On Maximum Signless Laplacian Estrada Indices of Graphs with Given Parameters

Signless Laplacian Estrada index of a graph $G$, defined as $SLEE(G)=\sum^{n}_{i=1}e^{q_i}$, where $q_1, q_2, \cdots, q_n$ are the eigenvalues of the matrix $\mathbf{Q}(G)=\mathbf{D}(G)+\mathbf{A}(G)$. We determine the unique graphs with maximum signless Laplacian Estrada indices among the set of graphs with given number of cut edges, pendent vertices, (vertex) connectivity and edge connectivity.Comment: 14 pages, 3 figure

Optimizirano određivanje lorazepama u humanom serumu visokotlačnom tekućinskom kromatografijom

The present research was designated to evaluate a rapid and sensitive method for determining low concentrations of the highly active drug lorazepam in serum. Isolation of the drug from biological fluid after addition of nordazepam as the internal standard was achieved using a simple liquid-liquid extraction with dichloromethane and the extracted compounds were quantified by high-performance liquid chromatography. Chromatographic separation on a reversed phase column containing a stationary phase with low silanol activity resulted in a perfectly symmetrical peak with a tailing factor of 1.0. The limit of quantitation in serum is 2.5 ng mL-1 for both lorazepam and internal standard. The procedure is rapid and sensitive enough for determination of lorazepam in serum.U radu se vrednuje brza i osjetljiva metoda za određivanje niskih koncentracija lorazepama u serumu. Lorazepam je izoliran iz seruma ekstrakcijom diklormetanom, zajedno s nordazepamom, koji je upotrebljen kao interni standard i zatim određivan visokotlačnom tekućinskom kromatografijom. Kromatografskim razdjeljivanjem na reverzno-faznoj koloni sa stacionarnom fazom s niskom aktivnošću silanola dobiveni su potpuno simetrični signali faktorom završnog povlačenja 1,0. Granica određivanja u serumu je 2,5 ng mL-1 za lorazepam i interni standard. Metoda je brza i dovoljno osjetljiva za određivanje lorazepama u serumu

Spatial mapping of the provenance of storm dust: Application of data mining and ensemble modelling

Spatial modelling of storm dust provenance is essential to mitigate its on-site and off-site effects in the arid and semi-arid environments of the world. Therefore, the main aim of this study was to apply eight data mining algorithms including random forest (RF), support vector machine (SVM), bayesian additive regression trees (BART), radial basis function (RBF), extreme gradient boosting (XGBoost), regression tree analysis (RTA), Cubist model and boosted regression trees (BRT) and an ensemble modelling (EM) approach for generating spatial maps of dust provenance in the Khuzestan province, a main region with active sources for producing dust in southwestern Iran. This study is the first attempt at predicting storm dust provenance by applying individual data mining models and ensemble modelling. We identified and mapped in a geographic information system (GIS), 12 potential effective factors for dust emissions comprising two for climate (wind speed, precipitation), five soil characteristics (texture, bulk density, Ec, organic matter (OM), available water capacity (AWC)), a normalized difference vegetation index (NDVI), land use, geology, a digital elevation model (DEM) and land type, and used a mean decrease accuracy measure (MDAM) to determine the corresponding importance scores (IS). A multicollinearity test (including the variance inflation factor (VIF) and tolerance coefficient (TC)) was applied to assess relationships between the effective factors, and an existing map of dust provenance was randomly categorized into two groups consisting of training (70%) and validation (30%) data. The individual data mining models were validated using the area under the curve (AUC). Based on the TC and VIF results, no collinearity was detected among the 12 effective factors for dust emissions. The prediction accuracies of the eight data mining models and an EM assessed by the AUC were as follows: EM (with AUC=99.8%) > XGBoost>RBF > Cubist>RF > BART>SVM > BRT > RTA (with AUC=79.1%). Among all models, the EM was found to provide the highest accuracy for predicting storm dust provenance. Using the EM, areas classified as being low, moderate, high and very high susceptibility for storm dust provenance comprised 36, 13, 23 and 28% of the total mapped area, respectively. Based on MDAM results, the highest and lowest IS were obtained for the wind speed (IS=23) and geology (IS=6.5) factors, respectively. Overall, the modelling techniques used in this research are helpful for predicting storm dust provenance and thereby targeting mitigation. Therefore, we recommend applying data mining EM approaches to the spatial mapping of storm dust provenance worldwide

TW-TOA based positioning in the presence of clock imperfections

This manuscript studies the positioning problem based on two-way time-of-arrival (TW-TOA) measurements in semi-asynchronous wireless sensor networks in which the clock of a target node is unsynchronized with the reference time. Since the optimal estimator for this problem involves difficult nonconvex optimization, two suboptimal estimators are proposed based on the squared-range least squares and the least absolute mean of residual errors. We formulated the former approach as an extended general trust region subproblem (EGTR) and propose a simple technique to solve it approximately. The latter approach is formulated as a difference of convex functions programming (DCP), which can be solved using a concave–convex procedure. Simulation results illustrate the high performance of the proposed techniques, especially for the DCP approach

TuNet: End-to-end Hierarchical Brain Tumor Segmentation using Cascaded Networks

Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord. In addition to having a high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-to-end cascaded network for semantic segmentation that utilizes the hierarchical structure of the tumor sub-regions with ResNet-like blocks and Squeeze-and-Excitation modules after each convolution and concatenation block. By utilizing cross-validation, an average ensemble technique, and a simple post-processing technique, we obtained dice scores of 88.06, 80.84, and 80.29, and Hausdorff Distances (95th percentile) of 6.10, 5.17, and 2.21 for the whole tumor, tumor core, and enhancing tumor, respectively, on the online test set.Comment: Accepted at MICCAI BrainLes 201

TDOA based positioning in the presence of unknown clock skew

Cataloged from PDF version of article.This paper studies the positioning problem of a single target node based on time-difference-of-arrival (TDOA) measurements in the presence of clock imperfections. Employing an affine model for the behaviour of a local clock, it is observed that TDOA based approaches suffer from a parameter of the model, called the clock skew. Modeling the clock skew as a nuisance parameter, this paper investigates joint clock skew and position estimation. The maximum likelihood estimator (MLE) is derived for this problem, which is highly nonconvex and difficult to solve. To avoid the difficulty in solving the MLE, we employ suitable approximations and relaxations and propose two suboptimal estimators based on semidefinite programming and linear estimation. To further improve the estimation accuracy, we also propose a refining step. In addition, the Cramer-Rao ´ lower bound (CRLB) is derived for this problem as a benchmark. Simulation results show that the proposed suboptimal estimators can attain the CRLB for sufficiently high signal-to-noise ratios

Cooperative Wireless Sensor Network Positioning via Implicit Convex Feasibility

We propose a distributed positioning algorithm to estimate the unknown positions of a number of target nodes, given distance measurements between target nodes and between target nodes and number of reference nodes at known positions. Based on a geometric interpretation, we formulate the positioning problem as an implicit convex feasibility problem in which some of the sets depend on the unknown target positions, and apply a parallel projection onto convex sets approach to estimate the unknown target node positions. The proposed technique is suitable for parallel implementation in which every target node in parallel can update its position and share the estimate of its location with other targets. We mathematically prove convergence of the proposed algorithm. Simulation results reveal enhanced performance for the proposed approach compared to available techniques based on projections, especially for sparse networks