In this task, you're given a paragraph from the research paper and your task is to generate a suitable title for the research paper based on the given paper. Under 100 words is a good title length.

a b s t r a c t H1N1 is an infectious virus which, when spread affects a large volume of the population. It is an airborne disease that spreads easily and has a high death rate. Development of healthcare support systems using cloud computing is emerging as an effective solution with the benefits of better quality of service, reduced costs and flexibility. In this paper, an effective cloud computing architecture is proposed which predicts H1N1 infected patients and provides preventions to control infection rate. It consists of four processing components along with secure cloud storage medical database. The random decision tree is used to initially assess the infection in any patient depending on his/her symptoms. Social Network Analysis (SNA) is used to present the state of the outbreak. The proposed architecture is tested on synthetic data generated for two million users. The system provided 94% accuracy for the classification and around 81% of the resource utilization on Amazon EC2 cloud. The key point of the paper is the use of SNA graphs to calculate role of an infected user in spreading the outbreak known as Outbreak Role Index (ORI). It will help government agencies and healthcare departments to present, analyze and prevent outbreak effectively.
Smart monitoring and controlling of Pandemic Influenza A (H1N1) using Social Network Analysis and cloud computing

Business integration is an important determinant of business value and firm performance which enables a firm to respond to pressing competitive forces. Given its significant role, business integration in general has been an enduring research topic over the years. However, due to its complex nature, the ways to achieve this important organisational capability in firms are still not clearly understood. Hence, the present study is an attempt to 'open this black box' and examine the relevant issues through a case study of M.com, one of the largest e-commerce firms in China. The findings of this case study emphasise that the ability to coordinate organisational interdependencies and asset orchestration according to the needs of business processes is challenging but necessary in order to achieve business integration in e-commerce firms. It is found that asset orchestration in a firm is contingent on the organisational interdependencies such that different interdependence types demand different coordination methods at the very least. It also shows the significant role of IT in asset orchestration and the coordination of organisational interdependencies during business integration. With these capabilities, firms can experience growth and improve the quality of conformance to customer needs.
The role of organisational interdependencies and asset orchestration in business integration: A case study of M.com

Background: False negative results of SARS-CoV-2 nucleic acid detection pose threats to COVID-19 patients and medical workers alike. Objective: To develop multivariate models to determine clinical characteristics that contribute to false negative results of SARS-CoV-2 nucleic acid detection, and use them to predict false negative results as well as time windows for testing positive. Design: Retrospective Cohort Study (Ethics number of Tongji Hospital Measurements: Clinical information on CT imaging reports, blood routine tests, and clinic symptoms were collected. A multivariate logistic regression was used to explain and predict false negative testing results of SARS-CoV-2 detection. A multivariate accelerated failure model was used to analyze and predict delayed time windows for testing positive. Results: Of the 1,324 outpatients who diagnosed of COVID-19, 633 patients tested positive in their first SARS-CoV-2 nucleic acid test (47.8%), with a mean age of 51 years (SD=14.9); the rest, which had a mean age of 47 years (SD=15.4), tested negative in the first test. "Ground glass opacity" in a CT imaging report was associated with a lower chance of false negatives (aOR, 0.56), and reduced the length of time window for testing positive by 26%. "Consolidation" was associated with a higher chance of false negatives (aOR, 1.57), and extended the length of time window for testing positive by 44%. In blood routine tests, basophils (aOR, 1.28) and eosinophils (aOR, 1.29) were associated with a higher chance of false negatives, and were found to extend the time window for testing positive by 23% and 41%, respectively. Age and gender also affected the significantly. Limitation: Data were generated in a large single-center study. Testing outcome and positive window of SARS-CoV-2 detection for COVID-19 patients were associated with CT imaging results, blood routine tests, and clinical symptoms. Taking into account relevant information in CT imaging reports, blood routine tests, and clinical symptoms helped reduce a false negative testing outcome. The predictive AFT model, what we believe to be one of the first statistical models for predicting time window of SARS-CoV-2 detection, could help clinicians improve the accuracy and efficiency of the diagnosis, and hence, optimizes the timing of nucleic acid detection and alleviates the shortage of nucleic acid detection kits around the world. All rights reserved. No reuse allowed without permission. author/funder, who has granted medRxiv a license to display the preprint in perpetuity.

Analysis and Prediction of COVID-19 Patients' False Negative Results for SARS-CoV-2 Detection with Pharyngeal Swab Specimen: A Retrospective Study Running Title: Prediction of False Negative Results for SARS-CoV-2 Detection