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A computational perspective on meta-Fibonacci recurrence relations
In that Phd thesis, we explore meta-Fibonacci recurrence relations concept in a new approach thanks to detailed computational investigations that are provided by a computer algebra system PARI/GP. It is well known that nested recurrence relations are considered one of the most resistant subclasses of nonlinear recurrences to known proof techniques of difference equations. In that direction, we introduce and analyse not only selected spesific variants but also certain solution families while we prove their properties based on new types of structural connections between systems of nested recurrence relations. In particular, we provide remarkable computational evidences on the existence of infinitely many different solutions for a notable class of nested recurrences while this is also provable for certain families of slow solutions. Additionally, generational methods for erratic meta-Fibonacci sequences, and related statistical properties of successive generations are studied in the scope of the thesis. Keywords: Hofstadter sequence, Hofstadter-Huber family, nested recurrences
Technology and energy management for cogeneration and trigeneration systems based on multi-objective optimization in integrated structures
Since the middle of the 19th century, the world's energy consumption has increased significantly due to the rapidly growing world population and improving quality of life after the industrial revolution. Global warming is one of the most critical problems for the world's future due to the high share of fossil fuels covering the ever-increasing energy demand and the carbon emissions released into the atmosphere. Although renewable energy sources seem to be an alternative to the solution of this problem, the estimates show that the dependence on fossil resources will continue to increase in the near future. The importance of energy efficiency in generation as well as in consumption is evident since 71% of the energy source of integrated structures, which have a significant share in Turkey's total energy consumption, is based on fossil fuels. Although the integrated structures differ according to their types, they are similar in terms of energy demand consisting of electricity, heating, domestic hot water, and cooling. Cogeneration and trigeneration systems are among the most effective alternatives to meet this demand. When the studies in the literature are examined, it is seen that one-dimensional solutions such as energy efficiency or energy saving are generally developed for a particular type of building. Unlike the examples in the literature, this thesis study aims to develop a valid model for all integrated structures by considering the energy requirement of integrated structures on a common denominator, and process analyses based on thermodynamic parameters have been examined on the basis of the first law of thermodynamics. In this context, it has been aimed to define the effective working intervals of the operational optimization of cogeneration and trigeneration systems depending on the genetic algorithm. Within this frame of reference, techno-economic analyzes of cogeneration and trigeneration options for the reference integrated structures have been made using the first law of thermodynamics analyses. In the study, the most effective operational processes have been defined as one generation using the multi-objective optimization method based on a genetic algorithm. In this process, measurable technical, economic, environmental, legislative, and social criteria and boundary conditions depending on the building type have been determined. The results of the multi-objective optimization showed that the problem does not have a single analytical solution; on the contrary, it contains a Pareto set of solutions depending on the objective function, constraints, and variability. According to the analysis results obtained from case studies consisting of a hospital, hotel, and shopping mall; 26-28% from primary energy sources, 25-34% savings in energy costs, and carbon dioxide emission savings of 450-500 tons/year, 230-280 tons/year, 72-78 tons/year, respectively, have been achieved. In addition, the Sustainable Heat Management Index, which was formed as a concrete contribution to the literature, has been developed to determine the priority of the heating or cooling system in the utilization of the heat released in the trigeneration system. This thesis consists of seven chapters. In the first chapter, there are analyzes on the conceptual information of the thesis, while a comprehensive literature research and publication review based on keywords are presented in the second chapter. Energy management and technology management for integrated structures, which form the main structure of the thesis, are discussed in the third and fourth chapters, respectively. The framework model developed in the study and the related methodological infrastructure are given in the fifth chapter. In the sixth chapter of the study, the techno-economic assessment and optimization results based on the framework model formed for the integrated structures taken as reference are explained in detail. In the conclusion part, specific findings obtained from the analyzes are given, and predictions and evaluations related to them are made. At the end of the study, suggestions regarding the issues to be addressed in the future are presented.
Dynamical analysis of chaotic circuits with computational algebra method
This study analyses a novel autonomous non- linear continuous-time system derived from Bao's simple third-order inductor-free memristive chaotic circuit for chaotic dynamics via computer algebra. Bao et al. designed the Chua memristor emulator with resistors, capacitors, and opamps to avoid the negative effects of the inductor [1]. This simple third-order inductor-free chaotic circuit utilizes reasonably functional discrete components, which are significant with the number of dynamically simplified elements for mathematical representation of chaotic behaviour. Our proposed model aims to overcome the negative long-term effects on the electrical properties of the capacitor and reveal experimental results depending on an additional variable that behaves like an electrical inductor. Numerical examples are given to study bifurcation properties in the modified chaotic circuit concerning the results with Lyapunov regimes and also to identify limit cycles of period-1, period-2, period-4, and chaotic attractors in the proposed circuit.
Chaotic system analysis with genetic algorithm approach
Dynamical analysis of epidemiological models gained significant attention from applied mathematicians after the Covid-19 pandemic caused a global catastrophe. Although studies involving the decision of the suitable model to describe the epidemiologic data and the predictive analysis of the disease spread have been carried out in-depth during this phase, studies that will expose the unrecognized dynamics through predictive analysis by investigating the algebraic properties of the models such as the invariant spaces, the center manifolds, and the Lyapunov functions in the general parameter space, still have the potential to contribute vastly to comprehending the spread dynamics. This work implements the triangular probabilistic distribution to an epidemic delay differential equation as a distributed continuous delay to determine the recovery rate based on characteristic model parameters. In this way, we propose a way to reduce the model parameters that need to be obtained during model fitting by defining generic coefficients derivable from field research and population characteristics.
Comparison of statistical and machine learning methods for prediction LME metal prices
It is intended to provide a comprehensive analysis of the daily zinc prices around the world in the wake of the Covid-19 pandemic and the impact it has had on global financial zinc markets. Through the implementation of ARIMA modelling on the original zinc prices, we have gained valuable insights into the complex dynamics. According to the results, ARIMA works efficiently when attempting to fit the stationary data than the other models. This study has provided an important contribution to the existing body of knowledge regarding pandemic-related factors in financial markets assessment and strongly highlights the need to integrate such factors into the assessment of these markets as a result of the results of this study. We also apply chaotic time series analysis to the LME zinc data to demonstrate the interaction with chaos theory.
Robust high-frequency solvers
This work concerns the numerical solutions of the direct obstacle scattering problem in $\mathbb{R}^2$. To this end, we formulate the problem as an equivalent integral equation. We then review the fundamental numerical methods such as Nyström, collocation and Galerkin methods, for integral equations of the second kind. We establish convergence results and error estimates for these methods, and incorporate numerical examples considering different integral equations. Although these methods are very efficient for low frequencies, they can not be utilized for high frequency scenarios as the computational cost grows linearly with the wave number $k$. In this connection, we propose a robust convergent algorithm based on a Galerkin formulation utilizing the geometrical optics ansatz to adapt the approximation spaces to high frequency scattering (by convex obstacles) which overcomes this type of growth in complexity requiring only $\mathcal{O}(k^{\varepsilon})$, $\varepsilon>0$, increase in the degrees of freedom to maintain a given accuracy. Numerical experiments demonstrate the efficiency of our method by exhibiting numerical errors and condition numbers in several scenarios.
Numerical modeling of sloshing using the radial basis function collocation method
A series of numerical models have been developed to study sloshing in 2D and 3D Rigid Tanks. The problem is approached with Potential Flow Theory and Laplace?s equation is solved with Neumann type lateral and bottom boundary conditions and with Dynamic and Kinematic free surface boundary conditions. For the numerical method to solve the initial-boundary value problem; Meshless Radial Basis Function Collocation Method is used and a predictor-corrector type time marching scheme is used with Adams-Bashworth predictors and Adams-Moulton correctors. The 2D models were verified by performing some numerical tests and comparing the results with available second or third order asymptotic analytical formulae from the literature, numerical experiments from the literature, and with lab experiments from the literature. For the 3D models, linear analytic formulae were used for verification. The results were supported with laboratory experiments, and by employing another numerical model developed using the Virtual Mass technique. Fully nonlinear boundary conditions with deformable boundaries have been used in the 2D sloshing models. The numerical tanks have been tested with linear standing wave, lateral excitation, vertical excitation and combined lateral and vertical excitation inputs. Fast convergence and excellent accuracy is obtained for the low amplitude test cases. For the high amplitude sloshing cases, some experimental techniques are used and a good accuracy is achieved. For the 3D sloshing studies, linearized mathematical formulation was used; and the RBFCM, Virtual Mass, and the Laboratory Experiment models were benchmarked by demonstrating their performances in estimating the sloshing mode shape frequencies. Finally, a sample tank from the industry was analyzed by applying real earthquake motions using the RBFCM and Virtual Mass models, and the calculated maximum wave amplitudes were compared. The numerical experiments show that the models created in this study may be utilized for the research on the optimum tank design; which may eventually lead to improvements on the design code.
The projection of mean temperature and precipitation for Ethiopia, Bangladesh, and Netherlands
Climate change is an important problem for today and it will also continue to be an important problem for the future. The purpose of this thesis is to analyze the future of the climate through climate modelling in 3 countries which are geographically signifi- cant, namely Ethiopia, Bangladesh and the Netherlands. First of all, we simulated todays climate for the years between 1970 ? 2000 by using RegCM 4.3.5.5 which is the regional climate modelling for analysis of regional climate change. We used ECHAM5, which is the atmospheric global climate model, as the forcing data when making this simulation and analyzed average surface temperature and precipitation variables. We compared model simulation for 1970 ? 2000 obtained by using RegCM 4.3.5.5 with the data from CRU which is the observational station data to examine correctness of our model. As a result, it was concluded that geographical features, climate events and topography have a signifi- cant effect in climate modelling as well. Differences which occurred especially in modelling average precipitation volume indicated us that it is more difficult to model precipitation than temperature. Following the verification of todays data for Ethiopia, Bangladesh and the Netherlands, data of ECHAM5 A1B scenario was used and thus seasonal average sur- face temperature and precipitation values obtained for the climate model for the years of 2020 ? 2050 being the future time by using RegCM 4.3.5.5. As a result of comparison of todays and futures data, it was found that considerable increases will occur in temperature between the years of 2020 ? 2050.
Application of mixed coarse-grained elastic network model to protein-ligand interactions
The aim of this thesis is to explore the effect of ligand binding on vibrational dynamics of proteins specifically the collective modes that are biologically relevant.
Computational analysis of cyclonic separation
Cyclonic separation is a method to remove particles from a fluid flow using a vortex for separation. This method of separation can be used to separate fine droplets of liquid or particles from a gas flow. This device is a kind of stationary mechanical device that uses centrifugal force to separate any kind of particles from a fluid stream. There are two fundamental types of air cyclones according to the direction in which the cleaned gas leaves the cyclone. The reverse flow cyclone is one of them and it is actually the most frequently used type in industry. While the clean gas leaves from upper lid, separated particles can exit through a bottom apex. Cyclones have simple manufacturing, low energy requirements, capability to work at high pressures and temperatures. In spite of the simplicity in manufacturing and operation, the mathematical formulations used for anticipating the separation efficiency are very complicated. In contrast to most of the previous studies which use a single input, in this study two slot type cyclone separators are considered and compared together, for both laminar and turbulent flows. In this research, mathematical modeling of cyclonic separation is based on Muschelknautz Method formulation (MM) and the computational fluid dynamics (CFD) analysis is performed using the COMSOL Multi-Physics program. Some of the well-known models such as Muschelknautz D type, Swift High Efficiency, Stairmand High Efficiency and Lapple General Purpose with single and double inputs are analyzed and compared. A model with different input sizes with single and double inputs is introduced and parameterized in terms of pressure drop profile, velocity magnitude, number of particles that each cyclone can separate, separation efficiency and stream lines in laminar and turbulent streams. The particle tracing module of COMSOL is used for that purpose.
Investigation of allosteric coupling in beta-2 adrenergic receptor through molecular dynamics simulations
The purpose of this thesis is to investigate the allosteric coupling between intra- and extracellular parts of G-protein coupled human β2 adrenergic receptor (β2AR) in the presence of intracellular loop ICL3, which is missing in all crystallographic structures. In a recent study, a 1 μs MD run showed that closure of the G-protein binding site due to tight packing and closure of ICL3 under the receptor was coupled with the expansion of the ligand binding site. In this study, two independent 500 ns runs, which started from the final snapshot of the original 1 μs MD run with closed ICL3, showed that ICL3 stayed mostly in closed position, pointing out that it is a stable state blocking the binding cavity of G-protein. This allosteric coupling between ICL3 opening/closure and ligand binding site compression/expansion was further investigated by imposing specific distance constraints on the ligand binding site of the receptor. A total of seven constraints were applied between the residues on helices 3, 5, 6 and 7 that form the binding pocket. Specifically, these constraints were between residue couples Ser203Oγ-Asp113Cγ, Ser204Oγ-Asp113Cγ, Ser207Oγ-Asp113Cγ, Asn293Cβ-Asp113Cβ, Phe289Cβ-Asp113Cβ, Asn312Cβ-Asp113Cβ, and Phe289Cβ-Asp312Cβ. Constraining the binding site to an open position forced ICL3 to close from its fully open position. Constraining the binding site to a relatively closed position kept ICL3 around its open form. Using one extra constraint on backbone atoms (between residues Ser207Cα-Asp113Cα of helices 5 and 3) facilitated the opening of the binding site, and thus accelerated the closure of ICL3 from a relatively open position. However, attempting to compress the binding pocket did not lead to a fully open ICL3. Through this work, the allosteric coupling between the ligand binding regions in the extracellular parts of H5 and H6 and the intracellular part of the receptor including ICL3 was demonstrated one more time.
Meshless modeling of wave induced flows inside sandy sea beds
Ocean waves have been research focus for scientists and engineers for the last several decades, being one of the most dominant physical phenomena that affect the coastal zones. Varieties of wave theories have been developed to understand the complex nature of this phenomenon. With the emergence of computers in the midst of twentieth century and the introduction of computational approach in science, ocean wave modeling has become an integral part of the studies on ocean waves. Among several numerical methods offered, developed and applied to the ocean wave problems, one of the most recent and practical is a meshless method called Radial Basis Function Collocation Method.The motivation behind the proposed modeling effort is the development of an understanding of how oxygen entrained in coastal waters penetrates into sandy sea bottoms. To understand the related phenomena, a linear wave propagation model over a porous sandy sea bed was developed using a meshless method, the Radial Basis Function Collocation Method (RBFCM). The Green's function of the Laplace equation was used as the Radial Basis Function (RBF). The solution of equations on the boundaries was used to obtain the values inside the solution domain using the RBF interpolation. The results indicate that the method used here, a boundary-only RBFCM solution, is an efficient method for solving the problem at hand.
Numerical analysis of multiplicity and transition phenomenon in natural convection
This research is devoted to the numerical investigation of the transition and multiplicity phenomena in natural convection. The numerical simulation is performed for a 2-D closed domain using FLUENT, a commercial computational uid dynamics package. In this study, the effects of different parameters such as Rayleigh number (Ra), aspect ratio (AR), and Prandtl number (Pr) are numerically analyzed. The complex phenomenon of onset of instability is studied to find the critical Ra numbers and bifurcation points. In a specific range of critical Ra numbers, transition and multiple results are observed using Rayleigh, boundary condition and pseudo-transient continuation methods. This method uses higher order temperature boundary conditions as an initial value on vertical walls. Multiple steady state results are observed at different Ra numbers. In a range of Ra numbers, the transition and multiple steady state results can completely change the convective ow pattern within the domain.
Affect analysis of communication texts in Turkish language
As digital technologies expanded our range of online communication, textual affect analysis become the focus of considerable interest in several disciplines. While many studies have been conducted in English language, there are only a few tools specific for the Turkish language. One reason for that is the lack of lexical resources for affect analysis and annotated corpora for an accurate evaluation. In this thesis, we develop an approach for continuous and dimensional affect analysis of Turkish communications by combining several tools. We conduct experiments on various text corpora in Turkish including online multi-party chat records, psychotherapy records, Twitter data, movie reviews, and teachers' comments on high school students. Analyzing such texts brings challenges like non-standard word usage, grammatical irregularities, abbreviation usage, and spelling mistakes. We propose several pre-processing steps to deal with these. Then, we adapt an affective word dictionary from English to Turkish, and by expanding it with synsets, obtain 15,200 words with annotations for valence, arousal, and dominance. We also employ a list of frequently used abbreviations, emoticons, interjections, modifiers (intensifiers and diminishers), and other linguistic indicators to capture the overall affective state at the sentence level. We recruit and train annotators to obtain affective ground truth specifically for multi-party chat records. Our results show that the proposed system is useful, yet there is much room for improvement at different stages.
Machine learning methods in natural language processing
There is a large number of algorithms for keyword extraction and text summarization in natural language processing, as we discuss some of these in this thesis. We started with a survey on automatic text summarization in order to understand the state of the art methods. Also we proposed a new and efficient method for keyword extraction task using Word2Vec and PageRank algorithms. In this thesis, we investigated two different graph based text summarization algorithms for both single and multi-document settings on different types of texts where we used LexRank for multi-document summarization and TextRank for single document summarization. We also investigated a number of keyword extraction methods. Almost every keyword extraction method use high dimensional vectors to define words in a vector space. We approached the problem of automatic extraction of keywords from text as a unsupervised learning task and we treat each word in the document as a low dimensional vector. We developed a new keyword extraction method using Word2Vec and PageRank algorithms. Our results show that summarization algorithms give best result on news texts, usable results on legal texts while they give less than optimal results for short stories. On the other hand, we also compared differences in using one-hot-representation and Word2Vec representation but we observed no significant differences between these methods.
Facial expression recognition in the wild using improved trajectories and fisher vector encoding
Automatic video data analysis has been a growing interest in order to improve human computer interaction. One of the most challenging parts in video analysis is the ability of evaluating human emotion robustly. Vast applications of human facial expression recognition can be seen everywhere from educational systems to treatment of Asperger's and surveillance. In this thesis, we explore facial expression recognition on both laboratory and realistic videos. After studying recent works about face detection, facial alignment, video description and classification, we present our novel approach in, which our proposed pipeline including facial alignment in combination with improved dense trajectory, geometric, encoded with Fisher vector encoding and LGBP-TOP features are fed to extreme learning machine. It is the first time that improved dense trajectory features are used in facial expression recognition. Furthermore, we extensively study each step of our pipeline in a comparative manner. We evaluate our approach on CK+ and EmotiW 2015 challenge datasets. Videos in first dataset are captured in laboratory settings and start from neutral state and end with peak expression while the second one is selected from movies with realistic conditions, spontaneous emotions, complicated background and challenging illumination variations. On Ck+ dataset, we obtained 94.80\% and 95.79\% (without contempt) accuracy, which is among the best results obtained on the CK+. On EmotiW 2015 challenge dataset, we got 43.39\% accuracy, which is higher than the baseline of the challenge considerably. In both datasets we were able to obtain the state-of-the-art results. Our results show that using appropriate pipeline of face alignment combined with efficient visual descriptors can result in a robust system with high ability of recognition.
Sfingozin kinaz 1 (SK1)'in ortosterik ve allosterik inhibitörlerinin in siliko yöntemlerle belirlenmesi
The sphingosine kinase 1 (SK1)/sphingosine-1-phosphate (S1P) signaling pathway is a crucial target for numerous human diseases from cancer to cardiovascular diseases. Even though the discovery of SK1 inhibitors has been considered as effective chemotherapeutic agents, early SK1 inhibitors that target the ATP or lipid-binding sites have suffered from poor potency, selectivity and pharmacokinetic properties. Thus, there is an urgent need for the discovery of in vivo stable orthosteric SK1 inhibitors with improved potency and selectivity. Also, the selectivity issue of the kinases, which share highly-conserved ATP-pocket, can be overcome by targeting the less-conserved allosteric sites. SK1 is known to function minimally as a dimer; however, the crystal structure of the SK1 dimer has not been determined. In this study, a template-based algorithm implemented in PRISM was used to predict the SK1 dimer structure and then the possible allosteric sites at the dimer interface were determined via SiteMap. Both the predicted putative allosteric site(s) and the lipid-binding site were used in an integrated workflow of ligand-based and structure-based pharmacophore modeling, virtual screening, molecular docking, re-screening of common scaffolds to propose a series of compounds with different scaffolds as potential orthosteric and allosteric SK1 inhibitors. The stability of the SK1-ligand complexes was analyzed by molecular dynamics simulations. As a final outcome, ligand 5 having a 1H-benzimidazol-2-ol scaffold was found to be a potential selective orthosteric inhibitor as well as ligand 7 having a 4,9-dihydro-1H-purine scaffold and ligand 12 sharing a 2,3,4,9-tetrahydro-1H-β-carboline scaffold were found to be potential selective allosteric inhibitors for SK1.
Metabolic modeling of daphnia for environmental toxicity studies
Daphnia, a genus of small planktonic freshwater crustaceans commonly referred to as "water fleas," plays a critical role in aquatic ecosystems and has long served as a model organism in ecotoxicological studies. Owing to its sensitivity to pollutants, short life cycle, ease of laboratory cultivation, and transparent body, Daphnia pulex has become a widely adopted test species for acute and chronic toxicity assessments. As a heterotrophic organism, Daphnia's growth and survival are highly dependent on food availability and quality, particularly essential nutrients such as phosphorus, polyunsaturated fatty acids (PUFAs), and sterols. Among various feed sources, Chlorella vulgaris has demonstrated considerable nutritional value for Daphnia cultivation, exhibiting high protein content and the ability to effectively remove selenium from wastewater. In this study, a genome-scale metabolic model of Daphnia pulex was constructed using the COBRA Toolbox within MATLAB, enabling simulation of its metabolic behavior under different nutrient conditions. The model was employed to evaluate the organism's growth response to various dietary inputs and to identify key metabolic pathways involved in nutrient assimilation. In the final stage, toxicological assessments were carried out by integrating metal concentration data from Lake Beyşehir with the metabolic model. Heavy metal compounds commonly found in freshwater environments were analyzed in terms of their potential impact on Daphnia's metabolic performance. The study presents a comprehensive framework that combines metabolic modeling with toxicological evaluation, offering novel insights into the metabolic adaptability of Daphnia pulex under both nutrient-driven and chemically stressed conditions. The results support the potential use of this integrated modeling approach for predicting the ecological consequences of pollutant exposure and assessing algal-based feed viability in contaminated water systems.
Rarest precipitation events in Turkiye willsee the greatest relative increase inmagnitude under future climate change
Changes in precipitation regimes occur due to global climate change, and these changes may occur in the form of increases in extreme precipitation events and drought as a result of decreased precipitation. This study undertakes a critical examination of T¨urkiye's rarest precipitation events and their anticipated changes under future climate change scenarios, employing the Regional Climate Model (RegCM4.3.5) and focusing on RCP 4.5 and RCP 8.5 scenarios. It evaluates precipitation in T¨urkiye for return periods of 1, 10, 100, 1000, and 10000 years during the periods 2026-2050, 2051-2075, and 2076-2100, based on MPI-ESM-MR scenario predictions under CMIP5, tailored for the region. Employing a range of distributions, including log-normal, GEV, and Gumbel, the log-normal distribution was found most accurate, particularly for the 2026-2050 RCP8.5 scenario. Under the RCP4.5 scenario, the Mediterranean region is expected to see a significant increase in extreme precipitation events with 100-year return periods, while Central Anatolia may experience a decrease. Under the RCP8.5 scenario, a substantial intensification of extreme precipitation events is projected for the Mediterranean and Aegean regions throughout the century. These findings highlight the urgent need for region-specific adaptation strategies in water management, flood defense, and sustainable urban planning, especially in the most affected Mediterranean and Aegean regions. This research not only offers important insights for future policy and research but also emphasizes the need for proactive measures in response to evolving climate patterns.
Scalability of projected patterns of extreme weather events over Europe using regional climate model projections
Climate change leads to significant increases in the frequency and severity of extreme weather events. Combating climate change and developing adaptation strategies is of great importance for a sustainable future. The effects of climate change need to be understood and addressed, and climate models are used for this end. However, due to differences in climate models and systematic errors in model calculations, different techniques such as "pattern scaling" are also used. The study used a pattern scaling approach, extracted from different global climate models, to examine how changes in temperature extremes and total precipitation in the EURO-CORDEX region respond to increases in global temperatures using certain extreme weather indices. According to the results, the yearly minimum value of daily minimum temperature (TNn) increases more than the yearly maximum value of daily maximum temperature (TXx) for Europe. The multi-model mean of the changes in scaled patterns of extreme temperatures emerges early, around 2020, even before it becomes robust. Additionally, the total annual precipitation amount (PRCPTOT) in the northern regions increases by up to 6-9% compared to the reference period, while decreases of up to 12-15% can be observed in North Africa. Finally, using the ERA5 dataset, the change in patterns of four extreme temperature indices was examined to determine whether end-of-century scaled patterns could be detected for the conditions that were observed. The observed patterns of change closely resemble the end-of-century scaled patterns identified by the GCM-RCM sample, indicating that end-century patterns are already emerging in some parts of the domain.
Market capitalization-based portfolio weighting in BorsaIstanbul: A comparative analysis of historical performanceagainst established benchmarks
This thesis examines the medium- to long-term performance of market-capitalization-weighted portfolios covering the entire Borsa Istanbul equity universe, adjusted for free-float shares and rebalanced at varying frequencies (1-day to 8-week). Using a comprehensive dataset spanning January 2011 to October 2024 with daily prices and actual free-float ratios, the study evaluates whether such portfolios can outperform benchmarks like BIST~30 and BIST~100. A total of 754 simulations per configuration enables robust comparisons across horizons from 12 to 172 months. Results indicate that full-market portfolios, when dynamically rebalanced, tend to outperform benchmarks in median and upper-quartile returns from the 24-month horizon onward. However, this advantage is limited in scale and does not yield significantly higher cumulative returns. Despite a nearly fifty-fold increase in total market capitalization, the portfolios captured only a modest portion and consistently underperformed the gold benchmark (XAUTRY) over long horizons. Risk metrics such as Sharpe Ratios and Maximum Drawdowns support these findings, with 10-day to 2-week rebalancing frequencies showing better risk-adjusted performance. The methodology avoids biases like overfitting, calendar effects, and survivorship. Overall, broader, systematically rebalanced portfolios appear to offer viable long-term alternatives to conventional Turkish equity benchmarks.
The effect of wind pattern change on the environmental/climatic conditions within cities
The labor force demand by the Industrial Revolution has led to an increase in the population of cities. This situation was followed by rapid development of urban areas with starting of nineteenth century to accommodate labor force around the industry. This situation has ended up with altering more land surfaces to buildings, car parks, roads or other structure types. However, it cannot be said that all the urbanization steps are taken properly. Thus, these anthropogenic developments have affected wind flow in urban. Arbitrary built urban areas restrict the wind flow in street canyons and make it slower than the flow above the building blocks, hence reduce the cooling effect of wind at near-ground levels. However, increasing the wind speed in streets can improve thermal comfort of people by the means of convective heat transfer through the skin. Thermal comfort and wind flow patterns are important environmental issues when designing new urban areas. Starting from this point, this thesis focuses on computational fluid dynamics (CFD) models to observe wind flow and thermal comfort of arbitrary built urban areas in Mecidiyeköy, Istanbul. Mecidiyeköy is an arbitrarily urbanized and one of the most crowded hubs of Istanbul, which is modelled as it is and compared with alternative design scenarios in wind flow and thermal comfort results. In general, this thesis analyzes the impact of buildings on wind flow, hence thermal comfort in cities.
Genome-scale metabolic reconstruction of freshwater organism daphnia pulex
The aim of this study is the reconstruction of the genome-scale model of an ecological organism and investigation of its cellular functionality towards toxicity by using computational systems biology approaches. Daphnia pulex is one of the keystone species of the toxicity tests for the environment, and hence it is here chosen as a model organism. The study on D. pulex genome was first published in February 2011 and contributed to numerous scientific researches. In the present study, manually curated genome-scale metabolic model of D. pulex is reconstructed by using KEGG and UniProt databases along with the literature search. The completed model includes 1051 reactions (985 internal, 66 exchange) and 774 metabolites (222 mitochondrial, 485 in the cytosol, 67 externals) related to 697 genes. Based on the constraint-based modeling, biomass is chosen as the objective function to inspect the cellular fluxes through the growth of D. pulex. The robustness analysis is performed on selected compounds such as L-isoleucine, selenite and sulfate so as to monitor their effects on biomass. L-isoleucine has a positive impact on the growth of D. pulex. The applicability of the reconstructed model of D. pulex to toxicity studies is tested by investigating the relation between selenate and sulfate uptakes, where these fluxes are estimated by flux balance analysis. The computational results demonstrate that when sulfate uptake is getting close to zero flux, selenate uptake begins. This observation on selenate and sulfate uptake mechanism indicates that predicted results are in agreement with the literature. Consequently, the reconstructed metabolic model of D. pulex is able to predict the cellular behavior and can be used for metabolic toxicity studies.
Climate change projections of Cyprus using regcm4.4
The scientific motivation of this thesis is a substantial presence of climate change which is one of the most significant issues in the world. There have been happening many natural disasters because of climate change. In particularly, coastal regions and islands, such as Cyprus, are more vulnerable to any possible hazards. Humankind start to contribute the climate change, especially, after indus- trial revolution. Burning fossil fuels causes the increase of CO2 emission to the atmosphere. In fact, at the beginning of the 18th century, before the Industrial Revolution, CO2 concentration in the atmosphere was 280 ppm; however, today, this value is 410 ppm. The unprecedented CO2 increase in the atmosphere since Industrial Revolution gives rise to global climate change. The Mediterranean region is predicted to be affected by climate changes in terms of air temperature rise. In this study, air temperature (◦C) and precipitation (mm/day) estimations of Cyprus were investigated with the regional climate model. The climatology model is run for the three periods of 2011–2040, 2041–2070, and 2071–2100, with respect to the control period of 1971–2005 for Cyprus domain via regional climate model simulations. Regional Climate Model (RegCM4.4) of ICTP (International Centre for Theoretical Physics) was run by using two different global climate models. MPI- ESM-MR global climate model of the Max Planck Institute for Meteorology and HadGEM2 of the Met Office Hadley Center were dynamically downscaled to 10 km resolution by using double nesting. RCP4.5 and RCP8.5, the emission scenarios, of the IPCC (Intergovernmental Panel of Climate Change) were used for projections.
Statistical return periods of extreme weather events
The Middle East & North Africa (MENA) region is one of the most populated areas on Earth in terms of city population. There are 643 cities in MENA region with population of urban agglomerations with 300,000 or more in 2018 according to United Nations. In this thesis, return periods of extreme temperature events are calculated using the probability density functions. For this purpose, Global Climate Model (GCM) outputs of Max Plank Institute Earth System Model Mixed Resolution (MPI-ESM-MR) and Hadley Global Environment Model 2 - Earth System (HadGEM2-ES) are dynamically downscaled to 50 km for the MENA region by using the Regional Climate Model v4.4 (RegCM4.4) for 2 different Representative Concentration Pathways scenarios, namely RCP 4.5 and RCP 8.5. Elevation correction is applied to each point for sea level. Temperatures at city centers are calculated from the nearest 4 grid points using inverse distance squared interpolation method. Daily maximum temperatures histograms are plotted for each city and future predictions of return periods are compared with the reference period of 1971-2000 using the means and standard deviations obtained from Gaussian Mixture Model. The results show that the frequency of extreme events increases for all cities between 2070 and 2099. Peaks in temperature distribution are diverging from each other which will cause more severe extreme events. This divergence would cause cities to have shorter transition seasons and their climate would transform into only 2 seasons.
Design of donor-acceptor conjugated polymers for high-performance organic solar cells: a quantum mechanical approach
In this study, the design of donor-acceptor conjugated polymers for high-performance organic solar cells is intended by investigating electronic, geometrical and optical properties of a specific set of donors that are used in polymer solar cells. 4 different donors including benzo [1,2-b:4,5b'] dithiophene (BDT), 3,3'-difluoro-2,2'-bithiophene (diF2T), naphtho [1,2-b:5,6-b'] dithiophene (NDT), thieno[3,2-b] thiophene (TT) and 1 acceptor named 2-propyl-5,6-difluorobenzo[d][1,2,3]triazole (ffTAZ) are studied in order to set a benchmark. The 4 different donors studied for benchmarking and 7 different acceptors including benzo [c][1,2,5] thiadiazole (cop1), [1,2,5]thiadiazolo [3,4-g] quinoxaline (cop2), [1,2,5] thiadiazolo [3,4-d] pyri-dazine (cop3), [1,2,5]thiadiazolo [3,4-c] pyridine (cop4), [1,2,5] oxadiazolo [3,4-d] pyri-dazine (cop5), fluorinated naphtho [1,2-c:5,6c'] bis [1,2,5] thiadiazole (FNTz), fluori-nated [2,1,3] benzobisthiadiazole (FBTz) are combined and investigated for 28 different predictions. Geometrical and optical characteristics were calculated using Density Functional Theory (DFT). Distortion energies and reorganizational energies for electron and hole transfer were calculated using B3LYP/6-311G*. In conclusion, 2 out of 28 molecules were chosen as the most effective molecules to be used in high-performing organic solar cells. The molecules chosen contain NDT and TT as donors and FBTz as acceptor.
Likelihood free particle filtering with approximate Bayesian computation for parameter estimation in cosmic ray air shower studies
Highly energetic particles called cosmic rays arrive at earth and interact with the atmosphere to cause other particles to emerge and fuel a chain interaction of particle production until the energy is dissipated. These cascades emerging from a highly energetic initial particle and producing secondary particles that spread over a large area at the ground are called Extensive Air Showers. These showers are studied to find anisotropy in their arrival direction and energy to unveil their source and production mechanisms in the universe. In this study we utilize the likelihood free particle filtering with Approximate Bayesian Computation to estimate the incident angle and energy of the primary particle. ABC method makes use of comparison between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood function of particle physics.
Modeling volatility dynamics in financial time series: Ananalysis of econometric models and machine learningmethods
In this thesis we present an analysis of different volatility dynamics in financial and cryptocurrency markets. We focus on the Borsa Istanbul stock exchange index BIST 100 (XU100), NASDAQ index, and Bitcoin/USD exchange rate (BTCUSD). Our aim is to understand the risk profiles of the data, forecast future risk using historical data, and statistically analyze structural characteristics of the volatility in the markets. In our analysis we used logarithmic returns of each financial asset we considered. Our first step of the investigation was to determine the best fitting ARIMA models, and analyze the parameters of these models using tools such as ADF-test, ACF- and PACF-plots, QQ-plots, and Kolmogorov-Smirnov test. Our working hypothesis is that if a model successfully explains the behaviour of an asset then the residual signal must be close to white noise. However, we found significant autocorrelations in the residuals which indicates that the ARIMA models we used did not fully capture all underlying patterns. In the next step, we used GARCH models to analyze the dynamics of the volatility inherent in each asset. While the risk profiles of the datasets varied, the structure coefficients of the models were consistent across all datasets. We finish the thesis by outlining how one might expand our study using endogeneous and exegeneous data, and different machine learning methods.
Üretken modellerde boyutsallik ve mimari uzerine analiz
Deep generative models are powerful class of machine learning models. How- ever, a significant amount of computing power and technical knowledge is required to conduct the training process. Even searching for hyperparameters requires a high com- putational cost. Moreover, there is still ongoing research on methods for evaluating generative models, and owing to the lack of a robust and consistent metric, there are limited comparisons between generative model architectures and algorithms. In this study, we attempted to compare two types of generative model architectures, Gener- ative Adversarial Networks (GANs) and Real-valued Non-Volume-Preserving (NVP) flows, with synthetic datasets as well as with a well known image dataset MNIST. We evaluate their data capturing ability according to data dimensionality and variability. We propose an Minimum Description Length (MDL) based metric to examine the ef- fect of model complexity which is measured as model's parameter count. We provide estimated Kullback-Leibler (KL) divergence and propsed MDL-based metric results. Our findings indicate that NVP models have the capability to encode more data variability while utilizing fewer parameters when contrasted with GANs for lower di- mensional datasets. The proposed MDL-based metric, facilitates selecting suitable architecture in terms of model complexity for a given dataset considering its variability and dimensionality.
Variance reduction in Markov chain Monte Carlo algorithms
In a simulation study, the output of the simulation is used to estimate target parameters. The estimated parameters typically has a variance, which impacts the precision of the estimation. In Monte Carlo simulations, the standard approach to increase the precision of the estimation is to increase the sample size. However, we can increase simulation size until a certain point due to resource restrictions. This is where Variance Reduction (VR) techniques help. These techniques use simulations as an input and return another simulation with almost the same expected value and less variance value. VR techniques are widely studied in the context of Monte Carlo simulations. However, the research on the application of these algorithms in Markov Chain Monte Carlo (MCMC) simulations is limited. In this thesis, we study the effectiveness of VR techniques for MCMC algorithms. We created an experiment environment that allows us to control the difficulty of the problems that are solved by MCMC algorithms. In our study, we use the Random-Walk Metropolis-Hastings algorithm with different types of proposal distributions. We first evaluate the convergence of the MCMC algorithms, select chains with good convergence properties, and then apply the VR techniques. The VR techniques of interest are {\it{general}} VR techniques, which are Antithetic Variates, Control Variates, and Latin Hypercube Sampling. We study the effectiveness of these algorithms using problems with varying difficulties. We address the problem of calculating the standard error of the MCMC simulation outputs, evaluate the accuracy of the estimated values, and we evaluate the correctness of the standard error calculations by calculating the coverage probabilities of the confidence intervals and RMSE/MAE values of the estimations. We show that VR techniques are successful at decreasing the variance of the MCMC simulations without introducing any bias.
Example-dependent cost-sensitive gradient boosting machines for credit scoring
Although most of machine learning algorithms try to minimize cost-insensitive losses, many real world applications require cost-sensitive approaches where misclassification costs among classes differ from each other. In addition to misclassification costs, examples in data sets may have nonidentical costs which is a case of example-dependent cost-sensitive learning. For example in credit scoring, mistakenly rejecting a good borrower and approving a bad client with financial distress result in different costs. Additionally, providing variety of credit amounts to applicants makes the credit scoring example-dependent. In other words, falsely approving 100M$ and 1M$ loans produce unequal costs. To overcome this problem, this thesis proposes an example-dependent cost-sensitive loss function. With the introduced loss function, cost sensitivity is handled during the learning process. This is achieved by changing the traditional loss function of Gradient Boosting Machines with the proposed one to make it Example Dependent Cost-Sensitive Gradient Boosting Machines. The proposed algorithm is tested on two real world data sets that include credit amounts and synthetically generated data sets. The algorithm is compared with cost-insensitive learners, previously proposed example-dependent cost-sensitive classifiers that handles cost-sensitivity during learning, a post-processing method called Thresholding and a pre-processing method Oversampling to make cost-insensitive classifiers cost-sensitive. Results show that our method outperforms those four methods in terms of financial savings.
Short-term forecast methodologies and case studies in traffic flow
This thesis gives case studies on short-term traffic flow forecasting strategies within a time series framework. After discussing the traditional, machine learning and deep learning methods, one of main goals is to experiment on the uses of hybrid methods. Besides analyzing approaches that were already used in the traffic flow literature, we also introduce and test distinct strategies. Further, we supplement our point forecast results with interval forecasts. In particular, quantiles regression based intervals such as quantile regression averaging and quantile regression neural network are implemented. Both point and interval forecasts are evaluated via several evaluation metrics, and an extensive comparison is provided among the methodologies studied.
Probabilistic discriminative region descriptor for time series classification
Detecting discriminivative regions is a recent promising concept in many different domains for various dataset types such as image, text and time series. In time series domain, time series might be large and high dimensional because of the developing storage capacities. Although computational capacities are improved, storage and computation costs are increased. Therefore recent attempts are focused on the decreasing the computational and run time complexities. To decrease the complexity of the models, instead of using raw data, construction of the new feature representation by using the distinctive sub-sequences of the time series is the most common approach. Discriminative sub-sequences are called as shapelets in time series reflect the characteristics of the class of time series. Shapelets provide interpretable results and shapelet-based classifiers have superior accuracy on many time series datasets. Many researchers have proposed shapelet extraction methodologies for classification purpose. This study proposes a novel local feature extraction framework for time series and shapelet-based time series classification pipeline. Proposed framework provides model selection flexibility to describe the time-observation space to find local discriminative regions. After obtaining the discriminative regions, shapelets are extracted on the time-observation space by thresholding the class probability estimates to construct a new feature representation. New feature representation is calculated by the Euclidean distance between shapelets and time series. Finally, a classifier is trained by the new feature representation. Experimental results show that shapelet-based time series classification by using proposed Probabilistic Discriminative Region Descriptor (PDRD) provides competitive results on benchmark datasets.
A tree based categorical variable encoding strategy in supervised learning tasks
Categorical variables are present in most real-world datasets, often consisting of a high number of levels, referred to as high-cardinality categorical variables. Most machine learning algorithms do not have an innate mechanism to deal with categorical variables, hence, their encoding is necessary. Categorical variable encoding is the general term for the conversion of nominal independent variables to a numerical format. Many encoding strategies exist, and they are discussed in this thesis. This thesis presents a novel encoding strategy, categorical split encoding, and also provides an analysis of existing encoding methods. Categorical split encoding uses primary and surrogate split information as the vector representation for categorical variables, through a tree-based algorithm, this method outputs binary columns for each categorical variable making use of target information. Missing values are imputed by using surrogate information, while clustering similar values together based on the path they take through the decision tree algorithm. Various existing encoding strategies are benchmarked for comparison with the proposed strategy. The performance of categorical split encoding and other encoding methods is compared with three different machine learning algorithms (generalized linear models, random forest and xgboost) using datasets from regression, binary and multiclass classification settings. Datasets used are made publicly available for replication purposes. As a result, categorical split encoding provides competitive results compared to existing encoding strategies in various datasets.
A comparative evaluation of machine learning algorithms for statistical downscaling of monthly mean temperature data over a European region
Climate change is the most vital environmental change that has already started to affect many ecosystems. It is caused by greenhouse gas emissions which are increasing since the pre-industrial era, and populated areas become more vulnerable to disasters due to climate change. It has never been more crucial to model the climate effects on local regions. Organizations like Intergovernmental Panel on Climate Change (IPCC) use global climate models (GCMs) to project future changes in climate on a continental scale. Although these models are becoming more accurate, downscaling these models to smaller scales is an important task that is studied by climate scientists. The two main downscaling methods are dynamical and statistical downscaling. Statistical downscaling studies are more reachable and important to develop when compared to dynamical downscaling due to its lower costs. The use of machine learning algorithms in statistical downscaling is a new area. Studies that implement machine learning to make local scale projections of surface temperature are numbered. In this paper, four different machine learning algorithms were tested on downscaling of two different surface temperature datasets over a European region with different resolutions. The best performing algorithm was also tested augmenting elevation data. The results show that Gaussian process regression performs the best with MAE of 0.04 - 0.51 as compared to the other machine learning algorithms tested. In conclusion, machine learning algorithms such as Gaussian process regression can be a suitable approach when downscaling spatial monthly mean surface temperature data.
Predicting countries' vulnerability to climate change
Many countries are subject to the consequences of global climate change at varying degrees, and their vulnerability varies according to socioeconomic and environmental factors. Individuals, societies, and countries must be aware of the effects of climate change. Knowledge and understanding of how exposed they are to hazards, their level of vulnerability, and what must be done are critical for the continuation of basic vital activities. Accurately predicting how much a country will be affected by climate change in the future or which life-supporting sectors will suffer is crucial for countries to take precautions. Therefore, in this study, The Notre Dame Global Adaptation Initiative (ND-GAIN) Country Index's data is used for predicting countries' vulnerability to climate change. It's an open-source index that displays how vulnerable a nation is to climate disruptions. The data for this index includes scores for vulnerability and six areas that support life, including food, water, health, ecosystem services, human habitat, and infrastructure, for 182 nations during a 26-year period from 1995 to 2020. Long short-term memory (LSTM) network-based model is built to predict seven countries' six years of data from 2021 to 2026. These countries are Turkey, Australia, Germany, Portugal, Sudan, Georgia, and Kyrgyzstan. The results showed that the model predicts an increase in the vulnerability scores of all countries except Sudan for 2021, a slight decrease in Germany and Australia, and a decrease in Turkey, Portugal, and Kyrgyzstan after 2021. The model predicts a decrease in Sudan and an increase in Georgia for all years. The model's successes are tested using data from 2010 to 2020. Although time series forecasting is challenging, forecasted values are close to actual values. This study is novel since no other studies have predicted countries' future years' vulnerability to climate change.
Life cycle analysis of different cuisines
The extensive global food system is responsible for approximately 30% of greenhouse gas emissions. While the steps of the global food system such as production, packaging, transportation, distribution, storage, and disposal are followed, environmental effects remain intangible. With the life cycle assessment (LCA), environmental impacts are seen in concrete form at every stage of the system. In this study, the environmental impacts of different cuisines were investigated through life cycle assessment. Three menus have been created, consisting of Turkish, Far East, and Mediterranean cuisines, which are known and have a wide variety of food. Each menu has been chosen in accordance with the culture of the cuisine it has. The menus consist of soup, main course, side course and dessert. As a result of the life cycle assessment made on the menus selected for 3 cuisines, it has been determined that the environmental impact of the Mediterranean cuisine is quite low. The reason why the environmental impact is very low compared to the Turkish and Far Eastern cuisines, mainly agricultural foods are included in the Mediterranean cuisine and animal- based meals are not preferred much. On a food basis, the environmental impact of animal- based foods is greater than that of plant foods. As a result of the study, Turkish cuisine, in which animal-based meals are predominant, is the cuisine with the most environmental impact.
Interpretation of compound fragments via attentive recursive tree
The discovery of new drug-like chemicals with desired properties is a challenging and costly process in the pharmaceutical industry. To facilitate this process in the preclinical phase, many different neural network models have been proposed for different tasks (e.g., drug-target affinity prediction, molecular property prediction, target-specific molecule generation). Despite producing successful results, they usually lack interpretability. To comprehend the significance of each fragment in the relevant compounds, we employed the Attention Recursive Tree (AR-Tree) model. Thanks to its task-specific attention mechanism, AR-Tree highlights the significant fragments of compounds by positioning them closer to the root of the tree structure. In this way, the identified significant fragments can be used to design new compounds with desired properties in future research. We experimented with five different classification and four different regression tasks of the MoleculeNet as benchmark tasks. The results of the experiments show that the proposed architecture succeeded in finding chemically meaningful fragments for the corresponding tasks.
Convolutional ensemble learning for edge intelligence
Deep Edge Intelligence targets the deployment of deep learning algorithms in the edge network. While training deep networks requires computational resources, edge devices frequently lack high computational power. Decentralized learning methods such as federated learning provide a solution for gathering limited information from edge devices and collectively improving prediction performance. However, a drawback of such methods is that they often require multiple rounds of network communication, which increases communication time and the risk of communication errors. Another drawback is that the same model architecture is often used on all edge devices, which makes it mandatory to work with devices above a level of computational capacity. This thesis proposes a hybrid learning approach that employs ensemble learning with a convolutional scheme for different edge model architectures, except for a selected fully connected layer of the same dimensionality. Initially, shallow neural networks are trained on edge devices until a certain level of performance is achieved. Next, the feature representations obtained by the shallow models are transferred to an ensemble model. Subsequently, the proposed convolutional ensemble model is trained to boost the prediction performance. This method facilitates the completion of the system training with a one-way data transfer between edge devices and the server. Variational auto-encoders are also utilized to generate feature vectors in case transferring the required representations from the edge devices fails. Extensive experiments demonstrate that the suggested method outperforms state-of-the-art techniques in terms of accuracy while requiring fewer communications and a lower amount of data in various training scenarios.
A multi-class approach to next session and in-session purchase prediction with real-time e-commerce data using machine learning techniques
Advances in machine learning yield implications for the rapidly growing e-commerce industry. Retailers are looking for ways to better understand and predict complex customer behavior. Two crucial problems that exist in the domain are predicting customers' platform engagement and purchase intent. Different techniques are employed in the literature addressing the problems separately, but the two tasks are highly dependent on each other since the ultimate goal for session engagement is purchase. Understanding if a next session will be made with a high purchase motive is critical to diversify the business actions taken. The main aim of the thesis is to develop a multi-class model that successfully distinguishes the next sessions with and without purchase intention. 38 million e-commerce sessions are collected for the specific task. Following the application of state-of-the-art LightGBM and LSTM algorithms, their results are compared, where LightGBM outperformed the latter. Additionally, a simple ensembling technique is used to increase the performance, leading to a 68% F1 score for the predictions of no session, 71% for the predictions of sessions without purchase and 59% for the predictions of sessions with purchase. Furthermore, an undersampling technique is employed to handle the imbalance differently than the technique used by LightGBM and LSTM, increasing F1 scores to 75%, 72% and 74% respectively.
Forecasting the energy consumption of sectors under different NGFS scenarios and analyzing the effects on Türkiye's GDP
Physical and transition risks of climate change will have an impact on countries' economies and sectors. With proper planning and taking the necessary steps, these impacts can be mitigated. Therefore, academic studies and analyzes in this field are important. In this study, it is examined how T¨urkiye's GDP will be affected by physical risks and transition risks under different climate scenarios. In addition, within the scope of these scenarios, it has been forecasted how the energy consumption of the sectors in T¨urkiye will be in the future. In cases where current policies are continued or the necessary measures are not taken at the right time, the impact of climate change on T¨urkiye's GDP will be huge. At this point, it is of great importance to limit GHG emissions and not to increase the global average temperatures compared to the preindustrial revolution. Because the increase in the number of extreme weather events or the occurrence of irreversible physical events such as sea level rise can seriously affect the economies. The steps in transitioning to a low carbon economy and combating the effects of climate change will also be a huge burden for the economies. In this context, the use of renewable energy sources should be increased in Turkey and practices that can reduce emissions such as carbon tax should be introduced. In energy production, fossil fuel consumption should be reduced and alternative energy types should be used. It can be said that the Oil and Gas, Transportation and Automotive sectors will be more affected by this situation. In these sector renewable energy types may need to be used more in energy production.
Future predictions of global hotspots for temperature and precipitation extremes with CMIP6 model
This study used a climate change hotspot technique using population-weighted Standard Euclidean Distance (SED) measurements to assess climate change risks across regions. The analysis shows changes in the world's climatology for the years 2026-2050 and 2076-2099 under the SSP2-4.5 and SSP3-7.0 scenarios. In the SSP2-4.5 scenario (2026-2050), equatorial regions like China, Japan, and Indonesia are hotspots that would experience increasing sea levels, extreme weather events, and heat waves because of a changing climate and population density. The Americas, Southern Africa, the Mediterranean, Western Europe, and Asia are considered to be risk zones. Concerns include droughts and unpredictable precipitation, which affect economies and food security. Ecosystems and communities must use customized adaptation strategies to meet these difficulties. Moderate adaption issues are anticipated under SSP2-4.5, necessitating focused measures. Distant future trends (2076-1999) align with near-future tendencies, which calls for further efforts. Similar indicator changes may cause a lack of differences between scenarios in the near future. This study enhances our understanding of hotspots and emphasizes the importance of thorough approaches. Innovations and a variety of situations improve climate resilience and adaptation. Future work should focus on improving assessment techniques, improving used data and models, and including socioeconomic issues for effective climate plans.
Tactile rendering of digital buttons and shapes on touchscreens using novel surface haptics technologies
Touchscreens are integrated in every aspect of our daily life as mobile phones, ATMs, tablets, vending machines, and car navigation systems. These screens provide an intuitive interface for touch interactions with no or limited haptic feedback. As a result, eye-free interaction is nearly impossible and users have to rely on visual and audio feedback to perform a task, which reduces the users' performance and experience. However, with the recent advances in surface haptics technologies, it is now becoming possible to generate complex tactile effects on touchscreens and enhance the user interactions by displaying more sophisticated haptic feedback. The overall goal of this thesis is to understand how to render realistic tactile buttons and shapes on touchscreens using novel surface haptics technologies. In this regard, the first part of the thesis focuses on creating vibrotactile feedback on a touchscreen that simulates the feeling of physical buttons using piezo actuators attached to the screen. For that purpose, we first recorded and analyzed the force, acceleration, and voltage data from twelve participants interacting with three different physical buttons: latch, toggle, and push buttons. Then, a button-specific vibrotactile stimulus was generated for each button based on the recorded data. Our results showed that participants were able to match the three digital buttons with their physical counterparts with a success rate of 83%. In addition, participants rated the degree of their subjective feelings using seven adjective pairs for all the physical and digital buttons investigated in this study. Our results showed that there exist at least three adjective pairs for which participants have rated two out of three digital buttons similar to their physical counterparts. In the second part, we investigated the recognition rate and time of five tactile shapes (i.e., triangle, square, pentagon, hexagon, and octagon) rendered by electrovibration on a touchscreen using three different methods and displayed in prototypical orientations and non-prototypical orientations (i.e., 15 degrees CW and CCW to the prototypical orientation). The results showed that the correct recognition rate of the shapes was higher when the haptically active area (area where electrovibration was on) was larger. However, as the number of edges increased, the recognition time increased and the recognition rate dropped significantly, arriving to a value slightly higher than the chance rate of 20% for non-prototypical octagon. Moreover, the recognition time for inside rendering condition was significantly shorter as compared to edge and outside rendering conditions and edge rendering condition led to the longest recognition time. Our analyses of exploration strategies revealed that participants first used global scanning to extract the coarse features of the displayed shapes, and then they applied local scanning to identify finer details, but needed another global scan for final confirmation in the case of non-prototypical shapes. We also observed that it was highly difficult to follow the edges of shapes and recognize shapes with more than five edges under electrovibration when a single finger was used for exploration. The results of these studies show that richer haptic stimuli is necessary for realistic rendering of digital buttons and shapes on touchscreens. For example, lack of kinesthetic feedback in rendering buttons and displaying haptic feedback in tangential direction only in rendering shapes adversely affected the perception of the participants in our study. These results are considered as a starting point for the development of tactile stimuli, haptically improved user interfaces, and touchscreen applications. These findings can also provide some guidance to haptic interface designers in developing techniques for efficient and effective interaction with graphical elements on touchscreens.
Extensive coarse-grained molecular dynamics simulations of soft matter: From RNA to hydrogels
Coarse-grained molecular dynamics (CG-MD) simulations are indispensable for investigating the dynamical response of macromolecular structures to external probes. The purpose of this thesis is to utilize CG-MD in order to determine (a) the response of a hydrogel to a time-varying external electric field, (b) the melting time of an RNA hairpin structure as a function of the temperature and the molecule size. Controlling the mechanical response of polyelectrolyte hydrogels to external electric fields is of great importance for hydrogel-based soft actuation systems. The first part of the thesis work involves the study of a semi-infinite polyelectrolyte hydrogel slab to a transient and spatially nonuniform half-sinusoidal electric field by means of both implicit and explicit solvent models. Results with an implicit solvent model demonstrated that an electric field confined to a small volumetric section of the hydrogel slab induces a reversible contraction of the entire slab in the direction perpendicular to the field. The hydrogel initially contracts by almost half of its field-free length and then retracts to its original size, with repeating contraction/retraction cycles exponentially decaying in magnitude akin to an underdamped oscillator. In contrast, almost no contraction occurs when the field is applied uniformly on the whole hydrogel slab. Analyses of contraction times and efficiencies for varying backbone charge fractions, dielectric constants, and salt concentrations confirm the robustness of the phenomenon. Further, by tuning the electric-field frequency and amplitude, both the contraction time and the efficiency can be controlled. Also, this phenomenon is demonstrated by using the explicit solvent model. Results showed that in realistic water concentrations (0.7 M), the contraction of the hydrogel decreases up to 35\%. Although this contraction is observed when an electric field is applied to the hydrogel slab partially like in the implicit solvent model, the field area increased to half of its initial size instead of 10\% for the implicit model. Concluded that our results of the mechanical behavior of the PE hydrogel slab are unconstrained by the solvent model, hydrogel's chemical properties, and electric field parameters. In the second part of the thesis, the melting behavior of an RNA hairpin, another macromolecular system distinguished by its palindromic sequence, investigated again by means of CG-MD simulations. Determination of the structure and dynamical behavior of nucleic acids as a function of temperature is a fundamental problem in polymer physics and is relevant for understanding the intracellular processes in thermophilic bacteria. The characteristic helical structure of these macromolecules is known to play an important role in their folding and melting dynamics. The main focus is on the melting dynamics upon an increase in the ambient temperature. Our results suggest the presence of two distinct dynamical regimes. The "critical regime" close to the equilibrium melting temperature displays an unexpected, non-monotonous dependence of the melting time on the temperature. The second, high-temperature regime is characterized by the entropic competition of the two denatured ends of the linear duplex structure. The scaling behavior of the melting time in both regimes investigated and identified the crossover boundary separating the two as a function of temperature and molecule size.
1000 genom projesinde yer alan CLOCK geni tek nükleotid polimorfizmlerinin in silico ve in vitro analizleri
Circadian rhythm is an internal process regulating ~24-h physiological and behavioral processes in organisms. In mammals, circadian rhythm is generated by transcription and translational feedback loop (TTFL) mechanism as a result of the interaction between core clock proteins. In TTFL, CLOCK and BMAL1 interact with each other and form a heterodimer to bind E-box sequences within the promoter region to initiate the transcription of the clock-controlled genes, including Period (Per) and Cryptochrome (Cry). Within the time, CRYs and PERs accumulate in the cytosol and then translocate into the nucleus with Casein Kinase Iε and repress BMAL1: CLOCK driven transcription. There are other auxiliary TTFLs exist that control circadian rhythm. Genetics and epidemiolocal studies suggest factors that disturb circadian rhythm result in susceptibility or may directly cause several diseases such as obesity, diabetes, cardiovascular diseases, aging, cancer, mood, and sleep disorders. Several single nucleotide polymorphisms (SNPs) for core clock genes have been identified and shown to be associated with different types of diseases. However, whether these SNPs contribute to the different type of the disease are ill-defined. One of the challenges in these approaches is that genome-wide association sequences (GWAS) studies have limitations in functional prediction. To address that, I developed using in vitro studies following the in-silico techniques to identify and characterize functional CLOCK SNPs from 1000 Genomes Ensemble to show the effect of a particular missense mutation on CLOCK protein on function. Such systematic approaches would allow us to discover SNPs with pathological effects and understand how these SNPs affect proteins' function. In this thesis, I performed a functional characterization of rare CLOCK missense variations (p.Phe104Cys, p.Leu118Arg, p.Asp119Val, p.Gly120Val, and p.Phe121Cys) identified from the Ensembl database. I initially analyzed these variations using computational tools. Results revealed that variants are located on the functionally important region of CLOCK. I used in vitro experimental approach and showed p.Leu118Arg, p.Asp119Val, and p.Phe121Cys CLOCK had reduced transactivation activity while p.Gly120Val CLOCK had increased transactivation along with BMAL1. However, p.Phe104Cys CLOCK had not been comparable transcriptional activity. To attrubitue these functional difference on the affinity between CLOCK SNPs and BMAL1, I performed co-immunoprecipitation between them Results indicated that p.Leu118Arg, p. Asp119Val, p.Gly120Val CLOCKs had reduced affinity to BMAL1and interestingly p.Phe121Cys CLOCK had increased the affinity to BMAL1. To gain more insight I further showed that the CRY1 had reduced repressor activity on p.Leu118Arg and p.Phe121Cys CLOCKs. Meanwhile, the estimation binding energy analysis of MD simulations supported the biochemical results, and binding energy analysis per residues was used to examine the mechanism of such SNPs effects. Collectively, I discovered that even single nucleotide changes in CLOCK directly affect the CLOCK functions. Hence, illumination of the effects of CLOCK SNPs would also help develop novel treatment strategies for diseases related to clock disruption for further studies and provide valuable information for the structure-function of CLOCK in circadian clock mechanism.
Investigating the role of nuclear receptor enhancers on gene transcription
When activated, the type-I steroid nuclear receptors (NR) translocate into the nucleus, altering the gene expression profile through non-coding cis-regulatory elements (CRE). Yet how they induce gene transcription is largely unknown. The androgen receptor (AR) and glucocorticoid receptor (GR) are well-studied examples of these nuclear receptors. Previously the AR enhancers were divided into three separate functional classes, and only a small subset of them was induced. We observed that those inducible AR enhancers are chromatin hubs and interact more with promoters of up-regulated genes upon hormone treatment. By integrating several independent publicly available functional genomic datasets, initially, we confirmed these classes for GR enhancers. We, then, investigated the intrinsic functional features of these enhancers, such as promoter activity. We discovered one particular enhancer class, which does not require NR occupancy for activity, had elevated promoter activity besides their enhancer activity. They were also accessible for binding various transcription factors (TF) in many tissues. Later, we described each factor's CRE interaction networks (cistrome) as graph diagrams and integrated the message passing algorithm to develop a scoring method for differential transcription factor occupancy on individual elements in these networks. As a proof of concept, this method effectively captured the AR or GR enrichment on the promoter of up-regulated genes; this couldn't be observed otherwise. We anticipate that our conceptual framework will improve our understanding of CRE function and gene expression regulation.
Assessing gas separation performances of COF membranes, COF/polymer MMMs, and dual filler-incorporated polymer membranes via high-throughput computational screening
Hundreds of covalent organic frameworks (COFs) have been synthesized and thousands of them have been computationally designed. However, it is impractical to experimentally test each material as membranes for gas separations. In this work, we focused on the membrane-based gas separation performances of experimentally synthesized COFs and hypothetical COFs (hypoCOFs). Gas permeabilities of COFs were computed by combining the results of grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) simulations and many COFs were found to overcome the upper bound of polymeric membranes for He/H2, N2/CH4, H2/N2, He/CH4, H2/CH4, and He/N2 separations. We then examined the structure-permeability relations of the COF membranes that are above the upper bound for each of the six gas separations and based on these relations we proposed an efficient approach for the selection of best hypoCOFs from a very large database. Molecular simulations showed that 120 hypoCOFs identified to be promising. They exceed the upper bound for He/CH4, He/N2, H2/CH4, H2/N2 separations. Both COFs and hypoCOFs were then studied as fillers in 25 polymers leading to a total of 29,020 COF/polymer and hypoCOF/polymer mixed matrix membranes (MMMs), representing the largest number of COF-based MMMs investigated to date. Permeabilities and selectivities of COF/polymer MMMs were computed for six different gas separations and results revealed that 18 of the 25 polymers can be carried above the upper bound when COFs were used as fillers. In the second part of thesis, we developed computational methods to identify the best-performing MOF-COF pairs to be used as dual fillers in polymer membranes for target gas separations. We focused on COF/polymer MMMs located below the upper bound due to their low gas selectivity for five industrially important gas separations, CO2/N2, CO2/CH4, H2/N2, H2/CH4, H2/CO2. Results showed that for polymers having a relatively high gas permeability (≥104 Barrer) but low selectivity (≤2.5) such as PTMSP, addition of the MOF as the second filler can have a dramatic effect on the final gas permeability and selectivity of the MMM. Property-performance relations of MOF and COFs were analyzed to understand effects of structural and chemical properties of the fillers on the permeability of the resulting MMMs and MOFs having Zn, Cu, and Cd metals were found to lead to the highest increase in gas permeability of MMMs. The methods in this thesis will provide insight into COFs and hypoCOFs in future computational studies and to find out promising MOF-COF pairs to be used in dual filler-incorporated polymer membranes.
Leveraging the molecular signatures of cancer for dynamic network
Drug resistance poses a significant challenge to the effectiveness of therapies, driven by accumulation of molecular alterations within dynamic cellular networks. In this thesis, we used a discrete dynamic model, Graph-based Cellular Automata (GCA), to reveal the network-based history of tumor progression and causal association between network modules and drug resistance by data integration. GCA can simulate dynamic systems using initial static information, set of states and simple transition rules. The reference graph is a tissue-specific interactome that consists of both protein-protein interactions and the regulatory network of the transcription factor to gene interactions composed of 8,228 nodes and 63,574 edges. By incorporating known biology and statistical rules of molecular alterations, including stimulations, repressions, and (non)-linear pairwise molecular correlations, GCA simulates molecular signalling and propagates mutation effects downstream of signalling pathways and complexes. Eventually, GCA gives a trajectory of subnetwork models for each context. In comparisons of simulations with and without mutations at the node level, we detected functional subnetworks within the dynamic network structure. We used publicly available omics data from a well-established cancer cell line repository to optimize the GCA model and construct dynamic networks for each cell-line-drug pair for interpreting drug resistance mechanisms at the pathway level. The accuracy of these drug representative networks were evaluated by cross-validation and on an independent test from Patient-Derived Xenografts (PDX). Notably, we found context-specific pathways (e.g. MAPK signalling) involving proteins from drug-resistant cell lines and PDX samples, thereby linking them to investigated drug resistance mechanisms. Overall, this approach, from molecular alterations to dynamic networks, transforms already available large datasets to gain new clinically relevant insights about drug resistance, offering potential implications for cancer therapy.
Predicting drug response through learning from network-based integration of multi-omics data in cancer cell lines
Predicting the treatment response continues to be a significant hurdle due to the inherent heterogeneity among tumours, leading to divergent responses to identical therapeutic approaches. This thesis introduces a strategy aiming to overcome this challenge by integrating a network-based hybrid machine learning model to detect drug response patterns, potentially elucidating the intricate biological mechanisms at play. Toward this goal, our approach deploys baseline omics data from more than 900 cancer cell lines. We assemble multi-omics data, including mutation, proteomic, and transcriptomic profiles from a broad spectrum of cell lines, along with their drug sensitivity data and protein targets of the drugs. The collected information is integrated onto a human protein-protein interactome, and using a personalized PageRank network propagation technique, we generate around 28,000 unique context-specific subnetworks, each representing a distinct drug-cell line pair. From this pool, we filtered out the grey zone contexts and focused on 1,194 pairs involving 426 cell lines and 45 drugs, labelled as either sensitive or resistant, facilitating more accurate predictions. Subsequently, these complex networks are converted into a format compatible with machine learning models via Graph2Vec, an unsupervised graph embedding algorithm, resulting in vectorized network representations encapsulating their topological and attribute-related features into a vector. These transformed networks serve as the input for a machine learning classifier, enabling it to predict drug responses based on the distinct properties inherent to each drug-cell line combination. Validity of this prediction model is thoroughly examined through a stringent testing, including a 10-fold cross-validation, resulting in an accuracy of 0.83 and an Area Under the Precision-Recall Curve (AUC) of 0.90. Moreover, additional 10-fold blind testing on independent cell line, drug, and tissue sets yielded an AUC of 0.72, 0.89, and 0.81, respectively. These findings underscore the model's potential in enhancing the precision of drug response predictions and illuminating the causal network modules underpinning drug resistance.
Robotic learning of haptic skills from expert demonstration for contact-rich manufacturing tasks
In this study, we propose a learning from demonstration (LfD) approach that utilizes an interaction (admittance) controller and two force sensors for the robot to learn the force applied by an expert from demonstrations in contact-rich tasks such as robotic polishing. Our goal is to equip the robot with the haptic expertise of an expert by using a machine learning (ML) approach while providing the flexibility for the user to intervene in the task at any point when necessary by using an interaction controller. The utilization of two force sensors, a pivotal concept in this study, allows us to gather environmental data crucial for effectively training our system to accommodate workpieces with diverse material and surface properties and maintain the contact of polisher with their surfaces. In the demonstration phase of our approach where an expert guiding the robot to perform a polishing task, we record the force applied by the human and the interaction force via two separate force sensors for the polishing trajectory followed by the expert to extract information about the environment. An admittance controller, which takes the interaction force as the input is used to output a reference velocity to be tracked by the internal motion controller (PID) of the robot to regulate the interactions between the polisher and the surface of a workpiece. A multilayer perceptron (MLP) model was trained to learn the human force profile based on the inputs of Cartesian position and velocity of the polisher, environmental force, and friction coefficient between the polisher and the surface to the model. During the deployment phase, in which the robot executes the task autonomously, the human force estimated by our system is utilized to balance the reaction forces coming from the environment and calculate the force needs to be inputted to the admittance controller to generate a reference velocity trajectory for the robot to follow. We designed three use-case scenarios to demonstrate the benefits of the proposed system: we first compare the performance of an expert polisher with a naive user to show the importance of haptic skills in polishing. In the second use-case, we show that the proposed system can successfully learn the intrinsic changes in human force profile from the expert user for autonomous robotic polishing of workpieces of different material and surface properties. The last use-case scenario involves human intervention during the robotic polishing for the regions on the workpiece requiring more polishing. The presented use-cases highlight the capability of the proposed pHRI (physical Human Robots Interaction) system to learn from human expertise and adjust its force based on material and surface variations during automated operations, while still accommodating manual interventions as needed.