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Heterogeneous catalysts usually consist of porous, inorganic materials. Especially in the mobile application, e.g., in exhaust gas catalysis as a part of the gas line of an engine, there are loads due to vibrations and high gas velocities. It can be assumed that physical stress will lead to a loss of the catalytically active coating. The present contribution describes experimental setups and methods for a systematic and reproducible investigation of the main influences to the mass loss due to vibrational and fluid‐mechanical stress caused by particle‐free gas flows.
Sound and music are well-studied aspects of the quality of experience in restaurants; the role of the room acoustical conditions, their influence on the visitors’ soundscape evaluation and their impact on the overall customer satisfaction in restaurants, however, has received less scientific attention. The present field study therefore investigated whether sound pressure level, reverberation time, and soundscape pleasantness can predict factors associated with overall restaurant quality. In total, 142 persons visiting 12 restaurants in Berlin rated relevant acoustical and non-acoustical factors associated with restaurant quality. Simultaneously, the A-weighted sound pressure level (LA,eq,15) was measured, and the reverberation time in the occupied state (T20,occ) was obtained by measurements performed in the unoccupied room and a subsequent calculation of the occupied condition according to DIN 18041. Results from linear mixed-effects models revealed that both the LA,eq,15 and T20,occ had a significant influence on soundscape pleasantness and eventfulness, whereby the effect of T20,occ was meditated by the LA,eq,15. Also, the LA,eq,15 as well as soundscape pleasantness were significant predictors of overall restaurant quality. A comprehensive structural equation model including both acoustical and non-acoustical factors, however, indicates that the effect of soundscape pleasantness on overall restaurant quality is mediated by the restaurant’s atmosphere. Our results support and extend previous findings which suggest that the acoustical design of restaurants involves a trade-off between comfort and liveliness, depending on the desired character of the place.
Poor young people more often face health difficulties, (learning) disabilities, and are overrepresented in special schools. Consequently, youth from poor households disproportionately frequently participate in disability‐specific programs aiming to improve their educational levels and labor market opportunities. They face a double burden of disability and poverty. In our study, we look at poor and non‐poor youth with disabilities (YPWD) who participate in vocational rehabilitation (VR) and whether VR helps them (a) in transitioning into employment and (b) in leaving poverty. We examine the association between the receipt of initial basic income support (BIS) as a poverty indicator, later labor market outcomes, and earned vocational qualification using administrative data. We make use of a sample of all persons accepted for VR in 2010 (N = 36,645). We employ logit models on VR attendees’ labor market outcomes three and five years after being accepted for VR as well as on their earned vocational qualifications. Beside initial poverty status, we control for educational level, type, and degree of disability and program pattern during the VR process. Our findings show that YPWD from poor households have a decreased likelihood of a vocational certificate and employment. Additionally, they are more likely to receive BIS than young people not from poor households and thus more likely to remain poor. In conclusion, VR seems to support poor YPWD less in their school‐to‐work transitions. Thus, disability‐specific programs should be more tailored to the social situations of participants, and counsellors should be more sensitive to their social backgrounds.
Thermoelectric generators (TEGs) have the ability to convert waste heat into electrical energy under unfavorable conditions and are becoming increasingly popular in academia, but have not yet achieved a broad commercial success, due to the still comparably low efficiency. To increase the efficiency and economic viability of TEGs, research is performed on the materials on one hand and on the system connection on the other. In the latter case, the net output power of the cooling system plays a key role. At first glance, passive cooling seems preferable to active cooling because it does not affect the net electrical output power. However, as shown in the present review, the active cooling is to be preferred for net output power. The situation is similar in air and water-cooling. Even though air-cooling is easier to set up, the water-cooling should be preferred to achieve higher net output power. It is shown that microchannel cooling has similar hydraulic performance to conventional cooling and inserts increase the net output power of TEG. As the review reveals that active water-cooling should be the method of choice to achieve high net output power, it also shows that a careful optimization is necessary to exploit the potential.
We present a novel metric Spatial Recall Index to assess the performance of machine-learning (ML) algorithms for automotive applications, focusing on where in the image which performance occurs. Typical metrics like intersection-over-union (IoU), precisionrecallcurves or average precision (AP) quantify the performance over a whole database of images, neglecting spatial performance variations. But as the optics of camera systems are spatially variable over the field of view, the performance of ML-based algorithms is also a function of space, which we show in simulation: A realistic objective lens based on a Cooke-triplet that exhibits typical optical aberrations like astigmatism and chromatic aberration, all variable over field, is modeled. The model is then applied to a subset of the BDD100k dataset with spatially-varying kernels. We then quantify local changes in the performance of the pre-trained Mask R-CNN algorithm. Our examples demonstrate the spatial dependence of the performance of ML-based algorithms from the optical quality over field, highlighting the need to take the spatial dimension into account when training ML-based algorithms, especially when looking forward to autonomous driving applications.