bluuebunny/crossref_metadata_2025 huggingface dataset:
Genomic Sequencing and Comparison of Sacbrood Viruses from Apis cerana and Apis mellifera in Taiwan
Authors: Chang Ju-Chun , Chang Zih-Ting , Ko Chong-Yu , Chen Yue-Wen , Nai Yu-Shin
Published: December 2020

Sacbrood virus (SBV) was the first identified bee virus and shown to cause serious epizootic infections in the population of Apis cerana in Taiwan in 2015. Herein, the whole genome sequences of SBVs in A. cerana and A. mellifera were decoded and designated AcSBV-TW and AmSBV-TW, respectively. The whole genomes of AcSBV-TW and AmSBV-TW were 8776 and 8885 bp, respectively, and shared 90% identity. Each viral genome encoded a polyprotein, which consisted of 2841 aa in AcSBV-TW and 2859 aa in AmSBV-TW, and these sequences shared 95% identity. Compared to 54 other SBVs, the structural protein and protease regions showed high variation, while the helicase was the most highly conserved region among SBVs. Moreover, a 17-amino-acid deletion was found in viral protein 1 (VP1) region of AcSBV-TW compared to AmSBV-TW. The phylogenetic analysis based on the polyprotein sequences and partial VP1 region indicated that AcSBV-TW was grouped into the SBV clade with the AC-genotype (17-aa deletion) and was closely related to AmSBV-SDLY and CSBV-FZ, while AmSBV-TW was grouped into the AM-genotype clade but branched independently from other AmSBVs, indicating that the divergent genomic characteristics of AmSBV-TW might be a consequence of geographic distance driving evolution, and AcSBV-TW was closely related to CSBV-FZ, which originated from China. This 17-amino-acid deletion could be found in either AcSBV or AmSBV in Taiwan, indicating cross-infection between the two viruses. Our data revealed geographic and host specificities between SBVs. The amino acid difference in the VP1 region might serve as a molecular marker for describing SBV cross-infection.
Inhibition of DOT1L by Half-Selenopsammaplin A Analogs Suppresses Tumor Growth and EMT-Mediated Metastasis in Triple-Negative Breast Cancer
Authors: Byun Woong Sub , Lee Gyu Ho , Park Hyeung-geun , Lee Sang Kook
Published: December 2020

Due to a lack of hormone receptors, current treatment strategies for triple-negative breast cancer (TNBC) are limited with frequent disease recurrence and metastasis. Recent findings have suggested that aberrant methylation of histone H3 lysine 79 residue (H3K79me) by the histone methyltransferase disruptor of telomeric silencing 1-like (DOT1L) is a potential therapeutic target for TNBC clinical management. Therefore, we developed DOT1L inhibitors as potential antitumor agents against TNBC cells. We reveal that a synthetic half-selenopsammaplin A analog 9l (subsequently known as 9l) exhibited inhibitory activity against DOT1L-mediated H3K79 methylation, and showed antitumor activity in TNBC cells. The analog 9l also significantly inhibited TNBC invasion and migration via the modulation of epithelial-mesenchymal transition (EMT) markers, including N-cadherin and vimentin downregulation and E-cadherin upregulation. In an MDA-MB-231/Luc-implanted orthotopic mouse metastasis model, treatment with 9l effectively inhibited tumor growth and lung metastasis via DOT1L regulatory activity and EMT processes. Taken together, these findings highlight the potential of 9l as a novel therapeutic candidate for treating metastatic TNBC via DOT1L modulation.
Machinability of the Thermoplastic Polymers: PEEK, PI, and PMMA
Authors: Yan Ying , Mao Yu , Li Bo , Zhou Ping
Published: December 2020

The thermoplastic polymer such as poly(methyl methacrylate) (PMMA), polyetheretherketone (PEEK), and polyimide (PI) is a kind of polymer material with properties of good mechanical strength. It has been widely used in the fields of aerospace, optical engineering, and microfluidics, etc. Thermoplastic polymers are considered to be one of the most promising engineering plastics in the future. Therefore, it is necessary to further study its mechanical properties and machinability, especially in ultra-precision machining. Furthermore, mechanical property and machinability were studied in this work. Through the dynamic mechanical analysis experiment, the elastic modulus and temperature effect of PMMA, PEEK, and PI are analyzed. In addition, the high-speed micromilling experiment is conducted to show that the surface roughness, burrs, and cutting chip characteristics in the high-speed micromilling process. In general, the surface quality of the brittle removal is generally better than that of the viscoelasticity state. The results show that PMMA, PEEK, and PI have good mechanical properties and machinability. Base on the results, the material will be in a viscoelastic state as the temperature increases. The surface quality of the brittle removal is generally better than the viscoelastic state.
Calcium Silicate-Activated Gelatin Methacrylate Hydrogel for Accelerating Human Dermal Fibroblast Proliferation and Differentiation
Authors: Lin Fong-Sian , Lee Jian-Jr , Lee Alvin Kai-Xing , Ho Chia-Che , Liu Yen-Ting , Shie Ming-You
Published: December 2020

Wound healing is a complex process that requires specific interactions between multiple cells such as fibroblasts, mesenchymal, endothelial, and neural stem cells. Recent studies have shown that calcium silicate (CS)-based biomaterials can enhance the secretion of growth factors from fibroblasts, which further increased wound healing and skin regeneration. In addition, gelatin methacrylate (GelMa) is a compatible biomaterial that is commonly used in tissue engineering. However, it has low mechanical properties, thus restricting its fullest potential for clinical applications. In this study, we infused Si ions into GelMa hydrogel and assessed for its feasibility for skin regeneration applications by observing for its influences on human dermal fibroblasts (hDF). Initial studies showed that Si could be successfully incorporated into GelMa, and printability was not affected. The degradability of Si-GelMa was approximately 20% slower than GelMa hydrogels, thus allowing for better wound healing and regeneration. Furthermore, Si-GelMa enhanced cellular adhesion and proliferation, therefore leading to the increased secretion of collagen I other important extracellular matrix (ECM) remodeling-related proteins including Ki67, MMP9, and decorin. This study showed that the Si-GelMa hydrogels were able to enhance the activity of hDF due to the gradual release of Si ions, thus making it a potential candidate for future skin regeneration clinical applications.
Preparation and Performance of Supercritical Carbon Dioxide Thickener
Authors: Liu Bin , Wang Yanling , Liang Lei
Published: December 2020

The low sand-carrying problem caused by the low viscosity of supercritical carbon dioxide (SC–CO2) limits the development of supercritical CO2 fracturing technology. In this study, a molecular simulation method was used to design a fluorine-free solvent-free SC–CO2 thickener 1,3,5,7-tetramethylcyclotetrasiloxane (HBD). Simulations and experiments mutually confirm that HBD-1 and HBD-2 have excellent solubility in SC–CO2. The apparent viscosity of SC–CO2 after thickening was evaluated with a self-designed and assembled capillary viscometer. The results show that when the concentration of HBD-2 is 5 wt.% (305.15 K, 10 MPa), the viscosity of SC–CO2 increases to 4.48 mPa·s. Combined with the capillary viscometer and core displacement device, the low damage of SC–CO2 fracturing fluid to the formation was studied. This work solves the pollution problems of fluoropolymers and co-solvents to organisms and the environment and provides new ideas for the molecular design and research of SC–CO2 thickeners.
Social vs. Individual Centrality of Religiosity: Research in Religious and Non-Religious Settings in Russia
Authors: Prutskova Elena
Published: December 2020

Most of the current approaches to measuring religiosity operate with indicators of individual religiosity. One of the central ideas of the current paper is that religiosity is a social phenomenon. The Centrality of Religiosity Scale (CRS) developed by S. Huber is applied to measure the individual component of religiosity. A modification of the CRS (CRS-SOC) has been developed to include the social component of religiosity with two aspects: social connections with lay members of religious communities and with the clergy. The analysis is based on the data of two surveys conducted in Russia: an on-line survey with a general population sample (1768 respondents) and a survey of parishioners of four Christian denominations: Orthodox, Catholic, “traditional” Protestant (Lutheran, Baptist), and the “new” Protestant (Pentecostal) (1192 respondents). The structure of religiosity among parishioners of different Christian denominations is discussed. The results, which revealed that the level of religiosity among Orthodox parishioners is slightly lower, are interpreted using the theory of religious economy.
The Missing Orientation
Authors: Levinson Paul
Published: December 2020

Humans last walked on the Moon in 1972. We not only have gone no further with in-person expeditions to places off Planet Earth, we have not even been back to the Moon. The main motive for getting to the Moon back then, Cold War competition, may have subsided, but competition for economic and scientific advantage among nations has continued, and has failed to ignite further human exploration of worlds beyond our planet. Nor has the pursuit of science, and the pursuit of commerce and tourism, in their own rights. This essay explores those failures, and argues for the integration of a missing ingredient in our springboard to space: the desire of every human being to understand more of what we are doing in this universe, why we are here, our place and part in the cosmos. Although science may answer a part of this, the deepest parts are the basis of every religion. Although the answers provided by different religions may differ profoundly, the orientation of every religion is to shed some light on what part we play in this universe. This orientation, which also can be called a sense of wonder, may be precisely what has been missing, and just what is needed, to at last extend our humanity beyond this planet on a permanent basis.
Constructing and Contesting the Shrine: Tourist Performances at Seimei Shrine, Kyoto
Authors: Tillonen Mia
Published: December 2020

Japanese Shinto shrines are popular pilgrimage sites not only for religious reasons, but also because of their connections to popular culture. This study discusses how tourism is involved in the construction of the shrine space by focusing on the material environment of the shrine, visitor performances, and how the shrine is contested by different actors. The subject of the study, Seimei Shrine, is a shrine dedicated to the legendary figure Abe no Seimei (921–1005), who is frequently featured in popular culture. Originally a local shrine, Seimei Shrine became a tourist attraction for fans of the novel series Onmyōji (1986–) and the movie adaptation (2001). Since then, the shrine has branded itself by placing themed statues, which realize the legend of Abe no Seimei in material form, while also attracting religious and touristic practices. On the other hand, visitors also bring new meanings to the shrine and its objects. They understand the shrine through different kinds of interactions with the objects, through performances such as touching and remembering. However, the material objects, their interpretation and performances are also an arena of conflict and contestation, as different actors become involved through tourism. This case study shows how religion and tourism are intertwined in the late-modern consumer society, which affects both the ways in which the shrine presents and reinvents itself, as well as how visitors understand and perform within the shrine.
U2-ONet: A Two-Level Nested Octave U-Structure Network with a Multi-Scale Attention Mechanism for Moving Object Segmentation
Authors: Wang Chenjie , Li Chengyuan , Liu Jun , Luo Bin , Su Xin , Wang Yajun , Gao Yan
Published: December 2020

Most scenes in practical applications are dynamic scenes containing moving objects, so accurately segmenting moving objects is crucial for many computer vision applications. In order to efficiently segment all the moving objects in the scene, regardless of whether the object has a predefined semantic label, we propose a two-level nested octave U-structure network with a multi-scale attention mechanism, called U2-ONet. U2-ONet takes two RGB frames, the optical flow between these frames, and the instance segmentation of the frames as inputs. Each stage of U2-ONet is filled with the newly designed octave residual U-block (ORSU block) to enhance the ability to obtain more contextual information at different scales while reducing the spatial redundancy of the feature maps. In order to efficiently train the multi-scale deep network, we introduce a hierarchical training supervision strategy that calculates the loss at each level while adding knowledge-matching loss to keep the optimization consistent. The experimental results show that the proposed U2-ONet method can achieve a state-of-the-art performance in several general moving object segmentation datasets.
Seafloor Topography Estimation from Gravity Anomaly and Vertical Gravity Gradient Using Nonlinear Iterative Least Square Method
Authors: Fan Diao , Li Shanshan , Li Xinxing , Yang Junjun , Wan Xiaoyun
Published: December 2020

Currently, seafloor topography inversion based on satellite altimetry gravity data provides the principal means to predict the global seafloor topography. Researchers often use sea surface geoid height or gravity anomaly to predict sea depth in the space domain. In this paper, a comprehensive discussion on seafloor topography inversion formulas in the space domain is presented using sea surface geoid height, gravity anomaly and introduces an approach that uses vertical gravity gradient. This would be the first study to estimate seafloor topography by vertical gravity gradient in the space domain. Further, a nonlinear iterative least-square inversion process is discussed. Using the search area for the Malaysia Airlines Flight MH370 as study site, we used the DTU17 gravity anomaly model and SIO V29.1 vertical gravity gradient to generate the seafloor topography. The results of the proposed bathymetric models were analyzed and compared with the DTU18 and SIO V20.1 bathymetric models. The experimental results show that the gravity anomaly and vertical gravity gradient in the study area are strongly correlated with the seafloor topography in the 20–200 km wavelength range. The optimal initial iteration values for seafloor topography variance and correlation length are 0.6365 km2 and 10.5′, respectively. Shipborne measurements from SONAR data were used as external checkpoints to evaluate the bathymetric models. The results show that the RMS for BAT_VGG_ILS (inversion model constructed by vertical gravity gradient) is smaller than for BAT_GA_ILS (inversion model constructed by gravity anomaly) and BAT_GA_VGG_ILS (inversion model constructed by gravity anomaly and vertical gravity gradient). The relative accuracy of the DTU18 bathymetry model was 9.27%, while the relative accuracy of the proposed seafloor models was higher than 4%. Within the 200 m difference range, the proportion of checkpoints for BAT_VGG_ILS was close to 95%, about 80% for BAT_GA_ILS and BAT_GA_VGG_ILS, and less than 50% for the DTU18. The results show that the nonlinear iterative least square method in the space domain is feasible.
Long Baseline Tightly Coupled DGNSS Positioning with Ionosphere-Free Inter-System Bias Calibration
Authors: Cheng Jianhua , Jiang Chao , Li Liang , Jia Chun , Qi Bing , Li Jiaxiang
Published: December 2020

Based on the statistical stability of the inter-system bias (ISB), we propose a tightly coupled Differential Global Navigation Satellite System (DGNSS) positioning method by using ionosphere-free combination for the long baseline applications. The proposed method is compatible with the traditional Radio Beacon (RBN) base station implementation. The tightly coupled DGNSS positioning method is utilized at the long baseline rover by eliminating the effect of ionosphere delay with ionosphere-free (IF) based differential ISB calibration. The improved positioning model strength can be obtained with the proposed method when compared with the traditional loosely coupled method, particularly under the satellite-deprived environment. GNSS datasets of different baselines were collected to test the proposed method. The results of the ISB stability show that the ISB has long-term stability and needs to be calibrated when the receiver is rebooted. The positioning results show that when compared with the IF-based loosely coupled method, the IF-based tightly coupled DGNSS method based on ISB calibration can obtain better positioning performance of accuracy and continuity within 240 km baselines.
Thermal Imaging for Plant Stress Detection and Phenotyping
Authors: Pineda Mónica , Barón Matilde , Pérez-Bueno María-Luisa
Published: December 2020

In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.
Application of the Infrared Thermography and Unmanned Ground Vehicle for Rescue Action Support in Underground Mine—The AMICOS Project
Authors: Szrek Jarosław , Zimroz Radoslaw , Wodecki Jacek , Michalak Anna , Góralczyk Mateusz , Worsa-Kozak Magdalena
Published: December 2020

Extraction of raw materials, especially in extremely harsh underground mine conditions, is irrevocably associated with high risk and probability of accidents. Natural hazards, the use of heavy-duty machines, and other technologies, even if all perfectly organized, may result in an accident. In such critical situations, rescue actions may require advanced technologies as autonomous mobile robot, various sensory system including gas detector, infrared thermography, image acquisition, advanced analytics, etc. In the paper, we describe several scenarios related to rescue action in underground mines with the assumption that searching for sufferers should be done considering potential hazards such as seismic, gas, high temperature, etc. Thus, possibilities of rescue team activities in such areas may be highly risky. This work reports the results of testing of a UGV robotic system in an underground mine developed in the frame of the AMICOS project. The system consists of UGV with a sensory system and image processing module that are based on an adaptation of You Only Look Once (YOLO) and Histogram of Oriented Gradients (HOG) algorithms. The experiment was very successful; human detection efficiency was very promising. Future work will be related to test the AMICOS technology in deep copper ore mines.
Moving to Automated Tree Inventory: Comparison of UAS-Derived Lidar and Photogrammetric Data with Manual Ground Estimates
Authors: Ramalho de Oliveira Luiz Felipe , Lassiter H. Andrew , Wilkinson Ben , Whitley Travis , Ifju Peter , Logan Stephen R. , Peter Gary F. , Vogel Jason G. , Martin Timothy A.
Published: December 2020

Unmanned aircraft systems (UAS) have advanced rapidly enabling low-cost capture of high-resolution images with cameras, from which three-dimensional photogrammetric point clouds can be derived. More recently UAS equipped with laser scanners, or lidar, have been employed to create similar 3D datasets. While airborne lidar (originally from conventional aircraft) has been used effectively in forest systems for many years, the ability to obtain important tree features such as height, diameter at breast height, and crown dimensions is now becoming feasible for individual trees at reasonable costs thanks to UAS lidar. Getting to individual tree resolution is crucial for detailed phenotyping and genetic analyses. This study evaluates the quality of three three-dimensional datasets from three sensors—two cameras of different quality and one lidar sensor—collected over a managed, closed-canopy pine stand with different planting densities. For reference, a ground-based timber cruise of the same pine stand is also collected. This study then conducted three straightforward experiments to determine the quality of the three sensors’ datasets for use in automated forest inventory: manual mensuration of the point clouds to (1) detect trees and (2) measure tree heights, and (3) automated individual tree detection. The results demonstrate that, while both photogrammetric and lidar data are well-suited for single-tree forest inventory, the photogrammetric data from the higher-quality camera is sufficient for individual tree detection and height determination, but that lidar data is best. The automated tree detection algorithm used in the study performed well with the lidar data, detecting 98% of the 2199 trees in the pine stand, but fell short of manual mensuration within the lidar point cloud, where 100% of the trees were detected. The manually-mensurated heights in the lidar dataset correlated with field measurements at r = 0.95 with a bias of −0.25 m, where the photogrammetric datasets were again less accurate and precise.
A Fully Automated Three-Stage Procedure for Spatio-Temporal Leaf Segmentation with Regard to the B-Spline-Based Phenotyping of Cucumber Plants
Authors: Harmening Corinna , Paffenholz Jens-André
Published: December 2020

Plant phenotyping deals with the metrological acquisition of plants in order to investigate the impact of environmental factors and a plant’s genotype on its appearance. Phenotyping methods that are used as standard in crop science are often invasive or even destructive. Due to the increase of automation within geodetic measurement systems and with the development of quasi-continuous measurement techniques, geodetic techniques are perfectly suitable for performing automated and non-invasive phenotyping and, hence, are an alternative to standard phenotyping methods. In this contribution, sequentially acquired point clouds of cucumber plants are used to determine the plants’ phenotypes in terms of their leaf areas. The focus of this contribution is on the spatio-temporal segmentation of the acquired point clouds, which automatically groups and tracks those sub point clouds that describe the same leaf. The application on example data sets reveals a successful segmentation of 93% of the leafs. Afterwards, the segmented leaves are approximated by means of B-spline surfaces, which provide the basis for the subsequent determination of the leaf areas. In order to validate the results, the determined leaf areas are compared to results obtained by means of standard methods used in crop science. The investigations reveal consistency of the results with maximal deviations in the determined leaf areas of up to 5%.
Environmental Drivers of Water Use for Caatinga Woody Plant Species: Combining Remote Sensing Phenology and Sap Flow Measurements
Authors: Paloschi Rennan A. , Ramos Desirée Marques , Ventura Dione J. , Souza Rodolfo , Souza Eduardo , Morellato Leonor Patrícia Cerdeira , Nóbrega Rodolfo L. B. , Coutinho Ítalo Antônio Cotta , Verhoef Anne , Körting Thales Sehn , Borma Laura De Simone
Published: December 2020

We investigated the water use of Caatinga vegetation, the largest seasonally dry forest in South America. We identified and analysed the environmental phenological drivers in woody species and their relationship with transpiration. To monitor the phenological evolution, we used remote sensing indices at different spatial and temporal scales: normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and green chromatic coordinate (GCC). To represent the phenology, we used the GCC extracted from in-situ automated digital camera images; indices calculated based on sensors included NDVI, SAVI and GCC from Sentinel-2A and B satellites images, and NDVI products MYD13Q1 and MOD13Q1 from a moderate-resolution imaging spectroradiometer (MODIS). Environmental drivers included continuously monitored rainfall, air temperature, soil moisture, net radiation, and vapour pressure deficit. To monitor soil water status and vegetation water use, we installed soil moisture sensors along three soil profiles and sap flow sensors for five plant species. Our study demonstrated that the near-surface GCC data played an important role in permitting individual monitoring of species, whereas the species’ sap flow data correlated better with NDVI, SAVI, and GCC than with species’ near-surface GCC. The wood density appeared to affect the transpiration cessation times in the dry season, given that species with the lowest wood density reach negligible values of transpiration earlier in the season than those with high woody density. Our results show that soil water availability was the main limiting factor for transpiration during more than 80% of the year, and that both the phenological response and water use are directly related to water availability when relative saturation of the soil profile fell below 0.25.
Underwater Topography Detection and Analysis of the Qilianyu Islands in the South China Sea Based on GF-3 SAR Images
Authors: Huang Longyu , Yang Jungang , Meng Junmin , Zhang Jie
Published: December 2020

Shallow sea underwater topography plays an important role in the development of islands and reefs. The Qilianyu Islands, located in Xisha, South China Sea, are a key area for the development and utilization of the South China Sea. Compared with traditional underwater topography detection methods, synthetic aperture radar (SAR) has the advantages of low cost, short time consumption, and the large-scale detection of shallow water topography. The GF-3 satellite is the first SAR satellite launched by China, and its ability to probe shallow sea topography has never been assessed. To detect the underwater topography of the Qilianyu Islands and test the application of GF-3 SAR data in shallow sea underwater topography detection, this paper implements the SAR shallow sea underwater topography detection model, the tidal information corresponding to the imaging time of the SAR image, and six GF-3 SAR images to detect the underwater topography of the Qilianyu island and reefs. The detection results have been analyzed from different imaging times, different water depths and different polarization modes, and the first four SAR images show promising detection results. The average absolute error (MAE) and average relative error (MRE) of the results are 1.5 m and 14.33%, respectively, which demonstrates that GF-3 SAR images have an impressive performance in underwater topography detection of South China Sea island reefs.
Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications
Authors: Hu Tianyu , Sun Xiliang , Su Yanjun , Guan Hongcan , Sun Qianhui , Kelly Maggi , Guo Qinghua
Published: December 2020

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.
Continuous-Time Laser Frames Associating and Mapping via Multilayer Optimization
Authors: Hu Shaoxing , Xiao Shen , Zhang Aiwu , Deng Yiming , Wang Bingke
Published: December 2020

To achieve the ability of associating continuous-time laser frames is of vital importance but challenging for hand-held or backpack simultaneous localization and mapping (SLAM). In this study, the complex associating and mapping problem is investigated and modeled as a multilayer optimization problem to realize low drift localization and point cloud map reconstruction without the assistance of the GNSS/INS navigation systems. 3D point clouds are aligned among consecutive frames, submaps, and closed-loop frames using the normal distributions transform (NDT) algorithm and the iterative closest point (ICP) algorithm. The ground points are extracted automatically, while the non-ground points are automatically segmented to different point clusters with some noise point clusters omitted before 3D point clouds are aligned. Through the three levels of interframe association, submap matching and closed-loop optimization, the continuous-time laser frames can be accurately associated to guarantee the consistency of 3D point cloud map. Finally, the proposed method was evaluated in different scenarios, the experimental results showed that the proposed method could not only achieve accurate mapping even in the complex scenes, but also successfully handle sparse laser frames well, which is critical for the scanners such as the new Velodyne VLP-16 scanner’s performance.
A New Paper-Based Microfluidic Device for Improved Detection of Nitrate in Water
Authors: Charbaji Amer , Heidari-Bafroui Hojat , Anagnostopoulos Constantine , Faghri Mohammad
Published: December 2020

In this paper, we report a simple and inexpensive paper-based microfluidic device for detecting nitrate in water. This device incorporates two recent developments in paper-based technology suitable for nitrate detection and has an optimized microfluidic design. The first technical advancement employed is an innovative fibrous composite material made up of cotton fibers and zinc microparticles that can be incorporated in paper-based devices and results in better nitrate reduction. The second is a detection zone with an immobilized reagent that allows the passage of a larger sample volume. Different acids were tested—citric and phosphoric acids gave better results than hydrochloric acid since this acid evaporates completely without leaving any residue behind on paper. Different microfluidic designs that utilize various fluid control technologies were investigated and a design with a folding detection zone was chosen and optimized to improve the uniformity of the signal produced. The optimized design allowed the device to achieve a limit of detection and quantification of 0.53 ppm and 1.18 ppm, respectively, for nitrate in water. This accounted for more than a 40% improvement on what has been previously realized for the detection of nitrate in water using paper-based technology.
Automatic Functional Shoulder Task Identification and Sub-Task Segmentation Using Wearable Inertial Measurement Units for Frozen Shoulder Assessment
Authors: Chang Chih-Ya , Hsieh Chia-Yeh , Huang Hsiang-Yun , Wu Yung-Tsan , Chen Liang-Cheng , Chan Chia-Tai , Liu Kai-Chun
Published: December 2020

Advanced sensor technologies have been applied to support frozen shoulder assessment. Sensor-based assessment tools provide objective, continuous and quantitative information for evaluation and diagnosis. However, the current tools for assessment of functional shoulder tasks mainly rely on manual operation. It may cause several technical issues to the reliability and usability of the assessment tool, including manual bias during the recording and additional efforts for data labeling. To tackle these issues, this pilot study aims to propose an automatic functional shoulder task identification and sub-task segmentation system using inertial measurement units to provide reliable shoulder task labeling and sub-task information for clinical professionals. The proposed method combines machine learning models and rule-based modification to identify shoulder tasks and segment sub-tasks accurately. A hierarchical design is applied to enhance the efficiency and performance of the proposed approach. Nine healthy subjects and nine frozen shoulder patients are invited to perform five common shoulder tasks in the lab-based and clinical environments, respectively. The experimental results show that the proposed method can achieve 87.11% F-score for shoulder task identification, and 83.23% F-score and 427 mean absolute time errors (milliseconds) for sub-task segmentation. The proposed approach demonstrates the feasibility of the proposed method to support reliable evaluation for clinical assessment.
A Novel Algorithm for Detecting Pedestrians on Rainy Image
Authors: Liu Yuhang , Ma Jianxiao , Wang Yuchen , Zong Chenhong
Published: December 2020

Pedestrian detection is widely used in cooperative vehicle infrastructure systems. Traditional pedestrian detection methods perform sufficiently well under sunny scenarios and obtain trustworthy traffic data. However, the detection drastically decreases under rainy scenarios. This study proposes a pedestrian detection algorithm with a de-raining module that improves detection accuracy under various rainy scenarios. Specifically, this algorithm determines the density information of rain and effectively removes rain streaks through the de-raining module. Then the algorithm detects pedestrians as a pair of keypoints through the pedestrian detection module to solve the problem of occlusion. Furthermore, a new pedestrian dataset containing rain density labels is established and used to train the algorithm. For the scenarios of light, medium, and heavy rain, extensive experiments on synthetic datasets demonstrate that the proposed algorithm increases AP (average precision) of pedestrian detection by 21.1%, 48.1%, and 60.9%. Moreover, the proposed algorithm performs well on real datasets and achieves improvements over the state-of-the-art methods, which reveals that the proposed algorithm can significantly improve the accuracy of pedestrian detection in rainy scenarios.
Transfer of Learning from Vision to Touch: A Hybrid Deep Convolutional Neural Network for Visuo-Tactile 3D Object Recognition
Authors: Rouhafzay Ghazal , Cretu Ana-Maria , Payeur Pierre
Published: December 2020

Transfer of learning or leveraging a pre-trained network and fine-tuning it to perform new tasks has been successfully applied in a variety of machine intelligence fields, including computer vision, natural language processing and audio/speech recognition. Drawing inspiration from neuroscience research that suggests that both visual and tactile stimuli rouse similar neural networks in the human brain, in this work, we explore the idea of transferring learning from vision to touch in the context of 3D object recognition. In particular, deep convolutional neural networks (CNN) pre-trained on visual images are adapted and evaluated for the classification of tactile data sets. To do so, we ran experiments with five different pre-trained CNN architectures and on five different datasets acquired with different technologies of tactile sensors including BathTip, Gelsight, force-sensing resistor (FSR) array, a high-resolution virtual FSR sensor, and tactile sensors on the Barrett robotic hand. The results obtained confirm the transferability of learning from vision to touch to interpret 3D models. Due to its higher resolution, tactile data from optical tactile sensors was demonstrated to achieve higher classification rates based on visual features compared to other technologies relying on pressure measurements. Further analysis of the weight updates in the convolutional layer is performed to measure the similarity between visual and tactile features for each technology of tactile sensing. Comparing the weight updates in different convolutional layers suggests that by updating a few convolutional layers of a pre-trained CNN on visual data, it can be efficiently used to classify tactile data. Accordingly, we propose a hybrid architecture performing both visual and tactile 3D object recognition with a MobileNetV2 backbone. MobileNetV2 is chosen due to its smaller size and thus its capability to be implemented on mobile devices, such that the network can classify both visual and tactile data. An accuracy of 100% for visual and 77.63% for tactile data are achieved by the proposed architecture.
Microcantilever: Dynamical Response for Mass Sensing and Fluid Characterization
Authors: Mouro João , Pinto Rui , Paoletti Paolo , Tiribilli Bruno
Published: December 2020

A microcantilever is a suspended micro-scale beam structure supported at one end which can bend and/or vibrate when subjected to a load. Microcantilevers are one of the most fundamental miniaturized devices used in microelectromechanical systems and are ubiquitous in sensing, imaging, time reference, and biological/biomedical applications. They are typically built using micro and nanofabrication techniques derived from the microelectronics industry and can involve microelectronics-related materials, polymeric materials, and biological materials. This work presents a comprehensive review of the rich dynamical response of a microcantilever and how it has been used for measuring the mass and rheological properties of Newtonian/non-Newtonian fluids in real time, in ever-decreasing space and time scales, and with unprecedented resolution.
DEDNet: Offshore Eddy Detection and Location with HF Radar by Deep Learning
Authors: Liu Fangyuan , Zhou Hao , Wen Biyang
Published: December 2020

Oceanic eddy is a common natural phenomenon that has large influence on human activities, and the measurement and detection of offshore eddies are significant for oceanographic research. The previous classical detecting methods, such as the Okubo–Weiss algorithm (OW), vector geometry algorithm (VG), and winding angles algorithm (WA), not only depend on expert’s experiences to set an accurate threshold, but also need heavy calculations for large detection regions. Differently from the previous works, this paper proposes a deep eddy detection neural network with pixel segmentation skeleton on high frequency radar (HFR) data, namely, the deep eddy detection network (DEDNet). An offshore eddy detection dataset is firstly constructed, which has origins from the sea surface current data measured by two HFR systems on the South China Sea. Then, a spatial globally optimum and strong detail-distinguishing pixel segmentation network is presented to automatically detect and localize offshore eddies in a flow chart. An eddy detection network based on fully convolutional networks (FCN) is also presented for comparison with DEDNet. Experimental results show that DEDNet performs better than the FCN-based eddy detection network and is competitive with the classical statistics-based methods.
Environmental Sustainability Enhancement of Waste Disposal Sites in Developing Countries through Controlling Greenhouse Gas Emissions
Authors: Sohoo Ihsanullah , Ritzkowski Marco , Kuchta Kerstin , Cinar Senem Önen
Published: December 2020

Sustainable management of municipal solid waste is one of the major challenges for authorities in developing countries. Current waste disposal methods in Pakistan and other developing countries are not meeting standards of any proper waste management system opted for in the developed world. This mismanagement of waste is leading to serious environmental problems at local as well as global levels. This study aims to investigate the methane emissions from waste dumpsites in the city of Karachi, Pakistan, and to propose an effective approach to enhance their environmental sustainability. The methane emissions from waste disposal sites were assessed by simulating four different landfill situations during the landfill simulation reactor experiment. The residual methane reduction potential of each waste disposal approach was assessed by a biochemical methane test of waste after the experiment. It is estimated that in the present situation, about 11,500 tons of CO2-eq methane is released annually from waste disposal sites in Karachi. The convectional anaerobic landfill with methane capturing facilities and post-aeration operation was found to be the most environmentally sustainable approach with controlling 65% of residual methane emissions in comparison with the present scenario. For the development of new landfill sites, we recommend the bioreactor landfill approach with methane recovery and post-care (in-situ aeration).
Philanthropy and the Contribution of Andrew Carnegie to Corporate Social Responsibility
Authors: Janowski Andrzej
Published: December 2020

The idea of corporate social responsibility (CSR) has attracted the interests of both practitioners and scientists, particularly since 1953, when H. R. Bowen published The Social Responsibilities of the Businessman. Over the years, the CSR concept evolved and became a managerial phenomenon; it was applied to different sectors with supposedly excellent effects. Unfortunately, there was discourse around the meaning of CSR. In the world of science, there is no agreement as to the semantic area of CSR. Academics face absolute, undisturbed freedom in the formulation of its elements and definitions. That abovementioned ambiguity determined the situation the recent CSR literature is vague and biased, and an extensive analysis of the latest contributions are lacking. To address this gap, there has been proposed a systematic literature review and bibliometrics of 119 articles published in 45 peer-reviewed, high-quality academic journals and 19 books, from January 1950 to July 2020. There are three objectives of this paper: to analyze the recent CSR definitions in the context of Carnegie’s principles, to identify trends in that field and evaluate the utility of the scientific efforts in the abovementioned context, and to indicate the future research paths in the context of corporate social responsibility.
Governmental Intervention and Its Impact on Growth, Economic Development, and Technology in OECD Countries
Authors: Sadeh Arik , Radu Claudia Florina , Feniser Cristina , Borşa Andrei
Published: December 2020

The governments’ intervention in the economy impacts technological performance and sustainability. This role has become even more critical due to the COVID-19 situation and in the context of the continuous increase in resource consumption, which requires finding alternative solutions. We provide a comprehensive literature review about the state’s economic functions, redistribution of resources in society, and the role of state intervention in sustainability-related issues, giving a full description of the opinions and concepts primarily of economists. We propose to study governments’ interventions in their economy using budgetary resources on public expenditure, highlighting the leading factors in government policies using a suggested intervention index. The state’s intervention policy’s stability is measured via the intervention index’s partial autocorrelation function over the years. We collected data from OECD data sets and conducted a descriptive statistical analysis followed by panel data analysis. Subsequently, two questions are explored about the state’s intervention and its technical performance and technology-related sustainability issues. Results show that economic strength positively affects the intervention. Expenditures on education may lead to better technological outcomes, unlike expenses on health. The tax burden inhibits innovation and technological progress, but total governmental revenues positively affect technological performance.
Techno-Economic Assessment of Energy Retrofitting Educational Buildings: A Case Study in Saudi Arabia
Authors: Hamida Mohammad B. , Ahmed Wahhaj , Asif Muhammad , Almaziad Faris Abdullah
Published: December 2020

The buildings and construction sector accounts for the majority of the energy consumption in the Kingdom of Saudi Arabia (KSA). For a sustainable future, energy consumption in the sector should be reduced and existing buildings need to be energy retrofitted. A number of studies present energy retrofitting of residential buildings in KSA; however, there is a lack of studies presenting retrofitting of educational buildings. Thus, the aim of this study is to adopt a BIM-based approach to assess Energy Conservation Measures (ECMs) in a prototypical Government-built educational building in Dammam, KSA. The methodology consists of six prime steps, (1) case study data collection, (2) energy auditing, (3) proposing ECMs, (4) BIM model development, (5) energy assessment, and (6) economic assessment. The energy audit revealed several inefficiencies in the building construction and operation and four ECMs were proposed and simulated. It was found that annual energy consumption can be reduced by 22.7% in the educational building, and the investment for the four ECMs is paid back in 2.7 years only. Therefore, implementing the proposed ECMs is a viable option to energy retrofit such educational buildings in the country, and the presented BIM-based approach can be adopted to efficiently conduct the energy retrofitting process.