Browsing by Author "Wang T"
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- Item3D Printing of Textured Soft Hybrid Meat Analogues(MDPI (Basel, Switzerland), 2022-02-06) Wang T; Kaur L; Furuhata Y; Aoyama H; Singh J; Mirade PSMeat analogue is a food product mainly made of plant proteins. It is considered to be a sustainable food and has gained a lot of interest in recent years. Hybrid meat is a next generation meat analogue prepared by the co-processing of both plant and animal protein ingredients at different ratios and is considered to be nutritionally superior to the currently available plant-only meat analogues. Three-dimensional (3D) printing technology is becoming increasingly popular in food processing. Three-dimensional food printing involves the modification of food structures, which leads to the creation of soft food. Currently, there is no available research on 3D printing of meat analogues. This study was carried out to create plant and animal protein-based formulations for 3D printing of hybrid meat analogues with soft textures. Pea protein isolate (PPI) and chicken mince were selected as the main plant protein and meat sources, respectively, for 3D printing tests. Then, rheology and forward extrusion tests were carried out on these selected samples to obtain a basic understanding of their potential printability. Afterwards, extrusion-based 3D printing was conducted to print a 3D chicken nugget shape. The addition of 20% chicken mince paste to PPI based paste achieved better printability and fibre structure.
- ItemForecasting Eruptions at Poorly Known Volcanoes Using Analogs and Multivariate Renewal Processes(John Wiley and Sons, Inc on behalf of the American Geophysical Union, 2022-06-28) Wang T; Bebbington M; Cronin S; Carman JForecasting future destructive eruptions from re-awakening volcanoes remains a challenge, mainly due to a lack of previous event data. This sparks a search for similar volcanoes to provide additional information, especially those with better compiled and understood event records. However, we show that some of the most obviously geologically comparable volcanoes have differing statistical occurrence patterns. Using such matches produces large forecasting uncertainties. We created a statistical tool to identify and test the compatibility of potential analogue volcanoes based on repose-time characteristics from world-wide datasets. Selecting analogue volcanoes with compatible behavior for factors being forecast, such as repose time, significantly reduces forecasting uncertainties. Applying this tool to Tongariro volcano (NZ), there is a 5% probability for a Volcanic Explosivity Index (VEI) ≥ 3 explosive eruption in the next 50 years. Using pre-historic geological records of a smaller available set of analogs, we forecast a 1% probability of a VEI ≥ 4 eruption in the next 50 years.
- ItemManipulating the alpha level cannot cure significance testing – comments on "Redefine statistical significance"(PeerJ Preprints, 2017-11-14) Trafimov D; Amrhein V; Areshenkoff CN; Barrera - Causil C; Beh EJ; Bilgiç Y; Bono R; Bradley MT; Briggs WM; Cepeda - Freyre HA; Chaigneau SE; Ciocca DR; Correa JC; Cousineau D; de Boer MR; Dhar SS; Dolgov I; Gómez - Benito J; Grendar M; Grice J; Guerrero - Gimenez ME; Gutiérrez A; Huedo - Medina TB; Jaffe K; Janyan A; Karimnezhad A; Korner - Nievergelt F; Kosugi K; Lachmair M; Ledesma R; Limongi R; Liuzza MT; Lombardo R; Marks M; Meinlschmidt G; Nalborczyk L; Nguyen HT; Ospina R; Perezgonzalez JD; Pfister R; Rahona JJ; Rodríguez - Medina DA; Romão X; Ruiz - Fernández S; Suarez I; Tegethoff M; Tejo M; van de Schoot R; Vankov I; Velasco - Forero S; Wang T; Yamada Y; Zoppino FCM; Marmolejo - Ramos FWe argue that depending on p-values to reject null hypotheses, including a recent call for changing the canonical alpha level for statistical significance from .05 to .005, is deleterious for the finding of new discoveries and the progress of science. Given that blanket and variable criterion levels both are problematic, it is sensible to dispense with significance testing altogether. There are alternatives that address study design and determining sample sizes much more directly than significance testing does; but none of the statistical tools should replace significance testing as the new magic method giving clear-cut mechanical answers. Inference should not be based on single studies at all, but on cumulative evidence from multiple independent studies. When evaluating the strength of the evidence, we should consider, for example, auxiliary assumptions, the strength of the experimental design, or implications for applications. To boil all this down to a binary decision based on a p-value threshold of .05, .01, .005, or anything else, is not acceptable.