Stability analysis of a hyperbolic stochastic Galerkin formulation for the Aw-Rascle-Zhang model with relaxation

We investigate the propagation of uncertainties in the Aw-Rascle-Zhang model, which belongs to a class of second order traffic flow models described by a system of nonlinear hyperbolic equations. The stochastic quantities are expanded in terms of wavelet-based series expansions. Then, they are projected to obtain a deterministic system for the coefficients in the truncated series. Stochastic Galerkin formulations are presented in conservative form and for smooth solutions also in the corresponding non-conservative form. This allows to obtain stabilization results, when the system is relaxed to a first-order model. Computational tests illustrate the theoretical results

Tail and ear necrosis in piglets of sows with increased weight loss over the suckling period.

A farm belonging to a Swiss sow pool system reported increased cases of necrosis on the base of the tail or ears in their piglets. Therefore, herd examination was performed in February 2021, and it was found that about half of all examined litters included piglets with necrosis of different locations, and that the sows of these piglets were rather thin. Upon instruction, the farmer then documented the body condition score (BCS) and weight before farrowing and after weaning, and the number of liveborn piglets affected by necrosis of the tail or ear of the next four farrowing batches. In total, data of 97 sows with 1214 liveborn piglets were evaluated. Sows were retrospectively allocated into two groups: Those with piglets with ear and/or tail necrosis (NE), and those without (WN). Of the 97 litters, 40 included piglets with necrosis, with 28 of them having piglets only with tail necrosis, 8 only with ear necrosis, and 4 litters included piglets with both types of necrosis. The group NE lost significantly more weight and BCS points over the suckling period than the group WN, with a tendency of having a lower BCS after weaning (2,0 vs. 2,25/5,0). Blood samples of five sows were analyzed and tested positive for the Fusarium mycotoxin deoxynivalenol (DON). It could be possible that the sows previously consumed DON contaminated feed, which was then stored in their fat tissue, and released again into the blood stream during increased weight loss. Since DON can be transferred from the sow to her piglets during gestation or lactation, this release might have affected the piglets, leading to tail or ear necrosis. However, causative studies are needed to confirm this hypothesis

Hazardous substances in frequently used professional cleaning products

A growing number of studies have identified cleaners as a group at risk for adverse health effects of the skin and the respiratory tract. Chemical substances present in cleaning products could be responsible for these effects. Currently, only limited information is available about irritant and health hazardous chemical substances found in cleaning products. We hypothesized that chemical substances present in cleaning products are known health hazardous substances that might be involved in adverse health effects of the skin and the respiratory tract. We performed a systematic review of cleaning products used in the Swiss cleaning sector. We surveyed Swiss professional cleaning companies (n = 1476) to identify the most used products (n = 105) for inclusion. Safety data sheets (SDSs) were reviewed and hazardous substances present in cleaning products were tabulated with current European and global harmonized system hazard labels. Professional cleaning products are mixtures of substances (arithmetic mean 3.5 +/- 2.8), and more than 132 different chemical substances were identified in 105 products. The main groups of chemicals were fragrances, glycol ethers, surfactants, solvents; and to a lesser extent, phosphates, salts, detergents, pH-stabilizers, acids, and bases. Up to 75% of products contained irritant (Xi), 64% harmful (Xn) and 28% corrosive (C) labeled substances. Hazards for eyes (59%) and skin (50%), and hazards by ingestion (60%) were the most reported. Cleaning products potentially give rise to simultaneous exposures to different chemical substances. As professional cleaners represent a large workforce, and cleaning products are widely used, it is a major public health issue to better understand these exposures. The list of substances provided in this study contains important information for future occupational exposure assessment studies

Separating neural oscillations from aperiodic 1/f activity: Challenges and recommendations

Electrophysiological power spectra typically consist of two components: An aperiodic part usually following an 1/f power law [Formula: see text] and periodic components appearing as spectral peaks. While the investigation of the periodic parts, commonly referred to as neural oscillations, has received considerable attention, the study of the aperiodic part has only recently gained more interest. The periodic part is usually quantified by center frequencies, powers, and bandwidths, while the aperiodic part is parameterized by the y-intercept and the 1/f exponent [Formula: see text]. For investigation of either part, however, it is essential to separate the two components. In this article, we scrutinize two frequently used methods, FOOOF (Fitting Oscillations & One-Over-F) and IRASA (Irregular Resampling Auto-Spectral Analysis), that are commonly used to separate the periodic from the aperiodic component. We evaluate these methods using diverse spectra obtained with electroencephalography (EEG), magnetoencephalography (MEG), and local field potential (LFP) recordings relating to three independent research datasets. Each method and each dataset poses distinct challenges for the extraction of both spectral parts. The specific spectral features hindering the periodic and aperiodic separation are highlighted by simulations of power spectra emphasizing these features. Through comparison with the simulation parameters defined a priori, the parameterization error of each method is quantified. Based on the real and simulated power spectra, we evaluate the advantages of both methods, discuss common challenges, note which spectral features impede the separation, assess the computational costs, and propose recommendations on how to use them