bluuebunny/crossref_metadata_2025 huggingface dataset:
Radiation Limit for the Energy Gain of the p–11B Reaction
Authors: Chirkov Alexei Yu. , Kazakov Kirill D.
Published: July 2023

The feasibility of positive energy yield in systems with the p–11B reaction is considered here by considering refined (optimistic) data on the reaction rate. The analysis was carried out within the traditional framework for magnetic confinement systems, but without taking into account a particular type of plasma configuration. The energy balance was considered both for the ions and electrons. The balance of particles includes all species as well as the products of fusion (alpha particles). Calculations have shown that accounting for the content of thermalized reaction products (alpha particles) leads to an increase in radiation losses and a decrease in gain to Q < 1. In the steady-state scenario, the energy gain Q~5–10 can be obtained in p–11B plasma, if only the fast (high-energy) population of fusion alpha particles is considered. For pulsed modes, the gain value is proportional to the content of alpha particles, and it is limited by the complete burn of one of the fuel components (boron), so it does not exceed unity. In the analysis we did not rely on any assumptions about the theoretically predicted mechanisms for increasing the cross section and the reaction rate, and only radiation losses (primarily bremsstrahlung) dramatically affect the gain Q. Thus, the regimes found can be considered as limiting in the framework of the classical concepts of processes in hot fusion plasma.
Thermal and Dielectric Investigations of Polystyrene Nanoparticles as a Viable Platform—Toward the Next Generation of Fillers for Nanocomposites
Authors: Asandulesa Mihai , Solonaru Ana-Maria , Resmerita Ana-Maria , Honciuc Andrei
Published: July 2023

Nanoparticles are often used as fillers for enhancing various properties of polymer composites such as mechanical, electrical, or dielectric. Among them, polymer nanoparticles are considered ideal contenders because of their compatibility with a polymer matrix. For this reason, it is important that they are synthesized in a surfactant-free form, to obtain predictable surface and structural properties. Here, we synthesized a series of polystyrene nanoparticles (PS NPs), by emulsion polymerization of styrene, using varying amounts of divinylbenzene as a crosslinking agent and sodium 4-vinylbenzenesulfonate as a copolymerizing monomer surfactant—“surfmer”. Using “surfmers” we obtained surfactant-free nanoparticles that are monodisperse, with a high degree of thermal stability, as observed by scanning electron microscopy and thermogravimetric investigations. The prepared series of NPs were investigated by means of broadband dielectric spectroscopy and we demonstrate that by fine-tuning their chemical composition, fine changes in their dielectric and thermal properties are obtained. Further, we demonstrate that the physical transformations in the nanoparticles, such as the glass transition, can be predicted by performing the first derivative of dielectric permittivity for all investigated samples. The glass transition temperature of PS NPs appears to be inversely correlated with the dielectric permittivity and the average diameter of NPs.
The Effect of Various Ti6Al4V Powders on the Behavior of Particle-Reinforced Polyester Matrix Composites
Authors: Teke Erdoğan , Seydibeyoğlu Elif Alyamaç , Seydibeyoğlu Mehmet Özgür
Published: July 2023

In this study, recycled and commercial Ti6Al4V powder was added to polyester composites at various filling ratios by weight fraction. Three weight fractions of Ti6Al4V particles, 0%, 5%, and 10%, were chosen for study. By examining the mechanical and microstructural properties of polyester composites, the effects of the Ti6Al4V powder proportion by weight fraction and particle size parameters were investigated. With the filler loading, a 39% increase in the tensile strength of the composites was achieved. A minor decrease in flexural strength was observed at 5% filler weight fraction. The addition of the recycled Ti6Al4V powder to the polyester matrix slightly reduced the thermal conductivity of the composite over that of the neat polymer. However, the incorporation of the commercial Ti6Al4V powder fillers in the polyester matrix considerably increased the thermal conductivity of the composites, suggesting several potential uses. The presence of high levels of oxygen in the powder led to reduced thermal conductivity of the composites due to the reduction in phonon scattering.
Advances and Applications of Block Copolymers
Authors: Politakos Nikolaos , Avgeropoulos Apostolos
Published: July 2023

Polymers are materials that have constantly evolved from the beginning of their discovery until the present day [...]
Design of Nano-Catalyst for Removal of Phenolic Compounds from Wastewater by Oxidation Using Modified Digital Basket Baffle Batch Reactor: Experiments and Modeling
Authors: Nawaf Amer T. , Humadi Jasim I. , Jarullah Aysar T. , Ahmed Mustafa A. , Hameed Shymaa Ali , Mujtaba Iqbal M.
Published: July 2023

Removal of phenol and phenolic compounds from wastewater using various techniques has received considerable attention in recent years. In this work, the removal of phenol from a model solution of phenol via catalytic oxidation is investigated with oxidant H2O2. For this purpose, we designed a new nano-catalyst (8% Fe2O3/AC) by loading iron oxide nanoparticles over nano-activated carbon via the impregnation process. We modified the recently developed digital basket baffle batch reactor (DBBBR) and used it for the catalytic oxidation process in order to examine the activity of the prepared nano-catalyst. The highest efficiency of phenol removal was found to be 95.35% under the following parameters: oxidation time of 120 min, oxidation temperature at 85 °C, and stirrer speed of 600 rpm. The minimization of the sum of the squared error between the experimental data and predicted results of phenol removal was considered as a base for the optimization technique to estimate the optimal parameters for the kinetic process. The predicted conversion of phenol excellently agreed with the experimental results (absolute average errors < 5%) for a wide range of process parameters.
An Overview of Carbon Emission Mitigation in the Food Industry: Efforts, Challenges, and Opportunities
Authors: Liu Ting-Chun , Wu Yi-Ching , Chau Chi-Fai
Published: July 2023

The food system plays a significant role in anthropogenic greenhouse gas (GHG) emissions, contributing to over one-third of these emissions. However, there has been limited attention given in the literature on how the food industry can effectively address the carbon issue. This review aims to bridge this research gap through providing a comprehensive overview of anthropogenic GHG emissions and exploring the role of carbon markets in mitigating climate change, with a specific emphasis on the food industry. It delves into the introduction of emission hotspots within the food industry, examines ongoing efforts in GHG emissions mitigation, and addresses the challenges associated with GHG verification and offsetting. Notably, emission hotspots are primarily found in the farm, manufacturing, and post-production stages of the food industry. The emissions from the farm stage, which are often overlooked, make a significant contribution to overall emissions. Carbon verification encounters limitations due to a lack of standardized methodologies, inaccurate data, and insufficient reporting of emissions. Currently, achieving carbon neutrality without relying on carbon offsets presents a significant challenge for the entire food industry. Comprehensive mitigation strategies and collaboration across agricultural producers and the food manufacturing industry are considered potential solutions to achieve genuine sustainability.
Folic Acid and Its Role in Oral Health: A Narrative Review
Authors: Albu Cristina-Crenguța , Bencze Maria-Angelica , Dragomirescu Anca-Oana , Suciu Ioana , Tănase Mihaela , Albu Ştefan-Dimitrie , Russu Emily-Alice , Ionescu Ecaterina
Published: July 2023

Vitamins, exogenous organic compounds that play a vital role in metabolic reactions, and fundamental powerful antioxidants with a crucial role in the genetic transcription process, are considered essential nutritional factors. Folic acid (FA), also known as folate, or Vitamin B9, plays an indispensable role in various intracellular reactions, being the main pawn, with a strong impact on medical and dental science. The aim of this paper mainly focuses on presenting the latest and most advanced aspects related to the following topics: (1) the resonance that FA, and more specifically FA deficiency, has at the level of the oral cavity; (2) the elements involved in the molecular landscape, which reflect the interaction and the possible mechanisms of action, through which FA influences oral health; and (3) the particular processes by which FA deficiency causes certain clinical conditions. Moreover, we aim to draw the attention and trigger the curiosity of health professionals on the need to know the specific host–environment interactions, particularly the linkage between individual genotype and phenotypic variability, which in the future could represent the basis of novel and effective treatment methods. From this perspective, we begin by providing an overview of the general radar echo of the human body induced by FA deficiency, before focusing on the genetic strategic substrate and biochemical processes involved in the molecular mechanisms through which FA acts at the cellular level. Finally, we reflect on the resulting conclusions: (1) the complex interrelationships between different types of cytokines (CKs) and abnormal folate metabolism are involved in the occurrence of neural tube defects (NTDs) and orofacial clefts (OFCs); (2) increased oxidative stress, endothelial dysfunction, and genomic instability, induced by folate deficiency, have a major impact on periodontal health; and (3) glutamate carboxypeptidase II, GCP2 1561C>T allelic variant, constitutes the main pawn, which specifically influences the bioavailability of natural folates and FA, as the main actors, with essential roles in oral health.
Study on the Fracture Propagation in Multi-Horizontal Well Hydraulic Fracturing
Authors: Ran Qiquan , Zhou Xin , Dong Jiaxin , Xu Mengya , Ren Dianxing , Li Ruibo
Published: July 2023

Multi-horizontal well hydraulic fracturing is a widely employed and highly effective method for stimulating tight and shale reservoirs. However, most existing studies primarily focus on investigating the impact of intra-well interference on fracture propagation while neglecting the influence of inter-well interference. Here, a multi-well hydraulic-fracture-propagation model is established to examine the effects of inter-well interference on fracture propagation within a multi-well system. In this study, based on the bilinear T-S criterion, the stiffness degradation is used to describe the damage and evolution process of fracture, the coupling process of fluid flow and solid damage and deformation is realized, and the dynamic distribution of inter-fracture flow is realized by using Kirchhoff function on the basis of the cohesive zone method (CZM) finite element model. Finally, the fracture-propagation model of multiple horizontal wells is established. Based on this model, the mechanism of inter-well interference on fracture propagation is studied, and the influence law of Young’s modulus and fracture displacement on fracture propagation in multi-wells is investigated. The results show that the reservoir can be divided into self-influence area, tension area and compression area according to the stress distribution state in the hydraulic fracture propagation of multi-wells. The propagation rate of hydraulic fractures in horizontal wells is significantly accelerated when they propagate to the local tension area generated by the fracture tip of neighboring wells, and rapidly decreases as the hydraulic fractures continue to propagate to the compression area of neighboring wells. Rocks with a lower Young’s modulus tend to be more plastic, forming hydraulic fractures with usually lower fracture lengths and usually larger fracture widths. The hydraulic fracture has an inhibitory effect on the propagation of fractures closer to each other in neighboring wells, and this inhibitory effect gradually increases as the distance decreases. The dominance of the dominant fracture to propagate in the self-influence area gradually decreases under inter-well and intra-well interference. As the dominant fracture propagates into the tension and compression areas of the neighboring well fractures, the feed fluid will show a brief rise and then eventually stabilize. This study quantifies the effect of inter-well interference on fracture propagation and lays the foundation for treatment optimization of small well spacing hydraulic fracturing.
A Fault Diagnosis Method for Drilling Pump Fluid Ends Based on Time–Frequency Transforms
Authors: Tang Aimin , Zhao Wu
Published: July 2023

Drilling pumps are crucial for oil and gas operations. Timely diagnosis and troubleshooting of fluid end faults is crucial to ensure the safe and stable operation of drilling pumps and prevent further deterioration of faults. Hence, from a data-driven perspective, this study proposes a fault diagnosis method for the fluid end of drilling pumps based on the generalized S transform (GST) and convolutional neural networks (CNN), using the vibration signal of the fluid end. To address the issue of noise pollution in the vibration signal resulting in unclear feature information and difficult feature extraction, the vibration signal is transformed into a time–frequency diagram based on GST, which more accurately characterizes the fault characteristics of the vibration signal. An AlexNet model, improved by introducing batch normalization and optimizing the number of neurons in the fully connected layer, is used to analyze the recognition performance of the model for the normal, minor damage, and severe damage states of the fluid end of the drilling pump. Finally, the diagnosis results are compared to other methods, with the results showing that the proposed method has the highest fault diagnosis accuracy. With an average recognition rate of 99.21% for the nine types of fluid end, the method proposed in this study provides a way to accurately diagnose fluid end failures, thus supporting the safe and efficient operation of drilling pumps.
Comparing Frequency and Severity Ratings for ME/CFS versus Controls
Authors: Jason Leonard A. , Benner Sage , Hansel Nicole
Published: July 2023

Most questionnaires for somatic symptoms focus either on occurrence, frequency, or severity, and in doing so, they might not be able to comprehensively assess the burden that symptoms present to patients. For example, a symptom might occur at a high frequency but only a minimal severity, so that it is less likely to be a burden on a patient; whereas a symptom that has both a high frequency and severity is more likely to be negatively impacting a patient. Study 1 examined frequency and severity scores for classic Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) symptoms among patients with ME/CFS versus a control group. Findings in Study 1 indicate there were more frequency/severity discrepancies for individuals with ME/CFS versus the control group. Study 1 concluded that collecting data on both measures of symptom burden provides unique indicators that can better assess the burden of the symptoms on patients. In a separate data set, Study 2 reported reliability data on slight differences in the time period and the way the severity was assessed. Study 2 findings indicated high levels of reliability for these changes in the time period and the way questions were asked. These studies provide important psychometric properties that could lead to more reliable and valid assessments of patients with post-viral illnesses.
Development History of the Loess–Paleosol Profiles of Pécel, Kisdorog and Bonyhádvarasd, Hungary
Authors: Makó László , Cseh Péter , Nagy Balázs , Sümegi Pál , Molnár Dávid
Published: July 2023

This study covers the examination of four loess–paleosol profiles in Hungary through grain size composition, organic matter, carbonate content and magnetic susceptibility measurements. One of the profiles (with a thickness of 25.72 m) can be found in the Gödöllő hills, on the border of town Pécel, and the other three profiles (Kisdorog-West—5.60 m, Kisdorog-East—6.40 and Bonyhádvarasd—8.16 m) are located in the Tolna hills of the Transdanubia region. The sections were continuously sampled with an interval of 4 cm. The same interval was also applied to the other three profiles. During the field exploration of the Pécel profile, we were able to study the complete loess wall, which was deposited on the sediment of the nearby Rákos stream. Based on the Ostracod fauna of the clay sediment beneath, the fluvial deposit can be considered as originating from the Upper Miocene. In the case of the Transdanubian sections, a significant change can be observed in the prevailing wind direction based on the grain size analyses. In addition, the results of magnetic susceptibility measurements suggest that the development of the Pécel profile took place during MIS 9–10, while the age of the three Transdanubian sections can be assumed to be the MIS 2–4.
Filial Piety in Fluidity: The Tension between the Textual and Visual Traditions of Śyāma Jātaka in Early Medieval China
Authors: Zhao Jinchao
Published: July 2023

The Śyāma jātaka is renowned for its portrayal of a devoted son who cared for his blind parents. The story has been translated into various textual versions and depicted in reliefs and murals, gaining wide circulation in the Buddhist world. Previous scholarship on the story’s transmission in China has primarily focused on its representation of filial piety and its resonance with the Chinese context. However, a careful examination of surviving visual depictions of jātaka stories brings to light historical and regional disparities that have often been overlooked in relation to the reception of Śyāma jātaka’s didactic teachings in early medieval China. While the story has flourished in North China (including the Central Plain and the Hexi Corridor) from the late fifth century onwards, it was intriguingly absent from the region during the first half of the sixth century. This absence of the Śyāma jātaka stands in contrast to the popularity of other jātakas such as Sudāna and Mahasattva, which were widely circulated in China. In this article, I explore the uneven adaptation of the Śyāma jātaka within Chinese visual culture by placing the story’s textual and visual traditions within the broader historical milieu of depicting Buddhist stories and filial paragons in the sixth century. My study demonstrates that the story’s theme in multiple dimensions was simplified to filial piety during the textual translation process of the story in third- and fourth-century China. Moreover, it reveals that the story’s visual legacy faced challenges and negotiations when integrating into the local teaching of filial piety. This reluctance can be attributed to two historical factors: the revival of pre-existing visual traditions depicting Chinese filial sons, and the growing preference for other jātakas that embodied teachings on generosity in early sixth-century North China. Furthermore, this study sheds light on the tension between textual and visual traditions when incorporating Buddhist teachings into a new social context. While various rhetoric strategies were developed in text translation to integrate Buddhist teachings into existing Chinese thought, the visual tradition posed separate questions regarding its necessity, the didactic intentions of patrons, and the visual logic understood by viewers.
Oneness and Mending the World in Arthur Green’s Neo-Hasidism
Authors: Meir Ephraim
Published: July 2023

This article describes and discusses Green’s mystical neo-Hasidic thought, his reshaping of Judaism and his combination of scholarship and existential engagement. I showcase how his vision on the Oneness of all and on the unity in plurality leads him to an appreciation of evolution and to the promotion of love energy in all, to ecological care and to a deep concern for what happens in Israel and Palestine.
RETRACTED: A Newly-Designed Wearable Robotic Hand Exoskeleton Controlled by EMG Signals and ROS Embedded Systems
Authors: Abdallah Ismail , Bouteraa Yassine
Published: July 2023

One of the most difficult parts of stroke therapy is hand mobility recovery. Indeed, stroke is a serious medical disorder that can seriously impair hand and locomotor movement. To improve hand function in stroke patients, new medical technologies, such as various wearable devices and rehabilitation therapies, are being developed. In this study, a new design of electromyography (EMG)-controlled 3D-printed hand exoskeleton is presented. The exoskeleton was created to help stroke victims with their gripping abilities. Computer-aided design software was used to create the device’s 3D architecture, which was then printed using a polylactic acid filament. For online classifications, the performance of two classifiers—the support vector machine (SVM) and the K-near neighbor (KNN)—was compared. The Robot Operating System (ROS) connects all the various system nodes and generates the decision for the hand exoskeleton. The selected classifiers had high accuracy, reaching up to 98% for online classification performed with healthy subjects. These findings imply that the new wearable exoskeleton, which could be controlled in accordance with the subjects’ motion intentions, could aid in hand rehabilitation for a wider motion range and greater dexterity.
Analysis of the Superposition Effect of Land Subsidence and Sea-Level Rise in the Tianjin Coastal Area and Its Emerging Risks
Authors: Yu Hairuo , Gong Huili , Chen Beibei
Published: July 2023

Tianjin is a coastal city of China. However, the continuous rise of the relative sea-level has brought huge hidden danger to Tianjin’s economic and social development. The land subsidence is the most important factor that influences relative sea-level rise. By analyzing the current situation of subsidence in Tianjin through PS-InSAR, it was found that the subsidence rate of the southern plain of Tianjin is slowing down as a whole. In addition, Wuqing and Jinghai sedimentary areas as well as other several subsidence centers have been formed. By establishing a regular grid of land subsidence and ground water to construct a geo-weighted regression model (GWR), it was found that Wuqing sedimentary area as a whole is positively correlated with TCA. According to the relative sea-level change, it can be predicted that the natural coastline of Tianjin will recede by about 87 km2 in 20 years. Based on the research results above, this paper, by using machine-learning method (XGBoost), has evaluated Tianjin’s urban safety and analyzed high-risk areas and main contributing factors. Potential risks to urban safety brought about by relative sea-level rise have been analyzed, which will improve the resilience of coastal areas to disasters.
Correction: Lee, J.H.; Lindenschmidt, K.-E. Bias-Corrected RADARSAT-2 Soil Moisture Dynamics Reveal Discharge Hysteresis at an Agricultural Watershed. Remote Sens. 2023, 15, 2677
Authors:   Remote Sensing Editorial Office
Published: July 2023

There was an error in the original publication [...]
Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods
Authors: Xie Dongbo , Huang Hongchao , Feng Linyan , Sharma Ram P. , Chen Qiao , Liu Qingwang , Fu Liyong
Published: July 2023

Aboveground biomass (AGB) of shrub communities in the desert is a basic quantitative characteristic of the desert ecosystem and an important index to measure ecosystem productivity and monitor desertification. An accurate and efficient method of predicting the AGB of a shrub community is essential for studying the spatial patterns and ecological functions of the desert region. Even though there are several entries in the literature on the AGB prediction of desert shrub communities using remote sensing data, the applicability and accuracy of airborne LiDAR data and prediction methods have not been well studied. We first extracted the elevation, density and intensity variables based on the airborne LiDAR, and then sample plot-level AGB prediction models were constructed using the parametric regression (nonlinear regression) and nonparametric methods (Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting Machine, and Multivariate adaptive regression splines). We evaluated accuracies of all the AGB prediction models we developed based on the fit statistics. Results showed that: (1) the elevation, density and intensity variables obtained from LiDAR point cloud data effectively predicted the AGB of the desert shrub community at a sample plot level, (2) the kappa coefficient of nonlinear mixed-effects (NLME) model obtained was 0.6977 with an improvement by 13% due to the random effects included into the model, and (3) the nonparametric model, such as Support Vector Machine showed the best fit statistics (R2 = 0.8992), which is 28% higher than the NLME-model, and effectively reduced the heteroscedasticity. The AGB prediction model presented in this paper, which is based on the airborne LiDAR data and machine learning algorithm, will provide a valuable tool to the managers and researchers for evaluating desert ecosystem productivity and monitoring desertification.
Significant Stratospheric Moistening Following Extreme El Niño Events
Authors: Chen Quanliang , Liao Yujing , Zhou Xin , Duan Ting , Xue Xiaotian , Zhang Ziqi , Dong Dandan , Feng Wuhu
Published: July 2023

The moistening impact of El Niño on the tropical lower stratosphere has been extensively studied, yet a long-standing challenge is its potential nonlinearities regarding the strength of El Niño. Extreme El Niño’s hydration in 2015/2016 was unprecedented in the satellite era, providing a great opportunity to distinguish the differential response of water vapor to extreme and moderate El Niño. Using ERA5 and MERRA-2 reanalysis data from 1979–2019, we compare the composite tropical lower stratospheric water vapor anomalies throughout all extreme and moderate El Niño episodes since the satellite era. We validate the variations in the lower stratospheric water vapor during the two distinct El Niño episodes using a three-dimensional chemistry transport model simulating the same period. The model reproduces the observed pattern in lower stratospheric water vapor. Both demonstrate that robust moistening during extreme El Niño events occurs throughout the tropical lower stratosphere. However, moderate El Niño events seem to have a weak effect on lower stratospheric water vapor. In comparison to moderate El Niño, the strong convective activities induced by extreme El Niño release large amounts of latent heat, causing extensive and intense warming in the tropical upper troposphere and lower stratosphere, thus greatly increasing the water vapor content in the tropical lower stratosphere. Additionally, moderate El Niño events have strong seasonality in their hydration effect in the tropics, whereas the intense moistening effect of extreme El Niño events prevails in all seasons during their episodes.
Reconstruction of a Monthly 1 km NDVI Time Series Product in China Using Random Forest Methodology
Authors: Sun Mengmeng , Gong Adu , Zhao Xiang , Liu Naijing , Si Longping , Zhao Siqing
Published: July 2023

The normalized difference vegetation index (NDVI) is one of the most common metrics used to describe vegetation dynamics. Unfortunately, low-quality pixels resulting from contamination (by features including clouds, snow, aerosols, and mixed factors) have impeded NDVI products’ widespread application. Researchers have thought of several ways to improve NDVI quality when contamination occurs. However, most of these algorithms are based on the noise-negative deviation principle, which aligns low-value NDVI products to an upper line but ignores cases where absolute surface values are low. Consequently, to fill in these research gaps, in this article, we use the random forest model to produce a set of high-quality NDVI products to represent actual surface characteristics more accurately and naturally. Climate and geographical products are used as model inputs to describe environmental factors. They represent the random forest (RF) model that establishes relationships between MODIS NDVI products and meteorological products in high-quality areas. In addition, auxiliary data and empirical knowledge are employed to meet filling requirements. Notably, the random forest (RF) algorithm exhibits a mean absolute error (MAE) of 0.024 and a root mean squared error (RMSE) of 0.034, in addition to a coefficient of determination (R2) value of 0.974. Furthermore, the MAE and RMSE of the RF-based method decreased by 0.014 and 0.019, respectively, when compared to those of the STSG (spatial–temporal Savitzky–Golay) plan and by 0.013 and 0.015, respectively, when compared to the LSTM (long short-term memory) method. R2 increased by 0.039 and 0.027, respectively, compared to the STSG and LSTM methods. We introduced a novel series of NDVI products that demonstrated consistent spatial and temporal connectivity. The novel product exhibits enhanced adaptability to intricate environmental conditions and promises the potential for utilization in investigating vegetation dynamics within the Chinese region.
CRNN: Collaborative Representation Neural Networks for Hyperspectral Anomaly Detection
Authors: Duan Yuxiao , Ouyang Tongbin , Wang Jinshen
Published: July 2023

Hyperspectral anomaly detection aims to separate anomalies and backgrounds without prior knowledge. The collaborative representation (CR)-based hyperspectral anomaly detection methods have gained significant interest and development because of their interpretability and high detection rate. However, the traditional CR presents a low utilization rate for deep latent features in hyperspectral images, making the dictionary construction and the optimization of weight matrix sub-optimal. Due to the excellent capacity of neural networks for generation, we formulate the deep learning-based method into CR optimization in both global and local streams, and propose a novel hyperspectral anomaly detection method based on collaborative representation neural networks (CRNN) in this paper. In order to gain a complete background dictionary and avoid the pollution of anomalies, the global dictionary is collected in the global stream by optimizing the dictionary atom loss, while the local background dictionary is obtained by using a sliding dual window. Based on the two dictionaries, our two-stream networks are trained to learn the global and local representation of hyperspectral data by optimizing the objective function of CR. The detection result is calculated by the fusion of residual maps of original and represented data in the two streams. In addition, an autoencoder is introduced to obtain the hidden feature considered as the dense expression of the original hyperspectral image, and a feature extraction network is concerned to further learn the comprehensive features. Compared with the shallow learning CR, the proposed CRNN learns the dictionary and the representation weight matrix in neural networks to increase the detection performance, and the fixed network parameters instead of the complex matrix operations in traditional CR bring a high inference efficiency. The experiments on six public hyperspectral datasets prove that our proposed CRNN presents the state-of-the-art performance.
Soil Salinity Estimation in Cotton Fields in Arid Regions Based on Multi-Granularity Spectral Segmentation (MGSS)
Authors: Fan Xianglong , Kang Xiaoyan , Gao Pan , Zhang Ze , Wang Jin , Zhang Qiang , Zhang Mengli , Ma Lulu , Lv Xin , Zhang Lifu
Published: July 2023

Soil salinization seriously threatens agricultural production and ecological environments in arid areas. The accurate and rapid monitoring of soil salinity and its spatial variability is of great significance for the amelioration of saline soils. In this study, 191 soil samples were collected from cotton fields in southern Xinjiang, China, to obtain spectral reflectance and electrical conductivity (EC) indoors. Then, multi-granularity spectral segmentation (MGSS) and seven conventional spectral preprocessing methods were employed to preprocess the spectral data, followed by the construction of partial least squares regression (PLSR) models for soil EC estimation. Finally, the performance of the models was compared. The results showed that compared with conventional spectral preprocessing methods, MGSS could greatly improve the correlation between spectrum and soil EC, extract the weak spectral information of soil EC, and expand the spectral utilization range. The model validation results showed that the PLSR model based on the second-order derivative (2nd-der-PLSR) had the highest estimation accuracy among the models constructed by conventional methods. However, the PLSR model based on MGSS (MGSS-PLSR) had the highest estimation accuracy among all models, with Rp2 (0.901) and RPD (3.080) being 0.151 and 1.302 higher than those of the 2nd-der-PLSR model, respectively, and nRMSEP (5.857%) being 4.29% lower than that of the 2nd-der-PLSR model. The reason for the high accuracy of the MGSS-PLSR model is as follows: In the continuous segmentation of the raw spectrum by MGSS, the bands with strong and weak correlations with respect to soil EC were concentrated during low granularity segmentation. With the increase in granularity level, the spectral features decreased and were distributed discretely. In addition, the locations of spectral features were also different at different granularity levels. Therefore, the spectral features of soil EC can be effectively extracted by the MGSS, which significantly improves the spectral estimation accuracy of soil salinity. This study provides a new technical means for soil salinity estimation in arid areas.
New Methodology for Intertidal Seaweed Biomass Estimation Using Multispectral Data Obtained with Unoccupied Aerial Vehicles
Authors: Borges Débora , Duarte Lia , Costa Isabel , Bio Ana , Silva Joelen , Sousa-Pinto Isabel , Gonçalves José Alberto
Published: July 2023

Seaweed assemblages include a variety of structuring species providing habitats, food and shelter for organisms from different trophic levels. Monitoring intertidal seaweed traditionally involves targeting small areas to collect data on species’ biological traits, which is often labour intensive and covers only a small area of the rocky reef under study. Given the various applications for seaweeds and their compounds, there has been an increase in demand for biomass triggered by the development of new markets. Such biomass demand generates new challenges for biomass quantification and the definition of future in-take harvesting commercial quotas by regulating agencies. The use of Unoccupied Aerial Vehicles (UAVs) as a low-cost yet efficient monitoring solution, combined with new sensors such as multispectral cameras, has been proposed for mapping intertidal reefs and seaweed in particular. In this study, a new methodology was developed and validated to quantify intertidal seaweed biomass based on multispectral UAV imagery, which was made available through an easy-to-use QGIS plugin (named SWUAV_BIO) that automates such biomass estimation. This tool was applied to a case study where the standing stock of Fucus spp. beds located at Viana do Castelo rocky shore (northern Portugal) was assessed using UAV multispectral imagery, providing a reference for future UAV-based ecological studies. Although comparison with the in situ assessments showed that biomass was underestimated by 36%, the SWUAV_BIO plugin is a valuable tool, as it provides an expedited (albeit conservative) seaweed standing stock assessment that can be used to monitor seaweed populations, their changes, and assess the effect of harvesting. These data can be used for an informed and sustainable management of seaweed resources by the competent authorities.
Reconstruction of Forest and Grassland Cover for the Conterminous United States from 1000 AD to 2000 AD
Authors: Wang Yafei , Yang Fan , He Fanneng
Published: July 2023

Spatially explicit reconstruction of historical land cover change is a prerequisite for a more comprehensive understanding of environmental changes. Anthropogenic activities have dramatically altered the land cover of the conterminous United States (CONUS), encroaching heavily on the primary vegetation. However, few datasets exist that depict the historical trajectory of forest and grassland cover changes in CONUS over the last millennium, and previous efforts have only focused on reconstructions for the last four centuries. By integrating remote sensing-derived land use/cover change (LUCC) data and potential vegetation data, we determined the potential extent of natural forest (PENF) and grassland (PENG) in CONUS. Based on a qualitative analysis of the trends and driving forces of forest and grassland changes, we devised a method of subtracting reconstructed historical cropland (1000–2000 AD) and built-up land (1850–2000 AD) from PENG and PENF to reconstruct a 5 min × 5 min grid dataset of forest and grassland cover at 13 time-points over the past millennium. The results showed that forest and grassland cover in CONUS underwent a slow decline (1000–1600 AD), an accelerated decline (1600–1800 AD), a dramatic decline (1800–1950 AD), and finally, a recovery (1950–2000 AD) over the study period. The modelled forest fraction decreased from 49% in 1000 AD to 33% in 2000 AD, representing a 32% area reduction, whereas the modelled grassland fraction decreased from 37% to 22%, representing a 42% area reduction. The reduction occurred primarily in the last 200 years, with forest and grassland reductions accounting for 86% and 97% of the total reduction over the millennium, respectively. Spatially, more than 80% of the land was originally covered by forests and grasslands, and the loss occurred mainly in the eastern CONUS and Great Plains over the past millennium. After the 1930s, farmland abandonment began in central and eastern CONUS, simultaneously with environmental protection laws. Federal government regeneration programs for forest and grassland resources and the Shelterbelt Project all contributed to a slowdown in forest and grassland decline and recovery in cover.
Unveiling Air Pollution in Crimean Mountain Rivers: Analysis of Sentinel-5 Satellite Images Using Google Earth Engine (GEE)
Authors: Tabunschik Vladimir , Gorbunov Roman , Gorbunova Tatiana
Published: July 2023

This article presents an assessment of atmospheric pollutant concentrations based on state-of-the-art geoinformation research methods that utilize Sentinel-5 satellite imagery, the cloud computing platform Google Earth Engine (GEE), and ArcGIS 10.8 software. The spatial distributions of some pollutants (nitrogen dioxide, sulfur dioxide, formaldehyde, carbon monoxide, methane) in the atmosphere are analyzed on the example of the basins of the Zapadnyy Bulganak, Alma, Kacha, Belbek, and Chernaya rivers on the north-western slope of the Crimean Mountains. The concentrations of the average annual and average monthly values of pollutants for each catchment area are compared. The GEE (Google Earth Engine) platform is used for extracting annual and monthly average rasters of pollutant substances, while ArcGIS is utilized for enhanced data visualization and in-depth analytical processing. Background concentrations of pollutants within protected natural areas are calculated. By comparing the spatial and temporal distribution of pollutant values with the background concentrations within these protected areas, a complex index of atmospheric pollution is constructed. The spatial and temporal variability of nitrogen dioxide (NO2) concentrations has been thoroughly examined. Based on the regression analysis (R > 0.85), the field of values of the total amount of emissions (which are analyzed for only six points in the study area and in the surrounding areas) was restored on the basis of the spatial and temporal heterogeneity of the field of distribution of nitrogen dioxide values (NO2). Since air pollution can have negative consequences, both for human health and for the ecosystem as a whole, this study is of great importance for assessing the ecological situation within the river basins of the north-western slope of the Crimean Mountains. This work also contributes to a general understanding of the problem of gas emissions, whose study is becoming increasingly relevant. The aim of this research is to assess the potential application of Sentinel-5 satellite imagery for air quality assessment and pollution analysis within the river basins of the north-western slopes of the Crimean Mountains. The significance of this study lies in the innovative use of Sentinel-5 satellite imagery to investigate air pollution in extensive regions where a regular network of observation points is lacking.
Identification of Abandoned Logging Roads in Point Reyes National Seashore
Authors: Wiskes William , Blesius Leonhard , Hines Ellen
Published: July 2023

Temporary roads are often placed in mountainous regions for logging purposes but then never decommissioned and removed. These abandoned forest roads often have unwanted environmental consequences. They can lead to altered hydrological regimes, excess erosion, and mass wasting events. These events can affect sediment budgets in streams, with negative consequences for anadromous fish populations. Maps of these roads are frequently non-existent; therefore, methods need to be created to identify and locate these roads for decommissioning. Abandoned logging roads in the Point Reyes National Seashore in California, an area partially under heavy forest canopy, were mapped using object-based image processing in concert with machine learning. High-resolution Q1 LiDAR point clouds from 2019 were used to create a bare earth model of the region, from which a slope model was derived. This slope model was then subjected to segmentation algorithms to identify and isolate regions of differing slopes. Regions of differing slopes were then used in a convolutional neural network (CNN), and a maximum likelihood classifier was used to delineate the historic road network. The accuracy assessment was conducted using historic aerial photos of the state of the region post-logging, along with ground surveys to verify the presence of logging roads in areas of question. This method was successfully able to identify road networks with a precision of 0.991 and an accuracy of 0.992. It was also found that the CNN was able to identify areas of highest disturbance to the slope gradient. This methodology is a valuable tool for decision makers who need to identify areas of high disturbance in order to mitigate adverse effects.
Validation of Landsat-9 and Landsat-8 Surface Temperature and Reflectance during the Underfly Event
Authors: Eon Rehman , Gerace Aaron , Falcon Lucy , Poole Ethan , Kleynhans Tania , Raqueño Nina , Bauch Timothy
Published: July 2023

With the launch of Landsat-9 on 27 September 2021, Landsat continues its fifty-year continuity mission of providing users with calibrated Earth observations. It has become a requirement that an underflight experiment be performed during commissioning to support sensor cross-calibration. In this most recent experiment, Landsat-9 flew under Landsat-8 for nearly three days with over 50% ground overlap, from 13 to 15 November 2021. To address the scarcity of reference data that are available to support calibration and validation early-on in the mission, a ground campaign was planned and executed by the Rochester Institute of Technology (RIT) on 14 November 2021 to provide full spectrum measurements for early mission comparisons. The primary experiment was conducted in the Outer Banks, North Carolina at Jockey’s Ridge Sand Dunes. Full-spectrum ground-based measurements were acquired with calibrated reference equipment, while a novel Unmanned Aircraft System (UAS)-based platforms acquired hyperspectral visible and near-infrared (VNIR)/Short-wave infrared (SWIR) imagery data and coincident broadband cooled thermal infrared (TIR) imagery. Results of satellite/UAS/ground comparisons were an indicator, during the commissioning phase, that Landsat-9 is behaving consistently with Landsat-8, ground reference, and UAS measurements. In the thermal infrared, all measurements agree to be within 1 K over water and to within 2 K over sand, which represents the most challenging material for estimating surface temperature. For the surface reflectance product(s), Landsat-8 and -9 are in good agreement and only deviate slightly from ground reference in the SWIR bands; a deviation of 2% in the VNIR and 5–8% in the SWIR regime. Subsequent longer-term studies indicate that Landsat 9 continues to perform as expected. The behavior of Thermal Infrared Sensor-2 (TIRS-2) against reference is also shown for the first year of the mission to illustrate its consistent performance.
Detection of Cotton Verticillium Wilt Disease Severity Based on Hyperspectrum and GWO-SVM
Authors: Zhang Nannan , Zhang Xiao , Shang Peng , Ma Rui , Yuan Xintao , Li Li , Bai Tiecheng
Published: July 2023

In order to address the challenge of early detection of cotton verticillium wilt disease, naturally infected cotton plants in the field, which were divided into five categories based on the degree of disease severity, have been investigated in this study. Canopies of infected cotton plants were analyzed with spectral data measured, and various preprocessing techniques, including multiplicative scatter correction (MSC) and MSC-continuous wavelet analysis algorithms, were used to predict the disease severity. With a combination of support vector machine (SVM) models with such optimization algorithms as genetic algorithm (GA), grid search (GS), particle swarm optimization (PSO), and grey wolf optimizer (GWO), a grading model of cotton verticillium wilt disease was established in this study. The study results show that the MSC-PSO-SVM model outperforms the other three models in terms of classification accuracy, and the accuracy, macro precision, macro recall, and macro F1-score of this model are 80%, 81.26%, 80%, and 79.57%, respectively. Among those eight models constructed on the basis of continuous wavelet analyses using mexh and db3, the MSC-db3(23)-PSO-SVM and MSC-db3(23)-GWO-SVM models perform best, with the latter having a shorter running time. An overall evaluation shows that the MSC-db3(23)-GWO-SVM model is an optimal model, with values of its accuracy, macro precision, macro recall, and macro F1-score indicators being 91.2%, 92.02%, 91.2%, and 91.16%, respectively. Moreover, under this model, the prediction accuracy on disease levels 1 and 5 has achieved the highest rate of 100%, with a prediction accuracy rate of 88% on disease level 2 and the lowest prediction accuracy rate of 84% on both disease levels 3 and 4. These results demonstrate that it is effective to use spectral technology in classifying the cotton verticillium wilt disease and satisfying the needs of field detection and grading. This study provides a new approach for the detection and grading of cotton verticillium wilt disease and offered a theoretical basis for early prevention, precise drug application, and instrument development for the disease.