Neural Architecture Search for Biomedical Image Classification: A Comparative Study Across Data Modalities

Deep neural networks have significantly advanced medical image classification across various modalities and tasks. However, manually designing these networks is often time-consuming and suboptimal. Neural Architecture Search (NAS) automates this process, potentially finding more efficient and effective models. This study provides a comprehensive comparative analysis of our two NAS methods, PBC-NAS and BioNAS, across multiple biomedical image classification tasks using the MedMNIST dataset. Our experiments evaluate these methods based on classification performance (Accuracy (ACC) and Area Under the Curve (AUC)) and computational complexity (Floating Point Operation Counts). Results demonstrate that BioNAS models slightly outperform PBC-NAS models in accuracy, with BioNAS-2 achieving the highest average accuracy of 0.848. However, PBC-NAS models exhibit superior computational efficiency, with PBC-NAS-2 achieving the lowest average FLOPs of 0.82 GB. Both methods outperform state-of-the-art architectures like ResNet-18 and ResNet- 50 and AutoML frameworks such as auto-sklearn, AutoKeras, and Google AutoML. Additionally, PBC-NAS and BioNAS outperform other NAS studies in average ACC results (except MSTF-NAS), and show highly competitive results in average AUC. We conduct extensive ablation studies to investigate the impact of architectural parameters, the effectiveness of fine-tuning, search space efficiency, and the discriminative performance of generated architectures. These studies reveal that larger filter sizes and specific numbers of stacks or modules enhance performance. Fine-tuning existing architectures can achieve nearly optimal results without separating NAS for each dataset. Furthermore, we analyze search space efficiency, uncovering patterns in frequently selected operations and architectural choices. This study highlights the strengths and efficiencies of PBCNAS and BioNAS, providing valuable insights and guidance for future research and practical applications in biomedical image classification

RG‑ACA: Efficient and Adaptive Routing Method for Internet of Things Based on Metaheuristic Approach

In Internet of Things (IoT) systems that use various and limited devices, efficient use of resources is critical. However, since this problem is inherently complex, an enhanced meta-heuristic approach is proposed. In this paper, a new method called Reverse Gauss Ant Colony Algorithm (RG-ACA) is suggested for the design of efficient routing protocol for these systems. It is analyzed on the network lifetime, throughput and packet delivery parameters and the results are compared with HEEL, I-HEEL, EES-LEACH and iABC algorithms. The RG-ACA algorithm ranked first in all categories compared to other related current studies with 94%, 96 Kbps and 91% performance in these three parameters, respectively

Machine Learning Regression for Assessing Sensing Performance and Anticancer Potential of Oolong Tea-Derived Silver Nanoparticles

In this study, machine learning (ML) algorithms were employed to predict analyte concentrations using sensing results and evaluate the anticancer effects of nanostructures. Multifunctional oolong tea extract-mediated silver nanoparticles (OTE-Ag NPs) were synthesized via a photo/ultrasound method and utilized in various applications, including a smartphone-based H2O2 sensor and electrochemical sensors for urea and fructose. Key features were extracted from electrochemical results, and feature importance analysis was used to select the most predictive features. The artificial neural network (ANN) model provided accurate predictions, particularly strong for urea (R2 = 0.8575, RMSE = 0.4266, MAE = 0.3380). The study revealed the selective toxicity of OTE-Ag NPs to MCF-7 breast cancer cells through analyses of cytotoxicity, apoptosis, cell cycle phases, and CD44 surface marker expression using Annexin V/PI dye and flow cytometry. Experimental results demonstrated that OTE-Ag NPs suppressed MCF-7 cell proliferation while exhibiting lower cytotoxicity in normal HUVEC cells (46% cell death). OTE-Ag NPs arrested MCF-7 cells in the G2/M phase, induced apoptosis, and reduced CD44 expression, suggesting metastasis suppression. The CD44+/CD24- ratio decreased from 84.79% in control MCF-7 cells to 47.7% in OTE-Ag NP-treated cells. Overall, OTE-Ag NPs significantly inhibited MCF-7 cell proliferation through the apoptotic pathway by regulating the cell cycle in the G2/M phase

First Photometric Investigation of V517 Cam Combined with Ground-Based and TESS Data

The observations of eclipsing binary systems are of great importance in astrophysics, as they allow direct measurements of fundamental stellar parameters. By analysing high-quality space-based observations with ground-based photometric data, it becomes possible to detect these fundamental parameters with greater precision using multicolour photometry. Here, we report the first photometric analysis results of the V517 Cam eclipsing binary system by combining the Transiting Exoplanet Survey Satellite (TESS) light curve and new CCD observations in BVRI filters, obtained with a 60 cm robotic telescope (T60) at the TÜBİTAK National Observatory

Resveratrol-Loaded PCL-PEG/GO/HAP Biocomposite Bone Membranes: Evaluation of Mechanical Properties, Release Kinetics and Cellular Response

In this study, biocomposite membranes were developed by incorporating resveratrol (RSV)-loaded PCL-PEG composites, modified with graphene oxide (GO) and hydroxyapatite (HAP). The aim was to enhance hydrophilicity with GO and improve bioactivity with HAP. The release kinetics of RSV was evaluated by using Franz diffusion cells and compared with various kinetic models, including Korsmeyer-Peppas, Higuchi, and Baker, all of which showed high correlation coefficients (R²) close to 0.99. Mechanical tests was performed to determine the suitability of these membranes for tissue engineering applications. The composite membrane modified with GO and HAP exhibited tensile strength of 105.2 ± 5.8 MPa, tensile modulus of 3895 ± 159 MPa, elongation at break of 8.4 ± 0.9%, and toughness of 5.88 ± 0.46 MJ/m³. In vitro cell adhesion studies, visualized using DAPI fluorescence staining, demonstrated increased cell adhesion to the composite membranes over periods of 1, 3, 5, 7, and 14 days. These findings highlight the potential of the RSV-loaded PCL-PEG membranes, enhanced with GO and HAP, for applications in bone tissue engineering

Advances in Sand Cat Swarm Optimization: A Comprehensive Study

This study provides an in-depth review and analysis of the nature-inspired Sand Cat Swarm Optimization (SCSO) algorithm. The SCSO algorithm effectively focuses on exploring solution areas inspired by sand cat hearing and finding the most suitable solutions for their hunting behavior. This algorithm is easily adaptable to various problems due to its stability, low-cost, flexibility, simple implementation, simplicity, derivative-free mechanism, and reasonable computation time. For these reasons, although it was published recently, it has begun to attract the attention of researchers. SCSO-based research has been presented in prestigious international journals such as Elsevier, Springer, MDPI, and IEEE since its inception in 2022. The studies cited in this paper are examined in three categories: improved, hybrid, and adapted. Research trends show that 39, 21, and 40% of SCSO-based studies fall into these three categories, respectively. Additionally, research on solving various problems inspired by the SCSO algorithm is discussed from two different perspectives: global optimizations and real-world applications. Analysis of the applications shows that 15 and 85% of the studies belong to these two fields, respectively

Mechanical Performance and ANN-Based Prediction of Co-Cr Dental Alloys with Gyroid Cellular Structures Produced by LPBF Technology

Gyroid structures exhibit significant potential in the fields of lightweight structural design, heat transfer, energy absorption, and biological applications. The use of gyroid for implants in dentistry is currently not sufficiently widespread. The research encompasses design, compression testing, cellular investigation using a digital microscope, and the application of artificial neural networks (ANNs) using data gained from the compression test. The ANN study and the test phase in which gyroid geometries are addressed by dental three-point bending tests are novel in this field. In the field of dentistry, this study compares the usability of five distinct gyroid design characteristics, including one model without a gyroid structure. During the testing, we found that the m1 model had an average maximum strength of 600 N, while the m3 model achieved 230 N. The remaining models achieved an approximate strength of 200 N. In the mechanical performance evaluation of the samples, a 40% weight reduction was achieved. An ANN model has been developed to predict the force experienced by gyroid structures under certain deformations depending on the infill ratio. This model was trained with data obtained from a three-point bending test. Using grid search and Monte Carlo cross-validation, the optimal multilayer perceptron structure was determined to have 12 neurons in the hidden layer, a mini-batch size of 8, and a learning rate of 0.0001. The Adam optimization algorithm was used to train the ANN model, which was constructed using the TensorFlow library. Evaluation metrics were used to test the model’s performance, and the results showed strong generalization capability and high accuracy with coefficient of determination (R2) of 0.997, mean squared error (MSE) of 3.337E-05 kN2, root mean square error of (RMSE) 0.005777 kN, and mean absolute error (MAE) of 0.003633 kN on the test dataset

Çocuk Araştırmalarında Mimarlık, Yapılı Çevre ve Kentsel Alanda Metodoloji ve Teknikler

The built environment's design greatly influences children's growth, well-being, and daily experiences. As urbanization advances and the number of children worldwide grows, there is an increasing need to examine how urban and architectural spaces might be adapted to meet the special needs of children and encourage their holistic development. In recent decades, a large body of literature has evolved from various disciplines, including architecture, urban and regional planning, and interior design, that investigates children's perspectives on their personal experiences. This paper presents a summary of methodological and ethical factors that researchers should consider when designing research projects with children, as well as methodologies and procedures for extracting their ideas in architecture. The publication invites researchers to think critically about these methodological concerns and the processes they choose to apply in this article, as they are intended to have a scientific impact on data collection and analysis for methodology in children's research. A combination of techniques was employed in the research after doing a comprehensive literature review, scientific mapping, and content analysis. The study's findings indicate that concepts such as children, education, playground, and inclusive design are useful. Furthermore, extensive analyses of the methodology used in children research were offered.Yapılı çevrenin tasarımı, çocukların gelişimini, refahını ve günlük deneyimlerini şekillendirmede önemli bir rol oynar. Kentleşme ilerledikçe ve dünya çapındaki çocuk sayısı artmaya devam ettikçe, kentsel ve mimari alanların çocukların benzersiz gereksinimlerini karşılamak ve bütünsel büyümelerini desteklemek için nasıl uyarlanabileceğini araştırmak için artan bir zorunluluk vardır. Son yıllarda, mimarlık, kentsel ve bölgesel planlama ve iç mekan tasarımı dahil olmak üzere çeşitli disiplinlerden, çocukların kendi deneyimlerine ilişkin bakış açılarını inceleyen önemli bir literatür gövdesi ortaya çıkmıştır. Bu makale, araştırmacıların çocukları içeren araştırma çalışmalarını planlarken dikkate almaları gereken metodolojik ve etik hususların yanı sıra mimarlıkta düşüncelerini ortaya çıkarmak için yöntem ve prosedürlere genel bir bakış sunmaktadır. Makale, araştırmacıları bu metodolojik endişeler ve bu makalede kullanmayı seçtikleri prosedürler hakkında eleştirel düşünmeye teşvik etmektedir, çünkü bunların toplanan veriler ve çocuk araştırmaları için metodoloji üzerindeki veri analizi üzerinde bilimsel bir etkiye sahip olması amaçlanmaktadır. Araştırma yönteminde, sistematik literatür incelemesi, bilimsel haritalama ve içerik analizi yapıldıktan sonra karma bir yöntem kullanılmıştır. Araştırma bulguları, çocuklar, eğitim, oyun alanı ve kapsayıcı tasarım kelimelerinin etkili olduğunu göstermektedir. Ayrıca, çocuklarla yürütülen araştırmanın metodolojisi hakkında ayrıntılı analizler sunulmuştur

Examining the Effective Role of Artificial Intelligence in the Interconnected Crisis of Climate Change and Human Migration

Introduction: Climate change is a key driver of human migration, particularly in regions facing resource scarcity and extreme weather events. Understanding migration patterns is essential for effective policy responses. Objectives: This multidisciplinary study applies data mining techniques to identify key environmental and socioeconomic factors influencing climate-induced migration and enhance predictive modeling for policy decision-making. Methods: Machine learning techniques, including spatiotemporal clustering and regression analysis, are applied to migration data from UNDESA and IOM’s CLIMB Database. Climate indicators such as temperature anomalies, drought frequency, and water stress are analyzed. Results: Findings reveal strong correlations between climate stressors and migration trends. Water scarcity and prolonged droughts significantly drive displacement, with predictive models demonstrating high accuracy in forecasting migration flows. Conclusions: Data mining is a valuable tool for analyzing and predicting climate-induced migration. Findings emphasize the need for proactive climate adaptation strategies and data-driven migration policies. Future research should integrate real-time monitoring and geospatial AI to improve forecasting accuracy

Development and Characterization of a Polycaprolactone/Graphene Oxide Scaffold for Meniscus Cartilage Regeneration Using 3D Bioprinting

Meniscus injuries represent a critical challenge in orthopedic medicine due to the limited self-healing capacity of the tissue. This study presents the development and characterization of polycaprolactone/graphene oxide (PCL/GO) scaffolds fabricated using 3D bioprinting technology for meniscus cartilage regeneration.Methods: GO was incorporated at varying concentrations (1%, 3%, 5% w/w) to enhance the bioactivity, mechanical, thermal, and rheological properties of PCL scaffolds. Results: Rheological analyses revealed that GO significantly improved the storage modulus (G’) from 36.1 Pa to 97.1 Pa and the yield shear stress from 97.2 Pa to 507.1 Pa, demonstrating enhanced elasticity and flow resistance. Mechanical testing showed that scaffolds with 1% GO achieved an optimal balance, with an elasticmodulus of 614MPa and ultimate tensile strength of 46.3MPa, closely mimicking the native meniscus’s mechanical behavior. FTIR analysis confirmed the successful integration of GO into the PCL matrix without disrupting its chemical integrity, while DSC analysis indicated improved thermal stability, with increases in melting temperatures. SEManalysis demonstrated a roughened surface morphology conducive to cellular adhesion and proliferation. Fluorescence microscopy using DAPI staining revealed enhanced cell attachment and regular nuclear distribution on PCL/GO scaffolds, particularly at lower GO concentrations. Antibacterial assays exhibited larger inhibition zones against E. coli and S. aureus, while cytotoxicity tests confirmed the biocompatibility of the PCL/GO scaffolds with fibroblast cells. Conclusions: This study highlights the potential of PCL/GO 3D-printed scaffolds as biofunctional platforms for meniscus tissue engineering, combining favorable mechanical, rheological, biological, and antibacterial properties