Security represents one of the crucial concerns when it comes to DevOps methodology-empowered software development and service delivery process. Considering the adoption of Infrastructure as Code (IaC), even minor flaws could potentially cause fatal consequences, especially in sensitive domains such as healthcare and maritime applications. However, most of the existing solutions tackle either Static Application Security Testing (SAST) or run-time behavior analysis distinctly. In this paper, we propose a) IaC Scan Runner, an open-source solution developed in Python for inspecting a variety of state-of-the-art IaC languages in application design time and b) the run time anomaly detection tool called LOMOS. Both tools work in synergy and provide a valuable contribution to a DevSecOps tool set. The proposed approach is demonstrated and their results will be demonstrated on various case studies showcasing the capabilities of static analysis tool IaC Scan Runner combined with LOMOS - log analysis artificial intelligence-enabled framework.

With the rapid spread of the COVID-19 pandemic, the novel Meaningful Integration of Data Analytics and Services (MIDAS) platform quickly demonstrates its value, relevance and transferability to this new global crisis. The MIDAS platform enables the connection of a large number of isolated heterogeneous data sources, and combines rich datasets including open and social data, ingesting and preparing these for the application of analytics, monitoring and research tools. These platforms will assist public health author ities in: (i) better understanding the disease and its impact; (ii) monitoring the different aspects of the evolution of the pandemic across a diverse range of groups; (iii) contributing to improved resilience against the impacts of this global crisis; and (iv) enhancing preparedness for future public health emergencies. The model of governance and ethical review, incorporated and defined within MIDAS, also addresses the complex privacy and ethical issues that the developing pandemic has highlighted, allowing oversight and scrutiny of more and richer data sources by users of the system.

The class of skew lattices can be seen as an algebraic category. It models an algebraic theory in the category of sets where the Green’s relation

The environmental transition led by the opportunities that arise with the digital transformation is engaging most industry sectors and bringing several unprecedented challenges to energy consumption and sustainability. The increasing adherence to renewable energy sources by the industrial sector not only underscores a significant shift towards sustainability but also reflects a strategic alignment with global efforts to mitigate climate change impacts, thereby marking a pivotal transition in energy consumption patterns and environmental stewardship within the industry. On the other hand, the increasing amount of renewable energy sources is disrupting the energy grid, due to their fluctuating nature and issue that energy has to be instantly consumed. Our contribution to this paper is a novel method that applies the Temporal Fusion Transformer (TFT) to energy data to make accurate multi-target predictions of load demand, energy price and carbon emissions. By utilising TFT, the predictions of national load demand for Serbia were as low as 1.87% MAPE, which is 20% lower than the baseline model. Similarly, for the Slovenian load demand in 2022, the iterative model achieved a MAPE as low as 2.37%, representing a 13.1% improvement over the baseline. We were also able to predict carbon footprint with a MAPE of 18.14% and energy price with a MAPE of 16.28%. Using a multi-target approach, we were able to improve our approach by an additional 2%. Overall the results show an improvement in load demand prediction scenarios based on data from Slovenian and Serbia energy suppliers. The predictions are used as part of a novel scheduler designed to find optimal execution times of energy-intensive tasks, resulting in decreased carbon emissions and utility costs of operations.