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Impact of Beam Misalignment Fading on Terahertz Band Drone Communications

IEEE Wireless Communications Letters 2025年第6期14卷 1668-1672页

作者： Saeed, Akhtar Gurbuz, Ozgur Gerstacker, Wolfgang Roppelt, Isabella Sabanci University Electronics Engineering Program Faculty of Engineering and Natural Sciences Istanbul34956 Turkey Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

In this letter, we evaluate the impact of beam misalignment (BM) fading on Terahertz (THz) drone communications by deriving simple novel closed-form expressions for the outage probability and average bit error rate (BER). Additionally, we present a high signal-to-noise ratio (SNR) regime analysis, resulting in the diversity orders of the outage probability and the average BER under the BM fading, including their asymptotic limits as a function of distance. We show that the results via our proposed closed-form expressions depict good agreement with the results via simulations and alternative expressions from the literature, also highlighting the simplicity of our solutions, which leads to valuable insights via diversity order analysis. © 2025 IEEE. All rights reserved.

关键词： Outages

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Ultrathin carbon biphenylene network as an anisotropic thermoelectric material with high temperature stability under mechanical strain

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Physical Review Materials 2025年第2期9卷 024003-024003页

作者： Gözde Özbal Sargin Salih Demirci Kai Gong V. Ongun Özçelik Faculty of Engineering and Natural Sciences Sabanci University Istanbul 34956 Turkey Materials Science and Nano Engineering Program Sabanci University Istanbul 34956 Turkey Department of Physics Kirikkale University Kirikkale 71450 Turkey Department of Civil and Environmental Engineering Rice University Texas 77005 USA

Carbon biphenylene network (C-BPN), which is an ultrathin material consisting of carbon atoms arranged in square-hexagonal-octagonal (4-6-8) periodic rings, has intriguing properties for nano-scale device design due to its unique crystal structure. Here, using the Landauer formalism in combination with first-principles calculations, we show that C-BPN is a highly stable thermoelectric material at elevated temperatures under mechanical strain, where its thermoelectric efficiency can be anisotropically engineered. Transport calculations reveal that C-BPN's transmission spectrum has significant degrees of directional anisotropy and it undergoes a metal-insulator transition under strain, which leads to an increase in its Seebeck coefficient. C-BPN's lattice thermal conductance can be selectively tuned up to 35% bidirectionally at room temperature by strain engineering. Enhancement in its power factor and the suppression of its lattice thermal conductance improves the p-type figure of merit up to 0.31 and 0.46 at 300 and 500 K, respectively. Our findings reveal that C-BPN has high potency to be used in thermoelectric nanodevices with selective anisotropic properties at elevated temperatures.

关键词： Metal insulator transition

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Nanoparticle-based sustained drug delivery for the treatment of ocular neovascular diseases

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ACTA OPHTHALMOLOGICA 2024年第Sup279期102卷

作者： Durak, Saliha Kutlu, Ozlem Cetinel, Sibel Nanotechnology Research and Application Center (SUNUM) Sabanci University Istanbul Turkey Faculty of Engineering and Natural Sciences Molecular Biology Genetics and Bioengineering Program Sabanci University Istanbul Turkey

Aims/Purpose: Nanoparticles have shown great potential as a drug delivery system for the treatment of neovascular ocular diseases characterized by the growth of abnormal blood vessels in the retina. The current standard of therapy for neovascular ocular diseases involves administering monoclonal antibody-based anti-VEGF drugs via intravitreal injections on a periodic basis. The short half-lives of these drugs necessitate frequent injections, which can limit the effectiveness of the treatments. Hence, there is a need to develop new approaches for enhanced bioavailability and sustained and long-lasting delivery of anti-VEGF drugs. The aim of this study was to utilize poly (glycerol sebacate) (PGS) nanoparticles to achieve an extended release of anti-VEGF (anti-vascular endothelial growth factor) drugs, with the objective of reducing the frequency of intravitreal injections required for treatment. Methods: Double emulsion solvent evaporation method was utilized for the synthesis of PGS nanoparticles. Nanoparticle size and polydispersity index (PDI) were determined with dynamic light scattering method. Results: Nanoparticles were synthesized with a size of approximately 150 nm and PDI less than 0.3. The encapsulation efficiency of nanoparticles loaded with anti-VEGF drugs was found to be 74%. The 30% drug release from nanoparticles in the first 2 months indicates that nanoparticles are a promising candidate for sustained drug release applications. Cell-viability studies showed that anti-VEGF drug-loaded nanoparticles did not exhibit toxicity to ARPE-19 cells, but they did exhibit anti-angiogenesis properties by inhibiting the growth of HUVEC cells in a concentration-dependent manner. Conclusions: The development of new-generation materials for the storage and preservation of anti-VEGF drugs, as well as their enhancement with prolonged release, will considerably improve the efficacy of long-term treatment strategies for ocular neovascular diseases.

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FXAI: Fusing XAI for Predicting COVID-19 Using Diverse Chest X - Ray Images 18

FXAI: Fusing XAI for Predicting COVID-19 Using Diverse Chest...

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18th International Conference on Intelligent Systems and Knowledge engineering, ISKE 2023

作者： Saleh, Radhwan A. A. Farea, Shawqi Mohammed Al-Huda, Zaid Ertunc, Metin Kvak, Daniel Al-Antari, Mugahed A. Kocaeli University Department of Mechatronics Engineering Kocaeli41001 Turkey Sabanci University Faculty of Engineering and Natural Sciences Istanbul Turkey School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan China CEO and Leader of AI R&D Carebot Ltd. Prague12800 Czech Republic Sejong University Department of Artificial Intelligence DaeyangAICenter Seoul05006 Korea Republic of

ISBN: (纸本)9798350318401

Fusing explainable artificial intelligence (FXAI) is currently a prominent research topic in medical imaging interpretations. The proposed FXAI has the capability to provide the following benefits. Firstly, it can extract strong and reliable high-level deep features by combining various standard AI networks. Secondly, it can simultaneously generate visual explainable saliency maps associated with each chest X-ray (CXR) scan. Such heat maps not only demonstrate the most relevant regions of the AI decision-making process but also offer advantages to radiologists and patients. Thirdly, it enhances prediction performance to deliver an optimal intelligent solution for communities worldwide. These advantages can support the development of an optimal treatment plan, reduce medical costs, and enhance the capabilities of health care systems. We have trained and evaluated the proposed FXAI using a diverse benchmark medical CXR dataset that has been collected from various public resources. Our findings encourage researchers and stakeholders in the medical industry to validate this proposed framework in a practical manner. © 2023 IEEE.

关键词： Classification (of information)

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Ultra-thin Carbon Biphenylene Network as an Anisotropic Thermoelectric Material with High Temperature Stability Under Mechanical Strain

arXiv

引用

arXiv 2024年

作者： Sargın, Gözde Özbal Demirci, Salih Gong, Kai Özçelik, V. Ongun Faculty of Engineering and Natural Sciences Sabanci University Istanbul34956 Turkey Department of Physics Kırıkkale University Kırıkkale71450 Turkey Department of Civil and Environmental Engineering Rice University TX77005 United States Materials Science and Nano Engineering Program Sabanci University Istanbul34956 Turkey

Carbon biphenylene network (C-BPN), which is an ultra-thin material consisting of carbon atoms arranged in square-hexagonal-octagonal (4-6-8) periodic rings, has intriguing properties for nano-scale device design due to its unique crystal structure. Here, using the Landauer formalism in combination with first-principles calculations, we show that C-BPN is a highly stable thermoelectric material at elevated temperatures under mechanical strain, where its thermoelectric efficiency can be anisotropically engineered. Transport calculations reveal that C-BPN’s transmission spectrum has significant degrees of directional anisotropy and it undergoes a metal-insulator transition under strain, which leads to an increase in its Seebeck coefficient. C-BPN’s lattice thermal conductance can be selectively tuned up to 35% bidirectionally at room temperature by strain engineering. Enhancement in its power factor and the suppression of its lattice thermal conductance improves the p-type figure of merit up to 0.31 and 0.76 at 300 and 1000 K, respectively. Our findings reveal that C-BPN has high potency to be used in thermoelectric nano devices with selective anisotropic properties at elevated temperatures. Copyright © 2024, The Authors. All rights reserved.

关键词： Metal insulator transition

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Rigorous Perturbation Bounds for the QX Decomposition for Centrosymmetric Matrices

arXiv

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arXiv 2025年

作者： Farooq, Aamir Khan, Rewayat Rani, Uzma Rahim, M. Tariq Department of Mathematics Zhejiang Normal University Jinhua321004 China Faculty of Mechatronics Informatics and Interdisciplinary Studies Institute of Information Technologies and Electronics Technical University of Liberec Liberec Czech Republic Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey Department of Mathematics Abbottabad University of Science and Technology Abbottabad Pakistan

Konrad Burnik suggests a structure-preserving QR factorization for centrosymmetric matrices, known as QX factorization. In this article, we obtain the explicit expressions for rigorous perturbation bounds of the QX factorization when the original matrix is perturbed, either norm-wise or component-wise. First, using the matrix-equation approach, weak rigorous perturbation bounds are derived. Then, strong rigorous perturbation bounds are obtained by combining the modified matrix-vector equation approach with the strategy for the Lyapunov majorant function and the Banach fixed-point theorem. The mixed and component-wise condition numbers and their upper bounds are also explicitly expressed. Numerical tests illustrate the validity of the obtained *** Codes 15A23, 15A45 © 2025, CC BY.

关键词： Lyapunov functions

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Energy Optimization of Wind Turbines via a Neural Control Policy Based on Reinforcement Learning Markov Chain Monte Carlo Algorithm

arXiv

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arXiv 2022年

作者： Aghaei, Vahid Tavakol Agababaoglu, Arda Bawo, Biram Naseradinmousavi, Peiman Yıldırım, Sinan Yeşilyurt, Serhat Onat, Ahmet Istinye University Faculty of Engineering and Natural Sciences Electrical and Electronics Engineering Istanbul Turkey Sabanci University Orhanli Tuzla Istanbul34956 Turkey Delivers AI ITU Arı Technological Park Istanbul Turkey Sabanci University Faculty of Engineering and Natural Sciences Mechatronics Engineering Istanbul Turkey San Diego State University Dynamic Systems and Control Laboratory San Diego United States Sabanci University Faculty of Engineering and Natural Sciences Industrial Engineering Istanbul Turkey Istanbul Technical University Control and Automation Engineering Istanbul Turkey

This study focuses on the numerical analysis and optimal control of vertical-axis wind turbines (VAWT) using Bayesian reinforcement learning (RL). We specifically address small-scale wind turbines, which are well-suited to local and compact production of electrical energy on a small scale, such as urban and rural infrastructure installations. Existing literature concentrates on large scale wind turbines which run in unobstructed, mostly constant wind profiles. However urban installations generally must cope with rapidly changing wind patterns. To bridge this gap, we formulate and implement an RL strategy using the Markov chain Monte Carlo (MCMC) algorithm to optimize the long-term energy output of a wind turbine. Our MCMC-based RL algorithm is a model-free and gradient-free algorithm, in which the designer does not have to know the precise dynamics of the plant and its uncertainties. Our method addresses the uncertainties by using a multiplicative reward structure, in contrast with additive reward used in conventional RL approaches. We have shown numerically that the method specifically overcomes the shortcomings typically associated with conventional solutions, including, but not limited to, component aging, modeling errors, and inaccuracies in the estimation of wind speed patterns. Our results show that the proposed method is especially successful in capturing power from wind transients;by modulating the generator load and hence the rotor torque load, so that the rotor tip speed quickly reaches the optimum value for the anticipated wind speed. This ratio of rotor tip speed to wind speed is known to be critical in wind power applications. The wind to load energy efficiency of the proposed method was shown to be superior to two other methods;the classical maximum power point tracking method and a generator controlled by deep deterministic policy gradient (DDPG) method. © 2022, CC BY-NC-ND.

关键词： Reinforcement learning

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Solving Multi-Period Line Planning with Resource Transfers: Logic-Based Benders Decomposition

SSRN

引用

SSRN 2024年

作者： Digehsara, Amin Ahmadi Şahin, Güvenç Borndörfer, Ralf Faculty of Management University of British Columbia 1137 Alumni Avenue KelownaBCV1V 1V7 Canada Faculty of Engineering and Natural Sciences Industrial Engineering Program Sabanci University Istanbul Tuzla34956 Turkey Zuse Institute Berlin Takustrasse 7 Berlin14195 Germany

The multi-period line planning problem with vehicle rotation is an integrated optimization problem seeking to determine the optimal combination of lines and their service frequencies. It incorporates vehicle rotation strategies, periodically rotating vehicles between designated pairs of lines to optimize resource utilization and adapt to passenger fluctuations. While this integration offers advantages, it also notably increases computational complexities, especially for medium-to-large scale problem instances. Our study addresses these computational hurdles by introducing two novel logic-based decomposition algorithms that leverage specific problem features. The first algorithm, a constraint propagation method, systematically narrows down feasible solutions by efficiently propagating constraints throughout the problem space. The second approach applies the max-flow min-cut theorem, a powerful tool traditionally used in network flow problems, to enhance the efficiency of the solution process. Extensive computational experiments were conducted on real-world public transportation networks of varying sizes, providing a robust testbed for our algorithms. The results are compelling: both algorithms outperform existing commercial solvers in terms of solution quality and computational time. Moreover, the max-flow min-cut decomposition consistently outperforms the Constraint Propagation method in both solution accuracy and computational efficiency. © 2024, The Authors. All rights reserved.

关键词： Urban transportation

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Designing blend polymersomes co-loaded with HRH peptide and sphingosine for the treatment of ocular neovascularization

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Journal of Drug Delivery Science and Technology 2025年 111卷

作者： Abuzer Alp Yetisgin Ozlem Kutlu Sibel Cetinel Nanotechnology Research and Application Center (SUNUM) Sabanci University Istanbul 34956 Turkey Faculty of Engineering and Natural Sciences Materials Science and Nano-Engineering Program Sabanci University Istanbul 34956 Turkey Faculty of Engineering and Natural Sciences Molecular Biology Genetics and Bioengineering Program Sabanci University Istanbul 34956 Turkey

Ocular neovascularization threatens millions around the world, and current anti-VEGF drugs could not satisfy the clinical needs. Polymersomes, which could penetrate ocular barriers, have recently gained attention as ocular drug delivery systems. In our previous study, high therapeutic efficiency of HA-Sph polymersomes for ocular neovascularization was demonstrated with sphingosine-associated toxicity towards corneal cells. In this study, we developed a novel blend polymersome system composed of two hyaluronan-based amphiphilic polymers that decreases the final sphingosine concentration. The blend polymersomes were incorporated with a second anti-angiogenic compound (HRH peptide) along with sphingosine and exhibited size, zeta potential, and encapsulation efficiency of 141.3 ± 12.2 nm, −25.8 ± 6.2 mV, and 79.8 ± 6.8 %, respectively. The final blend polymersomes system released 35.2 % HRH peptide over a week and this amount is doubled (70.5 %) via enzymatic degradation. Notably, we found that HRH-loaded blend polymersomes significantly reduced the proliferation of HUVECs while not impacting the viability of retinal pigment epithelial and corneal cells. Moreover, in vitro tube formation was significantly reduced by using HRH-loaded polymersomes, compared to HRH alone and ranibizumab groups. The overall tube length was reduced by 29.6 % and 38.1 % compared to normal and excess VEGF controls, respectively. We further observed the self-targeting capability of polymersomes through uptake via CD44 receptors on endothelial cells. Overall, this dual-drug-loaded, blend polymersomes system introduces a safer and more effective alternative to previous HA-Sph platforms and presents a promising, non-invasive approach for treating ocular neovascularization.

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FXAI: Fusing XAI for Predicting COVID-19 Using Diverse Chest X - Ray Images

FXAI: Fusing XAI for Predicting COVID-19 Using Diverse Chest...

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International Conference on Intelligent System and Knowledge engineering, ISKE

作者： Radhwan A. A. Saleh Shawqi Mohammed Farea Zaid Al-Huda Metin Ertunc Daniel Kvak Mugahed A. Al-antari Department of Mechatronics Engineering Kocaeli University Kocaeli Turkey Faculty of Engineering and Natural Sciences Sabanci University Istanbul Turkey School of Computing and Artificial Intelligence Southwest Jiaotong University Sichuan China CEO and Leader of AI R&D Carebot Ltd. Prague Czech Republic Department of Artificial Intelligence DaeyangAICenter Sejong University Seoul Republic of Korea

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