This article contributes to IR scholarship on the domestic politicisation of international affairs, focusing on transnational actors and mechanisms through which international issues can become politicised domestically. It conceptualises transnational politicisation as cross-boundary processes that draw international issues into the realm of domestic political choice and contestation. The analysis zooms in on diaspora groups as an exemplary case of such transnational politicisation, given their unique position in the transnational space. We argue that diasporas play a powerful role in the transnational politicisation of bilateral issues between homeland and hostland within host country domestic politics, either as politicising agents or conduits of politicisation attempts by home or host country political actors. Transnational processes of politicisation can generate intergovernmental friction, reducing the scope for bilateral cooperation, if they lead to widespread domestic politicisation in the host countries, raising identity-related and sovereignty concerns. The article presents two heuristic case studies on the Turkish diaspora in Germany, focusing on the 2016 ‘Armenia resolution’ of the German Bundestag and the 2017/2018 cycle of German and Turkish elections. The case studies detail the diaspora actors involved, explicate the transnational mechanisms through which bilateral issues become politicised in German domestic politics, and discuss consequences for German-Turkish relations.
Unloading processes denote the extraction of finished goods and raw materials from transport units and their subsequent conveyance to designated locations. The efficiency of unloading processes is vital in supply chain and logistics management, regarded as an essential component. Delays in unloading operations result in numerous challenges, including heightened operational expenses, diminished labour efficiency, and supply chain bottlenecks. Consequently, it is essential to ascertain unloading times beforehand to mitigate these challenges, resulting in diminished idle time, enhanced overall efficiency, and optimized scheduling. Therefore, precise prediction of unloading times is critically significant. The novelty of this study lies in the application of machine learning techniques to improve operational efficiency by accurately predicting unloading time. To that end, this study employed LightGBM and XGBoost to predict the unloading time in a real case. The unloading time can be predicted with R2 score greater than 0.99 utilizing both models. Subsequently, the SHapley Additive exPlanations (SHAP) methodology was used to ascertain how each input feature contributed to the model’s output. The load of leg significantly influences the unloading time more than the gross weight of truck and the leg distance.
Here, a catalyst-free route is reported for fabricating self-standing and robust paper-based adsorbents for methylene blue (MB) removal from aqueous solutions through the attachment of montmorillonite (MMT) to filter paper (FP) substrate surfaces using polydimethylsiloxane (PDMS) chains. Characterization results showed that the thickness of the combined PDMS and MMT layer was 4.0 +/- 0.7 g m-2, with MMT accounting for 2.2 +/- 0.3 wt% of the composite. The specific surface area of MMT, FP, and PDMS/MMT/FP composite was 203.4, 1.3, and 1.8 m2 g-1, respectively. A point of zero charge of 3.84 was measured for PDMS/MMT/FP. Spectroscopic analysis suggested interactions among MMT, PDMS, and FP, including the possible formation of Si-O-C bonds between PDMS and cellulose, the principal component of FP. Scanning electron microscopy (SEM) images revealed clay particles distributed uniformly across the fibers. MB adsorption tests conducted under identical conditions (100 mL, 5 mg L-1; 6 cm x 6 cm sheets) showed that the PDMS/MMT/FP composite removed 90.0% of the dye (corresponding to a qe of 1.4 mg g-1), almost doubling the 51.3% removal performance of pristine FP, despite the low MMT loading of only 2.2 wt% in the composite. The PDMS/MMT/FP composite closely followed the pseudo-second-order kinetic model and showed a better fit to the Langmuir isotherm. Even after 15 days of continuous shaking in MB solution at 150 rpm, the PDMS/MMT/FP composite retained its integrity, still exhibiting MMT particles across its surface and similar Al contents before and after the MB adsorption test, as evidenced by SEM and spectroscopic studies. These findings demonstrate that our simple and environmentally benign route can produce low-cost, self-standing paper-based adsorbents with clear promise for wastewater-treatment applications.
Sediment suspension and motion due to vessel traffic in navigation channels is a challenging problem in the design and management of waterways, ports and navigation channels. This study utilised computational fluid dynamics (CFD) simulations to investigate the impact of vessel motion on bed morphology and sediment suspension. The work was carried out for a navigation channel with sloped banks, subject to motion-induced currents and waves generated by a rectangular barge with a blunt bow, towed at constant speed. A fine sand channel bottom was modelled. The simulations, consisting of six different scenarios with two bank slope angles, two tow speeds and two vessel widths, have been carried out by FLOW-3D Hydro software. It was found out that the most significant parameter affecting sediment motion is the tow speed of the vessel, which is coupled with the vessels squat. The study further shows that ship width also plays a critical role in predicting the risk of sediment accumulation in harbour and navigation channel projects, as an increase of only 15 % in vessel width results in a large increase of 72 % in the total amount of suspended sediment. An increase in bank slopes, on the other hand, is found to have a smaller effect (15 %) on the total suspended sediment concentration.
Crowdfunding platforms like Kickstarter have reshaped early-stage financing by allowing entrepreneurs to connect directly with potential supporters. As a fast-expanding part of digital commerce, crowdfunding offers significant opportunities but also substantial risks for both entrepreneurs and platform operators, making predictive analytics an essential capability. Although crowdfunding shares some operational features with traditional e-commerce, its mix of financial uncertainty, emotionally charged storytelling, and fast-evolving social interactions makes it a distinct and more challenging forecasting problem. Accurately predicting campaign outcomes is especially difficult because of the high-dimensionality and diversity of the underlying textual and behavioral data. These factors highlight the need for scalable, intelligent data science methods that can jointly exploit structured and unstructured information. To address these issues, this study proposes a novel AI-based predictive framework that integrates a Convolutional Block Attention Module (CBAM)-enhanced symmetric autoencoder for compressing high-dimensional Generative AI (GenAI) BERT embeddings with meta-heuristic feature selection and advanced classification models. The framework systematically couples attention-driven feature compression with optimization techniques—Genetic Algorithm (GA), Jaya, and Artificial Rabbit Optimization (ARO)—and then applies Long Short-Term Memory (LSTM) and Gradient Boosting Machine (GBM) classifiers. Experiments on a large-scale Kickstarter dataset demonstrate that the proposed approach attains 77.8% accuracy while reducing feature dimensionality by more than 95%, surpassing standard baseline methods. In addition to its technical merits, the study yields practical insights for platform managers and campaign creators, enabling more informed choices in campaign design, promotional tactics, and backer targeting. Overall, this work illustrates how advanced AI methodologies can strengthen predictive analytics in digital commerce, thereby enhancing the strategic impact and long-term sustainability of crowdfunding ecosystems.
