Kadirli Uygulamalı Bilimler Fakültesi Dergisi

Heat Transfer Analysis of Circular and Annular Jet Impinging on a Wavy Surface

2025-03-24T11:13:24+03:00

This study numerically analyzes the surface cooling performance of circular and annular jets simultaneously impinging on a wavy target surface.The numerical study was carried out with RNG k-ε turbulance model by the ANSYS Fluent software. The jet-target surface distance was kept constant as H/D=2. The heat transfer on the target surface was calculated with the combinations of circular and annular jets at different Reynolds numbers (4000≤Re≤12000). In addition, the study was compared with the cases where only the circular jet and only the annular jet were active. As a result of the study, the velocity and temperature distributions obtained at different Reynolds numbers were presented. The findings showed that the simultaneous impingement of the circular and annular jets on the target surface provided higher heat transfer compared to only the circular and only the annular jets. For the combination of circular jet Reynolds number Re_in=12000 and annular jet Reynolds number Re_out=4000, it was observed that the heat transfer was improved by 2.49 compared to the circular jet and by 1.53 compared to the annular jet. The results show that the simultaneously impinging circular and annular jet has a important potential in cooling a wavy surface.

A Multi-Criteria Decision Support Model for Supplier Evaluation and Selection in the Die Industry

2025-05-11T22:35:52+03:00

The die manufacturing process is a lengthy and critical phase in ensuring the successful production of the final part in compliance with customer standards and expectations, as well as facilitating a seamless transition to mass production. Supplier selection plays a crucial role in the success of this process. This study examines the activities carried out during the die manufacturing stages, their significance within the process, their impact on production, and the role of suppliers at each stage. Furthermore, it underscores the importance of supplier selection and aims to identify the most suitable suppliers using decision support systems (DSS). In this context, the evaluation process is based on data derived from supplier performance and capacity assessments, which are conducted through a scoring system by the team. Subsequently, a comparative analysis is performed using Multi-Criteria Decision Making (MCDM) methods, leading to the development of a model for establishing a decision support mechanism in supplier selection. Within this framework, factors such as time, cost, and technical requirements in die manufacturing processes are assessed through a holistic approach to enhance supplier selection and improve overall process efficiency.

The Role of Industry 4.0 Components on the Circular Economy: An Empirical Study Based on Regression Analysis

2025-07-05T21:30:35+03:00

This study aims to examine the relationship between Industry 4.0 and circular economy practices through a regression model. The potential of Industry 4.0 to optimize manufacturing processes through advanced technologies and digitalization, when combined with circular economy principles, offers significant opportunities to enhance resource efficiency and reduce waste. The study employs a quantitative regression model to analyze how the applications of Industry 4.0 are integrated with circular economy principles and the impact of this integration on economic, environmental, and operational outcomes. In the model developed for the research, circular economy performance is defined as the dependent variable, while the usage of IoT, robotic automation, and digital manufacturing are considered as independent variables. According to the obtained regression model, digital manufacturing emerges as the most effective variable in increasing recycling rates, with this effect being both statistically significant and positively correlated. The results highlight the role of Industry 4.0 technologies in achieving the objectives of circular economy, potentially providing a roadmap for the design of sustainable production systems.

Convergence and Data Dependence for Fractional Integro-Differential Equations with Picard’s Three-Step Iteration Algorithm

2025-07-30T21:09:55+03:00

Fractional calculus, which involves derivatives and integrals of non-integer order, is widely used in modeling real-world problems in science and engineering. This study focuses on solving fractional integro-differential equations using Picard's three-step iteration algorithm. We apply the Picard's three-step iteration algorithm to approximate solutions and show that it converges strongly. We also investigate the data dependence of the solution. Therefore, we present new results for approximating the solution of the Riemann-Liouville fractional integro-differential equation and for data dependence.

Essential Oil Characterization of Paeonia arietina G. Anderson (Paeoniaceae)

2025-07-20T12:41:19+03:00

This study was conducted to determine the essential oil composition of Paeonia arietina G. Anderson. Plant samples were collected from the sloped areas near Dikme Village in Bingöl Province, and the essential oil was obtained through hydrodistillation. The resulting oil was analyzed using GC-MS, revealing 30 compounds comprising 82.70% of the total composition. The major constituent was methyl salicylate (27.34%), followed by palmitic acid (14.73%) and salicylaldehyde (10.0%). Additionally, biologically active compounds such as thymol (3.20%), stearic acid (6.82%), trans-myrtenal (2.20%), and α-methyl cinnamaldehyde (2.30%) were identified. Given the known antimicrobial, antioxidant, and anti-inflammatory properties of these constituents, the essential oil of P. arietina appears to have promising pharmacological and therapeutic potential. This research aims to contribute to the phytotherapeutic evaluation of this species.

Assesment of Graphene Nanoparticles as an Additive in Biodiesel: A Multi-Criteria Decision Making Approach

2025-05-13T19:32:36+03:00

The transition toward sustainable energy sources has accelerated research into biodiesel as a renewable alternative to fossil fuels. Recent studies have examined the use of nanomaterial additives—particularly graphene—to improve the performance and emissions of biodiesel blends. This study evaluates graphene-enhanced biodiesel-diesel blends using performance and emission data collected from the literature. Six Multi-Criteria Decision-Making (MCDM) methods—TOPSIS, COPRAS, MAIRCA, MOORA, MAUT, and MOOSRA—were applied to rank the blends based on various criteria. The final ranking was obtained using the BORDA aggregation method. Results indicate that the B20G90 blend consistently outperformed others, offering a favorable balance between efficiency and emissions. The study demonstrates the applicability of integrated MCDM techniques for systematic, data-driven evaluation of alternative fuel blends.

A Comparison of Ruminant Livestock and Pasture Resources in the Neighborhoods of İpekyolu District, Van Province Using Artificial Intelligence-Based Analysis

2025-07-23T14:24:59+03:00

The sustainability of rural livestock production depends on maintaining a balance between pasture availability and animal density. In this study, ruminant livestock populations and pasture resources across 43 neighborhoods in the İpekyolu district of Van Province were evaluated using Artificial Intelligence (AI) assisted clustering and outlier detection methods. Data retrieved from the national TURKVET system were used to classify the neighborhoods based on variables such as cattle and small ruminant numbers and available pasture area. Three main clusters were identified in the analysis: Cluster 0 included neighborhoods with high numbers of small ruminants and large pasture areas; Cluster 1 consisted of neighborhoods with a balanced distribution of cattle and pasture resources; and Cluster 2 encompassed neighborhoods with high cattle density but limited pasture availability. Additionally, Erçek, Karagündüz and Yalınağaç were identified as outliers in terms of cattle population and were excluded from the clustering analysis. Visual analyses such as boxplots, scatterplots and dendrograms were employed to intuitively reveal structural differences between the neighborhoods. The results indicate that in some neighborhoods, despite a high number of livestock enterprises, pasture resources are limited, while in others, extensive pasture areas exist alongside a relatively low number of enterprises. This reflects the variation in livestock production systems at the neighborhood level ranging from grazing based to more confined, feed based operations. These findings offer important insights for pasture management, subsidy allocation policies and the formulation of rural development strategies

Ruled Surfaces with〖 t〗_α ζ_1^α -Smarandache Base Curve Derived from the Bishop Frame

2025-08-10T18:15:42+03:00

In this study, a class of ruled surfaces defined by unit vector fields whose rulings consist of Smarandache-type curves has been investigated. The base curve of these surfaces is taken as the adjoint curve, which is obtained by integrating a Smarandache curve constructed through the combination of the tangent vector and the first Bishop vector defined in the context of the Bishop frame. Closed-form expressions for the Gauss and mean curvatures of the surfaces have been derived, and their fundamental geometric properties have been analyzed in detail. The study also examines the conditions under which these surfaces become developable, minimal, or singular. Moreover, the geodesic and asymptotic behaviors of certain parametric curves on the surface are evaluated, and the necessary and sufficient conditions for these properties are determined.

The Potential Use of Mineral Contents in Aronia (Aronia melanocarpa) Leaves from Different Harvest Periods in Animal Nutrition

2025-08-04T16:56:15+03:00

This study aimed to determine the mineral composition of Aronia (Aronia melanocarpa) leaves at different phenological stages and evaluate their potential use in animal nutrition. Leaf samples were collected from Aronia plants cultivated in Erzincan (Türkiye) during three different harvest periods and analyzed using ICP-MS to determine the concentrations of 19 elements (Na, K, Ca, Mg, Fe, Zn, Cu, Mn, Co, Ni, Al, Cr, V, Sr, As, Cd, Pb, Hg, Ag). Statistical evaluation was performed to assess differences among harvest times. The results revealed that Aronia leaves are particularly rich in macro minerals (Na, K, Ca, Mg) and micro minerals (Mn, Zn, Cu), indicating their potential as a complementary natural mineral source in ruminant feeding. While concentrations of As, Cd, Pb, and Ag remained well below the maximum tolerable limits established by the European Food Safety Authority (EFSA), mercury (Hg) levels exceeded the recommended safety threshold. This highlights the influence of environmental factors on toxic element accumulation. In conclusion, Aronia leaves exhibit a balanced mineral profile and may serve as a sustainable and safe feed additive in animal nutrition, provided that regular monitoring and quality control are implemented to manage heavy metal risks.

Use of Artificial Intelligence-Supported Learning Management System and Digital Transformation

2025-06-22T13:49:46+03:00

This study examines the transformation of learning management systems (LMS) with artificial intelligence (AI) technologies and the effects of this transformation on educational processes. AI-based applications have the potential to increase student interaction, offer personalized learning paths and improve teaching processes. Supporting LMSs with AI includes technological innovations such as learning analytics, intelligent recommendation systems, automatic assessment tools and chatbots, and offers more efficient teaching environments to faculty members. The use of AI-supported LMS in digitalized educational environments reshapes both teacher and student experiences and provides a more dynamic and interactive structure to teaching-learning processes. This research aims to evaluate these technological developments on the basis of literature and shed light on future applications.

The Use of Hypochlorous Acid in Chemistry: Bibliometric Analysis of Scientific Literature

2025-05-20T11:10:00+03:00

Historically, chemicals such as alcohols, aldehydes, chlorinated compounds and hypochlorous acid have been used to eliminate or inactivate infectious agents. Recognizing the potential of bibliometrics as a robust tool for analyzing scientific literature, we conducted a bibliometric study on research related to the application of hypochlorous acid in infections. This analysis aimed to identify emerging trends, reveal common patterns, examine collaborations and networks, and predict future research directions. Methods: Using the Scopus database, we searched for documents published between 1982 and 2024 with the keywords “hypochlorous acid,” “infection,” and “use.” Data analysis was conducted in R-Studio 2024.09.0 Build 375, employing a machine learning-based bibliometric approach through the bibliometrix R package. Author relevance was assessed by analyzing the number and fractional contribution of publications, while Lotka's law evaluated author productivity. Bradford's law was used to pinpoint the core journals covering the topic. The thematic landscape was classified into niche themes, emerging themes, engine themes, and core themes. Results: A total of 574 documents were identified, with original articles accounting for the majority (74%). The annual growth rate of publications was calculated at 5.08%. Overall, 424 journals published more than one document. Most of the corresponding authors work in the United States. Neutrophils, animals and hydrogen peroxide are emerging themes, while hypochlorous acid, human, and article represent the basic themes. Conclusions/Applications for Practice: The interests of infection specialists have diversified over time and metabolism, reactive oxygen metabolites, anti-infective agents have been added.

Zinc Finger Proteins in Colorectal and Breast Cancer: Diagnostic, Prognostic, and Therapeutic Approaches with a Current Literature Review

2025-08-08T19:50:22+03:00

Cancer is one of the leading causes of death worldwide and poses a significant burden on healthcare systems. ZFPs play crucial roles in regulating gene expression and are involved in key processes in cancer biology. This study examines the molecular functions of ZFPs in colorectal and breast cancers and their potential as therapeutic targets. In colorectal cancer, ZFP36 acts as a tumor suppressor, while ZFPs such as ZNF143, ZNF460, ZNF677, ZNF281, and ZNF384 regulate processes such as cell proliferation, metastasis, epithelial-mesenchymal transition (EMT), and drug resistance. In breast cancer, ZFPs like ZEB1, ZNF703, and ZBTB33 are directly associated with tumor progression, treatment resistance, and poor prognosis. Clinical and experimental data indicate that these proteins are considered crucial biomarkers and targets in diagnostic and therapeutic strategies. The functional and structural diversity of ZFPs deepens the molecular understanding of cancer and provides a strong foundation for personalized medical approaches