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

A self-organizing winner-takes-all mechanism based on heterosynaptic plasticity

Bu çalışmada, biyolojik sinir sistemlerinden esinlenerek tasarlanan, heterosinaptik plastisiteye dayalı özdüzenleyici bir Kazanan-Her-Şeyi-Alır mekanizması sunulmaktadır. Önerilen modelde, sinaptik ögrenme, ateşleme zamanlamasına bağlı plastisite yöntemleri yerine kalsiyum-temelli Sinaptik Etiketleme ve Yakalama mekanizması ile gerçekleştirilmiştir. Bu sayede sinaptik güncellemeler, yalnızca lokal etkinliklere degil, aynı zamanda hücre düzeyinde sentezlenen proteinlerin seçici baglanmasına bağlı hale getirilmiştir. Oluşturulan model üç farklı örüntüyü yüksek dogrulukla sınıflandırabilen ateşleyen sinir agları üzerinde test edildi. Eğitim sürecinde postsinaptik nöronlar belirli örüntülere özgüleşmiş ve ateşleme-temelli etiketleme yöntemiyle örüntü tahmini yapılmıştır. Sonuçlar, denetimsiz, biyolojik olarak anlamlı bir ögrenme mekanizmasıyla Kazanan-Her-Şeyi-Alır benzeri ağ yapılarının etkin biçimde oluşturulabilecegini ortaya koymaktadır.In this study, a self-organizing Winner-Takes-All mechanism inspired by biological neural systems and based on heterosynaptic plasticity is proposed. Unlike spike-timingdependent plasticity, synaptic learning in the proposed model is governed by a calcium-based Synaptic Tagging and Capture mechanism. This enables synaptic updates to depend not only on local activity, but also on the selective binding of proteins synthesized at the cellular level. The model was tested on spiking neural networks capable of classifying three distinct patterns with high accuracy. During training, postsynaptic neurons became specialized for specific patterns, and pattern prediction was performed using a spike-based labeling strategy. The results demonstrate that biologically plausible, unsupervised learning mechanisms can effectively lead to the emergence of Winner- Takes-All-like network structures. © 2025 IEEE

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

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

Computational persuasion technologies, explainability, and ethical-legal implications: A systematic literature review

This paper conducts a systematic literature review (SLR) to evaluate the effectiveness of computational persuasion technology (CPT) in the eHealth domain. Over the past fifteen years, CPT has been used in various scenarios, from promoting healthy diets to supporting chronic disease management. Despite the proliferation of intelligent systems and Web-based applications, the ethical and legal nuances of these technologies have become increasingly significant. The review follows a structured methodology, assessing 92 primary studies through sixteen research questions covering demographics, application scenarios, user requirements, objectives, functionalities, technologies, advantages, limitations, proposed solutions, ethical and legal implications, and the role of explainable AI (XAI). The findings indicate that while CPT holds promise in inducing behavioral change, many prototypes remain untested on a large scale (60% of surveyed studies only developed at a conceptual level), and long-term effectiveness is still uncertain (36% report attaining their goals, but none focuses on long-term assessment). The study highlights the need for more comparative analyses of persuasion models and tailored approaches to meet diverse user needs. Ethical and legal concerns, such as patient consent, data privacy, and potential for users' manipulation, are under-explored and require deeper investigation. The paper recommends a bottom-up regulatory approach to create more effective and flexible ethical and legal guidelines for CPT applications. In conclusion, significant advancements have been made in CPT for eHealth, but ongoing research is essential to address current limitations, enhance user acceptability and adherence, and ensure ethical and legal soundness

The new media art world in Turkey: Boundary-work in action

In an age marked by the Post-media condition, the term ‘New Media Art’ remains in use in reference to a professional territory not autonomous, but also different from the territory of Contemporary Art, and its members use various rhetorical tactics to maintain and expand their professional authority. This study aims at analyzing the professional ideology of the New Media Art territory in Turkey using Thomas F. Gieryn’s concept of the ‘boundary-work’. The data obtained via semi-structured in-depth interviews conducted with a purposively sampled population of artists, curators and producers are used to assess whether a common cultural repertoire exists in this territory and whether it conforms to or contradicts its global counterpart. The study finds that since the mid-1990s, the professional ideology of the New Media Art territory in Turkey has developed in parallel with the world, but not monolithically. The curators/producers’ ideology is defined by potential for democratization, intersections of art, science and technology, interaction/interactivity and interdisciplinary collaboration. However, they exhibit two repertoire variants positively correlated with the subjects’ career orientations. The artists’ common ideology is similar to the curators’/producers’, but their cultural repertoires are more flexible thanks to the variety of their individual practices. The study concludes that this duality emerges due to the limited funding sources in Turkey.Ozyeğin University Institute of Social Sciences, Department of Design, Technology and Societ

Assessment of firm capacity in hybrid systems: A Dubai case study on ess sizing

Aiming for carbon-free power systems for future grids has challenges in providing the necessary firm capacity from a power engineering perspective. During capacity planning studies, the variability of renewables can lead to periods of zero firmness, necessitating nonrenewable generation technologies to be added to the candidate list to ensure firmness. However, hybrid systems can provide limited firmness and lower the need for nonrenewable resources. This paper investigates the temporal firmness of the various sizes of energy storage systems combined with a 1MWp photovoltaic system. Using real-Time data from a site in Dubai, the framework simulates system performance hourly and monthly over an entire year. The analysis reveals that hybrid system firmness is affected by seasonal variations in PV output and is highly sensitive to the storage capacity. The study further demonstrates how different storage capacities affect the system's ability to maintain firm capacity. © 2025 IEEE

Differential privacy preserving based framework using blockchain for internet-of-things

The Internet of Things (IoT) has enabled the collection of vast amounts of data that can be used to improve various aspects of our lives. However, the astronomical volume of data generated by these IoT devices has raised significant concerns pertaining to privacy preservation. The amalgamation of the Internet of Things (IoT) with blockchain technology has engendered a promising solution for securing and managing IoT data, but it is still susceptible to privacy breaches. Recently, differential privacy (DP) has been proposed as a promising technique to alleviate these issues. In this paper, we design and propound a complete end-to-end blockchain-based architecture by implementing differential privacy at the stream level generated by IoT devices by deploying Laplace noise and Gaussian noise utilizing low complex cryptography mechanism and fast convergence consensus protocol to surmount the privacy preservation issues in IoT based blockchain network. Our novel DP-based framework introduces the concept of privacy levels as low, medium, and high as set by the data owner and also analyzes the impact of different parameters on the effectiveness of the approach and provides recommendations for tuning them. The workflow of our proposed framework consists of three phases: Data generation phase, Data Sharing phase, and Data Analysis phase. During the Data generation phase, the data owner will first determine the desired level of privacy protection (low, medium, high) and set the privacy budget (epsilon) and sensitivity (delta) of the data. Based on the budget value, the privacy module will generate noise from either Laplace or Gaussian distribution as requested by the data owner. The Data Sharing phase is mainly responsible for transmitting and processing the transactions inside the blockchain network. This is followed by the data analysis phase, which will check for the budget value and the amount of noise added to the data before the noisy data is handed over to the end user. We demonstrate the efficacy of our approach through multiple experimental evaluations and simulation results evince that our approach attains high levels of privacy preservation while upholding data utility and blockchain consistency. Overall, our proposed framework provides a promising solution to the privacy challenges in IoT-based blockchain systems, offering adjustable privacy levels to accommodate different privacy requirements. This DP-based approach and the adjustable privacy levels ensure alignment with the growing regulatory requirements for data privacy, such as GDPR, demonstrating compliance with these regulations and building trust with customers. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024

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

Toward self-sustainable airborne communication networks: Comprehensive modeling and analysis of energy consumption and harvesting

Airborne networks build upon the use of unmanned aerial vehicles (UAVs) and high-altitude platform stations (HAPSs) for wireless access and backhauling and are expected to be instrumental in providing global coverage and ubiquitous connectivity. They can offer a range of benefits, including lower latency and higher data rate capacity per unit area, making them an attractive alternative or complementary solution to low-earth orbit satellites in future non-terrestrial networks. The practical deployment of airborne nodes is restricted by onboard energy limitations, motivating the use of energy harvesting techniques. In this paper, we present an in-depth examination of the power consumption of HAPSs and rotary-wing UAVs. We delve into consumption patterns across various flight phases, shedding light on the multifaceted impact of diverse system and operational parameters on overall energy utilization. We then present a thorough analysis of energy harvesting methods. First, we examine solar energy harvesting and demonstrate its dependence on factors such as operational altitude, geographical location, climate conditions, and daylight duration. Subsequently, we introduce laser power beaming as a more predictable and controllable energy source. Thereafter, we discuss the feasibility of self-sustainable airborne networks based on these energy harvesting techniques and typical energy consumption patterns. © 2005-2012 IEEE.Tamkeen under the Research Institute NYUA