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Mathematical Modelling Study Of Infrared Dried Prickly Pear
No full textDrying of food products enhances product stability by preventing microbial spoilage, while also improving the efficiency of transportation, storage, and packaging. Commonly known as prickly pear, Opuntia ficusindica consists of cladodes, flowers, fruits, and seeds. Cladodes are rich in fiber, minerals, flavonoids, phenolic compounds. The fruit, composed of pulp, peel, and seeds, contains glucose, fructose, pectin, and notable bioactive compounds such as ascorbic acid, flavonoids, betalains, and phenolics. Infrared drying offers several advantages over conventional drying methods, including reduced processing time, uniform surface heating, minimized quality degradation, equipment simplicity and compactness, versatility, and substantial energy savings. In this study, the infrared drying kinetics of prickly pear (Opuntia) samples were investigated, and the experimental data were evaluated using established mathematical drying models. Prickly pear fruits were sliced into semi-circular shapes and dried in an infrared dryer at temperatures of 50, 60, and 70 C. The drying behavior was evaluated using a range of commonly applied mathematical models, including Aghbashlo et al., Alibas, Henderson et al., Jena and Das, Lewis, Logarithmic, Midilli and Kucuk, Page, Parabolic, Verma et al., Wang and Singh, Weibull, Two-Term Exponential. The drying process was completed within 240 to 555 minutes. Among the applied mathematical models Jena and Das, Midilli and Kucuk, and Aghbashlo et al. models exhibited the best performance for the 70 °C-dried samples with their high coefficients of determination (R2) values over 0.999 and low chi-square (x2) and root mean square error (RMSE) values. Two-Term Exponential, Midilli and Kucuk, and Aghbashlo et al. models provided the closest agreement with the experimental data of 60 C-dried samples with R2 values greater than 0.9999. For the samples dried at 50 C, the Two-Term Exponential, Weibull, and Aghbashlo et al. models demonstrated the best conformity with the observed drying behavior. These models yielded the highest R2 values and the lowest x2 and RMSE values, with R2 values also found to be greater than 0.9999.</p
Design of a novel pH sensor for flap monitoring and evaluation of its efficacy in an in vivo flap model
No full textAdaptive Speed Control of Climbing Robots Using Modified MRAC: From Simulation to Real-Time Application
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Integrating Google Search Trends with Advanced Predictive Models for Forecasting German Tourist Arrivals to Turkiye: A Decade-Long Analysis
No full textOver recent decades, tourism has played an increasingly vital role in the socioeconomic development of various regions, including Turkiye. This study focuses on forecasting the number of German tourists entering Turkiye every month over the past decade. The dataset, obtained from the Ministry of Culture and Tourism, includes the monthly number of German passport holders entering Turkiye, with data spanning ten years from January 2014 to November 2024 that incorporates the COVID-19 pandemic. Additionally, correlations between these numbers and the popularity of selected Google search keywords from Germany are analyzed. Predictive models are developed by utilizing the search popularity of these keywords alongside monthly tourist entry data. The models employed include Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Seasonal Autoregressive Integrated Moving Average Exogenous (SARIMAX), Support Vector Regression (SVR), and PROPHET. The inclusion of pandemic data offers a comprehensive perspective on the impact of global disruptions on travel trends. Initial results highlight the effectiveness of these models in capturing complex patterns and seasonality in the data, paving the way for more accurate and dynamic forecasting methods. This study contributes to the development of a practical tool for tourism analytics and emphasizes the potential of integrating search engine data into predictive models for enhanced decision-making in the tourism sector
Sustainable Lubrication Strategies in Eco-friendly Machining of AISI 4140 Steel: Performance and Environmental Impact Analysis Using Machine Learning
No full textThis study encompasses extensive analysis for different aspects of industrially important 4140 steel during dry and minimum quantity lubrication-assisted turning operations. Surface roughness, tool wear, cutting forces, chip morphology and cutting temperatures were considered as technological parameters while carbon emissions and energy consumption were handled as the ecological parameters. The environmental analysis indicates that increased cutting speeds and greater depths of cut result in a substantial rise in energy consumption, with levels reaching up to 50% higher than those seen in alternative configurations. In the case of high cutting speeds, carbon emissions can potentially increase by as much as 60%. Conversely, at low cutting speeds and parameters, energy consumption emissions decrease by 42%. In terms of carbon emissions, dry machining offers a distinct advantage over MQL. Machine learning (decision tree model) is utilized to model the effects of input and output parameters to determine the optimum values of these parameters. It has provided the relationship between the dependent variables and the independent variables for sustainable machining of an industrially important material. The decision tree ML model for cutting force results showed that RMSE values are 8.7 and 11.89 for dry and MQL environments, while it was 6.83 and 1.15 for cutting temperature in dry and MQL environments, respectively. Finally, RMSE values of surface roughness are 0.19 and 0.16 for dry and MQL environments, respectively
Geotechnical reconnaissance of the February 6, 2023, Pazarcık Mw = 7.7 and Elbistan Mw = 7.6, Kahramanmaraş-Türkiye earthquakes
No full textOn February 6, 2023, a moment magnitude (Mw) of 7.7 earthquake occurred on the East Anatolian Fault Zone at Pazarcık-Kahramanmaraş-Türkiye. Following this earthquake, approximately 9 hours later, another earthquake with a magnitude of Mw = 7.6, whose epicenter was in the Elbistan district of the same city, occurred on the Çardak Fault, which branches from the East Anatolian Fault. These two earthquakes, which occurred at a distance of approximately 90 km from each other, were followed by numerous aftershocks. The earthquakes were effective in an area of approximately 400 km long and 100-200 km wide in the southwest and northeast directions. In this paper, the failures and damages observed in buildings, building foundations, retaining structures, highways, railways, slopes, and tunnels were evaluated for four cities located in these two fault rupture regions. The investigated sites were chosen by considering the characteristics of fault lines, geology, and structural systems. In this way, the observations were associated with the site effects based on the soil properties and distance to the fault of the region
Novel Adsorbents for Canola Oil Physical Refining: Mesoporous Calcium and Magnesium Silica Aerogel
No full textThe refining process of edible oil is essential for extending its shelf life by removing contaminants that negatively affect quality and consumer acceptance. This study evaluates the effectiveness of calcium silica aerogel, magnesium silica aerogel, a combination of both, bentonite, Trisyl, and various aerogel combinations in a single-stage physical refining process of crude canola oil. Calcium and magnesium silica aerogels were synthesized via the precipitation method from water glass and subsequently dried under ambient pressure using an air dryer. The synthesized aerogels were characterized using scanning electron microscopy, Fourier-transform infrared spectroscopy, N2 adsorption-desorption analysis and bulk density measurements. The surface areas of the calcium and magnesium aerogels were found to be 45.67 m2/g and 616.46 m2/g, with densities of 0.15 g/cm3 and 0.18 g/cm3, respectively. The adsorption capacities of these adsorbents for free fatty acids, peroxides, and color pigments in crude canola oil were examined. The aerogels reduced free fatty acid levels by 14% to 47%, and their use in the refining process produced oil with a lighter color. Notably, the most effective peroxide removal, reaching 53.4%, was achieved with a 50:50 combination of aerogels. These findings demonstrate the potential of calcium and magnesium silica aerogels as effective adsorbents for removing impurities from edible oils
Türkiye'de Tütün Piyasasının Yapay Sinir Ağları ile Tahmin Edilmesi
No full textBu çalışma, Türkiye'deki tütün politikalarının gelecekteki dinamiklerini yapay sinir ağları kullanarak öngörmeyi amaçlamaktadır. 1961–2022 yılları arasındaki tütün üretimi, hasat alanı ve verim verileri, bu değişkenler arasındaki karmaşık ilişkileri anlamak amacıyla kapsamlı bir şekilde analiz edilmiştir. Sonuçlar, 2023 ile 2027 yılları arasında tütün üretimi ve hasat alanının kademeli olarak azalmasının beklendiğini, buna karşılık verimin önemli ölçüde artacağını göstermektedir. Bu eğilim, teknolojik gelişmelerin ve etkili tarım politikalarının olumlu etkilerini yansıtmaktadır. Zaman serisi tahminleri derin dendritik yapay sinir ağları (DeepDenT) yazılımı kullanılarak gerçekleştirilmiştir. Bu tahminler, tütün tarımının sürdürülebilirliği ve stratejik planlaması açısından değerli bilgiler sunmaktadır. Tahminlerin yanı sıra, çalışmada değişkenler arasındaki ilişkileri değerlendirmek amacıyla doğrusal Granger nedensellik testi uygulanmıştır. Ancak, istatistiksel olarak anlamlı bir nedensellik bulunamamıştır; bu durum, tütün üretiminin karmaşık ve doğrusal olmayan dinamiklerden etkilendiğini göstermektedir. Bu da geleneksel doğrusal modellerin üretim sürecinin gerçek doğasını yeterince yansıtamayabileceğini ima etmektedir. Genel olarak, bu çalışma tütün tarımındaki uzun vadeli eğilimlere dair kritik bulgular sunmakta ve politika geliştirme süreçlerine katkı sağlamaktadır. Üreticilerin bilinçli ve stratejik kararlar almalarına destek olmakta ve sektörün sürdürülebilirliği ile ekonomik istikrarına ilişkin anlayışı derinleştirmektedir. Böylece, veri temelli yaklaşımlar ve ileri düzey modelleme teknikleriyle üretim süreçlerinin optimize edilmesine yönelik yeni bir bakış açısı sunmaktadır.This study aims to forecast the future dynamics of tobacco policies in Türkiye using artificial neural networks. Tobacco production, area harvested, and yield data from 1961 to 2022 were comprehensively analyzed to understand the complex relationships among these variables. The results indicate that, while tobacco production and harvested area are expected to decline gradually between 2023 and 2027, yield will significantly increase. This trend reflects the positive impact of technological advancements and effective agricultural policies. Time series forecasting was conducted using DeepDenT software. These forecasts provide valuable insights for the sustainability and strategic planning of tobacco farming. In addition to forecasting, the study applied the linear Granger causality test to assess relationships between the variables. However, no statistically significant causality was found, suggesting that tobacco production is influenced by complex, non-linear dynamics. This implies that conventional linear models may be insufficient to capture the true nature of the production process. Overall, the study offers critical insights into long-term trends in tobacco agriculture and contributes to policy development. It supports producers in making informed, strategic decisions and enhances understanding of the sector’s sustainability and economic stability. Thus, the study offers a new perspective on optimizing production through data-driven approaches and advanced modeling