Number Of Available Experiments Research Articles

TiO2@lipophilic Porphyrin Composites: New Insights into Tuning the Photoreduction of Cr(VI) to Cr(III) in Aqueous Phase

Metal-free and Cu(II)-lipophilic porphyrins [H2Pp and Cu(II)Pp] loaded on titanium dioxide in the anatase phase (TiO2) were prepared and used as a heterogeneous catalyst for the photoreduction of Cr(VI) to Cr(III) in aqueous suspensions under UV–Vis light irradiation. TiO2 impregnated with copper(II) porphyrin [TiO2@Cu(II)Pp] was the most effective in photocatalyst reduction of toxic chromate Cr(VI) to non-toxic chromium Cr(III). We further evaluated an experimental design with the scope of fast optimization of the process conditions related to the use of TiO2 or TiO2-porphyrin based photocatalysts. A full factorial design as a chemometric tool was successfully employed for screening the affecting factors involved in photoconversion catalysis, with the modification of TiO2 both with porphyrin H2Pp and Cu(II)Pp. The studied experimental factors were the catalyst amount, the concentration of Cr(VI) ions, and the pH of the medium. The performed multivariate approach was successfully used for fast fitting and better evaluation of significant factors affecting the experimental responses, with the advantage of reducing the number of available experiments. Thus, the stability of the optimized TiO2 embedded Cu(II)Pp was investigated, confirming the high reproducibility and suitability for environmental purposes.

Thermodynamic and transport properties of meteor melt constituents from ab initio simulations: MgSiO3, SiO2, and MgO

Recent experiments have suggested that melt flow plays a critical role in the ablation of meteoroids during atmospheric entry. Thus, modeling ablation requires knowledge of the melt properties of meteoritic constituents. These properties, however, are poorly understood and difficult to obtain with experimental techniques at entry conditions. An alternative means of obtaining high-temperature melt properties is through ab initio molecular dynamics (AIMD) simulations. Such simulations are performed here to characterize the melt properties of enstatite (MgSiO3), which is prevalent in certain types of chondrites, and its constitutive oxides (SiO2 and MgO). The structure, thermodynamic properties (density, bulk modulus, heat capacity, and coefficient of thermal expansion), and transport properties (diffusion and viscosity) are computed across the entire liquid phase and agree well with the limited number of available experiments. The high fidelity AIMD results are compared against less accurate models for melt property determination, which include classical molecular dynamics simulations and empirical mixture rules. Properties obtained from these alternative models generally show large relative errors compared to experiment, with viscosity, in particular, having errors of up to 98%. The present results highlight the potential of AIMD simulations to provide “quantitatively accurate” properties for melts of complex silicates found in meteorites and terrestrial rocks.

A modular RANS approach for modeling hypersonic flow transition on a scramjet-forebody configuration

In this study, the laminar-turbulent flow transition scenario on a 20%-scale X-51A forebody configuration, is simulated with a recently proposed RANS (Reynolds-Averaged Navier–Stokes) transition/turbulence model, which takes into account of the rational effects of compressibility, crossflow and flow-separation characteristics on different instability modes in 3-D boundary-layer flows (Fu and Wang 2013). First, we perform model validation with a number of available experiments on boundary layer transition including hypersonic flows past sharp leading-edge flat plates and cones, nose blunted cones and double ramps. Moreover, a new formula is established to convert measured free-stream noise level to FSTI (free-stream turbulence intensity) that is more adopted by engineering transition approaches. The results show generally good agreement with experimental data, proving that the present model responds accurately to both FSTI and leading edge radii. Then, a simplified analysis is proposed to characterize the present model behavior in pre-transitional flow regions. The results identify the model capability of the transition onset prediction with regard to the influences of nose bluntness as well as roughness elements. Finally, the success in its application to the forebody configuration (with/without trips) under both quiet and noisy conditions, indicates that the mixed-mode transition scenario indeed benefits from such a modular prediction approach, which embodies current conceptual understanding of the transition process.

A Novel Knowledge-Driven Systems Biology Approach for Phenotype Prediction upon Genetic Intervention

Deciphering the biological networks underlying complex phenotypic traits, e.g., human disease is undoubtedly crucial to understand the underlying molecular mechanisms and to develop effective therapeutics. Due to the network complexity and the relatively small number of available experiments, data-driven modeling is a great challenge for deducing the functions of genes/proteins in the network and in phenotype formation. We propose a novel knowledge-driven systems biology method that utilizes qualitative knowledge to construct a Dynamic Bayesian network (DBN) to represent the biological network underlying a specific phenotype. Edges in this network depict physical interactions between genes and/or proteins. A qualitative knowledge model first translates typical molecular interactions into constraints when resolving the DBN structure and parameters. Therefore, the uncertainty of the network is restricted to a subset of models which are consistent with the qualitative knowledge. All models satisfying the constraints are considered as candidates for the underlying network. These consistent models are used to perform quantitative inference. By in silico inference, we can predict phenotypic traits upon genetic interventions and perturbing in the network. We applied our method to analyze the puzzling mechanism of breast cancer cell proliferation network and we accurately predicted cancer cell growth rate upon manipulating (anti)cancerous marker genes/proteins.

Development of an Intermittency Equation for the Modeling of the Supersonic/Hypersonic Boundary Layer Flow Transition

An intermittency transport equation is developed in this study to model the laminar-turbulence boundary layer transition at supersonic and hypersonic conditions. The model takes into account the effects of different instability modes associated with the variations in Mach numbers. The model equation is based on the intermittency factor γ concept and couples with the well-known SST k–ω eddy-viscosity model in the solution procedures. The particular features of the present model approach are that: (1) the fluctuating kinetic energy k includes the non-turbulent, as well as turbulent fluctuations; (2) the proposed transport equation for the intermittency factor γ triggers the transition onset through a source term; (3) through the introduction of a new length scale normal to wall, the present model employs the local variables only avoiding the use of the integral parameters, like the boundary layer thickness δ, which are often cost-ineffective with the modern CFD methods; (4) in the fully turbulent region, the model retreats to SST model. This model is validated with a number of available experiments on boundary layer transition including the incompressible, supersonic and hypersonic flows past flat plates, straight/flared cones at zero incidences, etc. It is demonstrated that the present model can be successfully applied to the engineering calculations of a variety of aerodynamic flow transition with a reasonably wide range of Mach numbers.

Modelling flow transition in a hypersonic boundary layer with Reynolds-averaged Navier-Stokes approach

Based on Reynolds-averaged Navier-Stokes approach, a laminar-turbulence transition model is proposed in this study that takes into account the effects of different instability modes associated with the variations in Mach numbers of compressible boundary layer flows. The model is based on k-ω-γ three-equation eddy-viscosity concept with k representing the fluctuating kinetic energy, ω the specific dissipation rate and the intermittency factor γ. The particular features of the model are that: 1) k includes the non-turbulent, as well as turbulent fluctuations; 2) a transport equation for the intermittency factor γ is proposed here with a source term set to trigger the transition onset; 3) through the introduction of a new length scale normal to wall, the present model employs the local variables only avoiding the use of the integral parameters, like the boundary layer thickness δ, which are often cost-ineffective with the modern CFD (Computational Fluid Dynamics) methods; 4) in the fully turbulent region, the model retreats to the well-known k-ω SST (Shear Stress Transport) model. This model is validated with a number of available experiments on boundary layer transitions including the incompressible, supersonic and hypersonic flows past flat plates, straight/flared cones at zero incidences, etc. It is demonstrated that the present model can be successfully applied to the engineering calculations of a variety of aerodynamic flow transition.

Classification methods and inductive learning rules: what we may learn from theory

Inductive learning methods allow the system designer to infer a model of the relevant phenomena of an unknown process by extracting information from experimental data. A wide range of inductive learning methods is nowadays available, potentially ensuring different levels of accuracy on different problem domains. In this critical review of theoretic results gained in the last decade, we address the problem of designing an inductive classification system with optimal accuracy when domain knowledge is limited and the number of available experiments is-possibly-small. By analyzing the formal properties of consistent learning methods and of accuracy estimators, we wish to convey to the reader the message that the common practice of aggressively pursuing error minimization with different training algorithms and classification families is unjustified

Gene‐CBR: A CASE‐BASED REASONIG TOOL FOR CANCER DIAGNOSIS USING MICROARRAY DATA SETS

Gene expression profiles are composed of thousands of genes at the same time, representing the complex relationships between them. One of the well‐known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability of design methods capable of overcoming current limitations of state‐of‐the‐art algorithms is crucial to the development of successful applications. This paper presents gene‐CBR, a hybrid model that can perform cancer classification based on microarray data. The system employs a case‐based reasoning model that incorporates a set of fuzzy prototypes, a growing cell structure network and a set of rules to provide an accurate diagnosis. The hybrid model has been implemented and tested with microarray data belonging to bone marrow cases from forty‐three adult patients with cancer plus a group of six cases corresponding to healthy persons.

Existentialism in Counseling: THE SCIENTIFIC VIEW

The behavioral scientist can readily agree that existentialism is a powerful and meaningful movement for the counseling profession. However, to achieve its fullest usefulness it must grow to meet the scrutiny characteristic of the behavioral scientists' methods. Its weakness from this point of view include the reluctance to formulate public definitions of its most prized concepts and its rejection of rigor in methodology. The possibilities of a scientific existentialism which by no means destroys the magnificence of its concepts are demonstrated in a number of available experiments. Counseling as a helping profession cannot responsibly accept existentialism wholly as it comes from contemporary philosophy but must extrapolate and develop it into an adequate science of man.

Absorption of corticoids and related steroids from subcutaneously implanted pellets in the guinea pig.

IN FORMER papers from this Department absorption of various steroid hormones from subcutaneously implanted pellets, calculated per square millimeter of the pellet (tablet), has been given for estradiol and its esters (1), for testosterone and its derivatives, and for progesterone (2).1 Occasionally absorption per square millimeter has been given also for various corticoids (3, 4, 5). In the present paper new results, especially with substances hitherto not dealt with, are reported. As to the methods used, and as to the errors to be considered, we refer to a former paper (2). It is remarkable how little the results with progesterone in the present series (Table 1) differ from those in the former series (see Table 2 of (2)). The same is true for other substances mentioned in former papers; notable differences occur only when the number of available experiments is small (3, 4, 5).