Ostim Technical University

Bu makalede, Arife Kalender poetikasında dişil söylem ve kadının konumlanışı postyapısalcı feminist eleştirinin yapısöküm tekniğiyle incelenmektedir. Kalender’in şiirlerinde dişil yazma stratejilerinin çözümlenmesinde Hélène Cixous, Luce Irigaray ve Julia Kristeva’nın dil, beden ve kültür üzerine görüşleri esas alınmaktadır. Feminist kuramcılar, kadınların bedenleriyle yaşadıkları ilişkilerin dilsel ve kültürel olarak belirlenmişliğine odaklanır. Dil ve ataerkil düzen bağlantısı, kültür/doğa, zihin/beden gibi ikilikler aracılığıyla kurulur. İlkinin ikincisine üstün olduğu bu anlam hiyerarşisi eril/dişil karşıtlığından ileri gelmektedir. Kalender şiirlerinde öznenin kendi bedenini anlatmasıyla açığa çıkan dişil dil, ataerkil dili sorgulayan ya da yerinden eden bir yapıdadır. Bu şiirlerde özne, bedeni üzerinde konuşurken eril temsil hiyerarşileriyle hesaplaşır. Kalender, doğum, cinsellik, kürtaj, ameliyat gibi süreçler bağlamında bedenin norm dışı hâllerini resmeder. Feminist kurama göre, eril kültürün öteki olanı dışarıda bırakan ya da baskılayan düzeneklerine karşılık, dişil beden saygı ve kapsayıcılığı üreterek farklılık kültürünü inşa eder. Kalender’de bu algı, ikizlik izleğinde vücut bulur. Şair, eril sistemin tahakküm pratikleriyle karşılaşan dişil bedenlerin ortaklaştığı ve eşitlendiği bir farklılık kültürü tahayyül eder. Kültürün dışına itilen dişil duyarlılığı tarih ve mitolojiden beslenerek ortaya çıkarır. Böylece Anadolu ve Mezopotamya uygarlıklarını merkeze alan bir anaerkil geçmiş kurgusuna ulaşır. Bu yönüyle Kalender şiiri, eril dil kodlarına paralel olarak eril tarih yazımını da yapısöküme uğratan bir şiirdir.

This study investigates the potential of generative artificial intelligence–based text-to-image models as early-stage decision-support tools in sustainable tiny house design. Within the conceptual design process, the study systematically models key components—formal schema development, material selection, and spatial atmosphere—through a controlled prompt framework that enables structured comparison of design alternatives. Design outputs generated by diffusion-based models, including ChatGPT (DALL·E 3), Copilot, and Gemini AI, are evaluated using a multi-criteria framework encompassing sustainability principles (use of natural materials, compact planning, energy-efficient openings), typological consistency, and overall design quality. To examine how prompt variations shape design outcomes, three analytical dimensions are defined: (1) morphological decisions (plan layout, roof typology, form), (2) material decisions (timber, recycled composites, hybrid systems), and (3) spatial atmosphere (daylighting, color palette, interior warmth). The findings show that text-to-image workflows not only accelerate the generation of design alternatives but also enable a structured interpretation of how different design options align with specified criteria. In this sense, generated outputs function as interpretable design propositions that can be comparatively assessed within a transparent evaluation logic. The results further suggest that generative AI supports designers by externalizing design reasoning, facilitating the exploration, comparison, and justification of early-stage decisions. These results position generative AI as a viable component of next-generation design decision-support systems, particularly in contexts where rapid iteration, multi-criteria evaluation, and the articulation of design reasoning are critical.

Based solely on the First Law of Thermodynamics, which addresses energy efficiency, heat pumps are considered to be a major asset for achieving the decarbonization goals of the Paris Agreement. Yet, this approach alone overestimates the potential for decarbonization and conceals certain root causes of carbon dioxide emissions arising from exergy destruction. This paper redefines the decarbonization potential of heat pumps holistically by applying the First and Second Laws of thermodynamics together. This research shows that power-to-heat energy conversion systems are responsible for exergy destruction, leading to additional carbon dioxide emissions, as the destroyed exergy must be offset by other means, possibly by burning fossil fuels. An exergy-based model was developed and used for different scenarios. Results were compared with boilers in heating mode fueled by fossil fuels or biogas, and with adsorption machines in cooling mode. Thermal energy storage systems and their optimal operational regimes for minimum environmental footprint have also been considered and modeled in accordance with the Second Law of Thermodynamics. A correction factor for the coefficient of performance of heat pumps has been developed to account for exergy destruction and emission avoidance associated with typical heat pump operation under electrical and ambient thermal inputs. Results show that today’s coefficient of performance must be higher to meet the emissions-mitigation and total electrification targets to mitigate global warming. The Second Law of thermodynamics shows that the case study about the comfort cooling system of a nature center building with ground source heat pumps and photovoltaic panels is responsible for emissions by 0.81 kg CO2 emissions/kWen-h, rather than saving 1.62 kg CO2/kWen-h by a no-heat pump case, comprising solar photovoltaic panels, adsorption cooling, and desiccant wheel. This case study also shows that the carbon dioxide emission responsibility resulting from exergy destruction during the power-to-heat process is as important as the direct emissions from electric power use. This paper concludes that the exergy destruction-related carbon footprint should not be neglected across all design and application phases to foster better awareness and establish exergy-rational strategy planning towards the Paris Agreement goals.

ABSTRACT This study presents a validated multicriteria decision-making (MCDM) framework for supplier selection that integrates large language models (LLMs) to enhance the evaluation process. Traditional supplier selection methods often rely on simplified scoring mechanisms that may overlook nuanced supplier characteristics. We propose a hybrid approach combining weighted scoring methods with DistilGPT-2 to generate interpretable supplier assessments. Using a data set of 362 Vietnamese textile and apparel companies with 64 evaluation features, our method calculates composite scores based on six key performance categories: cost, delivery, quality assurance, service, customer service, and sustainability. The LLM component processes these numerical scores to generate contextual analyses that aid decision-makers in understanding supplier strengths and weaknesses. Validation against expert human evaluations shows 78.3 % agreement in selection decisions, with Cohen’s κ = 0.672 indicating substantial inter-rater reliability. Statistical analysis reveals significant correlations between sustainability and quality assurance (r = 0.543, p < 0.001), and delivery performance with overall supplier acceptability (r = 0.621, p < 0.001). A comparison with the traditional technique for order preference by similarity to the ideal solution and analytic hierarchy process methods demonstrates superior interpretability while maintaining comparable decision accuracy. Results show that 23 suppliers (6.4 %) met selection criteria, whereas 339 (93.6 %) required further evaluation, highlighting the complexity of supplier assessment. This work demonstrates how LLMs can augment traditional MCDM approaches by providing interpretable narratives alongside quantitative metrics.

• Abnormal weather shocks cause delayed but persistent declines in U.S. municipal bond returns. • Municipal bond returns fall about 35 bps after 4–6 months and 40 bps after one year. • Republican-led states show sharper short-term bond return declines after weather shocks. • Medium-term effects converge, indicating widespread fiscal costs across all states. This paper investigates how abnormal weather shocks influence U.S. state-level municipal bond returns, offering evidence on the pricing of climate risk in local public debt markets. Using a composite index of standardized weather anomalies and a panel local projections framework, we find delayed but persistent negative effects of abnormal weather shocks on municipal bond returns. Returns remain stable initially but decline by about 35 basis points after four to six months and by 40 basis points after one year, indicating gradual repricing as fiscal and credit conditions adjust. Furthermore, we document partisan asymmetries: bonds issued by Republican-led states exhibit sharper short-term declines, reflecting weaker climate policies and adaptation efforts. Over the medium term, the effects converge across states, suggesting that abnormal weather shocks ultimately impose real and widespread fiscal costs on municipalities, regardless of political orientation.

An exergy-based thermohydraulic model has been developed to minimize carbon dioxide emissions from heat tracing of oil pipelines. This model determines an optimal fluid flow velocity by balancing the increase in the pipe-to-fluid convection coefficient on the one hand and the increase in pumping power demand on the other. Exergy destruction is considered in both cases, which contradict each other regarding responsibility for carbon dioxide emissions. Electric and hydronic heating are considered. A case study has shown that the optimal solution for the flow velocity at a unit mass flow rate, including varying the pipe diameter, can reduce carbon dioxide emission responsibility by 40%. Considering projections for the completion of new land-based pipeline construction worldwide, the total decarbonization potential of employing the new model is estimated at 0.13 Gton CO2 per year, once new pipeline installations totaling 11,947 km are completed by 2030. The option of replacing online electric power generators at pumping substations with combined heat and power systems, thereby shifting heat tracing from electric cables or mats to thermal power with hydronic heating with heat pipes, thereby using the generated electric power for pumps or compressors, has also been investigated, and it has been shown that a 64% emission saving is possible relative to generator-driven electric power heat tracing.

Purpose Istanbul, as the world’s second-most visited city by international tourists, plays a critical role in global tourism. This study aims to identify the key factors contributing to the estimation of tourist arrivals in Istanbul across three periods between 2009 and 2024. Design/methodology/approach Tourist arrivals are forecasted using Gradient Boosting, XGBoost and Random Forest models. Predictors include average temperature, real effective exchange rate (REER), Google Trends, terrorist attacks and the Producer Price Index (PPI) for air transportation. Model performance is evaluated through root mean squared error and R², while SHAP analysis interprets the relative influence and direction of each variable. Findings XGBoost demonstrates the best overall predictive performance among the tested models. PPI, temperature and REER are the most influential predictors, while terrorist attacks have a limited impact. SHAP analysis highlights the positive role of Google Trends and temperature and shows that currency depreciation stimulates tourism by increasing affordability. Originality/value This study enriches tourism forecasting research by integrating economic, climatic and digital indicators within a machine learning framework. It demonstrates the value of nonlinear models and real-time data sources, such as Google Trends, for producing accurate, timely forecasts that can inform strategic tourism management and policy.

Abstract Students often struggle to stay engaged and effectively regulate their learning in online environments, which can negatively impact their learning experiences. Despite the established importance of self-regulation of learning skills (SRLs) in maintaining engagement, many students face significant challenges in developing and implementing these skills due to a lack of adequate feedback. This is primarily due to tutors' high workloads and the difficulties inherent in engaging students in online settings. This study examines the impact of Learning Analytics (LA)-driven interventions to improve students' SRLs in online learning environments. Specifically, it compares the impact of feedback from Generative Artificial Intelligence (GenAI) and human tutors in a nine-week statistics course delivered via MOODLE at a higher education institution, employing LA and clustering techniques to model SRLs based on Ye & Pennisi’s (2022) framework. In a quasi-experimental design, participants with varying SRLs were assigned to either a tutor-feedback or GenAI-feedback group. Feedback readability and reliability evaluations indicate that LA-driven GenAI-produced feedback was significantly more readable than human tutor feedback (p < 0.01) and demonstrated higher reliability than tutor-generated feedback. Results show that students in the low SRLs cluster receiving GenAI feedback exhibited statistically significant improvements in goal-setting skills (p < 0.05) and overall SRLs levels (p < 0.05) compared to the tutor-feedback group. In contrast, no significant differences were observed among high SRLs cluster students. This study underscores the potential of LA-driven GenAI feedback to be able to develop tailored, scalable feedback, improving SRLs performance of low SRLs students in online higher education contexts. Future research should explore these effects across diverse student groups and investigate the collaborative potential of semi-automated feedback systems that include tutors.