Evaluating soil nitrate dynamics in an intercropping dripped ecosystem using HYDRUS-2D.

The competition mechanisms between crop species for water and nutrients, especially nitrate (NO3-N), in intercropping ecosystems are still poorly understood. Therefore, an experiment involving high (300 kg ha-1 for corn and 250 kg ha-1 for tomato), medium (210 kg ha-1 for corn and 175 kg ha-1 for tomato), and low (150 kg ha-1 for corn and 125 kg ha-1 for tomato) N-fertilizer applications (HF, MF, LF, respectively) was conducted in the corn and tomato intercropping ecosystem during 2014 (a calibration period for modeling) and 2015 (a validation period for modeling). The modified HYDRUS-2D code was used to analyze soil NO3-N concentrations (SNC) in the middle between corn rows (Pc), between corn and tomato rows (Pb), and between tomato rows (Pt), NO3-N exchange in the horizontal direction between different regions, NO3-N leaching from the corn, the bare, and the tomato region, and N uptake by crops. Simulated SNCs were in good agreement with measurements, with RMSE, NSE, and MRE of 0.01-0.06 mg cm-3, 0.75-0.98, and 8.7-19.1%, respectively, during the validation period (2015). Average SNCs in the 0-40 cm soil layer were different between Pc, Pt, and Pb. Intensive NO3-N exchange in the horizontal direction occurred during the second stage (Day After Sowing [DAS] 37-113 in 2014; DAS 29-120 in 2015). NO3-N exchange between the corn and bare regions was lower than between the tomato and bare regions due to smaller concentration gradients. However, in the vertical direction, NO3-N leaching from the corn region in both years was 4.1 and 8.8 times larger, respectively, than from the tomato region under HF since NO3-N mainly moved from the tomato region to the corn region. Our results reveal the competition between corn and tomato for N and provide a rationale for formulating and optimizing different fertilizer regimes for different crops in the intercropping ecosystem

Developing High-Throughput Chemical Approaches For Proteomic Profiling Of Aspartic Proteases And Protein Kinases

Ph.DDOCTOR OF PHILOSOPH

Dynamic behavior analysis and time delay feedback control of gear pair system with backlash non-smooth characteristic

The present work investigates the non-smooth vibration characteristic and time delay feedback control of a gear pair system involving backlash and time-varying mesh stiffness. Firstly, a gear pair model with backlash non-smooth characteristic is established. Then in combination with the discontinuity mapping method, Floquet theory is presented to determine the stability and bifurcation of periodic response, and the period doubling bifurcation has been accurately predicted. Moreover, the maximal Lyapunov exponent is obtained to determine the chaos state in gear pair system which is conform to the bifurcation diagram and Poincare section. Finally, a time delay feedback is introduced to control the dynamic behaviors of the system, and numerical simulation results show that the system can be effectively controlled from chaotic motion into stable periodic motion by increasing the delay feedback gain or delay time

Agents meet OKR: An Object and Key Results Driven Agent System with Hierarchical Self-Collaboration and Self-Evaluation

In this study, we introduce the concept of OKR-Agent designed to enhance the capabilities of Large Language Models (LLMs) in task-solving. Our approach utilizes both self-collaboration and self-correction mechanism, facilitated by hierarchical agents, to address the inherent complexities in task-solving. Our key observations are two-fold: first, effective task-solving demands in-depth domain knowledge and intricate reasoning, for which deploying specialized agents for individual sub-tasks can markedly enhance LLM performance. Second, task-solving intrinsically adheres to a hierarchical execution structure, comprising both high-level strategic planning and detailed task execution. Towards this end, our OKR-Agent paradigm aligns closely with this hierarchical structure, promising enhanced efficacy and adaptability across a range of scenarios. Specifically, our framework includes two novel modules: hierarchical Objects and Key Results generation and multi-level evaluation, each contributing to more efficient and robust task-solving. In practical, hierarchical OKR generation decomposes Objects into multiple sub-Objects and assigns new agents based on key results and agent responsibilities. These agents subsequently elaborate on their designated tasks and may further decompose them as necessary. Such generation operates recursively and hierarchically, culminating in a comprehensive set of detailed solutions. The multi-level evaluation module of OKR-Agent refines solution by leveraging feedback from all associated agents, optimizing each step of the process. This ensures solution is accurate, practical, and effectively address intricate task requirements, enhancing the overall reliability and quality of the outcome. Experimental results also show our method outperforms the previous methods on several tasks. Code and demo are available at https://okr-agent.github.io

Study on Modification of Lignin as Dispersant of Aqueous Graphene Suspension and Corrosion Performance in Waterborne G/Epoxy Coating

Though graphene (G) as an excellent protective material for metal, it can aggravate metal corrosion in other side. The modification of sodium lignin sulfonate was achieved by using itaconic acid and acrylamide，which was proved by UV-vis and Raman spectra. The modified sodium lignin sulfonate (LAI) with more carboxylic groups can be used as the dispersant for aqueous graphene suspension. The commercial graphene can be dispersed uniformly and stability in water via π-π interaction with LAI at high concentration (6 mg/mL)，and the LAI-G system can be used as an inhibitor in waterborne epoxy coatings too. Electrochemical impedance spectroscope (EIS) and Tafel polarization curves showed that the corrosion performance of waterborne epoxy system with well-dispersed G (0.5 wt %) was remarkably improved compared with pure epoxy coating