bluuebunny/crossref_metadata_2025 huggingface dataset:
A Combinatorial Approach towards Antibacterial and Antioxidant Activity Using Tartaric Acid Capped Silver Nanoparticles
Authors: Begum Irshad , Shamim Sana , Ameen Fuad , Hussain Zahid , Bhat Sartaj Ahmad , Qadri Tahir , Hussain Mumtaz
Published: April 2022

The convenient synthetic strategy for the one-pot synthesis of silver nanoparticles capped by tartaric acid with a controlled size is reported here. Their characterization is revealed through spectroscopic protocols, such as UV/Vis and FTIR, while SEM, DLS and a Zetasizer revealed the surface morphology, size distribution and surface charge on the nanoparticles. The surface plasmon resonance (SPR) band was observed at 406 nm with 1.07 a.u absorbance, the image for SEM shows that the particles were monodispersed and spherical in shape, while the z-average size distribution of AgNPs/TA in a colloidal solution was found to be 79.20 nm and the surface charge was monitored as −28.2 mV. The antibacterial activities of these capped nanoparticles alone and in synergism with selected fluoroquinolones (ofloxacin, sparfloxacin, ciprofloxacin and gemifloxacin) and macrolides (erythromycin and azithromycin) were assessed on selected Gram-negative as well as Gram-positive organisms by employing the disc diffusion method. Antioxidant activity against the DPPH (1,1-diphenyl-2-picrylhydrazyl) was also evaluated using the standard assay method. The antibacterial activity of the antibiotics has been increased against studied microorganisms, showing the positive synergistic effect of the capped nanoparticles. A potential therapeutic application of AgNPs/TA in combination with antibiotics is determined from the results of the present research. These capped nanoparticles also possess good antioxidant activity and, therefore, can be used in various fields of biomedical sciences.
Photodegradation of Decabrominated Diphenyl Ether in Soil Suspensions: Kinetics, Mechanisms and Intermediates
Authors: Huang Kaibo , Lin Haozhong , Tao Xueqin , Zou Mengyao , Lu Guining
Published: April 2022

Pollution by polybrominated diphenyl ethers (PBDEs) is a major concern due to their bioaccumulation, persistence, and carcinogenicity. This study aimed to investigate the decabrominated diphenyl ether (BDE-209) photodegradation in soil suspensions. The results indicate BDE-209 can degrade in soil suspensions and its degradation follows pseudo-first-order kinetics. The light sources and intensity effects were studied and the photodegradation rates were 500 W Mercury Lamp > 300 W Mercury Lamp > 500 W Xenon Lamp > 300 W Xenon Lamp, which indicates UV light is the main reason for BDE-209 degradation. Soil particle inhibits BDE-209 photodegradation due to the light-shielding effect. BDE-209 photodegradation rates increased from 0.055 to 0.071 h−1 with pH value increasing from 3.5 to 9.5. This may be because the products are more easily produced in higher pH soil suspensions. The presence of humic acid (HA) may inhibit BDE-209 photodegradation by photo-shielding. Fe3+ and Cu2+ have an adverse effect on BDE-209 photodegradation due to the photo competition. The •OH and 1O2 were detected in soil solutions. Analysis of the photoproducts of BDE-209 by gas chromatography mass spectrometry (GC-MS) and liquid chromatography time of flight mass spectrometry (LC-TOF-MS) showed that BDE-209 was mainly debrominated to the lower-brominated BDEs and the reactive oxygen radicals may not lead to BDE-209 degradation.
Thermodynamic Optimization of Aircraft Environmental Control System Using Modified Genetic Algorithm
Authors: Liu Qihang , Zhuang Laihe , Wen Jie , Dong Bensi , Liu Zhiwei
Published: April 2022

This paper presents an optimization method for the civil aircraft environmental control system (ECS) mainly involving two airstreams: the ram airstream for cooling and the bleed airstream for supplying the cabin. The minimum total fuel energy consumption rate (FECR), defined as the weighted sum of the shaft power extraction and propulsive power loss, is obtained under the precondition of the constant outputs in the cooling capacity and outlet pressure. A modified genetic algorithm (GA) is proposed to acquire the optimal values of the heat transfer areas, temperature ratio of bleed air, mass flow rate of ram air, and pressure ratios of the turbine, compressor, and fan. The statistical results show that the multipoint crossover and continuity improvement implemented in the modified GA improve convergence and distribution performance. The probability of reaching a satisfactory result using modified GA is 62.4% higher than standard GA. Due to the decrease of inlet parameters of bleed air and the elimination of power input in the compressor, the FECR of the optimization case can be lowered by 11.0%. In general, the evaluation method considering energy quality together with the modified optimization technique is proved effective in energy-saving design for such energy systems such as ECS with multiple inputs and outputs.
Adjusting the Structure of a Peptide Nucleic Acid (PNA) Molecular Beacon and Promoting Its DNA Detection by a Hybrid with Quencher-Modified DNA
Authors: Shigeto Hajime , Kishi Takamasa , Ishii Koki , Ohtsuki Takashi , Yamamura Shohei , Kitamatsu Mizuki
Published: April 2022

In this study, we performed an elaborate adjustment of the structure of peptide nucleic acid (PNA) molecular beacons as probes for detecting nucleic acids. We synthesized the PNA beacons with various numbers of Glu, Lys, and dabcyl (Dab) quenchers in them, and we investigated their fluorescence changes (F1/1/F0) with and without full-match DNA. As the numbers of Glu/Lys or Dab increased, the F1/1/F0 tended to decrease. Among the different beacons, the PNA beacon with one Glu and one Lys (P1Q1) showed the largest F1/1/F0. On the other hand, a relatively large F1/1/F0 was obtained when the number of Glu/Lys and the number of Dab were the same, and the balance between the numbers of Glu/Lys and Dab seemed to affect the F1/1/F0. We also investigated the DNA detection by the prehybrid of P1Q1, which consists of the T790M base sequence, [P1Q1(T790M)], with quencher-modified DNA (Q-DNA). We examined the DNA detection with single-base mismatch by P1Q1(T790M), and we clarified that there was difficulty in detecting the sequence with P1Q1 alone, but that the sequence was successfully detected by the prehybrid of P1Q1 with the Q-DNA.
Cognitive Biases and Addictive Disorders: A Bibliometric Review
Authors: Zhang Melvyn W. B.
Published: April 2022

Introduction: Since the early 2000s, there have been extensive investigations into cognitive biases in addictive disorders. The advances in the field have led to the discovery that cognitive bias exists in substance disorders and could in turn be modified. To date, there have been primary studies and meta-analysis demonstrating the existence of these biases and the effectiveness of cognitive bias modification (i.e., whereby such biases are retrained). There remains a lack of understanding of how the field has progressed and the research gaps, in light of the evidences provided by these primary studies. Objectives: A bibliometric analysis of the publications to date was performed to provide a map of the work that has been done so far. This would help researchers to better understand the development of cognitive bias research, the direction of the research, and the recent trends. Methods: For the purposes of this bibliometric research, Web of Science (WOS) was used in the identification of relevant articles. To identify the relevant articles, the following search strategy was implemented, that of ((((((TS = (“cognitive bias”)) OR TS = (“attention bias”)) OR TS = (“approach bias”)) OR TS = (“avoidance bias)) OR TS = (“interpretative bias”))). Bibliometric data analysis was conducted based on the identified articles. Results: A total of 161 citations were eventually included. These citations were published between 1994 and 2022. The average number of citations per documents was 26.73. Of these 161 citations, 122 were articles, 2 were editorials, 3 were corrections to the original manuscript, 5 were reviews, and 29 were meeting abstracts. The analysis of the trend of topics has shown that researchers were focused on understanding and gaining insights into cognitive biases and potentially examining the association between cognitive biases and cravings and aggression in the early days. Over the years, there has been an evolution into examining specific unconscious biases, namely, that of attention and approach biases. In the most recent years, the investigations have been more focused on examining bias modification/retraining. Conclusions: From our knowledge, this is the first bibliometric analysis that has been undertaken to explore all the publications related to cognitive bias in the field of addiction. The insights gained from this article could inform future research.
Increasing Importance of Risk Management in the Context of Solid Waste Sphere Reforming in Russian Regions
Authors: Degtereva Viktoria , Liubarskaia Maria , Merkusheva Viktoria , Artemiev Alexey
Published: April 2022

This article analyzes the risk factors influencing the achievement of solid waste sphere reforming goals in Russia. The given arguments present the current state of the reform as not very effective from an economic, environmental, and social perspective. The authors identify four groups of risk factors and put forward, as a critical condition for successful reform, the availability of reliable information, as well as risk management incorporated in the decision-making process. The basis for the research execution was the information from the regional solid waste sphere Master Plans and expert opinions on the readiness to achieve the reform goal regarding 100% MSW sorting based on the staff performance, public awareness, technology availability, and tariff validity assessments. The authors use a decision tree method and MSW sorting system development scenarios to provide the pessimistic and optimistic evaluation on the potential for fulfilling the reform tasks. The conclusions indicate the unattainability of the goals set by the Russian authorities for MSW sorting by 2030. The authors propose to change the status of risk factors through the implementation of certain measures for the transition from a negative to a positive scenario of reforming and to set the realistic goals for MSW sorting in Russia.
Satellite Observed Spatial and Temporal Variabilities of Particulate Organic Carbon in the East China Sea
Authors: Chen Dongxing , Zeng Lin , Boot Katherine , Liu Qianqian
Published: April 2022

In this study, we investigate spatial and temporal variabilities of particulate organic carbon (POC) in the East China Sea (ECS) and explore the dominant influencing factors for its three subregions—coastal, continental shelf and open sea—by analyzing satellite derived POC from 2001 to 2011 after calibration against in situ samplings. The analysis reveals that the largest POC occurs in spring at the coastal region with a value of 297.5 mg m−3, and the smallest in summer at the open sea region with a value of 108.9 mg m−3. POC in the coastal region is dominated by biological activity related to river discharge of nutrients and organic matter, and is regulated by sediment discharge variation due to the Three Gorge Project and chemical fertilizer application along the Changjiang River watershed; the open sea region is dominated by water exchange with the Kuroshio current, which results in the highest sea surface temperature and the lowest chlorophyll and POC among the three subregions; POC in the continental shelf region is driven by the seasonal competition between the influence of the coastal ocean and the Kuroshio current through biological activity and water exchange. A method to estimate the organic matter reserve in the ECS was developed based on satellite data and in situ samplings. Organic carbon storage was estimated at 4.08 × 1013 g, which could be used as a baseline for future estimates. An improved estimation could be found with additional in situ data and with the use of a more sophisticated algorithm for satellite POC analysis.
Mapping Crop Distribution Patterns and Changes in China from 2000 to 2015 by Fusing Remote-Sensing, Statistics, and Knowledge-Based Crop Phenology
Authors: Wang Yue , Zhang Zengxiang , Zuo Lijun , Wang Xiao , Zhao Xiaoli , Sun Feifei
Published: April 2022

Maps of different kinds of crops offer information about both crop distribution and crop mix, which support analyses on food security, environmental change, and climate change. Despite the growing capability for mapping specific crops, the majority of studies have focused on a few dominant crops, whereas maps with a greater diversity of crops lack research. Combining cropping seasons derived from MODIS EVI data, regional crop calendar data, and agricultural statistical surveys, we developed an allocation model to map 14 major crops at a 1 km resolution across China for the years 2000, 2010, and 2015. The model was verified based on the fitness between the area of the three typical combinations of region, crop/crop group derived from remote sensing data, and statistical data. The R2, indicating fitness, ranged from 0.51 to 0.75, with a higher value for the crops distributed in plain regions and a lower value in regions with topographically diverse landscapes. Within the same combination of region and crop/crop group, the larger harvest area a province has, the higher its fitness, suggesting an overall reliable result at the national level. A comparison of paddy rice between our results and the National Land Use/Cover Database of China showed a relatively high R2 and slope of fitness (0.67 and 0.71, respectively). Compared with the commonly used average allocation model, and without lending cropping season information, the diversity index of the results from our model is about 30% higher, indicating crop maps with greater spatial details. According to the spatial distribution analysis of the four main crops, the grids showing decreased trends accounted for 74.92%, 57.32%, and 59.00% of the total changed grid for wheat, rice, and soybean crops, respectively, while accounting for only 37.71% for maize. The resulting data sets can be used to improve assessments for nutrient security and sustainability of cropping systems, as well as their resilience in a changing climate.
A Framework of Generating Land Surface Reflectance of China Early Landsat MSS Images by Visibility Data and Its Evaluation
Authors: Zhao Cong , Wu Zihua , Qin Qiming , Ye Xin
Published: April 2022

The Landsat time-series dataset is one of the most widely used datasets for land surface research due to its long time-series and Land Surface Reflectance (LSR) product. Though the United States Geological Survey (USGS) provides Landsat LSR products for later Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Operational Land Imager (OLI), no early Landsat 1–5 Multispectral Scanner System (MSS) LSR product is generated currently, limiting the research traced back to the 1970s. Atmospheric correction is one of the necessary preprocesses for generating LSR products. However, it is challenging for MSS images, not only because the image quality is lower and bands are different compared with the current sensors, but also because of the multiple effects of other preprocesses, such as radiometric calibration. Based on the Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) model, we propose a novel framework for generating Landsat 1–5 MSS LSR data of China. Ground-based visibility records are introduced to replace the images-based aerosol optical depth (AOD) to effectively generate MSS LSR data of the 1970s. We evaluate the generated MSS LSR data by the cross-validation of the simultaneous observation of MSS and TM sensors in Landsat 4 and Landsat 5 using Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) surface reflectance product as the truth value. The evaluation result shows that the generated MSS LSR data is comparable with the later Landsat TM LSR product, with slightly larger uncertainties. In addition, it shows that the non-atmospheric factors (e.g., the difference of relative spectral responses of TM and MSS, the georegistration errors, the radiometric calibration uncertainty, and image noises) bring larger uncertainties than the atmospheric factors (e.g., the AOD retrieval method by visibility) to the cross-validation results. We apply the MSS LSR data generated by the proposed framework on time series analysis in the regions of interest (ROIs) of the spectral-stable land cover in China for all the MSS sensors. The application demonstrates the potential and promise of the MSS LSR data generated by the proposed framework.
Applying a Portable Backpack Lidar to Measure and Locate Trees in a Nature Forest Plot: Accuracy and Error Analyses
Authors: Xie Yuyang , Yang Tao , Wang Xiaofeng , Chen Xi , Pang Shuxin , Hu Juan , Wang Anxian , Chen Ling , Shen Zehao
Published: April 2022

Accurate tree positioning and measurement of structural parameters are the basis of forest inventory and mapping, which are important for forest biomass calculation and community dynamics analyses. Portable backpack lidar that integrates the simultaneous localization and mapping (SLAM) technique with a global navigation satellite system receiver has greater flexibility for tree inventory than terrestrial laser scanning, but it has never been used to measure and map forest structure in a large area (>101 hectares) with high tree density. In the present study, we used the LiBackpack DG50 backpack lidar system to obtain the point cloud data of a 10 ha plot of subtropical evergreen broadleaved forest, and applied these data to quantify errors and related factors in the diameter at breast height (DBH) measurements and positioning for more than 1900 individual trees. We found an average error of 4.19 cm in the DBH measurements obtained by lidar, compared with manual field measurements. The incompleteness of the tree stem point clouds was the main factor that caused the DBH measurement errors, and the field DBH measurements and density of the point clouds also had significant impacts. The average tree positioning error was 4.64 m, and it was significantly affected by the distance and route length from the measured trees to the data acquisition start position, whereas it was affected little by the habitat complexity and characteristics of tree stems. The tree positioning measurement error led to increases in the mean value and variability of paired-tree distance error as the sample plot scale increased. We corrected the errors based on the estimates of predictive models. After correction, the DBH measurement error decreased by 31.3%, the tree positioning error decreased by 44.3%, and the paired-tree distance error decreased by 56.3%. As the sample plot scale increased, the accumulated paired-tree distance error stabilized gradually.
Developing High-Resolution Crop Maps for Major Crops in the European Union Based on Transductive Transfer Learning and Limited Ground Data
Authors: Luo Yuchuan , Zhang Zhao , Zhang Liangliang , Han Jichong , Cao Juan , Zhang Jing
Published: April 2022

Precise and timely information on crop spatial distribution over large areas is paramount to agricultural monitoring, food security, and policy development. Currently, automatically classifying crop types at a large scale is challenging due to the scarcity of ground data. Although previous studies have indicated that transductive transfer learning (TTL) is a promising method to address this problem, it performs poorly within regions where crop compositions and phenology differ largely. Here we transferred random forest classifiers trained in limited regions with diversified growing conditions and land covers to the rest of the study area where ground data are scarce, with more than 130,000 Sentinel-2 images processed using the Google Earth Engine (GEE) platform. We established the 10 m crop maps for four major crops (i.e., maize, rapeseed, winter, and spring Triticeae crops) across 10 European Union (EU) countries from 2018 to 2019. The final crop maps had a high accuracy with overall accuracy generally greater than 0.89, with user’s accuracy and producer’s accuracy ranging from 0.72 to 0.98. Moreover, the resulting maps were consistent with the NUTS-2 level official statistics, with R2 consistently greater than 0.9. We further analyzed the crop rotation patterns and found that the rotation intervals across these EU countries were generally at least one year. Maize was dominantly rotated with winter Triticeae crops or converted to other land covers in the following year. Rapeseed was generally grown in rotation with winter Triticeae crops, whereas the rotation patterns of winter and spring Triticeae crops were more diversified. Red Edge Position (REP) and Normalized Difference Yellow Index (NDYI) played significant roles in crop classification across the EU. This study highlights the potential of the developed TTL method for crop classification over large spatial extents where labeled data are limited and the differences in crop compositions and phenology are relatively large.
Nitrous Oxide Profiling from Infrared Radiances (NOPIR): Algorithm Description, Application to 10 Years of IASI Observations and Quality Assessment
Authors: Vandenbussche Sophie , Langerock Bavo , Vigouroux Corinne , Buschmann Matthias , Deutscher Nicholas M. , Feist Dietrich G. , García Omaira , Hannigan James W. , Hase Frank , Kivi Rigel , Kumps Nicolas , Makarova Maria , Millet Dylan B. , Morino Isamu , Nagahama Tomoo , Notholt Justus , Ohyama Hirofumi , Ortega Ivan , Petri Christof , Rettinger Markus , Schneider Matthias , Servais Christian P. , Sha Mahesh Kumar , Shiomi Kei , Smale Dan , Strong Kimberly , Sussmann Ralf , Té Yao , Velazco Voltaire A. , Vrekoussis Mihalis , Warneke Thorsten , Wells Kelley C. , Wunch Debra , Zhou Minqiang , De Mazière Martine
Published: April 2022

Nitrous oxide (N2O) is the third most abundant anthropogenous greenhouse gas (after carbon dioxide and methane), with a long atmospheric lifetime and a continuously increasing concentration due to human activities, making it an important gas to monitor. In this work, we present a new method to retrieve N2O concentration profiles (with up to two degrees of freedom) from each cloud-free satellite observation by the Infrared Atmospheric Sounding Interferometer (IASI), using spectral micro-windows in the N2O ν3 band, the Radiative Transfer for TOVS (RTTOV) tools and the Tikhonov regularization scheme. A time series of ten years (2011–2020) of IASI N2O profiles and integrated partial columns has been produced and validated with collocated ground-based Network for the Detection of Atmospheric Composition Change (NDACC) and Total Carbon Column Observing Network (TCCON) data. The importance of consistency in the ancillary data used for the retrieval for generating consistent time series has been demonstrated. The Nitrous Oxide Profiling from Infrared Radiances (NOPIR) N2O partial columns are of very good quality, with a positive bias of 1.8 to 4% with respect to the ground-based data, which is less than the sum of uncertainties of the compared values. At high latitudes, the comparisons are a bit worse, due to either a known bias in the ground-based data, or to a higher uncertainty in both ground-based and satellite retrievals.
A Population Spatialization Model at the Building Scale Using Random Forest
Authors: Wang Mengqi , Wang Yinglin , Li Bozhao , Cai Zhongliang , Kang Mengjun
Published: April 2022

Population spatialization reveals the distribution and quantity of the population in geographic space with gridded population maps. Fine-scale population spatialization is essential for urbanization and disaster prevention. Previous approaches have used remotely sensed imagery to disaggregate census data, but this approach has limitations. For example, large-scale population censuses cannot be conducted in underdeveloped countries or regions, and remote sensing data lack semantic information indicating the different human activities occurring in a precise geographic location. Geospatial big data and machine learning provide new fine-scale population distribution mapping methods. In this paper, 30 features are extracted using easily accessible multisource geographic data. Then, a building-scale population estimation model is trained by a random forest (RF) regression algorithm. The results show that 91% of the buildings in Lin’an District have absolute error values of less than six compared with the actual population data. In a comparison with a multiple linear (ML) regression model, the mean absolute errors of the RF and ML models are 2.52 and 3.21, respectively, the root mean squared errors are 8.2 and 9.8, and the R2 values are 0.44 and 0.18. The RF model performs better at building-scale population estimation using easily accessible multisource geographic data. Future work will improve the model accuracy in densely populated areas.
Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI
Authors: Amin Eatidal , Belda Santiago , Pipia Luca , Szantoi Zoltan , El Baroudy Ahmed , Moreno José , Verrelst Jochem
Published: April 2022

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on time series of green Leaf Area Index (LAI) generated from NASA’s Harmonized Landsat 8 (L8) and Sentinel-2 (S2) surface reflectance dataset from 2016 to 2019. LAI time series were processed for each satellite dataset, which were used separately and combined to identify seasonal dynamics for a selection of crop types (wheat, clover, maize and rice). For the combination of L8 with S2 LAI products, we proposed two time series smoothing and fitting methods: (1) the Savitzky–Golay (SG) filter and (2) the Gaussian Processes Regression (GPR) fitting function. Single-sensor and L8-S2 combined LAI time series were used for the calculation of key crop Land Surface Phenology (LSP) metrics (start of season, end of season, length of season), whereby the detection of cropland growing seasons was based on two established threshold methods, i.e., a seasonal or a relative amplitude value. Overall, the developed phenology extraction scheme enabled identifying up to two successive crop cycles within a year, with a superior performance observed for the seasonal than for the relative threshold method, in terms of consistency and cropland season detection capability. Differences between the time series collections were analyzed by comparing the phenology metrics per crop type and year. Results suggest that L8-S2 combined LAI data streams with GPR led to a more precise detection of the start and end of growing seasons for most crop types, reaching an overall detection of 74% over the total planted crops versus 69% with S2 and 63% with L8 alone. Finally, the phenology mapping allowed us to evaluate the spatial and temporal evolution of the croplands over the agroecosystem in the Nile Delta.
All-Sky 1 km MODIS Land Surface Temperature Reconstruction Considering Cloud Effects Based on Machine Learning
Authors: Cho Dongjin , Bae Dukwon , Yoo Cheolhee , Im Jungho , Lee Yeonsu , Lee Siwoo
Published: April 2022

A high spatio-temporal resolution land surface temperature (LST) is necessary for various research fields because LST plays a crucial role in the energy exchange between the atmosphere and the ground surface. The moderate-resolution imaging spectroradiometer (MODIS) LST has been widely used, but it is not available under cloudy conditions. This study proposed a novel approach for reconstructing all-sky 1 km MODIS LST in South Korea during the summer seasons using various data sources, considering the cloud effects on LST. In South Korea, a Local Data Assimilation and Prediction System (LDAPS) with a relatively high spatial resolution of 1.5 km has been operated since 2013. The LDAPS model’s analysis data, binary MODIS cloud cover, and auxiliary data were used as input variables, while MODIS LST and cloudy-sky in situ LST were used together as target variables based on the light gradient boosting machine (LightGBM) approach. As a result of spatial five-fold cross-validation using MODIS LST, the proposed model had a coefficient of determination (R2) of 0.89–0.91 with a root mean square error (RMSE) of 1.11–1.39 °C during the daytime, and an R2 of 0.96–0.97 with an RMSE of 0.59–0.60 °C at nighttime. In addition, the reconstructed LST under the cloud was evaluated using leave-one-station-out cross-validation (LOSOCV) using 22 weather stations. From the LOSOCV results under cloudy conditions, the proposed LightGBM model had an R2 of 0.55–0.63 with an RMSE of 2.41–3.00 °C during the daytime, and an R2 of 0.70–0.74 with an RMSE of 1.31–1.36 °C at nighttime. These results indicated that the reconstructed LST has higher accuracy than the LDAPS model. This study also demonstrated that cloud cover information improved the cloudy-sky LST estimation accuracy by adequately reflecting the heterogeneity of the relationship between LST and input variables under clear and cloudy skies. The reconstructed all-sky LST can be used in a variety of research applications including weather monitoring and forecasting.
Surface Current Variations and Hydrological Characteristics of the Penghu Channel in the Southeastern Taiwan Strait
Authors: Hsu Po-Chun
Published: April 2022

Coastal ocean dynamics application radar (CODAR) SeaSonde high-frequency (HF) radars deployed along the coast of Taiwan were used to reveal ocean surface current variations both hourly and through climatological seasons in the Penghu Channel (PHC), southeastern Taiwan Strait (TS), from December 2014 to December 2020. The ocean current in the PHC has a semidiurnal tidal cycle, and the seasonal main flow, wind direction, and wind strength significantly affect the direction and speed of the flow passing through the PHC. The speed of the tidal current in the PHC area can reach more than 1 m/s, and the monthly average flow speed in the PHC is between 0.12 (winter) and 0.24 m/s (summer). Several buoys indicated that the southward flow along the western coast of Taiwan drifted through the PHC in fall and winter. The HF radar observations confirmed the same, implying that this occurred during the strong northeastern monsoon. For a weak northerly wind or even southerly wind, the flow in the PHC can be northward. Different wind directions can affect the speed of the flow passing through the PHC and the branch flow in the northern PHC. The HF radar results are highly consistent with the spatial characteristics of satellite data regarding the sea surface temperature, sea surface salinity, and chlorophyll concentrations; however, there are significant differences from the satellite-derived ocean current.
S-NER: A Concise and Efficient Span-Based Model for Named Entity Recognition
Authors: Yu Jie , Ji Bin , Li Shasha , Ma Jun , Liu Huijun , Xu Hao
Published: April 2022

Named entity recognition (NER) is a task that seeks to recognize entities in raw texts and is a precondition for a series of downstream NLP tasks. Traditionally, prior NER models use the sequence labeling mechanism which requires label dependency captured by the conditional random fields (CRFs). However, these models are prone to cascade label misclassifications since a misclassified label results in incorrect label dependency, and so some following labels may also be misclassified. To address the above issue, we propose S-NER, a span-based NER model. To be specific, S-NER first splits raw texts into text spans and regards them as candidate entities; it then directly obtains the types of spans by conducting entity type classifications on span semantic representations, which eliminates the requirement for label dependency. Moreover, S-NER has a concise neural architecture in which it directly uses BERT as its encoder and a feed-forward network as its decoder. We evaluate S-NER on several benchmark datasets across three domains. Experimental results demonstrate that S-NER consistently outperforms the strongest baselines in terms of F1-score. Extensive analyses further confirm the efficacy of S-NER.
Detecting Faults at the Edge via Sensor Data Fusion Echo State Networks
Authors: Bruneo Dario , De Vita Fabrizio
Published: April 2022

The pervasive use of sensors and actuators in the Industry 4.0 paradigm has changed the way we interact with industrial systems. In such a context, modern frameworks are not only limited to the system telemetry but also include the detection of potentially harmful conditions. However, when the number of signals generated by a system is large, it becomes challenging to properly correlate the information for an effective diagnosis. The combination of Artificial Intelligence and sensor data fusion techniques is a valid solution to address this problem, implementing models capable of extracting information from a set of heterogeneous sources. On the other hand, the constrained resources of Edge devices, where these algorithms are usually executed, pose strict limitations in terms of memory occupation and models complexity. To overcome this problem, in this paper we propose an Echo State Network architecture which exploits sensor data fusion to detect the faults on a scale replica industrial plant. Thanks to its sparse weights structure, Echo State Networks are Recurrent Neural Networks models, which exhibit a low complexity and memory footprint, which makes them suitable to be deployed on an Edge device. Through the analysis of vibration and current signals, the proposed model is able to correctly detect the majority of the faults occurring in the industrial plant. Experimental results demonstrate the feasibility of the proposed approach and present a comparison with other approaches, where we show that our methodology is the best trade-off in terms of precision, recall, F1-score and inference time.
Emotion Recognizing by a Robotic Solution Initiative (EMOTIVE Project)
Authors: D’Onofrio Grazia , Fiorini Laura , Sorrentino Alessandra , Russo Sergio , Ciccone Filomena , Giuliani Francesco , Sancarlo Daniele , Cavallo Filippo
Published: April 2022

Background: Emotion recognition skills are predicted to be fundamental features in social robots. Since facial detection and recognition algorithms are compute-intensive operations, it needs to identify methods that can parallelize the algorithmic operations for large-scale information exchange in real time. The study aims were to identify if traditional machine learning algorithms could be used to assess every user emotions separately, to relate emotion recognizing in two robotic modalities: static or motion robot, and to evaluate the acceptability and usability of assistive robot from an end-user point of view. Methods: Twenty-seven hospital employees (M = 12; F = 15) were recruited to perform the experiment showing 60 positive, negative, or neutral images selected in the International Affective Picture System (IAPS) database. The experiment was performed with the Pepper robot. Concerning experimental phase with Pepper in active mode, a concordant mimicry was programmed based on types of images (positive, negative, and neutral). During the experimentation, the images were shown by a tablet on robot chest and a web interface lasting 7 s for each slide. For each image, the participants were asked to perform a subjective assessment of the perceived emotional experience using the Self-Assessment Manikin (SAM). After participants used robotic solution, Almere model questionnaire (AMQ) and system usability scale (SUS) were administered to assess acceptability, usability, and functionality of robotic solution. Analysis wasperformed on video recordings. The evaluation of three types of attitude (positive, negative, andneutral) wasperformed through two classification algorithms of machine learning: k-nearest neighbors (KNN) and random forest (RF). Results: According to the analysis of emotions performed on the recorded videos, RF algorithm performance wasbetter in terms of accuracy (mean ± sd = 0.98 ± 0.01) and execution time (mean ± sd = 5.73 ± 0.86 s) than KNN algorithm. By RF algorithm, all neutral, positive and negative attitudes had an equal and high precision (mean = 0.98) and F-measure (mean = 0.98). Most of the participants confirmed a high level of usability and acceptability of the robotic solution. Conclusions: RF algorithm performance was better in terms of accuracy and execution time than KNN algorithm. The robot was not a disturbing factor in the arousal of emotions.
Collaborative Robot Precision Task in Medical Microbiology Laboratory
Authors: Baumkircher Aljaz , Seme Katja , Munih Marko , Mihelj Matjaž
Published: April 2022

This study focuses on the feasibility of collaborative robot implementation in a medical microbiology laboratory by demonstrating fine tasks using kinesthetic teaching. Fine tasks require sub-millimetre positioning accuracy. Bacterial colony picking and identification was used as a case study. Colonies were picked from Petri dishes and identified using matrix-assisted laser desorption/ionization (MALDI) time-of-flight (TOF) mass spectrometry. We picked and identified 56 colonies (36 colonies of Gram-negative Acinetobacter baumannii and 20 colonies of Gram-positive Staphylococcus epidermidis). The overall identification error rate was around 11%, although it was significantly lower for Gram-positive bacteria (5%) than Gram-negative bacteria (13.9%). Based on the identification scores, it was concluded that the system works similarly well as a manual operator. It was determined that tasks were successfully demonstrated using kinesthetic teaching and generalized using dynamic movement primitives (DMP). Further improvement of the identification error rate is possible by choosing a different deposited sample treatment method (e.g., semi-extraction, wet deposition).
Fatigue Measurements in an Existing Highway Concrete Bridge
Authors: Schuler Harald , Müller Martin
Published: April 2022

Knowing the actual fatigue effects helps to assess the structural safety of bridges more accurately. Especially when the actions change or increase, it is important to be able to determine this. This paper deals with the measurement and recording of actual fatigue loads at a critical point of an existing bridge. It shows how long-term effects can be separated from short-term effects and how amplitudes from passing trucks can be counted. The paper also highlights the challenges that arise when measuring actual fatigue loads.
Load-Balancing of Kubernetes-Based Edge Computing Infrastructure Using Resource Adaptive Proxy
Authors: Nguyen Quang-Minh , Phan Linh-An , Kim Taehong
Published: April 2022

Kubernetes (K8s) is expected to be a key container orchestration tool for edge computing infrastructures owing to its various features for supporting container deployment and dynamic resource management. For example, its horizontal pod autoscaling feature provides service availability and scalability by increasing the number of replicas. kube-proxy provides traffic load-balancing between replicas by distributing client requests equally to all pods (replicas) of an application in a K8s cluster. However, this approach can result in long delays when requests are forwarded to remote workers, especially in edge computing environments where worker nodes are geographically dispersed. Moreover, if the receiving worker is overloaded, the request-processing delay can increase significantly. To overcome these limitations, this paper proposes an enhanced load balancer called resource adaptive proxy (RAP). RAP periodically monitors the resource status of each pod and the network status among worker nodes to aid in load-balancing decisions. Furthermore, it preferentially handles requests locally to the maximum extent possible. If the local worker node is overloaded, RAP forwards its requests to the best node in the cluster while considering resource availability. Our experimental results demonstrated that RAP could significantly improve throughput and reduce request latency compared with the default load-balancing mechanism of K8s.
Reliability of Repeated Isometric Neck Strength in Rugby Union Players Using a Load Cell Device
Authors: Chavarro-Nieto Christian , Beaven Martyn , Gill Nicholas , Hébert-Losier Kim
Published: April 2022

Concussion is the most common injury in professional Rugby Union (RU) players, with increasing incidence and severity each year. Strengthening the neck is an intervention used to decrease concussion incidence and severity, which can only be proven effective if strength neck measures are reliable. We conducted a repeated-measures reliability study with 23 male RU players. Neck strength was assessed seated in a ‘make’ test fashion in flexion, extension, and bilateral-side flexion. Flexion-to-extension and left-to-right side ratios were also computed. Three testing sessions were undertaken over three consecutive weeks. Intrasession and intersession reliabilities were assessed using typical errors, coefficient of variations (CV), and intraclass correlation coefficients (ICC). Intrasession reliability demonstrated good-to-excellent relative (ICC > 0.75) and good absolute (CV ≤ 20%) reliability in all directions (ICC = 0.86–0.95, CV = 6.4–8.8%), whereas intersession reliability showed fair relative (ICC: 0.40 to 0.75) and acceptable absolute (CV ≤ 20%) reliability for mean and maximal values (ICC = 0.51–0.69, CV = 14.5–19.8%). Intrasession reliability for flexion-to-extension ratio was good (relative, ICC = 0.86) and acceptable (absolute, CV = 11.5%) and was fair (relative, ICC = 0.75) and acceptable (absolute, CV = 11.5%) for left-to-right ratio. Intersession ratios from mean and maximal values were fair (relative, ICC = 0.52–0.55) but not always acceptable (absolute, CV = 16.8–24%). Assessing isometric neck strength with a head harness and a cable with a load cell device seated in semi-professional RU players is feasible and demonstrates good-to-excellent intrasession and fair intersession reliability. We provide data from RU players to inform practice and assist standardisation of testing methods.
Persistent Periodic Uplink Scheduling Algorithm for Massive NB-IoT Devices
Authors: Wu Tin-Yu , Hwang Ren-Hung , Vyas Abhishek , Lin Chia-Yiu , Huang Chi-Ruei
Published: April 2022

Narrowband Internet of Things (NB-IoT) is one of the low-power wide-area network (LPWAN) technologies that aim to support enormous connections, featuring wide-area coverage, low power consumption, and low costs. NB-IoT could serve a massive number of IoT devices, but with very limited radio resources. Therefore, how to enable a massive number of IoT devices to transmit messages periodically, and with low latency, according to transmission requirements, has become the most crucial issue of NB-IoT. Moreover, IoT devices are designed to minimize power consumption so that the device battery can last for a long time. Similarly, the NB-IoT system must configure different power-saving mechanisms for different types of devices to prolong their battery lives. In this study, we propose a persistent periodic uplink scheduling algorithm (PPUSA) to assist a plethora of Internet of Things (IoT) devices in reporting their sensing data based on their sensing characteristics. PPUSA explicitly considers the power-saving mode and connection suspend/resume procedures to reduce the IoT device’s power consumption and processing overhead. PPUSA allocates uplink resource units to IoT devices systematically so that it can support the periodic–uplink transmission of a plethora of IoT devices while maintaining low transmission latency for bursty data. The simulation results show that PPUSA can support up to 600,000 IoT devices when the NB-IoT uplink utilization is 80%. In addition, it takes only one millisecond for the transmission of the bursty messages.
Rethinking the Role of Normalization and Residual Blocks for Spiking Neural Networks
Authors: Ikegawa Shin-ichi , Saiin Ryuji , Sawada Yoshihide , Natori Naotake
Published: April 2022

Biologically inspired spiking neural networks (SNNs) are widely used to realize ultralow-power energy consumption. However, deep SNNs are not easy to train due to the excessive firing of spiking neurons in the hidden layers. To tackle this problem, we propose a novel but simple normalization technique called postsynaptic potential normalization. This normalization removes the subtraction term from the standard normalization and uses the second raw moment instead of the variance as the division term. The spike firing can be controlled, enabling the training to proceed appropriately, by conducting this simple normalization to the postsynaptic potential. The experimental results show that SNNs with our normalization outperformed other models using other normalizations. Furthermore, through the pre-activation residual blocks, the proposed model can train with more than 100 layers without other special techniques dedicated to SNNs.
A New Fusion Fault Diagnosis Method for Fiber Optic Gyroscopes
Authors: Zhang Wanpeng , Zhang Dailin , Zhang Peng , Han Lei
Published: April 2022

The fiber optic gyroscope (FOG) is a high precision inertial navigation device, and it is necessary to ensure its reliability for effective use. However, the extracted fault features are easily distorted due to the interference of vibrations when the FOG is in operation. In order to minimize the influence of vibrations to the greatest extent, a fusion diagnosis method was proposed in this paper. It extracted features from fault data with Fast Fourier Transform (FFT) and wavelet packet decomposition (WPD), and built a strong diagnostic classifier with a sparse auto encoder (SAE) and a neural network (NN). Then, a fusion neural network model was established based on the diagnostic output probabilities of the two primary classifiers, which improved the diagnostic accuracy and the anti-vibration capability. Then, five fault types of the FOG under random vibration conditions were established. Fault data sets were collected and generated for experimental comparison with other methods. The results showed that the proposed fusion fault diagnosis method could perform effective and robust fault diagnosis for the FOG under vibration conditions with a high diagnostic accuracy.