Maxwell's role in turning the concept of model into the methodology of modeling

This is a contribution towards a history and philosophy of modeling in its early stages in electromagnetism. In 1873, James Clerk Maxwell (1831–1879) hinted at the methodology of modeling at the end of his Treatise on Electricity and Magnetism. We focus on Maxwell's impact on physicists who immediately followed him, specifically Oliver Lodge (1851–1940) and George Francis FitzGerald (1851–1901). We begin with the role that the scientific concept of model played in the late nineteenth century, as assessed by Ludwig Boltzmann (1844–1906). We then discuss the role of hypothesis as a methodology, the appeal to (dynamical) illustration, and the way Maxwell applied model and working model in his studies of electromagnetism. We show that for Maxwell these key terms were kept distinct, but Lodge did not maintain these distinctions and, in this regard, FitzGerald followed Lodge. Notwithstanding Lodge's influence, Fitzgerald modified Maxwell's theory based on the mechanical model he designed, thereby implicitly taking the first step towards modeling. This methodology consists in drawing consequences from the (mechanical) model to the (electrodynamic) theory and modifying the latter in light of the functioning of the former. At the core of our argument is the thesis that it was a methodological novelty to move from the concept of model to the methodology of modeling. The introduction of modeling as a new methodology into physics in the late nineteenth century was a major event which deserves proper recognition.     

Little-known and phylogenetically obscure South African estuarine microgastropods (Mollusca: Truncatelloidea) as living animals

Areas of the Knysna estuarine bay in the Western Cape are dominated by three endemic South African truncatelloid microgastropods, temporarily known as ‘Hydrobia’ knysnaensis (Krauss), ‘Assiminea’ capensis (Sowerby) and ‘Assiminea’ globulus Connolly. Although first described 80–170 years ago and present in abundance (up to 100,000 m^?2), they remain surrounded by confusion and still await taxonomic assignment, largely because they appear most atypical members of their groups by virtue of anatomy and/or biogeography and/or habitat. This study contributes in-life perspectives to morphological and phylogenetic analyses known to be on-going. At Knysna, they are syntopic: at least two occurring in >85% and all three in >40% of individual 0.0026 m² samples from their region of dominance. Nevertheless, they tend to greater abundance in divergent microhabitats; ‘A.’ globulus dominating higher tidal levels, and ‘A.’ capensis and ‘Hydrobia’ lower ones; the former especially unvegetated sediment, the latter, if anything, seagrass. Interspecific feeding interactions appear unlikely to be responsible for these patterns, other evidence suggesting that all are maintained below carrying capacity. Field biology of ‘H.’ knysnaensis generally appears equivalent to that of northern-hemisphere intertidal hydrobiids and that of ‘A.’ globulus is typically assimineid, albeit at atypically low shore height. Unlike assimineids, however, ‘A.’ capensis is truly aquatic. The success of these truncatelloids in unusual circumstances may be consequent on the absence from South Africa of other microgastropod groups that fill their niches elsewhere in the southern hemisphere.     

Constructing and evaluating core inflation measures from component‐level inflation data

This paper undertakes a comprehensive examination of 10 measures of core inflation and evaluates which measure produces the best forecast of headline inflation out‐of‐sample. We use the Personal Consumption Expenditure Price Index as our measure of inflation. We use two sets of components (17 and 50) of the Personal Consumption Expenditure Price Index to construct these core inflation measures and evaluate these measures at the three time horizons (6, 12 and 24 months) most relevant for monetary policy decisions. The best measure of core inflation for both sets of components and over all time horizons uses weights based on the first principal component of the disaggregated (component‐level) prices. Interestingly, the results vary by the number of components used; when more components are used the weights based on the persistence of each component is statistically equivalent to the weights generated by the first principal component. However, those forecasts using the persistence of 50 components are statistically worse than those generated using the first principal component of 17 components. The statistical superiority of the principal component method is due to the fact that it extracts (in the first principal component) the common source of variation in the component level prices that accurately describes trend inflation over the next 6–24 months.     

Methods for backcasting,nowcasting and forecasting using factor‐MIDAS: With an application to Korean GDP

We utilize mixed‐frequency factor‐MIDAS models for the purpose of carrying out backcasting, nowcasting, and forecasting experiments using real‐time data. We also introduce a new real‐time Korean GDP dataset, which is the focus of our experiments. The methodology that we utilize involves first estimating common latent factors (i.e., diffusion indices) from 190 monthly macroeconomic and financial series using various estimation strategies. These factors are then included, along with standard variables measured at multiple different frequencies, in various factor‐MIDAS prediction models. Our key empirical findings as follows. (i) When using real‐time data, factor‐MIDAS prediction models outperform various linear benchmark models. Interestingly, the “MSFE‐best” MIDAS models contain no autoregressive (AR) lag terms when backcasting and nowcasting. AR terms only begin to play a role in “true” forecasting contexts. (ii) Models that utilize only one or two factors are “MSFE‐best” at all forecasting horizons, but not at any backcasting and nowcasting horizons. In these latter contexts, much more heavily parametrized models with many factors are preferred. (iii) Real‐time data are crucial for forecasting Korean gross domestic product, and the use of “first available” versus “most recent” data “strongly” affects model selection and performance. (iv) Recursively estimated models are almost always “MSFE‐best,” and models estimated using autoregressive interpolation dominate those estimated using other interpolation methods. (v) Factors estimated using recursive principal component estimation methods have more predictive content than those estimated using a variety of other (more sophisticated) approaches. This result is particularly prevalent for our “MSFE‐best” factor‐MIDAS models, across virtually all forecast horizons, estimation schemes, and data vintages that are analyzed.     

Environmental Friendly Route and Wool Shrink Proofing

The shrink proofing on wool with treatment of protease named Argaenzyme STL was studied. The various pretreating auxiliaries, different parameters for protease treating process and the effect of stabilizer were discussed in detail. The varieties of some properties before and after protease treatment were also investigated.     

Photon emission of phagocytes in relation to stress and disease.

Phagocytes, the first-line cells of the body's defence mechanisms against invading pathogens, kill microorganisms by means of lysosomal degradative enzymes and highly toxic reactive oxygen intermediates. The reactive oxygen compounds are produced, in a process called the 'respiratory burst', by the NADPH oxidase complex in plasma membranes, and by myeloperoxidase in phagolysosomes after degranulation. These processes generate electronically excited states which, on relaxation, emit photons, giving rise to phagocyte chemiluminescence (CL). This paper describes the conditions for the measurement of CL, and reviews the activity of phagocytes from individuals undergoing stress or disease. The capability of phagocytes to emit photons reflects remarkably well the pathophysiological state of the host. In many cases even the magnitude of the stress, the presence of a pathogen in the body, or the activity of the disease can be estimated. Physiological changes, e.g. in the reproductive cycle, can also be predicted.