Risk-focused operational strategies for humanitarian supply chain stress testing management: a two-stage stochastic programming approach using F-tag tool

This study presents a holistic, lean-based methodology that adopts a continuous improvement philosophy to evaluate the stress level of the Humanitarian Supply Chain under uncertain demand increases following an earthquake. The proposed methodology integrates the value stream mapping (VSM) technique and a two-stage stochastic programming model, incorporating the F-tag tool. A case study focusing on a potential earthquake in Istanbul, Türkiye, is conducted by considering possible scenarios based on the devastating 2023 Kahramanmaraş, Türkiye earthquake. In this regard, VSM is employed to visualise the immediate post-earthquake state for Istanbul, presenting both the current and future states. Based on the optimisation model, a comparison between centralised and decentralised distribution strategies is conducted to enhance network resilience and contribute to overall supply chain sustainability during the ripple effects caused by the disruption. A design of experiments (DOE) setting is established to analyse the controlled factors and their interactions, using the Red Tag Ratio as the response variable.

The role of communication in expatriate management at SMEs: Evidence from foreign employees in Turkey

Being an employee of an small and medium-sized enterprise (SME) has distinctive features regarding performance, motivation, and work environment. Along with workers’ education, skills, and competencies, work relations and managerial communication are central to success, especially for foreigners working in a socially and culturally different country. This article aims to understand the role of communication in the performance and satisfaction of employees in expat-loaded and small business settings. The originality of the article is the primary data collected through a fresh population whose voices about their work lives as refugees in a foreign country are hardly heard. A sample of 302 foreign employees from Arab and Gulf countries working at SMEs in Turkey is surveyed and descriptive and inferential analysis is conducted. The article underlines the importance of communication in performance and employee well-being. Another finding is the differentiating impact of age in the better work relations between management and employees in SMEs.

Simulation of atom trajectories in the original Stern–Gerlach experiment

EP6.91 Assessment Of The Role Of ChatGPT As A Supplementary Tool For Providing Information Regarding Hip Arthroscopy

pC-SAC: A method for high-resolution 3D genome reconstruction from low-resolution Hi-C data

Influence of infrapatellar fat pad size on the development and severity of chondromalacia patella

ScatTR: Estimating the Size of Long Tandem Repeat Expansions from Short-Reads

Heterogeneous and novel transcript expression in single cells of patient-derived clear cell renal cell carcinoma organoids

The Role of Multiple Data Characteristics in EEG-Based Biometric Recognition: The Impact of States, Channels, and Frequencies

Enhancing Motor Imagery EEG Classification in Brain-Computer Interfaces via TransformerNet

Brain-computer interfaces (BCIs) offer promising solutions for assisting individuals with disabilities, supporting neurorehabilitation, and enhancing human capabilities. However, the limited decoding accuracy of EEG-based motor imagery (MI) signals poses a major challenge for the practical deployment of BCI systems. A common approach involves using signals from opposite hemispheres to boost classification accuracy. Yet, to control devices such as robotic hands or prosthetics in a human-like manner, it is essential to accurately classify hand opening and closing tasks using EEG signals from the same motor cortex region. This study introduces TransformerNet, a novel deep learning architecture specifically designed to classify hand open-close MI tasks from the same brain region. The task is particularly challenging due to the high similarity and overlapping nature of the EEG signals. TransformerNet combines a convolutional module, inspired by EEGNet, to extract local spatial features, with a Transformer encoder that captures long-range temporal dependencies. Furthermore, a channel attention mechanism enhances the model’s ability to focus on the most informative features. In experimental evaluations, TransformerNet achieved an average classification accuracy of 85.97%, outperforming traditional deep learning methods. The model effectively captures high-level temporal-spectral patterns and uncovers hidden dependencies within the EEG signals. These results demonstrate the potential of integrating attention mechanisms with Transformer-based architectures to improve MI-based BCI performance. This advancement holds promise for real-world applications such as brain-controlled prosthetics, assistive devices, and human-computer interaction, moving BCI technologies closer to practical and reliable implementation.