A graph-theoretic approach to modeling metabolic pathways

Summary The metabolic pathways of medazepam, oxazepam, and diazepam were modeled using graph-theoretic transforms which are incorporable into computer-assisted metabolic analysis programs. The information, represented in the form of a graph-theoretic transform kit, which was obtained from these pathways was then used to predict the metabolites of other benzodiazepine compounds. The transform kits gave statistically significant predictions with respect to a statistical method for evaluating the performance of the transform kits.     

Radiative models of laser-induced plasma and pump-probe diagnostics relevant to laser-induced breakdown spectroscopy

The paper describes past and present efforts in modeling of laser-induced plasma and overviews plasma diagnostics carried out by pump-probe techniques. Besides general information on existing plasma models, the emphasis is given to models relevant to spectrochemical analysis, i.e. models of radiating plasma. Special attention is paid to collisional-radiative (CR) and collisional-dominated (CD) plasma models where radiative processes play an important role. Also, calibration-free (CF) models are considered which may endow with the possibility for standardless spectroscopic analysis. In the diagnostic part, only methods based on the use of additional diagnostic tools (auxiliary lasers, optics, and probes) are described omitting those based on plasma own radiation. A short review is provided on image-based diagnostics (shadowgraphy, schlieren, and interferometry), absorption and fluorescence, Langmuir probe, and less frequently used cavity ringdown and Thomson scattering methods.     

高放废物处置库选址中低渗透介质地质研究的几个问题

低渗透介质是阻碍有害物质在地下迁移良好的天然屏障, 因此成为高放废物处置库围岩类型的首选。本文通过对高放废物处置库选址中地质研究的回顾, 阐述了低渗透介质地质研究的特点, 对地质参数测定、取样、水流模拟、地球化学模拟进行了重点介绍。     

Neural networks for prediction of acoustical properties of polyurethane foams

This paper presents a study of neural networks for prediction of acoustical properties of polyurethane foams. The proposed neural network model of the foam uses easily measured parameters such as frequency, airflow resistivity and density to predict multiple acoustical properties including the sound absorption coefficient and the surface impedance. Such a model is quite robust in the sense that it can be used to develop models for many different classes of materials with different sets of input and output parameters. The current neural network model of the foam is empirical and provides a useful complement to the existing analytical and numerical approaches.     

Dynamic optimization of chemical cleaning in dead-end ultra filtration

In this paper a control strategy is formulated that minimizes the costs for a single chemical cleaning of a dead-end ultra filtration membrane. From the process model, the performance index and the constraints it can be derived that dynamic optimization will lead to a ‘maximum effort control problem’, in which the controls (cleaning flow and cleaning agent concentration) are either zero or maximum. The change from maximum to zero is called the switching point. This switching point depends on the overall cleaning time and the requested cleaning effectiveness. From the calculated optimal control strategy it follows that cleaning time can be significantly reduced, compared to conventional cleaning.     

Structural fluctuations in ensembles of nanoparticles

Caused by the interaction between the particles, structural fluctuations influence thermodynamics and order of transformation of an ensemble of nanoparticles. A stringed thermodynamic analysis revealed that, in fluctuating ensembles, the ratio of particle numbers in the equilibrium over the one in the non-equilibrium phase is independent of any metastable in between. Structural transformations in such ensembles, connected to latent heat, are of infinite order. These findings are summarized in a set of theorems ruling structural fluctuations. Finally, the consequences of fluctuations are demonstrated by an example.     

Updating beliefs about variables given new information on how those variables relate

Bayesian techniques specify how to update beliefs about a variable given information on that variable or related variables. In many cases, statistical analyses also provide information about the relationship between variables, but the Borel Paradox prohibits many natural ways of updating beliefs conditioned on information about a relationship. This paper presents a method by which beliefs can be updated without violating the Borel Paradox under certain circumstances. We apply our approach to relationships specified by a statistical model (i.e., regression), and relationships described by statistical simulation.