Identifying tranquil environments and quantifying impacts

The UK has recently recognized the importance of tranquil spaces in the National Planning Policy Framework. This policy framework places considerable emphasis on sustainable development with the aim of making planning more streamlined, localized and less restrictive. Specifically it states that planning policies and decisions should aim to "identify and protect areas of tranquillity which have remained relatively undisturbed by noise and are prized for their recreational and amenity value for this reason". This is considered by some (e.g. National Park Authorities) to go beyond merely identifying quiet areas based on relatively low levels of mainly transportation noise, as the concept of tranquillity implies additionally a consideration of visual intrusion of man-made structures and buildings into an otherwise perceived natural landscape. In the first instance this paper reports on applying a method for predicting the perceived tranquillity of a place and using this approach to classify the level of tranquillity in existing areas. It then seeks to determine the impact of a new build, by taking the example of the construction of wind turbines in the countryside. For this purpose; noise level measurements, photographs and jury assessments of tranquillity at a medium sized land based wind turbine were made. It was then possible to calculate the decrement of noise levels and visual prominence with distance in order to determine the improvement of tranquillity rating with increasing range. The point at which tranquillity was restored in the environment allowed the calculation of the position of the footprint boundary

Developing Unique Engineering Solutions to Improve Patient Safety

Many efforts to improve healthcare safety have focused on redesigning processes of care or retraining clinicians. Far less attention has been focused on the use of new technologies to improve safety. We present the results of a unique collaboration between the VA National Center for Patient Safety (NCPS) and the Thayer School of Engineering at Dartmouth College. Each year, the NCPS identifies safety problems across the VA that could be addressed with newly-engineered devices. Teams of Thayer students and faculty participating in a senior design course evaluate and engineer a solution for one of the problems. Exemplar projects have targeted surgical sponge retention, nosocomial infections, surgical site localization, and remote monitoring of hospitalized patients undergoing diagnostic testing and procedures. The program has served as an avenue for engineering students and health care workers to solve problems together. The success of this academic-clinical partnership could be replicated in other settings

High-Q-factor Al [subscript 2]O[subscript 3] micro-trench cavities integrated with silicon nitride waveguides on silicon

We report on the design and performance of high-Q integrated optical micro-trench cavities on silicon. The microcavities are co-integrated with silicon nitride bus waveguides and fabricated using wafer-scale silicon-photonics-compatible processing steps. The amorphous aluminum oxide resonator material is deposited via sputtering in a single straightforward post-processing step. We examine the theoretical and experimental optical properties of the aluminum oxide micro-trench cavities for different bend radii, film thicknesses and near-infrared wavelengths and demonstrate experimental Q factors of > 10[superscript 6]. We propose that this high-Q micro-trench cavity design can be applied to incorporate a wide variety of novel microcavity materials, including rare-earth-doped films for microlasers, into wafer-scale silicon photonics platforms

Virtual Consultations Through the Veterans Administration SCAN‐ECHO Project Improves Survival for Veterans With Liver Disease

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146654/1/hep30074-sup-0001-SupInfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146654/2/hep30074.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146654/3/hep30074_am.pd

Guidelines for the use and reuse of animals for teaching within veterinary medical education programs

Use and reuse of animals for educational purposes could adversely affect animal welfare. Guidelines for quantifying, monitoring and planning the use and reuse of animals have been developed. Within this framework animals are assigned points for usage, with more points being allocated to procedures that may have a greater adverse effect on animal welfare. Usage of individual animals is limited to a maximum of 8 points in a calendar week, 24 points in a month or 60 points within a 16-week study period and any associated examination period. Advantages and disadvantages of the system are discussed while modification is expected as knowledge emerges on the impact of procedures on animal welfare

Gender, choice and constraint in call centre employment

This paper examines the genderised experience of employment in call centres. While existing studies have acknowledged structural and agential constraints on women in the workplace, this paper goes further by illustrating the gendered nature of career choice and progression in a context which, in certain respects, appears to have benefitted women's desires for advancement. Drawing on quantitative and in-depth qualitative data from four Scottish call centres, the study provides evidence of gender inequality shaped by structural and ideological workplace and household constraints

Fitting a geometric graph to a protein-protein interaction network

Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm that takes PPI interaction data and embeds proteins into a low-dimensional Euclidean space, under the premise that connectivity information corresponds to Euclidean proximity, as in geometric-random graphs.We judge the sensitivity and specificity of the fit by computing the area under the Receiver Operator Characteristic (ROC) curve. The network embedding algorithm is based on multi-dimensional scaling, with the square root of the path length in a network playing the role of the Euclidean distance in the Euclidean space. The algorithm exploits sparsity for computational efficiency, and requires only a few sparse matrix multiplications, giving a complexity of O(N2) where N is the number of proteins.The algorithm has been verified in the sense that it successfully rediscovers the geometric structure in artificially constructed geometric networks, even when noise is added by re-wiring some links. Applying the algorithm to 19 publicly available PPI networks of various organisms indicated that: (a) geometric effects are present and (b) two-dimensional Euclidean space is generally as effective as higher dimensional Euclidean space for explaining the connectivity. Testing on a high-confidence yeast data set produced a very strong indication of geometric structure (area under the ROC curve of 0.89), with this network being essentially indistinguishable from a noisy geometric network. Overall, the results add support to the hypothesis that PPI networks have a geometric structure

Fitting a geometric graph to a protein-protein interaction network

Finding a good network null model for protein-protein interaction (PPI) networks is a fundamental issue. Such a model would provide insights into the interplay between network structure and biological function as well as into evolution. Also, network (graph) models are used to guide biological experiments and discover new biological features. It has been proposed that geometric random graphs are a good model for PPI networks. In a geometric random graph, nodes correspond to uniformly randomly distributed points in a metric space and edges (links) exist between pairs of nodes for which the corresponding points in the metric space are close enough according to some distance norm. Computational experiments have revealed close matches between key topological properties of PPI networks and geometric random graph models. In this work, we push the comparison further by exploiting the fact that the geometric property can be tested for directly. To this end, we develop an algorithm that takes PPI interaction data and embeds proteins into a low-dimensional Euclidean space, under the premise that connectivity information corresponds to Euclidean proximity, as in geometric-random graphs.We judge the sensitivity and specificity of the fit by computing the area under the Receiver Operator Characteristic (ROC) curve. The network embedding algorithm is based on multi-dimensional scaling, with the square root of the path length in a network playing the role of the Euclidean distance in the Euclidean space. The algorithm exploits sparsity for computational efficiency, and requires only a few sparse matrix multiplications, giving a complexity of O(N2) where N is the number of proteins.The algorithm has been verified in the sense that it successfully rediscovers the geometric structure in artificially constructed geometric networks, even when noise is added by re-wiring some links. Applying the algorithm to 19 publicly available PPI networks of various organisms indicated that: (a) geometric effects are present and (b) two-dimensional Euclidean space is generally as effective as higher dimensional Euclidean space for explaining the connectivity. Testing on a high-confidence yeast data set produced a very strong indication of geometric structure (area under the ROC curve of 0.89), with this network being essentially indistinguishable from a noisy geometric network. Overall, the results add support to the hypothesis that PPI networks have a geometric structure