bluuebunny/crossref_metadata_2025 huggingface dataset:
Description of an Arabica Coffee Ideotype for Agroforestry Cropping Systems: A Guideline for Breeding More Resilient New Varieties
Authors: Breitler Jean-Christophe , Etienne Hervé , Léran Sophie , Marie Lison , Bertrand Benoit
Published: August 2022

Climate change (CC) is already impacting Arabica coffee cultivation in the intertropical zone. To deal with this situation, it is no longer possible to manage this crop using industrial agriculture techniques, which has been the main strategy implemented since the Green Revolution. Developing a more sustainable agriculture system that respects people and the environment is essential to guarantee future generations’ access to natural resources. In the case of Arabica coffee, the solution has been found. Agroforestry is proposed as an ecosystem-based strategy to mitigate and adapt to CC. At least 60% of Arabica coffee is produced in agroforestry systems (AFSs), which are the most sustainable way to produce coffee. Nevertheless, AFS coffee cultivation is currently uncompetitive partly because all modern varieties, selected for full-sun intensive cropping systems, have low yields in shaded environments. Here we review the reasons why agroforestry is part of the solution to CC, and why no breeding work has been undertaken for this cropping system. Based on the literature data, for breeding purposes we also define for the first time one possible coffee ideotype required for AFS coffee cultivation. The four main traits are: (1) productivity based on F1 hybrid vigor, tree volume and flowering intensity under shade; (2) beverage quality by using wild Ethiopian accessions as female progenitors and selecting for this criterion using specific biochemical and molecular predictors; (3) plant health to ensure good tolerance to stress, especially biotic; and (4) low fertilization to promote sustainable production. For each of these traits, numerous criteria with threshold values to be achieved per trait were identified. Through this research, an ecosystem-based breeding strategy was defined to help create new F1 hybrid varieties within the next 10 years.
Grapevine Badnavirus 1: Detection, Genetic Diversity, and Distribution in Croatia
Authors: Jagunić Martin , Diaz-Lara Alfredo , Rwahnih Maher Al , Preiner Darko , Stevens Kristian , Zdunić Goran , Hwang Minsook , Vončina Darko
Published: August 2022

Grapevine badnavirus 1 (GBV-1) was recently discovered in grapevine using high throughput sequencing. In order to carry out large-scale testing that will allow for better insights into virus distribution, conventional and real-time PCR assays were developed using sequences both from previously known, and four newly characterized isolates. Throughout the growing season and dormancy, GBV-1 can be detected by real-time PCR using available tissue, with the possibility of false-negative results early in vegetation growth. GBV-1 real-time PCR analysis of 4302 grapevine samples from the Croatian continental and coastal wine-growing regions revealed 576 (~13.4%) positive vines. In the continental wine-growing region, virus incidence was confirmed in only two collection plantations, whereas in the coastal region, infection was confirmed in 30 commercial vineyards and one collection plantation. Infection rates ranged from 1.9 to 96% at the different sites, with predominantly autochthonous grapevine cultivars infected. Conventional PCR products obtained from 50 newly discovered GBV-1 isolates, containing the 375 nucleotides long portion of the reverse transcriptase gene, showed nucleotide and amino acid identities ranging from 94.1 to 100% and from 92.8 to 100%, respectively. The reconstructed phylogenetic tree positioned the GBV-1 isolates taken from the same vineyard close to each other indicating a possible local infection event, although the tree nodes were generally not well supported.
Synthesis and Properties of Thermally Self-Healing PET Based Linear Polyurethane Containing Diels–Alder Bonds
Authors: Xu Minghui , Liu Ning , Mo Hongchang , Lu Xianming , Dou Jinkang , Tan Bojun
Published: August 2022

A Diels–Alder (DA) bond containing poly(tetrahydrofuran)-co-(ethyleneoxide) (PET) based linear polyurethane (PET-DA-PU) was synthesized via a prepolymer process using PET as raw material, DA diol as chain extender agent, and toluene-2,4-diisocyanate (TDI) as coupling agent. The structure of PET-DA-PU was characterized by attenuated total reflectance-Fourier transform-infrared spectroscopy (ATR-FTIR), proton nuclear magnetic resonance spectrometry (1H NMR) and carbon nuclear magnetic resonance spectrometry (13C NMR). The thermal performance and self-healing behavior of PET-DA-PU were investigated by differential scanning calorimetry (DSC), polarized optical microscope, universal testing machine, scanning electron microscopy (SEM) and NMR, respectively. The glass transition temperature of PET-DA-PU was found to be −59 °C. Under the heat treatment at 100 °C, the crack on PET-DA-PU film completely disappeared in 9 min, and the self-healing efficiency that was determined by the recovery of the largest tensile strength after being damaged and healed at 100 °C for 20 min can reach 89.1%. SEM images revealed the micro-cracks along with the blocky aggregated hard segments which were the important reasons for fracture. NMR spectroscopy indicated that the efficiency of retro DA reaction of PET-DA-PU was 70% after 20 min heating treatment at 100 °C. Moreover, the PET-DA-PU/Al/Na2SO4 composite was also prepared to simulate propellant formulation and investigated by universal testing machine and SEM; its healing efficiency was up to 87.8% under the same heat treatment process and exhibits good self-healing ability. Therefore, PET-DA-PU may serve as a promising thermally self-healing polymeric binder for future propellant formulations.
Recent Advances in Cellulose-Based Hydrogels for Tissue Engineering Applications
Authors: Chen Chao , Xi Yuewei , Weng Yunxuan
Published: August 2022

In recent years, cellulose has attracted much attention because of its excellent properties, such as its hydrophilicity, mechanical properties, biodegradability, biocompatibility, low cost and low toxicity. In addition, cellulose and its derivatives contain abundant hydrophilic functional groups (such as hydroxyl, carboxyl and aldehyde groups), which are good raw materials for synthesizing biocompatible hydrogels. In this paper, the application prospects of cellulose and its derivatives-based hydrogels in biomedical tissue engineering are summarized and discussed through the analysis of recent research. Firstly, we discuss the structure and properties of cellulose, nano celluloses (NC) from different sources (including cellulose nanocrystals (CNC), cellulose nanofibrils (CNF) and bacterial nano celluloses (BNC)) and cellulose derivatives (including cellulose ethers and cellulose esters) obtained by different modification methods. Then, the properties and preparation methods of physical and chemical cellulose hydrogels are described, respectively. The application of cellulose-based hydrogels as a tissue engineering scaffold (skin, bone and cartilage) in the biomedical field is introduced. Finally, the challenges and prospects of cellulose-based hydrogels in tissue engineering are summarized.
Study on the Disinfection Efficiency of the Combined Process of Ultraviolet and Sodium Hypochlorite on the Secondary Effluent of the Sewage Treatment Plant
Authors: Lu Hai , Wang Xiaoyan , Li Xueqi , Zhang Xiaoyu
Published: August 2022

The combined disinfection process of ultraviolet and sodium hypochlorite has more advantages than the single disinfection method in reducing the disinfectant dosage, shortening the reaction time, and resisting the impact of water quality changes and inhibiting the light reactivation of microorganisms. Given this, using the secondary effluent of a sewage plant as the research object, the disinfection efficiency of the combined process of ultraviolet and sodium hypochlorite was investigated. The experimental results showed that the inactivation effect of UV followed by sodium hypochlorite on fecal coliform and the inhibition of microbial photoreactivation was more significant than that of simultaneous disinfection of UV and sodium hypochlorite disinfection. When the UV dose was 24 mJ/cm2, after disinfection with UV followed by sodium hypochlorite, only 1 mg/L of sodium hypochlorite was required to be added, and a contact reaction time of 1 min for the fecal coliform index to meet the first-Class A emission standard. After disinfection, the effluent’s maximum reactivation rate of fecal coliform was 26.96%. However, the simultaneous disinfection of ultraviolet and sodium hypochlorite required the addition of 3 mg/L of sodium hypochlorite. After disinfection, the maximum reactivation rate of the fecal coliform group reached 30.81%.
Sustained Rise in Retractions in the Life Sciences Literature during the Pandemic Years 2020 and 2021
Authors: Yeo-Teh Nicole Shu Ling , Tang Bor Luen
Published: August 2022

The COVID-19 pandemic has been devastating to all human endeavors, and scientific research has not been spared. We queried how the retraction of publications might have been affected during the pandemic years 2020–2021. Searches performed with Retraction Watch Database (RWD) revealed that the total number of retractions (as proxied by retraction-related notices) rose steadily from 2013 into the pandemic years 2020–2021. Interestingly, while retractions in the physical and social sciences tapered during 2020–2021, those of the basic life sciences and health sciences showed robust increases in 2020, with the former maintaining a steep rise in 2021. This rise in retractions belied a tapering of total relevant publications in the same year and is confirmed with a complementary search strategy in Scopus. The retraction rate in the medical sciences, particularly those relating to infectious disease, is clearly affected by the anomalous high retraction rate of COVID-19-related papers. However, the sustained increase in the retraction rate of the basic life sciences papers, could be due, at least partly, to retraction spikes in several journals. The rise in retractions in the life and medical sciences could be attributed to heightened post-publication peer review of papers in online platforms such as PubPeer, where numerous problematic papers have been revealed.
Resourcing Future Generations Requires a New Approach to Material Stewardship
Authors: Nickless Edmund , Yakovleva Natalia
Published: August 2022

The paper aims to examine sector policies for securing mineral resources for future generations in ways that are economically, environmentally and socially responsible, guided by the Sustainable Development Goals by 2030 and carbon neutrality by 2050. This study proposes a roadmap for responsible mineral production, looking at drivers and goals, trends in resource demand and supply, technological and responsibility issues as well as current solutions. We conclude that while adopting the principles of a circular economy by minimizing waste, improving design for recovery, recycling alone will not be sufficient to meet the demand for an increasingly complex range of metals, and consequently primary mining will be needed for the foreseeable future. Various authors have proposed top-down approaches for sustainable mineral sourcing and co-ordination of global supply, but there is no evidence of these being taken up. Instead, to accelerate the transition towards sustainable, responsible and low-carbon mineral production, we suggest there is a role for ‘material stewardship’ defined and implemented through the actions of various players in the life cycle or value chain of specific minerals and metals. That bottom-up process has begun, though there is as yet no common definition of the term.
Reshaping Hyperspectral Data into a Two-Dimensional Image for a CNN Model to Classify Plant Species from Reflectance
Authors: Yuan Shaoxiong , Song Guangman , Huang Guangqing , Wang Quan
Published: August 2022

Leaf-level hyperspectral-based species identification has a long research history. However, unlike hyperspectral image-based species classification models, convolutional neural network (CNN) models are rarely used for the one-dimensional (1D) structured leaf-level spectrum. Our research focuses on hyperspectral data from five laboratories worldwide to test the general use of effective identification of the CNN model by reshaping 1D structure hyperspectral data into two-dimensional greyscale images without principal component analysis (PCA) or downscaling. We compared the performance of two-dimensional CNNs with the deep cross neural network (DCN), support vector machine, random forest, gradient boosting machine, and decision tree in individual tree species classification from leaf-level hyperspectral data. We tested the general performance of the models by simulating an application phase using data from different labs or years as the unseen data for prediction. The best-performing CNN model had validation accuracy of 98.6%, prediction accuracy of 91.6%, and precision of 74.9%, compared to the support vector machine, with 98.6%, 88.8%, and 66.4%, respectively, and DCN, with 94.0%, 85.7%, and 57.1%, respectively. Compared with the reference models, CNNs more efficiently recognized Fagus crenata, and had high accuracy in Quercus rubra identification. Our results provide a template for a species classification method based on hyperspectral data and point to a new way of reshaping 1D data into a two-dimensional image, as the key to better species prediction. This method may also be helpful for foliar trait estimation.
Spatial-Statistical Analysis of Landscape-Level Wildfire Rate of Spread
Authors: Schag Gavin M. , Stow Douglas A. , Riggan Philip J. , Nara Atsushi
Published: August 2022

The objectives of this study were to evaluate spatial sampling and statistical aspects of landscape-level wildfire rate of spread (ROS) estimates derived from airborne thermal infrared imagery (ATIR). Wildfire progression maps and ROS estimates were derived from repetitive ATIR image sequences collected during the 2017 Thomas and Detwiler wildfire events in California. Three separate landscape sampling unit (LSU) sizes were used to extract remotely sensed environmental covariates known to influence fire behavior. Statistical relationships between fire spread rates and landscape covariates were analyzed using (1) bivariate regression, (2) multiple stepwise regression, (3) geographically weighted regression (GWR), (4) eigenvector spatial filtering (ESF) regression, (5) regression trees (RT), and (6) and random forest (RF) regression. GWR and ESF regressions reveal that relationships between covariates and ROS estimates are substantially non-stationary and suggest that the global association of fire spread controls are locally differentiated on landscape scales. Directional slope is by far the most strongly associated covariate of ROS for the imaging sequences analyzed and the size of LSUs has little influence on any of the covariate relationships.
3D Convolutional Neural Network for Low-Light Image Sequence Enhancement in SLAM
Authors: Quan Yizhuo , Fu Dong , Chang Yuanfei , Wang Chengbo
Published: August 2022

Typical visual simultaneous localization and mapping (SLAM) systems rely on front-end odometry for feature extraction and matching to establish the relations between adjacent images. In a low-light environment, the image obtained by a camera is dim and shows scarce information, hindering the extraction of sufficient stable feature points, consequently undermining visual SLAM. Most existing methods focus on low-light enhancement of a single image, neglecting the strong temporal correlation across images in visual SLAM. We propose a method that leverages the temporal information of an input image sequence to enhance the low-light image and employed the enhanced result to improve the feature extraction and matching quality of visual SLAM. Our method trains a three-dimensional convolutional neural network to estimate pixelwise grayscale transformation curves to obtain a low-light enhancement image. Then, the grayscale transformation curves are iteratively applied to obtain the final enhanced result. The training process of the network does not require any paired reference images. We also introduced a spatial consistency loss for the enhanced image to retain the content and texture of the original image. We further integrated our method into VINS-Mono and compared with similar low-light image enhancement methods on the TUM-VI public dataset. The proposed method provides a lower positioning error. The positioning root-mean-squared error of our method is 19.83% lower than that of Zero-DCE++ in low-light environments. Moreover, the proposed network achieves real-time operation, being suitable for integration into a SLAM system.
Analysis of Spatial–Temporal Variations and Driving Factors of Typical Tail-Reach Wetlands in the Ili-Balkhash Basin, Central Asia
Authors: Cao Yijie , Ma Yonggang , Liu Tie , Li Junli , Zhong Ruisen , Wang Zheng , Zan Chanjuan
Published: August 2022

The Ili River Delta (IRD) is the largest delta in the arid zone of Central Asia. Since the 1970s, the entire delta system has undergone a series of changes due to climate change and the impoundment of the Kapchagay Reservoir upstream of the delta, triggering an ecological crisis. Wetlands play a crucial ecological role in biodiversity conservation. Most studies have mainly focused on the response of vegetation and soil microbial to ecological changes in the delta, ignoring the dynamic processes of wetlands changes. Hence, such changes in the IRD and the underlying mechanisms need to be investigated in depth. In this study, wetlands in the IRD from 1975 to 2020 were extracted based on Landsat images using the object-oriented method; changes in the wetland area, wetland landscape pattern, NDVI, and NPP were analyzed; and the contributions of natural and human factors to wetland evolution were quantified. The results indicated the following: (1) From 1975 to 2020, the wetland area of the IRD showed an increasing trend, and changes in the wetland area were mainly found in the middle part of the delta near the Saryesik Peninsula. (2) The wetland landscape pattern in the IRD changed markedly from 1975 to 2020. The dominant patches of the wetland in the middle of the delta continued to expand; the patch aggregation index (AI) increased, and the landscape fragmentation index (LFI) decreased. (3) From 2000 to 2020, the average annual normalized difference vegetation index (NDVI) and net primary productivity (NPP) in the IRD increased, which is consistent with the change in wetland expansion. (4) Inflow to the delta from the Ili River and the water level of Balkhash Lake are significantly correlated with the wetland area, which are the dominant factors driving wetland evolution; and water evaporation from the Kapchagay Reservoir and irrigation water diversion on the left bank of the reservoir obviously intensified the process of lake water level decline and wetland degradation during 1970 to 1985. These results can provide scientific background for making informed ecological protection decisions in the IRD under the impacts of climate change and human activities.
Direct Assimilation of Radar Reflectivity Data Using Ensemble Kalman Filter Based on a Two-Moment Microphysics Scheme for the Analysis and Forecast of Typhoon Lekima (2019)
Authors: Luo Jingyao , Li Hong , Xue Ming , Zhu Yijie
Published: August 2022

We investigate the impact of directly assimilating radar reflectivity data using an ensemble Kalman filter (EnKF) based on a double-moment (DM) microphysics parameterization (MP) scheme in GSI-EnKF data assimilation (DA) framework and WRF model for a landfall typhoon Lekima (2019). Observations from a single operational coastal Doppler are quality-controlled and assimilated. Compared with the baseline experiment initialized by GFS analysis, the reflectivity data assimilation experiment (Z-DA) resulted in an obvious improvement in both structural analysis and typhoon forecast skills in terms of intensity, precipitation, and track. Sensitivity experiments were conducted to evaluate the ability of EnKF to update certain state variables considering that the degree of freedom of analytical variables increased with a DM MP scheme. When either the total number concentration or other large-scale state variables that are not directly linked to reflectivity observations via the observation operator are not updated, the tendency of RMSIs and PS to be imbalanced is significantly increased during DA cycles compared to those of Z-DA with updating a full set of state variables, resulting in increased intensity and track forecast errors. These results indicate that the reliable ensemble covariance could handle the underconstraint issue associated with the DM scheme, and helps in obtaining more physically balanced analytical fields.
GPS, BDS-3, and Galileo Inter-Frequency Clock Bias Deviation Time-Varying Characteristics and Positioning Performance Analysis
Authors: Chen Yibiao , Mi Jinzhong , Gu Shouzhou , Li Bo , Li Hongchao , Yang Lijun , Pang Yuqi
Published: August 2022

Multi-frequency observations are now available from GNSSs, thereby bringing new opportunities for precise point positioning (PPP). However, they also introduce new challenges, such as inter-frequency clock bias (IFCB) between the new frequencies and the original dual-frequency observations due to triple-frequency observations, which severely impact the PPP. In this paper, we studied the estimation and correction methods of uncombined inter-frequency clock bias of GPS, BDS-3, and Galileo, analyzed the time-varying characteristics and short-term stability of IFCB, and analyzed the influence of IFCB on the positioning of the GPS, BDS-3, and Galileo, based on a triple-frequency un-differential non-combined PPP model. The obtained results show that the amplitude of Block IIF satellites of the GPS can reach up to 10–20 cm, and the IFCB in BDS-3, Galileo, and GPS Block III satellites can be neglected. After correction by IFCB, the 3D positioning accuracy of the GPS triple-frequency PPP was 1.73 cm and 4.75 cm in the static and kinematic modes, respectively, while the convergence time was 21.64 min and 39.61 min. Compared with the triple-frequency GPS PPP without any correction with IFCB, the static and kinematic 3D positioning accuracy in this work was improved by 27.39% and 17.34%, and the corresponding convergence time was improved by 10.55% and 15.22%, respectively. Furthermore, the delayed IFCB was also used for positioning processing, and it was found that a positioning performance comparable to that of the same day can be obtained. The standard deviation of IFCB for a single satellite was found to be no more than 1 cm, when the IFCB value of a neighboring day was subtracted from the IFCB value of same day, which proves the short-term stability of IFCB.
Impact of Training Set Configurations for Differentiating Plantation Forest Genera with Sentinel-2 Imagery and Machine Learning
Authors: Higgs Caley , van Niekerk Adriaan
Published: August 2022

Forest plantations in South Africa impose genus-specific demands on limited soil moisture. Hence, plantation composition and distribution mapping is critical for water conservation planning. Genus maps are used to quantify the impact of post-harvest genus-exchange activities in the forestry sector. Collecting genus data using in situ methods is costly and time-consuming, especially when performed at regional or national scales. Although remotely sensed data and machine learning show potential for mapping genera at regional scales, the efficacy of such methods is highly dependent on the size and quality of the training data used to build the models. However, it is not known what sampling scheme (e.g., sample size, proportion per genus, and spatial distribution) is most effective to map forest genera over large and complex areas. Using Sentinel-2 imagery as inputs, this study evaluated the effects of different sampling strategies (e.g., even, uneven, and area-proportionate) for training the random forests machine learning classifier to differentiate between Acacia, Eucalyptus, and Pinus trees in South Africa. Sample size (s) was related to the number of input features (n) to better understand the potential impact of sample sparseness. The results show that an even sample with maximum size (100%, s~91n) produced the highest overall accuracy (76.3%). Although larger training set sizes (s > n) resulted in higher OAs, a saturation point was reached at s~64n.
Vegetation Dynamics under Rapid Urbanization in the Guangdong–Hong Kong–Macao Greater Bay Area Urban Agglomeration during the Past Two Decades
Authors: Geng Shoubao , Zhang Huamin , Xie Fei , Li Lanhui , Yang Long
Published: August 2022

Detection of long-term vegetation dynamics is important for identifying vegetation improvement and degradation, especially for rapidly urbanizing regions with intensive land cover conversions. The Guangdong–Hong Kong–Macao Greater Bay Area (GBA) urban agglomeration has experienced rapid urbanization during the past decades with profound impacts on vegetation, so there is an urgent need to evaluate vegetation dynamics across land use/cover change (LUCC). Based on the normalized difference vegetation index (NDVI) during 2001–2020, we used coefficient of variation, Theil–Sen median trend analysis, and Hurst exponent to analyze the spatiotemporal change and future consistency of vegetation growth among the main LUCC in the GBA. Results demonstrated that low NDVI values with high fluctuations were mainly distributed in the central urban areas, whereas high NDVI values with low fluctuations were primarily located in the peripheral hilly mountains. The area-averaged NDVI showed an overall increasing trend at a rate of 0.0030 year−1, and areas with vegetation improvement (82.99%) were more than four times those with vegetation degradation (17.01%). The persistent forest and grassland and the regions converted from built-up to vegetation displayed the most obvious greening; NDVI in over 90% of these areas showed an increasing trend. In contrast, vegetation browning occurred in more than 60% of the regions converted from vegetation to built-up. Future vegetation change in most areas (91.37%) will continue the existing trends, and 80.06% of the GBA was predicted to develop in a benign direction, compared to 19.94% in a malignant direction. Our results contribute to in-depth understanding of vegetation dynamics during rapid urbanization in the GBA, which is crucial for vegetation conservation and land-use optimization.
Microwave Hyperthermia of Brain Tumors: A 2D Assessment Parametric Numerical Study
Authors: Redr Jan , Pokorny Tomas , Drizdal Tomas , Fiser Ondrej , Brunat Matous , Vrba Jan , Vrba David
Published: August 2022

Due to the clinically proven benefit of hyperthermia treatments if added to standard cancer therapies for various tumor sites and the recent development of non-invasive temperature measurements using magnetic resonance systems, the hyperthermia community is convinced that it is a time when even patients with brain tumors could benefit from regional microwave hyperthermia, even if they are the subject of a treatment to a vital organ. The purpose of this study was to numerically analyze the ability to achieve a therapeutically relevant constructive superposition of electromagnetic (EM) waves in the treatment of hyperthermia targets within the brain. We evaluated the effect of the target size and position, operating frequency, and the number of antenna elements forming the phased array applicator on the treatment quality. In total, 10 anatomically realistic 2D human head models were considered, in which 10 circular hyperthermia targets with diameters of 20, 25, and 30 mm were examined. Additionally, applicators with 8, 12, 16, and 24 antenna elements and operating frequencies of 434, 650, 915, and 1150 MHz, respectively, were analyzed. For all scenarios considered (4800 combinations), the EM field distributions of individual antenna elements were calculated and treatment planning was performed. Their quality was evaluated using parameters applied in clinical practice, i.e., target coverage (TC) and the target to hot-spot quotient (THQ). The 12-antenna phased array system operating at 434 MHz was the best candidate among all tested systems for HT treatments of glioblastoma tumors. The 12 antenna elements met all the requirements to cover the entire target area; an additional increase in the number of antenna elements did not have a significant effect on the treatment quality.
Robot-Based Calibration Procedure for Graphene Electronic Skin
Authors: Klimaszewski Jan , Wildner Krzysztof , Ostaszewska-Liżewska Anna , Władziński Michał , Możaryn Jakub
Published: August 2022

The paper describes the semi-automatised calibration procedure of an electronic skin comprising screen-printed graphene-based sensors intended to be used for robotic applications. The variability of sensitivity and load characteristics among sensors makes the practical use of the e-skin extremely difficult. As the number of active elements forming the e-skin increases, this problem becomes more significant. The article describes the calibration procedure of multiple e-skin array sensors whose parameters are not homogeneous. We describe how an industrial robot equipped with a reference force sensor can be used to automatise the e-skin calibration procedure. The proposed methodology facilitates, speeds up, and increases the repeatability of the e-skin calibration. Finally, for the chosen example of a nonhomogeneous sensor matrix, we provide details of the data preprocessing, the sensor modelling process, and a discussion of the obtained results.
Estimation of Soil Organic Carbon Using Vis-NIR Spectral Data and Spectral Feature Bands Selection in Southern Xinjiang, China
Authors: Bai Zijin , Xie Modong , Hu Bifeng , Luo Defang , Wan Chang , Peng Jie , Shi Zhou
Published: August 2022

Soil organic carbon (SOC) plays an important role in the global carbon cycle and soil fertility supply. Rapid and accurate estimation of SOC content could provide critical information for crop production, soil management and soil carbon pool regulation. Many researchers have confirmed the feasibility and great potential of visible and near-infrared (Vis-NIR) spectroscopy in evaluating SOC content rapidly and accurately. Here, to evaluate the feasibility of different spectral bands variable selection methods for SOC prediction, we collected a total of 330 surface soil samples from the cotton field in the Alar Reclamation area in the southern part of Xinjiang, which is located in the arid region of northwest China. Then, we estimated the SOC content using laboratory Vis-NIR spectral. The Particle Swarm optimization (PSO), Competitive adaptive reweighted sampling (CARS) and Ant colony optimization (ACO) were adopted to select SOC feature bands. The partial least squares regression (PLSR), random forest (RF) and convolutional neural network (CNN) inversion models were constructed by using full-bands (400–2400 nm) spectra (R) and feature bands, respectively. And we also analyzed the effects of spectral feature band selection methods and modeling methods on the prediction accuracy of SOC. The results indicated that: (1) There are significant differences in the feature bands selected using different methods. The feature bands selected methods substantially reduced the spectral variable dimensionality and model complexity. The models built by the feature bands selected by CARS, PSO and ACO methods showed the different potential of improvement in model accuracy compared with the full-band models. (2) The CNN model had the best performance for predicting SOC. The R2 of the optimal CNN model is 0.90 in the validation, which was improved by 0.05 and 0.04 in comparison with the PLSR and RF model, respectively. (3) The highest prediction accuracy was archived by the CNN model using the feature bands selected by CARS (validation set R2 = 0.90, RMSE = 0.97 g kg−1, RPD = 3.18, RPIQ = 3.11). This study indicated that using the CARS method to select spectral feature bands, combined with the CNN modeling method can well predict SOC content with higher accuracy.
Socially-Assistive Robots to Support Learning in Students on the Autism Spectrum: Investigating Educator Perspectives and a Pilot Trial of a Mobile Platform to Remove Barriers to Implementation
Authors: Silvera-Tawil David , Bruck Susan , Xiao Yi , Bradford DanaKai
Published: August 2022

Technology offers educators tools that can tailor learning to students’ learning styles and interests. Research into the use of socially-assistive robots as a learning support for children on the autism spectrum are showing promising results. However, to date, few schools have introduced these robots to support learning in students on the autism spectrum. This paper reports on a research project that investigated the barriers to implementing socially-assistive robot supported learning, and the expectations, perceived benefits and concerns of school teachers and therapists of students on the autism spectrum and adults on the autism spectrum. First, three focus groups were conducted with six adults on the autism spectrum, and 13 teachers and therapists of students from two autism-specific schools. During the focus groups, there was cautious optimism from participants about the value of socially-assistive robots for teaching support. While the data showed that participants were in favour of trialling socially-assistive robots in the classroom, they also raised several concerns and potential barriers to implementation, including the need for teacher training. In response to their concerns, the second part of the project focussed on developing a software platform and mobile application (app) to support the introduction of robots into autism-specific classrooms. The software platform and app were then trialled in two schools (n = 7 teachers and therapists). Results from focus groups indicated that participants believe socially-assistive robots could be useful for learning support, as the mobile app provides an easy to use tool to support preparing and conducting lessons that would motivate them to trial robots in the classroom.
Low-Light Image Enhancement Based on Constraint Low-Rank Approximation Retinex Model
Authors: Li Xuesong , Shang Jianrun , Song Wenhao , Chen Jinyong , Zhang Guisheng , Pan Jinfeng
Published: August 2022

Images captured in a low-light environment are strongly influenced by noise and low contrast, which is detrimental to tasks such as image recognition and object detection. Retinex-based approaches have been continuously explored for low-light enhancement. Nevertheless, Retinex decomposition is a highly ill-posed problem. The estimation of the decomposed components should be combined with proper constraints. Meanwhile, the noise mixed in the low-light image causes unpleasant visual effects. To address these problems, we propose a Constraint Low-Rank Approximation Retinex model (CLAR). In this model, two exponential relative total variation constraints were imposed to ensure that the illumination is piece-wise smooth and that the reflectance component is piece-wise continuous. In addition, the low-rank prior was introduced to suppress the noise in the reflectance component. With a tailored separated alternating direction method of multipliers (ADMM) algorithm, the illumination and reflectance components were updated accurately. Experimental results on several public datasets verify the effectiveness of the proposed model subjectively and objectively.
Biomechanical Analysis Suggests Myosuit Reduces Knee Extensor Demand during Level and Incline Gait
Authors: Kim Jaewook , Kim Yekwang , Kang Seonghyun , Kim Seung-Jong
Published: August 2022

An FDA-approved soft wearable robot, the Myosuit, which was designed to provide hip and knee extension torque has recently been commercialized. While studies have reported reductions in metabolic costs, increased gait speeds, and improvements in clinical test scores, a comprehensive analysis of electromyography (EMG) signals and joint kinematics is warranted because the recruitment of appropriate muscle groups during physiological movement patterns facilitates effective motor learning. Here, we compared the lower limb joint kinematics and EMG patterns while wearing the Myosuit with that of unassisted conditions when performing level overground and incline treadmill gait. The level overground gait sessions (seven healthy subjects) were performed at self-selected speeds and the incline treadmill gait sessions (four healthy subjects) were performed at 2, 3, 4, and 5 km/h. In order to evaluate how the user is assisted, we conducted a biomechanical analysis according to the three major gait tasks: weight acceptance (WA), single-limb support, and limb advancement. The results from the gait sessions suggest that Myosuit not only well preserves the users’ natural patterns, but more importantly reduce knee extensor demand during the WA phase for both level and incline gait.
RIS-Assisted Multi-Antenna AmBC Signal Detection Using Deep Reinforcement Learning
Authors: Jing Feng , Zhang Hailin , Gao Mei , Xue Bin , Cao Kunrui
Published: August 2022

Signal detection is one of the most critical and challenging issues in ambient backscatter communication (AmBC) systems. In this paper, a multi-antenna AmBC signal detection method is proposed based on reconfigurable intelligent surface (RIS) and deep reinforcement learning. Firstly, an efficient multi-antenna AmBC system is developed based on RIS, which can achieve information transmission and energy collection simultaneously. Secondly, a smart twin delayed deep deterministic (TD3) AmBC signal detection method is presented, based on deep reinforcement learning. Extensive quantitative and qualitative experiments are performed, which show that the proposed method is more compelling than the outstanding comparison methods.
A Survey of GIS and AR Integration: Applications
Authors: Safari Bazargani Jalal , Zafari Mostafa , Sadeghi-Niaraki Abolghasem , Choi Soo-Mi
Published: August 2022

Augmented Reality (AR) is a technology that enhances a person’s sensory perception by overlaying virtual objects in the user’s immediate surroundings. Furthermore, with the development of technologies, devices such as smartphones and head-mounted displays are being launched and are expanding the AR technology application sectors from research labs to a wide range of domains. On the other hand, Geospatial Information System (GIS) is capable of dealing with geospatial information, so it can be beneficial in most AR systems mainly because those systems are connected to location and information related to locations. The ultimate integrated solution could be beneficial for Sustainable Development Goals (SDGs). This paper investigates the combination of AR and GIS. Specifically, it studies the advantages of integration to address the challenges available in systems employing merely one of the technologies. The presented findings would assist researchers in future studies on utilizing GIS and AR simultaneously by giving an overview of the current applications and challenges.
Rethinking Sustainability Hotel Branding: The Pathways from Hotel Services to Brand Engagement
Authors: Tsou Hung-Tai , Hou Chieh-Chih , Chen Ja-Shen , Ngo Minh-Chau
Published: August 2022

Brand experience has received attention from scholars and researchers, especially in experiential marketing and management. Due to the high market competition and sustainability branding trends, hotel brands need to improve and enhance their hotel services to meet customers’ expectations and retain their engagement and loyalty to the brand. This study explores the relationships between hotel services (servicescape and employee service), brand experience (internal and behavioral responses), brand engagement, and brand loyalty. Web-based and paper-based surveys were used to collect data from the customers who have experienced hotel services in Vietnam. A total of 390 responses were used and analyzed by AMOS. The results show that there are positive relationships between hotel services, internal responses, and brand engagement. In addition, the moderating effects of brand loyalty on the relationship between brand experience and brand engagement were found. Although prior studies have been conducted in the servicescape and employee service fields, there remains less empirical research addressing the relationships between hotel services and brand experience. In addition, the examination of brand loyalty playing a moderator in the relationship between brand experience and brand engagement has not been adequately explored, so this study contributes to the brand loyalty literature.
Students’ Intention toward Self-Regulated Learning under Blended Learning Setting: PLS-SEM Approach
Authors: Jiang Yujun , Wang Ping , Li Qiang , Li Yingji
Published: August 2022

This research explores the influencing factors of students’ attitude toward self-regulated learning (SRL) within blended learning setting (BLS). The theoretical model is developed based on the Theory of Planned Behavior (TPB) and two dimensions are proposed: major satisfaction (MS) and perceived teacher support (PTS). It studies how these variables affect students’ attitude toward SRL within BLS, directly or indirectly. The proposed model is validated by employing a partial least squares structural equation modeling approach (PLS-SEM) based on the valid data collected from 604 students from three private universities in China. The results suggest that (a) the constructs, including the PTS and TPB (attitude, subjective norm and perceived behavior control), have a positive impact on student attitude toward SRL within BLS; (b) SRL attitude has a positive intermediary effect on the relationship between PTS and SRL intention, and between MS and SRL intention, respectively; (c) perceived behavior control (PBC) has a positive intermediary effect on the relationship between MS and SRL intention; (d) subjective norm (SN) has a positive intermediary effect on the relationship between PTS and SRL intention. The findings have useful implications for teachers, higher institution administrators, researchers, and higher education policy-makers in enhancing students’ learning within the blended learning context.
Bioinspired Pattern-Driven Single-Material 4D Printing for Self-Morphing Actuators
Authors: Alshebly Yousif Saad , Mustapha Khameel B. , Zolfagharian Ali , Bodaghi Mahdi , Mohamed Ali Mohamed Sultan , Almurib Haider Abbas , Nafea Marwan
Published: August 2022

Four-dimensional (4D) printing of shape memory polymers is a leading research field due to the possibilities allowed by using these materials. The strain difference in the structures that is caused by the different stiffness profiles can be used to influence the shape-memory effect in the actuators. In this study, the influence of patterns on the strain is tested in polylactic acid (PLA) actuators using patterns made of different shapes. Five bioinspired geometrical shapes, namely, circles, squares, hexagons, rhombuses, and triangles, are used in the three-dimensional (3D) printing of the actuators. The use of shapes of different sizes along with combinations of different patterns in the PLA actuators is carried out to develop 40 actuators with different designs. The effects of the patterns and their characteristics are analysed and compared. The self-bending angles of the actuators range from 6.19° to 30.86°, depending on the patterns and arrangement used. To demonstrate the feasibility of utilizing the proposed designs in practical applications, a hand-like shaped gripper is developed. The results show that the gripper can grip objects with uniform and non-uniform cross-sections. The developed gripper demonstrates that the proposed concept can be implemented in various applications, including self-morphing structures and soft robotics.
Spatial Pattern and Habitat Changes of Amphibian Species in the Priority Area for Biodiversity Conservation in the South Hengduan Mountains
Authors: Shi Nana , Guo Ningning , Wang Qi , Han Yu , Yan Haiming , Xiao Nengwen
Published: August 2022

Amphibian species, as typical objects to study the evolution of vertebrates from aquatic to terrestrial, have faced serious threats in recent decades. This study revealed changes in the spatial distribution pattern of amphibians and their habitats in the priority area for biodiversity conservation in the south of the Hengduan Mountains based on remote sensing and field survey data. Results showed that 71 species of amphibians in 27 genera of 10 families of 2 orders were recorded in the study area, among which 46 species were endemic to China. In particular, there were four national protected species and two newly found species. The amphibian species richness was overall higher in the northern part and lower in the southern part of the study area. There was an obvious variation in the species composition in five major geographic zones in the study area, and the species richness, number, and endemism were all the highest in areas between 2000 and 3000 m. There was a higher abundance of some endemic species, i.e., the Amolops, Bufo, and Scutiger. The main habitats of amphibian species included the forest, farmland, and grassland. There was remarkable fragmentation of these habitats, which was mainly due to land use conversion. There are currently 14 national nature reserves in the study area, covering 39 amphibian species, but there are still 32 amphibian species outsides these nature reserves. It is recommended to carry out long-term located monitoring, improve the existing protection network, and fill the protection gaps to achieve effective protection of these valuable biological resources.
A Multi-Objective Demand/Generation Scheduling Model-Based Microgrid Energy Management System
Authors: Jasim Ali M. , Jasim Basil H. , Kraiem Habib , Flah Aymen
Published: August 2022

In recent years, microgrids (MGs) have been developed to improve the overall management of the power network. This paper examines how a smart MG’s generation and demand sides are managed to improve the MG’s performance in order to minimize operating costs and emissions. A binary orientation search algorithm (BOSA)-based optimal demand side management (DSM) program using the load-shifting technique has been proposed, resulting in significant electricity cost savings. The proposed optimal DSM-based energy management strategy considers the MG’s economic and environmental indices to be the key objective functions. Single-objective particle swarm optimization (SOPSO) and multi-objective particle swarm optimization (MOPSO) were adopted in order to optimize MG performance in the presence of renewable energy resources (RERs) with a randomized natural behavior. A PSO algorithm was adopted due to the nonlinearity and complexity of the proposed problem. In addition, fuzzy-based mechanisms and a nonlinear sorting system were used to discover the optimal compromise given the collection of Pareto-front space solutions. To test the proposed method in a more realistic setting, the stochastic behavior of renewable units was also factored in. The simulation findings indicate that the proposed BOSA algorithm-based DSM had the lowest peak demand (88.4 kWh) compared to unscheduled demand (105 kWh); additionally, the operating costs were reduced by 23%, from 660 USD to 508 USD, and the emissions decreased from 840 kg to 725 kg, saving 13.7%.