Istanbul Bilgi University

Understanding the focus, scope, and outcomes of postgraduate theses on teacher professional development is essential for informing future research agendas and strengthening evidence-based practices in English language teaching. This study examines postgraduate theses focusing on the professional development of English teachers in Türkiye, identifies emerging trends, and offers insights for future research. Employing a qualitative document analysis approach, the study analyzed 92 theses accessed through the Turkish Council of Higher Education National Thesis Center, including 56 master’s theses and 36 doctoral dissertations. The findings reveal that professional development activities reported in these theses positively influenced teachers’ classroom practices, professional growth, and stakeholder awareness. In addition, such activities were found to enhance teacher motivation and foster professional collaboration. Based on the recurring patterns identified across the theses, the study highlights the importance of aligning professional development initiatives with teachers’ contextual needs, promoting reflective practices, and encouraging further research to deepen understanding of effective professional development in the field.

Decision Support Systems facilitate timely and informed decision-making by integrating heterogeneous data sources, analytical models, and user interfaces into cohesive systems. This study introduces a general-purpose and scalable Decision Support System framework that supports just-in-time, data-driven decision-making processes across diverse application domains. The proposed system architecture incorporates essential technologies, including Apache Kafka for parallel data streaming, a Python-based distributed data analytics module, a role-based access control system for authentication and authorization, and a WebSocket interface to deliver predictions in real-time. The Iris dataset was utilized for model validation, with logistic regression employed as the predictive model. Experimental evaluations were conducted under simulated load conditions using the Artillery tool to assess system scalability and responsiveness. Results demonstrate significant performance improvements when utilizing Apache Kafka consumer group with associated worker modules for parallel processing, achieving lower mean, median, P95, and P99 latencies. These findings highlight the effectiveness of the proposed architecture in enabling highly scalable and responsive Decision Support System applications that support just-in-time decision-making.

ABSTRACT Background and Purpose Chronic pain affects approximately 63% of people with multiple sclerosis (pwMS), contributing to fatigue, depression, anxiety, poor sleep, reduced quality of life, and cognitive decline. Within the biopsychosocial model, music therapy has emerged as a promising intervention to address these complex symptoms. This study aims to examine the effects of heart rate‐synchronized music therapy combined with motor imagery practice on pain, autonomic and cognitive functions, and psychosocial outcomes in patients with pwMS. We hypothesize that the combined intervention will lead to greater improvements than music therapy alone or routine care. Methods A double‐blind, randomized, and three‐arm parallel trial will be conducted with 45 patients with pwMS experiencing chronic pain. Participants will be randomly assigned to one of three groups: (1) heart rate‐synchronized music therapy combined with motor imagery, (2) heart rate‐synchronized music therapy alone, or (3) a control group receiving routine care. Interventions will be delivered twice weekly for 8 weeks, with each session lasting 20–30 min. The experimental groups will receive music therapy via videoconferencing. Assessments will be conducted at baseline, post‐intervention (week 8), and follow‐up (week 12). The primary outcome is pain intensity. Secondary outcomes include neuropathic pain, central sensitization, heart rate variability, anxiety, depression, fatigue, sleep quality, quality of life, and cognitive function. Sample size was calculated using G*Power; HRV data will be analyzed with Kubios software. Statistical analyses will be performed using SPSS and GraphPad Prism 10. Results Following randomization, baseline data will be collected. Blinded assessors will evaluate all outcomes at follow‐up points. An independent researcher will perform statistical analyses to assess changes across time and between groups. Discussion This study may provide evidence supporting a novel, non‐pharmacological, and telehealth‐compatible intervention for chronic pain in pwMS. Trial Registration NCT06800144, 24th January 2025

• Introduces CircularB-DfC, a design-stage framework for assessing and prioritising circularity in buildings • Integrates 35 circularity indicators and 20 enabling factors across design and value-chain processes • Provides Delphi-weighted scoring and a practitioner-oriented Excel tool for decision support • Demonstrates framework applicability through three contrasting regional and structural scenarios • Reveals how design strategies and enabling conditions jointly shape circular performance Circularity is increasingly recognised as a critical paradigm for sustainability in the built environment, yet existing efforts to assess it—whether focused on material flow analysis, design-for-disassembly strategies, durability metrics, or carbon accounting—remain fragmented and operate at different scales. Despite numerous indicator sets, the literature lacks an integrated framework that combines both technical design factors and the enabling organisational conditions required to support circular outcomes at the building level. This paper introduces CircularB-DfC (CircularB COST Action – Design for Circularity), a decision-Support Tool with a structured matrix for prioritising building design factors to enhance circular Material flows. The framework consolidates insights from a systematic literature review and a multi-stage expert engagement process, resulting in 35 technical indicators and 20 enabling factors . These are organised into four technical categories: Material Selection; Design for Disassembly; Embodied Energy and Carbon Footprint; Waste Minimisation, and one enabling category, Circular Construction Management, including Governance, Certification, Stakeholder Engagement, Digitalisation, and Socio-economic aspects. Indicators and enablers are aggregated into a Design Score and an Enabler Score to support early decision-making. The tool was applied to three illustrative scenarios: a reinforced-concrete industrial hall in the Western Balkans, a steel office building in Central Europe, and a timber residential project in East London. The steel scenario achieved the highest Design and Enabler Scores, the concrete scenario performed strongest in Waste Minimisation through prefabrication and site-based strategies, and the timber scenario scored lowest overall due to limited reuse and disassembly provisions in the original design. While CircularB-DfC offers a simple and transparent basis for integrating circularity in design, it is limited by the subjectivity of expert-based weighting and its static structure. Future research will focus on dynamic modelling, integration with digital tools, and broader validation to enhance applicability.

This study investigates strategic decision-making for integrating artificial intelligence–based predictive safety systems into occupational health and safety (OHS) management. The aim is to develop and apply a rigorous, transparent multi-criteria decision framework that helps organizations select among competing AI-driven safety solutions under uncertainty. The core research question is: Which AI-based predictive safety alternative offers the best balance of safety improvement, organizational feasibility, and strategic fit for OHS management? An integrated fuzzy MCDM approach combines Fuzzy Best–Worst Method (FBWM) to elicit criterion weights with MARCOS to prioritize alternatives evaluated by domain experts across technical performance, human factors, legal/regulatory fit, cost, and implementation readiness. The analysis highlights the dominant influence of safety impact and technological readiness on final rankings, while cost and legal compliance act as moderating considerations. Sensitivity tests across weighting schemes indicate stable priority orders without critical rank reversals, supporting managerial robustness. The findings provide actionable guidance for investment and OHS committees, demonstrate the practicality of a hybrid fuzzy model for high-risk settings, and clarify both the study’s aim and its central research question for future replications.

Abstract This study presents a self-tuning optical MEMS microscanner based on a low-cost polyimide structure integrated with a piezoresistive feedback mechanism. The system automatically detects and tracks the torsional resonance frequency (≈ 81 Hz), compensating for spring softening effects that otherwise degrade scan performance. A custom frequency sweep algorithm, combined with a closed-loop control, dynamically adjusts the actuation signal to maintain resonance-locked operation, enabling stable mechanical scan angles of 10–12 ∘ ( ≈ 42 ∘ total optical scan angle (TOSA)) at drive levels of 8–10 V pp and 60–80 mA ( ≈ 0.5 z − 0.8 W ). This approach ensures stable displacement and total optical scan angle over time, even under frequency drift. In high-power characterization sweeps, the device demonstrates optical scan angles up to 100 ∘ TOSA. The proposed architecture offers a practical and scalable solution for energy-efficient, high-resolution optical scanning using inexpensive and easily manufacturable polymer-based MEMS devices.

ABSTRACT In recent years, the evolving socio-political landscape of New Türkiye has presented significant challenges to women’s empowerment, particularly in sports. As gender equality dynamics change, initiatives such as Kızlar Sahada have become instrumental in empowering women in football through the Footballer Development Program, an initiative that closely resembles Sport for Development and Peace (SDP) interventions. This study explores how such programs shape women footballers’ empowerment by integrating Northern and Southern approaches, blending the individual-focused, postfeminist, and neoliberal models of the North with development-oriented, structural frameworks of the South. Employing a basic qualitative study approach, we conducted interviews with nine women serving as volunteer trainers, consultants, and officials and analyzed 20 program-related documents. Our findings inform a novel empowerment model for the context of New Türkiye. The study concludes that non-governmental organizations in New Türkiye have taken on roles traditionally associated with government support, using SDP-like programs with Northern methodologies to empower women footballers, following a process similar to the empowerment model we have identified. These approaches, in turn, reflect the strategies that women footballers have historically relied on to sustain their initiatives.

Purpose Service experiences are shaped through interactions with and perceptions of service providers. This study aims to investigate the impact of customer perceptions of service robots’ incivility in restaurants on customer emotions and service outcomes, addressing a gap in the current literature on human–robot interactions in hospitality settings. Design/methodology/approach Two experimental studies were used to examine the effects of robots, mechanoids and human server incivility on customer emotions, word-of-mouth intentions and expected service quality. Data were collected from US participants using online surveys. Findings The results reveal that service technologies’ physical and interactional capabilities influence customers’ perceptions of service environments; customers feel more anger, and service quality expectations are lower when a robot is uncivil (vs human); and when customers witness incivility by a robot (vs mechanoid), their positive emotions are more likely to be reduced. Originality/value This research addresses a clear gap in the literature by exploring the unique impacts of customers’ perceptions of service robot incivility in restaurants. It applies and tests the social exchange theory within human–robot interactions, highlighting how robot characteristics such as human-likeness influence customer responses to perceived incivility. The findings offer practical insights for optimizing robot use in hospitality to enhance customer experiences and service quality.