A critical insight on nanofluids for heat transfer enhancement.

There are numerous reports and publications in reputable scientific and engineering journals that attribute substantial enhancement in heat transfer capabilities for heat exchangers once they employ nanofluids as working fluids. By definition, a nanofluid is a working fluid that has a small volume fraction (5% or less) of a solid particle with dimensions in the nanoscale. The addition of this solid material has a reported significant impact on convective heat transfer in heat exchangers. This work investigates the significance of the reported enhancements in many recent related publications. Observations on these publications' geographical origins, fundamental heat transfer calculations, experimental setups and lack of potential applications are critically made. Heat transfer calculations based on methodologies outlined in random selection of available papers were conducted along with a statistical analysis show paradoxically inconsistent conclusion as well as an apparent lack of complete comprehension of convective heat transfer mechanism. In some of the surveyed literature for example, heat transfer coefficient enhancements were reported to be up to 27% and 48%, whereas the recalculations presented in this work restrain proclaimed enactments to ~ 3.5% and - 4% (no enhancement), respectively. This work aims at allowing a healthy scientific debate on whether nanofluids are the sole answer to enhancing convective heat transfer in heat exchangers. The quantity of literature that confirms the latter statement have an undeniable critical mass, but this volition could be stemming from and heading to the wrong direction. Finally, the challenges imposed by the physical nature of nanoparticles, as well as economic limitations caused by the high price of conventional nanoparticles such as gold (80$/g), diamond (35$/g), and silver (6$/g) that hinder their commercialization, are presented.

Economic statistical model of the np chart for monitoring defectives.

When monitoring manufacturing processes, managing an attribute quality characteristic is easier and faster than a variable quality characteristic. Yet, the economic-statistical design of attribute control charts has attracted much less attention than variable control charts in the literature. This study develops an algorithm for optimizing the economic-statistical performance of the np chart for monitoring defectives, based on Duncan's economic model. This algorithm has the merit of the economic model to minimize expected total cost, and the benefit of the statistical design to enhance the effectiveness of detecting increasing shifts in defectives. The effectiveness of the developed np chart is investigated under different operational scenarios. The results affirm a considerable superiority of the proposed np chart over the traditional np chart. Real-life data are used to demonstrate the applicability of the proposed np scheme, in comparison to the traditional np chart.

SEIR Model to address the impact of face masks amid COVID-19 pandemic.

Early in the pandemic of coronavirus disease 2019 (COVID-19), face masks were used extensively by the general public in several Asian countries. The lower transmission rate of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in Asian countries compared with Western countries suggested that the wider community use of face masks has the potential to decrease transmission of SARS-CoV-2. A risk assessment model named Susceptible, Exposed, Infectious, Recovered (SEIR) model is used to quantitatively evaluate the potential impact of community face masks on SARS-CoV-2 reproduction number (R0 ) and peak number of infectious persons. For a simulated population of one million, the model showed a reduction in R0 of 49% and 50% when 60% and 80% of the population wore masks, respectively. Moreover, we present a modified model that considers the effect of mask-wearing after community vaccination. Interestingly mask-wearing still provided a considerable benefit in lowering the number of infectious individuals. The results of this research are expected to help public health officials in making prompt decisions involving resource allocation and crafting legislation.

Use of Statistical Process Control Methods for Early Detection of Healthcare Facility-Associated Nontuberculous Mycobacteria Outbreaks: A Single-Center Pilot Study.

BACKGROUND: Nontuberculous mycobacteria (NTM) are emerging pathogens increasingly implicated in healthcare facility-associated (HCFA) infections and outbreaks. We analyzed the performance of statistical process control (SPC) methods in detecting HCFA NTM outbreaks. METHODS: We retrospectively analyzed 3 NTM outbreaks that occurred from 2013 to 2016 at a tertiary care hospital. The outbreaks consisted of pulmonary Mycobacterium abscessus complex (MABC) acquisition, cardiac surgery-associated extrapulmonary MABC infection, and a bronchoscopy-associated pseudo-outbreak of Mycobacterium avium complex (MAC). We analyzed monthly case rates of unique patients who had positive respiratory cultures for MABC, non-respiratory cultures for MABC, and bronchoalveolar lavage cultures for MAC, respectively. For each outbreak, we used these rates to construct a pilot moving average (MA) SPC chart with a rolling baseline window. We also explored the performance of numerous alternative control charts, including exponentially weighted MA, Shewhart, and cumulative sum charts. RESULTS: The pilot MA chart detected each outbreak within 2 months of outbreak onset, preceding actual outbreak detection by an average of 6 months. Over a combined 117 months of pre-outbreak and post-outbreak surveillance, no false-positive SPC signals occurred (specificity, 100%). Prospective use of this chart for NTM surveillance could have prevented an estimated 108 cases of NTM. Six high-performing alternative charts detected all outbreaks during the month of onset, with specificities ranging from 85.7% to 94.9%. CONCLUSIONS: SPC methods have potential to substantially improve HCFA NTM surveillance, promoting early outbreak detection and prevention of NTM infections. Additional study is needed to determine the best application of SPC for prospective HCFA NTM surveillance in other settings.

Monitoring scheme for early detection of coronavirus and other respiratory virus outbreaks.

In December 2019, an outbreak of pneumonia caused by a novel coronavirus (severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2]) began in Wuhan, China. SARS-CoV-2 exhibited efficient person-to-person transmission of what became labeled as COVID-19. It has spread worldwide with over 83,000,000 infected cases and more than 1,800,000 deaths to date (December 31, 2020). This research proposes a statistical monitoring scheme in which an optimized np control chart is utilized by sentinel metropolitan airports worldwide for early detection of coronavirus and other respiratory virus outbreaks. The sample size of this chart is optimized to ensure the best overall performance for detecting a wide range of shifts in the infection rate, based on the available resources, such as the inspection rate and the allowable false alarm rate. The effectiveness of the proposed optimized np chart is compared with that of the traditional np chart with a predetermined sample size under both sampling inspection and 100% inspection. For a variety of scenarios including a real case, the optimized np control chart is found to substantially outperform its traditional counterpart in terms of the average number of infections. Therefore, this control chart has potential to be an effective tool for early detection of respiratory virus outbreaks, promoting early outbreak investigation and mitigation.

Performance of statistical process control methods for regional surgical site infection surveillance: a 10-year multicentre pilot study.

BACKGROUND: Traditional strategies for surveillance of surgical site infections (SSI) have multiple limitations, including delayed and incomplete outbreak detection. Statistical process control (SPC) methods address these deficiencies by combining longitudinal analysis with graphical presentation of data. METHODS: We performed a pilot study within a large network of community hospitals to evaluate performance of SPC methods for detecting SSI outbreaks. We applied conventional Shewhart and exponentially weighted moving average (EWMA) SPC charts to 10 previously investigated SSI outbreaks that occurred from 2003 to 2013. We compared the results of SPC surveillance to the results of traditional SSI surveillance methods. Then, we analysed the performance of modified SPC charts constructed with different outbreak detection rules, EWMA smoothing factors and baseline SSI rate calculations. RESULTS: Conventional Shewhart and EWMA SPC charts both detected 8 of the 10 SSI outbreaks analysed, in each case prior to the date of traditional detection. Among detected outbreaks, conventional Shewhart chart detection occurred a median of 12 months prior to outbreak onset and 22 months prior to traditional detection. Conventional EWMA chart detection occurred a median of 7months prior to outbreak onset and 14 months prior to traditional detection. Modified Shewhart and EWMA charts additionally detected several outbreaks earlier than conventional SPC charts. Shewhart and SPC charts had low false-positive rates when used to analyse separate control hospital SSI data. CONCLUSIONS: Our findings illustrate the potential usefulness and feasibility of real-time SPC surveillance of SSI to rapidly identify outbreaks and improve patient safety. Further study is needed to optimise SPC chart selection and calculation, statistical outbreak detection rules and the process for reacting to signals of potential outbreaks.