Removal of odour and ammnia in ventilation air from growing-finishing pig units using vertical biofilters

[Abstract] The aim of this study was to investigate the removal of odour and ammonia from outlet air using vertical biofilters in two units with growing-finishing pigs in the winter. Woodchips were used as media in the wall of the biofilters. The air from the pig units was humidified by a high-pressure water system before it reached the biofilters. A total of 56 odour and ammonia measurements were taken at an average outdoor temperature of 5.4 C. The biofilters significantly reduced the odour concentration (OUE/m3) in the outlet air (P<0.001). The measured odour removal efficiency averaged 60 %. In contrast, the biofilters did not reduce the ammonia concentration (ppm) significantly in the outlet air. The hedonic tone of the odour of the air was determined before and after the biofilter. The untreated air was recorded as more unpleasant than the air that had passed through the biofilters. In conclusion, the biofilters were capable of reducing the odour concentration in the outlet air from units with growing-finishing pigs in the winter. The biofilters’ treatment of the air made the odour less unpleasant. However, the biofilters were not capable of reducing the ammonia concentration in the outlet air in the winte

Efficient Charge Separation in 2D Janus van der Waals Structures with Build-in Electric Fields and Intrinsic p-n Doping

Janus MoSSe monolayers were recently synthesised by replacing S by Se on one side of MoS$_2$ (or vice versa for MoSe$_2$). Due to the different electronegativity of S and Se these structures carry a finite out-of-plane dipole moment. As we show here by means of density functional theory (DFT) calculations, this intrinsic dipole leads to the formation of built-in electric fields when the monolayers are stacked to form $N$-layer structures. For sufficiently thin structures ($N<4$) the dipoles add up and shift the vacuum level on the two sides of the film by $\sim N \cdot 0.7$ eV. However, for thicker films charge transfer occurs between the outermost layers forming atomically thin n- and p-doped electron gasses at the two surfaces. The doping concentration can be tuned between about $5\cdot 10^{12}$ e/cm$^{2}$ and $2\cdot 10^{13}$ e/cm$^{2}$ by varying the film thickness. The surface charges counteract the static dipoles leading to saturation of the vacuum level shift at around 2.2 eV for $N>4$. Based on band structure calculations and the Mott-Wannier exciton model, we compute the energies of intra- and interlayer excitons as a function of film thickness suggesting that the Janus multilayer films are ideally suited for achieving ultrafast charge separation over atomic length scales without chemical doping or applied electric fields. Finally, we explore a number of other potentially synthesisable 2D Janus structures with different band gaps and internal dipole moments. Our results open new opportunities for ultrathin opto-electronic components such as tunnel diodes, photo-detectors, or solar cells

Hidden Neural Networks: A Framework for HMM/NN Hybrids

This paper presents a general framework for hybrids of Hidden Markov models (HMM) and neural networks (NN). In the new framework called Hidden Neural Networks (HNN) the usual HMM probability parameters are replaced by neural network outputs. To ensure a probabilistic interpretation the HNN is normalized globally as opposed to the local normalization enforced on parameters in standard HMMs. Furthermore, all parameters in the HNN are estimated simultaneously according to the discriminative conditional maximum likelihood (CML) criterion. The HNNs show clear performance gains compared to standard HMMs on TIMIT continuous speech recognition benchmarks. On the task of recognizing five broad phoneme classes an accuracy of 84% is obtained compared to 76% for a standard HMM. Additionally, we report a preliminary result of 69% accuracy on the TIMIT 39 phoneme task. 1. INTRODUCTION Among speech research scientists it is widely believed that HMMs are one of the best and most successful modelling..

EEG source imaging assists decoding in a face recognition task

EEG based brain state decoding has numerous applications. State of the art decoding is based on processing of the multivariate sensor space signal, however evidence is mounting that EEG source reconstruction can assist decoding. EEG source imaging leads to high-dimensional representations and rather strong a priori information must be invoked. Recent work by Edelman et al. (2016) has demonstrated that introduction of a spatially focal source space representation can improve decoding of motor imagery. In this work we explore the generality of Edelman et al. hypothesis by considering decoding of face recognition. This task concerns the differentiation of brain responses to images of faces and scrambled faces and poses a rather difficult decoding problem at the single trial level. We implement the pipeline using spatially focused features and show that this approach is challenged and source imaging does not lead to an improved decoding. We design a distributed pipeline in which the classifier has access to brain wide features which in turn does lead to a 15% reduction in the error rate using source space features. Hence, our work presents supporting evidence for the hypothesis that source imaging improves decoding