Optimal control under uncertainty and Bayesian parameters adjustments

We propose a general framework for studying optimal impulse control problem in the presence of uncertainty on the parameters. Given a prior on the distribution of the unknown parameters, we explain how it should evolve according to the classical Bayesian rule after each impulse. Taking these progressive prior-adjustments into account, we characterize the optimal policy through a quasi-variational parabolic equation, which can be solved numerically. The derivation of the dynamic programming equation seems to be new in this context. The main difficulty lies in the nature of the set of controls which depends in a non trivial way on the initial data through the filtration itself

Research on the Application of E-commerce to Small and Medium Enterprises (SMEs): the Case of India

SMEs account for a large proportion and play an important role in the development of each country in the world, including India. The globalization will bring many advantages for enterprises however SMEs will face fierce competition at the local, national and International level. In order to maintain and promote the important role of SMEs in the context of increased competition, SMEs have to change and adopt new technologies. E-commerce and digital technologies are bringing opportunities to help SMEs improve their competitiveness, narrow the gap with big enterprises thanks to their fairness and flexibility of the digital business environment.       According to UNIDO (2017), India is one of the countries successfully applying e-commerce to SMEs. Contributing to this success is the important role of the Indian government. Therefore, this paper focuses on researching the application of e-commerce to SMEs in terms of the role of government in promoting and creating an ecosystem for SMEs and e-commerce development

An Efficient Method for GPS Multipath Mitigation Using the Teager-Kaiser-Operator-based MEDLL

An efficient method for GPS multipath mitigation is proposed. The motivation for this proposed method is to integrate the Teager-Kaiser Operator (TKO) with the Multipath Estimating Delay Lock Loop (MEDLL) module to mitigate the GPS multipath efficiently. The general implementation process of the proposed method is that we first utilize the TKO to operate on the received signal’s Auto-Correlation Function (ACF) to get an initial estimate of the multipaths. Then we transfer the initial estimated results to the MEDLL module for a further estimation. Finally, with a few iterations which are less than those of the original MEDLL algorithm, we can get a more accurate estimate of the Line-Of-Sight (LOS) signal, and thus the goal of the GPS multipath mitigation is achieved. The simulation results show that compared to the original MEDLL algorithm, the proposed method can reduce the computation load and the hardware and/or software consumption of the MEDLL module, meanwhile, without decreasing the algorithm accuracy

Shear banding of colloidal glasses - a dynamic first order transition?

We demonstrate that application of an increasing shear field on a glass leads to an intriguing dynamic first order transition in analogy to equilibrium transitions. By following the particle dynamics as a function of the driving field in a colloidal glass, we identify a critical shear rate upon which the diffusion time scale of the glass exhibits a sudden discontinuity. Using a new dynamic order parameter, we show that this discontinuity is analogous to a first order transition, in which the applied stress acts as the conjugate field on the system's dynamic evolution. These results offer new perspectives to comprehend the generic shear banding instability of a wide range of amorphous materials.Comment: 4 pages, 4 figure

Pairing effect on the giant dipole resonance width at low temperature

The width of the giant dipole resonance (GDR) at finite temperature T in Sn-120 is calculated within the Phonon Damping Model including the neutron thermal pairing gap determined from the modified BCS theory. It is shown that the effect of thermal pairing causes a smaller GDR width at T below 2 MeV as compared to the one obtained neglecting pairing. This improves significantly the agreement between theory and experiment including the most recent data point at T = 1 MeV.Comment: 8 pages, 5 figures to be published in Physical Review

Ultra-high sensitivity magnetic field and magnetization measurements with an atomic magnetometer

We describe an ultra-sensitive atomic magnetometer using optically-pumped potassium atoms operating in spin-exchange relaxation free (SERF) regime. We demonstrate magnetic field sensitivity of 160 aT/Hz$^{1/2}$ in a gradiometer arrangement with a measurement volume of 0.45 cm$^3$ and energy resolution per unit time of $44 \hbar$. As an example of a new application enabled by such a magnetometer we describe measurements of weak remnant rock magnetization as a function of temperature with a sensitivity on the order of 10$^{-10}$ emu/cm$^3$/Hz$^{1/2}$ and temperatures up to 420$^\circ$C

Numerical Assessment of Fibre Inclusion in a Load Transfer Platform for Pile-Supported Embankments over Soft Soil

© 2016 ASCE. This study presents the results of a numerical investigation in the performance of natural fibre reinforced load transfer platform (NFRLTP) for pile supported embankment construction over soft soil. A numerical analysis based on finite element method (FEM) was carried out on an NFRLTP pile-supported embankment in a two-dimensional plane strain condition. The effects of natural fibre inclusion in the load transfer platform on the stress transfer mechanism, generation and dissipation of excess pore water pressure have been analyzed and discussed in detail. The findings indicate that natural fibre reinforced soil as a load transfer platform facilitated the load transfer process from the embankment to piles, while decreases the intensity of load transferred to soft soil, the excess pore water pressure and the overall settlement

Modeling reactivity to biological macromolecules with a deep multitask network

Most small-molecule drug candidates fail before entering the market, frequently because of unexpected toxicity. Often, toxicity is detected only late in drug development, because many types of toxicities, especially idiosyncratic adverse drug reactions (IADRs), are particularly hard to predict and detect. Moreover, drug-induced liver injury (DILI) is the most frequent reason drugs are withdrawn from the market and causes 50% of acute liver failure cases in the United States. A common mechanism often underlies many types of drug toxicities, including both DILI and IADRs. Drugs are bioactivated by drug-metabolizing enzymes into reactive metabolites, which then conjugate to sites in proteins or DNA to form adducts. DNA adducts are often mutagenic and may alter the reading and copying of genes and their regulatory elements, causing gene dysregulation and even triggering cancer. Similarly, protein adducts can disrupt their normal biological functions and induce harmful immune responses. Unfortunately, reactive metabolites are not reliably detected by experiments, and it is also expensive to test drug candidates for potential to form DNA or protein adducts during the early stages of drug development. In contrast, computational methods have the potential to quickly screen for covalent binding potential, thereby flagging problematic molecules and reducing the total number of necessary experiments. Here, we train a deep convolution neural networkthe XenoSite reactivity modelusing literature data to accurately predict both sites and probability of reactivity for molecules with glutathione, cyanide, protein, and DNA. On the site level, cross-validated predictions had area under the curve (AUC) performances of 89.8% for DNA and 94.4% for protein. Furthermore, the model separated molecules electrophilically reactive with DNA and protein from nonreactive molecules with cross-validated AUC performances of 78.7% and 79.8%, respectively. On both the site- and molecule-level, the model’s performances significantly outperformed reactivity indices derived from quantum simulations that are reported in the literature. Moreover, we developed and applied a selectivity score to assess preferential reactions with the macromolecules as opposed to the common screening traps. For the entire data set of 2803 molecules, this approach yielded totals of 257 (9.2%) and 227 (8.1%) molecules predicted to be reactive only with DNA and protein, respectively, and hence those that would be missed by standard reactivity screening experiments. Site of reactivity data is an underutilized resource that can be used to not only predict if molecules are reactive, but also show where they might be modified to reduce toxicity while retaining efficacy. The XenoSite reactivity model is available at http://swami.wustl.edu/xenosite/p/reactivity