Reusable Polyacrylonitrile-Sulfur Extractor of Heavy Metal Ions from Wastewater

Mercury, lead, and cadmium are among the most toxic and carcinogenic heavy metal ions (HMIs), posing serious threats to the sustainability of aquatic ecosystems and public health. There is an urgent need to remove these ions from water by a cheap but green process. Traditional methods have insufficient removal efficiency and reusability. Structurally robust, large surface-area adsorbents functionalized with high-selectivity affinity to HMIs are attractive filter materials. Here, an adsorbent prepared by vulcanization of polyacrylonitrile (PAN), a nitrogen-rich polymer, is reported, giving rise to PAN-S nanoparticles with cyclic π-conjugated backbone and electronic conductivity. PAN-S can be coated on ultra-robust melamine (ML) foam by simple dipping and drying. In agreement with hard/soft acid/base theory, N- and S-containing soft Lewis bases have strong binding to Hg²⁺, Pb²⁺, Cu²⁺, and Cd²⁺, with extraordinary capture efficiency and performance stability. Furthermore, the used filters, when collected and electrochemically biased in a recycling bath, can release the HMIs into the bath and electrodeposit on the counter-electrode as metallic Hg⁰, Pb⁰, Cu⁰, and Cd⁰, and the PAN-S@ML filter can then be reused at least 6 times as new. The electronically conductive PAN-S@ML filter can be fabricated cheaply and holds promise for scale-up applications.     

An approach to reduce energy consumption and performance losses on heterogeneous servers using power capping

Journal of Scheduling - Rapid growth of demand for remote computational power, along with high energy costs and infrastructure limits, has led to treating power usage as a primary constraint in...     

Gossip-based aggregation of trust in decentralized reputation systems

Decentralized Reputation Systems have recently emerged as a prominent method of establishing trust among self-interested agents in online environments. A key issue is the efficient aggregation of data in the system; several approaches have been proposed, but they are plagued by major shortcomings. We put forward a novel, decentralized data management scheme grounded in gossip-based algorithms. Rumor mongering is known to possess algorithmic advantages, and indeed, our framework inherits many of their salient features: scalability, robustness, a global perspective, and simplicity. We demonstrate that our scheme motivates agents to maintain a very high reputation, by showing that the higher an agent’s reputation is above the threshold set by its peers, the more transactions it would be able to complete within a certain time unit. We analyze the relation between the amount by which an agent’s average reputation exceeds the threshold and the time required to close a deal. This analysis is carried out both theoretically, and empirically through a simulation system called GossipTrustSim. Finally, we show that our approach is inherently impervious to certain kinds of attacks. A preliminary version of this article appeared in the proceedings of IJCAI 2007.     

A structural linearization principle for processes

In [11], an induction principle for processes was given which allows one to apply model-checking techniques to parameterized families of processes. A limitation of the induction principle is that it does not apply to the case in which one process depends directly upon a parameterized number of processes, which grows without bound. This would seem to preclude its application to families ofN processes interconnected in a star topology. Nonetheless, we show that if the dependency can be computed incrementally, then the direct dependency upon the parameterized number of processes may be re-expressed recursively in terms of a linear cascade of processes, yielding in effect a linearization of the inter-process dependencies and allowing the induction principle to apply.A previous version of this paper appears in the Proceedings of CAV 1993 (LNCS 697).     

Context‐Dependent Crowd Evaluation

Many times, even if a crowd simulation looks good in general, there could be some specific individual behaviors which do not seem correct. Spotting such problems manually can become tedious, but ignoring them may harm the simulation's credibility. In this paper we present a data‐driven approach for evaluating the behaviors of individuals within a simulated crowd. Based on video‐footage of a real crowd, a database of behavior examples is generated. Given a simulation of a crowd, an analog analysis is performed on it, defining a set of queries, which are matched by a similarity function to the database examples. The results offer a possible objective answer to the question of how similar are the simulated individual behaviors to real observed behaviors. Moreover, by changing the video input one can change the context of evaluation. We show several examples of evaluating simulated crowds produced using different techniques and comprising of dense crowds, sparse crowds and flocks.     

A new fast prototype selection method based on clustering

In supervised classification, a training set T is given to a classifier for classifying new prototypes. In practice, not all information in T is useful for classifiers, therefore, it is convenient to discard irrelevant prototypes from T. This process is known as prototype selection, which is an important task for classifiers since through this process the time for classification or training could be reduced. In this work, we propose a new fast prototype selection method for large datasets, based on clustering, which selects border prototypes and some interior prototypes. Experimental results showing the performance of our method and comparing accuracy and runtimes against other prototype selection methods are reported.     

Wireless network design via 3-decompositions

We consider some network design problems with applications for wireless networks. The input for these problems is a metric space (X,d) and a finite subset UX of terminals. In the Steiner Tree with Minimum Number of Steiner Points (STMSP) problem, the goal is to find a minimum size set SX−U of points so that the unit-disc graph of S+U is connected. Let Δ be the smallest integer so that for any finite VX for which the unit-disc graph is connected, this graph contains a spanning tree with maximum degree Δ. The best known approximation ratio for STMSP was Δ−1 [I.I. Măndoiu, A.Z. Zelikovsky, A note on the MST heuristic for bounded edge-length Steiner trees with minimum number of Steiner points, Information Processing Letters 75 (4) (2000) 165–167]. We improve this ratio to (Δ+1)/2+1+ε.In the Minimum Power Spanning Tree (MPST) problem, V=X is finite, and the goal is to find a “range assignment” on the nodes so that the edge set contains a spanning tree, and ∑_vVp(v) is minimized. We consider a particular case {0,1}-MPST of MPST when the distances are in {0,1}; here the goal is to find a minimum size set SV of “active” nodes so that the graph (V,E₀+E₁(S)) is connected, where , and E₁(S) is the set the edges in with both endpoints in S. We will show that the (5/3+ε)-approximation scheme for MPST of [E. Althaus, G. Calinescu, I. Măndoiu, S. Prasad, N. Tchervenski, A. Zelikovsky, Power efficient range assignment for symmetric connectivity in static ad hoc wireless networks, Wireless Networks 12 (3) (2006) 287–299] achieves a ratio 3/2 for {0,1}-distances. This answers an open question posed in [E. Lloyd, R. Liu, S. Ravi, Approximating the minimum number of maximum power users in ad hoc networks, Mobile Networks and Applications 11 (2006) 129–142].     

Co-design of H∞ jump observers for event-based measurements over networks

This work presents a strategy to minimise the network usage and the energy consumption of wireless battery-powered sensors in the observer problem over networks. The sensor nodes implement a periodic send-on-delta approach, sending new measurements when a measure deviates considerably from the previous sent one. The estimator node implements a jump observer whose gains are computed offline and depend on the combination of available new measurements. We bound the estimator performance as a function of the sending policies and then state the design procedure of the observer under fixed sending thresholds as a semidefinite programming problem. We address this problem first in a deterministic way and, to reduce conservativeness, in a stochastic one after obtaining bounds on the probabilities of having new measurements and applying robust optimisation problem over the possible probabilities using sum of squares decomposition. We relate the network usage with the sending thresholds and propose an iterative procedure for the design of those thresholds, minimising the network usage while guaranteeing a prescribed estimation performance. Simulation results and experimental analysis show the validity of the proposal and the reduction of network resources that can be achieved with the stochastic approach.     

Inconsistent heuristics in theory and practice

In the field of heuristic search it is usually assumed that admissible heuristics are consistent, implying that consistency is a desirable attribute. The term “inconsistent heuristic” has, at times, been portrayed negatively, as something to be avoided. Part of this is historical: early research discovered that inconsistency can lead to poor performance for A^? (nodes might be re-expanded many times). However, the issue has never been fully investigated, and was not re-considered after the invention of IDA^?.This paper shows that many of the preconceived notions about inconsistent heuristics are outdated. The worst-case exponential time of inconsistent heuristics is shown to only occur on contrived graphs with edge weights that are exponential in the size of the graph. Furthermore, the paper shows that rather than being something to be avoided, inconsistent heuristics often add a diversity of heuristic values into a search which can lead to a reduction in the number of node expansions. Inconsistent heuristics are easy to create, contrary to the common perception in the AI literature. To demonstrate this, a number of methods for achieving effective inconsistent heuristics are presented.Pathmax is a way of propagating inconsistent heuristic values in the search from parent to children. This technique is generalized into bidirectional pathmax (BPMX) which propagates values from a parent to a child node, and vice versa. BPMX can be integrated into IDA^? and A^?. When inconsistent heuristics are used with BPMX, experimental results show a large reduction in the search effort required by IDA^?. Positive results are also presented for A^? searches.     

A multicriteria approach to two-level hierarchy scheduling in grids

In this paper we address a multicriteria scheduling problem for computational Grid systems. We focus on the two-level hierarchical Grid scheduling problem, in which at the first level (the Grid level) a Grid broker makes scheduling decisions and allocates jobs to Grid nodes. Jobs are then sent to the Grid nodes, where local schedulers generate local schedules for each node accordingly. A general approach is presented taking into account preferences of all the stakeholders of Grid scheduling (end-users, Grid administrators, and local resource providers) and assuming a lack of knowledge about job time characteristics. A single-stakeholder, single-criterion version of the approach has been compared experimentally with the existing approaches.