A QoS-Aware Routing Protocol for Real-time Applications in Wireless Sensor Networks

The paper presents a quality of service aware routing protocol which provides low latency for high priority packets. Packets are differentiated based on their priority by applying queuing theory. Low priority packets are transferred through less energy paths. The sensor nodes interact with the pivot nodes which in turn communicate with the sink node. This protocol can be applied in monitoring context aware physical environments for critical applications.Comment: 10 pages. arXiv admin note: text overlap with arXiv:1001.5339 by other author

The effect of distance on observed mortality, childhood pneumonia and vaccine efficacy in rural Gambia.

We investigated whether straight-line distance from residential compounds to healthcare facilities influenced mortality, the incidence of pneumonia and vaccine efficacy against pneumonia in rural Gambia. Clinical surveillance for pneumonia was conducted on 6938 children living in the catchment areas of the two largest healthcare facilities. Deaths were monitored by three-monthly home visits. Children living >5 km from the two largest healthcare facilities had a 2·78 [95% confidence interval (CI) 1·74-4·43] times higher risk of all-cause mortality compared to children living within 2 km of these facilities. The observed rate of clinical and radiological pneumonia was lower in children living >5 km from these facilities compared to those living within 2 km [rate ratios 0·65 (95% CI 0·57-0·73) and 0·74 (95% CI 0·55-0·98), respectively]. There was no association between distance and estimated pneumococcal vaccine efficacy. Geographical access to healthcare services is an important determinant of survival and pneumonia in children in rural Gambia

Statistically Motivated Second Order Pooling

Second-order pooling, a.k.a.~bilinear pooling, has proven effective for deep learning based visual recognition. However, the resulting second-order networks yield a final representation that is orders of magnitude larger than that of standard, first-order ones, making them memory-intensive and cumbersome to deploy. Here, we introduce a general, parametric compression strategy that can produce more compact representations than existing compression techniques, yet outperform both compressed and uncompressed second-order models. Our approach is motivated by a statistical analysis of the network's activations, relying on operations that lead to a Gaussian-distributed final representation, as inherently used by first-order deep networks. As evidenced by our experiments, this lets us outperform the state-of-the-art first-order and second-order models on several benchmark recognition datasets.Comment: Accepted to ECCV 2018. Camera ready version. 14 page, 5 figures, 3 table

Ultrasensitive detection of lipoarabinomannan with plasmonic grating biosensors in clinical samples of HIV negative patients with tuberculosis.

BACKGROUND:Timely diagnosis of tuberculosis disease is critical for positive patient outcomes, yet potentially millions go undiagnosed or unreported each year. Sputum is widely used as the testing input, but limited by its complexity, heterogeneity, and sourcing problems. Finding methods to interrogate noninvasive, non-sputum clinical specimens is indispensable to improving access to tuberculosis diagnosis and care. In this work, economical plasmonic gratings were used to analyze tuberculosis biomarker lipoarabinomannan (LAM) from clinical urine samples by single molecule fluorescence assay (FLISA) and compared with gold standard sputum GeneXpert MTB/ RIF, culture, and reference ELISA testing results. METHODS AND FINDINGS:In this study, twenty sputum and urine sample sets were selected retrospectively from a repository of HIV-negative patient samples collected before initiation of anti-tuberculosis therapy. GeneXpert MTB/RIF and culture testing of patient sputum confirmed the presence or absence of pulmonary tuberculosis while all patient urines were reference ELISA LAM-negative. Plasmonic gratings produced by low-cost soft lithography were bound with anti-LAM capture antibody, incubated with patient urine samples, and biotinylated detection antibody. Fluorescently labeled streptavidin revealed single molecule emission by epifluorescence microscope. Using a 1 fg/mL baseline for limit of detection, single molecule FLISA demonstrated good qualitative agreement with gold standard tests on 19 of 20 patients, including accurately predicting the gold-standard-negative patients, while one gold-standard-positive patient produced no observable LAM in urine. CONCLUSIONS:Single molecule FLISA by plasmonic grating demonstrated the ability to quantify tuberculosis LAM from complex urine samples of patients from a high endemic setting with negligible interference from the complex media itself. Moreover, agreement with patient diagnoses by gold standard testing suggests that single molecule FLISA could be used as a highly sensitive test to diagnose tuberculosis noninvasively

Second-order Democratic Aggregation

Aggregated second-order features extracted from deep convolutional networks have been shown to be effective for texture generation, fine-grained recognition, material classification, and scene understanding. In this paper, we study a class of orderless aggregation functions designed to minimize interference or equalize contributions in the context of second-order features and we show that they can be computed just as efficiently as their first-order counterparts and they have favorable properties over aggregation by summation. Another line of work has shown that matrix power normalization after aggregation can significantly improve the generalization of second-order representations. We show that matrix power normalization implicitly equalizes contributions during aggregation thus establishing a connection between matrix normalization techniques and prior work on minimizing interference. Based on the analysis we present {\gamma}-democratic aggregators that interpolate between sum ({\gamma}=1) and democratic pooling ({\gamma}=0) outperforming both on several classification tasks. Moreover, unlike power normalization, the {\gamma}-democratic aggregations can be computed in a low dimensional space by sketching that allows the use of very high-dimensional second-order features. This results in a state-of-the-art performance on several datasets

Ten Years of Experience Training Non-Physician Anesthesia Providers in Haiti.

Surgery is increasingly recognized as an effective means of treating a proportion of the global burden of disease, especially in resource-limited countries. Often non-physicians, such as nurses, provide the majority of anesthesia; however, their training and formal supervision is often of low priority or even non-existent. To increase the number of safe anesthesia providers in Haiti, Médecins Sans Frontières has trained nurse anesthetists (NAs) for over 10 years. This article describes the challenges, outcomes, and future directions of this training program. From 1998 to 2008, 24 students graduated. Nineteen (79%) continue to work as NAs in Haiti and 5 (21%) have emigrated. In 2008, NAs were critical in providing anesthesia during a post-hurricane emergency where they performed 330 procedures. Mortality was 0.3% and not associated with lack of anesthesiologist supervision. The completion rate of this training program was high and the majority of graduates continue to work as nurse anesthetists in Haiti. Successful training requires a setting with a sufficient volume and diversity of operations, appropriate anesthesia equipment, a structured and comprehensive training program, and recognition of the training program by the national ministry of health and relevant professional bodies. Preliminary outcomes support findings elsewhere that NAs can be a safe and effective alternative where anesthesiologists are scarce. Training non-physician anesthetists is a feasible and important way to scale up surgical services resource limited settings

Emergency and critical care services in Tanzania: a survey of ten hospitals.

While there is a need for good quality care for patients with serious reversible disease in all countries in the world, Emergency and Critical Care tends to be one of the weakest parts of health systems in low-income countries. We assessed the structure and availability of resources for Emergency and Critical Care in Tanzania in order to identify the priorities for improving care in this neglected specialty. Ten hospitals in four regions of Tanzania were assessed using a structured data collection tool. Quality was evaluated with standards developed from the literature and expert opinion. Important deficits were identified in infrastructure, routines and training. Only 30% of the hospitals had an emergency room for adult and paediatric patients. None of the seven district and regional hospitals had a triage area or intensive care unit for adults. Only 40% of the hospitals had formal systems for adult triage and in less than one third were critically ill patients seen by clinicians more than once daily. In 80% of the hospitals there were no staff trained in adult triage or critical care. In contrast, a majority of equipment and drugs necessary for emergency and critical care were available in the hospitals (median 90% and 100% respectively. The referral/private hospitals tended to have a greater overall availability of resources (median 89.7%) than district/regional hospitals (median 70.6). Many of the structures necessary for Emergency and Critical Care are lacking in hospitals in Tanzania. Particular weaknesses are infrastructure, routines and training, whereas the availability of drugs and equipment is generally good. Policies to improve hospital systems for the care of emergency and critically ill patients should be prioritised

Multi-model evaluation of short-lived pollutant distributions over East Asia during summer 2008

The ability of seven state of the art chemistry-aerosol models to reproduce distributions of tropospheric ozone and its precursors, as well as aerosols over eastern Asia in summer 2008 is evaluated. The study focuses on the performance of models used to assess impacts of pollutants on climate and air quality as part of the EU ECLIPSE project. Models, run using the same ECLIPSE emissions, are compared over different spatial scales to in-situ surface, vertical profile and satellite data. Several rather clear biases are found between model results and observations including overestimation of ozone at rural locations downwind of the main emission regions in China as well as downwind over the Pacific. Several models produce too much ozone over polluted regions which is then transported downwind. Analysis points to different factors related to the ability of models to simulate VOC limited regimes over polluted regions and NOx limited regimes downwind. This may also be linked to biases compared to satellite NO2 indicating overestimation of NO2 over and to the north of the northern China Plain emission region. On the other hand, model NO2 is too low to the south and east of this region and over Korean/Japan. Overestimation of ozone is linked to systematic underestimation of CO particularly at rural sites and downwind of the main Chinese emission regions. This is likely to be due to enhanced destruction of CO by OH. Overestimation of Asian ozone and its transport downwind implies that radiative forcing from this source may be overestimated. Model-observation discrepancies over Beijing do not appear to be due to emission controls linked to the Olympic Games in summer 2008. With regard to aerosols, most models reproduce the satellite-derived AOD patterns over eastern China. Our study nevertheless reveals an overestimation of ECLIPSE model-mean surface BC and sulphate aerosols in urban China in summer 2008. The effect of the short-term emission mitigation in Beijing is too weak to explain the differences between the models. Our results rather point to an overestimation of SO2 emissions, in particular, close to the surface in Chinese urban areas. However, we also identify a clear underestimation of aerosol concentrations over northern India, suggesting that the rapid recent growth of emissions in India, as well as their spatial extension, is underestimated in emission inventories. Model deficiencies in the representation of pollution accumulation due to the Indian monsoon may also be playing a role. Comparison with vertical aerosol lidar measurements highlights a general underestimation of scattering aerosols in the boundary layer associated with overestimation in the free troposphere pointing to modeled aerosol lifetimes that are too long. This is likely linked to a too strong vertical transport and/or insufficient deposition efficiency during transport or export from the boundary layer, rather than chemical processing (in the case of sulphate aerosols). Underestimation of sulphate in the boundary layer implies potentially large errors in simulated aerosol-cloud interactions, via impacts on boundary-layer clouds. This evaluation has important implications for accurate assessment of air pollutants on regional air quality and global climate based on global model calculations. Ideally, models should be run at higher resolution over source regions to better simulate urban-rural pollutant gradients/chemical regimes, and also to better resolve pollutant processing and loss by wet deposition as well as vertical transport. Discrepancies in vertical distributions requires further quantification and improvement since this is a key factor in the determination of radiative forcing from short-lived pollutants

Current model capabilities for simulating black carbon and sulfate concentrations in the Arctic atmosphere: a multi-model evaluation using a comprehensive measurement data set

The concentrations of sulfate, black carbon (BC) and other aerosols in the Arctic are characterized by high values in late winter and spring (so-called Arctic Haze) and low values in summer. Models have long been struggling to capture this seasonality and especially the high concentrations associated with Arctic Haze. In this study, we evaluate sulfate and BC concentrations from eleven different models driven with the same emission inventory against a comprehensive pan-Arctic measurement data set over a time period of 2 years (2008–2009). The set of models consisted of one Lagrangian particle dispersion model, four chemistry transport models (CTMs), one atmospheric chemistry-weather forecast model and five chemistry climate models (CCMs), of which two were nudged to meteorological analyses and three were running freely. The measurement data set consisted of surface measurements of equivalent BC (eBC) from five stations (Alert, Barrow, Pallas, Tiksi and Zeppelin), elemental carbon (EC) from Station Nord and Alert and aircraft measurements of refractory BC (rBC) from six different campaigns. We find that the models generally captured the measured eBC or rBC and sulfate concentrations quite well, compared to previous comparisons. However, the aerosol seasonality at the surface is still too weak in most models. Concentrations of eBC and sulfate averaged over three surface sites are underestimated in winter/spring in all but one model (model means for January–March underestimated by 59 and 37 % for BC and sulfate, respectively), whereas concentrations in summer are overestimated in the model mean (by 88 and 44 % for July–September), but with overestimates as well as underestimates present in individual models. The most pronounced eBC underestimates, not included in the above multi-site average, are found for the station Tiksi in Siberia where the measured annual mean eBC concentration is 3 times higher than the average annual mean for all other stations. This suggests an underestimate of BC sources in Russia in the emission inventory used. Based on the campaign data, biomass burning was identified as another cause of the modeling problems. For sulfate, very large differences were found in the model ensemble, with an apparent anti-correlation between modeled surface concentrations and total atmospheric columns. There is a strong correlation between observed sulfate and eBC concentrations with consistent sulfate/eBC slopes found for all Arctic stations, indicating that the sources contributing to sulfate and BC are similar throughout the Arctic and that the aerosols are internally mixed and undergo similar removal. However, only three models reproduced this finding, whereas sulfate and BC are weakly correlated in the other models. Overall, no class of models (e.g., CTMs, CCMs) performed better than the others and differences are independent of model resolution