Browsing by Author "Demir, A."

Now showing 1 - 3 of 3

Citation - WoS: 9
Citation - Scopus: 6
Identification of the Unknown Coefficient in a Quasi-Linear Parabolic Equation by a Semigroup Approach Proceedings of the International Congress in Honour of Professor Hari M. Srivastava
(Springer International Publishing Ag, 2013) Ozbilge, E.; Demir, A.
This article presents a semigroup approach to the mathematical analysis of the inverse coefficient problems of identifying the unknown coefficient [InlineEquation not available: see fulltext.] in the quasi-linear parabolic equation [InlineEquation not available: see fulltext.] with Dirichlet boundary conditions [InlineEquation not available: see fulltext.], [InlineEquation not available: see fulltext.]. It is shown that the unknown coefficient [InlineEquation not available: see fulltext.] can be approximately determined via the semigroup approach. © 2013 Ozbilge and Demir; licensee Springer. © 2013 Elsevier B.V., All rights reserved.
Peptide-Nanoparticle Platforms for Antisense Therapeutics: A Coarse-Grained Modeling Approach to Brain Delivery
(Elsevier Ltd, 2026) Uner, B.Y.; Demir, A.; Zhou, P.; Taşkiran, E.Z.; Wassenaar, T.
Traumatic brain injury (TBI) is a leading cause of long-term neurological deficits, often resulting in complex, unresolved molecular and cellular dysfunctions. Among these, gene–circuit disruptions—particularly those affecting neuroinflammation, oxidative stress, and mitochondrial dynamics—have emerged as critical mediators of post-traumatic neuropathology. In this study, we utilized artificial intelligence (AI)-driven proteomics and RNA sequence integration to map altered signaling pathways following TBI. Computational predictions identified specific gene–circuit nodes susceptible to therapeutic intervention, including redox-sensitive mitochondrial regulators and genes involved in the neuroimmune interface. Importantly, although our analyses are derived from rodent models, the conserved signaling pathways and regulatory circuits identified here provide a translational window with strong relevance to human TBI pathophysiology, thereby bridging preclinical findings with potential therapeutic application. Based on these insights, we designed a suite of responsive nanoparticle formulations optimized in silico for targeted delivery to dysregulated brain regions. These carriers incorporated ligands targeting disrupted circuits and incorporated redox-sensitive release mechanisms. Our platform demonstrates the feasibility of a closed-loop, data-guided strategy that integrates AI-based gene network profiling with rational nanocarrier design. This approach provides a scalable framework for precision neurotherapeutics, particularly for complex disorders such as TBI where conventional monotherapies have proven inadequate. © 2026 Elsevier Ltd.
Water Consumption Dynamics in Izmir: Analyzing Influences of District, Seasonality, and External Events
(Institute of Electrical and Electronics Engineers Inc., 2024) Arslan, A.; Titiz, I.E.; Gürcan, E.C.; Demir, A.
This study investigates the patterns of water consumption in Izmir from January 2015 to January 2024, using a dataset provided by Izmir's Open Data Portal. The research aims to understand how different factors such as district, seasons, and external events like the COVID-19 pandemic influence water usage across the city. By employing statistical analysis and machine learning models including Decision Trees and K-means, this project identifies significant spatial and temporal variations in water consumption. This analysis not only aids in the efficient management of water resources but also serves as a foundation for future predictive modeling and sustainability efforts in urban settings. © 2024 IEEE.