bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing Conditions for Microwave-Assisted Extraction of Polyphenolic Content and Antioxidant Activity of Barleria lupulina Lindl.
Authors: Ismail-Suhaimy Noor Wahida , Gani Siti Salwa Abd , Zaidan Uswatun Hasanah , Halmi Mohd Izuan Effendi , Bawon Paiman
Published: April 2021

Barleria lupulina Lindl. (Acanthaceae) as an ornamental plant has been widely used in folklore medicine due to its abundancy in polyphenolic compounds. The present study examined conditions for optimal extraction of antioxidants from B. lupulina leaf extracts by using the microwave-assisted extraction (MAE) method. The effects of ethanol concentrations, microwave power, and extraction time on total phenolic content (TPC), total flavonoid content (TFC), 1-diphenyl-2-picrylhydrazyl (DPPH), and 2,20-azino-bis (3-ethylbenzothizoline-6-sulfonic acid) (ABTS) were investigated by single-factor experiments. Response surface methodology (RSM) was applied to observe interactions of three independent variables (ethanol concentrations, microwave power, and extraction time) on the dependent variables (TPC, TFC, DPPH, and ABTS) to establish optimal extraction conditions. Quadratic polynomial equations in all experimental models yielded favorably with fitted models with R2 and R2adj of more than 0.90 and a non-significant lack of fit at p > 0.05. The optimal conditions for the extraction of antioxidant activity were established at 80% (v/v) ethanol, 400 W, and 30 s with TPC (238.71 mg gallic acid equivalent (GAE)/g sample), TFC (58.09 mg QE/g sample), DPPH (87.95%), and ABTS (89.56%). Analysis by ultra-high-performance liquid chromatography–quadrupole time-of-flight mass spectrometry (UHPLC-QTOF/MS) successfully identified four new phenylethanoid glycoside compounds in the species.
Enhanced Ion Cluster Size of Sulfonated Poly (Arylene Ether Sulfone) for Proton Exchange Membrane Fuel Cell Application
Authors: Sharma Prem P. , Tinh Vo Dinh Cong , Kim Dukjoon
Published: April 2021

A successful approach towards enhancement in ion cluster size of sulfonated poly (arylene ether sulfone) (SPAES)-based membranes has been successfully carried out by encapsulating basic pendent branches as side groups. Modified SPAES was synthesized by condensation polymerization followed by bromination with N-bromosuccinamide (NBS) and sulfonation by ring opening reaction. Various molar ratios of branched polyethyleneimine (PEI) were added to the SPAES and the developed polymer was designated as SPAES-x-PEI-y, where x denoted the number of sulfonating acid group per polymer chain and y represents the amount of PEI concentration. Polymer synthesis was characterized by 1H-NMR (Nuclear magnetic resonance) and FT-IR (Fourier-transform infrared spectroscopy) analysis. A cumulative trend involving enhanced proton conductivity of the membranes with an increase in the molar ratio of PEI has been observed, clearly demonstrating the formation of ionic clusters. SPAES-140-PEI-3 membranes show improved proton conductivity of 0.12 Scm−1 at 80 °C. Excellent chemical stability was demonstrated by the polymer with Fenton’s test at 80 °C for 24 h without significant loss in proton conductivity, owing to the suitability of the synthesized hybrid membrane for electrochemical application. Moreover, a single cell degradation test was conducted at 80 °C showing a power density at a 140 mWcm−2 value, proving the stable nature of synthesized membranes for proton exchange membrane fuel cell application.
Antioxidant Films from Cassava Starch/Gelatin Biocomposite Fortified with Quercetin and TBHQ and Their Applications in Food Models
Authors: Tongdeesoontorn Wirongrong , Mauer Lisa J. , Wongruong Sasitorn , Sriburi Pensiri , Reungsang Alissara , Rachtanapun Pornchai
Published: April 2021

Edible and active packaging are attractive for use in food packaging applications due to their functionality and sustainability. This research developed new antioxidant active food packaging materials from cassava starch/gelatin (7:3 w/w) composite films with varied antioxidant types (quercetin and tertiary butylhydroquinone (TBHQ)) and concentrations (0–200 mg/200 mL film-forming solution) and evaluated their properties. Antioxidant addition altered the mechanical and barrier properties of the films. At 34% relative humidity (RH), increasing the concentration of quercetin increased the tensile strength and decreased the elongation at break of the composite films. Increasing quercetin and TBHQ contents increased the film water solubility and water vapor transmission rate. Intermolecular interactions between the antioxidants and films, as found in Fourier transform infrared (FT-IR) spectra and XRD micrographs, were related to the changed film functionalities. In food application studies, the cassava starch/gelatin films containing quercetin and TBHQ retarded the oxidation of lard (more than 35 days) and delayed the redness discoloration of pork. Cassava starch/gelatin composite films integrated with quercetin and TBHQ can be utilized as active packaging that delays oxidation in foods.
New Light in Polymer Science: Photoinduced Reversible Addition-Fragmentation Chain Transfer Polymerization (PET-RAFT) as Innovative Strategy for the Synthesis of Advanced Materials
Authors: Bellotti Valentina , Simonutti Roberto
Published: April 2021

Photochemistry has attracted great interest in the last decades in the field of polymer and material science for the synthesis of innovative materials. The merging of photochemistry and reversible-deactivation radical polymerizations (RDRP) provides good reaction control and can simplify elaborate reaction protocols. These advantages open the doors to multidisciplinary fields going from composite materials to bio-applications. Photoinduced Electron/Energy Transfer Reversible Addition-Fragmentation Chain-Transfer (PET-RAFT) polymerization, proposed for the first time in 2014, presents significant advantages compared to other photochemical techniques in terms of applicability, cost, and sustainability. This review has the aim of providing to the readers the basic knowledge of PET-RAFT polymerization and explores the new possibilities that this innovative technique offers in terms of industrial applications, new materials production, and green conditions.
Photogrammetry Using UAV-Mounted GNSS RTK: Georeferencing Strategies without GCPs
Authors: Štroner Martin , Urban Rudolf , Seidl Jan , Reindl Tomáš , Brouček Josef
Published: April 2021

Georeferencing using ground control points (GCPs) is the most common strategy in photogrammetry modeling using unmanned aerial vehicle (UAV)-acquired imagery. With the increased availability of UAVs with onboard global navigation satellite system–real-time kinematic (GNSS RTK), georeferencing without GCPs is becoming a promising alternative. However, systematic elevation error remains a problem with this technique. We aimed to analyze the reasons for this systematic error and propose strategies for its elimination. Multiple flights differing in the flight altitude and image acquisition axis were performed at two real-world sites. A flight height of 100 m with a vertical (nadiral) image acquisition axis was considered primary, supplemented with flight altitudes of 75 m and 125 m with a vertical image acquisition axis and two flights at 100 m with oblique image acquisition axes (30° and 15°). Each of these flights was performed twice to produce a full double grid. Models were reconstructed from individual flights and their combinations. The elevation error from individual flights or even combinations yielded systematic elevation errors of up to several decimeters. This error was linearly dependent on the deviation of the focal length from the reference value. A combination of two flights at the same altitude (with nadiral and oblique image acquisition) was capable of reducing the systematic elevation error to less than 0.03 m. This study is the first to demonstrate the linear dependence between the systematic elevation error of the models based only on the onboard GNSS RTK data and the deviation in the determined internal orientation parameters (focal length). In addition, we have shown that a combination of two flights with different image acquisition axes can eliminate this systematic error even in real-world conditions and that georeferencing without GCPs is, therefore, a feasible alternative to the use of GCPs.
3D Sensor-Fusion for the Documentation of Rural Heritage Buildings
Authors: Castilla F. J. , Ramón A. , Adán A. , Trenado A. , Fuentes D.
Published: April 2021

3D computer vision techniques are now required for the virtual reconstruction of ancient buildings and monuments in urban environments. In this paper, we include a new subfield within the broad field of Urban Heritage that we denominate as Rural Heritage (RH), and which is focused on recovering 3D models of small buildings and facilities of significance in rural environments. We, therefore, present a multi-sensory approach whose objective is to create complete architectural documentation of the dovecotes in an extended region of central Spain. This kind of aviary construction was very common in Spanish rural environments during the 19th century and the first half of the 20th century and is representative of an RH building. Sensory fusion was developed using color cameras, 3D terrestrial laser scanners, and photogrammetric techniques with Unmanned Aerial Vehicles (UAV) and achieves precise indoor and outdoor 3D models. The sensory fusion here also refers to the fact that the information coming from different sensors is integrated into a common documentation framework. A total of 80 dovecotes have been referenced and made available to the public in open access resources. The successful results and applicability of our method lead us to believe that the current documentation and the safeguard technologies in the RH field should evolve towards the use of these 3D computer vision techniques.
Quantification of the Environmental Impacts of Highway Construction Using Remote Sensing Approach
Authors: Feng Shuailong , Liu Shuguang , Jing Lei , Zhu Yu , Yan Wende , Jiang Bingchun , Liu Maochou , Lu Weizhi , Ning Ying , Wang Zhao , Li Qinyuan , Jia Jingni
Published: April 2021

Highways provide key social and economic functions but generate a wide range of environmental consequences that are poorly quantified and understood. Here, we developed a before–during–after control-impact remote sensing (BDACI-RS) approach to quantify the spatial and temporal changes of environmental impacts during and after the construction of the Wujing Highway in China using three buffer zones (0–100 m, 100–500 m, and 500–1000 m). Results showed that land cover composition experienced large changes in the 0–100 m and 100–500 m buffers while that in the 500–1000 m buffer was relatively stable. Vegetation and moisture conditions, indicated by the normalized difference vegetation index (NDVI) and the normalized difference moisture index (NDMI), respectively, demonstrated obvious degradation–recovery trends in the 0–100 m and 100–500 m buffers, while land surface temperature (LST) experienced a progressive increase. The maximal relative changes as annual means of NDVI, NDMI, and LST were about −40%, −60%, and 12%, respectively, in the 0–100m buffer. Although the mean values of NDVI, NDMI, and LST in the 500–1000 m buffer remained relatively stable during the study period, their spatial variabilities increased significantly after highway construction. An integrated environment quality index (EQI) showed that the environmental impact of the highway manifested the most in its close proximity and faded away with distance. Our results showed that the effect distance of the highway was at least 1000 m, demonstrated from the spatial changes of the indicators (both mean and spatial variability). The approach proposed in this study can be readily applied to other regions to quantify the spatial and temporal changes of disturbances of highway systems and subsequent recovery.
Assessment of k-Nearest Neighbor and Random Forest Classifiers for Mapping Forest Fire Areas in Central Portugal Using Landsat-8, Sentinel-2, and Terra Imagery
Authors: Pacheco Admilson da Penha , Junior Juarez Antonio da Silva , Ruiz-Armenteros Antonio Miguel , Henriques Renato Filipe Faria
Published: April 2021

Forest fires threaten the population’s health, biomass, and biodiversity, intensifying the desertification processes and causing temporary damage to conservation areas. Remote sensing has been used to detect, map, and monitor areas that are affected by forest fires due to the fact that the different areas burned by a fire have similar spectral characteristics. This study analyzes the performance of the k-Nearest Neighbor (kNN) and Random Forest (RF) classifiers for the classification of an area that is affected by fires in central Portugal. For that, image data from Landsat-8, Sentinel-2, and Terra satellites and the peculiarities of each of these platforms with the support of Jeffries–Matusita (JM) separability statistics were analyzed. The event under study was a 93.40 km2 fire that occurred on 20 July 2019 and was located in the districts of Santarém and Castelo Branco. The results showed that the problems of spectral mixing, registration date, and those associated with the spatial resolution of the sensors were the main factors that led to commission errors with variation between 1% and 15.7% and omission errors between 8.8% and 20%. The classifiers, which performed well, were assessed using the receiver operating characteristic (ROC) curve method, generating maps that were compared based on the areas under the curves (AUC). All of the AUC were greater than 0.88 and the Overall Accuracy (OA) ranged from 89 to 93%. The classification methods that were based on the kNN and RF algorithms showed satisfactory results.
A Low-Rank Group-Sparse Model for Eliminating Mixed Errors in Data for SRTM1
Authors: Ge Chenyu , Wang Mengmeng , Zhang Hongming , Chen Huan , Sun Hongguang , Chang Yi , Yang Qinke
Published: April 2021

The elimination of mixed errors is a key preprocessing technology for the area of digital elevation model data analysis, which is important for further applying data. We associated group sparsity with the low-rank uniqueness of local transformations of mixing errors to effectively remove mixing errors in data from Shuttle Radar Topography Mission 1 (SRTM 1) based on the sparseness of low-rank groups. First, the stripe-error structure that appeared globally in multiple directions was able to be better represented locally using group-sparse regularization and the uniqueness of the data in the low-rank direction of the local range and using variational ideas to constrain the gradient direction of the data to avoid redundant elimination. Second, the nonlocal self-similarity of the weighted kernel norm was used to remove random noise. Finally, the proposed model for eliminating mixed errors was solved using an algorithm based on the multiplier method of alternating direction. Experiments using simulated and real data found that the proposed low-rank group-sparse method (LRGS) eliminated mixed errors in both visual and quantitative evaluations better than the most recent processing methods and existing dataset products.
Monitoring and Evaluating Restoration Vegetation Status in Mine Region Using Remote Sensing Data: Case Study in Inner Mongolia, China
Authors: Wang Wei , Liu Rongyuan , Gan Fuping , Zhou Ping , Zhang Xiangwen , Ding Ling
Published: April 2021

The ecological restoration of mining areas is very important, and repeated field surveys are inefficient in large-scale vegetation monitoring. The coal mining industry is currently facing the challenge of the lack of appropriate methods for monitoring restoration processes. This study used an open pit coal mine in Dongsheng District, Inner Mongolia, China as an example, and used the 2011–2018 Landsat TM/ETM+ and OLI images to monitor and evaluate vegetation restoration activity of the coal mine. The average value of the monthly maximum value of vegetation index in the growing season was selected as the basic indicator for studying vegetation and bare soil changes. The growth root normalized differential vegetation index (GRNDVI) and GRNDVI anomaly method indicated that the constructed land type change factor was used to study the growth of mine vegetation and change of the range of bare land in the entire mining region. We found that westward mining activities started from 2012, and vegetation was restored in the eastern original mining region from 2013. The restoration vegetation areas from 2015 to 2016 and from 2017 to 2018 were larger than those in the other restoration years. Moreover, areas of expanded bare land from 2011 to 2012, and from 2017 to 2018 were larger than those in the other expansion years. The restoration vegetation growth changes were compared with those of the natural vegetation growth. Results showed that the restoration vegetation growth trend was considerably similar with that of the natural vegetation. Inter-annual restoration effects were analyzed by constructing the effect of the area-average factor and using vegetation growth data. Accordingly, the restoration vegetation effects were best in 2014 and 2016. Comprehensive restoration effect was analyzed using the weighted evaluation method to obtain the overall restoration effects of the coal mine. Results showed that the comprehensive restoration effect is inclined to the inferior growth state. This study conducted a preliminary evaluation of mine restoration vegetation, thereby providing a promising way for the future monitoring and evaluation of such processes.
Estimating the Impact of COVID-19 on the PM2.5 Levels in China with a Satellite-Driven Machine Learning Model
Authors: Li Qiulun , Zhu Qingyang , Xu Muwu , Zhao Yu , Narayan K. M. Venkat , Liu Yang
Published: April 2021

China implemented an aggressive nationwide lockdown procedure immediately after the COVID-19 outbreak in January 2020. As China emerges from the impact of COVID-19 on national economic and industrial activities, it has become the site of a large-scale natural experiment to evaluate the impact of COVID-19 on regional air quality. However, ground measurements of fine particulate matters (PM2.5) concentrations do not offer comprehensive spatial coverage, especially in suburban and rural regions. In this study, we developed a machine learning method with satellite aerosol remote sensing data, meteorological fields and land use parameters as major predictor variables to estimate spatiotemporally resolved daily PM2.5 concentrations in China. Our study period consists of a reference semester (1 November 2018–30 April 2019) and a pandemic semester (1 November 2019–30 April 2020), with six modeling months in each semester. Each period was then divided into subperiod 1 (November and December), subperiod 2 (January and February) and subperiod 3 (March and April). The reference semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.79 (17.55 μg/m3) and the pandemic semester model obtained a 10-fold cross-validated R2 (RMSE) of 0.83 (13.48 μg/m3) for daily PM2.5 predictions. Our prediction results showed high PM2.5 concentrations in the North China Plain, Yangtze River Delta, Sichuan Basin and Xinjiang Autonomous Region during the reference semester. PM2.5 levels were lowered by 4.8 μg/m3 during the pandemic semester compared to the reference semester and PM2.5 levels during subperiod 2 decreased most, by 18%. The southeast region was affected most by the COVID-19 outbreak with PM2.5 levels during subperiod 2 decreasing by 31%, followed by the Northern Yangtze River Delta (29%) and Pearl River Delta (24%).
Quantitatively Assessing Ecological Stress of Urbanization on Natural Ecosystems by Using a Landscape-Adjacency Index
Authors: Lin Meixia , Lin Tao , Jones Laurence , Liu Xiaofang , Xing Li , Sui Jinling , Zhang Junmao , Ye Hong , Liu Yuqin , Zhang Guoqin , Lu Xin
Published: April 2021

Urban spatial expansion poses a threat to regional ecosystems and biodiversity directly through altering the size, shape, and interconnectivity of natural landscapes. Monitoring urban spatial expansion using traditional area-based metrics from remote sensing provides a feasible way to quantify this regional ecological stress. However, variation in landscape-adjacency relationships (i.e., the adjacency between individual landscape classes) caused by urban expansion is often overlooked. In this study, a novel edge-based index (landscape-adjacency index, LAdI) was proposed based on the spatial-adjacency relationship between landscape patches to measure the regional ecological stress of urban expansion on natural landscapes. Taking the entire Yangtze River Delta Urban Agglomerations (YRD) as a study area, we applied the LAdI for individual landscape classes (Vi) and landscape level (LV) to quantitatively assess change over time in the ecological stress of YRD from 1990 to 2015 at two spatial scales: municipal scale and 5 km-grid scale. The results showed that the vulnerable zones (LV ≥ 0.6) were mainly distributed in the north of the YRD, and cultivated land was the most vulnerable natural landscape (Vi ≥ 0.6) at the 5 km-grid scale. The most vulnerable landscape at the municipal scale was cultivated land in 19 of 26 cities in each period, and that in the remaining 7 cities varied at distinct urbanization stages. We used scatter diagrams and Pearson correlation analysis to compare the edge-based LAdI with an area-based index (percent of built-up area, PB) and found that: LV and PB had a significant positive correlation at both the municipal scale and 5 km-grid scale. But there were multiple LVs with different values corresponding to one PB with the same value at the 5 km-grid scale. Both indexes could represent the degree of urban expansion; however, the edge-based metric better quantified ecological stress under different urban-sprawl patterns sharing the same percent of built-up area. As changes in land use affect both the size and edge effect among landscape patches, the area-based PB and the edge-based LAdI should be applied together when assessing the ecological stress caused by urbanization.
Leaf Age Matters in Remote Sensing: Taking Ground Truth for Spectroscopic Studies in Hemiboreal Deciduous Trees with Continuous Leaf Formation
Authors: Neuwirthová Eva , Kuusk Andres , Lhotáková Zuzana , Kuusk Joel , Albrechtová Jana , Hallik Lea
Published: April 2021

We examined the seasonal changes in biophysical, anatomical, and optical traits of young leaves, formed throughout the vegetative season due to sylleptic growth, and mature leaves formed by proleptic growth in spring. Leaf developmental categories contribute to the top-of-canopy reflectance and should be considered when taking ground truth for remote sensing studies (RS). Deciduous tree species, Betula pendula, Populus tremula, and Alnus incana, were sampled from May to October 2018 in an Estonian hemiboreal forest. Chlorophyll and carotenoid content were detected biochemically; leaf anatomical traits (leaf, palisade, and spongy mesophyll thickness) were measured on leaf cross-sections; leaf reflectance was measured by a spectroradiometer with an integrating sphere (350–2500 nm). Biophysical and anatomical leaf traits were related to 64 vegetation indices (VIs). Linear models based on VIs for all tested leaf traits were more robust if both juvenile and mature leaves were included. This study provides information on which VIs are interchangeable or independent. Pigment and leaf thickness sensitive indices formed PC1; water and structural trait related VIs formed an independent group associated with PC3. Type of growth and leaf age could affect the validation of biophysical and anatomical leaf trait retrieval from the optical signal. It is, therefore, necessary to sample both leaf developmental categories—young and mature—in RS, especially if sampling is only once within the vegetation season.
Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification
Authors: Farmakis Ioannis , Bonneau David , Hutchinson D. Jean , Vlachopoulos Nicholas
Published: April 2021

Reality capture technologies, also known as close-range sensing, have been increasingly popular within the field of engineering geology and particularly rock slope management. Such technologies provide accurate and high-resolution n-dimensional spatial representations of our physical world, known as 3D point clouds, that are mainly used for visualization and monitoring purposes. To extract knowledge from point clouds and inform decision-making within rock slope management systems, semantic injection through automated processes is necessary. In this paper, we propose a model that utilizes a segmentation procedure which delivers segments ready to classify and be retained or rejected according to complementary knowledge-based filter criteria. First, we provide relevant voxel-based features based on the local dimensionality, orientation, and topology and partition them in an assembly of homogenous segments. Subsequently, we build a decision tree that utilizes geometrical, topological, and contextual information and enables the classification of a multi-hazard railway rock slope section in British Columbia, Canada into classes involved in landslide risk management. Finally, the approach is compared to machine learning integrating recent featuring strategies for rock slope classification with limited training data (which is usually the case). This alternative to machine learning semantic segmentation approaches reduces substantially the model size and complexity and provides an adaptable framework for tailored decision-making systems leveraging rock slope semantics.
Intensity Values of Terrestrial Laser Scans Reveal Hidden Black Rock Art Pigment
Authors: Jalandoni Andrea , Winans W. , Willis Mark
Published: April 2021

The intensity values of terrestrial laser scanning (TLS) can be used to reveal painted black rock art behind graffiti and moss. The effect was observed in Gumahon cave in Peñablanca, Philippines where previously unnoticed black pigment was exposed underneath moss, red and white painted graffiti, and etched name graffiti. The application of TLS intensity values for this purpose has not, to our knowledge, been previously reported. The significance of this finding is that archaeologists are provided a new method of detecting obfuscated rock art that can aid interpretation. The method can be applied in similar contexts as black painted rock art is common in limestone caves across Southeast Asia and Micronesia, but also ubiquitous globally.
Validation of Plantar Pressure and Reaction Force Measured by Moticon Pressure Sensor Insoles on a Concept2 Rowing Ergometer
Authors: Barratt Georgina Kate , Bellenger Clint , Robertson Eileen Yule , Lane Jason , Crowther Robert George
Published: April 2021

The purpose of this study was to determine the reliability and validity of plantar pressure and reaction force measured using the Moticon and Pedar-x sensor insoles while rowing on a Concept2 ergometer. Nineteen participants performed four 500 m trials of ergometer rowing at 22–24 strokes/min; two trials wearing Moticon insoles and two wearing Pedar-x insoles in a randomised order. Moticon and Pedar-x insoles both showed moderate to strong test–retest reliability (ICC = 0.57–0.92) for mean and peak plantar pressure and reaction force. Paired t-test demonstrated a significant difference (p < 0.001) between Moticon and Pedar-x insoles, effect size showed a large bias (ES > 1.13), and Pearson’s correlation (r < 0.37) showed poor agreement for all plantar pressure and reaction force variables. Compared to Pedar-x, the Moticon insoles demonstrated poor validity, however, the Moticon insoles had strong reliability. Due to poor validity, caution should be used when considering Moticon insoles to assess changes in pressure and force reliably over time, across multiple trials or sessions. Moticon’s wireless and user-friendly application would be beneficial for assessing and monitoring biomechanical parameters in rowing if validity between measures of interest and Moticon’s results can be established.
Vision and RTLS Safety Implementation in an Experimental Human—Robot Collaboration Scenario
Authors: Slovák Juraj , Melicher Markus , Šimovec Matej , Vachálek Ján
Published: April 2021

Human–robot collaboration is becoming ever more widespread in industry because of its adaptability. Conventional safety elements are used when converting a workplace into a collaborative one, although new technologies are becoming more widespread. This work proposes a safe robotic workplace that can adapt its operation and speed depending on the surrounding stimuli. The benefit lies in its use of promising technologies that combine safety and collaboration. Using a depth camera operating on the passive stereo principle, safety zones are created around the robotic workplace, while objects moving around the workplace are identified, including their distance from the robotic system. Passive stereo employs two colour streams that enable distance computation based on pixel shift. The colour stream is also used in the human identification process. Human identification is achieved using the Histogram of Oriented Gradients, pre-learned precisely for this purpose. The workplace also features autonomous trolleys for material supply. Unequivocal trolley identification is achieved using a real-time location system through tags placed on each trolley. The robotic workplace’s speed and the halting of its work depend on the positions of objects within safety zones. The entry of a trolley with an exception to a safety zone does not affect the workplace speed. This work simulates individual scenarios that may occur at a robotic workplace with an emphasis on compliance with safety measures. The novelty lies in the integration of a real-time location system into a vision-based safety system, which are not new technologies by themselves, but their interconnection to achieve exception handling in order to reduce downtimes in the collaborative robotic system is innovative.
User Experience of the Mobile Terminal Customization System: The Influence of Interface Design and Educational Background on Personalized Customization
Authors: Yi Minzhe , Wang Ying , Tian Xiaoxue , Xia Huichao
Published: April 2021

The study verified the role that different interface designs and users’ educational backgrounds play in the task performance and subjective evaluation of mobile terminal customization system. Interface type (based on scroll, alternative, and attribute) and user group (college students and industrial workers) were employed as the variables. A total of 72 users were included in the study, and an analysis of 3 × 2 between-participants design indicated that (1) Different interface designs of customization systems had a significant difference in task performance, the alternative based interface had the best results in the task performance, and there was no significant difference between the attribute-based and scroll-based interfaces in task performance; (2) The matching between educational background and interface type will affect the users’ evaluation on system usability. Industrial workers thought that the scroll-based and alternative-based interfaces were more useable, while college students preferred attribute-based interface design; (3) Different interfaces had a significant difference in user task load. The scroll-based interface had the lowest mental demand on the users, while alternative-based had the lowest physical demand on the users, though it consumed more effort; (4) Different educational backgrounds had a significant difference in user task load. Industrial workers showed lower effort in the scroll-based and alternative-based interfaces, while college students had lower effort in the attribution-based interface; (5) A correlation analysis showed that there was a significant negative correlation between the system usability score and the effort in task load. This study results have a positive significance for interface design. With educational background and layout as two important factors in our interface design, we may obtain the most appropriate design principles for enhancing the online customization experiences of different groups of consumers. The more important is that this study is based on the actual needs of the industry. For the first time, we take suitcase as an online customized product, which may not only help local manufacturers to extend their traditional offline distribution channels to online, but also provide a constructive thinking concerning interface design for customization of a single product.
Time Series Forecasting of Univariate Agrometeorological Data: A Comparative Performance Evaluation via One-Step and Multi-Step Ahead Forecasting Strategies
Authors: Suradhaniwar Saurabh , Kar Soumyashree , Durbha Surya S. , Jagarlapudi Adinarayana
Published: April 2021

High-frequency monitoring of agrometeorological parameters is quintessential in the domain of Precision Agriculture (PA), where timeliness of collected observations and the ability to generate ahead-of-time predictions can substantially impact the crop yield. In this context, state-of-the-art internet-of-things (IoT)-based sensing platforms are often employed to generate, pre-process and assimilate real-time data from heterogeneous sensors and streaming data sources. Simultaneously, Time-Series Forecasting Algorithms (TSFAs) are responsible for generating reliable forecasts with a pre-defined forecast horizon and confidence. These TSFAs often rely on modelling the correlation between endogenous variables, the impact of exogenous variables on latent form and structural properties of data such as autocorrelation, periodicity, trend, pattern, and causality to approximate the model parameters. Traditionally, TSFAs such as the Holt–Winters (HW) and Autoregressive family of models (ARIMA) apply a linear and parametric approach towards model approximation, whilst models like Support Vector Regression (SVRs) and Neural Networks (NNs) adhere to a non-linear, non-parametric approach for modelling the historical data. Recently, Deep-Learning-based TSFAs such as Recurrent Neural Networks (RNNs), and Long-Short-Term-Memory (LSTMS) have gained popularity due to their capability to model long sequences of highly non-linear and stochastic data effectively. However, the evolution of TSFAs for predicting agrometeorological parameters pivots around one-step-ahead forecasting, which often overestimates the performance metrics defined for validating forecast capabilities of potential TSFAs. Hence, this paper attempts to evaluate and compare the performance of different machine learning (ML) and deep learning (DL) based TSFAs under one-step and multi-step-ahead forecast scenarios, thereby estimating the generalization capabilities of TSFA models over unseen data. The data used in this study are collected from an Automatic Weather Station (AWS), sampled at an interval of 15 min, and range over one month. Temperature (T) and Humidity (H) observations from the AWS are further converted into univariate, supervised time-series diurnal data profiles. Finally, walk-forward validation is used to evaluate recursive one-step-ahead forecasts until the desired prediction horizon is achieved. The results show that the Seasonal Auto-Regressive Integrated Moving Average (SARIMA) and SVR models outperform their DL-based counterparts in one-step and multi-step ahead settings with a fixed forecast horizon. This work aims to present a baseline comparison between different TSFAs to assist the process of model selection and facilitate rapid ahead-of-time forecasting for end-user applications.
Robust Coarse-to-Fine Registration Scheme for Mobile Laser Scanner Point Clouds Using Multiscale Eigenvalue Statistic-Based Descriptor
Authors: Fu Yongjian , Li Zongchun , Wang Wenqi , He Hua , Xiong Feng , Deng Yong
Published: April 2021

To overcome the drawbacks of pairwise registration for mobile laser scanner (MLS) point clouds, such as difficulty in searching the corresponding points and inaccuracy registration matrix, a robust coarse-to-fine registration method is proposed to align different frames of MLS point clouds into a common coordinate system. The method identifies the correct corresponding point pairs from the source and target point clouds, and then calculates the transform matrix. First, the performance of a multiscale eigenvalue statistic-based descriptor with different combinations of parameters is evaluated to identify the optimal combination. Second, based on the geometric distribution of points in the neighborhood of the keypoint, a weighted covariance matrix is constructed, by which the multiscale eigenvalues are calculated as the feature description language. Third, the corresponding points between the source and target point clouds are estimated in the feature space, and the incorrect ones are eliminated via a geometric consistency constraint. Finally, the estimated corresponding point pairs are used for coarse registration. The value of coarse registration is regarded as the initial value for the iterative closest point algorithm. Subsequently, the final fine registration result is obtained. The results of the registration experiments with Autonomous Systems Lab (ASL) Datasets show that the proposed method can accurately align MLS point clouds in different frames and outperform the comparative methods.
A Comparative Analysis of Honeypots on Different Cloud Platforms
Authors: Kelly Christopher , Pitropakis Nikolaos , Mylonas Alexios , McKeown Sean , Buchanan William J.
Published: April 2021

In 2019, the majority of companies used at least one cloud computing service and it is expected that by the end of 2021, cloud data centres will process 94% of workloads. The financial and operational advantages of moving IT infrastructure to specialised cloud providers are clearly compelling. However, with such volumes of private and personal data being stored in cloud computing infrastructures, security concerns have risen. Motivated to monitor and analyze adversarial activities, we deploy multiple honeypots on the popular cloud providers, namely Amazon Web Services (AWS), Google Cloud Platform (GCP) and Microsoft Azure, and operate them in multiple regions. Logs were collected over a period of three weeks in May 2020 and then comparatively analysed, evaluated and visualised. Our work revealed heterogeneous attackers’ activity on each cloud provider, both when one considers the volume and origin of attacks, as well as the targeted services and vulnerabilities. Our results highlight the attempt of threat actors to abuse popular services, which were widely used during the COVID-19 pandemic for remote working, such as remote desktop sharing. Furthermore, the attacks seem to exit not only from countries that are commonly found to be the source of attacks, such as China, Russia and the United States, but also from uncommon ones such as Vietnam, India and Venezuela. Our results provide insights on the adversarial activity during our experiments, which can be used to inform the Situational Awareness operations of an organisation.
Continuous Sign Language Recognition through a Context-Aware Generative Adversarial Network
Authors: Papastratis Ilias , Dimitropoulos Kosmas , Daras Petros
Published: April 2021

Continuous sign language recognition is a weakly supervised task dealing with the identification of continuous sign gestures from video sequences, without any prior knowledge about the temporal boundaries between consecutive signs. Most of the existing methods focus mainly on the extraction of spatio-temporal visual features without exploiting text or contextual information to further improve the recognition accuracy. Moreover, the ability of deep generative models to effectively model data distribution has not been investigated yet in the field of sign language recognition. To this end, a novel approach for context-aware continuous sign language recognition using a generative adversarial network architecture, named as Sign Language Recognition Generative Adversarial Network (SLRGAN), is introduced. The proposed network architecture consists of a generator that recognizes sign language glosses by extracting spatial and temporal features from video sequences, as well as a discriminator that evaluates the quality of the generator’s predictions by modeling text information at the sentence and gloss levels. The paper also investigates the importance of contextual information on sign language conversations for both Deaf-to-Deaf and Deaf-to-hearing communication. Contextual information, in the form of hidden states extracted from the previous sentence, is fed into the bidirectional long short-term memory module of the generator to improve the recognition accuracy of the network. At the final stage, sign language translation is performed by a transformer network, which converts sign language glosses to natural language text. Our proposed method achieved word error rates of 23.4%, 2.1% and 2.26% on the RWTH-Phoenix-Weather-2014 and the Chinese Sign Language (CSL) and Greek Sign Language (GSL) Signer Independent (SI) datasets, respectively.
Overuse-Related Injuries of the Musculoskeletal System: Systematic Review and Quantitative Synthesis of Injuries, Locations, Risk Factors and Assessment Techniques
Authors: Orejel Bustos Amaranta , Belluscio Valeria , Camomilla Valentina , Lucangeli Leandro , Rizzo Francesco , Sciarra Tommaso , Martelli Francesco , Giacomozzi Claudia
Published: April 2021

Overuse-related musculoskeletal injuries mostly affect athletes, especially if involved in preseason conditioning, and military populations; they may also occur, however, when pathological or biological conditions render the musculoskeletal system inadequate to cope with a mechanical load, even if moderate. Within the MOVIDA (Motor function and Vitamin D: toolkit for risk Assessment and prediction) Project, funded by the Italian Ministry of Defence, a systematic review of the literature was conducted to support the development of a transportable toolkit (instrumentation, protocols and reference/risk thresholds) to help characterize the risk of overuse-related musculoskeletal injury. The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach was used to analyze Review papers indexed in PubMed and published in the period 2010 to 2020. The search focused on stress (overuse) fracture or injuries, and muscle fatigue in the lower limbs in association with functional (biomechanical) or biological biomarkers. A total of 225 Review papers were retrieved: 115 were found eligible for full text analysis and led to another 141 research papers derived from a second-level search. A total of 183 papers were finally chosen for analysis: 74 were classified as introductory to the topics, 109 were analyzed in depth. Qualitative and, wherever possible, quantitative syntheses were carried out with respect to the literature review process and quality, injury epidemiology (type and location of injuries, and investigated populations), risk factors, assessment techniques and assessment protocols.
Controlling an Industrial Robot Using a Graphic Tablet in Offline and Online Mode
Authors: Kaczmarek Wojciech , Lotys Bartłomiej , Borys Szymon , Laskowski Dariusz , Lubkowski Piotr
Published: April 2021

The article presents the possibility of using a graphics tablet to control an industrial robot. The paper presents elements of software development for offline and online control of a robot. The program for the graphic tablet and the operator interface was developed in C# language in Visual Studio environment, while the program controlling the industrial robot was developed in RAPID language in the RobotStudio environment. Thanks to the development of a digital twin of the real robotic workstation, tests were carried out on the correct functioning of the application in offline mode (without using the real robot). The obtained results were verified in online mode (on a real production station). The developed computer programmes have a modular structure, which makes it possible to easily adapt them to one’s needs. The application allows for changing the parameters of the robot and the parameters of the path drawing. Tests were carried out on the influence of the sampling frequency and the tool diameter on the quality of the reconstructed trajectory of the industrial robot. The results confirmed the correctness of the application. Thanks to the new method of robot programming, it is possible to quickly modify the path by the operator, without the knowledge of robot programming languages. Further research will focus on analyzing the influence of screen resolution and layout scale on the accuracy of trajectory generation.
Women Mayors in Spain: An Analysis of Gender Differences in the Management and Quality of Information on Municipal Websites
Authors: Herrero-Gutiérrez Francisco-Javier , Simelio Núria , Carrascosa Puertas Lara
Published: April 2021

This article analyses the differences in the level of transparency and quality of information on the institutional websites of Spanish councils depending on whether the mayor’s office is held by a woman or a man. We focus on 605 Spanish municipalities with more than 10,000 inhabitants in which women mayors represent 26.3% (n = 159) of the total. The method is based on 52 quantitative indicators divided into two thematic blocks related to municipal transparency and to information for participation. The principal results reveal that municipalities led by women yield better results in terms of compliance with the indicators evaluated, with 50.68%, compared to the municipalities in which the mayor is a man, with 38.13%. In addition, a more detailed analysis reveals how women mayors obtain better results in all groups of indicators: transparency, quality of the information published on municipal websites and tools provided for citizen participation. The study reveals how the increase in elected women has a positive influence on the implementation of policies that allow for more transparent information and communication and that promote greater inclusion of citizens in the public debate.
An Evaluation of Optical Absorbance Kinetics for the Detection of Micro-Porosity in Molecularly Doped Polymer Thin-Films
Authors: Goldie David Mitchell , Skelton Orlaith , Bailey Thomas James
Published: April 2021

The use of optical absorbance kinetics to identify micro-porous regions in doped polymer films is evaluated. Data are presented for a series of hydrazone doped polymer films which are found to optically bleach upon exposure to an ultra violet (UV) radiation source. The UV absorbance kinetics are found to exhibit distinctive characteristics for the various polymers studied, with changes in film absorbance occurring either in a fast (<103 s) or slow (>104 s) timescale. An interpretation of these distinctive timescales based upon a cellular-automata model of the absorbance kinetics suggests that the underlying photo-oxidation of the hydrazone is highly sensitive to underlying micro-porosity in the films which controls the necessary supply of absorbed oxygen for photo-cyclic reaction.
An Integrated Framework to Measure the Performance of Inter-Organizational Programme on Health Technology Assessment
Authors: Vainieri Milena , Ferrè Francesca , Manetti Stefania
Published: April 2021

Combining insights from collaborative governance, performance management, and health technology assessment (HTA) literature, this study develops an integrated framework to systematically measure and monitor the performance of HTA network programmes. This framework is validated throughout an action research carried out in the Italian HTA network programme for medical devices. We found that when building up collaborative performance management systems, some elements such as the participation in the design and the use of context specific performance assessment framework, facilitate their acceptance by managers and policy makers especially in high professionalized and sector-specific organizations because it reflects their distinctive language and culture. The hybrid framework may help health authorities and policymakers to understand the HTA network, monitor its performance, and ensure network sustainability over time.