University of Lincoln

About: University of Lincoln is a education organization based out in Lincoln, Lincolnshire, United Kingdom. It is known for research contribution in the topics: Population & Context (language use). The organization has 2341 authors who have published 7025 publications receiving 124797 citations.

The Use and Performance of Household Refrigerators: A Review.

Abstract: The domestic refrigerator is now a common household device with very few households in the developed world not possessing 1, or more, for the storage of chilled foods. Domestic storage is the last, and in many respects the most important, link in the food chill chain. Inadequate domestic refrigeration or cooling is frequently cited as a factor in incidents of food poisoning. The authors reviewed the temperature performance of refrigerators in 2008. This new review builds on that review, covering studies that have been published since (and those that were unfortunately missed in the first review), and also seeks to put this important stage of the food cold chain in its context. It is clear from the published data that many refrigerators throughout the world are running at higher than recommended temperatures. It is also clear that, despite improvements in energy use, the temperature performance and use of refrigerators have not changed significantly in the last 40 or so years. Many householders still remain unaware of the recommended refrigeration temperature range, how to ensure that the correct refrigeration temperature range is achieved, the importance of monitoring that it is being maintained, and the potential hazards of temperature abuse.

Deep convolutional neural networks for image-based Convolvulus sepium detection in sugar beet fields

Abstract: Convolvulus sepium (hedge bindweed) detection in sugar beet fields remains a challenging problem due to variation in appearance of plants, illumination changes, foliage occlusions, and different growth stages under field conditions. Current approaches for weed and crop recognition, segmentation and detection rely predominantly on conventional machine-learning techniques that require a large set of hand-crafted features for modelling. These might fail to generalize over different fields and environments. Here, we present an approach that develops a deep convolutional neural network (CNN) based on the tiny YOLOv3 architecture for C. sepium and sugar beet detection. We generated 2271 synthetic images, before combining these images with 452 field images to train the developed model. YOLO anchor box sizes were calculated from the training dataset using a k-means clustering approach. The resulting model was tested on 100 field images, showing that the combination of synthetic and original field images to train the developed model could improve the mean average precision (mAP) metric from 0.751 to 0.829 compared to using collected field images alone. We also compared the performance of the developed model with the YOLOv3 and Tiny YOLO models. The developed model achieved a better trade-off between accuracy and speed. Specifically, the average precisions (APs@IoU0.5) of C. sepium and sugar beet were 0.761 and 0.897 respectively with 6.48 ms inference time per image (800 × 1200) on a NVIDIA Titan X GPU environment. The developed model has the potential to be deployed on an embedded mobile platform like the Jetson TX for online weed detection and management due to its high-speed inference. It is recommendable to use synthetic images and empirical field images together in training stage to improve the performance of models.

A qualitative study of systemic influences on paramedic decision making: care transitions and patient safety

Abstract: OBJECTIVES: Paramedics routinely make critical decisions about the most appropriate care to deliver in a complex system characterized by significant variation in patient case-mix, care pathways and linked service providers. There has been little research carried out in the ambulance service to identify areas of risk associated with decisions about patient care. The aim of this study was to explore systemic influences on decision making by paramedics relating to care transitions to identify potential risk factors. METHODS: An exploratory multi-method qualitative study was conducted in three English National Health Service (NHS) Ambulance Service Trusts, focusing on decision making by paramedic and specialist paramedic staff. Researchers observed 57 staff across 34 shifts. Ten staff completed digital diaries and three focus groups were conducted with 21 staff. RESULTS: Nine types of decision were identified, ranging from emergency department conveyance and specialist emergency pathways to non-conveyance. Seven overarching systemic influences and risk factors potentially influencing decision making were identified: demand; performance priorities; access to care options; risk tolerance; training and development; communication and feedback and resources. CONCLUSIONS: Use of multiple methods provided a consistent picture of key systemic influences and potential risk factors. The study highlighted the increased complexity of paramedic decisions and multi-level system influences that may exacerbate risk. The findings have implications at the level of individual NHS Ambulance Service Trusts (e.g. ensuring an appropriately skilled workforce to manage diverse patient needs and reduce emergency department conveyance) and at the wider prehospital emergency care system level (e.g. ensuring access to appropriate patient care options as alternatives to the emergency department).