Mocking enactments: a case study of multimodal stance-stacking.

Although research into multimodal stance-taking has gained momentum over the past years, the multimodal construction of so-called stacked stances has not yet received systematic attention in the literature. Mocking enactments are a prime example of such complex social actions as they are layered both interactionally and stance-related, and they rely significantly on the use of bodily visual resources, depicting rather than describing events and stances. Using Du Bois' Stance Triangle as a framework, this study investigates mocking enactments as a case study to unravel the multimodal aspects of layered stance expressions. Drawing on three data sets-music instruction in Dutch, German, and English, spontaneous face-to-face interactions among friends in Dutch, and narrations on past events in Flemish Sign Language (VGT)-this study provides a qualitative exploration of mocking enactments across different communicative settings, languages, and modalities. The study achieves three main objectives: (1) illuminating how enactments are used for mocking, (2) identifying the layers of stance-taking at play, and (3) examining the multimodal construction of mocking enactments. Our analysis reveals various different uses of enactments for mocking. Aside from enacting the target of the mockery, participants can include other characters and viewpoints, highlighting the breadth of the phenomenon under scrutiny. Second, we uncover the layered construction of stance on all axes of the Stance Triangle (evaluation, positioning, and alignment). Third, we find that mocking enactments are embedded in highly evaluative contexts, indexed by the use of bodily visual resources. Interestingly, not all mocking enactments include a multimodally exaggerated depiction, but instead, some merely allude to an absurd hypothetical scenario. Our findings contribute to the growing body of literature on multimodal stance-taking, by showing how a nuanced interpretation of the Stance Triangle can offer a useful framework for analyzing layered stance acts.

Determination of humic acids content in composts by means of near- and mid-infrared spectroscopy and partial least squares regression models.

Humic acids are part of the stable organic matter fraction in soils and composts. Due to their favorable properties for soils and plants, and their role in carbon sequestration, they are considered a quality criterion of composts. Time-consuming chemical extraction of humic acids and the inherent source of errors require alternative approaches for humic acids quantification. Different measurement techniques in the mid-infrared (MIR: KBr pellet technique) and near-infrared (NIR: fiber probe as well as an integrating sphere with a sample rotator) regions were applied. Partial least squares regression (PLSR) models based on infrared spectra were developed to determine humic acids contents in composts. As the wavenumber regions used (NIR: 6105-5380 cm(-1) and 4360-4220 cm(-1), MIR: 1745-1685 cm(-1) and 1610-1567 cm(-1)) represent different molecular vibrations, the importance of the methylene-group-derived vibrations for the NIR models is discussed. The correlation coefficients obtained for the KBr pellet technique, the NIR fiber probe technique, and the NIR integrating sphere (r = 0.94, 0.93, and 0.94) and the root mean square errors of cross-validation (RMSECV = 2.2% organic dry matter (ODM), 2.5% ODM, and 2.2% ODM) make the models appropriate for application in composting practice.

The applicability of Fourier transform infrared (FT-IR) spectroscopy in waste management.

State and stability or reactivity of waste materials are important properties that must be determined to obtain information about the future behavior and the emission potential of the materials. Different chemical and biological parameters are used to describe the stage of organic matter in waste materials. Fourier transform infrared spectroscopy provides information about the chemistry of waste materials in a general way. Several indicator bands that are referred to functional groups represent components or metabolic products. Their presence and intensity or their absence shed light on the phase of degradation or stabilization. The rapid assessment of the stage of organic matter decomposition is a very important field of application. Therefore, infrared spectroscopy is an appropriate tool for process and quality control, for the assessment of abandoned landfills and for checking of the successful landfill remediation. A wide range of applications are presented in this study for different waste materials. Progressing stages of a typical yard/kitchen waste composting process are shown. The fate of anaerobically "stabilized" leftovers in a subsequent liquid aerobic process is revealed by spectroscopic characteristics. A compost that underwent the biological stabilization process is distinguished from a "substrate" that comprises immature biogenic waste mixed with mineral compounds. Infrared spectra of freeze-dried leachate from untreated and aerated landfill material prove the effect of the aerobic treatment during 10 weeks in laboratory-scale experiments.

Prediction of humic acid content and respiration activity of biogenic waste by means of Fourier transform infrared (FTIR) spectra and partial least squares regression (PLS-R) models.

Fourier transform infrared (FTIR) spectroscopy has been proven to be an appropriate analytical method for the qualitative assessment of compost stability. This study focuses on quantitative determination of two time-consuming parameters: humic acid (HA) contents and respiration activity. Reactivity/stability and humification were quantified by respiration activities (oxygen uptake) and humic acid contents. These features are also reflected by a specific infrared spectroscopic pattern. Based on this relationship partial least squares regression (PLS-R) models for the prediction of respiration activities and humic acid contents were calculated. Characteristic wavenumber regions that are assigned to the biological/chemical parameter were selected for multivariate data analysis. The coefficient of determination (R(2)) obtained for the humic acid prediction model from infrared spectra was 87% with a root mean square error of cross-validation (RMSECV) of 2.6% organic dry matter (ODM). The prediction model for respiration activity resulted in a R(2) of 94% and a RMSECV for oxygen uptake of 2.9mgg(-1) dry matter (DM).