Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network for Remote Sensing Image Super-Resolution

Remote sensing image super-resolution (RSISR) plays a vital role in enhancing spatial detials and improving the quality of satellite imagery. Recently, Transformer-based models have shown competitive performance in RSISR. To mitigate the quadratic computational complexity resulting from global self-attention, various methods constrain attention to a local window, enhancing its efficiency. Consequently, the receptive fields in a single attention layer are inadequate, leading to insufficient context modeling. Furthermore, while most transform-based approaches reuse shallow features through skip connections, relying solely on these connections treats shallow and deep features equally, impeding the model's ability to characterize them. To address these issues, we propose a novel transformer architecture called Cross-Spatial Pixel Integration and Cross-Stage Feature Fusion Based Transformer Network (SPIFFNet) for RSISR. Our proposed model effectively enhances global cognition and understanding of the entire image, facilitating efficient integration of features cross-stages. The model incorporates cross-spatial pixel integration attention (CSPIA) to introduce contextual information into a local window, while cross-stage feature fusion attention (CSFFA) adaptively fuses features from the previous stage to improve feature expression in line with the requirements of the current stage. We conducted comprehensive experiments on multiple benchmark datasets, demonstrating the superior performance of our proposed SPIFFNet in terms of both quantitative metrics and visual quality when compared to state-of-the-art methods

Volatiles from cotton aphid (Aphis gossypii) infested plants attract the natural enemy Hippodamia variegata

The Aphis gossypii is a major threat of cotton worldwide due to its short life cycle and rapid reproduction. Chemical control is the primary method used to manage the cotton aphid, which has significant environmental impacts. Therefore, prioritizing eco-friendly alternatives is essential for managing the cotton aphid. The ladybird, Hippodamia variegata, is a predominant predator of the cotton aphid. Its performance in cotton plantation is directly linked to chemical communication, where volatile compounds emitted from aphid-infested plants play important roles in successful predation. Here, we comprehensively studied the chemical interaction between the pest, natural enemy and host plants by analyzing the volatile profiles of aphid-infested cotton plants using gas chromatography-mass spectrometry (GC-MS). We then utilized the identified volatile compounds in electrophysiological recording (EAG) and behavioral assays. Through behavioral tests, we initially demonstrated the clear preference of both larvae and adults of H. variegata for aphid-infested plants. Subsequently, 13 compounds, namely α-pinene, cis-3-hexenyl acetate, 4-ethyl-1-octyn-3-ol, β-ocimene, dodecane, E-β-farnesene, decanal, methyl salicylate, β-caryophyllene, α-humulene, farnesol, DMNT, and TMTT were identified from aphid-infested plants. All these compounds were electrophysiologically active and induced detectable EAG responses in larvae and adults. Y-tube olfactometer assays indicated that, with few exceptions for larvae, all identified chemicals were attractive to H. variegata, particularly at the highest tested concentration (100 mg/ml). The outcomes of this study establish a practical foundation for developing attractants for H. variegata and open avenues for potential advancements in aphid management strategies by understanding the details of chemical communication at a tritrophic level

A Spin-dependent Machine Learning Framework for Transition Metal Oxide Battery Cathode Materials

Owing to the trade-off between the accuracy and efficiency, machine-learning-potentials (MLPs) have been widely applied in the battery materials science, enabling atomic-level dynamics description for various critical processes. However, the challenge arises when dealing with complex transition metal (TM) oxide cathode materials, as multiple possibilities of d-orbital electrons localization often lead to convergence to different spin states (or equivalently local minimums with respect to the spin configurations) after ab initio self-consistent-field calculations, which causes a significant obstacle for training MLPs of cathode materials. In this work, we introduce a solution by incorporating an additional feature - atomic spins - into the descriptor, based on the pristine deep potential (DP) model, to address the above issue by distinguishing different spin states of TM ions. We demonstrate that our proposed scheme provides accurate descriptions for the potential energies of a variety of representative cathode materials, including the traditional Li$_x$TMO$_2$ (TM=Ni, Co, Mn, $x$=0.5 and 1.0), Li-Ni anti-sites in Li$_x$NiO$_2$ ($x$=0.5 and 1.0), cobalt-free high-nickel Li$_x$Ni$_{1.5}$Mn$_{0.5}$O$_4$ ($x$=1.5 and 0.5), and even a ternary cathode material Li$_x$Ni$_{1/3}$Co$_{1/3}$Mn$_{1/3}$O$_2$ ($x$=1.0 and 0.67). We highlight that our approach allows the utilization of all ab initio results as a training dataset, regardless of the system being in a spin ground state or not. Overall, our proposed approach paves the way for efficiently training MLPs for complex TM oxide cathode materials

Studies on solid phase synthesis,characterization and fluorescent property of the new rare earth complexes

Rare earth-β-diketone ligand complex luminescent material has stable chemical properties and excellent luminous property. Using europium oxide and (γ-NTA) as raw materials, novel rare earth-β-dione complexes are synthesized by solid state coordination chemistry. The synthesis temperature and milling time are discussed for optimization. Experimental results show that the suitable reaction situation is at 50 ℃ and 20 h for solid-phase synthesis. The compositions and structures of the complexes are characterized by means of elemental analysis, UV-Vis and FTIR methods, and the phase stability of the complex is determined by using TG-DTA technique. It is proved that preparation of waterless binary rare earth complexes by the solid phase reaction method results in a higher product yield. The fluorescence spectra show that between Eu (Ⅲ) and γ-NTA, there exists efficient energy transfer, and the rare earth complexes synthesis is an excellent red bright light-emitting material with excellent UV excited luminescence properties

Application Development of Lignin in Valuable Engineering Polymers Via Mild Thermochemical and Mechanochemical Process

NARA researchers developed new technologies for the preparation of engineering polymers from NARA lignin and explore the applications

High-Yield Production of Lignin-Derived Functional Carbon Nanosheet for Dye Adsorption

In this article, we report the preparation of lignin-derived carbon nanosheet (L-CNS) by direct thermal treatment of lignin without activation operation and the functions of the L-CNS as an adsorbent for rhodamine dye. The L-CNSs are fabricated by freeze-drying (FD) methods of lignin followed by high-temperature carbonization. It is found that lower frozen temperature in FD or lower concentration of lignin aqueous solution renders L-CNSs' more porous morphology and higher specific surface area (SSA), allowing a promising application of the L-CNSs as an efficient adsorbent for organic pollutants. In particular, the alkaline hydroxide catalyst helps to increase the SSA of carbon products, leading to a further improved adsorption capacity. On the other hand, p-toluenesulfonic acid (TsOH) catalyzed pyrolysis, which dramatically increased the L-CNS product yield, and provided a high-yield approach for the production of pollutant absorbent

High-Yield Exfoliation of Monolayer 1T '-MoTe2 as Saturable Absorber for Ultrafast Photonics

10.1021/acsnano.1c08093ACS NANO151118448-1845