Search Results (4)

Renewable fuels produced from animal- and plant-based edible oils have emerged as an alternative to oil and natural gas. Burgeoning interest in renewables can be attributed to the rapid depletion of fossil fuels caused by the global energy demand and the environmental advantages of renewables, specifically reduced emissions of greenhouse gases. An important property of the feedstock that is crucial for the conversion of edible oils to renewable fuels is the total acid number (TAN), as even a small increase in TAN for the feedstock can lead to corrosion of the catalyst in the refining process. Currently, the TAN is determined by potentiometric titration, which is time-consuming, expensive, and requires the preparation of reagents. As part of an effort to promote the use of renewable fuels, a partial least squares regression method with orthogonal signal correction to remove spectral information related to the sample background was developed to determine the TAN from the mid-infrared (IR) spectra of the feedstock. Digitally blended mid-IR spectral data were generated to fill in regions of the PLS calibration where there were very few samples. By combining experimental and digitally blended mid-IR spectral data to ensure adequate sample representation in all regions of the spectra–property calibration and better understand the spectra–property relationship through the identification of sample outliers in the original data that can be difficult to detect because of swamping, a PLS regression model for TAN (R² = 0.992, cross-validated root mean square error = 0.468, and bias = 0.0036) has been developed from 118 experimental and digitally blended mid-IR spectra of commercial feedstock. Thus, feedstock whose TAN value is too high for refining can be flagged using the proposed mid-IR method, which is faster and easier to use than the current titrimetric method. Full article

In paleobiology and macroevolution research, the spatial aggregation of fossil data can be influenced by the modifiable areal unit problem (MAUP), wherein the selection of different grid-cell sizes for data aggregation can lead to variations in statistical results. This study presents a case analysis focused on the spatial extent of marine bivalves and brachiopods over time across three Areas of Interest (AOIs) to evaluate the potential impact of the MAUP in grid-based fossil data processing. By employing rectangular grid matrices with cell sizes of 50, 100, 200, and 400 km, this research assesses the MAUP-related sensitivity of two commonly used grid-based proxies for species’ spatial distribution. The results reveal that the proxy based on the number of occupied grid cells (OGCs) is particularly sensitive to changes in cell size, whereas the proxy based on minimum-spanning-tree distance (MST distance) demonstrates greater robustness across varying grid scales. This study underscores that when constructing proxies for species’ spatial distribution ranges using grid matrices, the OGC method is more susceptible to MAUP effects than the MST distance method, warranting increased caution in studies employing the OGC approach. Full article

Phyllodocida is a clade of errantiate annelids characterized by having ventral sensory palps, anterior enlarged cirri, axial muscular proboscis, compound chaetae (if present) with a single ligament, and of lacking dorsolateral folds. Members of most families date back to the Carboniferous, although the earliest fossil was dated from the Devonian. Phyllodocida holds 27 well-established and morphologically homogenous clades ranked as families, gathering more than 4600 currently accepted nominal species. Among them, Syllidae and Polynoidae are the most specious polychaete groups. Species of Phyllodocida are mainly found in the marine benthos, although a few inhabit freshwater, terrestrial and planktonic environments, and occur from intertidal to deep waters in all oceans. In this review, we (1) explore the current knowledge on species diversity trends (based on traditional species concept and molecular data), phylogeny, ecology, and geographic distribution for the whole group, (2) try to identify the main knowledge gaps, and (3) focus on selected families: Alciopidae, Goniadidae, Glyceridae, Iospilidae, Lopadorrhynchidae, Polynoidae, Pontodoridae, Nephtyidae, Sphaerodoridae, Syllidae, Tomopteridae, Typhloscolecidae, and Yndolaciidae. The highest species richness is concentrated in European, North American, and Australian continental shelves (reflecting a strong sampling bias). While most data come from shallow coastal and surface environments most world oceans are clearly under-studied. The overall trends indicate that new descriptions are constantly added through time and that less than 10% of the known species have molecular barcode information available. Full article

The fossil record is notorious for being incomplete and distorted, frequently conditioning the type of knowledge that can be extracted from it. In many cases, this often leads to issues when performing complex statistical analyses, such as classification tasks, predictive modelling, and variance analyses, such as those used in Geometric Morphometrics. Here different Generative Adversarial Network architectures are experimented with, testing the effects of sample size and domain dimensionality on model performance. For model evaluation, robust statistical methods were used. Each of the algorithms were observed to produce realistic data. Generative Adversarial Networks using different loss functions produced multidimensional synthetic data significantly equivalent to the original training data. Conditional Generative Adversarial Networks were not as successful. The methods proposed are likely to reduce the impact of sample size and bias on a number of statistical learning applications. While Generative Adversarial Networks are not the solution to all sample-size related issues, combined with other pre-processing steps these limitations may be overcome. This presents a valuable means of augmenting geometric morphometric datasets for greater predictive visualization. Full article