bluuebunny/crossref_metadata_2025 huggingface dataset:
How to Steer and Control ERK and the ERK Signaling Cascade Exemplified by Looking at Cardiac Insufficiency
Authors: Breitenbach Tim , Lorenz Kristina , Dandekar Thomas
Published: May 2019

Mathematical optimization framework allows the identification of certain nodes within a signaling network. In this work, we analyzed the complex extracellular-signal-regulated kinase 1 and 2 (ERK1/2) cascade in cardiomyocytes using the framework to find efficient adjustment screws for this cascade that is important for cardiomyocyte survival and maladaptive heart muscle growth. We modeled optimal pharmacological intervention points that are beneficial for the heart, but avoid the occurrence of a maladaptive ERK1/2 modification, the autophosphorylation of ERK at threonine 188 (ERK Thr 188 phosphorylation), which causes cardiac hypertrophy. For this purpose, a network of a cardiomyocyte that was fitted to experimental data was equipped with external stimuli that model the pharmacological intervention points. Specifically, two situations were considered. In the first one, the cardiomyocyte was driven to a desired expression level with different treatment strategies. These strategies were quantified with respect to beneficial effects and maleficent side effects and then which one is the best treatment strategy was evaluated. In the second situation, it was shown how to model constitutively activated pathways and how to identify drug targets to obtain a desired activity level that is associated with a healthy state and in contrast to the maleficent expression pattern caused by the constitutively activated pathway. An implementation of the algorithms used for the calculations is also presented in this paper, which simplifies the application of the presented framework for drug targeting, optimal drug combinations and the systematic and automatic search for pharmacological intervention points. The codes were designed such that they can be combined with any mathematical model given by ordinary differential equations.
MicroRNA Dysregulation in Cutaneous Squamous Cell Carcinoma
Authors: García-Sancha Natalia , Corchado-Cobos Roberto , Pérez-Losada Jesús , Cañueto Javier
Published: May 2019

Cutaneous squamous cell carcinoma (CSCC) is the second most frequent cancer in humans and it can be locally invasive and metastatic to distant sites. MicroRNAs (miRNAs or miRs) are endogenous, small, non-coding RNAs of 19–25 nucleotides in length, that are involved in regulating gene expression at a post-transcriptional level. MicroRNAs have been implicated in diverse biological functions and diseases. In cancer, miRNAs can proceed either as oncogenic miRNAs (onco-miRs) or as tumor suppressor miRNAs (oncosuppressor-miRs), depending on the pathway in which they are involved. Dysregulation of miRNA expression has been shown in most of the tumors evaluated. MiRNA dysregulation is known to be involved in the development of cutaneous squamous cell carcinoma (CSCC). In this review, we focus on the recent evidence about the role of miRNAs in the development of CSCC and in the prognosis of this form of skin cancer.
Analysis of Expression Pattern of snoRNAs in Different Cancer Types with Machine Learning Algorithms
Authors: Pan Xiaoyong , Chen Lei , Feng Kai-Yan , Hu Xiao-Hua , Zhang Yu-Hang , Kong Xiang-Yin , Huang Tao , Cai Yu-Dong
Published: May 2019

Small nucleolar RNAs (snoRNAs) are a new type of functional small RNAs involved in the chemical modifications of rRNAs, tRNAs, and small nuclear RNAs. It is reported that they play important roles in tumorigenesis via various regulatory modes. snoRNAs can both participate in the regulation of methylation and pseudouridylation and regulate the expression pattern of their host genes. This research investigated the expression pattern of snoRNAs in eight major cancer types in TCGA via several machine learning algorithms. The expression levels of snoRNAs were first analyzed by a powerful feature selection method, Monte Carlo feature selection (MCFS). A feature list and some informative features were accessed. Then, the incremental feature selection (IFS) was applied to the feature list to extract optimal features/snoRNAs, which can make the support vector machine (SVM) yield best performance. The discriminative snoRNAs included HBII-52-14, HBII-336, SNORD123, HBII-85-29, HBII-420, U3, HBI-43, SNORD116, SNORA73B, SCARNA4, HBII-85-20, etc., on which the SVM can provide a Matthew’s correlation coefficient (MCC) of 0.881 for predicting these eight cancer types. On the other hand, the informative features were fed into the Johnson reducer and repeated incremental pruning to produce error reduction (RIPPER) algorithms to generate classification rules, which can clearly show different snoRNAs expression patterns in different cancer types. The analysis results indicated that extracted discriminative snoRNAs can be important for identifying cancer samples in different types and the expression pattern of snoRNAs in different cancer types can be partly uncovered by quantitative recognition rules.
Assessing Natural and Mechanical Dune Performance in a Post-Hurricane Environment
Authors: Ellis Jean T. , Román-Rivera Mayra A.
Published: May 2019

The purpose of this study is to document the geomorphic evolution of a mechanical dune over approximately one year following its installation and compare it to the recovery of a natural dune following the impact of Hurricane Matthew (2016). During the study period, the dunes’ integrity was tested by wave and wind events, including king tides, and a second hurricane (Irma, 2017), at the end of the study period. Prior to the impact of the second hurricane, the volumetric increase of the mechanical and natural dune was 32% and 75%, respectively, suggesting that scraping alone is not the optimal protection method. If scraping is employed, we advocate that the dune should be augmented by planting. Ideally, the storm-impacted dune should naturally recover. Post-storm vegetation regrowth was lower around the mechanical dune, which encouraged aeolian transport and dune deflation. Hurricane Irma, an extreme forcing event, substantially impacted the dunes. The natural dune was scarped and the mechanical dune was overtopped; the system was essentially left homogeneous following the hurricane. The results from this study question the current practice of sand scraping along the South Carolina coast, which occurs post-storm, emplacement along the former primary dune line, and does not include the planting of vegetation.
Weather-Routing System Based on METOC Navigation Risk Assessment
Authors: Fabbri Tommaso , Vicen-Bueno Raul
Published: May 2019

This paper presents a novel weather-routing system based on a multi-criteria setup. The set of 3 conflicting criteria is: travel time, ship navigation added resistance (caused by wind and waves), and navigation risk/safety. To this aim, the International Maritime Organization (IMO) safety guidelines are exploited for the design of navigation risk criterion as a function of the METeorological and OCeanographic (METOC) and sailing conditions. This risk is directly integrated into the multi-criteria setup, as an innovative alternative to the systems proposed in the open literature. The proposed methodology is tested in a real operational scenario in the Mediterranean Sea. The obtained results show how the proposed system provides alternative routes with minimum risk to the decision-makers, as well as other different alternative routes minimizing the other criteria.
New Diketopiperazines from a Marine-Derived Fungus Strain Aspergillus versicolor MF180151
Authors: Hu Jiansen , Li Zheng , Gao Jieyu , He Hongtao , Dai Huanqin , Xia Xuekui , Liu Cuihua , Zhang Lixin , Song Fuhang
Published: May 2019

Six new diketopiperazines, (±)-7,8-epoxy-brevianamide Q ((±)-1), (±)-8-hydroxy-brevianamide R ((±)-2), and (±)-8-epihydroxy-brevianamide R ((±)-3), together with four known compounds, (±)-brevianamide R ((±)-4), versicolorin B (5) and averufin (6), were isolated from a marine-derived fungus strain Aspergillus versicolor MF180151, which was recovered from a sediment sample collected from the Bohai Sea, China. The chemical structures were established by 1D- and 2D-NMR spectra and HR-ESI-MS. 1 is the first sample of brevianamides with an epoxy moiety. Their bioactivities were evaluated against Candida albicans, Bacillus subtilis, Staphylococcus aureus, methicillin-resistant S. aureus, Pseudomonas aeruginosa, and Bacillus Calmette-Guérin. Compounds 1–4 showed no activities against the pathogens, and compounds 5 and 6 showed moderate activities against S. aureus and methicillin-resistant S. aureus.
Effects of Platycodins Folium on Depression in Mice Based on a UPLC-Q/TOF-MS Serum Assay and Hippocampus Metabolomics
Authors: Wang Cuizhu , Lin Hongqiang , Yang Na , Wang Han , Zhao Yan , Li Pingya , Liu Jinping , Wang Fang
Published: May 2019

Major depressive disorder (MDD), also known as depression, is a state characterized by low mood and aversion to activity. Platycodins Folium (PF) is the dried leaf of Platycodon grandiflorum, with anti-inflammatory and antioxidative activities. Our previous research suggested that PF was rich in flavonoids, phenols, organic acids, triterpenoid saponins, coumarins and terpenoids. This study aimed to investigate the antidepressant effect of PF using lipopolysaccharide (LPS)-induced depressive mice. Several behavior tests (sucrose preference test (SPT), forced swimming test (FST) and tail suspension test (TST)) and biochemical parameters (IL-6, TNF-α and SOD levels) were used to evaluate the antidepressive effect of PF on LPS-induced depression model. Furthermore, a UPLC-Q/TOF-MS-based metabolomics approach was applied to explore the latent mechanism of PF in attenuating depression. As a result, a total of 21 and 11 metabolites that potentially contribute to MDD progress and PF treatment were identified in serum and hippocampus, respectively. The analysis of metabolic pathways revealed that lipid metabolism, amino acid metabolism, energy metabolism, arachidonic acid metabolism, glutathione metabolism and inositol phosphate metabolism were disturbed in a model of mice undergoing MDD and PF treatment. These results help us to understand the pathogenesis of depression in depth, and to discover targets for clinical diagnosis and treatment. They also provide the possibility of developing PF into an anti-depressantive agent.
QM/MM Benchmarking of Cyanobacteriochrome Slr1393g3 Absorption Spectra
Authors: Wiebeler Christian , Schapiro Igor
Published: May 2019

Cyanobacteriochromes are compact and spectrally diverse photoreceptor proteins that are promising candidates for biotechnological applications. Computational studies can contribute to an understanding at a molecular level of their wide spectral tuning and diversity. In this contribution, we benchmark methods to model a 110 nm shift in the UV/Vis absorption spectrum from a red- to a green-absorbing form of the cyanobacteriochrome Slr1393g3. Based on an assessment of semiempirical methods to describe the chromophore geometries of both forms in vacuo, we find that DFTB2+D leads to structures that are the closest to the reference method. The benchmark of the excited state calculations is based on snapshots from quantum mechanics/molecular mechanics molecular dynamics simulations. In our case, the methods RI-ADC(2) and sTD-DFT based on CAM-B3LYP ground state calculations perform the best, whereas no functional can be recommended to simulate the absorption spectra of both forms with time-dependent density functional theory. Furthermore, the difference in absorption for the lowest energy absorption maxima of both forms can already be modelled with optimized structures, but sampling is required to improve the shape of the absorption bands of both forms, in particular for the second band. This benchmark study can guide further computational studies, as it assesses essential components of a protocol to model the spectral tuning of both cyanobacteriochromes and the related phytochromes.
Recent Studies on Fluorinated Silica Nanometer-Sized Particles
Authors: Iacono Scott T. , Jennings Abby R.
Published: May 2019

Since initially being reported, fluorinated silica nanometer-sized particles (F-SiNPs) have gained much interest in the scientific community, due to their unique properties. These properties, include, low surface energies, increased mechanical strength, thermal robustness, and chemical resistance, and are a direct result of the incorporation of fluorine with a nanometer-sized silica network. This review aims to summarize the synthetic methods that have, and are still, being utilized to prepare these specialized materials. Following this, applications for F-SiNPs, with an emphasis on recent examples, will be presented in further detail.
Printing the Ultra-Long Ag Nanowires Inks onto the Flexible Textile Substrate for Stretchable Electronics
Authors: Ke Sheng-Hai , Xue Qing-Wen , Pang Chuan-Yuan , Guo Pan-Wang , Yao Wei-Jing , Zhu He-Ping , Wu Wei
Published: May 2019

Printing technology offers a simple and cost-effective opportunity to develop all-printed stretchable circuits and electronic devices, possibly providing ubiquitous, low-cost, and flexible devices. To successfully prepare high-aspect-ratio Ag nanowires (NWs), we used water and anhydrous ethanol as the solvent and polyvinylpyrrolidone (PVP) as the viscosity regulator to obtain a water-soluble Ag NWs conductive ink with good printability. Flexible and stretchable fabric electrodes were directly fabricated through screen printing. After curing at room temperature, the sheet resistance of the Ag NW fabric electrode was 1.5 Ω/sq. Under a tensile strain of 0–80% and with 20% strains applied for 200 cycles, good conductivity was maintained, which was attributed to the inherent flexibility of the Ag NWs and the intrinsic structure of the interlocked texture.
Molecular Responses in THP-1 Macrophage-Like Cells Exposed to Diverse Nanoparticles
Authors: Brzicova Tana , Javorkova Eliska , Vrbova Kristyna , Zajicova Alena , Holan Vladimir , Pinkas Dominik , Philimonenko Vlada , Sikorova Jitka , Klema Jiri , Topinka Jan , Rossner Pavel
Published: May 2019

In the body, engineered nanoparticles (NPs) may be recognized and processed by immune cells, among which macrophages play a crucial role. We evaluated the effects of selected NPs [NM-100 (TiO2), NM-110 (ZnO), NM-200 (SiO2), and NM-300 K (Ag)] on THP-1 macrophage-like cells. The cells were exposed to subcytotoxic concentrations of NPs (1–25 µg/mL) and the expression of immunologically relevant genes (VCAM1, TNFA, CXCL8, ICAM1, CD86, CD192, and IL1B) was analyzed by RT-qPCR. The expression of selected cytokines, growth factors and surface molecules was assessed by flow cytometry or ELISA. Generation of reactive oxygen species and induction of DNA breaks were also analyzed. Exposure to diverse NPs caused substantially different molecular responses. No significant effects were detected for NM-100 treatment. NM-200 induced production of IL-8, a potent attractor and activator of neutrophils, growth factors (VEGF and IGF-1) and superoxide. NM-110 triggered a proinflammatory response, characterized by the activation of transcription factor NF-κB, an enhanced production of proinflammatory cytokines (TNF-α) and chemokines (IL-8). Furthermore, the expression of cell adhesion molecules VCAM-1 and ICAM-1 and hepatocyte growth factor (HGF), as well as superoxide production and DNA breaks, were affected. NM-300 K enhanced IL-8 production and induced DNA breaks, however, it decreased the expression of chemokine receptor (CCR2) and CD86 molecule, indicating potential immunosuppressive activity. The toxicity of ZnO and Ag NPs was probably caused by their intracellular dissolution, as indicated by transmission electron microscopy imaging. The observed effects in macrophages might further influence both innate and adaptive immune responses by promoting neutrophil recruitment via IL-8 release and enhancing the adhesion and stimulation of T cells by VCAM-1 and ICAM-1 expression.
Channeled ECM-Based Nanofibrous Hydrogel for Engineering Vascularized Cardiac Tissues
Authors: Arvatz Smadar , Wertheim Lior , Fleischer Sharon , Shapira Assaf , Dvir Tal
Published: May 2019

Hydrogels are widely used materials for cardiac tissue engineering. However, once the cells are encapsulated within hydrogels, mass transfer to the core of the engineered tissue is limited, and cell viability is compromised. Here, we report on the development of a channeled ECM-based nanofibrous hydrogel for engineering vascularized cardiac tissues. An omentum hydrogel was mixed with cardiac cells, patterned to create channels and closed, and then seeded with endothelial cells to form open cellular lumens. A mathematical model was used to evaluate the necessity of the channels for maintaining cell viability and the true potential of the vascularized hydrogel to form a viable cardiac patch was studied.
Microfluidic Synthesis of Vinblastine-Loaded Multifunctional Particles for Magnetically Responsive Controlled Drug Release
Authors: Huang Keng-Shiang , Yang Chih-Hui , Wang Ya-Chin , Wang Wei-Ting , Lu Yen-Yi
Published: May 2019

Vinblastine (VBL) is a major chemotherapeutic drug; however, in some cases, it may cause severe side effects in patients with cancer. Designing a novel VBL pharmaceutical formulation is a crucial and emerging concern among researchers for reducing the use of VBL. This study developed a stimuli-responsive controlled VBL drug release system from magnetically sensitive chitosan capsules. A magnetically responsive controlled drug release system was designed by embedding superparamagnetic iron oxide (SPIO) nanoparticles (NPs) in a chitosan matrix and an external magnet. In addition, droplet microfluidics, which is a novel technique for producing polymer spheres, was used for manufacturing monodispersed chitosan microparticles. The prepared VBL and SPIO NPs-loaded chitosan microparticles were characterized and analyzed using Fourier transform infrared spectroscopy, transmission electron microscopy, scanning electron microscopy, a superconducting quantum interference device, and a biocompatibility test. The drug encapsulation efficiency was 67%–69%. The in vitro drug release test indicated that the VBL could be 100% released from chitosan composite particles in 80–130 min under magnetic stimulation. The pulsatile magnetically triggered tests showed individual and distinctive controlled release patterns. Thus, the timing and dose of VBL release was controllable by an external magnet. The results presume that using a magnetically responsive controlled drug release system offers a valuable opportunity for VBL drug delivery, where the delivery system is an active participant, rather than a passive vehicle, in the optimization of cancer treatment. The proposed actively targeted magnetic drug delivery system offers many advantages over conventional drug delivery systems by improving the precision and timing of drug release, easy operation, and higher compliance for pharmaceutical applications.
Prediction of CO2 Solubility in Ionic Liquids Based on Multi-Model Fusion Method
Authors: Xia Luyue , Wang Jiachen , Liu Shanshan , Li Zhuo , Pan Haitian
Published: May 2019

Reducing the emissions of greenhouse gas is a worldwide problem that needs to be solved urgently for sustainable development in the future. The solubility of CO2 in ionic liquids is one of the important basic data for capturing CO2. Considering the disadvantages of experimental measurements, e.g., time-consuming and expensive, the complex parameters of mechanism modeling and the poor stability of single data-driven modeling, a multi-model fusion modeling method is proposed in order to predict the solubility of CO2 in ionic liquids. The multiple sub-models are built by the training set. The sub-models with better performance are selected through the validation set. Then, linear fusion models are established by minimizing the sum of squares of the error and information entropy method respectively. Finally, the performance of the fusion model is verified by the test set. The results showed that the prediction effect of the linear fusion models is better than that of the other three optimal sub-models. The prediction effect of the linear fusion model based on information entropy method is better than that of the least square error method. Through the research work, an effective and feasible modeling method is provided for accurately predicting the solubility of CO2 in ionic liquids. It can provide important basic conditions for evaluating and screening higher selective ionic liquids.
Assessment of Optimal Transport for Operational Land-Cover Mapping Using High-Resolution Satellite Images Time Series without Reference Data of the Mapping Period
Authors: Tardy Benjamin , Inglada Jordi , Michel Julien
Published: May 2019

Land-cover map production using remote-sensing imagery is governed by data availability. In our case, data sources are two-fold: on one hand, optical data provided regularly by satellites such as Sentinel-2, and on the other hand, reference data which allow calibrating mapping methods or validating the results. The lengthy delays due to reference data collection and cleansing are one of the main issues for applications. In this work, the use of Optimal Transport (OT) is proposed. OT is a Domain Adaptation method that uses past data, both images and reference data, to produce the land-cover map of the current period without updated reference data. Seven years of Formosat-2 image time series and the corresponding reference data are used to evaluate two OT algorithms: conventional EMD transport and regularized transport based on the Sinkhorn distance. The contribution of OT to a classification fusion strategy is also evaluated. The results show that with a 17-class nomenclature the problem is too complex for the Sinkhorn algorithm, which provides maps with an Overall Accuracy (OA) of 30%. In contrast, with the EMD algorithm, an OA close to 70% is obtained. One limitation of OT is the number of classes that can be considered at the same time. Simplification schemes are proposed to reduce the number of classes to be transported. Cases of improvement are shown when the problem is simplified, with an improvement in OA varying from 5% and 20%, producing maps with an OA near 79%. As several years are available, the OT approaches are compared to standard fusion schemes, like majority voting. The gain in voting strategies with OT use is lower than the gain obtained with standard majority voting (around 5%).
Improved Estimation of the Intrinsic Dimension of a Hyperspectral Image Using Random Matrix Theory
Authors: Berman Mark
Published: May 2019

Many methods have been proposed in the literature for estimating the number of materials/endmembers in a hyperspectral image. This is sometimes called the “intrinsic” dimension (ID) of the image. A number of recent papers have proposed ID estimation methods based on various aspects of random matrix theory (RMT), under the assumption that the errors are uncorrelated, but with possibly unequal variances. A recent paper, which reviewed a number of the better known methods (including one RMT-based method), has shown that they are all biased, especially when the true ID is greater than about 20 or 30, even when the error structure is known. I introduce two RMT-based estimators ( R M T G , which is new, and R M T K N , which is a modification of an existing estimator), which are approximately unbiased when the error variances are known. However, they are biased when the error variance is unknown and needs to be estimated. This bias increases as ID increases. I show how this bias can be reduced. The results use semi-realistic simulations based on three real hyperspectral scenes. Despite this, when applied to the real scenes, R M T G and R M T K N are larger than expected. Possible reasons for this are discussed, including the presence of errors which are either deterministic, spectrally and/or spatially correlated, or signal-dependent. Possible future research into ID estimation in the presence of such errors is outlined.
Symmetry of Gait in Underweight, Normal and Overweight Children and Adolescents
Authors: Cimolin Veronica , Cau Nicola , Sartorio Alessandro , Capodaglio Paolo , Galli Manuela , Tringali Gabriella , Leban Bruno , Porta Micaela , Pau Massimiliano
Published: May 2019

Abnormal excess or lack of body mass can influence gait patterns, but in some cases such differences are subtle and not easy to detect, even with quantitative techniques for movement analysis. In these situations, the study of trunk accelerations may represent an effective way to detecting gait anomalies in terms of symmetry through the calculation of Harmonic Ratio (HR), a parameter obtained by processing trunk accelerations in the frequency domain. In the present study we used this technique to assess the existence of differences in HR during gait in a cohort of 75 healthy children and early adolescents (aged 7–14 years) stratified into 3 equally-sized age and gender-matched groups (Underweight: UW; Normal Weight: NW; Overweight: OW). The accelerometric signal, acquired using a single wearable inertial sensor, was processed to calculate stride length, speed, cadence and HR in antero-posterior, vertical and medio-lateral directions. No differences in spatio-temporal parameters were found among groups, while the HR in the medio-lateral direction was found significantly lower in UW children, while OW exhibited the highest values. On the basis of the results obtained, HR appears capable of discriminating gait symmetry in children with different body mass even when conventional gait parameters are unchanged.
A Hybrid Multi-Objective Optimization Model for Vibration Tendency Prediction of Hydropower Generators
Authors: Zhou Kai-Bo , Zhang Jian-Yu , Shan Yahui , Ge Ming-Feng , Ge Zi-Yue , Cao Guan-Nan
Published: May 2019

The hydropower generator unit (HGU) is a vital piece of equipment for frequency and peaking modulation in the power grid. Its vibration signal contains a wealth of information and status characteristics. Therefore, it is important to predict the vibration tendency of HGUs using collected real-time data, and achieve predictive maintenance as well. In previous studies, most prediction methods have only focused on enhancing the stability or accuracy. However, it is insufficient to consider only one criterion (stability or accuracy) in vibration tendency prediction. In this paper, an intelligence vibration tendency prediction method is proposed to simultaneously achieve strong stability and high accuracy, where vibration signal preprocessing, feature selection and prediction methods are integrated in a multi-objective optimization framework. Firstly, raw sensor signals are decomposed into several modes by empirical wavelet transform (EWT). Subsequently, the refactored modes can be obtained by the sample entropy-based reconstruction strategy. Then, important input features are selected using the Gram-Schmidt orthogonal (GSO) process. Later, the refactored modes are predicted through kernel extreme learning machine (KELM). Finally, the parameters of GSO and KELM are synchronously optimized by the multi-objective salp swarm algorithm. A case study and analysis of the mixed-flow HGU data in China was conducted, and the results show that the proposed model performs better in terms of predicting stability and accuracy.
Classifying Image Stacks of Specular Silicon Wafer Back Surface Regions: Performance Comparison of CNNs and SVMs
Authors: Kofler Corinna , Muhr Robert , Spöck Gunter
Published: May 2019

In this work, we compare the performance of convolutional neural networks and support vector machines for classifying image stacks of specular silicon wafer back surfaces. In these image stacks, we can identify structures typically originating from replicas of chip structures or from grinding artifacts such as comets or grinding grooves. However, defects like star cracks are also visible in those images. To classify these image stacks, we test and compare three different approaches. In the first approach, we train a convolutional neural net performing feature extraction and classification. In the second approach, we manually extract features of the images and use these features to train support vector machines. In the third approach, we skip the classification layers of the convolutional neural networks and use features extracted from different network layers to train support vector machines. Comparing these three approaches shows that all yield an accuracy value above 90%. With a quadratic support vector machine trained on features extracted from a convolutional network layer we achieve the best compromise between precision and recall rate of the class star crack with 99.3% and 98.6%, respectively.
A Convolution Component-Based Method with Attention Mechanism for Travel-Time Prediction
Authors: Ran Xiangdong , Shan Zhiguang , Fang Yufei , Lin Chuang
Published: May 2019

Deep learning approaches have been recently applied to traffic prediction because of their ability to extract features of traffic data. While convolutional neural networks may improve the predictive accuracy by transiting traffic data to images and extracting features in the images, the convolutional results can be improved by using the global-level representation that is a direct way to extract features. The time intervals are not considered as aspects of convolutional neural networks for traffic prediction. The attention mechanism may adaptively select a sequence of regions and only process the selected regions to better extract features when aspects are considered. In this paper, we propose the attention mechanism over the convolutional result for traffic prediction. The proposed method is based on multiple links. The time interval is considered as the aspect of attention mechanism. Based on the dataset provided by Highways England, the experimental results show that the proposed method can achieve better accuracy than the baseline methods.
An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms
Authors: Kocić Jelena , Jovičić Nenad , Drndarević Vujo
Published: May 2019

In this paper, one solution for an end-to-end deep neural network for autonomous driving is presented. The main objective of our work was to achieve autonomous driving with a light deep neural network suitable for deployment on embedded automotive platforms. There are several end-to-end deep neural networks used for autonomous driving, where the input to the machine learning algorithm are camera images and the output is the steering angle prediction, but those convolutional neural networks are significantly more complex than the network architecture we are proposing. The network architecture, computational complexity, and performance evaluation during autonomous driving using our network are compared with two other convolutional neural networks that we re-implemented with the aim to have an objective evaluation of the proposed network. The trained model of the proposed network is four times smaller than the PilotNet model and about 250 times smaller than AlexNet model. While complexity and size of the novel network are reduced in comparison to other models, which leads to lower latency and higher frame rate during inference, our network maintained the performance, achieving successful autonomous driving with similar efficiency compared to autonomous driving using two other models. Moreover, the proposed deep neural network downsized the needs for real-time inference hardware in terms of computational power, cost, and size.
Tracing Air Pollutant Emissions in China: Structural Decomposition and GVC Accounting
Authors: Chen Yuyi , Li Yunong , Yan Jie
Published: May 2019

The depth and breadth of China’s participation in global value chains have an important impact on the emissions of air pollutants from the production side, consumption side, and trade implications in China’s industries. Based on the global value chain accounting framework, this paper examines the path of China’s major air pollutant emissions in production and consumption during 1995–2009 and structurally decomposes the factors affecting air pollutant emissions. The results show that, firstly, both the air pollutant emissions on the production side and the air pollution emissions on the consumption side have increased significantly, and the production-side emissions have been higher than the consumption-side emissions. Secondly, the export of intermediate products shows a trend of “high pollution”, and this trend was more obvious after China’s accession to the world trade organization (WTO). Thirdly, the expansion of economic growth was the most important factor in the rapid emission of air pollutants in China and the reduction of pollution efficiency in Chinese industries depends on the increase in service inputs.
Evaluation of Sustainable Development of Tourism in Selected Cities in Turkey and Poland
Authors: Kurt Konakoglu Sultan Sevinc , Hełdak Maria , Kurdoglu Banu Cicek , Wysmułek Joanna
Published: May 2019

The paper analyses the sustainable development of tourism in selected cities located in Turkey and in Poland. The starting point for the conducted research was the adoption of indicators used to assess the sustainable development of tourism in the cities of Amasya (Turkey) and Zakopane (Poland). Ultimately, the authors used slightly modified indicators developed by the Voluntary Initiative for Sustainability in Tourism (VISIT). The studies demonstrated that in Amasya the development of tourism is quite stable and revealed quite a lot of deficiencies in Zakopane. Sustainable development was assessed as poor in terms of environmental obligations, in particular waste water management solutions and municipal waste management as well as in terms of water consumption. The research revealed that some of the indicators were not recorded in central statistical offices, which hindered the assessment considerably. This refers mainly to indicators related to social and cultural considerations. In general, the results are rather surprising, as the analysis revealed significant differences in the development of technological infrastructure and land use intensity. Unfortunately, in these aspects, Zakopane seems to have lost the competition.
Privatization of a Tourism Event: Do Attendees Perceive it as a Risky Cultural Lottery?
Authors: Attanasi Giuseppe , Passarelli Francesco , Urso Giulia , Cosic Hana
Published: May 2019

Individuals might have different views about the benefits and the costs of privatizing a cultural event. On the one hand, privatization may increase the quality of the event due to expanding investments. On the other hand, it may lead to the dissipation of important cultural and traditional connotations. Since benefits and costs are uncertain, we frame an individual’s choice regarding privatization as a lottery choice, where risk aversion and other individual traits play a role. We empirically investigate attendees’ preferences for privatizing a mass gathering festival in Italy. The festival is attended by almost 100,000 tourists each year. Over a three-year period, we collected a large dataset of survey questions. We find that willingness to accept privatization is decreasing in tourists’ risk aversion, while it is increasing in their sensitivity to the festival’s quality. Cultural tourists perceive a higher risk of commodification in the case of privatization. Authenticity-seeking tourists act as gatekeepers of the genuine roots of local traditions. They demand original values, ultimately contributing to the festival’s cultural sustainability. The purpose of attracting visitors is in fact commonly assumed to alter local culture, resulting in a staged authenticity; and privatization of cultural goods is often associated with commodification.
The Influence of Attitudes towards Cycling and Walking on Travel Intentions and Actual Behavior
Authors: García Jesús , Arroyo Rosa , Mars Lidón , Ruiz Tomás
Published: May 2019

The design and implementation of effective transport policies to reduce car use in urban areas requires a deep comprehension of the factors that influence travel behavior. In this context, psychological factors play an important role in explaining travel-related decisions. The purpose of this paper is to present a study on the effects of cognitive, affective and behavioral attitudes towards the use of walking and cycling on both intentions and real use of cars, public transport, bicycles, and walking. The data used was obtained from an on-line survey carried out in 2017. Analyses included reliability and validity of the questionnaire, exploratory and confirmatory factor analyses, and structural equation models. Results indicate that cycling and walking are evaluated differently in terms of feelings of freedom, pleasure, and relaxation. Positive evaluation of elements related to past walking behavior are negatively associated to both the intention to walk and actual walking. Transport policies to encourage cycling should be different from those with the aim of promoting walking. Positive attitudes towards walking are not enough to increase real walking.
Optimization and Application of Integrated Land Use and Transportation Model in Small- and Medium-Sized Cities in China
Authors: Ma Shuhong , Zhang Yan , Sun Chaoxu
Published: May 2019

Integrated land use and transportation models are helpful when policy, planning, or environment impacts are being evaluated, but the strengths and limitations in these models must be optimized. To optimize the ITLUP (Integrated Transportation and Land-Use Planning) model and apply it in small- and medium-sized cities in China, this study considered the constraints of land use intensity and introduced two critical indicators (the maximum number of households and maximum employment) to characterize the land capacity and improve the practicality of the model. Then, Monte Carlo simulation analysis was used to analyze the uncertainty factors using the coefficient of variation (C.V) and standardized regression coefficient (SRC). The results suggest that the maximum future employment and households may exceed the land limit and must be adjusted to a new zone, and the model operation simulation was closer to the actual situation of small- and medium-sized cities. The C.V value of the model output showed the increasing trend of the uncertainty of the model output variable over time, especially affected by DRAM model parameters, traffic demand forecasting model parameters and the peak hourly flow ratio. Such findings are meaningful for policymakers, planners, and others when the ITLUP model is used to anticipate the zonal employment and household allocation and to further explore the interaction between land use and transportation.
Circular Business Models for the Bio-Economy: A Review and New Directions for Future Research
Authors: Reim Wiebke , Parida Vinit , Sjödin David R.
Published: May 2019

Circular and bio-economy represents a political and industrial initiative to ensure that our society can rely on renewable biological sources while achieving economic growth. However, there is a need to critical review how realistic and feasible such initiatives are towards fulfilling the promised benefits of this economy. The literature on bio-economy often discusses the importance of innovative business models and their role in a successful shift to a bio-economy. Still, much of the discussion that is related to circular business models is fragmented and immature. Therefore, the purpose of this study is to conduct a systematic literature review of circular business model activities and the barriers to a bio-economy. Further, this review provides future research directions for a shift to a bio-economy. This study is based on a systematic review of 42 scientific journal articles and book chapters on a forest-based bio-economy. The business model canvas is used to provide a structured aggregation of the existing circular business models activities being used by the forestry sector. In addition, we develop a framework that describes the barriers to bio-economy-based circular business models and suggest new directions for future research. The study highlights the need for alignment among the elements of a business model as a key condition for its successful implementation in a bio-economy.
Physical Education Teachers and Their ICT Training Applied to Students with Disabilities. The Case of Spain
Authors: Fernández-Batanero José María , Sañudo Borja , Montenegro-Rueda Marta , García-Martínez Inmaculada
Published: May 2019

(1) Background: The introduction of Information and communication technologies (ICTs) in the area of Physical Education is a very recent phenomenon, and its implementation is a challenge for teachers in Spain. It implies pedagogic innovation for the improvement of the teaching and learning processes, especially for people with a disability (functional diversity). The aim of this study was to identify the degree of technological knowledge (ICT) of the physical education teachers for students with disabilities. (2) Methods: A sample of 341 Physical Education teachers from Spain volunteered for this cross-sectional study. A questionnaire named “DIFOTICyD”, created “ad hoc” was used as the data-gathering instrument. (3) Results: the results revealed the low training of the participants with respect to the use of the ICTs in the field of disability. The degree of training of the teachers was determined by the personal (gender, age), professional (teaching experience) and educational (training received) outcomes. (4) Conclusions: the findings of the study point to the need for didactic training that can be used to train the Physical Education teachers to use ICTs in order to facilitate the learning and educational innovation of students with disabilities.
Institutional Thickness and Regional Tourism Development: Lessons from Antioquia, Colombia
Authors: Restrepo Natalia , Anton Clavé Salvador
Published: May 2019

Researchers from different social sciences are increasingly interested in studying the role of institutions in regional development. Nevertheless, from the perspective of regional tourism development analysis, the role of institutions has been explored limitedly. Based on the institutional thickness approach, this study analyzed the role played by institutions in regional tourism development through a qualitative research procedure applied on a Latin American region. The study examined the institutional presence, the levels of interaction, the structures of domination and/or coalition patterns, and the common agendas in 28 institutions related to tourism in the region of Antioquia, Colombia. Results from this empirical analysis show that institutions play a decisive role in regional tourism development for reasons such as the allocation of economic resources, leadership, and interaction among stakeholders. Knowing these dynamics can be useful to boost better management and planning of tourism destinations throughout governance, coordination, and common agendas, and to enrich the debate on regional tourism development.
Mathematical Competence Scale (MCS) for Primary School: The Psychometric Properties and the Validation of an Instrument to Enhance the Sustainability of Talents Development through the Numeracy Skills Assessment
Authors: Bellini Diego , Crescentini Alberto , Zanolla Giovanna , Cubico Serena , Favretto Giuseppe , Faccincani Lorenzo , Ardolino Piermatteo , Gianesini Giovanna
Published: May 2019

The adequate development of the numeracy skills is a target of the fourth of the Sustainable Development Goals and is considered the basis for a financial literacy: both are competences needed for successful social and professional inclusion. Building on these goals, we carried out a unidimensional Mathematical Competence Scale (MCS) for primary school. The aim of this study was to present the psychometric properties and the validation process of MCS, designed basing on Item Response Theory. The final version of the scale, which measures different domains of mathematical knowledge (Data Analysis and Relationships, Geometry, Dimensions and Measurements, Numbers and Calculations), was validated on the entire population of 2935 fourth graders in Ticino Canton, Switzerland. The results reveal the high level of correlation between the six mathematical dimensions and confirm the assumption of a latent “mathematical construct”. However, even the multidimensional model could be considered a good model because it fitted the data significantly better than the one-dimensional model. In particular, the differences of the deviance between the two models are significant (χ2 (20) = 642.66, p < 0.001). Moreover, findings show a significant gender effect and a positive correlation between students’ actual school performance during the same academic year and MCS scores. MCS allows a reading of the learning and teaching process in the perspective of the psychology of sustainability and sustainable development and helps a teacher to sustain student talent through the development of numeracy skills; in fact, the scale is intended both as an assessment tool and an innovative approach for shaping the development of curriculum, and therefore has potential to serve as a bridge between empirical research, classroom practice and a positive (school and professional) career development.
A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks
Authors: Li Zhiwei , Lu Yu , Shi Yun , Wang Zengguang , Qiao Wenxin , Liu Yicen
Published: May 2019

Unmanned aerial vehicle (UAV) networks have a wide range of applications, such as in the Internet of Things (IoT), 5G communications, and so forth. However, the communications between UAVs and UAVs to ground control stations mainly use radio channels, and therefore these communications are vulnerable to cyberattacks. With the advent of software-defined radio (SDR), smart attacks that can flexibly select attack strategies according to the defender’s state information are gradually attracting the attention of researchers and potential attackers of UAV networks. The smart attack can even induce the defender to take a specific defense strategy, causing even greater damage. Inspired by symmetrical thinking, a solution using a software-defined network (SDN) to combat software-defined radio was proposed. We propose a network architecture which uses dual controllers, including a UAV flight controller and SDN controller, to achieve collaborative decision-making. Built on the top of the SDN, the state information of the whole network converges quickly and is fitted to an environment model used to develop an improved Dyna-Q-based reinforcement learning algorithm. The improved algorithm integrates the power allocation and track planning of UAVs into a unified action space. The simulation data showed that the proposed communication solution can effectively avoid smart jamming attacks and has faster learning efficiency and higher convergence performance than the compared algorithms.