bluuebunny/crossref_metadata_2025 huggingface dataset:
Detection and Monitoring of Bottom-Up Cracks in Road Pavement Using a Machine-Learning Approach
Authors: Praticò Filippo Giammaria , Fedele Rosario , Naumov Vitalii , Sauer Tomas
Published: March 2020

The current methods that aim at monitoring the structural health status (SHS) of road pavements allow detecting surface defects and failures. This notwithstanding, there is a lack of methods and systems that are able to identify concealed cracks (particularly, bottom-up cracks) and monitor their growth over time. For this reason, the objective of this study is to set up a supervised machine learning (ML)-based method for the identification and classification of the SHS of a differently cracked road pavement based on its vibro-acoustic signature. The method aims at collecting these signatures (using acoustic-sensors, located at the roadside) and classifying the pavement’s SHS through ML models. Different ML classifiers (i.e., multilayer perceptron, MLP, convolutional neural network, CNN, random forest classifier, RFC, and support vector classifier, SVC) were used and compared. Results show the possibility of associating with great accuracy (i.e., MLP = 91.8%, CNN = 95.6%, RFC = 91.0%, and SVC = 99.1%) a specific vibro-acoustic signature to a differently cracked road pavement. These results are encouraging and represent the bases for the application of the proposed method in real contexts, such as monitoring roads and bridges using wireless sensor networks, which is the target of future studies.
The Impact of Fertilization Regime on the Crop Performance and Chemical Composition of Potato (Solanum tuberosum L.) Cultivated in Central Greece
Authors: Petropoulos Spyridon A. , Fernandes Ângela , Polyzos Nikolaos , Antoniadis Vasileios , Barros Lillian , C.F.R. Ferreira Isabel
Published: March 2020

Potato cultivation is quite demanding in inorganic nutrients and adequate fertilization is a key factor for maximizing yield and producing tubers of high quality. In the present study, a field experiment was carried out to evaluate the effect of various forms of fertilization on crop performance and the nutritional value and chemical composition of two potato varieties (cv. Spunta and cv. Kennebec). For this purpose, five different fertilizer treatments were applied namely: control (C), standard fertilizer (T1), standard fertilizer + zeolite (T2), manure (T3) and slow release nitrogen fertilizer (T4). According to the results, it was observed that slow release treatment (T4) achieved the highest yield for both varieties, while the control treatment presented significantly lower yield compared to the studied fertilization regimes. The dry matter of leaves and shoots was higher in T1 treatment for cv. Kennebec and in T2 and T4 treatments for cv. Spunta, whereas the control treatment presented the highest dry matter content in tubers for cv. Kennebec and T2 and T3 treatments for cv. Spunta. A significant effect of the fertilization regime was also observed on the nutritional value of tubers and more specifically the protein, ash and fat content was increased by treatments T1 and T4, while carbohydrate content was also increased by T3 and T4 treatments for both varieties. Similarly, the total sugars, organic acids, β-carotene and lycopene content was increased in T3 treatment for the Spunta variety, while the antioxidant capacity showed a varied response depending on the fertilizer regime and the tested variety. In conclusion, the fertilization regime has a significant effect not only on the tuber yield but also on the quality of the final product and should be considered as an effective tool to increase the added value of potato crop.
Establishment of Acid Hydrolysis by Box–Behnken Methodology as Pretreatment to Obtain Reducing Sugars from Tiger Nut Byproducts
Authors: Razola-Díaz María del Carmen , Verardo Vito , Martín-García Beatriz , Díaz-de-Cerio Elixabet , García-Villanova Belén , Guerra-Hernández Eduardo Jesús
Published: March 2020

Carbohydrate-rich byproducts may be used as a growth substrate for microalgae. The tiger nut, compared to other tubers, has higher carbohydrate content, which mostly remain in the byproduct after the production of tiger nut milk (“Horchata”). The aim of this study is the optimization of a hydrolysis pre-treatment in tiger nut byproduct to obtain reducing sugars, objective framed in the European SUSPUFA (Sustainable production of health-promoting n-3 LC-PUFA using agro-food industry by-products through microalgae) Project. Two experimental 4-factor Box–Behnken designs (acid concentration (N), temperature (°C), time (min) and ratio acid/sample) were carried out with 27 experiments to optimize chemical hydrolysis conditions with H2SO4 and H3PO4 in duplicate. The sugars were determined by the Schoorl method and the extracts obtained by the optimal conditions were analyzed by high-performance liquid chromatography (HPLC). Protected designation of origin (PDO) and non-protected designation of origin (n-PDO) tiger nut byproducts were characterized by physicochemical methods. The validity of the experimental designs was confirmed by ANOVA. The optimum sugar values were obtained with H2SO4 2 N at 85 °C, 60 min and ratio 60 and H3PO4 21 N at 90 °C, 60 min and ratio 60. Glucose and xylose were the sugars detected in the hydrolyzed samples. In conclusion, almost 50% of the total carbohydrates present in the tiger nut byproduct were hydrolyzed to reducing sugars.
Development of Staphylococcus Enzybiotics: The Ph28 Gene of Staphylococcus epidermidis Phage PH15 Is a Two-Domain Endolysin
Authors: Muharram Magdy Mohamed , Abulhamd Ashraf Tawfik , Aldawsari Mohammed F. , Alqarni Mohamed Hamed , Labrou Nikolaos E.
Published: March 2020

Given the worldwide increase in antibiotic resistant bacteria, bacteriophage derived endolysins represent a very promising new alternative class of antibacterials in the fight against infectious diseases. Endolysins are able to degrade the prokaryotic cell wall, and therefore have potential to be exploited for biotechnological and medical purposes. Staphylococcus epidermidis is a Gram-positive multidrug-resistant (MDR) bacterium of human skin. It is a health concern as it is involved in nosocomial infections. Genome-based screening approach of the complete genome of Staphylococcus virus PH15 allowed the identification of an endolysin gene (Ph28; NCBI accession number: YP_950690). Bioinformatics analysis of the Ph28 protein predicted that it is a two-domain enzyme composed by a CHAP (22-112) and MurNAc-LAA (171-349) domain. Phylogenetic analysis and molecular modelling studies revealed the structural and evolutionary features of both domains. The MurNAc-LAA domain was cloned, and expressed in E. coli BL21 (DE3). In turbidity reduction assays, the recombinant enzyme can lyse more efficiently untreated S. epidermidis cells, compared to other Staphylococcus strains, suggesting enhanced specificity for S. epidermidis. These results suggest that the MurNAc-LAA domain from Ph28 endolysin may represent a promising new enzybiotic.
Prevalence and Therapeutic Challenges of Fungal Drug Resistance: Role for Plants in Drug Discovery
Authors: Marquez Lewis , Quave Cassandra L.
Published: March 2020

Antimicrobial resistance is a global issue that threatens the effective practice of modern medicine and global health. The emergence of multidrug-resistant (MDR) fungal strains of Candida auris and azole-resistant Aspergillus fumigatus were highlighted in the Centers for Disease Control and Prevention’s (CDC) 2019 report, Antibiotic Resistance Threats in the United States. Conventional antifungals used to treat fungal infections are no longer as effective, leading to increased mortality. Compounding this issue, there are very few new antifungals currently in development. Plants from traditional medicine represent one possible research path to addressing the issue of MDR fungal pathogens. In this commentary piece, we discuss how medical ethnobotany—the study of how people use plants in medicine—can be used as a guide to identify plant species for the discovery and development of novel antifungal therapies.
Correction: Biswas, M.S. et al. Inactivation of Carbonyl-Detoxifying Enzymes by H2O2 Is a Trigger to Increase Carbonyl Load for Initiating Programmed Cell Death in Plants. Antioxidants 2020, 9, 141
Authors: Biswas Md. Sanaullah , Terada Ryota , Mano Jun’ichi
Published: March 2020

The author wishes to make the following correction to this paper [...]
Characterization of a CholesteroNitrone (ISQ-201), a Novel Drug Candidate for the Treatment of Ischemic Stroke
Authors: Martínez-Alonso Emma , Escobar-Peso Alejandro , Ayuso Maria I. , Gonzalo-Gobernado Rafael , Chioua Mourad , Montoya Juan J. , Montaner Joan , Fernández Israel , Marco-Contelles José , Alcázar Alberto
Published: March 2020

Nitrones have a well-recognized capacity as spin-traps and are considered powerful free radical scavengers, which are two important issues in hypoxia-induced oxidative stress and cell death in brain ischemia. Consequently, nitrones have been proposed as therapeutic agents in acute ischemic stroke (AIS). In this paper, we update the biological and pharmacological characterization of ISQ-201, a previously identified cholesteronitrone hybrid with antioxidant and neuroprotective activity. This study characterizes ISQ-201 as a neuroprotective agent against the hypoxia-induced ischemic injury. Transitory four-vessel occlusion and middle cerebral artery occlusion (tMCAO) were used to induce cerebral ischemia. Functional outcomes were determined using neurofunctional tests. Infarct area, neuronal death, and apoptosis induction were evaluated. In addition, ISQ-201 reactivity towards free radicals was studied in a theoretical model. ISQ-201 significantly decreased the ischemia-induced neuronal death and apoptosis, in a dose-dependent manner, showing its therapeutic effect when administered up until 6 h after post-ischemic reperfusion onset, effects that remained after 3 months from the ischemic episode. Furthermore, ISQ-201 significantly reduced infarct volume, leading to recovery of the motor function in the tMCAO model. Finally, the theoretical study confirmed the reactivity of ISQ-201 towards hydroxyl radicals. The results reported here prompted us to suggest ISQ-201 as a promising candidate for the treatment of AIS.
A Detailed Investigation of Gear Body-Induced Tooth Deflections and Development of an Improved Analytical Solution
Authors: Zhang Hongsheng , Pan Jun , Rao Meng , Li Zhengwei , Liang Xihui
Published: March 2020

Many researchers have developed analytical methods to evaluate gear meshing stiffness. Some of these methods ignored the effect of the gear body while others used a simplified model to consider its effect. Until now, a detailed investigation of gear body-induced tooth deflections has been rare, especially for the double-tooth-pair meshing period. In this study, we present a detailed investigation of gear body-induced tooth deflections. To be specific, we will discuss how to accurately evaluate gear body-induced tooth deflections using the finite element analysis, and what are the effect of parameters such as loading and gear parameters on gear body-induced tooth deflections. Then, an improved solution is developed for evaluating the body-induced tooth deflection. In the single-tooth-pair meshing period, the improved formula is developed based on a popular formula proposed by Sainsot and Velex. This is achieved by optimizing the coefficients used in their formula to make the formula more accurate to evaluate gear body-induced tooth deflections. Meanwhile, we introduce a new term called affiliated body stiffness to evaluate the body-induced tooth deflections in the double-tooth-pair meshing period. The improved method can give higher accuracy in evaluating gear body-induced tooth deflections of spur gears with a pressure angle of 20°.
Multi-Scale and Multi-Dimensional Time Series InSAR Characterizing of Surface Deformation over Shandong Peninsula, China
Authors: Peng Mimi , Zhao Chaoying , Zhang Qin , Lu Zhong , Bai Lin , Bai Weiming
Published: March 2020

Shandong peninsula, the largest peninsula of China, is prone to severe land subsidence hazards along the coastline. In this paper, we provide, for the first time, multi-scale and multi-dimensional time series deformation measurements of the entire Shandong peninsula with advanced time series Interferometric Synthetic Aperture Radar (InSAR) techniques. We derive the spatiotemporal evolutions of the land subsidence by integrating multi-track Sentinel-1A/B and RADARSAT-2 satellite images. InSAR measurements are cross validated by the independent deformation rate results generated from different SAR tracks, reaching a precision of less than 1.3 cm/a. Two-dimensional time series over the Yellow River Delta (YRD) from 2017 to 2019 are revealed by integrating time series InSAR measurements from both descending and ascending tracks. Land subsidence zones are mainly concentrated on the YRD. In total, twelve typical localized subsidence zones are identified in the cities of Dongying (up to 290 mm/a; brine and groundwater exploitation for industrial usage), Weifang (up to 170 mm/a; brine exploitation for industrial usage), Qingdao (up to 70 mm/a; aquaculture and land reclamation), Yantai (up to 50 mm/a; land reclamation) and Rizhao (up to 60 mm/a; land reclamation). The causal factors of localized ground deformation are discussed, encompassing groundwater and brine exploitation, aquaculture and land reclamation. Multi-scale surveys of spatiotemporal deformation evolution and mechanism analysis are critical to make decisions on underground fluid exploitation and land reclamation.
Development and Assessment of a Sensor-Based Orientation and Positioning Approach for Decreasing Variation in Camera Viewpoints and Image Transformations at Construction Sites
Authors: Foroughi Sabzevar Mohsen , Gheisari Masoud , Lo James
Published: March 2020

Image matching techniques offer valuable opportunities for the construction industry. Image matching, a fundamental process in computer vision, is required for different purposes such as object and scene recognition, video data mining, reconstruction of three-dimensional (3D) objects, etc. During the image matching process, two images that are randomly (i.e., from different position and orientation) captured from a scene are compared using image matching algorithms in order to identify their similarity. However, this process is very complex and error prone, because pictures that are randomly captured from a scene vary in viewpoints. Therefore, some main features in images such as position, orientation, and scale of objects are transformed. Sometimes, these image matching algorithms cannot correctly identify the similarity between these images. Logically, if these features remain unchanged during the picture capturing process, then image transformations are reduced, similarity increases, and consequently, the chances of algorithms successfully conducting the image matching process increase. One way to improve these chances is to hold the camera at a fixed viewpoint. However, in messy, dusty, and temporary locations such as construction sites, holding the camera at a fixed viewpoint is not always feasible. Is there any way to repeat and retrieve the camera’s viewpoints during different captures at locations such as construction sites? This study developed and evaluated an orientation and positioning approach that decreased the variation in camera viewpoints and image transformation on construction sites. The results showed that images captured while using this approach had less image transformation in contrast to images not captured using this approach.
Representing Data Visualization Goals and Tasks through Meta-Modeling to Tailor Information Dashboards
Authors: Vázquez-Ingelmo Andrea , García-Peñalvo Francisco José , Therón Roberto , Conde Miguel Ángel
Published: March 2020

Information dashboards are everywhere. They support knowledge discovery in a huge variety of contexts and domains. Although powerful, these tools can be complex, not only for the end-users but also for developers and designers. Information dashboards encode complex datasets into different visual marks to ease knowledge discovery. Choosing a wrong design could compromise the entire dashboard’s effectiveness, selecting the appropriate encoding or configuration for each potential context, user, or data domain is a crucial task. For these reasons, there is a necessity to automatize the recommendation of visualizations and dashboard configurations to deliver tools adapted to their context. Recommendations can be based on different aspects, such as user characteristics, the data domain, or the goals and tasks that will be achieved or carried out through the visualizations. This work presents a dashboard meta-model that abstracts all these factors and the integration of a visualization task taxonomy to account for the different actions that can be performed with information dashboards. This meta-model has been used to design a domain specific language to specify dashboards requirements in a structured way. The ultimate goal is to obtain a dashboard generation pipeline to deliver dashboards adapted to any context, such as the educational context, in which a lot of data are generated, and there are several actors involved (students, teachers, managers, etc.) that would want to reach different insights regarding their learning performance or learning methodologies.
A Study on the Utilization of Clayey Soil as Embankment Material through Model Bearing Capacity Tests
Authors: Won Myoung-Soo , Langcuyan Christine P. , Gao Yu-Cong
Published: March 2020

The Saemangeum seawall, located on the western coast of Korea, is 33.8 km long and is known as the longest embankment in the world. The Saemangeum project is underway for road, railway, and port constructions for internal development. In the Saemangeum area, suitable granular soil for embankment material is difficult to obtain. However, silty clay is widely distributed. In this study, a series of model-bearing capacity tests were conducted as a basic study for using clayey soils as embankment materials. The model bearing capacity tests were carried out using a standard metal mold and a customized metal box. The test results showed that clayey soil, with normal moisture content (NMC), exhibited a large deformation and low bearing capacity. However, when the clay was well-compacted, with optimum moisture content (OMC), it exhibited a higher bearing capacity than dense sand. In addition, when crushed gravel and composite geotextiles were placed in the clayey soil with NMC, the bearing capacity was higher than that of dense sand. From the viewpoint of the bearing capacity, it is considered that clayey soil can be used as an embankment material when clay, crushed gravel, and composite geotextiles are properly combined.
Inorganic Waste Generated in Kraft Pulp Mills: The Transition from Landfill to Industrial Applications
Authors: Quina Margarida J. , Pinheiro Carolina T.
Published: March 2020

Kraft pulp mills produce the main raw material for paper, while several waste products are generated in large quantities in the process. This review study addresses four of the main inorganic wastes formed by this industry, namely green liquor dregs (GLD), slaker grits (SG), lime mud (LM) and boiler fly ash (BFA), which are still mostly discarded in landfills. A brief overview of a typical industrial process was included to outline the waste generation points. The main chemical and physical properties are indicated for highlighting the most relevant characteristics to determine which applications may be considered in each case. An in-depth literature review allowed the identification of the main applications that have been tested mainly at the laboratory scale and some at an industrial scale. The applications are grouped into construction materials, geotechnical, environmental, agricultural and others. This assessment shows that the circular economy and the sustainable development goals of the UN are important issues for organizations in general, and the pulp mill in particular. In fact, this industry has managed to close the chemicals loops, recover energy and reduce water consumption in the process. However, the current situation of inorganic waste can still be improved if industrial applications are developed to avoid landfill.
Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting
Authors: Lara-Benítez Pedro , Carranza-García Manuel , Luna-Romera José M. , Riquelme José C.
Published: March 2020

Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these types of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.
Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters
Authors: Renugadevi T. , Geetha K. , Muthukumar K. , Geem Zong Woo
Published: March 2020

Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.
Mechanical Properties and Explosive Spalling Behavior of Steel-Fiber-Reinforced Concrete Exposed to High Temperature—A Review
Authors: Zhang Peng , Kang Luoyi , Wang Juan , Guo Jinjun , Hu Shaowei , Ling Yifeng
Published: March 2020

Steel-fiber-reinforced concrete (SFRC) is being increasingly applied to various buildings and civil infrastructure as an advanced cementitious composite. In recent years, the requirements for SFRC in the construction industry have increased. Additionally, the fire resistance of SFRC has attracted attention; therefore, numerous investigations regarding the residual properties of SFRC have been conducted. This paper critically reviews the mechanical properties of SFRC subjected to elevated temperatures, including its residual compressive strength, flexural strength, tensile strength, elastic properties, fracture properties, and stress–strain relationships. The residual mechanical performance of SFRC and the action mechanism of steel fibers are reviewed in detail. Moreover, factors affecting the explosive spalling of concrete at high temperatures as well as the effect of steel fibers on the microstructure of heated concrete are discussed. It is demonstrated that, in general, SFRC exhibits better residual mechanical properties when exposed to elevated temperatures than plain concrete and can prevent the risk of explosive spalling more effectively. The purpose of this literature review is to provide an exhaustive insight into the feasibility of SFRC as a refractory building material; additionally, future research needs are identified.
Game Theory-Based Smart Mobile-Data Offloading Scheme in 5G Cellular Networks
Authors: Thien Huynh Thanh , Vu Van-Hiep , Koo Insoo
Published: March 2020

Mobile-data traffic exponentially increases day by day due to the rapid development of smart devices and mobile internet services. Thus, the cellular network suffers from various problems, like traffic congestion and load imbalance, which might decrease end-user quality of service. This work compensates for the problem of offloading in the cellular network by forming device-to-device (D2D) links. A game scenario is formulated where D2D-link pairs compete for network resources. In a D2D-link pair, the data of a user equipment (UE) is offloaded to another UE with an offload coefficient, i.e., the proportion of requested data that can be delivered via D2D links. Each link acts as a player in a cooperative game, with the optimal solution for the game found using the Nash bargaining solution (NBS). The proposed solution aims to present a strategy to control different parameters of the UE, including harvested energy which is stored in a rechargeable battery with a finite capacity and the offload coefficients of the D2D-link pairs, to optimize the performance of the network in terms of throughput and energy efficiency (EE) while considering fairness among links in the network. Simulation results show that the proposed game scheme can effectively offload mobile data, achieve better EE and improve the throughput while maintaining high fairness, compared to an offloading scheme based on a maximized fairness index (MFI) and to a no-offload scheme.
Oxidative Phosphorylation—an Update on a New, Essential Target Space for Drug Discovery in Mycobacterium tuberculosis
Authors: Foo Caroline Shi-Yan , Pethe Kevin , Lupien Andréanne
Published: March 2020

New drugs with new mechanisms of action are urgently required to tackle the global tuberculosis epidemic. Following the FDA-approval of the ATP synthase inhibitor bedaquiline (Sirturo®), energy metabolism has become the subject of intense focus as a novel pathway to exploit for tuberculosis drug development. This enthusiasm stems from the fact that oxidative phosphorylation (OxPhos) and the maintenance of the transmembrane electrochemical gradient are essential for the viability of replicating and non-replicating Mycobacterium tuberculosis (M. tb), the etiological agent of human tuberculosis (TB). Therefore, new drugs targeting this pathway have the potential to shorten TB treatment, which is one of the major goals of TB drug discovery. This review summarises the latest and key findings regarding the OxPhos pathway in M. tb and provides an overview of the inhibitors targeting various components. We also discuss the potential of new regimens containing these inhibitors, the flexibility of this pathway and, consequently, the complexity in targeting it. Lastly, we discuss opportunities and future directions of this drug target space.
The Feature Selection Effect on Missing Value Imputation of Medical Datasets
Authors: Liu Chia-Hui , Tsai Chih-Fong , Sue Kuen-Liang , Huang Min-Wei
Published: March 2020

In practice, many medical domain datasets are incomplete, containing a proportion of incomplete data with missing attribute values. Missing value imputation can be performed to solve the problem of incomplete datasets. To impute missing values, some of the observed data (i.e., complete data) are generally used as the reference or training set, and then the relevant statistical and machine learning techniques are employed to produce estimations to replace the missing values. Since the collected dataset usually contains a certain number of feature dimensions, it is useful to perform feature selection for better pattern recognition. Therefore, the aim of this paper is to examine the effect of performing feature selection on missing value imputation of medical datasets. Experiments are carried out on five different medical domain datasets containing various feature dimensions. In addition, three different types of feature selection methods and imputation techniques are employed for comparison. The results show that combining feature selection and imputation is a better choice for many medical datasets. However, the feature selection algorithm should be carefully chosen in order to produce the best result. Particularly, the genetic algorithm and information gain models are suitable for lower dimensional datasets, whereas the decision tree model is a better choice for higher dimensional datasets.
Automatic Detection of Pulmonary Nodules using Three-dimensional Chain Coding and Optimized Random Forest
Authors: Paing May Phu , Hamamoto Kazuhiko , Tungjitkusolmun Supan , Visitsattapongse Sarinporn , Pintavirooj Chuchart
Published: March 2020

The detection of pulmonary nodules on computed tomography scans provides a clue for the early diagnosis of lung cancer. Manual detection mandates a heavy radiological workload as it identifies nodules slice-by-slice. This paper presents a fully automated nodule detection with three significant contributions. First, an automated seeded region growing is designed to segment the lung regions from the tomography scans. Second, a three-dimensional chain code algorithm is implemented to refine the border of the segmented lungs. Lastly, nodules inside the lungs are detected using an optimized random forest classifier. The experiments for our proposed detection are conducted using 888 scans from a public dataset, and achieves a favorable result of 93.11% accuracy, 94.86% sensitivity, and 91.37% specificity, with only 0.0863 false positives per exam.
DUKMSVM: A Framework of Deep Uniform Kernel Mapping Support Vector Machine for Short Text Classification
Authors: Liu Zhaoying , Kan Haipeng , Zhang Ting , Li Yujian
Published: March 2020

This paper mainly deals with the problem of short text classification. There are two main contributions. Firstly, we introduce a framework of deep uniform kernel mapping support vector machine (DUKMSVM). The significant merit of this framework is that by expressing the kernel mapping function explicitly with a deep neural network, it is in essence an explicit kernel mapping instead of the traditional kernel function, and it allows better flexibility in dealing with various applications by applying different neural network structures. Secondly, to validate the effectiveness of this framework and to improve the performance of short text classification, we explicitly express the kernel mapping using bidirectional recurrent neural network (BRNN), and propose a deep bidirectional recurrent kernel mapping support vector machine (DRKMSVM) for short text classification. Experimental results on five public short text classification datasets indicate that in terms of classification accuracy, precision, recall rate and F1-score, the DRKMSVM achieves the best performance with the average values of accuracy, precision, recall rate, and F1-score of 87.23%, 86.99%, 86.13% and 86.51% respectively compared to traditional SVM, convolutional neural network (CNN), Naive Bayes (NB), and Deep Neural Mapping Support Vector Machine (DNMSVM) which applies multi-layer perceptron for kernel mapping.
Virtual Environments and Augmented Reality Applied to Heritage Education. An Evaluative Study
Authors: Ibañez-Etxeberria Alex , Gómez-Carrasco Cosme J. , Fontal Olaia , García-Ceballos Silvia
Published: March 2020

Technological advancements have provided heritage with new learning environments via the use of virtual and augmented reality, which can foster the accessibility and understanding of culture and propose new ways of interacting with heritage. Therefore, in this study, a systematic evaluation is carried out of n=197 heritage education programs listed in the database of the Observatorio de Educación patrimonial en España (OEPE) (the Spanish Heritage Education Observatory–SHEO) which, in their descriptions, integrate the use of virtual environments and/or augmented reality to promote learning on the part of the user. The objectives of this study are: (1) to analyse the state of the art, (2) to evaluate the quality of their educational designs via the “analysis and assessment sequential method for heritage education programs” (SAEPEP-OEPE) and (3) to identify variables which can be improved or which have a significant influence on the quality of the programs. Highlights of the results include: (a) the increasing implementation of these technologies in heritage education programs, with the greater presence of virtual resources than of learning environments, (b) the low level of the scope of educational quality in their designs, particularly their assessment, and (c) the inclusion of advanced technologies slightly decreases the specificity of the educational design.
Raman 2D Peak Line Shape in Epigraphene on SiC
Authors: Kunc Jan , Rejhon Martin
Published: March 2020

We measured a 2D peak line shape of epitaxial graphene grown on SiC in high vacuum, argon and graphene prepared by hydrogen intercalation from the so called buffer layer on a silicon face of SiC. We fitted the 2D peaks by Lorentzian and Voigt line shapes. The detailed analysis revealed that the Voigt line shape describes the 2D peak line shape better. We have determined the contribution of the homogeneous and inhomogeneous broadening. The homogeneous broadening is attributed to the intrinsic lifetime. Although the inhomogeneous broadening can be attributed to the spatial variations of the charge density, strain and overgrown graphene ribbons on the sub-micrometer length scales, we found dominant contribution of the strain fluctuations. The quasi free-standing graphene grown by hydrogen intercalation is shown to have the narrowest linewidth due to both homogeneous and inhomogeneous broadening.
Electromyography Analysis of Muscle Activation During Stand-Up Paddle Boarding: A Comparison of Paddling in Kneeling and Standing Positions
Authors: Tsai Feng-Hua , Wu Wen-Lan , Chen Yu-Ju , Liang Jing-Min , Hou Yi-You
Published: March 2020

Background: This study aimed to understand individual muscle use in different paddling postures in stand-up paddle boarding (SUP). Methods: Sixteen college students were recruited in this study. Surface electromyography of 16 muscles on the dominant side was recorded. Results: In the time series, the biceps muscle exhibited a continuous activation pattern in the pull phase when kneeling, whereas when standing, the muscle contracted considerably in the exit and recovery phases, implying that it plays different roles in the two postures. The biceps also exhibited significantly higher muscle activation in the kneeling position than it did in the standing position. The maximum muscle activity levels of the external oblique abdominis and triceps were significantly higher when standing than when kneeling. In addition, an unstable SUP board activated the gastrocnemius to help paddlers maintain stability on a swaying surface. Moreover, additional power from the wrist flexor must be used in the recovery and catch phases to stabilize paddle control in the standing position. Conclusion: The knowledge that changes in SUP posture activate different muscle groups can enhance training efficiency and provide a reference for designing individualized training programs.
An Automated End-to-End Side Channel Analysis Based on Probabilistic Model
Authors: Hwang Jeonghwan , Yoon Ji Won
Published: March 2020

In this paper, we propose a new automated way to find out the secret exponent from a single power trace. We segment the power trace into subsignals that are directly related to recovery of the secret exponent. The proposed approach does not need the reference window to slide, templates nor correlation coefficients compared to previous manners. Our method detects change points in the power trace to explore the locations of the operations and is robust to unexpected noise addition. We first model the change point detection problem to catch the subsignals irrelevant to the secret and solve this problem with Markov Chain Monte Carlo (MCMC) which gives a global optimal solution. After separating the relevant and irrelevant parts in signal, we extract features from the segments and group segments into clusters to find the key exponent. Using single power trace indicates the weakest power level of attacker where there is a very slight chance of acquiring as many power traces as needed for breaking the key. We empirically show the improvement in accuracy even with presence of high level of noise.
Investigation of Dual-Flow Deep Learning Models LSTM-FCN and GRU-FCN Efficiency against Single-Flow CNN Models for the Host-Based Intrusion and Malware Detection Task on Univariate Times Series Data
Authors: Čeponis Dainius , Goranin Nikolaj
Published: March 2020

Intrusion and malware detection tasks on a host level are a critical part of the overall information security infrastructure of a modern enterprise. While classical host-based intrusion detection systems (HIDS) and antivirus (AV) approaches are based on change monitoring of critical files and malware signatures, respectively, some recent research, utilizing relatively vanilla deep learning (DL) methods, has demonstrated promising anomaly-based detection results that already have practical applicability due low false positive rate (FPR). More complex DL methods typically provide better results in natural language processing and image recognition tasks. In this paper, we analyze applicability of more complex dual-flow DL methods, such as long short-term memory fully convolutional network (LSTM-FCN), gated recurrent unit (GRU)-FCN, and several others, for the task specified on the attack-caused Windows OS system calls traces dataset (AWSCTD) and compare it with vanilla single-flow convolutional neural network (CNN) models. The results obtained do not demonstrate any advantages of dual-flow models while processing univariate times series data and introducing unnecessary level of complexity, increasing training, and anomaly detection time, which is crucial in the intrusion containment process. On the other hand, the newly tested AWSCTD-CNN-static (S) single-flow model demonstrated three times better training and testing times, preserving the high detection accuracy.
Improved Rapid Visual Earthquake Hazard Safety Evaluation of Existing Buildings Using a Type-2 Fuzzy Logic Model
Authors: Harirchian Ehsan , Lahmer Tom
Published: March 2020

Rapid Visual Screening (RVS) is a procedure that estimates structural scores for buildings and prioritizes their retrofit and upgrade requirements. Despite the speed and simplicity of RVS, many of the collected parameters are non-commensurable and include subjectivity due to visual observations. This might cause uncertainties in the evaluation, which emphasizes the use of a fuzzy-based method. This study aims to propose a novel RVS methodology based on the interval type-2 fuzzy logic system (IT2FLS) to set the priority of vulnerable building to undergo detailed assessment while covering uncertainties and minimizing their effects during evaluation. The proposed method estimates the vulnerability of a building, in terms of Damage Index, considering the number of stories, age of building, plan irregularity, vertical irregularity, building quality, and peak ground velocity, as inputs with a single output variable. Applicability of the proposed method has been investigated using a post-earthquake damage database of reinforced concrete buildings from the Bingöl and Düzce earthquakes in Turkey.
A Digital Twin for Automated Root-Cause Search of Production Alarms Based on KPIs Aggregated from IoT
Authors: Papacharalampopoulos Alexios , Giannoulis Christos , Stavropoulos Panos , Mourtzis Dimitris
Published: March 2020

A dashboard application is proposed and developed to act as a Digital Twin that would indicate the Measured Value to be held accountable for any future failures. The current study describes a method for the exploitation of historical data that are related to production performance and aggregated from IoT, to eliciting the future behavior of the production, while indicating the measured values that are responsible for negative production performance, without training. The dashboard is implemented in the Java programming language, while information is stored into a Database that is aggregated by an Online Analytical Processing (OLAP) server. This achieves easy Key Performance Indicators (KPIs) visualization through the dashboard. Finally, indicative cases of a simulated transfer line are presented and numerical examples are given for validation and demonstration purposes. The need for human intervention is pointed out.
Comparison of Iodide, Iodate, and Iodine-Chitosan Complexes for the Biofortification of Lettuce
Authors: Dávila Rangel Irma Esther , Trejo Téllez Libia Iris , Ortega Ortiz Hortensia , Juárez Maldonado Antonio , González Morales Susana , Companioni González Barbarita , Cabrera De la Fuente Marcelino , Benavides Mendoza Adalberto
Published: March 2020

Iodine is an essential trace nutrient for humans; its deficit can affect motor and cognitive development. Biofortifying crops with iodine is a way of promoting the adequate intake of this element. The uses of chitosan-iodine complexes for crop biofortification have not been previously studied. The present work evaluated the effects of KIO3 and KI salts, chitosan-KIO3 complex (Cs-KIO3), and chitosan-KI complex (Cs-KI) application on lettuce, with a chitosan-only treatment as a control and water as the absolute control. Each treatment involved the application of 0, 5, and 25 mg I kg−1 soil applied before transplanting or 25 mg I kg−1 soil applied as split doses of 12.5 mg kg−1, once immediately before transplanting and the second application 15 days later. Single application of Cs-KIO3 at 5 and 25 mg I kg−1 increased lettuce biomass while the split-dose application (SDA) of Cs-KI (25 mg I kg−1) led to a decrease in biomass. Maximum accumulation of iodine in lettuce was observed after the application of KIO3 (25 mg I kg−1) in two parts. This study shows that the use of chitosan complexes, especially Cs-KIO3, may be a viable alternative for crop biofortification with iodine without affecting crop yields.
Evaporation of Water/Alumina Nanofluid Film by Mixed Convection Inside Heated Vertical Channel
Authors: Mohamed A. Belhadj , Hdidi Walid , Tlili Iskander
Published: March 2020

In industrial devices like heat recovery systems, heat pumps, as well as symmetric and complex engineering systems, a nano fluid mixture is used. Regarding the nature of the energy sources (thermal or thermal and electrical), many physical systems could represent possible applications in manufactural activities. The presence of nanoparticles inside a solvent is of great interest in order to optimize the efficacy of the nano-technology systems. The present work deals with heat and mass transfer through a vertical channel where an alumina/water film mixture flows on one of its plates. For simulation, we use a numerical method under mixed convection during water/alumina nano fluid evaporation. We heat the flown plate uniformly while the other is dry and exchange heat with a constant coefficient. The gas mixture enters channel with a constant profile. Results show that an augmentation of the volume rate of the nanoparticle disadvantages evaporation if the heating is absent. Otherwise, if the heating exists, an increasing volume rate of the nanoparticle advantages evaporation. We found also that the film velocity behavior when the volume rate of the nanoparticle varies, independent of the heating.
Distributed Passive Actuation Schemes for Seismic Protection of Multibuilding Systems
Authors: Palacios-Quiñonero Francisco , Rubió-Massegú Josep , Rossell Josep M. , Karimi Hamid Reza
Published: March 2020

In this paper, we investigate the design of distributed damping systems (DDSs) for the overall seismic protection of multiple adjacent buildings. The considered DDSs contain interstory dampers implemented inside the buildings and also interbuilding damping links. The design objectives include mitigating the buildings seismic response by reducing the interstory-drift and story-acceleration peak-values and producing small interbuilding approachings to decrease the risk of interbuilding collisions. Designing high-performance DDS configurations requires determining convenient damper positions and computing proper values for the damper parameters. That allocation-tuning optimization problem can pose serious computational difficulties for large-scale multibuilding systems. The design methodology proposed in this work—(i) is based on an effective matrix formulation of the damped multibuilding system; (ii) follows an H ∞ approach to define an objective function with fast-evaluation characteristics; (iii) exploits the computational advantages of the current state-of-the-art genetic algorithm solvers, including the usage of hybrid discrete-continuous optimization and parallel computing; and (iv) allows setting actuation schemes of particular interest such as full-linked configurations or nonactuated buildings. To illustrate the main features of the presented methodology, we consider a system of five adjacent multistory buildings and design three full-linked DDS configurations with a different number of actuated buildings. The obtained results confirm the flexibility and effectiveness of the proposed design approach and demonstrate the high-performance characteristics of the devised DDS configurations.
Effects of Rear Angle on the Turbulent Wake Flow between Two in-Line Ahmed Bodies
Authors: Essel Ebenezer , Das Subhadip , Balachandar Ram
Published: March 2020

Understanding the wake characteristics between two in-line vehicles is essential for improving and developing new strategies for reducing in-cabin air pollution. In this study, Ahmed bodies are used to investigate the effects of the rear slant angle of a leading vehicle on the mean flow and turbulent statistics between two vehicles. The experiments were conducted with a particle image velocimetry at a fixed Reynolds number, R e H = 1.7 × 10 4 , and inter-vehicle spacing distance of 0.75 L , where H and L are the height and length of the model. The rear slant angles investigated were a reference square back, high-drag angle ( α = 25 ° ) and low-drag angle ( α = 35 ° ). The mean velocities, Reynolds stresses, production of turbulent kinetic energy and instantaneous swirling strength are used to provide physical insight into the wake dynamics between the two bodies. The results indicate that the recirculation region behind the square back Ahmed body increases while those behind the slant rear-end bodies decreases in the presence of a follower. For the square back models, the dominant motion in the wake region is a strong upwash of jet-like flow away from the road but increasing the rear slant angle induces a stronger downwash flow that suppresses the upwash and dominates the wake region.