bluuebunny/crossref_metadata_2025 huggingface dataset:
Genome Analysis of Phytophthora nicotianae JM01 Provides Insights into Its Pathogenicity Mechanisms
Authors: Yuan Xiao-Long , Zhang Cheng-Sheng , Kong Fan-Yu , Zhang Zhong-Feng , Wang Feng-Long
Published: August 2021

Phytophthora nicotianae is a widely distributed plant pathogen that can cause serious disease and cause significant economic losses to various crops, including tomatoes, tobacco, onions, and strawberries. To understand its pathogenic mechanisms and explore strategies for controlling diseases caused by this pathogen, we sequenced and analyzed the whole genome of Ph. nicotianae JM01. The Ph. nicotianae JM01 genome was assembled using a combination of approaches including shotgun sequencing, single-molecule sequencing, and the Hi-C technique. The assembled Ph. nicotianae JM01 genome is about 95.32 Mb, with contig and scaffold N50 54.23 kb and 113.15 kb, respectively. The average GC content of the whole-genome is about 49.02%, encoding 23,275 genes. In addition, we identified 19.15% of interspersed elements and 0.95% of tandem elements in the whole genome. A genome-wide phylogenetic tree indicated that Phytophthora diverged from Pythium approximately 156.32 Ma. Meanwhile, we found that 252 and 285 gene families showed expansion and contraction in Phytophthora when compared to gene families in Pythium. To determine the pathogenic mechanisms Ph. nicotianae JM01, we analyzed a suite of proteins involved in plant–pathogen interactions. The results revealed that gene duplication contributed to the expansion of Cell Wall Degrading Enzymes (CWDEs) such as glycoside hydrolases, and effectors such as Arg-Xaa-Leu-Arg (RXLR) effectors. In addition, transient expression was performed on Nicotiana benthamiana by infiltrating with Agrobacterium tumefaciens cells containing a cysteine-rich (SCR) protein. The results indicated that SCR can cause symptoms of hypersensitive response. Moreover, we also conducted comparative genome analysis among four Ph. nicotianae genomes. The completion of the Ph. nicotianae JM01 genome can not only help us understand its genomic characteristics, but also help us discover genes involved in infection and then help us understand its pathogenic mechanisms.
Development, Characterization and Cell Viability Inhibition of PVA Spheres Loaded with Doxorubicin and 4′-Amino-1-Naphthyl-Chalcone (D14) for Osteosarcoma
Authors: Seba Viviane , de Lima Gabriel Goetten , Pereira Bruno L. , Silva Gabriel , Reinhardt Luiza Steffens , Arantes Pablo Ricardo , Chee Bor Shin , dos Santos Mariana Bastos , França Suzelei C. , Regasini Luis Octavio , Fachin Ana Lúcia , Cao Zhi , Nugent Michael J. D. , Marins Mozart
Published: August 2021

Chalcones (1,3-diaryl-2-propen-1-ones) are naturally occurring polyphenols with known anticancer activity against a variety of tumor cell lines, including osteosarcoma (OS). In this paper, we present the preparation and characterization of spheres (~2 mm) from polyvinyl alcohol (PVA) containing a combination of 4′-Amino-1-Naphthyl-Chalcone (D14) and doxorubicin, to act as a new polymeric dual-drug anticancer delivery. D14 is a potent inhibitor of osteosarcoma progression and, when combined with doxorubicin, presents a synergetic effect; hence, physically crosslinked PVA spheres loaded with D14 and doxorubicin were prepared using liquid nitrogen and six freeze–thawing cycles. Physical-chemical characterization using a scanning electron microscope (SEM), differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) presented that the drugs were incorporated into the spheres via weak interactions between the drugs and the polymeric chains, resulting in overall good drug stability. The cytotoxicity activity of the PVA spheres co-encapsulating both drugs was tested against the U2OS human osteosarcoma cell line by 3- (4,5-dimethylthiazol-2-yl) -2,5-diphenyltetrazolium bromide (MTT) assay, and compared to the spheres carrying either D14 or doxorubicin alone. The co-delivery showed a cytotoxic effect 2.6-fold greater than doxorubicin alone, revealing a significant synergistic effect with a coefficient of drug interaction (CDI) of 0.49. The obtained results suggest this developed PVA sphere as a potential dual-drug delivery system that could be used for the prominent synergistic anticancer activity of co-delivering D14 and doxorubicin, providing a new potential strategy for improved osteosarcoma treatment.
Finite Element Analysis of Gas Diffusion in Polymer Nanocomposite Systems Containing Rod-like Nanofillers
Authors: Htira Thouaiba , Zid Sarra , Zinet Matthieu , Espuche Eliane
Published: August 2021

Polymer-based films with improved gas barrier properties are of great interest for a large range of applications, including packaging and coatings. The barrier effect is generally obtained via the addition of a sufficient amount of impermeable nanofillers within the polymer matrix. Due to their low environmental footprint, bio-based nanocomposites such as poly(lactic acid)–cellulose nanocrystal (PLA–CNC) nanocomposites seem to be an interesting alternative to synthetic-polymer-based nanocomposites. The morphology of such systems consists of the dispersion of impermeable rod-like fillers of finite length in a more permeable matrix. The aim of this work is to analyze, through finite element modeling (FEM), the diffusion behavior of 3D systems representative of PLA–CNC nanocomposites, allowing the determination of the nanocomposites’ effective diffusivity. Parametric studies are carried out to evaluate the effects of various parameters, such as the filler volume fraction, aspect ratio, polydispersity, and agglomeration, on the improvement of the barrier properties. The role of the filler–matrix interfacial area (or interphase) is also investigated and is shown to be particularly critical to the overall barrier effect for highly diffusive interphases.
Utilization of Antibacterial Nanoparticles in Photocurable Additive Manufacturing of Advanced Composites for Improved Public Health
Authors: Billings Christopher , Cai Changjie , Liu Yingtao
Published: August 2021

This paper presents the additive manufacturing and characterization of nanoparticle-reinforced photocurable resin-based nanocomposites with a potential antimicrobial function for improved public health applications. Two types of photocurable resins are reinforced by titanium dioxide (TiO2) or zinc oxide (ZnO) nanoparticles with average diameters in the 10–30 nm range to provide antimicrobial properties. The developed nanocomposites can be additively manufactured using the digital light processing method with an outstanding surface quality and precise geometrical accuracy. Experimental characterizations are conducted to investigate key mechanical properties of the 3D printed nanocomposites, including Young’s Modulus, tensile strength, and abrasion resistance. Specimens produced were observed to demonstrate the following characteristics during testing. Tensile strength increased by 42.2% at a maximum value of 29.53 MPa. The modulus of elasticity increased by 14.3%, and abrasion resistance increased by 15.8%. The proper dispersion of the nanoparticles within the cured resin is validated by scanning electron images. The wettability and water absorption testing results indicate that the developed nanocomposites have an outstanding water resistance capability. The pairing of digital light processing with these novel nanocomposites allows for the creation of complex composite geometries that are not capable through other manufacturing processes. Therefore, they have the potential for long-term usage to improve general public health with antimicrobial functionality. The pairing of an unmodified photocurable resin with a 1% ZnO concentration demonstrated the most promise for commercial applications.
Process Transferability of Friction Riveting of AA2024-T351/Polyetherimide (PEI) Joints Using Hand-Driven, Low-Cost Drilling Equipment
Authors: Feier Anamaria , Becheru Andrei , Brîndușoiu Mihai , Blaga Lucian
Published: August 2021

The present work deals with the transferability of Friction Riveting joining technology from laboratory equipment to adapted in-house, low-cost machinery. A G13 drilling machine was modified for the requirements of the selected joining technique, and joints were performed using polyethermide plates and AA2024 aluminum alloy rivets of 6 mm diameter. This diameter was not previously reported for Friction Riveting. The produced joints were mechanically tested under tensile loading (pullout tests) with ultimate tensile forces of 9500 ± 900 N. All tested specimens failed through full-rivet pullout, which is the weakest reported joint in Friction Riveting. In order to understand this behavior, FE models were created and analyzed. The models produced were in agreement with the experimental results, with failure initiated within the polymer under stress concentrations in the polymeric material above the deformed metallic anchor at an ultimate value of the stress of 878 MPa at the surface of the joint. Stresses decreased to less than half of the maximum value around the anchoring zone while the rivet was removed and towards the surface. The paper thus demonstrates the potential ease of applying and reproducing Friction Riveting with simple machinery, while contributing to an understanding of the mechanical behavior (initialization of failure) of joints.
God, New Natural Law Theory, and Human Rights
Authors: Tollefsen Christopher
Published: August 2021

Critics of the “New” Natural Law (NNL) theory have raised questions about the role of the divine in that theory. This paper considers that role in regard to its account of human rights: can the NNL account of human rights be sustained without a more or less explicit advertence to “the question of God’s existence or nature or will”? It might seem that Finnis’s “elaborate sketch” includes a full theory of human rights even prior to the introduction of his reflections on the divine in the concluding chapter of Natural Law and Natural Rights. But in this essay, I argue that an adequate account of human rights cannot, in fact, be sustained without some role for God’s creative activity in two dimensions, the ontological and the motivational. These dimensions must be distinguished from the epistemological dimension of human rights, that is, the question of whether epistemological access to truths about human rights is possible without reference to God’s existence, nature, or will. The NNL view is that such access is possible. However, I will argue, the epistemological cannot be entirely cabined off from the relevant ontological and motivational issues and the NNL framework can accommodate this fact without difficulty.
An Adjustment Approach for Aerosol Optical Depth Inferred from CALIPSO
Authors: Zeng Zhaoliang , Wang Zemin , Zhang Baojun
Published: August 2021

The verification and correction of CALIPSO aerosol products is key to understanding the atmospheric environment and climate change. However, CALIPSO often cannot detect the full profile of aerosol for the low instrument sensitivity near the surface. Thus, a correction scheme for the aerosol extinction coefficient (AECs) in the planetary boundary layer (PBL) is proposed to improve the quality of the CALIPSO-based aerosol optical depth (AOD) at 532 nm. This scheme assumed that the aerosol is vertically and uniformly distributed below the PBL, and that the AECs in the whole PBL are equal to those at the top of the PBL; then, the CALIPSO AOD was obtained by vertically integrating AECs throughout the whole atmosphere. Additionally, the CALIPSO AOD and corrected CALIPSO AOD were validated against seven ground-based sites across eastern China during 2007–2015. Our results show that the initial CALIPSO AOD obtained by cloud filtering was generally lower than that of the ground-based observations. After accounting for the AECs in the PBL, the adjustment method tended to improve the CALIPSO AOD data quality. The average R (slope) value from all sites was improved by 7% (46%). Further, the relative distance between the ground track of CALIPSO and the ground station exhibited an influence on the validation result of CALIPSO AOD. The retrieval precision of CALIPSO AOD worsened with the increase in water vapor in the atmosphere. Our findings indicate that our scheme significantly improves the accuracy of CALIPSO AOD, which will help to provide alternative AOD products in the presence of severe atmospheric pollution.
Semantic Segmentation of Urban Buildings Using a High-Resolution Network (HRNet) with Channel and Spatial Attention Gates
Authors: Seong Seonkyeong , Choi Jaewan
Published: August 2021

In this study, building extraction in aerial images was performed using csAG-HRNet by applying HRNet-v2 in combination with channel and spatial attention gates. HRNet-v2 consists of transition and fusion processes based on subnetworks according to various resolutions. The channel and spatial attention gates were applied in the network to efficiently learn important features. A channel attention gate assigns weights in accordance with the importance of each channel, and a spatial attention gate assigns weights in accordance with the importance of each pixel position for the entire channel. In csAG-HRNet, csAG modules consisting of a channel attention gate and a spatial attention gate were applied to each subnetwork of stage and fusion modules in the HRNet-v2 network. In experiments using two datasets, it was confirmed that csAG-HRNet could minimize false detections based on the shapes of large buildings and small nonbuilding objects compared to existing deep learning models.
FastAER Det: Fast Aerial Embedded Real-Time Detection
Authors: Wolf Stefan , Sommer Lars , Schumann Arne
Published: August 2021

Automated detection of objects in aerial imagery is the basis for many applications, such as search and rescue operations, activity monitoring or mapping. However, in many cases it is beneficial to employ a detector on-board of the aerial platform in order to avoid latencies, make basic decisions within the platform and save transmission bandwidth. In this work, we address the task of designing such an on-board aerial object detector, which meets certain requirements in accuracy, inference speed and power consumption. For this, we first outline a generally applicable design process for such on-board methods and then follow this process to develop our own set of models for the task. Specifically, we first optimize a baseline model with regards to accuracy while not increasing runtime. We then propose a fast detection head to significantly improve runtime at little cost in accuracy. Finally, we discuss several aspects to consider during deployment and in the runtime environment. Our resulting four models that operate at 15, 30, 60 and 90 FPS on an embedded Jetson AGX device are published for future benchmarking and comparison by the community.
Assimilating FY-4A Lightning and Radar Data for Improving Short-Term Forecasts of a High-Impact Convective Event with a Dual-Resolution Hybrid 3DEnVAR Method
Authors: Liu Peng , Yang Yi , Lai Anwei , Wang Yunheng , Fierro Alexandre O. , Gao Jidong , Wang Chenghai
Published: August 2021

A dual-resolution, hybrid, three-dimensional ensemble-variational (3DEnVAR) data assimilation method combining static and ensemble background error covariances is used to assimilate radar data, and pseudo-water vapor observations to improve short-term severe weather forecasts with the Weather Research and Forecast (WRF) model. The higher-resolution deterministic forecast and the lower-resolution ensemble members have 3 and 9 km horizontal resolution, respectively. The water vapor pseudo-observations are derived from the combined use of total lightning data and cloud top height from the Fengyun-4A(FY-4A) geostationary satellite. First, a set of single-analysis experiments are conducted to provide a preliminary performance evaluation of the effectiveness of the hybrid method for assimilating multisource observations; second, a set of cycling analysis experiments are used to evaluate the forecast performance in convective-scale high-frequency analysis; finally, different hybrid coefficients are tested in both the single and cycling experiments. The single-analysis results show that the combined assimilation of radar data and water vapor pseudo-observations derived from the lightning data is able to generate reasonable vertical velocity, water vapor and hydrometeor adjustments, which help to trigger convection earlier in the forecast/analysis and reduce the spin-up time. The dual-resolution hybrid 3DEnVAR method is able to adjust the wind fields and hydrometeor variables with the assimilation of lightning data, which helps maintain the triggered convection longer and partially suppress spurious cells in the forecast compared with the three-dimensional variational (3DVAR) method. A cycling analysis that introduced a large number of observations with more frequent small adjustments is able to better resolve the observed convective events than a single-analysis approach. Different hybrid coefficients can affect the forecast results, either in the single deterministic or cycling analysis experiments. Overall, we found that a static coefficient of 0.4 and an ensemble coefficient of 0.6 yields the best forecast skill for this event.
Coverage and Rainfall Response of Biological Soil Crusts Using Multi-Temporal Sentinel-2 Data in a Central European Temperate Dry Acid Grassland
Authors: Rieser Jakob , Veste Maik , Thiel Michael , Schönbrodt-Stitt Sarah
Published: August 2021

Biological soil crusts (BSCs) are thin microbiological vegetation layers that naturally develop in unfavorable higher plant conditions (i.e., low precipitation rates and high temperatures) in global drylands. They consist of poikilohydric organisms capable of adjusting their metabolic activities depending on the water availability. However, they, and with them, their ecosystem functions, are endangered by climate change and land-use intensification. Remote sensing (RS)-based studies estimated the BSC cover in global drylands through various multispectral indices, and few of them correlated the BSCs’ activity response to rainfall. However, the allocation of BSCs is not limited to drylands only as there are areas beyond where smaller patches have developed under intense human impact and frequent disturbance. Yet, those areas were not addressed in RS-based studies, raising the question of whether the methods developed in extensive drylands can be transferred easily. Our temperate climate study area, the ‘Lieberoser Heide’ in northeastern Germany, is home to the country’s largest BSC-covered area. We applied a Random Forest (RF) classification model incorporating multispectral Sentinel-2 (S2) data, indices derived from them, and topographic information to spatiotemporally map the BSC cover for the first time in Central Europe. We further monitored the BSC response to rainfall events over a period of around five years (June 2015 to end of December 2020). Therefore, we combined datasets of gridded NDVI as a measure of photosynthetic activity with daily precipitation data and conducted a change detection analysis. With an overall accuracy of 98.9%, our classification proved satisfactory. Detected changes in BSC activity between dry and wet conditions were found to be significant. Our study emphasizes a high transferability of established methods from extensive drylands to BSC-covered areas in the temperate climate. Therefore, we consider our study to provide essential impulses so that RS-based biocrust mapping in the future will be applied beyond the global drylands.
Estimation and Analysis of the Observable-Specific Code Biases Estimated Using Multi-GNSS Observations and Global Ionospheric Maps
Authors: Li Min , Yuan Yunbin
Published: August 2021

Observable-specific bias (OSB) parameterization allows observation biases belonging to various signal types to be flexibly addressed in the estimation of ionosphere and global navigation satellite system (GNSS) clock products. In this contribution, multi-GNSS OSBs are generated by two different methods. With regard to the first method, geometry-free (GF) linear combinations of the pseudorange and carrier-phase observations of a global multi-GNSS receiver network are formed for the extraction of OSB observables, and global ionospheric maps (GIMs) are employed to correct ionospheric path delays. Concerning the second method, satellite and receiver OSBs are converted directly from external differential code bias (DCB) products. Two assumptions are employed in the two methods to distinguish satellite- and receiver-specific OSB parameters. The first assumption is a zero-mean condition for each satellite OSB type and GNSS signal. The second assumption involves ionosphere-free (IF) linear combination signal constraints for satellites and receivers between two signals, which are compatible with the International GNSS Service (IGS) clock product. Agreement between the multi-GNSS satellite OSBs estimated by the two methods and those from the Chinese Academy of Sciences (CAS) is shown at levels of 0.15 ns and 0.1 ns, respectively. The results from observations spanning 6 months show that the multi-GNSS OSB estimates for signals in the same frequency bands may have very similar code bias characteristics, and the receiver OSB estimates present larger standard deviations (STDs) than the satellite OSB estimates. Additionally, the variations in the receiver OSB estimates are shown to be related to the types of receivers and antennas and the firmware version. The results also indicate that the root mean square (RMS) of the differences between the OSBs estimated based on the CAS- and German Aerospace Center (DLR)-provided DCB products are 0.32 ns for the global positioning system (GPS), 0.45 ns for the BeiDou navigation satellite system (BDS), 0.39 ns for GLONASS and 0.22 ns for Galileo.
Cereal Yield Forecasting with Satellite Drought-Based Indices, Weather Data and Regional Climate Indices Using Machine Learning in Morocco
Authors: Bouras El houssaine , Jarlan Lionel , Er-Raki Salah , Balaghi Riad , Amazirh Abdelhakim , Richard Bastien , Khabba Saïd
Published: August 2021

Accurate seasonal forecasting of cereal yields is an important decision support tool for countries, such as Morocco, that are not self-sufficient in order to predict, as early as possible, importation needs. This study aims to develop an early forecasting model of cereal yields (soft wheat, barley and durum wheat) at the scale of the agricultural province considering the 15 most productive over 2000–2017 (i.e., 15 × 18 = 270 yields values). To this objective, we built on previous works that showed a tight linkage between cereal yields and various datasets including weather data (rainfall and air temperature), regional climate indices (North Atlantic Oscillation in particular), and drought indices derived from satellite observations in different wavelengths. The combination of the latter three data sets is assessed to predict cereal yields using linear (Multiple Linear Regression, MLR) and non-linear (Support Vector Machine, SVM; Random Forest, RF, and eXtreme Gradient Boost, XGBoost) machine learning algorithms. The calibration of the algorithmic parameters of the different approaches are carried out using a 5-fold cross validation technique and a leave-one-out method is implemented for model validation. The statistical metrics of the models are first analyzed as a function of the input datasets that are used, and as a function of the lead times, from 4 months to 2 months before harvest. The results show that combining data from multiple sources outperformed models based on one dataset only. In addition, the satellite drought indices are a major source of information for cereal prediction when the forecasting is carried out close to harvest (2 months before), while weather data and, to a lesser extent, climate indices, are key variables for earlier predictions. The best models can accurately predict yield in January (4 months before harvest) with an R2 = 0.88 and RMSE around 0.22 t. ha−1. The XGBoost method exhibited the best metrics. Finally, training a specific model separately for each group of provinces, instead of one global model, improved the prediction performance by reducing the RMSE by 10% to 35% depending on the provinces. In conclusion, the results of this study pointed out that combining remote sensing drought indices with climate and weather variables using a machine learning technique is a promising approach for cereal yield forecasting.
A Practical 3D Reconstruction Method for Weak Texture Scenes
Authors: Yang Xuyuan , Jiang Guang
Published: August 2021

In recent years, there has been a growing demand for 3D reconstructions of tunnel pits, underground pipe networks, and building interiors. For such scenarios, weak textures, repeated textures, or even no textures are common. To reconstruct these scenes, we propose covering the lighting sources with films of spark patterns to “add” textures to the scenes. We use a calibrated camera to take pictures from multiple views and then utilize structure from motion (SFM) and multi-view stereo (MVS) algorithms to carry out a high-precision 3D reconstruction. To improve the effectiveness of our reconstruction, we combine deep learning algorithms with traditional methods to extract and match feature points. Our experiments have verified the feasibility and efficiency of the proposed method.
Evaluation of Soil Properties, Topographic Metrics, Plant Height, and Unmanned Aerial Vehicle Multispectral Imagery Using Machine Learning Methods to Estimate Canopy Nitrogen Weight in Corn
Authors: Yu Jody , Wang Jinfei , Leblon Brigitte
Published: August 2021

Management of nitrogen (N) fertilizers is an important agricultural practice and field of research to minimize environmental impacts and the cost of production. To apply N fertilizer at the right rate, time, and place depends on the crop type, desired yield, and field conditions. The objective of this study is to use Unmanned Aerial Vehicle (UAV) multispectral imagery, vegetation indices (VI), crop height, field topographic metrics, and soil properties to predict canopy nitrogen weight (g/m2) of a corn field in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models were evaluated for canopy nitrogen weight prediction from 29 variables. RF consistently had better performance than SVR, and the top-performing validation model was RF using 15 selected height, spectral, and topographic variables with an R2 of 0.73 and Root Mean Square Error (RMSE) of 2.21 g/m2. Of the model’s 15 variables, crop height was the most important predictor, followed by 10 VIs, three MicaSense band reflectance mosaics (blue, red, and green), and topographic profile curvature. The model information can be used to improve field nitrogen prediction, leading to more effective and efficient N fertilizer management.
Active Strike-Slip Faulting and Systematic Deflection of Drainage Systems along the Altyn Tagh Fault, Northern Tibetan Plateau
Authors: Chen Peng , Yan Bing , Liu Yuan
Published: August 2021

Systematic deflection of drainage systems along strike-slip faults is the combination of repeated faulting slipping and continuous headward erosion accumulated on the stream channels. The measurement and analysis of systematically deflected stream channels will enhance our understanding on the deformational behaviors of strike-slip faults and the relationship between topographic response and active strike-slip faulting. In this study, detailed interpretation and analysis of remote sensing images and DEM data were carried out along the Altyn Tagh Fault, one typical large-scale strike-slip fault in the northern Tibetan Plateau, and together with the statistical results of offset amounts of 153 stream channels, revealed that (i) the drainage systems have been systematically deflected and/or offset in sinistral along the active Altyn Tagh Fault; (ii) The offset amounts recorded by stream channels vary in the range of 7 m to 72 km, and indicate a positively related linear relationship between the upstream length L and the offset amount D, the channel with bedrock upstream generally has a better correlation between L and D than that of non-bedrock upstream; (iii) River capture and abandonment are commonly developed along the Altyn Tagh Fault, which probably disturbed the continuous accumulation of offset recorded on individual stream channel, suggesting that the real maximum cumulative displacement recorded by stream channels might be larger than 72 km (lower bound) along the Altyn Tagh Fault. Along with the cumulative displacements recorded by other regional-scale strike-slip faults in the Tibetan Plateau, these results demonstrate that the magnitude of tectonic extrusion along these first-order strike-slip faults after the collision of India–Asia plates might be limited.
Cascaded Deep Learning Neural Network for Automated Liver Steatosis Diagnosis Using Ultrasound Images
Authors: Rhyou Se-Yeol , Yoo Jae-Chern
Published: August 2021

Diagnosing liver steatosis is an essential precaution for detecting hepatocirrhosis and liver cancer in the early stages. However, automatic diagnosis of liver steatosis from ultrasound (US) images remains challenging due to poor visual quality from various origins, such as speckle noise and blurring. In this paper, we propose a fully automated liver steatosis prediction model using three deep learning neural networks. As a result, liver steatosis can be automatically detected with high accuracy and precision. First, transfer learning is used for semantically segmenting the liver and kidney (L-K) on parasagittal US images, and then cropping the L-K area from the original US images. The second neural network also involves semantic segmentation by checking the presence of a ring that is typically located around the kidney and cropping of the L-K area from the original US images. These cropped L-K areas are inputted to the final neural network, SteatosisNet, in order to grade the severity of fatty liver disease. The experimental results demonstrate that the proposed model can predict fatty liver disease with the sensitivity of 99.78%, specificity of 100%, PPV of 100%, NPV of 99.83%, and diagnostic accuracy of 99.91%, which is comparable to the common results annotated by medical experts.
Computer-Aided Colon Polyp Detection on High Resolution Colonoscopy Using Transfer Learning Techniques
Authors: Tang Chia-Pei , Chen Kai-Hong , Lin Tu-Liang
Published: August 2021

Colonoscopies reduce the incidence of colorectal cancer through early recognition and resecting of the colon polyps. However, the colon polyp miss detection rate is as high as 26% in conventional colonoscopy. The search for methods to decrease the polyp miss rate is nowadays a paramount task. A number of algorithms or systems have been developed to enhance polyp detection, but few are suitable for real-time detection or classification due to their limited computational ability. Recent studies indicate that the automated colon polyp detection system is developing at an astonishing speed. Real-time detection with classification is still a yet to be explored field. Newer image pattern recognition algorithms with convolutional neuro-network (CNN) transfer learning has shed light on this topic. We proposed a study using real-time colonoscopies with the CNN transfer learning approach. Several multi-class classifiers were trained and mAP ranged from 38% to 49%. Based on an Inception v2 model, a detector adopting a Faster R-CNN was trained. The mAP of the detector was 77%, which was an improvement of 35% compared to the same type of multi-class classifier. Therefore, our results indicated that the polyp detection model could attain a high accuracy, but the polyp type classification still leaves room for improvement.
Estimation of a Human-Maneuvered Target Incorporating Human Intention
Authors: Qin Yongming , Kumon Makoto , Furukawa Tomonari
Published: August 2021

This paper presents a new approach for estimating the motion state of a target that is maneuvered by an unknown human from observations. To improve the estimation accuracy, the proposed approach associates the recurring motion behaviors with human intentions, and models the association as an intention-pattern model. The human intentions relate to labels of continuous states; the motion patterns characterize the change of continuous states. In the preprocessing, an Interacting Multiple Model (IMM) estimation technique is used to infer the intentions and extract motions, which eventually construct the intention-pattern model. Once the intention-pattern model has been constructed, the proposed approach incorporate the intention-pattern model to estimation using any state estimator including Kalman filter. The proposed approach not only estimates the mean using the human intention more accurately but also updates the covariance using the human intention more precisely. The performance of the proposed approach was investigated through the estimation of a human-maneuvered multirotor. The result of the application has first indicated the effectiveness of the proposed approach for constructing the intention-pattern model. The ability of the proposed approach in state estimation over the conventional technique without intention incorporation has then been demonstrated.
Performance Analysis in Ski Jumping with a Differential Global Navigation Satellite System and Video-Based Pose Estimation
Authors: Elfmark Ola , Ettema Gertjan , Groos Daniel , Ihlen Espen A. F. , Velta Rune , Haugen Per , Braaten Steinar , Gilgien Matthias
Published: August 2021

This study investigated the explanatory power of a sensor fusion of two complementary methods to explain performance and its underlying mechanisms in ski jumping. A differential Global Navigation Satellite System (dGNSS) and a markerless video-based pose estimation system (PosEst) were used to measure the kinematics and kinetics from the start of the in-run to the landing. The study had two aims; firstly, the agreement between the two methods was assessed using 16 jumps by athletes of national level from 5 m before the take-off to 20 m after, where the methods had spatial overlap. The comparison revealed a good agreement from 5 m after the take-off, within the uncertainty of the dGNSS (±0.05m). The second part of the study served as a proof of concept of the sensor fusion application, by showcasing the type of performance analysis the systems allows. Two ski jumps by the same ski jumper, with comparable external conditions, were chosen for the case study. The dGNSS was used to analyse the in-run and flight phase, while the PosEst system was used to analyse the take-off and the early flight phase. The proof-of-concept study showed that the methods are suitable to track the kinematic and kinetic characteristics that determine performance in ski jumping and their usability in both research and practice.
DoA Estimation for FMCW Radar by 3D-CNN
Authors: Sang Tzu-Hsien , Chien Feng-Tsun , Chang Chia-Chih , Tseng Kuan-Yu , Wang Bo-Sheng , Guo Jiun-In
Published: August 2021

A method of direction-of-arrival (DoA) estimation for FMCW (Frequency Modulated Continuous Wave) radar is presented. In addition to MUSIC, which is the popular high-resolution DoA estimation algorithm, deep learning has recently emerged as a very promising alternative. It is proposed in this paper to use a 3D convolutional neural network (CNN) for DoA estimation. The 3D-CNN extracts from the radar data cube spectrum features of the region of interest (RoI) centered on the potential positions of the targets, thereby capturing the spectrum phase shift information, which corresponds to DoA, along the antenna axis. Finally, the results of simulations and experiments are provided to demonstrate the superior performance, as well as the limitations, of the proposed 3D-CNN.
Extended Spatially Localized Perturbation GAN (eSLP-GAN) for Robust Adversarial Camouflage Patches
Authors: Kim Yongsu , Kang Hyoeun , Suryanto Naufal , Larasati Harashta Tatimma , Mukaroh Afifatul , Kim Howon
Published: August 2021

Deep neural networks (DNNs), especially those used in computer vision, are highly vulnerable to adversarial attacks, such as adversarial perturbations and adversarial patches. Adversarial patches, often considered more appropriate for a real-world attack, are attached to the target object or its surroundings to deceive the target system. However, most previous research employed adversarial patches that are conspicuous to human vision, making them easy to identify and counter. Previously, the spatially localized perturbation GAN (SLP-GAN) was proposed, in which the perturbation was only added to the most representative area of the input images, creating a spatially localized adversarial camouflage patch that excels in terms of visual fidelity and is, therefore, difficult to detect by human vision. In this study, the use of the method called eSLP-GAN was extended to deceive classifiers and object detection systems. Specifically, the loss function was modified for greater compatibility with an object-detection model attack and to increase robustness in the real world. Furthermore, the applicability of the proposed method was tested on the CARLA simulator for a more authentic real-world attack scenario.
Application of NSGA-II to Obtain the Charging Current-Time Tradeoff Curve in Battery Based Underwater Wireless Sensor Nodes
Authors: Rodríguez García Daniel , Montiel-Nelson Juan-A. , Bautista Tomás , Sosa Javier
Published: August 2021

In this paper, a novel application of the Nondominated Sorting Genetic Algorithm II (NSGA II) is presented for obtaining the charging current–time tradeoff curve in battery based underwater wireless sensor nodes. The selection of the optimal charging current and times is a common optimization problem. A high charging current ensures a fast charging time. However, it increases the maximum power consumption and also the cost and complexity of the power supply sources. This research studies the tradeoff curve between charging currents and times in detail. The design exploration methodology is based on a two nested loop search strategy. The external loop determines the optimal design solutions which fulfill the designers’ requirements using parameters like the sensor node measurement period, power consumption, and battery voltages. The inner loop executes a local search within working ranges using an evolutionary multi-objective strategy. The experiments proposed are used to obtain the charging current–time tradeoff curve and to exhibit the accuracy of the optimal design solutions. The exploration methodology presented is compared with a bisection search strategy. From the results, it can be concluded that our approach is at least four times better in terms of computational effort than a bisection search strategy. In terms of power consumption, the presented methodology reduced the required power at least 3.3 dB in worst case scenarios tested.
Counterterrorism Evaluation and Citizens: More Than about Policing?
Authors: Balestrini Pierre Philippe
Published: August 2021

The police force is one of the few institutions still trusted by the public today. Yet, whilst the recent waves of terrorism have “stimulated” academic activity on the determinants of public fear of terrorism, much less academic effort has been focused on measuring and assessing the effectiveness of anti-terrorism strategies. The present article makes some contributions towards addressing this gap by investigating what shapes public attitudes towards the effectiveness of terrorism policing. Using Eurobarometer data, our results demonstrate that objective national economic, societal and political indicators do not tend to influence popular opinion on the effectiveness of the police in dealing with terrorism. They also show that individuals’ perceptions about the national socio-economic situation are better predictors of public opinion on terrorism policing than individuals’ financial and social positions or levels of education. The influence of these perceptions on public attitudes towards the effectiveness of counterterrorism seems to be more potent than the one on public fear of terrorism found in the extant literature. The implications of these findings are then considered.
Exploring the Impact of Crises on Food Security in Lebanon: Results from a National Cross-Sectional Study
Authors: Hoteit Maha , Al-Atat Youssef , Joumaa Hussein , Ghali Suheir El , Mansour Rania , Mhanna Reem , Sayyed-Ahmad Fatima , Salameh Pascale , Al-Jawaldeh Ayoub
Published: August 2021

This study aims to explore the prevalence and correlates of food insecurity among Lebanese households since the ordeals of COVID-19, economic crisis, and Beirut port explosions. At the core of the study, a mobile application entitled Nutrition Assessment System (NAS) that simplified the data collection was used as toolkit and a technical test was carried out in all Lebanese governorates between November 2020 and March 2021. Findings show that food insecurity is an immediate problem for households in Beirut and in many governorates in Lebanon. Nine in every 16 households ate less than 2 meals per day and more than 70% of them skipped their meals to spare food. Even though half the population studied had a low food consumption score, 82.4% of the people were not relying on livelihood coping strategies. However, more than three out of ten of these households relied on at least three food-based coping strategies. In addition, as for the livelihoods, this assessment found that most Lebanese households reported a drop in income along with an expansion in debt incurrence in the last 24 months to be able to buy food. Improving food security in Lebanon requires effort not only on the part of the government, but through regional and international actions.
CSR in China: Does Being Close to the Central or Local Government Matter?
Authors: Zhang Yongfeng , Wang Peng , Kwon Jongwook
Published: August 2021

The purpose of this research was to determine how geographic distance affects corporate social responsibility (CSR) activities within China. Our argument focuses on whether the impact of the distance between countries on CSR activities also applies to the impact of the distance within a country between Chinese enterprises and local governments and the central government on CSR. Most previous studies of the impact of geographic distance on CSR focused on the distance between countries, reporting opposing claims. Multinational enterprises (MNEs) need to overcome the liability of foreignness (LOF) in host countries: the greater the distance, the worse the disadvantage. MNEs will choose CSR to shorten the distance from the host country. Hence, CSR activities should have a positive relationship with the distance from the host country. Conversely, due to the various differences caused by distance, it is difficult for the national sovereignty of the home country and the host country to resonate, so the distance between CSR activities and the host country should be negatively related. In this study, 5147 companies listed on the Shanghai Stock Exchange and Shenzhen Stock Exchange from the China Stock Market and Accounting Research (CSMAR) database from 2009 to 2018 were selected as the research objects to analyze the impact of the geographical distance between companies and the Central Government and local governments on CSR activities within China. Our results show that the effect of the distance between countries on CSR also applies within countries, and our results support the argument that distance and CSR are negatively correlated. This research enriches the understanding of CSR practices of Chinese domestic companies and provides new inspiration for multinational companies that plan to enter China in overcoming LOF.
Dynamic Decision-Making Process of Evacuees during Post-Earthquake Evacuation near an Automatic Flap Barrier Gate System: A Broken Windows Perspective
Authors: Song Yu , Liu Jia , Liu Qian
Published: August 2021

The automatic flap barrier gate system (AFBGS) plays a critical role in building security, but it is more vulnerable to natural hazards than common exits (including power failure, due to earthquakes, and delayed evacuation, due to safety certification, etc.). This article considers a dynamic decision-making process of evacuees during post-earthquake evacuation near an AFBGS. An interesting metaphor, broken windows (BW), is utilized to interpret people’s actual behavior during evacuation. A multi-stage decision-making mechanism of evacuees is developed to characterize the instantaneous transition among three defined stages: Habitual, mild, and radical states. Then, we build a modified three-layer social force model to reproduce the interaction between evacuees based on an actual post-earthquake evacuation. The simulations reveal that BW provides a contextualized understanding of emergency evacuation with a similar effect to the traditional metaphor. An earlier appearance of a mild rule breaker leads to a higher crowd evacuation efficiency. If evacuees maintain the state of broken windows behavior (BWB), the crowd evacuation efficiency can be improved significantly. Contrary to the criminological interpretation, the overall effect of mild BWB is positive, but the radical BWB is encouraged under the command of guiders.
Board Games in Improving Pupils’ Speaking Skills: A Systematic Review
Authors: Wong Catherine Hui Tiing , Yunus Melor Md
Published: August 2021

English is a fundamental language to learn as it is used worldwide. The teaching and learning of English has been emphasized in Malaysia as English plays a major role in global communication. However, speaking performance was recorded as poor and weak among pupils in ESL classrooms. Previous researchers explored a myriad of communicative language activities to improve speaking skill. Board games are employed as one of the most useful tools to improve speaking skills among pupils. This systematic review was conducted to examine pupils’ perceptions on the use of board games in learning and speaking, as well as the usefulness of board games in improving their speaking skill. A total of 35 articles, from the period 2017–2021, were accessed through three databases: Google Scholar, ERIC and SAGE Journals. The review was conducted systematically based on the guidelines from the PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses). Based on the articles gathered, the results showed that the qualitative research design was mostly used to collect pupils’ opinions on the use of board games, while quantitative research design was mostly used to evaluate the usefulness of board games in improving speaking. Overall, the systematic review revealed that board games had several positive impacts in pupils’ speaking performances such as improving speaking competency, enhancing motivation in speaking and increasing interpersonal interaction among pupils. It is suggested that future studies could focus on investigating teachers’ opinions on the use of board games in teaching and speaking.
A Circularity Indicator Tool for Measuring the Ecological Embeddedness of Manufacturing
Authors: Trollman Hana , Colwill James , Jagtap Sandeep
Published: August 2021

Circularity in manufacturing is critical to reducing raw material usage and waste. Ecological embeddedness examines circular relationships intended to benefit both economic actors and the natural environment. By understanding circular relationships in the value chain, manufacturers can formulate strategies that are eco-effective. This work develops and validates an original circularity tool to measure the ecological embeddedness of manufacturers using exploratory and confirmatory factor analysis. The tool is tested on process manufacturers selling products in the United Kingdom. The three main results are that the tool is useful and comprehensive (87% of users), enables simple comparisons with competitors, and identifies weaknesses in strategies related to the five dimensions connecting manufacturers, consumers, and the environment: understanding, realising, utilising, negotiating, and reclaiming. Manufacturers may use the tool to improve their ecological embeddedness, and sector-based circularity levels may be established for policy development. The novelty of the tool is in the use of ecological relationships to support achievement of a circular economy.