bluuebunny/crossref_metadata_2025 huggingface dataset:
Mapping of Genetic Locus for Leaf Trichome Formation in Chinese Cabbage Based on Bulked Segregant Analysis
Authors: Zhang Rujia , Ren Yiming , Wu Huiyuan , Yang Yu , Yuan Mengguo , Liang Haonan , Zhang Changwei
Published: April 2021

Chinese cabbage is a leafy vegetable, and its leaves are the main edible organs. The formation of trichomes on the leaves can significantly affect its taste, so studying this phenomenon is of great significance for improving the quality of Chinese cabbage. In this study, two varieties of Chinese cabbage, W30 with trichome leaves and 082 with glabrous leaves, were crossed to generate F1 and F1 plants, which were self-fertilized to develop segregating populations with trichome or glabrous morphotypes. The two bulks of the different segregating populations were used to conduct bulked segregant analysis (BSA). A total of 293.4 M clean reads were generated from the samples, and plants from the trichome leaves (AL) bulk and glabrous leaves (GL) bulk were identified. Between the two DNA pools generated from the trichome and glabrous plants, 55,048 SNPs and 272 indels were generated. In this study, three regions (on chromosomes 6, 10 and scaffold000100) were identified, and the annotation revealed three candidate genes that may participate in the formation of leaf trichomes. These findings suggest that the three genes—Bra025087 encoding a cyclin family protein, Bra035000 encoding an ATP-binding protein/kinase/protein kinase/protein serine/threonine kinase and Bra033370 encoding a WD-40 repeat family protein–influence the formation of trichomes by participating in trichome morphogenesis (GO: 0010090). These results demonstrate that BSA can be used to map genes associated with traits and provide new insights into the molecular mechanism of leafy trichome formation in Chinese cabbage.
Effect of Molar Mass on Critical Specific Work of Flow for Shear-Induced Crystal Nucleation in Poly (l-Lactic Acid)
Authors: Du Mengxue , Jariyavidyanont Katalee , Kühnert Ines , Boldt Regine , Androsch René
Published: April 2021

The concept of specific work of flow has been applied for the analysis of critical shearing conditions for the formation of crystal nuclei in poly (l-lactic acid) (PLLA). Systematic variation in both time and rate of shearing the melt in a parallel-plate rheometer revealed that these parameters are interconvertible regarding the shear-induced formation of crystal nuclei; that is, low shear rate can be compensated for by increasing the shear time and vice versa. This result supports the view that critical shearing conditions can be expressed by a single quantity, providing additional options for tailoring polymer processing routes when enhanced nuclei formation is desired/unwanted. Analysis of PLLA of different mass-average molar masses of 70, 90, 120, and 576 kDa confirmed improved shear-induced crystal nucleation for materials of higher molar mass, with critical specific works of flow, above which shear-induced nuclei formation occurs, of 550, 60, 25, and 5 kPa, respectively.
The Investigation of the Structure and Properties of Ozone-Sterilized Nonwoven Biopolymer Materials for Medical Applications
Authors: Tyubaeva Polina , Zykova Anna , Podmasteriev Vyacheslav , Olkhov Anatoly , Popov Anatoly , Iordanskii Alexey
Published: April 2021

Nowadays, the development and research of nonwoven medical fibrous materials based on biopolymers is an area of a great practical interest. One of the most promising methods for producing nonwoven materials with a highly developed surface is electrospinning (ES). In this article, the possibility of efficient sterilization of ultrathin fibers based on polyhydroxybutyrate (PHB) by ozone treatment was considered. The purpose of this work was to select the most optimal morphology of nonwoven materials for medical purposes and to establish the correlation between the supramolecular structure and the physical properties of fibrous materials while under the influence of an ozone sterilization process.
Cross-Linked Polymer Brushes Containing N-Halamine Groups for Antibacterial Surface Applications
Authors: Kinali-Demirci Selin
Published: April 2021

Microbial contamination is a significant issue in various areas, especially in the food industry. In this study, to overcome microbial contamination, cross-linked polymer brushes containing N-halamine were synthesized, characterized, and investigated for antibacterial properties. The cross-linked polymer brushes with different N-halamine ratios were synthesized by in-situ cross-linking methods with reversible addition−fragmentation chain transfer (RAFT) polymerization using a bifunctional cross-linker. The RAFT agent was immobilized on an amine-terminated silicon wafer surface and utilized in the surface-initiated RAFT polymerization of [N-(2-methyl-1-(4-methyl-2,5-dioxoimidazolidin-4-yl)propane-2-yl)acrylamide] (hydantoin acrylamide, HA), and N-(2-hydroxypropyl)methacrylamide) (HPMA) monomers. Measurement of film thickness, contact angle, and surface morphology of the resulting surfaces were used to confirm the structural characteristics of cross-linked polymer brushes. The chlorine content of the three different surfaces was determined to be approximately 8–31 × 1013 atoms/cm2. At the same time, it was also observed that the activation–deactivation efficiency decreased during the recharge–discharge cycles. However, it was determined that the prepared N-halamine-containing cross-linked polymer brushes inactivated approximately 96% of Escherichia coli and 91% of Staphylococcus aureus. In conclusion, in the framework of this study, high-performance brush gels were produced that can be used on antibacterial surfaces.
Rapid and Local Self-Healing Ability of Polyurethane Nanocomposites Using Photothermal Polydopamine-Coated Graphene Oxide Triggered by Near-Infrared Laser
Authors: Ha Yu-Mi , Kim Young Nam , Jung Yong Chae
Published: April 2021

In this study, we report the self-healing ability of polyurethane (PU) nanocomposites based on the photothermal effect of polydopamine-coated graphene oxide (PDA–rGO). Polydopamine (PDA) was coated on the graphene oxide (GO) surface, while simultaneously reducing GO by the oxidation of dopamine hydrochloride in an alkaline aqueous solution. The PDA–rGO was characterized by Fourier-transform infrared spectroscopy, X-ray diffraction, Raman spectroscopy, thermogravimetric analysis, and scanning electron microscopy–energy-dispersive X-ray analysis. PDA–rGO/PU nanocomposites with nanofiller contents of 0.1, 0.5 and 1 wt% were prepared by ex situ mixing method. The photothermal effect of the PDA–rGO in the PU matrix was investigated at 0.1 W/cm2 using an 808 nm near-infrared (NIR) laser. The photothermal properties of the PDA–rGO/PU nanocomposites were superior to those of the GO/PU nanocomposites, owing to an increase in the local surface plasmon resonance effect by coating with PDA. Subsequently, the self-healing efficiency was confirmed by recovering the tensile stress of the damaged nanocomposites using the thermal energy generated by the NIR laser.
Modelling Methods and Validation Techniques for CFD Simulations of PEM Fuel Cells
Authors: d’Adamo Alessandro , Haslinger Maximilian , Corda Giuseppe , Höflinger Johannes , Fontanesi Stefano , Lauer Thomas
Published: April 2021

The large-scale adoption of fuel cells system for sustainable power generation will require the combined use of both multidimensional models and of dedicated testing techniques, in order to evolve the current technology beyond its present status. This requires an unprecedented understanding of concurrent and interacting fluid dynamics, material and electrochemical processes. In this review article, Polymer Electrolyte Membrane Fuel Cells (PEMFC) are analysed. In the first part, the most common approaches for multi-phase/multi-physics modelling are presented in their governing equations, inherent limitations and accurate materials characterisation for diffusion layers, membrane and catalyst layers. This provides a thorough overview of key aspects to be included in multidimensional CFD models. In the second part, advanced diagnostic techniques are surveyed, indicating testing practices to accurately characterise the cell operation. These can be used to validate models, complementing the conventional observation of the current–voltage curve with key operating parameters, thus defining a joint modelling/testing environment. The two sections complement each other in portraying a unified framework of interrelated physical/chemical processes, laying the foundation of a robust and complete understanding of PEMFC. This is needed to advance the current technology and to consciously use the ever-growing availability of computational resources in the next future.
A Systematic Literature Review of Populism, Religion and Emotions
Authors: Yilmaz Ihsan , Morieson Nicholas
Published: April 2021

This paper examines the existing literature on the relationship between religion and populism, and is intended as a starting point for further examination of the relationships between populism, religion, and emotions. This paper systematically reviews the various aspects of the populist phenomenon. After a discussion on different definitions of populism, this paper looks at how the literature discusses the causes of populism, mainly socio-economic factors and emotive factors. Then it discusses how religion and populism interact and can be divided in two broad categories of religious populism and identitarian populism. While, on the surface, the two share similarities, this paper reviews populist manifestations across the world to draw the distinct features between the two forms. Lastly, while pointing out the salient features of religious populism and identitarian populism, this study points out gaps in the research on the relationship between religious populism and other phenomena such as transnational populism, the psychology of populism, the role of emotions in creating support for populism, and populism in Western and non-Western contexts for future areas of research in the field.
A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks
Authors: Rew Jehyeok , Cho Yongjang , Hwang Eenjun
Published: April 2021

Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have been proposed to construct effective species distribution models, but with limited success due to spatial biases in presences and imbalanced presence-absences. We propose a novel species distribution model to address these problems based on bootstrap aggregating (bagging) ensembles of deep neural networks (DNNs). We first generate bootstraps considering presence-absence data on spatial balance to alleviate the bias problem. Then we construct DNNs using environmental data from presence and absence locations, and finally combine these into an ensemble model using three voting methods to improve prediction accuracy. Extensive experiments verified the proposed model’s effectiveness for species in South Korea using crowdsourced observations that have spatial biases. The proposed model achieved more accurate and robust prediction results than the current best practice models.
Local Development and Gentrification Resulting from the Rehabilitation of Singular Buildings: Analysis of Neural Networks
Authors: Uribe-Toril Juan , Galindo Alejandro , Torres José , De Pablo Jaime , Ruiz-Real José
Published: April 2021

The recovery of a built heritage and specifically of singular buildings is a key aspect of local development. The aim of this study was to understand the influence of these regenerations on their environment by transforming adjacent businesses and initiating parallel processes of gentrification and local development. The renewed attraction of these new businesses to the area can result in increased employment and production. The methodology used was based on self-organizing maps of neural networks with matrix architecture and competitive learning. Through the analysis of neural networks, we were able to identify common relationships and behaviors in commercial properties which are adjacent to singular buildings and that share common patterns and characteristics or attributes. The singular buildings analyzed are located along the Spanish Mediterranean coast in the cities of Almería, Barcelona, and Valencia. The results obtained were based on the following hypotheses: occupancy model and the classification based on total occupancy, total variation in occupancy, and the most common types of usage of a given ground floor commercial property. Among the conclusions, we highlight the existence of commercial premises that display anti-cyclical economic behavior and the presence of commercial premises considered to be “unfortunate” or with low potential.
Using Multi-Angular Hyperspectral Data to Estimate the Vertical Distribution of Leaf Chlorophyll Content in Wheat
Authors: Wu Bin , Huang Wenjiang , Ye Huichun , Luo Peilei , Ren Yu , Kong Weiping
Published: April 2021

Heterogeneity exists in the vertical distribution of the biochemical components of crops. A leaf chlorophyll deficiency occurs in the bottom- and middle-layers of crops due to nitrogen stress and leaf senescence. Some studies used multi-angular remote sensing data for estimating the vertical distribution of the leaf chlorophyll content (LCC). However, these studies performed LCC inversion of different vertical layers using a fixed view zenith angle (VZA), but rarely considered the contribution of the components of the non-target layers to the spectral response. The main goal of this work was to determine the LCC of different vertical layers of the canopy of winter wheat (Triticum aestivum L.), using multi-angular remote sensing and spectral vegetation indices. Different combinations of VZAs were used for obtaining the LCC of different layers. The results revealed that the responses of the transformed chlorophyll in reflectance absorption index (TCARI) and modified chlorophyll absorption in reflectance index (MCARI)/optimized soil-adjusted vegetation index (OSAVI) to the upper-layer LCC were strongest at VZA 10°. For the middle-layer LCC, the response was strongest at 30°, but the response was significantly lower than that of the upper-layer. For the bottom-layer LCC, the responses were weak due to the obscuring effect of the upper- and middle-layer; thus, the LCC inversion of the bottom-layer data was not optimal for a single VZA. The optimal VZA or VZA combinations for LCC estimation were VZA 10° for the upper-layer LCC (TCARI with coefficient of determination (R2) = 0.69, root mean square error (RMSE) = 4.80 ug/cm2, MCARI/OSAVI with R2 = 0.73, RMSE = 4.17 ug/cm2), VZA 10° and 30° for the middle-layer LCC (TCARI with R2 = 0.17, RMSE = 4.81 ug/cm2, MCARI/OSAVI with R2 = 0.17, RMSE = 4.76 ug/cm2), and VZA 10°, 30°, and 50° for the bottom-layer LCC (TCARI with R2 = 0.40, RMSE = 6.29 ug/cm2, MCARI/OSAVI with R2 = 0.40, RMSE = 6.36 ug/cm2). The proposed observation strategy provided a significantly higher estimation accuracy of the target layer LCC than the single VZA approach, and demonstrated the ability of canopy multi-angular spectral reflectance to accurately estimate the wheat canopy chlorophyll content vertical distribution.
A Hierarchical Clustering Method to Repair Gaps in Point Clouds of Powerline Corridor for Powerline Extraction
Authors: Fan Yongzhao , Zou Rong , Fan Xiaoyun , Dong Rendong , Xie Mengyou
Published: April 2021

Powerline detection is becoming a significant issue for powerline monitoring and maintenance, which further ensures transmission security. As an efficient method, laser scanning has attracted considerable attention in powerline detection for its high precision and robustness during the night period. However, due to occlusion and varying point density, gaps will appear in scans and greatly influence powerline detection by over–clustering, insufficient extraction, or misclassification in existing methods. Moreover, this situation will be worse in terrestrial laser scanning (TLS), because TLS suffers more from gaps due to its unique ground–based scanning mode compared to other laser scanning systems. Thereby, this paper explores a robust method to repair gaps for extracting powerlines from TLS data. Firstly, a hierarchical clustering method is used to extract the powerlines. During the clustering, gaps are repaired based on neighborhood relations of powerline candidates, and repaired gaps can create continuous neighborhood relations that ensure the execution of the clustering method in return. Test results show that the hierarchical clustering method is robust in powerline extraction with repaired gaps. Secondly, reconstruction is performed for further detection. Pylon–powerline connections are found by the slope change method, and powerlines with multi–span are successfully fitted using these connections. Experiment shows that it is feasible to find connections for multi–span reconstruction.
Mapping Alkaline Fens, Transition Mires and Quaking Bogs Using Airborne Hyperspectral and Laser Scanning Data
Authors: Szporak-Wasilewska Sylwia , Piórkowski Hubert , Ciężkowski Wojciech , Jarzombkowski Filip , Sławik Łukasz , Kopeć Dominik
Published: April 2021

The aim of this study is to evaluate the effectiveness of the identification of Natura 2000 wetland habitats (Alkaline fens—code 7230, and Transition mires and quaking bogs—code 7140) depending on various remotely sensed (RS) data acquired from an airborne platform. Both remote sensing data and botanical reference data were gathered for mentioned habitats in the Lower (LB) and Upper Biebrza (UB) River Valley and the Janowskie Forest (JF) in different seasonal stages. Several different classification scenarios were tested, and the ones that gave the best results for analyzed habitats were indicated in each campaign. In the final stage, a recommended term of data acquisition, as well as a list of remote sensing products, which allowed us to achieve the highest accuracy mapping for these two types of wetland habitats, were presented. Designed classification scenarios integrated different hyperspectral products such as Minimum Noise Fraction (MNF) bands, spectral indices and products derived from Airborne Laser Scanning (ALS) data representing topography (developed in SAGA), or statistical products (developed in OPALS—Orientation and Processing of Airborne Laser Scanning). The image classifications were performed using a Random Forest (RF) algorithm and a multi-classification approach. As part of the research, the correlation analysis of the developed remote sensing products was carried out, and the Recursive Feature Elimination with Cross-Validation (RFE-CV) analysis was performed to select the most important RS sub-products and thus increase the efficiency and accuracy of developing the final habitat distribution maps. The classification results showed that alkaline fens are better identified in summer (mean F1-SCORE equals 0.950 in the UB area, and 0.935 in the LB area), transition mires and quaking bogs that evolved on/or in the vicinity of alkaline fens in summer and autumn (mean F1-SCORE equals 0.931 in summer, and 0.923 in autumn in the UB area), and transition mires and quaking bogs that evolved on dystrophic lakes in spring and summer (mean F1-SCORE equals 0.953 in spring, and 0.948 in summer in the JF area). The study also points out that the classification accuracy of both wetland habitats is highly improved when combining selected hyperspectral products (MNF bands, spectral indices) with ALS topographical and statistical products. This article demonstrates that information provided by the synergetic use of data from different sensors can be used in mapping and monitoring both Natura 2000 wetland habitats for its future functional assessment and/or protection activities planning with high accuracy.
Application of Satellite Sentinel-2 Images to Study Alternate Sandbars Movement at Lower Vistula River (Poland)
Authors: Kryniecka Klaudia , Magnuszewski Artur
Published: April 2021

The lower Vistula River was regulated in the years 1856–1878, at a distance of 718–939 km. The regulation plan did not take into consideration the large transport of the bed load. The channel was shaped using simplified geometry—too wide for the low flow and overly straight for the stabilization of the sandbar movement. The hydraulic parameters of the lower Vistula River show high velocities of flow and high shear stress. The movement of the alternate sandbars can be traced on the optical satellite images of Sentinel-2. In this study, a method of sandbar detection through the remote sensing indices, Sentinel Water Mask (SWM) and Automated Water Extraction Index no shadow (AWEInsh), and the manual delineation with visual interpretation (MD) was used on satellite images of the lower Vistula River, recorded at the time of low flows (20 August 2015, 4 September 2016, 30 July 2017, 20 September 2018, and 29 August 2019). The comparison of 32 alternate sandbar areas obtained by SWM, AWEInsh, and MD manual delineation methods on the Sentinel-2 images, recorded on 20 August 2015, was performed by the statistical analysis of the interclass correlation coefficient (ICC). The distance of the shift in the analyzed time intervals between the image registration dates depends on the value of the mean discharge (MQ). The period from 30 July 2017 to 20 September 2018 was wet (MQ = 1140 m3 × s−1) and created conditions for the largest average distance of the alternate sandbar shift, from 509 to 548 m. The velocity of movement, calculated as an average shift for one day, was between 1.2 and 1.3 m × day−1. The smallest shift of alternate sandbars was characteristic of the low flow period from 20 August 2015 to 4 September 2016 (MQ = 306 m3 × s−1), from 279 to 310 m, with an average velocity from 0.7 to 0.8 m × day−1.
A Unified Framework for Anomaly Detection of Satellite Images Based on Well-Designed Features and an Artificial Neural Network
Authors: Wang Haibo , Yu Wenyong , You Jiangbin , Ma Ruolin , Wang Weilin , Li Bo
Published: April 2021

Nowadays, as the number of remote sensing satellites launched and applied in China has been mounting, relevant institutions’ workload of processing raw satellite images to be distributed to users is also growing. However, due to factors such as extreme atmospheric conditions, diversification of on-board device status, data loss during transmission and algorithm issues of ground systems, defect of image quality is inevitable, including abnormal color, color cast, data missing, obvious stitching line between Charge-Coupled Devices (CCDs), and inconstant radiation values between CCDs. Product application has also been impeded. This study presents a unified framework based on well-designed features an Artificial Neural Network (ANN) to automatically identify defective images. Samples were collected to form the dataset for training and validation, systematic experiments designed to verify the effectiveness of the features, and the optimal network architecture of ANN determined. Moreover, an effective method was proposed to explain the inference of ANN based on local gradient approximation. The recall of our final model reached 81.18% and F1 score 80.13%, verifying the effectiveness of our method.
Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning
Authors: Hu Xikun , Ban Yifang , Nascetti Andrea
Published: April 2021

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.
High-Order Wave-Damage Interaction Coefficients (WDIC) Extracted through Modal Decomposition
Authors: Mei Hanfei , Giurgiutiu Victor
Published: April 2021

This paper presents a new technique for the extraction of high-order wave-damage interaction coefficients (WDIC) through modal decomposition. The frequency and direction dependent complex-valued WDIC are used to model the scattering and mode conversion phenomena of guided wave interaction with damage. These coefficients are extracted from the harmonic analysis of local finite element model (FEM) mesh with non-reflective boundaries (NRB) and they are capable of describing the amplitude and phase of the scattered waves as a function of frequency and direction. To extract the WDIC of each wave mode, all the possible propagating wave modes are considered to be scattered simultaneously from the damage and propagate independently. Formulated in frequency domain, the proposed method is highly efficient, providing an overdetermined equation system for the calculation of mode participation factors, i.e., WDIC of each mode. Case studies in a 6-mm aluminum plate were carried out to validate the WDIC of: (1) a through-thickness hole and (2) a sub-surface crack. At higher frequency, scattered waves of high-order modes will appear and their WDIC can be successfully extracted through the modal decomposition.
Performance of Vehicular Visible Light Communications under the Effects of Atmospheric Turbulence with Aperture Averaging
Authors: Eso Elizabeth , Ghassemlooy Zabih , Zvanovec Stanislav , Sathian Juna , Abadi Mojtaba Mansour , Younus Othman Isam
Published: April 2021

In this paper, we investigate the performance of a vehicular visible light communications (VVLC) link with a non-collimated and incoherent light source (a light-emitting diode) as the transmitter (Tx), and two different optical receiver (Rx) types (a camera and photodiode (PD)) under atmospheric turbulence (AT) conditions with aperture averaging (AA). First, we present simulation results indicating performance improvements in the signal-to-noise ratio (SNR) under AT with AA with increasing size of the optical concentrator. Experimental investigations demonstrate the potency of AA in mitigating the induced signal fading due to the weak to moderate AT regimes in a VVLC system. The experimental results obtained with AA show that the link’s performance was stable in terms of the average SNR and the peak SNR for the PD and camera-based Rx links, respectively with <1 dB SNR penalty for both Rxs, as the strength of AT increases compared with the link with no AT.
Molecular Recognition: Perspective and a New Approach
Authors: Seitz W. Rudolf , Grenier Casey J. , Csoros John R. , Yang Rongfang , Ren Tianyu
Published: April 2021

This perspective presents an overview of approaches to the preparation of molecular recognition agents for chemical sensing. These approaches include chemical synthesis, using catalysts from biological systems, partitioning, aptamers, antibodies and molecularly imprinted polymers. The latter three approaches are general in that they can be applied with a large number of analytes, both proteins and smaller molecules like drugs and hormones. Aptamers and antibodies bind analytes rapidly while molecularly imprinted polymers bind much more slowly. Most molecularly imprinted polymers, formed by polymerizing in the presence of a template, contain a high level of covalent crosslinker that causes the polymer to form a separate phase. This results in a material that is rigid with low affinity for analyte and slow binding kinetics. Our approach to templating is to use predominantly or exclusively noncovalent crosslinks. This results in soluble templated polymers that bind analyte rapidly with high affinity. The biggest challenge of this approach is that the chains are tangled when the templated polymer is dissolved in water, blocking access to binding sites.
Deep Learning-Based Concrete Surface Damage Monitoring Method Using Structured Lights and Depth Camera
Authors: Bang Hyuntae , Min Jiyoung , Jeon Haemin
Published: April 2021

Due to the increase in aging structures and the decrease in construction workforce, there is an increasing interest in automating structural damage monitoring. Surface damage on concrete structures, such as cracks, delamination, and rebar exposure, is one of the important parameters that can be used to estimate the condition of the structure. In this paper, deep learning-based detection and quantification of structural damage using structured lights and a depth camera is proposed. The proposed monitoring system is composed of four lasers and a depth camera. The lasers are projected on the surface of the structures, and the camera captures images of the structures while measuring distance. By calculating an image homography, the captured images are calibrated when the structure and sensing system are not in parallel. The Faster RCNN (Region-based Convolutional Neural Network) with Inception Resnet v2 architecture is used to detect three types of surface damage: (i) cracks; (ii) delamination; and (iii) rebar exposure. The detected damage is quantified by calculating the positions of the projected laser beams with the measured distance. The experimental results show that structural damage was detected with an F1 score of 0.83 and a median value of the quantified relative error of less than 5%.
Study on Human Activity Recognition Using Semi-Supervised Active Transfer Learning
Authors: Oh Seungmin , Ashiquzzaman Akm , Lee Dongsu , Kim Yeonggwang , Kim Jinsul
Published: April 2021

In recent years, various studies have begun to use deep learning models to conduct research in the field of human activity recognition (HAR). However, there has been a severe lag in the absolute development of such models since training deep learning models require a lot of labeled data. In fields such as HAR, it is difficult to collect data and there are high costs and efforts involved in manual labeling. The existing methods rely heavily on manual data collection and proper labeling of the data, which is done by human administrators. This often results in the data gathering process often being slow and prone to human-biased labeling. To address these problems, we proposed a new solution for the existing data gathering methods by reducing the labeling tasks conducted on new data based by using the data learned through the semi-supervised active transfer learning method. This method achieved 95.9% performance while also reducing labeling compared to the random sampling or active transfer learning methods.
Cyber Physical Systems Dependability Using CPS-IOT Monitoring
Authors: Bagula Antoine , Ajayi Olasupo , Maluleke Hloniphani
Published: April 2021

Recently, vast investments have been made worldwide in developing Cyber-Physical Systems (CPS) as solutions to key socio-economic challenges. The Internet-of-Things (IoT) has also enjoyed widespread adoption, mostly for its ability to add “sensing” and “actuation” capabilities to existing CPS infrastructures. However, attention must be paid to the impact of IoT protocols on the dependability of CPS infrastructures. We address the issues of CPS dependability by using an epidemic model of the underlying dynamics within the CPS’ IoT subsystem (CPS-IoT) and an interference-aware routing reconfiguration. These help to efficiently monitor CPS infrastructure—avoiding routing oscillation, while improving its safety. The contributions of this paper are threefold. Firstly, a CPS orchestration model is proposed that relies upon: (i) Inbound surveillance and outbound actuation to improve dependability and (ii) a novel information diffusion model that uses epidemic states and diffusion sets to produce diffusion patterns across the CPS-IoT. Secondly, the proposed CPS orchestration model is numerically analysed to show its dependability for both sensitive and non-sensitive applications. Finally, a novel interference-aware clustering protocol called “INMP”, which enables network reconfiguration through migration of nodes across clusters, is proposed. It is then bench-marked against prominent IoT protocols to assess its impact on the dependability of the CPS.
Radiation Pattern Prediction for Metasurfaces: A Neural Network-Based Approach
Authors: Taghvaee Hamidreza , Jain Akshay , Timoneda Xavier , Liaskos Christos , Abadal Sergi , Alarcón Eduard , Cabellos-Aparicio Albert
Published: April 2021

As the current standardization for the 5G networks nears completion, work towards understanding the potential technologies for the 6G wireless networks is already underway. One of these potential technologies for the 6G networks is reconfigurable intelligent surfaces. They offer unprecedented degrees of freedom towards engineering the wireless channel, i.e., the ability to modify the characteristics of the channel whenever and however required. Nevertheless, such properties demand that the response of the associated metasurface is well understood under all possible operational conditions. While an understanding of the radiation pattern characteristics can be obtained through either analytical models or full-wave simulations, they suffer from inaccuracy and extremely high computational complexity, respectively. Hence, in this paper, we propose a neural network-based approach that enables a fast and accurate characterization of the metasurface response. We analyze multiple scenarios and demonstrate the capabilities and utility of the proposed methodology. Concretely, we show that this method can learn and predict the parameters governing the reflected wave radiation pattern with an accuracy of a full-wave simulation (98.8–99.8%) and the time and computational complexity of an analytical model. The aforementioned result and methodology will be of specific importance for the design, fault tolerance, and maintenance of the thousands of reconfigurable intelligent surfaces that will be deployed in the 6G network environment.
An Enhanced Indoor Positioning Algorithm Based on Fingerprint Using Fine-Grained CSI and RSSI Measurements of IEEE 802.11n WLAN
Authors: Wang Jingjing , Park Joongoo
Published: April 2021

Received signal strength indication (RSSI) obtained by Medium Access Control (MAC) layer is widely used in range-based and fingerprint location systems due to its low cost and low complexity. However, RSS is affected by noise signals and multi-path, and its positioning performance is not stable. In recent years, many commercial WiFi devices support the acquisition of physical layer channel state information (CSI). CSI is an index that can characterize the signal characteristics with more fine granularity than RSS. Compared with RSS, CSI can avoid the effects of multi-path and noise by analyzing the characteristics of multi-channel sub-carriers. To improve the indoor location accuracy and algorithm efficiency, this paper proposes a hybrid fingerprint location technology based on RSS and CSI. In the off-line phase, to overcome the problems of low positioning accuracy and fingerprint drift caused by signal instability, a methodology based on the Kalman filter and a Gaussian function is proposed to preprocess the RSSI value and CSI amplitude value, and the improved CSI phase is incorporated after the linear transformation. The mutation and noisy data are then effectively eliminated, and the accurate and smoother outputs of the RSSI and CSI values can be achieved. Then, the accurate hybrid fingerprint database is established after dimensionality reduction of the obtained high-dimensional data values. The weighted k-nearest neighbor (WKNN) algorithm is applied to reduce the complexity of the algorithm during the online positioning stage, and the accurate indoor positioning algorithm is accomplished. Experimental results show that the proposed algorithm exhibits good performance on anti-noise ability, fusion positioning accuracy, and real-time filtering. Compared with CSI-MIMO, FIFS, and RSSI-based methods, the proposed fusion correction method has higher positioning accuracy and smaller positioning error.
Rugged and Compact Three-Axis Force/Torque Sensor for Wearable Robots
Authors: Jeong Heeyeon , Choi Kyungjun , Park Seong Jun , Park Cheol Hoon , Choi Hyouk Ryeol , Kim Uikyum
Published: April 2021

In the field of robotics, sensors are crucial in enabling the interaction between robots and their users. To ensure this interaction, sensors mainly measure the user’s strength, and based on this, wearable robots are controlled. In this paper, we propose a novel three-axis force/torque sensor for wearable robots that is compact and has a high load capacity. The bolt and nut combination of the proposed sensor is designed to measure high-load weights, and the simple structure of this combination allows the sensor to be compact and light. Additionally, to measure the three-axis force/torque, we design three capacitance-sensing cells. These cells are arranged in parallel to measure the difference in capacitance between the positive and negative electrodes. From the capacitance change measured by these sensing cells, force/torque information is converted through deep neural network calibration. The sensing point can also be confirmed using the geometric and kinematic relation of the sensor. The proposed sensor is manufactured through a simple and inexpensive process using cheap and simply structured components. The performance of the sensor, such as its repeatability and capacity, is evaluated using several experimental setups. In addition, the sensor is applied to a wearable robot to measure the force of an artificial muscle.
Determination of the Dissociation Constants of 16 Active Ingredients in Medicinal Herbs Using a Liquid–Liquid Equilibrium Method
Authors: Wang Wanying , Zheng Baixiu , Wu Jiahao , Lv Weisong , Lin Peiying , Gong Xingchu
Published: April 2021

The dissociation constant is an important physicochemical property of drug molecules that affects the pharmacokinetic and pharmacodynamic properties of drugs. In this study, the distribution coefficients of 16 active ingredients extracted from herbal materials were determined at different pH values in liquid–liquid equilibrium systems; the active ingredients were sinomenine, aescin A, aescin B, aescin C, aescin D, chlorogenic acid, neochlorogenic acid, cryptochlorogenic acid, isochlorogenic acid A, isochlorogenic acid B, isochlorogenic acid C, baicalin, wogonoside, calycosin-7-glucoside, astraisoflavan-7-O-β-D-glucoside, and isomucronulatol 7-O-glucoside. The dissociation constants of these active ingredients were calibrated and compared with reported values. The dissociation constants obtained were close to those reported in other studies, which means that the results of this work are reliable.
Geography Education for Promoting Sustainability in Indonesia
Authors: Hawa Nabila Nurul , Zakaria Sharifah Zarina Syed , Razman Muhammad Rizal , Majid Nuriah Abd
Published: April 2021

Education for the environment and sustainable development is the one important thing for being studied. At formal school in Indonesia, it was integrated into the subject matter like social science, natural science, geography, or biology. The study about geography education for promoting sustainability had not been thoroughly done in Indonesia. Therefore, this study examined and investigated articles about geography learning and sustainability in Indonesia from 2010 to 2020. The materials were searched through the Garuda and ERIC application, by using keywords related to geography education. It was selected to published articles in journals indexed by Scopus and Sinta. The data were analyzed by a qualitative method with a content analysis approach. The foci of analysis are topics of sustainability elements, learning methods and models, learning media and resources, and students’ assessment in geography learning. All of these were investigated in concern to their relationships and achievements to sustainable development goals (SDGs). As a result, the topics of geography education in Indonesia have contained three main elements of sustainability, namely environment which the most stated, then social, and economic. The learning model and method that was mostly used was outdoor learning with contextual method. The learning media that mostly mentioned were maps, while the learning resources were local wisdom. Aspects of students’ assessment that mostly studied in geography education for promoting sustainability were students’ knowledge, attitude, and skills. Overall, this study has shown that the topics related to sustainability elements contained in the articles on geography education in Indonesia includes their learning components.
Influence of Anthropogenic Loads on Surface Water Status: A Case Study in Lithuania
Authors: Česonienė Laima , Šileikienė Daiva , Marozas Vitas , Čiteikė Laura
Published: April 2021

Twenty-six water bodies and 10 ponds were selected for this research. Anthropogenic loads were assessed according to pollution sources in individual water catchment basins. It was determined that 50% of the tested water bodies had Ntotal values that did not correspond to the good and very good ecological status classes, and 20% of the tested water bodies had Ptotal values that did not correspond to the good and very good ecological status classes. The lake basins and ponds received the largest amounts of pollution from agricultural sources with total nitrogen at 1554.13 t/year and phosphorus at 1.94 t/year, and from meadows and pastures with total nitrogen at 9.50 t/year and phosphorus at 0.20 t/year. The highest annual load of total nitrogen for lake basins on average per year was from agricultural pollution from arable land (98.85%), and the highest total phosphorus load was also from agricultural pollution from arable land (60%).
The Corporate Responsibility Paradox: A Multi-National Investigation of Business Traveller Attitudes and Their Sustainable Travel Behaviour
Authors: Walsh Philip R. , Dodds Rachel , Priskin Julianna , Day Jonathon , Belozerova Oxana
Published: April 2021

The implementation of sustainability practices in the tourism system requires the participation of a variety of actors. While much research has focused on supply-side issues associated with sustainable tourism, there has been less focus on supply-side issues associated with consumer behaviour and business-related travel. This paper addresses the behaviours of this significant market segment. As behavioural change is seen as a key mechanism for achieving emission reduction, this paper focuses on behaviours of business travels from four countries: Canada, Switzerland, Russia and the U.S., using values-attitudes-behaviour (VAB) theory. We employ Principal Components Analysis to reduce the variables down to four factors and related factor scores. Stepwise multiple linear regression was then used to measure causal associations. The findings show how national cultures, demographics and values influence (although at different levels) the sustainable attitudes and behaviour of business travellers. These results have implications for future corporate travel policy. The recent impact of the COVID-19 global pandemic is also addressed.
Exploring Green Marketing Orientations toward Sustainability the Hospitality Industry in the COVID-19 Pandemic
Authors: Ho Chun-Yi , Tsai Bi-Huei , Chen Chiao-Shan , Lu Ming-Tsang
Published: April 2021

The effects of green marketing orientations for increasing the competitive advantage and improving the sustainability of the hospitality industry during the COVID-19 pandemic are receiving more attention. As the hospitality industry attempts to assimilate green marketing and move in the path of sustainable development, administrators need to expand their efforts for improving natural environmental orientation (NEO), market orientation, resource orientation, and brand orientation by applying their green marketing orientations to hospitality’s strategies during the COVID-19 pandemic. Only few studies have examined the improvement of green marketing orientations. The objective of the research is to address this issue, applying the methods of fuzzy mixture MCDM (multiple criteria decision-making), with fuzzy decision-making trials and an evaluation laboratory (DEMATEL), and fuzzy DEMATEL-based ANP (fuzzy DANP) to inspect the feedback and interdependent issues among numerous elements/dimensions of green marketing orientations. In an uncertain environment, an empirical case study of the hospitality industry is shown to demonstrate the recommended combined approaches and, finally, to state the best enhancement approaches for administrators. This result shows that the natural environmental orientation is the most important factor.
The Perceived Importance of Cybercrime Control among Police Officers: Implications for Combatting Industrial Espionage
Authors: Paek Seung-Yeop , Nalla Mahesh K. , Chun Yong-Tae , Lee Julak
Published: April 2021

The current research explored the predictors of how police officers perceived the importance of combatting cybercrime. This is an era in which industrial security is threatened by perpetrators who use advanced techniques to steal information online. Understanding how law enforcement officers view the control of cybercrimes, especially those that steal confidential business information, can inform industrial espionage prevention and help maintain a nation’s industrial competitiveness in the world market. We surveyed a convenience sample of South Korean police officers attending training at the Police Human Resources Development Institute (PHRDI) using a paper-and-pencil questionnaire. The results indicated that the officers’ perceptions of colleagues’ and organizational views on cybercrime control significantly impacted their attitudes. Additionally, officers’ perceptions of the seriousness of online theft (in this paper, we use the terms online theft and property cybercrime interchangeably) and their computer proficiency were also found to affect their views on the importance of combatting cybercrimes. We conclude by suggesting that the police take a proactive organizational approach to prevent and respond to online property crimes through education and public awareness programs, which could positively impact the prevention of industrial espionage.