bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Impregnation with Natural Shellac Polymer on the Mechanical Properties of Fast-Growing Chinese Fir
Authors: Zeng Qinzhi , Yu Xiya , Wei Nianfeng , Wu Zhiyong , Liu Qisong , Chen Nairong , Zhao Weigang
Published: September 2022

Fast-growing Chinese fir wood has shortfalls such as loose structure and low strength because it grows faster than natural trees. Resin impregnation is a great way to increase the strength of fast-growing fir. However, the resin used for impregnation is a kind of urea-formaldehyde resin, phenolic formaldehyde resin, melamine formaldehyde resin, and the like, which introduce harmful substances such as formaldehyde or phenolic into the wood. In this paper, Chinese fir wood was impregnated with natural shellac polymer, and the effects of impregnation variables on the mechanical properties of the wood were examined. The increase in strength in compression perpendicular to grain (SCPG) of wood samples impregnated with 15% shellac solution achieved a maximum value of 39.01%, but the modulus of rupture (MOR) was slightly reduced. The effects of the impregnation pressure, time, and their interaction were investigated by the response surface method (RSM). ANOVA analysis revealed that the impregnation pressure and time and the interaction between the two seemed to have a significant effect on ∆SCPG. Based on the response face model, the corresponding optimal parameters obtained are 1.0 MPa and 16.0 min for impregnation pressure and time, respectively. By impregnating fir wood with the above optimal conditions, the SCPG increased by 85.78%, whereas the MOR decreased by the least amount.
Synthesis of Water-Dispersed Sulfobetaine Methacrylate–Iron Oxide Nanoparticle-Coated Graphene Composite by Free Radical Polymerization
Authors: Perumal Suguna , Atchudan Raji , Lee Yong Rok
Published: September 2022

Research on the synthesis of water-soluble polymers has accelerated in recent years, as they are employed in many bio-applications. Herein, the synthesis of poly[2-(methacryloyloxy)ethyl]dimethyl-(3-sulfopropyl)ammonium hydroxide (PSB) by free radical polymerization in a sonication bath is described. PSB and iron oxide nanoparticles (IONPs) were simultaneously stabilized on the graphene surface. Graphene surfaces with PSB (GPSB) and graphene surfaces with PSB and IONPs (GPSBI) were prepared. Since PSB is a water-soluble polymer, the hydrophobic nature of graphene surfaces converts to hydrophilic nature. Subsequently, the prepared graphene composites, GPSB and GPSBI, were well-dispersed in water. The preparation of GPSB and GPSBI was confirmed by X-ray diffraction, Raman spectroscopy, field emission scanning electron microscopy, transmission electron microscopy, X-ray photoelectron spectroscopy, and thermogravimetric analysis. The impacts of PSB and IONPs on the graphene surfaces were studied systematically.
Special Issue—“Bio-Based Materials: Contribution to Advancing Circular Economy”
Authors: John Maya Jacob , Thomas Sabu
Published: September 2022

Bio-based materials have a significant role to play in the implementation of a functional circular economy [...]
Analysis of Microplastics in Aquatic Shellfish by Pyrolysis–Gas Chromatography/Mass Spectrometry after Alkali Digestion and Solvent Extraction
Authors: Zhong Yingying , Bao Qibei , Yuan Lifeng , Liu Jiawen , Cai Yan , Chen Xianfeng
Published: September 2022

Microplastics are harmful to both marine life and humans. Herein, a pyrolysis–gas chromatography–mass spectrometry (Py-GC/MS) technique for the detection of microplastics in aquatic shellfish is demonstrated. The organic matter in aquatic shellfish was removed by alkali digestion. Subsequently, using hexafluoroisopropanol as the extraction solvent, the extraction method was optimized. The influence of the digestion process on the nature of microplastics was investigated by analyzing the samples before and after the alkali treatment via infrared spectrometry, laser particle sizing, and scanning electron microscopy. Spiked recovery experiments and an analysis of actual samples were performed using PA6 and PA66 as analytes. A quantitative analysis of the characteristic ion fragment produced by high-temperature cracking was performed after chromatographic separation and mass spectrometry identification. The linear range of this method for PA6 and PA66 was 2–64 μg. The limits of detection of PA6 and PA66 were 0.2 and 0.6 μg, while the limits of quantitation were 0.6 and 2.0 μg, respectively. Recovery ranged from 74.4 to 101.62%, with a precision of 4.53–7.56%. The results suggest that the Py-GC/MS technique is suitable for the analysis and detection of trace microplastics in aquatic shellfish.
Synthesis and Characterization of a Self-Polycondensation Diazaphthalanone Monomer and Its Polymers from Polycondensation Reactions
Authors: Liu Xin , Zhang Xiaozhou , Jiang Jiawei , Jia Hongge , Jian Xigao , Wang Jinyan
Published: September 2022

Polyether ketone (PEK) plastics are linear thermoplastic polymers connected by at least one ether bond and at least one ketone bond on the aryl group. The reason for their excellent heat resistance, rigidity, and mechanical strength is that their main molecular chain contains plenty of aromatic rings and polar carbonyl groups, and their molecular chain presents a large rigidity and strong intermolecular force. In addition, the main chain contains a considerable number of ether bonds, resulting in a certain toughness. However, polyether ketone materials have the disadvantage of poor solubility because of their excellent rigidity. To improve the solubility of polyether ketone, the preparation method of a novel nitrogenous heterocyclic polyaromatic ether monomer, 2-(4-chlorophenyl)-2,3-dihydrophthalazine-1,4-dione (CDD), was proposed, and its activity of polymerization was studied. The average molecular weight of the poly(aryl ether ketone) containing a nitrogenous heterocyclic polyaromatic ether group obtained by self-polycondensation of CDD was 4.181 × 103 kg/mol, and the yield was 90.5%. In order to further explore the activity of monomers, novel copolymerized poly(aryl ether ketone) (PBCD) containing a nitrogenous heterocyclic polyaromatic ether structure was prepared by ternary copolymerization with 4,4-difluorobenzophenone (DFBP) and bisphenol fluorene (BHPF) with high activity. The average molecular weight of PBCD was 72.793 × 103 kg/mol, the molecular weight distribution was 2.344, and the yield was 88.1%. Fourier transform infrared spectroscopy (FT-IR) and 1H NMR were used to confirm the structure of the obtained polymer. Through thermogravimetric analysis (TGA), the determined weight loss temperature of 5% under nitrogen was higher than 500 °C, indicating excellent thermal stability. Compared with the solubility of the binary copolymer containing fluorenyl poly(aryl ether ketone) (PBD), the polymer showed reasonable solubility in selective solvents such as chloroform and N,N-dimethylacetamide.
Application of Ensemble Machine Learning Methods to Estimate the Compressive Strength of Fiber-Reinforced Nano-Silica Modified Concrete
Authors: Anjum Madiha , Khan Kaffayatullah , Ahmad Waqas , Ahmad Ayaz , Amin Muhammad Nasir , Nafees Afnan
Published: September 2022

In this study, compressive strength (CS) of fiber-reinforced nano-silica concrete (FRNSC) was anticipated using ensemble machine learning (ML) approaches. Four types of ensemble ML methods were employed, including gradient boosting, random forest, bagging regressor, and AdaBoost regressor, to achieve the study’s aims. The validity of employed models was tested and compared using the statistical tests, coefficient of determination (R2), and k-fold method. Moreover, a Shapley Additive Explanations (SHAP) analysis was used to observe the interaction and effect of input parameters on the CS of FRNSC. Six input features, including fiber volume, coarse aggregate to fine aggregate ratio, water to binder ratio, nano-silica, superplasticizer to binder ratio, and specimen age, were used for modeling. In predicting the CS of FRNSC, it was observed that gradient boosting was the model of lower accuracy and the AdaBoost regressor had the highest precision in forecasting the CS of FRNSC. However, the performance of random forest and the bagging regressor was also comparable to that of the AdaBoost regressor model. The R2 for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 0.82, 0.91, 0.91, and 0.92, respectively. Also, the error values of the models further validated the exactness of the ML methods. The average error values for the gradient boosting, random forest, bagging regressor, and AdaBoost regressor models were 5.92, 4.38, 4.24, and 3.73 MPa, respectively. SHAP study discovered that the coarse aggregate to fine aggregate ratio shows a greater negative correlation with FRNSC’s CS. However, specimen age affects FRNSC CS positively. Nano-silica, fiber volume, and the ratio of superplasticizer to binder have both positive and deleterious effects on the CS of FRNSC. Employing these methods will promote the building sector by presenting fast and economical methods for calculating material properties and the impact of raw ingredients.
Effect of Bacterial Cellulose Plasma Treatment on the Biological Activity of Ag Nanoparticles Deposited Using Magnetron Deposition
Authors: Vasil’kov Alexander , Budnikov Alexander , Gromovykh Tatiana , Pigaleva Marina , Sadykova Vera , Arkharova Natalia , Naumkin Alexander
Published: September 2022

New functional medical materials with antibacterial activity based on biocompatible bacterial cellulose (BC) and Ag nanoparticles (Ag NPs) were obtained. Bacterial cellulose films were prepared by stationary liquid-phase cultivation of the Gluconacetobacter hansenii strain GH-1/2008 in Hestrin–Schramm medium with glucose as a carbon source. To functionalize the surface and immobilize Ag NPs deposited by magnetron sputtering, BC films were treated with low-pressure oxygen–nitrogen plasma. The composition and structure of the nanomaterials were studied using transmission (TEM) and scanning (SEM) electron microscopy and X-ray photoelectron spectroscopy (XPS). Using electron microscopy, it was shown that on the surface of the fibrils that make up the network of bacterial cellulose, Ag particles are stabilized in the form of aggregates 5–35 nm in size. The XPS C 1s spectra show that after the deposition of Ag NPs, the relative intensities of the C-OH and O-C-O bonds are significantly reduced. This may indicate the destruction of BC oxypyran rings and the oxidation of alcohol groups. In the Ag 3d5/2 spectrum, two states at 368.4 and 369.7 eV with relative intensities of 0.86 and 0.14 are distinguished, which are assigned to Ag0 state and Ag acetate, respectively. Nanocomposites based on plasma-treated BC and Ag nanoparticles deposited by magnetron sputtering (BCP-Ag) exhibited antimicrobial activity against Aspergillus niger, S. aureus and Bacillus subtilis.
Numerical Simulation of the Operating Conditions for the Reduction of Iron Ore Powder in a Fluidized Bed Based on the CPFD Method
Authors: Wan Zi-wei , Huang Jin-yu , Zhu Guo-min , Xu Qi-yan
Published: September 2022

In this work, the computational particle fluid dynamics (CPFD) method is used to simulate the high-pressure visual fluidized bed experimental equipment independently designed and developed by the experimentation of the fluidized reduction process of iron ore powder. A numerical model for reducing iron ore fines in a three-dimensional fluidized bed is established, and the model is verified by combining numerical simulation and experimental testing. Moreover, the influences of different reducing factors on the reduction effect in the process of the fluidized reduction of iron ore fines are simulated in detail. Via the CPFD simulation of the fluidized reduction of iron ore fines, the optimal reduction pressure is found to be 0.2 MPa, and the optimal reducing gas is found to be H2. Moreover, the optimal gas velocity is 0.6 m/s, and the optimal reduction temperature is 923 K. This conclusion is consistent with the experimental measurements, so the simulation results can be used to verify the reliability of the optimal operating conditions.
Inhibition of Cholinesterases by Benzothiazolone Derivatives
Authors: Alagöz Mehmet Abdullah , Kim Seong-Min , Oh Jong Min , Arslan Gülnur , Özdemir Zeynep , Sari Suat , Özçelik Azime Berna , Önkol Tijen , Trisciuzzi Daniela , Nicolotti Orazio , Kim Hoon , Mathew Bijo
Published: September 2022

Thirteen benzothiazolone derivatives (M1–M13) were synthesized and evaluated for their inhibitory activity against cholinesterases (ChEs) and monoamine oxidases (MAOs). All the compounds inhibited ChEs more effectively than MAOs. In addition, most of the compounds showed higher inhibitory activities against butyrylcholinesterase (BChE) than acetylcholinesterase (AChE). Compound M13 most potently inhibited BChE with an IC50 value of 1.21 μM, followed by M2 (IC50 = 1.38 μM). Compound M2 had a higher selectivity index (SI) value for BChE over AChE (28.99) than M13 (4.16). The 6-methoxy indole group of M13 was expected to have a greater effect on BChE inhibitory activity than the other groups. Kinetics and reversibility tests showed that M13 was a reversible noncompetitive BChE inhibitor with a Ki value of 1.14 ± 0.21 μM. In a docking simulation, M13 is predicted to form a hydrogen bond with the backbone carbonyl group of Ser287 of BChE through its methoxy indole moiety and π−π interactions between its benzothiazolone group and the side chain of Trp82 with the five-membered pyrrole ring and with the six-membered benzene ring. From these results, it is suggested that M13 is a BChE inhibitor and a potential candidate agent for the treatment of Alzheimer’s disease.
Efficient Synthesis of Furfuryl Alcohol from Corncob in a Deep Eutectic Solvent System
Authors: Qin Lizhen , Di Junhua , He Yucai
Published: September 2022

As a versatile and valuable intermediate, furfuryl alcohol (FOL) has been widely used in manufacturing resins, vitamin C, perfumes, lubricants, plasticizers, fuel additives, biofuels, and other furan-based chemicals. This work developed an efficient hybrid strategy for the valorization of lignocellulosic biomass to FOL. Corncob (75 g/L) was catalyzed with heterogenous catalyst Sn-SSXR (2 wt%) to generate FAL (65.4% yield) in a deep eutectic solvent ChCl:LA–water system (30:70, v/v; 180 °C) after 15 min. Subsequently, the obtained FAL liquor containing FAL and formate could be biologically reduced to FOL by recombinant E. coli CF containing aldehyde reductase and formate dehydrogenase at pH 6.5 and 35 °C, achieving the FOL productivity of 0.66 g FOL/(g xylan in corncob). The formed formate could be used as a cosubstrate for the bioreduction of FAL into FOL. In addition, other biomasses (e.g., sugarcane bagasse and rice straw) could be converted into FOL at a high yield. Overall, this hybrid strategy that combines chemocatalysis and biocatalysis can be utilized to efficiently valorize lignocellulosic materials into valuable biofurans.
Spatial Nonlinear Simulation Analysis on the Temperature Shrinkage Effect of a Super-Long Frame Structure Considering the Construction Process
Authors: Jia Yigang , Lu Liangjian , Wu Guangyu , Liu Ying , Mo Xuan
Published: September 2022

The temperature shrinkage effect is a very important factor causing the cracking of super-long frame structures. Current design codes and recommendations for reinforced concrete (RC) structures consider the influence of the construction process on the temperature shrinkage effect by adopting a uniform reduction coefficient and neglect the influence of the construction method. However, different construction processes can vary the temperature stress of the structure. In this paper, under cooling action, the temperature stress of a one-layer, super-long frame structure with different quantities and indwelling times of post-cast strips is calculated with a spatial nonlinear simulation analysis program by adopting the degenerated spatial solid virtual laminated element method. With this approach, the internal force state of each construction stage during the construction process is accounted for in a nonlinear mechanical model of the structure. The results show that the quantity and the indwelling time of post-cast strips can effectively vary the temperature stress of the structure. Meanwhile, the quantity of the post-cast strips can have a more obvious effect than the indwelling time. Therefore, the construction process is an important factor affecting the temperature shrinkage effect of the structures. The research results can provide a valid reference for the design and construction of super-long frame structures.
Construction of Edge Computing Platform Using 3D LiDAR and Camera Heterogeneous Sensing Fusion for Front Obstacle Recognition and Distance Measurement System
Authors: Chen Pi-Yun , Lin Hsu-Yung , Pai Neng-Sheng , Huang Jing-Bin
Published: September 2022

This research aims to utilise heterogeneous sensor fusion using 3D Light Detection and Ranging (LiDAR) and cameras, combined with an object recognition system and a ranging system, to construct an edge computing platform such that a vehicle equipped with the platform can perform computations offline in real time. This work comprises two main sections: the first is heterogeneous fusion, and the second is obstacle recognition and ranging detection. To achieve heterogeneous sensor fusion, 3D–3D point matching was used to find rigid body transformation between two sensors and finally project the LiDAR 3D point cloud image onto the 2D image. For object recognition, YOLOv4-Tiny was used as the detection network. A lightweight network architecture and high computational speed could be effectively used on edge computing hardware with limited performance. Further, by drawing the bounding box, we could detect the point cloud within the bounding box to estimate the distance to the obstacle. For detecting distance, we conducted experiments in two ways: ‘minimum point in box’ and ‘median point in box’ and compared the results. With heterogeneous sensor fusion, object recognition and the ranging system, detecting the category and distance of obstacles ahead of the vehicle was possible in real time. Furthermore, integrating the edge computing platform architecture enabled moving the entire system offline, making it an independent system that returns results in real time. Finally, a dynamic test was conducted on a road. The experiment showed that the detection speed of YOLOv4-Tiny in the dynamic test was higher than 60 FPS, and the accuracy rate surpassed 70%. Furthermore, the distance detection error of the 3D LiDAR was less than 3 cm, which is sufficiently accurate to be applied to complex environments on roads.
Evaluation of Relative Permittivity and Loss Factor of 3D Printing Materials for Use in RF Electronic Applications
Authors: Picha Tomas , Papezova Stanislava , Picha Stepan
Published: September 2022

3D printing is more and more often used for the development and manufacturing of electronic devices and components. These applications require knowledge about the dielectric properties of the used materials—in particular minimal and stable values of relative permittivity and dielectric losses. The paper deals with the testing of the relative permittivity and loss factor of materials as follows: PLA (in three dye modifications), PET-G, and ABS and ASA in the frequency range 1–100 MHz. It was found that relative permittivity varied between 2.88–3.48 and the loss factor was in the range 0.03–4.31%. In terms of relative permittivity, all tested materials manifested a slight decline with increasing frequency. Concerning loss factor PLA (colorless) and ABS were proven to be more suitable for electrotechnical application due to the lower values and frequency dependences of the loss factor. Different results were observed in PLA-Silver and PLA-Metallic green. These materials showed a higher frequency dependency of loss factor with increasing frequency. The reasonable influence of added dyes was found. A study of the internal structure of the tested materials has not proven any significant defects (air gaps) that could affect the material’s dielectric properties.
Acid System and Stimulation Efficiency of Multistage Acid Fracturing in Porous Carbonate Reservoirs
Authors: Zhu Dawei , Wang Yunjin , Cui Mingyue , Zhou Fujian , Wang Yaocong , Liang Chong , Zou Honglan , Yao Fei
Published: September 2022

With little to no natural fracture development and the high calcite content in porous carbonate reservoirs, for multistage acid fracturing, different fluids are used to form a viscous fingering in the fracture, thus enhancing the degree of nonuniform etching. However, existing studies on multistage acid fracturing mainly focused on the combination of fracturing fluid and acid, which is less specific for porous carbonate rocks. Here, the rheological properties of five fluids, including guar-based fluid, cross-linked guar, gelled acid, cross-linked acid, and diverting acid, were studied at each temperature condition, and the viscosity relationship between each fluid was clarified. Based on the rheological properties, the differences between the seven liquid combinations on the etched morphology of the fracture walls were studied and analyzed. The conductivity of the seven acid-etched fractures under different closure stress was simulated. The experimental results showed that the viscosity relationships between the fluids at different temperatures were cross-linked guar > cross-linked acid > diverting acid (spent acid) > gelled acid > guar-based liquid > diverting acid (fresh acid). Because cross-linked acid has higher viscosity than gelled acid, it can form more obvious viscous fingering with a variety of liquids, which is more suitable for acid fracturing stimulation of porous carbonate reservoirs. In addition, the combination of cross-linked and diverting acids was screened out. The multistage alternate injection of this fluid combination could form tortuous and complex etching channels, and its acid-etching fracture conductivity was significantly higher than that of other fluid combinations at different closure stress. In this study, we optimized the fluid combination of porous carbonates and clarified the effect and mechanism of nonuniform etching to provide guidance for the fluid combination selection of multistage alternate acid fracturing process for porous carbonate reservoirs.
Productivity Improvement Using Simulated Value Stream Mapping: A Case Study of the Truck Manufacturing Industry
Authors: Poswa Fikile , Adenuga Olukorede Tijani , Mpofu Khumbulani
Published: September 2022

The accumulation of process waste in the production line causes fluctuations, bottlenecks, and increased inventory in workstations disrupting process flow. In this paper, the optimal process flow that will improve productivity using simulated value stream mapping (SVSM) for decision-making to provide consistency, minimise errors and non-value adding times in the implementation phase of VSM in the truck manufacturing industry. The proposed methodology applied a discrete event simulation for production process operations improvement to eliminate non-value adding times and provide good quality products at the lowest cost and highest efficiency. The results are the analysis of the current state of the production system in a South African truck manufacturing industry as a potential solution for the production system’s future state. The identified non-value adding times in the six most critical workstations were eliminated by SVSM resulting in a productivity improvement of 4%, most importantly bringing the productivity to 95% and total cycle time improvement to 451 for small units and 466 for large units. The results proposed combined VSM and simulation techniques based on empirical data from the observation during time measurement. The Yamazumi confirms the issues observed and the NVA recorded by showing how close the process cycle times are to the TAKT time, which enhance the LEAN application by DES to increase productivity and performance improvement to remain competitive in the global economy.
Cost Analysis of Synchronous Condenser Transformed from Thermal Unit Based on LCC Theory
Authors: Li Chenghao , Liu Mingyang , Guo Yi , Ma Hanqing , Wang Hua , Yuan Xiaoling
Published: September 2022

With the development of large-scale renewable energy consumption and multi-infeed high voltage direct current (HVDC) systems, the demand of a system for the synchronous condensers with a strong dynamic reactive power support capacity and a strong short-time overload capacity is increasing. Meanwhile, with the reuse of a large number of retired thermal units, the most practical and economic way is to transform thermal units into synchronous condensers. The cost difference in the life-cycle of the synchronous condenser transformed from a thermal unit (SCTTU) and the newly established synchronous condenser (NESC) is a key factor that affects the decision-making and construction of the transformation from thermal unit to synchronous condenser. However, the life-cycle cost (LCC) of the synchronous condenser transformed from a thermal unit and the newly established synchronous condenser contains many uncertain factors, which affect the accuracy of the LCC estimation value. In order to quantify the impact of the blind information on the cost of the synchronous condenser station, blind number theory is introduced to establish the blind number model of the LCC of the synchronous condenser transformed from a thermal unit and the newly established synchronous condenser. Additionally, the LCC of the NESC and SCTTU with a different life-cycle under the capacity of 2 × 300 MVar are estimated. The results show that the cost of the SCTTU with a long service life of more than 15 years is significantly lower than that of the NESC and, thus, the SCTTU has better economic performance. The economic performance of the SCTTU with a life-cycle of less than 15 years is not better than that of the NESC. Compared with the traditional calculation method of a single cost value, the blind number model can obtain the possible distribution interval of LCC and the reliability of the corresponding interval, which makes the estimation results more valuable for practical engineering reference.
Study on Transient Flow and Dynamic Characteristics of Dual Disc Check Valve Mounted in Pipeline System during Opening and Closing
Authors: Chang Zhengbai , Jiang Jin
Published: September 2022

Check valves are used extensively in industrial piping systems. Based on dynamic mesh technology, this study uses the RNG k-ε turbulence model to numerically calculate the dual disc check valve’s three-dimensional transient flow. The dynamic characteristics of the check valve in the pipeline system are also experimentally studied. To this end, the two discs are opened synchronously during the valve-opening process, including four stages: opening discs at a constant angular velocity, opening slowing down discs, slowly returning discs to the balance point, and discs maintaining oscillation. However, the movements of the two discs are asynchronous in the valve-closing process. As the downstream pressure increases, the valve disc begins to close, and the flow gradually stops; reverse flow takes shape, and the reverse flow stops until the discs are fully closed, and slamming of the check valve occurs. The non-dimensional dynamic characteristic curve of this type of dual disc check valve has a slope of about 1.624, which mirrors the response of the check valve closing to the occurrence of the water hammer in the system. Knowing the dynamic behavior can be convenient in designing and selecting a check valve and regulating piping system working conditions.
Ghrelin and Obestatin in Adolescent Patients with Anorexia Nervosa: Is There an Association with Disordered Eating, Depression, and Obsessive-Compulsive Symptoms?
Authors: Dutkiewicz Agata , Tyszkiewicz-Nwafor Marta , Bilska Karolina , Paszyńska Elżbieta , Roszak Magdalena , Zwolińska Weronika , Pytlińska Natalia , Słopień Agnieszka , Dmitrzak-Węglarz Monika
Published: September 2022

Anorexia nervosa (AN) is an eating disorder characterized by restrictive eating and significant weight loss. In the course of AN, changes are observed in appetite regulation, including orexigenic ghrelin and potentially anorexigenic obestatin. The study aimed to determine if any changes in serum ghrelin and obestatin levels during treatment of AN are observed, while investigating the correlations between these peptides and the severity of disturbed eating attitudes, depression, and anxiety. Thirty adolescent inpatients with AN (examined twice: before hospitalization treatment AN-BT and after treatment AN-AT) and thirty healthy age- and height-matched girls (CG) participated in the study. Anthropometric, serum ghrelin and obestatin concentrations and psychometric evaluations (Eating Attitudes Test 26 Item-EAT-26, Beck Depression Inventory-BDI, Hamilton Depression Rating Scale-HDRS, and Yale Brown Obsessive-Compulsive Scale-Y-BOCS) were performed. The study revealed significantly higher ghrelin and obestatin levels in AN-BT than in AN-AT. A trend toward lower levels during treatment provided partial normalizations. Analyzing correlations in the AN-BT vs. CG group, correlations of peptides with EAT-26, BDI, and HDRS scores were detected. These results suggest a potential role for ghrelin and obestatin in the context of defense mechanisms regulating appetite and body weight in the course of AN and in terms of psychopathological changes co-occurring with this eating disorder.
Spirituality/Religiosity of Sexual and Gender Minorities in Brazil: Assessment of Spiritual Resources and Religious Struggles
Authors: Rosa Zoé Tiago Silva da , Esperandio Mary Rute Gomes
Published: September 2022

Stigma and discriminatory attitudes create hostile and stressful environments that impact the mental health of marginalized populations. In view of these stressful situations, empirical research was undertaken with the objective of assessing the spiritual/religious resources employed by sexual and gender minorities to manage such situations, identifying the presence of religious and spiritual struggles and the style of attachment to God. This is a cross-sectional quantitative exploratory-descriptive study. The applied instruments were a sociodemographic questionnaire, the Centrality of Religiosity Scale, the Brief SRCOPE Scale, the Religious and Spiritual Struggles (RSS) Scale, and the Attachment to God Inventory. In total, 308 people participated in the study. The sample was categorized as religious (M = 3.37, SD = 1.10), and the use of positive spiritual/religious coping strategies was considered medium (M = 2.83, SD = 1.18). They scored 2.10 on the RSS Scale (SD = 0.65), which is considered a modest level, and the predominant attachment style was avoidant. The results indicate that this group has specific stressors resulting from the minority status and that a small number of people use spiritual/religious resources to deal with stressful situations. Religious transit and, mainly, the process of religious deidentification seem to work as coping strategies to deal with struggles experienced in religious environments that are not welcoming of sexual and gender diversity. In these transition and migration processes, “religious residues” (i.e., previous modes of thinking and feeling persist following religious deidentification) may be present. Thus, identifying whether such residues are made up of beliefs that negatively affect the mental health of sexual and gender minorities is a process that needs to be looked at carefully by faith communities, health professionals, and spiritual caregivers.
Patagonian Andes Landslides Inventory: The Deep Learning’s Way to Their Automatic Detection
Authors: Morales Bastian , Garcia-Pedrero Angel , Lizama Elizabet , Lillo-Saavedra Mario , Gonzalo-Martín Consuelo , Chen Ningsheng , Somos-Valenzuela Marcelo
Published: September 2022

Landslide inventories are crucial to studying the dynamics, associated risks, and effects of these geomorphological processes on the evolution of mountainous landscapes. The production of landslide maps is mainly based on manual visual interpretation methods of aerial and satellite images combined with field surveys. In recent times, advances in machine learning methods have made it possible to explore new semi-automated landslide detection methodologies using remotely detected images. In this sense, developing new artificial intelligence models based on Deep Learning (DL) opens up an excellent opportunity to automate this arduous process. Although the Andes mountain range is one of the most geomorphologically active areas on the planet, the few investigations that use DL mainly focus on mountain ranges in Europe and Asia. One of the main reasons is the low density of landslide data available in the Andean areas, making it difficult to experiment with DL models requiring large data volumes. In this work, we seek to narrow the existing gap in the availability of landslide inventories in the area of the Patagonian Andes. In addition, the feasibility and efficiency of DL techniques are studied to develop landslide detection models in the Andes from the generated datasets. To achieve this goal, we generated in a manual process a datasets of 10,000 landslides for northern Chilean Patagonia (42–45°S), being the largest freely accessible landslide datasets in this region. We implement a machine learning model, through DL, to detect landslides in optical images of the Sentinel-2 constellation using a model based on the DeepLabv3+ architecture, a state-of-the-art deep learning network for semantic segmentation. Our results indicate that the algorithm detects landslides with an accuracy of 0.75 at the object level. For its part, the segmentation reaches a precision of 0.86, a recall of 0.74, and an F1-score of 0.79. The correlation of the segmentation measured through the Matthews correlation coefficient shows a value of 0.59, and the geometric similarity of the correctly detected landslides measured through the Jaccard score reaches 0.70. Although the model shows a good response in the testing area, errors are generated that can be explained by geometric and spectral relationships, which should be solved through new training approaches and data sets.
Selection of Whole-Moon Landing Zones Based on Weights of Evidence and Fractals
Authors: Cao Yaqin , Wang Yongzhi , Liu Jianzhong , Zeng Xiaojia , Wang Juntao
Published: September 2022

At present, the selection of lunar landing areas is mostly determined by experts’ argumentation and experience. Generally, it is artificially limited to a small zone, and there are few effective quantitative models for landing areas. Under the premise that big data, artificial intelligence, and other technologies are becoming increasingly mature, with in-depth analysis and the mining of lunar-related digital data, it is possible to automatically optimize the landing zones in the whole moon. Factors such as engineering constraints, scientific goals, and resource requirements are comprehensively considered. This paper proposes a new method that strategically applies the weights of evidence (WoE) and fractals to optimize the landing area of the detector in the whole moon. The method takes the thickness of the lunar crust, roughness, slope, digital elevation model, gravity gradient, iron oxide distribution, and lunar soil optical maturity as evidence layers, and known landing sites as the target layer. After all moon data are divided into grids, the prior probability of each evidence factor, the in-cell weight of each evidence factor, and the Bayesian posterior probability are calculated. According to the semi-parabolic distribution in the fuzzy distribution, the fuzzy membership degree of the impact crater radius is presented and the complexity of the number of impact craters in a cell is calculated. The distribution complexity of impact craters in each cell is calculated according to the fractal. The result of the weights of evidence is further constrained by the complexity of the number of cells and the complexity of the distribution, and the posterior probability map of suitable landings is finally obtained. When comparing and analyzing the posterior probability map of the landing zones with the known landing points and the artificially preferred landing zones, it is found that 84% and 82.6% fall within the suitable landing zones, respectively. Among them, the first gradient is 58% and 58.7%, and the second gradient is 26% and 23.9%. The results at different resolutions are relatively stable and are consistent with the distribution of craters or basins in the lunar mantle and the spatial distribution of olivine, which proves the effectiveness and feasibility of this method. This method is a typical application of lunar big-data-driven knowledge discovery and will help promote the transformation of lunar landing area selection from traditional qualitative analyses to automated intelligence optimization.
Global Evaluation of SMAP/Sentinel-1 Soil Moisture Products
Authors: Mohseni Farzane , Mirmazloumi S. Mohammad , Mokhtarzade Mehdi , Jamali Sadegh , Homayouni Saeid
Published: September 2022

SMAP/Sentinel-1 soil moisture is the latest SMAP (Soil Moisture Active Passive) product derived from synergistic utilization of the radiometry observations of SMAP and radar backscattering data of Sentinel-1. This product is the first and only global soil moisture (SM) map at 1 km and 3 km spatial resolutions. In this paper, we evaluated the SMAP/Sentinel-1 SM product from different viewpoints to better understand its quality, advantages, and likely limitations. A comparative analysis of this product and in situ measurements, for the time period March 2015 to January 2022, from 35 dense and sparse SM networks and 561 stations distributed around the world was carried out. We examined the effects of land cover, vegetation fraction, water bodies, urban areas, soil characteristics, and seasonal climatic conditions on the performance of active–passive SMAP/Sentinel-1 in estimating the SM. We also compared the performance metrics of enhanced SMAP (9 km) and SMAP/Sentinel-1 products (3 km) to analyze the effects of the active–passive disaggregation algorithm on various features of the SMAP SM maps. Results showed satisfactory agreement between SMAP/Sentinel-1 and in situ SM measurements for most sites (r values between 0.19 and 0.95 and ub-RMSE between 0.03 and 0.17), especially for dense sites without representativeness errors. Thanks to the vegetation effect correction applied in the active–passive algorithm, the SMAP/Sentinel-1 product had the highest correlation with the reference data in grasslands and croplands. Results also showed that the accuracy of the SMAP/Sentinel-1 SM product in different networks is independent of the presence of water bodies, urban areas, and soil types.
A Novel Multi-Candidate Multi-Correlation Coefficient Algorithm for GOCI-Derived Sea-Surface Current Vector with OSU Tidal Model
Authors: Cui He , Chen Jianyu , Cao Zhenyi , Huang Haiqing , Gong Fang
Published: September 2022

The maximum cross-coefficient (MCC) algorithm based on the template matching technique is a typical algorithm for obtaining the sea-surface currents (SSCs) in marginal seas. However, this algorithm has mismatches between images in highly turbid water. In this study, we implemented the MCC algorithm to Geostationary Ocean Color Imager-derived total suspended matter to obtain the SSCs in the Yellow Sea and the East China Sea. We propose a novel vector optimization algorithm, which is combined with the accurate estimate of tidal ellipses from the OSU tidal model. This method considers the three greatest candidate acquisitions from multi-correlation coefficients as potential vectors. The rotation direction of the vector within the tidal oscillation is used to identify and substitute for the spurious vector. The obtained average speed of SSC reached 0.60 m/s, which was close to the buoy-measured average speed of 0.58 m/s. Compared with the existing spurious vector eliminating method, the average angular error was improved by 20%, and the average relative amplitude error was improved by 4% in our case study. On the basis of ensuring data integrity, the inversion accuracy was improved.
Relative Contributions of Climate Change and Human Activities on Vegetation Productivity Variation in National Nature Reserves on the Qinghai–Tibetan Plateau
Authors: Zhou Jia , Lu Tao
Published: September 2022

National nature reserves (NNRs) are at the forefront of conservation efforts on the Qinghai–Tibetan Plateau (QTP). However, few studies have examined the vegetation dynamics and their driving forces at the whole QTP scale. In this study, we used potential Net Primary Productivity (PNPP), actual NPP (ANPP), and human-activity-induced NPP (HNPP) to analyze the vegetation dynamics of 42 NNRs on the QTP. Further, we determined the driving factors of vegetation dynamics from 2000 to 2020. The results indicate that, during the 21 years studied, ANPP increased at 83.4% of the NNRs area on the QTP. Additionally, the contributions of climate change and anthropogenic factors to ANPP variation were 59.53% and 40.47%, respectively. The contribution of temperature to ANPP variation was considered high and stable, whereas the contribution of precipitation was relatively lower and variable. Residual analysis showed that human activities had both positive (51.30%) and negative effects (48.70%) on ANPP. Using Hurst exponent analysis, we found that 31.60% of the vegetation for the NNRs on the QTP will likely remain a persistent trend, and 65.4% will be stochastic in the future. By contrast, 3.00% of the vegetation mainly located in southern QTP would show a reverse trend, with most of them distributing in southern QTP, which deserves more attention. This study may help policymakers understand the relative impacts of climate change and human activities on vegetation in the different nature reserves on the QTP.
Importance of Calibration for Improving the Efficiency of Data Assimilation for Predicting Forest Characteristics
Authors: Lindgren Nils , Nyström Kenneth , Saarela Svetlana , Olsson Håkan , Ståhl Göran
Published: September 2022

Data assimilation (DA) is often used for merging observations to improve the predictions of the current and future states of characteristics of interest. In forest inventory, DA has so far found limited use, although dense time series of remotely sensed (RS) data have become available for estimating forest characteristics. A problem in forest inventory applications based on RS data is that errors from subsequent predictions tend to be strongly correlated, which limits the efficiency of DA. One reason for such a correlation is that model-based predictions, using techniques such as parametric or non-parametric regression, are normally biased conditional on the actual ground conditions, although they are unbiased conditional on the RS predictor variables. A typical case is that predictions are shifted towards the mean, i.e., small true values are overestimated, and large true values are underestimated. In this study, we evaluated if the classical calibration of RS-based predictions could remove this type of bias and improve DA results. Through a simulation study, we mimicked growing stock volume predictions from two different sensors: one from a metric strongly correlated with growing stock volume, mimicking airborne laser scanning, and one from a metric slightly less correlated with growing stock volume, mimicking data obtained from 3D digital photogrammetry. Consistent with previous findings, in areas such as chemistry, we found that classical calibration made the predictions approximately unbiased. Further, in most cases, calibration improved the DA results, evaluated in terms of the root mean square error of predicted volumes, evaluated at the end of a series of ten RS-based predictions.
Multi-Source Remote Sensing Pretraining Based on Contrastive Self-Supervised Learning
Authors: Liu Chenfang , Sun Hao , Xu Yanjie , Kuang Gangyao
Published: September 2022

SAR-optical images from different sensors can provide consistent information for scene classification. However, the utilization of unlabeled SAR-optical images in deep learning-based remote sensing image interpretation remains an open issue. In recent years, contrastive self-supervised learning (CSSL) methods have shown great potential for obtaining meaningful feature representations from massive amounts of unlabeled data. This paper investigates the effectiveness of CSSL-based pretraining models for SAR-optical remote-sensing classification. Firstly, we analyze the contrastive strategies of single-source and multi-source SAR-optical data augmentation under different CSSL architectures. We find that the CSSL framework without explicit negative sample selection naturally fits the multi-source learning problem. Secondly, we find that the registered SAR-optical images can guide the Siamese self-supervised network without negative samples to learn shared features, which is also the reason why the CSSL framework outperforms the CSSL framework with negative samples. Finally, we apply the CSSL pretrained network without negative samples that can learn the shared features of SAR-optical images to the downstream domain adaptation task of optical transfer to SAR images. We find that the choice of a pretrained network is important for downstream tasks.
How Well Do CMIP6 Models Simulate the Greening of the Tibetan Plateau?
Authors: Liu Jiafeng , Lu Yaqiong
Published: September 2022

The “warm-humid” climate change across the Tibetan Plateau (TP) has promoted grassland growth and an overall greening trend has been observed by remote sensing products. Many of the current generations of Earth System Models (ESMs) incorporate advanced process-based vegetation growth in the land surface module that can simulate vegetation growth, but the evaluation of their performance has not received much attention, especially over hot spots where projections of the future climate and vegetation growth are greatly needed. In this study, we compare the leaf area index (LAI) simulations of 35 ESMs that participated in CMIP6 to a remote-sensing-derived LAI product (GLASS LAI). The results show that about 40% of the models overestimated the Tibetan Plateau’s greening, 48% of the models underestimated the greening, and 11% of the models showed a declining LAI trend. The CMIP6 models generally produced poor simulations of the spatial distribution of LAI trend, and overestimated the LAI trend of alpine vegetation, grassland, and forest, but underestimated meadow and shrub. Compared with other vegetation types, simulations of the forest LAI trend were the worst, the declining trend in forest pixels on the TP was generally underestimated, and the greening of the meadow was underestimated as well. However, the greening of the grassland, was greatly overestimated. For the Tibetan Plateau’s averaged LAI, more than 70% of the models overestimated this during the growing seasons of 1981–2014. Similar to the forest LAI trend, the performance of the forest LAI simulation was the worst among the different vegetation types, and the forest LAI was underestimated as well.