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Financial reform and mortgage lending by systemically important financial institutions
No full textWe use proprietary transaction-level data from Intercontinental Exchange to examine how the Dodd–Frank Wall Street Reform and Consumer Protection Act (DFA) affected mortgage risk-taking by the six largest US financial institutions (SIFIs). Following DFA, these banks originated fewer mortgages, with lower average loan-to-value (LTV) ratios, and fewer high-LTV mortgages compared to other lenders. Our findings suggest that DFA curtailed risk taking among SIFIs but coincided with increased high-LTV lending by non-SIFIs, indicating a redistribution of risk to less regulated institutions. We provide the first transaction-level evidence linking DFA to measurable shifts in mortgage risk.Published versio
How has the interplay between state-led capitalism/Sheikhdom and private sector initiatives shaped Dubai into a global hub?
No full textEconomic
The Relationships Between Partial Tidal Disruption Events and Stellar Evolution in Main Sequence Stars
No full textPhysic
Effectiveness of Drones in Monitoring Plants of Which Goats are Tasked with Grazing
No full textEnvironmental Science and Sustainabilit
Electrochemistry-based assay for monitoring of adherent macrophages and foam cells on ab-cd36 modified electrospun nanofibers
No full textAtherosclerosis, a major cause of heart attacks, is a chronic inflammatory disease marked by the accumulation of lipid-laden foam cells and immune cells in the arterial wall. Here, for the first time, the adhesions of macrophages and foam cells toward the developed polystyrene/graphene oxide-3-aminopropyltriethoxysilane/Anti-CD36 (PS@GAPTES@Ab-CD36) electrospun nanofiber (ESNF)-based biofunctional surface was investigated using electrochemical measurements and fluorescence imaging. After the oxidative modification of high-density lipoprotein (HDL) was carried out, macrophage cells were incubated with different concentrations of oxidative HDL (ox-HDL) to determine the most suitable concentration of ox-HDL for obtaining foam cells. Afterward, electrochemical measurements were carried out using PS@GAPTES@Ab-CD36 modified screen-printed carbon electrode (SPCE) in the presence of foam cells and macrophages. The linear range of both cell types was 10-103 cells mL-1. The limit of detection (LOD) was calculated as 15 and 17 cells mL-1 for the foam cells and macrophages, respectively. It was observed that foam cells' adhesion to the PS@GAPTES@Ab-CD36 biofunctional surface was relatively high compared to macrophage cells because of the enhanced CD36 expression on the surface of foam cells. Finally, macrophage and foam cells were seeded on PS@GAPTES@Ab-CD36 and PS@GAPTES (control) ESNFs, and DAPI staining was carried out for fluorescence imaging
Utilizing advanced language models to identify industrial symbiosis opportunities within the circular economy: Capabilities and challenges
No full textPurposeThe aim of this study is to investigate the application of advanced language models, particularly ChatGPT-4, in identifying and utilizing industrial symbiosis opportunities within the circular economy. It examines how the model can aid in promoting sustainable industrial practices by processing data from the MAESTRI project database, which includes various symbiotic relationships, as well as randomly selected waste codes not included in the database. The research involves structured queries related to industrial symbiosis, circular economy, waste codes and potential opportunities. By assessing the model's accuracy in response generation, the study seeks to uncover both the capabilities and limitations of the language model in resource efficiency and waste reduction, emphasizing the need for ongoing refinement and expert oversight.Design/methodology/approachThe study adopts a mixed-methods approach, combining qualitative and quantitative analyses to explore the potential of ChatGPT-4 in identifying industrial symbiosis opportunities. Data from the EU-funded MAESTRI project database, which includes existing symbiotic relationships, as well as randomly selected waste codes not included in the database, are used as the primary sources. The language model is queried with structured questions on industrial symbiosis, circular economy and specific waste codes utilizing the model's advanced functions such as file upload. Responses are evaluated by comparing them with the MAESTRI database and official European Waste Catalogue (EWC) codes.FindingsThe study finds that ChatGPT-4 possesses a solid understanding of fundamental concepts related to industrial symbiosis and the circular economy. However, it encounters challenges in accurately describing EWC codes, with a notable portion of descriptions found to be incorrect. Despite these inaccuracies, the model shows potential in suggesting symbiotic opportunities, although its effectiveness is limited. Interestingly, the study reveals that the model can occasionally identify correct symbiotic relationships even with initial inaccuracies. These findings highlight the need for expert oversight and further development of the language model to improve its utility in complex, regulated fields like industrial symbiosis.Originality/valueThis study's originality lies in its exploration of advanced language models, particularly ChatGPT-4, for identifying industrial symbiosis opportunities within the circular economy framework. Unlike previous research, which primarily focuses on specific sectors and AI's role in general resource efficiency, this study specifically examines the capabilities and limitations of the language model in handling specialized and regulated information, such as EWC codes across various sectors. It employs a novel approach by comparing AI-generated responses with an established symbiosis database, which is comprehensive and spans all sectors rather than being limited to a single industry, as well as with randomly selected waste codes not included in the database. The study contributes to understanding how AI tools can support sustainable industrial practices, emphasizing the importance of refining these models for practical applications in environmental and industrial contexts
Effects of biostimulant priming a on seed germinatıon and seedling quality of carrot seeds
No full textBu çalışmada, havuç tohumlarında çimlenme ve erken fide gelişim döneminde kalite ve performansı arttırmak amacıyla deniz yosunu (DY) ve mikroalg (MA) çözeltileri ile yapılan priming uygulamalarının kullanım olanakları araştırılmıştır. Araştırmada, havuç türüne ait genotipler için ayrı ayrı en uygun priming protokollerinin belirlenmesi amaçlanmıştır. Bu amaçla, denemede “Dragon” ve “HY1” havuç genotiplerine ait tohumlar kullanılmıştır. Priming uygulamaları, farklı konsantrasyonlarda (0, 250, 500, 1000, 1500 ve 2000 ppm) hazırlanan oksijence zenginleştirilmiş DY ve MA çözeltilerinde 16 °C sıcaklıkta ve karanlıkta 1 gün süre ile yapılmıştır. Uygulama yapılmayan tohumlar ise kontrol grubu olarak değerlendirilmiştir. Priming uygulamaları sonrasında tohumlar, başlangıç nem kapsamlarına kadar kurutularak, 20±1 °C sıcaklıkta çimlendirme testlerine alınmışlardır. Havuç tohumlarının priming uygulamalarına olan tepkileri canlılık [Normal çimlenme oranı (NÇO)] ve farklı güç [Ortalama çimlenme süresi (OÇS), Çimlenme indeksi (Çİ), Fide güç indeksi (FGİ) ve fide kuru ağırlıkları] parametreleri doğrultusunda incelenmiştir. Dragon genotipi havuç tohumlarında DY çözeltileri ile yapılan priming uygulamaları sonucunda en iyi sonucu veren uygulamaların 1000 ppm; MA uygulamaları arasında ise 1500 ppm uygulaması olmuştur. HY1 genotipi havuç tohumlarında DY çözeltileri ile yapılan priming uygulamaları sonucunda en iyi sonucu veren uygulama grubunun 500 ppm; MA uygulamaları arasında ise 1000 ppm’lik uygulamalar olduğu tespit edilmiştir. Böylece deniz yosunu ekstraktlarının yanı sıra mikroalg ekstraktlarının da havuç tohumlarında kalite ve performans artışı sağlaması bakımından kullanılabileceği ortaya konmuştur. Çalışmadan elde edilen sonuçlar tohum endüstrilerine önerilebilir niteliktedir.This study examined the effectiveness of priming treatments using seaweed (SW) and microalgae (MA) solutions to improve seed quality and early seedling development in carrot (Daucus carota L.) seeds. The aim was to identify optimal priming protocols for two carrot genotypes: “Dragon” and “HY1”. Seeds were soaked in oxygen-enriched SW and MA solutions at concentrations of 0, 250, 500, 1000, 1500, and 2000 ppm for 24 hours at 16 °C in darkness. Untreated seeds served as the control group. After priming, seeds were dried to their original moisture content and tested for germination at 20 ± 1 °C. Seed performance was assessed through viability [(Normal Germination Rate (NGR)] and vigor indicators including Mean Germination Time (MGT), Germination Index (GI), Seedling Vigor Index (SVI), and seedling dry weight. Dragon showed the highest improvement with SW priming at 1000 ppm, and MA priming at 1500 ppm. HY1 responded best to SW at 500 ppm and MA at 1000 ppm. The results demonstrate that both seaweed and microalgae extracts are effective in enhancing carrot seed quality and vigor. These findings suggest that such priming treatments can be beneficial for commercial seed production and are recommended for use in the seed industry