Mining data from intensive care patients

In this paper we describe the application of data mining methods for predicting the evolution of patients in an intensive care unit. We discuss the importance of such methods for health care and other application domains of engineering. We argue that this problem is an important but challenging one for the current state of the art data mining methods and explain what improvements on current methods would be useful. We present a promising study on a preliminary data set that demonstrates some of the possibilities in this area.     

Population balances for particulate processes—a volume approach

Population balance models have been used in chemical engineering since the 1960s and have evolved to become the most important tools for design and control of particulate processes. In this paper we show that the intrinsic particle parameter that determines changes in the process and should thus be included in the population balance is the particle volume. The basic population that is modeled should be the mass distribution, or the volume distribution if the density is constant. The population balance thus describes the change of the volume distribution of volume with time. Furthermore, we suggest that the “birth” and “death” terms that are often used to describe discrete events in particulate processes can almost always be replaced by a rate of change term.To design and control existing and future processes, a multi-dimensional population balance model is required. We propose a volume-based model in which the particle properties that are modeled are the volumes of solid, liquid, and air, respectively. In the most general case the model will consist of a properties vector and a distribution tensor. Depending on the complexity of the process, one or more of the properties may be omitted from the model. This is shown in three examples of increasing complexity: comminution, sintering, and granulation.     

大规模体积压裂诱发的地质灾变问题研究综述

页岩储层大规模体积压裂对储层实施改造,是实现非常规油气资源工业化开发的核心技术,但目前该技术的机理研究和应用都处于起步发展阶段。大规模体积压裂可能带来潜在的地质灾害风险,主要包括淡水资源的消耗、浅水层的污染、断层活化诱发地质灾害和地表侵蚀、甲烷泄露致空气污染、植被破坏等问题。从体积压裂机理入手,研究了上述存在的地质灾变风险及灾害发生机制,并从勘探调研合理的页岩气开采区块、设计科学的钻井完井方式、压裂液注入速率方案、实时监测断层破裂带应力及滑移状态以及废水废气回收处理等角度提出对应的科学指导和防范措施,对国内页岩气顺利开展工业化开采、避免潜在的地质环境灾变对人类产生危害具有重要的指导意义。     

Generalized ordering-search for learning directed probabilistic logical models

Recently, there has been an increasing interest in directed probabilistic logical models and a variety of formalisms for describing such models has been proposed. Although many authors provide high-level arguments to show that in principle models in their formalism can be learned from data, most of the proposed learning algorithms have not yet been studied in detail. We introduce an algorithm, generalized ordering-search, to learn both structure and conditional probability distributions (CPDs) of directed probabilistic logical models. The algorithm is based on the ordering-search algorithm for Bayesian networks. We use relational probability trees as a representation for the CPDs. We present experiments on a genetics domain, blocks world domains and the Cora dataset. Editors: Stephen Muggleton, Ramon Otero, Simon Colton.     

增强战斗力的有效途径:虚拟战争

在军事领域中,随着军事斗争的复杂性的不断增加,人们一直在寻求一种能在实验条件下“预先实践战争”的方法,来深入地研究未来战争的规律。现代科学技术的飞速发展,使得人们这一愿望得以实现。在虚拟现实技术基础上发展起来的“虚拟战争”方法,正是研究增强军队战斗力的有效途径。本文对这一课题作了概要的阐述。     

Optimal attacks on qubit-based Quantum Key Recycling

Quantum Key Recycling (QKR) is a quantum cryptographic primitive that allows one to reuse keys in an unconditionally secure way. By removing the need to repeatedly generate new keys, it improves communication efficiency. ?kori? and de Vries recently proposed a QKR scheme based on 8-state encoding (four bases). It does not require quantum computers for encryption/decryption but only single-qubit operations. We provide a missing ingredient in the security analysis of this scheme in the case of noisy channels: accurate upper bounds on the required amount of privacy amplification. We determine optimal attacks against the message and against the key, for 8-state encoding as well as 4-state and 6-state conjugate coding. We provide results in terms of min-entropy loss as well as accessible (Shannon) information. We show that the Shannon entropy analysis for 8-state encoding reduces to the analysis of quantum key distribution, whereas 4-state and 6-state suffer from additional leaks that make them less effective. From the optimal attacks we compute the required amount of privacy amplification and hence the achievable communication rate (useful information per qubit) of qubit-based QKR. Overall, 8-state encoding yields the highest communication rates.     

Towards Automated Binding Affinity Prediction Using an Iterative Linear Interaction Energy Approach

Binding affinity prediction of potential drugs to target and off-target proteins is an essential asset in drug development. These predictions require the calculation of binding free energies. In such calculations, it is a major challenge to properly account for both the dynamic nature of the protein and the possible variety of ligand-binding orientations, while keeping computational costs tractable. Recently, an iterative Linear Interaction Energy (LIE) approach was introduced, in which results from multiple simulations of a protein-ligand complex are combined into a single binding free energy using a Boltzmann weighting-based scheme. This method was shown to reach experimental accuracy for flexible proteins while retaining the computational efficiency of the general LIE approach. Here, we show that the iterative LIE approach can be used to predict binding affinities in an automated way. A workflow was designed using preselected protein conformations, automated ligand docking and clustering, and a (semi-)automated molecular dynamics simulation setup. We show that using this workflow, binding affinities of aryloxypropanolamines to the malleable Cytochrome P450 2D6 enzyme can be predicted without a priori knowledge of dominant protein-ligand conformations. In addition, we provide an outlook for an approach to assess the quality of the LIE predictions, based on simulation outcomes only.     

Sequential enzymatic quantification of two sugars in a single microchannel

This paper presents the implementation of a multiple analyte enzyme assay, based on the sequential injection of the different enzyme solutions, in an electrokinetic driven microfluidic chip. The assay methodology for the simultaneous quantification of d-glucose and d-fructose was reported in previous publications but here the real integration of both enzyme assays was achieved. When assays were executed separately, good reproducibility was observed with average CV values of 5.2% and 4.5% for the d-glucose and d-fructose assay, respectively. Next, the assays for the quantification of d-glucose and d-fructose were integrated simultaneously on chip, where each assay was executed consecutively in the same microreactor by applying a specific sequence of potentials at the reservoirs. This article proves the integration of a sequential based quantification approach in continuous microfluidic chips with electrokinetic actuation.     

Renewable linear alpha olefins by selective ethenolysis of decarboxylated unsaturated fatty acids

A two‐step concept for the production of linear alpha olefins from biomass is reported. As a starting material an internally unsaturated C17 alkene was used, which was obtained by the decarboxylation of oleic acid. Here, we report on the ethenolysis of this bio‐based product, using commercially available metathesis catalysts. The desired alpha olefin products, 1‐nonene and 1‐decene, were obtained in excellent yield (96%) and selectivity (96%). Practical applications: The two‐step conversion described in this contribution, starting from unsaturated fatty acids, provides a method for the production of industrially important linear alpha olefins. These valuable products are widely used as starting materials for the production of surfactants and polymers such as linear low density polyethylene (LLDPE).