Covalent organic frameworks (COFs) with redox-active units are a class of ideal materials for electrochemical-energy-storage devices. A novel two-dimensional (2D) PDC–MA–COF with redox-active triazine units was prepared via aldehyde–amine condensation reaction by using 1,4-piperazinedicarboxaldehyde (PDC) and melamine (MA) as structural units, which possessed high specific surface area (S BET = 748.2 m2 g–1), narrow pore width (1.9 nm), large pore volume (1.21 cm3 g–1), and high nitrogen content (47.87%), for pseudocapacitance application. The interlayer C–H···N hydrogen bonding can “lock” the relative distance between two adjacent layers to avoid an interlayer slip, which is more conducive to maintaining the ordered pore structure of the COF and improving a fast charge transfer between the electrode interface and triazine units. The PDC–MA–COF exhibited an excellent electrochemical performance with the highest specific capacitance of 335 F g–1 along with 19.71% accessibility of the redox-active triazine units in a three-electrode system and 94 F g–1 in a two-electrode system at 1.0 A g–1 current density. Asymmetric supercapacitor of PDC–MA–COF//AC assembled using PDC–MA–COF and activated carbon (AC) as positive and negative electrode materials, respectively, exhibited a high energy density of 29.2 W h kg–1 with a power density of 750 W kg–1. At the same time, it also showed an excellent cyclic stability and could retain 88% of the initial capacitance after 20 000 charge–discharge cycles, which was better than those of the most of the analogous materials reported previously. This study provided a new strategy for designing redox-active COFs for pseudocapacitive storage.

Our work targets a network architecture and accompanying algorithms for countering distributed denial-of-service (DDoS) attacks directed at an Internet server. The basic mechanism is for a server under stress to install a router throttle at selected upstream routers. The throttle can be the leaky-bucket rate at which a router can forward packets destined for the server. Hence, before aggressive packets can converge to overwhelm the server, participating routers proactively regulate the contributing packet rates to more moderate levels, thus forestalling an impending attack. In allocating the server capacity among the routers, we propose a notion of level-max-min fairness. We first present a control-theoretic model to evaluate algorithm convergence under a varitey of system parameters. In addition, we present packet network simulation results using a realistic global network topology, and various models of good user and attacker distributions and behavior. Using a generator model of web requests parameterized by empirical data, we also evaluate the impact of throttling in protecting user access to a web server. First, for aggressive attackers, the throttle mechanism is highly effective in preferentially dropping attacker traffic over good user traffic. In particular, level-max-min fairness gives better good-user protection than recursive pushback of max-min fair rate limits proposed in the literature. Second, throttling can regulate the experienced server load to below its design limit -in the presence of user dynamics -so that the server can remain operational during a DDoS attack. Lastly, we present implementation results of our prototype on a Pentium III/866 MHz machine. The results show that router throttling has low deployment overhead in time and memory.Index Terms-Congestion control, distributed denial of service, network security, router throttling.

We present a network architecture and accompanying algorithms for countering distributed denial-of-service (DDoS) attacks directed at an Internet server. The basic mechanism is for a server under stress to install a router throttle at selected upstream routers. The throttle can be the leaky-bucket rate at which a router can forward packets destined for the server. Hence, before aggressive packets can converge to overwhelm the server, participating routers proactively regulate the contributing packet rates to more moderate levels, thus forestalling an impending attack. In allocating the server capacity among the routers, we propose a notion of level-max-min fairness. We present a controltheoretic model to evaluate algorithm convergence under a varitey of system parameters. In addition, we present packet network simulation results using a realistic global network topology, and various models of good user and attacker distributions and behavior. Using a generator model of web requests parameterized by empirical data, we also evaluate the impact of throttling in protecting user access to a web server. First, for aggressive attackers, the throttle mechanism is highly effective in preferentially dropping attacker traffic over good user traffic. In particular, level-max-min fairness gives better good-user protection than recursive pushback of max-min fair rate limits proposed in the literature. Second, throttling can regulate the experienced server load to below its design limit -in the presence of user dynamics -so that the server can remain operational during a DDoS attack.

The research of organic materials thrives for lithium-ion and sodium-ion batteries but lacks for aqueous Zn 2 + batteries. Herein, we prepared 1,4,5,8-naphthalene diimide (NTCDI), which is derived from 1,4,5,8-naphthalenetetracarboxylic dianhydride, through nitrogen substitution with the assistance of ammonia solution. NTCDI is first applied in aqueous Zn 2 + batteries, which can give a high reversible capacity of 240 mAh g À 1 at 0.1 A g À 1 .The electrochemical kinetics analysis indicates that the diffusion-control process dominates in the electrode reaction. NTCDI can keep 73.7 % of the initial capacity of 152 mAh g À 1 after 2000 continuous charge/discharge cycles at 1 A g À 1 , showing great potential for zinc storage.

With the development of cloud computing, the coexistence of multiple cloud service providers appears in the current cloud market. Due to heterogeneous instance types, different bandwidths and various price models among multiple clouds, it is a challenging issue to schedule a deadline-constrained scientific workflow across multiple clouds. Existing research for workflow scheduling are mostly in the traditional distributed computing environment (such as grid), and only a few primal contributions are made in the cloud environment. This paper proposes a scheduling strategy for a deadline-constrained scientific workflow across multiple clouds. In order to minimize the execution cost of the workflow while meeting its deadline, our strategy utilizes the discrete particle swarm optimization technique, and adopts randomly two-point crossover operator and randomly single point mutation operator of the genetic algorithm. Besides, the strategy optimizes the performance for both computation cost and data transfer cost across multiple clouds. Our strategy is evaluated through well-known workflows, and experimental results show that it performs better than other state-of-the-art strategies.