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Eğitimsel Veri Madenciliği: Öğrencilerin Performansını Tahmin Etmek İçin Ağaç Tabanlı Bir Modelin İnşası
No full textEducational data mining is a research field that probes undercover patterns in educational data. In this paper, machine learning algorithms have been applied to the dataset that consists of major features so as to predict students' final grade performances. Thus, the most significant features and the highest-performance machine learning algorithm have been also tried to be detected. To this end, univariate feature selection, tree-based feature selection, and L1-based feature selection methods have been used for the feature selection process. Classification and regression trees, k-nearest neighbors, naive Bayes, random forest, and support vector machines have been employed to build the learning models. The L1-based feature selection and classification and regression trees have delivered the best performance for the feature selection and the model creation processes, respectively. The experimental results demonstrate that the proposed model reached a classification accuracy of 0.7700 and an F1-score of 0.7888 on average. The L1-based feature selection method has selected only 4 features: these are scholarship type, total salary, transportation to the university, and cumulative grade point average in the last semester. In consequence, there exist lots of indicators that impact students' academic successes, the success or failure that emerges after the measurement process can be estimated by regarding these features in advance. Such a task will enable the relationship mechanism between the educational inputs and outputs to be understandable and eliminate shortcomings concerning the education process.Eğitimsel veri madenciliği, eğitim verilerindeki gizli örüntüleri keşfeden bir araştırma alanıdır. Bu çalışmada öğrencilerin final not performanslarını tahmin etmek amacıyla en temel özelliklerden oluşan bir veri setine makine öğrenmesi algoritmaları uygulanmıştır. Böylece en önemli özellikler ve en yüksek performanslı makine öğrenmesi algoritması da tespit edilmeye çalışılmıştır. Bu amaçla özellik seçim sürecinde tek değişkenli özellik seçimi, ağaç tabanlı özellik seçimi ve L1 tabanlı özellik seçimi yöntemleri kullanılmıştır. Öğrenme modellerini oluşturmak için sınıflandırma ve regresyon ağaçları, k-en yakın komşular, naive Bayes, rastgele orman ve destek vektör makineleri kullanılmıştır. L1 tabanlı özellik seçimi ve sınıflandırma ve regresyon ağaçları, sırasıyla özellik seçimi ve model oluşturma süreçlerinde en iyi performansı sağlamıştır. Deneysel sonuçlar, önerilen modelin ortalama 0,7700 sınıflandırma doğruluğuna ve 0,7888 F1 puanına ulaştığını göstermektedir. L1 tabanlı özellik seçme yönteminde yalnızca 4 özellik seçilmiştir: bunlar burs türü, toplam maaş, üniversiteye ulaşım ve son yarıyıldaki genel not ortalamasıdır. Sonuç olarak öğrencilerin akademik başarılarını etkileyen pek çok gösterge mevcut olup, ölçme süreci sonrasında ortaya çıkan başarı ya da başarısızlık, bu özellikler dikkate alınarak önceden tahmin edilebilmektedir. Böyle bir görev, eğitimsel girdi ve çıktılar arasındaki ilişki mekanizmasının anlaşılmasını sağlayacak ve eğitim sürecine ilişkin eksiklikleri ortadan kaldıracaktır
ON LACUNARY AI-STATISTICAL CONVERGENCE OF FUZZY TRIPLE SEQUENCES OF ORDER ?
No full textIn this study, we propose the concepts of f-lacunary AI-statistical convergence of order ? and strongly f-lacunary AI-summability of order ? for triple sequences of fuzzy numbers. Additionally, we explore fundamental connections between these convergence notions. As a practical application, we apply this newly defined convergence to establish a fuzzy Korovkin-type approximation theorem concerning triple sequences of fuzzy positive linear operators. To highlight the effectiveness of our result, we provide an example that demonstrates the superiority of the established theorem over its classical counterpart. © 2025 Elsevier B.V., All rights reserved
Effects of High-Dose Boric Acid on Hormonal Status, Oxidative Stress, and DNA Damage in Female Rats During the Menstrual Cycles
No full textBoron, found in nature in the form of compounds, has beneficial effects on the organism. However, information about the toxic effects of boron or its compounds is limited. This study aims to assess the effects of high dose boric acid on hormonal balance, oxidative stress, and DNA damage in female rats during the menstrual cycle. A total of 56 female Wistar albino rats were randomly allocated into two groups of equal size: a control group and a boric acid group (350 mg/kg, i.p.). These groups were subdivided into four subgroups based on the menstrual cycle phases (estrus, proestrus, diestrus, and metestrus), with seven rats in each subgroup. At the end of the 14-day experimental phase, biochemical, hormonal, and oxidative stress parameters and DNA damage on blood and tissue (liver, ovary, and kidney) samples were also studied. Additionally, histopathological examinations were performed on the liver, ovary, and kidney. High-dose boric acid did not affect biochemical parameters, including glucose, cholesterol, triglyceride, high-density lipoprotein, and low-density lipoprotein levels. However, it increased serum follicle-stimulating hormone levels, while luteinizing hormone, progesterone, and estrogen levels decreased. Furthermore, malondialdehyde levels were found to be elevated in blood, liver, and kidneys, whereas glutathione levels, as well as superoxide dismutase and catalase enzyme activities, were reduced. Although high-dose boric acid did not induce DNA damage, it caused mild tissue damage in the liver, ovary, and kidneys. These findings indicate that a high dose of boric acid induces oxidative stress and alters hormonal balance in female rats
Knowledge development of empowerment in nursing research: A bibliometric analysis using reference publication year spectroscopy
No full textAimThis bibliometric study aims to provide a comprehensive analysis of the history of empowerment in nursing using Reference Publication Year Spectroscopy (RPYS).MethodsDescriptive and bibliometric analyses were conducted. The study is based on 762 publications from 1983 to 2024, with 14,582 cited references in the Web of Science. The reference publication period was divided into three sub-periods, with a total of 17 peaks identified: four from the first period (earliest to 1980), six from the second (1981-2000), and seven from the last period (2001-2024). Data analysis was performed using RPYS with the CRExplorer.ResultsTen significant historical root publications, dating back to 1859, were identified, focusing on leadership, professionalism, social psychology, and philosophy of education. The earliest roots are linked to Florence Nightingale. In the recent period, there was a greater number of nursing-based root publications cited.ConclusionThis study is the first to identify the origins of influential nursing empowerment publications using RPYS. The earliest publications referenced in nursing empowerment literature originate from nursing. The RPYS proved to be a valuable approach for analyzing the historical roots of knowledge in nursing empowerment
The importance of transparency in preventing deforesta-tion and forest degradation: a comparison of discourse and facts in Türkiye
No full textThe forest area in T & uuml;rkiye has increased from 21.2 million hectares in 2005 to 23.4 million hectares in 2023. Citing this and other official forest data, politicians and bureaucrats have been claiming that T & uuml;rkiye is among the most successful countries in the world in forestry. However, these claims are misguiding discourses that aim to conceal from the public the true extent of deforestation and forest degradation in the country as, when scientific and official findings are compared with these discourses, it becomes evident that there are large discrepancies between discourses and facts. The government has prioritized economic benefits over protecting forest ecosystems. Public information about forestry is therefore manipulated which in turn prevents effective solutions to forest problems. This is considered to be a type of greenwashing. This study discussed how the discourse of politicians and forest administration in T & uuml;rkiye overlaps with current forestry work, and the importance of transparency in preventing deforestation and forest degradation. For the sake of transparency, a system should be established to verify the accuracy of information in reports submitted by countries to the FAO and similar organizations, and sanctions should be imposed on countries that provide misleading information
The importance of transparency in preventing deforestation and forest degradation: a comparison of discourse and facts in Türkiye
No full textThe forest area in Türkiye has increased from 21.2 million hectares in 2005 to 23.4 million hectares in 2023. Citing this and other official forest data, politicians and bureaucrats have been claiming that Türkiye is among the most successful countries in the world in forestry. However, these claims are misguiding discourses that aim to conceal from the public the true extent of deforestation and forest degradation in the country as, when scientific and official findings are compared with these discourses, it becomes evident that there are large discrepancies between discourses and facts. The government has prioritized economic benefits over protecting forest ecosystems. Public information about forestry is therefore manipulated which in turn prevents effective solutions to forest problems. This is considered to be a type of greenwashing. This study discussed how the discourse of politicians and forest administration in Türkiye overlaps with current forestry work, and the importance of transparency in preventing deforestation and forest degradation. For the sake of transparency, a system should be established to verify the accuracy of information in reports submitted by countries to the FAO and similar organizations, and sanctions should be imposed on countries that provide misleading information. © 2025 Elsevier B.V., All rights reserved
Impacts of Si extension on the singlet states of phenylethynyl anthracene
No full textThis study reports the synthesis, photophysical, and electrochemical characterization of a new silyl-extended fluorescent emitter in comparison to commercial BPEA with similar pi-conjugation structure. The structure of 9,10-Bis(TPhSi)A-Ant was anticipated to reduce aggregation tendency while also extending pi-conjugation. The singlet state behaviors of 9,10-Bis(TPhSi)A-Ant and BPEA in solution and film were investigated by photluminescence (PL), emission lifetime (ns TCSPC), fluorescence quantum yield (PLQY), and time resolved-PL spectroscopy. Compared to BPEA the addition of triphenylsilane was found to shift the fluorescence peak from 475 to 484 nm, and reduce the Stokes shift from 552.7 cm-1 to 303.2 cm-1. While both molecules exhibited almost 100 % PLQY in solution, both were found to suffer similar levels of concentration-induced emission quenching in films. 9,10-Bis(TPhSi)A-Ant despite its steric Si groups had lower film PLQY - likely due to increased reabsorption - while time-resolved PL spectroscopy confirmed a lower aggregation tendency. These results demonstrate both the steric and electronic effects of silyl extension, with relevance for the development of new photonic materials.Scientific and Technological Research Council of Turkiye (TUBITAK) [123F247]The financial support by the Scientific and Technological Research Council of Turkiye (TUBITAK, Project No: 123F247) is gratefully acknowledged. EA also thanks Prof. Dr. Canan Varlikli, Dr. Andrew Danos, Prof. Andrew P. Monkman, Dr. Burak Gultekin and Basak Turgut for their support
Effectiveness in the furniture industry: artificial intelligence, big data and sustainable design
No full textPurposeThis research aims to investigate the interaction between artificial intelligence (AI) capability, big data capabilities, sustainability design and organizational effectiveness in the context of the furniture industry. It aims to explore how investments in AI and big data technologies can spur sustainability-focused innovation and ultimately increase corporate performance.Design/methodology/approachBased on data collected from businesses operating in the furniture industry, this research uses a quantitative approach to analyze the relationships between independent variables (AI capability and big data features), mediating variable (sustainability design) and dependent variable (organizational effectiveness). The structural equation modeling (SEM) technique was used to test the proposed theoretical model and hypotheses. The SmartPLS program was used for analysis.FindingsAnalysis results show a significant positive relationship between AI capability, big data capabilities, sustainability design and organizational effectiveness in the furniture industry. Moreover, sustainability design demonstrates its important role in translating technological advances into tangible performance results by mediating the relationship between AI capability, big data capabilities and organizational effectiveness.Research limitations/implicationsAlthough this research contributes valuable insights, it also has limitations. It would not be appropriate to make a general assessment of the generalizability of the findings due to the focus on the furniture industry and the fact that the data of the research were collected from furniture-producing companies in Istanbul. Future research could explore additional industries and incorporate qualitative methods to provide a deeper understanding of the underlying mechanisms driving the observed relationships.Practical implicationsThe findings offer valuable insights to industry practitioners seeking to leverage the potential of AI and big data technologies to increase sustainable organizational effectiveness. Practical implications include strategic recommendations for integrating sustainability principles into organizational strategies, leveraging data-driven decision-making processes and encouraging innovation through technological investments.Originality/valueThe originality of this research lies in its comprehensive examination of the intertwined dynamics between AI capability, big data capabilities, sustainability design and organizational effectiveness, especially in the context of the furniture industry. By combining knowledge from multiple disciplines, this research offers a new perspective on the strategic implications of technological innovation for sustainable business practices
New benzimidazole-indole-amide derivatives as potent ?-glucosidase and acetylcholinesterase inhibitors
No full textNew derivatives 6a-m with benzimidazole-indole-amide scaffold were developed, synthesized, and assessed for potential inhibitory effects on alpha-glucosidase and acetylcholinesterase (AChE). These compounds were synthesized by various amine derivatives. With the exception of two compounds, the alpha-glucosidase inhibitory activities of the title derivatives were more than that of the positive control acarbose. Moreover, the anti-AChE activity of these compounds, with the exception of one compound, was better than that of tacrine (standard inhibitor). The most potent compound against alpha-glucosidase was 3-methylphenyl derivative 6i and the most potent compound against AChE was 3,4-dimethoxyphenethyl derivative 6m. All the synthesized compounds were placed in the active sites of alpha-glucosidase and AChE by in silico docking method and the obtained binding energies were approximately in agreement with the in vitro observed data. Interaction modes of the most potent compounds 6i and 6m demonstrated that these compounds interacted with important residues of their target enzymes. Molecular dynamics simulation was conducted specifically on compound 6i in complex with alpha-glucosidase to obtain deeper insights into the behavior of this molecule. Furthermore, in silico pharmacokinetic and toxicity studies on the most potent compound predicted that these compounds have good profiles in terms of oral absorption and toxicity
