New insights on survivability in multi-domain optical networks

With the development of intelligent optical networks and the general multi-protocol label switching (GMPLS) technique, the seamless convergence between IP network and optical network is no longer be a dream but a practical reality. Similar to the Internet, current optical networks have been divided into multiple domains each of which has its own network provider and management policy. Therefore, the development of multi-domain optical networks will be the trend of new-generation intelligent optical networks, and GMPLS-based survivability for multi-domain optical networks will become a hot topic of research in the future. This paper provides a comprehensive review of the existing survivable schemes in multi-domain optical networks and analyzes the shortcomings of current research. Based on previous studies, we present possible challenges and propose new ideas to design efficient survivable schemes to guide the future work of researchers in multi-domain optical networks.     

Use of a dark object concept and support vector machines to automate forest cover change analysis

An automated method was developed for mapping forest cover change using satellite remote sensing data sets. This multi-temporal classification method consists of a training data automation (TDA) procedure and uses the advanced support vector machines (SVM) algorithm. The TDA procedure automatically generates training data using input satellite images and existing land cover products. The derived high quality training data allow the SVM to produce reliable forest cover change products. This approach was tested in 19 study areas selected from major forest biomes across the globe. In each area a forest cover change map was produced using a pair of Landsat images acquired around 1990 and 2000. High resolution IKONOS images and independently developed reference data sets were available for evaluating the derived change products in 7 of those areas. The overall accuracy values were over 90% for 5 areas, and were 89.4% and 89.6% for the remaining two areas. The user's and producer's accuracies of the forest loss class were over 80% for all 7 study areas, demonstrating that this method is especially effective for mapping major disturbances with low commission errors. IKONOS images were also available in the remaining 12 study areas but they were either located in non-forest areas or in forest areas that did not experience forest cover change between 1990 and 2000. For those areas the IKONOS images were used to assist visual interpretation of the Landsat images in assessing the derived change products. This visual assessment revealed that for most of those areas the derived change products likely were as reliable as those in the 7 areas where accuracy assessment was conducted. The results also suggest that images acquired during leaf-off seasons should not be used in forest cover change analysis in areas where deciduous forests exist. Being highly automatic and with demonstrated capability to produce reliable change products, the TDA-SVM method should be especially useful for quantifying forest cover change over large areas.     

嵌入式1394总线接口的设计和实现

本文介绍一种嵌入式处理器与1394总线接口设计方法,并给出基于现场可编程门阵列(FPGA)设计的VHDL代码结构、仿真时序和试验结果,最后提出了一种新的通用接口设计方法并指出了它的优势所在.     

白蚁提取液急性毒性及致突变性研究

本文采用大,小鼠急性经口毒性试验、Ａｍｅｓ试验、小鼠骨髓细胞微核试验及小鼠精子畸变试验对白蚁提取液的毒理学安全性进行了研究,结果表明,白蚁提取液大,小鼠急性经口ＬＤ５０均大于１０ｇ／ｋｇ体重,属“实际无毒”：体内（包括体细胞和生殖细胞）、体外致突变性试验系统均未检出其致突性。     

Modelling the removal of organic vehicle from ceramic or metal mouldings: The effect of gas permeation on the incidence of defects

A shrinking undegraded core and a porous outer layer result, if the organic vehicle used for shaping ceramic or metal powder mouldings recedes in the interparticle space of the moulded body during pyrolysis. In the present work, a numerical model has been used which simulates the undegraded shrinking core situation and quantifies degradation of the organic vehicle and the diffusion of the resulting products in solution in the organic phase during pyrolysis of a ceramic moulding. This model is extended to include gaseous mass transport in the porous outer layer for a moulding in the shape of an infinite cylinder. The effect of resistance to gaseous mass transport in the porous outer region on defects originating in inner regions was estimated. It is shown that the greatest obstruction to mass transport is diffusion of degradation products in solution in the organic phase. However, the permeability coefficient for gas transport in the outer region begins to affect the critical heating rate required for avoidance of defects only when it is less than 10^–15m².Nomenclature C Concentration,C=C (r, t), based on the total volume of ceramic suspension - d Effective molecular diameter of alphamethylstyrene - D Diffusion coefficient,D=D (C, T) - e Porosity of powder - E Activation energy for thermal degradation - h Remaining weight fraction of polymer - H_vap Enthalphy of vaporization - i Node number - I Pre-exponential constant in Equation 13 - j Time step - K _p Permeability coefficient - K ₀ Specific rate constant - m Mass of monomer displaced - M Mass of one alphamethylstyrene molecule - P Monomer vapour pressure - P _s Monomer vapour pressure at outer surface of the cylinder - P ₁ ⁰ Vapour pressure of monomer over its pure liquid - Q Rate of production of monomer, based on the total volume of ceramic suspension - r Radius of the cylinder - r _j Distance from central axis to the inner surface of the porous layer at time stepj - r ₀ Initial radius of the cylinder - R Universal gas constant - S ₀ Specific surface area of powder per unit solid volume - t Time - T Absolute temperature - T _c Temperature at maximum vapour pressure of monomer and atZ _c - V Volume of monomer - V _c Ceramic volume fraction - V _p Polymer volume fraction - w Mass of monomer stored in the porous annulus - Z Heating rate - Z _c Critical heating rate - ₁ Volume fraction of monomer in the polymer-monomer solution - Viscosity of the monomer vapour - _p Density of the polymer - Polymer-monomer interaction constant     

Single-Atom Metal Sites Anchored Hydrogen-Bonded Organic Frameworks for Superior “Two-In-One” Photocatalytic Reaction

Photoredox catalysis is a green solution for organics transformation and CO₂ conversion into valuable fuels, meeting the challenges of sustainable energy and environmental concerns. However, the regulation of single-atomic active sites in organic framework not only influences the photoredox performance, but also limits the understanding of the relationship for photocatalytic selective organic conversion with CO₂ valorization into one reaction system. As a prototype, different single-atomic metal (M) sites (M²⁺ = Fe²⁺, Co²⁺, Ni²⁺, Cu²⁺, and Zn²⁺) in hydrogen-bonded organic frameworks (M-HOF) backbone with bridging structure of metal-nitrogen are constructed by a typical “two-in-one” strategy for superior photocatalytic C N coupling reactions integrated with CO₂ valorization. Remarkably, Zn-HOF achieves 100% conversion of benzylamine oxidative coupling reactions, 91% selectivity of N-benzylidenebenzylamine and CO₂ conversion in one photoredox cycle. From X-ray absorption fine structure analysis and density functional theory calculations, the superior photocatalytic performance is attributed to synergic effect of atomically dispersed metal sites and HOF host, decreasing the reaction energy barriers, enhancing CO₂ adsorption and forming benzylcarbamic acid intermediate to promote the redox recycle. This work not only affords the rational design strategy of single-atom active sites in functional HOF, but also facilitates the fundamental insights upon the mechanism of versatile photoredox coupling reaction systems.     

Ligand Design in Atomically Precise Copper Nanoclusters and Their Application in Electrocatalytic Reactions

Metal nanoclusters (MNCs) are compositionally well-defined and also structurally precise materials with unique molecule-like properties and discrete electronic energy levels. Atomically precise ligand-protected Cu nanoclusters (LP-CuNCs) are one category of typical MNCs that usually demonstrate unique geometric and electronic structures to serve as electrocatalysts. However, the synthesis, application, as well as structure-performance relationship of LP-CuNCs are not adequately studied. Significantly, the ligands are essential to the geometric structure, crystal structure, size, and electronic structure of LP-CuNCs, which determine their physiochemical properties and applications. In this review, significant progress in the ligand design of LP-CuNCs, and their application in electrocatalytic reactions is introduced. The general basics of ligand-protected MNCs (LP-MNCs) are first introduced and the functions of ligands are emphasized. Subsequently, a series of different ligands for LP-CuNCs including thiolates, phosphines, alkynyl, polymers, and biomolecules are highlighted. Thereafter, their applications in different electrocatalytic reactions are discussed. It is believed that this review will not only inspire the design and synthesis of novel LP-CuNCs, but also contribute to the extension of their applications in electrocatalytic reactions and the establishment of accurate structure-performance relationships.     

Evaluation Model of Click Rate of Electronic Commerce Advertising Based on Fuzzy Genetic Algorithm

Mobile Networks and Applications - In order to improve the ability of quantitative evaluation of e-commerce advertising click rate, a model of e-commerce advertising click rate evaluation based on...     

Service placement strategy for joint network selection and resource scheduling in edge computing

The Journal of Supercomputing - In the edge computing, service placement refers to the process of installing service platforms, databases, and configuration files corresponding to computing tasks...     

A Survey on Long-Tailed Visual Recognition

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers.