bluuebunny/crossref_metadata_2025 huggingface dataset:
Performance of Multi-GNSS in the Asia-Pacific Region: Signal Quality, Broadcast Ephemeris and Precise Point Positioning (PPP)
Authors: Li Mengyuan , Huang Guanwen , Wang Le , Xie Wei , Yue Fan
Published: June 2022

Since BeiDou Navigation Satellite System (BDS) and Japan’s Quasi-Zenith Satellite System (QZSS) have more visible satellites in the Asia-Pacific region, and navigation satellites of Global Positioning System (GPS), Galileo satellite navigation system (Galileo), and GLONASS satellite navigation system (GLONASS) are uniformly distributed globally, the service level of multi-mode Global Navigation Satellite System (GNSS) in the Asia-Pacific region should represent the best service capability. Based on the observation data of 10 Multi-GNSS Experiment (MGEX) stations, broadcast ephemeris and precision ephemeris from 13 to 19 October 2021, this paper comprehensively evaluated the service capability of multi-GNSS in the Asia-Pacific region from three aspects of observation data quality, broadcast ephemeris performance, and precision positioning level. The results show that: (1) the carrier-to-noise-density ratio (C/N0) quality of the GPS and Galileo is the best, followed by BDS and GLONASS, and QZSS is the worst. GPS, BDS-2, GLONASS, and QZSS pseudorange multipath values range from 0 to 0.6 m, while Galileo system and BDS-3 pseudorange multipath values range from 0 to 0.8 m. (2) In terms of broadcast ephemeris accuracy, BDS-3 broadcast ephemeris has the best orbit, and the three-dimensional (3D) Root Mean Square (RMS) is 0.21 m; BDS-2 was the worst, with a 3D RMS of 1.99 m. The broadcast ephemeris orbits of GPS, Galileo, QZSS, and GLONASS have 3D RMS of 0.60 m, 0.62 m, 0.83 m, and 1.27 m, respectively. For broadcast ephemeris clock offset: Galileo has the best performance, 0.61 ns, GLONASS is the worst, standard deviation (STD) is 3.10 ns, GPS, QZSS, BDS-3 and BDS-2 are 0.65 ns, 0.75 ns, and 1.72 ns, respectively. For signal-in-space ranging errors (SISRE), the SISRE results of GPS and Galileo systems are the best, fluctuating in the range of 0 m–2 m, followed by QZSS, BDS-3, Galileo, and BDS-2. (3) GPS, BDS, GLONASS, Galileo, GPS/QZSS, and BDS/QZSS were used for positioning experiments. In static PPP, the convergence time and positioning accuracy of GPS show the best performance. The positioning accuracy of GPS/QZSS and BDS/QZSS is improved compared with that of GPS and BDS. In terms of kinematic PPP, the convergence time and positioning accuracy of GPS/QZSS and BDS/QZSS are improved compared with that of GPS and BDS. In addition to GLONASS and Galileo systems, the other combinations outperformed 3 cm, 3 cm, and 5 cm in the east, north, and up directions.
Atmospheric Formaldehyde Monitored by TROPOMI Satellite Instrument throughout 2020 over São Paulo State, Brazil
Authors: Freitas Arthur Dias , Fornaro Adalgiza
Published: June 2022

We aimed to study the daily formaldehyde (HCHO) columns over urban and forested areas in São Paulo State, Brazil, from rhe TROPOMI spectrometer onboard the Sentinel-5P satellite during 2020. Nineteen specific areas were defined in four regions: 3 areas in each of two preserved Atlantic Forests (PEMD and PETAR), 3 in a sugarcane growing region (NERG) and 10 in the Metropolitan Area of São Paulo (MASP), among which 2 areas are in the Morro Grande reserve, which is a significant remnant of Atlantic Forest outside the densely urbanized area of MASP. An analysis of variance and Tukey’s test showed that the mean annual columns over the Morro Grande reserve (1.69±1.05×10−4 mol/m² and 1.73±1.07×10−4 mol/m²) presented greater statistical similarity with the forest and rural areas of the state (<1.70×10−4 mol/m²) than with MASP (>2.00×10−4 mol/m²), indicating few effects from megacity anthropogenic emissions. Case studies addressing selected days in 2020 showed that fires in and around the state were related to episodes of maximum density of HCHO columns. The results showed significant seasonality, with lower concentrations during summer (wet season) and higher concentrations during winter and spring (dry and transition dry–wet seasons).
Estimation of Maize Yield and Flowering Time Using Multi-Temporal UAV-Based Hyperspectral Data
Authors: Fan Jiahao , Zhou Jing , Wang Biwen , de Leon Natalia , Kaeppler Shawn M. , Lima Dayane C. , Zhang Zhou
Published: June 2022

Maize (Zea mays L.) is one of the most consumed grains in the world. Within the context of continuous climate change and the reduced availability of arable land, it is urgent to breed new maize varieties and screen for the desired traits, e.g., high yield and strong stress tolerance. Traditional phenotyping methods relying on manual assessment are time-consuming and prone to human errors. Recently, the application of uncrewed aerial vehicles (UAVs) has gained increasing attention in plant phenotyping due to their efficiency in data collection. Moreover, hyperspectral sensors integrated with UAVs can offer data streams with high spectral and spatial resolutions, which are valuable for estimating plant traits. In this study, we collected UAV hyperspectral imagery over a maize breeding field biweekly across the growing season, resulting in 11 data collections in total. Multiple machine learning models were developed to estimate the grain yield and flowering time of the maize breeding lines using the hyperspectral imagery. The performance of the machine learning models and the efficacy of different hyperspectral features were evaluated. The results showed that the models with the multi-temporal imagery outperformed those with imagery from single data collections, and the ridge regression using the full band reflectance achieved the best estimation accuracies, with the correlation coefficients (r) between the estimates and ground truth of 0.54 for grain yield, 0.91 for days to silking, and 0.92 for days to anthesis. In addition, we assessed the estimation performance with data acquired at different growth stages to identify the good periods for the UAV survey. The best estimation results were achieved using the data collected around the tasseling stage (VT) for the grain yield estimation and around the reproductive stages (R1 or R4) for the flowering time estimation. Our results showed that the robust phenotyping framework proposed in this study has great potential to help breeders efficiently estimate key agronomic traits at early growth stages.
A UAS and Machine Learning Classification Approach to Suitability Prediction of Expanding Natural Habitats for Endangered Flora Species
Authors: Jurišić Mladen , Radočaj Dorijan , Plaščak Ivan , Rapčan Irena
Published: June 2022

In this study, we propose integrating unmanned aerial systems (UASs) and machine learning classification for suitability prediction of expanding habitats for endangered flora species to prevent further extinction. Remote sensing imaging of the protected steppe-like grassland in Bilje using the DJI P4 Multispectral UAS ensured non-invasive data collection. A total of 129 individual flora units of five endangered flora species, including small pasque flower (Pulsatilla pratensis (L.) Miller ssp. nigricans (Störck) Zämelis), green-winged orchid (Orchis morio (L.)), Hungarian false leopardbane (Doronicum hungaricum Rchb.f.), bloody cranesbill (Geranium sanguineum (L.)) and Hungarian iris (Iris variegate (L.)) were detected and georeferenced. Habitat suitability in the projected area, designated for the expansion of the current area of steppe-like grassland in Bilje, was predicted using the binomial machine learning classification algorithm based on three groups of environmental abiotic criteria: vegetation, soil, and topography. Four machine learning classification methods were evaluated: random forest, XGBoost, neural network, and generalized linear model. The random forest method outperformed the other classification methods for all five flora species and achieved the highest receiver operating characteristic (ROC) values, ranging from 0.809 to 0.999. Soil compaction was the least favorable criterion for the habitat suitability of all five flora species, indicating the need to perform soil tillage operations to potentially enable the expansion of their coverage in the projected area. However, potential habitat suitability was detected for the critically endangered flora species of Hungarian false leopardbane, indicating its habitat-related potential for expanding and preventing further extinction. In addition to the current methods of predicting current coverage and population count of endangered species using UASs, the proposed method could serve as a basis for decision making in nature conservation and land management.
Terrestrial and Airborne Structure from Motion Photogrammetry Applied for Change Detection within a Sinkhole in Thuringia, Germany
Authors: Petschko Helene , Zehner Markus , Fischer Patrick , Goetz Jason
Published: June 2022

Detection of geomorphological changes based on structure from motion (SfM) photogrammetry is highly dependent on the quality of the 3D reconstruction from high-quality images and the correspondingly derived point precision estimates. For long-term monitoring, it is interesting to know if the resulting 3D point clouds and derived detectable changes over the years are comparable, even though different sensors and data collection methods were applied. Analyzing this, we took images of a sinkhole terrestrially with a Nikon D3000 and aerially with a DJI drone camera in 2017, 2018, and 2019 and computed 3D point clouds and precision maps using Agisoft PhotoScan and the SfM_Georef software. Applying the “multiscale model to model cloud comparison using precision maps” plugin (M3C2-PM) in CloudCompare, we analyzed the differences between the point clouds arising from the different sensors and data collection methods per year. Additionally, we were interested if the patterns of detectable change over the years were comparable between the data collection methods. Overall, we found that the spatial pattern of detectable changes of the sinkhole walls were generally similar between the aerial and terrestrial surveys, which were performed using different sensors and camera locations. Although the terrestrial data collection was easier to perform, there were often challenges due to terrain and vegetation around the sinkhole to safely acquire adequate viewing angles to cover the entire sinkhole, which the aerial survey was able to overcome. The local levels of detection were also considerably lower for point clouds resulting from aerial surveys, likely due to the ability to obtain closer-range imagery within the sinkhole.
Multisource Data Fusion and Adversarial Nets for Landslide Extraction from UAV-Photogrammetry-Derived Data
Authors: He Haiqing , Li Changcheng , Yang Ronghao , Zeng Huaien , Li Lin , Zhu Yufeng
Published: June 2022

Most traditional methods have difficulty detecting landslide boundary accurately, and the existing methods based on deep learning often lead to insufficient training or overfitting due to insufficient samples. An end-to-end, semi-supervised adversarial network, which fully considers spectral and topographic features derived using unmanned aerial vehicle (UAV) photogrammetry, is proposed to extract landslides by semantic segmentation to address the abovementioned problem. In the generative network, a generator similar to pix2pix is introduced into the proposed adversarial nets to learn semantic features from UAV-photogrammetry-derived data by semi-supervised operation and a confrontational strategy to reduce the requirement of the number of labeled samples. In the discriminative network, DeepLabv3+ is improved by inserting multilevel skip connection architecture with upsampling operation to obtain the contextual information and retain the boundary information of landslides at all levels, and a topographic convolutional neural network is proposed to be inserted into the encoder to concatenate topographic features together with spectral features. Then, transfer learning with the pre-trained parameters and weights, shared with pix2pix and DeepLabv3+, is used to perform landslide extraction training and validation. In our experiments, the UAV-photogrammetry-derived data of a typical landslide located at Meilong gully in China are collected to test the proposed method. The experimental results show that our method can accurately detect the area of a landslide and achieve satisfactiory results based on several indicators including the Precision, Recall, F1 score, and mIoU, which are 13.07%, 15.65%, 16.96%, and 18.23% higher than those of the DeepLabV3+. Compared with state-of-the-art methods such as U-Net, PSPNet, and pix2pix, the proposed adversarial nets considering multidimensional information such as topographic factors can perform better and significantly improve the accuracy of landslide extraction.
Radar Interferometry as a Monitoring Tool for an Active Mining Area Using Sentinel-1 C-Band Data, Case Study of Riotinto Mine
Authors: Escayo Joaquin , Marzan Ignacio , Martí David , Tornos Fernando , Farci Angelo , Schimmel Martin , Carbonell Ramon , Fernández José
Published: June 2022

Soil instability is a major hazard facing the mining industry in its role of supplying the indispensable mineral resources that our societal challenges require. Aiming to demonstrate the monitoring potential of radar satellites in the mining sector, we analyze the deformation field in the Riotinto mine, Spain. We propose a new method for combining ascending and descending results into a common dataset that provides better resolution. We project the LOS measurements resulting from both geometries to a common reference system without applying any type of geometric restriction. As a projection system, we use the vertical direction in flat areas and the slope in steep topographies. We then identify and remove outliers and artifacts from the joint dataset to finally obtain a deformation map that combines the two acquisition perspectives. The results in the Atalaya pit are consistent with GNSS measurements. The movements observed in the rock dumps were unknown before this study. We demonstrate the great potential of the Sentinel-1 satellite as a complementary tool for monitoring systems in mining environments and we call for its use to be standardized to guarantee a safe and sustainable supply of mineral resources necessary for a just technological transition.
Applying a Wavelet Transform Technique to Optimize General Fitting Models for SM Analysis: A Case Study in Downscaling over the Qinghai–Tibet Plateau
Authors: Hu Zixuan , Chai Linna , Crow Wade T. , Liu Shaomin , Zhu Zhongli , Zhou Ji , Qu Yuquan , Liu Jin , Yang Shiqi , Lu Zheng
Published: June 2022

Soil moisture (SM) is an important land-surface parameter. Although microwave remote sensing is recognized as one of the most appropriate methods for retrieving SM, such retrievals often cannot meet the requirements of specific applications because of their coarse spatial resolution and spatiotemporal data gaps. A range of general models (GMs) for SM analysis topics (e.g., gap-filling, forecasting, and downscaling) have been introduced to address these shortcomings. This work presents a novel strategy (i.e., optimized wavelet-coupled fitting method (OWCM)) to enhance the fitting accuracy of GMs by introducing a wavelet transform (WT) technique. Four separate GMs are selected, i.e., elastic network regression, area-to-area regression kriging, random forest regression, and neural network regression. The fitting procedures are then tested within a downscaling analysis implemented between aggregated Global Land Surface Satellite products (i.e., LAI, FVC, albedo), Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST, and Random Forest Soil Moisture (RFSM) datasets in both the WT space and the regular space. Then, eight fine-resolution SM datasets mapped from the trained GMs and OWCMs are analyzed using direct comparisons with in situ SM measurements and indirect intercomparisons between the aggregated OWCM-/GM-derived SM and RFSM. The results demonstrate that OWCM-derived SM products are generally closer to the in situ SM observations, and better capture in situ SM dynamics during the unfrozen season, compared to the corresponding GM-derived SM product, which shows fewer time changes and more stable trends. Moreover, OWCM-derived SM products represent a significant improvement over corresponding GM-derived SM products in terms of their ability to spatially and temporally match RFSM. Although spatial heterogeneity still substantially impacts the fitting accuracies of both GM and OWCM SM products, the improvements of OWCMs over GMs are significant. This improvement can likely be attributed to the fitting procedure of OWCMs implemented in the WT space, which better captures high- and low-frequency image features than in the regular space.
Research on Stray-Light Suppression Method for Large Off-Axis Three-Mirror Anastigmatic Space Camera
Authors: Wei Lei , Yang Lin , Fan Yuan-Peng , Cong Shan-Shan , Wang Yan-Song
Published: June 2022

The stray-light suppression of a large off-axis three-mirror anastigmatic space camera has been a hot topic, and this study proposes a composite stray-light suppression strategy that effectively suppresses stray light using the combination of a baffle, retaining ring, and internal antistray light measures. Additionally, the light barrier of the third mirror with a three-layered structure was designed to further optimize the composite stray-light suppression system. At the stray-light simulation analysis stage, in view of the limitations of the Torrance–Sparrow scattering analysis model, an analysis model with wide adaptability is proposed, which can be applied to the stray-light simulation analysis of large-size mirrors with rough surfaces. The simulation results indicate that the point source transmittance of the composite stray-light suppression strategy proposed in this paper is of the order of 10−5 before installing the light barrier of the third mirror, and the veiling glare index of the full field of view is less than 5.8%. After installing the light barrier of the third mirror, the point source transmittance reached the order of 10−8, and the veiling glare index of the full field of view was less than 1.31%. Moreover, the influence of the light barrier of the third mirror on the modulation transfer function of the system was less than 2.3%. The modulation transfer function test of the large-width off-axis three-mirror anastigmatic space camera in a simulated vacuum on-orbit environment was completed, and the test results indicated that the negative impact of the light barrier of the third mirror on the modulation transfer function was less than 3.6%. Moreover, an out-of-field imaging test of the space camera was conducted and the results showed that the image was clear, and the SNR reached 80 dB. The simulation and experimental results prove that the solution in this study can effectively solve the problem of stray-light suppression for large off-axis three-mirror anastigmatic space cameras.
Battery-Free Wireless Light-Sensing Tag Based on a Long-Range Dual-Port Dual-Polarized RFID Platform
Authors: Wagih Mahmoud , Weddell Alex S. , Beeby Steve
Published: June 2022

Radio frequency identification (RFID) represents an emerging platform for passive RF-powered wireless sensing. Differential Multi-port RFID systems are widely used to enable multiple independent measurands to be gathered, or to overcome channel variations. This paper presents a dual-port/dual-integrated circuit (IC) RFID sensing tag based on a shared aperture dual-polarized microstrip antenna. The tag can be loaded with different sensors where the received signal strength indicator (RSSI) of one IC is modulated using a sensor, and the other acts as a measurand-insensitive reference, for differential sensing. The 868 MHz tag maintains a minimum unloaded read range of 14 m insensitive to deployment on metals or lossy objects, which represents the longest reported range of a multi-port RFID sensing tag. The tag is loaded with a light-dependent resistor (LDR) to demonstrate its functionality as a battery-less wireless RFID light sensor. Following detailed RF characterization of the LDR, it is shown that the impedance, and consequently the RSSI, of the sensing tag are modulated by changing the light intensity, whereas the reference port maintains a mostly unchanged response for a correlated channel. The proposed tag shows the potential for channel variations-tolerant differential RFID sensing platforms based on polarization-diversity antennas.
An Approach for Time Synchronization of Wireless Accelerometer Sensors Using Frequency-Squeezing-Based Operational Modal Analysis
Authors: Chen Yi , Zheng Xiaoqing , Luo Yaozhi , Shen Yanbin , Xue Yu , Fu Wenwei
Published: June 2022

Wireless sensor networks usually suffer from the issue of time synchronization discrepancy due to environmental effects or clock management collapse. This will result in time delays between the dynamic responses collected by wireless sensors. If non-synchronized dynamic response data are directly used for structural modal identification, it leads to the misestimation of modal parameters. To overcome the non-synchronization issue, this study proposes a time synchronization approach to detect and correct asynchronous dynamic responses based on frequency domain decomposition (FDD) with frequency-squeezing processing (FSP). By imposing the expected relationship between modal phase angles extracted from the first-order singular value spectrum, the time lags between different sensors can be estimated, and synchronization can be achieved. The effectiveness of the proposed approach is fully demonstrated by numerical and experimental studies, as well as field measurement of a large-span spatial structure. The results verify that the proposed approach is effective for the time synchronization of wireless accelerometer sensors.
Human Movement Quality Assessment Using Sensor Technologies in Recreational and Professional Sports: A Scoping Review
Authors: Venek Verena , Kranzinger Stefan , Schwameder Hermann , Stöggl Thomas
Published: June 2022

The use of sensor technology in sports facilitates the data-driven evaluation of human movement not only in terms of quantity but also in terms of quality. This scoping review presents an overview of sensor technologies and human movement quality assessments in ecologically-similar environments. We searched four online databases to identify 16 eligible articles with either recreational and/or professional athletes. A total of 50% of the studies used inertial sensor technology, 31% vision-based sensor technology. Most of the studies (69%) assessed human movement quality using either the comparison to an expert’s performance, to an exercise definition or to the athletes’ individual baseline performance. A total of 31% of the studies used expert-based labeling of the movements to label data. None of the included studies used a control group-based study design to investigate impact on training progress, injury prevention or behavior change. Although studies have used sensor technology for movement quality assessment, the transfer from the lab to the field in recreational and professional sports is still emerging. Hence, research would benefit from impact studies of technology-assisted training interventions including control groups as well as investigating features of human movement quality in addition to kinematic parameters.
Edge Computing of Online Bounded-Error Query for Energy-Efficient IoT Sensors
Authors: Chang Ray-I , Tsai Jui-Hua , Wang Chia-Hui
Published: June 2022

Since the power of transmitting one-bit data is higher than that of computing one thousand lines of code in IoT (Internet of Things) applications, it is very important to reduce communication costs to save battery power and prolong system lifetime. In IoT sensors, the transformation of physical phenomena to data is usually with distortion (bounded-error tolerance). It introduces bounded-error data in IoT applications according to their required QoS2 (quality-of-sensor service) or QoD (quality-of-decision making). In our previous work, we proposed a bounded-error data compression scheme called BESDC (Bounded-Error-pruned Sensor Data Compression) to reduce the point-to-point communication cost of WSNs (wireless sensor networks). Based on BESDC, this paper proposes an online bounded-error query (OBEQ) scheme with edge computing to handle the entire online query process. We propose a query filter scheme to reduce the query commands, which will inform WSN to return unnecessary queried data. It not only satisfies the QoS2/QoD requirements, but also reduces the communication cost to request sensing data. Our experiments use real data of WSN to demonstrate the query performance. Results show that an OBEQ with a query filter can reduce up to 88% of the communication cost when compared with the traditional online query process.
Deep Learning, Mining, and Collaborative Clustering to Identify Flexible Daily Activities Patterns
Authors: Chifu Viorica Rozina , Pop Cristina Bianca , Rancea Alexandru Miron , Morar Andrei , Cioara Tudor , Antal Marcel , Anghel Ionut
Published: June 2022

The monitoring of the daily life activities routine is beneficial, especially in old age. It can provide relevant information on the person’s health state and wellbeing and can help identify deviations that signal care deterioration or incidents that require intervention. Existing approaches consider the daily routine as a rather strict sequence of activities which is not usually the case. In this paper, we propose a solution to identify flexible daily routines of older adults considering variations related to the order of activities and activities timespan. It combines the Gap-BIDE algorithm with a collaborative clustering technique. The Gap-BIDE algorithm is used to identify the most common patterns of behavior considering the elements of variations in activities sequence and the period of the day (i.e., night, morning, afternoon, and evening) for increased pattern mining flexibility. K-means and Hierarchical Clustering Agglomerative algorithms are collaboratively used to address the time-related elements of variability in daily routine like activities timespan vectors. A prototype was developed to monitor and detect the daily living activities based on smartwatch data using a deep learning architecture and the InceptionTime model, for which the highest accuracy was obtained. The results obtained are showing that the proposed solution can successfully identify the routines considering the aspects of flexibility such as activity sequences, optional and compulsory activities, timespan, and start and end time. The best results were obtained for the collaborative clustering solution that considers flexibility aspects in routine identification, providing coverage of monitored data of 89.63%.
Development of an Exoskeleton Platform of the Finger for Objective Patient Monitoring in Rehabilitation
Authors: Wilhelm Nikolas Jakob , Haddadin Sami , Lang Jan Josef , Micheler Carina , Hinterwimmer Florian , Reiners Anselm , Burgkart Rainer , Glowalla Claudio
Published: June 2022

This paper presents the application of an adaptive exoskeleton for finger rehabilitation. The system consists of a force-controlled exoskeleton of the finger and wireless coupling to a mobile application for the rehabilitation of complex regional pain syndrome (CRPS) patients. The exoskeleton has sensors for motion detection and force control as well as a wireless communication module. The proposed mobile application allows to interactively control the exoskeleton, store collected patient-specific data, and motivate the patient for therapy by means of gamification. The exoskeleton was applied to three CRPS patients over a period of six weeks. We present the design of the exoskeleton, the mobile application with its game content, and the results of the performed preliminary patient study. The exoskeleton system showed good applicability; recorded data can be used for objective therapy evaluation.
A Temperature Compensation Method for aSix-Axis Force/Torque Sensor Utilizing Ensemble hWOA-LSSVM Based on Improved Trimmed Bagging
Authors: Li Xuhao , Gao Lifu , Cao Huibin , Sun Yuxiang , Jiang Man , Zhang Yue
Published: June 2022

The performance of a six-axis force/torque sensor (F/T sensor) severely decreased when working in an extreme environment due to its sensitivity to ambient temperature. This paper puts forward an ensemble temperature compensation method based on the whale optimization algorithm (WOA) tuning the least-square support vector machine (LSSVM) and trimmed bagging. To be specific, the stimulated annealing algorithm (SA) was hybridized to the WOA to solve the local entrapment problem, and an adaptive trimming strategy is proposed to obtain the optimal trim portion for the trimmed bagging. In addition, inverse quote error (invQE) and cross-validation are employed to estimate the fitness better in training process. The maximum absolute measurement error caused by temperature decreased from 3.34% to 3.9×10−3% of full scale after being compensated by the proposed method. The analyses of experiments illustrate the ensemble hWOA-LSSVM based on improved trimmed bagging improves the precision and stability of F/T sensors and possesses the strengths of local search ability and better adaptability.
Ratiometric Optical Fiber Dissolved Oxygen Sensor Based on Fluorescence Quenching Principle
Authors: Zhao Yongkun , Zhang Hongxia , Jin Qingwen , Jia Dagong , Liu Tiegen
Published: June 2022

In this study, a ratiometric optical fiber dissolved oxygen sensor based on dynamic quenching of fluorescence from a ruthenium complex is reported. Tris(4,7-diphenyl-1,10-phenanthrolin) ruthenium(II) dichloride complex (Ru(dpp)32+) is used as an oxygen-sensitive dye, and semiconductor nanomaterial CdSe/ZnS quantum dots (QDs) are used as a reference dye by mixing the two substances and coating it on the plastic optical fiber end to form a composite sensitive film. The linear relationship between the relative fluorescence intensity of the ruthenium complex and the oxygen concentration is described using the Stern–Volmer equation, and the ruthenium complex doping concentration in the sol-gel film is tuned. The sensor is tested in gaseous oxygen and aqueous solution. The experimental results indicate that the measurement of dissolved oxygen has a lower sensitivity in an aqueous environment than in a gaseous environment. This is due to the uneven distribution of oxygen in aqueous solution and the low solubility of oxygen in water, which results in a small contact area between the ruthenium complex and oxygen in solution, leading to a less-severe fluorescence quenching effect than that in gaseous oxygen. In detecting dissolved oxygen, the sensor has a good linear Stern–Volmer calibration plot from 0 to 18.25 mg/L, the linearity can reach 99.62%, and the sensitivity can reach 0.0310/[O2] unit. The salinity stability, repeatability, and temperature characteristics of the sensor are characterized. The dissolved oxygen sensor investigated in this research could be used in various marine monitoring and environmental protection applications.
Blockchain-Based Peer-to-Peer Transactive Energy Management Scheme for Smart Grid System
Authors: Kumari Aparna , Chintukumar Sukharamwala Urvi , Tanwar Sudeep , Raboaca Maria Simona , Alqahtani Fayez , Tolba Amr , Sharma Ravi , Aschilean Ioan , Mihaltan Traian Candin
Published: June 2022

In Smart Grid (SG), Transactive Energy Management (TEM) is one of the most promising approaches to boost consumer participation in energy generation, energy management, and establishing decentralized energy market models using Peer-to-Peer (P2P). In P2P, a prosumer produces electric energy at their place using Renewable Energy Resources (RES) such as solar energy, wind energy, etc. Then, this generated energy is traded with consumers (who need the energy) in a nearby locality. P2P facilitates energy exchange in localized micro-energy markets of the TEM system. Such decentralized P2P energy management could cater to diverse prosumers and utility business models. However, the existing P2P approaches suffer from several issues such as single-point-of-failure, network bandwidth, scalability, trust, and security issues. To handle the aforementioned issues, this paper proposes a Decentralized and Transparent P2P Energy Trading (DT-P2PET) scheme using blockchain. The proposed DT-P2PET scheme aims to reduce the grid’s energy generation and management burden while also increasing profit for both consumers and prosumers through a dynamic pricing mechanism. The DT-P2PET scheme uses Ethereum-blockchain-based Smart Contracts (SCs) and InterPlanetary File System (IPFS) for the P2P energy trading. Furthermore, a recommender mechanism is also introduced in this study to increase the number of prosumers. The Ethereum SCs are designed and deployed to perform P2P in real time in the proposed DT-P2PET scheme. The DT-P2PET scheme is evaluated based on the various parameters such as profit generation (for prosumer and consumer both), data storage cost, network bandwidth, and data transfer rate in contrast to the existing approaches.
Perception of Affordances for Dribbling in Soccer: Exploring Children as Architects of Skill Development Opportunity
Authors: Duncan Michael , Martins Ricardo , Noon Mark , Eyre Emma
Published: June 2022

This study examined affordance perception for soccer dribbling using a mixed-methods approach in male grassroots soccer players. We examined how children construct and perceive skills practices for dribbling in soccer. Fourteen boys aged 10–11 years (Mean ± SD = 10.8 ± 0.4 years) who were regularly engaged in grassroots soccer participated in the present study. Children were provided with ten soccer cones and asked to create their own soccer dribbling pattern that would enable them to maximise the number of touches with a football and then dribble the ball in the pattern they had created for a 1 min period. Children were interviewed to explore their perception of affordances for soccer dribbling. The test of gross motor development-3 was used to assess fundamental movement skills (FMS), and the UGent soccer dribbling test was used to assess soccer dribbling skills. Children self-rated their own ability for soccer dribbling, as did their coaches. Pearson’s correlations were employed to examine the associations between quantitative variables, and thematic analysis was used to explore qualitative data. Results of the present study suggest that those children who created patterns with less space between cones accrued more touches of the football in their dribbling task (r = −0.671, p = 0.03). Children with a higher perception of their own dribbling ability had higher scores for FMS (r = 0.604, p = 0.049). Those children who scored better in actual soccer dribbling had higher scores for FMS (r = −0.746, p = 0.012) and were rated as better dribblers by their coaches (r = −0.67, p = 0.03). Interview data suggest a feedback loop between perception of ability and actual ability, which influenced the dribbling patterns that were created. This suggests that dribbling performance is scaled to the (perceived) action capabilities of the children, and children can act as architects in their own skill development.
Big Data and Sustainability in the Tourism Industry
Authors: Wang Jue , Ban Hyun-Jeong , Kim Hak-Seon
Published: June 2022

This Special Issue (SI) of Sustainability titled “Big Data and Sustainability in the Tourism Industry” contains 11 papers [...]
Accident Frequency Prediction Model for Flat Rural Roads in Serbia
Authors: Mićić Spasoje , Vujadinović Radoje , Amidžić Goran , Damjanović Milanko , Matović Boško
Published: June 2022

Traffic accidents, by their nature, are random events; therefore, it is difficult to estimate the exact places and times of their occurrences and the true nature of their impacts. Although they are hard to precisely predict, preventative actions can be taken and their numbers (in a certain period) can be approximately predicted. In this study, we investigated the relationship between accident frequency and factors that affect accident frequency; we used accident data for events that occurred on a flat rural state road in Serbia. The analysis was conducted using five statistical models, i.e., Poisson, negative binomial, random effect negative binomial, zero-inflated Poisson, and zero-inflated negative binomial models. The results indicated that the random effect negative binomial model outperformed the other models in terms of goodness-of-fit measures; it was chosen as the accident prediction model for flat rural roads. Four explanatory variables—annual average daily traffic, segment length, number of horizontal curves, and access road density—were found to significantly affect accident frequency. The results of this research can help road authorities make decisions about interventions and investments in road networks, designing new roads, and reconstructing existing roads.
From Flipped Classroom to Personalised Learning as an Innovative Teaching Methodology in the Area of Sports Management in Physical Activity and Sport Sciences
Authors: Gallardo-Guerrero Ana-Ma , Maciá-Andreu Ma-José , Conde-Pascual Elena , Sánchez-Sáez Juan-Antonio , Zurita-Ortiz Benito , García-Tascón Marta
Published: June 2022

Application of new methodologies at Spanish universities is essential to improve the teaching–learning process, although their implementation for the initial training of students is not common. The aim of this study was to use the flipped classroom (FC) methodology in the sports management area of the bachelor’s degree in Physical Activity and Sport Sciences (DPASS). Students (n = 370) from private (n = 284) and public (n = 86) universities participated in different activities, ending with the completion of a survey in which the impact of an FC was analysed in three dimensions: (1) interaction, (2) learning, and (3) methodology and resources. The results show significant differences concerning ownership (private universities are more highly rated than public universities). In terms of gender (with no significant differences), the female students had a better perception of the FC. The most highly valued aspects in the dimensions were as follows: (1) they are more likely to collaborate with their classmates (3.95 + 1.05); (2) learning is more active and experiential (3.69 + 0.86); and (3) with an FC, they spent more time than in other subjects. The most used device was laptop (68.4%). This pioneering study is of relevance to faculty members in the field of sports as it enhances students’ engagement with their learning development.
Research on China’s Manufacturing Industry Moving towards the Middle and High-End of the GVC Driven by Digital Economy
Authors: Zhou Rongrong , Tang Decai , Da Dan , Chen Wenya , Kong Lin , Boamah Valentina
Published: June 2022

A country’s manufacturing industry is often an important route for national prosperity, but it is also a conduit by which a digital economy may become truly useful. This is so the deep integration of the digital economy and manufacturing industry can enhance independent innovation efficiencies, promote the development of advanced manufacturing clusters, and constantly spawn new models, forms of business, and industries. Consequently, it is crucial to improve China’s global manufacturing value chain. This article starts with an analysis of the development status and competitiveness of the digital economy in China and abroad. It establishes a structural equation and uses the latest data from the World Input-Output and Asian Development Bank databases. It introduces new variables, such as digitization, research and development (R&D) investment, and industrial scale to empirically analyze China’s manufacturing industry’s global value chain (GVC). The results show that the digitization of China’s manufacturing industry can increase the forward participation of GVC in the manufacturing industry to improve the division status of GVCs. Analyses suggest that due to insufficient R&D investment in the division of labor in the GVCs, China’s manufacturing industry is prone to low-end lock-in, inefficient industrial structures, and weak innovation ability. Consequently, the following suggestions are proposed: China’s manufacturing industry needs to accelerate digital transformation, increase R&D investment, actively participate in the division of labor in the GVCs, and enhance core competitiveness.
A Normalized Rich-Club Connectivity-Based Strategy for Keyword Selection in Social Media Analysis
Authors: Lian Ying , Lin Xiaofeng , Dong Xuefan , Hou Shengjie
Published: June 2022

In this paper, we present a study on keyword selection behavior in social media analysis that is focused on particular topics, and propose a new effective strategy that considers the co-occurrence relationships between keywords and uses graph-based techniques. In particular, we used the normalized rich-club connectivity considering the weighted degree, closeness centrality, betweenness centrality and PageRank values to measure a subgroup of highly connected “rich keywords” in a keyword co-occurrence network. Community detection is subsequently applied to identify several keyword combinations that are able to accurately and comprehensively represent the researched topic. The empirical results based on four topics and comparing four existing models confirm the performance of our proposed strategy in promoting the quantity and ensuing the quality of data related to particular topics collected from social media. Overall, our findings are expected to offer useful guidelines on how to select keywords for social media-based studies and thus further increase the reliability and validity of their respective conclusions.
Influence of Steel Fibers on the Interfacial Shear Strength of Ternary Blend Geopolymer Concrete Composite
Authors: Gopika Mohan , Ganesan Namasivayam , Indira Pookattu Vattarambath , Sathish Kumar Veerappan , Murali Gunasekaran , Vatin Nikolai Ivanovich
Published: June 2022

Sustainable development is a major issue confronting society today. Cement, a major constituent of concrete, is a key component of any infrastructure development. The major drawback of cement production is that it involves the emission of CO2, the predominant greenhouse gas causing global warming. The development of geopolymers has resulted in a decrease in cement production, as well as a reduction in CO2 emissions. During mass concrete production in the construction of very large structures, interfaces/joints are formed, which are potential failure sites of crack formation. Concrete may interface with other concrete of different strengths, or other construction materials, such as steel. To ensure the monolithic behavior of composite concrete structures, bond strength at the interface should be established. The monolithic behavior can be ensured by the usage of shear ties across the interface. However, an increase in the number of shear ties at the interface may reduce the construction efficiency. The present study aims to determine the interfacial shear strength of geopolymer concrete as a substrate, and high-strength concrete as an overlay, by adding 0.50%, 0.75%, and 1% crimped steel fibers, and two and three shear ties, at the interface of push-off specimens. It was found that three shear ties at the interface can be replaced by two shear ties and 0.75% crimped steel fibers. In addition, a method was proposed to predict the interface shear strength of the concrete composite, which was found to be comparable to the test results.
The Use of EU Territorial Cooperation Funds for the Sustainable Development of National and Ethnic Minorities in the Baltic Sea Region
Authors: Studzieniecki Tomasz , Meyer Beata
Published: June 2022

The integration processes taking place in the Baltic Sea Region (BSR) are contributing to the sustainable development of this unique territory. Intensive cooperation financed from EU funds under the cohesion policy and the neighborhood policy have provided an opportunity for the development of ethnic and national minorities, who are important but still marginalized stakeholders. The theoretical aim of the article is to identify the attributes of national and ethnic minorities and to indicate key determinants of their sustainable development. The authors seek to answer whether territorial cooperation in the BSR contributes to the development of national and ethnic minorities, and if so, how. In the article, concepts and definitions related to the term “minority” are verified, classifications are developed and a model of sustainable development of ethnic and national minorities is built. Official statistical data are used to present the national and ethnic structure of the BSR countries. Then, 22 national minorities and 17 ethnic minorities are identified and described. Quantitative and qualitative research was carried out on 126 territorial cooperation projects supporting the development of national and ethnic minorities totaling EUR 85.25 million in value and implemented within 38 BSR programs in 2000–2020. The Sami minority were the greatest beneficiaries of the cooperation. Territorial cooperation projects have been shown to contribute to the preservation of cultural heritage and the development of education, social support and political empowerment. Territorial cooperation is a powerful instrument of sustainable development. Unfortunately, it still contributes only moderately to the development of national and ethnic minorities. There is a need to strengthen this issue in future programs of the cohesion policy and the neighborhood policy and to develop systemic solutions enabling national and ethnic minorities to participate more actively in the implementation of territorial cooperation projects.
Analysis of Digital Leadership in School Management and Accessibility of Animation-Designed Game-Based Learning for Sustainability of Education for Children with Special Needs
Authors: Baglama Basak , Evcimen Emirali , Altinay Fahriye , Sharma Ramesh Chander , Tlili Ahmed , Altinay Zehra , Dagli Gokmen , Jemni Mohamed , Shadiev Rustam , Yucesoy Yucehan , Celebi Menil
Published: June 2022

With the rapid integration of technology into educational environments during the pandemic period, the teaching processes in classrooms and private education institutions began to be carried out with technology support. Game-based animation learning designs in technology-supported educational environments provide an interesting and motivation-enhancing learning experience in developing students’ education skills. In today’s educational environments, 2D and 3D animation game designs are the unique technology-supported learning environments in teaching many different skills, behaviors, and concepts to individuals with special needs. Visual designs and animations are one of the technologies necessary to prepare individuals with special needs for an independent life. The accessibility of animation designs for teachers and families has gained significant importance during the COVID-19 pandemic period. Technology education supports the visual, listening, reading, writing, social, and communication skills of individuals with special needs, facilitates their independent life skills, and contributes to their development as a guide. This allows individuals to learn the targeted information more easily, permanently, and quickly. In this context, this study provides information on the use and accessibility of animation technology in special education, offers suggestions for the benefit of visual design and animation, which are among the assistive technologies, and gives insights into how school management is ready for digital education.