Soil respiration in cucumber field under crop rotation in solar greenhouse

Crop residues are the primary source of carbon input in the soil carbon pool. Crop rotation can impact the plant biomass returned to the soil, and influence soil respiration. To study the effect of previous crops on soil respiration in cucumber (Cucumis statirus L.) fields in solar greenhouses, soil respiration, plant height, leaf area and yield were measured during the growing season (from the end of Sept to the beginning of Jun the following year) from 2007 to 2010. The cucumber was grown following fallow (CK), kidney bean (KB), cowpea (CP), maize for green manure (MGM), black bean for green manure (BGM), tomato (TM), bok choy (BC). As compared with CK, KB, CP, MGM and BGM may increase soil respiration, while TM and BC may decrease soil respiration at full fruit stage in cucumber fields. Thus attention to the previous crop arrangement is a possible way of mitigating soil respiration in vegetable fields. Plant height, leaf area and yield had similar variation trends under seven previous crop treatments. The ratio of yield to soil respiration revealed that MGM is the crop of choice previous to cucumber when compared with CK, KB, CP, BGM, TM and BC

Irányítás mint hajózás – a „governance” értelméről

„A végtelen sajátosságának tűnik, hogy mindenhol és mindenben jelen legyen.” (Aquinói Szt. Tamás: Summa Theologiae

Classification-Aided Robust Multiple Target Tracking Using Neural Enhanced Message Passing

We address the challenge of tracking an unknown number of targets in strong clutter environments using measurements from a radar sensor. Leveraging the range-Doppler spectra information, we identify the measurement classes, which serve as additional information to enhance clutter rejection and data association, thus bolstering the robustness of target tracking. We first introduce a novel neural enhanced message passing approach, where the beliefs obtained by the unified message passing are fed into the neural network as additional information. The output beliefs are then utilized to refine the original beliefs. Then, we propose a classification-aided robust multiple target tracking algorithm, employing the neural enhanced message passing technique. This algorithm is comprised of three modules: a message-passing module, a neural network module, and a Dempster-Shafer module. The message-passing module is used to represent the statistical model by the factor graph and infers target kinematic states, visibility states, and data associations based on the spatial measurement information. The neural network module is employed to extract features from range-Doppler spectra and derive beliefs on whether a measurement is target-generated or clutter-generated. The Dempster-Shafer module is used to fuse the beliefs obtained from both the factor graph and the neural network. As a result, our proposed algorithm adopts a model-and-data-driven framework, effectively enhancing clutter suppression and data association, leading to significant improvements in multiple target tracking performance. We validate the effectiveness of our approach using both simulated and real data scenarios, demonstrating its capability to handle challenging tracking scenarios in practical radar applications.Comment: 15 page