Investigation on sulphonated zinc oxide nanorod incorporated sulphonated poly (1,4-phenylene ether ether sulfone) nanocomposite membranes for improved performance of microbial fuel cell

Hydrothermally prepared zinc oxide nanorods are sulphonated (S–ZnO NR) and incorporated into 15% Sulphonated Poly (1,4-Phenylene Ether Ether Sulfone) (SPEES) to improve the hydrophilicity, water uptake and ion transfer capacity. Water uptake and ion transfer capacity increased to 34.6 ± 0.6% and 2.0 ± 0.05 meq g^?1 from 29.8 ± 0.3% and 1.4 ± 0.04 meq g^?1 by adding 7.5 wt% S–ZnO NR to SPEES. Morphological studies show the prepared S–ZnO NR is well dispersed in the polymer matrix. SPEES +7.5 wt% S–ZnO NR membrane exhibits optimum performance after three-weeks of continual operation in a fabricated microbial fuel cell (MFC) to produce a maximum power density of 142 ± 1.2 mW m^?2 with a reduced biofilm compared to plain SPEES (59 ± 0.8 mW m^?2), unsulphonated filler incorporated SPEES (SPEES + 7.5 wt% ZnO, 68 ± 1.1 mW m^?2) and Nafion (130 ± 1.5 mW m^?2) thereby suggesting its suitability as a sustainable and improved cation exchange membrane (CEM) for MFCs.     

Impact of power-supply noise on timing in high-frequency microprocessors

This paper analyzes the impact of power-supply noise on the performance of high-frequency microprocessors. First, delay models that take this noise into account are proposed for device-dominated and interconnect-dominated timing paths. For typical circuits, it is shown that the peak of the noise is largely irrelevant and that the average supply voltage during switching is more important. It is then argued that global differential noise can potentially have a greater timing impact than common-mode noise. Finally, realistic values for the model parameters are measured on a 2.53-GHz Pentium4 microprocessor using a 130-nm technology. These values imply that the power-supply noise present on the system board reduces clock frequency by 6.7%. The model suggests that the frequency penalty associated with this power-supply noise will steadily increase and reach 7.6% for the 90-nm technology generation.     

A general enthalpy method for modeling solidification processes

In the present work, a general implicit source-based enthalpy method is presented for the analysis of solidification systems. The proposed approach is both robust and efficient. The performance of the method is illustrated by application to a number of problems taken from recent metallurgical literature.     

Analysis of Caching and Replication Strategies for Web Applications

Developers often use replication and caching mechanisms to enhance Web application performance. The authors present a qualitative and quantitative analysis of state-of-the art replication and caching techniques used to host Web applications. Their analysis shows that selecting the best mechanism depends heavily on data workload and requires a careful review of the application's characteristics. They also propose a technique for Web practitioners to compare different mechanisms' performance on their own     

Acrylic-based fire-retardant coatings for steel protection: Employing the concept of in situ ceramization

Traditional intumescent coatings are widely used as passive fire-protective coatings for steel structures as they are capable of expanding in the range of 20–50 times the original thickness thereby providing excellent insulation. However, the fragile nature of such residue and susceptibility to thermo-oxidation given their carbonaceous nature are key problematic issues. The concept of in situ ceramization is explored in this work as a means to form inorganic cohesive char with improved rigidity and thermo-oxidative stability. Coating samples were prepared by incorporating ammonium polyphosphate, talc, Mg(OH)₂, and polydimethylsiloxane as additives into acrylic resin at different weight fractions. Thermal analysis and x-ray diffraction have confirmed the reactions between the additives to form various crystalline magnesium phosphate phases, and to a small extent, silicon phosphate, thereby ensuring the thermo-oxidative stability of the residue. This is reiterated by the fire performance tests (by exposing the coatings to a temperature profile in a furnace similar to ISO 834 fire curve). Despite the advantages of rigid char and its thermo-oxidative stability as a result of formation of inorganic phosphates, the lack of swelling has resulted in relatively poor insulation capabilities of the char, and subsequently, compromised the fire protection times (that are in the range of 45–55 min). However, pyrolysis flow combustion calorimeter results of the coatings are promising and have shown a significant drop of up to 70% in the peak of heat release rate values as compared to neat resin.     

Evaluation of Localized Semantics: Data, Methodology, and Experiments

We present a new data set of 1014 images with manual segmentations and semantic labels for each segment, together with a methodology for using this kind of data for recognition evaluation. The images and segmentations are from the UCB segmentation benchmark database (Martin et al., in International conference on computer vision, vol. II, pp. 416–421, 2001). The database is extended by manually labeling each segment with its most specific semantic concept in WordNet (Miller et al., in Int. J. Lexicogr. 3(4):235–244, 1990). The evaluation methodology establishes protocols for mapping algorithm specific localization (e.g., segmentations) to our data, handling synonyms, scoring matches at different levels of specificity, dealing with vocabularies with sense ambiguity (the usual case), and handling ground truth regions with multiple labels. Given these protocols, we develop two evaluation approaches. The first measures the range of semantics that an algorithm can recognize, and the second measures the frequency that an algorithm recognizes semantics correctly. The data, the image labeling tool, and programs implementing our evaluation strategy are all available on-line (kobus.ca//research/data/IJCV_2007). We apply this infrastructure to evaluate four algorithms which learn to label image regions from weakly labeled data. The algorithms tested include two variants of multiple instance learning (MIL), and two generative multi-modal mixture models. These experiments are on a significantly larger scale than previously reported, especially in the case of MIL methods. More specifically, we used training data sets up to 37,000 images and training vocabularies of up to 650 words. We found that one of the mixture models performed best on image annotation and the frequency correct measure, and that variants of MIL gave the best semantic range performance. We were able to substantively improve the performance of MIL methods on the other tasks (image annotation and frequency correct region labeling) by providing an appropriate prior.     

Energy and Trust Management Framework for MANET using Clustering Algorithm

In General, Mobile Ad-Hoc Network (MANET) has limited energy resources, and it cannot recharge itself. This research goal focuses on building a power management scheme that saves energy in the MANET. Due to power instability, there is a chance that cluster heads fail and function incorrectly in cluster-based routing. As a result, instability occurs with the cluster heads while collecting data and communicating with others effectively. This work focuses on detecting the unstable cluster heads, which are replaced by other nodes implementing the envisaged self-configurable cluster mechanism. A self-configurable cluster mechanism with a k-means protocol approach is proposed to designate cluster heads effectively. The proposed k-means procedure is based on periodic irregular cluster head rotations or altering the number of clusters. We also propose a trust management mechanism in this research to detect and avoid MANET vulnerabilities. Because of the continuously changing topology and limited resources (power, bandwidth, computing), the trust management algorithm should only use local data. Consequently, compared to traditional protocols, the proposed approach with the k-means procedure and its experimental results show lower power usage and provide an optimal system for trust management.

     

Scheduling on Unrelated Machines under Tree-Like Precedence Constraints

We present polylogarithmic approximations for the R|prec|C _max and R|prec|∑ _j w _j C _j problems, when the precedence constraints are “treelike”—i.e., when the undirected graph underlying the precedences is a forest. These are the first non-trivial generalizations of the job shop scheduling problem to scheduling with precedence constraints that are not just chains. These are also the first non-trivial results for the weighted completion time objective on unrelated machines with precedence constraints of any kind. We obtain improved bounds for the weighted completion time and flow time for the case of chains with restricted assignment—this generalizes the job shop problem to these objective functions. We use the same lower bound of “congestion + dilation”, as in other job shop scheduling approaches (e.g. Shmoys, Stein and Wein, SIAM J. Comput. 23, 617–632, 1994). The first step in our algorithm for the R|prec|C _max problem with treelike precedences involves using the algorithm of Lenstra, Shmoys and Tardos to obtain a processor assignment with the congestion + dilation value within a constant factor of the optimal. We then show how to generalize the random-delays technique of Leighton, Maggs and Rao to the case of trees. For the special case of chains, we show a dependent rounding technique which leads to a bicriteria approximation algorithm for minimizing the flow time, a notoriously hard objective function. A preliminary version of this paper appeared in the Proc. International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (APPROX), pages 146–157, 2005. V.S. Anil Kumar supported in part by NSF Award CNS-0626964. Part of this work was done while at the Los Alamos National Laboratory, and supported in part by the Department of Energy under Contract W-7405-ENG-36. M.V. Marathe supported in part by NSF Award CNS-0626964. Part of this work was done while at the Los Alamos National Laboratory, and supported in part by the Department of Energy under Contract W-7405-ENG-36. Part of this work by S. Parthasarathy was done while at the Department of Computer Science, University of Maryland, College Park, MD 20742, and in part while visiting the Los Alamos National Laboratory. Research supported in part by NSF Award CCR-0208005 and NSF ITR Award CNS-0426683. Research of A. Srinivasan supported in part by NSF Award CCR-0208005, NSF ITR Award CNS-0426683, and NSF Award CNS-0626636.     

A queue based mutual exclusion algorithm

A new elegant and simple algorithm for mutual exclusion of N processes is proposed. It only requires shared variables in a memory model where shared variables need not be accessed atomically. We prove mutual exclusion by reformulating the algorithm as a transition system (automaton), and applying simulation of automata. The proof has been verified with the higher-order interactive theorem prover PVS. Under an additional atomicity assumption, the algorithm is starvation free, and we conjecture that no competing process is passed by any other process more than once. This conjecture was verified by model checking for systems with at most five processes.