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3D Bioprinting Scaffold of Gelatine Reinforced-Zinc Nanoparticles Synthesized by Green Synthesis: Comparative Evaluation of Mechanical and Thermal Properties
No full textThe development of sustainable, biocompatible, and mechanically robust biomaterials is essential for nextgeneration
biomedical applications. In this study, zinc oxide nanoparticles (ZnONPs) were synthesized using a
green, gelatin-mediated approach and incorporated into gelatin-based bioinks to fabricate 3D-bioprinted composite
scaffolds. Structural analyses confirmed the successful formation of crystalline ZnONPs and their uniform
dispersion within the gelatin matrix. Mechanical testing demonstrated a clear concentration-dependent
enhancement, with Young’s modulus, tensile strength, and toughness increasing up to 67%, 67%, and 110%,
respectively, in Gel–ZnONPs(5) compared to pristine gelatin. Antibacterial assays revealed strong inhibition
against S. aureus and Escherichia coli, with zones reaching 23.1 mm and 20.2 mm, approaching the efficacy of
Gentamicin. Cytocompatibility remained high across all tested concentrations, with cell viability consistently
exceeding 85%, fulfilling ISO 10,993–5 non-cytotoxicity criteria. The 3D bioprinting process yielded structurally
stable scaffolds with precise geometry, demonstrating the synergistic advantages of combining green nanoparticle
synthesis with additive manufacturing. Overall, the results highlight Gel–ZnONPs composites as promising
candidates for tissue engineering, wound management, and antimicrobial biomedical devices, offering a
sustainable strategy to enhance functionality, mechanical integrity, and biological performance in biofabricated
materials
Heterogeneous Co-Movements in US State and Metropolitan Statistical Area Housing Prices: New Insights from Quantile Factor Models
No full textPrevious research demonstrates that housing prices frequently move in tandem across regions, underscoring the interconnectedness and correlation present within housing markets. Building on this foundation, our study advances the analysis by examining quantile comovements and the synchronization between local and national housing markets. Using the quantile factor model across the full distribution of housing prices, we identify distinct factor structures at the lower and upper tails that contrast with those observed in the middle of the distribution. This analytic framework enables the detection of previously hidden factors influencing housing markets. With this approach, we illuminate how housing price dynamics interact across market segments, price levels, and geographic areas. Our findings reveal that co-movements can vary substantially across low, stable, and high housing price regimes, thus providing more comprehensive and nuanced economic insights into the complex nature of housing price fluctuations
Revisiting Workplace Mobbing: Tweets and Qualitative Analysis in Türkiye Case
No full textThe globality of mobbing points to huge influence of economic issues over social and societal aspects in the
life dynamics of work. COVID-19 presents a new kind of crisis that transforms these factors and establishes
new norms in working life simultaneously. Mobbing is to be defined, in this perspective, as the modifications
of situation of work and expectations of workers retraining the boundaries and manifestation of mobbing. This
study examines the impact of dislocating mobbing, which is a kind of violence that deteriorates the quality of
life for employees as well as workplace productivity, in terms of the new dynamics of mobbing and existing
dimensions of mobbing-the COVID-19 perspective. Mixed methods research was carried out through macrolevel
collection and analysis of tweet data alongside micro-level focus group interviews. While macro findings
identified general mobbing dimensions, micro findings revealed more indirect, implicit and specific means
of power imbalance. The findings of the research identify emerging gaps in organisational practice regarding
diversity and inclusion via the lens of increasing and latent specific power imbalances. In both data analyses,
a new dimension of mobbing was identified: the perception of injustice. The emergence of injustice as a new
dimension provides a more comprehensive perspective on current practices. The findings of this research
are expected to provide valid approaches towards reiteration of existing organisational practices and human
resources training
An Adaptive Hybrid SCSOWOA Algorithm for Generalized Multi-level Thresholding in Multi-organ Medical Image Segmentation
No full textThis study presents a novel hybrid optimization model that combines the complementary aspects of Sand Cat Swarm Optimization (SCSO) and Whale Optimization Algorithm (WOA) to solve the multi-level image thresholding problem. The proposed approach utilizes an adaptive two-stage mechanism that balances the high exploration capacity of SCSO with the concentrated local search capability of WOA, aiming to maximize inter-class variance in the histogram-based thresholding process. Various experiments are conducted on lung cancer, prostate, and mixed medical image datasets. Results demonstrate that modified SCSOWOA achieves superior performance across all datasets. For LC25000, it attains PSNR 27.9453 dB, SSIM 0.9340, FSIM 0.9542, Dice coefficient 0.8901, and Jaccard index 0.8034 at T = 12. For prostate, PSNR reaches 28.3965 dB, SSIM 0.7532, FSIM 0.8170, Dice 0.9215, and Jaccard 0.8593. In the MSD dataset, SCSOWOA achieves PSNR 29.3244 dB, SSIM 0.7118, FSIM 0.7562, Dice 0.8901, and Jaccard 0.8034, indicating consistent performance across diverse organs and modalities. The method also demonstrates high computational efficiency, with an average execution time of 1.3221 s, offering up to 40% speed improvement over conventional metaheuristics such as PSO and GWO. Overall, proposed method provides high-accuracy, low-variance, and computationally efficient segmentation, preserving both structural and perceptual fidelity. These results confirm the method’s robustness, generalizability, and practical applicability for AI-assisted diagnostic systems across histopathological and medical imaging contexts, balancing precision, structural preservation, and speed for real-world clinical deployment
Discrete Puma Optimizer to Solve Combinatorial Optimization Problems
No full textDiscrete and combinatorial optimization problems such as routing, scheduling, and resource allocation present high computational complexity, limiting the effectiveness of classical exact optimization methods. Most existing metaheuristic (MH) algorithms are originally designed for continuous domains and require transformation procedures that often degrade performance when applied to discrete problems. This study introduces the discrete puma optimizer (DPO), a new variant metaheuristic algorithm developed to operate directly within discrete search spaces by employing discrete-specific operators and adaptive exploration–exploitation strategies. DPO is applied to 7 real-world optimization problems, including the Traveling Salesman Problem, Smart Grid Optimization, Factory Production Planning, Vehicle Routing Problem, Modern TSP, Team Orienteering Problem, and Electric Vehicle Charging Station Location Optimization, and evaluated on a total of 22 small-, medium-, and large-scale dataset instances. The performance of DPO is benchmarked against 9 various and well-known MH algorithms. Experimental results show that DPO attains superior best and mean solutions, lower variance, and faster stabilization in convergence behavior. Wilcoxon signed-rank tests confirm the statistical significance of the observed improvements, particularly in large-scale scenarios where competing methods show marked degradation. Comprehensive cross-problem rankings further illustrate DPO’s enhanced generalizability and scalability. These results position DPO as an effective and robust approach for real-world large-scale discrete optimization tasks
The Impact of Personality Traits, Differentiation of Self, Self-Reflection, and Insight on Emotional Authenticity: A Study on Young Adults
No full textBu çalışmanın amacı, benliğin ayrımlaşması, büyük beşli kişilik özellikleri, öz yansıtma ve içgörü değişkenlerinin duygusal otantiklik üzerindeki etkisinin incelenmesidir. Araştırma kapsamında, 18-35 yaş arası genç yetişkinden oluşan 401 katılımcı tarafından çevrimiçi ortamda Duygusal Otantiklik Ölçeği, Büyük Beş-50 Kişilik Testi, Benliğin Ayrımlaşması Ölçeği ve Kendine Yansıtma ve İçgörü Ölçeği ve çeşitli demografik özellikleri sorgulayan bir demografik form yanıtlanmıştır. Veri analizi için Duygusal Otantiklik Ölçeği toplam puanı, otantik davranış, duygusal kaçınma ve dış etkiyi kabullenme alt boyutları ile her bir bağımsız değişkenin alt boyutları arasındaki ilişkiyi incelemek amacıyla Pearson Korelasyon Analizi; bağımsız değişkenlerin alt boyutlarının Duygusal Otantiklik ve alt boyutlarını nasıl etkilediğini incelemek amacıyla Stepwise Tekniği ile Çoklu Doğrusal Regresyon Modelleri tahmin edilmiştir. Bulgular, duygusal otantikliği en iyi şekilde yordayan faktörlerin ben pozisyonu, duygusal kopma, içgörü, başkalarına bağımlılık ve öz yansıtma değişkenleri olduğunu; bu nedenle duygusal otantikliğin oldukça karmaşık bir yapıda olan ve birçok farklı faktörden etkilenen kapsamlı bir konsept olduğunu göstermektedir. Mevcut bulgular alanyazın ışığında tartışılmıştır.The aim of this study is to examine the impact of differentiation of self, Big Five personality traits, self-reflection, and insight on emotional authenticity. Within the scope of the research, a total of 401 participants aged 18-35 completed the Emotional Authenticity Scale, the Big Five-50 Personality Test, the Differentiation of Self Inventory, and the Self-Reflection and Insight Scale, along with an online demographic questionnaire. For data analysis, Pearson Correlation Analysis was conducted to examine the relationships between the total score of the Emotional Authenticity Scale—along with its subdimensions of authentic behavior, emotional avoidance, and acceptance of external influence—and the subdimensions of each independent variable. In addition, Stepwise Multiple Linear Regression Models were estimated to investigate how the subdimensions of the independent variables predict emotional authenticity and its subcomponents. The findings indicated that the most significant predictors of emotional authenticity are I-position, emotional cutoff, insight, fusion with others, and self-reflection. These results suggested that emotional authenticity is a multifaceted construct influenced by a complex interplay of psychological variables. The findings are discussed in light of the existing literature
An Adaptive Hybrid Metaheuristic Algorithm for Satellite Images in Remote Sensing Image Segmentation
No full textIn recent years, the effective processing of high-resolution color satellite images obtained through remote sensing technologies has become a critical requirement in key applications such as environmental monitoring, urban planning, and disaster management. Color satellite imagery offers rich information, enabling more detailed analysis of land use, vegetation cover, and other surface characteristics. In this context, multi-level image thresholding techniques are widely employed to enhance segmentation quality; however, achieving both high accuracy and low computational cost in complex scenes remains a significant challenge. To address these challenges, this study proposes the RSA-HGSO algorithm, an adaptive hybrid structure that combines the Reptile Search Algorithm (RSA) and Henry Gas Solubility Optimization (HGSO). By integrating RSA’s global exploration capabilities with HGSO’s local exploitation strengths, RSA-HGSO ensures both solution diversity and fast, stable convergence. Experimental analyses conducted on high-resolution color satellite images from the NASA Visible Earth dataset demonstrate the algorithm’s efficient performance.With average values of PSNR 24.59 dB, SSIM 0.9088, FSIM 0.9233, and NCC 0.9663, RSA–HGSO outperforms comparative methods in terms of both accuracy and structural integrity. Furthermore, correlation analyses indicate that the original pixel intensity ranking is preserved at a rate of 91% after segmentation (Pearson 0.9557, Spearman 0.9549), and neighborhood relationships are largely maintained (Pearson 0.8042, Spearman 0.8051). Ablation studies further confirm that RSA–HGSO not only integrates the performance of its component algorithms but also complements exploration and exploitation processes in a synergistic manner, resulting in more balanced and robust outcomes. The findings suggest that the RSA–HGSO algorithm offers a balanced, scalable, and computationally efficient solution to the problem of color multi-level satellite image thresholding and presents a practical
method applicable to real-world tasks such as disaster management, agricultural monitoring, and urban planning
Efficient Olive Leaf Disease Detection via Hybrid Artificial Rabbit Optimization and Genetic Algorithm-Based Deep Feature Selection
No full textArtificial intelligence (AI)-supported agricultural disease detection has become increasingly important for addressing global food security challenges. In this study, a hybrid metaheuristic optimization-based feature selection approach is proposed for the detection of peacock eye disease (Venturia oleaginea) on olive leaves. The proposed method combines Artificial Rabbit Optimization (ARO) and Genetic Algorithm (GA) strategies to balance global exploration and local exploitation during feature selection. Comprehensive experiments conducted on a dataset of 954 olive leaf images demonstrate that the proposed approach achieves an F1-score of 99.7% while reducing the feature dimensionality by 95%, selecting only 100 features from ResNet101. Statistical analysis confirms that the method significantly outperforms standalone GA and ARO approaches (p < 0.05, paired t-tests), demonstrating superior long-term convergence behavior and a 47–56% reduction in performance variance across repeated runs. Compared to existing approaches in the literature, the proposed method attains competitive or superior accuracy with substantially fewer features, indicating a marked reduction in computational complexity. These results suggest that the proposed hybrid feature selection framework has strong potential for deployment in resource-constrained agricultural monitoring scenarios, where efficient inference and reduced model complexity are critical
Children’s Participation Rights in Practice: Exploring Circle Time as a Space for Participation in Preschool Classrooms
No full textChildren’s Participation Rights (CPR) are central to international policy debates on early childhood education. However, the practical enactment remains contested due to complex adult– child interactions across contexts. This context-sensitive investigation examines Turkish preschool classrooms. Using a multiple case study design, it explores verbal teacher–child interactions during 16 h of circle time observations with two teachers and 45 children (60–72 months). Framed within Lundy’s Participation Model, deductive reflexive thematic analysis highlights that structural arrangements of the classroom environment and procedural practices of teacher–child interactions simultaneously function to make circle time a potentially participatory space. The findings affirm that the activation of CPR in practice mostly depends on teachers’ dispositions, internal pedagogical practices, and the facilitation of an active listening environment. Children’s expressions were mostly limited to teacher sanctions, reducing chances to incorporate their views into decision-making. Findings also revealed that teachers’ recognition of their internal barriers is equally as decisive as external constraints in shaping CPR enactment. By outlining complexities of CPR in practice, this study advances understanding of the pedagogical insights for reconsidering everyday routines as sites of participation, improving educational quality. It also informs future research for genuinely participatory educational environments
Trends of Relative Commodity Prices with Comovements and Structural Breaks
No full textThe Prebisch-Singer (1950, PS) hypothesis posits a long-term decline in the relative prices of primary commodities (natural resources) compared to manufactured goods. This hypothesis carries important implications for resource management, economic growth, and the terms of trade in developing countries. It raises two critical questions that are inherently interlinked: (1) Do relative commodity prices exhibit a negative trend? and (2) Are these prices non-stationary? When addressing these questions, prior analyses have often overlooked the potential influence of cross-correlations among relative commodity prices. In this study, we jointly address these questions while explicitly accounting for cross-correlations. To achieve this, we first employ a dynamic factor model, which effectively captures the shared variations across commodity prices. Additionally, we incorporate the Fourier function to model smooth structural breaks, allowing for the identification of gradual changes in the underlying trend. The combined use of these methods yields significant insights. Our findings provide robust evidence supporting the PS hypothesis over the period 1900–2020, highlighting the persistent and systematic decline in the relative prices of primary commodities