpose: The aim of the present study was to investigate the presence of ON symptoms, measured by means of the ORTO-R, in a sample of University students with or without AT, specifically focusing on evaluating the role of sex and of dietary habits in the association between ON and autism spectrum. Methods: Subjects were requested to anonymously fulfil the ORTO-R and the Adult Autism Subthreshold Autism Spectrum (AdAS Spectrum) through an online form. Results: 2140 students participated in the study. Subjects with significant AT reported significantly higher ORTO-R scores than subjects without AT. Females and subjects following a vegetarian/vegan diet reported significantly higher ORTO-R scores than males and than subjects following an omnivorous diet, respectively. Significant positive correlations were found between ORTO-R and AdAS Spectrum scores. A decision tree model, with sex, type of diet and presence of AT as independent variables and ORTO-R score as dependent variable, showed in the first step the presence of significantly higher ORTO-R scores among females than among males, and in the second step showed in both sexes the presence of higher ORTO-R scores among subjects with AT than in those without. A significant interaction of sex and presence/absence of AT was reported on ORTO-R score, with a higher increasing trend of ORTO-R score with the increase of AdAS Spectrum score among females than among males. Conclusions: Our results further highlighted the association between AT and ON, in particular among females.


Biology ◽  
2022 ◽  
Vol 11 (1) ◽  
pp. 94
Author(s):  
Edoardo Conticini ◽  
Miriana d’Alessandro ◽  
Laura Bergantini ◽  
Diego Castillo ◽  
Paolo Cameli ◽  
...  

Background: ANCA-associated vasculitis (AAV) are small vessel vasculitis distinguished between microscopic polyangiitis (MPA) and granulomatosis with polyangiitis (GPA). The former may have interstitial lung disease (ILD) associated with high morbidity and mortality. Here, Krebs von den Lungen-6 (KL-6), a marker of fibrotic ILD, was assessed for distinguishing AAV patients with ILD from those without ILD, and whether its changes over time are correlated with disease activity. Materials and Methods: Thirteen AAV patients (eight females, mean age 61 ± 14.8 years) were enrolled: six MPA and six GPA. Serum samples were assayed for KL-6 concentrations (Fujirebio Europe, Belgium). To investigate potential binary classifiers for diagnosis of AAV-ILD, we constructed a regression decision tree model. Results: Higher serum KL-6 were in AAV-ILD compared with those without ILD (972.8 ± 398.5 vs 305.4 ± 93.9, p = 0.0040). Area under the receiver operating characteristics curve showed 100% of the diagnostic performance of KL-6 for identifying the ILD involvement (accuracy 91.7%) and the best cutoff value of 368 U/mL (sensitivity 100% and specificity 87.5%). The decision tree model showed a 33% improvement in class purity using a cut-off value of 513 U/mL to distinguish AAV patients with and without ILD. Stratifying AAV patients as MPA and GPA with and without ILD considering T0 and T1 KL-6, the model obtained an improvement of 40% for classifying GPA non-ILD with a T0 serum KL-6 cut-off value of 513 U/mL and a T1 KL-6 cut-off of 301 U/mL. A direct correlation was found between serum T0 KL-6 and T0 BVAS (r = 0.578, p = 0.044). Conclusion: Our multicenter study demonstrated KL-6 as a reliable, non-invasive, and easy-to-perform marker of ILD in AAV patients and its helpfulness for disease activity assessment. Changes in serum concentrations of KL-6 over time could be useful for monitoring AAV patients. Further study of KL-6 as a marker of response to therapy during long-term follow-up would also be worthwhile.


2022 ◽  
Vol 12 ◽  
Author(s):  
Anna Mascellani ◽  
Kirsten Leiss ◽  
Johanna Bac-Molenaar ◽  
Milan Malanik ◽  
Petr Marsik ◽  
...  

Powdery mildew is a common disease affecting the commercial production of gerbera flowers (Gerbera hybrida, Asteraceae). Some varieties show a certain degree of resistance to it. Our objective was to identify biomarkers of resistance to powdery mildew using an 1H nuclear magnetic resonance spectroscopy and chemometrics approach in a complex, fully factorial experiment to suggest a target for selection and breeding. Resistant varieties were found to differ from those that were susceptible in the metabolites of the polyketide pathway, such as gerberin, parasorboside, and gerberinside. A new compound probably involved in resistance, 5-hydroxyhexanoic acid 3-O-β-D-glucoside, was described for the first time. A decision tree model was built to distinguish resistant varieties, with an accuracy of 57.7%, sensitivity of 72%, and specificity of 44.44% in an independent test. Our results suggest the mechanism of resistance to powdery mildew in gerbera and provide a potential tool for resistance screening in breeding programs.


2022 ◽  
Author(s):  
Jonathan Karnon

Objective: Easy and equitable access to testing is a cornerstone of the public health response to COVID-19. Currently in Australia, testing using Polymerase Chain Reaction (PCR) tests for COVID-19 is free-to-the-user, but the public purchase their own Rapid Antigen Tests (RATs). We conduct an economic analysis of government-funded RATs in Australia. Design: An interactive decision tree model was developed to compare one policy in which government-funded RATs are free-to-the-user, and one in which individuals purchase their own RATs. The decision tree represents RAT and PCR testing pathways for a cohort of individuals without COVID-19-like symptoms, to estimate the likelihood of COVID-19 positive individuals isolating prior to developing symptoms and the associated costs of testing, from a government perspective. Data sources: Test costs and detection rates were informed by published studies, other input parameter values are unobservable and uncertain, for which a range of scenario analyses are presented. Data synthesis: Assuming 10% prevalence of COVID-19 in a cohort of 10,000 individuals who would use government-funded RATs, the model estimates an additional 464 individuals would isolate early at a cost to the government of around $52,000. Scenario analyses indicate that the incremental cost per additional COVID-19 positive individual isolating with no symptoms remains at a few hundred dollars at 5% prevalence, rising to $2,052 at 1% prevalence. Conclusions: Based on the presented decision tree model, even only minor reductions in COVID-19 transmission rates due to early isolation would justify the additional costs associated with a policy of government-funded RATs.


Author(s):  
Muthupriya Vasudevan ◽  
Revathi Sathya Narayanan ◽  
Sabiyath Fatima Nakeeb ◽  
Abhishek Abhishek

Customer relationship management (CRM) is an important element in all forms of industry. This process involves ensuring that the customers of a business are satisfied with the product or services that they are paying for. Since most businesses collect and store large volumes of data about their customers; it is easy for the data analysts to use that data and perform predictive analysis. One aspect of this includes customer retention and customer churn. Customer churn is defined as the concept of understanding whether or not a customer of the company will stop using the product or service in future. In this paper a supervised machine learning algorithm has been implemented using Python to perform customer churn analysis on a given data-set of Telco, a mobile telecommunication company. This is achieved by building a decision tree model based on historical data provided by the company on the platform of Kaggle. This report also investigates the utility of extreme gradient boosting (XGBoost) library in the gradient boosting framework (XGB) of Python for its portable and flexible functionality which can be used to solve many data science related problems highly efficiently. The implementation result shows the accuracy is comparatively improved in XGBoost than other learning models.


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