Crop growth in sandy soils is usually limited by plant-available nutrients and water contents. This study was conducted to determine whether these limiting factors could be improved through applications of compost and biochar. For this purpose, a maize (Zea mays L.) field trial was established at 1 ha area of a Dystric Cambisol in Brandenburg, NE Germany. Five treatments (control, compost, and three biochar-compost mixtures with constant compost amount (32.5 Mg ha -1 ) and increasing biochar amount, ranging from 5-20 Mg ha -1 ) were compared. Analyses comprised total organic C (TOC), total N (TN), plant-available nutrients, and volumetric soil water content for 4 months under field conditions during the growing season 2009. In addition, soil water-retention characteristics were analyzed on undisturbed soil columns in the laboratory. Total organic-C content could be increased by a factor of 2.5 from 0.8 to 2% (p < 0.01) at the highest biochar-compost level compared with control while TN content only slightly increased. Plant-available Ca, K, P, and Na contents increased by a factor of 2.2, 2.5, 1.2, and 2.8, respectively. With compost addition, the soil pH value significantly increased by up to 0.6 (p < 0.05) and plant-available soil water retention increased by a factor of 2. Our results clearly demonstrated a synergistic positive effect of compost and biochar mixtures on soil organic-matter content, nutrients levels, and water-storage capacity of a sandy soil under field conditions.

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We consider incorporating topic information into a sequence-to-sequence framework to generate informative and interesting responses for chatbots. To this end, we propose a topic aware sequence-to-sequence (TA-Seq2Seq) model. The model utilizes topics to simulate prior human knowledge that guides them to form informative and interesting responses in conversation, and leverages topic information in generation by a joint attention mechanism and a biased generation probability. The joint attention mechanism summarizes the hidden vectors of an input message as context vectors by message attention and synthesizes topic vectors by topic attention from the topic words of the message obtained from a pre-trained LDA model, with these vectors jointly affecting the generation of words in decoding. To increase the possibility of topic words appearing in responses, the model modifies the generation probability of topic words by adding an extra probability item to bias the overall distribution. Empirical studies on both automatic evaluation metrics and human annotations show that TA-Seq2Seq can generate more informative and interesting responses, significantly outperforming state-of-the-art response generation models.

In most cases, to obtain high‐performance electrode materials for lithium‐ion batteries (LIBs), it is necessary to optimize both their molecular structure and morphology. Normally, the molecular structure of covalent organic frameworks (COFs) can be well engineered by chemical design, while their morphology is mainly optimized by post‐processing. Herein, by introducing a flexible building unit containing sp3 N redox‐active centers, a bipolar‐type TP‐TA COF assembled by uniform 2D hexagonal nanosheets is synthesized in a one‐step reaction without any post‐processing, achieving the highly challenging simultaneous optimization of both molecular structure and morphology required for high‐performance electrode materials. Thus, when used as cathode material for LIBs, its combined optimized chemical structure and favorable morphology of TP‐TA COF synergistically render a high capacity (207 mA h g−1 at 200 mA g−1), excellent rate performance (129 mA h g−1 at 5.0 A g−1), and cycling stability (93% capacity retention after 1500 cycles at 5.0 A g−1).

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