Chinese postman problem approach for a large-scale conventional rail network in Turkey

Svake se godine željezničke pruge periodički pregledavaju zbog ispitivanja stanja tračnica i osiguranja sigurnosti vlakova u Turskoj. Takvi se projekti moraju obaviti s nekoliko specijaliziranih lokomotiva i skupom opremom. Pronalaženje optimalnog puta takve lokomotive, vozeći na svim prugama, od bitne je važnosti zbog troškova i udaljenosti. U postojećoj praksi, određivanje rute tih lokomotiva uglavnom je manualno i oslanja se prvenstveno na znanje i procjenu stručnjaka. U ovom se radu za rješavanje problema usmjeravanja prometa inspekcijskom lokomotivom, predlaže "problem kineskog poštara" - Chinese Postman Problem (CPP). Cilj je minimizirati ukupnu prijeđenu udaljenost pronalaženjem najkraćeg pravca. Predloženi je model primijenjen na veliki problem u stvarnom svijetu. U usporedbi s postojećim stanjem predloženim se modelom smanjuje prijeđeni put za 20,76%.Every year, railways are inspected periodically to examine the status of rail tracks and ensure the safety of train operations of the railroad networks in Turkey. These inspection projects must be performed by several specialized machines with large and expensive equipment. Finding the optimum route of inspection machines that control through travelling all lines is critically important in terms of cost and distance. In current practice, determining the route of these machines is largerly manual and primarily relies on the knowledge and judgment of experts. This paper proposes Chinese Postman Problem (CPP) to solve the inspection machine routing problem. The objective is to minimize the total travel distance on the railroads by finding the shortest route. The proposed model is applied to a large-scale real world problem. Compared to the current practice the proposed approach significantly outperforms the reduced objective value by 20,76%

MaxDIA enables library-based and library-free data-independent acquisition proteomics

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies—BoxCar acquisition and trapped ion mobility spectrometry—both lead to deep and accurate proteome quantification.publishedVersio

Landslide susceptibility mapping at VAZ watershed (Iran) using an artificial neural network model: a comparison between multilayer perceptron (MLP) and radial basic function (RBF) algorithms

Landslide susceptibility and hazard assessments are the most important steps in landslide risk mapping. The main objective of this study was to investigate and compare the results of two artificial neural network (ANN) algorithms, i.e., multilayer perceptron (MLP) and radial basic function (RBF) for spatial prediction of landslide susceptibility in Vaz Watershed, Iran. At first, landslide locations were identified by aerial photographs and field surveys, and a total of 136 landside locations were constructed from various sources. Then the landslide inventory map was randomly split into a training dataset 70 % (95 landslide locations) for training the ANN model and the remaining 30 % (41 landslides locations) was used for validation purpose. Nine landslide conditioning factors such as slope, slope aspect, altitude, land use, lithology, distance from rivers, distance from roads, distance from faults, and rainfall were constructed in geographical information system. In this study, both MLP and RBF algorithms were used in artificial neural network model. The results showed that MLP with Broyden–Fletcher–Goldfarb–Shanno learning algorithm is more efficient than RBF in landslide susceptibility mapping for the study area. Finally the landslide susceptibility maps were validated using the validation data (i.e., 30 % landslide location data that was not used during the model construction) using area under the curve (AUC) method. The success rate curve showed that the area under the curve for RBF and MLP was 0.9085 (90.85 %) and 0.9193 (91.93 %) accuracy, respectively. Similarly, the validation result showed that the area under the curve for MLP and RBF models were 0.881 (88.1 %) and 0.8724 (87.24 %), respectively. The results of this study showed that landslide susceptibility mapping in the Vaz Watershed of Iran using the ANN approach is viable and can be used for land use planning

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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MaxDIA enables library-based and library-free data-independent acquisition proteomics

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA—hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA’s bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies—BoxCar acquisition and trapped ion mobility spectrometry—both lead to deep and accurate proteome quantification