Highly luminescent and thermally stable lanthanide coordination polymers designed from 4-(dipyridin-2-yl)aminobenzoate: efficient energy transfer from Tb3+ to Eu3+ in a mixed lanthanide coordination compound

Herein, a new aromatic carboxylate ligand, namely, 4-(dipyridin-2-yl)aminobenzoic acid (HL), has been designed and employed for the construction of a series of lanthanide complexes (Eu3+ = 1, Tb3+ = 2, and Gd3+ = 3). Complexes of 1 and 2 were structurally authenticated by single-crystal X-ray diffraction and were found to exist as infinite 1D coordination polymers with the general formulas {Eu(L)(3)(H2O)(2)]}(n) (1) and {Tb(L)(3)(H2O)]center dot(H2O)}(n) (2). Both compounds crystallize in monoclinic space group C2/c. The photophysical properties demonstrated that the developed 4-(dipyridin-2-yl)aminobenzoate ligand is well suited for the sensitization of Tb3+ emission (Phi(overall) = 64%) thanks to the favorable position of the triplet state ((3)pi pi*) of the ligand the energy difference between the triplet state of the ligand and the excited state of Tb3+ (Delta E) = (3)pi pi* - D-5(4) = 3197 cm(-1)], as investigated in the Gd3+ complex. On the other hand, the corresponding Eu3+ complex shows weak luminescence efficiency (Phi(overall) = 7%) due to poor matching of the triplet state of the ligand with that of the emissive excited states of the metal ion (Delta E = (3)pi pi* - D-5(0) = 6447 cm(-1)). Furthermore, in the present work, a mixed lanthanide system featuring Eu3+ and Tb3+ ions with the general formula {Eu0.5Tb0.5(L)(3)(H2O)(2)]}(n) (4) was also synthesized, and the luminescent properties were evaluated and compared with those of the analogous single-lanthanide-ion systems (1 and 2). The lifetime measurements for 4 strongly support the premise that efficient energy transfer occurs between Tb3+ and Eu3+ in a mixed lanthanide system (eta = 86%).

Efficient Algorithms for Linear Summed Error Structural SVMs

Structural Support Vector Machines (SSVMs) have become a popular tool in machine learning for predicting structured objects like parse trees, Part-of-Speech (POS) label sequences and image segments. Various efficient algorithmic techniques have been proposed for training SSVMs for large datasets. The typical SSVM formulation contains a regularizer term and a composite loss term. The loss term is usually composed of the Linear Maximum Error (LME) associated with the training examples. Other alternatives for the loss term are yet to be explored for SSVMs. We formulate a new SSVM with Linear Summed Error (LSE) loss term and propose efficient algorithms to train the new SSVM formulation using primal cutting-plane method and sequential dual coordinate descent method. Numerical experiments on benchmark datasets demonstrate that the sequential dual coordinate descent method is faster than the cutting-plane method and reaches the steady-state generalization performance faster. It is thus a useful alternative for training SSVMs when linear summed error is used.

Retention characteristics of Cu2+, Pb2+, and Zn2+ from aqueous solutions by two types of low lime fly ashes

The technical feasibility of utilization of fly ash as a low-cost adsorbent for the removal of metals from water has been studied. For two types of fly ashes, the retention capacities of copper, lead, and zinc metal ions have been studied. Contact time, initial concentration, and pH have been varied and their effect on retention mechanism has been studied. The dominant mechanisms responsible for retention are found to be precipitation due to the presence of calcium hydroxide, and adsorption due to the presence of silica and alumina oxide surfaces in the fly ash. First-order kinetic plots have revealed that the rate constant increases with increase in the initial concentration and pH. Langmuir adsorption isotherms have been plotted to study the maximum adsorption capacities for metal ions considered under different conditions. X-ray diffraction studies revealed the formation of new peaks corresponding to respective metal ions precipitates under alkaline conditions.

GFT projection NMR for efficient H-1/C-13 sugar spin system identification in nucleic acids

A newly implemented G-matrix Fourier transform (GFT) (4,3)D HC(C)CH experiment is presented in conjunction with (4,3)D HCCH to efficiently identify H-1/C-13 sugar spin systems in C-13 labeled nucleic acids. This experiment enables rapid collection of highly resolved relay 4D HC(C)CH spectral information, that is, shift correlations of C-13-H-1 groups separated by two carbon bonds. For RNA, (4,3)D HC(C)CH takes advantage of the comparably favorable 1'- and 3'-CH signal dispersion for complete spin system identification including 5'-CH. The (4,3)D HC(C)CH/HCCH based strategy is exemplified for the 30-nucleotide 3'-untranslated region of the pre-mRNA of human U1A protein.

Efficient external memory structures for range-aggregate queries

We present external memory data structures for efficiently answering range-aggregate queries. The range-aggregate problem is defined as follows: Given a set of weighted points in R-d, compute the aggregate of the weights of the points that lie inside a d-dimensional orthogonal query rectangle. The aggregates we consider in this paper include COUNT, sum, and MAX. First, we develop a structure for answering two-dimensional range-COUNT queries that uses O(N/B) disk blocks and answers a query in O(log(B) N) I/Os, where N is the number of input points and B is the disk block size. The structure can be extended to obtain a near-linear-size structure for answering range-sum queries using O(log(B) N) I/Os, and a linear-size structure for answering range-MAX queries in O(log(B)(2) N) I/Os. Our structures can be made dynamic and extended to higher dimensions. (C) 2012 Elsevier B.V. All rights reserved.

Efficient Methods for Robust Classification Under Uncertainty in Kernel Matrices

In this paper we study the problem of designing SVM classifiers when the kernel matrix, K, is affected by uncertainty. Specifically K is modeled as a positive affine combination of given positive semi definite kernels, with the coefficients ranging in a norm-bounded uncertainty set. We treat the problem using the Robust Optimization methodology. This reduces the uncertain SVM problem into a deterministic conic quadratic problem which can be solved in principle by a polynomial time Interior Point (IP) algorithm. However, for large-scale classification problems, IP methods become intractable and one has to resort to first-order gradient type methods. The strategy we use here is to reformulate the robust counterpart of the uncertain SVM problem as a saddle point problem and employ a special gradient scheme which works directly on the convex-concave saddle function. The algorithm is a simplified version of a general scheme due to Juditski and Nemirovski (2011). It achieves an O(1/T-2) reduction of the initial error after T iterations. A comprehensive empirical study on both synthetic data and real-world protein structure data sets show that the proposed formulations achieve the desired robustness, and the saddle point based algorithm outperforms the IP method significantly.

Exploiting the Structure of the Constraint Graph for Efficient Points-to Analysis

Points-to analysis is a key compiler analysis. Several memory related optimizations use points-to information to improve their effectiveness. Points-to analysis is performed by building a constraint graph of pointer variables and dynamically updating it to propagate more and more points-to information across its subset edges. So far, the structure of the constraint graph has been only trivially exploited for efficient propagation of information, e.g., in identifying cyclic components or to propagate information in topological order. We perform a careful study of its structure and propose a new inclusion-based flow-insensitive context-sensitive points-to analysis algorithm based on the notion of dominant pointers. We also propose a new kind of pointer-equivalence based on dominant pointers which provides significantly more opportunities for reducing the number of pointers tracked during the analysis. Based on this hitherto unexplored form of pointer-equivalence, we develop a new context-sensitive flow-insensitive points-to analysis algorithm which uses incremental dominator update to efficiently compute points-to information. Using a large suite of programs consisting of SPEC 2000 benchmarks and five large open source programs we show that our points-to analysis is 88% faster than BDD-based Lazy Cycle Detection and 2x faster than Deep Propagation. We argue that our approach of detecting dominator-based pointer-equivalence is a key to improve points-to analysis efficiency.

Prioritizing constraint evaluation for efficient points-to analysis

Pervasive use of pointers in large-scale real-world applications continues to make points-to analysis an important optimization-enabler. Rapid growth of software systems demands a scalable pointer analysis algorithm. A typical inclusion-based points-to analysis iteratively evaluates constraints and computes a points-to solution until a fixpoint. In each iteration, (i) points-to information is propagated across directed edges in a constraint graph G and (ii) more edges are added by processing the points-to constraints. We observe that prioritizing the order in which the information is processed within each of the above two steps can lead to efficient execution of the points-to analysis. While earlier work in the literature focuses only on the propagation order, we argue that the other dimension, that is, prioritizing the constraint processing, can lead to even higher improvements on how fast the fixpoint of the points-to algorithm is reached. This becomes especially important as we prove that finding an optimal sequence for processing the points-to constraints is NP-Complete. The prioritization scheme proposed in this paper is general enough to be applied to any of the existing points-to analyses. Using the prioritization framework developed in this paper, we implement prioritized versions of Andersen's analysis, Deep Propagation, Hardekopf and Lin's Lazy Cycle Detection and Bloom Filter based points-to analysis. In each case, we report significant improvements in the analysis times (33%, 47%, 44%, 20% respectively) as well as the memory requirements for a large suite of programs, including SPEC 2000 benchmarks and five large open source programs.

NUcache: an efficient multicore cache organization based on next-use distance

The effectiveness of the last-level shared cache is crucial to the performance of a multi-core system. In this paper, we observe and make use of the DelinquentPC - Next-Use characteristic to improve shared cache performance. We propose a new PC-centric cache organization, NUcache, for the shared last level cache of multi-cores. NUcache logically partitions the associative ways of a cache set into MainWays and DeliWays. While all lines have access to the MainWays, only lines brought in by a subset of delinquent PCs, selected by a PC selection mechanism, are allowed to enter the DeliWays. The PC selection mechanism is an intelligent cost-benefit analysis based algorithm that utilizes Next-Use information to select the set of PCs that can maximize the hits experienced in DeliWays. Performance evaluation reveals that NUcache improves the performance over a baseline design by 9.6%, 30% and 33% respectively for dual, quad and eight core workloads comprised of SPEC benchmarks. We also show that NUcache is more effective than other well-known cache-partitioning algorithms.

Efficient Management of Fruit Pests by Pheromone Nanogels

Environment-friendly management of fruit flies involving pheromones is useful in reducing the undesirable pest populations responsible for decreasing the yield and the crop quality. A nanogel has been prepared from a pheromone, methyl eugenol (ME) using a low-molecular mass gelator. This was very stable at open ambient conditions and slowed down the evaporation of pheromone significantly. This enabled its easy handling and transportation without refrigeration, and reduction in the frequency of pheromone recharging in the orchard. Notably the involvement of the nano-gelled pheromone brought about an effective management of Bactrocera dorsalis, a prevalent harmful pest for a number of fruits including guava. Thus a simple, practical and low cost green chemical approach is developed that has a significant potential for crop protection, long lasting residual activity, excellent efficacy and favorable safety profiles. This makes the present invention well-suited for pest management in a variety of crops.

Effect of Metal Ions (Ag, Co, Ni, and Pd) on the Visible Light Degradation of Rhodamine B by Carbon-Covered Alumina-Supported TiO2 in Aqueous Solutions

Metal-ion (Ag, Co, Ni, and Pd) doped TiO2 nanocatalysts were successfully embedded on carbon-covered alumina supports. The CCA-embedded catalysts were crystalline and had a high surface area compared to the free metal-ion doped titania nanocatalysts while they still retained the anatase phase of the core TiO2. These catalysts were photocatalytically active under solar light irradiation. Rhodamine B was used as a model pollutant and the reactivity followed a pseudo-first-order reaction kinetics. The reaction rate of the CCA-supported catalysts was Pd > Ag > Co > Ni. Among the ratios of the CCA:catalyst used, it was found that the 1:1 ratio had the fastest reaction rate, followed by the 1:2 ratio, while the 2:1 ratio exhibited the lowest reaction rate. The CCA/metal-ion doped titania were found to have photocatalytic activities comparable with those of CCA-supported titania.

Cadmium deoxycholate: a new and efficient precursor for highly luminescent CdSe nanocrystals

We have demonstrated that cadmium deoxycholate (1), a Cd-salt, provides a convenient and inexpensive route to high quality CdSe nanocrystals with photoluminescence (PL) in the blue to red region of the visible spectrum, with reproducible quantum yields as high as similar to 47%. Owing to the high thermal stability of the bile acid based cadmium precursor (decomposition point: 332 degrees C), it was possible to achieve high injection and growth temperatures (similar to 300 degrees C) for the nanocrystals, which was essential for obtaining larger CdSe nanocrystals emitting in the red region (625-650 nm) with a sharp full width at half maximum (FWHM) (23 nm) and multiple (6-7) excitonic absorption features. The as-prepared CdSe nanocrystals synthesized from cadmium deoxycholate represent a series of highly efficient emitters with pure colours and controllable sizes, shapes and structures.

MULTI-OBJECTIVE GENETIC ALGORITHM FOR EFFICIENT POINT MATCHING IN MULTI-SENSOR SATELLITE IMAGE

This paper investigates a new approach for point matching in multi-sensor satellite images. The feature points are matched using multi-objective optimization (angle criterion and distance condition) based on Genetic Algorithm (GA). This optimization process is more efficient as it considers both the angle criterion and distance condition to incorporate multi-objective switching in the fitness function. This optimization process helps in matching three corresponding corner points detected in the reference and sensed image and thereby using the affine transformation, the sensed image is aligned with the reference image. From the results obtained, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient.

Energy-efficient synthesis of ferrite powders and films

In recent years, there has been significant effort in the synthesis of nanocrystalline spinel ferrites due to their unique properties. Among them, zinc ferrite has been widely investigated for countless applications. As traditional ferrite synthesis methods are energy- and time-intensive, there is need for a resource-effective process that can prepare ferrites quickly and efficiently without compromising material quality. We report on a novel microwave-assisted soft-chemical synthesis technique in the liquid medium for synthesis of ZnFe2O4 powder below 100 °C, within 5 min. The use of β-diketonate precursors, featuring direct metal-to-oxygen bonds in their molecular structure, not only reduces process temperature and duration sharply, but also leads to water-soluble and non-toxic by-products. As synthesized powder is annealed at 300 °C for 2 hrs in a conventional anneal (CA) schedule. An alternative procedure, a 2-min rapid anneal at 300 °C (RA) is shown to be sufficient to crystallize the ferrite particles, which show a saturation magnetization (MS) of 38 emu/g, compared with 39 emu/g for a 2-hr CA. This signifies that our process is efficient enough to reduce energy consumption by ∼85% just by altering the anneal scheme. Recognizing the criticality of anneal process to the energy budget, a more energy-efficient variation of the reaction process was developed, which obviates the need for post-synthesis annealing altogether. It is shown that the process also can be employed to deposit crystalline thin films of ferrites.

Packet scheduling for priority based transmission in energy harvesting sensors

In this paper, we determine packet scheduling policies for efficient power management in Energy Harvesting Sensors (EHS) which have to transmit packets of high and low priorities over a fading channel. We assume that incoming packets are stored in a buffer and the quality of service for a particular type of message is determined by the expected waiting time of packets of that type of message. The sensors are constrained to work with the energy that they garner from the environment. We derive transmit policies which minimize the sum of expected waiting times of the two types of messages, weighted by penalties. First, we show that for schemes with a constant rate of transmission, under a decoupling approximation, a form of truncated channel inversion is optimal. Using this result, we derive optimal solutions that minimize the weighted sum of the waiting times in the different queues.