HAMK Häme University of Applied Sciences (Finnish: Hämeen ammattikorkeakoulu) is an institution of higher education with seven locations in Finland. Its programmes are coordinated with industry and commerce. HAMK also offers research and development services, professional teacher education, further and continuing education and studies in the Open University of Applied Sciences.HAMK operates at seven locations: Evo, Forssa, Hämeenlinna, Lepaa, Mustiala, Riihimäki and Valkeakoski. There are approximately 8,000 students and 600 staff members..
Generative Artificial Intelligence (Gen-AI) has gained significant traction in larger firms, yet its adoption among micro-firms remains underexplored particularly in contexts marked by resource scarcity and heightened operational risk. This study addresses this gap by investigating how high-tech micro-firms adopt Gen-AI for risk management and growth. Drawing on semi-structured interviews with decision-makers from eight Finnish micro-firms, the research applies the Technology-Organization-Environment (TOE) framework to identify critical enablers and barriers. The findings highlight five key dimensions influencing adoption: technological readiness, leadership engagement, regulatory compliance, data-driven decision-making, and competitive pressures. While Gen-AI fosters operational resilience and strategic agility, its impact is constrained by limited data quality and high implementation costs. By offering a holistic and theoretically grounded perspective, this study advances understanding of Gen-AI adoption in micro-firms and contributes to literature on digital transformation under resource constraints. The insights also inform policymakers and practitioners aiming to enhance AI accessibility and governance for micro-enterprises.
The widespread adoption of edge computing and Internet of Things (IoT) applications has significantly increased the demand for efficient task scheduling in resource-constrained environments. These environments often rely on low-power, cost-effective hardware, making it challenging to maintain optimal performance while managing computational workloads. Traditional load-balancing approaches, such as round robin and least-loaded strategies, frequently struggle to optimize resource allocation in distributed systems with limited processing power, such as Raspberry Pi clusters. These conventional methods often fail to adapt dynamically to fluctuating workloads, leading to inefficient resource utilization, increased response times, and potential system bottlenecks. To address these challenges, this research introduces a novel Resource Conscious Predictive Load Balancing (RCP-LB) framework, which leverages machine learning techniques to enhance task allocation efficiency. Specifically, the framework employs Support Vector Machines (SVM) to analyze real-time CPU and memory usage data, enabling the system to predict the optimal task allocation dynamically. By proactively distributing workloads based on resource availability, the proposed approach reduces latency, improves response times, and maximizes overall system performance. Comprehensive performance evaluations compare RCP-LB with traditional load-balancing techniques, demonstrating its superior efficiency, scalability, and adaptability in handling dynamic workloads. The experimental results indicate that the RCP-LB framework significantly enhances system responsiveness and resource efficiency, making it particularly well-suited for real-time IoT applications and edge computing environments. By providing a robust, intelligent, and resource-aware task scheduling mechanism, this research contributes to the advancement of distributed computing frameworks, offering a practical and effective solution for modern, re-source-constrained systems.
Drug shortages are a current issue and often originate from problems in raw material supply. Even though they are more related to active ingredients the reason could be the lack of excipient(s) due to a major disruption to normal supply chains. Lactose is one of the most used excipients in oral solid dosage forms such as tablets. The aim of this study was to utilize lactose from a dairy side-stream in wet granulation and tableting and evaluate its behaviour in a formulation in comparison to a commercial reference. The possibility to use this type of lactose would be a sustainable option to ensure domestic availability during crises. The study design included different lactose proportions of fillers as well as liquid to solid ratios to investigate whether the resulting granules show similar responses when using side-stream or commercial lactose. The responses to changes were rather similar regardless of the lactose grade but liquid to solid ratio needed to be decreased (0.6 to 0.8. vs. 0.4 to 0.6) when using side-stream lactose. The formulations using side-stream lactose exhibited adequate flowability (3rd best classification in Ph.Eur) which was slightly inferior to that with commercial but tabletability was better due to its amorphous nature. However, disintegration (about 20 min vs. 2 min) and drug release (60-80 % vs. 100 %) were slower when using the side-stream lactose. This was most likely due to remaining milk proteinsin the lactose. Overall, the side-stream lactose provided promise for the use of such a side-stream material during supply disruptions.
