bluuebunny/crossref_metadata_2025 huggingface dataset:
Oxygen-Scavenging Multilayered Biopapers Containing Palladium Nanoparticles Obtained by the Electrospinning Coating Technique
Authors: Cherpinski Adriane , Szewczyk Piotr K. , Gruszczyński Adam , Stachewicz Urszula , Lagaron Jose M.
Published: February 2019

The main goal of this study was to obtain, for the first time, highly efficient water barrier and oxygen-scavenging multilayered electrospun biopaper coatings of biodegradable polymers over conventional cellulose paper, using the electrospinning coating technique. In order to do so, poly(3-hydroxybutyrate) (PHB) and polycaprolactone (PCL) polymer-containing palladium nanoparticles (PdNPs) were electrospun over paper, and the morphology, thermal properties, water vapor barrier, and oxygen absorption properties of nanocomposites and multilayers were investigated. In order to reduce the porosity, and to enhance the barrier properties and interlayer adhesion, the biopapers were annealed after electrospinning. A previous study showed that electrospun PHB-containing PdNP did show significant oxygen scavenging capacity, but this was strongly reduced after annealing, a process that is necessary to form a continuous film with the water barrier. The results in the current work indicate that the PdNP were better dispersed and distributed in the PCL matrix, as suggested by focus ion beam-scanning electron microscopy (FIB-SEM) experiments, and that the Pd enhanced, to some extent, the onset of PCL degradation. More importantly, the PCL/PdNP nanobiopaper exhibited much higher oxygen scavenging capacity than the homologous PHB/PdNP, due to most likely, the higher oxygen permeability of the PCL polymer and the somewhat higher dispersion of the Pd. The passive and active multilayered biopapers developed here may be of significant relevance to put forward the next generation of fully biodegradable barrier papers of interest in, for instance, food packaging.
Photochemical Synthesis of Nanosheet Tin Di/Sulfide with Sunlight Response on Water Pollutant Degradation
Authors: Matmin Juan , Jalani Mohamad Azani , Osman Hazwanee , Omar Qistina , Ab’lah NorulNazilah , Elong Kelimah , Kasim Muhd Firdaus
Published: February 2019

The photochemical synthesis of two-dimensional (2D) nanostructured from semiconductor materials is unique and challenging. We report, for the first time, the photochemical synthesis of 2D tin di/sulfide (PS-SnS2-x, x = 0 or 1) from thioacetamide (TAA) and tin (IV) chloride in an aqueous system. The synthesized PS-SnS2-x were characterized by X-ray diffraction (XRD), energy dispersive X-ray spectroscopy (EDX), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), a particle size distribution analyzer, X-ray photoelectron spectroscopy (XPS), Fourier-transform infrared spectroscopy (FTIR), thermal analysis, UV–Vis diffuse reflectance spectroscopy (DR UV–Vis), and photoluminescence (PL) spectroscopy. In this study, the PS-SnS2-x showed hexagonally closed-packed crystals having nanosheets morphology with the average size of 870 nm. Furthermore, the nanosheets PS-SnS2-x demonstrated reusable photo-degradation of methylene blue (MB) dye as a water pollutant, owing to the stable electronic conducting properties with estimated bandgap (Eg) at ~2.5 eV. Importantly, the study provides a green protocol by using photochemical synthesis to produce 2D nanosheets of semiconductor materials showing photo-degradation activity under sunlight response.
Free Fatty Acids’ Level and Nutrition in Critically Ill Patients and Association with Outcomes: A Prospective Sub-Study of PermiT Trial
Authors: Arabi Yaseen M. , Tamimi Waleed , Jones Gwynne , Jawdat Dunia , Tamim Hani , Al-Dorzi Hasan M. , Sadat Musharaf , Afesh Lara , Sakhija Maram , Al-Dawood Abdulaziz
Published: February 2019

Objectives: The objectives of this study were to evaluate the clinical and nutritional correlates of high free fatty acids (FFAs) level in critically ill patients and the association with outcomes, and to study the effect of short-term caloric restriction (permissive underfeeding) on FFAs level during critical illness. Patients/Method: In this pre-planned sub-study of the PermiT (Permissive Underfeeding vs. Target Enteral Feeding in Adult Critically Ill Patients) trial, we included critically ill patients who were expected to stay for ≥14 days in the intensive care unit. We measured FFAs level on day 1, 3, 5, 7, and 14 of enrollment. Of 70 enrolled patients, 23 (32.8%) patients had high FFAs level (baseline FFAs level >0.45 mmol/L in females and >0.6 mmol/L in males). Results: Patients with high FFAs level were significantly older and more likely to be females and diabetics and they had lower ratio of partial pressure of oxygen to the fraction of inspired oxygen, higher creatinine, and higher total cholesterol levels than those with normal FFAs level. During the study period, patients with high FFAs level had higher blood glucose and required more insulin. On multivariable logistic regression analysis, the predictors of high baseline FFAs level were diabetes (adjusted odds ratio (aOR): 5.36; 95% confidence interval (CI): 1.56, 18.43, p = 0.008) and baseline cholesterol level (aOR, 4.29; 95% CI: 11.64, 11.19, p = 0.003). Serial levels of FFAs did not differ with time between permissive underfeeding and standard feeding groups. FFAs level was not associated with 90-day mortality (aOR: 0.49; 95% CI: 0.09, 2.60, p = 0.40). Conclusion: We conclude that high FFAs level in critically ill patients is associated with features of metabolic syndrome and is not affected by short-term permissive underfeeding.
International System of Units (SI) Traceable Noise-Equivalent Power and Responsivity Characterization of Continuous Wave ErAs:InGaAs Photoconductive Terahertz Detectors
Authors: Fernandez Olvera Anuar , Roggenbuck Axel , Dutzi Katja , Vieweg Nico , Lu Hong , Gossard Arthur , Preu Sascha
Published: February 2019

A theoretical model for the responsivity and noise-equivalent power (NEP) of photoconductive antennas (PCAs) as coherent, homodyne THz detectors is presented. The model is validated by comparison to experimental values obtained for two ErAs:InGaAs PCAs. The responsivity and NEP were obtained from the measured rectified current, the current noise floor in the PCAs, and the incoming THz power for the same conditions. Since the THz power measurements are performed with a pyroelectric detector calibrated by the National Metrology Institute of Germany (PTB), the experimentally obtained values are directly traceable to the International System of Units (SI) for the described conditions. The agreement between the presented model and the experimental results is excellent using only one fitting parameter. A very low NEP of 1.8 fW/Hz at 188.8 GHz is obtained at room temperature.
Fabrication of Core-Shell Magnetic Molecularly Imprinted Nanospheres towards Hypericin via Click Polymerization
Authors: Wang Xinxin , Pei Yuxin , Hou Yong , Pei Zhichao
Published: February 2019

The core-shell structure molecularly imprinted magnetic nanospheres towards hypericin (Fe3O4@MIPs) were prepared by mercapto-alkyne click polymerization. The shape and size of nanospheres were characterized by dynamic light scattering (DLS) and transmission electron microscope (TEM). The nanospheres were analyzed by FTIR spectroscopy to verify the thiol-yne click reaction in the presence or absence of hypericin. The Brunauer–Emmet–Teller (BET) method was used for measuring the average pore size, pore volume and surface area. The Fe3O4@MIPs synthesized displayed a good adsorption capacity (Q = 6.80 µmol·g−1). In addition, so-prepared Fe3O4@MIPs showed fast mass transfer rates and good reusability. The method established for fabrication of Fe3O4@MIPs showed excellent reproducibility and has broad potential for the fabrication of other core-shell molecularly imprinted polymers (MIPs).
Influence of MoS2 Nanosheet Size on Performance of Drilling Mud
Authors: Hong Sung Hyun , Jo Hae Jin , Choi Min-Ju , Jang Ho Won , Kim Young Ju , Hwang Wook Ryol , Kim Soo Young
Published: February 2019

Water-based drilling mud (WBM) is a non-Newtonian fluid that has a variety of applications such as in transporting cuttings during drilling, protecting the borehole, and cooling the drill bit. With the development of nano-technology, various nanoparticles have been synthesized and have been added to WBM to improve its performance. Shear thinning is the most important factor in drilling mud and this attribute can be improved when two-dimensional particles are added. MoS2 nanoparticles, which represent a typical two-dimensional material, are easy to synthesize in large quantities and have a high thermal conductivity and low coefficient of friction. Since the two-dimensional structure, thermal conductivity, and low coefficient of friction of MoS2 would improve the performance of WBM, we experimented with MoS2 nanosheets as an additive, under optimal conditions, using various samples each with uniform sizes and thicknesses of nanosheets. A large amount of MoS2 nanosheets was synthesized, sorted by thickness and diameter, and added to drilling mud. The diameter of MoS2 was divided into a small diameter group (about 100–400 nm) and a big diameter group (about 300–650 nm), and the thickness was divided into 1–2 nm and 5–10 nm groups. Experimental results showed that when MoS2 is added to WBM, shear thinning occurs more strongly. In addition, the addition of MoS2 with a thickness of 1–2 nm and a diameter of 300–650 nm resulted in the highest increase in viscosity and thermal conductivity of WBM. As a result, we experimentally confirmed that MoS2 can be used as an additive to increase the thermal conductivity and viscosity of WBM and to make shear thinning phenomenon more.
Anti-Fouling and Anti-Bacterial Modification of Poly(vinylidene fluoride) Membrane by Blending with the Capsaicin-Based Copolymer
Authors: Shen Xiang , Liu Peng , Xia Shubiao , Liu Jianjun , Wang Rui , Zhao Hua , Liu Qiuju , Xu Jiao , Wang Fan
Published: February 2019

Membrane fouling induced by the adsorption of organic matter, and adhesion and propagation of bacteria onto the surfaces, is the major obstacle for the wide application of membrane technology. In this work, the capsaicin-based copolymer (PMMA-PACMO-Capsaicin) was synthesized via radical copolymerization using methyl methacrylate (MMA), N-acrylomorpholine (ACMO) and 8-methyl-N-vanillyl-6-nonenamide (capsaicin) as monomers. Subsequently, the capsaicin-based copolymer was readily blended with PVDF to fabricate PVDF/PMMA-PACMO-Capsaicin flat sheet membrane via immersed phase inversion method. The effects of copolymer concentration on the structure and performance of resultant membranes were evaluated systematically. With increase of PMMA-PACMO-Capsaicin copolymer concentration in the casting solution, the sponge-like layer at the membrane cross-section transfers to macroviod, and the pore size and porosity of membranes increase remarkably. The adsorbed bovine serum albumin protein (BSA) amounts to PVDF/PMMA-PACMO-Capsaicin membranes decrease significantly because of the enhanced surface hydrophilicty. During the cycle filtration of pure water and BSA solution, the prepared PVDF/PMMA-PACMO-Capsaicin membranes have a higher flux recovery ratio (FFR) and lower irreversible membrane fouling ratio (Rir), as compared with pristine PVDF membrane. PVDF/PMMA-PACMO-Capsaicin membrane is found to suppress the growth and propagation of Staphylococcus aureus bacteria, achieving an anti-bacterial efficiency of 88.5%. These results confirm that the anti-fouling and anti-bacterial properties of PVDF membrane are enhanced obviously by blending with the PMMA-PACMO-Capsaicin copolymer.
Pyrolytic Behavior of Major Biomass Components in Waste Biomass
Authors: Ben Haoxi , Wu Zhihong , Han Guangting , Jiang Wei , Ragauskas Arthur
Published: February 2019

The pyrolytic behavior of several biomass components including cellulose, hemicellulose, lignin, and tannin, from two sources of waste biomass (i.e., pine bark and pine residues) were examined. Compared to the two aromatic-based components in the biomass, carbohydrates produced much less char but more gas. Surprisingly, tannin produced a significant amount of water-soluble products; further analysis indicated that tannin could produce a large amount of catechols. The first reported NMR chemical shift databases for tannin and hemicellulose pyrolysis oils were created to facilitate the HSQC analysis. Various C–H functional groups (>30 different C–H bonds) in the pyrolysis oils could be analyzed by employing HSQC-NMR. The results indicated that most of the aromatic C–H and aliphatic C–H bonds in the pyrolysis oils produced from pine bark and pine residues resulted from the lignin and tannin components. A preliminary study for a quantitative application of HSQC-NMR on the characterization of pyrolysis oil was also done in this study. Nevertheless, the concepts established in this work open up new methods to fully characterize the whole portion of pyrolysis oils produced from various biomass components, which can provide valuable information on the thermochemical mechanisms.
Lightweight High-Performance Polymer Composite for Automotive Applications
Authors: Volpe Valentina , Lanzillo Sofia , Affinita Giovanni , Villacci Beniamino , Macchiarolo Innocenzo , Pantani Roberto
Published: February 2019

The automotive industry needs to produce plastic products with high dimensional accuracy and reduced weight, and this need drives the research toward less conventional industrial processes. The material that was adopted in this work is a glass-fiber-reinforced polyamide 66 (PA66), a material of great interest for the automotive industry because of its excellent properties, although being limited in application because of its relatively high cost. In order to reduce the cost of the produced parts, still preserving the main properties of the material, the possibility of applying microcellular injection molding process was explored in this work. In particular, the influence of the main processing parameters on morphology and performance of PA66 + 30% glass-fiber foamed parts was investigated. An analysis of variance (ANOVA) was employed to identify the significant factors that influence the morphology of the molded parts. According to ANOVA results, in order to obtain homogeneous foamed parts with good mechanical properties, an injection temperature of 300 °C, a high gas injection pressure, and a large thickness of the parts should be adopted.
Self-Association Behavior of Cell Membrane-Inspired Amphiphilic Random Copolymers in Water
Authors: Ohshio Maho , Ishihara Kazuhiko , Yusa Shin-ichi
Published: February 2019

Water-soluble and amphiphilic random copolymers (P(MPC/DMAx)) composed of hydrophilic 2-methacryloyloxyethyl phosphorylcholine (MPC) and hydrophobic n-dodecyl methacrylate (DMA) were prepared via reversible addition-fragmentation chain transfer (RAFT) controlled radical polymerization. The compositions of DMA unit (x) in the copolymer were in the range of 0 to 38 unit mol %. The degree of polymerization of P(MPC/DMAx) was adjusted to about 200. Since the monomer reactivity ratios of MPC and DMA are 1.01 and 1.00, respectively, ideal free radical copolymerization occurred. In aqueous solutions, interpolymer aggregation occurred due to the hydrophobic pendant n-dodecyl groups. The aggregation number (Nagg) increased with an increasing x. The mobilities of the DMA and MPC pendant groups in aqueous solutions were restricted, as confirmed by 1H NMR relaxation time measurements, because a part of the MPC units were trapped in the hydrophobic microdomain formed from the pendant n-dodecyl groups. The polarity of the hydrophobic microdomain formed from P(MPC/DMA38) in water was similar to that of ethyl acetate according to fluorescence probe experiments. No specific interactions were found in water between P(MPC/DMAx) and bovine serum albumin because the surface of the interpolymer aggregates contained only hydrophilic MPC units.
Effects of Cavity Structure on Tuning Properties of Polymer Lasers in a Liquid Environment
Authors: Cao Fengzhao , Zhang Shuai , Tong Junhua , Chen Chao , Niu Lianze , Zhai Tianrui , Zhang Xinping
Published: February 2019

The effect of cavity structures on the tuning properties of polymer lasers was investigated in two common distributed-feedback cavities. The configurations of the two cavities are substrate/grating/active waveguide and substrate/active waveguide/grating, respectively. The polymer lasers were operated in the liquid environment, and the laser wavelength was tuned dynamically by changing the refractive index of the liquid. Polymer lasers based on the substrate/grating/active waveguide structure showed a higher tunability than those based on the substrate/active waveguide/grating structure due to a larger electric field distribution of the laser mode in the liquid environment. It is expected that these results will be useful in the development of tunable laser sources.
Building A High-Resolution Vegetation Outlook Model to Monitor Agricultural Drought for the Upper Blue Nile Basin, Ethiopia
Authors: Bayissa Yared , Tadesse Tsegaye , Demisse Getachew
Published: February 2019

To reduce the impacts of drought, developing an integrated drought monitoring tool and early warning system is crucial and more effective than the crisis management approach that is commonly used in developing countries like Ethiopia. The overarching goal of this study was to develop a higher-spatial-resolution vegetation outlook (VegOut-UBN) model that integrates multiple satellite, climatic, and biophysical input variables for the Upper Blue Nile (UBN) basin. VegOut-UBN uses current and historical observations in predicting the vegetation condition at multiple leading time steps of 1, 3, 6, and 9 dekades. VegOut-UBN was developed to predict the vegetation condition during the main crop-growing season locally called “Kiremt” (June to September) using historical input data from 2001 to 2016. The rule-based regression tree approach was used to develop the relationship between the predictand and predictor variables. The results for the recent historic drought (2009 and 2015) and non-drought (2007) years are presented to evaluate the model accuracy during extreme weather conditions. The result, in general, shows that the predictive accuracy of the model decreases as the prediction interval increases for the cross-validation years. The coefficient of determination (R2) of the predictive and observed vegetation condition shows a higher value (R2 > 0.8) for one-month prediction and a relatively lower value (R2 ≅ 0.70) for three-month prediction. The result also reveals strong spatial integrity and similarity of the observed and predicted maps. VegOut-UBN was evaluated and compared with the Standardized Precipitation Index (SPI) (derived from independent rainfall datasets from meteorological stations) at different aggregate periods and with a food security status map. The result was encouraging and indicative of the potential application of VegOut-UBN for drought monitoring and prediction. The VegOut-UBN model could be informative in decision-making processes and could contribute to the development of operational drought monitoring and predictive models for the UBN basin.
How Groundwater Level Fluctuations and Geotechnical Properties Lead to Asymmetric Subsidence: A PSInSAR Analysis of Land Deformation over a Transit Corridor in the Los Angeles Metropolitan Area
Authors: Khorrami Mohammad , Alizadeh Babak , Ghasemi Tousi Erfan , Shakerian Mahyar , Maghsoudi Yasser , Rahgozar Peyman
Published: February 2019

Los Angeles has experienced ground deformations during the past decades. These ground displacements can be destructive for infrastructure and can reduce the land capacity for groundwater storage. Therefore, this paper seeks to evaluate the existing ground displacement patterns along a new metro tunnel in Los Angeles, known as the Sepulveda Transit Corridor. The goal is to find the most crucial areas suffering from subsidence or uplift and to enhance the previous reports in this metropolitan area. For this purpose, we applied a Persistent Scatterer Interferometric Synthetic Aperture Radar using 29 Sentinel-1A acquisitions from June 2017 to May 2018 to estimate the deformation rate. The assessment procedure demonstrated a high rate of subsidence in the Inglewood field that is near the study area of the Sepulveda Transit Corridor with a maximum deformation rate of 30 mm/yr. Finally, data derived from in situ instruments as groundwater level variations, GPS observations, and soil properties were collected and analyzed to interpret the results. Investigation of geotechnical boreholes indicates layers of fine-grained soils in some parts of the area and this observation confirms the necessity of more detailed geotechnical investigations for future constructions in the region. Results of investigating line-of-sight displacement rates showed asymmetric subsidence along the corridor and hence we proposed a new framework to evaluate the asymmetric subsidence index that can help the designers and decision makers of the project to consider solutions to control the current subsidence.
Monitoring Drought Effects on Vegetation Productivity Using Satellite Solar-Induced Chlorophyll Fluorescence
Authors: Zhang Lifu , Qiao Na , Huang Changping , Wang Siheng
Published: February 2019

Around the world, the increasing drought, which is exacerbated by climate change, has significant impacts on vegetation carbon assimilation. Identifying how short-term climate anomalies influence vegetation productivity in a timely and accurate manner at the satellite scale is crucial to monitoring drought. Satellite solar-induced chlorophyll fluorescence (SIF) has recently been reported as a direct proxy of actual vegetation photosynthesis and has more advantages than traditional vegetation indices (e.g., the Normalized Difference Vegetation Index, NDVI and the Enhanced Vegetation Index, EVI) in monitoring vegetation vitality. This study aims to evaluate the feasibility of SIF in interpreting drought effects on vegetation productivity in Victoria, Australia, where heat stress and drought are often reported. Drought-induced variations in SIF and absorbed photosynthetically active radiation (APAR) estimations based on NDVI and EVI were investigated and validated against results indicated by gross primary production (GPP). We first compared drought responses of GPP and vegetation proxies (SIF and APAR) during the 2009 drought event, considering potential biome-dependency. Results showed that SIF exhibited more consistent declines with GPP losses induced by drought than did APAR estimations during the 2009 drought period in space and time, where APAR had obvious lagged responses compared with SIF, especially in evergreen broadleaf forest land. We then estimated the sensitivities of the aforementioned variables to meteorology anomalies using the ARx model, where memory effects were considered, and compared the correlations of GPP anomaly with the anomalies of vegetation proxies during a relatively long period (2007–2013). Compared with APAR, GPP and SIF are more sensitive to temperature anomalies for the general Victoria region. For crop land, GPP and vegetation proxies showed similar sensitivities to temperature and water availability. For evergreen broadleaf forest land, SIF anomaly was explained better by meteorology anomalies than APAR anomalies. GPP anomaly showed a stronger linear relationship with SIF anomaly than with APAR anomalies, especially for evergreen broadleaf forest land. We showed that SIF might be a promising tool for effectively evaluating short-term drought impacts on vegetation productivity, especially in drought-vulnerable areas, such as Victoria.
Simulation of Sedimentation in Lake Taihu with Geostationary Satellite Ocean Color Data
Authors: He Anpeng , He Xianqiang , Bai Yan , Zhu Qiankun , Gong Fang , Huang Haiqing , Pan Delu
Published: February 2019

In this study, the goal is to estimate the sedimentation on the bottom bed of Lake Taihu using numerical simulation combined with geostationary satellite ocean color data. A two-dimensional (2D) model that couples the dynamics of shallow water and sediment transport is presented. The shallow water equations are solved using a semi-implicit finite difference method with an Alternating Direction Implicit (ADI) method. Suspended sediment transport is simulated by solving the general convection-diffusion equation with resuspension and deposition terms using a second-order explicit central difference method in space and two-step Adams–Bashforth method in time. Moreover, the total suspended particulate matter (TSM) is retrieved by the world’s first geostationary satellite ocean color sensor Geostationary Ocean Color Imager (GOCI) using atmospheric correction algorithm for turbid waters using ultraviolet wavelengths (UV-AC) and regional empirical TSM algorithm. The 2D model and GOCI-retrieved TSM are applied to study the sediment transport and sedimentation in Lake Taihu. Validation results show rationale TSM concentration retrieved by GOCI, and the simulated TSM concentrations are consistent with GOCI observations. In addition, simulated sedimentation results reveal the dangerous locations that must be observed and desilted.
Retrieval of Forest Vertical Structure from PolInSAR Data by Machine Learning Using LIDAR-Derived Features
Authors: Brigot Guillaume , Simard Marc , Colin-Koeniguer Elise , Boulch Alexandre
Published: February 2019

This paper presents a machine learning based method to predict the forest structure parameters from L-band polarimetric and interferometric synthetic aperture radar (PolInSAR) data acquired by the airborne UAVSAR system over the Réserve Faunique des Laurentides in Québec, Canada. The main objective of this paper is to show that relevant parameters of the PolInSAR coherence region can be used to invert forest structure indicators computed from the airborne LIDAR sensor Laser Vegetation and Ice Sensor (LVIS). The method relies on the shape of the observed generalized PolInSAR coherence region that is related to the three-dimensional structure of the scene. In addition to parameters describing the coherence shape, we consider the impact of acquisition parameters such as the interferometric baseline, ground elevation and local surface slope. We use the parameters as input a multilayer perceptron model to infer canopy features as estimated from LIDAR waveform. The output features are canopy height, cover and vertical profile class. Canopy height and canopy cover are estimated with a normalized RMSE of 13%, 15% respectively. The vertical profile was divided into 3 distinct classes with 66% accuracy.
Deep Attention Models for Human Tracking Using RGBD
Authors: Rasoulidanesh Maryamsadat , Yadav Srishti , Herath Sachini , Vaghei Yasaman , Payandeh Shahram
Published: February 2019

Visual tracking performance has long been limited by the lack of better appearance models. These models fail either where they tend to change rapidly, like in motion-based tracking, or where accurate information of the object may not be available, like in color camouflage (where background and foreground colors are similar). This paper proposes a robust, adaptive appearance model which works accurately in situations of color camouflage, even in the presence of complex natural objects. The proposed model includes depth as an additional feature in a hierarchical modular neural framework for online object tracking. The model adapts to the confusing appearance by identifying the stable property of depth between the target and the surrounding object(s). The depth complements the existing RGB features in scenarios when RGB features fail to adapt, hence becoming unstable over a long duration of time. The parameters of the model are learned efficiently in the Deep network, which consists of three modules: (1) The spatial attention layer, which discards the majority of the background by selecting a region containing the object of interest; (2) the appearance attention layer, which extracts appearance and spatial information about the tracked object; and (3) the state estimation layer, which enables the framework to predict future object appearance and location. Three different models were trained and tested to analyze the effect of depth along with RGB information. Also, a model is proposed to utilize only depth as a standalone input for tracking purposes. The proposed models were also evaluated in real-time using KinectV2 and showed very promising results. The results of our proposed network structures and their comparison with the state-of-the-art RGB tracking model demonstrate that adding depth significantly improves the accuracy of tracking in a more challenging environment (i.e., cluttered and camouflaged environments). Furthermore, the results of depth-based models showed that depth data can provide enough information for accurate tracking, even without RGB information.
Using Inertial Sensors to Quantify Postural Sway and Gait Performance during the Tandem Walking Test
Authors: Kim Kyoung Jae , Gimmon Yoav , Millar Jennifer , Schubert Michael C.
Published: February 2019

Vestibular dysfunction typically manifests as postural instability and gait irregularities, in part due to inaccuracies in processing spatial afference. In this study, we have instrumented the tandem walking test with multiple inertial sensors to easily and precisely investigate novel variables that can distinguish abnormal postural and gait control in patients with unilateral vestibular hypofunction. Ten healthy adults and five patients with unilateral vestibular hypofunction were assessed with the tandem walking test during eyes open and eyes closed conditions. Each subject donned five inertial sensors on the upper body (head, trunk, and pelvis) and lower body (each lateral malleolus). Our results indicate that measuring the degree of balance and gait regularity using five body-worn inertial sensors during the tandem walking test provides a novel quantification of movement that identifies abnormalities in patients with vestibular impairment.
An Unsupervised Learning Technique to Optimize Radio Maps for Indoor Localization
Authors: Trogh Jens , Joseph Wout , Martens Luc , Plets David
Published: February 2019

A major burden of signal strength-based fingerprinting for indoor positioning is the generation and maintenance of a radio map, also known as a fingerprint database. Model-based radio maps are generated much faster than measurement-based radio maps but are generally not accurate enough. This work proposes a method to automatically construct and optimize a model-based radio map. The method is based on unsupervised learning, where random walks, for which the ground truth locations are unknown, serve as input for the optimization, along with a floor plan and a location tracking algorithm. No measurement campaign or site survey, which are labor-intensive and time-consuming, or inertial sensor measurements, which are often not available and consume additional power, are needed for this approach. Experiments in a large office building, covering over 1100 m2, resulted in median accuracies of up to 2.07 m, or a relative improvement of 28.6% with only 15 min of unlabeled training data.
Axial Compression Behaviours of Pultruded GFRP–Wood Composite Columns
Authors: Qi Yujun , Xie Lei , Bai Yu , Liu Weiqing , Fang Hai
Published: February 2019

An innovative pultruded fiber reinforced polymer (FRP)–wood composite (PFWC) column with a lightweight southern pine wood core confined by outer FRP sheets was manufactured using an improved pultrusion process. Axial compression tests with both ends pinned as boundary conditions were employed to investigate the mechanical performance of such PFWC columns under concentric load. Through experimental investigations, the effects of the slenderness ratio on the failure modes and the axial load bearing capacities of the PFWC columns were evaluated. The failure modes showed that the specimens with a slenderness ratio less than 43.2 failed through compressive failure at junctions on FRP sheets, while those with slenderness ratios larger than 57.6 showed global buckling. Strain responses on specimens with different slenderness ratios are consistent with the observed failure modes. Finite element analysis was carried out to validate the experimental results, and satisfactory agreement was found between the failure modes and load–displacement curves. An empirical equation was developed with a new factor taking 0.65 into account to predict the load bearing capacities of the PFWC columns, and good agreement was found.
UAV Sensor Fault Detection Using a Classifier without Negative Samples: A Local Density Regulated Optimization Algorithm
Authors: Guo Kai , Liu Liansheng , Shi Shuhui , Liu Datong , Peng Xiyuan
Published: February 2019

Fault detection for sensors of unmanned aerial vehicles is essential for ensuring flight security, in which the flight control system conducts real-time control for the vehicles relying on the sensing information from sensors, and erroneous sensor data will lead to false flight control commands, causing undesirable consequences. However, because of the scarcity of faulty instances, it still remains a challenging issue for flight sensor fault detection. The one-class support vector machine approach is a favorable classifier without negative samples, however, it is sensitive to outliers that deviate from the center and lacks a mechanism for coping with them. The compactness of its decision boundary is influenced, leading to the degradation of detection rate. To deal with this issue, an optimized one-class support vector machine approach regulated by local density is proposed in this paper, which regulates the tolerance extents of its decision boundary to the outliers according to their extent of abnormality indicated by their local densities. The application scope of the local density theory is narrowed to keep the internal instances unchanged and a rule for assigning the outliers continuous density coefficients is raised. Simulation results on a real flight control system model have proved its effectiveness and superiority.
Improvement of Sensing Performance of Impedancemetric C2H2 Sensor Using SmFeO3 Thin-Films Prepared by a Polymer Precursor Method
Authors: Tasaki Tomohisa , Takase Satoko , Shimizu Youichi
Published: February 2019

A sensitive an impedancemetric acetylene (C2H2) gas sensor device could be fabricated by using perovskite-type SmFeO3 thin-film as a sensor material. The uniform SmFeO3 thin-films were prepared by spin-coating and focusing on the effects of polymer precursor solutions. The prepared precursors and thin-films were characterized by means of thermal analysis, Fourier-transform infrared spectroscopy, ultraviolet–visible spectroscopy, X-ray diffraction analysis, scanning electron microscopy and X-ray photoelectron spectroscopy. It was found that particle growth and increase in homogeneity of the prepared thin-film could be accelerated by the addition of acetyl acetone (AcAc) as a coordination agent in the polymer precursor solution. Moreover, the highly crystallized thin-film-based sensor showed good response properties and stabilities to a low C2H2 concentration between 0.5 and 2.0 ppm.
Optical Oxygen Sensing with Artificial Intelligence
Authors: Michelucci Umberto , Baumgartner Michael , Venturini Francesca
Published: February 2019

Luminescence-based sensors for measuring oxygen concentration are widely used in both industry and research due to the practical advantages and sensitivity of this type of sensing. The measuring principle is the luminescence quenching by oxygen molecules, which results in a change of the luminescence decay time and intensity. In the classical approach, this change is related to an oxygen concentration using the Stern-Volmer equation. This equation, which in most cases is non-linear, is parameterized through device-specific constants. Therefore, to determine these parameters, every sensor needs to be precisely calibrated at one or more known concentrations. This study explored an entirely new artificial intelligence approach and demonstrated the feasibility of oxygen sensing through machine learning. The specifically developed neural network learns very efficiently to relate the input quantities to the oxygen concentration. The results show a mean deviation of the predicted from the measured concentration of 0.5% air, comparable to many commercial and low-cost sensors. Since the network was trained using synthetically generated data, the accuracy of the model predictions is limited by the ability of the generated data to describe the measured data, opening up future possibilities for significant improvement by using a large number of experimental measurements for training. The approach described in this work demonstrates the applicability of artificial intelligence to sensing technology and paves the road for the next generation of sensors.
Improved Current Sensor for Water Diffusion Testing of Composite Insulators
Authors: Zhang Zhonghao , Yin Fanghui , Wang Liming , Mei Hongwei
Published: February 2019

An improved current sensor aimed at measuring currents of different parts in composite insulator samples was proposed. Conventional current sensors used in water diffusion tests aim to examine the performance of composite insulators, however, it is difficult for the conventional current sensors to locate the defects. Thus, we designed a new electrode structure to measure the currents of different components in short samples of composite insulators. Based on a finite analysis method, the influence of relative permittivity and conductivity on the current was analyzed. New samples with different interfaces and samples after operation were tested using the new and conventional current sensors. The performance of a certain part in short samples can be diagnosed by analyzing the current and phase information extracted from the test results. By comparing the test results of new and traditional current sensors, it was proved that the new electrode structure is more effective in locating the defects of insulators.
A Novel Ionospheric Sounding Network Based on Complete Complementary Code and Its Application
Authors: Liu Tongxin , Yang Guobin , Hu Yaogai , Jiang Chunhua , Lan Ting , Zhao Zhengyu , Ni Binbin
Published: February 2019

In this paper, complete complementary code (CCC) sequences are applied to a High Frequency (HF) ionospheric sounding network. Ionosondes distributed at multiple locations use the mutually orthogonal CCC sequences to conduct vertical soundings synchronously. At the same time, thanks to the omnidirectional antennas, every station can receive the oblique echoes transmitted from the others. Due to the orthogonality between the code sequences, both vertical and oblique ionograms can be simultaneously obtained and completely separated. Through this method, the sounding efficiency can be enhanced, and the inversion difficulty can be reduced. Further, by using the data assimilation method, vertical and oblique sounding results can be combined to obtain a wide range of regional ionospheric characteristics. To verify the performance of this kind of sounding network, validation experiments are implemented to demonstrate that vertical and oblique ionograms can be obtained independently at the same time and inverted separately and that the maps of foF2 parameters obtained by using the data assimilation method provide more details than single vertical or oblique sounding.
Detecting Multi-Resolution Pedestrians Using Group Cost-Sensitive Boosting with Channel Features
Authors: Zhu Chao , Yin Xu-Cheng
Published: February 2019

Significant progress has been achieved in the past few years for the challenging task of pedestrian detection. Nevertheless, a major bottleneck of existing state-of-the-art approaches lies in a great drop in performance with reducing resolutions of the detected targets. For the boosting-based detectors which are popular in pedestrian detection literature, a possible cause for this drop is that in their boosting training process, low-resolution samples, which are usually more difficult to be detected due to the missing details, are still treated equally importantly as high-resolution samples, resulting in the false negatives since they are more easily rejected in the early stages and can hardly be recovered in the late stages. To address this problem, we propose in this paper a robust multi-resolution detection approach with a novel group cost-sensitive boosting algorithm, which is derived from the standard AdaBoost algorithm to further explore different costs for different resolution groups of the samples in the boosting process, and to place greater emphasis on low-resolution groups in order to better handle the detection of multi-resolution targets. The effectiveness of the proposed approach is evaluated on the Caltech pedestrian benchmark and KAIST (Korea Advanced Institute of Science and Technology) multispectral pedestrian benchmark, and validated by its promising performance on different resolution-specific test sets of both benchmarks.
Computational Psychometrics Using Psychophysiological Measures for the Assessment of Acute Mental Stress
Authors: Cipresso Pietro , Colombo Desirée , Riva Giuseppe
Published: February 2019

The goal of this study was to provide reliable quantitative analyses of psycho-physiological measures during acute mental stress. Acute, time-limited stressors are used extensively as experimental stimuli in psychophysiological research. In particular, the Stroop Color Word Task and the Arithmetical Task have been widely used in several settings as effective mental stressors. We collected psychophysiological data on blood volume pulse, thoracic respiration, and skin conductance from 60 participants at rest and during stressful situations. Subsequently, we used statistical univariate tests and multivariate computational approaches to conduct comprehensive studies on the discriminative properties of each condition in relation to psychophysiological correlates. The results showed evidence of a greater discrimination capability of the Arithmetical Task compared to the Stroop test. The best predictors were the short time Heart Rate Variability (HRV) indices, in particular, the Respiratory Sinus Arrhythmia index, which in turn could be predicted by other HRV and respiratory indices in a hierarchical, multi-level regression analysis. Thus, computational psychometrics analyses proved to be an effective tool for studying such complex variables. They could represent the first step in developing complex platforms for the automatic detection of mental stress, which could improve the treatment.
Optimal Energy-Delay in Energy Harvesting Wireless Sensor Networks with Interference Channels
Authors: Jiao Dongbin , Ke Liangjun , Liu Shengbo , Chan Felix T.S.
Published: February 2019

In this work, we investigate the capacity allocation problem in the energy harvesting wireless sensor networks (WSNs) with interference channels. For the fixed topologies of data and energy, we formulate the optimization problem when the data flow remains constant on all data links and each sensor node harvests energy only once in a time slot. We focus on the optimal data rates, power allocations and energy transfers between sensor nodes in a time slot. Our goal is to minimize the total delay in the network under two scenarios, i.e., no energy transfer and energy transfer. Furthermore, since the optimization problem is non-convex and difficult to solve directly, by considering the network with the relatively high signal-to-interference-plus-noise ratio (SINR), the non-convex optimization problem can be transformed into a convex optimization problem by convex approximation. We attain the properties of the optimal solution by Lagrange duality and solve the convex optimization problem by the CVX solver. The experimental results demonstrate that the total delay of the energy harvesting WSNs with interference channels is more than that in the orthogonal channel; the total network delay increases with the increasing data flow for the fixed energy arrival rate; and the energy transfer can help to decrease the total delay.
Biochar Particle Size and Post-Pyrolysis Mechanical Processing Affect Soil pH, Water Retention Capacity, and Plant Performance
Authors: Liao Wenxi , Thomas Sean C.
Published: February 2019

It has become common practice in soil applications of biochar to use ground and/or sieved material to reduce particle size and so enhance mixing and surface contact between soils and char particles. Smaller particle sizes of biochars have been suggested to enhance liming effects and nutrient exchange, and potentially to increase water storage capacity; however, data remains scarce and effects on plant growth responses have not been examined. We manipulated biochar particle size by sieving or grinding to generate particles in two size ranges (0.06–0.5 mm and 2–4 mm), and examined effects on soil pH, soil water retention, and plant physiological and growth performance of two test species (ryegrass: Lolium multiflorum, and velvetleaf: Abutilon theophrasti) grown in a granitic sand culture. The small particle sieved biochar had the largest liming effect, increasing substrate pH values by an additional ~0.3 pH units compared to other biochars. Small particle size biochar showed enhanced water retention capacity, and sieved biochars showed 91%–258% larger water retention capacity than ground biochars of similar particle size, likely because sieved particles were more elongated than ground particles, and thus increased soil interpore volume. The two plant species tested showed distinct patterns of response to biochar treatments: ryegrass showed a better growth response to large biochar particles, while velvetleaf showed the highest response to the small, sieved biochar treatment. We show for the first time that post-processing of biochars by sieving and grinding has distinct effects on biochar chemical and physical properties, and that resulting differences in properties have large but strongly species-specific effects on plant performance in biochar-amended substrates.
Scenarios and Indicators for Sustainable Development: Towards a Critical Assessment of Achievements and Challenges
Authors: Spangenberg Joachim H.
Published: February 2019

The global ecosphere is a complex, evolving system, and the anthroposphere another, more rapidly evolving one [...]
The Role of Individual- and Contextual-Level Social Capital in Product Boycotting: A Multilevel Analysis
Authors: Zasuwa Grzegorz
Published: February 2019

Product boycotts represent an important form of sustainable consumption, as withholding purchasing can restrain firms from damaging the natural environment or breaking social rules. However, our understanding of consumer participation in these protests is limited. Most previous studies have focused on the psychological and economic determinants of product boycotting. Drawing on social capital literature, this study builds a framework that explains how individual- and contextual-level social capital affects consumer participation in boycotts of products. A multilevel logistic regression analysis of 29 country representative samples derived from the European Social Survey (N = 54221) shows that at the individual level product boycotting is associated with a person’s social ties, whereas at the country level, generalized trust and social networks positively affect consumer decisions to take part in these protests. These results suggest that to better understand differences among countries in consumer activism, it is necessary to consider the role of social capital as an important predictor of product boycotting.