bluuebunny/crossref_metadata_2025 huggingface dataset:
The Challenges and Prospects of Recovering Fine Copper Sulfides from Tailings Using Different Flotation Techniques: A Review
Authors: Bilal Muhammad , Park Ilhwan , Hornn Vothy , Ito Mayumi , Hassan Fawad , Jeon Sanghee , Hiroyoshi Naoki
Published: May 2022

Flotation is a common mineral processing method used to upgrade copper sulfide ores; in this method, copper sulfide mineral particles are concentrated in froth, and associated gangue minerals are separated as tailings. However, a significant amount of copper is lost into tailings during the processing; therefore, tailings can be considered secondary resources or future deposits of copper. Particle–bubble collision efficiency and particle–bubble aggregate stability determines the recovery of target particles; this attachment efficiency plays a vital role in the selectivity process. The presence of fine particles in the flotation circuit is because of excessive grinding, which is to achieve a higher degree of liberation. Complex sulfide ores of markedly low grade further necessitate excessive grinding to achieve the maximum degree of liberation. In the flotation process, fine particles due to their small mass and momentum are unable to collide with rising bubbles, and their rate of flotation is very slow, further lowering the recovery of target minerals. This collision efficiency mainly depends on the particle–bubble size ratio and the concentration of particles present in the pulp. To overcome this problem and to maintain a favorable particle–bubble size ratio, different techniques have been employed by researchers to enhance particle–bubble collision efficiency either by increasing particle size or by decreasing bubble size. In this article, the mechanism of tailing loss is discussed in detail. In addition, flotation methods for fine particles recovery such as microbubble flotation, column flotation, nanobubble flotation, polymer flocculation, shear flocculation, oil agglomeration, and carrier flotation are reviewed, and their applications and limitations are discussed in detail.
Timing and Provenance Transition of the Neoproterozoic Wuling Unconformity and Xihuangshan Unconformity of the Yangtze Block: Responses to Peripheral Orogenic Events
Authors: Li Dinghua , Yang Zhenning , Liu Yu , Yang Kunguang , Wu Dehai , Cai Pingting
Published: May 2022

Middle Neoproterozoic sedimentary strata are widely distributed on the periphery of the Yangtze Block. In the western Jiangnan Orogen, they are divided into the Lengjiaxi and Banxi groups by the “Wuling unconformity,” and the Banxi Group is further divided into the Madiyi Formation and Wuqiangxi Formation by the “Xihuangshan unconformity.” However, the timing and tectonic significance of the Wuling and Xihuangshan unconformities remain unclear, which hampers our understanding of the Precambrian tectonic evolution of the Yangtze Block. Zircon U–Pb dating and Lu–Hf isotopic analysis were performed on the sedimentary rocks above and below the two unconformity boundaries in the western Jiangnan Orogen. These data were used to trace sedimentary provenance and provide new insights into the basin evolution and tectonic significance of the unconformities. Combined with previous studies, the Wuling unconformity is bracketed to have formed between ~830 and 813 Ma, and the provenance of the sediments above the unconformity remained unchanged. The detrital zircons from the upper parts of the Lengjiaxi Group and lower parts of the Banxi Group show the primary peak ages of 800–1000 Ma, 1.0–1.30 Ga, 1.40–1.90 Ga, and 2.30–2.60 Ga, and the provenance mainly derived from the southwestern margin of the Yangtze Block, Cathaysia Block, and Jiangnan Orogen. The provenance from the Cathaysia Block occurred in the upper part of the Lengjiaxi Group, indicating that the Yangtze Block and Cathaysia Block merged in the western Jiangnan Orogen earlier than the formation time of the Wuling unconformity (~830–813 Ma) and the collisional time in the eastern Jiangnan Orogen (~820–800 Ma). Thus, the collision between the Yangtze and Cathaysia blocks may have undergone a scissor-like closure process from west to east. The formation time of the Xihuangshan unconformity was at ~800–779 Ma. The field contact relationships changed from an angular unconformity to a disconformity and then to conformity, from north to south, indicating that the Xihuangshan unconformity was controlled by tectonic movement in the north. The provenance of the sedimentary strata changed above the Xihuangshan unconformity. The detrital zircon age peaks of the upper Banxi Group are 755–1000 Ma, 1.90–2.10 Ga, and 2.35–2.70 Ga, and the detritus were derived from the northern margin of the Yangtze Block and the Jiangnan Orogen. This unconformity is coeval with that of the ~800–780 Ma collisional orogeny at the northern and northwestern margins of the Yangtze Block. Thus, the Xihuangshan unconformity is likely a response to the collision orogeny in the northern and northwestern margins of the Yangtze Block and induces the transition of sedimentary provenance.
A New Strategy to Fabricate Nanoporous Gold and Its Application in Photodetector
Authors: Yu Shunlin , Liu Chuan , Han Songjia
Published: May 2022

Nanoporous gold (NPG) plays an important role in high-performance electronic devices, including sensors, electrocatalysis, and energy storage systems. However, the traditional fabricating methods of NPG, dealloying technique or electrochemical reduction technique, usually require complex experimental procedures and sophisticated equipment. In this work, we reported a unique and simple method to prepare the NPG through a low-temperature solution process. More importantly, the structure of the NPG-based electrode can be further controlled by using the post-treatment process, such as thermal treatment and plasma treatment. Additionally, we also demonstrate the application of the resulting NPG electrodes in flexible photodetectors, which performs a higher sensitivity than common planar photodetectors. We believe that our work opens a possibility for the nanoporous metal in future electronics that is flexible, large scale, with facile fabrication, and low cost.
Direct Synthesis of MoS2 Nanosheets in Reduced Graphene Oxide Nanoscroll for Enhanced Photodetection
Authors: Wu Zhikang , Li Feifei , Li Xiya , Yang Yang , Huang Xiao , Li Hai
Published: May 2022

Due to their unique tubular and spiral structure, graphene and graphene oxide nanoscrolls (GONS) have shown extensive applications in various fields. However, it is still a challenge to improve the optoelectronic application of graphene and GONS because of the zero bandgap of graphene. Herein, ammonium tetrathiomolybdate ((NH4)2MoS4) was firstly wrapped into the ((NH4)2MoS4@GONS) by molecular combing the mixture of (NH4)2MoS4 and GO solution on hydrophobic substrate. After thermal annealing, the (NH4)2MoS4 and GO were converted to MoS2 nanosheets and reduced GO (RGO) simultaneously, and, thus, the MoS2@RGONS was obtained. Raman spectroscopy and high-resolution transmission electron microscopy were used to confirm the formation of MoS2 nanosheets among the RGONS. The amount of MoS2 wrapped in RGONS increased with the increasing height of GONS, which is confirmed by the atomic force microscopy and Raman spectroscopy. The as-prepared MoS2@RGONS showed much better photoresponse than the RGONS under visible light. The photocurrent-to-dark current ratios of photodetectors based on MoS2@RGONS are ~570, 360 and 140 under blue, red and green lasers, respectively, which are 81, 144 and 35 times of the photodetectors based on RGONS. Moreover, the MoS2@RGONS-based photodetector exhibited good power-dependent photoresponse. Our work indicates that the MoS2@RGONS is expected to be a promising material in the fields of optoelectronic devices and flexible electronics.
Broadening the Action Spectrum of TiO2-Based Photocatalysts to Visible Region by Substituting Platinum with Copper
Authors: Saraev Andrey A. , Kurenkova Anna Yu. , Gerasimov Evgeny Yu. , Kozlova Ekaterina A.
Published: May 2022

In this study, TiO2-based photocatalysts modified with Pt and Cu/CuOx were synthesized and studied in the photocatalytic reduction of CO2. The morphology and chemical states of synthesized photocatalysts were studied using UV-Vis diffuse reflectance spectroscopy, high-resolution transmission electron microscopy, and X-ray photoelectron spectroscopy. A series of light-emitting diodes (LEDs) with maximum intensity in the range of 365–450 nm was used to determine the action spectrum of photocatalysts. It is shown for, the first time, that the pre-calcination of TiO2 at 700 °C and the use of Cu/CuOx instead of Pt allow one to design a highly efficient photocatalyst for CO2 transformation shifting the working range to the visible light (425 nm). Cu/CuOx/TiO2 (calcined at 700 °C) shows a rate of CH4 formation of 1.2 ± 0.1 µmol h−1 g−1 and an overall CO2 reduction rate of 11 ± 1 µmol h−1 g−1 (at 425 nm).
Therapeutic Role of Carotenoids in Blood Cancer: Mechanistic Insights and Therapeutic Potential
Authors: Hussain Yaseen , Abdullah  , Alsharif Khalaf F. , Aschner Michael , Theyab Abdulrahman , Khan Fazlullah , Saso Luciano , Khan Haroon
Published: May 2022

Blood cancers are characterized by pathological disorders causing uncontrolled hematological cell division. Various strategies were previously explored for the treatment of blood cancers, including chemotherapy, Car-T therapy, targeting chimeric antigen receptors, and platelets therapy. However, all these therapies pose serious challenges that limit their use in blood cancer therapy, such as poor metabolism. Furthermore, the solubility and stability of anticancer drugs limit efficacy and bio-distribution and cause toxicity. The isolation and purification of natural killer cells during Car-T cell therapy is a major challenge. To cope with these challenges, treatment strategies from phyto-medicine scaffolds have been evaluated for blood cancer treatments. Carotenoids represent a versatile class of phytochemical that offer therapeutic efficacy in the treatment of cancer, and specifically blood cancer. Carotenoids, through various signaling pathways and mechanisms, such as the activation of AMPK, expression of autophagy biochemical markers (p62/LC3-II), activation of Keap1-Nrf2/EpRE/ARE signaaling pathway, nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), increased level of reactive oxygen species, cleaved poly (ADP-ribose) polymerase (c-PARP), c-caspase-3, -7, decreased level of Bcl-xL, cycle arrest at the G0/G1 phase, and decreasing STAT3 expression results in apoptosis induction and inhibition of cancer cell proliferation. This review article focuses the therapeutic potential of carotenoids in blood cancers, addressing various mechanisms and signaling pathways that mediate their therapeutic efficacy.
Impact of Food-Based Weight Loss Interventions on Gut Microbiome in Individuals with Obesity: A Systematic Review
Authors: Bliesner Aleisha , Eccles-Smith Jade , Bates Claire , Hayes Olivia , Ho Jet Yee , Martins Catia , Truby Helen , Nitert Marloes Dekker
Published: May 2022

The observation that the gut microbiota is different in healthy weight as compared with the obese state has sparked interest in the possible modulation of the microbiota in response to weight change. This systematic review investigates the effect of food-based weight loss diets on microbiota outcomes (α-diversity, β-diversity, relative bacterial abundance, and faecal short-chain fatty acids, SCFAs) in individuals without medical comorbidities who have successfully lost weight. Nineteen studies were included using the keywords ‘obesity’, ‘weight loss’, ‘microbiota’, and related terms. Across all 28 diet intervention arms, there were minimal changes in α- and β-diversity and faecal SCFA concentrations following weight loss. Changes in relative bacterial abundance at the phylum and genus level were inconsistent across studies. Further research with larger sample sizes, detailed dietary reporting, and consistent microbiota analysis techniques are needed to further our understanding of the effect of diet-induced weight loss on the gut microbiota.
Food Insecurity and the Association between Perceptions and Trust of Food Advertisements and Consumption of Ultra-Processed Foods among U.S. Parents and Adolescents
Authors: Chiong Reah , Figueroa Roger
Published: May 2022

Adolescents exposed to food and beverage advertisements (FBAs) typically low in nutrient density can be influenced in their food choices, eating behaviors, and health. This study examines the association between perceptions and trust of FBAs (key predictor) and the outcome of daily consumption of ultra-processed foods (UPFs) in parent-adolescent dyads, with risk of food insecurity as a potential moderator. Cross-sectional data from the Family, Life, Activity, Sun, Health and Eating (FLASHE) study was used to test actor and partner effects using structural equation modeling. The final model was adjusted for parent sex and education level, and effects were compared between dyads at risk of food insecurity (n = 605) and dyads not at risk (n = 1008). In the unadjusted model, actor effects (parent: b = 0.23, p = 0.001; adolescent b = 0.12, p = 0.001) and parent-partner effects were found (b = 0.08, p = 0.004). The final comparative model produced similar results for dyads not at risk of food insecurity (parent actor: b = 0.27, p = 0.001; parent partner: b = 0.10, p = 0.01; adolescent actor: b = 0.11, p = 0.003). For dyads at risk of food insecurity, only actor effects were significant (parent: b = 0.22, p = 0.001; adolescent: b = 0.11, p = 0.013). These findings suggest that parents’ favorability towards FBAs influence parent-adolescent unhealthy food consumption, and that this association is different when accounting for risk of food insecurity.
Bifidobacterium animalis ssp. lactis MG741 Reduces Body Weight and Ameliorates Nonalcoholic Fatty Liver Disease via Improving the Gut Permeability and Amelioration of Inflammatory Cytokines
Authors: Do Moon Ho , Oh Mi-Jin , Lee Hye-Bin , Kang Chang-Ho , Yoo Guijae , Park Ho-Young
Published: May 2022

Diet-induced obesity is one of the major causes of the development of metabolic disorders such as insulin resistance and nonalcoholic fatty liver disease (NAFLD). Recently, specific probiotic strains have been found to improve the symptoms of NAFLD. We examined the effects of Bifidobacterium animalis ssp. lactis MG741 (MG741) on NAFLD and weight gain, using a mouse model of high-fat-diet (HFD)-induced obesity. HFD-fed mice were supplemented daily with MG741. After 12 weeks, MG741-administered mice exhibited reduced fat deposition, and serum metabolic alterations, including fasting hyperinsulinemia, were modulated. In addition, MG741 regulated Acetyl-CoA carboxylase (ACC), fatty acid synthase (FAS), sterol regulatory element-binding protein 1 (SREBP-1), and carbohydrate-responsive element-binding protein (ChREBP) expression and lipid accumulation in the liver, thereby reducing the hepatic steatosis score. To determine whether the effects of MG741 were related to improvements in gut health, MG741 improved the HFD-induced deterioration in gut permeability by reducing toxic substances and inflammatory cytokine expression, and upregulating tight junctions. These results collectively demonstrate that the oral administration of MG741 could prevent NAFLD and obesity, thereby improving metabolic health.
Determinants of Acceptance of Weight Management Applications in Overweight and Obese Individuals: Using an Extended Unified Theory of Acceptance and Use of Technology Model
Authors: Bäuerle Alexander , Frewer Anna-Lena , Rentrop Vanessa , Schüren Lynik Chantal , Niedergethmann Marco , Lortz Julia , Skoda Eva-Maria , Teufel Martin
Published: May 2022

Overweight and obesity carry a tremendous burden in terms of physiological and psychological comorbidities. There is a great variety of weight management applications to support weight reduction, but a systematical analysis of individuals’ needs and requirements to adopt sustaining lifestyle changes is missing so far. This study aimed to assess the acceptance of such applications and its underlying predictors in individuals with overweight/obesity. A cross-sectional study was conducted, including 439 overweight/obese individuals. Health-related internet use and acceptance of weight management applications were examined using a modified questionnaire based on the Unified Theory of Acceptance and Use of Technology (UTAUT). The general acceptance of weight management applications was high, with significant age differences. Compared to older individuals, younger ones showed a higher acceptance. BMI was not significantly associated with acceptance. Besides psychometric data and eHealth-related data, performance expectancy, effort expectancy, and social influence proved to be significant predictors for acceptance. The total variance explanation provided by the extended UTAUT model was 61.2%. The knowledge of the influencing factors on acceptance might be useful in developing, optimizing, and establishing weight management applications. For determining acceptance and its predictors of weight management applications, the UTAUT model is a valid approach.
Future Prospects of Positron Emission Tomography–Magnetic Resonance Imaging Hybrid Systems and Applications in Psychiatric Disorders
Authors: Son Young-Don , Kim Young-Bo , Kim Jong-Hoon , Kim Jeong-Hee , Kwon Dae-Hyuk , Lee Haigun , Cho Zang-Hee
Published: May 2022

A positron emission tomography (PET)–magnetic resonance imaging (MRI) hybrid system has been developed to improve the accuracy of molecular imaging with structural imaging. However, the mismatch in spatial resolution between the two systems hinders the use of the hybrid system. As the magnetic field of the MRI increased up to 7.0 tesla in the commercial system, the performance of the MRI system largely improved. Several technical attempts in terms of the detector and the software used with the PET were made to improve the performance. As a result, the high resolution of the PET–MRI fusion system enables quantitation of metabolism and molecular information in the small substructures of the brainstem, hippocampus, and thalamus. Many studies on psychiatric disorders, which are difficult to diagnose with medical imaging, have been accomplished using various radioligands, but only a few studies have been conducted using the PET–MRI fusion system. To increase the clinical usefulness of medical imaging in psychiatric disorders, a high-resolution PET–MRI fusion system can play a key role by providing important information on both molecular and structural aspects in the fine structures of the brain. The development of high-resolution PET–MR systems and their potential roles in clinical studies of psychiatric disorders were reviewed as prospective views in future diagnostics.
Virtual Screening Based on Machine Learning Explores Mangrove Natural Products as KRASG12C Inhibitors
Authors: Luo Lianxiang , Zheng Tongyu , Wang Qu , Liao Yingling , Zheng Xiaoqi , Zhong Ai , Huang Zunnan , Luo Hui
Published: May 2022

Mangrove secondary metabolites have many unique biological activities. We identified lead compounds among them that might target KRASG12C. KRAS is considered to be closely related to various cancers. A variety of novel small molecules that directly target KRAS are being developed, including covalent allosteric inhibitors for KRASG12C mutant, protein–protein interaction inhibitors that bind in the switch I/II pocket or the A59 site, and GTP-competitive inhibitors targeting the nucleotide-binding site. To identify a candidate pool of mangrove secondary metabolic natural products, we tested various machine learning algorithms and selected random forest as a model for predicting the targeting activity of compounds. Lead compounds were then subjected to virtual screening and covalent docking, integrated absorption, distribution, metabolism and excretion (ADME) testing, and structure-based pharmacophore model validation to select the most suitable compounds. Finally, we performed molecular dynamics simulations to verify the binding mode of the lead compound to KRASG12C. The lazypredict function package was initially used, and the Accuracy score and F1 score of the random forest algorithm exceeded 60%, which can be considered to carry a strong ability to distinguish the data. Four marine natural products were obtained through machine learning identification and covalent docking screening. Compound 44 and compound 14 were selected for further validation after ADME and toxicity studies, and pharmacophore analysis indicated that they had a favorable pharmacodynamic profile. Comparison with the positive control showed that they stabilized switch I and switch II, and like MRTX849, retained a novel binding mechanism at the molecular level. Molecular dynamics analysis showed that they maintained a stable conformation with the target protein, so compound 44 and compound 14 may be effective inhibitors of the G12C mutant. These findings reveal that the mangrove-derived secondary metabolite compound 44 and compound 14 might be potential therapeutic agents for KRASG12C.
Infrared Thermography for Monitoring of Freeze Drying Processes—Part 2: Monitoring of Temperature on the Surface and Vertically in Cuvettes during Freeze Drying of a Pharmaceutical Formulation
Authors: Emteborg Håkan , Charoud-Got Jean , Seghers John
Published: May 2022

The coupling of an infrared (IR) camera to a freeze dryer for monitoring of the temperature of a pharmaceutical formulation (sucrose/mannitol solution, 4:1%, m/m) during freeze-drying has been exploited further. The new development allows monitoring of temperatures simultaneously at the surface as well as vertically, (e.g., in depth) along the side using custom-made cuvettes. The IR camera was placed on the chamber roof of a process-scale freeze dryer. Monitoring of cuvettes containing the formulation took place from above where one side of each cuvette was equipped with a germanium window. The Ge-window was placed next to an IR mirror having a 45° angle. The long-wave infrared radiation (LWIR) coming from the inside of the cuvette was reflected upwards toward the IR camera. Accurate recording of the temperature along the cuvettes’ depth profile was therefore possible. Direct imaging from −40 °C to 30 °C took place every 60 s on the surface and on the side with a 2 × 2 mm resolution per IR pixel for 45 h resulting in 2700 thermograms. Results are presented for freeze-drying of a pharmaceutical formulation as a function of time and spatially for the entire side (depth) of the cuvette. As the sublimation process was progressing, the spatial resolution (84 IR pixels for the side-view and 64 pixels for the surface-view) was more than sufficient to reveal lower temperatures deeper down in the material. The results show that the pharmaceutical formulation (a true solution at the onset) dries irregularly and that the sublimation front does not progress evenly through the material. During secondary drying, potential evaporative cooling of upper layers could be detected thanks to the high thermal and spatial resolution.
A Highly Sensitive, Ultra-Durable, Eco-Friendly Ionic Skin for Human Motion Monitoring
Authors: Li Zhaoxin , Xu Haoyan , Jia Na , Li Yifei , Zhu Liangkuan , Sun Zhuangzhi
Published: May 2022

Ionic conductive hydrogels have shown great potential in areas such as wearable devices and electronic skins. Aiming at the sensitivity and biodegradability of the traditional flexible hydrogel electronic skin, this paper developed an ionic skin (S−iSkin) based on edible starch–sodium alginate (starch–SA), which can convert the external strain stimulus into a voltage signal without an external power supply. As an excellent ion conductive polymer, S−iSkin exhibited good stretchability, low hydrophilicity and outstanding electrochemical and sensing properties. Driven by sodium ions, the ion charge transfer resistance of S−iSkin is reduced by 4 times, the capacitance value is increased by 2 times and its conductivity is increased by 7 times. Additionally, S−iSkin has excellent sensitivity and linearity (R2 = 0.998), a long service life and good biocompatibility. Under the action of micro-stress, it can produce a voltage change ratio of 2.6 times, and its sensitivity is 52.04. The service life test showed that it can work stably for 2000 s and work more than 200 stress–voltage response cycles. These findings provide a foundation for the development of health monitoring systems and micro-stress sensing devices based on renewable biomass materials.
Extraction Optimization of Mucilage from Seeds of Mimosa pudica by Response Surface Methodology
Authors: Bukhari Syed Nasir Abbas , Ali Arshad , Hussain Muhammad Ajaz , Tayyab Muhammad , Alotaibi Nasser F. , Elsherif Mervat A. , Junaid Kashaf , Ejaz Hasan
Published: May 2022

Mimosa pudica seed mucilage (MPM) is composed of glucuronoxylan, which is a swellable, pH-responsive and non-toxic biomaterial. Herein, we aimed to extract MPM from M. pudica seeds (MP seeds) to ascertain optimization of extraction conditions to get highest yield by response surface methodology, via Box-Behnken design (RSM-BBD). MPM was extracted from MP seeds by a hot water extraction method. The effects of four different parameters on the extraction yield of MPM were evaluated: pH of the extraction medium (1–10), seed/water contact time (1–12 h), the temperature of extraction medium (30–90 °C), and seed/water ratio (1:5–1:35 w/v). The maximum yield of MPM obtained by Design-Expert software was 10.66% (10.66 g/100 g) at pH 7, seed/water contact time of 6 h, extraction temperature of 50 °C, and seed/water ratio of 1:20 w/v. The p values of ANOVA were found to be less than 0.0001, which indicated that the extraction yield of MPM was significantly affected by all the study parameters. The results revealed that pH and extraction temperature were the most significant factors affecting the yield of MPM. MPM in compressed tablet form showed pH-responsive on–off switching behavior at pH 7.4 and 1.2 in a reversible manner. MPM in compressed tablet form sustained the release of itopride for 16 h following a super case-II transport mechanism and zero-order release kinetics.
Functionalization of PEG-AgNPs Hybrid Material to Alleviate Biofouling Tendency of Polyethersulfone Membrane
Authors: Fahrina Afrillia , Arahman Nasrul , Aprilia Sri , Bilad Muhammad Roil , Silmina Silmina , Sari Widia Puspita , Sari Indah Maulana , Gunawan Poernomo , Pasaoglu Mehmet Emin , Vatanpour Vahid , Koyuncu Ismail , Rajabzadeh Saeid
Published: May 2022

Membrane-based processes are a promising technology in water and wastewater treatments, to supply clean and secure water. However, during membrane filtration, biofouling phenomena severely hamper the performance, leading to permanent detrimental impacts. Moreover, regular chemical cleaning is ineffective in the long-run for overcoming biofouling, because it weakens the membrane structure. Therefore, the development of a membrane material with superior anti-biofouling performance is seen as an attractive option. Hydrophilic-anti-bacterial precursor polyethylene glycol-silver nanoparticles (PEG-AgNPs) were synthesized in this study, using a sol-gel method, to mitigate biofouling on the polyethersulfone (PES) membrane surface. The functionalization of the PEG-AgNP hybrid material on a PES membrane was achieved through a simple blending technique. The PES/PEG-AgNP membrane was manufactured via the non-solvent induced phase separation method. The anti-biofouling performance was experimentally measured as the flux recovery ratio (FRR) of the prepared membrane, before and after incubation in E. coli culture for 48 h. Nanomaterial characterization confirmed that the PEG-AgNPs had hydrophilic-anti-bacterial properties. The substantial improvements in membrane performance after adding PEG-AgNPs were evaluated in terms of the water flux and FRR after the membranes experienced biofouling. The results showed that the PEG-AgNPs significantly increased the water flux of the PES membrane, from 2.87 L·m−2·h−1 to 172.84 L·m−2·h−1. The anti-biofouling performance of the PES pristine membrane used as a benchmark showed only 1% FRR, due to severe biofouling. In contrast, the incorporation of PEG-AgNPs in the PES membrane decreased live bacteria by 98%. It enhanced the FRR of anti-biofouling up to 79%, higher than the PES/PEG and PES/Ag membranes.
Recent Advances in Fluorescence Recovery after Photobleaching for Decoupling Transport and Kinetics of Biomacromolecules in Cellular Physiology
Authors: Cai Ning , Lai Alvin Chi-Keung , Liao Kin , Corridon Peter R. , Graves David J. , Chan Vincent
Published: May 2022

Among the new molecular tools available to scientists and engineers, some of the most useful include fluorescently tagged biomolecules. Tools, such as green fluorescence protein (GFP), have been applied to perform semi-quantitative studies on biological signal transduction and cellular structural dynamics involved in the physiology of healthy and disease states. Such studies focus on drug pharmacokinetics, receptor-mediated endocytosis, nuclear mechanobiology, viral infections, and cancer metastasis. In 1976, fluorescence recovery after photobleaching (FRAP), which involves the monitoring of fluorescence emission recovery within a photobleached spot, was developed. FRAP allowed investigators to probe two-dimensional (2D) diffusion of fluorescently-labelled biomolecules. Since then, FRAP has been refined through the advancements of optics, charged-coupled-device (CCD) cameras, confocal microscopes, and molecular probes. FRAP is now a highly quantitative tool used for transport and kinetic studies in the cytosol, organelles, and membrane of a cell. In this work, the authors intend to provide a review of recent advances in FRAP. The authors include epifluorescence spot FRAP, total internal reflection (TIR)/FRAP, and confocal microscope-based FRAP. The underlying mathematical models are also described. Finally, our understanding of coupled transport and kinetics as determined by FRAP will be discussed and the potential for future advances suggested.
Research on the Method of Rainfall Field Retrieval Based on the Combination of Earth–Space Links and Horizontal Microwave Links
Authors: Zhao Yingcheng , Liu Xichuan , Pu Kang , Ye Jin , Xian Minghao
Published: May 2022

High-precision retrieval of rainfall over large areas is of great importance for the research of atmospheric detection and the social life. With the rapid development of communication satellite constellations and 5G communication networks, the use of widely distributed networks of earth–space links (ESLs) and horizontal microwave links (HMLs) to retrieve rainfall over large areas has great potential for obtaining high-precision rainfall fields and complementing traditional instruments of rainfall measurement. In this paper, we carry out the research of combining multiple ESLs with HMLs to retrieve rainfall fields. Firstly, a rainfall detection network for retrieving rainfall fields is built based on the atmospheric propagation model of ESL and HML. Then, the ordinary Kriging interpolation (OK) and radial basis function (RBF) neural network are applied to the reconstruction of rainfall fields. Finally, the performance of the joint network of ESLs and HMLs to retrieve rainfall fields in the area is validated. The results show that the joint network of ESLs and HMLs based on OK algorithm and RBF neural network is capable of retrieving the distribution of rain rates in different rain cells with high accuracy, and the root mean square error (RMSE) of retrieving the rain rates of real rainfall fields is lower than 0.56 mm/h, and the correlation coefficient (CC) is higher than 0.996. In addition, the CC for retrieving stratiform rainfall and convective rainfall by the joint network of ESLs and HMLs is higher than 0.949, indicating that the characteristics of the two different types of rainfall events can be accurately monitored.
A Machine Learning Strategy Based on Kittler’s Taxonomy to Detect Anomalies and Recognize Contexts Applied to Monitor Water Bodies in Environments
Authors: Dias Maurício Araújo , Marinho Giovanna Carreira , Negri Rogério Galante , Casaca Wallace , Muñoz Ignácio Bravo , Eler Danilo Medeiros
Published: May 2022

Environmental monitoring, such as analyses of water bodies to detect anomalies, is recognized worldwide as a task necessary to reduce the impacts arising from pollution. However, the large number of data available to be analyzed in different contexts, such as in an image time series acquired by satellites, still pose challenges for the detection of anomalies, even when using computers. This study describes a machine learning strategy based on Kittler’s taxonomy to detect anomalies related to water pollution in an image time series. We propose this strategy to monitor environments, detecting unexpected conditions that may occur (i.e., detecting outliers), and identifying those outliers in accordance with Kittler’s taxonomy (i.e., detecting anomalies). According to our strategy, contextual and non-contextual image classifications were semi-automatically compared to find any divergence that indicates the presence of one type of anomaly defined by the taxonomy. In our strategy, models built to classify a single image were used to classify an image time series due to domain adaptation. The results 99.07%, 99.99%, 99.07%, and 99.53% were achieved by our strategy, respectively, for accuracy, precision, recall, and F-measure. These results suggest that our strategy allows computers to recognize contexts and enhances their capabilities to solve contextualized problems. Therefore, our strategy can be used to guide computational systems to make different decisions to solve a problem in response to each context. The proposed strategy is relevant for improving machine learning, as its use allows computers to have a more organized learning process. Our strategy is presented with respect to its applicability to help monitor environmental disasters. A minor limitation was found in the results caused by the use of domain adaptation. This type of limitation is fairly common when using domain adaptation, and therefore has no significance. Even so, future work should investigate other techniques for transfer learning.
Bayesian Cloud Detection over Land for Climate Data Records
Authors: Bulgin Claire E. , Embury Owen , Maidment Ross I. , Merchant Christopher J.
Published: May 2022

Cloud detection is a necessary step in the generation of land surface temperature (LST) climate data records (CDRs) and affects data quality and uncertainty. We present here a sensor-independent Bayesian cloud detection algorithm and show that it is suitable for use in the production of LST CDRs. We evaluate the performance of the cloud detection with reference to two manually masked datasets for the Advanced Along-Track Scanning Radiometer (AATSR) and find a 7.9% increase in the hit rate and 4.9% decrease in the false alarm rate when compared to the operational cloud mask. We then apply the algorithm to four instruments aboard polar-orbiting satellites, which together can produce a global, 25-year LST CDR: the second Along-Track Scanning Radiometer (ATSR-2), AATSR, the Moderate Resolution Spectroradiometer (MODIS Terra) and the Sea and Land Surface Temperature Radiometer (SLSTR-A). The Bayesian cloud detection hit rate is assessed with respect to in situ ceilometer measurements for periods of overlap between sensors. The consistency of the hit rate is assessed between sensors, with mean differences in the cloud hit rate of 4.5% for ATSR-2 vs. AATSR, 4.9% for AATSR vs. MODIS, and 2.5% for MODIS vs. SLSTR-A. This is important because consistent cloud detection performance is needed for the observational stability of a CDR. The application of a sensor-independent cloud detection scheme in the production of CDRs is thus shown to be a viable approach to achieving LST observational stability over time.
Infrared Dim and Small Target Detection from Complex Scenes via Multi-Frame Spatial–Temporal Patch-Tensor Model
Authors: Hu Yuxin , Ma Yapeng , Pan Zongxu , Liu Yuhan
Published: May 2022

Infrared imaging plays an important role in space-based early warning and anti-missile guidance due to its particular imaging mechanism. However, the signal-to-noise ratio of the infrared image is usually low and the target is moving, which makes most of the existing methods perform inferiorly, especially in very complex scenes. To solve these difficulties, this paper proposes a novel multi-frame spatial–temporal patch-tensor (MFSTPT) model for infrared dim and small target detection from complex scenes. First, the method of simultaneous sampling in spatial and temporal domains is adopted to make full use of the information between multi-frame images, establishing an image-patch tensor model that makes the complex background more in line with the low-rank assumption. Secondly, we propose utilizing the Laplace method to approximate the rank of the tensor, which is more accurate. Third, to suppress strong interference and sparse noise, a prior weighted saliency map is established through a weighted local structure tensor, and different weights are assigned to the target and background. Using an alternating direction method of multipliers (ADMM) to solve the model, we can accurately separate the background and target components and acquire the detection results. Through qualitative and quantitative analysis, experimental results of multiple real sequences verify the rationality and effectiveness of the proposed algorithm.
Landslide Segmentation with Deep Learning: Evaluating Model Generalization in Rainfall-Induced Landslides in Brazil
Authors: Soares Lucas Pedrosa , Dias Helen Cristina , Garcia Guilherme Pereira Bento , Grohmann Carlos Henrique
Published: May 2022

Automatic landslide mapping is crucial for a fast response in a disaster scenario and improving landslide susceptibility models. Recent studies highlighted the potential of deep learning methods for automatic landslide segmentation. However, only a few works discuss the generalization capacity of these models to segment landslides in areas that differ from the ones used to train the models. In this study, we evaluated three different locations to assess the generalization capacity of these models in areas with similar and different environmental aspects. The model training consisted of three distinct datasets created with RapidEye satellite images, Normalized Vegetation Index (NDVI), and a digital elevation model (DEM). Here, we show that larger patch sizes (128 × 128 and 256 × 256 pixels) favor the detection of landslides in areas similar to the training area, while models trained with smaller patch sizes (32 × 32 and 64 × 64 pixels) are better for landslide detection in areas with different environmental aspects. In addition, we found that the NDVI layer helped to balance the model’s results and that morphological post-processing operations are efficient for improving the segmentation precision results. Our research highlights the potential of deep learning models for segmenting landslides in different areas and is a starting point for more sophisticated investigations that evaluate model generalization in images from various sensors and resolutions.
Reconstruction of Vegetation Index Time Series Based on Self-Weighting Function Fitting from Curve Features
Authors: Zhu Wenquan , He Bangke , Xie Zhiying , Zhao Cenliang , Zhuang Huimin , Li Peixian
Published: May 2022

Vegetation index (VI) time series derived from satellite sensors have been widely used in the estimation of vegetation parameters, but the quality of VI time series is easily affected by clouds and poor atmospheric conditions. The function fitting method is a widely used effective noise reduction technique for VI time series, but it is vulnerable to noise. Thus, ancillary data about VI quality are utilized to alleviate the interference of noise. However, this approach is limited by the availability, accuracy, and application rules of ancillary data. In this paper, we aimed to develop a new reconstruction method that does not require ancillary data. Based on the assumptions that VI time series follow the gradual growth and decline pattern of vegetation dynamics, and that clouds or poor atmospheric conditions usually depress VI values, we proposed a reconstruction method for VI time series based on self-weighting function fitting from curve features (SWCF). SWCF consists of two major procedures: (1) determining a fitting weight for each VI point based on the curve features of the VI time series and (2) implementing the weighted function fitting to reconstruct the VI time series. The double logistic function, double Gaussian function, and polynomial function were tested in SWCF based on a simulated dataset. The results indicate that the weighted function fitting with SWCF outperformed the corresponding unweighted function fitting with the root-mean-square error (RMSE) significantly reduced by 26.82–52.44% (p < 0.05), and it also outperformed the Savitzky–Golay filtering with the RMSE significantly reduced by 13.98–54.04% (p < 0.05) for 270 sample points selected in mid-high latitudes of the Northern Hemisphere. Moreover, SWCF showed excellent robustness and applicability in regional applications.
Evaluation of Nearshore QuikSCAT 4.1 and ERA-5 Wind Stress and Wind Stress Curl Fields over Eastern Boundary Currents
Authors: Strub P. Ted , James Corinne
Published: May 2022

Fields of coastal wind stress and wind stress curl in the 10–100 km next to the land control the processes of upwelling and downwelling of nutrients and water properties that are vital to highly productive coastal marine ecosystems. Here we ask the question: Do the present surface wind stress products from a satellite-borne scatterometer (QuikSCAT) and an atmospheric reanalysis model (ERA-5) systematically overestimate the magnitude of wind speed and stress in the 10–50 km next to the coast? We compare QuikSCAT wind speed retrievals to the relatively unused wind speed retrievals from satellite altimeters, which are able to approach closer to the coast than scatterometers without land reflections, due to their smaller radar footprints. Altimeter data on tracks approaching and crossing the coast indicate that the increases in coastal QuikSCAT wind speed values and ERA-5 coastal wind stress values are unrealistic. For analyses of wind speed and stress requiring high accuracy, especially those involving wind stress curl, we suggest considering individual Level 2B scatterometer wind retrievals as suspect at distances of 10 km and less from the coast, along with use of the Poor Coastal Processing flag. We found that similar increases in wind stress values next to the coast in gridded ERA-5 fields are not due to errors in the model physics or wind speeds. They are created during the interpolation of wind stress from the original model grid to a regular rectangular grid. We recommend that researchers who are analyzing wind stress and wind stress curl should calculate wind stress themselves from the gridded ERA-5 vector wind speed fields, rather than using the interpolated model wind stress or curl fields.
Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data
Authors: Pejak Branislav , Lugonja Predrag , Antić Aleksandar , Panić Marko , Pandžić Miloš , Alexakis Emmanouil , Mavrepis Philip , Zhou Naweiluo , Marko Oskar , Crnojević Vladimir
Published: May 2022

Agriculture is the backbone and the main sector of the industry for many countries in the world. Assessing crop yields is key to optimising on-field decisions and defining sustainable agricultural strategies. Remote sensing applications have greatly enhanced our ability to monitor and manage farming operation. The main objective of this research was to evaluate machine learning system for within-field soya yield prediction trained on Sentinel-2 multispectral images and soil parameters. Multispectral images used in the study came from ESA’s Sentinel-2 satellites. A total of 3 cloud-free Sentinel-2 multispectral images per year from specific periods of vegetation were used to obtain the time-series necessary for crop yield prediction. Yield monitor data were collected in three crop seasons (2018, 2019 and 2020) from a number of farms located in Upper Austria. The ground-truth database consisted of information about the location of the fields and crop yield monitor data on 411 ha of farmland. A novel method, namely the Polygon-Pixel Interpolation, for optimal fitting yield monitor data with satellite images is introduced. Several machine learning algorithms, such as Multiple Linear Regression, Support Vector Machine, eXtreme Gradient Boosting, Stochastic Gradient Descent and Random Forest, were compared for their performance in soya yield prediction. Among the tested machine learning algorithms, Stochastic Gradient Descent regression model performed better than the others, with a mean absolute error of 4.36 kg/pixel (0.436 t/ha) and a correlation coefficient of 0.83%.
Landslide Susceptibility Assessment Model Construction Using Typical Machine Learning for the Three Gorges Reservoir Area in China
Authors: Cheng Junying , Dai Xiaoai , Wang Zekun , Li Jingzhong , Qu Ge , Li Weile , She Jinxing , Wang Youlin
Published: May 2022

The Three Gorges Reservoir region in China is the Yangtze River Economic Zone’s natural treasure trove. Its natural environment has an important role in development. The unique and fragile ecosystem in the Yangtze River’s Three Gorges Reservoir region is prone to natural disasters, including soil erosion, landslides, debris flows, landslides, and earthquakes. Therefore, to better alleviate these threats, an accurate and comprehensive assessment of the susceptibility of this area is required. In this study, based on the collection of relevant data and existing research results, we applied machine learning models, including logistic regression (LR), the random forest model (RF), and the support vector machine (SVM) model, to analyze landslide susceptibility in the Yangtze River’s Three Gorges Reservoir region to analyze landslide events in the whole study region. The models identified five categories (i.e., topographic, geological, ecological, meteorological, and human engineering activities), with nine independent variables, influencing landslide susceptibility. The accuracy of landslide susceptibility derived from different models and raster cells was then verified by the accuracy, recall, F1-score, ROC curve, and AUC of each model. The results illustrate that the accuracy of different machine learning algorithms is ranked as SVM > RF > LR. The LR model has the lowest generalization ability. The SVM model performs well in all regions of the study area, with an AUC value of 0.9708 for the entire Three Gorges Reservoir area, indicating that the SVM model possesses a strong spatial generalization ability as well as the highest robustness and can be adapted as a real-time model for assessing regional landslide susceptibility.