Creating a new Europe through contemporary art:Manifesta and its relation to art, society and politics

Manifesta - European Biennial of Contemporary Art is a nomadic biennial that takes place every two years in a different city of Europe. In this dissertation, I focus on Manifesta's political, economic, urban and artistic relationships with its host cities and regions as well as the changes that occur in its own goals, discourse and organization over time. I argue that Manifesta is one of the new institutions of neoliberal governance in the field of art in the post-Cold War era. During the dissertation, I evaluate each edition of the biennial within its own context, adopt theoretical approaches suitable for this context and compare editions to find common points. I identify three significant periods within the history of Manifesta. In its first period, namely from its birth as an idea in the early 90’s to the Ljubljana edition held in 2000, Manifesta aimed to reach to the post-communist regions. Later, until its ninth edition held in Genk - Limburg (2012), Manifesta shifted its attention from the East-West axis to the North-South axis within Europe and concentrated more on the promotion of regions than cities. In its last and on-going period that started in 2014 in St. Petersburg, it went beyond the EU zone after the global systemic crisis and acted pragmatically in terms of its discourse. By investigating each editions’ complex set of relations in detail, this dissertation contributes to a better understanding of both Manifesta and the phenomenon of contemporary art biennials

Batch and continuous removal of heavy metals from industrial effluents using microbial consortia

Bio-removal of heavy metals, using microbial biomass, increasingly attracting scientific attention due to their significant role in purification of different types of wastewaters making it reusable. Heavy metals were reported to have a significant hazardous effect on human health, and while the conventional methods of removal were found to be insufficient; microbial biosorption was found to be the most suitable alternative. In this work, an immobilized microbial consortium was generated using Statistical Design of Experiment (DOE) as a robust method to screen the efficiency of the microbial isolates in heavy metal removal process. This is the first report of applying Statistical DOE to screen the efficacy of microbial isolates to remove heavy metals instead of screening normal variables. A mixture of bacterial biomass and fungal spores was used both in batch and continuous modes to remove Chromium and Iron ions from industrial effluents. Bakery yeast was applied as a positive control, and all the obtained biosorbent isolates showed more significant efficiency in heavy metal removal. In batch mode, the immobilized biomass was enclosed in a hanged tea bag-like cellulose membrane to facilitate the separation of the biosorbent from the treated solutions, which is one of the main challenges in applying microbial biosorption at large scale. The continuous flow removal was performed using fixed bed mini-bioreactor, and the process was optimized in terms of pH (6) and flow rates (1 ml/min) using Response Surface Methodology. The most potential biosorbent microbes were identified and characterized. The generated microbial consortia and process succeeded in the total removal of Chromium ions and more than half of Iron ions both from standard solutions and industrial effluents

Problematic online behaviors among adolescents and emerging adults: associations between cyberbullying perpetration, problematic social media use, and psychosocial factors

Over the past two decades, young people's engagement in online activities has grown markedly. The aim of the present study was to examine the relationship between two specific online behaviors (i.e., cyberbullying perpetration, problematic social media use) and their relationships with social connectedness, belongingness, depression, and self-esteem among high school and university students. Data were collected from two different study groups via two questionnaires that included the Cyberbullying Offending Scale, Social Media Use Questionnaire, Social Connectedness Scale, General Belongingness Scale, Short Depression-Happiness Scale, and Single Item Self-Esteem Scale. Study 1 comprised 804 high school students (48% female; mean age 16.20 years). Study 2 comprised 760 university students (60% female; mean age 21.48 years). Results indicated that problematic social media use and cyberbullying perpetration (which was stronger among high school students) were directly associated with each other. Belongingness (directly) and social connectedness (indirectly) were both associated with cyberbullying perpetration and problematic social media use. Path analysis demonstrated that while age was a significant direct predictor of problematic social media use and cyberbullying perpetration among university students, it was not significant among high school students. In both samples, depression was a direct predictor of problematic social media use and an indirect predictor of cyberbullying perpetration. However, majority of these associations were relatively weak. The present study significantly adds to the emerging body of literature concerning the associations between problematic social media use and cyberbullying perpetration

CYCLE-GAN BASED FEATURE TRANSLATION FOR OPTICAL-SAR DATA IN BURNED AREA MAPPING

For the management of the forest and the assessment of impacts on ecosystems, post-fire burned area mapping is crucial for sustainable environment and forestry. While optical remote sensing data has been extensively used for monitoring forest fires due to its spatial and temporal resolutions, it is susceptible to limitations imposed by poor weather conditions. To overcome this challenge, the complementary use of optical and Synthetic Aperture Radar (SAR) data is beneficial, as SAR can penetrate clouds and capture images in all-weather conditions. However, SAR lacks the necessary spectral features for comprehensive forest fire monitoring and burned area mapping. To overcome these limitations, this study proposes a Cycle-Consistent Generative Adversarial Networks (Cycle-GAN) based deep feature translation method for burned area mapping by combining optical and SAR data. This approach allows for the retrieval of precise information of interest with a level of precision that cannot be achieved by either optical or SAR data alone. The Cycle-GAN uses a cyclic structure to transfer data from one domain (optical) to another domain (SAR) into the same feature space. As a result, it can maintain its spectral characteristics while providing ongoing and current information for monitoring forest fires. For this purpose, Burn Area Index (BAI), Mid Infrared Burn Index (MIRBI), Normalised Burn Ratio (NBR) were determined using optical data and image translation was performed with Cycle-GAN on SAR data. The accuracy of the fake BAI, MIRBI and NBR spectral burn indices determined from the SAR was established by correlating the original spectral burn indices determined from the optical data. The results demonstrate a significant correlation between the real and generated fake burn indices, particularly with a noteworthy correlation coefficient of 0.93 observed for the NBR index. In addition, the findings validate the effectiveness of the generated indices in accurately representing and quantifying the extent of burned areas

Investigation of antimicrobial activity of some Turkish pleurocarpic mosses

In this study, the antimicrobial activities of different extracts from the five pleurocarpic mosses (Platyhypnidium riparioides (Hedw.) Dixon, Leucodon sciuroides (Hedw.) Schwägr., Hypnum cupressiforme Hedw., Homalothecium sericeum (Hedw.) Br.Eur., and Anomodon viticulosus (Hedw.) Hook & Taylor.) were tested aganist eight bacterial and fungal strains. For the extraction, four different solvents (ethyl alcohol, methyl alcohol, chloroform and acetone) were used. While methanolic extracts of P. riparioides showed the highest antibacterial effect against the Gram-negative bacterium Pseudomonas aeroginosa ATCC 27853, acetone extract of A. viticulosus showed the highest antifungal effect against the fungus Saccharomyces cerevisiae ATCC. All the results were compared with standard antibiotic discs: ketoconazole (50 μg), amphicillin (10 μg), eritromycin (15 μg), penicillin (10 μg) and vancomycin (30 μg).Key words: Moss, pleurocarpic, antimicrobial activity

Antifungal and antibacterial effects of some acrocarpic mosses

In this study, the antifungal and antibacterial effect of 6 different  acrocarpous mosses were tested in vitro aganist 8 different  microorganisms. For the extraction, ethyl alcohol, methyl alcohol, acetone and chloroform were used as solvents. While the highest antimicrobial effect was seen in methyl alcohol extracts, extracts of chloroform showed the lowest level of antimicrobial effect. Grimmia anodon Bruch & Schimp. which is one of the acrocarp mosses used in this study, showed the highest activity in termsof the number of microorganism affected. Tortella tortuosa (Hedw.) Limpr. only has effect on Candida albicans ATCC 16231 strain. All the results were compared with standard antibiotic discs, ketoconazole (50 ìg), ampicillin (10 ìg), eritromycin (15 ìg) and vancomycin (30 ìg).Key words: Moss, acrocarpous, antimicrobial effect

A proposal for a CT driven classification of left colon acute diverticulitis

Computed tomography (CT) imaging is the most appropriate diagnostic tool to confirm suspected left colonic diverticulitis. However, the utility of CT imaging goes beyond accurate diagnosis of diverticulitis; the grade of severity on CT imaging may drive treatment planning of patients presenting with acute diverticulitis. The appropriate management of left colon acute diverticulitis remains still debated because of the vast spectrum of clinical presentations and different approaches to treatment proposed. The authors present a new simple classification system based on both CT scan results driving decisions making management of acute diverticulitis that may be universally accepted for day to day practice

Modeling of the number of divorce in Turkey using the Generalized Poisson, Quasi-Poisson and Negative Binomial Regression

In this study, it has been aimed to model the numbers of divorce in Turkey between years 2001- 2009 using Generalized Poisson, Quasi-Poisson and Negative Binomial Regression methods. Data set of this study has been based on the data obtained from Turkish Statistical Institute (TUIK). Response variable-the annual rate of divorce- has been categorized into four groups with respect to the length of ex-married life of divorced couples. Explanatory variables have been designated as average age of the first marriage of men and women, the professional work life ratio of married women, the percentage of university graduates in both men and women. For Poisson models, overdispersion parameters have been detected respectively 32.413, 7.277, 16.158 and 26.361. Furthermore Pearson and G2 statistics have revealed that Poisson models are not appropriate for data set. When Quasi Poisson regression was employed, it has been detected that residual deviances are rather close to Poisson residuals. Finally, Negative binomial regression has been conducted. Overdispersion is a common phenomenon in Poisson modeling. In such data sets certain generalizations of Poisson regression and negative binomial regression modeling are used. In present study negative binomial regression has been detected as approved method.Bu çalışmada, 2001-2009 yılları arasında Türkiye’deki boşanma sayılarının Genelleştirilmiş Poisson, Quasi Poisson ve Negatif Binomiyal Regresyon metotlarına gore modellenmesi amaçlanmıştır. Çalışmanın veri seti, Türk İstatistik Kurumu (TÜİK)’den elde edilmiştir. Cevap değişkeni olan yıllık boşanma sayısı, boşanmış çiftlerin evil kalma sürelerine göre dört gruba ayrılmıştır. Çalışmanın veri seti Türkiye İstatistik Kurumu (TÜİK)’ndan elde edilen bilgiler ile oluşturulmuştur. Cevap değişkeni olan yıllık boşanma sayısı, boşanan çiftlerin evli kalma sürelerine göre dört gruba ayrılmıştır. Açıklayıcı değişkenler olarak, erkek ve kadınların ilk evlilik yaşı ortalamaları, evli kadının iş hayatına katılma oranı, erkek ve kadınlarda yüksek okul mezunu olma oranları ele alınmıştır. Poisson modelleri için aşırı yayılım parametresi sırasıyla 32.413, 7.277, 16.158 ve 26.361 olarak belirlenmiştir. Ayrıca Pearson ve G2 istatistikleri de Poisson modellerinin veri seti için uygun olmadığını göstermiştir. Quasi Poisson uygulandığında ise artıkların dağılımı Poisson modellerine çok yakın çıkmıştır. Sonuç olarak Negatif Binomiyal Regresyon kullanılmıştır. Aşırıyayılım, Poisson modellemesinde yaygın bir fenomendir. Bu gibi veri setlerinde Poisson Regresyonun çeşitli genelleştirmeleri ve Negatif Binomiyal Regresyon kullanılır. Bu çalışmada Negatif Binomiyal Regresyonun uygun olduğuna karar verilmiştir

An Intelligent Approach for Predicting Mechanical Properties of High-Volume Fly Ash (HVFA) Concrete

Plastic waste (PW) is a major soild waste, which its generation continues to increase globally year in and year out. Proper management of the PW is still a challenge due to its non-biodegradable nature. One of the most convenient ways of managing plastic waste is by using it in concrete as a partial substitute for natural aggregate. However, the main shortcomings of adding plastic waste to concrete are a reduction in strength and durability. Hence, to reduce the undesirable impact of the PW in concrete, highly reactive additives are normally added. In this research, 240 experimental datasets were used to train an artificial neural network (ANN) model using Levenberg Marquadt algorithms for the prediction of the mechanical properties and durability of high-volume fly ash (HVFA) concrete containing fly ash and PW as partial substitutes for cement and coarse aggregate, respectively, and graphene nanoplatlets (GNP) as additives to cementitious materials. The optimized model structure has five input parameters, 17 hidden neurons, and one output layer for each of the physical parameters. The results were analyzed graphically and statistically. The obtained results revealed that the generated network model can forecast with deviations less than 0.48%. The efficiency of the ANN model in predicting concrete properties was compared with that of the SVR (support vector regression) and SWLR (stepwise regression) models. The ANN outperformed SVR and SWLR for all the models by up to 6% and 74% for SVR and SWLR, respectively, in the confirmation stage. The graphical analysis of the results further demonstrates the higher prediction ability of the ANN. Doi: 10.28991/CEJ-2023-09-09-04 Full Text: PD