173 research outputs found
Study of Export Trade Financing in India with Particular Reference to Commercial Banks: Problems and Prospects
Exports are instrumental in the development of an economy, particularly developing nations. The Indian Financial System, through commercial bank offer financial resources for promoting exports by providing both pre and post shipment finances. LERMS and Full –convertibility on trade account of Indian rupee have provided importers to export financing, so also the New Trade Policy, provides a Favourable climate for exports.
The present paper spells out the role and share of commercial banks in export financing and issues in export financing i.e. aspirations and requirements of borrowers and discontentment of banks with the present regulation of export credit. The paper suggests for increasing the flow of bank credit to export sector, restructuring the interest rates. It also calls for a change in the attitudes of banks conservative and risk avers. The need for coordination between banks and financial institutions, role of EIGC in timely settlement of claims are impetus for a favorable export business. The stress is on introducing the new innovative services of counter trade, overseas borrowings, international factoring and banker’s acceptance for accelerating promotion of exports
Quantum characterization of superconducting photon counters
We address the quantum characterization of photon counters based on
transition-edge sensors (TESs) and present the first experimental tomography of
the positive operator-valued measure (POVM) of a TES. We provide the reliable
tomographic reconstruction of the POVM elements up to 11 detected photons and
M=100 incoming photons, demonstrating that it is a linear detector.Comment: 3 figures, NJP (to appear
Rank-based model selection for multiple ions quantum tomography
The statistical analysis of measurement data has become a key component of
many quantum engineering experiments. As standard full state tomography becomes
unfeasible for large dimensional quantum systems, one needs to exploit prior
information and the "sparsity" properties of the experimental state in order to
reduce the dimensionality of the estimation problem. In this paper we propose
model selection as a general principle for finding the simplest, or most
parsimonious explanation of the data, by fitting different models and choosing
the estimator with the best trade-off between likelihood fit and model
complexity. We apply two well established model selection methods -- the Akaike
information criterion (AIC) and the Bayesian information criterion (BIC) -- to
models consising of states of fixed rank and datasets such as are currently
produced in multiple ions experiments. We test the performance of AIC and BIC
on randomly chosen low rank states of 4 ions, and study the dependence of the
selected rank with the number of measurement repetitions for one ion states. We
then apply the methods to real data from a 4 ions experiment aimed at creating
a Smolin state of rank 4. The two methods indicate that the optimal model for
describing the data lies between ranks 6 and 9, and the Pearson test
is applied to validate this conclusion. Additionally we find that the mean
square error of the maximum likelihood estimator for pure states is close to
that of the optimal over all possible measurements.Comment: 24 pages, 6 figures, 3 table
Quantum teleportation on a photonic chip
Quantum teleportation is a fundamental concept in quantum physics which now
finds important applications at the heart of quantum technology including
quantum relays, quantum repeaters and linear optics quantum computing (LOQC).
Photonic implementations have largely focussed on achieving long distance
teleportation due to its suitability for decoherence-free communication.
Teleportation also plays a vital role in the scalability of photonic quantum
computing, for which large linear optical networks will likely require an
integrated architecture. Here we report the first demonstration of quantum
teleportation in which all key parts - entanglement preparation, Bell-state
analysis and quantum state tomography - are performed on a reconfigurable
integrated photonic chip. We also show that a novel element-wise
characterisation method is critical to mitigate component errors, a key
technique which will become increasingly important as integrated circuits reach
higher complexities necessary for quantum enhanced operation.Comment: Originally submitted version - refer to online journal for accepted
manuscript; Nature Photonics (2014
Spectral thresholding quantum tomography for low rank states
The estimation of high dimensional quantum states is an important statistical problem arising in current quantum technology applications. A key example is the tomography of multiple ions states, employed in the validation of state preparation in ion trap experiments (Häffner et al 2005 Nature 438 643). Since full tomography becomes unfeasible even for a small number of ions, there is a need to investigate lower dimensional statistical models which capture prior information about the state, and to devise estimation methods tailored to such models. In this paper we propose several new methods aimed at the efficient estimation of low rank states and analyse their performance for multiple ions tomography. All methods consist in first computing the least squares estimator, followed by its truncation to an appropriately chosen smaller rank. The latter is done by setting eigenvalues below a certain 'noise level' to zero, while keeping the rest unchanged, or normalizing them appropriately. We show that (up to logarithmic factors in the space dimension) the mean square error of the resulting estimators scales as where r is the rank, is the dimension of the Hilbert space, and N is the number of quantum samples. Furthermore we establish a lower bound for the asymptotic minimax risk which shows that the above scaling is optimal. The performance of the estimators is analysed in an extensive simulations study, with emphasis on the dependence on the state rank, and the number of measurement repetitions. We find that all estimators perform significantly better than the least squares, with the 'physical estimator' (which is a bona fide density matrix) slightly outperforming the other estimators
Identification of plasma lipid biomarkers for prostate cancer by lipidomics and bioinformatics
Background:
Lipids have critical functions in cellular energy storage, structure and signaling. Many individual lipid molecules have been associated with the evolution of prostate cancer; however, none of them has been approved to be used as a biomarker. The aim of this study is to identify lipid molecules from hundreds plasma apparent lipid species as biomarkers for diagnosis of prostate cancer.
Methodology/Principal Findings:
Using lipidomics, lipid profiling of 390 individual apparent lipid species was performed on 141 plasma samples from 105 patients with prostate cancer and 36 male controls. High throughput data generated from lipidomics were analyzed using bioinformatic and statistical methods. From 390 apparent lipid species, 35 species were demonstrated to have potential in differentiation of prostate cancer. Within the 35 species, 12 were identified as individual plasma lipid biomarkers for diagnosis of prostate cancer with a sensitivity above 80%, specificity above 50% and accuracy above 80%. Using top 15 of 35 potential biomarkers together increased predictive power dramatically in diagnosis of prostate cancer with a sensitivity of 93.6%, specificity of 90.1% and accuracy of 97.3%. Principal component analysis (PCA) and hierarchical clustering analysis (HCA) demonstrated that patient and control populations were visually separated by identified lipid biomarkers. RandomForest and 10-fold cross validation analyses demonstrated that the identified lipid biomarkers were able to predict unknown populations accurately, and this was not influenced by patient's age and race. Three out of 13 lipid classes, phosphatidylethanolamine (PE), ether-linked phosphatidylethanolamine (ePE) and ether-linked phosphatidylcholine (ePC) could be considered as biomarkers in diagnosis of prostate cancer.
Conclusions/Significance:
Using lipidomics and bioinformatic and statistical methods, we have identified a few out of hundreds plasma apparent lipid molecular species as biomarkers for diagnosis of prostate cancer with a high sensitivity, specificity and accuracy
Evolution of ZnS Nanoparticles via Facile CTAB Aqueous Micellar Solution Route: A Study on Controlling Parameters
Synthesis of semiconductor nanoparticles with new photophysical properties is an area of special interest. Here, we report synthesis of ZnS nanoparticles in aqueous micellar solution of Cetyltrimethylammonium bromide (CTAB). The size of ZnS nanodispersions in aqueous micellar solution has been calculated using UV-vis spectroscopy, XRD, SAXS, and TEM measurements. The nanoparticles are found to be polydispersed in the size range 6–15 nm. Surface passivation by surfactant molecules has been studied using FTIR and fluorescence spectroscopy. The nanoparticles have been better stabilized using CTAB concentration above 1 mM. Furthermore, room temperature absorption and fluorescence emission of powdered ZnS nanoparticles after redispersion in water have also been investigated and compared with that in aqueous micellar solution. Time-dependent absorption behavior reveals that the formation of ZnS nanoparticles depends on CTAB concentration and was complete within 25 min
Multiple order-up-to policy for mitigating bullwhip effect in supply chain network
This paper proposes a multiple order-up-to policy based inventory replenishment scheme to mitigate the bullwhip effect in a multi-stage supply chain scenario, where various transportation modes are available between the supply chain (SC) participants. The proposed policy is similar to the fixed order-up-to policy approach where replenishment decision “how much to order” is made periodically on the basis of the predecided order-up-to inventory level. In the proposed policy, optimal multiple order-up-to levels are assigned to each SC participants, which provides decision making reference point for deciding the transportation related order quantity. Subsequently, a mathematical model is established to define optimal multiple order-up-to levels for each SC participants that aims to maximize overall profit from the SC network. In parallel, the model ensures the control over supply chain pipeline inventory, high satisfaction of customer demand and enables timely utilization of available transportation modes. Findings from the various numerical datasets including stochastic customer demand and lead times validate that—the proposed optimal multiple order-up-to policy based inventory replenishment scheme can be a viable alternative for mitigating the bullwhip effect and well-coordinated SC. Moreover, determining the multiple order-up-to levels is a NP hard combinatorial optimization problem. It is found that the implementation of new emerging optimization algorithm named bacterial foraging algorithm (BFA) has presented superior optimization performances. The robustness and applicability of the BFA algorithm are further validated statistically by employing the percentage heuristic gap and two-way ANOVA analysis
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