An audit of blood cross-match ordering practices at the Aga Khan University Hospital: first step towards a Maximum Surgical Blood Ordering Schedule

Objective: In the absence of an explicit maximum blood order policy, ordering for blood transfusion is frequently based on subjective anticipation of blood loss instead of evidence based estimates of average requirement in a particular procedure. This study was done to assess current practice and the feasibility of a prospective randomized work to develop practice guidelines.METHOD: We audited transfusion data for elective surgical procedures in our hospital during the last 2 years. Cross-matched to transfused ratio (C/T ratio) and Transfusion Index (Ti) for each of the elective surgical procedures was performed during the study period. C/T ratio is used as a measure of the efficiency of blood ordering practice. It should ideally be between 2 and 2.5. We compared our results with the ideal.Results: Data was analyzed for 32 elective surgical procedures in 2131 patients. Majority (2079) (97.56%) of the patients had C/T ratios higher than 2.5. Only 12 in 450 (21.11%) patients, had a Transfusion Index (Ti) higher than 0.5. There were 13 procedures in which both C/T ratio was greater than 2.5 and Ti less than or equal to 0.5.CONCLUSION: In vast majority of elective surgical procedures routine cross match is not necessary. We propose a draft Maximum Surgical Blood Ordering Schedule (MSBOS). It provides guidelines for frequently performed elective surgical procedures by recommending the maximum number of units of blood to be cross-matched preoperatively. Implementation of MSBOS will result in about 60% reduction of cost to the patients

Parallel Perceptrons, Activation Margins and Imbalanced Training Set Pruning

The final publication is available at Springer via http://dx.doi.org/10.1007/11492542_6Proceedings of Second Iberian Conference, IbPRIA 2005, Estoril, Portugal, June 7-9, 2005, Part IIA natural way to deal with training samples in imbalanced class problems is to prune them removing redundant patterns, easy to classify and probably over represented, and label noisy patterns that belonging to one class are labelled as members of another. This allows classifier construction to focus on borderline patterns, likely to be the most informative ones. To appropriately define the above subsets, in this work we will use as base classifiers the so–called parallel perceptrons, a novel approach to committee machine training that allows, among other things, to naturally define margins for hidden unit activations. We shall use these margins to define the above pattern types and to iteratively perform subsample selections in an initial training set that enhance classification accuracy and allow for a balanced classifier performance even when class sizes are greatly different.With partial support of Spain’s CICyT, TIC 01–572, TIN2004–0767

Effect of plant growth regulators on flowering behavior of cashew cv. Vengurla-4 grown in the hilly tracts of South Gujarat

A trial was conducted at Subhir and Chikhalda locations in Dang district of South Gujarat, India to assess the effect of Ethrel, NAA and GA3 on the flowering behavior of cashew cultivar Vengurla-4 during 2013-14. Three concentrations each of GA3 (50, 75, 100 ppm), Ethrel (10, 30, 50 ppm) and NAA (50, 75, 100ppm) were applied as foliar sprays 20 days before blossoming and 20 days after full bloom in twenty year old trees of cashew cultivar Vengurla-4. Trees sprayed with 50 ppm Ethrel had significantly the highest number of flowering panicles per squaremeter (13.09), number of perfect flowers per panicle (87.11) and sex ratio (0.24) across locations and in pooled data. However, this was at par with 10 ppm Ethrel which emerged as the second best treatment of the trial. This study demonstrated the potential of Ethrel in improving various flowering parameters of cashew which are important determinations in increasing nut production

Microstructural and environmental effects on stress corrosion and corrosion fatigue of 7075 aluminum alloy

The design and development of high performance structural materials requires a thorough understanding of the relationship between environment, mechanical stresses, microstructure, and properties. The corrosion and fatigue behavior of aluminum alloys is greatly influenced by environment and precipitate structure. A comprehensive, mechanistic understanding of the role of environment on cyclic fatigue of Al alloys is needed. The relationship between environmental and mechanical effects is not well understood. The driving force at the crack tip is clearly a combination of chemical and mechanical processes operating together. A synergy between these processes is also present. In this talk, the role of moisture on stress corrosion and corrosion-fatigue of 7075 Al alloy will be presented. Rolled 7075 Al alloy was heat-treated to underaged, peak-aged, and overaged conditions. To investigate the effects of corrosion and fatigue on peak-aged 7075 aluminum alloy, corroded samples were tested via in situ x-Ray tomography. The samples were mechanically polished, then soaked in covered 3.5 wt.% NaCl for fifteen days to allow for significant corrosion to occur. Then, they were fatigue tested in situ in 3.5 wt.% NaCl using synchrotron x-ray tomography to analyze the fatigue crack initiation and growth characteristics. Hydrogen bubbles were observed between the sample and the fluid upon crack initiation, indicating chemical changes in the sample during in situ corrosion fatigue. The effect of oxide layers forming during corrosion and 2nd phase inclusions, on fatigue initiation and propagation, will be discussed. The microstructure and morphology of the fracture surfaces were examined by scanning electron microscopy (SEM) and correlated with the crack growth behavior. The crack initiation, growth, and damage were also quantified by sophisticated three dimensional (3D) in situ x-ray synchrotron tomography technique. This technique provided interesting insights into the onset of crack initiation and growt

Covering problems in edge- and node-weighted graphs

This paper discusses the graph covering problem in which a set of edges in an edge- and node-weighted graph is chosen to satisfy some covering constraints while minimizing the sum of the weights. In this problem, because of the large integrality gap of a natural linear programming (LP) relaxation, LP rounding algorithms based on the relaxation yield poor performance. Here we propose a stronger LP relaxation for the graph covering problem. The proposed relaxation is applied to designing primal-dual algorithms for two fundamental graph covering problems: the prize-collecting edge dominating set problem and the multicut problem in trees. Our algorithms are an exact polynomial-time algorithm for the former problem, and a 2-approximation algorithm for the latter problem, respectively. These results match the currently known best results for purely edge-weighted graphs.Comment: To appear in SWAT 201

Competition of stress corrosion crack branches observed in-situ using time-lapse 3D x-ray synchrotron computed tomography

The progress of a stress corrosion crack in a sensitized AA7075 alloy was studied by in-situ x-ray synchrotron computed tomography. A load was applied to a pre-cracked specimen inside an environmental cell containing moist air and the propagation of the stress corrosion crack was observed. Measurements from the 3D image of the crack have already been shown to provide better quantification compared to observations of the crack from the outer surface. In this paper we study in detail the progress of the stress corrosion crack as it propagates through the material. We reveal how the formation of metal ligaments occurs and the competition of the ‘main’ crack and its branches. We have visualized these features to show the complexity of the local variation in crack morphology in a way that brings new insight into the interaction of the stress corrosion crack with the microstructure of the material

4D microstructural and electrochemical characterization of dissimilar metal corrosion in naval structural Joints

Dissimilar metal corrosion in aircraft and naval structures has proven to be a persistent challenge. Decades of research in the area have shown that such complex contact surfaces are subject to a combination of corrosive environments and mechanical loads. Hence, this multi-faceted problem must be understood from electrochemical, microstructural and mechanical standpoints to comprehensively understand corrosion damage in these systems. Please click Additional Files below to see the full abstract

The prediction of fatigue using speech as a biosignal

Automatic systems for estimating operator fatigue have application in safety-critical environments. We develop and evaluate a system to detect fatigue from speech recordings collected from speakers kept awake over a 60-hour period. A binary classification system (fatigued/not-fatigued) based on time spent awake showed good discrimination, with 80 % unweighted accuracy using raw features, and 90 % with speaker-normalized features. We describe the data collection, feature analysis, machine learning and cross-validation used in the study. Results are promising for real-world applications in domains such as aerospace, transportation and mining where operators are in regular verbal communication as part of their normal working activities

Software defect prediction: do different classifiers find the same defects?

Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.Peer reviewedFinal Published versio

Exploring the performance of resampling strategies for the class imbalance problem

The present paper studies the influence of two distinct factors on the performance of some resampling strategies for handling imbalanced data sets. In particular, we focus on the nature of the classifier used, along with the ratio between minority and majority classes. Experiments using eight different classifiers show that the most significant differences are for data sets with low or moderate imbalance: over-sampling clearly appears as better than under-sampling for local classifiers, whereas some under-sampling strategies outperform over-sampling when employing classifiers with global learning