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The Economic Well-Being of Nations Is Associated With Positive Daily Situational Experiences
No full textPeople in economically advantaged nations tend to evaluate their life as more positive overall and report greater well-being than people in less advantaged nations. But how does positivity manifest in the daily life experiences of individuals around the world? The present study asked 15,244 college students from 62 nations, in 42 languages, to describe a situation they experienced the previous day using the Riverside Situational Q-sort (RSQ). Using expert ratings, the overall positivity of each situation was calculated for both nations and individuals. The positivity of the average situation in each nation was strongly related to the economic development of the nation as measured by the Human Development Index (HDI). For individuals’ daily experiences, the economic status of their nation also predicted the positivity of their experience, even more than their family socioeconomic status. Further analyses revealed the specific characteristics of the average situations for higher HDI nations that make their experiences more positive. Higher HDI was associated with situational experiences involving humor, socializing with others, and the potential to express emotions and fantasies. Lower HDI was associated with an increase in the presence of threats, blame, and hostility, as well as situational experiences consisting of family, religion, and money. Despite the increase in a few negative situational characteristics in lower HDI countries, the overall average experience still ranged from neutral to slightly positive, rather than negative, suggesting that greater HDI may not necessarily increase positive experiences but rather decrease negative experiences. The results illustrate how national economic status influences the lives of individuals even within a single instance of daily life, with large and powerful consequences when accumulated across individuals within each nation. © 202
The Influence of Packaging Design Visual Elements on Consumers' Purchase Intention: a Comparison Study on Organic Food and Non-Food Products
No full textKiygi-Calli, Meltem/0000-0002-2979-9309; EL ORAIBY, MARYAM/0000-0003-0485-9325Consumers are showing a growing interest in organic products. This study investigates how visual packaging design elements influence the consumers' purchase intention toward organic food products compared to organic non-food products. We apply a questionnaire and ask respondents to rate the different packaging designs for organic dry pasta and soap bars. We use the orthogonal design method to obtain representative stimulus designs and conduct a conjoint analysis to determine the utilities of each design and assess the relative importance of color, material, and tagline typeface. Our results reveal that for the organic food and non-food products, packaging color is the most decisive factor, followed by material and typeface, independently to the participants' demographic characteristics. The combination of visual elements with the highest utility score includes plastic for the packaging material, the color cream, and the typewritten typeface for the organic claim for both organic pasta and organic soap. This study also integrates a benefit-based segmentation approach with conjoint analysis. Our study contributes to understanding consumers' preferences with valuable insights for the organic industry
Twıtter Görsellerinin Vatandaşları Kolektıf Eyleme Teşvik Potansiyeli
No full textBu deney, yalnızca görsel içeren Tweet’lerin yalnızca metin içeren Tweet'lerle karşılaştırıldığında, Beyaz Amerikalılar’ı Black Lives Matter protestoları için kolektif eylemi teşvik etmek potansiyelini test etmiştir. Bağımlı değişken olarak kolektif eyleme katılma isteği kullanılarak, 2 (Tweet modu: sadece görsel-sadece metinsel) x 3 (Tweet içerik türü: öfke uyandıran, etkinlik (efficacy) uyandıran, nötr) şeklinde denekler arası deney tasarım uygulanmıştır. Beklentinin aksine bulgularda, vatandaşları siyasete katılmaya motive etmede sadece görsel içeren Tweet'lerin sadece metinsel Tweet'lerden daha etkili olduğuna veya öfke uyandıran içeriğin uyandırmayan içeriklerden daha etkili olduğuna dair hiçbir kanıt bulunamamıştır. Öfke uyandıran, görsel temelli sosyal protesto Tweet'lerinin, Beyaz Amerikalılar arasında öfke uyandırmayan görsel Tweet'lere göre yalnızca marjinal olarak daha fazla harekete geçirici olduğu bulunmuştur. Bununla birlikte, sonuçlar, kolektif etkinlik (efficacy) uyandıran metinsel Tweet'lerin, etkinlik ortaya çıkaran görsel Tweet'lerden daha fazla kolektif eyleme katılım niyeti uyandırdığını gösterdi. Dahası, öfke uyandıran görsellere maruz kaldıktan sonra erkekler, kadınlardan daha fazla toplumsal protestolara katılma eğilimi göstermiştir. Öte yandan kadınlar, etkinlik ortaya çıkaran görsel içeriklere maruz kaldıktan sonra protesto eyleminde bulunma konusunda erkeklerden daha istek belirtmişlerdir
Driven and Non-Driven Surface Chaos in Spin-Glass Sponges
No full textArtun, Erbil Can/0000-0002-9624-3124; Berker, A/0000-0002-5172-2172A spin-glass system with a smooth or fractal outer surface is studied by renormalization-group theory, in bulk spatial dimension d = 3. Independently varying the surface and bulk random-interaction strengths, phase diagrams are calculated. The smooth surface does not have spin-glass ordering in the absence of bulk spin-glass ordering and always has spin-glass ordering when the bulk is spin-glass ordered. With fractal (d > 2) surfaces, a sponge is obtained and has surface spin-glass ordering also in the absence of bulk spin-glass ordering. The phase diagram has the only-surface-spin-glass ordered phase, the bulk and surface spin-glass ordered phase, and the disordered phase, and a special multicritical point where these three phases meet. All spin-glass phases have distinct chaotic renormalization-group trajectories, with distinct Lyapunov and runaway exponents which we have calculated.Kadir Has University Doctoral Studies Scholarship Fund; Academy of Sciences of Turkey (TUBA)Support by the Kadir Has University Doctoral Studies Scholarship Fund and by the Academy of Sciences of Turkey (TUBA) is gratefully acknowledged. All authors have equally shared in the research and preparation of this manuscript
A Radio Frequency Identification Reader Collision Avoidance Protocol for Dense Reader Environments in the Context of Industry 4.0
No full textIn the new industrial revolution known as Industry 4.0, radio frequency identification (RFID) systems are a key component of automatic detection. These systems have two main elements, namely Reader and Tag. In many Internet of Things (IoT) applications, the RFID system is used with lots of readers working together in a dense environment to read tags. The simultaneous operation of readers with a common sensory range increases the likelihood of reader-to-tag collision and reader-to-reader collision and reduces the number of successful reading and as a result, reduces network performance and average waiting time for each reader increased. Collisions happen when readers are in the interference range and start reading tags simultaneously, so it is necessary to use the right solution to control channel access in these systems. So far, various solutions have been proposed to control readers' access to the communication channel. Some of them have not considered the existing standards for this type of system or have not been efficient enough to be used in the IoT. In this study, we propose a method that, by considering the distance between readers and the number of neighbourhoods, and the possibility of information sharing, allows readers to successfully read more tags with fewer collisions in a certain time frame. The results of the performance study in a real-world environment showed that the suggested method outperformed similar methods in terms of network performance and has much better throughput, making it a superior choice for usage in IoT-based RFID systems
Anomaly Detection Via Machine Learning
No full textRetail companies monitor inventory stock levels regularly and manage stock levels based on forecasted sales to sustain their market position. The accuracy of inventory stocks is critical for retail companies to create a correct strategy. Many retail com- panies try to detect and prevent inventory record inaccuracy caused by employee or customer theft, damage or spoilage and wrong shipments. This study is aimed to detect inaccurate stocks using machine learning methods. It uses the real inven- tory stock data of Migros Ticaret A.S¸. of Turkey’s largest supermarket chains. A multiple of machine learning algorithms such as Isolation Forest (IF), Local Outlier Factor (LOF), One-Class Support Vector Machine (OCSVM) were used to detect abnormal stock values. On the other hand, generally, researchers use public data to develop methods, and it is challenging to apply machine learning algorithms to real-life data, especially in unsupervised learning. This thesis shows how to handle real-life data noises, missing values etc. The experimental findings show the perfor- mances of machine learning methods in detecting anomalies in low and high level inventory stock
The Role of Environmental, Social, Governance (ESG) Practices and Ownership on Firm Performance in Emerging Markets
No full textThis paper investigates: (i) the effect of environmental, social, and governance (ESG) engagement and ownership attributes on firm performance and (ii) whether different ownership attributes (institutional, foreign, and state ownership) moderate the association between ESG engagement and firm performance. Employing an extensive sample from 22 emerging countries worldwide, we provide cross-country evidence that ESG engagement and its three pillars, i.e. environmental, social, and governance pillars, enhance firm performance, proxied with ROA and Tobin's Q. Moreover, institutional and foreign ownership positively impact firm performance. We present novel evidence that the positive impact of superior ESG engagement on firm performance is lower for higher institutional ownership companies than lower institutional ownership companies, but greater for higher foreign ownership companies than lower foreign ownership companies
Cost-Effective Synthesis of Qca Logic Circuit Using Genetic Algorithm
No full textQuantum-dot cellular automata (QCA) is a field coupling nano-technology that has drawn significant attention for its low power consumption, low area overhead, and achieving a high speed over the CMOS technology. Majority Voter (MV) and QCA Inverter (INV) are the primitive logic in QCA for implementing any QCA circuit. The performance and cost of a QCA circuit directly depend on the number of QCA primitives and their interconnections. Their optimization plays a crucial role in optimizing the QCA logic circuit synthesis. None of the previous works considered elitism in GA, all the optimization objectives (MV, INV and Level), and the redundancy elimination approach. These profound issues lead us to propose a new methodology based on Genetic algorithm (GA) for the cost-effective synthesis of the QCA circuit of the multi-output boolean functions with an arbitrary number of inputs. The proposed method reduces the delay and gate count, where the worst-case delay is minimized in terms of the level. This methodology adapts elitism to preserve the best solutions throughout the intermediate generations. Here, MV, INV, and levels are optimized according to their relative cost factor in a QCA circuit. Moreover, new methodologies are proposed to create the initial population, maintain the variations, and eliminate redundant gates. Simulation results endorse the superiority of the proposed method.Young Faculty Research Fellowship (YFRF) of Visvesvaraya Ph.D. scheme [MLA/MUM/GA/ 10(37)]This work is sponsored by the Young Faculty Research Fellowship (YFRF) of Visvesvaraya Ph.D. scheme through the grant number MLA/MUM/GA/ 10(37)B
Analysis of Adaptive Mechanisms in Inhibitory Neurocircuitry Underlying Metabolic Control
No full text[Abstract Not Available
Veri Güdümlü Ağ Yeniden Yapılandırması
No full textGüç şebekeleri, ekosistem, iklim, nöron ağları ve bir hastalığın küresel ölçekte yayılması gibi hayatımızın temel bileşenlerinin bir ortak noktası vardır: karmaşık ağlar üzerinde etkileşen dinamik birimler olarak modellenebilmeleri. Pek çok örnekte, karmaşık sistemlerden elde edilen veriler doğal bir ağ yapısını temsil eder veya sistem özünde ağ yapısında olmasa bile bir ağ gibi modellenebilir. Ağ dinamiğini bilmek, bu karmaşık sistemlerden istenen işlevselliği elde etmek, dolayısıyla gelecekteki durumunu tahmin etmek ve kontrol etmek için çok önemlidir. Örneğin beynimizdeki nöron ağlarının etkileşimindeki normal olmayan değişiklikler patolojik durumlara yol açabileceğinden, bu ağlar insan sağlığı için önemli bir dinamik ağ sınıfını oluştururlar. Epilepsi krizleri nöron ağlarının etkileşimlerinin değişmesi ile beliren ağ senkronizasyonu ile ilişkilidir. Bu tip istenmeyen nöronal senkronizasyona kritik geçişleri önceden tahmin etmek ve erken uyarı sinyallerini tespit edecek teknolojileri icat etmek hayati önem taşır. Nöronların iç dinamikleri ve aralarındaki bağlantı şemasından oluşan nöron ağlarında, senkronizasyona kritik geçiş doğrudan belirlenemez. Bu nedenle amaç, parametre değişikliklerinden kaynaklanan kritik geçişleri tahmin etmek için ağ dinamiğinin denklemini her bir düğümden elde edilen ölçüm verisinden öğrenmektir. Bu doktora çalışması, dinamik sistemler teorisinden ortalama alan yaklaşımlarını istatistiksel öğrenme araçlarıyla birleştirerek zaman serisi gözlemlerinden dinamik bir ağı yeniden yapılandırma yaklaşımı sunar. Önerilen veri güdümlü yeniden yapılandırma yaklaşımı iki temel varsayımda bulunur: sinirbilimsel bir model ve tüm düğümlerin verisine tam erişim. Buna karşılık, düğümlerin iç dinamikleri, aralarındaki bağlantı yapısı ve etkileşim şekli bilinmez. Sinirbilimsel koşullar, nöronların iç dinamiğinin kaotik davranış göstermesi, zayıf bir etkileşimde olmaları ve ölçekten bağımsız bir ağ ile temsil edilmeleri olarak sıralanır. Metodolojimiz tüm bilinmeyenleri nispeten kısa zaman serileri kullanarak doğru bir şekilde öğrenir ve ağ boyutundan bağımsızdır. Kısa süreli ölçüm ve büyük ağlarda başarı gerçek dünya örneklerine yaklaşabilmemiz için önemli iki kısıt olarak ele alınmıştır. Sonuç olarak, veriden öğrenilmiş ağ modeli tüm parametreleri kontrol edebilmemize ve karmaşık ağın kolektif davranışını tahmin edebilmemize izin verir.Dynamical networks, including power grids, food webs, climate networks, and neuron networks, described by dynamical units oscillating on complex networks, are fundamental components of our everyday lives. The ability to regulate network dynamics is crucial for predicting, thus, controlling these systems' behavior to acquire the desired functionality. Neuron networks are an important class of dynamical networks for human wellness since the changes in the interaction can lead to undesired pathological situations. For instance, epileptic seizures are associated with emergent neural network synchronization when dynamic network parameters change. Consequently, it is vital to anticipate critical transitions to neuronal synchronization and invent predictive technologies to detect early warning signals to prevent potential tragedies. In the case of neuron network dynamics, consisting of intrinsic neuron function and the coupling scheme between neurons, the critical transitions to synchronization are not directly determinable. Therefore, the governing equation must be recovered from the observations of the nodes for forecasting the critical transitions due to parameter changes. Therefore, the governing equation must be recovered from the observations of the nodes for forecasting the critical transitions due to parameter changes. This PhD thesis develops a dynamical network reconstruction approach from time series observations by integrating mean-field approaches from dynamical systems theory with statistical learning tools. The proposed reconstruction approach assumes a neuroscientific setting and accessibility to all nodes' data while the local dynamics of the nodes, the coupling function between them and the interaction structure are unknown. Our methodology accurately identifies them using relatively short time series and is independent of the network size, which is vital since it is generally impossible to have long real-world observations, and real networks are large. Finally, the reconstructed model allows us to predict the emergent collective behavior of dynamical networks considering parameter change, which is crucial to avoid undesired behaviors for real-world applications such as epilepsy seizures