Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements

In manufacturing environments, real-time monitoring of yoghurt fermentation is required to maintain an optimal production schedule, ensure product quality, and prevent the growth of pathogenic bacteria. Ultrasonic sensors combined with machine learning models offer the potential for non-invasive process monitoring. However, methods are required to ensure the models are robust to changing ultrasonic measurement distributions as a result of changing process conditions. As it is unknown when these changes in distribution will occur, domain adaptation methods are needed that can be applied to newly acquired data in real-time. In this work, yoghurt fermentation processes are monitored using non-invasive ultrasonic sensors. Furthermore, a transmission based method is compared to an industrially-relevant non-transmission method which does not require the sound wave to travel through the fermenting yoghurt. Three machine learning algorithms were investigated including fully-connected neural networks, fully-connected neural networks with long short-term memory layers, and convolutional neural networks with long short-term memory layers. Three real-time domain adaptation strategies were also evaluated, namely; feature alignment, prediction alignment, and feature removal. The most accurate method (mean squared error of 0.008 to predict pH during fermentation) was non-transmission based and used convolutional neural networks with long short-term memory layers, and a combination of all three domain adaption methods

Acoustic attenuation spectroscopy and helium ion microscopy study of rehydration of dairy powder

Complete hydration is essential for the production of structured dairy products from powders. It is essential that the ingredients used hydrate completely. Determination of an end point of rehydration is non-trivial, but ultrasound-based methodologies have demonstrated potential in this area and are well suited to measuring bulk samples in situ. Here, acoustic attenuation spectroscopy (AAS) is used to monitor rehydration of skim milk powder, and recombined systems of micellar casein isolate with lactose and whey protein isolate. Dynamic light scattering, zeta-potential measurements and AAS as a function of pH characterise each component around its isoelectric point to assess its functionality. Scanning helium ion microscopy was used to image the dry powders, without any conductive coating, producing resolution equivalent to scanning electron microscopy, but with much larger focal lengths and fewer imaging artefacts. Imaging the powders provides information on particle size and morphology which can affect dissolution behaviour. Reconstituted skim milk powder and recombined samples were monitored showing there are changes occurring over several hours. Attenuation coefficients are shown to predict the end point of hydration. Model fitting is used to extract volume fractions and average particle sizes of large and small particle populations in recombined samples over time. AAS is demonstrated to be capable of tracking the dynamics in rehydrating dispersions over time. Physical parameters such as the volume fraction and particle size of the dispersed phase can be determined

Five Lenses on Team Tutor Challenges: A Multidisciplinary Approach

This chapter describes five disciplinary domains of research or lenses that contribute to the design of a team tutor. We focus on four significant challenges in developing Intelligent Team Tutoring Systems (ITTSs), and explore how the five lenses can offer guidance for these challenges. The four challenges arise in the design of team member interactions, performance metrics and skill development, feedback, and tutor authoring. The five lenses or research domains that we apply to these four challenges are Tutor Engineering, Learning Sciences, Science of Teams, Data Analyst, and Human–Computer Interaction. This matrix of applications from each perspective offers a framework to guide designers in creating ITTSs

The ABC130 barrel module prototyping programme for the ATLAS strip tracker

For the Phase-II Upgrade of the ATLAS Detector, its Inner Detector, consisting of silicon pixel, silicon strip and transition radiation sub-detectors, will be replaced with an all new 100 % silicon tracker, composed of a pixel tracker at inner radii and a strip tracker at outer radii. The future ATLAS strip tracker will include 11,000 silicon sensor modules in the central region (barrel) and 7,000 modules in the forward region (end-caps), which are foreseen to be constructed over a period of 3.5 years. The construction of each module consists of a series of assembly and quality control steps, which were engineered to be identical for all production sites. In order to develop the tooling and procedures for assembly and testing of these modules, two series of major prototyping programs were conducted: an early program using readout chips designed using a 250 nm fabrication process (ABCN-25) and a subsequent program using a follow-up chip set made using 130 nm processing (ABC130 and HCC130 chips). This second generation of readout chips was used for an extensive prototyping program that produced around 100 barrel-type modules and contributed significantly to the development of the final module layout. This paper gives an overview of the components used in ABC130 barrel modules, their assembly procedure and findings resulting from their tests.Comment: 82 pages, 66 figure

Net zero roadmap modelling for sustainable dairy manufacturing and distribution

Energy-derived carbon emissions from dairy manufacturing and distribution are significant. Meeting a net zero carbon target is a global priority, and to that end the dairy industry is engaging an emission-reduction strategy. Modelling tools that can predict energy consumption and related carbon-emissions can aid decision-making towards energy transitioning. This study presents an energy consumption model for dairy skimmed milk and cream manufacturing and distribution, which has been developed following a mechanistic modelling approach. This approach integrates chemical engineering process design, heat exchange principles, and empirical modelling to simulate energy consumption for each individual supply chain component, sequence-by-sequence. The model offers simulation flexibility, allowing the projection of the product’s embodied energy and carbon-emissions under diverse manufacturing and distribution scenarios. To investigate the model capabilities, scenario analysis was performed for 12 different scenarios. These scenarios resulted by testing the use of three fuel types for the heating requirements in manufacturing (oil, natural gas and hydrogen), two categories of refrigerated vehicles (diesel and electric), and two different distribution infrastructures (centralised and decentralised). The evaluated skimmed milk product-embodied energy ranged between 309 and 869 kJ/L. The scenarios were also simulated towards 2050 using the UK projections for electricity’s carbon conversion factor to predict the anticipated carbon-emission reductions. These 2050 projections allowed for roadmap planning towards decarbonising energy for skimmed milk and cream manufacturing and distribution, with outcomes demonstrating up to 90.2% carbon-emission reductions by 2050. The developed model can support safe decision-making and assist the dairy industry in meeting the net zero carbon target