Additional Pages in PSG Data Book For AU ExamsRegulations: 2013Department: B.E. Mechanical Engineering.Subject Code: ME 6503Subject: Design of Machine ElementsContent Details:The COE has Permitted Additional Pages for the Examinations along with the PSG Design Data Book for the Students Belongs to Regulations 2013. Some insufficiency of data found in the Data book and so few additional pages have been added which will be effect from the upcoming November/December 2015 Examinations.For Additional Pages Details:(Size: 7.86 MB / Downloads: 19,995).
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The book presents select proceedings of the International Conference on Materials, Design and Manufacturing (ICMDMSE 2022). The book covers recent trends in design and manufacturing practices relating to sustainability. Various topics covered in this book include materials design for sustainability, material characterization, tribology, finite element methods (FEM), computational fluid dynamics in designing materials, manufacturing techniques inclined to sustainability, additive manufacturing, energy, Industry 4.0, MEMS, green manufacturing, and optimization techniques. This book will be useful for researchers and professionals working in various fields of mechanical engineering.
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.
Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true. As shown above, the post-study probability that a finding is true (PPV) depends a lot on the pre-study odds (R). Thus, research findings are more likely true in confirmatory designs, such as large phase III randomized controlled trials, or meta-analyses thereof, than in hypothesis-generating experiments. Fields considered highly informative and creative given the wealth of the assembled and tested information, such as microarrays and other high-throughput discovery-oriented research [4,8,17], should have extremely low PPV.
These corollaries consider each factor separately, but these factors often influence each other. For example, investigators working in fields where true effect sizes are perceived to be small may be more likely to perform large studies than investigators working in fields where true effect sizes are perceived to be large. Or prejudice may prevail in a hot scientific field, further undermining the predictive value of its research findings. Highly prejudiced stakeholders may even create a barrier that aborts efforts at obtaining and disseminating opposing results. Conversely, the fact that a field is hot or has strong invested interests may sometimes promote larger studies and improved standards of research, enhancing the predictive value of its research findings. Or massive discovery-oriented testing may result in such a large yield of significant relationships that investigators have enough to report and search further and thus refrain from data dredging and manipulation.
For fields with very low PPV, the few true relationships would not distort this overall picture much. Even if a few relationships are true, the shape of the distribution of the observed effects would still yield a clear measure of the biases involved in the field. This concept totally reverses the way we view scientific results. Traditionally, investigators have viewed large and highly significant effects with excitement, as signs of important discoveries. Too large and too highly significant effects may actually be more likely to be signs of large bias in most fields of modern research. They should lead investigators to careful critical thinking about what might have gone wrong with their data, analyses, and results.
Second, most research questions are addressed by many teams, and it is misleading to emphasize the statistically significant findings of any single team. What matters is the totality of the evidence. Diminishing bias through enhanced research standards and curtailing of prejudices may also help. However, this may require a change in scientific mentality that might be difficult to achieve. In some research designs, efforts may also be more successful with upfront registration of studies, e.g., randomized trials [35]. Registration would pose a challenge for hypothesis-generating research. Some kind of registration or networking of data collections or investigators within fields may be more feasible than registration of each and every hypothesis-generating experiment. Regardless, even if we do not see a great deal of progress with registration of studies in other fields, the principles of developing and adhering to a protocol could be more widely borrowed from randomized controlled trials.
Nevertheless, most new discoveries will continue to stem from hypothesis-generating research with low or very low pre-study odds. We should then acknowledge that statistical significance testing in the report of a single study gives only a partial picture, without knowing how much testing has been done outside the report and in the relevant field at large. Despite a large statistical literature for multiple testing corrections [37], usually it is impossible to decipher how much data dredging by the reporting authors or other research teams has preceded a reported research finding. Even if determining this were feasible, this would not inform us about the pre-study odds. Thus, it is unavoidable that one should make approximate assumptions on how many relationships are expected to be true among those probed across the relevant research fields and research designs. The wider field may yield some guidance for estimating this probability for the isolated research project. Experiences from biases detected in other neighboring fields would also be useful to draw upon. Even though these assumptions would be considerably subjective, they would still be very useful in interpreting research claims and putting them in context.
HIGHLIGHTSInfection rate in cabins with previous case was not statistically different than those without.
Age of subject was found not to be a confounding factor.
Conflicting results from previous studies of this outbreak are largely explainable.
Airborne transmission through the HVAC system could explain the virus spread on the ship.
Recirculated air and inadequate air filtration likely increased airborne transmission.
ABSTRACT The Diamond Princess cruise ship is a unique COVID-19 transmission case because of the high testing capacity and the confined environment. This exploratory study aims to raise the hypothesis regarding the role of poor ventilation systems in the spread of COVID-19 by analysing count data collected by the onboard clinic during the outbreak, and considering the deck plan and design of the air conditioning system of the ship. Observed symptomatic infection rate after day 5 (incubation period median day) of the quarantine, in cabins without previous confirmed cases are compared to that in cabins with previous confirmed cases. Accordingly, the observed symptomatic infection rate in cabins without a previously confirmed case (1.2%) was higher than for cabins with a previously confirmed case (0.8%); however, the difference was not statistically significant. In addition, age did not appear to be a confounding variable. Airborne transmission of COVID-19 through the ventilation system onboard could explain the higher than expected virus spread into cabins without previously confirmed cases during the quarantine period; thus, this study provides further potential evidence of coronavirus transmission by aerosols. Conflicting results from other studies involving the Diamond Princess outbreak are also discussed in light of our results.
There is accumulating evidence of COVID-19 spreading widely in confined settings such as restaurants, hospitals, care homes, shops, gyms, public transport, offices, schools, prisons, etc. Ventilation system design, filters, and upgrades; natural ventilation (just using outside air and not recirculating it); and airflow (direction/speed) should be all considered and evaluated when deciding what intervention measure(s) is appropriate to reduce exposure and limit the transmission of COVID-19 in a confined setting. Keeping two meters distance between customers in a shop, for instance, is not an effective measure without considering the air flow inside the shop. Self-isolating residents of a care home inside the rooms is not an effective measure if the ventilation system in the care home does not have highly-efficient filters that can capture a virus as small as COVID-19, especially if the HVAC system is working on a save-energy mode and the air is kept recirculated inside the residence. 2ff7e9595c
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