NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image

This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole- scene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB images are themselves calculated numerically using the ground-truth HS images and supplied spectral sensitivity functions (ii) a "Real World" track, simulating capture by an uncalibrated and unknown camera, where the HS images are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever, natural hyperspectral image data set is presented, containing a total of 510 HS images. The Clean and Real World tracks had 103 and 78 registered participants respectively, with 14 teams competing in the final testing phase. A description of the proposed methods, alongside their challenge scores and an extensive evaluation of top performing methods is also provided. They gauge the state-of-the-art in spectral reconstruction from an RGB image

Effect of electroacupuncture at distal–proximal acupoint combinations on spinal interleukin-1 beta in a rat model of neuropathic pain

Objective: Pain from herniated disc is a common type of neuropathic pain. This study investigated whether electroacupuncture (EA) stimulation at distal–proximal combinations of acupoints in the rat model of neuropathic pain modulates spinal interleukin-1 beta (IL-1β) to induce acupuncture analgesia and possibly serve as a pain-relief modality for herniated disc. Methods: A rat model of neuropathic pain was established. Rats were randomly divided into normal, model, sham, EA 1, EA 2, and EA 3 groups. EA 1 rats were needled at bilateral Ex-B2, BL25, BL40, and BL60 acupoints. EA 2 rats were needled at bilateral BL40 and BL60. EA 3 rats were needled at bilateral L5 Ex-B2 and BL25. EA stimulation was administered once daily over 7 days. Mechanical withdrawal threshold from noxious mechanical stimulation was measured 1 day preoperatively and at 3, 5, and 7 days postoperatively. After 7 days of intervention, enzyme-linked immunosorbent assay (ELISA) was used to quantify IL-1β in the spinal cord. Results: Mechanical withdrawal threshold of rats in the model group decreased at 3 days postoperatively when compared with the normal group (P < 0.01), lasting 7 days postoperatively. Mechanical withdrawal thresholds in the EA 1, EA 2, and EA 3 groups were elevated over the model group (P < 0.05; P < 0.01). No obvious differences were found between EA 1, EA 2, and EA 3 groups. ELISA demonstrated an increase in IL-1β in the spinal cord of rats in the model group compared with the normal group (P < 0.01). EA treatment attenuated the increase in spinal IL-1β in the model group. Expression of spinal IL-1β was significantly lower in EA 1, EA 2, and EA 3 groups. Conclusion: EA at distal + proximal acupoints, distal points, as well as proximal points attenuated upregulation of spinal IL-1β, alleviated the extent of neuropathic pain hypersensitivity, and promoted mechanical withdrawal threshold, resulting in EA analgesia

Effect of Ce Doping on Catalytic Performance of Cu/TiO2 for CO Oxidation

CO is toxic and detrimental to human beings and the atmosphere. The presence of CO has become one of the important indicators for evaluating ambient air quality. Iron and steel sintering generate large amounts of flue gas emissions containing approximately 1% CO, which has become an important factor restricting the improvement of air quality in steel cities. The catalytic oxidation method is the simplest and most effective method for CO emission control. In this work, a series of CuxCey/TiO2 catalysts with different Ce doping levels were prepared by a one-step impregnation method and subsequently tested for catalytic CO oxidation. The Cu0.5Ce0.5/TiO2 sample has the best catalytic activity under the conditions of 1% CO and 10% H2O. The catalysts were next characterized by XRD, BET, TEM, H-2-TPR, XPS and in-situ DRIFTS methods. The characterization results show that Ce doping promotes the dispersion of copper species on the catalyst surface and improve the redox performance of the catalyst. The results of in-situ experiments showed that both Cu/TiO2 and Cu0.5Ce0.5/TiO2 catalysts followed Mars and van Krevelen mechanism, and Ce doping enhanced the adsorption and activation of oxygen, thus improving the low-temperature reaction activity of the catalysts. [GRAPHICS]

Optimizing Nitrogen Application for Chinese Ratoon Rice Based on Yield and Reactive Nitrogen Loss

Ratoon rice (RR) has been regarded as a labor-saving and beneficial production system. Nitrogen (N) surplus and reactive N losses (Nr losses) are effective environmental indicators used to evaluate the performance of N management. Few studies have assessed N surplus and Nr losses for Chinese RR. In this study, Chinese RR planting areas were divided into South China (SC), the southern part of East China (SEC), Central China (CC), the northern part of East China (NEC), and Southwest China (SW). N surplus and Nr losses were also calculated based on 782 studies using a quadratic model under optimized N management for the highest yield (OPT-yield), the highest N-use efficiency (NUE) (OPT-NUE), and the highest grain N uptake (OPT-N uptake). The RR yields in the five regions ranged from 9.98 to 13.59 t ha&minus;1. The high-yield record was observed in SEC, while the low-yield record was observed in NEC. The highest and the lowest Nr losses were found in NEC and SC, respectively. N surplus was reduced, while the yield was maintained in SEC, CC, NEC, and SW under OPT-yield and OPT-N uptake, and N surplus and Nr losses were reduced in the five regions when targeting the highest NUE. Farmers should be encouraged to plant RR in SEC and CC. RR was also a good choice when N management measures were conducted in three other regions. To achieve a win&ndash;win situation for both yield and the environment, OPT-yield could serve to improve the N management of current conventional practices

DIMA:Distributed cooperative microservice caching for internet of things in edge computing by deep reinforcement learning

The ubiquitous Internet of Things (IoTs) devices spawn growing mobile services of applications with computationally-intensive and latency-sensitive features, which increases the data traffic sharply. Driven by container technology, microservice is emerged with flexibility and scalability by decomposing one service into several independent lightweight parts. To improve the quality of service (QoS) and alleviate the burden of the core network, caching microservices at the edge of networks empowered by the mobile edge computing (MEC) paradigm is envisioned as a promising approach. However, considering the stochastic retrieval requests of IoT devices and time-varying network topology, it brings challenges for IoT devices to decide the caching node selection and microservice replacement independently without complete information of dynamic environments. In light of this, a MEC-enabled di stributed cooperative m icroservice ca ching scheme, named DIMA, is proposed in this paper. Specifically, the microservice caching problem is modeled as a Markov decision process (MDP) to optimize the fetching delay and hit ratio. Moreover, a distributed double dueling deep Q-network (D3QN) based algorithm is proposed, by integrating double DQN and dueling DQN, to solve the formulated MDP, where each IoT device performs actions independently in a decentralized mode. Finally, extensive experimental results are demonstrated that the DIMA is well-performed and more effective than existing baseline schemes

Extraction of connected river networks from multi-temporal remote sensing imagery using a path tracking technique

Precise delineation of river networks is important for accurate hydrological and flood modelling. Whilst remote sensing (RS) has showed great potential in monitoring hydrological changes over space and time, the existing RS-based methods extract river networks based on local morphologies and seldom take into account the overall hydrological connectivity of the rivers. The existing methods also commonly neglect the effect of seasonal variation of water surfaces and the existence of temporary water bodies, which deteriorate the precision of positioning river networks. To address these challenges, a new two-stage method is developed to Extract spatiotemporal variation of water surfaces based on Multi-temporal remote sensing Imagery and Delineate connected river networks with improved accuracy (EMID method for short) using a path tracking technique. The EMID method delineates connected river networks using (a) multi-temporal imagery and a Random Forest model to synoptically map the location and extent of water surfaces under different hydrological conditions, and (b) an optimization algorithm to find the best river paths based on water-occurrence frequency. Four drainage basins with various river morphologies are considered to validate EMID. Comparing with alternative methods, the EMID method consistently produces river network results with improved accuracy in terms of stream location, river coverage and network connectivity

Supplementary information files for Extraction of connected river networks from multi-temporal remote sensing imagery using a path tracking technique

Supplementary files for Extraction of connected river networks from multi-temporal remote sensing imagery using a path tracking technique. Precise delineation of river networks is important for accurate hydrological and flood modelling. Whilst remote sensing (RS) has showed great potential in monitoring hydrological changes over space and time, the existing RS-based methods extract river networks based on local morphologies and seldom take into account the overall hydrological connectivity of the rivers. The existing methods also commonly neglect the effect of seasonal variation of water surfaces and the existence of temporary water bodies, which deteriorate the precision of positioning river networks. To address these challenges, a new two-stage method is developed to Extract spatiotemporal variation of water surfaces based on Multi-temporal remote sensing Imagery and Delineate connected river networks with improved accuracy (EMID method for short) using a path tracking technique. The EMID method delineates connected river networks using (a) multi-temporal imagery and a Random Forest model to synoptically map the location and extent of water surfaces under different hydrological conditions, and (b) an optimization algorithm to find the best river paths based on water-occurrence frequency. Four drainage basins with various river morphologies are considered to validate EMID. Comparing with alternative methods, the EMID method consistently produces river network results with improved accuracy in terms of stream location, river coverage and network connectivity.</div

Nonlinear Water Quality Response to Numerical Simulation of In Situ Phosphorus Control Approaches

The nonlinear and heterogeneous responses of nutrients to eutrophication control measures are a major challenge for in situ treatment engineering design, especially for large water bodies. Tackling the problem calls for a full understanding of potential water quality responses to various treatment schemes, which cannot be fulfilled by empirical-based methods or small-scale tests. This paper presents a methodology for Phoslock application based on the idea of object-oriented intelligent engineering design (OOID), which includes numerical simulation to explore the features of responses to numerous assumed schemes. A large plateau lake in Southwestern China was employed as a case study to illustrate the characteristics of the water quality response and demonstrate the applicability of this new approach. It was shown by the simulation and scenario analysis that the water quality response to Phoslock application always reflected nonlinearity and spatiotemporal heterogeneity, and always varied with objects, boundary conditions, and engineering design parameters. It was also found that some design parameters, like release position, had a significant impact on efficiency. Thus, a remarkable improvement could be obtained by cost-effective analysis based on scenarios using combinations of design parameters