An optimized analytical method for the simultaneous detection of iodoform, iodoacetic acid, and other trihalomethanes and haloacetic acids in drinking water

An optimized method is presented using liquid-liquid extraction and derivatization for the extraction of iodoacetic acid (IAA) and other haloacetic acids (HAA9) and direct extraction of iodoform (IF) and other trihalomethanes (THM4) from drinking water, followed by detection by gas chromatography with electron capture detection (GC-ECD). A Doehlert experimental design was performed to determine the optimum conditions for the five most significant factors in the derivatization step: namely, the volume and concentration of acidic methanol (optimized values  = 15%, 1 mL), the volume and concentration of Na2SO4 solution (129 g/L, 8.5 mL), and the volume of saturated NaHCO3 solution (1 mL). Also, derivatization time and temperature were optimized by a two-variable Doehlert design, resulting in the following optimized parameters: an extraction time of 11 minutes for IF and THM4 and 14 minutes for IAA and HAA9; mass of anhydrous Na2SO4 of 4 g for IF and THM4 and 16 g for IAA and HAA9; derivatization time of 160 min and temperature at 40°C. Under optimal conditions, the optimized procedure achieves excellent linearity (R2 ranges 0.9990–0.9998), low detection limits (0.0008–0.2 µg/L), low quantification limits (0.008–0.4 µg/L), and good recovery (86.6%–106.3%). Intra- and inter-day precision were less than 8.9% and 8.8%, respectively. The method was validated by applying it to the analysis of raw, flocculated, settled, and finished waters collected from a water treatment plant in China

A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings

Learning word embeddings has received a significant amount of attention recently. Often, word embeddings are learned in an unsupervised manner from a large collection of text. The genre of the text typically plays an important role in the effectiveness of the resulting embeddings. How to effectively train word embedding models using data from different domains remains a problem that is underexplored. In this paper, we present a simple yet effective method for learning word embeddings based on text from different domains. We demonstrate the effectiveness of our approach through extensive experiments on various down-stream NLP tasks.Comment: 7 pages, accepted by EMNLP 201

A self-learning system for identifying harmful network information

Network information identification is a &ldquo;hot&rdquo; topic currently. This paper designs a self-learning system using neural network algorithm for identifying the harmful network messages of both Chinese and English languages. The system segments the message into words and creates key word vector which characterizes the harmful network information. The BP algorithm is taken advantage of to train the neural network. The result of training and studying of the neural network can be applied onto many network applications based on message identification. The result of experiments demonstrates that our system has a high degree of accuracy.<br /

Concatenating dynamical decoupling with decoherence-free subspaces for quantum computation

A scheme to implement a quantum computer subjected to decoherence and governed by an untunable qubit-qubit interaction is presented. By concatenating dynamical decoupling through bang-bang (BB) pulse with decoherence-free subspaces (DFSs) encoding, we protect the quantum computer from environment-induced decoherence that results in quantum information dissipating into the environment. For the inherent qubit-qubit interaction that is untunable in the quantum system, BB control plus DFSs encoding will eliminate its undesired effect which spoils quantum information in qubits. We show how this quantum system can be used to implement universal quantum computation.Comment: 6 pages,2 figures, 1 tabl

AC response of an atomic tunnel junction

By combining density functional analysis with the solution of a three-dimensional quantum scattering problem, and applying appropriate ac transport theory, we investigated the ac transport response of Si atomic tunnel junctions in the resonance tunneling regime. Our results show that transmission channels of both the leads and the atomic section contribute to the dynamic conductance. The ac response is found to be determined by the average channel transmission weighted by the corresponding density of states, explaining a counterintuitive result that at resonance tunneling the ac response may be capacitivelike. As the ac frequency is increased, the resonance peak of dynamic conductance can be split into two, a phenomenon that can be explained analytically.published_or_final_versio

Attacking practical quantum key distribution system with wavelength dependent beam splitter and multi-wavelength sources

Unconditional security of quantum key distribution protocol can be guaranteed by the basic property of quantum mechanics. Unfortunately, the practical quantum key distribution system always have some imperfections, and the practical system may be attacked if the imperfection can be controlled by the eavesdropper Eve. Applying the fatal security loophole introduced by the imperfect beam splitter's wavelength dependent optical property, we propose wavelength-dependent attacking model, which can be applied to almost all practical quantum key distribution systems with the passive state modulation and photon state detection after the practical beam splitter. Utilizing our attacking model, we experimentally demonstrate the attacking system based on practical polarization encoding quantum key distribution system with almost 100% success probability. Our result demonstrate that all practical devices require tightened security inspection for avoiding side channel attacks in practical quantum key distribution experimental realizations

Determining the Solution Space of Vertex-Cover by Interactions and Backbones

To solve the combinatorial optimization problems especially the minimal Vertex-cover problem with high efficiency, is a significant task in theoretical computer science and many other subjects. Aiming at detecting the solution space of Vertex-cover, a new structure named interaction between nodes is defined and discovered for random graph, which results in the emergence of the frustration and long-range correlation phenomenon. Based on the backbones and interactions with a node adding process, we propose an Interaction and Backbone Evolution Algorithm to achieve the reduced solution graph, which has a direct correspondence to the solution space of Vertex-cover. By this algorithm, the whole solution space can be obtained strictly when there is no leaf-removal core on the graph and the odd cycles of unfrozen nodes bring great obstacles to its efficiency. Besides, this algorithm possesses favorable exactness and has good performance on random instances even with high average degrees. The interaction with the algorithm provides a new viewpoint to solve Vertex-cover, which will have a wide range of applications to different types of graphs, better usage of which can lower the computational complexity for solving Vertex-cover

Stochastic Biological System-of-Systems Modelling for iPSC Culture

Large-scale manufacturing of induced pluripotent stem cells (iPSCs) is essential for cell therapies and regenerative medicines. Yet, iPSCs form large cell aggregates in suspension bioreactors, resulting in insufficient nutrient supply and extra metabolic waste build-up for the cells located at core. Since subtle changes in micro-environment can lead to cell stress and heterogeneous cell population, a novel Biological System-of-Systems (Bio-SoS) framework is proposed to characterize cell-to-cell interactions, spatial heterogeneity, and cell response to micro-environmental variation. Building on stochastic metabolic reaction network, aggregation kinetics, and reaction-diffusion mechanisms, the Bio-SoS model can quantify the impact of factors (i.e., aggregate size) on cell product health and quality heterogeneity, accounting for causal interdependencies at individual cell, aggregate, and cell population levels. This framework can accurately predict iPSC culture conditions for both monolayer and aggregate cultures, where these predictions can be leveraged to ensure the control of culture processes for successful cell growth and expansion.Comment: 36 pages, 10 figure

Symbolic Dynamics Analysis of the Lorenz Equations

Recent progress of symbolic dynamics of one- and especially two-dimensional maps has enabled us to construct symbolic dynamics for systems of ordinary differential equations (ODEs). Numerical study under the guidance of symbolic dynamics is capable to yield global results on chaotic and periodic regimes in systems of dissipative ODEs which cannot be obtained neither by purely analytical means nor by numerical work alone. By constructing symbolic dynamics of 1D and 2D maps from the Poincare sections all unstable periodic orbits up to a given length at a fixed parameter set may be located and all stable periodic orbits up to a given length may be found in a wide parameter range. This knowledge, in turn, tells much about the nature of the chaotic limits. Applied to the Lorenz equations, this approach has led to a nomenclature, i.e., absolute periods and symbolic names, of stable and unstable periodic orbits for an autonomous system. Symmetry breakings and restorations as well as coexistence of different regimes are also analyzed by using symbolic dynamics.Comment: 35 pages, LaTeX, 13 Postscript figures, uses psfig.tex. The revision concerns a bug at the end of hlzfig12.ps which prevented the printing of the whole .ps file from page 2