TED University

This study investigates the effects of three different drying methods - sun drying, shade drying and oven drying at 55 °C. Some physical, structural and chemical properties of Ziziphus jujuba Mill. fruit before and after drying are evaluated. Freshly harvested jujube fruits from the Elazığ (Turkey) region are subjected to each drying method, and changes in surface hardness, moisture content and internal structure are analyzed. Shore A hardness measurements indicate a significant increase in fruit firmness over time, with oven-dried samples showing the highest values, followed by sun-dried samples. Fourier transform infrared spectroscopy reveals marked chemical transformations during drying, including reductions in OH stretching bands and shifts in CO and CH regions, reflecting moisture loss and modifications in sugars and polysaccharides. Weight loss calculations determine that approximately 60-65 wt% of initial mass is lost to reach a final moisture content of around 16-18 wt%. These structural and chemical changes influence the fruit's texture, rehydration ability and consumer acceptability. Drying kinetic modeling using two-term models for both oven and sun/shade drying demonstrates excellent fit (R2 = 0.9995), with oven drying showing a higher effective diffusion coefficient (Deff = 4.57 × 10-10 m2 s-1) than sun/shade drying (Deff = 7.96 × 10-11 m2 s-1), indicating more efficient moisture transport. Response surface methodology is employed to optimize drying conditions and evaluate mass loss over time. © 2026 Society of Chemical Industry.

Abstract The growing need for enhanced thermal regulation in both electric and internal combustion engine vehicles has driven significant research efforts toward the development of next-generation heat exchanger technologies. Motivated by the pressing need for thermally consistent and high-fidelity prediction tools in contemporary automobile systems, this study introduces a novel hybrid framework by coupling empirical results from ZnO–water nanofluid experiments with physics-informed neural networks (PINN). This approach is crucial for connecting empirical data with fundamental physical rules for accurate thermal prediction. The experimental investigations were carried out across a range of operating parameters, including nanoparticle volume concentrations from 0 to 0.1%, inlet temperatures between 40 °C and 80 °C, and volume flow rates varying from 5 to 11 LPM. Major results revealed that the highest thermal enhancement was achieved at a ZnO concentration of 0.025%, which led to an increase of up to 213% in the convective heat transfer coefficient and a 214% improvement in the Nusselt number relative to pure water, particularly under low-temperature conditions. Beyond this concentration, performance deteriorated due to agglomeration and increased viscosity. Using the experimental data, both artificial neural network (ANN) and physics-informed neural network (PINN) models were developed to predict key parameters such as the Nusselt number and heat transfer coefficient. The PINN model outperformed the ANN, yielding approximately 7.6% higher R 2 for the heat transfer coefficient (0.9167 vs 0.8354) and 8.7% higher R 2 for Nusselt number predictions (0.8697 vs 0.7901). These findings highlight the importance of creating a high-fidelity digital twin architecture that addresses the ‘black-box’ constraints of traditional AI, offering a comprehensive solution for real-time optimization and monitoring of advanced automobile cooling systems.

Wikipedia serves as the world's largest and most popular online reference encyclopedia, rich in structured knowledge and authoritative citations. Recently, numerous works have leveraged large language models to automatically generate Wikipedia-like articles. However, existing approaches primarily focus on producing singular narrative-type content, overlooking higher information-density structured elements such as timeline and table. To address these limitations, we propose WikiMAG, a multi-agent guided framework for generating structured Wikipedia-like articles. This framework employs a collaborative multi-agent mechanism to orchestrate the creation process, featuring three synergistic core components: Progressive planner first constructs the coarse-grained outline framework and then annotate fine-grained types for outline units, encompassing narrative, timeline, and table formats; Reflective inspector dynamically curates high-quality references via multi-round interactive feedback, thereby enhancing the authority and relevance of citations; Versatile writer integrates fine-grained outline details and high-quality reference information to generate information-rich articles, incorporating the three annotated formats. We evaluate WikiMAG on two public datasets, FreshWiki and WikiGenBen, across outline, writing, and verifiability dimensions. Compared with the best baseline method, our method achieves an average improvement of 6.73 points and 4.39 points in Heading Soft Recall and the METEOR metric (a machine translation and text generation evaluation metric) respectively, and an average increase of 16.84 percentage points in Citation Rate.

Purpose: Within the framework of Acceptance and Commitment Therapy, the study aimed to examine the extent to which psychological flexibility predicted future anxiety among university students and whether core components of psychological flexibility, which are mindfulness, valuing (progress and obstruction), and cognitive defusion, explained additional variance in future anxiety beyond the psychological flexibility. Method: A correlational research design was used in this study. The sample consisted of 336 undergraduate students (213 females, 119 males, and four who did not disclose their gender) enrolled at universities in Ankara, Türkiye, with ages ranging from 18 to 29 years. Hierarchical regression analysis was conducted to test the proposed model. Findings: The results indicated that the final model accounted for 32% of the variance in future anxiety. Individuals with higher levels of psychological flexibility, mindfulness, and cognitive defusion reported lower levels of future anxiety, whereas those with greater value-related obstruction reported higher levels of future anxiety. Progress in values did not significantly predict future anxiety. Conclusion: The findings underscore the critical role of Acceptance and Commitment Therapy in understanding and addressing future anxiety among emerging adults.

The growing use of Large Language Models (LLMs) in healthcare raises important questions about the need for domain-specific training in medical applications. This study presents a detailed evaluation of medical-domain and general-purpose LLMs using three medical datasets (PubMedQA, BioASQ, and WikiDoc), which contain approximately 11,000 question-answer pairs. We evaluated four medical-domain models (Meditron-7B, BioMistral-7B, MedAlpaca-13B, and PMC-LLaMA-13B) against four general-purpose instruction-tuned models (Ministral-8B-Instruct, Gemma 2-9B-it, Vicuna-13B v1.5, and Llama 3-8B-Instruct). Across 182,944 prompts in both zero-shot and few-shot settings, our findings show that general-purpose models consistently outperformed their medical-specific counterparts on all evaluation metrics. Specifically, Ministral-8B-Instruct achieved the highest performance in few-shot settings with a BERTScore of 0.613, SimCSE of 0.764, and semantic similarity of 0.684. These scores were significantly higher than those of the best medical model, BioMistral-7B (0.545, 0.678, and 0.533, respectively). Furthermore, zero-shot performance often matched or surpassed few-shot results, as seen with Llama-3-8B-Instruct achieving a SimCSE score of 0.794. These findings challenge the common assumption that domain-specific pretraining is required for optimal performance in specialized tasks and have major implications for how resources are allocated in healthcare AI development.

This study seeks to analyse bilig’s publications from 2008 to 2024, with specific reference to the role of English in knowledge dissemination in the Turkic social sciences. To that end, the bibliometric indicators included in the study were the annual and citation distributions of articles written in Turkish and English, the national and institutional backgrounds of authors publishing in those languages, the countries with the most citations, and the language of the most cited publications. Employing a bibliometric methodology, the study performed descriptive and citational analyses using Biblioshiny – the web interface of Bibliometrix – and the Web of Science’s data analysis tool. The collected data were visualised and tabulated using various tools. The results indicated that (i) the articles written in Turkish outnumbered those in English, (ii) Turkish-written articles were cited more, (iii) authors from Türkiye and Turkish institutions prevailed, (iv) Türkiye received the highest citations, and (v) the articles with the most citations were published in English. The findings were discussed, implications were drawn, and recommendations were made. The study is expected to contribute to bilig’s broader recognition and higher citability at the international level.

This article re-examines the contemporary debate between moralistic and realist approaches in political theory through a sociological lens. Rather than treating the controversy as a purely methodological disagreement about whether practical judgement should begin from moral principles or from political reality, it argues that the central issue concerns the social organisation of practical reason. Political judgement is formed under specific social conditions. It is construed within institutional roles and incentives, informational infrastructures, professional vocabularies, and relations of authority that privilege some descriptions of reality while marginalising others. On this basis, the article develops a classificatory scheme that orders contemporary realisms by how they understand “the real” to bear on the practical question “What is to be done?” It distinguishes paralyzing realism, in which the real chiefly disables agency and leaves theory largely non-directive; neutral realism, which treats the real primarily as feasibility constraint and tends to underestimate the socially mediated character of political facts; and activist realism, which treats the real as a contested field of interpretation and a resource for improving judgement from within practice. The article then illustrates what this activist orientation looks like in practice through John Dunn’s account of democracy and the conditions of realistic political judgement. The article concludes that a non-metaphysical, non-foundational account of the theory–practice relation is most compatible with this activist orientation and provides the most plausible basis for reconnecting political theory and political practice without relying on an Archimedean standpoint.