bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Study of the Preparation of High-Molecular-Weight Fibroin by Degumming Silk with Several Neutral Proteases
Authors: Liu Xueping , Huang Qian , Pan Peng , Fang Mengqi , Zhang Yadong , Yang Shanlong , Li Mingzhong , Liu Yu
Published: August 2023

Removing sericin from the periphery of silk without damage to silk fibroin (SF) to obtain high-molecular-weight SF is a major challenge in the field of SF-based biomaterials. In this study, four neutral proteases, subtilisin, trypsin, bromelain and papain, were used to degum silk, and the degumming efficiency of the proteases and their influence on the molecular weight (MW) of regenerated silk fibroin were studied. The results indicated that all four neutral proteases could remove sericin from silk almost completely, and they caused less damage to SF fibers than Na2CO3 degumming did. The degumming efficiency of trypsin and papain was strong, but they caused relatively high damage to SF, whereas bromelain caused the least damage. The results of sodium dodecyl sulfate–polyacrylamide gel electrophoresis, gel permeation chromatography and shear viscosity showed that the MWs of regenerated SF derived from neutral protease degumming were significantly higher than that of SF derived from Na2CO3 degumming. The MW of regenerated SF derived from bromelain degumming was the highest, while the MWs of regenerated SF derived from papain and trypsin degumming were relatively low. This study provides an efficient and environmentally friendly biological degumming method for obtaining high-molecular-weight silk fibroin.
Environmental Stability of Additively Manufactured Thermoplastic Polyamide Composites
Authors: Imburgia Michael J. , Faust Jessica L. , Ospina Buitrago Johan , Enfield Rachael E. , Roy-Mayhew Joseph D.
Published: August 2023

As the adoption of additive manufacturing technologies for end-use parts continues to progress, the evaluation of environmental durability is essential for the qualification of manufactured articles in industries such as automotive, aerospace, and electrical. This study explores the effects of UV and water-spray exposure on the mechanical properties of an additively manufactured polyamide 6 blend reinforced with short carbon fiber and continuous carbon fiber. Fused-filament-fabrication-printed test samples were exposed to a Xenon-arc UV source following ASTM G155 Cycle 1 conditions for a duration of 1000 h. Tensile, flexural, and Izod impact tests were performed on exposed and unexposed test samples. While Exposed tensile and flexural samples maintained their strength (84–100% and 88–100%, of Control samples, respectively), Izod impact strength increased (104–201% of Controls). This study also examines the influence of coatings and finds that samples coated with Krylon® Fusion All-In-One® and JetFlex® Polyurethane Primer maintain similar mechanical properties and exhibit a better visual appearance as compared to uncoated, exposed samples.
Enhanced Antioxidant Activity and Reduced Cytotoxicity of Silver Nanoparticles Stabilized by Different Humic Materials
Authors: Zykova Maria V. , Volikov Alexander B. , Buyko Evgeny E. , Bratishko Kristina A. , Ivanov Vladimir V. , Konstantinov Andrey I. , Logvinova Lyudmila A. , Mihalyov Dmitrii A. , Sobolev Nikita A. , Zhirkova Anastasia M. , Maksimov Sergey V. , Perminova Irina V. , Belousov Mikhail V.
Published: August 2023

The current article describes the biological activity of new biomaterials combining the “green” properties of humic substances (HSs) and silver nanoparticles. The aim is to investigate the antioxidant activity (AOA) of HS matrices (macroligands) and AgNPs stabilized with humic macroligands (HS-AgNPs). The unique chemical feature of HSs makes them very promising ligands (matrices) for AgNP stabilization. HSs have previously been shown to exert many pharmacological effects mediated by their AOA. AgNPs stabilized with HS showed a pronounced ability to bind to reactive oxygen species (ROS) in the test with ABTS. Also, higher AOA was observed for HS-AgNPs as compared to the HS matrices. In vitro cytotoxicity studies have shown that the stabilization of AgNPs with the HS matrices reduces the cytotoxicity of AgNPs. As a result of in vitro experiments with the use of 2,7-dichlorodihydrofluorescein diacetate (DCFDA), it was found that all HS materials tested and the HS-AgNPs did not exhibit prooxidant effects. Moreover, more pronounced AOA was shown for HS-AgNP samples as compared to the original HS matrices. Two putative mechanisms of the pronounced AOA of the tested compositions are proposed: firstly, the pronounced ability of HSs to inactivate ROS and, secondly, the large surface area and surface-to-volume ratio of HS-AgNPs, which facilitate electron transfer and mitigate kinetic barriers to the reduction reaction. As a result, the antioxidant properties of the tested HS-AgNPs might be of particular interest for biomedical applications aimed at inhibiting the growth of bacteria and viruses and the healing of purulent wounds.
Study of the Thermochemical Effect on the Transport and Structural Characteristics of Heterogeneous Ion-Exchange Membranes by Combining the Cell Model and the Fine-Porous Membrane Model
Authors: Filippov Anatoly N. , Akberova Elmara M. , Vasil’eva Vera I.
Published: August 2023

For the first time, based on the joint application of the fine-porous and cell models, a theoretical analysis of the changing transport and structural characteristics of heterogeneous polymeric ion-exchange membranes (IEMs) MK-40, MA-40, and MA-41 after exposure to elevated temperatures in water and aggressive media (H2SO4 and NaOH solutions), as well as after long-term processing in electrodialyzers of various types, was carried out. The studied membranes are composites of ion-exchange polymers with polyethylene and nylon reinforcing mesh. The external influences provoke the aging of IEMs and the deterioration of their characteristics. The transport properties of IEMs are quantitatively described using five physicochemical parameters: counterion diffusion and equilibrium distribution coefficients in the membrane, characteristic exchange capacity, which depends on the microporosity of ion-exchanger particles, and macroscopic porosity at a known exchange capacity of IEMs. Calculations of the physicochemical parameters of the membranes were performed according to a specially developed fitting technique using the experimental concentration dependences of integral diffusion permeability and specific electrical conductivity, and their model analogs. This made it possible to identify and evaluate changes in the membrane micro- and macrostructure and examine the process of artificial aging of the IEM polymer material due to the abovementioned external impacts.
Quantifying the Residual Stiffness of Concrete Beams with Polymeric Reinforcement under Repeated Loads
Authors: Sultani Haji Akbar , Sokolov Aleksandr , Rimkus Arvydas , Gribniak Viktor
Published: August 2023

Current technology development ensures a variety of advanced materials and options for reinforcing concrete structures. However, the absence of a uniform testing methodology complicates the quantification and comparative analysis of the mechanical performance of the composite systems. The repeated mechanical loads further complicate the issue. This research extends the recently developed residual stiffness assessment concept to the repeated loading case. It provides an engineer with a simplified testing layout and analytical model to quantify the residual flexural stiffness of standardized laboratory specimens subjected to repeated cycling loads. This model explicitly relates the particular moment and curvature values, requiring neither iterative calculations nor the load history. Thus, this feature allows residual stiffness quantification under repeated loading conditions, including complete reloading of the beam samples imitating the structural strengthening procedure; the proposed technique is equally efficient in quantifying the residual stiffness of the beam samples with any combinations of fiber-reinforced polymer (FRP) reinforcements, i.e., embedded bars, near-surface-mounted strips, and externally bonded sheets. This study employs 12 flexural elements with various reinforcement and loading layouts to illustrate the proposed methodology’s efficiency in quantifying the residual strength of the tension concrete, which estimates the efficiency of the reinforcement system. The explicit quantifying of the residual resistance of the FRP reinforcement systems under repeated load cycles describes the essential novelty of this work.
Self-Vibration of a Liquid Crystal Elastomer Fiber-Cantilever System under Steady Illumination
Authors: Li Kai , Liu Yufeng , Dai Yuntong , Yu Yong
Published: August 2023

A new type of self-oscillating system has been developed with the potential to expand its applications in fields such as biomedical engineering, advanced robotics, rescue operations, and military industries. This system is capable of sustaining its own motion by absorbing energy from the stable external environment without the need for an additional controller. The existing self-sustained oscillatory systems are relatively complex in structure and difficult to fabricate and control, thus limited in their implementation in practical and complex scenarios. In this paper, we creatively propose a novel light-powered liquid crystal elastomer (LCE) fiber-cantilever system that can perform self-sustained oscillation under steady illumination. Considering the well-established LCE dynamic model, beam theory, and deflection formula, the control equations for the self-oscillating system are derived to theoretically study the dynamics of self-vibration. The LCE fiber-cantilever system under steady illumination is found to exhibit two motion regimes, namely, the static and self-vibration regimes. The positive work done by the tension of the light-powered LCE fiber provides some compensation against the structural resistance from cantilever and the air damping. In addition, the influences of system parameters on self-vibration amplitude and frequency are also studied. The newly constructed light-powered LCE fiber-cantilever system in this paper has a simple structure, easy assembly/disassembly, easy preparation, and strong expandability as a one-dimensional fiber-based system. It is expected to meet the application requirements of practical complex scenarios and has important application value in fields such as autonomous robots, energy harvesters, autonomous separators, sensors, mechanical logic devices, and biomimetic design.
Enhancing Strength and Sustainability: Evaluating Glass and Basalt Fiber-Reinforced Biopolyamide as Alternatives for Petroleum-Based Polyamide Composite
Authors: Bednarowski Dariusz , Bazan Patrycja , Kuciel Stanisław
Published: August 2023

This study aims to analyze strength properties and low-cycle dynamic tests of composite materials modified with glass and basalt fibers. Biopolyamide 4.10 was used as the matrix, and the fiber contents were 15, 30, and 50% by weight. Static tensile tests, impact tests, and determination of mechanical hysteresis loops were carried out as strength tests. The length of the fibers in the produced composites and their processing properties were determined. The composite materials were compared with commercially available glass fiber-reinforced composites with 30 and 50% fiber contents. The results showed that such composites can successfully replace composite materials based on petroleum-based polymeric materials, providing high strength properties and reducing the negative environmental impact by using renewable sources. Composites with 30% basalt fiber composition were characterized by higher tensile strength by about 60% compared to commercially available composites with 30% glass fiber composition and an almost doubly increased Young’s modulus. Increasing the content of basalt fibers to 50% results in a further increase in strength properties. Despite the lower tensile strength compared to polyamide 6 with 50% glass fiber content, basalt fibers provided an approximately 10% higher modulus of elasticity.
Solution Blow-Spun Poly (Ethylene Oxide)-Polysulfone Bicomponent Fibers—Characterization of Morphology, Structure, and Properties
Authors: Domínguez-Herrera José Ernesto , Maldonado-Saavedra Octavio , Grande-Ramírez José Roberto , Guarneros-Nolasco Luis Rolando , González-Benito Javier
Published: August 2023

Solution blow spinning was used to prepare nonwoven bicomponent fibers constituted by poly (ethylene oxide)-Polysulfone (PEO-PSF). As a new material, deep characterization was carried out to have a database to understand final performance regarding its multiple functions as a potential material for biomedical applications. The morphology was studied by field emission scanning electron and transmission electron microscopy and optical profilometry. Structural characterization was carried out by Fourier transform infrared spectroscopy and thermal degradation by thermogravimetric analysis. Additionally, wettability and mechanical behavior were studied by contact angle measurements and tensile tests, respectively. The bicomponent material was constituted of fibers with a structure mainly described by a core-shell structure, where the PSF phase is located at the center of the fibers, and the PEO phase is mainly located at the outer parts of the fibers, leading to a kind of shell wall. The study of possible interactions between different phases revealed them to be lacking, pointing to the presence of an interface core/shell more than an interphase. The morphology and roughness of the bicomponent material improved its wettability when glycerol was tested. Indeed, its mechanical properties were enhanced due to the PSF core provided as reinforcement material.
Structural Characteristics and Properties of Redissolved Silk Sericin
Authors: Lee Hye Gyeoung , Jang Mi Jin , Park Byung-Dae , Um In Chul
Published: August 2023

Silk sericin has garnered the attention of researchers as a promising biomaterial because of its good biocompatibility and high water retention. However, despite its useful properties, the poor storage stability of sericin has restricted its extensive use in biorelated applications. This study extracted sericin from silkworm cocoon, dried and stored it as a solid, and then dissolved it in hot water conditions to improve the storage stability of sericin for its use. The dissolution behavior of the extracted sericin solids was examined in conjunction with the structural characteristics and properties of dissolved sericin. Consequently, the results of solution viscosity, gel strength, crystallinity index, and thermal decomposition temperature indicated that the molecular weight (MW) of the dissolved sericin remained constant until a dissolution time of 5 min, following which deterioration was observed. The optimum condition of dissolution of the extracted sericin solid was 5 min at 90 °C. Conclusively, the extracted sericin could be stored in a dry state and dissolved to prepare redissolved sericin aqueous solution with the same MW as extracted sericin, thereby improving the storage stability of the sericin aqueous solution.
Hybrid Composite Materials Made of Recycled PET and Standard Polymer Blends Used in Civil Engineering
Authors: Papán Daniel , Lapašová Lenka , Papánová Zuzana
Published: August 2023

This paper presents an investigation of the tensile properties of two composites made from recycled polyethylene tetraphthalate, cement–concrete mix and standard polymer-based adhesive mixes, used in the construction industry. To describe tensile behavior, experimental measurements of each component of the resulting composite materials were processed in detail. It presents the possibilities of modifying materials suitable for building structures and at the same time provides an opportunity to get rid of polyethylene tetraphthalate (PET), which has already been recycled several times. Because the resulting composite contains a majority of the composite composition on a fragile basis, its use in practice depends on its simple thrust properties. In this paper, a study of the most important mechanical properties of a previously unused composite is presented. These properties were obtained experimentally using an innovative tensile test method.
Advancement in Microbial Fuel Cells Technology by Using Waste Extract as an Organic Substrate to Produce Energy with Metal Removal
Authors: Aleid Ghada Mohamed , Alshammari Anoud Saud , Ahmad Alamri Rahmah Dhahawi , Hussain Fida , Oh Sang-Eun , Ahmad Akil , Ibrahim Mohamad Nasir Mohamad , Umar Khalid
Published: August 2023

Energy generation using microbial fuel cells (MFC) and removing toxic metal ions is a potentially exciting new field of study as it has recently attracted a lot of interest in the scientific community. However, MFC technology is facing several challenges, including electron production and transportation. Therefore, the present work focuses on enhancing electron generation by extracting sugarcane waste. MFC was successfully operated in a batch mode for 79 days in the presence of 250 mg/L Pb2+ and Hg2+ ions. Sugarcane extract was regularly fed to it without interruption. On day 38, the maximum current density and power density were recorded, which were 86.84 mA/m2 and 3.89 mW/m2, respectively. The electrochemical data show that a sufficient voltage generation and biofilm formation produce gradually. The specific capacitance was found to be 11 × 10−4 F/g on day 79, indicating the steady growth of biofilm. On the other hand, Pb2+ and Hg2+ removal efficiencies were found to be 82% and 74.85%, respectively. Biological investigations such as biofilm analysis and a recent literature survey suggest that conductive-type pili species can be responsible for energy production and metal removal. The current research also explored the oxidation method of sugarcane extract by bacterial communities, as well as the metal removal mechanism. According to the parameter optimization findings, a neutral pH and waste produced extract can be an optimal condition for MFC operation.
Bearing Fault Diagnosis Based on ICEEMDAN Deep Learning Network
Authors: Liang Bo , Feng Wuwei
Published: August 2023

Bearing fault diagnosis has evolved from machine learning to deep learning, addressing the issues of performance degradation in deep learning networks and the potential loss of key feature information. This paper proposes a fault diagnosis method for rolling bearing faults based on ICEEMDAN combined with the Hilbert transform (ICEEMDAN-Hilbert) and a residual network (ResNet). Firstly, the collected fault vibration signals are classified as fault samples and randomly sampled with a fixed length. The IMF components obtained by decomposing the bearing fault vibration signals using ICEEMDAN are able to maximize the restoration of fault vibrations. Then, the IMF components are transformed from one-dimensional time-domain signals to two-dimensional time-frequency domain images using Hilbert transformation. The RGB color images can be directly used in deep learning models without the need for manual labeling of a large amount of data, thereby avoiding the loss of key feature information. The ResNet network incorporates the attention mechanism (CBAM) structure for the precise extraction of fault features, enabling a more detailed classification of fault features. Additionally, the residual network effectively addresses the problem of performance degradation in multi-layer network models. Finally, transfer learning is applied in the deep learning network by freezing the training layer parameters and training the fully connected layer. This effectively solves the problem of insufficient data in real operating conditions, which hinders deep training of the model, while also reducing the training time. By combining the ResNet network with the convolutional block attention module (CBAM) structure, the model completes the recognition and training of time-frequency images for rolling bearing faults. The results demonstrate that the ResNet with CBAM model has strong fault feature extraction capabilities, achieving higher accuracy, 7–12% higher than other conventional network models, and exhibiting superior diagnostic performance compared to other deep learning models.
Analysis of the Fuel Properties of the Seed Shell of the Neem Plant (Azadirachta indica)
Authors: Neto Francisco Simão , Melo Neta Maria , Sousa Ana , Damasceno Luana , Sousa Bruna , Medeiros Samuel , Melo Rafael , Lopes Ada , Santos José , Rios Maria
Published: August 2023

The energetic potential of the seed shell of the Neem plant (Azadirachta indica) was investigated using proximate analysis, Higher Heating Value (HHV), thermal analysis (TG-DTG and DSC) in inert and oxidative atmospheres, and X-ray fluorescence (XRF). The results of ash (3.80% ± 0.44), volatile matter (81.76% ± 1.30), fixed carbon (14.44% ± 1.74), and estimated HHV (18.791 MJ/kg: average value) are compatible with other biomasses already used as fuels in the bioenergy industry. Thermograms showed three main degradation events in synthetic air and two in nitrogen, attributed to the moisture, release of volatile materials, and decomposition of hemicellulose, cellulose, and lignin. The elements positively detected by the XRF were Ca, K, S, P, Fe, Ti, Zn, Rb, and Sr.
Natural Convection in a Circular Enclosure with Four Cylinders under Magnetic Field: Application to Heat Exchanger
Authors: Azzouz Raouia , Hamida Mohamed
Published: August 2023

This paper documents the 2D numerical study of magnetohydrodynamic unsteady natural convective heat transfer in a circular enclosure with four heating cylinders in both the horizontal and the vertical mid-plane. The fluid is an incompressible Newtonian fluid. The main transport equations based on the conservation of mass, momentum, and energy are calculated and solved using a finite element numerical solver with the following parameter ranges: dimensionless distance between cylinders S = 0.05–0.29, Rayleigh number Ra=103–106, and Hartmann number for Ha = 0–120. COMSOL Multiphysics, a numerical simulation program, was used to solve the governing equations. It was demonstrated that for lower Ra values, heat transfer through an applied magnetic field is unaffected for a specific S value because the mechanism of transport is diffusion, whereas for larger Ra, there is a complex interaction among magnetic field and physical thermal properties. The features of the heat transfer rate are determined by the interaction. The Nusselt number virtually stays constant as Ha rises at smaller Ra. However, at high Ra, the Nusselt number initially declines with Ha and thereafter essentially stays constant, and at high Ra values, the switch from conduction to convective heat transfer takes place. Additionally, Nu rises slightly with S at increasing Ra.
Thermal Treatment of Commercial Sweetener Solutions Modulates the Metabolic Responses in C57BL/6 Mice during a 24-Week-Long Exposition
Authors: Nieto-Mazzocco Elizabeth , Franco-Robles Elena , Saldaña-Robles Adriana , Pacheco Neith , Ozuna César
Published: August 2023

The purpose of this paper was to evaluate the effect of thermal treatment (TT: 121 ± 2 °C, 15 min) on the composition of commercial sweeteners diluted in water (10 °Brix). Additionally, we evaluated the impact of this TT on metabolic responses in C57BL/6 mice during a 24-week treatment. The sweeteners included in this study were sucrose (SC), glucose-63 (GLU63), agave syrup (AS), sucralose (SUC), and steviol glycosides (STG). HPLC analysis showed changes in the concentration of simple sugars of GLU63 and AS after TT. Importantly, in all sweeteners, TT modulated metabolic responses in mice. The mice drinking thermally treated sweetener solution showed an increase of 10–13% (p < 0.05) in food intake (AS, SUC, and STG), beverage intake (2–21%; SC and GLU63), weight gain (38%; SUC), energy (10–13%; AS, SUC, and STG), glucose levels (11–17%; SC and STG), GLP-1 (30%; SC) and insulin (88%; AS) release, and the generation of protein carbonyl (SC) and malondialdehyde (all sweeteners tested) compared to mice drinking solution without TT. In conclusion, TT of sweetener solutions accentuates the metabolic responses of healthy mice, which can be related to overweight and its comorbidities.
A New Explanation of Why the Euthyphro Dilemma Is a False Dilemma
Authors: Søvik Atle Ottesen
Published: August 2023

The article gives a new explanation for why the Euthyphro dilemma is a false dilemma and argues that it is a middle position that both the theist and the atheist could accept. The argument is that both the will of God and the preferences of individuals are necessary truthmakers for what the good is. Each of the components is insufficient on its own, but jointly they are sufficient. Individual preferences are necessary to provide the normativity of the good, while God is necessary for the objectivity of the good. It is the combination of individual preferences into a possible world that is valuated the most by the most that gives the normative goal for moral choices. It is the knowledge of God of what would most probably be valuated the most by the most that makes a concrete choice the morally right thing to do in a concrete situation.
Vivekananda: Indian Swami and Global Guru
Authors: Harris Ruth
Published: August 2023

This article seeks to integrate the “Indian swami” with the “global guru” and reflects upon why Vivekananda’s teaching was conveyed so differently to different audiences. It argues that Vivekananda’s distinctive form of “counter-preaching” had its roots in Adhikari-bheda, a tradition that seeks to tailor spiritual instruction to the needs and capacities of individual aspirants. I will show how he applied this technique to larger audiences because he believed that “truth” had a relative dimension that had to account for cultural difference. I investigate how instruction in Hindu “man-making” and spiritual democracy in India was matched by lessons designed to counter “muscular Christianity” in Euro-America. Vivekananda wanted both to reinforce a vision of eastern wisdom and counter western (and at times Indian) prejudices, whilst also attempting to shift entrenched but fallacious generalizations in each arena. In working within this seeming contradiction, I will show how his nationalism and universalism were inextricable, and also tied to his innovative formulations of Advaita Vedanta, karma yoga, and especially “practical Vedanta”. I will conclude by explaining how his methods generally sought to pull his audiences away from extremes. The kaleidoscopic qualities of his teachings, I will suggest, explain why his legacy has been so variously deployed by both the right and left in contemporary Indian political culture.
Trinomial: Return-Risk and Sustainability: Is Sustainability Valued by Investors? A Choice Experiment for Spanish Investors Applied to SDG 12
Authors: Díaz-Caro Carlos , Crespo-Cebada Eva , Goenechea Borja Encinas , Mirón Sanguino Ángel-Sabino
Published: August 2023

Traditionally, finance has paid attention to the risk-return trade-off. Recently, given the incorporation of the 2030 Agenda and climate change, a third pillar has been incorporated into the investment decision: sustainability. Socially responsible investment is an instrument that can incorporate all three pillars. This paper aims to assess sustainability by Spanish investors using a choice experiment by applying the Bayesian approach with Markov chain Monte Carlo sampling and obtain the willingness to pay (invest) for each attribute. The results show that profitability remains the most important factor, although risk is at the same level as sustainability.
Dynamic Response Measurement and Cable Tension Estimation Using an Unmanned Aerial Vehicle
Authors: Kim In-Ho , Jung Hyung-Jo , Yoon Sungsik , Park Jong Woong
Published: August 2023

Since all structures vibrate due to external loads, measuring and analyzing vibration data is a representative method of structural health monitoring. In this paper, we propose a non-contact cable estimation method using a vision sensor mounted on an unmanned aerial vehicle. A target cable among many cables can be identified through marker detection. In addition, the motion of the structure can be quickly captured using the extracted feature points. Although computer vision can be used to transform displacements of multiple axis, in this study, only the vertical displacement is considered to estimate tension. Finally, the cable tension can be estimated via the vibration method using the modal frequencies derived from the cable displacement. To verify the performance of the proposed method, lab-scale experiments were carried out and the results were compared with the conventional method based on the accelerometer. The proposed method showed a 3.54% error compared with the existing method and confirmed that the cable tension force can be estimated quickly at low cost.
Phenology-Based Maximum Light Use Efficiency for Modeling Gross Primary Production across Typical Terrestrial Ecosystems
Authors: Lv Yulong , Chi Hong , Shi Peichen , Huang Duan , Gan Jialiang , Li Yifan , Gao Xinyi , Han Yifei , Chang Cun , Wan Jun , Ling Feng
Published: August 2023

The maximum light use efficiency (LUE) (ε0) is a key essential parameter of the LUE model, and its accurate estimation is crucial for quantifying gross primary production (GPP) and better understanding the global carbon budget. Currently, a comprehensive understanding of the potential of seasonal variations of ε0 in GPP estimation across different plant functional types (PFTs) is still lacking. In this study, we used a phenology-based strategy for the estimation of ε0 to find the optimal photosynthetic responses of the parameter in different phenological stages. The start and end of growing season (SOS and EOS) from time series vegetation indices and the camera-derived greenness index were extracted across seven PFT flux sites using the methods of the hybrid generalized additive model (HGAM) and double logistic function (DLF). Optimal extractions of SOS and EOS were evaluated, and the ε0 was estimated from flux site observations during the optimal phenological stages with the light response equation. Coupled with other obligatory parameters of the LUE model, phenology-based GPP (GPPphe-based) was estimated over 21 site-years and compared with vegetation photosynthesis model (VPM)-based GPP (GPPVPM) and eddy covariance-measured GPP (GPPEC). Generally, GPPphe-based basically tracked both the seasonal dynamics and inter-annual variation of GPPEC well, especially at forest, cropland, and wetland flux sites. The R2 between GPPphe-based and GPPEC was stable between 0.85 and 0.95 in forest ecosystems, between 0.75 and 0.85 in cropland ecosystems, and around 0.9 in wetland ecosystems. Furthermore, we found that GPPphe-based was significantly improved compared to GPPVPM in cropland, grassland, and wetland ecosystems, implying that phenology-based ε0 is more appropriate in the GPP estimation of herbaceous plants. In addition, we found that GPPphe-based was significantly improved over GPPVPM in cropland, grassland, and wetland ecosystems, and the R2 between GPPphe-based and GPPEC was improved by up to 0.11 in cropland ecosystems and 0.05 in wetland ecosystems compared to GPPVPM, and RMSE was reduced by up to 5.90 and 2.11 g C m−2 8 day−1, respectively, implying that phenology-based ε0 in herbaceous plants is more appropriate for GPP estimation. This work highlights the potential of phenology-based ε0 in understanding the seasonal variation of vegetation photosynthesis and production.
LPMSNet: Location Pooling Multi-Scale Network for Cloud and Cloud Shadow Segmentation
Authors: Dai Xin , Chen Kai , Xia Min , Weng Liguo , Lin Haifeng
Published: August 2023

Among the most difficult difficulties in contemporary satellite image-processing subjects is cloud and cloud shade segmentation. Due to substantial background noise interference, existing cloud and cloud shadow segmentation techniques would result in false detection and missing detection. We propose a Location Pooling Multi-Scale Network (LPMSNet) in this study. The residual network is utilised as the backbone in this method to acquire semantic info on various levels. Simultaneously, the Location Attention Multi-Scale Aggregation Module (LAMA) is introduced to obtain the image’s multi-scale info. The Channel Spatial Attention Module (CSA) is introduced to boost the network’s focus on segmentation goals. Finally, in view of the problem that the edge details of cloud as well as cloud shade are easily lost, this work designs the Scale Fusion Restoration Module (SFR). SFR can perform picture upsampling as well as the acquisition of edge detail information from cloud as well as cloud shade. The mean intersection over union (MIoU) accuracy of this network reached 94.36% and 81.60% on the Cloud and Cloud Shadow Dataset and the five-category dataset L8SPARCS, respectively. On the two-category HRC-WHU Dataset, the accuracy of the network on the intersection over union (IoU) reached 90.51%. In addition, in the Cloud and Cloud Shadow Dataset, our network achieves 97.17%, 96.83%, and 97.00% in precision (P), recall (R), and F1 score (F1) in cloud segmentation tasks, respectively. In the cloud shadow segmentation task, precision (P), recall (R), and F1 score (F1) reached 95.70%, 96.38%, and 96.04%, respectively. Therefore, this method has a significant advantage over the current cloud and cloud shade segmentation methods.
Landsat-7 ETM+, Landsat-8 OLI, and Sentinel-2 MSI Surface Reflectance Cross-Comparison and Harmonization over the Mediterranean Basin Area
Authors: Perez Martina , Vitale Marcello
Published: August 2023

In the Mediterranean area, vegetation dynamics and phenology analysed over a long time can have an important role in highlighting changes in land use and cover as well as the effect of climate change. Over the last 30 years, remote sensing has played an essential role in bringing about these changes thanks to many types of observations and techniques. Satellite images are to be considered an important tool to grasp these dynamics and evaluate them in an inexpensive and multidisciplinary way thanks to Landsat and Sentinel satellite constellations. The integration of these tools holds a dual potential: on the one hand, allowing us to obtain a longer historical series of reflectance data, while on the other hand making data available with a higher frequency even within a specific timeframe. The study aims to conduct a comprehensive cross-comparison analysis of long-time-series pixel values in the Mediterranean regions. For this scope comparisons between Landsat-7 (ETM+), Landsat-8 (OLI), and Sentinel-2 (MSI) satellite sensors were conducted based on surface reflectance products. We evaluated these differences using Ordinary Least Squares (OLS) and Major Axis linear regression (RMA) analysis on points extracted from over 15,000 images across the Mediterranean basin area from 2017 to 2020. Minor but consistent differences were noted, necessitating the formulation of suitable adjustment equations to better align Sentinel-2 reflectance values with those of Landsat-7 or Landsat-8. The results of the analysis are compared with the most-used harmonization coefficients proposed in the literature, revealing significant differences. The root-mean-square deviation, the mean difference and the orthogonal distance regression (ODR) slope show an improvement of the parameters for both models used (OLS and RMA) in this study. The discrepancies in reflectance values leads to corresponding variations in the estimation of biophysical parameters, such as NDVI, showing an increase in the ODR slope of 0.3. Despite differences in spatial, spectral, and temporal characteristics, we demonstrate that integration of these datasets is feasible through the application of band-wise regression corrections for a sensitive and heterogeneous area like those of the Mediterranean basin area.
Space-Based THz Radar Fly-Around Imaging Simulation for Space Targets Based on Improved Path Tracing
Authors: Ning Qianhao , Wang Hongyuan , Yan Zhiqiang , Liu Xiang , Lu Yinxi
Published: August 2023

Aiming at the space target detection application of a space-based terahertz (THz) radar, according to the imaging mechanism of broadband THz radars, a THz radar imaging simulation method based on improved path tracing is proposed. Firstly, the characterization method of THz scattering characteristics based on Kirchhoff’s approximation method is introduced. The multi-parameter THz bidirectional reflectance distribution function (THz-BRDF) models of aluminum (Al), white-painted Al, and polyimide film at 0.215 THz are fitted according to the theoretical data, with fitting errors below 4%. Then, the THz radar imaging simulation method based on improved path tracing is presented in detail. The simulation method utilizes path tracing to simulate parallelized THz radar echo signal data, considering multi-path energy scattering based on the THz-BRDF model. Finally, we conducted THz radar imaging simulation experiments. The influences in the imaging process of different fly-around motions are analyzed, and a comparison experiment is conducted with the fast-physical optics (FPO) method. The comparative results indicate that the proposed method exhibits richer and more realistic features compared with the FPO method. The simulation experiments results demonstrate that the proposed method can provide a data source for ground algorithm testing of THz radars, particularly in evaluating the target detection and recognition algorithm based on deep learning, providing strong support for the application of space-based THz radars in the future.
Monitoring and Comparative Analysis of Hohhot Subway Subsidence Using StaMPS-PS Based on Two DEMS
Authors: Zhao Sihai , Li Peixian , Li Hairui , Zhang Tao , Wang Bing
Published: August 2023

The subway alleviates the traffic pressure in the city but also brings the potential risk of land subsidence. The land subsidence caused by the subway is a global problem that seriously affects the safety of subway operations and surrounding buildings. Therefore, it is very important to carry out long-term deformation monitoring on the subway system. StaMPS-PS is a time-series Interferometric Synthetic Aperture Radar (InSAR) technique that serves as an effective means for monitoring urban ground subsidence. However, the accuracy of external (Digital Elevation Models) DEM will affect the accuracy of StaMPS-PS monitoring, and previous studies have mostly used SRTM-1 arc DEM (30 m) as the external DEM. In this study, to obtain a more precise measurement of surface deformation caused by the excavation of the Hohhot subway, a total of 85 scenes of Sentinel-1A data from July 2015 to October 2021, as well as two different resolution digital elevation models (DEMs) (ALOS PALSAR DEM and SRTM-1 arc DEM), were used to calculate and analyze the subsidence along the subway line in Hohhot city. The StaMPS-PS monitoring results showed the ALOS PALSAR DEM, as an external DEM, had higher accuracy, and there was regional subsidence in both the construction processes of Line 1 and Line 2 of the Hohhot subway, with a maximum subsidence rate of −21.1 mm/year. The dynamic changes in subway subsidence were fitted using the Peck formula and the long short-term memory (LSTM) model. The Peck formula results showed the width and maximum subsidence of the settlement troughs gradually expanded during the construction of the subway. The predicted values of the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) of the LSTM model were less than 4 mm and 10%, respectively, consistent with the measured results. Furthermore, we discussed the factors that affect settlement along the subway line and the impact of two external DEMs on StaMPS-PS. The study results provide a scientific method for DEM selection and subsidence analysis calculations in the StaMPS-PS monitoring of urban subway subsidence.
Semi-FCMNet: Semi-Supervised Learning for Forest Cover Mapping from Satellite Imagery via Ensemble Self-Training and Perturbation
Authors: Chen Beiqi , Wang Liangjing , Fan Xijian , Bo Weihao , Yang Xubing , Tjahjadi Tardi
Published: August 2023

Forest cover mapping is of paramount importance for environmental monitoring, biodiversity assessment, and forest resource management. In the realm of forest cover mapping, significant advancements have been made by leveraging fully supervised semantic segmentation models. However, the process of acquiring a substantial quantity of pixel-level labelled data is prone to time-consuming and labour-intensive procedures. To address this issue, this paper proposes a novel semi-supervised-learning-based semantic segmentation framework that leverages limited labelled and numerous unlabelled data, integrating multi-level perturbations and model ensembles. Our framework incorporates a multi-level perturbation module that integrates input-level, feature-level, and model-level perturbations. This module aids in effectively emphasising salient features from remote sensing (RS) images during different training stages and facilitates the stability of model learning, thereby effectively preventing overfitting. We also propose an ensemble-voting-based label generation strategy that enhances the reliability of model-generated labels, achieving smooth label predictions for challenging boundary regions. Additionally, we designed an adaptive loss function that dynamically adjusts the focus on poorly learned categories and dynamically adapts the attention towards labels generated during both the student and teacher stages. The proposed framework was comprehensively evaluated using two satellite RS datasets, showcasing its competitive performance in semi-supervised forest-cover-mapping scenarios. Notably, the method outperforms the fully supervised approach by 1–3% across diverse partitions, as quantified by metrics including mIoU, accuracy, and mPrecision. Furthermore, it exhibits superiority over other state-of-the-art semi-supervised methods. These results indicate the practical significance of our solution in various domains, including environmental monitoring, forest management, and conservation decision-making processes.
Multi-Featured Sea Ice Classification with SAR Image Based on Convolutional Neural Network
Authors: Wan Hongyang , Luo Xiaowen , Wu Ziyin , Qin Xiaoming , Chen Xiaolun , Li Bin , Shang Jihong , Zhao Dineng
Published: August 2023

Sea ice is a significant factor in influencing environmental change on Earth. Monitoring sea ice is of major importance, and one of the main objectives of this monitoring is sea ice classification. Currently, synthetic aperture radar (SAR) data are primarily used for sea ice classification, with a single polarization band or simple combinations of polarization bands being common choices. While much of the current research has focused on optimizing network structures to achieve high classification accuracy, which requires substantial training resources, we aim to extract more information from the SAR data for classification. Therefore we propose a multi-featured SAR sea ice classification method that combines polarization features calculated by polarization decomposition and spectrogram features calculated by joint time-frequency analysis (JTFA). We built a convolutional neural network (CNN) structure for learning the multi-features of sea ice, which combines spatial features and physical properties, including polarization and spectrogram features of sea ice. In this paper, we utilized ALOS PALSAR SLC data with HH, HV, VH, and VV, four types of polarization for the multi-featured sea ice classification method. We divided the sea ice into new ice (NI), first-year ice (FI), old ice (OI), deformed ice (DI), and open water (OW). Then, the accuracy calculation by confusion matrix and comparative analysis were carried out. Our experimental results demonstrate that the multi-feature method proposed in this paper can achieve high accuracy with a smaller data volume and computational effort. In the four scenes selected for validation, the overall accuracy could reach 95%, 91%, 96%, and 95%, respectively, which represents a significant improvement compared to the single-feature sea ice classification method.
Deep Hybrid Compression Network for Lidar Point Cloud Classification and Segmentation
Authors: Zhao Zhi , Ma Yanxin , Xu Ke , Wan Jianwei
Published: August 2023

Extensive research on deep neural networks for LiDAR point clouds has contributed inexhaustible momentum to the development of computer 3D vision applications. However, storage and energy consumption have always been a challenge for deploying these deep models on mobile devices. Quantization provides a feasible route, of which current primary research is focused on uniform bit-width quantization without considering different layers or filters’ sensitivity to different bit-widths. This article proposes a novel hybrid compression method based on relaxed mixed-precision quantization, relaxed weights pruning, and knowledge distillation to overcome the limitations of uniform quantization illustrated above, while further improving model accuracy and reducing model memory consumption. It employs a differentiable searching method to search for the optimal bit allocation and weight sparsity, while conducting feature distillation, accordingly considering the feature degradation by pooling operation in point cloud deep models. The proposed method combines three compression techniques to balance the trade-off between compression accuracy and model size. Pruning alleviates the increasing memory consumption problem caused by mixed-precision quantization, while distillation improves compression accuracy without increasing model size. The experiments validate that the proposed method outperforms state-of-the-art typical uniform quantization methods in terms of accuracy with an acceptable and relatively competitive compression performance.
Precision Detection of Dense Litchi Fruit in UAV Images Based on Improved YOLOv5 Model
Authors: Xiong Zhangjun , Wang Lele , Zhao Yingjie , Lan Yubin
Published: August 2023

The utilization of unmanned aerial vehicles (UAVs) for the precise and convenient detection of litchi fruits, in order to estimate yields and perform statistical analysis, holds significant value in the complex and variable litchi orchard environment. Currently, litchi yield estimation relies predominantly on manual rough counts, which often result in discrepancies between the estimated values and the actual production figures. This study proposes a large-scene and high-density litchi fruit recognition method based on the improved You Only Look Once version 5 (YOLOv5) model. The main objective is to enhance the accuracy and efficiency of yield estimation in natural orchards. First, the PANet in the original YOLOv5 model is replaced with the improved Bi-directional Feature Pyramid Network (BiFPN) to enhance the model’s cross-scale feature fusion. Second, the P2 feature layer is fused into the BiFPN to enhance the learning capability of the model for high-resolution features. After that, the Normalized Gaussian Wasserstein Distance (NWD) metric is introduced into the regression loss function to enhance the learning ability of the model for litchi tiny targets. Finally, the Slicing Aided Hyper Inference (SAHI) is used to enhance the detection of tiny targets without increasing the model’s parameters or computational memory. The experimental results show that the overall AP value of the improved YOLOv5 model has been effectively increased by 22%, compared to the original YOLOv5 model’s AP value of 50.6%. Specifically, the APs value for detecting small targets has increased from 27.8% to 57.3%. The model size is only 3.6% larger than the original YOLOv5 model. Through ablation and comparative experiments, our method has successfully improved accuracy without compromising the model size and inference speed. Therefore, the proposed method in this paper holds practical applicability for detecting litchi fruits in orchards. It can serve as a valuable tool for providing guidance and suggestions for litchi yield estimation and subsequent harvesting processes. In future research, optimization can be continued for the small target detection problem, while it can be extended to study the small target tracking problem in dense scenarios, which is of great significance for litchi yield estimation.