İzmir Institute of Technology (Turkish: İzmir Yüksek Teknoloji Enstitüsü, commonly referred to as İYTE) is a public research university in İzmir, Turkey. İYTE maintains a strong emphasis on the natural sciences and engineering and is the only institute of its kind in Turkey with a special focus on scientific research. İzmir Institute of Technology is often cited among Turkey's top universities. The medium of instruction in all the departments of İYTE is English.
This study examines Triple Helix synergy in the context of place-based innovation and identifies the key factors associated with this synergy by analyzing university-industry-government interactions in the Turkish pharmaceutical industry. ANN, Kernel Density, Global Moran's I, OLS and Panel Data Analyses were respectively applied within the scope of the research. The first finding of the research is that Triple Helix actors contribute directly to place-based innovations by creating synergy in high-density city centers, OIZs, universities, and technoparks. The second finding is that Moran's I test results reveal that spatial proximity does not always have a decisive effect on place-based innovations. In other words, even though actors benefit from spatial proximity, it is discovered that it is not a sufficient factor for generating innovation. As the third finding, it has been found that the important factors associated with synergy are membership in local and global networks, qualified employment that generates innovation, other employment that indirectly contributes to production, and R&D investments. These findings reveal that place-based innovation processes gain meaning not only through physical clustering but also through interaction and collaboration between actors. The research clearly demonstrates that the key to making innovation production sustainable and effective lies in a strong, permanent, and locally context-appropriate Triple Helix synergy established between universities, industry, and government.
In this study, six new Pt(II) complexes containing pyridine- and thiophene-based oxime ligands were synthesized as potential antitumour active compounds and characterized as platinumoxime coordination compounds. 1(Pyridin-2-yl)ethanone oxime (2-acetylpyridine oxime, L1) and 1-(thiophen-2-yl)ethan-1-oxime (2-acetylthiophene oxime, L2) were employed as ligands. The L1-based complexes were formulated as [Pt(NH3)Cl(L1)]NO3 (Asy1), [Pt(L1)2]Cl2 (Bis1), and [PtCl2(L1)] (Cis1), whereas the L2-based complexes were obtained as [Pt(NH3)Cl (L2)]NO3 (Asy2), [Pt(L2)2]Cl2 (Bis2), and [PtCl2(L2)] (Cis2). Structural characterization was performed using elemental analysis, FT-IR, NMR, and mass spectrometry techniques. The interactions of the synthesized Pt(II) complexes with calf thymus DNA (ct-DNA) were investigated using electrochemical, spectroelectrochemical, and agarose gel electrophoresis methods. Spectral changes and electrochemical titration data suggested that the interaction with ct-DNA is dominated by electrostatic contributions, with binding constants (Kb) in the range of 2.62 & times; 102-3.07 & times; 103 M- 1. Gel electrophoresis experiments demonstrated that DNA cleavage activity strongly depends on coordination geometry, ligand structure, and experimental conditions, with cis-configured complexes exhibiting higher nuclease activity at lower concentrations. This study also investigated the effects of the synthesized Pt(II) complexes on antioxidant and detoxification enzymes (GST, GPX, and CAT). The L2-based complexes, especially Bis2 and Asy2, showed strong effects on these enzymes, highlighting the importance of sulfur donor ligands, such as thiphene oxime, and ligand design in improving platinum-based anticancer drugs.
Recent advances in Kolmogorov-Arnold Networks (KANs) have shown strong potential for functional representation learning; however, existing formulations remain limited in their ability to provide explicit geometric interpretability, curvature control, and deformation-aware adaptation for biomedical image analysis. This paper introduces q-FunKAN, a geometry-aware Functional Kolmogorov-Arnold Network that integrates learnable Lupas & cedil; q-B & eacute;zier inner functions and q-Hermite spectral parameterizations within a unified geometric-spectral framework for biomedical image enhancement and segmentation. The central novelty of the proposed model lies in embedding a learnable deformation parameter q into the KAN functional space, enabling curvature-adaptive modulation of local anatomical structures while preserving global smoothness through spectral regularization. The proposed framework combines three complementary mechanisms: B & eacute;zier-based control-point parameterization for interpretable local deformation, q-Hermite spectral expansion for stable global representation, and topology-preserving deformation regularization for anatomically plausible enhancement and segmentation. This design allows q-FunKAN to explicitly balance local boundary precision and global structural consistency, providing a transparent alternative to highly parameterized convolutional and transformer-based models. Extensive experiments on five benchmark MRI datasets, including BRATS 2021, CHAOS, fastMRI, IXI, and a controlled synthetic phantom dataset, demonstrate the effectiveness of the proposed framework. Compared with strong convolutional, transformer-based, restoration-oriented, and KAN-based baselines, q-FunKAN achieves consistent improvements in image fidelity, perceptual quality, and segmentation accuracy, including gains of up to (+1.3) dB PSNR, (+1.0%) Dice, and (-0.004) LPIPS over leading competing models. Ablation studies further confirm that learnable q-adaptivity, B & eacute;zier geometric modeling, Hermite spectral regularization, and Jacobianbased topology preservation make complementary contributions to performance and stability. Qualitative analyses show sharper anatomical boundaries, reduced artifacts, and interpretable q-heatmaps aligned with curvature-sensitive regions. By bridging q-calculus, geometric approximation theory, and functional neural representation learning, q-FunKAN establishes a mathematically grounded, interpretable, and geometry-aware framework for biomedical image enhancement and segmentation.
Within the contested terrains of post-neoliberal urbanism, what are the possibilities (and limits) of urban commons? The article focuses on the case of K & uuml;lt & uuml;rpark in I(center dot)zmir to provide critical answers by highlighting its counter-hegemonic potential of public-commons partnerships. Established as a cultural and ecological landmark, the park has recently become a site of intense struggles over urban futures: municipal projects seeking market-oriented valorization have clashed with civic mobilizations defending its ecological, cultural, and symbolic significance. These tensions created a fragile yet transformative process, compelling municipal authorities to experiment with more democratic, participatory, and solidarity-based governance practices. In this respect, K & uuml;lt & uuml;rpark offers instructive insights into understanding parks as a form of urban commonwealth, underlining the significance of grassroots urban movements and the capacity of local governments to develop policies and strategies grounded in these bottom-up claims. Drawing on qualitative case study methods-including in-depth interviews with the key actors including municipal officials, civil society representatives, and professional chambers, alongside document analysis and field observations-the study reveals how K & uuml;lt & uuml;rpark functions simultaneously as a terrain of contestation and a laboratory of urban emancipation. The case illustrates both the risks and potentials of new municipalism: the risk of co-optation and fragmentation, but also the capacity to nurture progressive institutional arrangements that safeguard urban commons and strengthen collective agency. Public-commons partnerships thus emerge as embryonic infrastructures of counter-hegemonic urban politics, offering a pathway toward transformative, post-neoliberal urban futures.
Active earth pressure acting on retaining walls can be significantly affected by tension cracks that develop within unsaturated marginal backfills. Existing analytical approaches typically idealize these cracks as a continuous zone of uniform depth, neglecting their stochastic nature and the resulting variability in active thrust. This study presents a physics-informed stochastic framework for evaluating active thrust in unsaturated backfills containing spatially variable tension cracks. A physically admissible random field is developed in which crack depth is characterized by its mean, coefficient of variation, and correlation length, while crack location is governed by mean crack spacing. The generated crack field is integrated into a limit-equilibrium formulation incorporating suction stress and a log-spiral failure mechanism. Deterministic analyses first identify an Influence Zone (IZ) along the backfill surface where tension cracks affect active thrust. Monte Carlo simulations are then performed to quantify the influence of crack variability on the 95th percentile of the active earth pressure coefficient, Ka,0.95. Results reveal the existence of a Transitional Regime Domain (TRD) within the IZ where active thrust exhibits maximum sensitivity to crack variability and cracking-zone extent. The location and extent of the TRD depend on the statistical characteristics of the crack field, particularly mean crack spacing and depth variability. Among the stochastic parameters considered, the coefficient of variation of crack depth exerts the strongest influence on Ka,0.95, whereas correlation length has a comparatively minor effect over the conditions and autocorrelation models investigated. The proposed framework provides a practical tool for probabilistic assessment and risk-informed design of retaining walls with unsaturated cracked backfills.
