Wireless data management system for environmental monitoring in livestock buildings

The impact of air quality on the health, welfare and productivity of livestock needs to be considered, especially when livestock are kept in enclosed buildings. The monitoring of such environmental factors allows for the development of appropriate strategies to reduce detrimental effects of sub-optimal air quality on the respiratory health of both livestock and farmers. In 2009, an environmental monitoring system was designed, developed and tested that allowed for the monitoring of a number of airborne pollutants. One limitation of the system was the manual collection of logged data from each unit. This paper identifies limitations of the current environmental monitoring system and suggests a range of networking technologies that can be used to increase usability. Consideration is taken for the networking of environmental monitoring units, as well as the collection of recorded data. Furthermore, the design and development of a software system that is used to collate and store recorded environmental data from multiple farms is explored. In order to design such a system, simplified software engineering processes and methodologies have been utilised. The main steps taken in order to complete the project were requirements elicitation with clients, requirements analysis, system design, implementation and finally testing. The outcome of the project provided a potential prototype for improving the environmental monitoring system and analysis informing the benefit of the implementation

Effectiveness of intervention strategies exclusively targeting reductions in children’s sedentary time: a systematic review of the literature

Methodological quality assessment of included studies. (DOCX 57 kb

In the Shadow of Asylum Decision-Making: The Knowledge Politics of Country-of-Origin Information

Country-of-origin information has secured a central place in European asylum systems, underpinning state decisions on the asylum status of refugee populations. All European states produce this type of information, and dedicated country-of-origin information units are increasingly common. This article analyzes the knowledge politics of country-of-origin information, with a focus on the relation between knowledge and decision. We are interested in this type of knowledge precisely because it is uneasily positioned in-between social scientific methodology and policy decision-making and is infused with a “pulsional normativity.” We distinguish three phases of country-of-origin information production: first, a phase of investigation, where foreign lands are reduced to stable and mobile forms so that they can be studied as research units; second, the concordance of information production, relying on standardized instruments and practical skill; and third, the consolidation phase, which involves the return of country information constructed inside research units back into the administrative and regulatory world. The final section of the article examines how complex and frail information about countries of origin becomes deployed as valid grounds for asylum decision-making

Discrimination of Helicobacter pullorum and Campylobacter lari by analysis of whole cell fatty acid extracts

Helicobacter pullorum and Campylobacter lari are rarely isolated from humans with acute enteritis. Hitherto the two species could only be identified by genotypic techniques. Gas liquid chromatography of whole cell fatty acid extracts is described as the first phenotypic method for discrimination of the two species. Cholesteryl glucoside, a characteristic feature of the genus Helicobacter, but seldom found in other bacteria, could not be detected in Helicobacter pullorum. Therefore, rapid determination of this glycolipid may serve as a discrimination marker for Helicobacter pullorum from most other Helicobacter specie

Discrimination of Helicobacter pullorum and Campylobacter lari by analysis of whole cell fatty acid extracts

Helicobacter pullorum and Campylobacter lari are rarely isolated from humans with acute enteritis. Hitherto the two species could only be identified by genotypic techniques. Gas liquid chromatography of whole cell fatty acid extracts is described as the first phenotypic method for discrimination of the two species. Cholesteryl glucoside, a characteristic feature of the genus Helicobacter, but seldom found in other bacteria, could not be detected in Helicobacter pullorum. Therefore, rapid determination of this glycolipid may serve as a discrimination marker for Helicobacter pullorum from most other Helicobacter species

A cerebellar internal model calibrates a feedback controller involved in sensorimotor control

Animals can adjust their behavior in response to changes in the environment when these changes can be predicted. Here the authors show the role of the cerebellum in zebrafish that change their swimming as they adjust to long-lasting changes in visual feedback Animals must adapt their behavior to survive in a changing environment. Behavioral adaptations can be evoked by two mechanisms: feedback control and internal-model-based control. Feedback controllers can maintain the sensory state of the animal at a desired level under different environmental conditions. In contrast, internal models learn the relationship between the motor output and its sensory consequences and can be used to recalibrate behaviors. Here, we present multiple unpredictable perturbations in visual feedback to larval zebrafish performing the optomotor response and show that they react to these perturbations through a feedback control mechanism. In contrast, if a perturbation is long-lasting, fish adapt their behavior by updating a cerebellum-dependent internal model. We use modelling and functional imaging to show that the neuronal requirements for these mechanisms are met in the larval zebrafish brain. Our results illustrate the role of the cerebellum in encoding internal models and how these can calibrate neuronal circuits involved in reactive behaviors depending on the interactions between animal and environment

Efficient Biomedical Image Segmentation on EdgeTPUs at Point of Care

The U-Net architecture is a state-of-the-art neural network for semantic image segmentation that is widely used in biomedical research. It is based on an encoder-decoder framework and its vanilla version shows already high performance in terms of segmentation quality. Due to its large parameter space, however, it has high computational costs on both, CPUs and GPUs. In a research setting, inference time is relevant, but not crucial for the results. However, especially in mobile, clinical applications a light and fast variant would allow deep-learning assisted, objective diagnosis at the point of care. In this work, we suggest an optimized, tiny-weight U-Net for an inexpensive hardware accelerator. We first mined the U-Net architecture to reduce computational complexity to increase runtime performance by simultaneously keeping the accuracy on a high level. Using an open, biomedical dataset for high-speed videoendoscopy (BAGLS), we show that we can dramatically reduce the parameter space and computations by over 99.8% while keeping the segmentation performance at 95% of our baseline. Using a custom upscaling routine, we further successfully deployed our optimized U-Net to an EdgeTPU hardware accelerator to gain cost-effective speed improvements on conventional computers and to showcase the applicability of EdgeTPUs for biomedical imaging processing of large images on portable devices. Combining the optimized architecture and the EdgeTPU, we gain a speedup of >79-times compared to our initial baseline while keeping high accuracy. This combination allows to provide immediate results to the clinician, especially in constrained computational environments, and an objective diagnosis at the point of care

Impact of Mixed Precision Techniques on Training and Inference Efficiency of Deep Neural Networks

In the deep learning community, increasingly large models are being developed, leading to rapidly growing computational costs and energy costs. Recently, a new trend has been arising, advocating that researchers should also report the energy efficiency besides their model’s performance in their papers. Previous research has shown that reduced precision can be helpful to improve energy efficiency. Based on this finding, we propose a simple practice to effectively improve the energy efficiency of training and inference, i.e., training the model with mixed precision and deploying it on Edge TPUs. We evaluated its effectiveness by comparing the speed-up of a state-of-the-art semantic segmentation architecture with respect to different typical usage scenarios, including using different devices, deep learning frameworks, model sizes, and batch sizes. Our results show that enabled mixed precision can gain up to a 1.9× speedup compared to the most common and default float32 data type on GPUs. Deploying the models on Edge TPU further boosted the inference by a factor of 6. Our approach allows researchers to accelerate their training and inference procedures without jeopardizing the model’s accuracy, meanwhile reducing energy consumption and electricity cost easily without changing their model architecture or retraining. Furthermore, our approach is helpful in reducing the carbon footprint used to train and deploy the neural network and thus has a positive effect on environmental resources