Copper toxicity has been a selective pressure on the sea-ice bacteria due to its widespread occur-rence in Antarctica. Here, with a combined biochemical and metabolomic approach, the Cu2+ ad-aptation mechanisms of Antarctic bacteria were analyzed. Heavy metal resistance pattern of Pb2+ > Cu2+ > Cd2+ > Hg2+ > Zn2+ was observed. Copper treatment did increase the activity of antioxidants and enzymes, maintaining cellular redox state balance and normal cell division and growth. Metabolomics analysis demonstrated that fatty acids, amino acids, and carbohydrates played dominant roles in copper stress adaptation. The results indicated that the adaptation mechanisms of strain O5 to copper stress included protein synthesis and repair, accumulation of organic per-meable substances, up-regulation of energy metabolism, and formation of fatty acids. This study increases the resistance mechanism understanding of Antarctic strains to heavy metals in extreme environments.

The main objective of this work is the design and implementation of self-adaptive capabilities in wireless sensors by applying control engineering and model-based design methodologies. It has been addressed the problem related to the changes in the flow of data packets through the network connection and the excess energy consumption that this causes in these devices. To design the solution, a systemic characterization of the scheduling and execution process of embedded tasks on the device has been carried out. This means defining cause-effect relationships in the system and its modelling theoretically and/or experimentally. In turn, these models facilitate the design of control strategies to improve the dynamic behavior of the system. As a solution, a self-adaptation strategy based on feedforward control algorithm has been designed and developed, which has been applied to improve the dynamic behavior and resource consumption. The developed solution has been satisfactorily evaluated experimentally.

In light of the recent pandemic, several COVID-19 vaccines were developed, tested and approved in a very short time, a process that otherwise takes many years. Above all, these efforts have also unmistakably revealed the capacity limits and potential for improvement in vaccine production. This review aims to emphasize recent approaches for targeted rapid adaptation and production of vaccines from an interdisciplinary multifaceted perspective. Using literature research, stakeholder analysis and a value proposition canvas, we reviewed technological innovations on the pharmacological level, formulation, validation and resilient vaccine production to supply bottlenecks and logistic networks. We identified four main drivers to accelerate the vaccine product life cycle: computerized candidate screening, modular production, digitized quality management and a resilient business model with corresponding transparent supply chains. In summary, the results presented here can serve as a guide and implementation tool for flexible, scalable vaccine production to respond swiftly to pandemic situations in the future.

In recent years, the individualized demand of customers brings small batches and diversification of orders towards enterprises. The application of enabling technologies in factory, such as the Industrial Internet of Things (IIoT) and Cloud Manufacturing (CMfg), enhances the ability of customer requirement automatic elicitation and the manufacturing process control. The job shop scheduling problem with random job arrival time dramatically increases the difficulty in process management. Thus, how to collaboratively schedule the production and logistics resources in the shop floor is very challenging, and it has a fundamental and practical significance of achieving the competitiveness for an enterprise. To address this issue, the real-time model of production and logistics resources is built firstly. Then, the task entropy model is built based on the task information. Finally, the real-time self-adaption collaboration of production and logistics resources is realized. The proposed algorithm is carried out based on a practical case to evaluate its effectiveness. Experimental results show that our proposed algorithm outperforms three existing algorithms.

Living walls (LWs) are a climate change adaptation strategy for cities, as they have a cooling effect. Previous studies of the cooling effect of LWs were carried out in different climatic zones. These studies differed in their experimental design, or simulated data via models. Plants’ cooling capacity is explained by shading and transpiration, and depends on physical plant parameters, environmental factors, and system-related influences. A three-year-long trial was carried out between 2017 and 2019 at an experimental garden in Geisenheim, Germany. We chose a textile-based LW system with high water demand and plants from a wet/fresh habitat. We assumed that this would achieve high evaporative cooling. The experimental setup included four experimental walls which were exposed to the north, south, east, and west, respectively. The plant choice was divided into three plant mix variants (Cascade, Ground cover, and Meadow) and a Control with no vegetation. We measured the temperature with sensors and a thermal imaging (IR) camera in different setups. The main results were that the measured vegetation temperature (TV) depends on air temperature (TA), measurement position, plant mix variant, and plant species. We could detect the cooling effect only at a small distance from the LW (microclimatic). Our methodological approaches should be continued in further studies.