Bayburt University

This study aims to reveal the mediating role of Machiavellianism in the effect of abusive supervision on knowledge-hiding behavior and the moderating role of moral identity in the relationship between Machiavellianism and knowledge-hiding behavior. The study adopts the Conservation of Resources (COR) Theory as its theoretical framework. Quantitative data were obtained from 408 healthcare professionals using the survey method. The analyses conducted via SPSS, AMOS, and Process Macro revealed that abusive supervision significantly increases knowledge-hiding behavior (beta = .855) and Machiavellianism (beta = .820). Furthermore, Machiavellianism acts as a mediator in the relationship between abusive supervision and knowledge-hiding. A notable finding is that moral identity acts as a moderator (beta = .134), strengthening the positive effect of Machiavellianism on knowledge-hiding behavior. Considering the analysis results, some suggestions specific to healthcare institutions, healthcare workers, and future studies were presented. Theoretically, this study contributes to the Dark Triad literature by demonstrating how abusive environments trigger resource conservation through knowledge-hiding. Practically, suggestions for preventing toxic leadership and improving ethical climates in healthcare institutions are presented. Health institutions should implement a reward system for employees who demonstrate helpfulness and teamwork to decrease Machiavellian behaviors.

PurposeThis study aimed to examine the association between ultra-processed food (UPF) consumption and premenstrual syndrome (PMS) across different body mass index (BMI) categories in women.Design/methodology/approachA total of 480 women aged 20-49 years participated in the study between May 2024 and May 2025 in 3 cities in T & uuml;rkiye. PMS symptoms were assessed using the Premenstrual Syndrome Scale (PMSS), and UPF intake was evaluated according to the NOVA classification using a food frequency questionnaire. Anthropometric measurements, including height and body weight, were taken by researchers, and BMI was calculated for each participant.FindingsThe mean BMI of the participants was 24.2 +/- 4.9 kg/m2, with an average daily UPF intake of 858.0 +/- 133.3 g. Higher UPF intake was significantly associated with increased reporting of PMS, with 7.0% higher odds among normal-weight women, 19.0% higher odds among overweight women, and 41.0% higher odds among women with obesity. Moreover, as UPF consumption increased from the lowest (Q1) to the highest quartile (Q4), the odds of experiencing PMS symptoms were 2.65 times (OR: 2.65, %95 CI: 1.52-4.61), 2.85 times (OR: 2.85, %95 CI:1.60-4.95), and 3.07 times (OR: 3.07, %95 CI: 1.71-5.49) higher, respectively (p = 0.001).Research limitations/implicationsThese findings underscore a significant association between UPF consumption and PMS symptoms, particularly among women with obesity. Given the high prevalence of PMS and its substantial impact on women's well-being, dietary interventions aimed at reducing UPF intake may represent a promising approach for PMS management and prevention.Originality/valueTo the best of our knowledge, this study is the first to examine the relationship between UPF intake and self-reported PMS presence across different BMI categories.

Formative assessment practices have been a significant focus in Social Studies education for many years. One commonly used technique in these practices is the diagnostic branched tree, which is designed to assess students' understanding through a branching structure based on their responses. While this technique shows promise for identifying students' learning status and gaps throughout the ongoing process, it presents several structural limitations. This research aims to address these limitations by integrating diagnostic branched trees with natural language understanding, a key AI technique in educational assessment, thereby enhancing their diagnostic capabilities and making them smarter tools in educational settings. Furthermore, this research seeks to evaluate the impact of this revised system in middle schools within a practical setting. Following the development phase, the results of the experimental study demonstrated that the revised system had a significantly positive impact on students' learning performance. Qualitative data, meanwhile, suggested that this success was largely due to the system's ability to provide formative feedback and personalized guidance, which fostered metacognition and self-regulated learning. At this point, considering the positive transformations brought about by technology, it is recommended that innovative technologies be developed to enhance formative assessment across various levels of K-12 education.

Historical masonry minarets are highly vulnerable to seismic actions due to their slender geometry, limited tensile capacity, and material heterogeneity. However, their response to near-fault ground motions characterized by velocity pulses remains insufficiently explored. This study investigates the seismic response of the historical Tavanl & imath; Mosque Minaret (1894, Trabzon, T & uuml;rkiye) subjected to pulse-like (PL) and non-pulse-like (NPL) near-fault ground motions. A three-dimensional finite element model (FEM) was developed in ANSYS Workbench and systematically calibrated using empirical formulations to represent the current dynamic condition of the structure. Seismic performance was evaluated through linear dynamic analyses in terms of displacement demands, principal stress distribution, and drift-ratio-based performance levels. The results indicate that model calibration significantly modifies the dynamic characteristics, increasing the fundamental frequency from 0.734 Hz to 1.126 Hz and reducing displacement demands by approximately 35-76% across the considered records. Despite this improvement, PL ground motions consistently generate more critical deformation demands than NPL motions, frequently exceeding Collapse Prevention (CP) limits even when Peak Ground Acceleration (PGA) values are relatively low. A key finding is that seismic demand cannot be reliably predicted by peak intensity measures or pulse-period ratios (Tp/T1) alone; rather, velocity-related parameters and pulse coherence govern the structural response. These results demonstrate that integrating empirical model calibration with pulse-sensitive seismic analysis is essential for reliable seismic assessment and conservation planning of slender historical masonry structures located in near-fault regions. The study offers a systematic framework that integrates model calibration and pulse-sensitive seismic analysis for evaluating the drift-controlled response of slender historical masonry minarets in near-fault regions.

The rapid advancement of Artificial Intelligence (AI) has transformed educational tools and practices. Integrating AI into instruction supports complex thinking, enriches teaching, and helps educators in material creation and visualization. Therefore, it is essential to equip both students and educators with AI literacy and digital material design competencies in order to support pedagogically sound and ethically responsible instructional practices. Within this context, the present study examines the extent to which an AI-integrated instructional technology course influences pre-service teachers’ AI literacy and digital material design competencies, as well as their perceptions and experiences of learning within an AI-integrated instructional environment. Using a mixed-methods approach, 50 pre-service social studies teachers (41 female, 9 male) participated in a semester-long course. Data were collected through the Artificial Intelligence Literacy Scale, Digital Material Design Competency Scale, and semi-structured interviews. Paired sample t-tests indicated significant gains in both AI literacy and digital material design competencies, with large effect sizes. Qualitative and quantitative findings showed that AI tools not only improved technical abilities but also enhanced pedagogical thinking. The study highlights the importance of AI-integrated curricula in preparing future teachers for modern educational challenges.