bluuebunny/crossref_metadata_2025 huggingface dataset:
Measurement of a Temperature Field Generated by a Synthetic Jet Actuator using Digital Holographic Interferometry
Authors: Dancova Petra , Psota Pavel , Vit Tomas
Published: March 2019

This paper shows the possibility of the measurement of a temperature field generated by heated fluid from a synthetic jet (SJ) actuator. Digital holographic interferometry (DHI) was the main measuring method used for the experiments. A single-projection DHI was used for the visualization of the temperature field as an average temperature along the optical axis. The DHI results are compared with data obtained from constant current anemometry (CCA) experiments for the validation of the method. Principle of 3D temperature distribution using a tomographic approach is also described in this paper. A single SJ actuator, multiple continual nozzle, and the SJ actuator with two output orifices are used as a testing device for the presented experiments. The experimental configuration can measure high-frequency synthetic jets with the use of a single slow-frame-rate camera. Due to the periodic character of the SJ flow, synchronization between the digital camera, and the external trigger driving the phenomenon is performed. This approach can also distinguish between periodic and random parts of the flow.
Energy Harvesting Performance of Plate Wing from Discrete Gust Excitation
Authors: Cheng Yun , Li Daochun , Xiang Jinwu , Da Ronch Andrea
Published: March 2019

Energy harvesting from aeroelastic response tends to have a wide application prospect, especially for small-scale unmanned aerial vehicles. Gusts encountered in flight can be treated as a potential source for sustainable energy supply. The plate model is more likely to describe a low aspect ratio, thin plate wing structure. In this paper, the Von Kármán plate theory and 3D doublet lattice method, coupled with a piezoelectric equation, are used to build a linear state-space equation. Under the load of “one-minus-cosine” discrete gust, the effects of flow speed and gust amplitude, thickness of piezoelectric ceramic transducer (PZTs) layers, and mounted load resistance are investigated. Results reveal that the PZTs layers on the wing root of the leading edge can obtain the highest electrical parameters. The flow velocity, thickness of the PZTs layers and load resistance are used to optimize energy harvesting data.
Impact of Row Spacing, Sowing Density and Nitrogen Fertilization on Yield and Quality Traits of chia (Salvia Hispanica L.) Cultivated in southwestern Germany
Authors: Grimes Samantha J. , Phillips Timothy D. , Capezzone Filippo , Graeff-Hönninger Simone
Published: March 2019

To obtain high chia seed yields and seed qualities, a suitable crop management system needs to be developed for the given growing conditions in southwestern Germany. Field experiments were conducted at the experimental station Ihinger Hof in two consecutive years (2016, 2017). The study aimed to evaluate yield and quality traits of chia depending on different (i) row spacing (35, 50 and 75 cm), (ii) sowing densities (1, 1.5 and 2 kg ha−1) and, (iii) N-fertilization rates (0, 20 and 40 kg N ha−1). It consisted of three independent, completely randomized field experiments with three replications. Results showed that chia seed yields ranged from 618.39 to 1171.33 kg ha−1 and that a thousand seed mass of 1.14 to 1.24 g could be obtained. Crude protein-, crude oil- and mucilage contents varied from 18.11–23.91%, 32.16–33.78% and 10.00–13.74%, respectively. Results indicated that the year of cultivation and the accompanied environmental conditions, like precipitation or temperature, influenced the determined traits more than the applied agronomic practices. As average seed yields exceeded those obtained in the countries of origin (Mexico, Guatemala) while having comparable quality characteristics, chia holds great potential as an alternative crop for farmers in southwestern Germany.
Exogenous Foliar Application of Glycine Betaine to Alleviate Water Deficit Tolerance in Two Indica Rice Genotypes under Greenhouse Conditions
Authors: Tisarum Rujira , Theerawitaya Cattarin , Samphumphung Thapanee , Takabe Teruhiro , Cha-um Suriyan
Published: March 2019

The aim of this investigation was to enhance overall growth, yield attributes as well as physio-biochemical adaptive strategies by exogenous foliar application of glycine betaine (GlyBet) in two rice varieties against water deficit stress under greenhouse conditions. Rice crop cvs. RD43 (low amylose content) and SPR1 (high amylose content) grown in clay pots containing garden soil until booting stage were chosen as the test plant material, sprayed by 0 (control) or 100 mM GlyBet and subsequently subjected to: MWD (mild water deficit by 8 d water withholding; 24.80% SWC; Soil water content) or SWD (severe water deficit by 14 d water withholding; 13.63% SWC) or WW (well-watered conditions or control). Free proline content in cv. RD43 was rapidly increased in relation to the degree of water deficit and suppressed by exogenous GlyBet, while free proline in cv. SPR1 was lower than cv. RD43. Overall growth performances and yield traits in both cultivars under MWD were maintained by exogenous application of GlyBet; however, these parameters declined under SWD even after the GlyBet application. Degradation of photosynthetic pigments and chlorophyll fluorescence in pretreated GlyBet plants under SWD were prevented, resulting in elevated net photosynthetic rate (Pn). Interestingly, Pn was very sensitive parameter that sharply declined under SWD in both RD43 and SPR1 genotypes. Positive relationships between physio-morphological and biochemical changes in rice genotypes were demonstrated with high correlation co-efficiency. Based on the key results, it is concluded that foliar GlyBet application may play an important role in drought-tolerant enhancement in rice crops.
Physiological and Growth Response of Pepper (Capsicum annum L.) Seedlings to Supplementary Red/Blue Light Revealed through Transcriptomic Analysis
Authors: Tang Zhongqi , Yu Jihua , Xie Jianming , Lyu Jian , Feng Zhi , Dawuda Mohammed Mujitaba , Liao Weibiao , Wu Yue , Hu Linli
Published: March 2019

Red and blue light have great effects on physiological processes and growth of plants. In this experiment, we investigated the physiological and growth response of pepper (Capsicum annuum L.) to supplementary red:blue (4:1) light for 1 h (T1), 3 h (T2), and 5 h (T3), and the full-spectrum light-emitting diodes, LEDs, as control (CK). Thirty-day-old seedlings were grown under these treatments for 20 days in a climate-controlled room before data measurement. The results showed that the light treatments significantly (p < 0.05) affected the photosynthesis and growth indexes as well as gene expression in the pepper seedlings. Plants under T2 generally had better performance in terms of seedling growth. A total of 124, 1283, and 1091 differentially expressed genes were found in CK vs. T1, CK vs. T2, and CK vs. T3, respectively. Among the treatments, T2 in comparison with CK had 705 upregulated and 578 downregulated differentially expressed genes (DEGs). We also found that CPRF2, Paggis, HLIPS, GIGANTEA, LSH1, and FTSH genes were expressed differently under the various light treatments. Based on GeneOntology (GO) enrichment analysis, DEGs were significantly enriched on 15 GO terms of which xyloglucan:xyloglucosyl transferase activity and apoplastic, cellular polysaccharide metabolic, and cellular carbohydrate metabolic processes were closely related to light responses. A total of 96 genes that are related to plant–pathogen interaction, zeatin biosynthesis, plant hormone signal transduction, and wax/cutin/suberine biosynthesis which are involved in the pathway of light reaction in plants were significantly enriched in T2 plants compared with plants under CK. The application of red:blue light at 4:1 for 3 h improved the growth of pepper seedlings better than the other treatments and this can be tested under the Chinese Solar Greenhouse condition.
Parasitic Nematode and Protozoa Status of Working Sheepdogs on the North Island of New Zealand
Authors: O’Connell Adam , Scott Ian , Cogger Naomi , Jones Boyd R. , Hill Kate E.
Published: March 2019

Working farm dogs in New Zealand may have a high parasitic challenge because of access to raw meat and close contact with other dogs. This cross-sectional study aimed to estimate the percentage of dogs with gastrointestinal nematode and protozoan parasite lifecycle stages present in their feces and to identify factors associated with the presence of parasites. A single researcher collected information about the dogs and their management via a questionnaire, body condition scored (BCS) the dogs, and collected fecal samples to determine the parasite burden. Fecal samples were collected from 171 dogs and 40% (95% CI 33.0% to 47.7%) contained parasite ova or (oo)cysts. There was no association between BCS and the presence of nematodes and parasites (p = 0.74) in the feces. The percentage of dogs with parasites present in their feces was not associated with BCS or the frequency with which anthelmintic drugs were reportedly administered (p = 0.61). The high percentage of dogs with parasites are of concern for the health of the dogs and their owners, given the zoonotic potential of some parasites. Further, research should also focus on understanding why reporting giving anthelmintic drugs at least every three months did not eliminate the infection.
Paulownia Leaves as A New Feed Resource: Chemical Composition and Effects on Growth, Carcasses, Digestibility, Blood Biochemistry, and Intestinal Bacterial Populations of Growing Rabbits
Authors: Al-Sagheer Adham A. , Abd El-Hack Mohamed E. , Alagawany Mahmoud , Naiel Mohammed A. , Mahgoub Samir A. , Badr Mohamed M. , Hussein Elsayed O. S. , Alowaimer Abdullah N. , Swelum Ayman A.
Published: March 2019

This experiment was conducted to study the effects of paulownia leaf meal (PLM) as a nontraditional feed on the growth, carcasses, digestibility, blood chemistry, and intestinal microbiota of growing rabbits. Sixty rabbits (5-weeks old) were randomly allotted to three dietary treatments containing three amounts of PLM (0%, 15%, and 30%). The results showed that PLM has a higher content of ether extract, organic matter, methionine, tyrosine, histidine, manganese, and zinc than alfalfa hay. Body weight gain decreased when 30% PLM was provided. The best feed conversion ratio was recorded in the rabbits fed 15% PLM. A notable increase in high-density lipoprotein levels with a significant decrease in low-density lipoprotein was noted in the rabbits fed the PLM diets. Total fungi and Enterobacteriaceae and total bacterial count in the feed were significantly reduced because of PLM. In the cecum, coliforms, Enterobacteriaceae species, and total bacterial count declined in the rabbits fed the PLM diets. Conclusively, up to 15% PLM can be used in rabbit diets without any deleterious effects on the performance, nutrient digestibility, and blood constituents. In addition, dietary inclusion of PLM has the potential to reduce cecal pathogenic bacteria in rabbits.
A Breath of Fresh Air in the Fog of Antimicrobial Resistance: Inhaled Polymyxins for Gram-Negative Pneumonia
Authors: Biagi Mark , Butler David , Tan Xing , Qasmieh Samah , Wenzler Eric
Published: March 2019

Despite advancements in therapy, pneumonia remains the leading cause of death due to infectious diseases. Novel treatment strategies are desperately needed to optimize the antimicrobial therapy of patients suffering from this disease. One such strategy that has recently garnered significant attention is the use of inhaled antibiotics to rapidly achieve therapeutic concentrations directly at the site of infection. In particular, there is significant interest in the role of inhaled polymyxins for the treatment of nosocomial pneumonia, including ventilator-associated pneumonia, due to their retained activity against multi-drug resistant Gram-negative pathogens, including Acinetobacter baumannii and Pseudomonas aeruginosa. This review will provide a comprehensive overview of the pharmacokinetic/pharmacodynamic profile, clinical outcomes, safety, and potential role of inhaled polymyxins in clinical practice.
Polyphenols as Potential Attenuators of Heat Stress in Poultry Production
Authors: Hu Ruizhi , He Yujia , Arowolo Muhammed Adebayo , Wu Shusong , He Jianhua
Published: March 2019

Heat stress is a non-specific physiological response of the body when exposed to high ambient temperatures, which can break the balance of body redox and result in oxidative stress that affects growth performance as well as the health of poultry species. Polyphenols have attracted much attention in recent years due to their antioxidant ability and thus, can be an effective attenuator of heat stress. In this paper, the potential mechanisms underlying the inhibitory effect of polyphenols on heat stress in poultry has been reviewed to provide a reference and ideas for future studies related to polyphenols and poultry production.
Mechanism of Rapid Nuclear Factor-E2-Related Factor 2 (Nrf2) Activation via Membrane-Associated Estrogen Receptors: Roles of NADPH Oxidase 1, Neutral Sphingomyelinase 2 and Epidermal Growth Factor Receptor (EGFR)
Authors: Ishii Tetsuro , Warabi Eiji
Published: March 2019

Membrane-associated estrogen receptors (ER)-α36 and G protein-coupled estrogen receptor (GPER) play important roles in the estrogen’s rapid non-genomic actions including stimulation of cell proliferation. Estrogen via these receptors induces rapid activation of transcription factor nuclear factor-E2-related factor 2 (Nrf2), a master regulator of detoxification and antioxidant systems, playing a key role in the metabolic reprogramming to support cell proliferation. This review highlights the possible mechanism underlying rapid Nrf2 activation via membrane-associated estrogen receptors by estrogen and phytoestrogens. Stimulation of ER-α36-GPER signaling complex rapidly induces Src-mediated transactivation of epidermal growth factor receptor (EGFR) leading to a kinase-mediated signaling cascade. We propose a novel hypothesis that ER-α36-GPER signaling initially induces rapid and temporal activation of NADPH oxidase 1 to generate superoxide, which subsequently activates redox-sensitive neutral sphingomyelinase 2 generating the lipid signaling mediator ceramide. Generation of ceramide is required for Ras activation and ceramide-protein kinase C ζ-casein kinase 2 (CK2) signaling. Notably, CK2 enhances chaperone activity of the Cdc37-Hsp90 complex supporting activation of various signaling kinases including Src, Raf and Akt (protein kinase B). Activation of Nrf2 may be induced by cooperation of two signaling pathways, (i) Nrf2 stabilization by direct phosphorylation by CK2 and (ii) EGFR-Ras-PI 3 kinase (PI3K)-Akt axis which inhibits glycogen synthase kinase 3β leading to enhanced nuclear transport and stability of Nrf2.
Incorporation of Torsional & Higher-Mode Responses in Displacement-Based Seismic Design of Asymmetric RC Frame Buildings
Authors: Abebe Beka Hailu , Lee Jong Seh
Published: March 2019

Direct Displacement-Based Design (DDBD) is currently a widely used displacement-based seismic design method. DDBD accounts for the torsional response of Reinforced Concrete (RC) frame buildings by using semi-empirical equations formulated for wall-type buildings. Higher-mode responses are incorporated by using equations obtained from only a few parametric studies of regular planar frames. In this paper, there is an attempt to eliminate torsional responses by proportioning frames’ secant stiffnesses so that the centers of rigidity and supported mass (the mass on and above each story) coincide. Once the torsional rotations are significantly reduced and only translational motions are achieved, higher-mode responses are included using a technique developed by the authors in their recent paper. The efficiency of the proposed design procedure in fulfilling the intended performance objective is checked by two plan-asymmetric 20-story RC frame building cases. Case-I has the same-plan configuration while Case-II has a different-plan configuration along the height. Both cases have different bay widths in orthogonal directions. Verification of the case studies by Nonlinear Time History Analysis (NTHA) has shown that the proposed method results in designs that satisfy the performance objective with reasonable accuracy without redesigning members. It is believed that a step forward is undertaken toward rendering design verification by NTHA less necessary, thereby saving computational resources and effort.
Generalization of an Optical ASA Switch
Authors: Lin Bey-Chi
Published: March 2019

An arrayed waveguide grating (AWG) is a kind of passive wavelength router, and it is the most promising technology for developing large optical switches. However, AWGs have poor scalability, and using small AWGs to construct a large switch has been done in many prior works. A novel AWG-based switch called ASA (AWG, Space switching, AWG) that does not use wavelength converters has been proposed. It can expand the switch size from N to N2 by using N × N AWGs. In this paper, we generalize the ASA switch by using only N × N AWGs, N × N space switches, and N wavelengths such that the switch size is expanded to Nt for any positive integer t. Since each port of an N × N AWG can transmit up to N wavelengths simultaneously, the total capacity of the generalized ASA switch is extended to be close to Nt+1 × the bandwidth of a wavelength channel, provided that the inputs which are located in the same port position of each input AWG are destined to distinct outputs.
Experimental Investigation of Freezing and Melting Characteristics of Graphene-Based Phase Change Nanocomposite for Cold Thermal Energy Storage Applications
Authors: Sidney Shaji , Dhasan Mohan , C. Selvam , Harish Sivasankaran
Published: March 2019

In the present work, the freezing and melting characteristics of water seeded with chemically functionalized graphene nanoplatelets in a vertical cylindrical capsule were experimentally studied. The volume percentage of functionalized graphene nanoplatelets varied from 0.1% to 0.5% with an interval of 0.1%. The stability of the synthesized samples was measured using zeta potential analyzer. The thermal conductivity of the nanocomposite samples was experimentally measured using the transient hot wire method. A ~24% (maximum) increase in the thermal conductivity was observed for the 0.5% volume percentage in the liquid state, while a ~53% enhancement was observed in the solid state. The freezing and melting behavior of water dispersed with graphene nanoplatelets was assessed using a cylindrical stainless steel capsule in a constant temperature bath. The bath temperatures considered for studying the freezing characteristics were −6 °C and −10 °C, while to study the melting characteristics the bath temperature was set as 31 °C and 36 °C. The freezing and melting time decreased for all the test conditions when the volume percentage of GnP increased. The freezing rate was enhanced by ~43% and ~32% for the bath temperatures of −6 °C and −10 °C, respectively, at 0.5 vol % of graphene loading. The melting rate was enhanced by ~42% and ~63% for the bath temperatures of 31 °C and 36 °C, respectively, at 0.5 vol % of graphene loading.
Learning Deep CNN Denoiser Priors for Depth Image Inpainting
Authors: Li Zun , Wu Jin
Published: March 2019

Due to the rapid development of RGB-D sensors, increasing attention is being paid to depth image applications. Depth images play an important role in computer vision research. In this paper, we address the problem of inpainting for single depth images without corresponding color images as a guide. Within the framework of model-based optimization methods for depth image inpainting, the split Bregman iteration algorithm was used to transform depth image inpainting into the corresponding denoising subproblem. Then, we trained a set of efficient convolutional neural network (CNN) denoisers to solve this subproblem. Experimental results demonstrate the effectiveness of the proposed algorithm in comparison with three traditional methods in terms of visual quality and objective metrics.
Large-Area Ultraviolet Photodetectors Based on p-Type Multilayer MoS2 Enabled by Plasma Doping
Authors: Zhang Xiao-Mei , Tseng Sian-Hong , Lu Ming-Yen
Published: March 2019

Two-dimensional (2D) MoS2 has recently become of interest for applications in broad range photodetection due to their tunable bandgap. In order to develop 2D MoS2 photodetectors with ultrafast response and high responsivity, up-scalable techniques for realizing controlled p-type doping in MoS2 is necessary. In this paper, we demonstrate a p-type multilayer MoS2 photodetector with selective-area doping using CHF3 plasma treatment. Microscopic and spectroscopic characterization techniques, including atomic force microscopy (AFM) and X-ray photoelectron spectroscopy (XPS), are used to investigate the morphological and electrical modification of the p-type doped MoS2 surface after CHF3 plasma treatment. Back-gated p-type MoS2 field-effect transistors (FETs) are fabricated with an on/off current ratio in the order of 103 and a field-effect mobility of 65.2 cm2V−1s−1. They exhibit gate-modulated ultraviolet photodetection with a rapid response time of 37 ms. This study provides a promising approach for the development of mild plasma-doped MoS2 as a 2D material in post-silicon electronic and optoelectronic device applications.
Identification of Coal Geographical Origin Using Near Infrared Sensor Based on Broad Learning
Authors: Lei Meng , Rao Zhongyu , Li Ming , Yu Xinhui , Zou Liang
Published: March 2019

Geographical origin, an important indicator of the chemical composition and quality grading, is one essential factor that should be taken into account in evaluating coal quality. However, traditional coal origin identification methods based on chemistry experiments are not only time consuming and labour intensive, but also costly. Near-Infrared (NIR) spectroscopy is an effective and efficient way to measure the chemical compositions of samples and has demonstrated excellent performance in various fields of quantitative and qualitative research. In this study, we employ NIR spectroscopy to identify coal origin. Considering the fact that the NIR spectra of coal samples always contain a large amount of redundant information and the number of samples is small, the broad learning algorithm is utilized here as the modelling system to classify the coal geographical origin. In addition, the particle swarm optimization algorithm is introduced to improve the structure of the Broad Learning (BL) model. We compare the improved model with the other five multivariate classification methods on a dataset with 243 coal samples collected from five countries. The experimental results indicate that the improved BL model can achieve the highest overall accuracy of 97.05%. The results obtained in this study suggest that the NIR technique combined with machine learning methods has significant potential for further development of coal geographical origin identification systems.
A Cloud-Based Framework for Large-Scale Log Mining through Apache Spark and Elasticsearch
Authors: Li Yun , Jiang Yongyao , Gu Juan , Lu Mingyue , Yu Manzhu , Armstrong Edward M. , Huang Thomas , Moroni David , McGibbney Lewis J. , Frank Greguska , Yang Chaowei
Published: March 2019

The volume, variety, and velocity of different data, e.g., simulation data, observation data, and social media data, are growing ever faster, posing grand challenges for data discovery. An increasing trend in data discovery is to mine hidden relationships among users and metadata from the web usage logs to support the data discovery process. Web usage log mining is the process of reconstructing sessions from raw logs and finding interesting patterns or implicit linkages. The mining results play an important role in improving quality of search-related components, e.g., ranking, query suggestion, and recommendation. While researches were done in the data discovery domain, collecting and analyzing logs efficiently remains a challenge because (1) the volume of web usage logs continues to grow as long as users access the data; (2) the dynamic volume of logs requires on-demand computing resources for mining tasks; (3) the mining process is compute-intensive and time-intensive. To speed up the mining process, we propose a cloud-based log-mining framework using Apache Spark and Elasticsearch. In addition, a data partition paradigm, logPartitioner, is designed to solve the data imbalance problem in data parallelism. As a proof of concept, oceanographic data search and access logs are chosen to validate performance of the proposed parallel log-mining framework.
A Quantitative Investigation on the Peripheral Nerve Response within the Small Strain Range
Authors: Giannessi Elisabetta , Stornelli Maria Rita , Coli Alessandra , Sergi Pier Nicola
Published: March 2019

Peripheral nerves are very complex biological structures crucial to linking the central nervous system to the periphery of the body. However, their real behaviour is partially unknown because of the intrinsic difficulty of studying these structures in vivo. As a consequence, theoretical and computational tools together with in vitro experiments are widely used to approximate the mechanical response of the peripheral nervous tissue to different kind of solicitations. More specifically, particular conditions narrow the mechanical response of peripheral nerves within the small strain regime. Therefore, in this work, the mechanical response of nerves was investigated through the study of the relationships among strain, stress and displacements within the small strain range. Theoretical predictions were quantitatively compared to experimental evidences, while the displacement field was studied for different values of the tissue compressibility. This framework provided a straightforward computational assessment of the nerve response, which was needed to design suitable connections to biomaterials or neural interfaces within the small strain range.
Loop Closure Detection Based on Multi-Scale Deep Feature Fusion
Authors: Chen Baifan , Yuan Dian , Liu Chunfa , Wu Qian
Published: March 2019

Loop closure detection plays a very important role in the mobile robot navigation field. It is useful in achieving accurate navigation in complex environments and reducing the cumulative error of the robot’s pose estimation. The current mainstream methods are based on the visual bag of word model, but traditional image features are sensitive to illumination changes. This paper proposes a loop closure detection algorithm based on multi-scale deep feature fusion, which uses a Convolutional Neural Network (CNN) to extract more advanced and more abstract features. In order to deal with the different sizes of input images and enrich receptive fields of the feature extractor, this paper uses the spatial pyramid pooling (SPP) of multi-scale to fuse the features. In addition, considering the different contributions of each feature to loop closure detection, the paper defines the distinguishability weight of features and uses it in similarity measurement. It reduces the probability of false positives in loop closure detection. The experimental results show that the loop closure detection algorithm based on multi-scale deep feature fusion has higher precision and recall rates and is more robust to illumination changes than the mainstream methods.
Lessons Learned on Geosynthetics Applications in Road Structures in Silesia Mining Region in Poland
Authors: Kawalec Jacek , Grygierek Marcin , Koda Eugeniusz , Osiński Piotr
Published: March 2019

The paper aims at the research of pioneering applications of geosynthetic materials used for improvement of anthropogenic material for road contraction challenges in Poland. The presented case study concerns a road embankment construction process within an area of underground mining coal extraction for which significant deformations have been frequently recorded. To improve the bearing capacity of the structure base, the geosynthetic materials were used. The question; however, was how the anthropogenic materials, filling the embankment, will interact with each other over time. The assessment of the structural condition of the motorway surface was performed using the falling weight deflectometer and the calculated modules based on the back-analysis method. They confirmed the effectiveness of the geosynthetics used in the study. They also revealed that the mining exploitation, with simultaneous use of aggregate stabilization with geogrids, did not cause significant changes in the stiffness of the pavement layers. All observations, based on both field and laboratory tests, did not show any negative impact of anthropogenic soils on the structural behavior of geogrids.
Time-Domain Near-Infrared Spectroscopy and Imaging: A Review
Authors: Yamada Yukio , Suzuki Hiroaki , Yamashita Yutaka
Published: March 2019

This article reviews the past and current statuses of time-domain near-infrared spectroscopy (TD-NIRS) and imaging. Although time-domain technology is not yet widely employed due to its drawbacks of being cumbersome, bulky, and very expensive compared to commercial continuous wave (CW) and frequency-domain (FD) fNIRS systems, TD-NIRS has great advantages over CW and FD systems because time-resolved data measured by TD systems contain the richest information about optical properties inside measured objects. This article focuses on reviewing the theoretical background, advanced theories and methods, instruments, and studies on clinical applications for TD-NIRS including some clinical studies which used TD-NIRS systems. Major events in the development of TD-NIRS and imaging are identified and summarized in chronological tables and figures. Finally, prospects for TD-NIRS in the near future are briefly described.
Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
Authors: Li Yundong , Hu Wei , Dong Han , Zhang Xueyan
Published: March 2019

Using aerial cameras, satellite remote sensing or unmanned aerial vehicles (UAV) equipped with cameras can facilitate search and rescue tasks after disasters. The traditional manual interpretation of huge aerial images is inefficient and could be replaced by machine learning-based methods combined with image processing techniques. Given the development of machine learning, researchers find that convolutional neural networks can effectively extract features from images. Some target detection methods based on deep learning, such as the single-shot multibox detector (SSD) algorithm, can achieve better results than traditional methods. However, the impressive performance of machine learning-based methods results from the numerous labeled samples. Given the complexity of post-disaster scenarios, obtaining many samples in the aftermath of disasters is difficult. To address this issue, a damaged building assessment method using SSD with pretraining and data augmentation is proposed in the current study and highlights the following aspects. (1) Objects can be detected and classified into undamaged buildings, damaged buildings, and ruins. (2) A convolution auto-encoder (CAE) that consists of VGG16 is constructed and trained using unlabeled post-disaster images. As a transfer learning strategy, the weights of the SSD model are initialized using the weights of the CAE counterpart. (3) Data augmentation strategies, such as image mirroring, rotation, Gaussian blur, and Gaussian noise processing, are utilized to augment the training data set. As a case study, aerial images of Hurricane Sandy in 2012 were maximized to validate the proposed method’s effectiveness. Experiments show that the pretraining strategy can improve of 10% in terms of overall accuracy compared with the SSD trained from scratch. These experiments also demonstrate that using data augmentation strategies can improve mAP and mF1 by 72% and 20%, respectively. Finally, the experiment is further verified by another dataset of Hurricane Irma, and it is concluded that the paper method is feasible.
Research and Development of Automatic Monitoring System for Livestock Farms
Authors: Chen Chin-Shan , Chen Wei-Cheng
Published: March 2019

In this study we have developed an automatic monitoring system based on wireless communication networks in both dairy and pig farms to replace traditional manual data collection of the environmental conditions and manual controls of fans and water control valves in livestock farms to solve the man-power shortage problem for livestock farming. Firstly, sensors for detecting temperature, humidity, illumination, wind speed and the control circuit and communication system were installed. The monitoring programs were subsequently designed to transmit the data back to the user interface display of the office through RFU-400 wireless communication modules, and the data collected from the farm environment have been stored in a database for data analysis. Finally, the fans and water spray valves have been automatically activated duly to improve the temperature and humidity of the livestock farms. We analyzed the data collected from the sensors with regard to the lactation yields for dairy cows, and suggested optimized environmental parameters for dairy cows to increase their appetite and lactation yield, or increase the feed conversion rate of the pigs. We expect the process and results of this study can result in helpful reference to livestock farming, and help to achieve the best economic benefits in raising cattle, pigs and so forth.
Optimal Design of a Residential Photovoltaic Renewable System in South Korea
Authors: Shin Hyunkyung , Geem Zong Woo
Published: March 2019

An optimal design model for residential photovoltaic (PV) systems in South Korea was proposed. In the optimization formulation, the objective function is composed of three costs, including the monthly electricity bill, the PV system construction cost (including the government’s subsidy), and the PV system maintenance cost. Here, because the monthly electricity bill is not differentiable (it is a stepped piecewise linear function), it cannot be solved by using traditional gradient-based approaches. For details considering the residential electric consumption in a typical Korean household, consumption was broken down into four types (year-round electric appliances, seasonal electric appliances, lighting appliances, and stand-by power). For details considering the degree of PV generation, a monthly generation dataset with different PV tilt angles was analyzed. The optimal design model was able to obtain a global design solution (PV tilt angle and PV size) without being trapped in local optima. We hope that this kind of practical approach will be more frequently applied to real-world designs in residential PV systems in South Korea and other countries.
Estimating Human Body Dimensions Using RBF Artificial Neural Networks Technology and Its Application in Activewear Pattern Making
Authors: Wang Zhujun , Wang Jianping , Xing Yingmei , Yang Yalan , Liu Kaixuan
Published: March 2019

Nowadays, the popularity of the internet has continuously increased. Predicting human body dimensions intelligently would be beneficial to improve the precision and efficiency of pattern making for enterprises in the apparel industry. In this study, a new predictive model for estimating body dimensions related to garment pattern making is put forward based on radial basis function (RBF) artificial neural networks (ANNs). The model presented in this study was trained and tested using the anthropometric data of 200 adult males between the ages 20 and 48. The detailed body dimensions related to pattern making could be obtained by inputting four easy-to-measure key dimensions into the RBF ANN model. From the simulation results, when spreading parameter σ and momentum factor α were set to 0.012 and 1, the three-layer model with 4, 72, and 8 neurons in the input, hidden, and output layers, respectively, showed maximum accuracy, after being trained by a dataset with 180 samples. Moreover, compared with a classic linear regression model and the back propagation (BP) ANN model according to mean squared error, the predictive performance of the RBF ANN model put forward in this study was better than the other two models. Therefore, it is feasible for the presented predictive model to design garment patterns, especially for tight-fitting garment patterns like activewear. The estimating accuracy of the proposed model would be further improved if trained by more appropriate datasets in the future.
A Block Iteration with Parallelization Method for the Greedy Selection in Radial Basis Functions Based Mesh Deformation
Authors: Zhao Ran , Li Chao , Guo Xiaowei , Fan Sijiang , Wang Yi , Yang Canqun
Published: March 2019

Greedy algorithm is one of the important point selection methods in the radial basis function based mesh deformation. However, in large-scale mesh, the conventional greedy selection will generate expensive time consumption and result in performance penalties. To accelerate the computational procedure of the point selection, a block iteration with parallelization method is proposed in this paper. By the block iteration method, the computational complexities of three steps in the greedy selection are all reduced from O ( n 3 ) to O ( n 2 ) . In addition, the parallelization of two steps in the greedy selection separates boundary points into sub-cores, efficiently accelerating the procedure. Specifically, three typical models of three-dimensional undulating fish, ONERA M6 wing and three-dimensional Super-cavitating Hydrofoil are taken as the test cases to validate the proposed method and the results show that it improves 17.41 times performance compared to the conventional method.
Optical Properties of Buffers and Cell Culture Media for Optofluidic and Sensing Applications
Authors: Hoang Van Thuy , Stępniewski Grzegorz , Czarnecka Karolina H. , Kasztelanic Rafał , Long Van Cao , Xuan Khoa Dinh , Shao Liyang , Śmietana Mateusz , Buczyński Ryszard
Published: March 2019

Interactions between light and various cells in cultures, such as bacteria or mammalian cells, are widely applied for optical sensors and optofluidic systems. These microorganisms need to be kept in proper aqueous media, referred to as buffers or cell culture media, that are required, respectively, for stable storage or delivering biochemical nutrients for their growth. When experiments or numerical analyses on optical devices are performed, the properties of these media are usually considered to be similar to those of pure water, with negligible influence of biochemical compounds on the medium’s optical properties. In this work, we investigated the transmission, material dispersion, and scattering properties of selected and widely used buffers and cell culture media. We show that the optical properties of these media may significantly vary from those of water. Well-defined properties of buffers and cell culture media are essential for proper design of various optical sensing or future optofluidic systems dealing with biological structures.
Impedance Measurement and Detection Frequency Bandwidth, a Valid Island Detection Proposal for Voltage Controlled Inverters
Authors: Llonch-Masachs Marc , Heredero-Peris Daniel , Chillón-Antón Cristian , Montesinos-Miracle Daniel , Villafáfila-Robles Roberto
Published: March 2019

Anti-islanding detection methods have been part of a secure operation for distributed energy resource inverters, avoiding the creation of non-intentional energization when the mains are lost. These detection mechanisms were conceived historically for current-controlled inverters. New control possibilities have broken ground, and current- or voltage-controlled inverters are a reality; however, special attention must be paid to detection strategies when applied to the latter ones. This paper addresses two topics: it exposes the lack of effectiveness of those detection algorithms based on the voltage/frequency displacement concept under voltage-controlled inverters and evaluates the applicability limits of the others based on the impedance measurement (IM). The IM is presented as a valid mechanism to achieve the islanding detection, but the exploration of its limits drives the concept of detection frequency bandwidth (DFBW), introduced in this paper. The DFBW is suggested as a practical approach to select the proper injection frequency to measure. Therefore, an improved strategy based on the IM and DFBW is proposed to allow achieving the detection towards (non-)resonant loads considering low computational burden. The results were experimentally validated in a 90-kVA four-wire voltage-controlled inverter, offering detection times of less than 100 ms in any case.
Voltage/Frequency Deviations Control via Distributed Battery Energy Storage System Considering State of Charge
Authors: Hua Yongzhu , Shentu Xiangrong , Xie Qiangqiang , Ding Yi
Published: March 2019

In recent years, the installation of distributed generation (DG) of renewable energies has grown rapidly. When the penetration of grid-integrated DGs are getting high, the voltage and frequency of the power system may cause deviation. We propose an algorithm that reduces voltage and frequency deviation by coordinating the control of multiple battery energy storage systems (BESSs). The proposed algorithm reduces the total number of charging and discharging times by calculating the sensitivity coefficient of BESS at different nodes and then selecting the appropriate BESSs to operate. The algorithm is validated on a typical distribution testing system. The results show that the voltage and frequency are controlled within the permissible range, the state of charge of BESSs are controlled within the normal range, and the total number of charging and discharging cycles of BESSs are reduced.
Effect of Pyrolysis Temperature on Biochar Microstructural Evolution, Physicochemical Characteristics, and Its Influence on Biochar/Polypropylene Composites
Authors: Elnour Ahmed Y. , Alghyamah Abdulaziz A. , Shaikh Hamid M. , Poulose Anesh M. , Al-Zahrani Saeed M. , Anis Arfat , Al-Wabel Mohammad I.
Published: March 2019

Environmental management through effective utilization of biowastes has been a topic of intensive research in recent years. This study examines the effect of pyrolysis temperature on the physical and morphological characteristic of biochar (BC) derived from lignocellulosic wastes. The biochar was prepared by pyrolysing date palm biomass at various temperatures, i.e., 300, 400, 500, 600, and 700 °C. These pyrolysed biochars were then characterized for their carbon content, mineral compositions, chemical functionalities, and morphological structures, for understanding their physicochemical characteristics and microstructural evolution. It was revealed that the pyrolytic condition plays a key role in the formation of biochar microstructure. These biochar samples were then utilized without any further treatments/purifications for their practical application as reinforcement materials for polymer composites. They were blended with a polypropylene matrix by a melt mixing technique followed by injection molding process. The type of biochar was found to significantly affect the composites properties. Differences in microstructure, surface chemistry, and chemical compositions of BCs were observed to be determining factors affecting the compatibility and thermomechanical properties of resulted composites.
An Adaptive Neuro-Fuzzy Propagation Model for LoRaWAN
Authors: Hosseinzadeh Salaheddin , Larijani Hadi , Curtis Krystyna , Wixted Andrew
Published: March 2019

This article proposes an adaptive-network-based fuzzy inference system (ANFIS) model for accurate estimation of signal propagation using LoRaWAN. By using ANFIS, the basic knowledge of propagation is embedded into the proposed model. This reduces the training complexity of artificial neural network (ANN)-based models. Therefore, the size of the training dataset is reduced by 70% compared to an ANN model. The proposed model consists of an efficient clustering method to identify the optimum number of the fuzzy nodes to avoid overfitting, and a hybrid training algorithm to train and optimize the ANFIS parameters. Finally, the proposed model is benchmarked with extensive practical data, where superior accuracy is achieved compared to deterministic models, and better generalization is attained compared to ANN models. The proposed model outperforms the nondeterministic models in terms of accuracy, has the flexibility to account for new modeling parameters, is easier to use as it does not require a model for propagation environment, is resistant to data collection inaccuracies and uncertain environmental information, has excellent generalization capability, and features a knowledge-based implementation that alleviates the training process. This work will facilitate network planning and propagation prediction in complex scenarios.
Towards Hyper-Dimensional Variography Using the Product-Sum Covariance Model
Authors: Tadić Jovan M. , Williams Ian N. , Tadić Vojin M. , Biraud Sébastien C.
Published: March 2019

Modeling hyper-dimensional spatial variability is a complex task from both practical and theoretical standpoints. In this paper we develop a method for modeling hyper-dimensional covariance (variogram) structures using the product-sum covariance model initially developed to model spatio-temporal variability. We show that the product-sum model can be used recursively up to an arbitrarily large number of dimensions while preserving relative modeling simplicity and yielding valid covariance models. The method can be used to model variability in anisotropic conditions with multiple axes of anisotropy or when temporal evolution is involved, and thus is applicable to “full anisotropic 3D+time” situations often encountered in environmental sciences. It requires fewer assumptions than the traditional product-sum modeling approach. The new method also presents an alternative to classical approaches to modeling zonal anisotropy and requires fewer parameters to be estimated from data. We present an example by applying the method in conjunction with ordinary kriging to map photosynthetically-active radiation (PAR) for 2006, in Oklahoma, CA, USA and to explore effects of spatio-temporal variability in PAR on gross primary productivity (GPP).
Operator Ordering and Solution of Pseudo-Evolutionary Equations
Authors: Behr Nicolas , Dattoli Giuseppe , Lattanzi Ambra
Published: March 2019

The solution of pseudo initial value differential equations, either ordinary or partial (including those of fractional nature), requires the development of adequate analytical methods, complementing those well established in the ordinary differential equation setting. A combination of techniques, involving procedures of umbral and of operational nature, has been demonstrated to be a very promising tool in order to approach within a unifying context non-canonical evolution problems. This article covers the extension of this approach to the solution of pseudo-evolutionary equations. We will comment on the explicit formulation of the necessary techniques, which are based on certain time- and operator ordering tools. We will in particular demonstrate how Volterra-Neumann expansions, Feynman-Dyson series and other popular tools can be profitably extended to obtain solutions for fractional differential equations. We apply the method to several examples, in which fractional calculus and a certain umbral image calculus play a role of central importance.