Chemical Fractionation Of Cu And Zn And Their Impacts On Microbial Properties In Slightly Contaminated Soils

Chemical fractionation of Cu and Zn in bulk soil and its effects on soil microbial properties were determined in Cu and Zn contaminated soils (Cu: 35.57~46.37 mg·kg-1, Zn: 74.33~127.20 mg·kg-1) sampled from an agricultural field in outskirts of Zibo, China during the month of September, 2011. A sequential extraction technique (SET) was used for metals chemical fractionation analysis in soils and a correlation analysis was applied to determinate the effects of metal on soil microbial properties. Chemical speciation showed that Cu and Zn were mostly present in the residual fraction and their concentrations in the most labile fraction (acid soluble fraction) were the lowest in the investigated soils. However, the correlation analysis indicated that the labile forms of Cu/Zn, such as its acid soluble, reducible or oxidizable fractions, were usually significantly negatively correlated with the tested microbial activities at 0.05 or 0.01 probability levels. These results indicate that the metal labile fractions could exert an inhibitory effect on the soil microbial parameters even in the minor contaminated soils. Int. J. Agril. Res. Innov. & Tech. 3 (1): 20-25, June, 2013 DOI: http://dx.doi.org/10.3329/ijarit.v3i1.1604

Robust Switching Control Strategy for a Transmission System with Unknown Backlash

This paper proposes a robust switching control strategy for a transmission system with unknown backlash. Firstly, the adverse effects of backlash nonlinearity in the transmission system are analyzed. Then the backlash model and different operating modes of the transmission system with backlash are investigated. For each operating mode, an individual controller is designed to make the system robust against the unknown backlash and various frequencies of the input signal. Moreover, a supervisory controller is proposed to estimate the current mode of the transmission system and coordinate the controller switching between different modes. Simulations are done to verify that our switching control strategy can efficiently reduce the oscillation caused by backlash and is quite robust against the variation of the frequency of the input signal

Exploring Self- and Cross-Triplet Correlations for Human-Object Interaction Detection

Human-Object Interaction (HOI) detection plays a vital role in scene understanding, which aims to predict the HOI triplet in the form of <human, object, action>. Existing methods mainly extract multi-modal features (e.g., appearance, object semantics, human pose) and then fuse them together to directly predict HOI triplets. However, most of these methods focus on seeking for self-triplet aggregation, but ignore the potential cross-triplet dependencies, resulting in ambiguity of action prediction. In this work, we propose to explore Self- and Cross-Triplet Correlations (SCTC) for HOI detection. Specifically, we regard each triplet proposal as a graph where Human, Object represent nodes and Action indicates edge, to aggregate self-triplet correlation. Also, we try to explore cross-triplet dependencies by jointly considering instance-level, semantic-level, and layout-level relations. Besides, we leverage the CLIP model to assist our SCTC obtain interaction-aware feature by knowledge distillation, which provides useful action clues for HOI detection. Extensive experiments on HICO-DET and V-COCO datasets verify the effectiveness of our proposed SCTC