Development of Knowledge-Based Engineering System for Structural Size Optimization of External Fixation Device

The development process of the knowledge-based engineering (KBE) system for the structural size optimization of external fixation device is presented in this paper. The system is based on algorithms for generative modeling, finite element model (FEM) analysis, and size optimization. All these algorithms are integrated into the CAD/CAM/CAE system CATIA. The initial CAD/FEM model of external fixation device is verified using experimental verification on the real design. Experimental testing is done for axial pressure. Axial stress and displacements are measured using tensometric analysis equipment. The proximal bone segment displacements were monitored by a displacement transducer, while the loading was controlled by a force transducer. Iterative hybrid optimization algorithm is developed by integration of global algorithm, based on the simulated annealing (SA) method and a local algorithm based on the conjugate gradient (CG) method. The cost function of size optimization is the minimization of the design volume. Constrains are given in a form of clinical interfragmentary displacement constrains, at the point of fracture and maximum allowed stresses for the material of the external fixation device. Optimization variables are chosen as design parameters of the external fixation device. The optimized model of external fixation device has smaller mass, better stress distribution, and smaller interfragmentary displacement, in correlation with the initial model.

74. Evaluation of the Differences in Appropriateness of BioFire® FilmArray® Gastrointestinal Panel Testing between Emergency Department and Inpatient Services

Background Use of rapid molecular diagnostic panels in the evaluation of diarrhea provides increased sensitivity for organism identification and decreased time to results. However, their inappropriate use can lead to unnecessary expenditures and antimicrobial exposures. We aimed to characterize the appropriateness of testing using the BioFire® FilmArray® Gastrointestinal Panel (SFA) in different clinical settings and to describe the impact of SFA results on patient care. Methods Retrospective study of adult patients presenting to hospitals part of an integrated health system in Des Moines, Iowa, between July 30 and September 30, 2019, and who had a SFA ordered and collected in the Emergency Department (ED) or an inpatient service. The appropriateness of SFA testing was determined according to adherence to a local algorithm available through the hospital’s intranet (Figure 1). Reason for testing, appropriateness of SFA test, molecular targets identified, and antibiotic exposures were collected. Results We identified 257 patients, 111 (43.2%) who had SFA done in the ED and 146 (56.8%) as inpatients. Testing was deemed inappropriate in 46 (41.4%) of ED patients compared to 100 (68.5%) of inpatients (p< 0.0001). Documented indications for SFA are presented in Table 1. Among ED patients testing was most frequently considered inappropriate due to absence of diarrhea on the day of test collection (41.3%), and among inpatients due to the use of SFA for assessment of hospital-onset diarrhea (47.0%) (Table 2). Overall, there were 94 (36.6%) positive SFA (Figure 2). Among ED patients, the percentage of positive SFA samples was 30.4% and 50.8% for inappropriate and appropriate testing respectively (p=0.03), while for inpatients it was 33.0% for inappropriate orders and 30.4% for appropriate orders (p=0.76). Antibiotics were prescribed to 28.2% and 28.1% of patients tested in the ED and inpatient service respectively. Conclusion High proportions of inappropriate SFA testing were identified both in the ED and inpatient services, with distinct issues in each site. Characterization of the reasons underlying inappropriate use of SFA can aid in the design of diagnostic stewardship interventions tailored to each clinical setting. Disclosures All Authors: No reported disclosures

Predicting soil carbon by efficiently using variation in a mid-IR soil spectral library

Traditional laboratory methods of acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modelling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL; n = 4374) in the mid-infrared range includes soil samples from the Biodiversity Monitoring Program (BDM), arranged in a regularly spaced grid across Switzerland, and temporally-resolved data from the Swiss Soil Monitoring Network (NABO). Given the relatively low representation of organic soils and their organo-mineral diversity in the SSL, we aimed to develop both an efficient calibration sampling scheme and accurate modelling strategy to estimate soil carbon (SC) contents of heterogeneous samples between 0 m to 2 m depth from 26 locations within two drained peatland regions (HAFL dataset; n = 116). The focus was on minimizing the need for new reference analyses by efficiently mining the spectral information of SSL instances and their target-feature representations. We used partial least square regressions (PLSR) together with a 5 times repeated, grouped by location, 10-fold cross validation (CV) to predict SC ranging from 1 % to 52 % in the local HAFL dataset. We compared the validation performance of different calibration schemes involving local models (1), models using the entire SSL spiked with local samples (2) and 15 subsets of local and SSL samples using the RS-LOCAL algorithm (3). Using local and RS-LOCAL calibrations with at least 5 local samples, we achieved similar validation results for predictions of SC up to 52 % (R2 = 0.94–0.96, bias = −0.6–1.5, RMSE = 2.6 % to 3.5 % total carbon). However, calibrations of representative SSL and local samples using RS-LOCAL only required 5 local samples for very accurate models (RMSE = 2.9 % total carbon), while local calibrations required 50 samples for similarly accurate results (RMSE

Distributed link scheduling in wireless networks

This work investigates distributed transmission scheduling in wireless networks. Due to interference constraints, “neighboring links” cannot be simultaneously activated, otherwise transmissions will fail. Here, we consider any binary model of interference. We use the model described by Bui et al. in [L. X. Bui, S. Sanghavi and R. Srikant, Distributed link scheduling with constant overhead, IEEE/ACM Trans. Netw. 17(5) (2009) 1467–1480; S. Sanghavi, L. Bui and R. Srikant, Distributed link scheduling with constant overhead, in Proc. ACM Sigmetrics (San Diego, CA, USA, 2007), pp. 313–324.]. We assume that time is slotted and during each slot there are two phases: one control phase in which a link scheduling algorithm determines a set of non-interfering links to be activated, and a data phase in which data is sent through these links. We assume random arrivals on each link during each slot, so that a queue is associated to each link. Since nodes do not have a global knowledge of the queues sizes, our aim (like in [L. X. Bui, S. Sanghavi and R. Srikant, Distributed link scheduling with constant overhead, IEEE/ACM Trans. Netw. 17(5) (2009) 1467–1480; S. Sanghavi, L. Bui and R. Srikant, Distributed link scheduling with constant overhead, in Proc. ACM Sigmetrics (San Diego, CA, USA, 2007), pp. 313–324.]) is to design a distributed link scheduling algorithm. To be efficient, the control phase should be as short as possible; this is done by exchanging control messages during a constant number of mini-slots (constant overhead). In this paper, we design the first fully distributed local algorithm with the following properties: it works for any arbitrary binary interference model; it has a constant overhead (independent of the size of the network and the values of the queues), and it does not require any knowledge of the queue-lengths. We prove that this algorithm gives a maximal set of active links, where for any non-active link there exists at least one active link in its interference set. We also establish sufficient conditions for stability under general Markovian assumptions. Finally, the performance of our algorithm (throughput, stability) is investigated and compared via simulations to that of previously proposed schemes.

Impact of the Partitioning Method on Multidimensional Adaptive-Chemistry Simulations

The large number of species included in the detailed kinetic mechanisms represents a serious challenge for numerical simulations of reactive flows, as it can lead to large CPU times, even for relatively simple systems. One possible solution to mitigate the computational cost of detailed numerical simulations, without sacrificing their accuracy, is to adopt a Sample-Partitioning Adaptive Reduced Chemistry (SPARC) approach. The first step of the aforementioned approach is the thermochemical space partitioning for the generation of locally reduced mechanisms, but this task is often challenging because of the high-dimensionality, as well as the high non-linearity associated to reacting systems. Moreover, the importance of this step in the overall approach is not negligible, as it has effects on the mechanisms’ level of chemical reduction and, consequently, on the accuracy and the computational speed-up of the adaptive simulation. In this work, two different clustering algorithms for the partitioning of the thermochemical space were evaluated by means of an adaptive CFD simulation of a 2D unsteady laminar flame of a nitrogen-diluted methane stream in air. The first one is a hybrid approach based on the coupling between the Self-Organizing Maps with K-Means (SKM), and the second one is the Local Principal Component Analysis (LPCA). Comparable results in terms of mechanism reduction (i.e., the mean number of species in the reduced mechanisms) and simulation accuracy were obtained for both the tested methods, but LPCA showed superior performances in terms of reduced mechanisms uniformity and speed-up of the adaptive simulation. Moreover, the local algorithm showed a lower sensitivity to the training dataset size in terms of the required CPU-time for convergence, thus also being optimal, with respect to SKM, for massive dataset clustering tasks.

Monodromy and K-theory of Schubert curves via generalized jeu de taquin

International audience We establish a combinatorial connection between the real geometry and the K-theory of complex Schubert curves Spλ‚q, which are one-dimensional Schubert problems defined with respect to flags osculating the rational normal curve. In a previous paper, the second author showed that the real geometry of these curves is described by the orbits of a map ω on skew tableaux, defined as the commutator of jeu de taquin rectification and promotion. In particular, the real locus of the Schubert curve is naturally a covering space of RP1, with ω as the monodromy operator.We provide a fast, local algorithm for computing ω without rectifying the skew tableau, and show that certain steps in our algorithm are in bijective correspondence with Pechenik and Yong's genomic tableaux, which enumerate the K-theoretic Littlewood-Richardson coefficient associated to the Schubert curve. Using this bijection, we give purely combinatorial proofs of several numerical results involving the K-theory and real geometry of Spλ‚q.