Chemically-Passive Suppression of Laminar Premixed Hydrogen Flames in Microgravity

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76993/1/AIAA-2006-741-196.pd

A review on heterogeneous solid catalysts and related catalytic mechanisms for epoxidation of olefins with H2O2

The epoxidation reaction using heterogeneous solid catalysts with H2O2 as oxidants are environmentally friendly routes to produce extensively useful epoxides which are traditionally obtained from capital-intensive or environmentally polluted processes. In this paper, various types of solid catalysts for the epoxidation of olefins with H2O2 as oxidants are reviewed. The efficient catalysts reported include microporous and mesoporous molecular sieves, layered-type materials, inorganic oxides, supported catalysts, zeolite encapsulated metal complexes, polyoxometalates, and supported organometallic catalysts. The proposed reaction mechanisms over different solid catalysts are summarized. The problems and perspectives to further efficiently improve the catalytic performances of the concerned heterogeneous catalysts for epoxidation reaction are remarked

The microstructure and mechanical properties of friction stir welded Ti6Al4V titanium alloy under β transus temperature

Ti6Al4V titanium alloy is friction stir welded using a W-Re rotational tool. The effects of welding speed on the microstructure, tensile strength and fracture properties of weld are investigated. At the rotational velocity of 250 r/min, the peak temperature is lower than β transus temperature, and the weld nugget is made up of fine α phase and transformed β phase. The grain size of shoulder affected zone is bigger than that of weld nugget because of low thermal conductivity of Ti6Al4V titanium alloy. By increasing the welding speed, the grain size of weld nugget, the tensile strength and the ductility of weld all are decreased

Task-Distributionally Robust Data-Free Meta-Learning

Data-Free Meta-Learning (DFML) aims to efficiently learn new tasks by leveraging multiple pre-trained models without requiring their original training data. Existing inversion-based DFML methods construct pseudo tasks from a learnable dataset, which is inversely generated from the pre-trained model pool. For the first time, we reveal two major challenges hindering their practical deployments: Task-Distribution Shift (TDS) and Task-Distribution Corruption (TDC). TDS leads to a biased meta-learner because of the skewed task distribution towards newly generated tasks. TDC occurs when untrusted models characterized by misleading labels or poor quality pollute the task distribution. To tackle these issues, we introduce a robust DFML framework that ensures task distributional robustness. We propose to meta-learn from a pseudo task distribution, diversified through task interpolation within a compact task-memory buffer. This approach reduces the meta-learner's overreliance on newly generated tasks by maintaining consistent performance across a broader range of interpolated memory tasks, thus ensuring its generalization for unseen tasks. Additionally, our framework seamlessly incorporates an automated model selection mechanism into the meta-training phase, parameterizing each model's reliability as a learnable weight. This is optimized with a policy gradient algorithm inspired by reinforcement learning, effectively addressing the non-differentiable challenge posed by model selection. Comprehensive experiments across various datasets demonstrate the framework's effectiveness in mitigating TDS and TDC, underscoring its potential to improve DFML in real-world scenarios

A Semi-Empirical Inversion Model for Assessing Surface Soil Moisture using AMSR-E Brightness Temperatures

In 2004-2005, 2007 and 2009, three major drought disasters occurred in Guangdong Province of southern China, which caused serious economic losses. Hence, it has recently become an important research subject in China to monitor surface soil moisture (SSM) and the drought disaster quickly and accurately. SSM is an effective indicator for characterizing the degree of drought. First, using the brightness temperatures (T[subscript b]) of the Advanced Microwave Scanning Radiometer on the EOS Aqua Satellite (AMSR-E), a modified surface roughness index was developed to map the land surface roughness. Then by combining microwave polarization difference indices (MPDI)-based vegetation cover classification and the modified surface roughness index, a simple semi-empirical model of SSM was derived from the passive microwave radiative transfer equation using AMSR-E C-band T[subscript b] and observed surface soil temperature (T[subscript s]). The model was inverted to calculate SSM. The results show the ability to discriminate over a broad range of SSM (7%~73%) with an accuracy of 2.11% in bare ground and flat areas (R² =0.87), 2.89% in sparse vegetation and flat surface areas (R²=0.85), about 6%~9% in dense vegetation areas and rough surface areas (0.80≤R²≤0.83). The simulation results were also validated using in-situ SSM data (R²=0.87, RMSE=6.36%). Time series mapping of SSM from AMSR-E imageries further demonstrated that the presented method was effective to detect the initiation, duration and recovery of the drought events.This is the author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by Elsevier and can be found at: http://www.journals.elsevier.com/journal-of-hydrology/.Keywords: Drought disaster, Brightness temperature (T[subscript b]), South China, Semi-Empirical model, Surface soil moisture (SSM), AMSR-E, Passive microwave remote sensin

Riben cang Han ji di li wen xian zhen ben cong shu

Table of contents for "Riben cang Zhongguo shan shui ci miao zhi zhen ben hui kan

Riben cang han ji di li wen xian zhen ben cong shu

Table of contents for "Riben cang Zhongguo shui li wen xian zhen ben hui kan

Study of Information Supply Chain and Artificial Neural Network’s Related Application

Users become less and less patient with huge useless data today. One of the great challenges now most net searching engines meet is how to get valuable information from lots of data sets. Aiming to satisfy every user’s special demand, we need to integrate and optimize the whole course of data searching, including adjusting the users’ input keywords, searching original results from network, and further processing of these results. Learning from the idea of Supply Chain Management, we put forward the concept of Information Supply Chain (ISC) in this paper to generalize the course above .For ISC’s optimization, artificial neural network is chosen as a tool to find out the relationships between different keywords and paper categories, which are summarized and stored in knowledge base. Based on it, the process of selecting proper keywords and searching news information could be more efficient. A pruning method named MW-OBS is illustrated to train ANN as well. Some details about the framework and components are also mentioned, especially on how an individual step in ISC works, what’s the relationship between them, and how they coordinate to meet every personal demand. ISC, an integrated information processing in the interests of users’ individual need, has great advantages over simple searching from network with original keywords