Carnitine/choline acyltransferases play diverse roles in energy metabolism and neuronal signalling. Our knowledge of their evolutionary relationships, important for functional understanding, is incomplete. Therefore, we aimed to determine the evolutionary relationships of these eukaryotic transferases. We performed extensivephylogenetic and intron position analyses. We found that mammalian intramitochondrial CPT2 is most closely related to cytosolic yeast carnitine transferases (Sc-YAT1 and 2), whereas the other members of the family are related to intraorganellar yeast Sc-CAT2. Therefore, the cytosolically active CPT1 more closely resembles intramitochondrial ancestors than CPT2. The choline acetyltransferase is closely related to carnitine acetyltransferase and shows lower evolutionary rates than long chain acyltransferases. In the CPT1 family several duplications occurred during animal radiation, leading to the isoforms CPT1A, CPT1B and CPT1C. In addition, we found five CPT1-like genes in Caenorhabditis elegans that strongly group to the CPT1 family. The long branch leading to mammalian brain isoform CPT1C suggests that either strong positive or relaxed evolution has taken place on this node. The presented evolutionary delineation of carnitine/choline acyltransferases adds to current knowledge on their functions and provides tangible leads for further experimental research.
MULTIFILE
Airport management is frequently faced with a problem of assigning flights to available stands and parking positions in the most economical way that would comply with airline policies and suffer minimum changes due to any operational disruptions. This work presents a novel approach to the most common airport problem – efficient stand assignment. The described algorithm combines benefits of data-mining and metaheuristic approaches and generates qualitative solutions, aware of delay trends and airport performance perturbations. The presented work provides promising solutions from the starting moments of computation, in addition, it delivers to the airport stakeholders delay-aware stand assignment, and facilitates the estimation of risk and consequences of any operational disruptions on the slot adherence.
MULTIFILE
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
