bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of TMPA 3B42-V7 Product on Extreme Precipitation Estimates
Authors: Chen Jiachao , Wang Zhaoli , Wu Xushu , Lai Chengguang , Chen Xiaohong
Published: January 2021

Availability of precipitation data at high spatial and temporal resolution is crucial for the understanding of precipitation behaviors that are determinant for environmental aspects such as hydrology, ecology, and social aspects like agriculture, food security, or health issues. This study evaluates the performance of 3B42-V7 satellite-based precipitation product on extreme precipitation estimates in China, by using the Fuzzy C-Means algorithm and L-moment-based regional frequency analysis method. The China Gauge-based Daily Precipitation Analysis (CGDPA) product is employed to measure the estimation biases of 3B42-V7. Results show that: (1) for most regions of China, the Generalized Extreme Value and Generalized Normal distributions are preferable for extreme precipitation estimates; (2) the extreme precipitation estimations of 3B42-V7 for different return periods have a high correlation with those of CGDPA, with biases within 25% for a majority of China on extreme precipitation estimates.
A Distributed Modular Data Processing Chain Applied to Simulated Satellite Ozone Observations
Authors: Gai Marco , Barbara Flavio , Ceccherini Simone , Cortesi Ugo , Del Bianco Samuele , Tirelli Cecilia , Zoppetti Nicola , Belotti Claudio , Canessa Bruno , Farruggia Vincenzo , Masini Andrea , Keppens Arno , Lambert Jean-Christopher , Arola Antti , Lipponen Antti , Tuinder Olaf
Published: January 2021

Remote sensing of the atmospheric composition from current and future satellites, such as the Sentinel missions of the Copernicus programme, yields an unprecedented amount of data to monitor air quality, ozone, UV radiation and other climate variables. Hence, full exploitation of the growing wealth of information delivered by spaceborne observing systems requires addressing the technological challenges for developing new strategies and tools that are capable to deal with these huge data volumes. The H2020 AURORA (Advanced Ultraviolet Radiation and Ozone Retrieval for Applications) project investigated a novel approach for synergistic use of ozone profile measurements acquired at different frequencies (ultraviolet, visible, thermal infrared) by sensors onboard Geostationary Equatorial Orbit (GEO) and Low Earth Orbit (LEO) satellites in the framework of the Copernicus Sentinel-4 and Sentinel-5 missions. This paper outlines the main features of the technological infrastructure, designed and developed to support the AURORA data processing chain as a distributed data processing and describes in detail the key components of the infrastructure and the software prototype. The latter demonstrates the technical feasibility of the automatic execution of the full processing chain with simulated data. The Data Processing Chain (DPC) presented in this work thus replicates a processing system that, starting from the operational satellite retrievals, carries out their fusion and results in the assimilation of the fused products. These consist in ozone vertical profiles from which further modules of the chain deliver tropospheric ozone and UV radiation at the Earth’s surface. The conclusions highlight the relevance of this novel approach to the synergistic use of operational satellite data and underline that the infrastructure uses general-purpose technologies and is open for applications in different contexts.
Challenges of the Polarimetric Update on Operational Radars in China—Ground Clutter Contamination of Weather Radar Observations
Authors: Wu Chong , Liu Liping , Chen Chao , Zhang Chian , He Guangxin , Li Juan
Published: January 2021

China New Generation Doppler Weather Radar (CINRAD) plans to upgrade its hardware and software to achieve polarimetric function. However, the small-magnitude polarimetric measurements were negatively affected by the scattering characteristics of ground clutter and the filter’s response to the ground clutter. This polarimetric contamination was characterized by decreased differential reflectivity (ZDR) and cross-correlation coefficient (ρhv), as well as an increased standard deviation of the differential phase (ΦDP), generating a large-area and long-term observational anomaly for eight polarimetric radars in South China. Considering that outliers simultaneously appeared in the radar mainlobe and sidelobe, the variations in the reflectivity before and after clutter mitigation (ΔZH) and ρhv were used for quantitatively describing the random dispersion caused by mainlobe and sidelobe clutters. The performance of polarimetric algorithms was also reduced by clutter contamination. The deteriorated membership functions in the hydrometeor classification algorithm changed the proportion of classified echoes. The empirical relations of R(ZH, ZDR) and R(KDP) were broken in the quantitative precipitation estimation algorithm and the extra error considerably exceeded the uncertainty caused by the drop-size distribution (DSD) variability of R(ZH). The above results highlighted the negative impact of clutter contamination on polarimetric applications that need to be further investigated.
Exploring the Relationship between River Discharge and Coastal Erosion: An Integrated Approach Applied to the Pisa Coastal Plain (Italy)
Authors: Bini Monica , Casarosa Nicola , Luppichini Marco
Published: January 2021

Coastal erosion coupled with human-induced pressure has severely affected the coastal areas of the Mediterranean region in the past and continues to do so with increasing intensity today. In this context, the Pisa coastal plain shows a long history of erosion, which started at the beginning of the nineteenth century. In this work, shoreline positions derived from historical maps as well as airborne and DGPS (Differential Global Positioning System) surveys were analyzed in a GIS (Geographic Information System) environment to identify the main changes that have occurred in the last 142 years. These analyses were compared with 100 years of discharge data measured at the S. Giovanni alla Vena gauge to identify a possible correlation between the two sets of information. Finally, Sentinel-2 and Landsat images were studied to identify the dispersion of sediments transported by the Arno River. In particular, we found a minimum of fluvial discharge in the years 1954, 1978, and 2012 corresponding to a peak of erosion, while the reduced erosion rate and the fluvial discharge increased in the years 1928–1944, 1954–1975, and after 2012. The qualitative anticorrelation between discharge and erosion is particularly true if we take into account flood events with a value of discharge greater than 700 m3/s, which are those able to transport suspended sand. The remote sensing analyses of Sentinel-2 images acquired during the floods of 6 February 2019 and 3 December 2019, under the most typical wind and sea state conditions for this area (wind coming from SW and storms coming from W/SW and SW) show that during these events a consistent amount of sediment was transported by the river. However, the majority of these sediments are not deposited along the coastline but are dispersed offshore. Grain-size analyses on the transported sediment show that plumes are formed by coarse-to-medium sand, suitable for coastal nourishment, but the reconstructed sediment dispersion lines show that some sectors of the coastline are constantly in the shade. These areas are the most affected by erosion.
Real-Time Leak Detection for a Gas Pipeline Using a k-NN Classifier and Hybrid AE Features
Authors: Quy Thang Bui , Kim Jong-Myon
Published: January 2021

This paper introduces a technique using a k-nearest neighbor (k-NN) classifier and hybrid features extracted from acoustic emission (AE) signals for detecting leakages in a gas pipeline. The whole algorithm is embedded in a microcontroller unit (MCU) to detect leaks in real-time. The embedded system receives signals continuously from a sensor mounted on the surface of a gas pipeline to diagnose any leak. To construct the system, AE signals are first recorded from a gas pipeline testbed under various conditions and used to synthesize the leak detection algorithm via offline signal analysis. The current work explores different features of normal/leaking states from corresponding datasets and eliminates redundant and outlier features to improve the performance and guarantee the real-time characteristic of the leak detection program. To obtain the robustness of leak detection, the paper normalizes features and adapts the trained k-NN classifier to the specific environment where the system is installed. Aside from using a classifier for categorizing normal/leaking states of a pipeline, the system monitors accumulative leaking event occurrence rate (ALEOR) in conjunction with a defined threshold to conclude the state of the pipeline. The entire proposed system is implemented on the 32F746G-DISCOVERY board, and to verify this system, numerous real AE signals stored in a hard drive are transferred to the board. The experimental results show that the proposed system executes the leak detection algorithm in a period shorter than the total input data time, thus guaranteeing the real-time characteristic. Furthermore, the system always yields high average classification accuracy (ACA) despite adding a white noise to input signal, and false alarms do not occur with a reasonable ALEOR threshold.
An Odor Labeling Convolutional Encoder–Decoder for Odor Sensing in Machine Olfaction
Authors: Wen Tengteng , Mo Zhuofeng , Li Jingshan , Liu Qi , Wu Liming , Luo Dehan
Published: January 2021

Deep learning methods have been widely applied to visual and acoustic technology. In this paper, we propose an odor labeling convolutional encoder–decoder (OLCE) for odor identification in machine olfaction. OLCE composes a convolutional neural network encoder and decoder where the encoder output is constrained to odor labels. An electronic nose was used for the data collection of gas responses followed by a normative experimental procedure. Several evaluation indexes were calculated to evaluate the algorithm effectiveness: accuracy 92.57%, precision 92.29%, recall rate 92.06%, F1-Score 91.96%, and Kappa coefficient 90.76%. We also compared the model with some algorithms used in machine olfaction. The comparison result demonstrated that OLCE had the best performance among these algorithms.
A Deep Learning Method for the Impact Damage Segmentation of Curve-Shaped CFRP Specimens Inspected by Infrared Thermography
Authors: Wei Ziang , Fernandes Henrique , Herrmann Hans-Georg , Tarpani Jose Ricardo , Osman Ahmad
Published: January 2021

Advanced materials such as continuous carbon fiber-reinforced thermoplastic (CFRP) laminates are commonly used in many industries, mainly because of their strength, stiffness to weight ratio, toughness, weldability, and repairability. Structural components working in harsh environments such as satellites are permanently exposed to some sort of damage during their lifetimes. To detect and characterize these damages, non-destructive testing and evaluation techniques are essential tools, especially for composite materials. In this study, artificial intelligence was applied in combination with infrared thermography to detected and segment impact damage on curved laminates that were previously submitted to a severe thermal stress cycles and subsequent ballistic impacts. Segmentation was performed on both mid-wave and long-wave infrared sequences obtained simultaneously during pulsed thermography experiments by means of a deep neural network. A deep neural network was trained for each wavelength. Both networks generated satisfactory results. The model trained with mid-wave images achieved an F1-score of 92.74% and the model trained with long-wave images achieved an F1-score of 87.39%.
The Overview of Human Localization and Vital Sign Signal Measurement Using Handheld IR-UWB Through-Wall Radar
Authors: Yang Degui , Zhu Zhengliang , Zhang Junchao , Liang Buge
Published: January 2021

Obtaining information (e.g., position, respiration, and heartbeat rates) on humans located behind opaque and non-metallic obstacles (e.g., walls and wood) has prompted the development of non-invasive remote sensing technologies. Due to its excellent features like high penetration ability, short blind area, fine-range resolution, high environment adoption capabilities, low cost and power consumption, and simple hardware design, impulse radio ultra-wideband (IR-UWB) through-wall radar has become the mainstream primary application radar used for the non-invasive remote sensing. IR-UWB through-wall radar has been developed for nearly 40 years, and various hardware compositions, deployment methods, and signal processing algorithms have been introduced by many scholars. The purpose of these proposed approaches is to obtain human information more accurately and quickly. In this paper, we focus on IR-UWB through-wall radar and introduce the key advances in system design and deployment, human detection theory, and signal processing algorithms, such as human vital sign signal measurement methods and moving human localization. Meanwhile, we discuss the engineering pre-processing methods of IR-UWB through-wall radar. The lasts research progress in the field is also presented. Based on this progress, the conclusions and the development directions of the IR-UWB through-wall radar in the future are also preliminarily forecasted.
Nanozymes for Environmental Pollutant Monitoring and Remediation
Authors: Wong Elicia L. S. , Vuong Khuong Q. , Chow Edith
Published: January 2021

Nanozymes are advanced nanomaterials which mimic natural enzymes by exhibiting enzyme-like properties. As nanozymes offer better structural stability over their respective natural enzymes, they are ideal candidates for real-time and/or remote environmental pollutant monitoring and remediation. In this review, we classify nanozymes into four types depending on their enzyme-mimicking behaviour (active metal centre mimic, functional mimic, nanocomposite or 3D structural mimic) and offer mechanistic insights into the nature of their catalytic activity. Following this, we discuss the current environmental translation of nanozymes into a powerful sensing or remediation tool through inventive nano-architectural design of nanozymes and their transduction methodologies. Here, we focus on recent developments in nanozymes for the detection of heavy metal ions, pesticides and other organic pollutants, emphasising optical methods and a few electrochemical techniques. Strategies to remediate persistent organic pollutants such as pesticides, phenols, antibiotics and textile dyes are included. We conclude with a discussion on the practical deployment of these nanozymes in terms of their effectiveness, reusability, real-time in-field application, commercial production and regulatory considerations.
TIMA SLAM: Tracking Independently and Mapping Altogether for an Uncalibrated Multi-Camera System
Authors: Ince Omer Faruk , Kim Jun-Sik
Published: January 2021

We present a novel simultaneous localization and mapping (SLAM) system that extends the state-of-the-art ORB-SLAM2 for multi-camera usage without precalibration. In this system, each camera is tracked independently on a shared map, and the extrinsic parameters of each camera in the fixed multi-camera system are estimated online up to a scalar ambiguity (for RGB cameras). Thus, the laborious precalibration of extrinsic parameters between cameras becomes needless. By optimizing the map, the keyframe poses, and the relative poses of the multi-camera system simultaneously, observations from the multiple cameras are utilized robustly, and the accuracy of the shared map is improved. The system is not only compatible with RGB sensors but also works on RGB-D cameras. For RGB cameras, the performance of the system tested on the well-known EuRoC/ASL and KITTI datasets that are in the stereo configuration for indoor and outdoor environments, respectively, as well as our dataset that consists of three cameras with small overlapping regions. For the RGB-D tests, we created a dataset that consists of two cameras for an indoor environment. The experimental results showed that the proposed method successfully provides an accurate multi-camera SLAM system without precalibration of the multi-cameras.
Application of Skeleton Data and Long Short-Term Memory in Action Recognition of Children with Autism Spectrum Disorder
Authors: Zhang Yunkai , Tian Yinghong , Wu Pingyi , Chen Dongfan
Published: January 2021

The recognition of stereotyped action is one of the core diagnostic criteria of Autism Spectrum Disorder (ASD). However, it mainly relies on parent interviews and clinical observations, which lead to a long diagnosis cycle and prevents the ASD children from timely treatment. To speed up the recognition process of stereotyped actions, a method based on skeleton data and Long Short-Term Memory (LSTM) is proposed in this paper. In the first stage of our method, the OpenPose algorithm is used to obtain the initial skeleton data from the video of ASD children. Furthermore, four denoising methods are proposed to eliminate the noise of the initial skeleton data. In the second stage, we track multiple ASD children in the same scene by matching distance between current skeletons and previous skeletons. In the last stage, the neural network based on LSTM is proposed to classify the ASD children’s actions. The performed experiments show that our proposed method is effective for ASD children’s action recognition. Compared to the previous traditional schemes, our scheme has higher accuracy and is almost non-invasive for ASD children.
CNN-Based Suppression of False Contour and Color Distortion in Bit-Depth Enhancement
Authors: Peng Changmeng , Cai Luting , Huang Xiaoyang , Fu Zhizhong , Xu Jin , Li Xiaofeng
Published: January 2021

It is a challenge to transmit and store the massive visual data generated in the Visual Internet of Things (VIoT), so the compression of the visual data is of great significance to VIoT. Compressing bit-depth of images is very cost-effective to reduce the large volume of visual data. However, compressing the bit-depth will introduce false contour, and color distortion would occur in the reconstructed image. False contour and color distortion suppression become critical issues of the bit-depth enhancement in VIoT. To solve these problems, a Bit-depth Enhancement method with AUTO-encoder-like structure (BE-AUTO) is proposed in this paper. Based on the convolution-combined-with-deconvolution codec and global skip of BE-AUTO, this method can effectively suppress false contour and color distortion, thus achieving the state-of-the-art objective metric and visual quality in the reconstructed images, making it more suitable for bit-depth enhancement in VIoT.
Evaluation of Coating Thickness Using Lift-Off Insensitivity of Eddy Current Sensor
Authors: Meng Xiaobai , Lu Mingyang , Yin Wuliang , Bennecer Abdeldjalil , Kirk Katherine J.
Published: January 2021

Defect detection in ferromagnetic substrates is often hampered by nonmagnetic coating thickness variation when using conventional eddy current testing technique. The lift-off distance between the sample and the sensor is one of the main obstacles for the thickness measurement of nonmagnetic coatings on ferromagnetic substrates when using the eddy current testing technique. Based on the eddy current thin-skin effect and the lift-off insensitive inductance (LII), a simplified iterative algorithm is proposed for reducing the lift-off variation effect using a multifrequency sensor. Compared to the previous techniques on compensating the lift-off error (e.g., the lift-off point of intersection) while retrieving the thickness, the simplified inductance algorithms avoid the computation burden of integration, which are used as embedded algorithms for the online retrieval of lift-offs via each frequency channel. The LII is determined by the dimension and geometry of the sensor, thus eliminating the need for empirical calibration. The method is validated by means of experimental measurements of the inductance of coatings with different materials and thicknesses on ferrous substrates (dual-phase alloy). The error of the calculated coating thickness has been controlled to within 3% for an extended lift-off range of up to 10 mm.
Influence of a Biomass-Burning Event in PM2.5 Concentration and Air Quality: A Case Study in the Metropolitan Area of São Paulo
Authors: de Arruda Moreira Gregori , da Silva Andrade Izabel , Cacheffo Alexandre , da Silva Lopes Fábio Juliano , Calzavara Yoshida Alexandre , Gomes Antonio Arleques , da Silva Jonatan João , Landulfo Eduardo
Published: January 2021

Severe biomass burning (BB) events have become increasingly common in South America in the last few years, mainly due to the high number of wildfires observed recently. Such incidents can negatively influence the air quality index associated with PM2.5 (particulate matter, which is harmful to human health). A study performed in the Metropolitan Area of São Paulo (MASP) took place on selected days of July 2019, evaluated the influence of a BB event on air quality. Use of combined remote sensing, a surface monitoring system and data modeling and enabled detection of the BB plume arrival (light detection and ranging (lidar) ratio of (50 ± 34) sr at 532 nm, and (72 ± 45) sr at 355 nm) and how it affected the Ångström exponent (>1.3), atmospheric optical depth (>0.7), PM2.5 concentrations (>25 µg.m−3), and air quality classification. The utilization of high-order statistical moments, obtained from elastic lidar, provided a new way to observe the entrainment process, allowing understanding of how a decoupled aerosol layer influences the local urban area. This new novel approach enables a lidar system to obtain the same results as a more complex set of instruments and verify how BB events contribute from air masses aloft towards near ground ones.
A Digital-Analog Hybrid System-on-Chip for Capacitive Sensor Measurement and Control
Authors: Gao Zhenyi , Zhou Bin , Li Xiang , Yang Lei , Wei Qi , Zhang Rong
Published: January 2021

Sensors based on capacitance detection are common in the field of inertial measurement and have the potential for miniaturization and low power consumption. In order to control and process such sensors, a novel digital-analog hybrid system-on-chip (SoC) is designed and implemented. The system includes a capacitor to voltage (C/V) conversion circuit and a band-pass sigma-delta modulator (BPSDM) as the analog-to-digital converter (ADC). The digital signal is processed by the dedicated circuit module based on the least mean square error demodulation (LMSD) algorithm on the chip. The low-power Cortex-M3 processor supports software implementation of control algorithms and circuit parameter configuration. The control signal is output through a digital BPSDM. The chip was taped out under SMIC 180 nm Complementary Metal Oxide Semiconductor (CMOS) technology and tested for performance. The result shows that the maximum operating frequency of the chip is 105 MHz. The total area is 77.43 mm2. When the system clock is set to 51.2 MHz, the static power consumption and dynamic power consumption of the digital system are 18 mW and 54 mW respectively.
An IoE and Big Multimedia Data Approach for Urban Transport System Resilience Management in Smart Cities
Authors: Bellini Emanuele , Bellini Pierfrancesco , Cenni Daniele , Nesi Paolo , Pantaleo Gianni , Paoli Irene , Paolucci Michela
Published: January 2021

Today, the complexity of urban systems combined with existing and emerging threats constrains administrations to consider smart technologies and related huge amounts of data generated as a means to take timely and informed decisions. The smart city needs to be prepared for both expected and unexpected situations, and the possibility to mitigate the effect of the uncertainty behind the causes of disruptions through the analysis of all the possible data generated by the city open new possibility for resilience operationalization. This article aims at introducing a new conceptualization for resilience and presenting an innovative full stack solution to exploit Internet of Everything (IoE) and big multimedia data in smart cities to manage resilience of urban transport systems (UTS), which is one of the most critical infrastructures of the city. The approach is based on a novel data driven approach to resilience engineering and functional resonance analysis method (FRAM), to understand and model an UTS in the context of smart cities and to support evidence driven decision making. The paper proposes an architecture taking into account: (a) different kinds of available data generated in the smart city, (b) big data collection and semantic aggregation and enrichment; (c) data sense-making process composed by analytics of different data sources like social media, communication networks, IoT, user behavior; (d) tools for knowledge driven decisions able to combine different information generated by analytics, experience, and structural information of the city into a comprehensive and evidence driven decision model. The solution has been applied in Florence metropolitan city in the context of RESOLUTE H2020 research project of the European Commission.
Three-Dimensional Localization Algorithm Based on Improved A* and DV-Hop Algorithms in Wireless Sensor Network
Authors: Huang Xiaohu , Han Dezhi , Cui Mingming , Lin Guanghan , Yin Xinming
Published: January 2021

In the traditional wireless sensor networks (WSNs) localization algorithm based on the Internet of Things (IoT), the distance vector hop (DV-Hop) localization algorithm has the disadvantages of large deviation and low accuracy in three-dimensional (3D) space. Based on the 3DDV-Hop algorithm and combined with the idea of A* algorithm, this paper proposes a wireless sensor network node location algorithm (MA*-3DDV-Hop) that integrates the improved A* algorithm and the 3DDV-Hop algorithm. In MA*-3DDV-Hop, firstly, the hop-count value of nodes is optimized and the error of average distance per hop is corrected. Then, the multi-objective optimization non dominated sorting genetic algorithm (NSGA-II) is adopted to optimize the coordinates locally. After selection, crossover, mutation, the Pareto optimal solution is obtained, which overcomes the problems of premature convergence and poor convergence of existing algorithms. Moreover, it reduces the error of coordinate calculation and raises the localization accuracy of wireless sensor network nodes. For three different multi-peak random scenes, simulation results show that MA*-3DDV-Hop algorithm has better robustness and higher localization accuracy than the 3DDV-Hop, PSO-3DDV-Hop, GA-3DDV-Hop, and N2-3DDV-Hop.
The Benefits of Receiver Clock Modelling in Satellite Timing
Authors: Qin Weijin , Wang Xiao , Su Hang , Zhang Zhe , Li Xiao , Yang Xuhai
Published: January 2021

Satellite timing is an effective and convenient method that has been widely accepted in the time community. The key to satellite timing is obtaining a clean receiver clock offset. In this paper, instead of regarding the receiver clock offset as white noise, a two-state stochastic clock model involving three kinds of noise was conceived and used in PPP filter estimation. The influence of clock type and sampling time on satellite timing performance was first analysed. In addition, the kinematic scheme and static scheme were both investigated for meeting the demands of multi-occasional users. The values show that the model works well for both the kinematic scheme and static scheme; in contrast to that of the white noise model, the timing stability is enhanced at all the sampling times. For the six stations, especially when the averaging time is less than 1000 s, the average stability improvement values of the kinematic scheme are 75.53, 43.24, 75.00, 69.05, 40.57, and 25.45%, and the average improvement values of the static scheme are 65.49, 77.94, 56.71, 60.78, 64.41, and 39.41%. Furthermore, the enhancement magnitude is related to clock type. For a high-stability clock, the improvement of the kinematic scheme is greater than that of the static scheme, whereas for a low-stability clock, the improvement of the kinematic scheme is less than that of the static scheme.
Computational Load Analysis of a Galileo OSNMA-Ready Receiver for ARM-Based Embedded Platforms
Authors: Troglia Gamba Micaela , Nicola Mario , Motella Beatrice
Published: January 2021

Many GNSS applications have been experiencing some constantly growing needs in terms of security and reliability. To address some of them, both GPS and Galileo are proposing evolutions of their legacy civil signals, embedding features of authentication. This paper focuses on the Galileo Open Signal Navigation Message Authentication (OSNMA) and describes its implementation within a real-time software receiver for ARM-based embedded platforms. The innovative contributions of the paper include the software profiling analysis for the OSNMA add on, along with the comparison among performances obtained with different platforms. In addition, specific evaluations on the computational load of the whole receiver complete the analysis. The receiver used for the implementation belongs to the NGene receivers family—real-time fully-software GPS and Galileo receivers, tailored for different platforms and sharing the same core processing. In detail, the paper deals with the introduction of the OSNMA support inside the eNGene, the version of the receiver executable by ARM-based embedded platforms.
Differentiation of Heterogeneous Mouse Liver from HCC by Hyperpolarized 13C Magnetic Resonance
Authors: Lev-Cohain Naama , Sapir Gal , Uppala Sivaranjan , Nardi-Schreiber Atara , Goldberg Shraga , Adler-Levy Yael , Sosna Jacob , Gomori J. , Katz-Brull Rachel
Published: January 2021

The clinical characterization of small hepatocellular carcinoma (HCC) lesions in the liver and differentiation from heterogeneous inflammatory or fibrotic background is important for early detection and treatment. Metabolic monitoring of hyperpolarized 13C-labeled substrates has been suggested as a new avenue for diagnostic magnetic resonance. The metabolism of hyperpolarized [1-13C]pyruvate was monitored in mouse precision-cut liver slices (PCLS) of aged MDR2-KO mice, which served as a model for heterogeneous liver and HCC that develops similarly to the human disease. The relative in-cell activities of lactate dehydrogenase (LDH) to alanine transaminase (ALT) were found to be 0.40 ± 0.06 (n = 3) in healthy livers (from healthy mice), 0.90 ± 0.27 (n = 3) in heterogeneously inflamed liver, and 1.84 ± 0.46 (n = 3) in HCC. Thus, the in-cell LDH/ALT activities ratio was found to correlate with the progression of the disease. The results suggest that the LDH/ALT activities ratio may be useful in the assessment of liver disease. Because the technology used here is translational to both small liver samples that may be obtained from image-guided biopsy (i.e., ex vivo investigation) and to the intact liver (i.e., in a noninvasive MRI scan), these results may provide a path for differentiating heterogeneous liver from HCC in human subjects.
Dissolution Testing of Nicotine Release from OTDN Pouches: Product Characterization and Product-to-Product Comparison
Authors: Aldeek Fadi , McCutcheon Nicholas , Smith Cameron , Miller John H. , Danielson Timothy L.
Published: January 2021

In recent years, oral tobacco-derived nicotine (OTDN) pouches have emerged as a new oral tobacco product category. They are available in a variety of flavors and do not contain cut or ground tobacco leaf. The on!® nicotine pouches fall within this category of OTDN products and are currently marketed in seven (7) flavors with five (5) different nicotine levels. Evaluation of the nicotine release from these products is valuable for product assessment and product-to-product comparisons. In this work, we characterized the in vitro release profiles of nicotine from the 35 varieties of on!® nicotine pouches using a fit-for-purpose dissolution method, employing the U.S. Pharmacopeia flow-through cell dissolution apparatus 4 (USP-4). The nicotine release profiles were compared using the FDA’s Guidance for Industry: Dissolution Testing of Immediate Release Solid Oral Dosage Forms. The cumulative release profiles of nicotine show a dose dependent response for all nicotine levels. The on!® nicotine pouches exhibit equivalent percent nicotine release rates for each flavor variant across all nicotine levels. Furthermore, the nicotine release profiles from on!® nicotine pouches were compared to a variety of other commercially available OTDN pouches and traditional pouched smokeless tobacco products. The percent nicotine release rates were found to be dependent on the product characteristics, showing similarities and differences in the nicotine release profiles between the on!® nicotine pouches and other compared products.
Kumaraswamy Generalized Power Lomax Distributionand Its Applications
Authors: Nagarjuna Vasili B.V. , Vardhan R. Vishnu , Chesneau Christophe
Published: January 2021

In this paper, a new five-parameter distribution is proposed using the functionalities of the Kumaraswamy generalized family of distributions and the features of the power Lomax distribution. It is named as Kumaraswamy generalized power Lomax distribution. In a first approach, we derive its main probability and reliability functions, with a visualization of its modeling behavior by considering different parameter combinations. As prime quality, the corresponding hazard rate function is very flexible; it possesses decreasing, increasing and inverted (upside-down) bathtub shapes. Also, decreasing-increasing-decreasing shapes are nicely observed. Some important characteristics of the Kumaraswamy generalized power Lomax distribution are derived, including moments, entropy measures and order statistics. The second approach is statistical. The maximum likelihood estimates of the parameters are described and a brief simulation study shows their effectiveness. Two real data sets are taken to show how the proposed distribution can be applied concretely; parameter estimates are obtained and fitting comparisons are performed with other well-established Lomax based distributions. The Kumaraswamy generalized power Lomax distribution turns out to be best by capturing fine details in the structure of the data considered.
An Empirical Study on Students’ Academic Wellbeing and Sustainable Development in Live Webcast Classes
Authors: Liu Huani , Wang Minjuan , Wan Hengling , Lyu Yifan , Zhu Haorong
Published: January 2021

In recent years, live webcast classes have been increasingly used in China as an approach to alleviating educational poverty through equal access to high-quality education. Many schools in impoverished areas have managed to increase their proportions of students entering college by introducing the new model. While celebrating improved learning outcomes of a small percentage of students, educators should also be concerned about the overall academic wellbeing and sustainable development of less successful students. In the present study, academic wellbeing was conceptualized as a multidimensional construct covering seven dimensions, namely Empathy, Support, Responsiveness, Reliability, Tangibility, Self-efficacy and Buoyancy. Data were collected from 136 twelfth-grade students who had studied in live webcast classes. The results show that the overall academic wellbeing in live webcast classes was consistent among students of different academic performance levels, but the specific dimensions of academic wellbeing that they think mostly need improvement varied among different student groups. The findings of this study suggest that learner wellbeing and sustainability can be enhanced by closer collaboration between live webcast instructors and local teachers in instructional materials design, exercise and test questions’ compilation, as well as students’ self-study facilitation. The degree to which a local teacher should be involved in classroom teaching depends on the students’ academic level and learning needs.
Quality Assessment of the Services Delivered by a Court, Based on the Perceptions of Users, Magistrates, and Court Officials
Authors: Moura e Sá Patrícia , Rosa Maria João , Santinha Gonçalo , Valente Cátia
Published: January 2021

This paper aims to measure the quality of the services delivered by a court by assessing the satisfaction of court users and service providers, i.e., magistrates and court officials. For that purpose, a case study was carried out and data were collected by means of a questionnaire based on the SERVPERF instrument, in which perceived service quality is measured, considering court users, magistrates, and court officials’ perceptions of post-service performance. One hundred and fifty-eight questionnaires were successfully returned. An in-depth interview was later conducted to the court administrator to gain a richer understanding of the results achieved and ask follow-up questions. Overall, findings revealed that court users, magistrates, and court officials clearly have a positive view of the services provided, although improvement is needed, particularly in the court’s facilities and technological equipment. The current research sheds some light on the potentialities and difficulties of assessing service quality in the judiciary and contributes to the validation of the SERVPERF instrument in this context.
Indigenous Knowledge and Endogenous Actions for Building Tribal Resilience after Typhoon Soudelor in Northern Taiwan
Authors: Lee Su-Hsin , Chen Yin-Jen
Published: January 2021

Indigenous peoples often face significant vulnerabilities to climate risks, yet the capacity of a social-ecological system (SES) to resilience is abstracted from indigenous and local knowledge. This research explored how the Tayal people in the Wulai tribes located in typhoon disaster areas along Nanshi River used indigenous knowledge as tribal resilience. It applied empirical analysis from secondary data on disaster relief and in-depth interviews, demonstrating how indigenous people’s endogenous actions helped during post-disaster reconstructing. With the intertwined concepts of indigenous knowledge, SESs, and tribes’ cooperation, the result presented the endogenous actions for tribal resilience. In addition, indigenous knowledge is instigated by the Qutux Niqan of mutual assistance and symbiosis among the Wulai tribes, and there is a need to build joint cooperation through local residence, indigenous people living outside of their tribes, and religious or social groups. The findings of tribal resilience after a typhoon disaster of co-production in the Wulai, Lahaw, and Fushan tribes include the importance of historical context, how indigenous people turn to their local knowledge rather than just only participating in disaster relief, and how they produce indigenous tourism for indigenous knowledge inheritance. The paper contributes to contemporary tribal resilience research as well as cooperation actions among tribes through indigenous knowledge, all of which exhibit social, nature, and economy resilience from their own indigenous knowledge to address the possibility of governance and disaster adaptation.
Lake Atitlan: A Review of the Food, Energy, and Water Sustainability of a Mountain Lake in Guatemala
Authors: Neher Timothy P. , Soupir Michelle L. , Kanwar Rameshwar S.
Published: January 2021

This paper summarizes the findings of an extensive review of literature that was conducted to understand the historical state of the food, energy, and water nexus in the Lake Atitlan basin and to recommend incentive-based, long-term sustainable policies to become a significant driver to Guatemala’s tourism industry and GDP growth. The SWAT (Soil and Water Assessment Tool) was implemented in the basin to work towards the goal of simulating nutrient loading. A key conclusion of this review study is for the local population to have advocacy for the “zero wastewater discharge to Lake Atitlan” initiative to bring long-term benefits to lake water quality. One of the recommended policy decisions is to seek external financing from international agencies like the World Bank at low-cost interest (IDA Loans) to implement waste management systems and pay this external debt by putting a small but affordable tax on tourists visiting the lake. Once a culture of zero municipal effluent discharge to Lake Atitlan is adopted by the local population, the livelihood of residents will become sustainable and the standard of living will increase because of improved water and air quality, making Lake Atitlan a haven of tourism for Guatemala and lifting its economy.
A Social-Ecological Systems Understanding of Drivers of Degradation in the Tsitsa River Catchment to Inform Sustainable Land Management
Authors: Itzkin Adela , Scholes Mary C. , Clifford-Holmes Jai Kumar , Rowntree Kate , van der Waal Bennie , Coetzer Kaera
Published: January 2021

Understanding the interactions of the social and biophysical drivers of land degradation is crucial for developing adaptive management actions for future sustainability. A research-praxis project, the ‘Tsitsa Project’ (TP), applies a social-ecological systems (SES) approach where researchers, natural resource managers, and residents collaborate to support sustainable livelihoods and improved natural resource management for the degraded Tsitsa River Catchment (TRC) in South Africa. A system diagramming approach was coupled with findings from interviews, workshops, literature, and two conceptual frameworks. Data inputs were qualitatively integrated to provide a systemic snapshot of how the context-specific social and biophysical drivers are interlinked and how they interact, revealing multiple processes that operate simultaneously to cause and exacerbate land degradation. Physical and climatic variables, changes in land use and cover, and overgrazing were identified as key factors leading to degradation. Additionally, poverty and disempowerment were also important. While little can be done to influence the physical aspects (steep topography and duplex soils) and climatic variables (extreme rainfall and drought), carefully planned changes in land use and management could produce dual-benefits for improving landscape conditions and sustainable livelihoods. This analysis will inform integrated planning processes to monitor, avoid, reduce and reverse land degradation.
Quantitative Analysis of Sustainable Use of Construction Materials for Supply Chain Integration and Construction Industry Performance through Structural Equation Modeling (SEM)
Authors: Kamal Asad , Azfar Rai Waqas , Salah Bashir , Saleem Waqas , Abas Muhammad , Khan Razaullah , Pruncu Catalin I.
Published: January 2021

This research focuses on the mediating role of construction materials, sustainable use between the construction supply chain integration and the construction industry performance. In this concern, the case of Pakistan was considered specifically. The research design employed in this study was quantitative and a close-ended survey questionnaire was used as a research instrument. The sample size used is comprised of 300 participants and analysis was performed through the Structural Equation Modelling (SEM). The results revealed that the effect of the components of supply chain integration on the construction industry performance was statistically significant. Moreover, outcomes also substantiate the mediation role of using construction material sustainably. The scope of the research was limited to the construction industry of Pakistan; however, future research would focus on other countries and industries.
Public Green Space Policy Implementation: A Case Study of Krakow, Poland
Authors: Kwartnik-Pruc Anita , Trembecka Anna
Published: January 2021

Green space is essential for the implementation of the idea of sustainable urban development. This paper contains original research on the implementation of local government tasks in the development of public green space. The aim of this research was to analyse the actions taken by the municipal authorities regarding the development of public green space, including the acquisition of real properties, the regulation of their legal status, as well as the adoption of planning and programme documents. The Polish Central Statistical Office data on the public green space of the largest cities in Poland were analysed in order to determine the dynamics of changes. Then, the focus was placed on Krakow, where the authors analysed in detail the distribution and type of urban green space as well as the actions taken by the Municipality to both extend it and to protect it against building development. The criterion of green space accessibility to city residents was indicated as a necessary aspect to be considered in the overall assessment of the existing greenery. The conclusions include the assessment of the actions of the Krakow authorities and the observed trends in the development of public green space.
Effects of Racing Surface and Turn Radius on Fatal Limb Fractures in Thoroughbred Racehorses
Authors: Peterson Michael , Sanderson Wayne , Kussainov Nurlan , Hobbs Sarah Jane , Miles Patti , Scollay Mary C. , Clayton Hilary M.
Published: January 2021

North American Thoroughbred racing is conducted on three types of surfaces—dirt, turf, and synthetic. The tracks are oval, and races are run counterclockwise. The loading on right and left limbs is expected to differ as a function of turn radius, banking, surface, and gait asymmetry. Hind limbs and forelimbs also have different functions related to propulsion and turning, respectively. This study uses the Equine Injury Database for race starts from 1 January 2009 through 31 December 2014, to compare injury rates across participating North American racetracks. The data are limited to catastrophic injuries in which horses died or were euthanized due to a fracture within 72 h of the start of the race. Overall injury rates were lower on turf and synthetic surfaces and the pattern of limb injuries in left vs. right and fore vs. hind limbs were different. Regardless of surface, forelimbs were more likely to fracture. Dirt surfaces showed higher rates of forelimb injuries compared to other surfaces, hind limbs were more likely to experience a fatal fracture on turf than on dirt. The left fore and right hind limbs were more likely to experience a fatal fracture but only on dirt surfaces.
Country Differences in Determinants of Behavioral Intention towards Sustainable Apparel Products
Authors: Jung Hye Jung , Oh Kyung Wha , Kim HaeJung Maria
Published: January 2021

Increased demands for sustainable apparel products require research to understand better how to encourage sustainable buying behavior effectively, especially in the understudied areas of cross-cultural research. This study, which includes respondents from the UK, US, and China (total n = 711) who completed an online survey, explores determinants of behavioral intention toward sustainable apparel products (SAP). This paper contributes to examine both consumer characteristics (shopping values, consciousness of sustainability, perceived consumer effectiveness, and environmental knowledge) and marketing perspective (evaluation criteria of SAP) determinants for encouraging sustainable apparel consumption behaviors. Significant country differences also emerged, indicating the positive impact on behavioral intention to sustainable apparel products across three countries. Results of structural equation modeling analysis demonstrated there were differences and similarities in the effect of consumers’ characteristic factors and marketing perspective factors on SAP behavioral intention among three countries. The results validate that differentiated marketing strategies in the sustainable apparel industry are required when targeting global consumers to boost sustainable apparel consumption and successfully help to remedy the crisis facing our planet and further generation.