bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Selective Laser Melting Machine Source on the Dynamic Properties of AlSi10Mg Alloy
Authors: Amir Ben , Samuha Shmuel , Sadot Oren
Published: April 2019

Selective laser melting (SLM) AlSi10Mg alloy has been thoroughly investigated in terms of its microstructure and quasi-static properties, owing to its broad industrial applications. However, the effects of the SLM process on the dynamic behavior under impact conditions remain to be established. This research deals with the influences of manufacturing process parameters on the dynamic response of the SLM on AlSi10Mg at a high strain rate of 700 to 6700 s−1 by using a split Hopkinson pressure bar apparatus. Examinations were performed on vertically and horizontally built samples, processed individually by two manufacturers using a different laser scanning technique on the same powder composition. It was concluded that the fabrication technique does not influence the true stress–true strain dependency at strain rates of 700 to 2800 s−1. However, at higher strain rates (4000 to 6700 s−1), this study revealed different plastic behavior, which was associated only with the horizontally built samples. Moreover, this study found different failure demeanors at true strains exceeding 0.8. The dynamic response was correlated with the as-built microstructure and crystallographic texture, characterized using the electron backscattered diffraction technique.
Fully Biodegradable Composites: Thermal, Flammability, Moisture Absorption and Mechanical Properties of Natural Fibre-Reinforced Composites with Nano-Hydroxyapatite
Authors: Khalili Pooria , LIU Xiaoling , ZHAO Zirui , Blinzler Brina
Published: April 2019

Natural fibre-reinforced poly(lactic acid) (PLA) laminates were prepared by a conventional film stacking method from PLA films and natural fabrics with a cross ply layup of [0/90/0/90/0/90], followed by hot compression. Natural fibre (NF) nano-hydroxyapatite (nHA) filled composites were produced by the same manufacturing technique with matrix films that had varying concentrations of nHA in the PLA. Their flammability, thermal, moisture absorption and mechanical properties were analysed in terms of the amount of nHA. The flame behavior of neat PLA and composites evaluated by the UL-94 test demonstrated that only the composite containing the highest quantity of nHA (i.e., 40 wt% nHA in matrix) was found to achieve an FH-1 rating and exhibited no recorded burn rate, whereas other composites obtained only an FH-3. The thermal degradation temperature and mass residue were also observed, via thermogravimetric analysis, to increase when increasing concentrations of nHA were added to the NF composite. The tensile strength, tensile modulus and flexural modulus of the neat resin were found to increase significantly with the introduction of flax fibre. Conversely, moisture absorption was found to increase and mechanical properties to decrease with both the presence of NF and increasing concentrations of nHA, and subsequent mechanical properties experienced an obvious reduction.
Comparison of Butyric Acid Leaching Behaviors of Zinc from Three Basic Oxygen Steelmaking Filter Cakes
Authors: Wang Jingxiu , Wang Zhe , Zhang Zhongzhi , Zhang Guangqing
Published: April 2019

The selective leaching of zinc from three different basic oxygen steelmaking (BOS) filter cakes by butyric acid was investigated to compare the leaching behaviors of zinc and further to establish the correlation of the zinc leaching performances and the chemical compositions. The effects of acid concentration and the acid to solid (L/S) stoichiometric ratio were studied, with different optimal leaching conditions obtained. BOS-1 showed the lowest leachability with only less than 10% of zinc removed by 0.5 M acid concentration and 90% of the L/S stoichiometric ratio in 10 h. The best zinc selectivity was achieved with BOS-2 at 51.2% of zinc leaching efficiency, with only 0.47% of iron loss under optimal conditions of 1.5 M acid concentration and a 70% stoichiometric ratio. BOS-3 showed the highest leaching of zinc but the optimal conditions depend on the priority consideration. Using 1.0 M acid and 90% stoichiometric ratio for 10 h, the leaching efficiency of zinc was 84.6% with 20% iron loss. The filter cakes and the leaching residues were characterized. The results indicate different zinc and iron leaching behaviors, which were probably related to the storage conditions, zinc containing phases and the leaching parameters.
Solvent Front Position Extraction with Semi-Automatic Device as a Powerful Sample Preparation Procedure Prior to Quantitative Instrumental Analysis
Authors: Klimek-Turek Anna , Jaglińska Kamila , Imbierowicz Magdalena , Dzido Tadeusz Henryk
Published: April 2019

The new prototype device is applied to the Solvent Front Position Extraction (SFPE) sample preparation procedure. The mobile phase is deposited onto the chromatographic plate adsorbent layer by the pipette, which is moved, according to programmed movement path, by a 3D printer mechanism. The application of the prototype device to SFPE procedure leads to the increased repeatability of the results and significant reduction of the analysis time in comparison to the classical procedure of chromatogram development. Additionally, the new equipment allows use procedures that are not possible to run using the classic chromatogram development. In this paper, the results of manual and semi-automatic sample preparation with SFPE are compared and the possible application of this prototype device is discussed.
Ponatinib Inhibits Proliferation and Induces Apoptosis of Liver Cancer Cells, but Its Efficacy Is Compromised by Its Activation on PDK1/Akt/mTOR Signaling
Authors: Liu Chang , Mu Xiuli , Wang Xuan , Zhang Chan , Zhang Lina , Yu Baofeng , Sun Gongqin
Published: April 2019

Ponatinib is a multi-target protein tyrosine kinase inhibitor, and its effects on hepatocellular carcinoma cells have not been previously explored. In the present study, we investigated its effects on hepatocellular carcinoma cell growth and the underlying mechanisms. Toward SK-Hep-1 and SNU-423 cells, ponatinib induces apoptosis by upregulation of cleaved caspase-3 and -7 and promotes cell cycle arrest in the G1 phase by inhibiting CDK4/6/CyclinD1 complex and phosphorylation of retinoblastoma protein. It inhibits the growth-stimulating mitogen-activated protein (MAP) kinase pathway, the phosphorylation of Src on both negative and positive regulation sites, and Jak2 and Stat3 phosphorylation. Surprisingly, it also activates the PDK1, the protein kinase B (Akt), and the mechanistic target of rapamycin (mTOR) signaling pathway. Blocking mTOR signaling strongly sensitizes cells to inhibition by ponatinib and makes ponatinib a much more potent inhibitor of hepatocellular carcinoma cell proliferation. These findings demonstrate that ponatinib exerts both positive and negative effects on hepatocellular cell proliferation, and eliminating its growth-stimulating effects by drug combination or potentially by chemical medication can significantly improve its efficacy as an anti-cancer drug.
Piper Species: A Comprehensive Review on Their Phytochemistry, Biological Activities and Applications
Authors: Salehi Bahare , Zakaria Zainul Amiruddin , Gyawali Rabin , Ibrahim Salam A. , Rajkovic Jovana , Shinwari Zabta Khan , Khan Tariq , Sharifi-Rad Javad , Ozleyen Adem , Turkdonmez Elif , Valussi Marco , Tumer Tugba Boyunegmez , Monzote Fidalgo Lianet , Martorell Miquel , Setzer William N.
Published: April 2019

Piper species are aromatic plants used as spices in the kitchen, but their secondary metabolites have also shown biological effects on human health. These plants are rich in essential oils, which can be found in their fruits, seeds, leaves, branches, roots and stems. Some Piper species have simple chemical profiles, while others, such as Piper nigrum, Piper betle, and Piper auritum, contain very diverse suites of secondary metabolites. In traditional medicine, Piper species have been used worldwide to treat several diseases such as urological problems, skin, liver and stomach ailments, for wound healing, and as antipyretic and anti-inflammatory agents. In addition, Piper species could be used as natural antioxidants and antimicrobial agents in food preservation. The phytochemicals and essential oils of Piper species have shown strong antioxidant activity, in comparison with synthetic antioxidants, and demonstrated antibacterial and antifungal activities against human pathogens. Moreover, Piper species possess therapeutic and preventive potential against several chronic disorders. Among the functional properties of Piper plants/extracts/active components the antiproliferative, anti-inflammatory, and neuropharmacological activities of the extracts and extract-derived bioactive constituents are thought to be key effects for the protection against chronic conditions, based on preclinical in vitro and in vivo studies, besides clinical studies. Habitats and cultivation of Piper species are also covered in this review. In this current work, available literature of chemical constituents of the essential oils Piper plants, their use in traditional medicine, their applications as a food preservative, their antiparasitic activities and other important biological activities are reviewed.
In Vitro and In Silico Evaluation of Bikaverin as a Potent Inhibitor of Human Protein Kinase CK2
Authors: Haidar Samer , Aichele Dagmar , Birus Robin , Hielscher Janine , Laitinen Tuomo , Poso Antti , Jose Joachim
Published: April 2019

Protein kinase CK2 is an emerging target for therapeutic intervention in human diseases, particularly in cancer. Inhibitors of this enzyme are currently in clinical trials, indicating the druggability of human CK2. By virtual screening of the ZINC database, we found that the natural compound bikaverin can fit well in the ATP binding site of the target enzyme CK2. By further in vitro evaluation using CK2 holoenzyme, bikaverin turned to be a potent inhibitor with an IC50 value of 1.24 µM. In this work, the cell permeability of bikaverin was determined using a Caco-2 cell permeability assay as a prerequisite for cellular evaluation and the compound turned out to be cell permeable with a Papp- value of 4.46 × 10−6 cm/s. Bikaverin was tested for its effect on cell viability using a MTT assay and cell proliferation using an EdU assay in different cancer cell lines (MCF7, A427 and A431 cells). Cell viability and cell proliferation were reduced dramatically after treatment with 10 µM bikaverin for 24 h. Additionally the IncuCyte® live-cell imaging system was applied for monitoring the cytotoxicity of bikaverin in the three tested cancer cell lines. Finally, molecular dynamic studies were performed to clarify the ligand binding mode of bikaverin at the ATP binding site of CK2 and to identify the amino acids involved.
Long Non-Coding RNA Myoparr Regulates GDF5 Expression in Denervated Mouse Skeletal Muscle
Authors: Hitachi Keisuke , Nakatani Masashi , Tsuchida Kunihiro
Published: April 2019

Skeletal muscle is a highly plastic tissue and decreased skeletal muscle mass (muscle atrophy) results in deteriorated motor function and perturbed body homeostasis. Myogenin promoter-associated long non-coding RNA (lncRNA) Myoparr promotes skeletal muscle atrophy caused by surgical denervation; however, the precise molecular mechanism remains unclear. Here, we examined the downstream genes of Myoparr during muscle atrophy following denervation of tibialis anterior (TA) muscles in C57BL/6J mice. Myoparr knockdown affected the expression of 848 genes. Sixty-five of the genes differentially regulated by Myoparr knockdown coded secretory proteins. Among these 65 genes identified in Myoparr-depleted skeletal muscles after denervation, we focused on the increased expression of growth/differentiation factor 5 (GDF5), an inhibitor of muscle atrophy. Myoparr knockdown led to activated bone morphogenetic protein (BMP) signaling in denervated muscles, as indicated by the increased levels of phosphorylated Smad1/5/8. Our detailed evaluation of downstream genes of Myoparr also revealed that Myoparr regulated differential gene expression between myogenic differentiation and muscle atrophy. This is the first report demonstrating the in vivo role of Myoparr in regulating BMP signaling in denervated muscles. Therefore, lncRNAs that have inhibitory activity on BMP signaling may be putative therapeutic targets for skeletal muscle atrophy.
Size Separation of Silica Particles Using a Magnetite-Containing Gel-Packed Column
Authors: Miyoshi Manoka , Takayanagi Kosuke , Morisada Shintaro , Ohto Keisuke , Kawakita Hidetaka , Morita Shoichiro
Published: April 2019

A magnetite-containing gel was prepared by water-in-oil radical polymerization of N,N-dimethylacrylamide and N,N′-methylenebisacrylamide in the presence of magnetite. The size of the prepared gel particles was 86 µm. The obtained magnetite-containing gel was packed in a column and first permeated with water, which revealed that the gel displayed a nonlinear response to pressure drop with increasing flow rate. Thus, the gel particles at the bottom of the column felt more pressure from the fluid than those at the top, causing greater deformation of the gel particles at the bottom of the column than at the top. The gaps between the packed gel particles functioned as pores to filter particles of appropriate size and morphology. An industrial silica particle suspension with particle sizes of 300 nm, 800 nm, and 10 µm was permeated through the gel layer. The smallest (300 nm) silica particles passed through the column. The filtered silica particles were recovered from the gel layer by using a magnet to separate the magnetite-containing gel from the filtered silica particles. This magnetite-containing gel has wide application prospects for the separation of not only ceramics but also other colloids.
The Contribution of Energy Taxes to Climate Change Policy in the European Union (EU)
Authors: Liobikienė Genovaitė , Butkus Mindaugas , Matuzevičiūtė Kristina
Published: April 2019

Energy taxes are one of the main market-based tools directed toward mitigating climate change in the European Union (EU). Therefore, the aim of this article was to analyze whether energy taxes really contribute to the reduction of greenhouse gas (GHG) emissions and the successful implementation of climate change policy. Applying the Granger causality test on time series and using panel data analysis, the direct and indirect (via the reduction of fossil energy consumption (FEC) and energy intensity (EI), as well as the increase of renewable energy consumption (REN)) impacts of energy taxes on GHG emissions in EU countries were analyzed in the present study. The results showed that energy taxes did not Granger-cause fossil energy consumption, energy intensity, renewable energy consumption, and GHG emissions in almost all EU countries. Regarding the panel data analysis, the results showed that energy taxes did not, directly and indirectly, influence GHG emissions. Therefore, this paper shows that generally, energy tax policy in EU countries is ineffective. Thus, tax policy should be reformed and matched with an emissions trading system in seeking climate change mitigation.
A Proposal for Redesigning the Water Quality Network of the Tunjuelo River in Bogotá, Colombia through a Spatio-Temporal Analysis
Authors: Peña-Guzmán Carlos Andrés , Soto Lina , Diaz Angie
Published: April 2019

Bogotá is the capital of Colombia and represents the most important urban center in the country. Bogotá’s population and economic growth have accelerated exponentially in recent years and this growth has brought with it a variety of environmental impacts, including degradation of surface water quality. Government agencies have developed the water quality network of Bogotá that spans across four large rivers, including the Tunjuelo. According to measurements since 2009, water quality has changed in association with the dynamics of the city. This article utilizes a spatial and temporal analysis with multivariate statistics (Principal Components Analyses, dendograms, and Kruskal-Wallis) to propose a redesign of the Tunjuelo River water quality network. Based on these analyses, the number of monitoring stations can be reduced from nine to seven and the measurement frequency can be reduced. Together, the proposed spatial and temporal redesign would reduce the sample acquisition and analysis costs across the network by 50%.
A Random Forest Method to Forecast Downbursts Based on Dual-Polarization Radar Signatures
Authors: Medina Bruno L. , Carey Lawrence D. , Amiot Corey G. , Mecikalski Retha M. , Roeder William P. , McNamara Todd M. , Blakeslee Richard J.
Published: April 2019

The United States Air Force’s 45th Weather Squadron provides wind warnings, including those for downbursts, at the Cape Canaveral Air Force Station and Kennedy Space Center (CCAFS/KSC). This study aims to provide a Random Forest model that classifies thunderstorms’ downburst and null events using a 35-knot wind threshold to separate these two categories. The downburst occurrence was assessed using a dense network of wind observations around CCAFS/KSC. Eight dual-polarization radar signatures that are hypothesized to have physical implications for downbursts at the surface were automatically calculated for 209 storms and ingested into the Random Forest model. The Random Forest model predicted null events more correctly than downburst events, with a True Skill Statistic of 0.40. Strong downburst events were better classified than those with weaker wind magnitudes. The most important radar signatures were found to be the maximum vertically integrated ice and the peak reflectivity. The Random Forest model presented a more reliable performance than an automated prediction method based on thresholds of single radar signatures. Based on these results, the Random Forest method is suggested for continued operational development and testing.
Building Footprint Extraction from High-Resolution Images via Spatial Residual Inception Convolutional Neural Network
Authors: Liu Penghua , Liu Xiaoping , Liu Mengxi , Shi Qian , Yang Jinxing , Xu Xiaocong , Zhang Yuanying
Published: April 2019

The rapid development in deep learning and computer vision has introduced new opportunities and paradigms for building extraction from remote sensing images. In this paper, we propose a novel fully convolutional network (FCN), in which a spatial residual inception (SRI) module is proposed to capture and aggregate multi-scale contexts for semantic understanding by successively fusing multi-level features. The proposed SRI-Net is capable of accurately detecting large buildings that might be easily omitted while retaining global morphological characteristics and local details. On the other hand, to improve computational efficiency, depthwise separable convolutions and convolution factorization are introduced to significantly decrease the number of model parameters. The proposed model is evaluated on the Inria Aerial Image Labeling Dataset and the Wuhan University (WHU) Aerial Building Dataset. The experimental results show that the proposed methods exhibit significant improvements compared with several state-of-the-art FCNs, including SegNet, U-Net, RefineNet, and DeepLab v3+. The proposed model shows promising potential for building detection from remote sensing images on a large scale.
Long-Term Variations in the Pixel-to-Pixel Variability of NOAA AVHRR SST Fields from 1982 to 2015
Authors: Wu Fan , Cornillon Peter , Guan Lei , Kilpatrick Katherine
Published: April 2019

Sea surface temperature (SST) fields obtained from the series of space-borne five-channel Advanced Very High Resolution Radiometers (AVHRRs) provide the longest continuous time series of global SST available to date (1981–present). As a result, these data have been used for many studies and significant effort has been devoted to their careful calibration in an effort to provide a climate quality data record. However, little attention has been given to the local precision of the SST retrievals obtained from these instruments, which we refer to as the pixel-to-pixel (p2p) variability, a characteristic important in the ability to resolve structures such as ocean fronts characterized by small gradients in the SST field. In this study, the p2p variability is estimated for Level-2 SST fields obtained with the Pathfinder retrieval algorithm for AVHRRs on NOAA-07, 9, 11, 12 and 14-19. These estimates are stratified by year, season, day/night and along-scan/along-track. The overall variability ranges from 0.10 K to 0.21 K. For each satellite, the along-scan variability is between 10 and 20% smaller than the along-track variability (except for NOAA-16 nighttime for which it is approximately 30% smaller) and the summer and fall σ s are between 10 and 15% smaller than the winter and spring σ s. The differences between along-track and along-scan are attributed to the way in which the instrument has been calibrated. The seasonal differences result from the T 4 − T 5 term in the Pathfinder retrieval algorithm. This term is shown to be a major contributor to the p2p variability and it is shown that its impact could be substantially reduced without a deleterious effect on the overall p2p σ of the resulting products by spatially averaging it as part of the retrieval process. The AVHRR/3s (NOAA-15 through 19) were found to be relatively stable with trends in the p2p variability of at most 0.015 K/decade.
Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery
Authors: Ma Huiqin , Huang Wenjiang , Jing Yuanshu , Yang Chenghai , Han Liangxiu , Dong Yingying , Ye Huichun , Shi Yue , Zheng Qiong , Liu Linyi , Ruan Chao
Published: April 2019

Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied to data during a period of typical co-epidemic outbreak of winter wheat powdery mildew and aphids in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced training data set before model construction. Then, a back propagation neural network (BPNN) based on a new training data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original training data set-based BPNN and support vector machine (SVM) methods were used for comparison and testing of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphids at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest.
Optimized Contrast Enhancement for Infrared Images Based on Global and Local Histogram Specification
Authors: Liu Chengwei , Sui Xiubao , Kuang Xiaodong , Liu Yuan , Gu Guohua , Chen Qian
Published: April 2019

In this paper, an optimized contrast enhancement method combining global and local enhancement results is proposed to improve the visual quality of infrared images. Global and local contrast enhancement methods have their merits and demerits, respectively. The proposed method utilizes the complementary characteristics of these two methods to achieve noticeable contrast enhancement without artifacts. In our proposed method, the 2D histogram, which contains both global and local gray level distribution characteristics of the original image, is computed first. Then, based on the 2D histogram, the global and local enhanced results are obtained by applying histogram specification globally and locally. Lastly, the enhanced result is computed by solving an optimization equation subjected to global and local constraints. The pixel-wise regularization parameters for the optimization equation are adaptively determined based on the edge information of the original image. Thus, the proposed method is able to enhance the local contrast while preserving the naturalness of the original image. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the block-based methods for improving the visual quality of infrared images.
Robust Face Recognition Based on a New Supervised Kernel Subspace Learning Method
Authors: Khalili Mobarakeh Ali , Cabrera Carrillo Juan Antonio , Castillo Aguilar Juan Jesús
Published: April 2019

Face recognition is one of the most popular techniques to achieve the goal of figuring out the identity of a person. This study has been conducted to develop a new non-linear subspace learning method named “supervised kernel locality-based discriminant neighborhood embedding,” which performs data classification by learning an optimum embedded subspace from a principal high dimensional space. In this approach, not only nonlinear and complex variation of face images is effectively represented using nonlinear kernel mapping, but local structure information of data from the same class and discriminant information from distinct classes are also simultaneously preserved to further improve final classification performance. Moreover, in order to evaluate the robustness of the proposed method, it was compared with several well-known pattern recognition methods through comprehensive experiments with six publicly accessible datasets. Experiment results reveal that our method consistently outperforms its competitors, which demonstrates strong potential to be implemented in many real-world systems.
Application of Deep-Learning Methods to Bird Detection Using Unmanned Aerial Vehicle Imagery
Authors: Hong Suk-Ju , Han Yunhyeok , Kim Sang-Yeon , Lee Ah-Yeong , Kim Ghiseok
Published: April 2019

Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.
Ultrasonic Inspection of Localized Defects in Low-Porosity CFRP
Authors: Feng Wei , Zhou Xiaojun , Zeng Xiang , Yang Chenlong
Published: April 2019

A preliminary backscattered signal model of carbon-fiber-reinforced plastic (CFRP) laminate was established. The backscattered signal model was composed of three sub models, which were concerned with structural signal, scattering signal, and non-acoustic noise. Resonance in structural signal and echoes excited by defects (porosity and rich-resin) were studied. The results showed that: resonance would occur when there was sufficient bandwidth; when the CFRP laminate contained voids, the center frequency of the backscattered signal decreased; and the localized defects, including rich-resin and localized porosity, tended to generate apparent echoes where they located. A simplified backscattered signal model was subsequently put forward, showing certain potential in revealing time-frequency properties of backscattered signals. The newly proposed variational mode decomposition was used for defect modes extraction, successfully avoiding the mode mixing and false modes which easily exist in empirical mode decomposition. Subsequently, the generalized Stockwell transform was adopted for the defects localization. The simulation and experiment denoted the coincidence between the backscattered signal model and the experimental signal, and showed the effectiveness of variational mode decomposition and generalized Stockwell transform in localized defects detection.
Estimating Pavement Roughness by Fusing Color and Depth Data Obtained from an Inexpensive RGB-D Sensor
Authors: Mahmoudzadeh Ahmadreza , Golroo Amir , Jahanshahi Mohammad R. , Firoozi Yeganeh Sayna
Published: April 2019

Measuring pavement roughness and detecting pavement surface defects are two of the most important tasks in pavement management. While existing pavement roughness measurement approaches are expensive, the primary aim of this paper is to use a cost-effective and sufficiently accurate RGB-D sensor to estimate the pavement roughness in the outdoor environment. An algorithm is proposed to process the RGB-D data and autonomously quantify the road roughness. To this end, the RGB-D sensor is calibrated and primary data for estimating the pavement roughness are collected. The collected depth frames and RGB images are registered to create the 3D road surfaces. We found that there is a significant correlation between the estimated International Roughness Index (IRI) using the RGB-D sensor and the manual measured IRI using rod and level. By considering the Power Spectral Density (PSD) analysis and the repeatability of measurement, the results show that the proposed solution can accurately estimate the different pavement roughness.
A Flow Velocity Measurement Method Based on a PVDF Piezoelectric Sensor
Authors: Li Qi , Xing Junhua , Shang Dajing , Wang Yilin
Published: April 2019

To measure the flow velocity of a fluid without affecting its motion state, a method was proposed based on a polyvinylidene fluoride (PVDF) piezoelectric film sensor. A self-made PVDF piezoelectric sensor placed parallel with the flow direction was used to measure the flow velocity. First, the piezoelectric characteristics of PVDF were obtained theoretically. Next, the relationship between flow velocity and sound pressure was verified numerically. Finally, the relationship between flow velocity and the electrical output of the PVDF piezoelectric film was obtained experimentally. In conclusion, the proposed method was shown to be reliable and effective.
On the Image Sensor Processing for Lane Detection and Control in Vehicle Lane Keeping Systems
Authors: Kuo C.Y. , Lu Y.R. , Yang S.M.
Published: April 2019

Lane keeping systems for a keeping a vehicle in the desired lane is key to advanced driving assistance system in autonomous vehicles. This paper presents a cost-effective image sensor with efficient processing algorithm for lane detection and lane control applications to autonomous delivery systems. The algorithm includes (1) lane detection by inverse perspective mapping and random sample consensus parabola fitting and (2) lane control by pure pursuit steering controller and classical proportional integral speed controller based on a nonholonomic kinematic model. The image sensor experiments conducted on a 1/10 scale model car maneuvering in a straight–curve–straight lane validate the better processing performance before, during, and after the turning section over previous work. The image sensor with the processing algorithm achieves the average lane detection error within 5% and maximum cross-track error within 9% in real-time. The development shall pave the way to cost-effective autonomous delivery systems.
Delay-Tolerance-Based Mobile Data Offloading Using Deep Reinforcement Learning
Authors: Mochizuki Daisuke , Abiko Yu , Saito Takato , Ikeda Daizo , Mineno Hiroshi
Published: April 2019

The demand for mobile data communication has been increasing owing to the diversification of its purposes and the increase in the number of mobile devices accessing mobile networks. Users are experiencing a degradation in communication quality due to mobile network congestion. Therefore, improving the bandwidth utilization efficiency of cellular infrastructure is crucial. We previously proposed a mobile data offloading protocol (MDOP) for improving the bandwidth utilization efficiency. Although this method balances a load of evolved node B by taking into consideration the content delay tolerance, accurately balancing the load is challenging. In this paper, we apply deep reinforcement learning to MDOP to solve the temporal locality of a traffic. Moreover, we examine and evaluate the concrete processing while considering a delay tolerance. A comparison of the proposed method and bandwidth utilization efficiency of MDOP showed that the proposed method reduced the network traffic in excess of the control target value by 35% as compared with the MDOP. Furthermore, the proposed method improved the data transmission ratio by the delay tolerance range. Consequently, the proposed method improved the bandwidth utilization efficiency by learning how to provide the bandwidth to the user equipment when MDOP cannot be used to appropriately balance a load.
Comprehensive Investigation on Principle Component Large-Scale Wi-Fi Indoor Localization
Authors: Salamah Ahmed H. , Tamazin Mohamed , Sharkas Maha A. , Khedr Mohamed , Mahmoud Mohamed
Published: April 2019

The smartphone market is rapidly spreading, coupled with several services and applications. Some of these services require the knowledge of the exact location of their handsets. The Global Positioning System (GPS) suffers from accuracy deterioration and outages in indoor environments. The Wi-Fi Fingerprinting approach has been widely used in indoor positioning systems. In this paper, Principal Component Analysis (PCA) is utilized to improve the performance and to reduce the computation complexity of the Wi-Fi indoor localization systems based on a machine learning approach. The experimental setup and performance of the proposed method were tested in real indoor environments at a large-scale environment of 960 m2 to analyze the performance of different machine learning approaches. The results show that the performance of the proposed method outperforms conventional indoor localization techniques based on machine learning techniques.
Personalized Rehabilitation Recognition for Ubiquitous Healthcare Measurements
Authors: Kan Yao-Chiang , Kuo Yu-Chieh , Lin Hsueh-Chun
Published: April 2019

The physical therapeutic application needs personalized rehabilitation recognition (PRR) for ubiquitous healthcare measurements (UHMs). This study employed the adaptive neuro-fuzzy inference system (ANFIS) to generate a PRR model for a self-development system of UHM. The subjects wore a sensor-enabled wristband during physiotherapy exercises to measure the scheduled motions of their limbs. In the model, the sampling data collected from the scheduled motions are labeled by an arbitrary number within a defined range. The sample datasets are referred as the design of an initial fuzzy inference system (FIS) with data preprocessing, feature visualizing, fuzzification, and fuzzy logic rules. The ANFIS then processes data training to adjust the FIS for optimization. The trained FIS then can infer the motion labels via defuzzification to recognize the features in the test data. The average recognition rate was higher than 90% for the testing motions if the subject followed the sampling schedule. With model implementation, the middle section of motion datasets in each second is recommended for recognition in the UHM system which also includes a mobile App to retrieve the personalized FIS in order to trace the exercise. This approach contributes a PRR model with trackable diagrams for the physicians to explore the rehabilitation motions in details.
Achieving Neuroplasticity in Artificial Neural Networks through Smart Cities
Authors: Allam Zaheer
Published: April 2019

Through the Internet of things (IoT), as promoted by smart cities, there is an emergence of big data accentuating the use of artificial intelligence through various components of urban planning, management, and design. One such system is that of artificial neural networks (ANNs), a component of machine learning that boasts similitude with brain neurological networks and its functioning. However, the development of ANN was done in singular fashion, whereby processes are rendered in sequence in a unidimensional perspective, contrasting with the functions of the brain to which ANN boasts similitude, and in particular to the concept of neuroplasticity which encourages unique complex interactions in self-learning fashion, thereby encouraging more inclusive urban processes and render urban coherence. This paper takes inspiration from Christopher Alexander’s Nature of Order and dwells in the concept of complexity theory; it also proposes a theoretical model of how ANN can be rendered with the same plastic properties as brain neurological networks with multidimensional interactivity in the context of smart cities through the use of big data and its emerging complex networks. By doing so, this model caters to the creation of stronger, richer, and more complex patterns that support Alexander’s concept of “wholeness” through the connection of overlapping networks. This paper is aimed toward engineers with interdisciplinary interest looking at creating more complex and intricate ANN models, and toward urban planners and urban theorists working on the emerging contemporary concept of smart cities.
Comparative Analysis of Two Urban Microclimates: Energy Consumption and Greenhouse Gas Emissions
Authors: Santillán-Soto Néstor , García-Cueto O. , Lambert-Arista Alejandro , Ojeda-Benítez Sara , Cruz-Sotelo Samantha
Published: April 2019

This paper presents a hypothetical and comparative performance of a 5 ton air conditioner (AC) operating in two zones in different urban microclimates for 25 days. One site represents a type of homogeneous planned urbanism and the other is a traditional heterogeneous zone. Air temperature data was collected and then processed using a linear regression model included in the operating manual of the AC in order to obtain their energy consumption. Results indicate that for an area with 500 homes, a traditional urban complex requires 12,350 kWh of electrical energy more than a planned zone (1.89%). This extra energy amounts up to $1180 and adds 9191 kg of CO2 to the atmosphere. The increased energy consumption has implications that increase the cost and environmental aspects of two urban microclimates, so that urbanization without planning is less friendly to the environment. In this sense, this study highlights the effects of urban microclimates on domestic electricity consumption from air conditioning. In addition, for a city with an arid desert climate, the variation in electricity consumption is associated with changes in the urban mosaic. The results found represent scientific evidence that can be used as a reference to establish public policies that could be incorporated into the local construction regulations, oriented to reduce the energy consumption associated with the use of air conditioning equipment.
A Dominance-Based Network Method for Ranking Efficient Decision-Making Units in Data Envelopment Analysis
Authors: Lee Jiyoung , Choi Gyunghyun
Published: April 2019

Ranking of efficient decision-making units (DMUs) using data envelopment analysis (DEA) results is very important for various purposes. We propose a new comprehensive ranking method using network analysis for efficient DMUs to improve the discriminating power of DEA. This ranking method uses a measure, namely dominance value, which is a network centrality-based indicator. Thus far, existing methods exploiting DMU’s positional features use either the superiority, which considers the efficient DMUs’ relative position on the frontier compared to other DMUs, or the influence, which captures the importance of the DMUs’ role as benchmarking targets for inefficient DMUs. However, in this research, the dominance value is the compounded measure of both core positional features of DMUs. Moreover, a network representation technique has been used to ensure the performance of the dominance value compared to the superiority and influence. To demonstrate the proposed ranking method, we present two examples, research and development (R&D) efficiency of small and medium-sized enterprises (SMEs) and technical efficiency of plug-in hybrid electric vehicles (HEVs). Through these two examples, we can see how the known weaknesses and the unobserved points in the existing method differ in this new method. Hence, it is expected that the proposed method provides another new meaningful ranking result that can show different implications.
Consumer Needs for Hand-Touch Product Designs Based on the Experience Economy
Authors: Tu Jui-Che , Yang Cheng-Hsueh
Published: April 2019

Hand-touch products are products that emphasize tactile sensations. These products can generate sensory stimulation and create memorable experiences for consumers, thereby driving purchase decisions. The economic value of hand-touch products is undoubtedly associated with the experience economy. Consumers generally focus on how much experience value is created by the hand-touch product itself. This study adopted the experience economy perspective to investigate the development of hand-touch products. To gain further insight into consumers’ product needs and place the focus of product development on the most important aspects, this study primarily adopted a questionnaire survey and Kano’s two-dimensional model to classify quality factors. In addition, the relative majority method was used to classify quality factors into five major categories: Attractive quality, One-dimensional quality, Must-be quality, Indifferent quality, Reverse quality. The study results showed that: (1) Consumers valued the sense marketing of hand-touch products. (2) Timely use of other marketing approaches enables a product to effectively meet customers’ needs. (3) Use of Cross Analysis to Identify Top-Priority Factors. This study’s findings on the true needs of consumers for hand-touch products can enhance and improve product life cycle, thereby achieving the sustainable development of products.
Impact of Non-Agricultural Employment and Environmental Awareness on Farmers’ Willingness to Govern the Heavy Metal Pollution of Farmland: A Case Study of China
Authors: Lu Hua
Published: April 2019

Heavy metal pollution of farmland is a significant issue affecting the quality of agricultural products and human health. Farmers’ behaviors can have a direct impact on the level of heavy metal pollution affecting farmland in China. Whether the heavy metal pollution of farmland can be effectively governed at a low cost depends on the farmers. This paper analyzes the mechanism by which the extent of non-agricultural employment and environmental awareness influences farmers’ willingness to govern the heavy metal pollution of farmland using microdata for farmers in China and conducts an empirical analysis via a logit model. The results show that farmers in China display low willingness to govern the heavy metal pollution of farmland and that the increase in non-agricultural income will not significantly improve this willingness. Environmental awareness and farmers’ willingness to govern the heavy metal pollution of farmland are closely related: the higher the environmental awareness of farmers is, the stronger their willingness to govern heavy metal pollution, and the higher the probability of their participating in fallow land treatment. The government can introduce incentives to improve farmers’ environmental awareness of the heavy metal pollution of farmland. In addition, the government should strengthen publicity about the positive effects of fallow land treatment and encourage farmers to participate in the governance of heavy metal pollution of farmland. Given increasing non-agricultural employment opportunities and the transformation of agricultural production modes, agricultural technical training provided by governmental departments can enable them to be more scientific and rational in their agrochemical selection and application, thus reducing or avoiding the heavy metal pollution of farmland at the source. Attention should be paid to the differences between farmers to ultimately reduce the cost and improve the efficiency of treatment.
Belt and Road Initiative and Railway Sector Efficiency—Application of Networked Benchmarking Analysis
Authors: Li Weidong , Hilmola Olli-Pekka
Published: April 2019

In recent years, there has been a lot of attention paid to China’s Belt and Road Initiative (BRI), which aims to invest in better connecting China, South-East Asia, Central Asia, the Middle East and Europe. As countries that share the same continent, and are in many cases without proper sea connection (landlocked), the key mode of long-distance transportation is railways. However, numerous countries have different levels of past investments, labor productivity, transportation profile, and culture surrounding railways, and all of this leads to differences in overall efficiency. In this research, we apply well established and widely used data envelopment analysis (DEA) to evaluate the longitudinal efficiency of railway operations. This is the first time such an analysis is completed on the Belt and Road member countries. Efficiency itself hardly improved at all during the examination period, whether in passenger and freight or just freight transports. China itself represents an important benchmark for many countries, as its efficiency is all the time highest possible. In the network benchmarking analysis, it was shown that China, Estonia, Latvia, and Israel are often proposed benchmarks for the others to increase their efficiency in the future. From efficiency development perspective, Chinese railway sector is beneficial and more balanced to be benchmarked as compared to other significantly sized railway countries, like India or Russia.