bluuebunny/crossref_metadata_2025_split huggingface dataset:
Perspectives of the unmanned cargo transport with electric traction development under the conditions of the Russian Federation
Authors: Saikin A M 
Published: June 2019

Abstract
The development of road transport in the world is highly connected with increased requirements for safety and cost-effectiveness of carrying out passenger and cargo transportation, reducing the negative impact of road transport on people and the environment, as well as the desire of countries that do not have direct access to energy sources, “get off the oil needle”. To ensure the fulfillment of these requirements, the key development areas in the automotive industry of developed countries are unmanned vehicles and electric vehicles. Taking into account foreign experience, the article looks at the prospects and features of developing unmanned cargo vehicles with electric traction, at operation conditions of the Russian Federation, which differ markedly from the conditions of vehicle operation in Central Europe, North America and the countries of the Asian region.
Creation concept and architecture principle for vehicle motion control competitive systems under road and climatic conditions of the Russian Federation
Authors: Saikin A M , Buznikov S E , Barashkov A A , Elkin D S , Strukov V O , Shabanov N S , Lucenko E V 
Published: June 2019

Abstract
The urgency of traffic safety improvement is determined by the global significance level of this problem. Current research provides the scientific substantiation of the creation concept and building principles for competitive motion control systems (MCSs) under road and climatic conditions of the Russian Federation. The study was carried out using system analysis methods and modern control theory. The safe vehicle control task is formulated as prevention of collisions with moving and stationary obstacles on the route taking into account the road and climatic conditions of the Russian Federation. To estimate the quality of the dynamic stabilization solution modified quadratic functional is introduced. The results of the study are presented as an analysis of properties of the modified quadratic control quality functional and associated consumer indicators. Based on the analysis of results of the minimization problem for control quality quadratic functional and the multicriteria problem could be solved by software and hardware modification of intelligent systems realized in the minimal configuration of hardware. The novelty of the work is the scientific substantiation of the concept for the competitive vehicle MCSs creation based on virtual information sensors, the use of new mathematical models and control algorithms to solve the problem of collision avoidance.
Implementation of the interaction of the steering wheel loader control system of the remote-controlled wheeled vehicle operator interface with a real-time simulation model
Authors: Buzunov N V , Kotiev G O , Gorelov V A 
Published: June 2019

Abstract
A wheeled vehicle remote control workstation should include systems and devices that would provide steering feedback for the operator. The electric loader of the steering wheel with digital control can act as a system of this kind. Development and adjustment of the steering wheel loader can be performed with real-time simulation models sufficiently reflecting the behavior of the remote-controlled vehicle. The paper describes interaction of the real-time simulation model of an 8x8 wheeled chassis with the prototype of a steering wheel loader control system. The authors demonstrate compatibility of the software and hardware components in question, show adequacy of the model during interaction with the steering wheel loader control system, and propose using such models during the development of the remote control operator workstations.
Logic-based artificial intelligence in systems for monitoring the enforcing traffic regulations
Authors: Aladin D V , Varlamov O O , Chuvikov D A , Chernenkiy V M , Smelkova E A , Baldin A V 
Published: June 2019

Abstract
The paper touches upon the issues of monitoring and assessment of driver’s actions during driving. Its key idea is to prove that currently there are all necessary conditions to develop control systems of traffic regulations based on artificial intelligence with logical reasoning. The article gives a detailed analysis of advanced driver-assistance systems from the world’s leading companies and pays attention to the advantages and disadvantages of such solutions. As an engineering approach to solving the identified problems, it is proposed to use Mivar expert system. The purpose of this decision is to monitor the enforcing traffic regulations and inform the driver about violations. The article proposes the concept of practical implementation of Mivar system included in the driver assistance system. We conclude that Mivar approach provides excellent opportunities for creating the control system for monitoring the enforcing traffic regulations. The proposed solution can be used for reducing the number of accidents caused by drivers.
Planning of driverless vehicle motion trajectory taking into account its dimensional characteristics and under conditions of dynamically changing environment
Authors: Zabolotny A 
Published: June 2019

AbstractThis paper presents the solution for the task of the planning of the driverless vehicle (DV) driving trajectory for the modern road conditions. The rapidly-exploring random trees (RRT) algorithm consisting in the generation of the treetops under the equiprobability law and search for an optimum trajectory was used as a base. The minimum length acts as a criterion of optimality. The condition for the trajectory generation using the road map model is added to the new algorithm. Thereby, the problem of low processing (response) speed of the optimum trajectory search algorithm was solved.
Measurements of dust concentration at workplaces in a hard coal mine processing plants after installation of the NEPTUN spraying system
Authors: Bałaga D , Kalita M , Siegmund M , Klimek Z 
Published: June 2019

Abstract
The article presents the place and scope of testing the NEPTUN prototype spraying system intended for neutralization of total and respirable dust at workplaces in the processing plant of Bolesław Śmiały mine. The testing procedure and the results of dust concentration measurements at each workplace, covered by the operational range of the NEPTUN spraying system are presented. In the final part, the results of tests were analyzed and the reduction efficiency of total and respirable dust at the tested workplaces is given.
Bench tests of BWC pressure free heat exchanger - part 1
Authors: Suffner H , Lesiak K 
Published: June 2019

Abstract
A requirement to limit the temperature of diesel engine components results from the recommendations of the drive manufacturers as well as from the mining regulations. In order to meet these requirements it is necessary to reduce the temperature of the exhaust gases below 150 °C and in order to achieve this objective, in the exhaust system of the diesel engine various types of heat exchangers are used. The article presents the procedures and results of testing the pressure free heat exchanger for cooling the exhaust systems in underground mining machinery drives applied in hard coal mines.
The Influence of the Methodology for Slopes Forming in Open Pit Mines on their Stability
Authors: Mzyk T , Jendruś R 
Published: June 2019

Abstract
Open pit mines are frequently accumulating significant amounts of material in the form of dumping grounds, landfills or forming land for reclamation. Often the form of emerging dumping grounds is determined by stability analysis of their slopes at the design stage. During the operation of the mining site and the collection of material on the pile, only the geometry of the slope is a subject of control. In many cases, after making slopes of a dozen or so meters height or even up to several tens of meters, and after a certain time has elapsed since their formation, deformation of the escarpments can be observed. At this stage, the only option is to change the geometry, i.e. inclination of a slope or, in the worst case, rebuilding of the dump. In the paper the analysis of the impact of the method of forming slopes and material quality on stability of formed slopes and their safe exploitation has been presented. It also presents a proposal to normalize the methodology of design and construction of slopes in a manner ensuring stability and taking into account the variability of the material parameters from which the slope is to be formed.
Conformal embedding and twisted theta functions at level one
Authors: Mukhopadhyay Swarnava , Zelaci Hacen 
Published: June 2019

In this paper, we consider the conformal embedding of

s
o

(
r
)

\mathfrak {so}(r)

into

s
l

(
r
)

\mathfrak {sl}(r)

and study relations between level one

SO
⁡
(
r
)

\operatorname {SO}(r)

-theta functions and twisted

SL
⁡
(
r
)

\operatorname {SL}(r)

-theta functions coming from parahoric moduli spaces. In particular, we give another proof of a theorem by Pauly-Ramanan [J. London Math. Soc. (2) 63 (2001), pp. 513–532].
Sums with the Möbius function twisted by characters with powerful moduli
Authors: Banks William , Shparlinski Igor 
Published: June 2019

In a recent work, the authors (2016) have combined classical ideas of A. G. Postnikov (1956) and N. M. Korobov (1974) to derive improved bounds on short character sums for certain nonprincipal characters with powerful moduli. In the present paper, these results are used to bound sums with the Möbius function twisted by characters of the same type, which complements and improves some earlier work of B. Green (2012). To achieve this, we obtain a series of results about the size and zero-free region of

L
L

-functions with the same class of moduli.
Spatio-temporal effects of climate change on the geographical distribution and flowering phenology of hummingbird-pollinated plants
Authors: Correa-Lima Ana Paula Araujo , Varassin Isabela Galarda , Barve Narayani , Zwiener Victor Pereira 
Published: June 2019

AbstractBackgrounds and AimsTropical plant species are already suffering the effects of climate change and projections warn of even greater changes in the following decades. Of particular concern are alterations in flowering phenology, given that it is considered a fitness trait, part of plant species ecological niche, with potential cascade effects in plant–pollinator interactions. The aim of the study was to assess the potential impacts of climate change on the geographical distribution and flowering phenology of hummingbird-pollinated plants.MethodsWe implemented ecological niche modelling (ENM) to investigate the potential impacts of different climate change scenarios on the geographical distribution and flowering phenology of 62 hummingbird-pollinated plant species in the Brazilian Atlantic Forest.Key ResultsDistribution models indicate future changes in the climatic suitability of their current habitats, suggesting a tendency towards discontinuity, reduction and spatial displacement. Flowering models indicate that climate can influence species phenology in different ways: some species may experience increased flowering suitability whereas others may suffer decreased suitability.ConclusionsOur results suggest that hummingbird-pollinated species are prone to changes in their geographical distribution and flowering under different climate scenarios. Such variation may impact the community structure of ecological networks and reproductive success of tropical plants in the near future.
Building large updatable colored de Bruijn graphs via merging
Authors: Muggli Martin D , Alipanahi Bahar , Boucher Christina 
Published: June 2019

Abstract

Motivation
There exist several large genomic and metagenomic data collection efforts, including GenomeTrakr and MetaSub, which are routinely updated with new data. To analyze such datasets, memory-efficient methods to construct and store the colored de Bruijn graph were developed. Yet, a problem that has not been considered is constructing the colored de Bruijn graph in a scalable manner that allows new data to be added without reconstruction. This problem is important for large public datasets as scalability is needed but also the ability to update the construction is also needed.

Results
We create a method for constructing the colored de Bruijn graph for large datasets that is based on partitioning the data into smaller datasets, building the colored de Bruijn graph using a FM-index based representation, and succinctly merging these representations to build a single graph. The last step, merging succinctly, is the algorithmic challenge which we solve in this article. We refer to the resulting method as VariMerge. This construction method also allows the graph to be updated with new data. We validate our approach and show it produces a three-fold reduction in working space when constructing a colored de Bruijn graph for 8000 strains. Lastly, we compare VariMerge to other competing methods—including Vari, Rainbowfish, Mantis, Bloom Filter Trie, the method of Almodaresi et al. and Multi-BRWT—and illustrate that VariMerge is the only method that is capable of building the colored de Bruijn graph for 16 000 strains in a manner that allows it to be updated. Competing methods either did not scale to this large of a dataset or do not allow for additions without reconstruction.

Availability and implementation
VariMerge is available at https://github.com/cosmo-team/cosmo/tree/VARI-merge under GPLv3 license.

Supplementary information
Supplementary data are available at Bioinformatics online.
DNN-Dom: predicting protein domain boundary from sequence alone by deep neural network
Authors: Shi Qiang , Chen Weiya , Huang Siqi , Jin Fanglin , Dong Yinghao , Wang Yan , Xue Zhidong 
Published: June 2019

AbstractMotivationAccurate delineation of protein domain boundary plays an important role for protein engineering and structure prediction. Although machine-learning methods are widely used to predict domain boundary, these approaches often ignore long-range interactions among residues, which have been proven to improve the prediction performance. However, how to simultaneously model the local and global interactions to further improve domain boundary prediction is still a challenging problem.ResultsThis article employs a hybrid deep learning method that combines convolutional neural network and gate recurrent units’ models for domain boundary prediction. It not only captures the local and non-local interactions, but also fuses these features for prediction. Additionally, we adopt balanced Random Forest for classification to deal with high imbalance of samples and high dimensions of deep features. Experimental results show that our proposed approach (DNN-Dom) outperforms existing machine-learning-based methods for boundary prediction. We expect that DNN-Dom can be useful for assisting protein structure and function prediction.Availability and implementationThe method is available as DNN-Dom Server at http://isyslab.info/DNN-Dom/.Supplementary informationSupplementary data are available at Bioinformatics online.
An empirical Bayesian ranking method, with applications to high throughput biology
Authors: Ferguson John , Chang Joseph 
Published: June 2019

AbstractMotivationIn bioinformatics, genome-wide experiments look for important biological differences between two groups at a large number of locations in the genome. Often, the final analysis focuses on a P-value-based ranking of locations which might then be investigated further in follow-up experiments. However, this strategy may result in small effect sizes, with low P-values, being ranked more favorably than larger more scientifically important effects. Bayesian ranking techniques may offer a solution to this problem provided a good prior distribution for the collective distribution of effect sizes is available.ResultsWe develop an Empirical Bayes ranking algorithm, using the marginal distribution of the data over all locations to estimate an appropriate prior. In simulations and analysis using real datasets, we demonstrate favorable performance compared to ordering P-values and a number of other competing ranking methods. The algorithm is computationally efficient and can be used to rank the entirety of genomic locations or to rank a subset of locations, pre-selected via traditional FWER/FDR methods in a 2-stage analysis.Availability and implementationAn R-package, EBrank, implementing the ranking algorithm is available on CRAN.Supplementary informationSupplementary data are available at Bioinformatics online.
Sequence alignment using machine learning for accurate template-based protein structure prediction
Authors: Makigaki Shuichiro , Ishida Takashi 
Published: June 2019

Abstract

Motivation
Template-based modeling, the process of predicting the tertiary structure of a protein by using homologous protein structures, is useful if good templates can be found. Although modern homology detection methods can find remote homologs with high sensitivity, the accuracy of template-based models generated from homology-detection-based alignments is often lower than that from ideal alignments.

Results
In this study, we propose a new method that generates pairwise sequence alignments for more accurate template-based modeling. The proposed method trains a machine learning model using the structural alignment of known homologs. It is difficult to directly predict sequence alignments using machine learning. Thus, when calculating sequence alignments, instead of a fixed substitution matrix, this method dynamically predicts a substitution score from the trained model. We evaluate our method by carefully splitting the training and test datasets and comparing the predicted structure’s accuracy with that of state-of-the-art methods. Our method generates more accurate tertiary structure models than those produced from alignments obtained by other methods.

Availability and implementation
https://github.com/shuichiro-makigaki/exmachina.

Supplementary information
Supplementary data are available at Bioinformatics online.
Selection-adjusted inference: an application to confidence intervals forcis-eQTL effect sizes
Authors: Panigrahi Snigdha , Zhu Junjie , Sabatti Chiara 
Published: June 2019

SummaryThe goal of expression quantitative trait loci (eQTL) studies is to identify the genetic variants that influence the expression levels of the genes in an organism. High throughput technology has made such studies possible: in a given tissue sample, it enables us to quantify the expression levels of approximately 20 000 genes and to record the alleles present at millions of genetic polymorphisms. While obtaining this data is relatively cheap once a specimen is at hand, obtaining human tissue remains a costly endeavor: eQTL studies continue to be based on relatively small sample sizes, with this limitation particularly serious for tissues as brain, liver, etc.—often the organs of most immediate medical relevance. Given the high-dimensional nature of these datasets and the large number of hypotheses tested, the scientific community has adopted early on multiplicity adjustment procedures. These testing procedures primarily control the false discoveries rate for the identification of genetic variants with influence on the expression levels. In contrast, a problem that has not received much attention to date is that of providing estimates of the effect sizes associated with these variants, in a way that accounts for the considerable amount of selection. Yet, given the difficulty of procuring additional samples, this challenge is of practical importance. We illustrate in this work how the recently developed conditional inference approach can be deployed to obtain confidence intervals for the eQTL effect sizes with reliable coverage. The procedure we propose is based on a randomized hierarchical strategy with a 2-fold contribution: (1) it reflects the selection steps typically adopted in state of the art investigations and (2) it introduces the use of randomness instead of data-splitting to maximize the use of available data. Analysis of the GTEx Liver dataset (v6) suggests that naively obtained confidence intervals would likely not cover the true values of effect sizes and that the number of local genetic polymorphisms influencing the expression level of genes might be underestimated.
SOD1 deficiency: a novel syndrome distinct from amyotrophic lateral sclerosis
Authors: Park Julien H , Elpers Christiane , Reunert Janine , McCormick Michael L , Mohr Julia , Biskup Saskia , Schwartz Oliver , Rust Stephan , Grüneberg Marianne , Seelhöfer Anja , Schara Ulrike , Boltshauser Eugen , Spitz Douglas R , Marquardt Thorsten 
Published: June 2019

Abstract
Superoxide dismutase 1 (SOD1) is the principal cytoplasmic superoxide dismutase in humans and plays a major role in redox potential regulation. It catalyses the transformation of the superoxide anion (O2•−) into hydrogen peroxide. Heterozygous variants in SOD1 are a common cause of familial amyotrophic lateral sclerosis. In this study we describe the homozygous truncating variant c.335dupG (p.C112Wfs*11) in SOD1 that leads to total absence of enzyme activity. The resulting phenotype is severe and marked by progressive loss of motor abilities, tetraspasticity with predominance in the lower extremities, mild cerebellar atrophy, and hyperekplexia-like symptoms. Heterozygous carriers have a markedly reduced enzyme activity when compared to wild-type controls but show no overt neurologic phenotype. These results are in contrast with the previously proposed theory that a loss of function is the underlying mechanism in SOD1-related motor neuron disease and should be considered before application of previously proposed SOD1 silencing as a treatment option for amyotrophic lateral sclerosis.