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

Clustering-based negative sampling approaches for protein-protein interaction prediction

The lack of confirmed negative interactions poses a major challenge to the prediction of protein-protein interactions. The reliable selection of these negative samples within a dataset is crucial for a better understanding of the underlying patterns and dynamics. The random sampling method is the most widely used negative sampling method, where negative pairs are randomly selected from unlabelled samples (i.e., samples not experimentally confirmed as positive interactions). However, they tend to introduce inaccurately labelled negative samples, resulting in less reliable predictions, which may affect the efficiency of the learning process. Our study aims to assess the reliability of clustering-based negative sampling methods and highlight their fundamental differences from the widely used random sampling method. To achieve this goal, we propose a hierarchical clustering-based algorithm that uses different mechanisms to select negative instances from unlabelled instances. We investigated the effectiveness of our proposed approach compared to existing clustering-based negative sampling methods and random sampling on four different datasets. The results indicate that clustering-based methods surpass the commonly used random sampling method.TÜBİTA

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

Neuro-symbolic ai for supporting chronic disease diagnosis and monitoring

In remote areas or regions with limited access to medical specialists, there is often a high reliance on telemedicine and Artificial Intelligence (AI)-based diagnostic tools. However, misdiagnoses or inadequate care may occur if the AI system lacks domain knowledge, failing to adhere to medical protocols. Despite the incredible research efforts applying AI in medicine, only a few models have been routinely adopted in medicine, due to issues related to trustworthiness. To address these concerns, Symbolic Knowledge Injection (SKI) has been proposed as a solution: it integrates domain-specific expertise into Machine Learning (ML) models, to improve their predictive capabilities. Despite their promising results in other fields, applicability of SKI in healthcare scenarios has not been thoroughly investigated, yet. Accordingly, in this study, we explore the applicability of a SKI method on medical datasets to evaluate: (i) how the predictive capabilities of ML models changes, (ii) their adherence to the medical protocols, and (iii) their robustness w.r.t. data degradation. Results demonstrate the potential of integrating data-driven models with established medical guidelines by improving different clinically relevant metrics

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

Harnessing pore size in COF membranes: A concentration gradient-driven molecular dynamics study on enhanced H2/CH4 separation

This work presents a novel approach for accurately predicting the gas transport properties of covalent organic framework (COF) membranes using a nonequilibrium molecular dynamics (NEMD) methodology called concentration gradient-driven molecular dynamics (CGD-MD). We first simulated the flux of hydrogen (H2) and methane (CH4) across two distinct COF membranes, COF-300 and COF-320, for which experimental data are available in the literature. Our CGD-MD simulation results aligned closely with the experimentally measured gas permeability and selectivity of these COF membranes. Leveraging the same methodology, we discovered promising COF candidates for H2/CH4 separation, including NPN-1, NPN-2, NPN-3, TPE-COF-I, COF-303, DMTA-TPB2, 3D-Por-COF, COF-921, COF-IM AA, TfpBDH, and PCOF-2. We then compared our findings with simulations utilizing the well-known approach that merges grand canonical Monte Carlo (GCMC) and equilibrium molecular dynamics (EMD) to predict gas adsorption and diffusion parameters in COFs. Our results showed that when the pore sizes of COF membranes are below 10 & Aring;, the choice of the method plays a significant role in determining the performance of the membranes. The GCMC+EMD approach suggested that COFs tend to exhibit CH4 selectivity when their pore limiting diameters are below 10 & Aring;, whereas the CGD-MD results reveal a preference for H2. Density functional theory calculations indicate that H2 has a lower affinity for three promising COFs, NPN-1, NPN-2, and NPN-3, compared to CH4, which results in H2 remaining unbound, while CH4 occupies all of the adsorption sites, thereby facilitating the selective recovery of H2 at the end of the separation process. We proposed a relationship between adsorption time and diffusion time, highlighting the critical role of selecting an appropriate simulation method. This relationship underscores how adsorption and diffusion processes interplay, impacting material performance. Overall, these insights not only improve the accuracy of predictive models but also guide the development of more efficient COF-based membrane applications for future research and industrial applications.TÜBİTA

Advancing white balance correction through deep feature statistics and feature distribution matching

Auto-white balance (AWB) correction is a crucial process in digital imaging, ensuring accurate and consistent color correction across varying lighting conditions. This study presents an innovative AWB correction method that conceptualizes lighting conditions as the style factor, allowing for more adaptable and precise color correction. Previous studies predominantly relied on Gaussian distribution assumptions for feature distribution alignment, which can limit the ability to fully exploit the style information as a modifying factor. To address this limitation, we propose a U-shaped Transformer-based architecture, where the learning objective of style factor enforces matching deep feature statistics using the Exact Feature Distribution Matching algorithm. Our proposed method consistently outperforms existing AWB correction techniques, as evidenced by both extensive quantitative and qualitative analyses conducted on the Cube+ and a synthetic mixed-illuminant dataset. Furthermore, a systematic component-wise analysis provides deeper insights into the contributions of each element, further validating the robustness of the proposed approach

Numerical simulations of non-fluorescent states of carboxyfluoresceins in presence of heavy iodine ions

In the present research, the effects of heavy iodine ions on dark transient state populations of carboxyfluorescein derivatives, each with a different number of bromine atoms attached, were computationally examined via numerical simula tions adapted to a widefield fluorescence microscopy integrated with a microfluidics platform. Numerical simulations were car ried out by considering geometrical profile of excitation laser beam, microscopy and microfluidics parameters of a proposed experimental design as well as electronic transition rates of fluorescent molecules. Electronic state model of studied dyes was treated as a system of first order ordinary differential equa tions and time-dependent solutions of long-lived, non-fluorescent dark state populations were computed for each dye at varying potassium-iodine [KI] concentrations. Analytical solutions of state populations were then adapted to a proposed experimental setup to systematically analyze how dark state populations evolve when carboxyfluorescein derivatives pass through exci tation beam field in a microfluidics chip under different flow speeds. Computational experiments have successfully uncovered systematic changes in dark triplet and photo-oxidized state populations upon the addition of iodine ions into fluorophore solutions.TÜBİTAKPost prin