bluuebunny/crossref_metadata_2025 huggingface dataset:
Simultaneity and Time Reversal in Quantum Mechanics in Relation to Proper Time
Authors: Yasmineh Salim
Published: September 2022

In Newtonian physics, the equation of motion is invariant when the direction of time (t→−t) is flipped. However, in quantum physics, flipping the direction of time changes the sign of the Schrödinger equation. An anti-unitary operator is needed to restore time reversal in quantum physics, but this is at the cost of not having a consistent definition of time reversal applicable to all fundamental theories. On the other hand, a quantum system composed of a pair of entangled particles behaves in such a manner that when the state of one particle is measured, the second particle ‘simultaneously’ acquires a determinate state. A notion of absolute simultaneity seems to be inferred by quantum mechanics, even though it is forbidden by the postulates of relativity. We aim to point out that the above two problems can be overcome if the wavefunction is defined with respect to proper time, which in fact is the real physical time instead of ordinary time.
Healing Memory: A Bonaventurian Response to Pope Francis’ Fratelli Tutti
Authors: Currie Laura
Published: September 2022

In his encyclical Fratelli Tutti, Pope Francis identifies a need to “heal open wounds,” as well as a “need for peacemakers … prepared to work boldly and creatively to initiate processes of healing and renewed encounter.” This paper aims to address the Holy Father’s call to “heal wounds” within our societies by first identifying memory as the primary domain that needs healing, and then proposing the wisdom of St. Bonaventure as providing the remedy. Bonaventure’s Itinerarium Mentis in Deum reveals how affectus acts as a “healing agent” upon memory as the soul ascends to God; if understood primarily as a “healing journey”, Bonaventure’s Itinerarium can shed significant light on the “processes of healing” so desperately needed today. This paper will follow each of the three major “steps” in the soul’s journey to God, as identified in Bonaventure’s Itinerarium, by identifying the role of memory in the first step; the role of affectus and its interplay with memory in the second step; and how affectus acts as a healing agent upon memory in the third step. Concluding thoughts will be offered regarding how this path given by Bonaventure can provide a foundation for building peace in the world today.
Interpretive Trends and the Conceptual Construction of the Daodejing’s Dao in Russian Sinology: A Historical Overview
Authors: Zhang Hongyan , Luo Jing
Published: September 2022

“Dao” is not only a core concept in the Daodejing, but is also an important keyword in Chinese classical philosophy. It encompasses the origin of all things in the universe, the laws of nature, and the laws of dealing with the world. A historical review of Russian sinologists’ interpretation and conceptualization of “Dao” reveals the differences in philosophical understanding and translation strategies of those sinologists, and reflects the translation loss and compensation of Chinese classical philosophical keywords in the process of foreign dissemination. During the Imperial Russian Period, researchers regarded “Dao” as the revitalization of religious theology. During the Soviet Period, the aim of the Soviet researchers was to find the struggle between materialism and idealism in “Dao”. In the Post-Soviet Period, researchers gradually threw off the shackles of ideology, and began to conduct more diversified and multi-level research on “Dao” and the Daodejing. This article aims to discuss the research and translation of the Daodejing in Russia, paying particular attention to the dissemination and reception of “Dao” in Russia. It also endeavors to explore the interpretive trends of “Dao” in Russia and highlight the dissemination and understanding of Laozi thought in Russia.
Islamic Religious Education and Citizenship Education: Their Relationship According to Practitioners of Primary Islamic Religious Education in The Netherlands
Authors: Essabane Kamel , Vermeer Paul , Sterkens Carl
Published: September 2022

This article discusses how practitioners of Islamic Religious Education (IRE) in Dutch primary schools look at the relationship between IRE and citizenship education (CE). To what extent do they believe it is possible and desirable for IRE to contribute to CE? What would an integration of IRE and CE look like, and where do they see potential tensions between IRE and CE? In two extensive focus group discussions, with identity coordinators and experienced IRE teachers, the relationships between IRE and four citizenship dimensions, namely, identity, legal status, participation, and rights, were discussed. Qualitative content analysis of these discussions reveals that the integration of IRE with CE is desirable and possible, but in varying degrees based on the level of different citizenship dimensions. The extent of integration of IRE and CE also depends on the interpretations of Islamic key concepts. IRE and CE were also found to face similar challenges in seeking to achieve integration: both struggle with exclusive interpretations of Islam and citizenship.
Gamay at Hiyang: Reconstructing Fray Juan de Oliver’s Declaracion de la Doctrina Christiana en Idioma Tagalog as a Catechetical Paradigm
Authors: Eballo Arvin Dineros
Published: September 2022

During the American annexation of the Philippines, a classic English catechism designed in a question-and-answer format known as the Baltimore catechism became popular. Apparently, that said catechism served as the standard Catholic catechetical text (lingua franca) in the country from 1900 to the late 1960s. There is no single best method and approach in the ministry of catechesis. However, cultural appreciation of the people is essential to realize purposeful and meaningful catechesis. In the celebration of the Quincentenario of the arrival of Catholicism in the Philippines, it is a fitting tribute to recognize Fray Juan de Oliver, O.F.M. and revisit his Declaracion de la Doctrina Christian en Idioma Tagalog which he used from 1582 to 1591 when he was tasked to evangelize the locals of Balayan, Batangas during the early stages of the Catholic propagation in Luzon. The 188-page catechism attributed to de Oliver was all written in Tagalog which meant that he persevered in learning the local language to impart the Catholic teachings strikingly to the natives. Through historical and textual analysis, the proponent intends to reconstruct de Oliver’s adaptable and comfortable (gamay) methods and approaches of catechizing in a suitable way (hiyang) to the worldview, language, and culture of the locals. Likewise, de Oliver’s catechetical pedagogy may also serve as a pedagogical paradigm for priests, religious educators, and catechists in conducting contextualized catechesis.
The Connection between Buddhist Temples, the Landscape, and Monarchical Power: A Comparison between Tuoba Hong (471–499) from the Northern Wei Dynasty and Li Shimin (626–649) from the Tang Dynasty
Authors: Zhao Yi , Xu Chaoran , Liu Tingfeng
Published: September 2022

Since the Western Jin period, Buddhism has confronted bureaucratic power within the architectural landscape. In this study, historical records and archaeological reports of two Buddhist temples—the Siyuan Temple 思遠佛寺 built during the reign of Tuoba Hong 拓跋宏 and Emperor Xiaowen 孝文帝 of the Northern Wei dynasty, and the Zhaoren Temple 昭仁寺, built during the reign of Li Shimin 李世民 and Emperor Taizong 唐太宗 of the Tang dynasty—were examined. A comparison was made of the two temples’ geographic locations in relation to cities while considering period-specific phenomena. This study also considers mountains, water, and topographical features. The geographic information reflects differences in the ideas of the ruling class and monarchs of the two historical periods. The findings are that both Buddhist temples were close to the capital and both emperors demonstrated the supremacy of their power by building them. Therefore, the religious landscape owes its formation, development, and underlying significance to emperors and social groups.
Michel Henry’s Notion of Bodily-Ownness in the Context of the Ecological Crisis
Authors: Lind Andreas Gonçalves
Published: September 2022

Despite the suspicions of Gnosticism that have been cast against Michel Henry’s phenomenology, the truth is that his approach in no way belittles the earth, the nature we inhabit. The purpose of this article is to sketch an eco-phenomenology from Henry’s notion of bodily-ownness. In this sense, I show how Henry defends an intimate and intrinsic connection between the human and nature that risks being severed by the imposition of scientistic ideology. In doing so, an integral ecology emerges from Henry’s radical phenomenology. On the one hand, human beings shall abandon their selfish way of life, in the sense of ceasing to want to dominate, control and transform the world at will. On the other hand, they do so in order to regain their original connection with nature, where they exist in a radical passivity in which life is given to them and is realized more fully as a person in harmony with the earth. In this sense, contrary to some current ecological movements, the ecology that arises from Henry’s approach does not set human beings against nature. In fact, in order for nature to be respected, human beings do not have to disappear, withdraw or stop progressing in the life proper to them.
Locality from Hybridization to Integration: Cultural Politics and Space Production of Taiwan Mazu Temples in Mainland China
Authors: Zhou Yong
Published: September 2022

An upsurge in Taiwan-based Mazu temple buildings has been observed in China (Tianjin, Kunshan, and Xiamen, etc.) recently. This paper applies qualitative research methods, including participatory observation and semi-structured interviews, to explore the development of Mazu temples in Tianjin, Kunshan, and Xiamen, China in terms of cross-regional connectivity, materiality, and cross-regional locality, to explore the process of transplantation and construction in the mainland. This paper finds that Mazu culture is a reproduction of the vision of “one race one culture” in the cultural space, and this spatial reproduction is realized through cross-strait religious and cultural exchanges. Informed by the perspective of the political and cultural context, three interconnected dimensions—cross-regional connectivity, materiality, and cross-regional locality—have influenced why, where, and how Taiwan Mazu temples have been transplanted and constructed. This kind of hybrid locality is an integration of urban space and religion, as well as an organic, complex process that integrates religious functions, local politics, and the cultural economy. With the ingenious localization efforts of Taiwanese businessmen, these new Mazu temples promote homogenization in the sacred space and create a unique mixed locality—a more-than-state.
Isolated Central Nervous System Vasculitides in COVID-19: A Systematic Review of Case Reports and Series
Authors: Vecchio Domizia , Moretto Francesca , Padelli Samuel , Grossi Francesca , Cantello Roberto , Vaschetto Rosanna
Published: September 2022

Cerebral vasculitides, both isolated or in systemic disorders, could be triggered by infections, and few cases have been associated to coronavirus disease 2019 (COVID-19). This study searched for publications in Pubmed, EMBASE, and Cochrane library databases for case reports and series of isolated central nervous system (CNS) vasculitides triggered by severe acute respiratory syndrome coronavirus-2. We included 12 studies (published from June 2020 to July 2022) and collected 39 adult patients (5/39 pathologically or radiologically proven, 34/39 suggestive for primary CNS vasculitis or PCNSV). All cases had a positive real-time polymerase chain reaction on a nasopharyngeal swab or a respiratory tract specimen. About the 85% of the included cases were males, and disease onset occurred later than 50 years old in all but three subjects. In total, 33/39 patients presented severe COVID-19 pneumonia, frequently requiring intensive care unit care. The most common neurological features were headache, obnubilation, and coma. PCNSV was suspected mainly on radiological findings, whereas the cerebrospinal fluid analysis was minimally altered. Magnetic resonance imaging showed vessel wall enhancement in 32/39 cases, generally with the concomitant presence of microbleeds, subarachnoid haemorrhages, and/or multiple ischemic lesions. Despite the severe respiratory and neurological disease course, most cases (93%) improved spontaneously or after a course of high-dose intravenous steroids with no need for immunosuppression. In conclusion, PCNSV could rarely relate to COVID-19 and independently from pulmonary disease severity. Adults with COVID-19-related PCNSV could have a favourable prognosis.
The Effect of Corporate Governance Structure on Fraud and Money Laundering
Authors: Mousavi Maryam , Zimon Grzegorz , Salehi Mahdi , Stępnicka Nina
Published: September 2022

This paper aims to assess the effect of corporate governance mechanisms, including board members’ and audit committee members’ characteristics, particularly their independence, expertise in terms of finance and industry and efforts on the level of fraud and money laundering (ML) in financial statements of the listed firm on the Tehran Stock Exchange. The procedure of the study is descriptive correlation based on published information from firms listed on the Tehran Stock Exchange from 2014 to 2020, using a sample of 154 firms with 1071 observations. The method used for hypothesis testing is linear regression using panel data. The Benish model is used measure the level of fraud in financial statements, and for ML, the auditors’ opinion are used. The results show that board characteristics, including independence, financial expertise, industry expertise and board effort, as well as audit committee features, such as independence, financial expertise, industry expertise and audit committee effort, have a significant and negative impact on the fraudulent financial reporting and ML. Moreover, since this paper was carried out in an emerging financial market, particularly in Iran, to figure out the effect of corporate governance structures on financial statement fraud and ML, it can provide helpful information for investors and policymakers in this regard.
Effects of Land Use and Land Cover Change on Temperature in Summer over the Yellow River Basin, China
Authors: Ru Xutong , Song Hongquan , Xia Haoming , Zhai Shiyan , Wang Yaobin , Min Ruiqi , Zhang Haopeng , Qiao Longxin
Published: September 2022

As the main driving force of global climate change, land use and land cover change (LUCC) can affect the surface energy balance and the interaction between the surface and atmosphere. This effect will cause further surface temperature changes. The Yellow River Basin is an important ecological security barrier in China. Therefore, exploring the impact of its LUCC on temperature changes can provide certain help for future land-use planning in the Yellow River Basin. Here, we conducted two numerical simulation experiments (Case2015 and Case1995) by using the weather research and forecasting (WRF) model to quantify the effect of LUCC in the Yellow River Basin on the summer 2 m air temperature (T2 m). The results showed that LUCC led to an overall warming trend in T2 m in the Yellow River Basin. Urban expansion caused T2 m to rise by approximately 0.3 °C to 0.6 °C. A warming effect was also identified in the areas where farmland and bare areas were converted to grassland, with T2 m increasing by around 0.4 °C.
Deep Learning to Near-Surface Humidity Retrieval from Multi-Sensor Remote Sensing Data over the China Seas
Authors: Zhang Rongwang , Guo Weihao , Wang Xin
Published: September 2022

Near-surface humidity (Qa) is a key parameter that modulates oceanic evaporation and influences the global water cycle. Remote sensing observations act as feasible sources for long-term and large-scale Qa monitoring. However, existing satellite Qa retrieval models are subject to apparent uncertainties due to model errors and insufficient training data. Based on in situ observations collected over the China Seas over the last two decades, a deep learning approach named Ensemble Mean of Target deep neural networks (EMTnet) is proposed to improve the satellite Qa retrieval over the China Seas for the first time. The EMTnet model outperforms five representative existing models by nearly eliminating the mean bias and significantly reducing the root-mean-square error in satellite Qa retrieval. According to its target deep neural network selection process, the EMTnet model can obtain more objective learning results when the observational data are divergent. The EMTnet model was subsequently applied to produce 30-year monthly gridded Qa data over the China Seas. It indicates that the climbing rate of Qa over the China Seas under the background of global warming is probably underestimated by current products.
Estimation of Soil Organic Carbon Content in Coastal Wetlands with Measured VIS-NIR Spectroscopy Using Optimized Support Vector Machines and Random Forests
Authors: Song Jingru , Gao Junhai , Zhang Yongbin , Li Fuping , Man Weidong , Liu Mingyue , Wang Jinhua , Li Mengqian , Zheng Hao , Yang Xiaowu , Li Chunjing
Published: September 2022

Coastal wetland soil organic carbon (CW-SOC) is crucial for both “blue carbon” and carbon sequestration. It is of great significance to understand the content of soil organic carbon (SOC) in soil resource management. A total of 133 soil samples were evaluated using an indoor spectral curve and were categorized into silty soil and sandy soil. The prediction model of CW-SOC was established using optimized support vector machine regression (OSVR) and optimized random forest regression (ORFR). The Leave-One-Out Cross-Validation (LOO-CV) method was used to verify the model, and the performance of the two prediction models, as well as the models’ stability and uncertainty, was examined. The results show that (1) The SOC content of different coastal wetlands is significantly different, and the SOC content of silty soils is about 1.8 times that of sandy soils. Moreover, the characteristic wavelengths associated with SOC in silty soils are mainly concentrated in the spectral range of 500–1000 nm and 1900–2400 nm, while the spectral range of sandy soils is concentrated in the spectral range of 600–1400 nm and 1700–2400 nm. (2) The organic carbon prediction model of silty soil based on the OSVR method under the first-order differential of reflectance (R′) is the best, with the Adjusted-R2 value as high as 0.78, the RPD value is much greater than 2.0 and 5.07, and the RMSE value as low as 0.07. (3) The performance of the OSVR model is about 15~30% higher than that of the support vector machine regression (SVR) model, and the performance of the ORFR model is about 3~5% higher than that of the random forest regression (RFR) model. OSVR and ORFR are better methods of accurately predicting the CW-SOC content and provide data support for the carbon cycle, soil conservation, plant growth, and environmental protection of coastal wetlands.
Tensor Dictionary Self-Taught Learning Classification Method for Hyperspectral Image
Authors: Liu Fengshuang , Fu Jun , Wang Qiang , Zhao Rongqiang
Published: September 2022

Precise object classification based on Hyperspectral imagery with limited training data presents a challenging task. We propose a tensor-based dictionary self-taught learning (TDSL) classification method to provide some insight into these challenges. The idea of TDSL is to utilize a small amount of unlabeled data to improve the supervised classification. The TDSL trains tensor feature extractors from unlabeled data, extracts joint spectral-spatial tensor features and performs classification on the labeled data set. These two data sets can be gathered over different scenes even by different sensors. Therefore, TDSL can complete cross-scene and cross-sensor classification tasks. For training tensor feature extractors on unlabeled data, we propose a sparse tensor-based dictionary learning algorithm for three-dimensional samples. In the algorithm, we initialize dictionaries using Tucker decomposition and update these dictionaries based on the K higher-order singular value decomposition. These dictionaries are feature extractors, which are used to extract sparse joint spectral-spatial tensor features on the labeled data set. To provide classification results, the support vector machine as the classifier is applied to the tensor features. The TDSL with the majority vote (TDSLMV) can reduce the misclassified pixels in homogenous regions and at the edges of different homogenous regions, which further refines the classification. The proposed methods are evaluated on Indian Pines, Pavia University, and Houston2013 datasets. The classification results show that TDSLMV achieves as high as 99.13%, 99.28%, and 99.76% accuracies, respectively. Compared with several state-of-the-art methods, the classification accuracies of the proposed methods are improved by at least 2.5%.
Maneuvering Extended Object Tracking with Modified Star-Convex Random Hypersurface Model Based on Minimum Cosine Distance
Authors: Sun Lifan , Zhang Jinjin , Yu Haofang , Fu Zhumu , He Zishu
Published: September 2022

Maneuvering extended object tracking is a new research field due to the rapid development of modern sensor technology. Multiple measurements may be resolved from different unknown sources on an object by using a high-resolution radar. In this case, the object should be regarded as an extended one with object extension, e.g., its shape may be described by the star-convex random hypersurface model. This model is usually specified by a one-dimensional radial function. However, the divergence of the shape estimation and a high error of the kinematic state estimation are likely to occur when an extended object maneuvers. This is because the radial function may take a negative value after Fourier series expansion, which leads to unpredictable estimation results. Unfortunately, the model itself is unable to solve this problem via the subsequent iterations. In this paper, we proposed a modified shape estimation approach to track an extended object with a star-convex random hypersurface model based on minimum cosine distance. Both the extension state and kinematic state at the current time are reinitialized once the radial function takes a negative value. Moreover, a mathematical model was constructed by using the principle of minimum cosine distance, so as to obtain more reasonable weight distribution coefficients for the correction of the extension state. Simulation results in different scenarios demonstrated the effectiveness of the proposed tracking approach.
Spatio-Temporal Patterns and Driving Factors of Vegetation Change in the Pan-Third Pole Region
Authors: Yang Xuyan , Yang Qinke , Yang Miaomiao
Published: September 2022

The Pan-Third Pole (PTP) region, one of the areas with the most intense global warming, has seen substantial changes in vegetation cover. Based on the GIMMS NDVI3g and meteorological dataset from 1982 to 2015, this study evaluated the spatio-temporal variation in fractional vegetation coverage (FVC) by using linear regression analysis, standard deviation, correlation coefficient, and multiple linear regression residuals to explore its response mechanism to climate change and human activities. The findings showed that: (1) the FVC was progressively improved, with a linear trend of 0.003•10a−1. (2) The largest proportion of the contribution to FVC change was found in the unchanged area (39.29%), followed by the obvious improvement (23.83%) and the mild improvement area (13.53%). (3) The impact of both climate change and human activities is dual in FVC changes, and human activities are increasing. (4) The FVC was positively correlated with temperature and precipitation, with a stronger correlation with temperature, and the climate trend was warm and humid. The findings of the study serve to understand the impacts of climate change and human activities on the dynamic changes in the FVC and provide a scientific foundation for ecological conservation and sustainable economic development in the PTP region.
Long-Term Variation Study of Fine-Mode Particle Size and Regional Characteristics Using AERONET Data
Authors: Shin Juseon , Sim Juhyeon , Dehkhoda Naghmeh , Joo Sohee , Kim Taegyeong , Kim Gahyeong , Müller Detlef , Tesche Matthias , Shin Sung-Kyun , Shin Dongho , Noh Youngmin
Published: September 2022

To identify the long-term trend of particle size variation, we analyzed aerosol optical depth (AOD, τ) separated as dust (τD) and coarse-(τPC) and fine-pollution particles (τPF) depending on emission sources and size. Ångström exponent values are also identified separately as total and fine-mode particles (αT and αPF). We checked these trends in various ways; (1) first-order linear regression analysis of the annual average values, (2) percent variation using the slope of linear regression method, and (3) a reliability analysis using the Mann–Kendall (MK) test. We selected 17 AERONET sun/sky radiometer sites classified into six regions, i.e., Europe, North Africa, the Middle East, India, Southeast Asia, and Northeast Asia. Although there were regional differences, τ decreased in Europe and Asian regions and increased in the Middle East, India, and North Africa. Values of τPC and τPF, show that aerosol loading caused by non-dust aerosols decreased in Europe and Asia and increased in India. In particular, τPF considerably decreased in Europe and Northeast Asia (95% confidential levels in MK-test), and τPC decreased in Northeast Asia (Z-values for Seoul and Osaka are −2.955 and −2.306, respectively, statistically significant if |z| ≥ 1.96). The decrease in τPC seems to be because of the reduction of primary and anthropogenic emissions from regulation by air quality policies. The meaningful result in this paper is that the particle size became smaller, as seen by values of αT that decreased by −3.30 to −30.47% in Europe, North Africa, and the Middle East because αT provides information on the particle size. Particle size on average became smaller over India and Asian regions considered in our study due to the decrease in coarse particles. In particular, an increase of αPF in most areas shows the probability that the average particle size of fine-mode aerosols became smaller in recent years. We presumed the cause of the increase in αT is because relatively large-sized fine-mode particles were eliminated due to air quality policies.
Automatic Extraction of Marine Aquaculture Zones from Optical Satellite Images by R3Det with Piecewise Linear Stretching
Authors: Ma Yujie , Qu Xiaoyu , Yu Cixian , Wu Lianhui , Zhang Peng , Huang Hengda , Gui Fukun , Feng Dejun
Published: September 2022

In recent years, the development of China’s marine aquaculture has brought serious challenges to the marine ecological environment. Therefore, it is significant to classify and extract the aquaculture zone and spatial distribution in order to provide a reference for aquaculture management. However, considering the complex marine aquaculture environment, it is difficult for traditional remote sensing technology and deep learning to achieve a breakthrough in the extraction of large-scale aquaculture zones so far. This study proposes a method based on the combination of piecewise linear stretching and R3Det to classify and extract raft aquaculture and cage aquaculture zones. The grayscale value is changed by piecewise linear stretching to reduce the influence of complex aquaculture backgrounds on the extraction accuracy, to effectively highlight the appearance characteristics of the aquaculture zone, and to improve the image contrast. On this basis, the aquaculture zone is classified and extracted by R3Det. Taking the aquaculture zone of Sansha Bay as the research object, the experimental results showed that the accuracy of R3Det in extracting the number of raft aquaculture and cage aquaculture zones was 98.91% and 97.21%, respectively, and the extraction precision of the area of the aquaculture zone reached 92.08%. The proposed method can classify and extract large-scale marine aquaculture zones more simply and efficiently than common remote sensing techniques.
DHCAE: Deep Hybrid Convolutional Autoencoder Approach for Robust Supervised Hyperspectral Unmixing
Authors: Hadi Fazal , Yang Jingxiang , Ullah Matee , Ahmad Irfan , Farooque Ghulam , Xiao Liang
Published: September 2022

Hyperspectral unmixing (HSU) is a crucial method to determine the fractional abundance of the material (endmembers) in each pixel. Most spectral unmixing methods are affected by low signal-to-noise ratios because of noisy pixels and bands simultaneously, requiring robust HSU techniques that exploit both 3D (spectral–spatial dimension) and 2D (spatial dimension) domains. In this paper, we present a new method for robust supervised HSU based on a deep hybrid (3D and 2D) convolutional autoencoder (DHCAE) network. Most HSU methods adopt the 2D model for simplicity, whereas the performance of HSU depends on spectral and spatial information. The DHCAE network exploits spectral and spatial information of the remote sensing images for abundance map estimation. In addition, DHCAE uses dropout to regularize the network for smooth learning and to avoid overfitting. Quantitative and qualitative results confirm that our proposed DHCAE network achieved better hyperspectral unmixing performance on synthetic and three real hyperspectral images, i.e., Jasper Ridge, urban and Washington DC Mall datasets.
Forest Height Mapping Using Feature Selection and Machine Learning by Integrating Multi-Source Satellite Data in Baoding City, North China
Authors: Zhang Nan , Chen Mingjie , Yang Fan , Yang Cancan , Yang Penghui , Gao Yushan , Shang Yue , Peng Daoli
Published: September 2022

Accurate estimation of forest height is crucial for the estimation of forest aboveground biomass and monitoring of forest resources. Remote sensing technology makes it achievable to produce high-resolution forest height maps in large geographical areas. In this study, we produced a 25 m spatial resolution wall-to-wall forest height map in Baoding city, north China. We evaluated the effects of three factors on forest height estimation utilizing four types of remote sensing data (Sentinel-1, Sentinel-2, ALOS PALSAR-2, and SRTM DEM) with the National Forest Resources Continuous Inventory (NFCI) data, three feature selection methods (stepwise regression analysis (SR), recursive feature elimination (RFE), and Boruta), and six machine learning algorithms (k-nearest neighbor (k-NN), support vector machine regression (SVR), random forest (RF), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and categorical boosting (CatBoost)). ANOVA was adopted to quantify the effects of three factors, including data source, feature selection method, and modeling algorithm, on forest height estimation. The results showed that all three factors had a significant influence. The combination of multiple sensor data improved the estimation accuracy. Boruta’s overall performance was better than SR and RFE, and XGBoost outperformed the other five machine learning algorithms. The variables selected based on Boruta, including Sentinel-1, Sentinel-2, and topography metrics, combined with the XGBoost algorithm, provided the optimal model (R2 = 0.67, RMSE = 2.2 m). Then, we applied the best model to create the forest height map. There were several discrepancies between the generated forest height map and the existing map product, and the values with large differences between the two maps were mostly distributed in the steep areas with high slope values. Overall, we proposed a methodological framework for quantifying the importance of data source, feature selection method, and machine learning algorithm in forest height estimation, and it was proved to be effective in estimating forest height by using freely accessible multi-source data, advanced feature selection method, and machine learning algorithm.
Implementation of a Rainfall Normalization Module for GSMaP Microwave Imagers and Sounders
Authors: Yamamoto Munehisa K. , Kubota Takuji
Published: September 2022

This paper introduces the Method of Microwave Rainfall Normalization (MMN) for the Global Satellite Mapping of Precipitation (GSMaP) algorithm in its latest version (V05, algorithm version 8), released in December 2021. The method aims to mitigate the discrepancy of GSMaP rainfall estimates among passive microwave (PMW) imagers/sounders (MWIs/MWSs) due to differences in sensor specifications and retrieval algorithms. The basic idea of the MMN module is to calibrate target PMW sensors with reference sensors (the Global Precipitation Measurement (GPM) Microwave Imager (GMI) and the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)) using the cumulative distribution function (CDF) of the rain rate. Differences between the CDF and normalization table for MWSs are greater than MWIs due to different rain retrieval algorithms. More (less) MWS rainfall is detected over the ocean (land) than GMI rainfall. Matchup rainfall data between GMI and a target PMW sensor are compared to evaluate MMN performance. The monthly mean rainfall and mean bias error were improved for almost all PMW sensors. This study leaves open the possibility for further inter-calibration and improvement of rain detection and heavy rainfall retrievals.
Assessing Suitability of Auto-Selection of Hot and Cold Anchor Pixels of the UAS-METRIC Model for Developing Crop Water Use Maps
Authors: Molaei Behnaz , Peters R. Troy , Khot Lav R. , Stöckle Claudio O.
Published: September 2022

The METRIC energy balance model uses an auto-selection approach for identifying hot (dry, bare soil) and cold (fully transpiring crop) anchor pixels for the internal calibration of the model. When an unmanned aerial system (UAS) is used for imagery, the small image size and the varying crop and soil water status of agricultural fields make the identification of reliable hot and cold pixels challenging. In this study, we used an experimental spearmint field under three irrigation levels (75%, 100%, and 125% of crop evapotranspiration, ETc). As a way of providing diverse field conditions, six different extents (Extent 1 to Extent 6) were selected from each day of the seven days of UAS imagery campaigns of the same field for generating UAS-based ETc maps using auto-selection of hot and cold anchor pixels for the internal calibration of the model. Extent 1 had the smallest coverage area of the field, including only plants that were irrigated at 75% of ETc, while the fields of view of the other extents increased to where the Extent 6 covered the spearmint field and all the surroundings including trees, a nearby water canal, irrigated grass, and irrigated and non-irrigated soil. The results showed that different sizes of extent resulted in the selection of variable hot (bare, but moist soil in small extents, and dry bare soil at the larger extents) and cold anchor pixels (crop under water stress at the small extents, and tree canopy or grass alongside the water canal at the larger extents). This variation resulted in significantly different ETc estimation for the same spearmint crop field, indicative of a potential limitation for the use auto-selection of hot and cold pixels when using the UAS-METRIC model.
MV-GPRNet: Multi-View Subsurface Defect Detection Network for Airport Runway Inspection Based on GPR
Authors: Li Nansha , Wu Renbiao , Li Haifeng , Wang Huaichao , Gui Zhongcheng , Song Dezhen
Published: September 2022

The detection and restoration of subsurface defects are essential for ensuring the structural reliability of airport runways. Subsurface inspections can be performed with the aid of a robot equipped with a Ground Penetrating Radar (GPR). However, interpreting GPR data is extremely difficult, as GPR data usually contains severe clutter interference. In addition, many different types of subsurface defects present similar features in B-scan images, making them difficult to distinguish. Consequently, this makes later maintenance work harder as different subsurface defects require different restoration measures. Thus, to automate the inspection process and improve defect identification accuracy, a novel deep learning algorithm, MV-GPRNet, is proposed. Instead of traditionally using GPR B-scan images only, MV-GPRNet utilizes multi-view GPR data to robustly detect regions with defects despite significant interference. It originally fuses the 3D feature map in C-scan data and the 2D feature map in Top-scan data for defect classification and localization. With our runway inspection robot, a large number of real runway data sets from three international airports have been used to extensively test our method. Experimental results indicate that the proposed MV-GPRNet outperforms state-of-the-art (SOTA) approaches. In particular, MV-GPRNet achieves F1 measurements for voids, cracks, subsidences, and pipes at 91%, 69%, 90%, and 100%, respectively.
Convolutional Neural Network Algorithms for Semantic Segmentation of Volcanic Ash Plumes Using Visible Camera Imagery
Authors: Guerrero Tello José Francisco , Coltelli Mauro , Marsella Maria , Celauro Angela , Palenzuela Baena José Antonio
Published: September 2022

In the last decade, video surveillance cameras have experienced a great technological advance, making capturing and processing of digital images and videos more reliable in many fields of application. Hence, video-camera-based systems appear as one of the techniques most widely used in the world for monitoring volcanoes, providing a low cost and handy tool in emergency phases, although the processing of large data volumes from continuous acquisition still represents a challenge. To make these systems more effective in cases of emergency, each pixel of the acquired images must be assigned to class labels to categorise them and to locate and segment the observable eruptive activity. This paper is focused on the detection and segmentation of volcanic ash plumes using convolutional neural networks. Two well-established architectures, the segNet and the U-Net, have been used for the processing of in situ images to validate their usability in the field of volcanology. The dataset fed into the two CNN models was acquired from in situ visible video cameras from a ground-based network (Etna_NETVIS) located on Mount Etna (Italy) during the eruptive episode of 24th December 2018, when 560 images were captured from three different stations: CATANIA-CUAD, BRONTE, and Mt. CAGLIATO. In the preprocessing phase, data labelling for computer vision was used, adding one meaningful and informative label to provide eruptive context and the appropriate input for the training of the machine-learning neural network. Methods presented in this work offer a generalised toolset for volcano monitoring to detect, segment, and track ash plume emissions. The automatic detection of plumes helps to significantly reduce the storage of useless data, starting to register and save eruptive events at the time of unrest when a volcano leaves the rest status, and the semantic segmentation allows volcanic plumes to be tracked automatically and allows geometric parameters to be calculated.
Analysis of Characteristics of Fire Incident on 24 July 2021 in Jilin Province, China
Authors: Xu Liehao , Wang Yanning , Chen Jun
Published: September 2022

At 15:48 on 24 July 2021, a fire accident occurred in Changchun City, Jilin Province, China, resulting in 15 deaths and 25 injuries. After the accident happened, Jilin Province comprehensively organised an emergency rescue, saving more than 100 trapped people. Given the dangers posed by these events, it is recommended that we must strengthen our risk awareness. Combined with the general background and scene of the fire, in this study, we analysed the causes of the accident and described the on-site rescue actions. The direct cause of the accident was an arc fault, and the indirect cause was the dereliction of duty in the laying and installation of the circuit and the subsequent fire supervision. From our case analysis of electrical fires in public buildings, we found that electrical failure, human factors (illegal operation), ageing and short circuits of wires, poor contact of wires, and ignition of other combustibles by electrical appliances are the main factors causing electrical fire accidents in public buildings. To avoid serious fire accidents caused by arc faults, we proposed suggestions to reduce or prevent electric fire accidents caused by arc faults.
Impact of Phenol on Membranes during Bipolar Membrane Electrodialysis for High Salinity Pesticide Wastewater Treatment
Authors: Tang Yuanhui , Sun Wenwen , Lin Yakai , Wang Lin , Chen Hedi , Wu Huanhuan , Yu Lixin , Wang Xiaolin
Published: September 2022

To achieve a cleaner production, pesticide wastewater with concentrated NaCl can be treated by using a bipolar membrane electrodialysis (BMED) and converted to NaOH and HCl, which minimizes acid and alkali consumption in a pesticide production process. However, ion-exchange membranes (IEMs) are vulnerable to fouling by phenolic substances present in the concentrated NaCl solutions. This work aimed to understand the performance and fouling mechanism of BMED from phenol during the desalination of NaCl and explore an effective cleaning method. The results firstly showed that for the NaCl solutions with higher phenol concentrations, the selectivity of the IEMs was reduced after processing six successive batches of BMED, which led to reverse migration of ions, organics leakage, and an obvious increase in the energy consumption and the concentration of generated acid and alkali. Secondly, IEMs characterization analysis detected that the structure of the IEMs was deformed, while phenol fouling deposits were observed on the surface and interior of the IEMs, especially for the anion exchange membranes (AEMs). Then, the results of soaking tests proved that the phenol could bring about swelling-like degradation to the AEMs and 0.1 wt.% NaOH solution was studied to be the optimized cleaning agent since the performance of the fouled IEMs in the short-running process could be recovered after 5 h of in situ cleaning that removed the phenol fouling deposits efficiently. Finally, the results of a long-running BMED operation treating NaCl solution containing 10 g/L phenol concentration showed that the IEMs were severely fouled, and the fouling was firstly due to the swelling-like mechanism during the initial 12 successive batches, and then should belong to the blockage-like mechanism during the following 20 successive batches. The seriously fouled IEMs could no longer be recovered even after a deep in situ cleaning. This research proves that under appropriate pretreatment or operating conditions, the BMED process is an alternative way of treating wastewater with high salinity and the presence of phenol molecules.
Boosting the Liquid Chromatography Separation of Complex Bispecific Antibody Products by Using the Multi-Isocratic Elution Mode
Authors: Murisier Amarande , D’Atri Valentina , Larraillet Vincent , Pirner Sebastian , Guillarme Davy
Published: September 2022

In reversed-phase liquid chromatography (RPLC), the selectivity between major species and minor variants of protein biopharmaceutical products is always limited. Unfortunately, the stationary phase chemistry, type of mobile phase (organic modifier and salts) and temperature only have a very limited impact on selectivity. Therefore, instead of using a linear elution gradient, we evaluated a recently developed strategy, named the multi-isocratic elution mode, to improve the chromatographic resolution. In this contribution, a generic workflow involving the use of an Excel spreadsheet is provided for the rapid and successful development of multi-isocratic elution methods, without the need to use HPLC modeling software. This simple strategy was then successfully applied to very complex biopharmaceutical products; these included one reduced mAb-cytokine fusion protein and a mAb-domain-fusion (C-terminal) protein sample, containing numerous minor variants that were poorly separated from the major species. The addition of several isocratic steps during the chromatographic run provides a clear added value in terms of chromatographic selectivity for several variants, simplifying characterization of the sample with advanced MS tools. In addition to these advantages, some of the limitations of the multi-isocratic elution mode were also highlighted; these included the need to use a highly precise pumping device (preferably, a binary pumping system) and the need to prepare highly accurate mobile phases.
Antioxidant Activity and Phenolic Content of Sonication- and Maceration-Assisted Ethanol and Acetone Extracts of Cymbopogon citratus Leaves
Authors: Irfan Shafeeqa , Ranjha Muhammad , Nadeem Muhammad , Safdar Muhammad , Jabbar Saqib , Mahmood Shahid , Murtaza Mian , Ameer Kashif , Ibrahim Salam
Published: September 2022

Cymbopogon citratus is a medicinal and well-known aromatic plant which is usually used as a substitute for green-tea with extraordinary phytomedicinal potential. It is of great importance because it offers several promising health effects. The objective of the study was to investigate the antioxidant activity and total phenolic content of lemongrass leaves extracted by maceration and ultrasound-assisted extraction techniques. Different concentrations of both acetone and ethanol (50% and 70%) as solvents were tested for their radical scavenging activity against the stable free radical DPPH in quantization using a spectrophotometric assay. The ultrasound-assisted extraction technique had advantageous results in the polyphenols extraction yield (26.68%), TPC (61 mg GAE/g of extract) and DPPH (73.85%) compared to the maceration technique. Overall, the results were significant from 50% ethanol and the DPPH results were more significant from 70% ethanol than other concentrations. It is concluded that the ultrasound-assisted extraction is a better extraction technique for polyphenols than other conventional techniques.
Phenolic Composition, Antioxidant and Antibacterial Activities of Extract from Flowers of Rosa damascena from Morocco
Authors: Chroho Mounia , Bouymajane Aziz , Oulad El Majdoub Yassine , Cacciola Francesco , Mondello Luigi , Aazza Mustapha , Zair Touriya , Bouissane Latifa
Published: September 2022

Rosa damascena is referred to as the queen of roses due to its ornamental, flavoring, and perfuming uses, along with its recognized use in therapy. This study aimed to investigate the total phenols and flavonoids contents, the phenolic compounds, and study the antioxidant and antibacterial properties of the hydroethanolic extract from Rosa damascena flowers, collected from the Middle Atlas of Morocco (Khenifra). The total phenols and flavonoids were assessed using gallic acid and quercetin as standards, and the phenolic compounds were characterized using HPLC-PDA-ESI/MS. The antioxidant activity was evaluated by two methods, namely ferric reducing assay power and total antioxidant capacity. The broth microdilution method was employed to evaluate the antibacterial activity of extract against four bacteria (Escherichia coli, Salmonella typhimurium, Staphylococcus aureus, and Listeria monocytogenes). Up to 16 phenolic compounds belonging to tannins and flavonoids were positively identified in the Rosa damascena extract. The latter displayed high antioxidant activity and exhibited a bacteriostatic effect against Escherichia coli and a bactericidal effect against Salmonella typhimuriumn, Staphylococcus aureus, and Listeria monocytogenes. As a result, the flowers of Rosa damascena might be employed as natural agents in the pharmaceutical field.