High-fidelity inelastic post-buckling response for balanced design and performance improvement of X-braced moment resisting frames

In this paper, the nonlinear post buckling response of X-Braced Moment Resisting Frame (X-BMRF) systems are studied. The X-BMRF comprises of X-bracing diagonals attached to the moment frame by corner gusset plates to form the structural system acting as a dual frame. In common practice today, one of the X-bracing diagonal members is discontinuous, and a middle gusset plate is used to connect the diagonals to each other at the intersection. In this study, the effect of mid-connection details and different types sizes of corner gusset plate connection are well measured to evaluate behavioral characteristics of the above systems. An accurate and robust three-dimensional finite element modeling of the above systems validatedverified against available test data and numerical simulation are demonstrated. Then, a number of X-BMRFs are designed and analyzed under monotonic (and cyclic) loading(s), and later ductility values and energy dissipation ratios of such systems are appraised. The results are used to evaluate the secondary yield mechanisms, probable failure modes, and to quantify the loading share of story shear when different rigidity ratios between the X-bracing and moment frame systems are deliberated. Finally, the results can provide a suitable ground to present a new set of balanced design criteria which can improve nonlinear performance and assure maximum system ductility of such system

Noise and multistability in gene regulatory networks

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Physics, 2004.Includes bibliographical references (leaves 103-112).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Proteins are the functional machinery in living cells. Proteins interact with each other and bind to DNA to form so-called gene regulatory networks and in this way regulate the level, location and timing of expression of other proteins. Cells implement feedback loops to create a memory of their gene expression states. In this way, every differentiated cell in a multicellular organism remembers its expression profile throughout its life. On the other hand, biochemical reactions that take place during gene expression involve small numbers of molecules, and are therefore dominated by large concentration fluctuations. This intrinsic noise has the potential to corrupt memory storage and might result in random transitions between different gene expression states. In the first part of my thesis, I will discuss how the fluctuations in gene expression levels are regulated. The results provided the first experimental evidence that cells can regulate noise in their gene expression by tuning their genetic parameters. In the second half of my thesis, I will discuss how cells create memory by experimentally studying a gene regulatory network that implements a positive feedback loop. A positive feedback loop with nonlinear interactions creates two distinct stable gene expression states. A phase diagram, coupled with a mathematical model of the network, was used to quantitatively investigate the biochemical processes in this network. The response of the network depends on its previous history (hysteresis). Despite the fluctuations in the gene expression, the memory of the gene expression state is preserved for a long time for a broad range of system parameters.(cont.) On the other hand, for some of the parameters, noise causes random transitions of the cells between different gene expression states and results in a bimodal response. Finally, the hysteretic response of the natural system is experimentally converted to an ultrasensitive graded response as predicted by our model.by Ertugrul M. Ozbudak.Ph.D

MODELIRANJE GORIVA I POTENCIJALNO PONAŠANJE POŽARA U TURSKOJ

Description of fuel characteristics is an essential input to fire behavior models that can provide decision-support for fire management. Fuel models describe fuel characteristics for fire modeling systems based on Rothermel’s fire spread model. In this study, fire behavior data collected in field experiments in different fuel complexes in Turkey is used in the process of fuel model development. Nine fuel models were built for low and tall maquis, Anatolian black pine (P. nigra J.F. Arnold subsp. nigra var. caramanica (Loudon) Rehder), litter, and slash variable in age and load. BehavePlus simulations of fire rate of spread, flame length and fireline intensity for typical summer weather conditions highlight the quite different fire potential between the studied fuel types. The difficulty in dealing with fuel complexes dominated by live fuels was evident from the simulations. On the contrary, the model correctly predicted the observed temporal decrease of fire behavior in slash. This study shows the crucial importance of experimental fire data to parameterize fuel models.Opis karakteristika goriva je nužan podatak za modele ponašanja požara koji mogu dati podršku odlukama za požarno upravljanje. Modeli goriva opisuju karakteristike goriva za sustave modeliranja požara koji se temelje na Rothermelovom modelu širenja požara. U ovoj studiji, podaci o ponašanju požara prikupljeni u terenskim eksperimentima u različitim kompleksima goriva u Turskoj korišteni su u procesu razvoja modela goriva. Napravljeno je devet modela goriva za nisku i visoku makiju, anatolski crni bor (P. nigra J.F. Arnold subsp. nigra var. caramanica (Loudon) Rehder), stelju te otpad, varijable u starosti i težini. BehavePlus simulacije stope širenja požara, duljine plamena i intenziteta požarne fronte za tipične ljetne vremenske uvjete naglašavaju potpuno drukčiji potencijal požara između izučavanih tipova goriva. Iz simulacija su očite teškoće u bavljenju kompleksom goriva kojima dominira živo gorivo. Nasuprot tomu, model je točno predvidio privremeno smanjenje ponašanja požara u otpadu. Ova studija pokazuje ključnu važnost eksperimentalnih podataka o požaru, kako bi se izvršila parametrizacija modela goriva

Investigation of the Effects of Reaction Temperature in NiFe2O4 Nanoparticles Synthesis by Hydrothermal Method

In this experimental study was investigated the effect of reaction temperature in NiFe2O4 nanoparticles synthesis with hydrothermal method. An appropriate ratio of solutions nickel nitrate and ferric nitrate were dissolved in deionized water and poured into a crucible. Later, polyethylene glycol 600 (PEG 600) was added to this mixture. Samples were adjusted to pH 11 values using NaOH solution. Accordingly, experiments were made at 180, 200 and 250 oC, respectively. The other parameters, were fixed as reaction time 24 h and pH value 11. The structural and morphological properties of NiFe2O4 nanoparticles were determined by X-ray powder diffraction (XRD) and Scanning Electron microscopy (SEM). Results showed that increasing calcination temperature contributed to cyristallinity of NiFe2O4 nano particles. But also average particle size increased. As a result, average particle size was calculated by using Debye-Scherrer Formula as approximately 30 nm. However, this results was confirmed with SEM and TEM analysis. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/3502

BIOLOGY OF SILKWORM (BOMBYX MORI) IN TURKEY

According to oldest records the first time silkworm was cultivated and silk was obtained from cocoonat China. Silkworm eggs and mulberry seeds was brought to Istanbul illegally the year 552 at age of Byzantine Empire although China kept it as a secret. It started to spread Marmara regione specially Bursa and It’s neighbourhood. Then it was spreaded to allover the world. Sericulture have been economical, cultural and traditional cultivating sector at Turkey for 1500 years. Silkworm is cultivated at about 30 countries that include Turkey. Silk fiber is superior to other fibers in terms of stability, flexibility and brightness. Amount of need is approximately twice the amount of cultivating. In whole world Turkish silk fiber quality is at second rank after japanese silk. Silkworm is a general term that includes a range from worm to the butterfly. Silkworm is a kind of night butterflies. Butterflies are light cream colour have chubby bodies and have soft feathers. Wingspan is about 4-5 cm. Butterfly have lost flying ability because of domestication also have 2 or 3 days life and at that period doesn’t feed and doesn’t fly. Silkworm is fed with mulberry leaves. One cocoon is made from a single silk fiber it’s lenght is 800 meters. Real silkworm named “Bombyx Mori L” is bred at mulberry tree which is cultivated at China is white breed. Bombyx Mori L silkworm producesbest silk fiber amoung other genus and it is most special genus cultivated

Pyrolysis Mass Spectrometric Analysis of styrene butadiene block and random copolymers

Cataloged from PDF version of article.Direct pyrolysis mass spectrometric analysis of a styrene-butadiene-styrene block copolymer indicated that thermal decomposition of each block shows a resemblance to the related homopolymer, giving a possibility of differentiation of blocks. However, the random analog, the styrene butadiene rubber, degraded in a manner that is somewhat in between in nature of the thermal characteristics of both homopolymers. This technique shows promise to differentiate thermal behaviors of each sequence in block polymers if any exist. Indirect pyrolysis mass spectrometric analysis gave no clear evidence for differentiation of the nature and the composition of the copolymers. © 1997 Elsevier Science Ltd. All rights reserved

To Invest or Not to Invest: Using Vocal Behavior to Predict Decisions of Investors in an Entrepreneurial Context

Entrepreneurial pitch competitions have become increasinglypopular in the start-up culture to attract prospective investors. As theultimate funding decision often follows from some form of social interaction,it is important to understand how the decision-making processof investors is influenced by behavioral cues. In this work, we examinewhether vocal features are associated with the ultimate funding decisionof investors by utilizing deep learning methods.We used videos of individualsin an entrepreneurial pitch competition as input to predict whetherinvestors will invest in the startup or not. We proposed models that combinedeep audio features and Handcrafted audio Features (HaF) and feedthem into two types of Recurrent Neural Networks (RNN), namely LongShort-Term Memory (LSTM) and Gated Recurrent Units (GRU). Wealso trained the RNNs with only deep features to assess whether HaFprovide additional information to the models. Our results show that it ispromising to use vocal behavior of pitchers to predict whether investorswill invest in their business idea. Different types of RNNs yielded similarperformance, yet the addition of HaF improved the performance

To Invest or Not to Invest: Using Vocal Behavior to Predict Decisions of Investors in an Entrepreneurial Context

Entrepreneurial pitch competitions have become increasinglypopular in the start-up culture to attract prospective investors. As theultimate funding decision often follows from some form of social interaction,it is important to understand how the decision-making processof investors is influenced by behavioral cues. In this work, we examinewhether vocal features are associated with the ultimate funding decisionof investors by utilizing deep learning methods.We used videos of individualsin an entrepreneurial pitch competition as input to predict whetherinvestors will invest in the startup or not. We proposed models that combinedeep audio features and Handcrafted audio Features (HaF) and feedthem into two types of Recurrent Neural Networks (RNN), namely LongShort-Term Memory (LSTM) and Gated Recurrent Units (GRU). Wealso trained the RNNs with only deep features to assess whether HaFprovide additional information to the models. Our results show that it ispromising to use vocal behavior of pitchers to predict whether investorswill invest in their business idea. Different types of RNNs yielded similarperformance, yet the addition of HaF improved the performance

CASTNet: Community-Attentive Spatio-Temporal Networks for Opioid Overdose Forecasting

Opioid overdose is a growing public health crisis in the United States. This crisis, recognized as "opioid epidemic," has widespread societal consequences including the degradation of health, and the increase in crime rates and family problems. To improve the overdose surveillance and to identify the areas in need of prevention effort, in this work, we focus on forecasting opioid overdose using real-time crime dynamics. Previous work identified various types of links between opioid use and criminal activities, such as financial motives and common causes. Motivated by these observations, we propose a novel spatio-temporal predictive model for opioid overdose forecasting by leveraging the spatio-temporal patterns of crime incidents. Our proposed model incorporates multi-head attentional networks to learn different representation subspaces of features. Such deep learning architecture, called "community-attentive" networks, allows the prediction of a given location to be optimized by a mixture of groups (i.e., communities) of regions. In addition, our proposed model allows for interpreting what features, from what communities, have more contributions to predicting local incidents as well as how these communities are captured through forecasting. Our results on two real-world overdose datasets indicate that our model achieves superior forecasting performance and provides meaningful interpretations in terms of spatio-temporal relationships between the dynamics of crime and that of opioid overdose.Comment: Accepted as conference paper at ECML-PKDD 201