The development and pilot testing of a rapid assessment tool to improve local public health system capacity in Australia

<p>Abstract</p> <p>Background</p> <p>To operate effectively the public health system requires infrastructure and the capacity to act. Public health's ability to attract funding for infrastructure and capacity development would be enhanced if it was able to demonstrate what level of capacity was required to ensure a high performing system. Australia's public health activities are undertaken within a complex organizational framework that involves three levels of government and a diverse range of other organizations. The question of appropriate levels of infrastructure and capacity is critical at each level. Comparatively little is known about infrastructure and capacity at the local level.</p> <p>Methods</p> <p>In-depth interviews were conducted with senior managers in two Australian states with different frameworks for health administration. They were asked to reflect on the critical components of infrastructure and capacity required at the local level. The interviews were analyzed to identify the major themes. Workshops with public health experts explored this data further. The information generated was used to develop a tool, designed to be used by groups of organizations within discrete geographical locations to assess local public health capacity.</p> <p>Results</p> <p>Local actors in these two different systems pointed to similar areas for inclusion for the development of an instrument to map public health capacity at the local level. The tool asks respondents to consider resources, programs and the cultural environment within their organization. It also asks about the policy environment - recognizing that the broader environment within which organizations operate impacts on their capacity to act. Pilot testing of the tool pointed to some of the challenges involved in such an exercise, particularly if the tool were to be adopted as policy.</p> <p>Conclusion</p> <p>This research indicates that it is possible to develop a tool for the systematic assessment of public health capacity at the local level. Piloting the tool revealed some concerns amongst participants, particularly about how the tool would be used. However there was also recognition that the areas covered by the tool were those considered relevant.</p

The UCF Report, Vol. 19 No. 15, February 21, 1997

Community leaders gather on campuses for expert teach-ins; Chancellor Reed outlines the challenges facing the educational system in Florida

Quantitative risk analysis procedure for economic project sustainability.

The paper briefly explains the importance of quantitative procedures of risk analysis in large and medium scale projects for the sustenance of project economic sustainability. Globally, several construction projects are being descoped and tend to close out before attaining the initial project deliverables due to cost and schedule overruns. The quantitative methods can sieve the key factors and forecast the tangible impacts that can lead to schedule or cost variance in a project. It can guide the project stakeholders for timely decision making and mitigate the risks associated to achieve the project goals. Today, the quantitative methods are pivotal to analyze the impacts of Covid-19 crises in Construction industry which are increasingly apparent. Keywords- Project economic sustainability, Risk analysis, Quantitative method

ON THE USE OF DEEP NEURAL NETWORKS FOR THE DETECTION OF SMALL VEHICLES IN ORTHO-IMAGES

International audienceThis paper addresses the question of the detection of small targets (vehicles) in ortho-images. This question differs from the general task of detecting objects in images by several aspects. First, the vehicles to be detected are small, typically smaller than 20x20 pixels. Second, due to the multifarious-ness of the landscapes of the earth, several pixel structures similar to that of a vehicle might emerge (roof tops, shadow patterns, rocks, buildings), whereas within the vehicle class the inter-class variability is limited as they all look alike from afar. Finally, the imbalance between the vehicles and the rest of the picture is enormous in most cases. Specifically, this paper is focused on the detection tasks introduced by the VeDAI dataset [1]. This work supports an extensive study of the problems one might face when applying deep neural networks with low resolution and scarce data and proposes some solutions. One of the contributions of this paper is a network severely outperforming the state-of-the-art while being much simpler to implement and a lot faster than competitive approaches. We also list the limitations of this approach and provide several new ideas to further improve our results

Fast object detection in compressed JPEG Images

Object detection in still images has drawn a lot of attention over past few years, and with the advent of Deep Learning impressive performances have been achieved with numerous industrial applications. Most of these deep learning models rely on RGB images to localize and identify objects in the image. However in some application scenarii, images are compressed either for storage savings or fast transmission. Therefore a time consuming image decompression step is compulsory in order to apply the aforementioned deep models. To alleviate this drawback, we propose a fast deep architecture for object detection in JPEG images, one of the most widespread compression format. We train a neural network to detect objects based on the blockwise DCT (discrete cosine transform) coefficients {issued from} the JPEG compression algorithm. We modify the well-known Single Shot multibox Detector (SSD) by replacing its first layers with one convolutional layer dedicated to process the DCT inputs. Experimental evaluations on PASCAL VOC and industrial dataset comprising images of road traffic surveillance show that the model is about $2\times$ faster than regular SSD with promising detection performances. To the best of our knowledge, this paper is the first to address detection in compressed JPEG images

Development of a Coaxial 3D Printing Platform for Biofabrication of Implantable Islet-Containing Constructs

Over the last two decades, pancreatic islet transplantations have become a promising treatment for Type I diabetes. However, although providing a consistent and sustained exogenous insulin supply, there are a number of limitations hindering the widespread application of this approach. These include the lack of sufficient vasculature and allogeneic immune attacks after transplantation, which both contribute to poor cell survival rates. Here, these issues are addressed using a biofabrication approach. An alginate/gelatin-based bioink formulation is optimized for islet and islet-related cell encapsulation and 3D printing. In addition, a custom-designed coaxial printer is developed for 3D printing of multicellular islet-containing constructs. In this work, the ability to fabricate 3D constructs with precise control over the distribution of multiple cell types is demonstrated. In addition, it is shown that the viability of pancreatic islets is well maintained after the 3D printing process. Taken together, these results represent the first step toward an improved vehicle for islet transplantation and a potential novel strategy to treat Type I diabetes

An Expressive Deep Model for Human Action Parsing from A Single Image

This paper aims at one newly raising task in vision and multimedia research: recognizing human actions from still images. Its main challenges lie in the large variations in human poses and appearances, as well as the lack of temporal motion information. Addressing these problems, we propose to develop an expressive deep model to naturally integrate human layout and surrounding contexts for higher level action understanding from still images. In particular, a Deep Belief Net is trained to fuse information from different noisy sources such as body part detection and object detection. To bridge the semantic gap, we used manually labeled data to greatly improve the effectiveness and efficiency of the pre-training and fine-tuning stages of the DBN training. The resulting framework is shown to be robust to sometimes unreliable inputs (e.g., imprecise detections of human parts and objects), and outperforms the state-of-the-art approaches.Comment: 6 pages, 8 figures, ICME 201