bluuebunny/crossref_metadata_2025 huggingface dataset:
Semi-Interpenetrating Polymer Networks Based on Cyanate Ester and Highly Soluble Thermoplastic Polyimide
Authors: Liu Jingfeng , Fan Weifeng , Lu Gewu , Zhou Defeng , Wang Zhen , Yan Jingling
Published: May 2019

Thermoplastic polyimide (TPI) was synthesized via a traditional one-step method using 2,3,3′,4′-biphenyltetracarboxylic dianhydride (3,4′-BPDA), 4,4′-oxydianiline (4,4′-ODA), and 2,2′-bis(trifluoromethyl)benzidine (TFMB) as the monomers. A series of semi-interpenetrating polymer networks (semi-IPNs) were produced by dissolving TPI in bisphenol A dicyanate (BADCy), followed by curing at elevated temperatures. The curing reactions of BADCy were accelerated by TPI in the blends, reflected by lower curing temperatures and shorter gelation time determined by differential scanning calorimetry (DSC) and rheological measurements. As evidenced by scanning electron microscopy (SEM) images, phase separation occurred and continuous TPI phases were formed in semi-IPNs with a TPI content of 15% and 20%. The properties of semi-IPNs were systematically investigated according to their glass transition temperatures (Tg), thermo-oxidative stability, and dielectric and mechanical properties. The results revealed that these semi-IPNs possessed improved mechanical and dielectric properties compared with pure polycyanurate. Notably, the impact strength of semi-IPNs was 47%–320% greater than that of polycyanurate. Meanwhile, semi-IPNs maintained comparable or even slightly higher thermal resistance in comparison with polycyanurate. The favorable processability and material properties make TPI/BADCy blends promising matrix resins for high-performance composites and adhesives.
Development of A Novel Corrugated Polyvinylidene difluoride Membrane via Improved Imprinting Technique for Membrane Distillation
Authors: Mat Nawi Normi Izati , Bilad Muhammad Roil , Zolkhiflee Nurazrina , Nordin Nik Abdul Hadi , Lau Woei Jye , Narkkun Thanitporn , Faungnawakij Kajornsak , Arahman Nasrul , Mahlia Teuku Meurah Indra
Published: May 2019

Membrane distillation (MD) is an attractive technology for desalination, mainly because its performance that is almost independent of feed solute concentration as opposed to the reverse osmosis process. However, its widespread application is still limited by the low water flux, low wetting resistance and high scaling vulnerability. This study focuses on addressing those limitations by developing a novel corrugated polyvinylidene difluoride (PVDF) membrane via an improved imprinting technique for MD. Corrugations on the membrane surface are designed to offer an effective surface area and at the same time act as a turbulence promoter to induce hydrodynamic by reducing temperature polarization. Results show that imprinting of spacer could help to induce surface corrugation. Pore defect could be minimized by employing a dual layer membrane. In short term run experiment, the corrugated membrane shows a flux of 23.1 Lm−2h−1 and a salt rejection of >99%, higher than the referenced flat membrane (flux of 18.0 Lm−2h−1 and similar rejection). The flux advantage can be ascribed by the larger effective surface area of the membrane coupled with larger pore size. The flux advantage could be maintained in the long-term operation of 50 h at a value of 8.6 Lm−2h−1. However, the flux performance slightly deteriorates over time mainly due to wetting and scaling. An attempt to overcome this limitation should be a focus of the future study, especially by exploring the role of cross-flow velocity in combination with the corrugated surface in inducing local mixing and enhancing system performance.
Mental Health among Italian Nichiren Buddhists: Insights from a Cross-Sectional Exploratory Study
Authors: Bragazzi Nicola Luigi , Ballico Lorenzo , Del Puente Giovanni
Published: May 2019

Religiosity/spirituality is generally considered as a powerful tool for adjusting and coping with stressors, attributing purposes and meanings (either existential/philosophical, cognitive, or behavioral ones) to daily situations and contexts. While studies generally investigate these effects in Judaism and Christianity believers, there is a dearth of data concerning oriental religions. We sampled from Italian Nichiren Buddhists, the most widespread branch of Buddhism in Italy (n = 391). Participants were Buddhists on average since 5 years and self-defined moderate practitioners. Adaptive strategies exhibited higher scores than maladaptive ones. Specifically, the adaptive strategy of active coping positively correlated with self-evaluated degree of being a practicing Buddhist, as well as positive reframing and religion, while maladaptive strategies such as use of substances, venting and behavioral disengagement correlated negatively. Only the subscale of religion correlated significantly and positively with the time from which the participant had become Buddhist, while the use of emotional support correlated negatively. Most participants had a predominantly internal locus of control. External locus of control negatively correlated with time the participant became Buddhist and the self-reported degree of being a practicing Buddhist, whereas internal locus positively correlated only with the latter variable. Furthermore, Buddhist participants exhibited a low psychopathological profile when compared with the normative scores.
Development and Validation of a Scale for Christian Character Assessment for Young Children1
Authors: Kim Sungwon
Published: May 2019

The emphasis on character education that has emerged from the Nuri national curriculum and the Character Education Act is leading the direction of Korean education. However, the lack of a proper scale of character assessment—especially Christian character for young children—has caused uncertainty in related studies. This study aims to develop and validate a scale that assesses Christian character for young children. The data was collected from 257 (study 1) and 405 (study 2) Christian children who attend church and kindergarten or day care center. Within 12 factors, 67 questions were developed, which were subsequently refined to 39 questions by seven professors. The Christian character scale for young children was finalized to twenty-four questions through exploratory factor analysis and confirmatory factor analysis within four factors: piety, self-control/harmony, responsibility/independence, and caring/respect.
Label Noise Cleansing with Sparse Graph for Hyperspectral Image Classification
Authors: Leng Qingming , Yang Haiou , Jiang Junjun
Published: May 2019

In a real hyperspectral image classification task, label noise inevitably exists in training samples. To deal with label noise, current methods assume that noise obeys the Gaussian distribution, which is not the real case in practice, because in most cases, we are more likely to misclassify training samples at the boundaries between different classes. In this paper, we propose a spectral–spatial sparse graph-based adaptive label propagation (SALP) algorithm to address a more practical case, where the label information is contaminated by random noise and boundary noise. Specifically, the SALP mainly includes two steps: First, a spectral–spatial sparse graph is constructed to depict the contextual correlations between pixels within the same superpixel homogeneous region, which are generated by superpixel image segmentation, and then a transfer matrix is produced to describe the transition probability between pixels. Second, after randomly splitting training pixels into “clean” and “polluted,” we iteratively propagate the label information from “clean” to “polluted” based on the transfer matrix, and the relabeling strategy for each pixel is adaptively adjusted along with its spatial position in the corresponding homogeneous region. Experimental results on two standard hyperspectral image datasets show that the proposed SALP over four major classifiers can significantly decrease the influence of noisy labels, and our method achieves better performance compared with the baselines.
FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond
Authors: Frantz David
Published: May 2019

Ever increasing data volumes of satellite constellations call for multi-sensor analysis ready data (ARD) that relieve users from the burden of all costly preprocessing steps. This paper describes the scientific software FORCE (Framework for Operational Radiometric Correction for Environmental monitoring), an ‘all-in-one’ solution for the mass-processing and analysis of Landsat and Sentinel-2 image archives. FORCE is increasingly used to support a wide range of scientific to operational applications that are in need of both large area, as well as deep and dense temporal information. FORCE is capable of generating Level 2 ARD, and higher-level products. Level 2 processing is comprised of state-of-the-art cloud masking and radiometric correction (including corrections that go beyond ARD specification, e.g., topographic or bidirectional reflectance distribution function correction). It further includes data cubing, i.e., spatial reorganization of the data into a non-overlapping grid system for enhanced efficiency and simplicity of ARD usage. However, the usage barrier of Level 2 ARD is still high due to the considerable data volume and spatial incompleteness of valid observations (e.g., clouds). Thus, the higher-level modules temporally condense multi-temporal ARD into manageable amounts of spatially seamless data. For data mining purposes, per-pixel statistics of clear sky data availability can be generated. FORCE provides functionality for compiling best-available-pixel composites and spectral temporal metrics, which both utilize all available observations within a defined temporal window using selection and statistical aggregation techniques, respectively. These products are immediately fit for common Earth observation analysis workflows, such as machine learning-based image classification, and are thus referred to as highly analysis ready data (hARD). FORCE provides data fusion functionality to improve the spatial resolution of (i) coarse continuous fields like land surface phenology and (ii) Landsat ARD using Sentinel-2 ARD as prediction targets. Quality controlled time series preparation and analysis functionality with a number of aggregation and interpolation techniques, land surface phenology retrieval, and change and trend analyses are provided. Outputs of this module can be directly ingested into a geographic information system (GIS) to fuel research questions without any further processing, i.e., hARD+. FORCE is open source software under the terms of the GNU General Public License v. >= 3, and can be downloaded from http://force.feut.de.
Surfaces of Revolution (SORs) Reconstruction Using a Self-Adaptive Generatrix Line Extraction Method from Point Clouds
Authors: Liu Xianglei , Huang Ming , Li Shanlei , Ma Chaoshuai
Published: May 2019

This paper presents an automatic reconstruction algorithm of surfaces of revolution (SORs) with a self-adaptive method for generatrix line extraction from point clouds. The proposed method does not need to calculate the normal of point clouds, which can greatly improve the efficiency and accuracy of SORs reconstruction. Firstly, the rotation axis of a SOR is automatically extracted by a minimum relative deviation among the three axial directions for both tall-thin and short-wide SORs. Secondly, the projection profile of a SOR is extracted by the triangulated irregular network (TIN) model and random sample consensus (RANSAC) algorithm. Thirdly, the point set of a generatrix line of a SOR is determined by searching for the extremum of coordinate Z, together with overflow points processing, and further determines the type of generatrix line by the smaller RMS errors between linear fitting and quadratic curve fitting. In order to validate the efficiency and accuracy of the proposed method, two kinds of SORs, simple SORs with a straight generatrix line and complex SORs with a curved generatrix line are selected for comparison analysis in the paper. The results demonstrate that the proposed method is robust and can reconstruct SORs with a higher accuracy and efficiency based on the point clouds.
A Partition Modeling for Anthropogenic Heat Flux Mapping in China
Authors: Wang Shasha , Hu Deyong , Chen Shanshan , Yu Chen
Published: May 2019

Anthropogenic heat (AH) generated by human activities has a major impact on urban and regional climate. Accurately estimating anthropogenic heat is of great significance for studies on urban thermal environment and climate change. In this study, a gridded anthropogenic heat flux (AHF) estimation scheme was constructed based on socio-economic data, energy-consumption data, and multi-source remote sensing data using a partition modeling method, which takes into account the regional characteristics of AH emission caused by the differences in regional development levels. The refined AHF mapping in China was realized with a high resolution of 500 m. The results show that the spatial distribution of AHF has obvious regional characteristics in China. Compared with the AHF in provinces, the AHF in Shanghai is the highest which reaches 12.56 W·m−2, followed by Tianjin, Beijing, and Jiangsu. The AHF values are 5.92 W·m−2, 3.35 W·m−2, and 3.10 W·m−2, respectively. As can be seen from the mapping results of refined AHF, the high-value AHF aggregation areas are mainly distributed in north China, east China, and south China. The high-value AHF in urban areas is concentrated in 50–200 W·m−2, and maximum AHF in Shenzhen urban center reaches 267 W·m−2. Further, compared with other high resolution AHF products, it can be found that the AHF results in this study have higher spatial heterogeneity, which can better characterize the emission characteristics of AHF in the region. The spatial pattern of the AHF estimation results correspond to the distribution of building density, population, and industry zone. The high-value AHF areas are mainly distributed in airports, railway stations, industry areas, and commercial centers. It can thus be seen that the AHF estimation models constructed by the partition modeling method can well realize the estimation of large-scale AHF and the results can effectively express the detailed spatial distribution of AHF in local areas. These results can provide technical ideas and data support for studies on surface energy balance and urban climate change.
Hybrid Grasshopper Optimization Algorithm and Differential Evolution for Multilevel Satellite Image Segmentation
Authors: Jia Heming , Lang Chunbo , Oliva Diego , Song Wenlong , Peng Xiaoxu
Published: May 2019

An efficient satellite image segmentation method based on a hybrid grasshopper optimization algorithm (GOA) and minimum cross entropy (MCE) is proposed in this paper. The proposal is known as GOA–jDE, and it merges GOA with self-adaptive differential evolution (jDE) to improve the search efficiency, preserving the population diversity especially in the later iterations. A series of experiments is conducted on various satellite images for evaluating the performance of the algorithm. Both low and high levels of the segmentation are taken into account, increasing the dimensionality of the problem. The proposed approach is compared with the standard color image thresholding methods, as well as the advanced satellite image thresholding techniques based on different criteria. Friedman test and Wilcoxon’s rank sum test are performed to assess the significant difference between the algorithms. The superiority of the proposed method is illustrated from different aspects, such as average fitness function value, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature similarity index (FSIM), standard deviation (STD), convergence performance, and computation time. Furthermore, natural images from the Berkeley segmentation dataset are also used to validate the strong robustness of the proposed method.
Power Factor Compensation Using Teaching Learning Based Optimization and Monitoring System by Cloud Data Logger
Authors: Cano Ortega Antonio , Sánchez Sutil Francisco Jose , De la Casa Hernández Jesús
Published: May 2019

The main objective of this paper is to compensate power factor using teaching learning based optimization (TLBO), determine the capacitor bank optimization (CBO) algorithm, and monitor a system in real-time using cloud data logging (CDL). Implemented Power Factor Compensation and Monitoring System (PFCMS) calculates the optimal capacitor combination to improve power factor of the installation by measure of voltage, current, and active power. CBO algorithm determines the best solution of capacitor values to install, by applying TLBO in different phases of the algorithm. Electrical variables acquired by the sensors and the variables calculated are stored in CDL using Google Sheets (GS) to monitor and analyse the installation by means of a TLBO algorithm implemented in PFCMS, that optimizes the compensation power factor of installation and determining which capacitors are connected in real time. Moreover, the optimization of the power factor in facilities means economic and energy savings, as well as the improvement of the quality of the operation of the installation.
An Improved Two-Dimensional Direction-Of-Arrival Estimation Algorithm for L-Shaped Nested Arrays with Small Sample Sizes
Authors: Gao Xiaofeng , Hao Xinhong , Li Ping , Li Guolin
Published: May 2019

In this paper, an improved two-dimensional (2-D) direction of arrival (DOA) estimation algorithm for L-shaped nested arrays is proposed. Unlike the approach for a classical nested array, which use the auto-correlation matrix (ACM) to increase the degrees of freedom (DOF), we utilize the cross-correlation matrix (CCM) of different sub-arrays to generate two long consecutive virtual arrays. These acquire a large number of DOF without redundant elements and eliminate noise effects. Furthermore, we reconstruct the CCM based on the singular value decomposition (SVD) operation in order to reduce the perturbation of noise for small numbers of samples. To cope with the matrix rank deficiency of the virtual arrays, we construct the full rank equivalent covariance matrices by using the output and its conjugate vector of virtual arrays. The unitary estimation of signal parameters via rotational invariance technique (ESPRIT) is then performed on the covariance matrices to obtain the DOA of incident signals with low computational complexity. Finally, angle pairing is achieved by deriving the equivalent signal vector of the virtual arrays using the estimated angles. Numerical simulation results show that the proposed algorithm not only provides more accurate 2-D DOA estimation performance with low complexity, but also achieves angle estimation for small numbers of samples compared to existing similar methods.
Smart Fault-Detection Machine for Ball-Bearing System with Chaotic Mapping Strategy
Authors: Li Shih-Yu , Gu Kai-Ren
Published: May 2019

In this paper, a set of smart fault-detection approach with chaotic mapping strategy is developed for an industrial ball-bearing system. There are four main statuses in this ball-bearing system: normal, inner race fault, outer race fault, and ball fault. However, it is hard to simply classify each of them through their vibration signals in time-series. By developing a nonlinear error dynamic system as well as a chaotic mapping strategy, the signals in the time series can be converted into the chaotic domain, which are revealed in 3D phase portraits. Further, through collocation of clustering methods, such as Euclidean distance (ED) and the kernel method of K-means (KM), the proposed 3D phase portraits of each different state can be efficiently identified through checking the autonomously adjusted ranges of feature values. The experiment results show that the proposed smart detection approach is effective and feasible, and the accuracy of detection in the testing stage is close to 100%.
Large Area Nanohole Arrays for Sensing Fabricated by Interference Lithography
Authors: Valsecchi Chiara , Gomez Armas Luis Enrique , Weber de Menezes Jacson
Published: May 2019

Several fabrication techniques are recently used to produce a nanopattern for sensing, as focused ion beam milling (FIB), e-beam lithography (EBL), nanoimprinting, and soft lithography. Here, interference lithography is explored for the fabrication of large area nanohole arrays in metal films as an efficient, flexible, and scalable production method. The transmission spectra in air of the 1 cm2 substrate were evaluated to study the substrate behavior when hole-size, periodicity, and film thickness are varied, in order to elucidate the best sample for the most effective sensing performance. The efficiency of the nanohole array was tested for bulk sensing and compared with other platforms found in the literature. The sensitivity of ~1000 nm/RIU, achieved with an array periodicity in the visible range, exceeds near infrared (NIR) performances previously reported, and demonstrates that interference lithography is one of the best alternative to other expensive and time-consuming nanofabrication methods.
Highly Enhanced Inductance Sensing Performance of Dual-Quartz Crystal Converter
Authors: Matko Vojko , Milanovic Miro
Published: May 2019

This paper presents ways of inductance sensitivity improvement in a quartz crystal converter for low inductance measurement. To improve the converter’s sensitivity, two quartz crystals that were connected in parallel and additional capacitance connected to the two quartz crystals in the oscillator’s circuit are used. The new approach uses a converter with special switchable oscillator and multiplexer switches to compensate for the crystal’s natural temperature-frequency characteristics and any other influences, such as parasitic capacitances and parasitic inductances, which reduce them to a minimum. The experimental results demonstrate improved sensitivity and well-compensated dynamic temperature influence on the converter’s output frequency. The fundamental quartz crystal frequency-temperature characteristics in the temperature range between 0–40 °C are simultaneously compensated. Furthermore, the converter enables the measurement of the influence of its own hysteresis at different values of inductances at the selected sensitivity by parallel capacitances connected either to the single- or dual-quartz crystal unit. The results show that the converter converting inductances in the range between 85–100 μH to a frequency range between 1–150 kHz only has ±0.05 ppm frequency instability (during the temperature change between 0–40 °C), which gives the converter a resolution of 1 pH. As a result, the converter can be applied where low inductance measurement, nondestructive testing, impedance change measurement, and magnetic material properties measurement are important.
Demonstration of a Low-Cost and Portable Optical Cavity-Based Sensor through Refractive Index Measurements
Authors: Rho Donggee , Breaux Caitlyn , Kim Seunghyun
Published: May 2019

An optical cavity-based sensor using a differential detection method has been proposed for point-of-care diagnostics. We developed a low-cost and portable optical cavity-based sensor system using a 3D printer and off-the-shelf optical components. In this paper, we demonstrate the sensing capability of the portable system through refractive index measurements. Fabricated optical cavity samples were tested using the portable system and compared to simulation results. A referencing technique and digital low pass filtering were applied to reduce the noise of the portable system. The measurement results match the simulation results well and show the improved linearity and sensitivity by employing the differential detection method. The limit of detection achieved was 1.73 × 10−5 Refractive Index Unit (RIU), which is comparable to other methods for refractive index sensing.
A Comparison Study of Fatigue Behavior of Hard and Soft Piezoelectric Single Crystal Macro-Fiber Composites for Vibration Energy Harvesting
Authors: Peddigari Mahesh , Kim Ga-Yeon , Park Chan Hee , Min Yuho , Kim Jong-Woo , Ahn Cheol-Woo , Choi Jong-Jin , Hahn Byung-Dong , Choi Joon-Hwan , Park Dong-Soo , Hong Jae-Keun , Yeom Jong-Taek , Park Kwi-Il , Jeong Dae-Yong , Yoon Woon-Ha , Ryu Jungho , Hwang Geon-Tae
Published: May 2019

Designing a piezoelectric energy harvester (PEH) with high power density and high fatigue resistance is essential for the successful replacement of the currently using batteries in structural health monitoring (SHM) systems. Among the various designs, the PEH comprising of a cantilever structure as a passive layer and piezoelectric single crystal-based fiber composites (SFC) as an active layer showed excellent performance due to its high electromechanical properties and dynamic flexibilities that are suitable for low frequency vibrations. In the present study, an effort was made to investigate the reliable performance of hard and soft SFC based PEHs. The base acceleration of both PEHs is held at 7 m/s2 and the frequency of excitation is tuned to their resonant frequency (fr) and then the output power (Prms) is monitored for 107 fatigue cycles. The effect of fatigue cycles on the output voltage, vibration displacement, dielectric, and ferroelectric properties of PEHs was analyzed. It was noticed that fatigue-induced performance degradation is more prominent in soft SFC-based PEH (SS-PEH) than in hard SFC-based PEH (HS-PEH). The HS-PEH showed a slight degradation in the output power due to a shift in fr, however, no degradation in the maximum power was noticed, in fact, dielectric and ferroelectric properties were improved even after 107 vibration cycles. In this context, the present study provides a pathway to consider the fatigue life of piezoelectric material for the designing of PEH to be used at resonant conditions for long-term operation.
Delamination Detection in Polymeric Ablative Materials Using Pulse-Compression Thermography and Air-Coupled Ultrasound
Authors: Laureti Stefano , Khalid Rizwan Muhammad , Malekmohammadi Hamed , Burrascano Pietro , Natali Maurizio , Torre Luigi , Rallini Marco , Puri Ivan , Hutchins David , Ricci Marco
Published: May 2019

Ablative materials are used extensively in the aerospace industry for protection against high thermal stresses and temperatures, an example being glass/silicone composites. The extreme conditions faced and the cost-risk related to the production/operating stage of such high-tech materials indicate the importance of detecting any anomaly or defect arising from the manufacturing process. In this paper, two different non-destructive testing techniques, namely active thermography and ultrasonic testing, have been used to detect a delamination in a glass/silicone composite. It is shown that a frequency modulated chirp signal and pulse-compression can successfully be used in active thermography for detecting such a delamination. Moreover, the same type of input signal and post-processing can be used to generate an image using air-coupled ultrasound, and an interesting comparison between the two can be made to further characterise the defect.
Guided Wave Propagation in Detection of Partial Circumferential Debonding in Concrete Structures
Authors: Zima Beata
Published: May 2019

The following article presents results of investigating the damage detection in reinforced concrete beams with artificially introduced debonding between the rod and cover, using a non-destructive method based on elastic waves propagation. The primary aim of the research was to analyze the possible use of guided waves in partial circumferential debonding detection. Guided waves were excited and registered in reinforced concrete specimens with varying extents of debonding damage by piezoelectric sensors attached at both ends of the beams. Experimental results in the form of time–domain signals registered for variable extent of debonding were compared, and the relationships relating to the damage size and time of flight and average wave velocity were proposed. The experimental results were compared with theoretical predictions based on dispersion curves traced for the free rod of circular cross-section and rectangular reinforced concrete cross-section. The high agreement of theoretical and experimental data proved that the proposed method, taking advantage of average wave velocity, can be efficiently used for assessing debonding size in reinforced concrete structures. It was shown that the development of damage size in circumferential direction has a completely different impact on wave velocity than development of debonding length. The article contains a continuation of work previously conducted on the detection of delamination in concrete structures. The proposed relationship is the next essential step for developing a diagnostics method for detecting debondings of any size and orientation.
Directional Forgetting for Stable Co-Adaptation in Myoelectric Control
Authors: Yeung Dennis , Farina Dario , Vujaklija Ivan
Published: May 2019

Conventional myoelectric controllers provide a mapping between electromyographic signals and prosthetic functions. However, due to a number of instabilities continuously challenging this process, an initial mapping may require an extended calibration phase with long periods of user-training in order to ensure satisfactory performance. Recently, studies on co-adaptation have highlighted the benefits of concurrent user learning and machine adaptation where systems can cope with deficiencies in the initial model by learning from newly acquired data. However, the success remains highly dependent on careful weighting of these new data. In this study, we proposed a function driven directional forgetting approach to the recursive least-squares algorithm as opposed to the classic exponential forgetting scheme. By only discounting past information in the same direction of the new data, local corrections to the mapping would induce less distortion to other regions. To validate the approach, subjects performed a set of real-time myoelectric tasks over a range of forgetting factors. Results show that directional forgetting with a forgetting factor of 0.995 outperformed exponential forgetting as well as unassisted user learning. Moreover, myoelectric control remained stable after adaptation with directional forgetting over a range of forgetting factors. These results indicate that a directional approach to discounting past training data can improve performance and alleviate sensitivities to parameter selection in recursive adaptation algorithms.
A Novel FEM Based T-S Fuzzy Particle Filtering for Bearings-Only Maneuvering Target Tracking
Authors: Wang Xiaoli , Li Liangqun , Xie Weixin
Published: May 2019

In this paper, we propose a novel fuzzy expectation maximization (FEM) based Takagi-Sugeno (T-S) fuzzy particle filtering (FEMTS-PF) algorithm for a passive sensor system. In order to incorporate target spatial-temporal information into particle filtering, we introduce a T-S fuzzy modeling algorithm, in which an improved FEM approach is proposed to adaptively identify the premise parameters, and the model probability is adjusted by the premise membership functions. In the proposed FEM, the fuzzy parameter is derived by the fuzzy C-regressive model clustering method based on entropy and spatial-temporal characteristics, which can avoid the subjective influence caused by the artificial setting of the initial value when compared to the traditional FEM. Furthermore, using the proposed T-S fuzzy model, the algorithm samples particles, which can effectively reduce the particle degradation phenomenon and the parallel filtering, can realize the real-time performance of the algorithm. Finally, the results of the proposed algorithm are evaluated and compared to several existing filtering algorithms through a series of Monte Carlo simulations. The simulation results demonstrate that the proposed algorithm is more precise, robust and that it even has a faster convergence rate than the interacting multiple model unscented Kalman filter (IMMUKF), interacting multiple model extended Kalman filter (IMMEKF) and interacting multiple model Rao-Blackwellized particle filter (IMMRBPF).
Instant Mercury Ion Detection in Industrial Waste Water with a Microchip Using Extended Gate Field-Effect Transistors and a Portable Device
Authors: Sukesan Revathi , Chen Yi-Ting , Shahim Suman , Wang Shin-Li , Sarangadharan Indu , Wang Yu-Lin
Published: May 2019

Mercury ion selective membrane (Hg-ISM) coated extended gate Field Effect transistors (ISM-FET) were used to manifest a novel methodology for ion-selective sensors based on FET’s, creating ultra-high sensitivity (−36 mV/log [Hg2+]) and outweighing ideal Nernst sensitivity limit (−29.58 mV/log [Hg2+]) for mercury ion. This highly enhanced sensitivity compared with the ion-selective electrode (ISE) (10−7 M) has reduced the limit of detection (10−13 M) of Hg2+ concentration’s magnitude to considerable orders irrespective of the pH of the test solution. Systematical investigation was carried out by modulating sensor design and bias voltage, revealing that higher sensitivity and a lower detection limit can be attained in an adequately stronger electric field. Our sensor has a limit of detection of 10−13 M which is two orders lower than Inductively Coupled Plasma Mass Spectrometry (ICP-MS), having a limit of detection of 10−11 M. The sensitivity and detection limit do not have axiomatic changes under the presence of high concentrations of interfering ions. The technology offers economic and consumer friendly water quality monitoring options intended for homes, offices and industries.
Emotion Recognition from Multiband EEG Signals Using CapsNet
Authors: Chao Hao , Dong Liang , Liu Yongli , Lu Baoyun
Published: May 2019

Emotion recognition based on multi-channel electroencephalograph (EEG) signals is becoming increasingly attractive. However, the conventional methods ignore the spatial characteristics of EEG signals, which also contain salient information related to emotion states. In this paper, a deep learning framework based on a multiband feature matrix (MFM) and a capsule network (CapsNet) is proposed. In the framework, the frequency domain, spatial characteristics, and frequency band characteristics of the multi-channel EEG signals are combined to construct the MFM. Then, the CapsNet model is introduced to recognize emotion states according to the input MFM. Experiments conducted on the dataset for emotion analysis using EEG, physiological, and video signals (DEAP) indicate that the proposed method outperforms most of the common models. The experimental results demonstrate that the three characteristics contained in the MFM were complementary and the capsule network was more suitable for mining and utilizing the three correlation characteristics.
A Review of the Popular and Scholarly Accounts of Donald Trump’s White Working-Class Support in the 2016 US Presidential Election
Authors: Thompson Jack
Published: May 2019

Popular and scholarly accounts of Trump’s ascendency to the presidency of the United States on the part of the American white working-class use different variables to define the sociodemographic group because there is no “working-class White” variable available in benchmark datasets for researchers to code. To address this need, the Author ran a multinomial regression to assess whether income, education and racial identity predict working-class membership among white Americans, finding that income and education are statistically significant predictors of working-class whiteness, while racial identity is not. Arriving at a robust definition of “white working-class” in light of these findings, the paper next turns to a review of the extant literature. By retrieving studies from searches of computerised databases, hand searches and authoritative texts, the review critically surmises the explanatory accounts of Trump’s victory. Discussion of the findings from the review is presented in three principal sections. The first section explains how working-class White communities, crippled by a dearth of social and geographic mobility, have been “left behind” by the political elites. The second section examines how white Americans, whose dominant group position is threatened by demographic change, voted for Trump because of resonance between his populist rhetoric and their latent “racist” attitudes. The third and final section explores the implications of a changing America for native-born whites, and how America’s increasing ethnoracial diversity is eroding relations between its dominant and nondominant groups. The Author surmises by arguing that these explanatory accounts must be understood in the context of this new empirical approximation of “working-class White”.
Impact of Low-Income Home Energy-Efficiency Retrofits on Building Air Tightness and Healthy Home Indicators
Authors: Shrestha Prateek M. , Humphrey Jamie L. , Barton Kelsey E. , Carlton Elizabeth J. , Adgate John L. , Root Elisabeth D. , Miller Shelly L.
Published: May 2019

We studied 226 low-income households as a part of the Colorado Home Energy Efficiency and Respiratory Health (CHEER) study to investigate the relationship between energy-efficiency retrofits (EERs) specific to air sealing of residential building envelopes, annual average infiltration rates (AAIR), and qualitative indicators of “healthy” homes. Blower door tests quantified the leakage area in each home, which was used to estimate the AAIR. Walk-through inspections were used to record observations of air-sealing retrofits conducted as part of Colorado’s Weatherization Assistance Program and indirect indicators of poor indoor environmental quality (IEQ) in the homes, such as visible mold or stains, visible dust on hard surfaces, vapor condensation on windows, dampness, and perceived air quality. Results showed that building characteristics like age and volume affected AAIR more significantly than air-sealing EERs. Among the air-sealing EERs, homes with the air-handler ductwork sealed and windows weather-stripped were found to have significantly lower AAIR compared to the homes without these features. Mold growth, wall stains, notably higher levels of dust, and unacceptable odor levels were more frequently reported in homes with higher AAIR, showing that leakier homes do not necessarily have better IEQ.
Key Considerations When Designing Certification Systems for Urban Sustainability and Implications for The Swedish Post-Construction System Citylab
Authors: Lind Jonas , Malmqvist Tove , Wangel Josefin
Published: May 2019

Addressing sustainability in urban planning has led to an increasing number of certification systems to support such processes. Nevertheless, there is no commonly recognised framework listing what is important to consider when developing such systems. Citylab is a certification system that is used in several Swedish urban development projects. Today, Citylab certifies the planning process of urban areas but it will be extended with a post-construction part. This paper presents a three steps analysis of the design of such a post-construction certification system. First, a literature review was performed, which allowed for identifying three principles and 11 sub-principles that make up a generic framework for the design of similar certification systems. Second, 13 semi-structured interviews were conducted in Sweden with key urban development stakeholders to better specify the scope of a post-construction extension of Citylab. As a result, four alternatives emerge for the role and function of this system. Third, crossing the results of both previous steps allowed for an understanding of important considerations and implications for the Citylab post-construction certification system design. The paper concludes on the relevance of such a reflexive procedure for the design of certification systems in general, in which the use of the framework is a key to ensure transparency and enable deliberate choices and priorities.
An Integral Approach to the Modeling of Information Support for Local Sustainable Development—Experiences of a Serbian Enabling Leadership Experiment
Authors: Lalović Ksenija , Živković Jelena , Radosavljević Uroš , Đukanović Zoran
Published: May 2019

Collaborative strategic decision making has to be widely informed, communicated and knowledge-based in order to innovate transformations toward local and global sustainability. It is unimaginable that this process could be effective without computer-aided information support, but the research indicates the utilization constraints within human capacities to recognize their usability and usefulness. These constraints seem to be even more challenging within the intensively transitional social contexts, such as Serbia. We argue that understanding the relationships between sustainability, governance, and planning in a specific social context has profound importance to gain usefulness of information support and to ensure its increasing utilization. Identifying the practical path of information support modeling requires an operational framework that encompasses innovative and socially valid initiatives. Therefore, an integral theory framework was chosen to comprehend all social influences on the information support of successful utilization. This article presents the integral framework of the information support’s conceptual setting, which was used to build up community-based collaborative action research (CBCAR) as a transformative social learning process that enables information support utilization, and it was tested in six municipalities of Serbia. The implementation of pilot territorial information support (TIS) initiatives resulted in continuous and proactive local community efforts in information support development and usage.
Rapid Simulation of Optimally Responsive Façade during Schematic Design Phases: Use of a New Hybrid Metaheuristic Algorithm
Authors: Yi Hwang , Kim Mi-Jin , Kim Yuri , Kim Sun-Sook , Lee Kyu-In
Published: May 2019

Operation of environmentally responsive building components requires rapid prediction of the optimal adaptation of geometric shapes and positions, and such responsive configuration needs to be identified during the design process as early as possible. However, building simulation practices to characterize optimized shapes of various geometric design candidates are limited by complex simulation procedures, slow optimization, and lack of site information. This study suggests a practical approach to the design of responsive building façades by integrating on-site sensors, building performance simulation (BPS), machine-learning, and 3D geometry modeling on a unified design interface. To this end, a novel and efficient hybrid optimization algorithm, tabu-based adaptive pattern search simulated annealing (T-APSSA), was developed and integrated with wireless sensor data communication (using nRF24L01 and ESP8266 WiFi modules) on a parametric visual programming language (VPL) interface Rhino Grasshopper (0.9.0076, McNeel, Seattle, USA). The effectiveness of T-APSSA for early-stage BPS and optimal design is compared with other metaheuristic algorithms, and the proposed framework is validated by experimental optimal envelope (window shading) designs for single (daylight) and multiple (daylight and energy) objectives. Test results demonstrate the improved efficiency of T-APSSA in calculations (two to four times faster than other algorithms). This T-APSSA-integrated sensor-enabled design optimization practice supports rapid BPS and digital prototyping of responsive building façade design.
Trade Openness and Carbon Emissions: Evidence from Belt and Road Countries
Authors: Sun Huaping , Attuquaye Clottey Samuel , Geng Yong , Fang Kai , Clifford Kofi Amissah Joshua
Published: May 2019

The search for a green and low-carbon economy has been a guide to current energy and environmental research. Using current panel cointegration approaches, our study examines the interaction between trade and an environmental pollution proxy of carbon dioxide (CO2) emissions by integrating economic growth and energy usage as major potential determining factors in this relationship for 49 high-emission countries in Belt and Road regions over the period of 1991–2014. For a robust analysis, we further grouped these countries into income panels (high, middle, low) and various regions (East Asia, Southeast Asia, Central Asia, South Asia, the Middle East/Africa, and Europe). The results of the panel cointegration tests revealed that the four variables were stationary in the long run. Similarly, our panel results indicated that trade openness had both positive and negative impacts on environmental pollution, but the effect varied in these different groups of nations. The results of the vector error correction model (VECM) causality also showed a long-run causal effect between trade, economic growth, energy consumption, and environmental pollution in the Belt and Road, Europe, high-income, middle-income, and low-income panels. The environmental Kuznets curve (EKC) results further indicated the existence of an inverted U-form relationship between trade and carbon emissions. Finally, certain policy implications are discussed.
The Sustainability of an Urban Ritual in the Collective Memory: Bergama Kermesi
Authors: İnceköse Ülkü
Published: May 2019

Bergama Festival, locally known as Bergama Kermesi, is an annual festival which dates back to 22 May 1937 in the city. It came into existence as a result of Atatürk’s intention to introduce this, an extraordinary town with its historical and cultural properties, and promote it internationally. The Festival is an important element in the collective memory of the city. Initially, it was a civic event, a device in the formation process of the Turkish Republic. However, now, it is a civil event for national and international representatives, and a festival that allows locals and guests from different social, economic, and cultural backgrounds to mix freely and equally for a certain period. In the course of the Festival, the public buildings and the open spaces of the town become places of activity and entertainment. Parks, stadiums, the town square, and streets function as spaces for a variety of activities. Looking back at its 81-year history, one can notice some important changes in the Festival’s cultural and social practices, from an earlier state-dominated character into the current more publicly oriented one. This article studies the change of Bergama Festival as an ‘invented tradition’ into an element of the collective memory in town from the perspective of different public affairs that it introduces. In this regard, the article will also show how an urban ritual can maintain its sustainability by keeping itself fresh in the social life.
Incorporating Forests, Agriculture, and Energy Consumption in the Framework of the Environmental Kuznets Curve: A Dynamic Panel Data Approach
Authors: Parajuli Rajan , Joshi Omkar , Maraseni Tek
Published: May 2019

Based on country-specific panel data from 1990–2014 for 86 different countries, we quantify the effects of forests and agricultural land in CO2 emissions, using the framework of the Environmental Kuznets Curve (EKC). The results from the dynamic panel data method reveal that forests are an important determinant in reducing CO2 emissions globally, but the effects vary by region. All else constant, we estimate a 0.11% decline in CO2 emissions per 1% increase in the forest area globally. However, the agricultural sector is found to be a true CO2 emitter. Our study provides additional empirical evidence for the roles of forests in regulating atmospheric CO2, further reinforcing the importance of forests in global climate change policies.