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Production of sorbet with persimmon using green pea aquafaba: physicochemical characterization and bioaccessibility of bioactive compounds
No full textThe purpose of this study was to valorize green pea cooking water (aquafaba) as a foaming agent in sorbet samples using with persimmon. Significant amount (22.34% +/- 1.22) of protein was determined in GPA (green pea aquafaba) at 10 Brix as a dry basis. GPA was added to the homogeneous puree mixture in varying concentrations (2.5, - 5, - 7.5, - 10 and 15%) and sorbet was produced in ice cream machine. The results showed that protein and dietary fiber content increased with the increasing of GPA. While the L value increased with increasing of GPA, the overrun improved from 8 to 51%. According to the experimental data, the Herschel-Bulkley model was fitted to samples, and the flow behavior index values varied between 0.29 and 0.37 (R-2 > 0.97). Total phenolic content (TPC) and antioxidant activity (CUPRAC and DPPH) of the resulting sorbet samples were also determined. Moreover, bioaccessibility of phenolics and antioxidant activities were also determined using in vitro digestion. All of the bioactive parameters are positively affected at sorbet samples. Using GPA as a foaming agent improved the overrun as well as bioactive characteristics of the sorbets. It can be said that 10% GPA gives better results, especially when considering overrun and sensory evaluation.Gaziantep University [BIDEB-2211/A]; TUBITAKThis work was funded by TUBITAK (BIDEB-2211/A) which was support in terms of educational period
Functionalized p-cymene and pyrazine derivatives: Physicochemical, ADMT, drug-likeness, and DFT studies
No full textThe p-cymene and pyrazine derivatives functionalized with the hydroxy and methoxy group(s) were under the focus to explore the electronic structural properties, which would play a critical role in the biochemical reactivity features via performing systematic computational analyses. The DFT computations of the data set were performed by B3LYP/6-311 G* * level to predict the structural and electronic properties as well as the physicochemical values. The physicochemical properties such as lipophilicity and water solubility features were determined because these values should be in balance with each other in early-stage-drug-design research. The averaged lipophilicity of the p-cymene and pyrazine derivatives were calculated as CYM3 (2.39) CYM4 (10.2)> CYM1 (7.40)>CYM2 (5.16)> CYM (3.12) and PYZ (512)> PYZ1 (170)> PYZ3 (166)> PYZ2 (118)> PYZ4 (77.3), respectively. The ADMT properties of the data set were dealt with in detail to estimate the structural advantage or disadvantage because the possible side effects on human-health and the environment have to be considered in designing the novel agent in addition to the possible potencies. All compounds would be promising agents in terms of the Caco-2 and MDCK penetration and Pgp-inhibition potencies. According to the IGC(50), LC50FM, and LC50DM results, the p-cymene compounds could have lower (or no) risk than the glyphosate and pyrazine derivatives like being for BCF scores. The FMO analyses were performed to estimate the possible reactive region for nucleophilic or electrophilic attacks.Scientific Research Projects Department of Sivas Cumhuriyet University [EGT-2023-098]All calculations have been carried out at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TR-Grid e -Infrastructure). The author thanks to Scientific Research Projects Department of Sivas Cumhuriyet University (Project No: EGT-2023-098)
Biological and in silico studies of methyl 2-(2-methoxy-2oxoethyl)-4-methylfuran-3-carboxylate as a promising antimicrobial agent
No full textHerein, we report the biological and in silico investigations of synthesized furan derivative as a promised antimicrobial agent. The biological activity of synthesized targeted compound was investigated against opportunistic gram-positive (Bacillus mesentericus, B. subtilis and Staphylococcus aureus) and gram- negative (Acinetobacter baumannii, Escherichia coli, Klebsiella pneumoniae and Pseudomonas aeruginosa) bacteria, as well as yeast of genus Candida (C. albicans, C. guillermondii and C. tropicalis). The studied substance inhibited the growth of all bacteria and fungi at concentrations of 0.3-0.05%, whereas MIC in relation to the test organisms varied between 62.5 and 15.6 mu g mL showing the lowest value for S. aureus and A. baumannii. The obtained results were also compared with the activity of pristine antibiotics (gentamicin and fluconazole), which revealed the more potent activity of the targeted compound than that of antibiotics. Computational analyses of the studied compound are performed at M06-2X/6-31+G(d,p) level in the water. Molecular docking calculations revealed 2CCG (TMK) and 4FUV (CarO) proteins as target proteins in the case of S. aureus and A. baumannii respectively, whereas p450 cytochrome analyses demonstrated the inhibition of CYP2C9 protein. ADME properties and MM-GBSA analyses showed that the studied compound exhibits better results than pristine antibiotic as in the case of experimental analysis
Pattern-based deep learning natural stone classification design
No full textMermer, doğadan çıkarıldıktan sonra ticari olarak kullanılabilen en eski inşaat malzemelerinden biri olup, estetik ve sağlam yapısı sayesinde iç ve dış cephe kaplamalarında yaygın olarak kullanılmaktadır. Mermer plakalarının kalite sınıflandırması, dayanıklılık, cilalama oranı, renk homojenliği ve doku gibi teknolojik ve görsel parametrelere dayanarak yapılmaktadır. Bu sınıflandırma işlemi genellikle üretim hattının sonunda uzmanlar tarafından görsel olarak gerçekleştirilmekte ve uzmanların subjektif değerlendirmelerine bağlı olarak değişiklik gösterebilmektedir. Bu sınıflandırma işleminin doğruluğunu ve tutarlılığını olumsuz etkilemektedir. Gerçekleştirilen tez çalışmasında bu amaç doğrultusunda, Türkiye'nin Mersin-Erdemli bölgesinden elde edilen bir mermer türüne ait üç farklı desen sınıfının kalite seviyesini sınıflandırmak için özgün bir veriseti oluşturulmuştur. Çalışmada, enerji öznitelik katmanlarıyla atlama bağlantılarına sahip öznitelik katmanları (TCNN), görüntü sınıflandırmada yaygın olarak kullanılan AlexNet, ResNet ve DenseNet mimarilerine eklenerek sırasıyla AlexNet-TCNN, ResNet-TCNN ve DenseNet-TCNN olarak adlandırılan üç yeni model geliştirilmiştir. Geliştirilen modeller, ImageNet veriseti ile önceden eğitilmiş ve elde edilen ağırlıklar mermer sınıflandırma modellerine transfer edilmiştir. Tek tek eğitilen bu üç modelin test çıktılarının ortalamasını hesaplayan topluluk öğrenme tabanlı bir derin öğrenme modeli ile mermer kalitesini belirleyen nihai sonuçlar elde edilmiştir. Tez çalışmasında Kapsam Elektromekanik tarafından hazırlanan bir mermer plaka sınıflandırma makinesine bu derin öğrenme modelleri eklenerek mermer sınıflandırma sürecini daha objektif, tutarlı ve verimli hale getirmek hedeflenmiştir. Sonuçlar, geliştirilen TCNN modellerinin mermer kalitesini değerlendirme ve sınıflandırmada yüksek performans gösterdiğini ortaya koymaktadır.Marble, one of the oldest commercially used construction materials, is widely employed in interior and exterior cladding due to its aesthetic appeal and durable structure. The quality classification of marble slabs is based on technological and visual parameters such as durability, polish rate, color uniformity, and texture. However, this classification process is typically conducted visually by experts at the end of the production line and is subject to variations depending on the subjective evaluations of these experts. This situation negatively impacts the accuracy and consistency of the classification process. In this thesis, an original dataset was created to classify three different pattern quality levels of a type of marble obtained from the Mersin-Erdemli region of Turkey. The study developed three novel models named AlexNet-TCNN, ResNet-TCNN, and DenseNet-TCNN by integrating feature layers with skip connections into commonly used image classification architectures such as AlexNet, ResNet, and DenseNet. These models were pre-trained on the ImageNet dataset, and the obtained weights were transferred to the marble classification models. Final results determining marble quality were achieved through an ensemble learning-based deep learning model that averaged the test outputs of these individually trained models. Within the scope of the thesis, these deep learning models were integrated into a marble slab classification machine developed by Kapsam Electromechanical, aiming to make the marble classification process more objective, consistent, and efficient. The results demonstrate that the developed TCNN models exhibit high performance in assessing and classifying marble quality
Comparison of the bioaccumulation and biosorption of copper ions by Rhizopus delemar and Candida lipolytica
No full textIn this study, the bioaccumulation of Cu(II) ion by Rhizopus delemar and Candida lipolytica was investigated and the bioaccumulation and biosorption of Cu(II) ions by Candida lipolytica compared. The specific growth rate and the maximum microorganism concentration of both microorganisms increased with increasing initial molasses concentration whereas decreased with increasing initial Cu(II) ion concentrations up to 250 mg/L. Accoding to Monod equation, the maximum specific growth rate and saturation constant for C. lipolytica and R. delemar were determined as 0.335 h−1-32.330 g/L and 0.406 h−1-24.182 g/L, respectively. The bioaccumulation efficiencies of R. delemar and C. lipolytica were determined as 74.18% and 67.30% in the presence of 50 mg/L Cu(II), respectively. At pH 4.0, the maximum biosorption rate (4.72 mg/g min) and the biosorbed Cu(II) ion concentration (49.0 mg/L) were obtained for C. lipolytica and these values increased with increasing Cu(II) concentration. Cu(II) biosorption by C. lipolytica fitted well to the Langmuir isotherm model and pseudo-second-order kinetic model and maximum adsorption capacity was found as 114.942 mg/g. The bioaccumulated Cu(II) was found to be 21.5 mg/g whereas the amounts of biosorbed Cu(II) were calculated as 53 mg/g by C. lipolytica at a concentration of 100 mg/L of Cu(II). © 2024 Taylor & Francis Group, LLC
Primarily molecular detection and phylogenetic analyses of spotted fever group Rickettsia species in cats in Türkiye: With new host reports of Rickettsia aeschlimannii, Rickettsia slovaca, and Candidatus Rickettsia barbariae
No full textDomestic cats are companion animals that live with people in their households or outdoors, and strong relationships exist between cats and humans. However, this animal is also a host/reservoir of zoonotic pathogens, including Rickettsia species. In T & uuml;rkiye, cat ownership has increased over the years, but there is a lack of data on the pathogens in cats. In this study, 396 cat blood samples were collected from different parts of T & uuml;rkiye, and these samples were investigated for Rickettsia species with PCR assay. In addition, DNA sequences were performed for species identification and phylogenetic analyses of detected Rickettsia species. 24 out of 396 cat blood samples (6.06 %) were found to be infected with Rickettsia species. The DNA sequence analyses of all PCRpositive samples were done, and Ri. aeschlimannii was identified in 17 samples, Ri. slovaca in four, Candidatus Rickettsia barbariae in two, and Ri. raoultii in one sample. The phylogenetic analyses of obtained DNA from the above-mentioned species were performed. The sequence data belonging to the species were uploaded to the GenBank, and accession numbers for Rickettsia aeschlimannii (PP998242-PP998258), Ri. slovaca (PP998259PP998262), Candidatus Rickettsia barbariae (PP998263-PP998264), and Ri. raoultii (PP998265) were taken. This result provides the first molecular detection of Ri. aeschlimannii, Ri. slovaca, Candidatus Rickettsia barbariae, and Ri. raoultii in T & uuml;rkiye. Moreover, the DNA of Ri. aeschlimannii, Ri. slovaca, and Candidatus Rickettsia barbariae were identified in cat blood samples for the first time in the world, and the cats were a new host for these Rickettsia species. Detailed studies are, however, needed to determine the pathogenicity, biological characteristics, and vectors of these Rickettsia species in this new host
Identification of Industrial Occupational Safety Risks and Selection of Optimum Intervention Strategies: Fuzzy MCDM Approach
No full textOver 1.1 million deaths occur annually from workplace injuries and diseases, with higher risks in developing countries. Occupational safety studies commonly use quantitative or qualitative methods, but these often fail to address uncertainty. This research targets the Libyan Steel Company (LISCO), aiming to analyze safety risks and develop a structured approach to identify optimal risk mitigation strategies. To this end, the Fuzzy Weights by ENvelope and SLOpe (F-WENSLO) method was chosen to determine the weights of three main safety risks and a total of 18 sub-risks belonging to them, and the fuzzy Bonferroni mean aggregation operator is applied to synthesize expert opinions. The Fuzzy Alternative Ranking Technique based on Adaptive Standardized Intervals (F-ARTASI) method was used to identify and rank the most appropriate safety interventions. While the primary risks identified under the main criteria and sub-criteria are occupational diseases and noise-induced diseases, with weights of 0.4737 and 0.1313, respectively, the intervention strategy deemed most effective for enhancing occupational safety is behavioral safety programs, which hold a weight of 11.0341. The sensitivity test of the analysis results reveals that although the criteria weights and the parameters used in the analysis vary under various scenarios, the ranking of the alternatives remains consistent. Since the general ranking of the alternatives is the same in other methods, decision makers will reach similar results no matter which method they use. This shows that a flexible and reliable decision-making approach is adopted in the process of optimizing occupational safety risks. This research emphasizes the critical importance of prioritizing occupational diseases and natural hazards in the formulation of occupational safety strategies and thus aims to contribute to the protection of workers in industrial plants such as LISCO
Bubble energy nanogenerators
No full textDemands for sustainable and efficient energy solutions are increasing globally every day. This has led to significant advances in nanotechnology-based energy harvesting. Bubble Energy Nanogenerators (BuNGs) are one of the latest emerging technologies to convert the kinetic and potential energy of air bubbles in water into electrical energy. This review is based on a comprehensive review of theoretical principles, instability mechanisms, and recent technological developments in bubble-based nanogenerators, with a particular focus on triboelectric nanogenerators (TENGs), piezoelectric nanogenerators (PENGs) and hybrid nanogenerators. The article aims to critically evaluate bubble dynamics and stability by combining fundamental instability models, including Ledinegg, Taylor, and Henry instability theories, to improve the understanding of bubble-induced energy conversion. Additionally, advances in nanomaterial integration, such as using surface-modified electrodes, surface coatings, and hydrophobic nanostructures to optimize energy efficiency, are discussed. According to the literature, it is understood that BuNG designs can achieve high voltage outputs with large bubble sizes, but there are difficulties in controlling energy dissipation, unstable bubble behavior, and charge transfer efficiency. New approaches, pressure-induced bubble collapse, charge separation mechanisms, and modified surfaces for improved performance have been presented as solutions. This work is intended to bridge the gap between fundamental bubble physics and applied nanotechnology and draw a clear roadmap for future research on self-powered energy systems, underwater sensing, and renewable energy harvesting applications
International trade and logistics infrastructure relationship: An Econometric evaluation in Shanghai Pact and One Belt One Road Project countries
No full textKüresel ticaretin büyümesi ve ekonomik entegrasyonun derinleşmesiyle birlikte lojistik sektörünün stratejik önemi daha da artmıştır. Bu çalışmada, uluslararası ticaret ve lojistiğin birbirleriyle olan dinamik ilişkisini Şangay İşbirliği Örgütü ve Bir Kuşak Bir Yol Projesi ülkeleri kapsamında incelenmektedir. Yapılan bu çalışmada gayrisafi sabit sermaye oluşumları ve lojistik performans endeksinin alt kriterlerinden olan altyapı ve zamanlamanın bağımsız değişkenler olarak ayrı ayrı olarak ithalat ve ihracat üzerindeki etkileri incelenmiştir. Çalışmada kullanılan verilerin tamamı Dünya Bankası'ndan elde edilmiştir. Bağımsız değişkelerden altyapı ve zamanlamanın 2007,2010,2012,2014,2016,2018 ve 2022 yılları arasındaki verileri olduğu için diğer verilerin hareketli ortalamaları alınarak Tobit analizi yapılmıştır. Yapılan analizin sonuçlarına göre; Bağımsız değişkelerin ( Gayri safi sabit sermaye oluşumları, ihracat, altyapı ve zamanlama ) ithalat üzerinde anlamlı bir etkiye sahip olduğu gözlemlenmektedir. Aynı zamanda bağımsız değişkenlerden Gayri safi sabit sermaye oluşumları (GSSO) ve ihracatın pozitif yönde etkilediği, altyapı ve zamanlamanın negatif yönde etkilediği görülmektedir. Yapılan ikinci analizde bağımsız değişkenlerin ( Gayri safi sabit sermaye oluşumları, ihracat, altyapı ve zamanlama ) ihracat üzerindeki etkileri incelenmiştir. Yapılan bu analizde bağımsız değişkelerin tümünün ihracat üzerinde anlamlı bir etkiye sahip olduğu gözlemlenmiştir. Yine bağımsız değişkenlerin katsayılarına baktığımızda ithalat, altyapı ve zamanlamanın pozitif GSSO'nun ise negatif şekilde etkilediği görülmüştür. Şangay İşbirliği Örgütü (Şangay Paktı) ve Bir Kuşak Bir Yol (BKBY) projesine dahil ülkeler üzerinde gerçekleştirilen bu çalışmadan elde edilen bulgular, bu ülkeler için önemli politika önerileri sunmaktadır. Sonuç olarak, Şangay Paktı ve BKBY ülkelerindeki dış ticaretin belirleyicileri üzerine yapılan bu analizler, ithalat ve ihracat arasındaki dengelerin karmaşık olduğunu ve ülkelerin lojistik altyapılarını ve doğrudan yabancı yatırım politikalarını yeniden gözden geçirmeleri gerektiğini göstermektedir. Ekonomik büyüme ve kalkınma süreçlerinde, bu ülkelerin dış ticaret yapılarındaki değişimlerin küresel ticaret sistemine entegrasyonu sağlaması açısından kritik önemde olduğu açıktır. Gelecek çalışmalar için, bu tür analizlerin sektörel düzeyde ayrıntılı veri setleri kullanılarak gerçekleştirilmesi faydalı olacaktır. Şangay Paktı ve BKBY ülkelerinde farklı sektörlerin ithalat ve ihracat üzerindeki etkilerini detaylandırmak, politika yapıcılar için daha özelleşmiş ve etkin stratejiler geliştirilmesine imkan tanıyabilir.With the growth of global trade and deepening economic integration, the strategic importance of the logistics sector has increased even more. In this study, the dynamic relationship between international trade and logistics is examined within the scope of the Shanghai Cooperation Organization and the One Belt and Road Project countries. In this study, the effects of infrastructure and timing, which are sub-criteria of gross fixed capital formations and logistics performance index, on imports and exports separately as independent variables were examined. All data used were obtained from the World Bank. Since the independent variables; infrastructure and timing have data between the years 2007, 2010, 2012, 2014, 2016, 2018 and 2022, Tobit analysis was performed by taking the moving averages of other data. According to the results of the analysis; It is observed that the independent variables (gross fixed capital formations, exports, infrastructure and timing) have a significant effect on imports. At the same time, it is seen that GSSO and exports, which are independent variables, affect positively, while infrastructure and timing affect negatively. In the second analysis, the effects of independent variables on exports were examined. In this analysis, it was observed that all independent variables had a significant effect on exports. When we look at the coefficients of the independent variables, it is seen that import, infrastructure and timing have positive effects and GSSO has negative effects
Modelling urban growth in high resolution employing vector cellular automata approach
No full textM2024862 ve 124Y025Bu çalışma, coğrafi veri madenciliği (CVM) ile bütünleşik çalışan, vektör hücresel otomat (V-HO) tabanlı kentsel büyüme simülasyon modeli (KBSM) geliştirmeyi amaçlamaktadır. Coğrafi nesnelerin gerçek geometrilerini daha doğru şekilde temsil eden V-HO modelinin KBSM çalışmalarında kullanımı giderek yaygınlaşmaktadır. Ancak raster tabanlı HO algoritmasına kıyasla, vektör veri yapısının karmaşıklığı ve düzensizliği, V-HO modellerinin uygulanmasını zorlaştırmaktadır. Bu nedenle esnek komşuluk ve hücresel işlerliğin sağlanmasındaki kısıtlılıkları aşmak amacıyla büyüme vektörleri (BV) yöntemi önerilmiştir. Modelde, arazi örtüsü/kullanımı değişimlerini etkileyen mekânsal ve zamansal dinamikler Rastgele Orman (RO) algoritması ile analiz edilmiştir. Çalışma alanı olarak İstanbul’un Sancaktepe ilçesi seçilmiş, parsel seviyesinde arazi örtüsü/kullanımı değişimleri simüle edilerek %86 doğruluk oranı elde edilmiştir. Bulgularımız, vektör veri yapısının esnekliğinden yararlanılarak daha verimli, dinamik, doğru ve yüksek çözünürlükte simülasyonlar oluşturulabileceğini göstermektedir. 2040 yılına ait simülasyon sonuçları, mevcut kentleşme eğilimlerinin devam etmesi durumunda tarım alanlarında %25, orman alanlarında %3 ve açık arazilerde %21 oranında kayıplar yaşanabileceğini ortaya koymaktadır.This paper aims to create a vector cellular automata (V-CA)-based urban growth simulation model (UGSM) integrated with geographic data mining (GDM). V-CA-based models, which more accurately represent the actual geometries of geographic objects, are becoming prevalent in UGSM studies. However, compared to the raster-CA algorithm, the complexity and irregularity of the vector data structure make implementing V-CA models difficult. Therefore, the growth vectors (GV) method suggests overcoming the limitations of flexible neighborhood and cellular operability. The model examines the spatio-temporal dynamics driving land cover/land use changes with the Random Forest (RF) algorithm. Istanbul's Sancaktepe district was selected as the study area, achieving an 86% accuracy rate in simulating land cover/use changes at the parcel level. Our findings demonstrate that vector data structure's flexibility allows more efficient, dynamic, accurate, and high-resolution UGSMs. Simulation results for 2040 indicate that if current urbanization trends continue, agricultural areas could lose 25%, forest areas 3%, and open lands 21%.Sivas Cumhuriyet Üniversitesi ve TÜBİTA