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Digital Payments and Sustainable Economic Growth: Transmission Mechanisms and Evidence From an Emerging Economy, Turkey
No full textThis study investigates the impact of digital transactions on sustainable economic growth in Turkey, utilizing a vector autoregressive (VAR) model and quarterly data from 2006 to 2023. The results indicate a positive long-term association between digital payments and GDP. Granger causality tests and impulse response functions suggest a bidirectional relationship, highlighting mutual reinforcement between economic activity and digital financial adoption. The study also investigates three potential transmission channels linking digital payments to economic performance: household consumption, productivity, and financial intermediation. Evidence shows that digital payments are associated with increased consumption and financial sector activity, while the link to productivity is less conclusive. These findings imply that policymakers should prioritize digital financial infrastructure development and enhance regulatory frameworks to promote inclusive and sustainable economic growth. © 2025 by the authors
Search for Same-Charge Top-Quark Pair Production in pp Collisions at √s = 13 TeV with the ATLAS Detector
No full textA search for the production of top-quark pairs with the same electric charge (tt or tt¯) is presented. The analysis uses proton-proton collision data at s = 13 TeV, recorded by the ATLAS detector at the Large Hadron Collider, corresponding to an integrated luminosity of 140 fb−1. Events with two same-charge leptons and at least two b-tagged jets are selected. Neural networks are employed to define two selections sensitive to additional couplings beyond the Standard Model that would enhance the production rate of same-sign top-quark pairs. No significant signal is observed, leading to an upper limit on the total production cross-section of same-sign top-quark pairs of 1.6 fb at 95% confidence level. Corresponding limits on the three Wilson coefficients associated with the Otu1, OQu1, and OQu8 operators in the Standard Model Effective Field Theory framework are derived. © 2025 Elsevier B.V., All rights reserved
Ultrafast Pulse Propagation Time-Domain Dynamics in Dispersive One-Dimensional Photonic Waveguides
No full textUltrafast pulses, particularly those with durations under 100 fs, are crucial in achieving unprecedented precision and control in light-matter interactions. However, conventional on-chip photonic platforms are not inherently designed for ultrafast time-domain operations, posing a significant challenge in achieving essential parameters such as high peak power and high temporal resolution. This challenge is particularly pronounced when propagating through integrated waveguides with nonlinear and high-dispersion profiles. In addressing this challenge, we present a design methodology for ultrafast pulse propagation in dispersive integrated waveguides, specifically focused on enhancing the time-domain characteristics of one-dimensional grating waveguides (1DGWs). The proposed methodology aims to determine the optimal structural parameters for achieving maximum peak power, enhanced temporal resolution, and extended pulse storage duration during ultrafast pulse propagation. To validate this approach, we design and fabricate two specialized 1DGWs on a silicon-on-insulator (SOI) platform. A digital finite impulse response (FIR) model, trained with both transmission and phase measurement data, is employed to obtain ultrafast time-domain characteristics, enabling easy extraction of these results. Our approach achieves a 2.8-fold increase in peak power and reduces pulse broadening by 24 %, resulting in a smaller sacrifice in temporal resolution. These results can possibly pave the way for advanced light-matter interactions within dispersive integrated waveguides. © 2025 the author(s), published by De Gruyter, Berlin/Boston.Natural Sciences and Engineering Research Council of Canada, NSERC; UBCx Silicon Photonics Design; Silicon Electronic-Photonic Integrated Circuits; Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTAK, (122E566, BIDEB 2210A); Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTA
The Impact of Economic Factors on Brain Circulation Patterns
No full textThe concept of brain drain has long been a significant topic of discussion. Advancements in technology have significantly facilitated transportation and communication which in turn has led to increased human mobility over the last few decades. In particular, traveling to different countries for education and employment has become increasingly widespread. This study extracts academic talent migration data from a major academic publishing dataset and examines brain drain trends between countries in relation to their economic conditions..
A Stochastic Knapsack Model for Sustainable Safety Resource Allocation Under Interdependent Safety Measures
No full textOzkan, Gokhan/0000-0001-9565-0054The optimum choice of safety measures (SMs) within constraints is necessary for effective risk management in occupational health and safety (OHS). The stochastic nature of safety interventions is frequently overlooked by traditional approaches such as deterministic models and risk matrices. This study presents a novel stochastic knapsack model that maximizes the overall expected benefit during a risk assessment period considering budgetary constraints and the interdependencies between risks and safety measures. Two models are developed as follows: a one-to-one relationship model assuming independent risks and a multiple-relationship model accounting for interdependent safety measures. The suggested model's real-world implementation is illustrated through a case study in the retail industry. The results demonstrate the model's ability to efficiently prioritize SMs, showing an 18% reduction in objective function value and an average risk reduction of 29.5 per monetary unit invested, compared to 26.2 for the deterministic model. A more realistic and flexible framework for safety investment planning is offered by the analysis, which emphasizes the benefits of including stochastic components and interdependencies in decision-making. By addressing the significant drawbacks of deterministic models and providing a flexible, data-driven framework for safety optimization, this study adds to the body of literature. The suggested model is in line with the United Nations Sustainable Development Goals (SDGs), specifically SDGs 3, 8, 9, and 12. Its adaptability contributes to achieving SDG 13, emphasizing possible uses in risk management for climate change. This study shows how decision-making that is structured and aware of uncertainty can support safer, more sustainable industrial processes
The Analysis Description Language Ecosystem: Latest Developments and Physics Applications
No full textWe present latest developments in Analysis Description Language (ADL), a declarative domain-specific language describing the physics algorithm of a HEP data analysis decoupled from software frameworks. Analyses written in ADL can be integrated into any framework for various tasks. ADL is a multipurpose construct with uses ranging from analysis design to preservation, reinterpretation, queries, visualisation, combination, etc. The most advanced infrastructure to execute ADL on events is the CutLang runtime interpreter. Recent technical developments include an automated interface with different data types, generation of the abstract syntax tree, a visualization tool that that auto-converts analysis flows to graphs, incorporation of trained machine learning models and a Jupyter-based plotting tool. We also report physics implications including a large scale LHC analysis implementation and validation effort for beyond the standard model reinterpretation purposes and studies with ATLAS and CMS open data. © Copyright owned by the author(s) under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0).National Research Foundation of Korea, NRF; Ministry of Education, MOE, (NRF-2021R1I1A3048138, NRF-2018R1A6A1A06024970, NRF-2008-00460); Ministry of Education, MO
Multimodal Stock Price Prediction
No full textIn an era where financial markets are heavily influenced by many static and dynamic factors, it has become increasingly critical to carefully integrate diverse data sources with machine learning for accurate stock price prediction. This paper explores a multimodal machine learning approach for stock price prediction by combining data from diverse sources, including traditional financial metrics, tweets, and news articles. We capture real-time market dynamics and investor mood through sentiment analysis on these textual data using both ChatGPT-4o and FinBERT models. We look at how these integrated data streams augment predictions made with a standard Long Short-Term Memory (LSTM model) to illustrate the extent of performance gains. Our study's results indicate that incorporating the mentioned data sources considerably increases the forecast effectiveness of the reference model by up to 5%. We also provide insights into the individual and combined predictive capacities of these modalities, highlighting the substantial impact of incorporating sentiment analysis from tweets and news articles. This research offers a systematic and effective framework for applying multimodal data analytics techniques in financial time series forecasting that provides a new perspective for investors to leverage data for decision-making. © 2025 by SCITEPRESS – Science and Technology Publications, Lda
Modeling Techniques and Boundary Conditions in Abdominal Aortic Aneurysm Analysis: Latest Developments in Simulation and Integration of Machine Learning and Data-Driven Approaches
No full textResearch on abdominal aortic aneurysms (AAAs) primarily focuses on developing a clear understanding of the initiation, progression, and treatment of AAA through improved model accuracy. High-fidelity hemodynamic and biomechanical predictions are essential for clinicians to optimize preoperative planning and minimize therapeutic risks. Computational fluid dynamics (CFDs), finite element analysis (FEA), and fluid-structure interaction (FSI) are widely used to simulate AAA hemodynamics and biomechanics. However, the accuracy of these simulations depends on the utilization of realistic and sophisticated boundary conditions (BCs), which are essential for properly integrating the AAA with the rest of the cardiovascular system. Recent advances in machine learning (ML) techniques have introduced faster, data-driven surrogates for AAA modeling. These approaches can accelerate segmentation, predict hemodynamics and biomechanics, and assess disease progression. However, their reliability depends on high-quality training data derived from CFDs and FEA simulations, where BC modeling plays a crucial role. Accurate BCs can enhance ML predictions, increasing the clinical applicability. This paper reviews existing BC models, discussing their limitations and technical challenges. Additionally, recent advancements in ML and data-driven techniques are explored, discussing their current states, future directions, common algorithms, and limitations.This research was supported by TUBITAK (The Scientific and Technological Research Council of Turkiye) 3501-Career Development Program (Project number: 221M001). The publication of this article was funded by ADA University.TUBITAK (The Scientific and Technological Research Council of Turkiye) 3501-Career Development Program [221M001]ADA Universit
Energy Absorber Inspired by Spider Webs
No full textThe spider orb web has evolved to efficiently absorb the energy of flying insects colliding with it. In this study, a novel three-dimensional lattice structure inspired by the specific structural characteristics of the spider orb web was designed and optimized to create a new lattice design. The design was optimized for energy absorption and energy absorption efficiency using a size optimization procedure with numerical modeling based on beam elements under quasi-static compression loading. This optimized lattice was additively manufactured and subjected to quasi-static compression testing. Numerical results for energy absorption and compression behavior showed good agreement with experimental findings. Additionally, numerical analysis of the optimized lattice was performed using solid elements to predict the energy absorption behavior more accurately, and the results showed even better agreement with experimental data. The resulting lattice also demonstrated improved energy absorption performance compared to existing lattice structures.Turkish Aerospace Industries the Scientific and Technological Research Council of Turkiye (TUBTAK) [118C145]This work was supported by the Turkish Aerospace Industries the Scientific and Technological Research Council of Turkiye (TUBTAK) under the project number 118C145
Halis: A Hardware-Software Co-Designed Near-Cache Accelerator for Graph Pattern Mining
No full textErgin, Oguz/0000-0003-2701-3787Graph Pattern Mining (GPM) algorithms extract meaningful information within graph structures, making them fundamental building blocks for multiple application domains. However, their performance is bottlenecked by hard-to-predict divergence control, cache pollution, and low parallelism caused by index matching operations that dominate the execution time. To address these challenges, this paper introduces Halis, a hardware-software co-designed Near-Cache Accelerator for GPM workloads on commercial multi-core CPUs. By executing index matching operations near the Last-Level Cache (LLC), Halis reduces data movement and cache pollution in upper cache levels while minimizing divergence control and enhancing parallelism. To achieve this, Halis repurposes underutilized Content Addressable Memories (CAMs) in hardware data prefetchers, taking advantage of their efficient lookup capabilities for GPM workloads. Furthermore, Halis includes virtual memory support, ensuring compatibility with commodity operating systems. Designed as a decoupled programmable accelerator, it operates via memory-mapped registers. Our evaluation demonstrates that Halis outperforms software and hardware approaches by 26.9x and 2.4x respectively, while incurring a negligible area overhead of 0.05% over the CPU baseline