bluuebunny/crossref_metadata_2025 huggingface dataset:
Adsorption Kinetics of Arsenic (V) on Nanoscale Zero-Valent Iron Supported by Activated Carbon
Authors: Zhu Huijie , Shi Mingyan , Zhang Xiuji , Liu Bo , Yao Dahu
Published: September 2020

The presence of arsenic (As) in drinking water is of serious concern due to its negative impact on human health. This work reports on the kinetics of nanoscale zero-valent iron (Fe0) supported by activated carbon (NZVI/AC) for the removal of As (V) species from aqueous solutions. To better understand the factors affecting this process, we investigated the effects of various experimental parameters including initial As (V) concentration, adsorbent dosage, pH, temperature, and coexisting ions on the adsorption kinetics using a batch-adsorption method. The optimum conditions for As (V) removal by NZVI/AC were found to be: 318 K, pH 3.5, an adsorbent dosage of 1.5 g/L, and an equilibrium time of 72 h. A greater mass of NZVI/AC, lower concentration of As (V) and lower pH positively promoted adsorption kinetics. The presence of phosphate (PO43−) and silicate (SiO42−) markedly inhibited As (V) removal kinetics. However, in the presence of 4.5 g/L NZVI/AC, ≥99.9% of As (V) was removed from raw groundwater.
Supermagnetic Sugarcane Bagasse Hydrochar for Enhanced Osteoconduction in Human Adipose Tissue-Derived Mesenchymal Stem Cells
Authors: Kim Min , Jee Seung-Cheol , Sung Jung-Suk , Kadam Avinash A.
Published: September 2020

Hydrothermally carbonized sugarcane bagasse (SCB) has exceptional surface properties. Looking at the huge amount of SCB produced, its biocompatible nature, cheap-cost for carbonization, and its easy functionalization can give impeccable nano-biomaterials for tissue engineering applications. Herein, sugarcane bagasse was converted into hydrochar (SCB-H) by hydrothermal carbonation. The SCB-H produced was further modified with iron oxide (Fe3O4) nanoparticles (denoted as SCB-H@Fe3O4). Facile synthesized nano-bio-composites were characterized by SEM, HR-TEM, XRD, FT-IR, XPS, TGA, and VSM analysis. Bare Fe3O4 nanoparticles (NPs), SCB-H, and SCB-H@Fe3O4 were tested for cytocompatibility and osteoconduction enhancement of human adipose tissue-derived mesenchymal stem cells (hADMSCs). The results confirmed the cytocompatible and nontoxic nature of SCB-H@Fe3O4. SCB-H did not show enhancement in osteoconduction, whilst on the other hand, Fe3O4 NPs exhibited a 0.5-fold increase in the osteoconduction of hADMSCs. However, SCB-H@Fe3O4 demonstrated an excellent enhancement in osteoconduction of a 3-fold increase over the control, and a 2.5-fold increase over the bare Fe3O4 NPs. Correspondingly, the expression patterns assessment of osteoconduction marker genes (ALP, OCN, and RUNX2) confirmed the osteoconductive enhancement by SCB-H@Fe3O4. In the proposed mechanism, the surface of SCB-H@Fe3O4 might provide a unique topology, and anchoring to receptors of hADMSCs leads to accelerated osteogenesis. In conclusion, agriculture waste-derived sustainable materials like “SCB-H@Fe3O44” can be potentially applied in highly valued medicinal applications of stem cell differentiation.
How Does N Mineral Fertilizer Influence the Crop Residue N Credit?
Authors: Ferraz-Almeida Risely , da Silva Natália , Wendling Beno
Published: September 2020

In no-tillage systems, there is an accumulation of crop residues (CR), which plays an essential role in the availability of soil-N. A study was set up to provide information regarding the N credit and the influence of N mineral fertilizer. There was the addition of a similar rate of residue (10 Mg ha−1; sugarcane, soybean, and brachiaria) and N mineral fertilizer (urea; 120 kg N ha−1) in loam soil. After the stabilization of biological activity (73 days), soil and remaining residues were collected, and C and N monitored. The results showed that the N credit was positive with the application of soybean, sugarcane, and brachiaria. There was a positive balance of the soybean N credit in soil with a reduction from 2.49 to 0.90 g kg−1 of N in remaining residue, and a direct increase of 90% of soil-N. There is no need of N fertilizer to potentialize the soybean N credit, but it is required to potentialize N credit of brachiaria and sugarcane. The urea demonstrated to be an excellent enhancer of brachiaria N credit, but it was not adequate for sugarcane residues. Based on our result, the accumulation and incorporation of CR can be considered as N credit with a positive contribution in soil-N.
Assessment of Sodium Knowledge and Urinary Sodium Excretion among Regions of the United Arab Emirates: A Cross-Sectional Study
Authors: Jarrar Amjad H. , Stojanovska Lily , Apostolopoulos Vasso , Cheikh Ismail Leila , Feehan Jack , Ohuma Eric O. , Ahmad Ala Z. , Alnoaimi Asma A. , Al Khaili Latifa S. , Allowch Najah H. , Meqbaali Fatima T. Al , Souka Usama , Al Dhaheri Ayesha S.
Published: September 2020

Non-communicable diseases (NCDs) such as cardiovascular disease, cancer and diabetes, are increasing worldwide and cause 65% to 78% of deaths in the Gulf Cooperation Council (GCC). A random sample of 477 healthy adults were recruited in the United Arab Emirates (UAE) in the period March–June 2015. Demographic, lifestyle, medical, anthropometric and sodium excretion data were collected. A questionnaire was used to measure knowledge, attitude and practice regarding salt. Mean sodium and potassium excretion were 2713.4 ± 713 mg/day and 1803 ± 618 mg/day, respectively, significantly higher than the World Health Organization (WHO) recommendations for sodium (2300 mg/day) and lower for potassium (3150 mg/day). Two-thirds (67.4%) exceeded sodium guidelines, with males 2.6 times more likely to consume excessively. The majority of the participants add salt during cooking (82.5%) and whilst eating (66%), and 75% identified processed food as high source of salt. Most (69.1%) were aware that excessive salt could cause disease. Most of the UAE population consumes excess sodium and insufficient potassium, likely increasing the risk of NCDs. Despite most participants being aware that high salt intake is associated with adverse health outcomes, this did not translate into salt reduction action. Low-sodium, high-potassium dietary interventions such as the Mediterranean diet are vital in reducing the impact of NCDs in the UAE.
Immune Response to COVID-19: Can We Benefit from the SARS-CoV and MERS-CoV Pandemic Experience?
Authors: Sinderewicz Emilia , Czelejewska Wioleta , Jezierska-Wozniak Katarzyna , Staszkiewicz-Chodor Joanna , Maksymowicz Wojciech
Published: September 2020

The global range and high fatality rate of the newest human coronavirus (HCoV) pandemic has made SARS-CoV-2 the focus of the scientific world. Next-generation sequencing of the viral genome and a phylogenetic analysis have shown the high homology of SARS-CoV-2 to other HCoVs that have led to local epidemics in the past. The experience acquired in SARS and MERS epidemics may prove useful in understanding the SARS-CoV-2 pathomechanism and lead to effective treatment and potential vaccine development. This study summarizes the immune response to SARS-CoV, MERS-CoV, and SARS-CoV-2 and focuses on T cell response, humoral immunity, and complement system activation in different stages of HCoVs infections. The study also presents the quantity and frequency of T cell responses, particularly CD4+ and CD8+; the profile of cytokine production and secretion; and its relation to T cell type, disease severity, and utility in prognostics of the course of SARS, MERS, and COVID-19 outbreaks. The role of interferons in the therapy of these infections is also discussed. Moreover, the kinetics of specific antibody production, the correlation between humoral and cellular immune response and the immunogenicity of the structural HCoVs proteins and their utility in the development of a vaccine against SARS, MERS, and COVID-19 has been updated.
Nematocidal Effects of a Coriander Essential Oil and Five Pure Principles on the Infective Larvae of Major Ovine Gastrointestinal Nematodes In Vitro
Authors: Helal Mohamed A. , Abdel-Gawad Ahmed M. , Kandil Omnia M. , Khalifa Marwa M. E. , Cave Gareth W. V. , Morrison Alison A. , Bartley David J. , Elsheikha Hany M.
Published: September 2020

The anthelmintic effects of extracted coriander oil and five pure essential oil constituents (geraniol, geranyl acetate, eugenol, methyl iso-eugenol, and linalool) were tested, using larval motility assay, on the third-stage larvae (L3s) of Haemonchus contortus, Trichostrongylus axei, Teladorsagia circumcincta, Trichostrongylus colubriformis, Trichostrongylus vitrinus and Cooperia oncophora. Coriander oil and linalool, a major component of tested coriander oil, showed a strong inhibitory efficacy against all species, except C. oncophora with a half maximal inhibitory concentration (IC50) that ranged from 0.56 to 1.41% for the coriander oil and 0.51 to 1.76% for linalool. The coriander oil and linalool combinations conferred a synergistic anthelmintic effect (combination index [CI] <1) on larval motility comparable to positive control (20 mg/mL levamisole) within 24 h (p < 0.05), reduced IC50 values to 0.11–0.49% and induced a considerable structural damage to L3s. Results of the combined treatment were validated by quantitative fluorometric microplate-based assays using Sytox green, propidium iodide and C12-resazurin, which successfully discriminated live/dead larvae. Only Sytox green staining achieved IC50 values comparable to that of the larval motility assay. The cytotoxicity of the combined coriander oil and linalool on Madin–Darby Canine Kidney cells was evaluated using sulforhodamine-B (SRB) assay and showed no significant cytotoxic effect at concentrations < 1%. These results indicate that testing essential oils and their main components may help to find new potential anthelmintic compounds, while at the same time reducing the reliance on synthetic anthelmintics.
Incubation with a Complex Orange Essential Oil Leads to Evolved Mutants with Increased Resistance and Tolerance
Authors: Berdejo Daniel , Pagán Elisa , Merino Natalia , Pagán Rafael , García-Gonzalo Diego
Published: September 2020

Emergence of strains with increased resistance/tolerance to natural antimicrobials was evidenced after cyclic exposure to carvacrol, citral, and (+)-limonene oxide. However, no previous studies have reported the development of resistance and tolerance to complex essential oils (EOs). This study seeks to evaluate the occurrence of Staphylococcus aureus strains resistant and tolerant to a complex orange essential oil (OEO) after prolonged cyclic treatments at low concentrations. Phenotypic characterization of evolved strains revealed an increase of minimum inhibitory and bactericidal concentration for OEO, a better growth fitness in presence of OEO, and an enhanced survival to lethal treatments, compared to wild-type strain. However, no significant differences (p > 0.05) in cross-resistance to antibiotics were observed. Mutations in hepT and accA in evolved strains highlight the important role of oxidative stress in the cell response to OEO, as well as the relevance of the cell membrane in the cell response to these natural antimicrobials. This study demonstrates the emergence of S. aureus strains that are resistant and tolerant to EO (Citrus sinensis). This phenomenon should be taken into account to assure the efficacy of natural antimicrobials in the design of food preservation strategies, in cleaning and disinfection protocols, and in clinical applications against resistant bacteria.
Determination of Metals’ Content in Components Mounted on Printed Circuit Boards from End-of-Life Mobile Phones
Authors: Gorewoda Tadeusz , Eschen Marcus , Charasińska Jadwiga , Knapik Magdalena , Kozłowicz Sylwia , Anyszkiewicz Jacek , Jadwiński Michał , Potempa Martyna , Gawliczek Marta , Chmielarz Andrzej , Kurylak Witold
Published: September 2020

The electronic components mounted on the printed circuit boards (PCBs) of mobile phones represent a resource that is rich in metals, and after separation from the boards, these components could be considered secondary raw materials. The concentrations of the valuable metals are insignificant when compared with those of complete PCBs; however, they could be significantly higher in a fraction formed from the separated components. This study focused on the analysis of Ag, Au, Cu, Nd, Nb, Ni, Pb, Pd, Sn, and Ta in fractions produced by the separation of all the components mounted on PCBs from several types of mobile phones. Atomic absorption spectrometry, atomic emission spectrometry, and mass spectrometry techniques were utilized, and a comparison of five older models of “brick” phones and five modern smartphones was conducted. Additionally, 50 kg of PCBs from the current recycling market were analyzed in the same way to create a summary of the current recycling stream.
Have Christian Colleges and Universities Become More Inclusive of LGBTQ Students Since Obergefell v. Hodges?
Authors: Coley Jonathan S.
Published: September 2020

Due to rapid changes in societal attitudes toward LGBTQ (lesbian, gay, bisexual, transgender, and queer) people, as well as the U.S. Supreme Court’s 2015 decision Obergefell v. Hodges legalizing same-sex marriage nationwide, Christian colleges and universities are experiencing more pressure to become inclusive of LGBTQ students. This article draws on U.S. Department of Education data on all four-year, not-for-profit Christian colleges and universities, as well as an original longitudinal dataset of LGBTQ student groups across Christian colleges and universities, to describe the landscape of LGBTQ student inclusion on Christian campuses before and after Obergefell v. Hodges. In 2013, two years before the U.S. Supreme Court legalized same-sex marriage, just under half (45%) of Christian colleges and universities had LGBTQ student groups. However, the U.S. Supreme Court’s Obergefell decision has evidently had little effect on holdouts: in 2019, the percentage of Christian colleges and universities that were home to LGBTQ student groups was only slightly higher (47%). Logistic regression analyses reveal that Christian colleges and universities that have recently become home to LGBTQ student groups were already predisposed to having LGBTQ groups in the first place, given that they are associated with social justice-minded denominations, have large student bodies, and have higher percentages of women students. The article’s findings hold implications for ongoing research on the status of LGBTQ people within Christian institutions.
The Impact of Coal’s Petrographic Composition on Its Suitability for the Gasification Process: The Example of Polish Deposits
Authors: Bielowicz Barbara , Misiak Jacek
Published: September 2020

In this paper, we discuss the impact of the rank of coal, petrographic composition, and physico-chemical coal properties on the release and composition of syngas during coal gasification in a CO2 atmosphere. This study used humic coals (parabituminous to anthracite) and lithotypes (bright coal and dull coal). Gasification was performed at temperatures between 600 and 1100 °C. It was found that the gas release depends on the temperature and rank of coal, and the reactivity increases with the increasing rank of coal. It was shown that the coal lithotype does not affect the gas composition or the process. Until 900 °C, the most intense processes were observed for higher rank coals. Above 1000 °C, the most reactive coals had a vitrinite reflectance of 0.5–0.6%. It was confirmed that the gasification of low-rank coal should be performed at temperatures above 1000 °C, and the reactivity of coal depends on the petrographic composition and physico-chemical features. It was shown that inertinite has a negative impact on the H2 content; at 950 °C, the increase in H2 depends on the rank of coal and vitrinite content. The physicochemical properties of coal rely on the content of maceral groups and the rank of coal. An improved understanding these relationships will allow the optimal selection of coal for gasification.
Assessing Lava Flow Subpixel Surface Roughness and Particle Size Distribution for Improved Thermal Inertia Interpretations
Authors: Simurda Christine , Ramsey Michael , Scheidt Stephen
Published: September 2020

Apparent thermal inertia (ATI) is a remote sensing-based thermophysical approximation used to estimate surface properties such as particle size or soil moisture, but is subject to oversimplifications if uniform surface materials are assumed. Geological surfaces realistically contain multiple types of materials that are represented by mixed pixels in a digital image that can dramatically affect the derived thermal response. Thus, the current surface uniformity assumption can lead to erroneous calculations. To define how these mixed particle size surfaces affect ATI, a multi-instrument, multi-spectral study was conducted at the North Coulee rhyolite flow, Mono Domes (California). This flow is compositionally homogenous with particle sizes ranging from silt size to boulders, making it an ideal location to understand the relationship between particle size distributions and ATI. Multispectral data from orbital sensors with increasing spatial resolutions were analyzed in combination with samples, GPS, and photogrammetry data collected in the field. The surface particle size characteristics divided into three broad categories (fine, moderate, and coarse) were mapped based on WorldView-2 visible data and field observations. Broad categories were validated using a 3-dimensional point cloud derived from structure-from-motion (SfM) methods using field photographs. The areal percentage of each category within an ATI pixel was derived to populate a lookup table (LUT) with the corresponding ATI values to quantify the effect of mixed pixels. Lower ATI values are dominated by fine particles (sand and dust) as expected, however, surfaces with the highest values were predominately moderate-sized cobbles. Pixels with a high areal percentage of coarse sizes display an intermediate ATI value, suggesting that either self-shadowing or trapping of fines lowers the ATI value. Alternatively, areas with a majority of moderate particle sizes have less shadowing and efficient vertical sorting of smaller material, as seen in field data, leaving a thermal derived response indicative of the dominating particle size. This study demonstrates how a uniform property assumption can cause erroneous derived thermal modeling, particularly over surfaces with a high percentage of boulders and large particle sizes. Future thermophysical studies of Earth or other planetary surfaces can greatly benefit from a multi-sensor approach combined with a higher spatial resolution visible dataset.
Global Monitoring of the Vegetation Dynamics from the Vegetation Optical Depth (VOD): A Review
Authors: Frappart Frédéric , Wigneron Jean-Pierre , Li Xiaojun , Liu Xiangzhuo , Al-Yaari Amen , Fan Lei , Wang Mengjia , Moisy Christophe , Le Masson Erwan , Aoulad Lafkih Zacharie , Vallé Clément , Ygorra Bertrand , Baghdadi Nicolas
Published: September 2020

Vegetation is a key element in the energy, water and carbon balances over the land surfaces and is strongly impacted by climate change and anthropogenic effects. Remotely sensed observations are commonly used for the monitoring of vegetation dynamics and its temporal changes from regional to global scales. Among the different indices derived from Earth observation satellites to study the vegetation, the vegetation optical depth (VOD), which is related to the intensity of extinction effects within the vegetation canopy layer in the microwave domain and which can be derived from both passive and active microwave observations, is increasingly used for monitoring a wide range of ecological vegetation variables. Based on different frequency bands used to derive VOD, from L- to Ka-bands, these variables include, among others, the vegetation water content/status and the above ground biomass. In this review, the theoretical bases of VOD estimates for both the passive and active microwave domains are presented and the global long-term VOD products computed from various groups in the world are described. Then, major findings obtained using VOD are reviewed and the perspectives offered by methodological improvements and by new sensors onboard satellite missions recently launched or to be launched in a close future are presented.
Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data
Authors: Sun Yu , Zheng Sheng , Wu Yuzhe , Schlink Uwe , Singh Ramesh P.
Published: September 2020

China is one of the largest carbon emitting countries in the world. Numerous strategies have been considered by the Chinese government to mitigate carbon emissions in recent years. Accurate and timely estimation of spatiotemporal variations of city-level carbon emissions is of vital importance for planning of low-carbon strategies. For an assessment of the spatiotemporal variations of city-level carbon emissions in China during the periods 2000–2017, we used nighttime light data as a proxy from two sources: Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The results show that cities with low carbon emissions are located in the western and central parts of China. In contrast, cities with high carbon emissions are mainly located in the Beijing-Tianjin-Hebei region (BTH) and Yangtze River Delta (YRD). Half of the cities of China have been making efforts to reduce carbon emissions since 2012, and regional disparities among cities are steadily decreasing. Two clusters of high-emission cities located in the BTH and YRD followed two different paths of carbon emissions owing to the diverse political status and pillar industries. We conclude that carbon emissions in China have undergone a transformation to decline, but a very slow balancing between the spatial pattern of high-emission versus low-emission regions in China can be presumed.
Assessing Rapid Variability in Atmospheric Apparent Optical Depth with an Array Spectrometer System
Authors: González Josep-Abel , Calbó Josep
Published: September 2020

A method for determining rapid variations in atmospheric optical depth is proposed. The method is based upon computation of the ratio between close-time spectral measurements of solar direct flux. Use of the ratio avoids the need for absolute calibration of the instruments and minimizes the effects of changes in instrumental conditions (such as temperature or mechanical adjustments) and in air mass. The technique has been applied to some campaigns of measurement for sky conditions ranging from clear skies to scattered-to-broken cloudiness, performed at high frequency (~1Hz) with a system of three array spectrometers, capable of performing very rapid spectral acquisitions, in the 400 to 1700 nm band, thus covering the visible and extending to the near-infrared spectral ranges. Results demonstrate the capacity of this instrumentation and method to detect rapid variation of optical depth, as well as rapid changes in its spectral pattern. The optical depth variability depends on the particular state of the sky and is connected to particle condensation and evaporation processes and to the changes in water vapor content in the transition region between cloud-free and cloudy regions. Thus, the method is suitable for analyzing rapid processes involving particles, either aerosol or cloud droplets, and water vapor, in the cloud boundaries.
A Simple Approach for Mapping Forest Cover from Time Series of Satellite Data
Authors: Liu Yang , Liu Ronggao
Published: September 2020

Forest cover mapping based on multi-temporal satellite observations usually uses dozens of features as inputs, which requires huge training data and leads to many ill effects. In this paper, a simple but efficient approach was proposed to map forest cover from time series of satellite observations without using classifiers and training data. This method focuses on the key step of forest mapping, i.e., separation of forests from herbaceous vegetation, considering that the non-vegetated area can be easily identified by the annual maximum vegetation index. We found that the greenness of forests is generally stable during the maturity period, but a similar greenness plateau does not exist for herbaceous vegetation. It means that the mean greenness during the vegetation maturity period of forests should be larger than that of herbaceous vegetation, while its standard deviation should be smaller. A combination of these two features could identify forests with several thresholds. The proposed approach was demonstrated for mapping the extents of different forest types with MODIS observations. The results show that the overall accuracy ranges 91.92–95.34% and the Kappa coefficient is 0.84–0.91 when compared with the reference datasets generated from fine-resolution imagery of Google Earth. The proposed approach can greatly simplify the procedures of forest cover mapping.
New Insights into Long-Term Aseismic Deformation and Regional Strain Rates from GNSS Data Inversion: The Case of the Pollino and Castrovillari Faults
Authors: Cambiotti Gabriele , Palano Mimmo , Orecchio Barbara , Marotta Anna Maria , Barzaghi Riccardo , Neri Giancarlo , Sabadini Roberto
Published: September 2020

We present a novel inverse method for discriminating regional deformation and long-term fault creep by inversion of GNSS velocities observed at the spatial scale of intraplate faults by exploiting the different spatial signatures of these two mechanisms. In doing so our method provides a refined estimate of the upper bound of the strain accumulation process. As case study, we apply this method to a six year GNSS campaign (2003–2008) set up in the southern portion of the Pollino Range over the Castrovillari and Pollino faults. We show that regional deformation alone cannot explain the observed deformation pattern and implies high geodetic strain rate, with a WSW-ENE extension of 86±41×10−9/yr. Allowing for the possibility of fault creep, the modelling of GNSS velocities is consistent with their uncertainties and they are mainly explained by a shallow creep over the Pollino fault, with a normal/strike-slip mechanism up to 5 mm/yr. The regional strain rate decrease by about 70 percent and is characterized by WNW-ESE extension of 24±28×10−9/yr. The large uncertainties affecting our estimate of regional strain rate do not allow infering whether the tectonic regime of the area is extensional or strike-slip, although the latter is slightly more likely.
Fundamental Concepts and Evolution of Wi-Fi User Localization: An Overview Based on Different Case Studies
Authors: Retscher Guenther
Published: September 2020

Indoor positioning poses a number of challenges, especially in large and complex buildings. Several effects, such as signal attenuation, signal fluctuations, interference, and multipath play a decisive role in signal propagation. The severity of each challenge depends on the method and technology adopted to perform user localization. Wi-Fi is a popular method because of its ubiquity with already available public and private infrastructure in many environments and the ability for mobile clients, such as smartphones, to receive these signals. In this contribution, the fundamental concepts and basics and the evolution of Wi-Fi as the most widely used indoor positioning technology are reviewed and demonstrated using four different conducted case studies. Starting from an analysis of the properties of Wi-Fi signals and their propagation, suitable techniques are identified. The mathematical models of location fingerprinting and lateration are consolidated and assessed as well as new technology directions and developments highlighted. Results of the case studies demonstrate the capability of Wi-Fi for continuous user localization also in dynamic environments and kinematic mode where the user walks with a usual step speed. However, to achieve acceptable localization accuracy, calibration of the devices is required to mitigate the variance problems due to the device heterogeneity.
A Wearable Stethoscope for Long-Term Ambulatory Respiratory Health Monitoring
Authors: Yilmaz Gürkan , Rapin Michaël , Pessoa Diogo , Rocha Bruno M. , de Sousa Antonio Moreira , Rusconi Roberto , Carvalho Paulo , Wacker Josias , Paiva Rui Pedro , Chételat Olivier
Published: September 2020

Lung sounds acquired by stethoscopes are extensively used in diagnosing and differentiating respiratory diseases. Although an extensive know-how has been built to interpret these sounds and identify diseases associated with certain patterns, its effective use is limited to individual experience of practitioners. This user-dependency manifests itself as a factor impeding the digital transformation of this valuable diagnostic tool, which can improve patient outcomes by continuous long-term respiratory monitoring under real-life conditions. Particularly patients suffering from respiratory diseases with progressive nature, such as chronic obstructive pulmonary diseases, are expected to benefit from long-term monitoring. Recently, the COVID-19 pandemic has also shown the lack of respiratory monitoring systems which are ready to deploy in operational conditions while requiring minimal patient education. To address particularly the latter subject, in this article, we present a sound acquisition module which can be integrated into a dedicated garment; thus, minimizing the role of the patient for positioning the stethoscope and applying the appropriate pressure. We have implemented a diaphragm-less acousto-electric transducer by stacking a silicone rubber and a piezoelectric film to capture thoracic sounds with minimum attenuation. Furthermore, we benchmarked our device with an electronic stethoscope widely used in clinical practice to quantify its performance.
Feature Channel Expansion and Background Suppression as the Enhancement for Infrared Pedestrian Detection
Authors: Wang Shengzhe , Wang Bo , Wang Shifeng , Tang Yifeng
Published: September 2020

Pedestrian detection is an important task in many intelligent systems, particularly driver assistance systems. Recent studies on pedestrian detection in infrared (IR) imagery have employed data-driven approaches. However, two problems in deep learning-based detection are the implicit performance and time-consuming training. In this paper, a novel channel expansion technique based on feature fusion is proposed to enhance the IR imagery and accelerate the training process. Besides, a novel background suppression method is proposed to stimulate the attention principle of human vision and shrink the region of detection. A precise fusion algorithm is designed to combine the information from different visual saliency maps in order to reduce the effect of truncation and miss detection. Four different experiments are performed from various perspectives in order to gauge the efficiency of our approach. The experimental results show that the Mean Average Precisions (mAPs) of four different datasets have been increased by 5.22% on average. The results prove that background suppression and suitable feature expansion will accelerate the training process and enhance the performance of IR image-based deep learning models.
Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods
Authors: Guo Yahui , Yin Guodong , Sun Hongyong , Wang Hanxi , Chen Shouzhi , Senthilnath J. , Wang Jingzhe , Fu Yongshuo
Published: September 2020

Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. In this study, the scale effects were investigated by analyzing the linear relationships between VI calculated from red–green–blue (RGB) images from unmanned aerial vehicles (UAV) and ground leaf chlorophyll contents of maize measured using SPAD-502. The scale impacts were assessed by applying different flight altitudes and the highest coefficient of determination (R2) can reach 0.85. We found that the VI from images acquired from flight altitude of 50 m was better to estimate the leaf chlorophyll contents using the DJI UAV platform with this specific camera (5472 × 3648 pixels). Moreover, three machine-learning (ML) methods including backpropagation neural network (BP), support vector machine (SVM), and random forest (RF) were applied for the grid-based chlorophyll content estimation based on the common VI. The average values of the root mean square error (RMSE) of chlorophyll content estimations using ML methods were 3.85, 3.11, and 2.90 for BP, SVM, and RF, respectively. Similarly, the mean absolute error (MAE) were 2.947, 2.460, and 2.389, for BP, SVM, and RF, respectively. Thus, the ML methods had relative high precision in chlorophyll content estimations using VI; in particular, the RF performed better than BP and SVM. Our findings suggest that the integrated ML methods with RGB images of this camera acquired at a flight altitude of 50 m (spatial resolution 0.018 m) can be perfectly applied for estimations of leaf chlorophyll content in agriculture.
Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC–LSTM Deep Learning Model
Authors: Zhou Jianyin , Xiang Jie , Huang Sixun
Published: September 2020

Typhoons are some of the most serious natural disasters, and the key to disaster prevention and mitigation is typhoon level classification. How to better use data of satellite cloud pictures to achieve accurate classification of typhoon levels has become one of classification the hot issues in current studies. A new framework of deep learning neural network, Graph Convolutional–Long Short-Term Memory Network (GC–LSTM), is proposed, which is based on the data of satellite cloud pictures of the Himawari-8 satellite in 2010–2019. The Graph Convolutional Network (GCN) is used to process the irregular spatial structure of satellite cloud pictures effectively, and the Long Short-Term Memory (LSTM) network is utilized to learn the characteristics of satellite cloud pictures over time. Moreover, to verify the effectiveness and accuracy of the model, the prediction effect and model stability are compared with other models. The results show that: the algorithm performance of this model is better than other prediction models; the prediction accuracy rate of typhoon level classification reaches 92.35%, and the prediction accuracy of typhoons and super typhoons reaches 95.12%. The model can accurately identify typhoon eye and spiral cloud belt, and the prediction results are always kept in the minimum range compared with the actual results, which proves that the GC–LSTM model has stronger stability. The model can accurately identify the levels of different typhoons according to the satellite cloud pictures. In summary, the results can provide a theoretical basis for the related research of typhoon level classification.
A Tactile Method for Rice Plant Recognition Based on Machine Learning
Authors: Chen Xueshen , Mao Yuanyang , Ma Xu , Qi Long
Published: September 2020

Accurate and real-time recognition of rice plants is the premise underlying the implementation of precise weed control. However, achieving desired results in paddy fields using the traditional visual method is difficult due to the occlusion of rice leaves and the interference of weeds. The objective of this study was to develop a novel rice plant recognition sensor based on a tactile method which acquires tactile information through physical touch. The tactile sensor would be mounted on the paddy field weeder to provide identification information for the actuator. First, a flexible gasbag filled with air was developed, where vibration features produced by tactile and sliding feedback were acquired when this apparatus touched rice plants or weeds, allowing the subtle vibration data with identification features to be reflected through the voltage value of an air-pressured sensor mounted inside the gasbag. Second, voltage data were preprocessed by three algorithms to optimize recognition features, including dimensional feature, dimensionless feature, and fractal dimension. The three types of features were used to train and test a neural network classifier. To maximize classification accuracy, an optimum set of features (b (variance), f (kurtosis), h (waveform factor), l (box dimension), and m (Hurst exponent)) were selected using a genetic algorithm. Finally, the feature-optimized classifier was trained, and the actual performances of the sensor at different contact positions were tested. Experimental results showed that the recognition rates of the end, middle, and root of the sensor were 90.67%, 98%, and 96% respectively. A tactile-based method with intelligence could produce high accuracy for rice plant recognition, as demonstrated in this study.
Energy Efficient Pupil Tracking Based on Rule Distillation of Cascade Regression Forest
Authors: Kim Sangwon , Jeong Mira , Ko Byoung Chul
Published: September 2020

As the demand for human-friendly computing increases, research on pupil tracking to facilitate human–machine interactions (HCIs) is being actively conducted. Several successful pupil tracking approaches have been developed using images and a deep neural network (DNN). However, common DNN-based methods not only require tremendous computing power and energy consumption for learning and prediction; they also have a demerit in that an interpretation is impossible because a black-box model with an unknown prediction process is applied. In this study, we propose a lightweight pupil tracking algorithm for on-device machine learning (ML) using a fast and accurate cascade deep regression forest (RF) instead of a DNN. Pupil estimation is applied in a coarse-to-fine manner in a layer-by-layer RF structure, and each RF is simplified using the proposed rule distillation algorithm for removing unimportant rules constituting the RF. The goal of the proposed algorithm is to produce a more transparent and adoptable model for application to on-device ML systems, while maintaining a precise pupil tracking performance. Our proposed method experimentally achieves an outstanding speed, a reduction in the number of parameters, and a better pupil tracking performance compared to several other state-of-the-art methods using only a CPU.
Multi-Proxy Constraint Loss for Vehicle Re-Identification
Authors: Chen Xu , Sui Haigang , Fang Jian , Zhou Mingting , Wu Chen
Published: September 2020

Vehicle re-identification plays an important role in cross-camera tracking and vehicle search in surveillance videos. Large variance in the appearance of the same vehicle captured by different cameras and high similarity of different vehicles with the same model poses challenges for vehicle re-identification. Most existing methods use a center proxy to represent a vehicle identity; however, the intra-class variance leads to great difficulty in fitting images of the same identity to one center feature and the images with high similarity belonging to different identities cannot be separated effectively. In this paper, we propose a sampling strategy considering different viewpoints and a multi-proxy constraint loss function which represents a class with multiple proxies to perform different constraints on images of the same vehicle from different viewpoints. Our proposed sampling strategy contributes to better mine samples corresponding to different proxies in a mini-batch using the camera information. The multi-proxy constraint loss function pulls the image towards the furthest proxy of the same class and pushes the image from the nearest proxy of different class further away, resulting in a larger margin between decision boundaries. Extensive experiments on two large-scale vehicle datasets (VeRi and VehicleID) demonstrate that our learned global features using a single-branch network outperforms previous works with more complicated network and those that further re-rank with spatio-temporal information. In addition, our method is easy to plug into other classification methods to improve the performance.
Fabric Defect Detection Based on Illumination Correction and Visual Salient Features
Authors: Di Lan , Long Hanbin , Liang Jiuzhen
Published: September 2020

Aiming at the influence of uneven illumination on fabric feature extraction and the limitations of traditional frequency-based visual saliency algorithms, we propose a fabric defect detection method based on the combination of illumination correction and visual salient features—(1) Construct a multi-scale side window box (MS-BOX) filter to extract the illumination component of the image, then use the constructed two-dimensional gamma correction function to perform illumination correction on the image in the global angle, and finally enhance the local contrast of the image in the local angle; (2) Use the L0 gradient minimization method to remove the background texture of fabric images and highlight the defects; (3) Represent the fabric image as a quaternion image, where each pixel in the image is represented by a quaternion consisting of color, intensity and edge characteristics. The two-dimensional fractional Fourier transform (2D-FRFT) is used to obtain the saliency map of the quaternion image. Experiments show that our method has a higher overall recall rate for defect detection of star-patterned, box-patterned, and dot-patterned fabrics, and the overall recall-precision effect is better than other existing methods.
Identification of Factors Affecting the Performance of Rural Road Projects in Colombia
Authors: Gómez-Cabrera Adriana , Sanz-Benlloch Amalia , Montalban-Domingo Laura , Ponz-Tienda Jose Luis , Pellicer Eugenio
Published: September 2020

Rural roads play an indispensable role in economic and social well-being, especially in developing countries, contributing to achieving the Sustainable Development Goals. For this reason, it is necessary to plan these projects properly to guarantee their success. In this line, the objective of this research is to identify significant variables generating overruns in time and cost using empirical data of 535 rural road projects in Colombia from 2015 to 2018. Bivariate analysis, with statistical tools like Spearman’s Rho and Kruskal–Wallis, allowed identifying that higher values of variables like budget and project intensity are related to higher deviations in cost and time. Additionally, it was found that projects with shorter durations are reporting higher time overruns. The worst performers are projects executed in the year that council mayors start their terms, those developed in municipalities with more resources, and those awarded using a competitive bidding process. Multivariate analysis, through Random Forest, assessed the effect of considering all variables interacting simultaneously and ranking them in order of importance. The results demonstrated a relationship between cost and time performance, and that numerical variables are more significant than the categorical ones. This study contributes to a better understanding of the causes of delays and cost overruns on rural roads, providing useful insight for researchers and industry practitioners.
Intelligent Management of Distributed Energy Resources for Increased Resilience and Environmental Sustainability of Hospitals
Authors: Kyriakarakos George , Dounis Anastasios
Published: September 2020

There is a global trend towards zero-energy or even positive-energy buildings, including healthcare facilities. Energy efficiency activities have been investigated and applied successfully for more than 20 years in healthcare facilities in general and hospitals in particular. It is in the last decade that on-site energy production mainly from photovoltaics has been considered mainly as an extra revenue stream for healthcare facilities. Back-up systems are still diesel generator-based in most cases and only recently has there been interest in unifying the energy systems of healthcare facilities in order to integrate the operation of the main systems of the hospital with the on-site renewable energy production and the back-up systems. Hospitals play a very crucial role in our societies. There is a need to achieve the best results in terms of healthcare services but, at the same time, to reduce the cost of these services without affecting the quality level, to enhance resilience and to increase environmental sustainability. As far as energy is concerned, this is feasible and can be accomplished using energy efficiency interventions and on-site power generation and storage using renewable energy technologies. An Intelligent Energy Management System (IEMS) has to be in place in order to harvest the benefits of all the related subsystems allowing them to operate effectively and harmoniously, while at the same time ensuring the operation of the hospital under extreme conditions, e.g., after a natural disaster. The research concerning IEMSs for hospitals is at its first steps and needs to gain momentum.
Mechanical and Durability Properties of Cement-Stabilized Recycled Concrete Aggregate
Authors: Li Qingfu , Hu Jing
Published: September 2020

This research investigates the effect of using recycled concrete aggregate (RCA) as a partial replacement of natural aggregate (NA) on the mechanical and durability-related properties of a cement-stabilized recycled concrete aggregate (CSR) mixture. In this case, mixtures were prepared with 0%, 40%, 70%, and 100% (by weight) RCA to replace NA, and cement contents of 4%, 5%, and 6% were used in this study. Test parameters included the replacement ratio, cement content, and curing time. Tests were carried out to establish the unconfined compressive strength (UCS), indirect tensile strength (ITS), drying shrinkage, and water loss ratio of each mix proportion. The preliminary results of UCS and ITS tests indicated that the incorporation of RCA resulted in a decrease of strength compared with a cement-stabilized macadam (CSM) mixture, but the seven-day strength of the CSR mixture met the related requirements of road bases. The increase in cement content and curing time had an obvious effect on strength improvement. The drying shrinkage test showed that the drying shrinkage properties of the CSR mixture were obviously reduced with a high replacement ratio. It is evident that the CSM mixture presented a better drying shrinkage performance than that of the CSR mixture.
Modeling Collision Probability on Freeway: Accounting for Different Types and Severities in Various LOS
Authors: Yang Bo , Wu Yao , Zhang Weihua , Bao Jie
Published: September 2020

In this study, collision-related data were collected on the I-880 freeway of California in the United States from 2006 to 2011. Our objective was to study the collision probability of different collision types and severities in different traffic states. The traffic states were divided by the traditional level of service (LOS) method. Various Bayesian conditional logit models have been established to analyze the relationship between the collision probability of different collision patterns and LOSs. The results showed that LOS A had the best safety performance associated with all of the collision types and severities, LOS C had the worst safety performance associated with hit object collisions, LOS D had the worst safety performance associated with sideswipe collisions and rear end collisions, and LOS F had the worst safety performance associated with injury collisions. The five-stage Bayesian random parameter sequential logit model was established to quantify the effects of different variables on the collision probability of various collision types and severities. In addition to LOS, the visibility, road surface, weather, ramp, and number of lanes had significant effects on different collision types and severities.