bluuebunny/crossref_metadata_2025 huggingface dataset:
Interaction Insight of Pullulan-Mediated Gamma-Irradiated Silver Nanoparticle Synthesis and Its Antibacterial Activity
Authors: Salleh Mohd Shahrul Nizam , Ali Roshafima Rasit , Shameli Kamyar , Hamzah Mohd Yusof , Kasmani Rafiziana Md , Nasef Mohamed Mahmoud
Published: October 2021

The production of pure silver nanoparticles (Ag-NPs) with unique properties remains a challenge even today. In the present study, the synthesis of silver nanoparticles (Ag-NPs) from natural pullulan (PL) was carried out using a radiation-induced method. It is known that pullulan is regarded as a microbial polysaccharide, which renders it suitable to act as a reducing and stabilizing agent during the production of Ag-NPs. Pullulan-assisted synthesis under gamma irradiation was successfully developed to obtain Ag-NPs, which was characterized by UV-Vis, XRD, TEM, and Zeta potential analysis. Pullulan was used as a stabilizer and template for the growth of silver nanoparticles, while gamma radiation was modified to be selective to reduce silver ions. The formation of Ag-NPs was confirmed using UV–Vis spectra by showing a surface plasmon resonance (SPR) band in the region of 410–420 nm. As observed by TEM images, it can be said that by increasing the radiation dose, the particle size decreases, resulting in a mean diameter of Ag-NPs ranging from 40.97 to 3.98 nm. The XRD analysis confirmed that silver metal structures with a face-centered cubic (FCC) crystal were present, while TEM images showed a spherical shape with smooth edges. XRD also demonstrated that increasing the dose of gamma radiation increases the crystallinity at a high purity of Ag-NPs. As examined by zeta potential, the synthesized Ag-NP/PL was negatively charged with high stability. Ag-NP/PL was then analysed for antimicrobial activity against Staphylococcus aureus, and it was found that it had high antibacterial activity. It is found that the adoption of radiation doses results in a stable and green reduction process for silver nanoparticles.
Organosilica-Modified Multiblock Copolymers for Membrane Gas Separation
Authors: Davletbaeva Ilsiya M. , Alentiev Alexander Yu. , Faizulina Zulfiya Z. , Zaripov Ilnaz I. , Nikiforov Roman Yu. , Parfenov Victor V. , Arkhipov Alexander V.
Published: October 2021

Organosubstituted silica derivatives were synthesized and investigated as modifiers of block copolymers based on macroinitiator and 2,4-toluene diisocyanate. A peculiarity of the modified block copolymers is the existence in their structure of coplanar rigid polyisocyanate blocks of acetal nature (O-polyisocyanates). Organosubstituted silica derivatives have a non-additive effect on high-temperature relaxation and α-transitions of modified polymers and exhibit the ability to influence the supramolecular structure of block copolymers. The use of the developed modifiers leads to a change in the gas transport properties of block copolymers. The increase of the permeability coefficients is due to the increase of the diffusion coefficients. At the same time, the gas solubility coefficients do not change. An increase in the ideal selectivity for a number of gas pairs is observed. An increase in the selectivity for the CO2/N2 gas pair (from 25 to 39) by 1.5 times demonstrates the promising use of this material for flue gases separation.
Polyphenolic Characterization and Antioxidant Capacity of Laurus nobilis L. Leaf Extracts Obtained by Green and Conventional Extraction Techniques
Authors: Dobroslavić Erika , Elez Garofulić Ivona , Zorić Zoran , Pedisić Sandra , Dragović-Uzelac Verica
Published: October 2021

Laurus nobilis L. is an evergreen Mediterranean shrub whose leaves have been known for various health-promoting effects mainly attributed to polyphenols. Microwave- (MAE) and ultrasound-assisted extraction (UAE) are green extraction techniques that enable effective isolation of polyphenols from plant material. Therefore, the aim of this research was to optimize the extraction conditions of MAE (ethanol percentage, temperature, extraction time, microwave power) and UAE (ethanol percentage, extraction time, amplitude) of polyphenols from Laurus nobilis L. leaves and to assess their polyphenolic profile by ultra performance liquid chromatography- tandem mass spectrometry (UPLC-MS/MS) and antioxidant capacity by oxygen radical absorbance capacity (ORAC) assay. Optimal MAE conditions were 50% ethanol, 80 °C, 10 min and 400 W. Optimal UAE conditions were 70% ethanol, 10 min and 50% amplitude. Spectrophotometric analysis showed the highest total phenolic content in the extracts was obtained by MAE, compared to conventional heat-reflux extraction (CRE) and UAE. The polyphenolic profile of all obtained extracts included 29 compounds, with kaempferol and quercetin glycosides being the most abundant. UPLC-MS/MS showed the highest total phenolic content in the extracts obtained by CRE. ORAC assay showed the highest antioxidant capacity in extracts obtained by CRE, which is in agreement with the polyphenolic profile determined by UPLC-MS/MS.
An Evaluation of DIF Tests in Multistage Tests for Continuous Covariates
Authors: Debelak Rudolf , Debeer Dries
Published: October 2021

Multistage tests are a widely used and efficient type of test presentation that aims to provide accurate ability estimates while keeping the test relatively short. Multistage tests typically rely on the psychometric framework of item response theory. Violations of item response models and other assumptions underlying a multistage test, such as differential item functioning, can lead to inaccurate ability estimates and unfair measurements. There is a practical need for methods to detect problematic model violations to avoid these issues. This study compares and evaluates three methods for the detection of differential item functioning with regard to continuous person covariates in data from multistage tests: a linear logistic regression test and two adaptations of a recently proposed score-based DIF test. While all tests show a satisfactory Type I error rate, the score-based tests show greater power against three types of DIF effects.
The Christian Ideal of Sustainable Development and Its Perception among Young Adults: A Case Study from Slovakia
Authors: Tkáčová Hedviga , Slivka Daniel
Published: October 2021

In this article, we present the concept of the participation and responsibility of individuals in the sustainable development (SD) of the world from a Christian ethics point of view. The Christian ethics perspective is based on the biblical command to care for and protect creation. Nevertheless, errors have been repeated throughout history because of a misunderstanding of human responsibility for what has been entrusted to man from the beginning. The article explores not only these mistakes, but also the concept of a properly understood individual responsibility from the point of view of Christian ethics. The ideal from the perspective of Christian ethics is followed by our own qualitative research. The research sample consists of 120 young adults. During the research, the perception of a person’s individual responsibility tasks for SD is explored from the perspective of university students. The research findings answer the question of in which of the four areas of SD do respondents see the needs of their participation the most. The findings of the research also indicate the importance that the young respondents allocated to all four areas of SD, which were clearly defined by The National Strategic Document on Sustainable Development in Slovakia. Furthermore, we recorded a total of 12 main areas that the respondents considered to be important tasks of man for the world and its SD. We also evaluated the qualitative findings in the group of young adults that confirm the perception of each student’s individual participation and responsibility for the SD of society and the physical world.
Culture–Religion—Ethnicity (Alliance of Identities in the Environment of Foreign Slovak Communities)
Authors: Lenovský Ladislav , Slobodová Nováková Katarína
Published: October 2021

Is religion and faith in today’s economically and culturally globalized modern society just a conservative experience, tradition, and sentiment, or an anthropological universal, the primary essence of every individual and collective identity and a practical tool for realizing spiritual, cultural, and social needs? The community’s culture, religion, and ethnicity are most significant in contrast (or on the border) with others. The existence of minorities is determined by their being defined against the majority and characterized by differences. Contemporary Slovak communities living abroad, such as ethnic, cultural, and religious minorities, are a compelling object of research into the relationship between culture, religion, and ethnicity. Our paper examines the role this relationship plays in their identification, cultural, and revitalization processes.
Tracing the Influence of Ming-Qing Buddhism in Early Modern Japan: Yunqi Zhuhong’s Tract on Refraining from Killing and on Releasing Life and Ritual Animal Releases
Authors: Ambros Barbara R.
Published: October 2021

This essay traces the Japanese reception of Zhuhong’s Tract on Refraining from Killing and on Releasing Life in the early modern period. Ritual animal releases have a long history in Japan beginning in the seventh century, approximately two centuries after such rituals arose in China. From the mid-eighth century, the releases became large-scale state rites conducted at Hachiman shrines, which have been most widely studied and documented. By contrast, a different strand of life releases that emerged in the Edo period owing to the influence of late Ming Buddhism has received comparatively little scholarly attention despite the significance for the period. Not only may the publication of a Sino–Japanese edition of Zhuhong’s Tract in 1661 have been an impetus for Shogun Tokugawa Tsunayoshi’s Laws of Compassion in the late-seventeenth century, but also approximately thirty Japanese Buddhist texts inspired by Zhuhong’s Tract appeared over the next two and a half centuries. As Zhuhong’s ethic of refraining from killing and releasing life was assimilated over the course of the Edo and into the Meiji period, life releases became primarily associated with generating merit for the posthumous repose of the ancestors although they were also said to have a variety of vital benefits for the devotees and their families, such as health, longevity, prosperity, and descendants.
Robot Anticipation Learning System for Ball Catching
Authors: Carneiro Diogo , Silva Filipe , Georgieva Petia
Published: October 2021

Catching flying objects is a challenging task in human–robot interaction. Traditional techniques predict the intersection position and time using the information obtained during the free-flying ball motion. A common pain point in these systems is the short ball flight time and uncertainties in the ball’s trajectory estimation. In this paper, we present the Robot Anticipation Learning System (RALS) that accounts for the information obtained from observation of the thrower’s hand motion before the ball is released. RALS takes extra time for the robot to start moving in the direction of the target before the opponent finishes throwing. To the best of our knowledge, this is the first robot control system for ball-catching with anticipation skills. Our results show that the information fused from both throwing and flying motions improves the ball-catching rate by up to 20% compared to the baseline approach, with the predictions relying only on the information acquired during the flight phase.
Identification of Infiltration Features and Hydraulic Properties of Soils Based on Crop Water Stress Derived from Remotely Sensed Data
Authors: Brom Jakub , Duffková Renata , Haberle Jan , Zajíček Antonín , Nedbal Václav , Bernasová Tereza , Křováková Kateřina
Published: October 2021

Knowledge of the spatial variability of soil hydraulic properties is important for many reasons, e.g., for soil erosion protection, or the assessment of surface and subsurface runoff. Nowadays, precision agriculture is gaining importance for which knowledge of soil hydraulic properties is essential, especially when it comes to the optimization of nitrogen fertilization. The present work aimed to exploit the ability of vegetation cover to identify the spatial variability of soil hydraulic properties through the expression of water stress. The assessment of the spatial distribution of saturated soil hydraulic conductivity (Ks) and field water capacity (FWC) was based on a combination of ground-based measurements and thermal and hyperspectral airborne imaging data. The crop water stress index (CWSI) was used as an indicator of crop water stress to assess the hydraulic properties of the soil. Supplementary vegetation indices were used. The support vector regression (SVR) method was used to estimate soil hydraulic properties from aerial data. Data analysis showed that the approach estimated Ks with good results (R2 = 0.77) for stands with developed crop water stress. The regression coefficient values for estimation of FWC for topsoil (0–0.3 m) ranged from R2 = 0.38 to R2 = 0.99. The differences within the study sites of the FWC estimations were higher for the subsoil layer (0.3–0.6 m). R2 values ranged from 0.12 to 0.99. Several factors affect the quality of the soil hydraulic features estimation, such as crop water stress development, condition of the crops, period and time of imaging, etc. The above approach is useful for practical applications for its relative simplicity, especially in precision agriculture.
PS-InSAR-Based Validated Landslide Susceptibility Mapping along Karakorum Highway, Pakistan
Authors: Hussain Muhammad Afaq , Chen Zhanlong , Wang Run , Shoaib Muhammad
Published: October 2021

Landslide classification and identification along Karakorum Highway (KKH) is still challenging due to constraints of proposed approaches, harsh environment, detail analysis, complicated natural landslide process due to tectonic activities, and data availability problems. A comprehensive landslide inventory and a landslide susceptibility mapping (LSM) along the Karakorum Highway were created in recent research. The extreme gradient boosting (XGBoost) and random forest (RF) models were used to compare and forecast the association between causative parameters and landslides. These advanced machine learning (ML) models can measure environmental issues and risks for any area on a regional scale. Initially, 74 landslide locations were determined along the KKH to prepare the landslide inventory map using different data. The landslides were randomly divided into two sets for training and validation at a proportion of 7/3. Fifteen landslide conditioning variables were produced for susceptibility mapping. The interferometric synthetic aperture radar persistent scatterer interferometry (PS-InSAR) technique investigated the deformation movement of extracted models in the susceptible zones. It revealed a high line of sight (LOS) deformation velocity in both models’ sensitive zones. For accuracy comparison, the area under the curve (AUC) of the receiver operating characteristic (ROC) curve approach was used, which showed 93.44% and 92.22% accuracy for XGBoost and RF, respectively. The XGBoost method produced superior results, combined with PS-InSAR results to create a new LSM for the area. This improved susceptibility model will aid in mitigating the landslide disaster, and the results may assist in the safe operation of the highway in the research area.
Observation of Ionospheric Gravity Waves Introduced by Thunderstorms in Low Latitudes China by GNSS
Authors: Liu Tong , Yu Zhibin , Ding Zonghua , Nie Wenfeng , Xu Guochang
Published: October 2021

The disturbances of the ionosphere caused by thunderstorms or lightning events in the troposphere have an impact on global navigation satellite system (GNSS) signals. Gravity waves (GWs) triggered by thunderstorms are one of the main factors that drive short-period Travelling Ionospheric Disturbances (TIDs). At mid-latitudes, ionospheric GWs can be detected by GNSS signals. However, at low latitudes, the multi-variability of the ionosphere leads to difficulties in identifying GWs induced by thunderstorms through GNSS data. Though disturbances of the ionosphere during low-latitude thunderstorms have been investigated, the explicit GW observation by GNSS and its propagation pattern are still unclear. In this paper, GWs with periods from 6 to 20 min are extracted from band-pass filtered GNSS carrier phase observations without cycle-slips, and 0.2–0.8 Total Electron Content Unit (TECU) magnitude perturbations are observed when the trajectories of ionospheric pierce points fall into the perturbed region. The propagation speed of 102.6–141.3 m/s and the direction of the propagation indicate that the GWs are propagating upward from a certain thunderstorm at lower atmosphere. The composite results of disturbance magnitude, period, and propagation velocity indicate that GWs initiated by thunderstorms and propagated from the troposphere to the ionosphere are observed by GNSS for the first time in the low-latitude region.
Deep Ensembles for Hyperspectral Image Data Classification and Unmixing
Authors: Nalepa Jakub , Myller Michal , Tulczyjew Lukasz , Kawulok Michal
Published: October 2021

Hyperspectral images capture very detailed information about scanned objects and, hence, can be used to uncover various characteristics of the materials present in the analyzed scene. However, such image data are difficult to transfer due to their large volume, and generating new ground-truth datasets that could be utilized to train supervised learners is costly, time-consuming, very user-dependent, and often infeasible in practice. The research efforts have been focusing on developing algorithms for hyperspectral data classification and unmixing, which are two main tasks in the analysis chain of such imagery. Although in both of them, the deep learning techniques have bloomed as an extremely effective tool, designing the deep models that generalize well over the unseen data is a serious practical challenge in emerging applications. In this paper, we introduce the deep ensembles benefiting from different architectural advances of convolutional base models and suggest a new approach towards aggregating the outputs of base learners using a supervised fuser. Furthermore, we propose a model augmentation technique that allows us to synthesize new deep networks based on the original one by injecting Gaussian noise into the model’s weights. The experiments, performed for both hyperspectral data classification and unmixing, show that our deep ensembles outperform base spectral and spectral-spatial deep models and classical ensembles employing voting and averaging as a fusing scheme in both hyperspectral image analysis tasks.
Immunized Token-Based Approach for Autonomous Deployment of Multiple Mobile Robots in Burnt Area
Authors: Nantogma Sulemana , Ran Weizhi , Liu Pengfei , Yu Zhang , Xu Yang
Published: October 2021

Collaborative exploration, sensing and communication in previously unknown environments with high network latency, such as outer space, battlefields and disaster hit areas are promising in multi-agent applications. When disasters such as large fires or natural disasters occur, previously established networks might be destroyed or incapacitated. In these cases, multiple autonomous mobile robots (AMR) or autonomous unmanned ground vehicles carrying wireless devices and/or thermal sensors can be deployed to create an end-to-end communication and sensing coverage to support rescue efforts or access the severity of damage. However, a fundamental problem is how to rapidly deploy these mobile agents in such complex and dynamic environments. The uncertainties introduced by the operational environment and wide range of scheduling problem have made solving them as a whole challenging. In this paper, we present an efficient decentralized approach for practical mobile agents deployment in unknown, burnt or disaster hit areas. Specifically, we propose an approach that combines methods from Artificial Immune System (AIS) with special token messages passing for a team of interconnected AMR to decide who, when and how to act during deployment process. A distributed scheme is adopted, where each AMR makes its movement decisions based on its local observation and a special token it receives from its neighbors. Empirical evidence of robustness and effectiveness of the proposed approach is demonstrated through simulation.
Detecting and Analyzing the Displacement of a Small-Magnitude Earthquake Cluster in Rong County, China by the GACOS Based InSAR Technology
Authors: Zhao Liang , Liang Rubing , Shi Xianlin , Dai Keren , Cheng Jianhua , Cao Junxing
Published: October 2021

A series of small-magnitude earthquakes (Mw 2.9~Mw 4.9) occurred in Rong County, Sichuan Province, China between 30 March 2018 and December 2020, which threatened the safety of local residents. Determining the surface displacement and estimating the damage caused by these earthquakes are significant for earthquake relief, post-earthquake disaster assessment and hazard elimination. This paper integrates the Generic Atmospheric Correction Online Service (GACOS) with interferometry synthetic aperture radar (InSAR) to accurately detect the displacement of the series of small-magnitude earthquakes in Rong County based on 45 Sentinel-1 ascending/descending images acquired from January 2018 to December 2020. We analyze the influence of some factors involved in surface displacement, including earthquake magnitude, focal depth and the distance from the epicenter to the fault. The above measurement for small-magnitude earthquakes and statistics analysis for the displacement have not been performed before, so this can help better understand the displacement features of small-magnitude earthquakes, which are important for post-earthquake hazard assessment and disaster prevention.
SC-SM CAM: An Efficient Visual Interpretation of CNN for SAR Images Target Recognition
Authors: Feng Zhenpeng , Ji Hongbing , Stanković Ljubiša , Fan Jingyuan , Zhu Mingzhe
Published: October 2021

Convolutional neural networks (CNNs) have successfully achieved high accuracy in synthetic aperture radar (SAR) target recognition; however, the intransparency of CNNs is still a limiting or even disqualifying factor. Therefore, visually interpreting CNNs with SAR images has recently drawn increasing attention. Various class activation mapping (CAM) methods are adopted to discern the relationship between CNN’s decision and image regions. Unfortunately, most existing CAM methods are based on optical images; thus, they usually lead to a limiting visualization effect for SAR images. Although a recently proposed Self-Matching CAM can obtain a satisfactory effect for SAR images, it is quite time-consuming, due to there being hundreds of self-matching operations per image. G-SM-CAM reduces the time of such operation dramatically, but at the cost of visualization effect. Based on the limitations of the above methods, we propose an efficient method, Spectral-Clustering Self-Matching CAM (SC-SM CAM). Spectral clustering is first adopted to divide feature maps into groups for efficient computation. In each group, similar feature maps are merged into an enhanced feature map with more concentrated energy in a specific region; thus, the saliency heatmaps may more accurately tally with the target. Experimental results demonstrate that SC-SM CAM outperforms other SOTA CAM methods in both effect and efficiency.
A Manual for Monitoring Wild Boars (Sus scrofa) Using Thermal Infrared Cameras Mounted on an Unmanned Aerial Vehicle (UAV)
Authors: Kim Minyoung , Chung Ok-Sik , Lee Jong-Koo
Published: October 2021

This study proposes monitoring methods of wild boars in plains or mountain forest areas using unmanned aerial vehicles (UAVs) equipped with infrared cameras. It is recommended to monitor in winter when the canopy layer is thin and the temperature difference between the ambient temperature and the body temperature of the wild boar is distinct, so that the infrared camera can better detect the wild boars. In flat land, the plane movement of the UAV can be easily monitored using the line transect, the point count survey, the plot sampling method, and the belted transect. In the mountain forest, there are variations in elevation due to slopes. Therefore, we introduced the WAYPOINT function to reflect the difference in altitude. After the investigator designates the waypoint, various information can be modified according to the terrain, and the WAYPOINT can be transmitted to other UAVs, so the utilization is high. In this method, once a route is created using the WAYPOINT, there is no need for additional operation after the start of the flight, and it helps to re-monitor the site by using the WAYPOINT record repeatedly. Therefore, this technical note provides a more repeatedly sustainable and scalable monitoring method than the conventional UAV method.
Crossing the Great Divide: Bridging the Researcher–Practitioner Gap to Maximize the Utility of Remote Sensing for Invasive Species Monitoring and Management
Authors: Parker Kelsey , Elmes Arthur , Boucher Peter , Hallett Richard A. , Thompson John E. , Simek Zachary , Bowers Justin , Reinmann Andrew B.
Published: October 2021

Invasive species are increasingly present in our ecosystems and pose a threat to the health of forest ecosystems. Practitioners are tasked with locating these invasive species and finding ways to mitigate their spread and impacts, often through costly field surveys. Meanwhile, researchers are developing remote sensing products to detect the changes in vegetation health and structure that are caused by invasive species, which could aid in early detection and monitoring efforts. Although both groups are working towards similar goals and field data are essential for validating RS products, these groups often work independently. In this paper, we, a group of researchers and practitioners, discuss the challenges to bridging the gap between researchers and practitioners and summarize the literature on this topic. We also draw from our experiences collaborating with each other to advance detection, monitoring, and management of the Hemlock Woolly Adelgid (Adelges tsugae; HWA), an invasive forest pest in the eastern U.S. We conclude by (1) highlighting the synergies and symbiotic mutualism of researcher–practitioner collaborations and (2) providing a framework for facilitating researcher–practitioner collaborations that advance fundamental science while maximizing the capacity of RS technologies in monitoring and management of complex drivers of forest health decline such as invasive species.
An Algorithm for the Retrieval of High Temporal-Spatial Resolution Shortwave Albedo from Landsat-8 Surface Reflectance and MODIS BRDF
Authors: Yang Gang , Wang Jiyan , Xiong Junnan , Yong Zhiwei , Ye Chongchong , Sun Huaizhang , Liu Jun , Duan Yu , He Yufeng , He Wen
Published: October 2021

Variations in surface physicochemical properties and spatial structures can prominently transform surface albedo which conversely influence surface energy balances and global climate, making it crucial to continuously monitor and quantify surface dynamics at fine scales. Here, we made two improvements to propose an algorithm for the simultaneous retrieval of 30-m Landsat albedo, based on the coupling of Landsat-8 and MODIS BRDF. First, two kinds of prior knowledge were added to disaggregate BRDF, including the Anisotropic Flat Index (AFX) and the Albedo-to-Nadir reflectance ratio (AN ratio), from MODIS scales into Landsat scales. Second, a simplified data fusion method was used to simulate albedo for the same, subsequent, or antecedent dates. Finally, we validated the reliability and correlations of the algorithm at six sites of the Surface Radiation (SURFRAD) budget network and intercompared the results with another algorithm called the ‘concurrent approach’. The results showed that the proposed algorithm had favorable usability and robustness, with a root mean square error (RMSE) of 0.015 (8%) and a mean bias of −0.005; while the concurrent approach had a RMSE of 0.026 (14%) and a mean bias of −0.018. The results emphasized that the proposed algorithm has captured subtle changes in albedo over a 16-day period.
Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models
Authors: Eyre Riley , Lindsay John , Laamrani Ahmed , Berg Aaron
Published: October 2021

Accurate yield estimation and optimized agricultural management is a key goal in precision agriculture, while depending on many different production attributes, such as soil properties, fertilizer and irrigation management, the weather, and topography.The need for timely and accurate sensing of these inputs at the within field-scale has led to increased adoption of very high-resolution remote and proximal sensing technologies. With regard to topography attributes, greater attention is currently being devoted to LiDAR datasets (Light Detection and Ranging), mainly because numerous topographic variables can be derived at very high spatial resolution from these datasets. The current study uses LiDAR elevation data from agricultural land in southern Ontario, Canada to derive several topographic attributes such as slope, and topographic wetness index, which were then correlated to seven years of crop yield data. The effectiveness of each topographic derivative was independently tested using a moving-window correlation technique. Finally, the correlated derivatives were selected as explanatory variables for geographically weighted regression (GWR) models. The global coefficient of determination values (determined from an average of all the local relationships) were found to be R2 = 0.80 for corn, R2 = 0.73 for wheat, R2 = 0.71 for soybeans and R2 = 0.75 for the average of all crops. These results indicate that GWR models using topographic variables derived from LiDAR can effectively explain yield variation of several crop types on an entire-field scale.
Enhancing Animal Movement Analyses: Spatiotemporal Matching of Animal Positions with Remotely Sensed Data Using Google Earth Engine and R
Authors: Crego Ramiro , Masolele Majaliwa , Connette Grant , Stabach Jared
Published: October 2021

Movement ecologists have witnessed a rapid increase in the amount of animal position data collected over the past few decades, as well as a concomitant increase in the availability of ecologically relevant remotely sensed data. Many researchers, however, lack the computing resources necessary to incorporate the vast spatiotemporal aspects of datasets available, especially in countries with less economic resources, limiting the scope of ecological inquiry. We developed an R coding workflow that bridges the gap between R and the multi-petabyte catalogue of remotely sensed data available in Google Earth Engine (GEE) to efficiently extract raster pixel values that best match the spatiotemporal aspects (i.e., spatial location and time) of each animal’s GPS position. We tested our approach using movement data freely available on Movebank (movebank.org). In a first case study, we extracted Normalized Difference Vegetation Index information from the MOD13Q1 data product for 12,344 GPS animal locations by matching the closest MODIS image in the time series to each GPS fix. Data extractions were completed in approximately 3 min. In a second case study, we extracted hourly air temperature from the ERA5-Land dataset for 33,074 GPS fixes from 12 different wildebeest (Connochaetes taurinus) in approximately 34 min. We then investigated the relationship between step length (i.e., the net distance between sequential GPS locations) and temperature and found that animals move less as temperature increases. These case studies illustrate the potential to explore novel questions in animal movement research using high-temporal-resolution, remotely sensed data products. The workflow we present is efficient and customizable, with data extractions occurring over relatively short time periods. While computing times to extract remotely sensed data from GEE will vary depending on internet speed, the approach described has the potential to facilitate access to computationally demanding processes for a greater variety of researchers and may lead to increased use of remotely sensed data in the field of movement ecology. We present a step-by-step tutorial on how to use the code and adapt it to other data products that are available in GEE.
A Review of Crop Water Stress Assessment Using Remote Sensing
Authors: Ahmad Uzair , Alvino Arturo , Marino Stefano
Published: October 2021

Currently, the world is facing high competition and market risks in improving yield, crop illness, and crop water stress. This could potentially be addressed by technological advancements in the form of precision systems, improvements in production, and through ensuring the sustainability of development. In this context, remote-sensing systems are fully equipped to address the complex and technical assessment of crop production, security, and crop water stress in an easy and efficient way. They provide simple and timely solutions for a diverse set of ecological zones. This critical review highlights novel methods for evaluating crop water stress and its correlation with certain measurable parameters, investigated using remote-sensing systems. Through an examination of previous literature, technologies, and data, we review the application of remote-sensing systems in the analysis of crop water stress. Initially, the study presents the relationship of relative water content (RWC) with equivalent water thickness (EWT) and soil moisture crop water stress. Evapotranspiration and sun-induced chlorophyll fluorescence are then analyzed in relation to crop water stress using remote sensing. Finally, the study presents various remote-sensing technologies used to detect crop water stress, including optical sensing systems, thermometric sensing systems, land-surface temperature-sensing systems, multispectral (spaceborne and airborne) sensing systems, hyperspectral sensing systems, and the LiDAR sensing system. The study also presents the future prospects of remote-sensing systems in analyzing crop water stress and how they could be further improved.
Top-Down Pyramid Fusion Network for High-Resolution Remote Sensing Semantic Segmentation
Authors: Gu Yuhang , Hao Jie , Chen Bing , Deng Hai
Published: October 2021

In recent years, high-resolution remote sensing semantic segmentation based on data fusion has gradually become a research focus in the field of land classification, which is an indispensable task of a smart city. However, the existing feature fusion methods with bottom-up structures can achieve limited fusion results. Alternatively, various auxiliary fusion modules significantly increase the complexity of the models and make the training process intolerably expensive. In this paper, we propose a new lightweight model called top-down pyramid fusion network (TdPFNet) including a multi-source feature extractor, a top-down pyramid fusion module and a decoder. It can deeply fuse features from different sources in a top-down structure using high-level semantic knowledge guiding the fusion of low-level texture information. Digital surface model (DSM) data and open street map (OSM) data are used as auxiliary inputs to the Potsdam dataset for the proposed model evaluation. Experimental results show that the network proposed in this paper not only notably improves the segmentation accuracy, but also reduces the complexity of the multi-source semantic segmentation model.
Accurate Pupil Center Detection in Off-the-Shelf Eye Tracking Systems Using Convolutional Neural Networks
Authors: Larumbe-Bergera Andoni , Garde Gonzalo , Porta Sonia , Cabeza Rafael , Villanueva Arantxa
Published: October 2021

Remote eye tracking technology has suffered an increasing growth in recent years due to its applicability in many research areas. In this paper, a video-oculography method based on convolutional neural networks (CNNs) for pupil center detection over webcam images is proposed. As the first contribution of this work and in order to train the model, a pupil center manual labeling procedure of a facial landmark dataset has been performed. The model has been tested over both real and synthetic databases and outperforms state-of-the-art methods, achieving pupil center estimation errors below the size of a constricted pupil in more than 95% of the images, while reducing computing time by a 8 factor. Results show the importance of use high quality training data and well-known architectures to achieve an outstanding performance.
Application of Prandtl’s Theory in the Design of an Experimental Chamber for Static Pressure Measurements
Authors: Šabacká Pavla , Neděla Vilém , Maxa Jiří , Bayer Robert
Published: October 2021

Pumping in vacuum chambers is part of the field of environmental electron microscopy. These chambers are separated from each other by a small-diameter aperture that creates a critical flow in the supersonic flow regime. The distribution of pressure and shock waves in the path of the primary electron beam passing through the differentially pumped chamber has a large influence on the quality of the resulting microscope image. As part of this research, an experimental chamber was constructed to map supersonic flow at low pressures. The shape of this chamber was designed using mathematical–physical analyses, which served not only as a basis for the design of its geometry, but especially for the correct choice of absolute and differential pressure sensors with respect to the cryogenic temperature generated in the supersonic flow. The mathematical and physical analyses presented here map the nature of the supersonic flow with large gradients of state variables at low pressures at the continuum mechanics boundary near the region of free molecule motion in which the Environmental Electron Microscope and its differentially pumped chamber operate, which has a significant impact on the resulting sharpness of the final image obtained by the microscope. The results of this work map the flow in and behind the Laval nozzle in the experimental chamber and are the initial basis that enabled the optimization of the design of the chamber based on Prandtl’s theory for the possibility of fitting it with pressure probes in such a way that they can map the flow in and behind the Laval nozzle.
Measurement of Flow Fluctuation in the Flow Standard Facility Based on Singular Value Decomposition
Authors: Meng Tao , Wei Huanchang , Gao Feng , Shi Huichao
Published: October 2021

In order to accurately evaluate the flow stability of the flow standard facility, the flow fluctuation in the standard facility needs to be accurately measured. However, the flow fluctuation signal is always superimposed with the fluctuation signal of the measuring flowmeter or measurement system (mainly noise), which leads to inaccurate measurement of the flow fluctuation and even an unreliable evaluation result of the flow stability. In addition, when there are multiple fluctuation sources, flow fluctuations with different frequencies are superimposed together, which is extremely unfavorable for evaluating the impact of flow fluctuation with different single frequencies. In this paper, a new measuring method was proposed to obtain the fluctuation signal and the flow fluctuation based on singular value decomposition (SVD). Simulation experiments on the fluctuation signal (single frequency and multiple frequencies) under different levels of noise were conducted, and simulation results showed that the proposed method could accurately obtain the fluctuation signal and the flow fluctuation, even under high noise. Finally, an experimental platform was set-up based on a water flow standard facility and a flow fluctuation generator, and experiments on the output signal of a venturi flowmeter were carried out. The experiment results showed that the proposed method could effectively obtain the fluctuation signal and accurately measure the flow fluctuation.
Reflectance Imaging Spectroscopy (RIS) for Operation Night Watch: Challenges and Achievements of Imaging Rembrandt’s Masterpiece in the Glass Chamber at the Rijksmuseum
Authors: Gabrieli Francesca , Delaney John K. , Erdmann Robert G. , Gonzalez Victor , van Loon Annelies , Smulders Patrick , Berkeveld Roy , van Langh Robert , Keune Katrien
Published: October 2021

Visible and infrared reflectance imaging spectroscopy is one of the several non-invasive techniques used during Operation Night Watch for the study of Rembrandt’s iconic masterpiece The Night Watch (1642). The goals of this project include the identification and mapping of the artists’ materials, providing information about the painting technique used as well as documenting the painting’s current state and ultimately determining the possible conservation plan. The large size of the painting (3.78 m by 4.53 m) and the diversity of the technical investigations being performed make Operation Night Watch the largest research project ever undertaken at the Rijksmuseum. To construct a complete reflectance image cube at a high spatial resolution (168 µm2) and spectral resolution (2.54 to 6 nm), the painting was imaged with two high-sensitivity line scanning hyperspectral cameras (VNIR 400 to 1000 nm, 2.54 nm, and SWIR 900 to 2500 nm, 6 nm). Given the large size of the painting, a custom computer-controlled 3-D imaging frame was constructed to move each camera, along with lights, across the painting surface. A third axis, normal to the painting, was added along with a distance-sensing system which kept the cameras in focus during the scanning. A total of 200 hyperspectral image swaths were collected, mosaicked and registered to a high-resolution color image to sub-pixel accuracy using a novel registration algorithm. The preliminary analysis of the VNIR and SWIR reflectance images has identified many of the pigments used and their distribution across the painting. The SWIR, in particular, has provided an improved visualization of the preparatory sketches and changes in the painted composition. These data sets, when combined with the results from the other spectral imaging modalities and paint sample analyses, will provide the most complete understanding of the materials and painting techniques used by Rembrandt in The Night Watch.
Validation of 3-Space Wireless Inertial Measurement Units Using an Industrial Robot
Authors: Hislop Jaime , Isaksson Mats , McCormick John , Hensman Chris
Published: October 2021

Inertial Measurement Units (IMUs) are beneficial for motion tracking as, in contrast to most optical motion capture systems, IMU systems do not require a dedicated lab. However, IMUs are affected by electromagnetic noise and may exhibit drift over time; it is therefore common practice to compare their performance to another system of high accuracy before use. The 3-Space IMUs have only been validated in two previous studies with limited testing protocols. This study utilized an IRB 2600 industrial robot to evaluate the performance of the IMUs for the three sensor fusion methods provided in the 3-Space software. Testing consisted of programmed motion sequences including 360° rotations and linear translations of 800 mm in opposite directions for each axis at three different velocities, as well as static trials. The magnetometer was disabled to assess the accuracy of the IMUs in an environment containing electromagnetic noise. The Root-Mean-Square Error (RMSE) of the sensor orientation ranged between 0.2° and 12.5° across trials; average drift was 0.4°. The performance of the three filters was determined to be comparable. This study demonstrates that the 3-Space sensors may be utilized in an environment containing metal or electromagnetic noise with a RMSE below 10° in most cases.
Application of Dual-Frequency Self-Injection Locked DFB Laser for Brillouin Optical Time Domain Analysis
Authors: Lopez-Mercado Cesar A. , Korobko Dmitry A. , Zolotovskii Igor O. , Fotiadi Andrei A.
Published: October 2021

Self-injection locking to an external fiber cavity is an efficient technique enabling drastic linewidth narrowing of semiconductor lasers. Recently, we constructed a simple dual-frequency laser source that employs self-injection locking of a DFB laser in the external ring fiber cavity and Brillouin lasing in the same cavity. The laser performance characteristics are on the level of the laser modules commonly used with BOTDA. The use of a laser source operating two frequencies strongly locked through the Brillouin resonance simplifies the BOTDA system, avoiding the use of a broadband electrooptical modulator (EOM) and high-frequency electronics. Here, in a direct comparison with the commercial BOTDA, we explore the capacity of our low-cost solution for BOTDA sensing, demonstrating distributed measurements of the Brillouin frequency shift in a 10 km sensing fiber with a 1.5 m spatial resolution.
Biosignal-Based Human–Machine Interfaces for Assistance and Rehabilitation: A Survey
Authors: Esposito Daniele , Centracchio Jessica , Andreozzi Emilio , Gargiulo Gaetano D. , Naik Ganesh R. , Bifulco Paolo
Published: October 2021

As a definition, Human–Machine Interface (HMI) enables a person to interact with a device. Starting from elementary equipment, the recent development of novel techniques and unobtrusive devices for biosignals monitoring paved the way for a new class of HMIs, which take such biosignals as inputs to control various applications. The current survey aims to review the large literature of the last two decades regarding biosignal-based HMIs for assistance and rehabilitation to outline state-of-the-art and identify emerging technologies and potential future research trends. PubMed and other databases were surveyed by using specific keywords. The found studies were further screened in three levels (title, abstract, full-text), and eventually, 144 journal papers and 37 conference papers were included. Four macrocategories were considered to classify the different biosignals used for HMI control: biopotential, muscle mechanical motion, body motion, and their combinations (hybrid systems). The HMIs were also classified according to their target application by considering six categories: prosthetic control, robotic control, virtual reality control, gesture recognition, communication, and smart environment control. An ever-growing number of publications has been observed over the last years. Most of the studies (about 67%) pertain to the assistive field, while 20% relate to rehabilitation and 13% to assistance and rehabilitation. A moderate increase can be observed in studies focusing on robotic control, prosthetic control, and gesture recognition in the last decade. In contrast, studies on the other targets experienced only a small increase. Biopotentials are no longer the leading control signals, and the use of muscle mechanical motion signals has experienced a considerable rise, especially in prosthetic control. Hybrid technologies are promising, as they could lead to higher performances. However, they also increase HMIs’ complexity, so their usefulness should be carefully evaluated for the specific application.