(Z)-3-(3,4-Dimeth­oxy­phen­yl)-3-(4-fluoro­phen­yl)-1-morpholino­prop-2-en-1-one

The title compound, C21H22FNO4, is an isomer of flumorph (systematic name 4-[3-(3,4-dimethoxyphenyl)-3-(4-fluorophenyl)-1-oxo-2-propenyl]morpholine), which was developed by Shenyang research institute of chemical industry and used as fungicide. The mol­ecule adopts a Z configuration about the C=C double bond. The dihedral angle between the two benzene rings is 73.45 (11)°

Shubnikov-de Haas oscillations of a single layer graphene under dc current bias

Shubnikov-de Haas (SdH) oscillations under a dc current bias are experimentally studied on a Hall bar sample of single layer graphene. In dc resistance, the bias current shows the common damping effect on the SdH oscillations and the effect can be well accounted for by an elevated electron temperature that is found to be linearly dependent on the current bias. In differential resistance, a novel phase inversion of the SdH oscillations has been observed with increasing dc bias, namely we observe the oscillation maxima develop into minima and vice versa. Moreover, it is found that the onset biasing current, at which a SdH extremum is about to invert, is linearly dependent on the magnetic field of the SdH extrema. These observations are quantitatively explained with the help of a general SdH formula.Comment: 5 pages, 4 figures, A few references adde

Simulation Study of Swarm Intelligence Based on Life Evolution Behavior

Swarm intelligence (SI) is a new evolutionary computation technology, and its performance efficacy is usually affected by each individual behavior in the swarm. According to the genetic and sociological theory, the life evolution behavior process is influenced by the external and internal factors, so the mechanisms of external and internal environment change must be analyzed and explored. Therefore, in this paper, we used the thought of the famous American genetic biologist Morgan, “life = DNA + environment + interaction of environment + gene,” to propose the mutation and crossover operation of DNA fragments by the environmental change to improve the performance efficiency of intelligence algorithms. Additionally, PSO is a random swarm intelligence algorithm with the genetic and sociological property, so we embed the improved mutation and crossover operation to particle swarm optimization (PSO) and designed DNA-PSO algorithm to optimize single and multiobjective optimization problems. Simulation experiments in single and multiobjective optimization problems show that the proposed strategies can effectively improve the performance of swarm intelligence

Silicon Derived from Glass Bottles as Anode Materials for Lithium Ion Full Cell Batteries.

Every year many tons of waste glass end up in landfills without proper recycling, which aggravates the burden of waste disposal in landfill. The conversion from un-recycled glass to favorable materials is of great significance for sustainable strategies. Recently, silicon has been an exceptional anode material towards large-scale energy storage applications, due to its extraordinary lithiation capacity of 3579 mAh g-1 at ambient temperature. Compared with other quartz sources obtained from pre-leaching processes which apply toxic acids and high energy-consuming annealing, an interconnected silicon network is directly derived from glass bottles via magnesiothermic reduction. Carbon-coated glass derived-silicon (gSi@C) electrodes demonstrate excellent electrochemical performance with a capacity of ~1420 mAh g-1 at C/2 after 400 cycles. Full cells consisting of gSi@C anodes and LiCoO2 cathodes are assembled and achieve good initial cycling stability with high energy density

Numerical simulations for analyzing deformation characteristics of hydrate-bearing sediments during depressurization

Natural gas hydrates have been treated as a potential energy resource for decades. Understanding geomechanical properties of hydrate-bearing porous media is an essential to protect the safety of individuals and devices during hydrate production. In this work, a numerical simulator named GrapeFloater is developed to study the deformation behavior of hydrate-bearing porous media during depressurization, and the numerical simulator couples multiple processes such as conductive-convective heat transfer, two-phase fluid flow, intrinsic kinetics of hydrate dissociation, and deformation of solid skeleton. Then, a depressurization experiment is carried out to validate the numerical simulator. A parameter sensitivity analysis is performed to discuss the deformation behavior of hydrate-bearing porous media as well as its effect on production responses. Conclusions are drawn as follows: the numerical simulator named GrapeFloater predicts the experimental results well; the modulus of hydrate-bearing porous media has an obvious effect on production responses; final deformation increases with decreasing outlet pressure; both the depressurization and the modulus decrease during hydrate dissociation contribute to the deformation of hydrate-bearing porous media.Cited as: Liu, L., Lu, X., Zhang, X., et al. Numerical simulations for analyzing deformation characteristics of hydrate-bearing sediments during depressurization. Advances in Geo-Energy Research, 2017, 1(3): 135-147, doi: 10.26804/ager.2017.03.0

CLMFormer: Mitigating Data Redundancy to Revitalize Transformer-based Long-Term Time Series Forecasting System

Long-term time-series forecasting (LTSF) plays a crucial role in various practical applications. Transformer and its variants have become the de facto backbone for LTSF, offering exceptional capabilities in processing long sequence data. However, existing Transformer-based models, such as Fedformer and Informer, often achieve their best performances on validation sets after just a few epochs, indicating potential underutilization of the Transformer's capacity. One of the reasons that contribute to this overfitting is data redundancy arising from the rolling forecasting settings in the data augmentation process, particularly evident in longer sequences with highly similar adjacent data. In this paper, we propose a novel approach to address this issue by employing curriculum learning and introducing a memory-driven decoder. Specifically, we progressively introduce Bernoulli noise to the training samples, which effectively breaks the high similarity between adjacent data points. To further enhance forecasting accuracy, we introduce a memory-driven decoder. This component enables the model to capture seasonal tendencies and dependencies in the time-series data and leverages temporal relationships to facilitate the forecasting process. The experimental results on six real-life LTSF benchmarks demonstrate that our approach can be seamlessly plugged into varying Transformer-based models, with our approach enhancing the LTSF performances of various Transformer-based models by maximally 30%.Comment: Tech repor

Application of photo-crosslinkable gelatin methacryloyl in wound healing

Wound healing is a complex and coordinated biological process easily influenced by various internal and external factors. Hydrogels have immense practical importance in wound nursing because of their environmental moisturising, pain-relieving, and cooling effects. As photo-crosslinkable biomaterials, gelatine methacryloyl (GelMA) hydrogels exhibit substantial potential for tissue repair and reconstruction because of their tunable and beneficial properties. GelMA hydrogels have been extensively investigated as scaffolds for cell growth and drug release in various biomedical applications. They also hold great significance in wound healing because of their similarity to the components of the extracellular matrix of the skin and their favourable physicochemical properties. These hydrogels can promote wound healing and tissue remodelling by reducing inflammation, facilitating vascularisation, and supporting cell growth. In this study, we reviewed the applications of GelMA hydrogels in wound healing, including skin tissue engineering, wound dressing, and transdermal drug delivery. We aim to inspire further exploration of their potential for wound healing

Experimental study on evolution behaviors of triaxial-shearing parameters for hydrate-bearing intermediate fine sediment

Evolution behaviors of triaxial shearing parameters are very important for geo-technical re- sponse analysis during the process of extracting natural gas from hydrate-bearing reservoirs. In order to explore the effects of hydrate formation/decomposition on triaxial shearing behaviors of intermediate fine sediment, natural beach sand in Qingdao, China, which was sieved from 0.1 to 0.85 mm, was used and a series of triaxial shear tests were carried out in this paper. The principle of critical state was firstly used to explain the mechanism of strain softening and/or hardening failure mode. Moreover, an empirical model was provided for axial-lateral strain and corresponding model parameters calculation. Evolution rules of critical strength parameters were analyzed prominently. The results show that failure mode of sediment is controlled by several parameters, such as effective confining pressure, hydrate saturation, etc. Different axial-lateral strain model coefficients’ effect on strain relationships are different, probing into the physical meaning of each coefficient is essential for further understanding of strain relationships. Complex geo-technical response should be faced with the progress of producing natural gas from hydrate-bearing reservoir, because of sudden change of failure pattern and formation modulus. Further compressive study on critical condition of failure pattern is needed for proposed promising hydrate-bearing reservoirs.Cited as: Li, Y., Liu, C., Liu, L., Sun, J., Liu, H., Meng, Q. Experimental study on evolution behaviors of triaxial-shearing parameters for hydrate-bearing intermediate fine sediment. Advances in Geo-Energy Research, 2018, 2(1): 43-52, doi: 10.26804/ager.2018.01.0