bluuebunny/crossref_metadata_2025 huggingface dataset:
Calibration of Automatic Sun Photometer with Temperature Correction in Field Environment
Authors: Chen Shuyu , Li Yuan , Cao Fengmei , Zhang Yuxiang
Published: December 2021

Aerosol optical depth (AOD) is an important atmospheric correction parameter in remote sensing. In order to obtain AOD accurately, the surface-based automatic sun photometer needs to carry out calibration regularly. The normally used Langley method can be effective only when the AOD and the calibration coefficients of the instrument remain unchanged throughout the day. However, when observing the AOD with CE318 sun photometer in field environment, it was found that the AOD of silicon (Si) detector at 1020 nm and indium gallium arsenide (InGaAs) detector at 1639 nm was strongly influenced by temperature due to the large temperature difference at the Dunhuang site. Based on the corresponding relationship between AOD and wavelength, the model of the calibration coefficients varying with temperature was established by nonlinear regression method in field environment. By comparing the AOD before and after temperature correction with the theoretical one, the ratio of data with relative error (RE) less than 5% increased from 0.195 and 0.14 to 0.894 and 0.355, respectively. By this method, calibration can be carried out without the limit of constant AOD. In addition, it is simpler, more convenient, and less costly to perform temperature correction in a field environment than in a laboratory.
Estimation of Maximum Hail Diameters from FY-4A Satellite Data with a Machine Learning Method
Authors: Wu Qiong , Shou Yi-Xuan , Ma Lei-Ming , Lu Qifeng , Wang Rui
Published: December 2021

The magnitude of damage caused by hail depends on its size; however, direct observation or indirect estimation of hail size remains a significant challenge. One primary reason for estimations by proxy, such as through remote sensing methods, is that empirical relationships or statistical models established in one region may not apply to other areas. This study employs a machine learning method to build a hail size estimation model without assuming relations in advance. It uses FY-4A AGRI data to provide cloud-top information and ERA5 data to add vertical environment information. Before training the model, we conducted a principal component analysis (PCA) to analyze the highly influential factors on hail sizes. A total of 18 features, composed of four groups, namely brightness temperature (BT), the difference in BT (BTD), thermodynamics, and dynamics groups, were chosen from 29 original features. Dynamic and BTD features show superior performance in identifying large hail. Although the selected features are more closely correlated to hail sizes than unselected ones, the relationships are complicated and nonlinear. As a result, a two-layer regression back propagation neural network (BPNN) model with powerful fitting ability is trained with selected features to predict maximum hail diameter (MHD). The linear fitting R2 between predicted and observed MHDs is 0.52 on the test set, which signifies that our model performs well compared with other hail size estimation models. We also examine the model concerning all three hail cases in Shanghai, China, between 2019 and 2021. The model attained more satisfactory results than the radar-based maximum estimated hail size (MEHS) method, which overestimates the MHDs, thus further supporting the operational applications of our model.
Evaluation of Surface Reflectance Products Based on Optimized 6S Model Using Synchronous In Situ Measurements
Authors: Zhou Xiaocheng , Liu Xueping , Wang Xiaoqin , He Guojin , Zhang Youshui , Wang Guizhou , Zhang Zhaoming
Published: December 2021

Surface reflectance (SR) estimation is the most essential preprocessing step for multi-sensor remote sensing inversion of geophysical parameters. Therefore, accurate and stable atmospheric correction is particularly important, which is the premise and basis of the quantitative application of remote sensing. It can also be used to directly compare different images and sensors. The Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi-Spectral Instrument (MSI) surface reflectance products are publicly available and demonstrate high accuracy. However, there is not enough validation using synchronous spectral measurements over China’s land surface. In this study, we utilized Moderate Resolution Imaging Spectroradiometer (MODIS) atmospheric products reconstructed by Categorical Boosting (CatBoost) and 30 m ASTER Global Digital Elevation Model (ASTER GDEM) data to adjust the relevant parameters to optimize the Second Simulation of Satellite Signal in the Solar Spectrum (6S) model. The accuracy of surface reflectance products obtained from the optimized 6S model was compared with that of the original 6S model and the most commonly used Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) model. Surface reflectance products were validated and evaluated with synchronous in situ measurements from 16 sites located in five provinces of China: Fujian, Gansu, Jiangxi, Hunan, and Guangdong. Through the indirect and direct validation across two sensors and three methods, it provides evidence that the synchronous measurements have the higher and more reliable validation accuracy. The results of the validation indicated that, for Landsat-8 OLI and Sentinel-2 MSI SR products, the overall root mean square error (RMSE) calculated results of optimized 6S, original 6S and FLAASH across all spectral bands were 0.0295, 0.0378, 0.0345, and 0.0313, 0.0450, 0.0380, respectively. R2 values reached 0.9513, 0.9254, 0.9316 and 0.9377, 0.8822, 0.9122 respectively. Compared with the original 6S model and FLAASH model, the mean percent absolute error (MPAE) of the optimized 6S model was reduced by 32.20% and 15.86% for Landsat-8 OLI, respectively. On the other, for the Sentinel-2 MSI SR product, the MPAE value was reduced by 33.56% and 33.32%. For the two kinds of data, the accuracy of each band was improved to varying extents by the optimized 6S model with the auxiliary data. These findings support the hypothesis that reliable auxiliary data are helpful in reducing the influence of the atmosphere on images and restoring reality as much as is feasible.
Motion Blur Removal for Uav-Based Wind Turbine Blade Images Using Synthetic Datasets
Authors: Peng Yeping , Tang Zhen , Zhao Genping , Cao Guangzhong , Wu Chao
Published: December 2021

Unmanned air vehicle (UAV) based imaging has been an attractive technology to be used for wind turbine blades (WTBs) monitoring. In such applications, image motion blur is a challenging problem which means that motion deblurring is of great significance in the monitoring of running WTBs. However, an embarrassing fact for these applications is the lack of sufficient WTB images, which should include better pairs of sharp images and blurred images captured under the same conditions for network model training. To overcome the challenge of image pair acquisition, a training sample synthesis method is proposed. Sharp images of static WTBs were first captured, and then video sequences were prepared by running WTBs at different speeds. The blurred images were identified from the video sequences and matched to the sharp images using image difference. To expand the sample dataset, rotational motion blurs were simulated on different WTBs. Synthetic image pairs were then produced by fusing sharp images and images of simulated blurs. Finally, a total of 4000 image pairs were obtained. To conduct motion deblurring, a hybrid deblurring network integrated with DeblurGAN and DeblurGANv2 was deployed. The results show that the integration of DeblurGANv2 and Inception-ResNet-v2 provides better deblurred images, in terms of both metrics of signal-to-noise ratio (80.138) and structural similarity (0.950) than those obtained from the comparable networks of DeblurGAN and MobileNet-DeblurGANv2.
An Effectively Dynamic Path Optimization Approach for the Tree Skeleton Extraction from Portable Laser Scanning Point Clouds
Authors: Xu Sheng , Li Xin , Yun Jiayan , Xu Shanshan
Published: December 2021

One key step to the tree structure study is skeleton processing. Although there are lots of extraction approaches, the existing methods have paid less attention to extraction effectiveness, which highly use redundant points to formulate the skeleton and bring difficulties to the subsequent 3D modeling. This work proposes a four-step framework for the purpose of skeleton extraction. Firstly, candidate skeleton points are filtered from input data based on the spatial slice projection and grouped using the Euclidean distance analysis. Secondly, a key dynamic path optimization step is used to formulate a tree skeleton using the candidate point information. Thirdly, the optimized path is filled by interpolating points to achieve complete skeletons. Finally, short skeletons are removed based on the distance between branching points and ending points, and then, the extraction skeletons are smoothed for improving the visual quality. Our main contribution lies in that we find the global minimization cost path from every point to the root using a novel energy function. The formulated objective function contains a data term to constrain the distance between points and paths, and a smoothness term to constrain the direction continuities. Experimental scenes include three different types of trees, and input point clouds are collected by a portable laser scanning system. Skeleton extraction results demonstrate that we achieved completeness and correctness of 81.10% and 99.21%. respectively. Besides, our effectiveness is up to 79.26%, which uses only 5.82% of the input tree points in the skeleton representation, showing a promising effective solution for the tree skeleton and structure study.
Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada
Authors: Khare Siddhartha , Deslauriers Annie , Morin Hubert , Latifi Hooman , Rossi Sergio
Published: December 2021

Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R2 from 0.66 to 0.85) than NDVI (R2 from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather.
Satellite-Based Monitoring of Primary Production in a Mediterranean Islet Post Black Rat Eradication
Authors: Ibañez-Álvarez Miguel , Farràs Santasusana Pol , Calleja Juan Antonio , Rouco Carlos , Brolly Matthew , Burnside Niall G. , Baraza Elena , Bartolomé Jordi , Serrano Emmanuel
Published: December 2021

Invasive rodents have a detrimental impact on terrestrial ecosystem functioning, this is often exacerbated on small islands. Rat eradication campaigns are often used to deal with this environmental perturbation given their classification as invasive species. Studies assessing the effects of rodent control at ecosystem scale are scarce and thus little is known about the subsequent functional response of vegetation subsequent to rat control. In this work, we use remote sensing to assess the effects of black rat (Rattus rattus) eradication on Mediterranean vegetation productivity in the Sa Dragonera Islet, Mallorca (Spain). Rats feed on seeds, sprouts, and leaves of woody vegetation and hence we expect primary production to increase nine years after the rodenticide campaign. The Break Detection approach for additive season and trend (BFAST method) was adopted to examine changes in vegetation density before and after the eradication campaign in Sa Dragonera Islet (Balearic Islands), using a temporal series of monthly NDVI data extracted from Landsat imagery. The same temporal trends were examined for a control zone where no rat eradication took place, in order to control for weather-driven changes. The results of this study revealed changes across the 21-year monthly NDVI time series. However, the dates, magnitude, and trend of these changes could not be explicitly attributed to the action of rats, when compared to the historical changes on the islet and the changes found to co-occur within the control zone. These finding could, perhaps, be explained by the high resilience of Mediterranean shrubs to browsing including that of rat invasion. However, the results from the study appear to show that rat damage on specific plant species, with little contribution to global NDVI values, would be overshadowed by the effects of broader environmental factors in this remote sensing approach. The results suggest that the current passive restoration scheme imposed following eradication is not sufficient for effective ecosystem restoration.
Artifacts in EEG-Based BCI Therapies: Friend or Foe?
Authors: McDermott Eric James , Raggam Philipp , Kirsch Sven , Belardinelli Paolo , Ziemann Ulf , Zrenner Christoph
Published: December 2021

EEG-based brain–computer interfaces (BCI) have promising therapeutic potential beyond traditional neurofeedback training, such as enabling personalized and optimized virtual reality (VR) neurorehabilitation paradigms where the timing and parameters of the visual experience is synchronized with specific brain states. While BCI algorithms are often designed to focus on whichever portion of a signal is most informative, in these brain-state-synchronized applications, it is of critical importance that the resulting decoder is sensitive to physiological brain activity representative of various mental states, and not to artifacts, such as those arising from naturalistic movements. In this study, we compare the relative classification accuracy with which different motor tasks can be decoded from both extracted brain activity and artifacts contained in the EEG signal. EEG data were collected from 17 chronic stroke patients while performing six different head, hand, and arm movements in a realistic VR-based neurorehabilitation paradigm. Results show that the artifactual component of the EEG signal is significantly more informative than brain activity with respect to classification accuracy. This finding is consistent across different feature extraction methods and classification pipelines. While informative brain signals can be recovered with suitable cleaning procedures, we recommend that features should not be designed solely to maximize classification accuracy, as this could select for remaining artifactual components. We also propose the use of machine learning approaches that are interpretable to verify that classification is driven by physiological brain states. In summary, whereas informative artifacts are a helpful friend in BCI-based communication applications, they can be a problematic foe in the estimation of physiological brain states.
An Optimization-Based Orchestrator for Resource Access and Operation Management in Sliced 5G Core Networks
Authors: Hsiao Chiu-Han , Wen Yean-Fu , Lin Frank Yeong-Sung , Chen Yu-Fang , Huang Yennun , Su Yang-Che , Wu Ya-Syuan
Published: December 2021

Network slicing is a promising technology that network operators can deploy the services by slices with heterogeneous quality of service (QoS) requirements. However, an orchestrator for network operation with efficient slice resource provisioning algorithms is essential. This work stands on Internet service provider (ISP) to design an orchestrator analyzing the critical influencing factors, namely access control, scheduling, and resource migration, to systematically evolve a sustainable network. The scalability and flexibility of resources are jointly considered. The resource management problem is formulated as a mixed-integer programming (MIP) problem. A solution approach based on Lagrangian relaxation (LR) is proposed for the orchestrator to make decisions to satisfy the high QoS applications. It can investigate the resources required for access control within a cost-efficient resource pool and consider allocating or migrating resources efficiently in each network slice. For high system utilization, the proposed mechanisms are modeled in a pay-as-you-go manner. Furthermore, the experiment results show that the proposed strategies perform the near-optimal system revenue to meet the QoS requirement by making decisions.
Pilot Study on Analysis of Electroencephalography Signals from Children with FASD with the Implementation of Naive Bayesian Classifiers
Authors: Dyląg Katarzyna Anna , Wieczorek Wiktoria , Bauer Waldemar , Walecki Piotr , Bando Bozena , Martinek Radek , Kawala-Sterniuk Aleksandra
Published: December 2021

In this paper Naive Bayesian classifiers were applied for the purpose of differentiation between the EEG signals recorded from children with Fetal Alcohol Syndrome Disorders (FASD) and healthy ones. This work also provides a brief introduction to the FASD itself, explaining the social, economic and genetic reasons for the FASD occurrence. The obtained results were good and promising and indicate that EEG recordings can be a helpful tool for potential diagnostics of FASDs children affected with it, in particular those with invisible physical signs of these spectrum disorders.
Performance Index for Extrinsic Calibration of LiDAR and Motion Sensor for Mapping and Localization
Authors: Kim Gamin
Published: December 2021

Light Detection and Ranging (LiDAR) is a sensor that uses a laser to represent the surrounding environment in three-dimensional information. Thanks to the development of LiDAR, LiDAR-based applications are being actively used in autonomous vehicles. In order to effectively use the information coming from LiDAR, extrinsic calibration which finds the translation and the rotation relationship between LiDAR coordinate and vehicle coordinate is essential. Therefore, many studies on LiDAR extrinsic calibration are steadily in progress. The performance index (PI) of the calibration parameter is a value that quantitatively indicates whether the obtained calibration parameter is similar to the true value or not. In order to effectively use the obtained calibration parameter, it is important to validate the parameter through PI. Therefore, in this paper, we propose an algorithm to obtain the performance index for the calibration parameter between LiDAR and the motion sensor. This performance index is experimentally verified in various environments by Monte Carlo simulation and validated using CarMaker simulation data and real data. As a result of verification, the PI of the calibration parameter obtained through the proposed algorithm has the smallest value when the calibration parameter has a true value, and increases as an error is added to the true value. In other words, it has been proven that PI is convex to the calibration parameter. In addition, it is able to confirm that the PI obtained using the proposed algorithm provides information on the effect of the calibration parameters on mapping and localization.
The SALT—Readout ASIC for Silicon Strip Sensors of Upstream Tracker in the Upgraded LHCb Experiment
Authors: Abellan Beteta Carlos , Andreou Dimitra , Artuso Marina , Beiter Andy , Blusk Steven , Bugiel Roma , Bugiel Szymon , Carbone Antonio , Carli Ina , Chen Bo , Conti Nadim , De Benedetti Federico , Ding Shuchong , Ely Scott , Firlej Miroslaw , Fiutowski Tomasz , Gandini Paolo , Germann Danielle , Grieser Nathan , Idzik Marek , Jiang Xiaojie , Krupa Wojciech , Li Yiming , Li Zhuoming , Liang Xixin , Liu Shuaiyi , Lu Yu , Mackey Lauren , Moron Jakub , Mountain Ray , Petruzzo Marco , Pham Hang , Schmidt Burkhard , Sheng Shuqi , Spadaro Norella Elisabetta , Swientek Krzysztof , Szumlak Tomasz , Tobin Mark , Wang Jianchun , Wilkinson Michael , Wu Hangyi , Zhang Feihao , Zou Quan
Published: December 2021

SALT, a new dedicated readout Application Specific Integrated Circuit (ASIC) for the Upstream Tracker, a new silicon detector in the Large Hadron Collider beauty (LHCb) experiment, has been designed and developed. It is a 128-channel chip using an innovative architecture comprising a low-power analogue front-end with fast pulse shaping and a 40 MSps 6-bit Analog-to-Digital Converter (ADC) in each channel, followed by a Digital Signal Processing (DSP) block performing pedestal and Mean Common Mode (MCM) subtraction and zero suppression. The prototypes of SALT were fabricated and tested, confirming the full chip functionality and fulfilling the specifications. A signal-to-noise ratio of about 20 is achieved for a silicon sensor with a 12 pF input capacitance. In this paper, the SALT architecture and measurements of the chip performance are presented.
Performance Evaluation of an Autonomously Driven Agricultural Vehicle in an Orchard Environment
Authors: Han Joong-hee , Park Chi-ho , Jang Young Yoon , Gu Ja Duck , Kim Chan Young
Published: December 2021

To address the problems of inefficient agricultural production and labor shortages, there has been active research to develop autonomously driven agricultural machines, using advanced sensors and ICT technology. Autonomously driven speed sprayers can also reduce accidents such as the pesticide poisoning of farmers, and vehicle overturn that frequently occur during spraying work in orchards. To develop a commercial, autonomously driven speed sprayer, we developed a prototype of an autonomously driven agricultural vehicle, and conducted performance evaluations in an orchard environment. A prototype of the agricultural vehicle was created using a rubber-tracked vehicle equipped with two AC motors. A prototype of the autonomous driving hardware consisted of a GNSS module, a motion sensor, an embedded board, and an LTE module, and it was made for less than $1000. Additional software, including a sensor fusion algorithm for positioning and a path-tracking algorithm for autonomous driving, were implemented. Then, the performance of the autonomous driving agricultural vehicle was evaluated based on two trajectories in an apple farm. The results of the field test determined the RMS, and the maximums of the path-following errors were 0.10 m, 0.34 m, respectively.
Improvement of Performance for Raman Assisted BOTDR by Analyzing Brillouin Gain Spectrum
Authors: Huang Qiang , Sun Junqiang , Jiao Wenting , Kai Li
Published: December 2021

We propose a simplified partitioned Brillouin gain spectrum (BGS) analysis method to enhance the spatial resolution and measurement accuracy of a Brillouin optical time-domain reflectometer (BOTDR) assisted by a first-order Raman pump. We theoretically derive the mathematical model of the partitioned BGS and analyze the superposition process of sub-Brillouin signals within a theoretical spatial resolution range. We unified all the unknown constant parameters of the calculation process to simplify the partitioned BGS analysis method and the value of the uniform parameter is attained through the system test data and numerical analysis. Moreover, to automate data processing, the starting point of the temperature/strain change is determined by the first occurrence of the maximum Brillouin frequency shift (BFS), then the position where the partitioned BGS analysis method calculation begins is obtained. Using a 100 ns probe pulse and partitioned BGS analysis method, we obtain a spatial resolution of 0.4 m in the 78.45-km-long Raman-assisted BOTDR system, and the measurement accuracy is significantly improved. In addition, we achieve a strain accuracy of 5.6 με and a spatial resolution of 0.4 m in the 28.5-km-long BOTDR without Raman amplification.
Non-Linear Regression Models with Vibration Amplitude Optimization Algorithms in a Microturbine
Authors: Rodríguez-Abreo Omar , Rodríguez-Reséndiz Juvenal , Montoya-Santiyanes L. A. , Álvarez-Alvarado José Manuel
Published: December 2021

Machinery condition monitoring and failure analysis is an engineering problem to pay attention to among all those being studied. Excessive vibration in a rotating system can damage the system and cannot be ignored. One option to prevent vibrations in a system is through preparation for them with a model. The accuracy of the model depends mainly on the type of model and the fitting that is attained. The non-linear model parameters can be complex to fit. Therefore, artificial intelligence is an option for performing this tuning. Within evolutionary computation, there are many optimization and tuning algorithms, the best known being genetic algorithms, but they contain many specific parameters. That is why algorithms such as the gray wolf optimizer (GWO) are alternatives for this tuning. There is a small number of mechanical applications in which the GWO algorithm has been implemented. Therefore, the GWO algorithm was used to fit non-linear regression models for vibration amplitude measurements in the radial direction in relation to the rotational frequency in a gas microturbine without considering temperature effects. RMSE and R2 were used as evaluation criteria. The results showed good agreement concerning the statistical analysis. The 2nd and 4th-order models, and the Gaussian and sinusoidal models, improved the fit. All models evaluated predicted the data with a high coefficient of determination (85–93%); the RMSE was between 0.19 and 0.22 for the worst proposed model. The proposed methodology can be used to optimize the estimated models with statistical tools.
Computationally Efficient Direction-of-Arrival Estimation Algorithms for a Cubic Coprime Array
Authors: Gong Pan , Chen Xixin
Published: December 2021

In this paper, we investigate the problem of direction-of-arrival (DOA) estimation for massive multi-input multi-output (MIMO) radar, and propose a total array-based multiple signals classification (TA-MUSIC) algorithm for two-dimensional direction-of-arrival (DOA) estimation with a coprime cubic array (CCA). Unlike the conventional multiple signal classification (MUSIC) algorithm, the TA-MUSIC algorithm employs not only the auto-covariance matrix but also the mutual covariance matrix by stacking the received signals of two sub cubic arrays so that full degrees of freedom (DOFs) can be utilized. We verified that the phase ambiguity problem can be eliminated by employing the coprime property. Moreover, to achieve lower complexity, we explored the estimation of signal parameters via the rotational invariance technique (ESPRIT)-based multiple signal classification (E-MUSIC) algorithm, which uses a successive scheme to be computationally efficient. The Cramer–Rao bound (CRB) was taken as a theoretical benchmark for the lower boundary of the unbiased estimate. Finally, numerical simulations were conducted in order to demonstrate the effectiveness and superiority of the proposed algorithms.
Reversing Poor Safety Records: Identifying Best Practices to Improve Fleet Safety
Authors: Camden Matthew C. , Hickman Jeffrey S. , Hanowski Richard J.
Published: December 2021

Commercial motor vehicle safety is of utmost importance, as crashes involving commercial motor vehicles often result in significant property damage, injuries, fatalities, and financial loss for fleets. However, fleet managers are often unsure what strategies other fleets have used to successfully improve safety. To identify best practices, researchers completed case studies with nine commercial motor vehicle fleets that successfully improved their safety performance. A content analysis was performed, and the successful strategies were organized into the Haddon Matrix. Results showed that there was no one single strategy that fleets used to improve safety. Instead, fleets relied on a comprehensive approach focusing on pre-crash countermeasures, including addressing hiring practices, driver training, fleet safety culture, safety technologies, scheduling, and maintenance. However, an enhanced safety culture and advanced safety technology were identified as critical components to their safety improvement. Results from this study may help fleets understand what their peers have used to successfully improve safety and which strategies may not be as helpful.
Perception of “Fake News” and Potentially Manipulative Content in Digital Media—A Generational Approach
Authors: Trninić Dragana , Kuprešanin Vukelić Anđela , Bokan Jovana
Published: December 2021

The presence of “fake news” and potentially manipulative content in the media is nothing new, but this area has largely expanded with the emergence of the Internet and digital media, thus opening itself up to anyone who has online access. As a result, there is an increasing amount of such content in the media, especially in digital media. This paper deals with the perception of fake news and potentially manipulative content by various generations—in particular, the perceptions of the young and the middle-aged generations, with the focus being on their ability to recognise, verify, and relate to such content. The results of this study were gained by means of a qualitative methodology applied to focus groups in Bosnia and Herzegovina. The results are presented through a thematic analysis of the differences in perception of “fake news” between these generations, firstly in terms of their apprehension and interpretation of it, and secondly in terms of their relation to it. The authors conclude that both generations lack competence concerning media literacy, and that providing education in the field of digital media might offer a long-term solution for building resistance to “fake news” for future generations.
A Novel Monitoring System (AUT FIT) for Anthropometrics and Physical Fitness in Primary School Children in Austria: A Cross-Sectional Pilot Study
Authors: Jarnig Gerald , Jaunig Johannes , Kerbl Reinhold , Lima Rodrigo Antunes , van Poppel Mireille N. M.
Published: December 2021

Monitoring of anthropometric and physical fitness parameters in primary school children is important for the prevention of future health problems. Many of the existing test batteries that are useful for monitoring require expensive test materials, specialized test administrators, and a lot of space. This limits the usefulness of such tests for widespread use. The aim of this pilot study was to design and evaluate monitoring tools for anthropometrics and physical fitness tests in primary schools, called AUT FIT. The test battery consists of height, weight, and waist circumference measurement and eight fitness tests (6 min run, V sit-and-reach, jumping sideways, standing long jump, medicine ball throw, 4 × 10 m shuttle run, ruler drop, single leg stand). Data of 821 children aged 7 to 10 years were gathered. Most AUT FIT tests showed excellent test–retest and interrater reliability and were easy to implement. Criterion-related validity was evident by a strong correlation between physical education teacher rankings and rank scores for motor fitness. Nationwide implementation in the Austrian school system could be an important component for monitoring and improving the health and fitness of primary school children.
Path Analysis of Sea-Level Rise and Its Impact
Authors: Chung Jean , Tong Guanchao , Chao Jiayou , Zhu Wei
Published: December 2021

Global sea-level rise has been drawing increasingly greater attention in recent years, as it directly impacts the livelihood and sustainable development of humankind. Our research focuses on identifying causal factors and pathways on sea level changes (both global and regional) and subsequently predicting the magnitude of such changes. To this end, we have designed a novel analysis pipeline including three sequential steps: (1) a dynamic structural equation model (dSEM) to identify pathways between the global mean sea level (GMSL) and various predictors, (2) a vector autoregression model (VAR) to quantify the GMSL changes due to the significant relations identified in the first step, and (3) a generalized additive model (GAM) to model the relationship between regional sea level and GMSL. Historical records of GMSL and other variables from 1992 to 2020 were used to calibrate the analysis pipeline. Our results indicate that greenhouse gases, water, and air temperatures, change in Antarctic and Greenland Ice Sheet mass, sea ice, and historical sea level all play a significant role in future sea-level rise. The resulting 95% upper bound of the sea-level projections was combined with a threshold for extreme flooding to map out the extent of sea-level rise in coastal communities using a digital coastal tracker.
Back Analysis of a Horizontal Geothermal Plant Implemented in a Wine Production Process
Authors: Alimonti Claudio , Pecci Gabriele
Published: December 2021

The Salcheto winery has undertaken a process of reduction of its primary energy consumption and the implementation of green energy technologies. They adopted solar photovoltaic, wood biomass, and geothermal energy sources. A horizontal ground source heat exchanger (GSHE) plant is used to cool a part of the pressed grapes and control the wine production temperature. The goal of this work was to investigate some technical issues of the plant and to increase the efficiency of the whole system. The first step was the evaluation of the actual operating conditions of the GSHE plant, by performing a thermal response test. The results allowed us to find the thermal diffusivity of 3.5 × 10−7 m2/s, and the calculation with the IGSHPA standard indicated a cooling performance of about 6 kW. A survey during the harvest highlighted a peak power of 6 kW. Therefore, to improve the plant, some modifications were proposed and analyzed. In the new layout, the geothermal plant serves the condenser of the refrigeration unit, allowing cooling of the all production lines, instead of only one. The peak power was evaluated as 32 kW, and the GSHE can fulfil this, up to 18 kW. For higher power, the evaporative tower will supply the remainder, covering a maximum of 45%. Furthermore, the refrigeration unit may cover the cooling requirements of the entire residential and office building, without other plant improvements.
Response of Potential Indicators of Soil Quality to Land-Use and Land-Cover Change under a Mediterranean Climate in the Region of Al-Jabal Al-Akhdar, Libya
Authors: Alawamy Jamal Suliman , Balasundram Siva K. , Mohd. Hanif Ahmad Husni , Teh Boon Sung Christopher
Published: December 2021

Conversion of native lands into agricultural use, coupled with poor land management practices, generally leads to changes in soil properties. Understanding the undesirable effects of land-use and land-cover (LULC) changes on soil properties is essential when planning for sustainable land management. This study was conducted in Al Jabal Al Akhdar region, Libya, to assess the effects of land-use and land-cover changes on soil quality inferred by analyzing the relative changes in 17 chemical, physical, and biological soil properties in the upper layer (0–20 cm) of disturbed and undisturbed soil systems. Soil samples were collected from 180 sampling sites with 60 from each of the three types of LULC prevalent in the study area: natural Mediterranean forests (NMF), rainfed agriculture (RA), and irrigated crops (IC). The soil properties of the two agricultural land uses were compared with soil properties under an adjacent natural forest, which served as a control to assess changes in soil quality resulting from the cultivation of deforested land. The results indicate significant reductions in most soil quality indicators under rainfed agriculture as compared to native forest land. Under irrigated agriculture, there were significant changes (p ≤ 0.05) in most of the soil quality indicators, generally, indicating a significant reduction in soil quality, except for improvement of nitrogen and phosphorus levels due to frequent fertilizer application. Our data support the notion that changes in land use and land cover, in the absence of sustainable management measures, induce deterioration of soil properties and ultimately may lead to land degradation and productivity decline.
Connections of Transformative Education with Bhutan’s Pedagogical Ideas for Promoting Sustainability Education
Authors: Jeronen Eila , Ahonen Päivi , Korkeamäki Riitta-Liisa
Published: December 2021

The study aims to clarify how transformative education teaching and learning ideas have been incorporated into sustainable development-focused education in Bhutan. Sustainable development is included in various ways in the Educating for Gross National Happiness Training Manual (GNH TM) developed by the Ministry of Education of Bhutan in 2013. GNH-focused education aims at developing students’ respect and critical thinking for the well-being of human beings and the environment. The article provides an overview of 26 selected articles published in peer-reviewed scientific journals from 1991–2021. Altogether, 12 sustainable development-focused transformative education articles were analyzed in detail using qualitative content analysis. The results of the study show that transformative education is reflected in many ways in the teaching goals, objectives, contents, and methods introduced in the GNH TM units. Consequently, transformative education and teaching have become part of teaching in Bhutan’s schools, with an emphasis on sustainable development and protection of the environment. However, for a sustainable future, active student-centered teaching and learning methods should be used in a more diverse way.
A Cross-Sectional Survey of Personal Hygiene Positive Behavior Related to COVID-19 Prevention and Control among Indonesian Communities
Authors: Lestari Fatma , Kadir Abdul , Idham Muhammad , Azwar Fahrul , Ramadhany Ganis , Sembiring Fredy , Ghazmahadi Ghazmahadi , Hakim Abdul , Modjo Robiana , Widanarko Baiduri , Kusminanti Yuni
Published: December 2021

People’s behaviors can affect the spread of the COVID-19 virus. Public behaviors, including proper personal hygiene and healthy life practices, the use of appropriate masks, and the application of good disinfectants, have an important role in human health and protection towards prevention and control of COVID-19 spread. This study aims to perform a survey of public behavior and best practices related to COVID-19 prevention and control among Indonesian communities. A cross-sectional study was designed to collect information using an online survey. Respondents were gathered from 34 provinces in Indonesia through the Indonesia National Safety and Health Council network and Universities network. Respondents voluntarily submitted their response to a predesigned online questionnaire. The collected data was then analyzed using SPSS 24.0. A total of 771 respondent subjects (male 386; female 385) were recruited. The results show that 96% of participants implement positive behavior of personal hygiene, including wearing a mask when going outside their house, and almost 67.3% of respondents sprayed a disinfectant in their house. The majority of participants hold a door handle (37.2%) and desk (28.1%) at a frequency of more than six times a day. There were 8.2% of participants implementing good personal hygiene, including hand-washing at least once a day and maximum of more than six times after going to the toilet. At-risk behaviors were conducted by respondents in which they touched part of their face at a frequency of once during an hour. It was identified that males are less likely to perform better hand hygiene as opposed to females. In conclusion, the study showed that positive behavior of personal hygiene related to COVID-19 has been implemented among Indonesian communities. This result suggests that positive behavior based on community-based prevention and control needs to be continually maintained in order to prevent and control COVID-19 spread.
Aerosol Characteristics and Their Impact on the Himalayan Energy Budget
Authors: Chand Kesar , Kuniyal Jagdish Chandra , Kanga Shruti , Guleria Raj Paul , Meraj Gowhar , Kumar Pankaj , Farooq Majid , Singh Suraj Kumar , Nathawat Mahendra Singh , Sahu Netrananda , Kumar Raj
Published: December 2021

The extensive work on the increasing burden of aerosols and resultant climate implications shows a matter of great concern. In this study, we investigate the aerosol optical depth (AOD) variations in the Indian Himalayan Region (IHR) between its plains and alpine regions and the corresponding consequences on the energy balance on the Himalayan glaciers. For this purpose, AOD data from Moderate Resolution Imaging Spectroradiometer (MODIS, MOD-L3), Aerosol Robotic Network (AERONET), India, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) were analyzed. Aerosol radiative forcing (ARF) was assessed using the atmospheric radiation transfer model (RTM) integrated into AERONET inversion code based on the Discrete Ordinate Radiative Transfer (DISORT) module. Further, air mass trajectory over the entire IHR was analyzed using a hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We estimated that between 2001 and 2015, the monthly average ARF at the surface (ARFSFC), top of the atmosphere (ARFTOA), and atmosphere (ARFATM) were −89.6 ± 18.6 Wm−2, −25.2 ± 6.8 Wm−2, and +64.4 ± 16.5 Wm−2, respectively. We observed that during dust aerosol transport days, the ARFSFC and TOA changed by −112.2 and −40.7 Wm−2, respectively, compared with low aerosol loading days, thereby accounting for the decrease in the solar radiation by 207% reaching the surface. This substantial decrease in the solar radiation reaching the Earth’s surface increases the heating rate in the atmosphere by 3.1-fold, thereby acting as an additional forcing factor for accelerated melting of the snow and glacier resources of the IHR.
New Challenges Regarding the Intervention of Musculoskeletal Risk in Truck Service Garages
Authors: Oliver Hernández Coral , Li Shimeng , Aguado Benedí María José , Mateo Rodríguez Inmaculada
Published: December 2021

Background: The automotive industry is heavily affected by sick leaves caused by the handling of loads and using postures that produce musculoskeletal disorders. Research is needed to analyse their causes and find possible solutions to eliminate or mitigate these risks. Objective: Our objective was to analyse the level of musculoskeletal risk in the different work tasks performed by truck and bus mechanics. Our intention is also to analyse whether postural training and feedback can help reduce risk. Methods: The rapid entire body assessment (REBA) was used to assess the postures performed by 35 mechanics from eight branches throughout Spain. The participants were subsequently divided randomly into two groups (experimental group and control group). The experimental group (EG) was given training and feedback on their postures and the control group (CG) was not offered any type of intervention. A few months after the initial assessment, their postural load in the usual tasks was re-evaluated. Results: An overall average REBA Score: 10.49 ± 1.33. The main risk was found in the trunk and arms with sustained above-the-head postures. EG’s second results are significantly improved compared to the first (p = 0.026 *). Conclusions: These jobs have a high-risk level of musculoskeletal disorders. The course of action presented with postural training and feedback has shown satisfactory results. Nevertheless, given the size of the sample, further research will be needed to delve deeper into this possibility as a future line of intervention.
Local Route Planning for Collision Avoidance of Maritime Autonomous Surface Ships in Compliance with COLREGs Rules
Authors: Namgung Ho
Published: December 2021

A maritime autonomous surface ship (MASS) ensures safety and effectiveness during navigation using its ability to prevent collisions with a nearby target ship (TS). This avoids the loss of human life and property. Therefore, collision avoidance of MASSs has been actively researched recently. However, previous studies did not consider all factors crucial to collision avoidance in compliance with the International Regulations for Preventing Collisions at Sea (COLREGs) Rules 5, 7, 8, and 13–17. In this study, a local route-planning algorithm that takes collision-avoidance actions in compliance with COLREGs Rules using a fuzzy inference system based on near-collision (FIS-NC), ship domain (SD), and velocity obstacle (VO) is proposed. FIS-NC is used to infer the collision risk index (CRI) and determine the time point for collision avoidance. Following this, I extended the VO using the SD to secure the minimum safe distance between the MASS and the TS when they pass each other. Unlike previous methods, the proposed algorithm can be used to perform safe and efficient navigation in terms of near-collision accidents, inferred CRI, and deviation from the course angle route by taking collision-avoidance actions in compliance with COLREGs Rules 5, 7, 8, and 13–17.
Systematic Literature Review on Self-Assessment Inventory for Quality Teaching among Islamic Education Teachers
Authors: Masuwai Azwani , Zulkifli Hafizhah , Tamuri Ab
Published: December 2021

The impact of teaching behavior towards teaching quality and self-assessment has become an emerging drift in education, providing an option for teachers’ development. However, studies on teaching behavior and self-assessment are still scarce, despite their importance in improving education. Thus, this review thoroughly identifies the aspects of teaching behavior and teaching quality, as well as the instruments of self-assessment in the teaching profession. Initially, 116 articles published on Web of Science (WoS) and Scopus between 2017 to 2021 were identified, and 10 out of 116 articles were retained for review, based on the exclusion and inclusion criteria. The criteria include the significant findings listed the aspects of teaching behavior and teaching quality. Second, the approaches of self-assessment are emphasized in teaching. Third, the reliability of the instruments used for self-assessment mostly achieved the internal consistency criteria. These three criteria imply the importance of self-assessment for quality teaching and teacher development. In general, this review could help teachers cultivate quality teaching behavior and perform reflective practice for their personal development.