138 research outputs found
Characterization of protein-flavor interactions using inverse gas chromatography
No full textIn this research, we investigated the retention/release mechanism of selected flavor compounds on or from protein matrices by establishing quantitative design principles for these interactions. Thermodynamic parameters (partition coefficient Kp, free energy of adsorption ∆Gs and the enthalpy of adsorption ∆Hs) of the interaction between selected flavor compounds (hexane, hexanal, hexanol and d-limonene) and protein systems (soy protein isolate and zein) were determined by using inverse gas chromatography under different temperatures and relatively humid conditions. The inverse gas chromatography system was fitted with an additional humidification system that could maintain the relative humidity of the carrier gas, thus enabling the evaluation of the effect of relative humidity on the measured quantities. Increasing temperature and relative humidity led to less favorable interaction between selected flavors and proteins. Flavor retention at high relative humidity was less than at low relative humidity or at dry conditions. This suggests that flavor compounds and water molecules might be competing to bind to the available sides of the protein. Quantitative characterization of the mechanism and thermodynamics of flavor binding and release in protein matrices will benefit the food industry to efficiently develop flavored foods.M.S.Includes bibliographical referencesby Ozlem Dol
Risk-averse control of undiscounted transient Markov models
No full textThe classical optimal control problems for discrete-time, transient Markov processes are infinite horizon, undiscounted expected total cost or reward models. Some examples of these models are optimal stopping problems and stochastic shortest or longest path problems, which may have applications in health-care, finance, and maintenance. However, such expected value models implicitly assume the decision maker is risk-neutral, so they may not be appropriate for several real-life problems. In this study, we use Markov risk measures to formulate a risk-averse version of the optimal control problem for transient Markov processes with general state and compact control spaces. We derive risk-averse dynamic programming equations and show that they have a unique solution which is also the optimal value of the Markov control problem. Furthermore, it is shown that a randomized policy may be strictly better than deterministic policies, when risk measures are employed. We suggest two algorithms, value iteration and policy iteration methods, for solving the dynamic programming equations and show their convergence. In general, each policy evaluation step of the policy iteration algorithm requires solving a system of nonsmooth equations. We use a version of nonsmooth Newton method to solve these equations and show its global convergence. We further consider a risk-averse finite horizon Markov control problem under randomized policies and derive a value iteration method for its solution. Finally, we work on asset selling, organ transplant, and credit card examples to illustrate the theory for infinite horizon problem, and present numerical results.Ph. D.Includes bibliographical referencesIncludes vitaby Ozlem Cavu
Modeling traveler behavior via day-to-day learning dynamics
No full textTravel behavior lies at the core of analysis and evaluation of transportation related measures aiming to improve urban mobility, environmental quality and a wide variety of social objectives. A better understanding of travel behavior will improve travel demand forecasting and the assessment of emerging transport policies, and will improve our means to increase road safety. The day-to-day models reflect the travelers’ learning and forecasting mechanisms. These models predict travelers’ choices for any given day based on their experienced choices in the previous days. Day-to-day approaches allow the use of wide range of behavioral rules, and levels of aggregation, and capture the heterogeneity in users’ learning and adaptation processes, and behavioral characteristics. This thesis aims to develop a novel framework to model the interdependence between travelers’ choice decisions, learning and adaptation behavior and the day-to-day update mechanism of traffic flows. The novelty of this thesis is that the proposed approach combines traveler heterogeneity and rationality in a single framework to predict travelers’ day-to-day departure time and route decisions, and develops a novel day-to-day dynamic traffic assignment approach. The empirical results obtained from real transportation network, New Jersey Turnpike, confirm that the proposed day-to-day learning and dynamic traffic assignment framework model can successfully capture the significant learning dynamics, demonstrating the possibility of developing a psychological framework (i.e., learning models) as a viable approach to represent travel behavior. The other contributions of this thesis include a novel route choice set generation approach based on stochastic integer programming approach. The proposed methodology takes into account travel time variability and reliability in the transportation network. The path relevance criteria are directly incorporated into the optimization model by minimizing mean travel time, travel time variability and path overlap. Unlike previous approaches in the literature, proposed methodology eliminates the filtering step from the choice set generation and generates paths sets at desired dissimilarity level while minimizing the travel time and variability of these paths. Several case studies show the applicability of the proposed methodology on real transportation networks.Ph.D.Includes bibliographical referencesIncludes vitaby Ozlem Yanmaz-Tuze
Corrigendum to “The status of depression and anxiety in infertile Turkish couples” [Iran J Reprod Med 2011; 9: 99-104]
No full textThe publisher has been informed of an error that occurred on page 99 in which the second authors name must be changed to Ozlem Kayacik Gunday. On behalf of the author, the publisher wishes to apologize for this error. The online version of article has been updated on 31 August 2023 and can be found at https://doi.org/10.18502/ijrm.v9i2.104
Contributing factors to mortality rates of pulmonary tuberculosis in intensive care units
No full textHyperbolic tangent solution to the conformable time fractional Zakharov-Kuznetsov equation in 3D space
No full textA cubic subdomain Galerkin method over the geometrically graded mesh to the singularly perturbed problem
No full textAndrogen receptor-binding sites are highly mutated in prostate cancer
No full textAndrogen receptor (AR) signalling is essential in nearly all prostate cancers. Any alterations to AR-mediated transcription can have a profound effect on carcinogenesis and tumor growth. While mutations of the AR protein have been extensively studied, little is known about those somatic mutations that occur at the non-coding regions where AR binds DNA. Using clinical whole genome sequencing, we show that AR binding sites have a dramatically increased rate of mutations that is greater than any other transcription factor and specific to only prostate cancer. Demonstrating this may be common to lineage-specific transcription factors, estrogen receptor binding sites were also found to have elevated rate of mutations in breast cancer. We provide evidence that these mutations at AR binding sites, and likely other related transcription factors, are caused by faulty repair of abasic sites. Overall, this work demonstrates that non-coding AR binding sites are frequently mutated in prostate cancer and can impact enhancer activity.We thank Dogancan Ozturan, Firat Uyulur, and Kenan Sevinc for their helpful scientific discussions. Mehmet Gonen is supported by the Turkish Academy of Sciences (GEBIP; The Young Scientist Award Program) and the Science Academy of Turkey (BAGEP; The Young Scientist Award Program). Nathan Lack is supported by the Turkish Academy of Sciences (GEBIP; The Young Scientist Award Program).Lack, NA (reprint author), Koc Univ, Sch Med, TR-34450 Istanbul, Turkey, Univ British Columbia, Vancouver Prostate Ctr, Vancouver, BC V6H 3Z6, Canada, Koc Univ, Koc Univ Res Ctr Translat Med KUTTAM, TR-34450 Istanbul, Turkey.
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Higher order trigonometric B-spline algorithms to the solution of coupled Burgers’ equation
No full textEffects of Contemporary Information Technologies on Culture and Architectural Space
No full textAbstractChanges are inherent and inevitable in any living culture. We live in a fluid, changing world with increasingly blurred boundaries between local and global practices. Due to rapidly escalating technological developments in social interaction technology and digital communication, social contacts between people has changed. These technological developments have changed our habits and gradually our culture. The requirements and needs of the society are evolving and this can be seen on different scales. This new level of connectivity brings important concerns regarding privacy, protection and control. Like in our daily life and through living conditions etc. This in turn results in changes to spatial planning and architecture. Technological advances and cultural changes have increasingly demanded the new definition of space. Today architectural space becomes “the space of all dimensions”. This paper aims to investigate this evolution of space on different scales and means that caused by contemporary information technology through cultural and environmental aspects. The implementations of information technology on relations and different scales of interaction based on space will be discussed, and then the change and evolution of architectural concepts on will be addressed
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