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Our knowledge of the effects of exposure to indoor ultrafine particles (sub-100 nm, #/cm3 ) on human brain activity is very limited. The effects of cooking ultrafine particles (UFP) on healthy adults were assessed using an electroencephalograph (EEGs) for brain response. Peak ultrafine particle concentrations were approximately 3 × 105 particle/cm3, and the average level was 1.64 × 105 particle/cm3 . The average particle number emission rate (S) and the average number decay rate (a+k) for chicken frying in brain experiments were calculated to be 2.82 × 1012 (SD = 1.83 × 1012 , R2 = 0.91, p = 0.0013) particles/min, 0.47 (SD = 0.30, R2 = 0.90, p

This study is the first study that reports the cluster particle (1–3 nm) formation (CPF) in two modern preschools located in Nur-Sultan city of Kazakhstan from October 28 to November 27, 2019. The average particle number concentration and mode diameter values during major CPF events in Preschool I and Preschool II were found to be 1.90 × 106 (SD 6.43 × 106) particles/cm3 and 1.60 (SD 0.85) nm, and 1.11 × 109 (SD 5.46 × 109) particles/cm3 and 2.16 (SD 1.47) nm, respectively. The ultraviolet PM concentration reached as high as 7 μg/m3 in one of the measurement days. The estimated emission rate in Preschool I for CPF events was 9.57 × 109 (SD 1.92 × 109) particles/min. For Preschool II, the emission rate was 7.25 × 109 (SD 12.4 × 109) particles/min. We identified primary cluster particles (CPs) emitted directly from the sources such as candle burning, and secondary CPs formed as a result of the oxidation of indoor VOCs or smoking VOCs. The secondary CPs are likely to be SOA. Indoor VOCs were mainly emitted during cleaning activities as well as during painting and gluing. Indoor VOCs are the controlling factors in the CPF events. Changes in the training and cleaning programs may result in significant reductions in the exposure of the children to CPs.

Cataloged from PDF version of article. Thesis (Ph.D.): Bilkent University, Department of Industrial Engineering, İhsan Doğramacı Bilkent University, 2021. Includes bibliographical references (pages 97-102). In classical multi-armed bandit problem, the aim is to find a policy maximizing the expected total reward, implicitly assuming that the decision maker is risk-neutral. On the other hand, the decision makers are risk-averse in some real life applications. In this study, we design a new setting for the classical multi-armed bandit problem (MAB) based on the concept of dynamic risk measures, where the aim is to find a policy with the best risk adjusted total discounted outcome. We provide theoretical analysis of MAB with respect to this novel setting, and propose two different priority-index heuristics giving risk-averse allocation indices with structures similar to Gittins index. The first proposed heuristic is based on Lagrangian duality and the indices are expressed as the Lagrangian multiplier corresponding to the activation constraint. In the second part, we present a theoretical analysis based on Whittle’s retirement problem and propose a gener-alized version of restart-in-state formulation of the Gittins index to compute the proposed risk-averse allocation indices. Finally, as a practical application of the proposed methods, we focus on optimal design of clinical trials and we apply our risk-averse MAB approach to perform risk-averse treatment allocation based on a Bayesian Bernoulli model. We evaluate the performance of our approach against other allocation rules, including fixed randomization. by Milad Malekipirbazari Ph.D.

The global spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has challenged most countries worldwide. It was quickly recognized that reduced activities (lockdowns) during the Coronavirus Disease of 2019 (COVID-19) pandemic produced major changes in air quality. Our objective was to assess the impacts of COVID-19 lockdowns on ground-level PM2.5, NO2, and O3 concentrations on a global scale. We obtained data from 34 countries, 141 cities, and 458 air monitoring stations on 5 continents (few data from Africa). On a global average basis, a 34.0% reduction in NO2 concentration and a 15.0% reduction in PM2.5 were estimated during the strict lockdown period (until April 30, 2020). Global average O3 concentration increased by 86.0% during this same period. Individual country and continent-wise comparisons have been made between lockdown and business-as-usual periods. Universally, NO2 was the pollutant most affected by the COVID-19 pandemic. These effects were likely because its emissions were from sources that were typically restricted (i.e., surface traffic and non-essential industries) by the lockdowns and its short lifetime in the atmosphere. Our results indicate that lockdown measures and resulting reduced emissions reduced exposure to most harmful pollutants and could provide global-scale health benefits. However, the increased O3 may have substantially reduced those benefits and more detailed health assessments are required to accurately quantify the health gains. At the same, these restrictions were obtained at substantial economic costs and with other health issues (depression, suicide, spousal abuse, drug overdoses, etc.). Thus, any similar reductions in air pollution would need to be obtained without these extensive economic and other consequences produced by the imposed activity reductions.

Knowledge on the impact of the exposure to indoor ultrafine particles (UFPs) on the human brain is restricted. Twelve non-atopic, non-smoking, and healthy adults (10 female and 7 male, in average 22 years old) were monitored for brain physiological responses via electroencephalographs (EEGs) during cooking. Frying ground beef meat in sunflower oil using electric stove without ventilation was conducted. UFPs, particulate matter (PM) (PM1, PM2.5, PM4, PM10), CO2, indoor temperature, RH, oil and meat temperatures were monitored continuously throughout the experiments. The UFP peak concentration was recorded to be approximately 2.0 × 105 particles/cm3. EEGs were recorded before exposure, at end of cooking when PM peak concentrations were observed, and 30 min after the end of the cooking session (post-exposure). Brain electrical activity statistically significantly changed during post-exposure compared to the before exposure, suggesting the translocation of UFPs to the brain, occurring solely in the frontal and temporal lobes of the brain. Study participants older than 25 were more susceptible to UFPs compared to those younger than 25. Also, the brain abnormality was mainly driven by male rather than female study participants. The brain slow-wave band (delta) decreased while the fast-wave band (Beta3) increased similar to the pattern found in the literature for the exposure to smoking fumes and diesel exhaust.

Günümüzün veri odaklı dünyasnda çeşitli endüstriler rekabet üstünlüğü sağlamak ve devamlı gelişim için düzenli bir şekilde veri madenciligine başvurmaktadır. Bu tezde, iki uygulama alannda veri madenciliği tekniklerinin kullanlması ele alınmaktadır: sosyal kredilendirme ve demirleme planlama.Elektronik ticaret ve sosyal platformların gelişmesi ile sosyal kredilendirme, kredi verenlerinve kredi kullancılarının bankalar gibi kurumsal aracıların yardım olmadan iş yapabildiği gerçekçi bir platform olarak ortaya çıkmıştır. Sosyal kredilendirme kısa sürede milyarlarca dolarlk kredi sirkülasyonu sağlayan baz platformlarla birlikte son zamanlardaciddi bir ivme kazanmştır. Öte yandan, bu tür platformların sürdürülebilirliği ve yaygın bir şekilde kullanlmas bireysel kredi kullanclarnın riskinin doğru tahmin edilmesine bağldır. Bu amaçla, kredi kullanclarnın risk durumunu tahmin etmek için rasgele ormanlar (RO) tabanl bir snandırma yöntemi öneriyoruz. Popüler sosyal kredilendirme platformu Lending Club (LC) verileri üzerindeki çalşmalarmız, RO tabanl yöntemin iyi kredi kullanclarnın tanımlanmasında LC sonuçlarnın yanı sıra FICO kredi puanlarından da daha sağlıklı tahmin verdiğini göstermektedir.İkinci veri madenciliği uygulama alan olarak deniz taşımacılğı ele alnmaktadr. Özellikle, İstanbul'da son dokuz yılda toplanan demirleme bilgilerini içeren yeni bir veri seti üzerine kapsamlı bir analiz sunulmaktadır. Belirli bir gemi için demirleme süresinin tahmin edilebilmesi amacyla bir veri madenciliği yapısı sağlanmaktadır. Amacımız gemi demirlemesi ile ilgili önemli faktörlerden bir anlam çıkarmak ve verimli demirleme planlamas için önemli olan demirleme süresi tahmini için etkili bir yöntem geliştirmektir. Buna ek olarak, gemi tipi traği zamansal analizi ile birlikte istatistiksel ARIMA modeli kullanlarak önümüzdeki üç yıl için gemi tipi traği tahmin edilmiştir. Sonuçlarmız, gemi demirleme saysında genel bir düşüş, fakat LPG taşyclarında belirgin bir artşolduğunu göstermektedir. Bu sonuçlar oldukça önemlidir çünkü bu tür gemiler kazalara daha yatkındır ve herhangi bir kaza anında Boğaz için büyük tehlike teşkil etmektedir. In today's data-driven world, various industries resort to data mining on a regular basis for competitive advantage and sustained growth. In this thesis, we consider employment of data mining techniques in two application domains: social lending and anchorage planning.With the advance of electronic commerce and social platforms, social lending (also known as peer-to-peer lending) has emerged as a viable platform where lenders and borrowers can do business without the help of institutional intermediaries such as banks. Social lending has gained significant momentum recently, with some platforms reaching multibillion dollar loan circulation in a short amount of time. On the other hand, sustainability and possible widespread adoption of such platforms depend heavily on reliable risk attribution to individual borrowers. For this purpose, we propose a random forest (RF) based classification method for predicting borrower status. Our results on data from the popular social lending platform Lending Club (LC) indicate the RF-based method outperforms the FICO credit scores as well as LC grades in identification of good borrowers.The second data mining application domain we consider pertains to maritime transportation. In particular, we first provide a comprehensive statistical analysis on a new anchorage data set gathered for nine recent consecutive years in Istanbul anchorages. We introduce a data mining framework with the aim of identifying a good estimate for anchorage duration for a given vessel. Our goal is to develop an understanding of key factors relevant to vessel anchorage and devise an effective methodology for predicting anchorage duration, which is critical for efficient anchorage planning. In addition, along with a temporal analysis of vessel type taffic, we forecast vessel type trafficc for the next three years using the statistical ARIMA model. Our results suggest an overall decrease in berthing vessels, yet a pronounced increase in LPG barges. This finding is rather significant as this type of vessel is more prone to accidents and any such accident wouldpose a great danger to the Strait. 77

In pattern recognition, irrelevant and redundant features together with a large number of noisy instances in the underlying dataset decrease performance of trained models and make the training process considerably slower, if not practically infeasible. In order to combat this so-called curse of dimensionality, one option is to resort to feature selection (FS) methods designed to select the features that contribute the most to the performance of the model, and one other option is to utilize feature extraction (FE) methods that map the original feature space into a new space with lower dimensionality. These two methods together are called feature reduction (FR) methods. On the other hand, deploying an FR method on a dataset with massive number of instances can become a major challenge, from both memory and run time perspectives, due to the complex numerical computations involved in the process. The research question we consider in this study is rather a simple, yet novel one: do these FR methods really need the whole set of instances (WSI) available for the best performance, or can we achieve similar performance levels with selecting a much smaller random subset of WSI prior to deploying an FR method? In this work, we provide empirical evidence based on comprehensive computational experiments that the answer to this critical research question is in the affirmative. Specifically, with simple random instance selection followed by FR, the amount of data needed for training a classifier can be drastically reduced with minimal impact on classification performance. We also provide recommendations on which FS/ FE method to use in conjunction with which classifier.

This study is the first study that reports the cluster particle (1–3 nm) formation (CPF) in two modern preschools located in Nur-Sultan city of Kazakhstan from October 28 to November 27, 2019. The average particle number concentration and mode diameter values during major CPF events in Preschool I and Preschool II were found to be 1.90 × 106 (SD 6.43 × 106) particles/cm3 and 1.60 (SD 0.85) nm, and 1.11 × 109 (SD 5.46 × 109) particles/cm3 and 2.16 (SD 1.47) nm, respectively. The ultraviolet PM concentration reached as high as 7 μg/m3 in one of the measurement days. The estimated emission rate in Preschool I for CPF events was 9.57 × 109 (SD 1.92 × 109) particles/min. For Preschool II, the emission rate was 7.25 × 109 (SD 12.4 × 109) particles/min. We identified primary cluster particles (CPs) emitted directly from the sources such as candle burning, and secondary CPs formed as a result of the oxidation of indoor VOCs or smoking VOCs. The secondary CPs are likely to be SOA. Indoor VOCs were mainly emitted during cleaning activities as well as during painting and gluing. Indoor VOCs are the controlling factors in the CPF events. Changes in the training and cleaning programs may result in significant reductions in the exposure of the children to CPs.

In classical multiarmed bandit problem, the aim is to find a policy maximizing the expected total reward, implicitly assuming that the decision-maker is risk-neutral. On the other hand, the decision-makers are risk-averse in some real-life applications. In this article, we design a new setting based on the concept of dynamic risk measures where the aim is to find a policy with the best risk-adjusted total discounted outcome. We provide a theoretical analysis of multiarmed bandit problem with respect to this novel setting and propose a priority-index heuristic which gives risk-averse allocation indices having a structure similar to Gittins index. Although an optimal policy is shown not always to have index-based form, empirical results express the excellence of this heuristic and show that with risk-averse allocation indices we can achieve optimal or near-optimal interpretable policies.

Cooking is a major source of indoor particulate matter (PM), especially ultrafine particles (UFPs). Long-term exposure to fine and ultrafine particles (UFPs) has been associated with adverse human health effects. Toxicological studies have demonstrated that exposure to PM2.5 (particles with aerodynamic diameter smaller than 2.5 μm) may result in increased blood pressure (BP). Some clinical studies have shown that acute exposure to PM2.5 causes changes in systolic (SBP) and diastolic blood pressure (DBP), depending on the source of particles. Studies assessing the effect of exposure to cooking PM on BP and heart rate (HR) using electric or gas stoves are not well represented in the literature. The aim of this investigation was to perform controlled studies to quantify the exposure of 50 healthy volunteer participants to fine and ultrafine particles emitted from a low-emissions recipe for frying ground beef on an electric stove. The BP and heart rate (HR) of the volunteers were monitored during exposure and after the exposure (2 h post-exposure). Maximum UFP and PM2.5 concentrations were 6.5 × 104 particles/cm3 and 0.017 mg/m3, respectively. Exposure to UFPs from frying was associated with statistically significant increases in the SBP. The lack of food and drink during the 2 h post-cooking period was also associated with a statistically significant reduction in SBP. No statistically significant changes in DBP were observed. Physiological factors, including heat stress over the stove, movements and anxiety, could be responsible for an elevation in HR at the early stages of the experiments with a subsequent drop in HR after 90 min post-cooking, when study participants were relaxed in a living room.

With recent emergence of machine learning problems with massive number of features, feature selection (FS) has become an ever-increasingly important tool to mitigate the effects of the so-called curse of dimensionality. FS aims to eliminate redundant and irrelevant features for models that are faster to train, easier to understand, and less prone to overfitting. This study presents a wrapper FS method based on Simultaneous Perturbation Stochastic Approximation (SPSA) with Barzilai and Borwein (BB) non-monotone gains within a pseudo-gradient descent framework wherein performance is measured via cross-validation. We illustrate that SPSA with BB gains (SPSA-BB) provides dramatic improvements in terms of the number of iterations for convergence with minimal degradation in cross-validated error performance over the current state-of-the art approach with monotone gains (SPSA-MON). In addition, SPSA-BB requires only one internal parameter and therefore it eliminates the need for careful fine-tuning of numerous other internal parameters as in SPSA-MON or comparable meta-heuristic FS methods such as genetic algorithms (GA). Our particular implementation includes gradient averaging as well as gain smoothing for better convergence properties. We present computational experiments on various public datasets with Nearest Neighbors and Naive Bayes classifiers as wrappers. We present comparisons of SPSA-BB against full set of features, SPSA-MON, as well as seven popular meta-heuristics based FS algorithms including GA and particle swarm optimization. Our results indicate that SPSA-BB converges to a good feature set in about 50 iterations on the average regardless of the number of features (whether a dozen or more than 1000 features) and its performance is quite competitive. SPSA-BB can be considered extremely fast for a wrapper method and therefore it stands as a high-performing new feature selection method that is also computationally feasible in practice.

The global spread of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has challenged most countries worldwide. It was quickly recognized that reduced activities (lockdowns) during the Coronavirus Disease of 2019 (COVID-19) pandemic produced major changes in air quality. Our objective was to assess the impacts of COVID-19 lockdowns on ground-level PM2.5, NO2, and O3 concentrations on a global scale. We obtained data from 34 countries, 141 cities, and 458 air monitoring stations on 5 continents (few data from Africa). On a global average basis, a 34.0% reduction in NO2 concentration and a 15.0% reduction in PM2.5 were estimated during the strict lockdown period (until April 30, 2020). Global average O3 concentration increased by 86.0% during this same period. Individual country and continent-wise comparisons have been made between lockdown and business-as-usual periods. Universally, NO2 was the pollutant most affected by the COVID-19 pandemic. These effects were likely because its emissions were from sources that were typically restricted (i.e., surface traffic and non-essential industries) by the lockdowns and its short lifetime in the atmosphere. Our results indicate that lockdown measures and resulting reduced emissions reduced exposure to most harmful pollutants and could provide global-scale health benefits. However, the increased O3 may have substantially reduced those benefits and more detailed health assessments are required to accurately quantify the health gains. At the same, these restrictions were obtained at substantial economic costs and with other health issues (depression, suicide, spousal abuse, drug overdoses, etc.). Thus, any similar reductions in air pollution would need to be obtained without these extensive economic and other consequences produced by the imposed activity reductions., The authors of this study truly appreciate the funding provided by Nazarbayev University through the Collaborative Research Grant (grant number: 091019CRP2104). MAT, MN, EA, and NE are the members of the Chemical and Aerosol Research Team (CART) and also the Environment and Resource Efficiency Cluster (EREC) at Nazarbayev University, and they would like to acknowledge the resources provided by CART and EREC to conduct this research. For India, except for Delhi, daily and hourly averaged air quality data were obtained from the Central Pollution Control Board (CPCB) CCR, OpenAQ or Air quality historical data platform, and location of stations were obtained from the OpenAQ website. CPCB is gratefully acknowledged for making the data available. Further, the CPCB data were consolidated and curated by Mr Prem Maheshwarkar, Earth and Environmental Sciences, IISER Bhopal whose contribution is also acknowledged. In Chile, the contributions made by Mr. Roberto Martinez, Head of the Planning and the Standards, Department of the Air Quality and Climate Change Division, Ministry for the Environment to provide data to this study, are greatly appreciated. In Brazil, CETESB (Environmental Company of the State of Sao Paulo) is acknowledged for its contribution to data preparation. In Cyprus, the authors would like to thank the Department of Labor Inspection of the Ministry of Environment of the Republic of Cyprus for providing the observational data for their monitoring network. For New Zealand, authors acknowledge the Environment Canterbury as the source for the Christchurch data. For Spain, Barcelona, the authors would like to thank "Department of Environmental Quality, Generalitat de Catalunya" as the data source. Turkish Ministry of Environment and Urbanization and Turkish State of Meteorological Services provided air quality and meteorological data, respectively, that are greatly appreciated. In the Netherlands, data were obtained from the National Institute for Public Health and the Environment (RIVM) and contained additional data from the GGD Amsterdam (provided by Dave de Jonge) and the DCMR Environmental Protection Agency (provided by Ed van der Gaag) and National Institute for Public Health and the Environment (RIVM) that are greatly appreciated. The authors would like to thank Tehran Air Quality Control Company for providing the data for the city of Tehran.