Crop mapping is essential for agricultural management and food production monitoring, but challenges like limited crop labels and poor model generalization significantly hinder large-scale crop mapping. Here, we intro...
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Crop mapping is essential for agricultural management and food production monitoring, but challenges like limited crop labels and poor model generalization significantly hinder large-scale crop mapping. Here, we introduce a novel Contrastive Self-Training Network (CSTN), integrating a self-training strategy and contrastive domain adaptation (CDA) for cross-region crop mapping. CSTN uses pseudo-labels in the target region generated by the self-training strategy to assist supervised learning, and aligns features across regions using class-aware prototypes. Qualitative and quantitative evaluations demonstrate that CSTN significantly outperforms state-ofthe-art methods with a 12.29 % increase in average F1-score, particularly in maize identification. Moreover, CSTN also enables early-season crop classification for pre-harvest decision-making applications. The interpretability of the model is demonstrated through an in-depth analysis of feature map visualizations, attention map visualizations, and the effectiveness of the modules. This study provides a robust method for enhancing largescale crop mapping and facilitating more accurate and timely agricultural practices.
Removal of sulfur species from blast furnace gas is urgently needed due to the strict emission limits imposed on iron-steel industrial flue gas. Improving the sulfur capacity of H2S is a crucial challenge to reduce th...
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Removal of sulfur species from blast furnace gas is urgently needed due to the strict emission limits imposed on iron-steel industrial flue gas. Improving the sulfur capacity of H2S is a crucial challenge to reduce the operation cost. NiFe layered double hydroxide (LDH) adsorbents were synthesized using the hydrothermal method to strengthen the adsorption of H2S, achieving a high sulfur capacity of 133.6 mg/g at 50 degrees C. Characterization studies have revealed that the reaction pathway of H2S on the NiFe LDH surface involves adsorption, dissociation and oxidation. It has been clarified that the high sulfur capacity can be attributed to the abundant H2S dissociation sites and the excellent O2 activation sites. The dissociation sites of H2S encompass metal sites, -OH and CO32-. The interaction between O2 and the bridge site of asymmetric metal atoms significantly enhances the dissociation of O2. Strengthening the dissociation of H2S and O2 improves the sulfur capacity. The deactivation of adsorbents comes from the continuous consumption of oxygen species mainly composed of -OH and the deposition of sulfur species in the smaller mesopores ranging from 2 to 10 nm. This work provides useful insights into designing highly efficient iron-based adsorbents for the desulfurization of blast furnace gas.
A new Schiff-base fluorescence probe CS containing benzimidazole and coumarin fluorophores was synthesized and characterized by NMR, IR and ESI-MS. Fluorescence and absorption spectra show the unique changes in the pr...
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A new Schiff-base fluorescence probe CS containing benzimidazole and coumarin fluorophores was synthesized and characterized by NMR, IR and ESI-MS. Fluorescence and absorption spectra show the unique changes in the presence of Hg2+ and Cu2+. Hg2+ induces a remarkable fluorescence enhancement and results in mercury-promoted hydrolysis of a mercury-CS complex to convert the imine group to the aldehyde group. Cu2+ leads to a colorimetric change from colorless to pale yellow and causes formation of a copper-CS complex. Thus, CS can act as a fluorescent chemodosimeter for Hg2+ and a naked-eye chemosensor Cu2+. The cell imaging experiments for Hg2+ show a slow enhancement of a bright blue fluorescence with increasing incubation time, while Cu2+ instantly quenches the fluorescence and then the fluorescence intensity has no further change by extending the incubation time. Therefore, the probe CS shows its potential application for simultaneous differential detection of Hg2+ and Cu2+ in living cells based on the fluorescence response time. (C) 2016 Elsevier B.V. All rights reserved.
The thermal hydraulic characteristics and heat transfer of two-phase fluid in helical coil once-through steam generator under ocean conditions like rolling and heaving motions are studied numerically. A CFD model is e...
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The thermal hydraulic characteristics and heat transfer of two-phase fluid in helical coil once-through steam generator under ocean conditions like rolling and heaving motions are studied numerically. A CFD model is established to analyze the effects of motion parameters like amplitude, period and rolling radius and the heat flux distribution are discussed. The analysis shows that the overall heat transfer decreases under ocean conditions, but the overall deterioration is limited, indicting the helical coil pipe has the stability for ocean conditions. The additional forces like Coriolis, centrifugal and inertial forces introduced by rolling motion are analyzed theoretically and the result shows it detorts the original two main vortexes responsible for high heat transfer due to its spiral geometry, making heat transfer fluctuates up and down periodically near the steady state value. As rolling height increases, the heat transfer is nonlinear due to the competition between Coriolis force and centrifugal and inertial forces. As rolling amplitude increases, the heat transfer variation increases and the local heat transfer deterioration is severely aggravated, indicating high rolling amplitude should be avoided as possible. Increasing rolling period makes heat transfer behavior more approximate to static case as gravity and centrifugal force due to spiral geometry is dominate. Under heaving motion, the overall heat transfer decreases, and increasing heaving amplitude and decreasing period make the fluctuation more severe.
This paper presents the development and validation of numerical transient critical heat flux (CHF) prediction model based on the coupling of dynamic bubble simulation by Monte Carlo (MC) method and two-dimensional hea...
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This paper presents the development and validation of numerical transient critical heat flux (CHF) prediction model based on the coupling of dynamic bubble simulation by Monte Carlo (MC) method and two-dimensional heat conduction model of the substrate by finite volume method. The model aims to predict the transient CHF value, estimate the dynamic heat flux and temperature distribution and evaluate various phenomena of bubble behaviors during transient boiling heat transfer process under exponentially escalating heat power input. The dynamic bubble simulation is performed by modeling and tracking bubble nucleation, bubble growth, bubble departure and bubble coalescence procedures of individual bubbles in real time based on simplified fundamental bubble parameters including nucleation site density (NSD), bubble departure diameter, bubble growth and waiting time. The heat flux partitioning model was used to evaluate different heat fluxes based on the classification of microlayer and dry spot regions. According to the dynamic heat flux boundary due to dynamic bubble behavior and exponentially escalating heat input, the thermal conductivity model was coupled to feed back the wall temperature distribution for further updates and simulation of dynamic bubble behavior. Numerical simulation results showed good agreement with transient flow boiling experimental results with escalating periods ranging from 500 ms to 5 ms, and the results showed the trend that the transient CHF value increases with decreasing escalating period as observed in experiments. Sensitivity analysis of input bubble parameters was performed and showed that the model is more sensitive to bubble departure diameter and bubble waiting time.
Angkor Wat is one of the most important archaeological sites in the world. However, due to its changes in natural environment and human activities, the Angkor Wat and its surrounding environment are facing great chall...
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A DSC method was demonstrated to determine the temperature uniformity at transverse direction in BOPA annealing oven by utilizing the existence of small endothermic peak before the melting of alpha-form (221 ° C)...
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ISBN:
(纸本)9780985011277
A DSC method was demonstrated to determine the temperature uniformity at transverse direction in BOPA annealing oven by utilizing the existence of small endothermic peak before the melting of alpha-form (221 ° C) and/or gamma-form (214 ° C) nylon 6 on 1st heating thermograph.
Near-infrared (NIR) photothermal therapy (PTT) is a new approach to ablate cancer without affecting normal tissues. A pivotal concern of PPT is to develop photo-responsive agents with high biocompatibility as well as ...
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Near-infrared (NIR) photothermal therapy (PTT) is a new approach to ablate cancer without affecting normal tissues. A pivotal concern of PPT is to develop photo-responsive agents with high biocompatibility as well as effective photothermal conversion efficiency. Copper sulfide (CuS) nanoparticles prepared are characterized by their low synthesis cost, wide NIR absorption range, good biocompatibility and favorable NIR photothermal conversion efficiency. CuS nanoparticles were then coated with mesoporous silicon dioxide (SiO2) by the Stober method, and further loaded with anticancer drug doxorubicin (DOX). The nanocomposites obtained were named CuS@MSN-DOX. The infrared thermal imaging of CuS@MSN-DOX demonstrated its favorable photothermal efficacy. The potential of CuS@MSN-DOX utilized as a multifunctional platform for combined PPT and chemotherapy was exploited both at the cell level and in a mice model. The result demonstrated that CuS@MSN-DOX was endowed with the synergistic effect of chemo-photothermal therapy, which confirmed that it is a promising candidate for combined therapy of cancer.
In large-scale group decision-making (LSGDM) problems, clustering and feedback mechanisms play crucial roles in the consensus-building process. This study focuses on LSGDM problems that incorporate historical data in ...
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In large-scale group decision-making (LSGDM) problems, clustering and feedback mechanisms play crucial roles in the consensus-building process. This study focuses on LSGDM problems that incorporate historical data in dynamic social networks and introduces a novel consensus decision-making method. First, we define a picture fuzzy linguistic Euclidean distance metric, which effectively quantifies the distance between picture fuzzy linguistic information, providing a robust quantitative tool for related research. Second, we constructed a hybrid trust-based dynamic social network that enables decision-makers (DMs) to interact closely based on both trust relationships and preference similarities. This approach overcomes the limitations of existing studies that consider only trust relationships when constructing hybrid trust social networks. Additionally, we propose a novel clustering method that integrates hybrid trust networks and historical data. Furthermore, we address the identification and management of noncooperative behavior within the consensus feedback mechanism to enhance the efficiency of group consensus. Finally, a high-speed rail construction site selection problem is presented as a case study to demonstrate the effectiveness and superiority of the proposed method.
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