bluuebunny/crossref_metadata_2025 huggingface dataset:
Design of Web-to-Web Spacing for the Reduced Pressure Drop and Effective Depth Filtration
Authors: Roh Sanghyun , Park Kangsoo , Kim Jooyoun
Published: November 2019

The study aims at lowering the pressure drop and extending the service life at a given set of filter materials implementing a space between the filter layers. As design factors, the web-to-web space was implemented by inserting either a bulk air gap or porous spacer web between the filter webs. The effect of spacing, either by the air gap or by the spacer web, on the pressure drop reduction was apparent for 4-layer constructions, and the effect was greater at the higher face velocity. The use of spacer web was more effective than the air gap in reducing the pressure drop, because the porous, fluffy spacer web acted as an effective air flow channel between the compact filter layers. The loading capacity was also increased with the spacer web implementation, effectively delaying the clogging point and extending the service life. Employing both experimental investigation and numerical simulation, this study intended to provide a practical design solution to the important problem in the field of air filtration. The results of this study can be used as a practical design guide to reduce pressure drop via depth filtration.
Embedding Ultra-High-Molecular-Weight Polyethylene Fibers in 3D-Printed Polylactic Acid (PLA) Parts
Authors: Amza Cătălin Gheorghe , Zapciu Aurelian , Eyþórsdóttir Arnheiður , Björnsdóttir Auðbjörg , Borg Jonathan
Published: November 2019

This study aims to assess whether ultra-high-molecular-weight polyethylene (UHMWPE) fibers can be successfully embedded in a polylactic acid (PLA) matrix in a material extrusion 3D printing (ME3DP) process, despite the apparent thermal incompatibility between the two materials. The work started with assessing the maximum PLA extrusion temperatures at which UHMWPE fibers withstand the 3D printing process without melting or severe degradation. After testing various fiber orientations and extrusion temperatures, it has been found that the maximum extrusion temperature depends on fiber orientation relative to extrusion pathing and varies between 175 °C and 185 °C at an ambient temperature of 25 °C. Multiple specimens with embedded strands of UHMWPE fibers have been 3D printed and following tensile strength tests on the fabricated specimens, it has been found that adding even a small number of fiber strands laid in the same direction as the load increased tensile strength by 12% to 23% depending on the raster angle, even when taking into account the decrease in tensile strength due to reduced performance of the PLA substrate caused by lower extrusion temperatures.
Screening and Diversity Analysis of Aerobic Denitrifying Phosphate Accumulating Bacteria Cultivated from A2O Activated Sludge
Authors: Li Yong , Zhao Siyuan , Zhang Jiejie , He Yang , Zhang Jianqiang , Ge Rong
Published: November 2019

The aerobic denitrifying phosphate accumulating bacteria (ADPB) use NO3− as an electron acceptor and remove nitrate by denitrification and concomitant uptake of excessive phosphorus in aerobic conditions. Activated sludge was collected from the A2O aerobic biological pool of the sewage treatment plant at Hezuo Town, Chengdu City. The candidate ADPB strains were obtained by cultivation in the enriched denitrification media, followed by repeated isolation and purification on bromothymol blue (BTB) solid plates. The obtained candidates were further screened for ADPB strains by phosphorus uptake experiment, nitrate reduction test, metachromatic granules staining, and poly-β-hydroxybutyrate (PHB) staining. The 16 sedimentation ribosome deoxyribonucleic acid (16 S rDNA) molecular technique was used to determine their taxonomy. Further, the denitrification and dephosphorization capacities of ADPB strains were ascertained through their growth characteristics in nitrogen-phosphorus-rich liquid media. The results revealed a total of 25 ADPB strains screened from the activated sludge of the A2O aerobic biological pool of the sewage treatment plant at Hezuo Town. These strains belonged to two classes, four orders, and five genera. Among them, the strain SW18NP2 was a potentially new species in the Acinetobacter genus, while the strain SW18NP24 was a potential new species in the Pseudomonas genus at the time of their characterization. The Acinetobacter was the dominant genus. The obtained ADPB strains demonstrated a rich diversity. The ADPB strains had significant variations in denitrification and dephosphorization capacities. Twenty-three strains exhibited a total phosphorus removal rate of above 50%, and 19 strains exhibited a total nitrogen removal rate of above 50%. The strain SW18NP2 showed the best denitrifying phosphorus removal (DPR) capacity, with a dephosphorization rate of 82.32% and a denitrification rate of 73.73%. The ADPB in the A2O aerobic biological pool of the sewage treatment plant at Hezuo Town demonstrated a rich diversity and a strong DPR capacity.
Yah’s Exemplary Soldiers: African Hebrew Israelites in the Israel Defense Forces
Authors: Esensten Andrew
Published: November 2019

This article considers the process of identity formation among soldiers in the Israel Defense Forces (IDF) who were born into the African Hebrew Israelites of Jerusalem (AHIJ), more commonly known as the Black Hebrews. The AHIJ are a sect of African Americans who began settling in Israel in 1969 and who identify as direct descendants of the Biblical Israelites. Due to the group’s insular nature, the IDF is the primary state institution in which they fully participate, and their mandatory service is a source of both pride and consternation for community members and leaders. Considering the personal experiences of 14 African Hebrew soldiers who enlisted between 2009 and 2010, the article argues that while the soldiers by and large maintain their distinctive identity during the course of their service, they also internalize some of the language, attitudes, and cultural touchstones of the majority Israeli Jewish population. As a result, they experience a kind of “double consciousness”, the feeling of dislocation first described by the African American scholar W. E. B. Du Bois at the turn of the twentieth century.
Rethinking Anger as a Desire for Payback: A Modified Thomistic View
Authors: Rippentrop Schnell Jan , Fritz Cates Diana
Published: November 2019

This essay takes a fresh approach to a traditional Western philosophical account of anger, according to which anger is best defined as a desire for payback, namely, a desire to make an offender pay a price, in the currency of unwanted pain, for the pain he caused someone else. The essay focuses more specifically on the work of Thomas Aquinas, whose account of anger is often thought to center on a desire for ‘just vengeance.’ It analyzes and extends aspects of Aquinas’s account that have previously been treated too narrowly. It distinguishes three forms of anger, each of which has important features in common, which justify characterizing it as anger. Only one of these forms involves a desire to make an offender suffer for what he did. Even as this essay argues for articulating different forms of anger, it emphasizes the fluidity of anger’s forms, features, and relationships to other emotions. It briefly engages philosophical, psychological, and neuroscientific perspectives while working principally in the domain of religious ethics and moral psychology.
Semi-Coupled Convolutional Sparse Learning for Image Super-Resolution
Authors: Li Lingling , Zhang Sibo , Jiao Licheng , Liu Fang , Yang Shuyuan , Tang Xu
Published: November 2019

In the convolutional sparse coding-based image super-resolution problem, the coefficients of low- and high-resolution images in the same position are assumed to be equivalent, which enforces an identical structure of low- and high-resolution images. However, in fact the structure of high-resolution images is much more complicated than that of low-resolution images. In order to reduce the coupling between low- and high-resolution representations, a semi-coupled convolutional sparse learning method (SCCSL) is proposed for image super-resolution. The proposed method uses nonlinear convolution operations as the mapping function between low- and high-resolution features, and conventional linear mapping can be seen as a special case of the proposed method. Secondly, the neighborhoods within the filter size are used to calculate the current pixel, improving the flexibility of our proposed model. In addition, the filter size is adjustable. In order to illustrate the effectiveness of SCCSL method, we compare it with four state-of-the-art methods of 15 commonly used images. Experimental results show that this work provides a more flexible and efficient approach for image super-resolution problem.
Demonstration and Evaluation of 3D Winds Generated by Tracking Features in Moisture and Ozone Fields Derived from AIRS Sounding Retrievals
Authors: Santek David , Nebuda Sharon , Stettner Dave
Published: November 2019

For more than 15 years, polar winds from the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery have been generated by the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Meteorological Satellite Studies (CIMSS). These datasets are a NOAA National Environmental Satellite, Data, and Information Service (NESDIS) operational satellite product that is used at more than 10 major numerical weather prediction (NWP) centers worldwide. The MODIS polar winds product is composed of both infrared window (IR-W) and water vapor (WV) tracked features. The WV atmospheric motion vectors (AMV) yield a better spatial distribution than the IR-W since both cloud and clear-sky features can be tracked in the WV images. As the new generation polar satellite-era begins with the Suomi National Polar-orbiting Partnership (S-NPP), there is currently no WV channel on the Visible/Infrared Imager/Radiometer Suite (VIIRS), resulting in a data gap with only IR-W derived AMVs possible. This scenario presents itself as an opportunity to evaluate hyperspectral infrared moisture retrievals from consecutive overlapping satellite polar passes to extract atmospheric motion from clear-sky regions on constant (and known) pressure surfaces, i.e., estimating winds in retrieval space rather than radiance space. Perhaps most significantly, this method has the potential to provide vertical wind profiles, as opposed to the current MODIS-derived single-level AMVs. In this study, the winds technique is applied to Atmospheric Infrared Sounder (AIRS) moisture retrievals from NASA’s Aqua satellite. The resulting winds are assimilated into the Goddard Earth Observing System Model, Version 5 (GEOS-5). The results are encouraging, as the AIRS retrieval polar AMVs have a similar quality as the MODIS AMVs and exhibit a positive impact in the hemispheric Day 4.5 to 6.5 forecasts for a one-month experiment in July 2012.
On the Interest of Optical Remote Sensing for Seasonal Snowmelt Parameterization, Applied to the Everest Region (Nepal)
Authors: Bouchard Benjamin , Eeckman Judith , Dedieu Jean-Pierre , Delclaux François , Chevallier Pierre , Gascoin Simon , Arnaud Yves
Published: November 2019

In the central part of the Hindu Kush Himalayan region, snowmelt is one of the main inputs that ensures the availability of surface water outside the monsoon period. A common approach for snowpack modeling is based on the degree day factor (DDF) method to represent the snowmelt rate. However, the important seasonal variability of the snow processes is usually not represented when using a DDF method, which can lead to large uncertainties for snowpack simulation. The SPOT-VGT and the MODIS-Terra sensors provide valuable information for snow detection over several years. The aim of this work was to use those data to parametrize the seasonal variability of the snow processes in the hydrological distributed snow model (HDSM), based on a DDF method. The satellite products were corrected and combined in order to implement a database of 8 day snow cover area (SCA) maps over the northern part of the Dudh Koshi watershed (Nepal) for the period 1998–2017. A revisited version of the snow module of the HDSM model was implemented so as to split it into two parameterizations depending on the seasonality. Corrected 8 day SCA maps retrieved from MODIS-Terra were used to calibrate the seasonal parameterization, through a stochastic method, over the period of study (2013–2016). The results demonstrate that the seasonal parameterization reduces the error in the simulated SCA and increases the correlation with the MODIS SCA. The two-set version of the model improved the yearly RMSE from 5.9% to 7.7% depending on the basin, compared to the one-set version. The correlation between the model and MODIS passes from 0.73 to 0.79 in winter for the larger basin, Phakding. This study shows that the use of a remote sensing product can improve the parameterization of the seasonal dynamics of snow processes in a model based on a DDF method.
Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data
Authors: Immitzer Markus , Neuwirth Martin , Böck Sebastian , Brenner Harald , Vuolo Francesco , Atzberger Clement
Published: November 2019

Detailed knowledge about tree species composition is of great importance for forest management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented spectral, spatial and temporal resolution. Here, we investigated the potential benefits of using high temporal resolution data for classification of five coniferous and seven broadleaved tree species in a diverse Central European Forest. To run the classification, 18 cloud-free S2 acquisitions were analyzed in a two-step approach. The available scenes were first used to stratify the study area into six broad land-cover classes. Subsequently, additional classification models were created separately for the coniferous and the broadleaved forest strata. To permit a deeper analytical insight in the benefits of multi-temporal datasets for species identification, classification models were developed taking into account all 262,143 possible permutations of the 18 S2 scenes. Each model was fine-tuned using a stepwise recursive feature reduction. The additional use of vegetation indices improved the model performances by around 5 percentage points. Individual mono-temporal tree species accuracies range from 48.1% (January 2017) to 78.6% (June 2017). Compared to the best mono-temporal results, the multi-temporal analysis approach improves the out-of-bag overall accuracy from 72.9% to 85.7% for the broadleaved and from 83.8% to 95.3% for the coniferous tree species, respectively. Remarkably, a combination of six–seven scenes achieves a model quality equally high as the model based on all data; images from April until August proved most important. The classes European Beech and European Larch attain the highest user’s accuracies of 96.3% and 95.9%, respectively. The most important spectral variables to distinguish between tree species are located in the Red (coniferous) and short wave infrared (SWIR) bands (broadleaved), respectively. Overall, the study highlights the high potential of multi-temporal S2 data for species-level classifications in Central European forests.
Assessment of the Impacts of Image Signal-to-Noise Ratios in Impervious Surface Mapping
Authors: Xian George , Shi Hua , Anderson Cody , Wu Zhuoting
Published: November 2019

Medium spatial resolution satellite images are frequently used to characterize thematic land cover and a continuous field at both regional and global scales. However, high spatial resolution remote sensing data can provide details in landscape structures, especially in the urban environment. With upgrades to spatial resolution and spectral coverage for many satellite sensors, the impact of the signal-to-noise ratio (SNR) in characterizing a landscape with highly heterogeneous features at the sub-pixel level is still uncertain. This study used WorldView-3 (WV3) images as a basis to evaluate the impacts of SNR on mapping a fractional developed impervious surface area (ISA). The point spread function (PSF) from the Landsat 8 Operational Land Imager (OLI) was used to resample the WV3 images to three different resolutions: 10 m, 20 m, and 30 m. Noise was then added to the resampled WV3 images to simulate different fractional levels of OLI SNRs. Furthermore, regression tree algorithms were incorporated into these images to estimate the ISA at different spatial scales. The study results showed that the total areal estimate could be improved by about 1% and 0.4% at 10-m spatial resolutions in our two study areas when the SNR changes from half to twice that of the Landsat OLI SNR level. Such improvement is more obvious in the high imperviousness ranges. The root-mean-square-error of ISA estimates using images that have twice and two-thirds the SNRs of OLI varied consistently from high to low when spatial resolutions changed from 10 m to 20 m. The increase of SNR, however, did not improve the overall performance of ISA estimates at 30 m.
Constructing a High-Accuracy Geometric Model for Moon-Based Earth Observation
Authors: Ding Yixing , Guo Huadong , Liu Guang , Han Chunming , Shang Haolv , Ruan Zhixing , Lv Mingyang
Published: November 2019

The Moon provides us with a long-term, stable, and unique place for Earth observation. Space agencies of various countries, including the United States, China, and Italy, have made the realization of Moon-based Earth observation an objective of their lunar missions. To date, although some conceptual studies have been presented, an accurate geometric model for Moon-based Earth observation has not yet been described. This paper introduces such a geometric model, which connects the attitude of a Moon-based sensor with a corresponding field of view on the Earth’s surface. The aberration and light time correction are involved. Due to the lack of high-quality experimental data, one qualitative comparison with Chang’E lander images and another quantitative comparison with software output are made. The comparison results show good similarity. The overall model accuracy is evaluated to be better than 4″ at current stage and will be better than 1.5″ if the seleno-graphic position is accurately determined. In direct geolocation process, the aberration and light time will cause a total 0.7 km deviation on the ground near the sublunar point. In SAR range history simulation, the light time effect will lead to a linear error, as large as tens of meters, throughout the integration time.
Phenomic and Physiological Analysis of Salinity Effects on Lettuce
Authors: Adhikari Neil D. , Simko Ivan , Mou Beiquan
Published: November 2019

Salinity is a rising concern in many lettuce-growing regions. Lettuce (Lactuca sativa L.) is sensitive to salinity, which reduces plant biomass, and causes leaf burn and early senescence. We sought to identify physiological traits important in salt tolerance that allows lettuce adaptation to high salinity while maintaining its productivity. Based on previous salinity tolerance studies, one sensitive and one tolerant genotype each was selected from crisphead, butterhead, and romaine, as well as leaf types of cultivated lettuce and its wild relative, L. serriola L. Physiological parameters were measured four weeks after transplanting two-day old seedlings into 350 mL volume pots filled with sand, hydrated with Hoagland nutrient solution and grown in a growth chamber. Salinity treatment consisted of gradually increasing concentrations of NaCl and CaCl2 from 0 mM/0 mM at the time of transplanting, to 30 mM/15 mM at the beginning of week three, and maintaining it until harvest. Across the 10 genotypes, leaf area and fresh weight decreased 0–64% and 16–67%, respectively, under salinity compared to the control. Salinity stress increased the chlorophyll index by 4–26% in the cultivated genotypes, while decreasing it by 5–14% in the two wild accessions. Tolerant lines less affected by elevated salinity were characterized by high values of the chlorophyll fluorescence parameters Fv/Fm and instantaneous photosystem II quantum yield (QY), and lower leaf transpiration.
Heartbeat Sound Signal Classification Using Deep Learning
Authors: Raza Ali , Mehmood Arif , Ullah Saleem , Ahmad Maqsood , Choi Gyu Sang , On Byung-Won
Published: November 2019

Presently, most deaths are caused by heart disease. To overcome this situation, heartbeat sound analysis is a convenient way to diagnose heart disease. Heartbeat sound classification is still a challenging problem in heart sound segmentation and feature extraction. Dataset-B applied in this study that contains three categories Normal, Murmur and Extra-systole heartbeat sound. In the purposed framework, we remove the noise from the heartbeat sound signal by applying the band filter, After that we fixed the size of the sampling rate of each sound signal. Then we applied down-sampling techniques to get more discriminant features and reduce the dimension of the frame rate. However, it does not affect the results and also decreases the computational power and time. Then we applied a purposed model Recurrent Neural Network (RNN) that is based on Long Short-Term Memory (LSTM), Dropout, Dense and Softmax layer. As a result, the purposed method is more competitive compared to other methods.
Development of a Segmental Bioelectrical Impedance Spectroscopy Device for Body Composition Measurement
Authors: Cannon Thomas , Choi JungHun
Published: November 2019

Whole-body bioelectrical impedance analysis for measuring body composition has been well-explored but may not be sensitive enough to changes in the trunk compared to changes in the limbs. Measuring individual body segments can address this issue. A segmental bioelectrical impedance spectroscopy device (SBISD) was designed for body composition measurement and a prototype was implemented. Compensation was performed to adjust the measured values to correct for a phase difference at high frequencies and to counteract the hook effect when measuring the human body. The SBISD was used to measure five subjects and was compared against three existing analyzers. For most segmental measurements, the SBISD was within 10% of the R0 and R∞ values determined with a Bodystat Multiscan 5000 and an Impedimed SFB7. The impedance values from the third reference device, a Seca 514, differed significantly due to its eight-electrode measuring technique, meaning impedance measurements could not be compared directly.
Lightweight Convolutional Neural Network and Its Application in Rolling Bearing Fault Diagnosis under Variable Working Conditions
Authors: Liu Hengchang , Yao Dechen , Yang Jianwei , Li Xi
Published: November 2019

The rolling bearing is an important part of the train’s running gear, and its operating state determines the safety during the running of the train. Therefore, it is important to monitor and diagnose the health status of rolling bearings. A convolutional neural network is widely used in the field of fault diagnosis because it does not require feature extraction. Considering that the size of the network model is large and the requirements for monitoring equipment are high. This study proposes a novel bearing fault diagnosis method based on lightweight network ShuffleNet V2 with batch normalization and L2 regularization. In the experiment, the one-dimensional time-domain signal is converted into a two-dimensional Time-Frequency Graph (TFG) using a short-time Fourier transform, though the principle of graphics to enhance the TFG dataset. The model mainly consists of two units, one for extracting features and one for spatial down-sampling. The building units are repeatedly stacked to construct the whole model. By comparing the proposed method with the origin ShuffleNet V2, machine learning model and state-of-the-art fault diagnosis model, the generalization of the proposed method for bearing fault diagnosis is verified.
Metaheuristic Optimisation Algorithms for Tuning a Bioinspired Retinal Model
Authors: Crespo-Cano Rubén , Cuenca-Asensi Sergio , Fernández Eduardo , Martínez-Álvarez Antonio
Published: November 2019

A significant challenge in neuroscience is understanding how visual information is encoded in the retina. Such knowledge is extremely important for the purpose of designing bioinspired sensors and artificial retinal systems that will, in so far as may be possible, be capable of mimicking vertebrate retinal behaviour. In this study, we report the tuning of a reliable computational bioinspired retinal model with various algorithms to improve the mimicry of the model. Its main contribution is two-fold. First, given the multi-objective nature of the problem, an automatic multi-objective optimisation strategy is proposed through the use of four biological-based metrics, which are used to adjust the retinal model for accurate prediction of retinal ganglion cell responses. Second, a subset of population-based search heuristics—genetic algorithms (SPEA2, NSGA-II and NSGA-III), particle swarm optimisation (PSO) and differential evolution (DE)—are explored to identify the best algorithm for fine-tuning the retinal model, by comparing performance across a hypervolume metric. Nonparametric statistical tests are used to perform a rigorous comparison between all the metaheuristics. The best results were achieved with the PSO algorithm on the basis of the largest hypervolume that was achieved, well-distributed elements and high numbers on the Pareto front.
Deep Learning on Multi Sensor Data for Counter UAV Applications—A Systematic Review
Authors: Samaras Stamatios , Diamantidou Eleni , Ataloglou Dimitrios , Sakellariou Nikos , Vafeiadis Anastasios , Magoulianitis Vasilis , Lalas Antonios , Dimou Anastasios , Zarpalas Dimitrios , Votis Konstantinos , Daras Petros , Tzovaras Dimitrios
Published: November 2019

Usage of Unmanned Aerial Vehicles (UAVs) is growing rapidly in a wide range of consumer applications, as they prove to be both autonomous and flexible in a variety of environments and tasks. However, this versatility and ease of use also brings a rapid evolution of threats by malicious actors that can use UAVs for criminal activities, converting them to passive or active threats. The need to protect critical infrastructures and important events from such threats has brought advances in counter UAV (c-UAV) applications. Nowadays, c-UAV applications offer systems that comprise a multi-sensory arsenal often including electro-optical, thermal, acoustic, radar and radio frequency sensors, whose information can be fused to increase the confidence of threat’s identification. Nevertheless, real-time surveillance is a cumbersome process, but it is absolutely essential to detect promptly the occurrence of adverse events or conditions. To that end, many challenging tasks arise such as object detection, classification, multi-object tracking and multi-sensor information fusion. In recent years, researchers have utilized deep learning based methodologies to tackle these tasks for generic objects and made noteworthy progress, yet applying deep learning for UAV detection and classification is considered a novel concept. Therefore, the need to present a complete overview of deep learning technologies applied to c-UAV related tasks on multi-sensor data has emerged. The aim of this paper is to describe deep learning advances on c-UAV related tasks when applied to data originating from many different sensors as well as multi-sensor information fusion. This survey may help in making recommendations and improvements of c-UAV applications for the future.
A Novel Subspace Alignment-Based Interference Suppression Method for the Transfer Caused by Different Sample Carriers in Electronic Nose
Authors: Liang Zhifang , Tian Fengchun , Zhang Ci , Yang Liu
Published: November 2019

A medical electronic nose (e-nose) with 31 gas sensors is used for wound infection detection by analyzing the bacterial metabolites. In practical applications, the prediction accuracy drops dramatically when the prediction model established by laboratory data is directly used in human clinical samples. This is a key issue for medical e-nose which should be more worthy of attention. The host (carrier) of bacteria can be the culture solution, the animal wound, or the human wound. As well, the bacterial culture solution or animals (such as: mice, rabbits, etc.) obtained easily are usually used as experimental subjects to collect sufficient sensor array data to establish the robust predictive model, but it brings another serious interference problem at the same time. Different carriers have different background interferences, therefore the distribution of data collected under different carriers is different, which will make a certain impact on the recognition accuracy in the detection of human wound infection. This type of interference problem is called “transfer caused by different sample carriers”. In this paper, a novel subspace alignment-based interference suppression (SAIS) method with domain correction capability is proposed to solve this interference problem. The subspace is the part of space whose dimension is smaller than the whole space, and it has some specific properties. In this method, first the subspaces of different data domains are gotten, and then one subspace is aligned to another subspace, thereby the problem of different distributions between two domains is solved. From experimental results, it can be found that the recognition accuracy of the infected rat samples increases from 29.18% (there is no interference suppression) to 82.55% (interference suppress by SAIS).
Surface Deformation Analysis of the Wider Zagreb Area (Croatia) with Focus on the Kašina Fault, Investigated with Small Baseline InSAR Observations
Authors: Govorčin Marin , Pribičević Boško , Wdowinski Shimon
Published: November 2019

The wider Zagreb area is considered one of the few seismically active areas in the Republic of Croatia. During the period 1880–1906, moderate to strong seismic activity with three earthquakes magnitude ML ≥ 6 occurred on the NW-SE striking Kašina Fault and since then, the area has not experienced earthquakes exceeding magnitude ML = 5. In order to estimate the ongoing interseismic strain accumulation along the fault, we analyze Advanced Land Observing Satellite (ALOS) Phased Array L-band SAR (PALSAR) and Environmental Satellite (Envisat)-Advanced Synthetic Aperture Radar (ASAR) datasets acquired over the period 2007–2010 and 2002–2010, respectively. The data were analyzed using small baseline interferometry (SBI) technique and indicate very slow surface deformations in the area, within ±3.5 mm/year, which are in a good agreement with previous geodetic studies. Interseismic strain accumulation analysis was conducted on two 14 km long segments of the Kašina Fault, seismically active in the South and stable in the North. The analysis indicates an ongoing interseismic strain accumulation of 2.3 mm/year on the Southern segment and no detectable strain accumulation on the Northern segment. Taking into consideration the lack of moderate to strong seismic activity in the past 113 years combined with the preliminary geodetic analysis from this study, we can conclude that the Southern segment of the Kašina Fault has the potential to generate earthquake magnitude Mw < 6.
A Methodology to Model Integrated Smart City System from the Information Perspective
Authors: Muvuna Jules , Boutaleb Tuleen , Baker Keith J , Mickovski Slobodan B
Published: November 2019

Rapid urban population growth challenges cities and sustainable urban development. Despite the effort deployed with conventional urban design, the current solutions are unable to significantly respond to existing challenges. The concept of smart city (SC) has been gaining popularity and cities have developed strong interest for transformation into SCs. However, given that a city is both a complex system of systems and a dynamic complex environment, achieving a state of SC can be challenging. Urban transformation into smart city has been straggling and has shown contrasting and disconnected views. Many studies have covered the design and modelling of an SC, but their focus has been mostly thematic and lack an integrated view of a smart city system. This study presents a methodology helped by a Model-Based Systems Engineering (MBSE) approach and Systems Modelling Language (SysML) to develop a model of an integrated SC system. The model brings all subsystems to operate together in one system and focuses on the information perspective of a city system. Three scenarios are presented to illustrate how an integrated information platform can be a gateway and easy access to information in an SC system, as well as a starting point towards modelling an integrated SC system.
Cross-National Attunement to Popular Songs across Time and Place: A Sociology of Popular Music in the United States, Germany, Thailand, and Tanzania
Authors: Waters Tony , Philhour David
Published: November 2019

This paper explores empirically Edward T. Hall’s assertion about the role of musical elements, including rhythm recognition and what are called “ear worms” in popular culture. To test Hall’s assertion, data were collected from the United States, Germany, Tanzania, and Thailand in 2015–2017 using a 26 brief “song intros.” Data were also collected from exchange students from South Korea and Turkey. Survey responses were analyzed using factor analysis in order to identify patterns of recognition. It was found that there were indeed patterns of recognition apparently reflecting national boundaries for some song recognition, but others crossed boundaries. A separate analysis of patterned recognition comparing American youth under thirty, with elders over 60 indicated that there were also boundaries between age groups. Such experiments in music recognition are an effective methodology for Culture Studies given that musical elements are tied to issues of identity, culture, and even politics. Music recognition can be used to measure elements of such subconscious habitus.
Sustainable Approach to Eradicate the Inhibitory Effect of Free-Cyanide on Simultaneous Nitrification and Aerobic Denitrification during Wastewater Treatment
Authors: Mpongwana Ncumisa , Ntwampe Seteno K. O. , Omodanisi Elizabeth I. , Chidi Boredi S. , Razanamahandry Lovasoa C.
Published: November 2019

Simultaneous nitrification and aerobic denitrification (SNaD) is a preferred method for single stage total nitrogen (TN) removal, which was recently proposed to improve wastewater treatment plant design. However, SNaD processes are prone to inhibition by toxicant loading with free cyanide (FCN) possessing the highest inhibitory effect on such processes, rendering these processes ineffective. Despite the best efforts of regulators to limit toxicant disposal into municipal wastewater sewage systems (MWSSs), FCN still enters MWSSs through various pathways; hence, it has been suggested that FCN resistant or tolerant microorganisms be utilized for processes such as SNaD. To mitigate toxicant loading, organisms in SNaD have been observed to adopt a diauxic growth strategy to sequentially degrade FCN during primary growth and subsequently degrade TN during the secondary growth phase. However, FCN degrading microorganisms are not widely used for SNaD in MWSSs due to inadequate application of suitable microorganisms (Chromobacterium violaceum, Pseudomonas aeruginosa, Thiobacillus denitrificans, Rhodospirillum palustris, Klebsiella pneumoniae, and Alcaligenes faecalis) commonly used in single-stage SNaD. This review expatiates the biological remedial strategy to limit the inhibition of SNaD by FCN through the use of FCN degrading or resistant microorganisms. The use of FCN degrading or resistant microorganisms for SNaD is a cost-effective method compared to the use of other methods of FCN removal prior to TN removal, as they involve multi-stage systems (as currently observed in MWSSs). The use of FCN degrading microorganisms, particularly when used as a consortium, presents a promising and sustainable resolution to mitigate inhibitory effects of FCN in SNaD.
Uncertain Hybrid Multiple Attribute Group Decision of Offshore Wind Power Transmission Mode Based on theVIKOR Method
Authors: Pang Nansheng , Guo Wenjing
Published: November 2019

With the increasing scale of offshore wind power, large-capacity and long-distance offshore wind power will be a trend in the future development of wind power. However, compared with onshore wind power transmission, offshore wind power transmission is more difficult and costly. Therefore, it is of great practical significance and urgency to select the appropriate power transmission mode to realize the long-distance transmission of large-capacity offshore wind power. Since the selection of offshore wind power transmission mode is a multiple attribute decision making problem, in this paper, the technical characteristics of high voltage alternating current (HVAC) and voltage source converter based on high voltage direct current (VSC-HVDC) that are already in use and hybrid HVDC that is possible to be used in offshore wind power transmission in the future are discussed. Based on this analysis, the reliability, economy and construction difficulty of offshore wind power transmission mode are systematically analyzed, and VIKOR method is applied to group decision-making for the selection of offshore wind power transmission mode. The main contributions of this paper are as follows: (1) the characteristics of large-capacity and long-distance offshore wind power transmission are studied, and the evaluation system with multiple indexes including a large number of qualitative indexes is constructed from the technical, economic and social aspects. In order to deal with difficult problems of fuzzy information, interval numbers, language variables, intuitionistic fuzzy numbers and other data types are used to deal with them effectively. (2) In the VIKOR method, because the expert weights are unknown in group decision-making, the expert trust function is used to objectively determine the weight of each expert. (3) The group decision-making analysis of actual case of offshore wind power plant is carried out by using expert knowledge and VIKOR method. The case shows that this method is simple, reasonable and practical.
Global Variation in Climate, Human Development, and Population Density Has Implications for Urban Ecosystem Services
Authors: Richards Daniel , Masoudi Mahyar , Oh Rachel R. Y. , Yando Erik S. , Zhang Jingyuan , Friess Daniel A. , Grêt-Regamey Adrienne , Tan Puay Yok , Edwards Peter J.
Published: November 2019

Humans rely upon ecosystem services to regulate their environment and to provide resources and cultural benefits. As the world’s urban population grows, it becomes increasingly important to find ways of improving the provision of ecosystem services in urban areas. However, the kinds of ecosystem services that are most needed or demanded by urban populations, and the opportunities to provide these, vary widely in cities around the world. Here we explore variation in climate, Human Development Index (HDI), and population density, and discuss their implications for providing and managing urban ecosystem services. Using 221 published studies of urban ecosystem services, we analyse the extent to which existing research adequately covers global variation in climatic and social conditions. Our results reveal an under-representation of studies from tropical cities and from lower HDI countries, with implications for how we conceptualize and quantify urban ecosystem services, and how we transfer benefits across case studies. Future work should be aimed at correcting these deficits and determining the extent to which conclusions about urban ecosystem services are transferable from one city to another.
Identification of Overall Innovation Behavior by Using a Decision Tree: The Case of a Korean Manufacturer
Authors: Park Sunyoung
Published: November 2019

Based on the two recent consecutive Korean Innovation Surveys in 2014 and 2016, this research empirically identifies the influencing factors and overall behavior of innovation success and failure in the manufacturing industry by using decision-making tree analysis (DT). The influencing factors and behavior of a successful innovator are also investigated from the perspectives of financial contribution, innovation activity, and research and development (R&D) activity. By using DT, this study acquires comprehensive knowledge of the arguments on innovation factors and behaviors in different contexts over time while dealing with all the factors in a single statistical framework based on the Oslo manual. Results with around 80% predictive accuracy show that the role of R&D is crucial for innovation success. The larger the firm size and the older the firm, the higher the success achieved by the firm will be. Firms in a low-technology industry prefer other innovation activities rather than R&D. Concerning a successful innovator’s behavior, target market characteristics that drive a firm to seek market needs influence innovation behavior and the use of information for innovation. Firms prefer implementing low-cost R&D activities across sectors, but firms in low-technology sectors prefer non-R&D activities. Regional characteristics of well-established business environments help firms to focus on R&D activities and reduce costly non-R&D activities. Most firms having R&D institutes focus on conducting in-house R&D using their own information. Cooperative R&D is conducted for closing capability gaps, but absorptive capacity is required to complement cooperative R&D. These empirical findings reaffirm the arguments on innovation behavior and arrange them in the overall perspective; they also provide managerial and political implications. Establishing and strengthening private or public R&D support programs to increase the capability of both in-house and cooperative R&D through funding as well as leveling up the information environment on technology and the market is crucial to the national innovation system.
Location-Based Tracking Data and Customer Movement Pattern Analysis for Sustainable Fashion Business
Authors: Kim Jonghyuk , Hwangbo Hyunwoo , Kim Sung Jun , Kim Soyean
Published: November 2019

Retailers need accurate movement pattern analysis of human-tracking data to maximize the space performance of their stores and to improve the sustainability of their business. However, researchers struggle to precisely measure customers’ movement patterns and their relationships with sales. In this research, we adopt indoor positioning technology, including wireless sensor devices and fingerprinting techniques, to track customers’ movement patterns in a fashion retail store over four months. Specifically, we conducted three field experiments in three different timeframes. In each experiment, we rearranged one element of the visual merchandising display (VMD) to track and compare customer movement patterns before and after the rearrangement. For the analysis, we connected customers’ discrete location data to identify meaningful patterns in customers’ movements. We also used customers’ location and time information to match identified movement pattern data with sales data. After classifying individuals’ movements by time and sequences, we found that stay time in a particular zone had a greater impact on sales than the total stay time in the store. These results challenge previous findings in the literature that suggest that the longer customers stayed in a store, the more they purchase. Further, the results confirmed that effective store rearrangement could change not only customer movement patterns but also overall sales of store zones. This research can be a foundation for various practical applications of tracking data technologies.
Artificial Light at Night Influences Clock-Gene Expression, Activity, and Fecundity in the Mosquito Culex pipiens f. molestus
Authors: Honnen Ann-Christin , Kypke Janina L. , Hölker Franz , Monaghan Michael T.
Published: November 2019

Light is an important environmental cue, and exposure to artificial light at night (ALAN) may disrupt organismal physiology and behavior. We investigated whether ALAN led to changes in clock-gene expression, diel activity patterns, and fecundity in laboratory populations of the mosquito Culex pipiens f. molestus (Diptera, Culicidae), a species that occurs in urban areas and is thus regularly exposed to ALAN. Populations were kept under 16 h:8h light:dark cycles or were subjected to an additional 3.5 h of light (100–300 lx) in the evenings. ALAN induced significant changes in expression in all genes studied, either alone (period) or as an interaction with time (timeless, cryptochrome2, Clock, cycle). Changes were sex-specific: period was down-regulated in both sexes, cycle was up-regulated in females, and Clock was down-regulated in males. ALAN-exposed mosquitoes were less active during the extra-light phase, but exposed females were more active later in the night. ALAN-exposed females also produced smaller and fewer eggs. Our findings indicate a sex-specific impact of ALAN on the physiology and behavior of Culex pipiens f. molestus and that changes in clock-gene expression, activity, and fecundity may be linked.
Performance Evaluation and Comparison of Modified Spectral Mixture Analysis Method for Different Images of Landsat Series Satellites
Authors: Huang Xiaodong , Liu Wenkai , Han Yuping , Wang Chunying , Wang Han , Hu Sai
Published: November 2019

Urban impervious surface is considered one of main factors affecting urban heat island and urban waterlogging. It is commonly extracted utilizing the original linear spectral mixture analysis (LSMA) model. However, due to the deficiencies of this method, many improvements and modifications have been proposed. In this paper, a modified dynamic endmember linear spectral mixture analysis (DELSMA) model was introduced and tested in Zhengzhou, China, using different images of Landsat series satellites. The accuracy and performance of DELSMA model was evaluated in terms of R M S E , r and R 2 . Results show that (1) the DELSMA model performed equally well for Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced Thematic Mapper (ETM+) images, and obtained better accuracy by using Landsat-8 Operational Land Imager (OLI) than Landsat TM/ETM+; (2) the DELSMA model achieved a better performance than the original LSMA model consistently, using images of Landsat from different sensors. Based exclusively on the overall accuracy reports, the DELSMA model proved to be a more efficient method for extracting impervious surface. Our study will provide a reliable method of impervious surface estimation for the urban planner and management in monitoring urban expansion, revealing urban heat island, and estimating urban surface runoff, using time-series Landsat imagery.
Green Procurement in Romanian Construction Projects. A Cluster Analysis of the Barriers and Enablers to Green Procurement in Construction Projects from the Bucharest-Ilfov Region of Romania
Authors: Simion Cezar-Petre , Nicolescu Ciprian , Vrîncuț Mihai
Published: November 2019

The research presented in this paper aimed at identifying the most significant green procurement barriers and enablers for construction projects in the Bucharest-Ilfov region and grouping them into clusters. For this purpose, 14 barriers and 14 enablers were selected on the basis of the literature review and a questionnaire-based survey was carried out with members of the construction projects’ teams from the analyzed region. The cluster analysis resulted in eight clusters for barriers and seven clusters for enablers. In the case of barriers, the most significant cluster was the one that included the barrier regarding technical and technological difficulties related to the use of green building materials. Another significant barrier was the increase of project execution costs. Enablers from the most significant cluster had higher energy efficiency and use of green building materials as a competitive advantage. Another significant enabler identified was regarding the pressure to implement environmental protection policies/legislation. To explain and detail the results of the cluster analysis, semi-structured interviews were carried out with experts involved in projects. They indicated, in most cases, the same barriers and enablers as those obtained from the cluster analysis.