bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effects of Caffeine on Metabolomic Responses to Muscle Contraction in Rat Skeletal Muscle
Authors: Tsuda Satoshi , Hayashi Tatsuya , Egawa Tatsuro
Published: August 2019

Exercise has beneficial effects on our health by stimulating metabolic activation of skeletal muscle contraction. Caffeine is a powerful metabolic stimulant in the skeletal muscle that has ergogenic effects, including enhanced muscle power output and endurance capacity. In the present study, we aim to characterize the metabolic signatures of contracting muscles with or without caffeine stimulation using liquid chromatography-mass spectrometry and capillary electrophoresis coupled to mass spectrometry. Isolated rat epitrochlearis muscle was incubated in the presence or absence or of 3 mM caffeine for 30 min. Electrical stimulation (ES) was used to induce tetanic contractions during the final 10 min of incubation. Principal component analysis and hierarchical clustering analysis detected 184 distinct metabolites across three experimental groups—basal, ES, and ES with caffeine (ES + C). Significance Analysis of Microarray identified a total of 50 metabolites with significant changes in expression, and 23 metabolites significantly changed between the ES and ES + C groups. Changes were observed in metabolite levels of various metabolic pathways, including the pentose phosphate, nucleotide synthesis, β-oxidation, tricarboxylic acid cycle, and amino acid metabolism. In particular, D-ribose 5-phosphate, IMP, O-acetylcarnitine, butyrylcarnitine, L-leucine, L-valine, and L-aspartate levels were higher in the ES + C group than in the ES group. These metabolic alterations induced by caffeine suggest that caffeine accelerates contraction-induced metabolic activations, thereby contributing to muscle endurance performance and exercise benefits to our health.
Choline: Exploring the Growing Science on Its Benefits for Moms and Babies
Authors: Korsmo Hunter W. , Jiang Xinyin , Caudill Marie A.
Published: August 2019

The importance of ensuring adequate choline intakes during pregnancy is increasingly recognized. Choline is critical for a number of physiological processes during the prenatal period with roles in membrane biosynthesis and tissue expansion, neurotransmission and brain development, and methyl group donation and gene expression. Studies in animals and humans have shown that supplementing the maternal diet with additional choline improves several pregnancy outcomes and protects against certain neural and metabolic insults. Most pregnant women in the U.S. are not achieving choline intake recommendations of 450 mg/day and would likely benefit from boosting their choline intakes through dietary and/or supplemental approaches.
Diet in Irritable Bowel Syndrome (IBS): Interaction with Gut Microbiota and Gut Hormones
Authors: El-Salhy Magdy , Hatlebakk Jan Gunnar , Hausken Trygve
Published: August 2019

Diet plays an important role not only in the pathophysiology of irritable bowel syndrome (IBS), but also as a tool that improves symptoms and quality of life. The effects of diet seem to be a result of an interaction with the gut bacteria and the gut endocrine cells. The density of gut endocrine cells is low in IBS patients, and it is believed that this abnormality is the direct cause of the symptoms seen in IBS patients. The low density of gut endocrine cells is probably caused by a low number of stem cells and low differentiation progeny toward endocrine cells. A low fermentable oligo-, di-, monosaccharide, and polyol (FODMAP) diet and fecal microbiota transplantation (FMT) restore the gut endocrine cells to the level of healthy subjects. It has been suggested that our diet acts as a prebiotic that favors the growth of a certain types of bacteria. Diet also acts as a substrate for gut bacteria fermentation, which results in several by-products. These by-products might act on the stem cells in such a way that the gut stem cells decrease, and consequently, endocrine cell numbers decrease. Changing to a low-FODMAP diet or changing the gut bacteria through FMT improves IBS symptoms and restores the density of endocrine cells.
Financial Incentive Required for Pharmacy Students to Accept a Post-Graduation Position in Rural and Undesirable Pharmacy Settings
Authors: Ulrich Erin , Hurdelbrink Jonathan , Perepelkin Jason , Welter Kelli
Published: August 2019

Background: It has been estimated that in 2018, 20% of pharmacy students were unemployed following graduation. However, many pharmacy positions go vacant each year, with the majority of these positions existing in rural areas. Methods: Pharmacy students completed a one-time, anonymous, online questionnaire. Measures of interest included: subject characteristics and preference in a variety job offers. Discrete Choice Experiment methodology of questionnaire design was used and Conditional Logit models were conducted to analyze the data to determine the financial incentive required for pharmacy students to take a post-graduate job with particular traits. Conclusions: A total of 283 students completed questionnaires from Iowa, North Dakota, South Dakota, Saskatchewan, and Manitoba. The majority of subjects were female, P3 students, and from a non-rural hometown. American students would need to be paid an additional $18,738 in salary to practice in a rural area, while Canadian students would require an additional $17,156. Canadian respondents would require an additional $7125 in salary to work in a community pharmacy with a low level of patient interaction compared to a community position with a large amount of patient interaction. Overall, pharmacy student preferences in post-graduation job attributes vary significantly between states and provinces.
Effect of Thermospermine on the Growth and Expression of Polyamine-Related Genes in Rice Seedlings
Authors: Miyamoto Minaho , Shimao Satoshi , Tong Wurina , Motose Hiroyasu , Takahashi Taku
Published: August 2019

A mutant defective in the biosynthesis of thermospermine, acaulis5 (acl5), shows a dwarf phenotype with excess xylem vessels in Arabidopsis thaliana. Exogenous supply of thermospermine remarkably represses xylem differentiation in the root of seedlings, indicating the role of thermospermine in proper repression of xylem differentiation. However, the effect of thermospermine has rarely been investigated in other plant species. In this paper, we examined its effect on the growth and gene expression in rice seedlings. When grown with thermospermine, rice seedlings had no clearly enlarged metaxylem vessels in the root. Expression of OsACL5 was reduced in response to thermospermine, suggesting a negative feedback control of thermospermine biosynthesis like in Arabidopsis. Unlike Arabidopsis, however, rice showed up-regulation of phloem-expressed genes, OsHB5 and OsYSL16, by one-day treatment with thermospermine. Furthermore, expression of OsPAO2 and OsPAO6, encoding extracellular polyamine oxidase whose orthologs are not present in Arabidopsis, was induced by both thermospermine and spermine. These results suggest that thermospermine affects the expression of a subset of genes in rice different from those affected in Arabidopsis.
Leaf Anatomy, Morphology and Photosynthesis of Three Tundra Shrubs after 7-Year Experimental Warming on Changbai Mountain
Authors: Zhou Yumei , Deng Jifeng , Tai Zhijuan , Jiang Lifen , Han Jianqiu , Meng Gelei , Li Mai-He
Published: August 2019

Tundra is one of the most sensitive biomes to climate warming. Understanding plant eco-physiological responses to warming is critical because these traits can give feedback on the effects of climate-warming on tundra ecosystem. We used open-top chambers following the criteria of the International Tundra Experiment to passively warm air and soil temperatures year round in alpine tundra. Leaf size, photosynthesis and anatomy of three dominant species were investigated during the growing seasons after 7 years of continuous warming. Warming increased the maximal light-saturated photosynthetic rate (Pmax) by 43.6% for Dryas. octopetala var. asiatica and by 26.7% for Rhododendron confertissimum across the whole growing season, while warming did not significantly affect the Pmax of V. uliginosum. The leaf size of Dr. octopetala var. asiatica and Rh. confertissimum was increased by warming. No marked effects of warming on anatomical traits of Dr. octopetala var. asiatica were observed. Warming decreased the leaf thickness of Rh. confertissimum and Vaccinium uliginosum. This study highlights the species-specific responses to climate warming. Our results imply that Dr. octopetala var. asiatica could be more dominant because it, mainly in terms of leaf photosynthetic capacity and size, seems to have advantages over the other two species in a warming world.
Temperature-Controlled Solvent Vapor Annealing of Thin Block Copolymer Films
Authors: Cheng Xiao , Böker Alexander , Tsarkova Larisa
Published: August 2019

Solvent vapor annealing is as an effective and versatile alternative to thermal annealing to equilibrate and control the assembly of polymer chains in thin films. Here, we present scientific and practical aspects of the solvent vapor annealing method, including the discussion of such factors as non-equilibrium conformational states and chain dynamics in thin films in the presence of solvent. Homopolymer and block copolymer films have been used in model studies to evaluate the robustness and the reproducibility of the solvent vapor processing, as well as to assess polymer-solvent interactions under confinement. Advantages of utilizing a well-controlled solvent vapor environment, including practically interesting regimes of weakly saturated vapor leading to poorly swollen states, are discussed. Special focus is given to dual temperature control over the set-up instrumentation and to the potential of solvo-thermal annealing. The evaluated insights into annealing dynamics derived from the studies on block copolymer films can be applied to improve the processing of thin films of crystalline and conjugated polymers as well as polymer composite in confined geometries.
Manufacturing and Characterization of Functionalized Aliphatic Polyester from Poly(lactic acid) with Halloysite Nanotubes
Authors: Montava-Jorda Sergi , Chacon Victor , Lascano Diego , Sanchez-Nacher Lourdes , Montanes Nestor
Published: August 2019

This work reports the potential of poly(lactic acid)—PLA composites with different halloysite nanotube (HNTs) loading (3, 6 and 9 wt%) for further uses in advanced applications as HNTs could be used as carriers for active compounds for medicine, packaging and other sectors. This work focuses on the effect of HNTs on mechanical, thermal, thermomechanical and degradation of PLA composites with HNTs. These composites can be manufactured by conventional extrusion-compounding followed by injection molding. The obtained results indicate a slight decrease in tensile and flexural strength as well as in elongation at break, both properties related to material cohesion. On the contrary, the stiffness increases with the HNTs content. The tensile strength and modulus change from 64.6 MPa/2.1 GPa (neat PLA) to 57.7/2.3 GPa MPa for the composite with 9 wt% HNTs. The elongation at break decreases from 6.1% (neat PLA) down to a half for composites with 9 wt% HNTs. Regarding flexural properties, the flexural strength and modulus change from 116.1 MPa and 3.6 GPa respectively for neat PLA to values of 107.6 MPa and 3.9 GPa for the composite with 9 wt% HNTs. HNTs do not affect the glass transition temperature with invariable values of about 64 °C, or the melt peak temperature, while they move the cold crystallization process towards lower values, from 112.4 °C for neat PLA down to 105.4 °C for the composite containing 9 wt% HNTs. The water uptake has been assessed to study the influence of HNTs on the water saturation. HNTs contribute to increased hydrophilicity with a change in the asymptotic water uptake from 0.95% (neat PLA) up to 1.67% (PLA with 9 wt % HNTs) and the effect of HNTs on disintegration in controlled compost soil has been carried out to see the influence of HNTs on this process, which is a slight delay on it. These PLA-HNT composites show good balanced properties and could represent an interesting solution to develop active materials.
Principal Component Analysis of Blast Furnace Drainage Patterns
Authors: Roche Mauricio , Helle Mikko , Saxén Henrik
Published: August 2019

Monitoring and control of the blast furnace hearth is critical to achieve the required production levels and adequate process operation, as well as to extend the campaign length. Because of the complexity of the draining, the outflows of iron and slag may progress in different ways during tapping in large blast furnaces. To categorize the hearth draining behavior, principal component analysis (PCA) was applied to two extensive sets of process data from an operating blast furnace with three tapholes in order to develop an interpretation of the outflow patterns. Representing the complex outflow patterns in low dimensions made it possible to study and illustrate the time evolution of the drainage, as well as to detect similarities and differences in the performance of the tapholes. The model was used to explain the observations of other variables and factors that are known to be affected by, or affect, the state of the hearth, such as stoppages, liquid levels, and tap duration.
Understanding the Lack of Reactivity of 2,4-Dihydroxybenzaldehyde Towards the Biginelli Adduct Using Density Functional Theory Molecular Modeling
Authors: Flores-Morales Virginia , Ayala-Medrano Eduardo D. , García-Elías José , Martínez-Fierro Margarita L. , Marquez Edgar , Mora José
Published: August 2019

The Biginelli reaction is a multicomponent reaction for obtaining dihydropyrimidinthiones quickly, with multiple substitution patterns. The reaction mechanism remains unclear. Three possible pathways proposed for the reaction are the iminium route, an enamine intermediate, and the Knoevenagel pathway. However, when thiourea was used, no theoretical calculations were reported. Thus, based on the literature, the iminium pathway was used to obtain evidence explaining the lack of reactivity of 2,4-dihydroxybenzaldehyde towards the Biginelli adduct, compared with 4-hydroxybenzaldehyde. This computational study, carried out using the B3LYP/6-31++G(d,p) level of theory, showed an increment of 150 kJ/mol in the activation energy of the slowest pathway, due to the presence of a hydroxyl group in position 2 (ortho) of the aromatic aldehyde, decreasing its reactivity. Natural bond orbital (NBO) calculations suggest that the determinant steps are simultaneous, i.e., the polarization of the carbonyl group and its corresponding protonation by the hydrogen of the SH fragment of the thiourea tautomer. The activation enthalpy values suggest that the nucleophile attack takes place later on the compound 2,4-dihydroxybenzaldehyde compared to 4-hydroxybenzaldehyde-TS, confirming that the OH group in position 2 hinders the condensation reaction.
The Water Footprint of European Agricultural Imports: Hotspots in the Context of Water Scarcity
Authors: Dolganova Iulia , Mikosch Natalia , Berger Markus , Núñez Montserrat , Müller-Frank Andrea , Finkbeiner Matthias
Published: August 2019

This study investigates the Water Footprint (WF) resulting from the agricultural imports of the European Union (EU-28). Import trade statistics were compiled and linked with crop- and country-specific water consumption data and water scarcity factors. Within the study, the virtual water imports of 104 agricultural commodities for the baseline year 2015 were assessed and product and country hotspots were evaluated. It was shown that (a) Europe imported 100 million tons of agricultural goods and 11 km3 of associated virtual irrigation water; (b) the highest impacts of water consumption do not necessarily result from high import amounts, but from water-intensive goods produced in water scarce countries; (c) the largest external EU-28 water footprint occurred due to the product categories cotton, nuts and rice; and (d) the highest share of the EU external water footprint took place in the United States (US), Pakistan, Turkey, Egypt and India.
Towards Functional Mobile Microrobotic Systems
Authors: Adam Georges , Chowdhury Sagar , Guix Maria , Johnson Benjamin V. , Bi Chenghao , Cappelleri David
Published: August 2019

This paper presents our work over the last decade in developing functional microrobotic systems, which include wireless actuation of microrobots to traverse complex surfaces, addition of sensing capabilities, and independent actuation of swarms of microrobots. We will discuss our work on the design, fabrication, and testing of a number of different mobile microrobots that are able to achieve these goals. These microrobots include the microscale magnetorestrictive asymmetric bimorph microrobot (

μ

MAB), our first attempt at magnetic actuation in the microscale; the microscale tumbling microrobot (

μ

TUM), our microrobot capable of traversing complex surfaces in both wet and dry conditions; and the micro-force sensing magnetic microrobot (

μ

FSMM), which is capable of real-time micro-force sensing feedback to the user as well as intuitive wireless actuation. Additionally, we will present our latest results on using local magnetic field actuation for independent control of multiple microrobots in the same workspace for microassembly tasks.
A Novel Coarse-to-Fine Scheme for Remote Sensing Image Registration Based on SIFT and Phase Correlation
Authors: Yang Han , Li Xiaorun , Zhao Liaoying , Chen Shuhan
Published: August 2019

Automatic image registration has been wildly used in remote sensing applications. However, the feature-based registration method is sometimes inaccurate and unstable for images with large scale difference, grayscale and texture differences. In this manuscript, a coarse-to-fine registration scheme is proposed, which combines the advantage of feature-based registration and phase correlation-based registration. The scheme consists of four steps. First, feature-based registration method is adopted for coarse registration. A geometrical outlier removal method is applied to improve the accuracy of coarse registration, which uses geometric similarities of inliers. Then, the sensed image is modified through the coarse registration result under affine deformation model. After that, the modified sensed image is registered to the reference image by extended phase correlation. Lastly, the final registration results are calculated by the fusion of the coarse registration and the fine registration. High universality of feature-based registration and high accuracy of extended phase correlation-based registration are both preserved in the proposed method. Experimental results of several different remote sensing images, which come from several published image registration papers, demonstrate the high robustness and accuracy of the proposed method. The evaluation contains root mean square error (RMSE), Laplace mean square error (LMSE) and red–green image registration results.
Evaluation of Grass Quality under Different Soil Management Scenarios Using Remote Sensing Techniques
Authors: Askari Mohammad Sadegh , McCarthy Timothy , Magee Aidan , Murphy Darren J.
Published: August 2019

Hyperspectral and multispectral imagery have been demonstrated to have a considerable potential for near real-time monitoring and mapping of grass quality indicators. The objective of this study was to evaluate the efficiency of remote sensing techniques for quantification of aboveground grass biomass (BM) and crude protein (CP) in a temperate European climate such as Ireland. The experiment was conducted on 64 plots and 53 paddocks with varying quantities of nitrogen applied. Hyperspectral imagery (HSI) and multispectral imagery (MSI) were analyzed to develop the prediction models. The MSI data used in this study were captured using an unmanned aircraft vehicle (UAV) and the satellite Sentinel-2, while the HSI data were obtained using a handheld hyperspectral camera. The prediction models were developed using partial least squares regression (PLSR) and stepwise multi-linear regression (MLR). Eventually, the spatial distribution of grass biomass over plots and paddocks was mapped to assess the within-field variability of grass quality metrics. An excellent accuracy was achieved for the prediction of BM and CP using HSI (RPD > 2.5 and R2 > 0.8), and a good accuracy was obtained via MSI-UAV (2 < RPD < 2.5 and R2 > 0.7) for the grass quality indicators. The accuracy of the models calculated using MSI-Sentinel-2 was reasonable for BM prediction and insufficient for CP estimation. The red-edge range of the wavelengths showed the maximum impact on the predictability of grass BM, and the NIR range had the greatest influence on the estimation of grass CP. Both the PLSR and MLR techniques were found to be sufficiently robust for spectral modelling of aboveground BM and CP. The PLSR yielded a slightly better model than MLR. This study suggested that remote sensing techniques can be used as a rapid and reliable approach for near real-time quantitative assessment of fresh grass quality under a temperate European climate.
A Modeling and Measurement Approach for the Uncertainty of Features Extracted from Remote Sensing Images
Authors: Zhang Qi , Zhang Penglin , Xiao Yao
Published: August 2019

The reliability of remote sensing (RS) image classification is crucial for applying RS image classification results. However, it has received minimal attention, especially the uncertainty of features extracted from RS images. The uncertainty of image features constantly accumulates, propagates, and ultimately affects the reliability and accuracy of image classification results. Thus, research on the quantitative modeling and measurement of the feature uncertainty of RS images is very necessary. To make up for the lack of research on quantitative modeling and measurement of uncertainty of image features, this study first investigates and summarizes the appearance characteristics of the feature uncertainty of RS images in geospatial and feature space domains based on the source and formation mechanisms of feature uncertainty. Then, a modeling and measurement approach for the uncertainty of image features is proposed on the basis of these characteristics. In this approach, a new Local Adaptive Multi-Feature Weighting Method based on Information Entropy and the Local Distribution Density of Points is proposed to model and measure the feature uncertainty of an image in the geospatial and feature space domains. In addition, a feature uncertainty index is also constructed to comprehensively describe and quantify the feature uncertainty, which can also be used to refine the classification map to improve its accuracy. Finally, we propose two effectiveness verification schemes in two perspectives, namely, statistical analysis and image classification, to verify the validity of the proposed approach. Experimental results on two real RS images confirm the validity of the proposed approach. Our study on the feature uncertainty of images may contribute to the development of uncertainty control methods or reliable classification schemes for RS images.
Consecutive Dual-Vortex Interactions between Quadruple Typhoons Noru, Kulap, Nesat and Haitang during the 2017 North Pacific Typhoon Season
Authors: Liou Yuei-An , Liu Ji-Chyun , Liu Chung-Chih , Chen Chun-Hsu , Nguyen Kim-Anh , Terry James P.
Published: August 2019

This study utilizes remote sensing imagery, a differential averaging technique and empirical formulas (the ‘Liou–Liu formulas’) to investigate three consecutive sets of dual-vortex interactions between four cyclonic events and their neighboring environmental air flows in the Northwest Pacific Ocean during the 2017 typhoon season. The investigation thereby deepens the current understanding of interactions involving multiple simultaneous/sequential cyclone systems. Triple interactions between Noru–Kulap–Nesat and Noru–Nesat–Haitung were analyzed using geosynchronous satellite infrared (IR1) and IR3 water vapor (WV) images. The differential averaging technique based on the normalized difference convection index (NDCI) operator and filter depicted differences and generated a new set of clarified NDCI images. During the first set of dual-vortex interactions, Typhoon Noru experienced an increase in intensity and a U-turn in its direction after being influenced by adjacent cooler air masses and air flows. Noru’s track change led to Fujiwhara-type rotation with Tropical Storm Kulap approaching from the opposite direction. Kulap weakened and merged with Noru, which tracked in a counter-clockwise loop. Thereafter, in spite of a distance of 2000–2500 km separating Typhoon Noru and newly-formed Typhoon Nesat, the influence of middle air flows and jet flows caused an ‘indirect interaction’ between these typhoons. Evidence of this second interaction includes the intensification of both typhoons and changing track directions. The third interaction occurred subsequently between Tropical Storm Haitang and Typhoon Nesat. Due to their relatively close proximity, a typical Fujiwhara effect was observed when the two systems began orbiting cyclonically. The generalized Liou–Liu formulas for calculating threshold distances between typhoons successfully validated and quantified the trilogy of interaction events. Through the unusual and combined effects of the consecutive dual-vortex interactions, Typhoon Noru survived 22 days from 19 July to 9 August 2017 and migrated approximately 6900 km. Typhoon Noru consequently became the third longest-lasting typhoon on record for the Northwest Pacific Ocean. A comparison is made with long-lived Typhoon Rita in 1972, which also experienced similar multiple Fujiwhara interactions with three other concurrent typhoons.
Infrared Thermography Sensor for Disease Activity Detection in Rheumatoid Arthritis Patients
Authors: Pauk Jolanta , Wasilewska Agnieszka , Ihnatouski Mikhail
Published: August 2019

A recent review of thermography studies in rheumatoid arthritis shows limited data about disease activity and mostly focuses on differences between the thermography of rheumatoid arthritis patients and typical subjects. A retrospective study compared patients with high disease activity (n = 50), moderate disease activity (n = 16), and healthy participants (n = 42), taking into account demographic, clinical, laboratory, and thermography parameters. We applied an infrared thermography sensor and a fingers examination protocol. Outcomes included the mean temperature of five fingers of a hand: In static, post-cooling, post-rewarming, the total change in mean temperature of fingers due to cold provocation, the total change in mean temperature of fingers due to rewarming, the area under the cooling curve, the area under the heating curve, the difference between the area under the rewarming and the cooling curve, and temperature intensity distribution maps. For patients with high disease activity, a lower area under the heating curve and a lower difference between the area under the rewarming curve and the cooling curve were observed, as well as a smaller total change in mean temperature due to rewarming, compared to patients with moderate disease activity (p < 0.05). Our study findings could be helpful in patients with an equivocal clinical examination.
CBN-VAE: A Data Compression Model with Efficient Convolutional Structure for Wireless Sensor Networks
Authors: Liu Jianlin , Chen Fenxiong , Yan Jun , Wang Dianhong
Published: August 2019

Data compression is a useful method to reduce the communication energy consumption in wireless sensor networks (WSNs). Most existing neural network compression methods focus on improving the compression and reconstruction accuracy (i.e., increasing parameters and layers), ignoring the computation consumption of the network and its application ability in WSNs. In contrast, we pay attention to the computation consumption and application of neural networks, and propose an extremely simple and efficient neural network data compression model. The model combines the feature extraction advantages of Convolutional Neural Network (CNN) with the data generation ability of Variational Autoencoder (VAE) and Restricted Boltzmann Machine (RBM), we call it CBN-VAE. In particular, we propose a new efficient convolutional structure: Downsampling-Convolutional RBM (D-CRBM), and use it to replace the standard convolution to reduce parameters and computational consumption. Specifically, we use the VAE model composed of multiple D-CRBM layers to learn the hidden mathematical features of the sensing data, and use this feature to compress and reconstruct the sensing data. We test the performance of the model by using various real-world WSN datasets. Under the same network size, compared with the CNN, the parameters of CBN-VAE model are reduced by 73.88% and the floating-point operations (FLOPs) are reduced by 96.43% with negligible accuracy loss. Compared with the traditional neural networks, the proposed model is more suitable for application on nodes in WSNs. For the Intel Lab temperature data, the average Signal-to-Noise Ratio (SNR) value of the model can reach 32.51 dB, the average reconstruction error value is 0.0678 °C. The node communication energy consumption can be reduced by 95.83%. Compared with the traditional compression methods, the proposed model has better compression and reconstruction accuracy. At the same time, the experimental results show that the model has good fault detection performance and anti-noise ability. When reconstructing data, the model can effectively avoid fault and noise data.
People Counting by Dense WiFi MIMO Networks: Channel Features and Machine Learning Algorithms
Authors: Kianoush Sanaz , Savazzi Stefano , Rampa Vittorio , Nicoli Monica
Published: August 2019

Subject counting systems are extensively used in ambient intelligence applications, such as smart home, smart building and smart retail scenarios. In this paper, we investigate the problem of transforming an unmodified WiFi radio infrastructure into a flexible sensing system for passive subject counting. We first introduce the multi-dimensional channel features that capture the subject presence. Then, we compare Bayesian and neural network based machine learning tools specialized for subject discrimination and counting. Ensemble classification is used to leverage space-frequency diversity and combine learning tools trained with different channel features. A combination of multiple models is shown to improve the counting accuracy. System design is based on a dense network of WiFi devices equipped with multiple antennas. Experimental validation is conducted in an indoor space featuring up to five moving people. Real-time computing and practical solutions for cloud migration are also considered. The proposed approach for passive counting gives detection results with 99% average accuracy.
Data Mining and Statistical Approaches in Debris-Flow Susceptibility Modelling Using Airborne LiDAR Data
Authors: Lay Usman Salihu , Pradhan Biswajeet , Yusoff Zainuddin Bin Md , Abdallah Ahmad Fikri Bin , Aryal Jagannath , Park Hyuck-Jin
Published: August 2019

Cameron Highland is a popular tourist hub in the mountainous area of Peninsular Malaysia. Most communities in this area suffer frequent incidence of debris flow, especially during monsoon seasons. Despite the loss of lives and properties recorded annually from debris flow, most studies in the region concentrate on landslides and flood susceptibilities. In this study, debris-flow susceptibility prediction was carried out using two data mining techniques; Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) models. The existing inventory of debris-flow events (640 points) were selected for training 70% (448) and validation 30% (192). Twelve conditioning factors namely; elevation, plan-curvature, slope angle, total curvature, slope aspect, Stream Transport Index (STI), profile curvature, roughness index, Stream Catchment Area (SCA), Stream Power Index (SPI), Topographic Wetness Index (TWI) and Topographic Position Index (TPI) were selected from Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) data. Multi-collinearity was checked using Information Factor, Cramer’s V, and Gini Index to identify the relative importance of conditioning factors. The susceptibility models were produced and categorized into five classes; not-susceptible, low, moderate, high and very-high classes. Models performances were evaluated using success and prediction rates where the area under the curve (AUC) showed a higher performance of MARS (93% and 83%) over SVR (76% and 72%). The result of this study will be important in contingency hazards and risks management plans to reduce the loss of lives and properties in the area.
“Because of the Christian Fellowship, I Decided to Stay”: How Participating in a Christian Community Shapes the Social Experiences of Chinese International Students
Authors: Li Anke , Nguyen Chi , Choi Jinhee
Published: August 2019

This ethnographic study examines how participation in a Christian church community shapes Chinese international undergraduate students’ social experiences in an American university. Our findings reveal that Chinese international undergraduate students identify the church and its fellowship as (1) a social support community and (2) an informal learning community, one which fills in the gap in counseling services and interpersonal activities that the university fails to offer. Recommendations are made for higher education institutions to provide stronger support for international students, regardless of their nationalities and religions.
Impact of Leachate from Northern Landfill Site in Bloemfontein on Water and Soil Quality: Implications for Water and Food Security
Authors: Ololade Olusola O. , Mavimbela Sabelo , Oke Saheed A. , Makhadi Rinae
Published: August 2019

Solid waste management in developing cities is a threat to water and food security. The final disposal option for solid wastes is usually landfill sites. Possible contaminants and their impact on surface and groundwater, and soil quality, at the northern solid waste landfill in Bloemfontein city, South Africa, was investigated. Soil samples were analysed for basic cations and heavy metals. A one-point surface leachate, groundwater, and surface water samples were analysed for physicochemical and microbiological parameters. Hydrochemical speciation models were developed using these parameters to determine the influence of the leachate emanating from the landfill on the quality of the water samples. Findings from the study showed that the low metal content in the soil and water samples posed no immediate threat to food and water security. However, most of the other parameters were above the permissible limit of South African National Standard 241 (SANS241) and World Health Organisation (WHO) for drinking water, and the Department of Water Affairs and Forestry (DWAF) specification for irrigation, an indication that the groundwater was unfit for drinking, domestic and irrigation purposes. Metal concentrations in the soil also increased with distance downslope of the landfill along drainage lines. The implementation of a circular economy in Bloemfontein will translate to less pollution and enhance sustainable development.
Research on Frequency Fuzzy Adaptive Additional Inertial Control Strategy for D-PMSG Wind Turbine
Authors: Li Mudan , Wang Yinsong
Published: August 2019

The traditional additional inertial control (T-AIC) strategy can provide frequency support for the directly-driven wind turbine with a permanent magnet synchronous generator (D-PMSG). However, due to the fixed control coefficients, the frequency modulation effect is poor under load and wind speed disturbances. In order to improve the frequency transient response of D-PMSG, a fuzzy adaptive additional inertial control strategy (FA-AIC) is proposed in this paper. A simplified D-PMSG model is established for the complexity and low calculation speed. A single-machine grid-connected system composed of a D-PMSG and an equivalent synchronous generator set (ESGS) is taken as the background and analysis of the principle of T-AIC. The proportional and derivative coefficient initial values in T-AIC are tuned by simulating the static characteristics and inertial response characteristics of the conventional synchronous generator set, and fuzzy control technology is introduced to adjust the proportional and derivative coefficients adaptively based on the frequency deviation and the frequency deviation change rate under load or wind speed disturbances. The simulation verification indicates that T-AIC, kinetic energy (KE)-based gain-AIC and FA-AIC all can utilize the D-PMSG additional inertial response to provide frequency support for grid-connected systems. Compared with T-AIC and KE-based gain-AIC, the proposed FA-AIC can not only provide more effective frequency support during load disturbances, but also suppress the frequency fluctuation caused by the wind speed variation and displays a better dynamic frequency regulation effect.
Vehicle Routing and Scheduling Optimization of Ship Steel Distribution Center under Green Shipbuilding Mode
Authors: Li Jinghua , Guo Hui , Zhou Qinghua , Yang Boxin
Published: August 2019

Timeliness of steel distribution centers can effectively ensure the smooth progress of ship construction, but the carbon emissions of vehicles in the distribution process are also a major source of pollution. Therefore, when considering the common cost of vehicle distribution, taking the carbon emissions of vehicles into account, this paper establishes a Mixed Integer Linear Programming (MILP) model called green vehicle routing and scheduling problem with simultaneous pickups and deliveries and time windows (GVRSP-SPDTW). An intelligent water drop algorithm is designed and improved, and compared with the genetic algorithm and traditional intelligent water drop algorithm. The applicability of the improved intelligent water drop algorithm is proven. Finally, it is applied to a specific example to prove that the improved intelligent water drop algorithm can effectively reduce the cost of such problems, thereby reducing the carbon emissions of vehicles in the distribution process, achieving the goals of reducing environmental pollution and green shipbuilding.
Finance, Sustainability and Negative Externalities. An Overview of the European Context
Authors: Ziolo Magdalena , Filipiak Beata Zofia , Bąk Iwona , Cheba Katarzyna , Tîrca Diana Mihaela , Novo-Corti Isabel
Published: August 2019

The goal of the paper is to examine the relation between finance and sustainability, with a special emphasis on the impact of negative externalities. Sustainable development as a concept aims to mitigate negative externalities. Conventional finance offers no room for the environment and society. Therefore, three-dimensional sustainable finance has appeared. This paper is the first original attempt to examine the relationship between: financial, economic, environmental and social development indicators from the sustainability perspective, with a special focus on externalities. To study the disparities between the European Union (EU) countries belonging to the OECD in the field of sustainable development and sustainable finance, the multi-criteria taxonomy was used. The basis of the analyses was the indicators transformed according to the relative taxonomy method. The database, based on Eurostat, contains indicators describing pillars of sustainable development such as: economic (12 indicators), social (28), environmental (7) and sustainable finance (16). The study analyses the sample of 23 countries in 2007, 2013 and 2016. The results confirm a positive relationship among the analysed indicators. On the basis of 62 statistical features selected according to the statistical methods, 7 groups of countries were obtained in 2007 and 2013 and 8 groups in 2016. In the case of Scandinavian countries, one can observe a permanent separation of economic growth from its negative impact on the natural environment. Such dependencies are no longer so obvious in the case of other EU countries belonging to the Organization for Economic Cooperation and Development (OECD). Therefore, attention should be paid to the most economically developed countries in Western Europe, i.e., Belgium, Germany, Luxembourg, the Netherlands and the United Kingdom, whose high rankings in the case of economic, social and very often also financial results correspond to much worse results in the case of environmental development.
Development of Output Correction Methodology for Long Short Term Memory-Based Speech Recognition
Authors: Arslan Recep Sinan , Barışçı Necaattin
Published: August 2019

This paper presents a correction methodology for Long Short Term Memory (LSTM) based speech recognition. A strategy that validates with a reference database was developed for LSTM. It is conceptually simple but requires a large keyword database to match test templates. The correction method is based on the “most matching method” that is finding the word in which the system output is closest among the “Referenced Template Database”. Each LSTM model recognition output was corrected with the proposed new concept. Thus, system recognition performance was improved by correcting faulty outputs. The effectiveness, efficiency, and contribution of this approach to system performance were demonstrated by experiments. Tests carried out using different speech-text datasets and LSTM models yielded an average performance increase of 2.25%. With some advanced models, this ratio rises to 3.84%.
Understanding Visual Engagement with Urban Street Edges along Non-Pedestrianised and Pedestrianised Streets Using Mobile Eye-Tracking
Authors: Simpson James , Thwaites Kevin , Freeth Megan
Published: August 2019

Existing knowledge of street edge experience has often been constructed using methods that offer a limited opportunity to gain empirical insight from the first-hand perspective of pedestrians. In order to address this, mobile eye-tracking glasses were used during the current investigation to provide a detailed understanding of pedestrian visual engagement with street edges along both non-pedestrianised and pedestrianised urban streets. Through this, the current study advances empirical knowledge of street edge experience from a perspective that has previously been challenging to capture and quantify. The findings demonstrate that people visually engage with street edge ground floors more than their upper floors, that visual engagement is distributed more towards the street edge on the walked side of non-pedestrianised streets than the opposite side, and that visual engagement with street edges of pedestrianised streets is balanced across both sides. The study findings also highlight how the everyday activities of pedestrians and different streets being walked often influence the amount of visual engagement within these street edge areas. These insights provide a new understanding that develops existing knowledge of pedestrian street edge experience. Significantly, they also provide an empirical foundation from which to examine how design intervention can become more considerate of peoples’ routine use of and experiential engagement with street edges along non-pedestrianised and pedestrianised urban streets.
A Multi-Step Approach Framework for Freight Forecasting of River-Sea Direct Transport without Direct Historical Data
Authors: Guo Zhaoxia , Le Weiwei , Wu Youkai , Wang Wei
Published: August 2019

The freight forecasting of river-sea direct transport (RSDT) is crucial for the policy making of river-sea transportation facilities and the decision-making of relevant port and shipping companies. This paper develops a multi-step approach framework for freight volume forecasting of RSDT in the case that direct historical data are not available. First, we collect publicly available shipping data, including ship traffic flow, speed limit of each navigation channel, free-flow running time, channel length, channel capacity, etc. The origin–destination (O–D) matrix estimation method is then used to obtain the matrix of historical freight volumes among all O–D pairs based on these data. Next, the future total freight volumes among these O–D pairs are forecasted by using the gray prediction model, and the sharing rate of RSDT is estimated by using the logit model. The freight volume of RSDT is thus determined. The effectiveness of the proposed approach is validated by forecasting the RSDT freight volume on a shipping route of China.
Impact of Low-E Window Films on Energy Consumption and CO2 Emissions of an Existing UK Hotel Building
Authors: Amirkhani Shiva , Bahadori-Jahromi Ali , Mylona Anastasia , Godfrey Paulina , Cook Darren
Published: August 2019

In order to fulfil the UK government’s ambitious goal of 80% reductions in greenhouse gas emissions by 2050 compared to the levels of 1990s, unprecedented measures for improving the energy efficiency of buildings are needed. This study investigates the impact of a specific type of Low-emissivity (Low-E) window film—Thinsulate Climate Control 75—on the holistic energy consumption of an existing United Kingdom (UK) hotel building. Building modelling and energy simulation software EDSL TAS is used to conduct the study. The result of the simulations demonstrates that by applying Thinsulate films, savings in heating, cooling, and total energy consumptions are achieved by 3%, 20%, and 2.7%, respectively. Also 4.1% and 5.1% savings are achieved in annual CO2 emissions and total energy costs, respectively, while the initial costs may be an issue. This study found that application of Low-E window films results in slightly better energy performance of the hotel regarding its heating-dominant climate. The study also recommends using average annual actual energy consumption data for a time range, instead of picking a single year’s data for validating purposes.
Evaluation of Empirical Reference Evapotranspiration Models Using Compromise Programming: A Case Study of Peninsular Malaysia
Authors: Muhammad Mohd , Nashwan Mohamed , Shahid Shamsuddin , Ismail Tarmizi , Song Young , Chung Eun-Sung
Published: August 2019

Selection of appropriate empirical reference evapotranspiration (ETo) estimation models is very important for the management of agriculture, water resources, and environment. Statistical metrics generally used for performance assessment of empirical ETo models, on a station level, often give contradictory results, which make the ranking of methods a challenging task. Besides, the ranking of ETo estimation methods for a given study area based on the rank at different stations is also a difficult task. Compromise programming and group decision-making methods have been proposed in this study for the ranking of 31 empirical ETo models for Peninsular Malaysia based on four standard statistical metrics. The result revealed the Penman-Monteith as the most suitable method of estimation of ETo, followed by radiation-based Priestley and Taylor and the mass transfer-based Dalton and Meyer methods. Among the temperature-based methods, Ivanov was found the best. The methodology suggested in this study can be adopted in any other region for an easy but robust evaluation of empirical ETo models.