Location-based examination of the characteristics of university campuses in İstanbul

PurposeThe study investigates the connections that university buildings and campuses establish with their environments and the effects of their environments on universities. Many factors influence the location of universities in a particular region. The initial university settlement and the subsequent facilities needed by university branches, colleges, or faculties are related to each other by the time parameter Istanbul, a historical city that is also Turkey's largest, hosts several state and founding universities. This study investigates the connections between the urban areas where university campuses in Istanbul are located within the city and the specific characteristics of universities.Design/methodology/approachWe evaluate the data on university locations in the city to determine the relationships between their establishment date, size, type, place of location in the city, and unique characteristics. The analysis is based on data for occupancy rate, satisfaction level, university ranking, and space area per student.FindingsResults: Campus areas are affected by the availability of public transport and other amenities in urban areas. There are also interactions between meeting students' satisfaction, university ranking, and public perceptions of the university and campus location in the city.Originality/valueThis study investigates the connections between the urban areas where university campuses in Istanbul are located within the city and the specific characteristics of universities

Elucidating the role of processing parameters on microstructure, grain refinement, and mechanical features of Al/Ti laminated composite fabricated by accumulative roll bonding

Multilayered composites are widely used in automobile components. The properties of multilayered composites mainly depend on processing parameters. In this investigation, Al/Ti composites were fabricated by accumulative roll bonding (ARB). The influence of rolling parameters including rolling velocity (10 < v < 20 r/min), rolling cycle (7 cycles), and friction coefficient (0.08 < < 0.14) on the evolution of microstructure, grain refinement, variations of hardness, and tensile features were examined. The results of microstructural characterization showed that the layers became more discontinuous and the broken pieces of Ti were better distributed in the Al matrix when the rolling cycle and rolling friction increased and the rolling velocity decreased. In addition, the characterization of grain structure revealed grain refinement in Ti and Al layers after the 7(th) cycle. The grain structure was better refined when the friction coefficient was 0.14 and rolling velocity was 10 r/min. The hardness and strength values increased as the rolling cycle and friction coefficient increased and the rolling velocity decreased. When the friction coefficient was 0.14 and the rolling velocity was 20 r/min, the highest strength of 520 MPa was obtained and the maximum hardness of Ti and Al were 76 HV and 292 HV, respectively. Although the fracture surfaces showed delamination and different sizes of dimples, a larger number of dimples were observed at higher rolling velocities and lower friction coefficients

Development of the usage possibilities of adobe with computational design

In addition to improving the physical material properties of adobe, the ability to use it with today's design approach also plays an important role in this material being considered a contemporary building material. The development of computer-aided design technology not only changes the architectural design concept but also improves the usage possibilities of traditional building materials. The parametric structures created with computational design allow the use of traditional materials in different ways, leading to the emergence of innovative construction methods. With its easily accessible, economical, and sustainable features, adobe is a preferred material for contemporary designs, and it meets today's building production needs. It is a necessity of our age to investigate the adobe material, which increases the indoor air quality and creates healthy spaces, as a building material of today, as well as a material of the future. This study aims to consider how the usage possibilities and production methods of Adobe material can be improved by examining the innovations brought by computational design to Adobe material via parametrically designed Adobe building projects and structural elements. It is also important to do a benchmark in this study by examining the usage of other building materials used in computational design projects and establishing a relationship between these techniques and adobe

Comparative analysis on the phenomenological and artificial neural network modeling for flow curves of a beta titanium alloy

This study investigates the elevated temperature mechanical behavior of Ti-5V-5Mo-5Cr-4Al alloy through uniaxial tensile experiments conducted at temperatures ranging from room temperature to 550 degrees C and strain rates of 0.001, 0.01, and 0.1 s(-1). The results reveal that the dominant softening mechanism is dynamic recovery, whereas dynamic precipitation took place at the lowest rate of deformation and at temperatures ranging from 400 degrees C to 500 degrees C. To predict the mechanical behavior of this recent beta titanium alloy, artificial neural network (ANN) approach and modified Hensel-Spittel (m-HS) model were employed. In the prediction of flow curves using the m-HS model, a correlation coefficient (R) of 0.901 and an average absolute relative error (AARE) of 8.891 % were obtained. In contrast, the ANN approach yielded significantly better results, with an R value of 0.997 and an AARE of 2.3 %. The findings from this study provide routes for determining the hot workability of next-generation metastable beta titanium alloys.Ozyegin Universit

Asymptotically optimal energy consumption and inventory control in a make-to-stock manufacturing system

We study a make-to-stock manufacturing system in which a single server makes the production. The server consumes energy, and its power consumption depends on the server state: a busy server consumes more power than an idle server, and an idle server consumes more power than a turned-off server. When a server is turned on, it completes a costly set-up process that lasts a while. We jointly control the finished goods inventory and the server's energy consumption. The objective is to minimize the long-run average inventory holding, backorder, and energy consumption costs by deciding when to produce, when to idle or turn off the server, and when to turn on a turned-off server. Because the exact analysis of the problem is challenging, we consider the asymptotic regime in which the server is in the conventional heavy-traffic regime. We formulate a Brownian control problem (BCP) with impulse and singular controls. In the BCP, the impulse control appears due to server shutdowns, and the singular control appears due to server idling. Depending on the system parameters, the optimal BCP solution is either a control-band or barrier policy. We propose a simple heuristic control policy from the optimal BCP solution that can easily be implemented in the original (non-asymptotic) system. Furthermore, we prove the asymptotic optimality of the proposed control policy in a Markovian setting. Finally, we show that our proposed policy performs close to optimal in numerical experiments.TÜBİTAK ; European Union's Horizon 2020 ; Türkiye Bilimler Akademis

Digital transformation and cybersecurity risks

This study analyzes the impact of integrating cybersecurity measures on the financial performance of banks. Utilizing regression analysis with data from 100 financial institutions, the findings reveal that banks prioritizing cybersecurity perform better financially. This study demonstrates that it is the quality and strategic integration of cybersecurity measures, as revealed through disclosures, that significantly influence financial outcomes, rather than the sheer scale of investment. A subsample analysis suggests that larger banks appear more resilient to cybersecurity threats due to scale-related advantages, while smaller banks can also improve their financial performance by adopting proportionate, strategically aligned cybersecurity measures. Effective cybersecurity integration correlates with improved financial metrics such as return on assets and equity. Furthermore, the severity of cybersecurity incidents negatively impacts financial performance, emphasizing the importance of proactive risk management. This study underscores the critical role of cybersecurity in financial strategy, enabling banks to navigate digital transformation challenges effectively

Resistance under confinement: resilience of protests and their limits in authoritarian Turkey

In this paper, we examine the relationship between the process of autocratisation and protests, and argue that scholarship on electoral autocracies should not only focus on major protest cycles but also examine 'ordinary' protests to understand how social and political actors resist and push back against autocratisation. Using an original dataset of protest events from 2015 to 2021, we analyse the transformation of protests in Turkey as it experienced gradual but significant autocratisation. We discuss two mechanisms through which autocratisation might affect levels, actors and repertoires of protesting: first, via increasing repression; and, second, via the policy choices of the authoritarian regime. Our findings indicate that protests continued even under the state of emergency in Turkey, but with significant changes in levels and repertoires of protesting. The protest scene was dominated by protests using tactics that rely on a small number of individuals and are contained in their spatial reach and disruptiveness. This research underlines the importance of examining ordinary protests to analyse how autocratisation transforms protests, using original data from local sources.Bogazici Universit

Is hotel revenue performance effective for destination competitiveness? An assessment by wavelet coherence analysis

Hotel industry is crucial for destination competitiveness as part of quality tourist infrastructure. The high performance of the industry promises a positive effect on destination competitive. This study aims to develop a approach focusing on the hotel revenue-related performance data in the case of European cities (Istanbul, London, Madrid, Paris, and Rome). Wavelet coherence analysis was applied on revenue data of the years 2013–2022 from 4000+ hotels. Findings reveal time–frequency relationships between hotel revenue variables, highlighting consistent coherence in supply–demand relationships during the pandemic, except for Rome. Furthermore, the analysis detected differentiated patterns in supply–revenue coherence, with Istanbul’s market showing unique fluctuations. London is the city with higher revenue expertise. Key revenue performance indicators of hotels emerged as significant determinants of a city’s competitiveness. These insights present implications for policymakers, community stakeholders, and industry practitioners, emphasizing the pivotal role of adept revenue professionals in improving competitiveness in their destinations

Mask-to-height: A YOLOv11-based architecture for joint building instance segmentation and height classification from satellite imagery

Accurate building instance segmentation and height classification are critical for urban planning, 3D city modeling, and infrastructure monitoring. This paper presents a detailed analysis of YOLOv11, the recent advancement in the YOLO series of deep learning models, focusing on its application to joint building extraction and discrete height classification from satellite imagery. YOLOv11 builds on the strengths of earlier YOLO models by introducing a more efficient architecture that better combines features at different scales, improves object localization accuracy, and enhances performance in complex urban scenes. Using the DFC2023 Track 2 dataset - which includes over 125,000 annotated buildings across 12 cities - we evaluate YOLOv11's performance using metrics such as precision, recall, F1 score, and mean average precision (mAP). Our findings demonstrate that YOLOv11 achieves strong instance segmentation performance with 60.4% mAP @ 50 and 38.3% mAP @ 50-95 while maintaining robust classification accuracy across five predefined height tiers. The model excels in handling occlusions, complex building shapes, and class imbalance, particularly for rare high-rise structures. Comparative analysis confirms that YOLOv11 outperforms earlier multitask frameworks in both detection accuracy and inference speed, making it well-suited for real-time, large-scale urban mapping. This research highlights YOLOv11's potential to advance semantic urban reconstruction through streamlined categorical height modeling, offering actionable insights for future developments in remote sensing and geospatial intelligence. © 2025 IEEE.TÜBİTA

Reducción del riesgo de corrosión de materiales a base de cemento mezclados con agua de mar

The objective of this study is to reduce the amount of free chloride that causes corrosion by employing different binders and to attain the free chloride level permitted by the relevant standards. The chloride content was determined by the X-ray fluorescence method. It was observed that the chloride binding capacity increased when the CaO/Al₂O₃ ratio decreased up to 4-6. On the other hand, it was determined that the chloride binding capacity decreased slightly when the CaCO3/Al₂O₃ ratio increased from 0 to 0.3-0.5, though no significant effect was observed after 0.5. The majority of binders demonstrated results below the maximum chloride level determined by the standards, which is a great opportunity to use seawater as mixing water for reinforced concrete.Boğaziçi ÜniversitesiPublisher versio