Intelligent Industrial Cleaning: A Multi-Sensor Approach Utilising Machine Learning-Based Regression

Effectively cleaning equipment is essential for the safe production of food but requires a significant amount of time and resources such as water, energy, and chemicals. To optimize the cleaning of food production equipment, there is the need for innovative technologies to monitor the removal of fouling from equipment surfaces. In this work, optical and ultrasonic sensors are used to monitor the fouling removal of food materials with different physicochemical properties from a benchtop rig. Tailored signal and image processing procedures are developed to monitor the cleaning process, and a neural network regression model is developed to predict the amount of fouling remaining on the surface. The results show that the three dissimilar food fouling materials investigated were removed from the test section via different cleaning mechanisms, and the neural network models were able to predict the area and volume of fouling present during cleaning with accuracies as high as 98% and 97%, respectively. This work demonstrates that sensors and machine learning methods can be effectively combined to monitor cleaning processes

Reconstitution Properties of Thymus Stem Cells in Murine Fetal Liver

Injection of day-12 murine fetal liver cells into thymus lobes of Thy-1 congenic adult recipients results in a wave of thymocyte development. The kinetics of repopulation by donor cells reaches a peak after 20–25 days. The frequency of thymic stem cells (TSC) in day-12 fetal liver was estimated, by limit dilution, as 1 in 4x104 cells. Within 8 hr of injection into a thymus lobe, fetal liver TSC commit to T-cell development, losing stem-cell activity. When fetal liver cells are maintained in culture for 7 days, with no exogenous cytokines added, and then injected intra-thymically (I.T.), thymus recolonization is not observed. However, TSC can be maintained in culture for 7 days with IL-1β, IL-3, IL-6, or LIF added, alone or in combination, with steel factor (SLF). Poisson analysis of fetal liver cells cultured with SLF and IL-3 together revealed a precursor frequency of 1 in 1.8x 105 cells. In contrast, the frequency of TSC in adult bone marrow was estimated by limit dilution as 1 in 12,000 cells

The Effect of Light Intensity, Sensor Height, and Spectral Pre-Processing Methods When Using NIR Spectroscopy to Identify Different Allergen-Containing Powdered Foods

Food allergens present a significant health risk to the human population, so their presence must be monitored and controlled within food production environments. This is especially important for powdered food, which can contain nearly all known food allergens. Manufacturing is experiencing the fourth industrial revolution (Industry 4.0), which is the use of digital technologies, such as sensors, Internet of Things (IoT), artificial intelligence, and cloud computing, to improve the productivity, efficiency, and safety of manufacturing processes. This work studied the potential of small low-cost sensors and machine learning to identify different powdered foods which naturally contain allergens. The research utilised a near-infrared (NIR) sensor and measurements were performed on over 50 different powdered food materials. This work focussed on several measurement and data processing parameters, which must be determined when using these sensors. These included sensor light intensity, height between sensor and food sample, and the most suitable spectra pre-processing method. It was found that the K-nearest neighbour and linear discriminant analysis machine learning methods had the highest classification prediction accuracy for identifying samples containing allergens of all methods studied. The height between the sensor and the sample had a greater effect than the sensor light intensity and the classification models performed much better when the sensor was positioned closer to the sample with the highest light intensity. The spectra pre-processing methods, which had the largest positive impact on the classification prediction accuracy, were the standard normal variate (SNV) and multiplicative scattering correction (MSC) methods. It was found that with the optimal combination of sensor height, light intensity, and spectra pre-processing, a classification prediction accuracy of 100% could be achieved, making the technique suitable for use within production environments

Using citizen science to identify Australia’s least known birds and inform conservation action

Citizen science is a popular approach to biodiversity surveying, whereby data that are collected by volunteer naturalists may help analysts to understand the distribution and abundance of wild organisms. In Australia, birdwatchers have contributed to two major citizen science programs, eBird (run by the Cornell Lab of Ornithology) and Birdata (run by Birdlife Australia), which collectively hold more than 42 million records of wild birds from across the country. However, these records are not evenly distributed across space, time, or taxonomy, with particularly significant variation in the number of records of each species in these datasets. In this paper, we explore this variation and seek to determine which Australian bird species are least known as determined by rates of citizen science survey detections. We achieve this by comparing the rates of survey effort and species detection across each Australian bird species? range, assigning all 581 species to one of the four groups depending on their rates of survey effort and species observation. We classify 56 species into a group considered the most poorly recorded despite extensive survey effort, with Coxen?s Fig Parrot Cyclopsitta coxeni, Letter-winged Kite Elanus scriptus, Night Parrot Pezoporus occidentalis, Buff-breasted Buttonquail Turnix olivii and Red-chested Buttonquail Turnix pyrrhothorax having the very lowest numbers of records. Our analyses provide a framework to identify species that are poorly represented in citizen science datasets. We explore the reasons behind why they may be poorly represented and suggest ways in which targeted approaches may be able to help fill in the gaps.Publisher PDFPeer reviewe

AD|ARC: Construction of a research ready dataset to better understand farmers and farming households.

Objectives The AD|ARC Administrative Data: Agriculture Research Collection is an ambitious and original linkage project, bringing together information about farmers and farming households from several sources. When complete, this research-ready dataset will assist in addressing three broad themes: health and well-being, prosperity and resilience, and engagement with agri-environment. Approach The dataset is being constructed from information drawn from survey, census, and administrative sources. Necessarily, this includes working across government departments to ensure comprehensive coverage of farm, business, education, and health data. Similarly, data owners, processors, and researchers are working closely to ensure the resultant dataset meets expectations. Alongside this cross-sectoral aspect, the work is also cross-jurisdictional, with the intention being for the data to capture information about farms, farmers and farming households from across the UK. Results Rather than focus on the detail of the substantive research that AD|ARC will enable, this paper discusses some of the challenges and successes of this linkage project to date. Drawing on the experience of the teams from across the UK (England, Northern Ireland, Scotland, and Wales), the first part will discuss challenges faced in linkage of this multi-faceted project, alongside how the population census is being utilised to better understand farming communities, through the identification of both farming households and workers. Secondly, a broader discussion of the challenges and sensitivities of working across government departments and administrations will be presented, alongside ways of working developed to recognise and overcome these. Conclusion The AD|ARC project will result in an invaluable resource to better understand the farming community, which in turn will help to better inform policy debate and decision making. Alongside this, the process of creating the dataset has offered opportunities for learning and insight across a range of issues

CIRCE: Coordinated Ionospheric Reconstruction Cubesat Experiment

The Coordinated Ionospheric Reconstruction Cubesat Experiment (CIRCE) is a collaborative space mission between the UK Defence Science and Technology Laboratory (Dstl), and the US Naval Research Laboratory (NRL) in developing small satellite ionospheric physics capability. CIRCE will characterise space weather effects on a regional scale in the ionosphere/thermosphere system. Properly characterising the dynamic ionosphere is important for a wide range of both civil and defence applications such as GPS, communications, and sensing technology. Consisting of two near-identical 6U (2x3U) CubeSat buses, the CIRCE nanosatellites will fly in a lead-follow tandem configuration in co-planar near-polar orbits at 500km altitude. Provided by Blue Canyon Technologies (BCT), the two buses will use differential drag to achieve and maintain an in-track separation of between 250 and 500km, allowing short time-scale dynamics to be observed in-situ. These nanosatellites each carry a complement of 5 individual scientific instruments, contributed from academic, industrial, and government partners across the UK and US. Scheduled to launch in 2021 via the US Department of Defence Space Test Program, the two CIRCE satellites will provide observations to enable a greater understanding of the driving processes of geophysical phenomena in the ionosphere/thermosphere system, distributed across a wide range of latitudes, and altitudes, as the mission progresses

Solution structure of the inner DysF domain of myoferlin and implications for limb girdle muscular dystrophy type 2b

Mutations in the protein dysferlin, a member of the ferlin family, lead to limb girdle muscular dystrophy type 2B and Myoshi myopathy. The ferlins are large proteins characterised by multiple C2 domains and a single C-terminal membrane-spanning helix. However, there is sequence conservation in some of the ferlin family in regions outside the C2 domains. In one annotation of the domain structure of these proteins, an unusual internal duplication event has been noted where a putative domain is inserted in between the N- and C-terminal parts of a homologous domain. This domain is known as the DysF domain. Here, we present the solution structure of the inner DysF domain of the dysferlin paralogue myoferlin, which has a unique fold held together by stacking of arginine and tryptophans, mutations that lead to clinical disease in dysferlin