bluuebunny/crossref_metadata_2025 huggingface dataset:
Error Estimation of Pathfinder Version 5.3 Level-3C SST Using Extended Triple Collocation Analysis
Authors: Saha Korak , Dash Prasanjit , Zhao Xuepeng , Zhang Huai-min
Published: February 2020

Sea Surface Temperature (SST) is an essential climate variable (ECV) for monitoring the state and detecting changes in the climate. The concept of ECVs, developed by the Global Climate Observing System (GCOS) program of the World Meteorological Organization (WMO), has been broadly adopted in worldwide science and policy circles Besides being a climate change indicator, the global SST field is an essential input for atmospheric models, air-sea exchange studies, understanding marine ecosystems, operational weather, and ocean forecasting, military and defense operations, tourism, and fisheries research. It is, therefore, critical to understand the errors associated with SST measurements from both in situ measurements and satellite observations. The customary way of validating a satellite SST is to compare it with in situ measured SSTs. This method, however, will have inaccuracies due to uncertainties involving both types of measurements. A triple collocation (TC) error analysis can be implemented on three mutually independent error-prone measurements to estimate the root-mean-square error (RMSE) of each measurement. In this study, the error characterization for the Pathfinder SST version 5.3 (PF53) dataset is performed using an extended TC (ETC) method and reported to be in the range of 0.31 to 0.37 K. These values are reasonable, as is evident from corresponding very high (~0.98) unbiased signal-to-noise ratio (SNR) values.
Color Restoration for Full-Waveform Multispectral LiDAR Data
Authors: Wang Binhui , Song Shalei , Gong Wei , Cao Xiong , He Dong , Chen Zhenwei , Lin Xin , Li Faquan , Sun Jia
Published: February 2020

The current full-waveform data at a single wavelength can mainly retrieve the geometric attributes of targets along the light path by detecting waveform components, resulting in the lack of spectral or color attribute information. This kind of device relies on a digital camera for acquiring the color information, however, which is inevitably limited by the lighting conditions and geometric registration errors. With the development of multispectral light detection and ranging (LiDAR) or even hyperspectral LiDAR that often utilize a supercontinuum laser source covering the whole visible light band, including red, green and blue bands, the simultaneous acquisition of color and spatial information becomes possible and makes passive imaging data no longer necessary. In this study, we propose a color restoration method for a full-waveform multispectral LiDAR (FWMSL) system. Additionally, we develop a multispectral lognormal function to fit the tailing echoes measured by FWMSL further accurately. Experimental data from our FWMSL system are used to evaluate the performance of the proposed method. The relative standard deviation, correlation coefficient (R2) and color difference ( Δ E ) metrics suggest that the color restoration for the full-waveform multispectral data is feasible.
Quantitative Evaluation of Environmental Loading Induced Displacement Products for Correcting GNSS Time Series in CMONOC
Authors: Li Chenfeng , Huang Shengxiang , Chen Qiang , Dam Tonie van , Fok Hok Sum , Zhao Qian , Wu Weiwei , Wang Xinpeng
Published: February 2020

Mass redistribution within the Earth system deforms the surface elastically. Loading theory allows us to predict loading induced displacement anywhere on the Earth’s surface using environmental loading models, e.g., Global Land Data Assimilation System. In addition, different publicly available loading products are available. However, there are differences among those products and the differences among the combinations of loading models cannot be ignored when precisions of better than 1 cm are required. Many scholars have applied these loading corrections to Global Navigation Satellite System (GNSS) time series from mainland China without considering or discussing the differences between the available models. Evaluating the effects of different loading products over this region is of paramount importance for accurately removing the loading signal. In this study, we investigate the performance of these different publicly available loading products on the scatter of GNSS time series from the Crustal Movement Observation Network of China. We concentrate on five different continental water storage loading models, six different non-tidal atmospheric loading models, and five different non-tidal oceanic loading models. We also investigate all the different combinations of loading products. The results show that the difference in RMS reduction can reach 20% in the vertical component depending on the loading correction applied. We then discuss the performance of different loading combinations and their effects on the noise characteristics of GNSS height time series and horizontal velocities. The results show that the loading products from NASA may be the best choice for corrections in mainland China. This conclusion could serve as an important reference for loading products users in this region.
Use of Hyperion for Mangrove Forest Carbon Stock Assessment in Bhitarkanika Forest Reserve: A Contribution Towards Blue Carbon Initiative
Authors: Anand Akash , Pandey Prem Chandra , Petropoulos George P. , Pavlides Andrew , Srivastava Prashant K. , Sharma Jyoti K. , Malhi Ramandeep Kaur M.
Published: February 2020

Mangrove forest coastal ecosystems contain significant amount of carbon stocks and contribute to approximately 15% of the total carbon sequestered in ocean sediments. The present study aims at exploring the ability of Earth Observation EO-1 Hyperion hyperspectral sensor in estimating aboveground carbon stocks in mangrove forests. Bhitarkanika mangrove forest has been used as case study, where field measurements of the biomass and carbon were acquired simultaneously with the satellite data. The spatial distribution of most dominant mangrove species was identified using the Spectral Angle Mapper (SAM) classifier, which was implemented using the spectral profiles extracted from the hyperspectral data. SAM performed well, identifying the total area that each of the major species covers (overall kappa = 0.81). From the hyperspectral images, the NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) were applied to assess the carbon stocks of the various species using machine learning (Linear, Polynomial, Logarithmic, Radial Basis Function (RBF), and Sigmoidal Function) models. NDVI and EVI is generated using covariance matrix based band selection algorithm. All the five machine learning models were tested between the carbon measured in the field sampling and the carbon estimated by the vegetation indices NDVI and EVI was satisfactory (Pearson correlation coefficient, R, of 86.98% for EVI and of 84.1% for NDVI), with the RBF model showing the best results in comparison to other models. As such, the aboveground carbon stocks for species-wise mangrove for the study area was estimated. Our study findings confirm that hyperspectral images such as those from Hyperion can be used to perform species-wise mangrove analysis and assess the carbon stocks with satisfactory accuracy.
Improving the Applicability of Hydrologic Models for Food–Energy–Water Nexus Studies Using Remote Sensing Data
Authors: Koppa Akash , Gebremichael Mekonnen
Published: February 2020

Food, energy, and water (FEW) nexus studies require reliable estimates of water availability, use, and demand. In this regard, spatially distributed hydrologic models are widely used to estimate not only streamflow (SF) but also different components of the water balance such as evapotranspiration (ET), soil moisture (SM), and groundwater. For such studies, the traditional calibration approach of using SF observations is inadequate. To address this, we use state-of-the-art global remote sensing-based estimates of ET and SM with a multivariate calibration methodology to improve the applicability of a widely used spatially distributed hydrologic model (Noah-MP) for FEW nexus studies. Specifically, we conduct univariate and multivariate calibration experiments in the Mississippi river basin with ET, SM, and SF to understand the trade-offs in accurately simulating ET, SM, and SF simultaneously. Results from univariate calibration with just SF reveal that increased accuracy in SF at the cost of degrading the spatio-temporal accuracy of ET and SM, which is essential for FEW nexus studies. We show that multivariate calibration helps preserve the accuracy of all the components involved in calibration. The study emphasizes the importance of multiple sources of information, especially from satellite remote sensing, for improving FEW nexus studies.
Soil Moisture Calibration Equations for Active Layer GPR Detection—a Case Study Specially for the Qinghai–Tibet Plateau Permafrost Regions
Authors: Du Erji , Zhao Lin , Zou Defu , Li Ren , Wang Zhiwei , Wu Xiaodong , Hu Guojie , Zhao Yonghua , Liu Guangyue , Sun Zhe
Published: February 2020

Ground-penetrating radar (GPR) is a convenient geophysical technique for active-layer soil moisture detection in permafrost regions, which is theoretically based on the petrophysical relationship between soil moisture (θ) and the soil dielectric constant (ε). The θ–ε relationship varies with soil type and thus must be calibrated for a specific region or soil type. At present, there is lack of such a relationship for active-layer soil moisture estimation for the Qinghai–Tibet plateau permafrost regions. In this paper, we utilize the Complex Refractive Index Model to establish such a calibration equation that is suitable for active-layer soil moisture estimation with GPR velocity. Based on the relationship between liquid water, temperature, and salinity, the soil water dielectric constant was determined, which varied from 84 to 88, with an average value of 86 within the active layer for our research regions. Based on the calculated soil-water dielectric constant variation range, and the exponent value range within the Complex Refractive Index Model, the exponent value was determined as 0.26 with our field-investigated active-layer soil moisture and dielectric data set. By neglecting the influence of the soil matrix dielectric constant and soil porosity variations on soil moisture estimation at the regional scale, a simple active-layer soil moisture calibration curve, named CRIM, which is suitable for the Qinghai–Tibet plateau permafrost regions, was established. The main shortage of the CRIM calibration equation is that its calculated soil-moisture error will gradually increase with a decreasing GPR velocity and an increasing GPR velocity interpretation error. To avoid this shortage, a direct linear fitting calibration equation, named as υ-fitting, was acquired based on the statistical relationship between the active-layer soil moisture and GPR velocity with our field-investigated data set. When the GPR velocity interpretation error is within ±0.004 m/ns, the maximum moisture error calculated by CRIM is within 0.08 m3/m3. While when the GPR velocity interpretation error is larger than ±0.004 m/ns, a piecewise formula calculation method, combined with the υ-fitting equation when the GPR velocity is lower than 0.07 m/ns and the CRIM equation when the GPR velocity is larger than 0.07 m/ns, was recommended for the active-layer moisture estimation with GPR detection in the Qinghai–Tibet plateau permafrost regions.
Parametric Estimations Based on Homomorphic Deconvolution for Time of Flight in Sound Source Localization System
Authors: Park Yeonseok , Choi Anthony , Kim Keonwook
Published: February 2020

Vehicle-mounted sound source localization systems provide comprehensive information to improve driving conditions by monitoring the surroundings. The three-dimensional structure of vehicles hinders the omnidirectional sound localization system because of the long and uneven propagation. In the received signal, the flight times between microphones delivers the essential information to locate the sound source. This paper proposes a novel method to design a sound localization system based on the single analog microphone network. This article involves the flight time estimation for two microphones with non-parametric homomorphic deconvolution. The parametric methods are also suggested with Yule-walker, Prony, and Steiglitz-McBride algorithm to derive the coefficient values of the propagation model for flight time estimation. The non-parametric and Steiglitz-McBride method demonstrated significantly low bias and variance for 20 or higher ensemble average length. The Yule-walker and Prony algorithms showed gradually improved statistical performance for increased ensemble average length. Hence, the non-parametric and parametric homomorphic deconvolution well represent the flight time information. The derived non-parametric and parametric output with distinct length will serve as the featured information for a complete localization system based on machine learning or deep learning in future works.
Monitoring Movements of Ataxia Patient by Using UWB Technology
Authors: Zilani Tanjila Akter , Al-Turjman Fadi , Khan Muhammad Bilal , Zhao Nan , Yang Xiaodong
Published: February 2020

Internet of multimedia things (IoMT) driving innovative product development in health care applications. IoMT requires delay-sensitive and higher bandwidth devices. Ultra-wideband (UWB) technology is a promising solution to improve communication between devices, tracking and monitoring of patients. In the future, this technology has the capability to expand the IoMT world with new capabilities and more devices can be integrated. At the present time, some people face different types of physiological problems because of the damage in different areas of the central nervous system. Thus, they lose their balance coordination. One of these types of coordination problems is named Ataxia, in which patients are unable to control their body movements. This kind of coordination disorder needs a proper supervision system for the caretaker. Previous Ataxia assessment methods are cumbersome and cannot handle regular monitoring and tracking of patients. One of the most challenging tasks is to detect different walking abnormalities of Ataxia patients. In our paper, we present a technique for monitoring and tracking of a patient with the help of UWB technology. This method expands the real-time location systems (RTLS) in the indoor environment by placing wearable receiving tags on the body of Ataxia patients. The location and four different walking movement data are collected by UWB transceiver for the classification and prediction in the two-dimensional path. For accurate classification, we use a support vector machine (SVM) algorithm to clarify the movement variations. Our proposed examined result successfully achieved and the accuracy is above 95%.
Enhancing Security on Touch-Screen Sensors with Augmented Handwritten Signatures
Authors: Abazid Majd , Houmani Nesma , Garcia-Salicetti Sonia
Published: February 2020

We aim at enhancing personal identity security on mobile touch-screen sensors by augmenting handwritten signatures with specific additional information at the enrollment phase. Our former works on several available and private data sets acquired on different sensors demonstrated that there are different categories of signatures that emerge automatically with clustering techniques, based on an entropy-based data quality measure. The behavior of such categories is totally different when confronted to automatic verification systems in terms of vulnerability to attacks. In this paper, we propose a novel and original strategy to reinforce identity security by enhancing signature resistance to attacks, assessed per signature category, both in terms of data quality and verification performance. This strategy operates upstream from the verification system, at the sensor level, by enriching the information content of signatures with personal handwritten inputs of different types. We study this strategy on different signature types of 74 users, acquired in uncontrolled mobile conditions on a largely deployed mobile touch-screen sensor. Our analysis per writer category revealed that adding alphanumeric (date) and handwriting (place) information to the usual signature is the most powerful augmented signature type in terms of verification performance. The relative improvement for all user categories is of at least 93% compared to the usual signature.
Continuous Monitoring of Air Purification: A Study on Volatile Organic Compounds in a Gas Cell
Authors: Fathy Alaa , Pivert Marie Le , Kim Young Jai , Ba Mame Ousmane , Erfan Mazen , Sabry Yasser M. , Khalil Diaa , Leprince-Wang Yamin , Bourouina Tarik , Gnambodoe-Capochichi Martine
Published: February 2020

Air pollution is one of the major environmental issues that humanity is facing. Considering Indoor Air Quality (IAQ), Volatile Organic Compounds (VOCs) are among the most harmful gases that need to be detected, but also need to be eliminated using air purification technologies. In this work, we tackle both problems simultaneously by introducing an experimental setup enabling continuous measurement of the VOCs by online absorption spectroscopy using a MEMS-based Fourier Transform infrared (FTIR) spectrometer, while those VOCs are continuously eliminated by continuous adsorption and photocatalysis, using zinc oxide nanowires (ZnO-NWs). The proposed setup enabled a preliminary study of the mechanisms involved in the purification process of acetone and toluene, taken as two different VOCs, also typical of those that can be found in tobacco smoke. Our experiments revealed very different behaviors for those two gases. An elimination ratio of 63% in 3 h was achieved for toluene, while it was only 14% for acetone under same conditions. Adsorption to the nanowires appears as the dominant mechanism for the acetone, while photocatalysis is dominant in case of the toluene.
Determination of the Real Cracking Moment of Two Reinforced Concrete Beams through the Use of Embedded Fiber Optic Sensors
Authors: García Díaz Julián , Navarro Cano Nieves , Rúa Álvarez Edelmiro
Published: February 2020

This article investigates the possibility of applying weldable optic fiber sensors to the corrugated rebar in reinforced concrete structures to detect cracks and measure the deformation of the steel. Arrays have initially been designed comprised of two weldable optic fiber sensors, and one temperature sensor to compensate its effect in measuring deformations. A series of tests were performed on the structures to evaluate functioning of the sensors, and the results obtained from the deformation measures shown by the sensors have been stored using specific software. Two reinforced concrete beams simply resting on the support have been designed to perform the tests, and they have been monitored in the zones with maximum flexion moment. Different loading steps have been applied to the beams at the center of the span, using a loading cylinder, and the measurement of the load applied has been determined using a loading cell. The analysis of the deformation measurements of the corrugated rebar obtained by the optic fiber sensors has allowed us to determine the moment at which the concrete has cracked due to the effect of the loads applied and the deformation it has suffered by the effect of the different loading steps applied to the beams. This means that this method of measuring deformations in the corrugated rebar by weldable optic fiber sensors provides very precise results. Future lines of research will concentrate on determining an expression that indicates the real cracking moment of the concrete.
Learning Mobile Manipulation through Deep Reinforcement Learning
Authors: Wang Cong , Zhang Qifeng , Tian Qiyan , Li Shuo , Wang Xiaohui , Lane David , Petillot Yvan , Wang Sen
Published: February 2020

Mobile manipulation has a broad range of applications in robotics. However, it is usually more challenging than fixed-base manipulation due to the complex coordination of a mobile base and a manipulator. Although recent works have demonstrated that deep reinforcement learning is a powerful technique for fixed-base manipulation tasks, most of them are not applicable to mobile manipulation. This paper investigates how to leverage deep reinforcement learning to tackle whole-body mobile manipulation tasks in unstructured environments using only on-board sensors. A novel mobile manipulation system which integrates the state-of-the-art deep reinforcement learning algorithms with visual perception is proposed. It has an efficient framework decoupling visual perception from the deep reinforcement learning control, which enables its generalization from simulation training to real-world testing. Extensive simulation and experiment results show that the proposed mobile manipulation system is able to grasp different types of objects autonomously in various simulation and real-world scenarios, verifying the effectiveness of the proposed mobile manipulation system.
Fuzzy Self-tuning Tracking Differentiator for Motion Measurement Sensors and Application in Wide-Bandwidth High-accuracy Servo Control
Authors: Gao Yang , Tian Dapeng , Wang Yutang
Published: February 2020

Sensor differential signals are widely used in many systems. The tracking differentiator (TD) is an effective method to obtain signal differentials. Differential calculation is noise-sensitive. There is the characteristics of low-pass filter (LPF) in the TD to suppress the noise, but phase lag is introduced. For LPF, fixed filtering parameters cannot achieve both noise suppression and phase compensation lag compensation. We propose a fuzzy self-tuning tracking differentiator (FSTD) capable of adaptively adjusting parameters, which uses the frequency information of the signal to achieve a trade-off between the phase lag and noise suppression capabilities. Based on the frequency information, the parameters of TD are self-tuning by a fuzzy method, which makes self-tuning designs more flexible. Simulations and experiments using motion measurement sensors show that the proposed method has good filtering performance for low-frequency signals and improves tracking ability for high-frequency signals compared to fixed-parameter differentiator.
Person Re-Identification Using Deep Modeling of Temporally Correlated Inertial Motion Patterns
Authors: Gohar Imad , Riaz Qaiser , Shahzad Muhammad , Zeeshan Ul Hasnain Hashmi Muhammad , Tahir Hasan , Ehsan Ul Haq Muhammad
Published: February 2020

Person re-identification (re-ID) is among the essential components that play an integral role in constituting an automated surveillance environment. Majorly, the problem is tackled using data acquired from vision sensors using appearance-based features, which are strongly dependent on visual cues such as color, texture, etc., consequently limiting the precise re-identification of an individual. To overcome such strong dependence on visual features, many researchers have tackled the re-identification problem using human gait, which is believed to be unique and provide a distinctive biometric signature that is particularly suitable for re-ID in uncontrolled environments. However, image-based gait analysis often fails to extract quality measurements of an individual’s motion patterns owing to problems related to variations in viewpoint, illumination (daylight), clothing, worn accessories, etc. To this end, in contrast to relying on image-based motion measurement, this paper demonstrates the potential to re-identify an individual using inertial measurements units (IMU) based on two common sensors, namely gyroscope and accelerometer. The experiment was carried out over data acquired using smartphones and wearable IMUs from a total of 86 randomly selected individuals including 49 males and 37 females between the ages of 17 and 72 years. The data signals were first segmented into single steps and strides, which were separately fed to train a sequential deep recurrent neural network to capture implicit arbitrary long-term temporal dependencies. The experimental setup was devised in a fashion to train the network on all the subjects using data related to half of the step and stride sequences only while the inference was performed on the remaining half for the purpose of re-identification. The obtained experimental results demonstrate the potential to reliably and accurately re-identify an individual based on one’s inertial sensor data.
High-Refractive-Index Materials for Giant Enhancement of the Transverse Magneto-Optical Kerr Effect
Authors: Moncada-Villa Edwin , Mejía-Salazar J. Ricardo
Published: February 2020

The ability of plasmonic structures to confine and enhance light at nanometer length scales has been traditionally exploited to boost the magneto-optical effects in magneto-plasmonic structures. These platforms allows for light control via externally applied magnetic fields, which is of prime importance for sensing, data storage, optical-isolation, and telecommunications applications. However, applications are hindered by the high-level of ohmic losses associated to metallic and ferromagnetic components. Here, we use a lossless all-dielectric platform for giant enhancement of the magneto-optical effects. Our structure consists of a high-refractive index dielectric film on top of a magnetic dielectric substrate. We numerically demonstrate an extraordinarily enhanced transverse magneto-optical Kerr effect due to the Fabry–Perot resonances supported by the high-refractive index slab. Potential applications for sensing and biosensing are also illustrated in this work.
Noise Modeling and Simulation of Giant Magnetic Impedance (GMI) Magnetic Sensor
Authors: Jin Fang , Tu Xin , Wang JinChao , Yang Biao , Dong KaiFeng , Mo WenQin , Hui YaJuan , Peng JunWen , Jiang JieFeng , Xu Lei , Song JunLei
Published: February 2020

The detection resolution of a giant magneto-impedance (GMI) sensor is mainly limited by its equivalent input magnetic noise. The noise characteristics of a GMI sensor are evaluated by noise modeling and simulation, which can further optimize the circuit design. This paper first analyzes the noise source of the GMI sensor. It discusses the noise model of the circuit, the output sensitivity model and the modeling process of equivalent input magnetic noise. The noise characteristics of three modules that have the greatest impact on the output noise are then simulated. Finally, the simulation results are verified by experiments. By comparing the simulated noise spectrum curve and the experimental noise spectrum curve, it is demonstrated that the preamplifier and the multiplier contribute the most to the output white noise, and the low-pass filter plays a major role in the output 1/f noise. These modules should be given priority in the optimization of the noise of the conditioning circuit. The above results provide technical support for the practical application of low-noise GMI magnetometers.
Optimal Lead Position in Patch-Type Monitoring Sensors for Reconstructing 12-Lead ECG Signals with Universal Transformation Coefficient
Authors: Lee Dongseok , Kwon Hyunbin , Lee Hongji , Seo Chulhun , Park Kwangsuk
Published: February 2020

The aim of this study was to reconstruct a 12-lead electrocardiograph (ECG) with a universal transformation coefficient and find the appropriate electrode position and shape for designing a patch-type ECG sensor. A 35-channel ECG monitoring system was developed, and 14 subjects were recruited for the experiment. A feedforward neural network with one hidden layer was applied to train the transformation coefficient. Three electrode shapes (5 cm × 5 cm square, 10 cm × 10 cm square, and right-angled triangle) were considered for the patch-type ECG sensor. The mean correlation coefficient (CC) and minimum CC methods were applied to evaluate the reconstruction performance. The average CCs between the standard 12-lead ECG and reconstructed 12-lead ECG were 0.860, 0.893, and 0.893 for a 5 cm × 5 cm square, 10 cm × 10 cm square, and right-angled triangle shape. The right-angled triangle showed the highest performance among the considered shapes. The results also suggested that the bottom of the central area of the chest was the most suitable position for attaching the patch-type ECG sensor.
A Wavelet Adaptive Cancellation Algorithm Based on Multi-Inertial Sensors for the Reduction of Motion Artifacts in Ambulatory ECGs
Authors: Xiong Fan , Chen Dongyi , Huang Miao
Published: February 2020

Wearable electrocardiogram (ECG) devices are universally used around the world for patients who have cardiovascular disease (CVD). At present, how to suppress motion artifacts is one of the most challenging issues in the field of physiological signal processing. In this paper, we propose an adaptive cancellation algorithm based on multi-inertial sensors to suppress motion artifacts in ambulatory ECGs. Firstly, this method collects information related to the electrode motion through multi-inertial sensors. Then, the part that is not related to the electrode motion is removed through wavelet transform, which improves the correlation of the reference input signal. In this way, the ability of the adaptive cancellation algorithm to remove motion artifacts is improved in the ambulatory ECG. Subsequent experimentation demonstrated that the wavelet adaptive cancellation algorithm based on multi-inertial sensors can effectively remove motion artifacts in ambulatory ECGs.
Urban Vegetation in Air Quality Management: A Review and Policy Framework
Authors: Badach Joanna , Dymnicka Małgorzata , Baranowski Andrzej
Published: February 2020

Recent episodes of high air pollution concentration levels in many Polish cities indicate the urgent need for policy change and for the integration of various aspects of urban development into a common platform for local air quality management. In this article, the focus was placed on the prospects of improving urban air quality through proper design and protection of vegetation systems within local spatial planning strategies. Recent studies regarding the mitigation of air pollution by urban greenery due to deposition and aerodynamic effects were reviewed, with special attention given to the design guidelines resulting from these studies and their applicability in the process of urban planning. The conclusions drawn from the review were used to conduct three case studies: in Gdańsk, Warsaw, and Poznań, Poland. The existing local urban planning regulations for the management of urban greenery were critically evaluated in relation to the findings of the review. The results indicate that the current knowledge regarding the improvement of urban air quality by vegetation is not applied in the process of urban planning to a sufficient degree. Some recommendations for alternative provisions were discussed.
A Sustainable Approach to Innovation Adoption in Light-Rail Transport
Authors: Alade Taslim , Edelenbos Jurian , Gianoli Alberto
Published: February 2020

This article primarily aims to understand how the Light-Rail Transport (LRT) pricing and infrastructural innovations from a Chinese context have been adopted to the Addis-Ababa city context. Secondly, it wishes to show what were the economic, social, and environmental effects of these adapted innovations on passenger service delivery and Multi-National Corporations (MNCs), and what effects the resident’s proximity had on commercial activities along the LRT route. Thirdly, it identified re-adaptations to a more sustainable LRT with respect to the passengers, MNCs, and residents. This study has revealed economic, social, and environmental effects that may influence innovation adoption, such as the following: reduction in carbon emissions; fare evasion; inconvenience; affordability; less revenue; less proximity to commercial activities; and an increase in travel distances for pedestrians. A mixed method for a single case study was used, including semi-structured interviews with light-rail experts and a passenger survey. The results show that economic sustainability factors account for 12 out of 14 sustainability factors and 2 out of 14 social and environmental sustainability factors. The results are intended to be used as a decision support system for innovation adoption in other cities with similar context, in order to develop a sustainable approach to LRT planning.
Factors Affecting Farmers’ Access to Formal and Informal Credit: Evidence from Rural Afghanistan
Authors: Moahid Masaood , Maharjan Keshav Lall
Published: February 2020

Adequate access to credit is necessary for the sustainable development of agriculture. This study uses a double hurdle model to investigate what affects farming households’ credit participation and amount, and a Probit model to find out credit constraints. For this purpose, the data from a survey of 292 farming households in Afghanistan was utilized. The study finds that households obtain credit for their agricultural activities from various formal and informal sources. The results of the double hurdle model reveal that the financial activities of the households were positively determined by crop diversity, education, number of adults in a household, size of land, and access to extension. Non-agricultural income decreases the likelihood of participation. The results of the analysis of credit constraints indicate that formal credit did not help small-scale and remoter farming households; however, these households relied on informal credit, especially when they faced income shock. Furthermore, religious belief increased the chances of avoiding formal credit but not informal credit. It is suggested that formal credit should be expanded to rural areas, especially to small-scale farming households. Policy makers should also consider increasing access to extension. Formal financial institutions should provide Sharia-compliant credit, which increases the confidence level of households in using formal credit in Afghanistan.
Sustainability in Online Video Hosting Services: The Effects of Serendipity and Flow Experience on Prolonged Usage Time
Authors: Lu Hsi-Peng , Cheng Yi-Hsiu
Published: February 2020

Online video hosting services have been a highly competitive e-commerce application in recent years. The service providers constantly seek viable ways for users to prolong usage time as a model for sustainable business development. In this study, we propose a framework to verify the impact of perceived serendipity on flow experience and prolong usage time. In addition, we verify the factors of online video platforms that affect perceived serendipity, such as enabled connections, introduced unexpected information, and induced curiosity. This study used questionnaires to survey YouTube users and analyzed 405 samples using the Structural Equation Model (SEM) method. The result shows that enabled connections and introduced unexpected information significantly affect perceived serendipity, which significantly impacts on the flow experience. Although induced curiosity does not significantly affect perceived serendipity, it directly affects the flow experience. Moreover, both perceived serendipity and flow experience positively affect intentions to prolong usage time. The results validate that serendipity is another antecedent of flow experience, and support the assumption that increasing the likelihood of perceived serendipity will affect users’ intention to prolong usage time in online video hosting services. The implication of research and discussions provides references for online video hosting service providers in marketing and operations.
A Function-Based Approach for Life Cycle Management of Chemicals in the Textile Industry
Authors: Roos Sandra , Posner Stefan , Jönsson Christina , Olsson Elisabeth , Nilsson-Lindén Hanna , Schellenberger Steffen , Larsson Mikael , Hanning Anne-Charlotte , Arvidsson Rickard
Published: February 2020

Consumer products such as clothes and footwear sometimes contain chemical substances with properties that pose a risk to human health and the environment. These substances, restricted by law or company policy, are in focus for chemicals management processes by textile retailers. However, complex and non-transparent supply chains, and limited chemical knowledge, makes chemicals management challenging. Therefore, a function-based approach for life cycle management (LCM) of chemicals was developed, based on results of previous projects and evaluated using a two-step Delphi process. The resulting approach aims to help retailers identify and substitute hazardous substances in products, and consists of three parts: (i) a function-based chemicals management concept model for different levels of chemical information within the supply chain, (ii) tools for non-chemists which explain chemical information, and (iii) a continuous provision of knowledge to stakeholders (e.g., retailers) in a network. This approach is successfully implemented by over 100 retailers in the Nordic countries, providing the textile industry with practical and robust tools to manage and substitute hazardous chemicals in products and production processes. We conclude that the developed approach provides an explicit link, communication, and knowledge sharing between actors in the supply chain, which has proven important in chemicals LCM.
Turning Agri-Food Cooperative Vegetable Residues into Functional Powdered Ingredients for the Food Industry
Authors: Bas-Bellver Claudia , Barrera Cristina , Betoret Noelia , Seguí Lucía
Published: February 2020

Current food transformation processes must face the food waste issue by developing valorization processes to reintroduce by-products in the economic cycle and contribute to circular economy, generating social and economic value, and ensuring permanence of agricultural and rural activities. In the present paper, the results of a collaboration project between a regional agri-food cooperative and university are summarized. The project aimed to revalorize a series of vegetable wastes (carrot, leek, celery, and cabbage) from the fresh and ready-to-eat lines of the cooperative, by producing functional powders to be used as functional food ingredients. Vegetables residues were successfully transformed into functional ingredients by hot air drying or freeze-drying, and variables such as storage conditions and grinding intensity prior to drying were considered. Twenty-five vegetable powders were obtained and characterized in terms of physicochemical and antioxidant properties. Results showed that drying (mainly hot air drying) allowed obtaining stable powders, with very low water activity values, and a significantly increased functionality. Vegetable waste powders could be used in the food industry as coloring and flavoring ingredients, or natural preservatives, or either be used to reformulate processed foods in order to improve their nutritional properties.
Scientific Landscape of Sustainable Urban and Rural Areas Research: A Systematic Scientometric Analysis
Authors: Sheikhnejad Yahya , Yigitcanlar Tan
Published: February 2020

Urban and rural areas are the cradles of human development and civilization as all practical progress and feasible products have emerged from these places. Having a clear understanding of the sustainability of these areas, especially the progress of research in this field, is highly important. This study undertakes a comprehensive scientometric analysis to visualize and quantitatively measure the outcome of the scientific enterprise to construct sustainable urban and rural areas. The scientific productivity of recorded publications, their impacts, and their importance were systematically measured in diverse aspects by virtue of ten distinctive techniques. A critical assessment of the respected criteria for sustainable urban and rural areas is presented in order to further clarify its boundaries with other related topics, as well as providing a clear benchmark for future studies. After screening the search results between 1900 and 2018 on the Web of Science database, 7397 publications explicitly related to sustainable urban and rural areas were extracted and analyzed by the CiteSpace software via the relevant bibliometric indicators. The results of this study shed light on the trends and patterns, consisting of kernel research areas, countries, journals, institutions, and how these entities have been concatenated, inside the bibliographic records around sustainable urban and rural areas. The findings facilitate in identifying the barriers and weakness in the current sustainable urban and rural areas research, and consequently help in identifying auspicious directions for prospective investigations.
Urban Function as a New Perspective for Adaptive Street Quality Assessment
Authors: Hu Feng , Liu Wei , Lu Junyu , Song Chengpeng , Meng Yuan , Wang Jun , Xing Hanfa
Published: February 2020

Street networks are considered to be one significant component of urban structures that serve various urban functions. Assessing the quality of each street is important for managing natural and public resources, organizing urban morphologies and improving city vitality. While current research focuses on particular street assessment indices, such as accessibility and connectivity, they ignore biases in street assessment caused by differences in urban functions. To address this issue, an adaptive approach to assessing street quality from the perspective of the variation in urban functions is proposed. First, an adaptive urban function detection model is established, with street-level element segmenting using PSPNet and semantic urban function extraction using LDA topic modelling. On this basis, an urban function-driven street quality assessment is proposed to adaptively evaluate multilevel urban streets. Taking Tianhe District in Guangzhou, Guangdong Province, as the study area, experiments using street view images and points of interest (POIs) are applied to validate the proposed approach. The experiment results in a model for adaptive urban function detection with an overall accuracy of 64.3%, showing that streets with different urban functions, including traffic, commercial, and residential functions, can be assessed. The experimental results can facilitate urban function organization and urban land-use planning.
Mapping Worldwide Environmental Suitability for Artemisia annua L.
Authors: Ding Fangyu , Ma Tian , Hao Mengmeng , Wang Qian , Chen Shuai , Wang Di , Huang Luqi , Zhang Xiaobo , Jiang Dong
Published: February 2020

Artemisinin, which is isolated from the naturally occurring plant Artemisia annua L. (A. annua; Qinghao in traditional Chinese medicine), is considered to be the active ingredient in the most effective treatment for malaria. Current malaria eradication plans rely on an affordable and robust supply of artemisinin, resulting in the demand to expand the area of A. annua under cultivation. However, there is no reliable assessment of the potential land resources suitable for planting A. annua at the global scale. By explicitly incorporating the assembled contemporary occurrence records of A. annua with various spatial predictor variables, a species distribution modelling procedure was adopted to produce the first global environmental suitability map for A. annua with high geographic detail (5 × 5 km2). The estimated map reveals that the total amount of potential land resources suitable for planting A. annua is approximately 1496.56 million hectares, mainly distributed in Asia (516.50 million hectares), Europe (378.82 million hectares), North America (354.56 million hectares) and South America (172.01 million hectares). The relationships between the relevant variables and A. annua were explored, and these illustrated that the most noteworthy predictor variables were meteorological factors, followed by solar radiation factors, soil factors and topographical factors. The map provides a rigorous environmental niche baseline to support the reasonable expansion of the A. annua cultivation area.
Leveraged Trading, Irrational Sentiment and Sustainability in the Stock Market: Evidence from China
Authors: Peng Zhen , Hu Changsheng
Published: February 2020

Leveraged trading exhibits the characteristics of “strong margin trading and weak short selling” in the Chinese stock market. On the basis of monthly data on leveraged trading in the Chinese stock market from January 2014 to December 2016, we aim to empirically examine the relationship between leveraged trading and investor sentiment, and analyze the characteristics of investor sentiment contained in the leverage ratio. The results show that (1) as the leverage ratio increases, the pattern of investor trading changes from the positive feedback trading of “chasing up and down” to the negative feedback trading of “selling high and buying low”; (2) leveraged trading has the typical characteristics of irrational sentiment; (3) inverse arbitrage strategies based on leverage ratios is effective in one month in the Chinese market. The findings in this paper provide empirical support for clarifying the influence mechanism between leveraged trading and investor sentiment, and can serve as a useful reference for reducing the impact of leveraged trading on volatility and maintaining the sustainability of the stock market.
Density through the Prism of Supertall Residential Skyscrapers: Urbo-Architectural Type in Global Megacities
Authors: Radović Vuk
Published: February 2020

Since their inauguration in 2005, supertall residential skyscrapers have established themselves as a truly new, 21st century phenomenon. Their uniqueness spans the spectrum of critically important issues, ranging from discrete ways of conceptualization, production, and delivery, introduction of latest technologies, strict organizational and spatial rules and practices, all the way to various socio-cultural impacts, which include the peculiar, often invisible ways of cultural accommodation. This paper presents parts of a larger research project into this urbo-architectural type, focusing on the capacity of these skyscrapers to address numerous issues related to residential density, especially in fast-growing megacities. While a substantial number of research projects explore economic, architectural, engineering, and environmental attributes of these buildings by focusing on measurable aspects of their production and use, the holistic comparisons and qualitative elaboration of the significance of the residential supertall phenomenon are still lacking. This paper attempts to fill that gap and open a new approach into investigations of supertall residential skyscrapers. The starting position is that these are not simply bigger and taller, but fundamentally different urban artefacts, which have an untapped capacity to reach another kind of quality. Definition and recognition of that difference will enable us to better capitalize upon the qualities which it brings and help avoid the problems which it generates.
Alternative Hydrophobic Core in Proteins—The Effect of Specific Synergy
Authors: Fabian Piotr , Stapor Katarzyna , Banach Mateusz , Ptak-Kaczor Magdalena , Konieczny Leszek , Roterman Irena
Published: February 2020

Proteins with a high degree of sequence similarity representing different structures provide a key to understand how protein sequence codes for 3D structure. An analysis using the fuzzy oil drop model was carried out on two pairs of proteins with different secondary structures and with high sequence identities. It has been shown that distributions of hydrophobicity for these proteins are approximated well using single 3D Gaussian function. In other words, the similar sequences fold into different 3D structures, however, alternative structures also have symmetric and monocentric hydrophobic cores. It should be noted that a significant change in the helical to beta-structured form in the N-terminal section takes places in the fragment much preceding the location of the mutated regions. It can be concluded that the final structure is the result of a complicated synergy effect in which the whole chain participates simultaneously.