Integration of biofuel production. How pyrolysis and torrefaction can be integrated

The main goal of this work is to clarify the idea of two thermochemical conversion processes of biomass - pyrolysis and torrefaction and to identify possible ways how and where exactly these processes can be integrated. Integration into CHP power plant process was chosen as one of the most promising ways. Multiple product development was determined by means of this integration concept. The analysis of the possible pros and cons was made based on some experimental data collected from the previous studies related to the topic of my work. In addition, one real integrated case was represented in the last part of the work. Finally, to highlight the main idea brief summarizing was done.

Pharmacology and toxicology of diphenyl diselenide in several biological models

The pharmacology of synthetic organoselenium compounds indicates that they can be used as antioxidants, enzyme inhibitors, neuroprotectors, anti-tumor and anti-infectious agents, and immunomodulators. In this review, we focus on the effects of diphenyl diselenide (DPDS) in various biological model organisms. DPDS possesses antioxidant activity, confirmed in several in vitro and in vivo systems, and thus has a protective effect against hepatic, renal and gastric injuries, in addition to its neuroprotective activity. The activity of the compound on the central nervous system has been studied since DPDS has lipophilic characteristics, increasing adenylyl cyclase activity and inhibiting glutamate and MK-801 binding to rat synaptic membranes. Systemic administration facilitates the formation of long-term object recognition memory in mice and has a protective effect against brain ischemia and on reserpine-induced orofacial dyskinesia in rats. On the other hand, DPDS may be toxic, mainly because of its interaction with thiol groups. In the yeast Saccharomyces cerevisiae, the molecule acts as a pro-oxidant by depleting free glutathione. Administration to mice during cadmium intoxication has the opposite effect, reducing oxidative stress in various tissues. DPDS is a potent inhibitor of d-aminolevulinate dehydratase and chronic exposure to high doses of this compound has central effects on mouse brain, as well as liver and renal toxicity. Genotoxicity of this compound has been assessed in bacteria, haploid and diploid yeast and in a tumor cell line.

Integration of cooking and vacuum cooling of carrots in a same vessel

Cooked vegetables are commonly used in the preparation of ready-to-eat foods. The integration of cooking and cooling of carrots and vacuum cooling in a single vessel is described in this paper. The combination of different methods of cooking and vacuum cooling was investigated. Integrated processes of cooking and vacuum cooling in a same vessel enabled obtaining cooked and cooled carrots at the final temperature of 10 ºC, which is adequate for preparing ready-to-eat foods safely. When cooking and cooling steps were performed with the samples immersed in boiling water, the effective weight loss was approximately 3.6%. When the cooking step was performed with the samples in boiling water or steamed, and the vacuum cooling was applied after draining the boiling water, water loss ranged between 15 and 20%, which caused changes in the product texture. This problem can be solved with rehydration using a small amount of sterile cold water. The instrumental textural properties of carrots samples rehydrated at both vacuum and atmospheric conditions were very similar. Therefore, the integrated process of cooking and vacuum cooling of carrots in a single vessel is a feasible alternative for processing such kind of foods.

Raw-appearing Restructured fish models made with Sodium alginate or Microbial transglutaminase and effect of chilled storage

Restructuring by adding Sodium Alginate or Microbial Transglutaminase (MTGase) using cold gelation technology make it possible to obtain many different raw products from minced and/or chopped fish muscle that are suitable for being used as the basis of new restructured products with different physicochemical properties and even different compositions. Special consideration must be given to their shelf-life and the changes that may take place during chilling, both in visual appearance and physicochemical properties. After chilled storage, the restructured models made with different muscular particle size and composition at low temperature (5 °C), it was observed that microbial growth limited the shelf-life to 7-14 days. Mechanical properties increased (p < 0.05) during that time, and higher values were observed in samples elaborated by joining small muscle particle size than in those elaborated by homogenization. There was no clear increase in the cooking yield and purge loss, and no significant colour change (p > 0.05) was detected during storage.

Vulnerability to adverse consequences of drinking and problem drinker status as predicted by risky drinking behaviours, drug use, sex differences and affect : a test of multiple models /

Although alcohol problems and alcohol consumption are related, consumption does not fully account for differences in vulnerability to alcohol problems. Therefore, other factors should account for these differences. Based on previous research, it was hypothesized that risky drinking behaviours, illicit and prescription drug use, affect and sex differences would account for differences in vulnerability to alcohol problems while statistically controlling for overall alcohol consumption. Four models were developed that were intended to test the predictive ability of these factors, three of which tested the predictor sets separately and a fourth which tested them in a combined model. In addition, two distinct criterion variables were regressed on the predictors. One was a measure of the frequency that participants experienced negative consequences that they attributed to their drinking and the other was a measure of the extent to which participants perceived themselves to be problem drinkers. Each of the models was tested on four samples from different populations, including fIrst year university students, university students in their graduating year, a clinical sample of people in treatment for addiction, and a community sample of young adults randomly selected from the general population. Overall, support was found for each of the models and each of the predictors in accounting for differences in vulnerability to alcohol problems. In particular, the frequency with which people become intoxicated, frequency of illicit drug use and high levels of negative affect were strong and consistent predictors of vulnerability to alcohol problems across samples and criterion variables. With the exception of the clinical sample, the combined models predicted vulnerability to negative consequences better than vulnerability to problem drinker status. Among the clinical and community samples the combined model predicted problem drinker status better than in the student samples.

Development and characterization of concentric capillary nebulizer used in inductively coupled plasma analysis /

A simple, low-cost concentric capillary nebulizer (CCN) was developed and evaluated for ICP spectrometry. The CCN could be operated at sample uptake rates of 0.050-1.00 ml min'^ and under oscillating and non-oscillating conditions. Aerosol characteristics for the CCN were studied using a laser Fraunhofter diffraction analyzer. Solvent transport efficiencies and transport rates, detection limits, and short- and long-term stabilities were evaluated for the CCN with a modified cyclonic spray chamber at different sample uptake rates. The Mg II (280.2nm)/l\/lg 1(285.2nm) ratio was used for matrix effect studies. Results were compared to those with conventional nebulizers, a cross-flow nebulizer with a Scott-type spray chamber, a GemCone nebulizer with a cyclonic spray chamber, and a Meinhard TR-30-K3 concentric nebulizer with a cyclonic spray chamber. Transport efficiencies of up to 57% were obtained for the CCN. For the elements tested, short- and long-term precisions and detection limits obtained with the CCN at 0.050-0.500 ml min'^ are similar to, or better than, those obtained on the same instrument using the conventional nebulizers (at 1.0 ml min'^). The depressive and enhancement effects of easily ionizable element Na, sulfuric acid, and dodecylamine surfactant on analyte signals with the CCN are similar to, or better than, those obtained with the conventional nebulizers. However, capillary clog was observed when the sample solution with high dissolved solids was nebulized for more than 40 min. The effects of data acquisition and data processing on detection limits were studied using inductively coupled plasma-atomic emission spectrometry. The study examined the effects of different detection limit approaches, the effects of data integration modes, the effects of regression modes, the effects of the standard concentration range and the number of standards, the effects of sample uptake rate, and the effect of Integration time. All the experiments followed the same protocols. Three detection limit approaches were examined, lUPAC method, the residual standard deviation (RSD), and the signal-to-background ratio and relative standard deviation of the background (SBR-RSDB). The study demonstrated that the different approaches, the integration modes, the regression methods, and the sample uptake rates can have an effect on detection limits. The study also showed that the different approaches give different detection limits and some methods (for example, RSD) are susceptible to the quality of calibration curves. Multicomponents spectral fitting (MSF) gave the best results among these three integration modes, peak height, peak area, and MSF. Weighted least squares method showed the ability to obtain better quality calibration curves. Although an effect of the number of standards on detection limits was not observed, multiple standards are recommended because they provide more reliable calibration curves. An increase of sample uptake rate and integration time could improve detection limits. However, an improvement with increased integration time on detection limits was not observed because the auto integration mode was used.

Understanding the Integration of Living Skills Through the Context of Health and Physical Education: A Case Study of Educators’ Experiences

The research presented is a qualitative case study of educators’ experiences in integrating living skills in the context of health and physical education (HPE). In using semi-structured interviews the study investigated HPE educators’ experiences and revealed their insights relative to three major themes; professional practice, challenges and support systems. Professional practice experiences detailed the use of progressive lesson planning, reflective and engaging activities, explicit student centered pedagogy as well as holistic teaching philosophies. Even further, the limited knowledge and awareness of living skills, conflicting teaching philosophies, competitive environments between subject areas and lack of time and accessibility were four major challenges that emerged throughout the data. Major supportive roles for HPE educators in the integration process included other educators, consultants, school administration, public health, parents, community programs and professional organizations. The study provides valuable discussion and suggestions for improvement of pedagogical practices in teaching living skills in the HPE setting.

Phonon spectra and temperature variation of thermodynamic properties of fcc metals via Finnis-Sinclair type many body potentials: Sutton-Chen and improved Sutton-Chen models

Volume(density)-independent pair-potentials cannot describe metallic cohesion adequately as the presence of the free electron gas renders the total energy strongly dependent on the electron density. The embedded atom method (EAM) addresses this issue by replacing part of the total energy with an explicitly density-dependent term called the embedding function. Finnis and Sinclair proposed a model where the embedding function is taken to be proportional to the square root of the electron density. Models of this type are known as Finnis-Sinclair many body potentials. In this work we study a particular parametrization of the Finnis-Sinclair type potential, called the "Sutton-Chen" model, and a later version, called the "Quantum Sutton-Chen" model, to study the phonon spectra and the temperature variation thermodynamic properties of fcc metals. Both models give poor results for thermal expansion, which can be traced to rapid softening of transverse phonon frequencies with increasing lattice parameter. We identify the power law decay of the electron density with distance assumed by the model as the main cause of this behaviour and show that an exponentially decaying form of charge density improves the results significantly. Results for Sutton-Chen and our improved version of Sutton-Chen models are compared for four fcc metals: Cu, Ag, Au and Pt. The calculated properties are the phonon spectra, thermal expansion coefficient, isobaric heat capacity, adiabatic and isothermal bulk moduli, atomic root-mean-square displacement and Gr\"{u}neisen parameter. For the sake of comparison we have also considered two other models where the distance-dependence of the charge density is an exponential multiplied by polynomials. None of these models exhibits the instability against thermal expansion (premature melting) as shown by the Sutton-Chen model. We also present results obtained via pure pair potential models, in order to identify advantages and disadvantages of methods used to obtain the parameters of these potentials.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

QUANTIFICATION AND EXTRACTION OF SURFACE FEATURES FROM DIGITAL TERRAIN MODELS

Digital Terrain Models (DTMs) are important in geology and geomorphology, since elevation data contains a lot of information pertaining to geomorphological processes that influence the topography. The first derivative of topography is attitude; the second is curvature. GIS tools were developed for derivation of strike, dip, curvature and curvature orientation from Digital Elevation Models (DEMs). A method for displaying both strike and dip simultaneously as colour-coded visualization (AVA) was implemented. A plug-in for calculating strike and dip via Least Squares Regression was created first using VB.NET. Further research produced a more computationally efficient solution, convolution filtering, which was implemented as Python scripts. These scripts were also used for calculation of curvature and curvature orientation. The application of these tools was demonstrated by performing morphometric studies on datasets from Earth and Mars. The tools show promise, however more work is needed to explore their full potential and possible uses.

High-Spin and Emissive Molecular Materials: Synthesis and Characterization of New Polynuclear Ni(II) Complexes from the Use of Aromatic Schiff Bases as Bridging Ligands

The employment of the bridging/chelating Schiff bases, N-salicylidene-4-methyl-o-aminophenol (samphH2) and N-naphthalidene-2-amino-5-chlorobenzoic acid (nacbH2), in nickel cluster chemistry has afforded eight polynuclear Ni(II) complexes with new structural motifs, interesting magnetic and optical properties, and unexpected organic ligand transformations. In the present thesis, Chapter 1 deals with all the fundamental aspects of polynuclear metal complexes, molecular magnetism and optics, while research results are reported in Chapters 2 and 3. In the first project (Chapter 2), I investigated the coordination chemistry of the organic chelating/bridging ligand, N-salicylidene-4-methyl-o-aminophenol (samphH2). The general NiII/tBuCO2-/samphH2 reaction system afforded two new tetranuclear NiII clusters, namely [Ni4(samph)4(EtOH)4] (1) and [Ni4(samph)4(DMF)2] (2), with different structural motifs. Complex 1 possessed a cubane core while in complex 2 the four NiII ions were located at the four vertices of a defective dicubane. The nature of the organic solvent was found to be of pivotal importance, leading to compounds with the same nuclearity, but different structural topologies and magnetic properties. The second project, the results of which are summarized in Chapter 3, included the systematic study of a new optically-active Schiff base ligand, N-naphthalidene-2-amino-5-chlorobenzoic acid (nacbH2), in NiII cluster chemistry. Various reactions between NiX2 (X- = inorganic anions) and nacbH2 were performed under basic conditions to yield six new polynuclear NiII complexes, namely (NHEt3)[Ni12(nacb)12(H2O)4](ClO4) (3), (NHEt3)2[Ni5(nacb)4(L)(LH)2(MeOH)] (4), [Ni5(OH)2(nacb)4(DMF)4] (5), [Ni5(OMe)Cl(nacb)4(MeOH)3(MeCN)] (6), (NHEt3)2[Ni6(OH)2(nacb)6(H2O)4] (7), and [Ni6(nacb)6(H2O)3(MeOH)6] (8). The nature of the solvent, the inorganic anion, X-, and the organic base were all found to be of critical importance, leading to products with different structural topologies and nuclearities (i.e., {Ni5}, {Ni6} and {Ni12}). Magnetic studies on all synthesized complexes revealed an overall ferromagnetic behavior for complexes 4 and 8, with the remaining complexes being dominated by antiferromagnetic exchange interactions. In order to assess the optical efficiency of the organic ligand when bound to the metal centers, photoluminescence studies were performed on all synthesized compounds. Complexes 4 and 5 show strong emission in the visible region of the electromagnetic spectrum. Finally, the ligand nacbH2 allowed for some unexpected organic transformations to occur; for instance, the pentanuclear compound 5 comprises both nacb2- groups and a new organic chelate, namely the anion of 5-chloro-2-[(3-hydroxy-4-oxo-1,4-dihydronaphthalen-1-yl)amino]benzoic acid. In the last section of this thesis, an attempt to compare the NiII cluster chemistry of the N-naphthalidene-2-amino-5-chlorobenzoic acid ligand with that of the structurally similar but less bulky, N-salicylidene-2-amino-5-chlorobenzoic acid (sacbH2), was made.

Computation and Analysis of Multiple Structural-Change Models

In a recent paper, Bai and Perron (1998) considered theoretical issues related to the limiting distribution of estimators and test statistics in the linear model with multiple structural changes. In this companion paper, we consider practical issues for the empirical applications of the procedures. We first address the problem of estimation of the break dates and present an efficient algorithm to obtain global minimizers of the sum of squared residuals. This algorithm is based on the principle of dynamic programming and requires at most least-squares operations of order O(T 2) for any number of breaks. Our method can be applied to both pure and partial structural-change models. Secondly, we consider the problem of forming confidence intervals for the break dates under various hypotheses about the structure of the data and the errors across segments. Third, we address the issue of testing for structural changes under very general conditions on the data and the errors. Fourth, we address the issue of estimating the number of breaks. We present simulation results pertaining to the behavior of the estimators and tests in finite samples. Finally, a few empirical applications are presented to illustrate the usefulness of the procedures. All methods discussed are implemented in a GAUSS program available upon request for non-profit academic use.

Aggregations and Marginalization of GARCH and Stochastic Volatility Models

The GARCH and Stochastic Volatility paradigms are often brought into conflict as two competitive views of the appropriate conditional variance concept : conditional variance given past values of the same series or conditional variance given a larger past information (including possibly unobservable state variables). The main thesis of this paper is that, since in general the econometrician has no idea about something like a structural level of disaggregation, a well-written volatility model should be specified in such a way that one is always allowed to reduce the information set without invalidating the model. To this respect, the debate between observable past information (in the GARCH spirit) versus unobservable conditioning information (in the state-space spirit) is irrelevant. In this paper, we stress a square-root autoregressive stochastic volatility (SR-SARV) model which remains true to the GARCH paradigm of ARMA dynamics for squared innovations but weakens the GARCH structure in order to obtain required robustness properties with respect to various kinds of aggregation. It is shown that the lack of robustness of the usual GARCH setting is due to two very restrictive assumptions : perfect linear correlation between squared innovations and conditional variance on the one hand and linear relationship between the conditional variance of the future conditional variance and the squared conditional variance on the other hand. By relaxing these assumptions, thanks to a state-space setting, we obtain aggregation results without renouncing to the conditional variance concept (and related leverage effects), as it is the case for the recently suggested weak GARCH model which gets aggregation results by replacing conditional expectations by linear projections on symmetric past innovations. Moreover, unlike the weak GARCH literature, we are able to define multivariate models, including higher order dynamics and risk premiums (in the spirit of GARCH (p,p) and GARCH in mean) and to derive conditional moment restrictions well suited for statistical inference. Finally, we are able to characterize the exact relationships between our SR-SARV models (including higher order dynamics, leverage effect and in-mean effect), usual GARCH models and continuous time stochastic volatility models, so that previous results about aggregation of weak GARCH and continuous time GARCH modeling can be recovered in our framework.