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A better classroom than we have: children imagine and draw their ideal classroom
No full textThis study uses a qualitative approach to examine the classroom environment that children imagine, analyzing the drawings and interviews of 84 children aged 5-6 to understand what they expect to see in their learning environment, and attempts to understand the differences between real and ideal classroom environments. Three main themes emerged: (1) Aesthetic preferences and classroom design, (2) Learning resources and play materials, and (3) Connecting with nature. The findings suggest that children desire more functional furniture and space for movement and play. Additionally, they expressed a need for more technological tools, play and learning materials, and natural elements in their classrooms. These insights are valuable for designing learning environments that support children's holistic development. The study's findings have significant implications for teachers, school principals and policymakers, offering guidance on creating educational environments that align with children's preferences and developmental needs. By understanding and incorporating children's ideas, more effective and engaging learning spaces can be designed, fostering their overall development
Yetersiz Dayanıma Sahip Mevcut Betonarme Binalar için Yenilikçi Sismik Dış İskelet Sisteminin Tam Ölçekli Deneyleri
No full textÖzet. Standart altı mevcut betonarme binalarda düşük beton dayanımı, düz do-natı kullanımı, yetersiz detaylandırma ve boyutlandırma gibi zayıflıklar büyükdepremler neticesinde binalarda yapısal performans açısından kabul edilemeye-cek hasara ve yıkıma sebep olmaktadır. Genellikle perde duvar kullanılmayan veyetersiz yanal rijitliğe sahip bu binaların deprem davranışında tasarımda dikkatealınmayan dolgu duvarlar önemli rol oynamaktadır. Bu çalışmada uygulama sı-rasında bina kullanımına engel olmayan, bina cephelerindeki betonarme çerçeveelemanlara ve dolgu duvarlara kimyasal ankraj ile bağlanan hafif çelik dış iskeletsisteminin performansı ODTÜ Uğur Ersoy Yapı Mekaniği Laboratuvarı’nda ya-pılan laboratuvar deneyleriyle incelenmiştir. 2000 yılı öncesi betonarme yapı sto-ğunu temsil eden, dolgu duvarlı, tam ölçekli iki betonarme çerçeve üzerinde ger-çekleştirilen çevrimsel yükleme deneyleri ile sismik dış iskelet sisteminin daya-nım, rijitlik, süneklik ve hasar dağılımı gibi yapısal davranış ve performans pa-rametrelerine etkisi incelenmiştir. Hızlı uygulanan, pratik ve yenilikçi bir güçlen-dirme alternatifi olan sismik dış iskelet sistemi test edilen standart altı dolgu du-varlı betonarme çerçevenin yanal yük, nihai ötelenme ve enerji sönümleme ka-pasitelerini önemli ölçüde arttırmış, yapısal elemanlarda ve dolgu duvarlarda ha-sarı azaltarak depreme daha dirençli hale getirmiştir.</p
Unraveling Roman mobility: Archaeogenomic insights from Anatolia to the Italian peninsula
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A PV Module with Embedded Humidity Sensors to Detect Moisture Ingress
No full textMoisture ingress in photovoltaic (PV) modules reduces power output, accelerates material degradation, and poses safety risks, including electric shock, arcing, and fire hazards, by exposing high-voltage components. PV modules commonly experience challenges with moisture ingress, as many of the encapsulation materials used are not impermeable to air and water vapor. To address these challenges, research has focused on improving encapsulation materials and sealing techniques, alongside developing early-detection of moisture increase to prevent failures. This paper presents the design and fabrication of a PV module with embedded humidity sensors. While other papers have proposed similar ideas, this paper provides complete design details, including lessons learned from the experimental work, and experimental data to validate the technique. The proposed PV module facilitates early moisture ingress detection, supports R&D efforts to enhance durability, verify efficacy of moisture detection techniques, enables moisture trend analysis, and provides manufacturers with reliable moisture exposure data for warranty assessments
Microplastics in soil: a comprehensive review of analytical techniques
No full textMicroplastics (MPs) pollution has increasingly been recognized as a critical environmental issue impacting terrestrial ecosystems, particularly soil matrices. This review comprehensively evaluates existing identification techniques for MPs in soil, highlighting the complexities associated with soil matrices, such as heterogeneity, organic matter content, and diverse particle sizes. Current methods, including sieving, filtration, density separation, chemical digestion, and spectroscopic analysis (e.g., FTIR, Raman spectroscopy), are critically assessed for efficiency, reliability, and applicability. Our analysis identifies significant methodological inconsistencies across studies, emphasizing the urgent need for standardized analytical protocols to enable reliable comparative assessments. Recommendations include the implementation of stringent quality assurance/quality control measures to mitigate cross-contamination and enhance data quality. Given the projected increase in global plastic production and consequent MPs pollution, it is imperative to develop standardized, scalable, and cost-effective methodologies for monitoring MPs in soil environments
İcat çıkarma şimdi”, “Bu yollardan geçtik”, “Zaten bildiğim şeyler” gibi laflara kulak asmayan Öğretmen Ağı Değişim Elçileri
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A fast seismic assessment technique for reinforced concrete buildings: Machine learning-based Hassan Index
No full textAssessing large inventories of reinforced concrete structures in urban areas with high seismicity is a daunting task that requires tools that can be applied quickly to produce reliable results. The first goal should be to identify the most vulnerable structures that require rapid intervention. Existing assessment standards are often too complex for this purpose. In the literature, the index-based methods, for example the Hassan Index, provide more efficient assessment options based on simple geometric parameters. The question addressed here is whether machine learning (ML) algorithms trained to use the same parameters can better match field observations. The developed algorithm has been trained and tested on survey data from 1320 low- to mid-rise buildings, the model achieved 74 % accuracy on a held-out test set with 5 % “risk” (false negatives) and 21 % “cost” (false positives), improving over the simple index-threshold baseline (61 % accuracy, 8 % risk, 31 % cost). On an external dataset from the 2024 Taiwan earthquake, performance remained comparable (73 % accuracy, 3 % risk, 24 % cost). The approach is intended to prioritize structures for detailed assessment and early intervention; its applicability is limited to buildings whose attributes fall within the training data domain in terms of statistical properties
