Utility of stream mesocosms for climate change research

The utility of stream mesocosms was examined in a study of replicability of water physicochemstry and benthic macroinvertebrate assemblages in an array of artificial flumes near the River Itchen in southern U.K. High quality groundwater supply and similar exposure to the environment lead water physicochemistry to be highly replicate across all channels. The within- and between-flume replicate design reduced macroinvertebrate assemblages’ variability temporally, but the structure of macroinvertebrate assemblages in mesocosms shift seasonally. The highly temporal replicability of mesocosms allowed a long-term (i.e. 1- year) study of drought in these stream mesocosms. Seven water depth treatments were applied in a series (n=21) of artificial flumes to construct a linear varying drought gradient so that each treatment was replicated three times. The drought experiment lasted a course from August, 2013 to August, 2014. Algal growth and the abundance of three grazer taxa were negatively correlated with both drought intensity and drought duration. Additionally, the drought intensity impact on algal growth shifted with drought duration. Conversely, drought intensity had a fixed negative impact on decomposition process. Shredder community structure was altered by drought impact reducing shredder abundance and shredding efficiency. However, the shredding efficiency in freshwater ecosystem was more related to shredding efficiency of specialist shredder rather than shredder abundance. The mesocosms could mimic freshwater ecosystem physiochemistry environment and macroinvertebrate assemblage effectively and comprehensively, which provided an access to study the impact of natural disturbance on freshwater ecosystem. This study developed the understanding of the drought effect on the entire freshwater ecosystem

Causality-based Dual-Contrastive Learning Framework for Domain Generalization

Domain Generalization (DG) is essentially a sub-branch of out-of-distribution generalization, which trains models from multiple source domains and generalizes to unseen target domains. Recently, some domain generalization algorithms have emerged, but most of them were designed with non-transferable complex architecture. Additionally, contrastive learning has become a promising solution for simplicity and efficiency in DG. However, existing contrastive learning neglected domain shifts that caused severe model confusions. In this paper, we propose a Dual-Contrastive Learning (DCL) module on feature and prototype contrast. Moreover, we design a novel Causal Fusion Attention (CFA) module to fuse diverse views of a single image to attain prototype. Furthermore, we introduce a Similarity-based Hard-pair Mining (SHM) strategy to leverage information on diversity shift. Extensive experiments show that our method outperforms state-of-the-art algorithms on three DG datasets. The proposed algorithm can also serve as a plug-and-play module without usage of domain labels

1D NiHPO4 nanotubes prepared using dissolution equilibrium as bifunctional electrocatalyst for high-efficiency water splitting

In this work, one-dimensional NiHPO4 nanotubes are successfully fabricated on nickel foam by hydrothermal reaction, in which a dissolution equilibrium between phosphates is controlled by tuning the proportion of the mixed solvent and amounts of KOH. As the dissolution equilibrium is broken, the morphology of NiHPO4 transfers from solid nanowires to hollow nanotubes. The resulting 1D NiHPO4 nanotubes exhibit good electrocatalytic activity and stability in oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Notably, a water-splitting voltage of 1.62 V at a current density of 10 mA cm 2 is obtained in an electrolyzer setup assembled using 1D NiHPO4 nanotubes as cathode and anode, demonstrating NiHPO4 nanotubes are promising catalysts for overall water splitting. Moreover, the revealed mechanism of forming tube morphology can be extended to fabricate other metal phosphates with hollow structures

The second fusion of laser and aerospace—an inspiration for high energy lasers

Since the first laser was invented, the pursuit of high-energy lasers (HELs) has always been enthusiastic. The first revolution of HELs was pushed by the fusion of laser and aerospace in the 1960s, with the chemical rocket engines giving fresh impetus to the birth of gas flow and chemical lasers, which finally turned megawatt lasers from dream into reality. Nowadays, the development of HELs has entered the age of electricity as well as the rocket engines. The properties of current electric rocket engines are highly consistent with HELs’ goals, including electrical driving, effective heat dissipation, little medium consumption and extremely light weight and size, which inspired a second fusion of laser and aerospace and motivated the exploration for potential HELs. As an exploratory attempt, a new configuration of diode pumped metastable rare gas laser was demonstrated, with the gain generator resembling an electric rocket-engine for improved power scaling ability

Position of meristems and the angles of the cell division plane regulate the uniqueness of lateral organ shape

花びらの形が葉と違う仕組みを解明. 京都大学プレスリリース. 2022-12-12.Leaf meristem is a cell proliferative zone present in the lateral organ primordia. In this study, we examined how cell proliferative zones in primordia of planar floral organs and polar auxin transport inhibitor (PATI)-treated leaf organs differ from those of non-treated foliage leaves of Arabidopsis thaliana, with a focus on the accumulation pattern of ANGUSTIFOLIA3 (AN3) protein, a key element for leaf meristem positioning. We found that PATI-induced leaf shape changes were correlated with cell division angle but not with meristem positioning/size or AN3 localisation. In contrast, different shapes between sepals and petals compared with foliage leaves were associated with both altered meristem position, due to altered AN3 expression patterns, and different distributions of cell division angles. A numerical simulation showed that meristem position majorly affected the final shape but biased cell division angles had a minor effect. Taken together, these results suggest that the unique shapes of different lateral organs depend on the position of the meristem in the case of floral organs and cell division angles in the case of leaf organs with different auxin flow

Which Channel to Ask My Question? Personalized Customer Service Request Stream Routing using Deep Reinforcement Learning

Customer services are critical to all companies, as they may directly connect to the brand reputation. Due to a great number of customers, e-commerce companies often employ multiple communication channels to answer customers' questions, for example, chatbot and hotline. On one hand, each channel has limited capacity to respond to customers' requests, on the other hand, customers have different preferences over these channels. The current production systems are mainly built based on business rules, which merely considers tradeoffs between resources and customers' satisfaction. To achieve the optimal tradeoff between resources and customers' satisfaction, we propose a new framework based on deep reinforcement learning, which directly takes both resources and user model into account. In addition to the framework, we also propose a new deep-reinforcement-learning based routing method-double dueling deep Q-learning with prioritized experience replay (PER-DoDDQN). We evaluate our proposed framework and method using both synthetic and a real customer service log data from a large financial technology company. We show that our proposed deep-reinforcement-learning based framework is superior to the existing production system. Moreover, we also show our proposed PER-DoDDQN is better than all other deep Q-learning variants in practice, which provides a more optimal routing plan. These observations suggest that our proposed method can seek the trade-off where both channel resources and customers' satisfaction are optimal.Comment: 13 pages, 7 figure