Application of Multi Criteria Decision Making (MCDM) to Analyse the Impact of External Environment on Innovation Ecosystem

The Innovation ecosystem has become an emerging area of academic research within the last 15 years Grandstrand and Holgersson, 2020) and this has been recognized as a key element to achieve value proposition in particular for innovative firms (Talmar et al. 2020). However, no comprehensive approach has been developed in order to understand the extent to which this ecosystem is affected by external environment and if any remedial action needs to be taken by the firms in order to bring the ecosystem back to its normal status following a major external event/shock with a severe impact or potential disruption

The Prevalence of Risk Factors for the Development of Bacteraemia in Children

AIM: The objective of this study was to evaluate the frequency of risk factors for bacteremia in children less than 15 years of age was determined in Bahrami Hospital during 2013-2016. METHODS: This study conducted on 84 children aged 3 months’ to15 years old, who hospitalised in the pediatrics ward and the PICU in Bahrami Hospital from 2012 to 2016. Our study consisted of 46 boys (54.2%) and 38 girls. Moreover, 24.1% of subjects (20 patients) were entered in the study as young as three months old, followed by three months to three years (49.4 %; 41 subjects), and 3 to 15 years of age (26.5%; 22 individuals). RESULTS: The average hospitalization duration was determined to be 15.30 ± 8.75 days. Moreover, our results revealed that a history of blood transfusion in 11.2% of patients. On the other hand, 35.7% of cases were determined to be positive for blood cultures. The microorganisms reported from positive blood cultures include Enterobacter (81.48%), Escherichia coli (11.11%) and Klebsiella (3.70%). Also, 50% of patients were hospitalised in the internal ward, 12% received immunosuppressive drugs, and 96.4% of the patients had a history of vaccination. CONCLUSION: Pediatric severe sepsis remains a burdensome public health problem, with prevalence, morbidity, and mortality rates similar to those reported in critically ill adult populations. International clinical trials targeting children with severe sepsis are warranted

Urinary antigene and PCR can both be used to detect Legionella pneumophila in childrens hospital-acquired pneumonia

Legionella pneumophila is the causative agent of more than 95 cases of severe Legionella pneumonia. Nosocomial pneumonias in different hospital wards is an important medical and pharmaceutical concern. This study aimed to detect Legionella with two methods: polymerase chain reaction PCR and detection of urine antigenic test UAT in patients suffering from nosocomial pneumonia admitted to pediatric intensive care unit PICU of children hospitals. This study was conducted in PICU wards of Rasool Akram and Bahrami children hospitals, Tehran, Iran during 2013- 2014. In patients diagnosed with hospital-acquired pneumonia, intratracheal secretion samples for PCR and urine sample for urinary antigen test UTA were taken. Simultaneously, PCR and urinary antigen test were conducted using commercial kits. The results of urinary antigen test and PCR were analyzed by SPSS v.19 for statistical comparison. In this study, 96 patients aging 2.77 years on average with two age peaks of less than 1 year and 7-8 year were enrolled. More than half of the patients were under 1 year old. The most common underlying diseases were seizure, Acute Lymphoblastic Lymphoma, Down syndrome and metabolic syndromes. The positivity rate of Legionella urinary antigen test was 16.7% and positivity rate of PCR test was 19.8%. There were no significant associations between the results obtained by both assays with age, gender or underlying diseases. In conclusion, PCR is a better detection method for Legionella infection than urinary antigen test, but the difference between the two methods was not significant. © 2019 PAGEPress Publications. All rights reserved

Urinary antigene and PCR can both be used to detect Legionella pneumophila in children's hospital-acquired pneumonia

Legionella pneumophila is the causative agent of more than 95% cases of severe Legionella pneumonia. Nosocomial pneumonias in different hospital wards is an important medical and pharmaceutical concern. This study aimed to detect Legionella with two methods: polymerase chain reaction [PCR] and detection of urine antigenic test [UAT] in patients suffering from nosocomial pneumonia admitted to pediatric intensive care unit [PICU] of children hospitals. This study was conducted in PICU wards of Rasool Akram and Bahrami children hospitals, Tehran, Iran during 2013 - 2014. In patients diagnosed with hospital-acquired pneumonia, intratracheal secretion samples for PCR and urine sample for urinary antigen test [UTA] were taken. Simultaneously, PCR and urinary antigen test were conducted using commercial kits. The results of urinary antigen test and PCR were analyzed by SPSS v.19 for statistical comparison. In this study, 96 patients aging 2.77 years on average with two age peaks of less than 1 year and 7-8 year were enrolled. More than half of the patients were under 1 year old. The most common underlying diseases were seizure, Acute Lymphoblastic Lymphoma, Down syndrome and metabolic syndromes. The positivity rate of Legionella urinary antigen test was 16.7% and positivity rate of PCR test was 19.8%. There were no significant associations between the results obtained by both assays with age, gender or underlying diseases. In conclusion, PCR is a better detection method for Legionella infection than urinary antigen test, but the difference between the two methods was not significant

Machine Learning-enabled Decision Support System (ML-DSS) for Asset Condition Monitoring

Condition monitoring (CM) is a critical component of industrial asset maintenance and management, particularly in the manufacturing context. CM identifies significant changes in a piece of machinery’s performance which could be indicative of a developing fault and potentially lead to significant operational cost and even major disruption in manufacturing and production.Implementation of CM in a typical industrial environment requires support by a system of interconnected software and hardware elements. Traditionally, these systems were developed merely for the specific task of asset health monitoring. However, the digitalisation wave of Industry 4.0 and wider application of artificial intelligence-based (smart) technologies has provided a great opportunity for further development of these systems, thereby making substantial contributions to the efficiency of manufacturing and production.As a part of a UK Government (InnovateUK)-funded project, an intelligent condition monitoring system (called JANUS) was designed and developed in the research and development (R&amp;D) division of Monition Limited (now RS Group plc) in order to contribute to operational efficiency not only by means of reducing asset downtime via more accurate and on-time prediction of asset health condition but more efficient use of technicians/labour resources. In order to meet these objectives, JANUS used supervised learning-based machine learning (ML) algorithms along with multi-criteria decision-making techniques to develop an ML-enabled decision support system for analysis of asset condition monitoring data.<br/