Effects of ohmic heating on technological properties of whole egg

The aim of this work was to study the effects of different ohmic heating conditions on color, rheology, foaming, and gelling properties of whole egg. Industrial products treated by conventional heat pasteurization and the corresponding raw materials were also evaluated. Ohmic treatments accomplished in a static cell (65.5 \ub0C 73 min, 70 \ub0C 71 min, and 67 \ub0C 74.5 min) increased whole egg apparent viscosity (up to 190%), but also foam overrun (up to 28%) and gel hardness (up to 15%). The performance improvement was confirmed by treatments carried out in a continuous pilot plant (71 \ub0C 70.6 min, 68 \ub0C 71.4 min) and the products resulted stable during storage at 4 \ub0C for 30 days. In conclusion, this study demonstrated that ohmic heating is a suitable alternative to conventional pasteurization. Low temperature treatments are preferable to avoid possible rheological issues due to protein denaturation. Industrial relevance: Whole egg is a protein ingredient with multiple technological properties, used in many foods. Due to safety reasons, food manufacturers often use pasteurized liquid egg products, microbiologically safer and easier to handle with respect to shell eggs. In order to satisfy the required sanitary levels for liquid egg products, thermal pasteurization treatments are needed. However, since egg proteins are very sensitive to high temperatures, attention must be paid to avoid coagulation entailing deleterious effects against egg quality. In this study, different ohmic heating treatments were evaluated as milder alternatives to conventional pasteurization. The lab- and pilot-scale experiments and the subsequent statistical analyses of the obtained results contributed to assess the effects of the different ohmic treatments on technological features (e.g. color, rheology, foaming, and gelling properties) of liquid whole egg. This study demonstrated that ohmic heating is a suitable technology for whole egg treatment, paving the way for new opportunities in order to produce safe food ingredients with improved technological functionalities

3D AUDIO-VISUAL SPEAKER TRACKING WITH AN ADAPTIVE PARTICLE FILTER

reserved4siWe propose an audio-visual fusion algorithm for 3D speaker tracking from a localised multi-modal sensor platform composed of a camera and a small microphone array. After extracting audio-visual cues from individual modalities we fuse them adaptively using their reliability in a particle filter framework. The reliability of the audio signal is measured based on the maximum Global Coherence Field (GCF) peak value at each frame. The visual reliability is based on colour-histogram matching with detection results compared with a reference image in the RGB space. Experiments on the AV16.3 dataset show that the proposed adaptive audio-visual tracker outperforms both the individual modalities and a classical approach with fixed parameters in terms of tracking accuracy.Qian, Xinyuan; Brutti, Alessio; Omologo, Maurizio; Cavallaro, AndreaQian, Xinyuan; Brutti, Alessio; Omologo, Maurizio; Cavallaro, Andre

Audio-visual tracking of concurrent speakers

Audio-visual tracking of an unknown number of concurrent speakers in 3D is a challenging task, especially when sound and video are collected with a compact sensing platform. In this paper, we propose a tracker that builds on generative and discriminative audio-visual likelihood models formulated in a particle filtering framework. We localize multiple concurrent speakers with a de-emphasized acoustic map assisted by the image detection-derived 3D video observations. The 3D multimodal observations are either assigned to existing tracks for discriminative likelihood computation or used to initialize new tracks. The generative likelihoods rely on color distribution of the target and the de-emphasized acoustic map value. Experiments on AV16.3 and CAV3D datasets show that the proposed tracker outperforms the uni-modal trackers and the state-of-the-art approaches both in 3D and on the image plane

Super hygroscopic non-stoichiometric cerium oxide particles as electrode component for PEM fuel cells

The design of highly efficient promoters for the oxygen reduction reaction (ORR) is an important challenge in the large-scale distribution of proton exchange membrane (PEM) fuel cells. Hygroscopic cerium oxide (CeO2) is here proposed as co-catalyst in combination with Pt. Physical chemical characterizations, by means of X-ray diffraction, vibrational spectroscopy, morphological and thermal analyses, were carried out, demonstrating high water affinity of the synthesized CeO2 nanoparticles. Composite catalysts (i. e., Pt : CeO2 1 : 0.5 and 1 : 1 wt:wt), were studied by either rotating disk electrode (RDE) and fuel cell tests performed at 80 °C and 110 °C. Interestingly, the cell adopting the Pt : CeO2 1 : 0.5 catalyst enabled the achievement of high power densities reaching ∼80 and ∼35 mW cm−2 under low relative humidity and high temperatures. This result demonstrates that tuning material surface properties (e. g. oxygen vacancies) could significantly boost the electrochemical performance of cathodes as a combined result of optimized water retention and improved ORR kinetic

Dynamics of Mn3+ in off-stoichiometric LiMn1.5Ni0.5O4

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ConflictNET: End-to-End Learning for Speech-Based Conflict Intensity Estimation

Computational paralinguistics aims to infer human emotions, personality traits and behavioural patterns from speech signals. In particular, verbal conflict is an important example of human-interaction behaviour, whose detection would enable monitoring and feedback in a variety of applications. The majority of methods for detection and intensity estimation of verbal conflict apply off-the-shelf classifiers/regressors to generic hand-crafted acoustic features. Generating conflict-specific features requires refinement steps and the availability of metadata, such as the number of speakers and their speech overlap duration. Moreover, most techniques treat feature extraction and regression as independent modules, which require separate training and parameter tuning. To address these limitations, we propose the first end-to-end convolutional-recurrent neural network architecture that learns conflict-specific features directly from raw speech waveforms, without using explicit domain knowledge or metadata. Additionally, to selectively focus the model on portions of speech containing verbal conflict instances, we include a global attention interface that learns the alignment between layers of the recurrent network. Experimental results on the SSPNet Conflict Corpus show that our end-to-end architecture achieves state-of-the-art performance in terms of Pearson Correlation Coefficient

Testing for nonlinearity in the choice of a freight transport service

Manufacturing firms buy transport services with the aim of minimizing their total logistics cost. There is a large amount of literature analyzing how shippers value the various characteristics of a transport service, mostly performed by collecting stated-preference data and estimating discrete choice models. Most of the empirical studies specify the deterministic part of the utility functions as linear in the observed attributes. This implicitly constrains the characteristics of the analyzed transport service to be perfect substitutes, and to have a constant substitutability ratio. Such an assumption is inconsistent with the standard microeconomic theory, typically assuming inputs’ decreasing marginal productivity, and may not be realistic. The paper tests the linearity assumption for freight rate, travel time, probability of having damaged and lost freight, frequency, flexibility, mode and punctuality on a sample of Italian small- and medium-sized manufacturing enterprises (SME). Our findings suggest that the linearity-in-the-attributes assumption should be rejected and that the marginal impact on the utility-of-profit of the attributes is not constant. More specifically travel time and freight rate produce decreasing marginal reductions of the utility-of-profit; while safety (percentage of not damaged or lost shipments) and punctuality (percentage of shipments on time) are responsible for increasing marginal contributions to the utility-of-profit. The substitutability ratios between (a) freight rate and loss and damage, (b) freight rate and travel time, (c) freight rate and punctuality, (d) travel time and damage and loss and (e) travel time and punctuality are estimated and found not constant. Finally, it is found that the willingness to pay for the qualitative attributes obtained with a linearly specified model tend to be overestimated

Multi-speaker tracking from an audio-visual sensing device

Compact multi-sensor platforms are portable and thus desirable for robotics and personal-assistance tasks. However, compared to physically distributed sensors, the size of these platforms makes person tracking more difficult. To address this challenge, we propose a novel 3D audio-visual people tracker that exploits visual observations (object detections) to guide the acoustic processing by constraining the acoustic likelihood on the horizontal plane defined by the predicted height of a speaker. This solution allows the tracker to estimate, with a small microphone array, the distance of a sound. Moreover, we apply a color-based visual likelihood on the image plane to compensate for misdetections. Finally, we use a 3D particle filter and greedy data association to combine visual observations, color-based and acoustic likelihoods to track the position of multiple simultaneous speakers. We compare the proposed multimodal 3D tracker against two state-of-the-art methods on the AV16.3 dataset and on a newly collected dataset with co-located sensors, which we make available to the research community. Experimental results show that our multimodal approach outperforms the other methods both in 3D and on the image plane