bluuebunny/crossref_metadata_2025 huggingface dataset:
Application of Multispectral Camera in Monitoring the Quality Parameters of Fresh Tea Leaves
Authors: Chen Longyue , Xu Bo , Zhao Chunjiang , Duan Dandan , Cao Qiong , Wang Fan
Published: September 2021

The production of high-quality tea by Camellia sinensis (L.) O. Ktze is the goal pursued by both producers and consumers. Rapid, nondestructive, and low-cost monitoring methods for monitoring tea quality could improve the tea quality and the economic benefits associated with tea. This research explored the possibility of monitoring tea leaf quality from multi-spectral images. Threshold segmentation and manual sampling methods were used to eliminate the image background, after which the spectral features were constructed. Based on this, the texture features of the multi-spectral images of the tea canopy were extracted. Three machine learning methods, partial least squares regression, support vector machine regression, and random forest regression (RFR), were used to construct and train multiple monitoring models. Further, the four key quality parameters of tea polyphenols, total sugars, free amino acids, and caffeine content were estimated using these models. Finally, the effects of automatic and manual image background removal methods, different regression methods, and texture features on the model accuracies were compared. The results showed that the spectral characteristics of the canopy of fresh tea leaves were significantly correlated with the tea quality parameters (r ≥ 0.462). Among the sampling methods, the EXG_Ostu sampling method was best for prediction, whereas, among the models, RFR was the best fitted modeling algorithm for three of four quality parameters. The R2 and root-mean-square error values of the built model were 0.85 and 0.16, respectively. In addition, the texture features extracted from the canopy image improved the prediction accuracy of most models. This research confirms the modeling application of a combination of multi-spectral images and chemometrics, as a low-cost, fast, reliable, and nondestructive quality control method, which can effectively monitor the quality of fresh tea leaves. This provides a scientific reference for the research and development of portable tea quality monitoring equipment that has general applicability in the future.
Typhoon Warm-Core Structures Derived from FY-3D MWTS-2 Observations
Authors: Niu Zeyi , Zou Xiaolei , Huang Wei
Published: September 2021

In this study, the three-dimensional (3D) warm-core structures of the Northwest Pacific typhoons Francisco, Lekima, and Krosa in August 2019 are retrieved from the Fengyun-3D (FY-3D) microwave temperature sounder-2 (MWTS-2) observations of brightness temperature. Due to the lack of two window channels at 23.8 GHz and 31.4 GHz, an empirical cloud detection algorithm based on 50.3 GHz bias-corrected observations-minus-backgrounds is applied to obtain clear-sky observations for the multiple linear regression retrieval algorithm. The MWTS-2 cloud-affected channels 3–5 are not used to retrieve temperatures under cloudy conditions to eliminate low-tropospheric cold anomalies. The multiple linear regression coefficients are obtained based on MWTS-2 brightness temperatures and the temperatures from the European Centre for Medium-Range Weather Forecasts Reanalysis-5 (ERA5) in the training period of three weeks before the month of targeted typhoons. The proposed MWTS-2 warm-core retrieval can well capture the radial and vertical temporal evolutions of the temperature anomalies of the typhoons Francisco, Lekima, and Krosa. The sizes of the warm-core anomalies of typhoons Lekima and Krosa retrieved by the MWTS-2 are horizontally and vertically similar to and stronger than those of the ERA5. Compared with the ERA5 reanalysis in August 2019, the biases for MWTS-2 temperature retrievals are smaller than ±0.25 K, with root-mean-square errors (RMSEs) smaller than and 2.0 K at all altitudes. Additionally, the location of the 250-hPa maximum temperature anomaly retrieved by the MWTS-2 is closer to the best track than that of the ERA5. A weak warm-core around 200 hPa and a cold-core anomaly in the middle troposphere are also found in the outer rain bands region due to the effect of evaporation of rainfall.
DGFNet: Dual Gate Fusion Network for Land Cover Classification in Very High-Resolution Images
Authors: Guo Yongjie , Wang Feng , Xiang Yuming , You Hongjian
Published: September 2021

Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on land cover classification thanks to their outstanding nonlinear feature extraction ability. DCNNs are usually designed as an encoder–decoder architecture for the land cover classification in very high-resolution (VHR) remote sensing images. The encoder captures semantic representation by stacking convolution layers and shrinking image spatial resolution, while the decoder restores the spatial information by an upsampling operation and combines it with different level features through a summation or skip connection. However, there is still a semantic gap between different-level features; a simple summation or skip connection will reduce the performance of land-cover classification. To overcome this problem, we propose a novel end-to-end network named Dual Gate Fusion Network (DGFNet) to restrain the impact of the semantic gap. In detail, the key of DGFNet consists of two main components: Feature Enhancement Module (FEM) and Dual Gate Fusion Module (DGFM). Firstly, the FEM combines local information with global contents and strengthens the feature representation in the encoder. Secondly, the DGFM is proposed to reduce the semantic gap between different level features, effectively fusing low-level spatial information and high-level semantic information in the decoder. Extensive experiments conducted on the LandCover dataset and the ISPRS Potsdam dataset proved the effectiveness of the proposed network. The DGFNet achieves state-of-art performance 88.87% MIoU on the LandCover dataset and 72.25% MIoU on the ISPRS Potsdam dataset.
Global Change in Terrestrial Ecosystem Detected by Fusion of Microwave and Optical Satellite Observations
Authors: Nara Hideharu , Sawada Yohei
Published: September 2021

The detection of global land change via satellite observation is a major challenge in improving the understanding of global environmental change. In this study, we develop a new vegetation index which can be used as a proxy for the fractions of tree canopy and short vegetation, based on the simple linear regression between microwave vegetation optical depth (VOD) and optical leaf area index (LAI). Although we use no high-resolution reference data, the newly developed vegetation index successfully detects global land change which has been reported by previous estimations based on high-resolution reference data. We find that the relationship between VOD and LAI is non-stationary and the temporal change in the VOD-LAI relationship is an important signal for detecting global change in the terrestrial ecosystem.
Performance of Single-Epoch EWL/WL/NL Ambiguity-Fixed Precise Point Positioning with Regional Atmosphere Modelling
Authors: Gao Wang , Zhao Qing , Meng Xiaolin , Pan Shuguo
Published: September 2021

Precise point positioning (PPP) with ambiguity resolution (AR) can improve positioning accuracy and reliability. The narrow-lane (NL) AR solution can reach centimeter-level accuracy but there is a certain initialization time. In contrast, extra-wide-lane (EWL) or wide-lane (WL) ambiguity can be fixed instantaneously. However, due to the limited correction accuracy of the empirical atmospheric model, the positioning accuracy is only a few decimeters. In order to further improve the real-time performance of PPP while ensuring accuracy, we developed a multi-system multi-frequency uncombined PPP single-epoch EWL/WL/NL AR method with regional atmosphere modelling. In the proposed method, the precise atmosphere, including zenith wet-troposphere delay (ZWD) and the slant ionosphere, is extracted through multi-frequency stepwise AR, which then is both interpolated and broadcast to users. By adding regional atmosphere constraints, users can achieve single-epoch PPP AR with centimeter-level accuracy. To verify the algorithm, four sets of reference networks with different inter-station distances are used for experiments. With atmosphere constraints, the accuracy of the single-epoch WL solution can be improved from the decimeter level to a few centimeters, with an improvement of more than 90%, and the epoch fix rate can also be improved to varying degrees, especially for the dual-frequency case. Due to the enlarged noise of the EWL combination, its accuracy is at the decimeter level, while the accuracy of the WL/NL solution can reach several centimeters. However, reliable NL ambiguity-fixing tightly relies on atmosphere constraints with sufficiently high accuracy. When the modelling of the atmosphere correction is not accurate enough, the NL AR performance is degraded, although this situation can be improved to a certain extent through the multi-GNSS combination. In contrast, in this case, the WL ambiguity can be successfully fixed and can support the precise positioning with an accuracy of several centimeters.
Landscape Structure and Seasonality: Effects on Wildlife Species Richness and Occupancy in a Fragmented Dry Forest in Coastal Ecuador
Authors: Haro-Carrión Xavier , Johnston Jon , Bedoya-Durán María Juliana
Published: September 2021

Despite high fragmentation and deforestation, little is known about wildlife species richness and occurrence probabilities in tropical dry forest (TDF) landscapes. To fill this gap in knowledge, we used a Sentinel-2-derived land-cover map, Normalized Difference Vegetation Index (NDVI) data and a multi-species occupancy model to correct for detectability to assess the effect of landscape characteristics on medium and large mammal occurrence and richness in three TDF areas that differ in disturbance and seasonality in Ecuador. We recorded 15 species of medium and large mammals, distributed in 12 families; 1 species is critically Endangered, and 2 are Near-Threatened. The results indicate that species occupancy is related to low forest cover and high vegetation seasonality (i.e., high difference in NDVI between the wet and dry seasons). We believe that the apparent negative effect of forest cover is an indicator of species tolerance for disturbance. The three sampling areas varied from 98% to 40% forest cover, yet species richness and occupancy were not significantly different among them. Vegetation seasonality indicates that more seasonal forests (i.e., those where most tree species lose their leaves during the dry season) tend to have higher mammal species occupancy compared to less seasonal, semi-deciduous forests. Overall, occupancy did not vary between the dry and wet seasons, but species-specific data indicate that some species exhibit higher occupancy during the wet season. This research offers a good understanding of mammal species’ responses to habitat disturbance and fragmentation in TDFs and provides insights to promote their conservation.
Development and Validation of Machine-Learning Clear-Sky Detection Method Using 1-Min Irradiance Data and Sky Imagers at a Polluted Suburban Site, Xianghe
Authors: Liu Mengqi , Xia Xiangao , Fu Disong , Zhang Jinqiang
Published: September 2021

Clear-sky detection (CSD) is of critical importance in solar energy applications and surface radiative budget studies. Existing CSD methods are not sufficiently validated due to the lack of high-temporal resolution and long-term CSD ground observations, especially at polluted sites. Using five-year high resolution ground-based solar radiation data and visual inspected Total Sky Imager (TSI) measurements at polluted Xianghe, a suburban site, this study validated 17 existing CSD methods and developed a new CSD model based on a machine-learning algorithm (Random Forest: RF). The propagation of systematic errors from input data to the calculated global horizontal irradiance (GHI) is confirmed with Mean Absolute Error (MAE) increased by 99.7% (from 20.00 to 39.93 W·m−2). Through qualitative evaluation, the novel Bright-Sun method outperforms the other traditional CSD methods at Xianghe site, with high accuracy score 0.73 and 0.92 under clear and cloudy conditions, respectively. The RF CSD model developed by one-year irradiance and TSI data shows more robust performance, with clear/cloudy-sky accuracy score of 0.78/0.88. Overall, the Bright-Sun and RF CSD models perform satisfactorily at heavy polluted sites. Further analysis shows the RF CSD model built with only GHI-related parameters can still achieve a mean accuracy score of 0.81, which indicates RF CSD models have the potential in dealing with sites only providing GHI observations.
Quantitative Assessment of Riverbed Planform Adjustments, Channelization, and Associated Land Use/Land Cover Changes: The Ingauna Alluvial-Coastal Plain Case (Liguria, Italy)
Authors: Mandarino Andrea , Pepe Giacomo , Cevasco Andrea , Brandolini Pierluigi
Published: September 2021

The active-channel planform adjustments that have occurred along the Centa, lower Arroscia and lower Neva rivers since 1930, along with the riverbed channelization processes and the land-use and land-cover changes in disconnected riverine areas, were investigated through a multitemporal analysis based on remote sensing and geographical information systems (GIS). These watercourses flow through the largest Ligurian alluvial-coastal plain in a completely anthropogenic landscape. This research is based on the integrated use of consolidated and innovative metrics for riverbed planform analysis. Specific indices were introduced to assess active-channel lateral migration in relation to the active-channel area abandonment and formation processes. The Arroscia and Neva riverbeds experienced narrowing, progressive stabilization, and braiding phenomena disappearance from 1930 to the early 1970s, and then slight narrowing up to the late 1980s. Subsequently, generalized stability was observed. Conversely, the Centa was not affected by relevant planform changes. Recently, all rivers underwent a slight to very slight width increase triggered by the November 2016 high-magnitude flood. The active-channel adjustments outlined in this paper reflect the relevant role in conditioning the river morphology and dynamics played by channelization works built from the 1920s to the early 1970s. They (i) narrowed, straightened, and stabilized the riverbed and (ii) reduced the floodable surface over the valley-floor. Thus, large disconnected riverine areas were occupied by human activities and infrastructures, resulting in a progressive increase in vulnerable elements exposed to hydrogeomorphic hazards. The outlined morphological dynamics (i) display significant differences in terms of chronology, type, and magnitude of active-channel planform adjustments with respect to the medium- and short-term morphological evolution of most Italian rivers and (ii) reflect the widespread urbanization of Ligurian major valley floors that occurred over the 20th century. The outcomes from this study represent an essential knowledge base from a river management perspective; the novel metrics enlarge the spectrum of available GIS tools for active-channel planform analysis.
Progress and Trends in the Application of Google Earth and Google Earth Engine
Authors: Zhao Qiang , Yu Le , Li Xuecao , Peng Dailiang , Zhang Yongguang , Gong Peng
Published: September 2021

Earth system science has changed rapidly due to global environmental changes and the advent of Earth observation technology. Therefore, new tools are required to monitor, measure, analyze, evaluate, and model Earth observation data. Google Earth (GE) was officially launched by Google in 2005 as a ”geobrowser”, and Google Earth Engine (GEE) was released in 2010 as a cloud computing platform with substantial computational capabilities. The use of these two tools or platforms in various applications, particularly as used by the remote sensing community, has developed rapidly. In this paper, we reviewed the applications and trends in the use of GE and GEE by analyzing peer-reviewed articles, dating up to January 2021, in the Web of Science (WoS) core collection using scientometric analysis (i.e., by using CiteSpace) and meta-analysis. We found the following: (1) the number of articles describing the use of GE or GEE increased substantially from two in 2006 to 530 in 2020. The number of GEE articles increased much faster than those concerned with the use of GE. (2) Both GE and GEE were extensively used by the remote sensing community as multidisciplinary tools. GE articles covered a broader range of research areas (e.g., biology, education, disease and health, economic, and information science) and appeared in a broader range of journals than those concerned with the use of GEE. (3) GE and GEE shared similar keywords (e.g., “land cover”, “water”, “model”, “vegetation”, and “forest”), which indicates that their application is of great importance in certain research areas. The main difference was that articles describing the use of GE emphasized its use as a visual display platform, while those concerned with GEE placed more emphasis on big data and time-series analysis. (4) Most applications of GE and GEE were undertaken in countries, such as the United States, China, and the United Kingdom. (5) GEE is an important tool for analysis, whereas GE is used as an auxiliary tool for visualization. Finally, in this paper, the merits and limitations of GE and GEE, and recommendations for further improvements, are summarized from an Earth system science perspective.
An Application of Sea Ice Tracking Algorithm for Fast Ice and Stamukhas Detection in the Arctic
Authors: Selyuzhenok Valeria , Demchev Denis
Published: September 2021

For regional environmental studies it is important to know the location of the fast ice edge which affects the coastal processes in the Arctic. The aim of this study is to develop a new automated method for fast ice delineation from SAR imagery. The method is based on a fine resolution hybrid sea ice tracking algorithm utilizing advantages of feature tracking and cross-correlation approaches. The developed method consists of three main steps: drift field retrieval at sub-kilometer scale, selection of motionless features and edge delineation. The method was tested on a time series of C-band co-polarized (HH) ENVISAT ASAR and Sentinel-1 imagery in the Laptev and East Siberian Seas. The comparison of the retrieved edges with the operational ice charts produced by the Arctic and Antarctic Research Institute (Russia) showed a good agreement between the data sets with a mean distance between the edges of <15 km. Thanks to the high density of the ice drift product, the method allows for detailed fast ice edge delineation. In addition, large stamukhas with horizontal size of tens of kilometers can be detected. The proposed method can be applied for regional fast ice mapping and large stamukhas detection to aid coastal research. Additionally, the method can serve as a tool for operational sea ice mapping.
Research on Multi-Object Sorting System Based on Deep Learning
Authors: Zhang Hongyan , Liang Huawei , Ni Tao , Huang Lingtao , Yang Jinsong
Published: September 2021

As a complex task, robot sorting has become a research hotspot. In order to enable robots to perform simple, efficient, stable and accurate sorting operations for stacked multi-objects in unstructured scenes, a robot multi-object sorting system is built in this paper. Firstly, the training model of rotating target detection is constructed, and the placement state of five common objects in unstructured scenes is collected as the training set for training. The trained model is used to obtain the position, rotation angle and category of the target object. Then, the instance segmentation model is constructed, and the same data set is made, and the instance segmentation network model is trained. Then, the optimized Mask R-CNN instance segmentation network is used to segment the object surface pixels, and the upper surface point cloud is extracted to calculate the normal vector. Then, the angle obtained by the normal vector of the upper surface and the rotation target detection network is fused with the normal vector to obtain the attitude of the object. At the same time, the grasping order is calculated according to the average depth of the surface. Finally, after the obtained object posture, category and grasping sequence are fused, the performance of the rotating target detection network, the instance segmentation network and the robot sorting system are tested on the established experimental platform. Based on this system, this paper carried out an experiment on the success rate of object capture in a single network and an integrated network. The experimental results show that the multi-object sorting system based on deep learning proposed in this paper can sort stacked objects efficiently, accurately and stably in unstructured scenes.
An Unsupervised Learning-Based Multi-Organ Registration Method for 3D Abdominal CT Images
Authors: Yang Shaodi , Zhao Yuqian , Liao Miao , Zhang Fan
Published: September 2021

Medical image registration is an essential technique to achieve spatial consistency geometric positions of different medical images obtained from single- or multi-sensor, such as computed tomography (CT), magnetic resonance (MR), and ultrasound (US) images. In this paper, an improved unsupervised learning-based framework is proposed for multi-organ registration on 3D abdominal CT images. First, the explored coarse-to-fine recursive cascaded network (RCN) modules are embedded into a basic U-net framework to achieve more accurate multi-organ registration results from 3D abdominal CT images. Then, a topology-preserving loss is added in the total loss function to avoid a distortion of the predicted transformation field. Four public databases are selected to validate the registration performances of the proposed method. The experimental results show that the proposed method is superior to some existing traditional and deep learning-based methods and is promising to meet the real-time and high-precision clinical registration requirements of 3D abdominal CT images.
A Novel Handwritten Digit Classification System Based on Convolutional Neural Network Approach
Authors: Yahya Ali Abdullah , Tan Jieqing , Hu Min
Published: September 2021

An enormous number of CNN classification algorithms have been proposed in the literature. Nevertheless, in these algorithms, appropriate filter size selection, data preparation, limitations in datasets, and noise have not been taken into consideration. As a consequence, most of the algorithms have failed to make a noticeable improvement in classification accuracy. To address the shortcomings of these algorithms, our paper presents the following contributions: Firstly, after taking the domain knowledge into consideration, the size of the effective receptive field (ERF) is calculated. Calculating the size of the ERF helps us to select a typical filter size which leads to enhancing the classification accuracy of our CNN. Secondly, unnecessary data leads to misleading results and this, in turn, negatively affects classification accuracy. To guarantee the dataset is free from any redundant or irrelevant variables to the target variable, data preparation is applied before implementing the data classification mission. Thirdly, to decrease the errors of training and validation, and avoid the limitation of datasets, data augmentation has been proposed. Fourthly, to simulate the real-world natural influences that can affect image quality, we propose to add an additive white Gaussian noise with σ = 0.5 to the MNIST dataset. As a result, our CNN algorithm achieves state-of-the-art results in handwritten digit recognition, with a recognition accuracy of 99.98%, and 99.40% with 50% noise.
Comparison of Phenolic Compounds in Olive Leaves by Different Drying and Storage Methods
Authors: Feng Shiling , Zhang Chunyan , Liu Li , Xu Zhou , Chen Tao , Zhou Lijun , Yuan Ming , Li Tian , Ding Chunbang
Published: September 2021

Oleuropein, a bitter substance that exists in olive leaves, can be hydrolyzed to hydroxytyrosol. These are the main phenolic compounds, and they have beneficial properties to human bodies. In this study, we established a simple and new method to determine oleuropein and hydroxytyrosol quickly by HPLC. HPLC conditions were set as follows: water (A) acetonitrile (B) as mobile phase, gradient elution orders: 90%A–10%B for 0–10 min, 80%A–20%B for 14–30 min, and then change to 90%A–10%B for 30–33 min; detection wavelength: 280 nm. Compared with other detection methods, the method simplified the elution procedure and shortened the time. Additionally, we provided a better drying method and preservation of olive leaves in tea drinking production that were air-dried at room temperature of 25 °C.
Artemisinin DNA Base Interaction Studies in Presence of Fe(II): LC/TOF MS Separation of Reaction Products
Authors: Oke Kogila , Mugweru Amos
Published: September 2021

Artemisinin (ART) is a sesquiterpene lactone and a popular malaria drug with potential anticancer properties. In this work, LC/TOF/MS, was used to investigate the reaction of ART with DNA bases. ART-deoxyadenosine and ART-deoxycytidine interactions, were studied in the presence of iron II ions. ART-deoxyadenosine and ART-deoxycytidine reaction mixtures gave chromatographic signatures that remained fairly unchanged at room temperature but grew after incubation at 37 °C. The change in temperature from room temperature to 37 °C was the main driver of adduct formation in these reactions. ART was found to react with Fe(II) ions as observed from several new chromatographic peaks. ART-deoxyadenosine as well as ART-deoxycytidine in the presence of Fe(II) ions resulted in formation of new chromatographic signatures of adducts consisting of DNA bases and ART. It was clear that addition of iron (II) to DNA base-ART mixtures gave rise to new reaction products mediated by a different reaction mechanism. Studies of ART reactions with DNA in vitro is key in elucidating elusive mechanism of this drug.
The Economic Consequences of Decriminalizing Sex Work in Washington, DC—A Conceptual Model
Authors: Srsic Amanda , Dubas-Jakóbczyk Katarzyna , Kocot Ewa
Published: September 2021

(1) Under repressive policies, sex workers are at disproportionate risk for violence and sexually transmitted infections. The decriminalization of sex work provides increased social and health benefits to both sex workers and society. This is the first research that complements human rights-based messages with a quantifiable economic impact of such a law and a model for future calculations. (2) This research assesses the potential economic consequences of decriminalizing sex work in the District of Columbia (DC) in three areas: (A) income tax revenue, (B) criminal justice system savings, and (C) health sector savings (due to averted cases of violence, HIV, gonorrhea, and herpes). (3) An economic model is developed and utilized based on data from a literature search and agency records. (4) Decriminalizing sex work in DC will generate USD5348.68 per sex worker and USD2.53 per client annually, plus USD20,118.48 in criminal justice system savings a year. Per sex worker, USD5058.08 will be gained from income tax revenue, and USD290.60 will be generated through health sector savings (USD274.65, 0.02, 15.64, and 0.29 from averted cases of violence, HIV, gonorrhea, and herpes, respectively). Per client, decriminalization will generate USD0.05, 2.32, and 0.16 from averted cases of HIV, gonorrhea, and herpes, respectively, or USD8462.35 annually, after considering the total number of clients. Estimates are reported in 2020 USD. (5) The potential economic impact of decriminalizing sex work is widespread. The presented model, in conjunction with a rights-based foundation, should urgently be used by advocates, sex workers, decision makers, and other researchers.
Relationship between Step Characteristics and Race Performance during 5000-m Race
Authors: Ueno Hiromasa , Nakazawa Sho , Takeuchi Yohsuke , Sugita Masaaki
Published: September 2021

This study examined the relationship between step characteristics and race time in a 5000-m race. Twenty-one male Japanese endurance runners performed a 5000-m race. Step length, step frequency, contact time, and flight time of two gait cycles (i.e., four consecutive ground contacts) were measured every 400-m by using high-speed video image. Moreover, step length was normalized to body height to minimize the effect of body size. In addition to step characteristics on each lap, the averages of all laps and the per cent change from the first half to the second half were calculated. The average step frequency and step length normalized to body height correlated significantly with the 5000-m race time (r = −0.611, r = −0.575, respectively, p < 0.05 for both). Per cent changes in contact time and step length correlated significantly with the 5000-m race time (r = 0.514, r = −0.486, respectively, p < 0.05 for both). These findings suggest that, in addition to higher step frequency and step length normalized to body height, smaller changes in step length during a given race may be an important step characteristic to achieving superior race performance in endurance runners.
Study on the Optical Properties of the Point-Focus Fresnel System
Authors: Shen Fei , Huang Weidong
Published: September 2021

The characteristic analysis of the flux formed by the heliostat in the optical system is the basis in design and optimization of the whole system. In this paper, our research subject is a pilot installation of the point-focus Fresnel system, which is a new optical design between the tower system and the dish system. For the optical system, it is very important to accurately calculate the solar flux density distribution on the receiver plane. Aiming at the case that the focal length of the heliostat is not equal to the distance from the center of the heliostat to the center of the receiver plane, based on the projection, an approximate calculation method is proposed. Using the method to calculate the solar flux density distribution of the point-focus Fresnel system, and the results are compared with that calculated by SolTrace code, it is found that the solar flux density distribution of both is relatively consistent in shape and numerical value, which verifies the accuracy of the method and it can be used for design and optimization of the point-focus Fresnel system.
Constructed Wetlands in South Korea: Current Status and Performance Assessment
Authors: Choi Hyeseon , Reyes Nash Jett DG. , Jeon Minsu , Kim Lee-Hyung
Published: September 2021

The efficiency of nature-based facilities is mostly evaluated in terms of their pollutant removal capabilities; however, apart from water purification functions, constructed wetlands also perform ecological, cultural, and environmental education functions. A multi-criteria performance index was developed in this study to evaluate the overall efficiency of constructed wetlands. A total of 54 constructed wetlands installed across South Korea were monitored to evaluate the pollutant removal performance of the facilities. A comparison between the conventional pollutant removal-based evaluation and the developed multi-criteria index was also performed to determine the key changes in the results of evaluation when different methods are employed. Among the different types of wetlands studied, hybrid systems were found to be the most effective in terms of pollutant removal due to their complex configurations and functions. Newly constructed treatment wetlands have unstable performance and thus, a stabilization period ranging from two to five years is required to assess the facility’s pollutant removal capabilities. As compared to the conventional pollutant removal-based efficiency evaluation, the comprehensive evaluation method provided a more strategic tool for identifying the facilities’ strengths and weaknesses. Generally, the multi-criteria performance index developed in this inquiry can be utilized as a general tool for evaluating the sustainability of similar nature-based facilities.
Sustainability in Hospitality Marketing during the COVID-19 Pandemic. Content Analysis of Consumer Empirical Research
Authors: Šerić Maja , Šerić Mario
Published: September 2021

This study performs content analysis of consumer empirical research dealing with sustainability issues in hospitality marketing literature during the outbreak of COVID-19. Papers published in the Social Sciences Citation Index (SSCI) ranked hospitality journals from January 2020 up to and including May 2021 are reviewed. The total of 46 papers met the search criteria and were subject to content analysis. The reviewed papers are classified based on research topics, variables, and themes; method and study design; data analysis; sample; industry; and location. Consumer perceptions are the dominant research theme, followed by technology innovation, communication and media, consumer emotions and psychological conditions, and other themes. Quantitative research is the main method with online surveys mostly used for study design. Analysis of moderation/mediation is the most frequently employed analytical method, whereas the majority of investigated samples have more than 300 cases. The restaurant industry received the highest attention, followed by hotel and other lodging industries, while the USA and Greater China are the most investigated geographical areas. Research findings are discussed according to the identified research themes and specific directions for future research are provided.
Locomotion of Slope Geohazards Responding to Climate Change in the Qinghai-Tibetan Plateau and Its Adjacent Regions
Authors: Jia Yiru , Liu Jifu , Guo Lanlan , Deng Zhifei , Li Jiaoyang , Zheng Hao
Published: September 2021

Slope geohazards, which cause significant social, economic and environmental losses, have been increasing worldwide over the last few decades. Climate change-induced higher temperatures and shifted precipitation patterns enhance the slope geohazard risks. This study traced the spatial transference of slope geohazards in the Qinghai-Tibet Plateau (QTP) and investigated the potential climatic factors. The results show that 93% of slope geohazards occurred in seasonally frozen regions, 2.6% of which were located in permafrost regions, with an average altitude of 3818 m. The slope geohazards are mainly concentrated at 1493–1988 m. Over time, the altitude of the slope geohazards was gradually increased, and the mean altitude tended to spread from 1984 m to 2562 m by 2009, while the slope gradient varied only slightly. The number of slope geohazards increased with time and was most obvious in spring, especially in the areas above an altitude of 3000 m. The increase in temperature and precipitation in spring may be an important reason for this phenomenon, because the results suggest that the rate of air warming and precipitation at geohazard sites increased gradually. Based on the observation of the spatial location, altitude and temperature growth rate of slope geohazards, it is noted that new geohazard clusters (NGCs) appear in the study area, and there is still a possibility of migration under the future climate conditions. Based on future climate forecast data, we estimate that the low-, moderate- and high-sensitivity areas of the QTP will be mainly south of 30° N in 2030, will extend to the south of 33° N in 2060 and will continue to expand to the south of 35° N in 2099; we also estimate that the proportion of high-sensitivity areas will increase from 10.93% in 2030 to 14.17% in 2060 and 17.48% in 2099.
Progress on the Stereoselective Synthesis of Chiral Molecules Based on Metal-Catalyzed Dynamic Kinetic Resolution of Alcohols with Lipases
Authors: Ferraccioli Raffaella
Published: September 2021

Metal/lipase-combo catalyzed dynamic kinetic resolution (DKR) of racemic chiral alcohols is a general and practical process to obtain the corresponding enantiopure esters R with quantitative conversion. The use of known Ru-catalysts as well as newly developed homogeneous and heterogeneous metal catalysts (Fe, V) contributed to make the DKR process more sustainable and to expand the substrate scope of the reaction. In addition to classical substrates, challenging allylic alcohols, tertiary alcohols, C1-and C2-symmetric biaryl diols turned out to be competent substrates. Synthetic utility further emerged from the integration of this methodology into cascade reactions leading to linear/cyclic chiral molecules with high ee through the formation of multiple bonds, in a one-pot procedure.
p-Adic q-Twisted Dedekind-Type Sums
Authors: Bayad Abdelmejid , Simsek Yilmaz
Published: September 2021

The main purpose of this paper is to define p-adic and q-Dedekind type sums. Using the Volkenborn integral and the Teichmüller character representations of the Bernoulli polynomials, we give reciprocity law of these sums. These sums and their reciprocity law generalized some of the classical p-adic Dedekind sums and their reciprocity law. It is to be noted that the Dedekind reciprocity laws, is a fine study of the existing symmetry relations between the finite sums, considered in our study, and their symmetries through permutations of initial parameters.
Delay Risk Assessment Models for Road Projects
Authors: Antoniou Fani
Published: September 2021

Significant universal research regarding causes of delay in road projects has been carried out based on expert opinion. This study classifies and standardizes all road construction delay factors found in the literature by developing a common risk breakdown structure (RBS) to allow for comparison between real project delay factors, and the study also proposes two delay risk assessment models (DRAMs) based on delay information from 120 real projects constructed in a similar environment with akin procurement and contract policies. The first calculates the risk priority number (RPN) and the second applies the Technique for Order Preference by Similarity to Ideal Situation (TOPSIS) multi-criteria decision-making method (MCDM). The results showed that four specific delay factors dominate in Greek road construction for which relevant mitigation proposals are made. The proposed DRAMs, while calibrated for application in Greece, can be adapted to any construction environment for which real project data is available to provide a tool for transferring experience from past projects to future projects and from accomplished to novice public client decision makers.
Radiomics for Everyone: A New Tool Simplifies Creating Parametric Maps for the Visualization and Quantification of Radiomics Features
Authors: Kim Damon , Jensen Laura J. , Elgeti Thomas , Steffen Ingo G. , Hamm Bernd , Nagel Sebastian N.
Published: September 2021

Aim was to develop a user-friendly method for creating parametric maps that would provide a comprehensible visualization and allow immediate quantification of radiomics features. For this, a self-explanatory graphical user interface was designed, and for the proof of concept, maps were created for CT and MR images and features were compared to those from conventional extractions. Especially first-order features were concordant between maps and conventional extractions, some even across all examples. Potential clinical applications were tested on CT and MR images for the differentiation of pulmonary lesions. In these sample applications, maps of Skewness enhanced the differentiation of non-malignant lesions and non-small lung carcinoma manifestations on CT images and maps of Variance enhanced the differentiation of pulmonary lymphoma manifestations and fungal infiltrates on MR images. This new and simple method for creating parametric maps makes radiomics features visually perceivable, allows direct feature quantification by placing a region of interest, can improve the assessment of radiological images and, furthermore, can increase the use of radiomics in clinical routine.
Rutin Exerts Cytotoxic and Senescence-Inducing Properties in Human Melanoma Cells
Authors: Pinzaru Iulia , Chioibas Raul , Marcovici Iasmina , Coricovac Dorina , Susan Razvan , Predut Denisa , Georgescu Doina , Dehelean Cristina
Published: September 2021

Malignant melanoma represents the deadliest type of skin cancer with narrow treatment options in advanced stages. Herbal constituents possessing anticancer properties occupy a particular spot in melanoma research as potential chemotherapeutics. Rutin (RUT) is a natural compound exerting antioxidant, antimicrobial, anti-inflammatory, UV-filtering, and SPF-enhancing activities that are beneficial to the skin; however, its effect as an anti-melanoma agent is less investigated. The current study is focused on assessing the cytotoxic potential of RUT against two different human melanoma cell lines: RPMI-7951 and SK-MEL-28 by evaluating its impact in terms of cell viability, cells’ morphology, and nuclear aspect assessment, and senescence-inducing properties. The results indicate a dose-dependent decrease in the viability of both cell lines, with calculated IC50 values of 64.49 ± 13.27 µM for RPMI-7951 cells and 47.44 ± 2.41 µM for SK-MEL-28, respectively, accompanied by a visible reduction in the cell confluency and apoptotic features within the cell nuclei. RUT exerted a senescence-inducing property highlighted by the elevated expression of senescent-associated beta-galactosidase (SA-β-gal) in SK-MEL-28 cells. Despite the in vitro anti-melanoma effect revealed by our results, further studies are required to elucidate the mechanisms of RUT-induced cytotoxicity and senescence in melanoma cells.
Insecticide Exposure and Risk of Asthmatic Symptoms: A Systematic Review and Meta-Analysis
Authors: Chittrakul Jiraporn , Sapbamrer Ratana , Sirikul Wachiranun
Published: September 2021

The incidence of respiratory disease is increasing. In relation to this, in addition to infection, factors associated with working with chemical insecticides are a cause for concern. Some of the chemicals involved have been shown to affect the respiratory system, and consequentially workers are at increased risk of conditions such as asthma. However, medical opinion around this area is still controversial; therefore, the objective of this study is to investigate the association between exposure to insecticides and asthma by means of a systematic review of the relevant literature. Relevant literature was identified, and a systematic review was conducted to investigate the association between exposure to insecticides and asthma. A total of five studies (three cross sectional and two cohort) including 45,435 subjects were identified as relevant. The summary odds ratios related to the impact of exposure to specific insecticides on asthma were organophosphates 1.31 (95%CI = 1.17–1.48, I2 = 27%, p = 0.172), carbamates 1.44 (95%CI 1.08–1.92, I2 = 56.7%, p = 0.031) and organochlorines 1.31 (95%CI 1.19–1.64, I2 = 37.3%, p = 0.131). Farmers exposed to certain insecticides may have an increased risk of asthma and asthmatic symptoms, but further research on that issue is urgently needed.
Bee Venom Alleviated Edema and Pain in Monosodium Urate Crystals-Induced Gouty Arthritis in Rat by Inhibiting Inflammation
Authors: Goo Bonhyuk , Lee Jeeyoun , Park Chansol , Yune Taeyoung , Park Yeoncheol
Published: September 2021

Bee venom (BV) acupuncture has anti-inflammatory and analgesic effects; therefore, it was used as a traditional Korean medicine for various musculoskeletal disorders, especially arthritis. In this study, we investigated the effect of BV on monosodium urate (MSU) crystal-induced acute gouty rats. An intra-articular injection of MSU crystal suspension (1.25 mg/site) was administered to the tibiotarsal joint of the hind paw of Sprague Dawley rats to induce MSU crystal-induced gouty arthritis. Colchicine (30 mg/kg) was orally administered 1 h before MSU crystal injection as a positive control, and BV (0.5 mg/kg) was injected into the tibiotarsal joint immediately after MSU crystal injection. The ankle thickness, mechanical allodynia, and expression of proinflammatory cytokines (TNF-α, IL-1β, IL6, COX2 and iNOS) and chemokines (MIP-1α, MIP-1β, MCP-1, GRO-α, MIP-2α) were then evaluated. BV reduced the expression of proinflammatory cytokines and chemokines, which are important mediators of MSU crystal-induced inflammatory responses. This anti-inflammatory effect was also confirmed histologically to attenuate synovitis and neutrophil infiltration. We demonstrated that BV markedly ameliorated ankle edema and mechanical allodynia in gouty rats. These results suggest that BV acupuncture is a potential clinical therapy for acute gouty management.
Is Toxin-Producing Planktothrix sp. an Emerging Species in Lake Constance?
Authors: Fournier Corentin , Riehle Eva , Dietrich Daniel R. , Schleheck David
Published: September 2021

Recurring blooms of filamentous, red-pigmented and toxin-producing cyanobacteria Planktothrix rubescens have been reported in numerous deep and stratified prealpine lakes, with the exception of Lake Constance. In a 2019 and 2020 Lake Constance field campaign, we collected samples from a distinct red-pigmented biomass maximum below the chlorophyll-a maximum, which was determined using fluorescence probe measurements at depths between 18 and 20 m. Here, we report the characterization of these deep water red pigment maxima (DRM) as cyanobacterial blooms. Using 16S rRNA gene-amplicon sequencing, we found evidence that the blooms were, indeed, contributed by Planktothrix spp., although phycoerythrin-rich Synechococcus taxa constituted most of the biomass (>96% relative read abundance) of the cyanobacterial DRM community. Through UPLC–MS/MS, we also detected toxic microcystins (MCs) in the DRM in the individual sampling days at concentrations of ≤1.5 ng/L. Subsequently, we reevaluated the fluorescence probe measurements collected over the past decade and found that, in the summer, DRM have been present in Lake Constance, at least since 2009. Our study highlights the need for a continuous monitoring program also targeting the cyanobacterial DRM in Lake Constance, and for future studies on the competition of the different cyanobacterial taxa. Future studies will address the potential community composition changes in response to the climate change driven physiochemical and biological parameters of the lake.
Hantaviruses in Agricultural and Forestry Workers: Knowledge, Attitudes and Practices in Italian Physicians
Authors: Riccò Matteo , Ferraro Pietro , Peruzzi Simona , Balzarini Federica , Ranzieri Silvia
Published: September 2021

Hantaviruses are viral pathogens usually endemic in rodent populations. Human exposure follows inhalation of dusts contaminated with rodent excreta, and most individuals have been infected in occupational settings heavily contaminated with rodent droppings, such as agricultural and forestry. To date, knowledge, attitudes and practices of medical professionals, especially occupational physicians (OP), regarding hantavirus disease in at-risk workers have been scarcely investigated. We investigated these topics through a structured questionnaire administered through an online survey of 223 medical professionals (42.2% of them working as OP). Adequate general knowledge of hantavirus disease was found in 48.9% of respondents, with OP exhibiting a better understanding of clinical features of human hantavirus infections. OP aware of the endemic status of hantavirus in North-Eastern Italy exhibited higher risk perception for agricultural workers (odds ratio 21,193, 95% confidence interval 3.666–122.505). On the contrary, a better knowledge of hantaviruses was association with acknowledging an increased risk of hantavirus infection in forestry workers (odds ratio 5.880, 95% confidence interval 1.620–21.343). Hantavirus in Italy represent an often-overlooked biological risk in occupational settings. The lack of preventive immunization, the inappropriate risk perception and the unsatisfying awareness of hantavirus issues collectively stress the importance of appropriate information campaigns among health care providers.