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Multi-period hub network design from a dual perspective: An integrated approach considering congestion, demand uncertainty, and service quality optimization
No full textThis study introduces a hub network design problem that considers three key factors: congestion, demand uncertainty, and multi-periodicity. Unlike classical models, which tend to address these factors separately, our model considers them simultaneously, providing a more realistic representation of hub network design challenges. Our model also incorporates service level considerations of network users, extending beyond the focus on transportation costs. Service quality is evaluated using two measures: travel time and the number of hubs visited during travel. Moreover, our model allows for adjustments in capacity levels and network structure throughout the planning horizon, adding a dynamic and realistic aspect to the problem setting. The inherent nonlinear nonconvex integer programming problem is reformulated into a mixed-integer second-order cone programming (SOCP) problem. To manage the model's complexity, we propose an exact solution algorithm based on Benders decomposition, where the sub-problems are solved using a column generation technique. The efficacy of the solution approach is demonstrated through extensive computational experiments. Additionally, we discuss the benefits of each considered feature in terms of transportation costs and their impact on network structure, providing insights for the field
Ultrafast Laser Synthesis of Zeolites
No full textResearch demonstrates that zeolite nucleation and growth can be controlled by fine-tuning chemical composition, temperature, and pressure, resulting in structures with diverse porosities and functionalities. Nevertheless, current energy delivery methods lack the finesse required to operate on the femto- and picosecond timescales of silica polymerization and depolymerization, limiting their ability to direct synthesis with high precision. To overcome this limitation, an ultrafast laser synthesis technique is introduced, capable of delivering energy at these timescales with unprecedented spatiotemporal precision. Unlike conventional or emerging approaches, this method bypasses the need for specific temperature and pressure settings, as nucleation and growth are governed by dynamic phenomena arising from nonlinear light–matter interactions, such as convective flows, cavitation bubbles, plasma formation, and shock waves. These processes can be initiated, paused, and resumed within fractions of a second, effectively “freezing” structures at any stage of self-assembly. Using this approach, the entire nucleation and growth pathway of laser-synthesized TPA-silicate-1 zeolites is traced, from early oligomer formation to fully developed crystals. The unprecedented spatiotemporal control of this technique unlocks new avenues for manipulating reaction pathways and exploring the vast configurational space of zeolites
Şehir Planlama ve Endüstriyel Karbonsuzlaşmanın Entegrasyonu: Yeşil Mutabakat Çerçevesinde Sınırda Karbon Düzenleme Mekanizmasının (SKDM) Rolü
No full textThe European Green Deal aims to achieve climate neutrality by 2050 through transformative policies designed to curb carbon emissions and support sustainable economic development. Among its initiatives, the Carbon Border Adjustment Mechanism (CBAM) serves as a critical tool for preventing carbon leakage by imposing tariffs on imports from countries and regions with less strict environmental regulations. Although its primary focus is industrial decarbonization, CBAM has far-reaching implications for urban planning and regional development.This paper examines the interconnection between the Green Deal, CBAM, and urban planning, emphasizing the strategies required to align industrial activity with climate objectives. The adoption of CBAM is expected to shift industrial practices, potentially reshaping the geographic distribution of production and trade. Urban centers, as key nodes of industry and logistics, must adapt by implementing sustainable land-use policies, optimizing transport systems, and advancing circular economy principles.Additionally, CBAM presents an opportunity for urban planners to promote green industrial zones and integrate renewable energy initiatives into local development strategies. However, it also raises critical socio-economic concerns, particularly for carbon-intensive regions that may face significant challenges in transitioning to new regulatory environments, potentially exacerbating economic inequality.This study explores case studies of cities and regions within and beyond the European Union, showcasing how urban planning can address the effects of CBAM. By focusing on governance, stakeholder engagement, and innovative solutions, this research underscores the essential role of urban planning in balancing industrial transformation with environmental sustainability and equitable development.</p
Dual <i>p</i>-adic Diophantine approximation on manifolds
No full textThe Generalised Baker-Schmidt Problem (1970) concerns the Hausdorff measure of the set of -approximable points on a non-degenerate manifold. Beresnevich-Dickinson-Velani (in 2006, for the homogeneous setting) and Badziahin-Beresnevich-Velani (in 2013, for the inhomogeneous setting) proved the divergence part of this problem for dual approximation on arbitrary non-degenerate manifolds. The divergence part has also been resolved for the -adic setting by Datta-Ghosh in 2022, for the inhomogeneous setting. The corresponding convergence counterpart represents a challenging open question. In this paper, we prove the homogeneous -adic convergence result for hypersurfaces of dimension at least three with some mild regularity condition, as well as for some other classes of manifolds satisfying certain conditions. We provide similar, slightly weaker results for the inhomogeneous setting. We do not restrict to monotonic approximation functions
AIRCRAFT PERFORMANCE AND FLIGHT TRAJECTORY ANALYSIS OF A LAND-BASED JET AIRCRAFT FOR ASSESSING ITS FEASIBILITY IN CARRIER-BASED OPERATIONS
No full textA Comparative Study of Multi-Task Learning Approaches on Disjoint Datasets Ayri sik Veri Setlerinde ok G revli grenme Yakla simlarinin Kar sila stirilmasi
No full textMulti-task learning is an approach that aims to use resources more efficiently during inference by concurrently learning multiple tasks through a single model. This method seeks to improve model generalization and performance by leveraging shared feature extraction, rather than using separate models for different tasks. However, when working with disjoint datasets, completing the missing labels for each task becomes a costly and time-consuming process. In this study, we compare the simultaneous utilization of independent datasets and different multi-task learning methods by adding a classification task to an unsupervised trained segmentation model. Our proposed approach offers a scalable solution by preserving the original labeling structure of the datasets and eliminating the need for multi-label annotation
Understanding future opportunities in robotics technology: a comprehensive analysis of research and innovation trends
No full textThis study investigates the future opportunities and development trajectories of robotics technology through comprehensive analysis of research trends and technological developments. By examining over 268.000 scientific publications and 343.000 patents in the robotics domain, the research provides insights into emerging technological patterns and their potential societal implications. Moreover, this study provides a methodological framework for future-oriented technology analysis (FTA) that extends beyond robotics to other technological domains. The analysis, supported by advanced text mining techniques including Topic Modeling and semantic analysis, identified 91 distinct research topics and 98 patent topics, revealing significant trends in robotics development across various sectors including manufacturing, healthcare, service robotics, and human–robot interaction. The findings highlight the increasing convergence of artificial intelligence with robotics, the growing importance of collaborative robots, and the emergence of novel applications in healthcare and social assistance. The study demonstrates how robotics technology is evolving beyond traditional industrial applications into more sophisticated and socially integrated systems. By analyzing these patterns within the framework of FTA, this research offers valuable data-driven insights for policymakers, industries, and researchers in understanding and preparing for future developments in robotics technology. The findings contribute to both theoretical understanding of robotics evolution and practical applications, providing strategic insights for stakeholders navigating the future landscape of robotics technology
Optical gating in photodetector for femtosecond time-resolved imaging
No full textNon-degenerate two-photon femtosecond gating directly within a photosensor has been investigated in both external and internal photoeffect configurations, with the aim of enabling fast time-resolved imaging applications. This method of gating eliminates the need for the bulky and alignment-sensitive conventional setup that requires separate elements for gating and sensing. For a K2CsSb photocathode, the energy conditions of the participating photons have been established, and the method's time resolution and sensitivity have been quantitatively evaluated. For the first time, the use of non-degenerate two-photon absorption in a multi-pixel CMOS sensor for ballistic imaging through a scattering medium—without mechanical scanning—has been experimentally demonstrated, revealing a significant improvement in image contrast over conventional single-photon imaging
Comparison of UiO-66 Nanoparticles Synthesized via Micro-fluidic vs. Macro-Stationary Routes for Aqueous Phosphate Removal
No full textPhosphate contamination in wastewater is a critical environmental concern, driving eutrophication. This study investigates the synthesis of zirconium-based UiO-66 metal organic framework (MOF) nanoparticles via conventional macro-stationary batch and droplet-based micro-fluidic systems for phosphate removal from aqueous solutions. Characterization using XRD and SEM revealed that micro-fluidically synthesized UiO-66 nanoparticles exhibited smaller crystallites and higher crystallinity compared to those from the macro-stationary method. Adsorption kinetics followed a pseudo-second-order model and the Freundlich isotherm provided a better fit to the equilibrium data. Thermodynamic analysis indicated a spontaneous physisorption process. Comparative adsorption studies showed that UiO-66 nanoparticles synthesized via the micro-fluidic route demonstrated slightly enhanced phosphate removal efficiency. This work highlights the potential of micro-fluidic synthesis for producing MOF nanoparticles with improved properties for environmental applications
Navigating online microteaching: pre-service teachers’ experiences and insights
No full textThis study explores pre-service English language teachers’ (PELTs) experiences with online microteaching (OMT) during the COVID-19 pandemic. Using a qualitative case study design, semi-structured interviews were conducted with six pre-service English teachers to investigate their perceptions of OMT and its influence on their instructional skills and professional development. The findings show that OMT provided valuable opportunities for practice, feedback, and reflection, contributing to their digital literacy and enhanced confidence in using online instructional methods. Participants noted mixed emotional experiences, while some found comfort in the familiar home environment, others encountered significant stress and anxiety because of the lack of face-to-face interaction and technical challenges. The data identify key challenges, including student engagement, managing technological issues, and handling restricted classroom interaction. Despite these difficulties, pre-service English language teachers demonstrated adaptability and problem-solving skills. This study highlights the role of incorporating strong support mechanisms, developing digital literacy, and resilience to prepare prospective teachers for an evolving educational environment. This study contributes to the dynamic discourse on teacher education in the context of online learning and teaching settings. Recommendations for further research might include longitudinal studies on the long-term impact of OMT and comparative studies between traditional and online microteaching to enhance teacher training programs