Informatics

This paper presents a hybrid deep learning framework for real-time sign language recognition (SLR) tailored to Internet of Things (IoT)-enabled environments, enhancing accessibility for Deaf communities. The proposed system integrates a Long Short-Term Memory (LSTM) network for static gesture recognition and a 3D Convolutional Neural Network (3D CNN) for dynamic gesture recognition. Implemented on a Raspberry Pi device using MediaPipe for landmark extraction, the system supports low-latency, on-device inference suitable for resource-constrained edge computing. Experimental results demonstrate that the LSTM model achieves its highest stability and performance for static signs at 1000 training epochs, yielding an average F1-score of 0.938 and an accuracy of 86.67%. In contrast, at 2000 epochs, the model exhibits a catastrophic performance collapse (F1-score of 0.088) due to overfitting and weight instability, highlighting the necessity of careful training regulation. Despite this, the overall system achieves consistently high classification performance under controlled conditions. In contrast, the 3D CNN component maintains robust and consistent performance across all evaluated training phases (500–2000 epochs), achieving up to 99.6% accuracy on dynamic signs. When deployed on a Raspberry Pi platform, the system achieves real-time performance with a frame rate of 12–15 FPS and an average inference latency of approximately 65 ms per frame. The hybrid architecture effectively balances recognition accuracy with computational efficiency by routing static gestures to the LSTM and dynamic gestures to the 3D CNN. This work presents a detailed epoch-wise comparative analysis of model stability and computational feasibility, contributing a practical and scalable IoT-enabled solution for inclusive, real-time sign-to-text communication in intelligent environments.

The deployment of large language models (LLMs) in commercial environments depends critically on the availability of robust digital infrastructure, scalable computing resources, and mature cloud architectures. This study examines how macro-level digital infrastructure, in particular cloud computing adoption, conditions the ability of the European retail sector to deploy and benefit from large language models (LLMs). Using a country-year panel of EU member states from 2017 to 2023, we estimate fixed-effects regressions to quantify the association between enterprise cloud use and retail trade volume growth, and implement an event-study design to explore dynamic responses around changes in cloud uptake. The results show that increases in cloud adoption are significantly associated with higher retail trade growth added and productivity, with especially strong effects in emerging Eastern European markets. We identify a digital threshold of around 20% of enterprises using cloud services, above which the marginal impact on retail performance becomes notably larger. These findings highlight cloud infrastructure as a key enabling condition for LLM-enabled retail applications and inform EU digital and industrial policy targeting regional digital disparities.

This study explores the virtualizability of team processes and their implications for team performance during the COVID-19 pandemic. The main research question was: What is the effect of the ease of virtualizing team processes on the outcomes of teams that have shifted from in-person to virtual work? A survey method was employed, and the data were analyzed using Structural Equation Modeling (SEM). Building on the frameworks based on literature review, the study defined sensory, relational, and synchronization requirements, along with the mechanisms of reach and representation. Results show that sensory requirements negatively influence the virtualizability of team processes, while relational and synchronization requirements do not have a statistically significant impact. Although the mechanisms of reach and representation do not moderate the relationships between constructs, they do have a direct positive effect on susceptibility to virtualization. Contrary to initial expectations, virtualizability positively affects both tangible and emotional outcomes, indicating that cohesion and satisfaction can be maintained—or even improved—in virtual teams. These findings enhance the theoretical understanding of team processes and virtualizability and offer practical insights for managing distributed teams.