Optimal dynamic predictive cruise control for differential driven electric vehicles

This paper describes a method to predict the energy consumption of dual driven electric vehicles (EVs) over a predefined trajectory. This way the voltages that need to be applied on the motors are calculated to optimize the trip for minimal energy consumption and traveling time. Since traveling time and energy consumption are conflicting constraints, the goal is to decrease energy consumption, without entailing a drastic increase in traveling time. An algorithm was designed to get a weighted optimization for these two constraints, taking into account the trajectory and the aerodynamics of the vehicle. For the aerodynamics also wind velocity and direction are taken into account, combined with the heading of the car. Concerning the trajectory, the route to follow is inserted as a constraint, and the slopes along this route and its environment are taken into account. Using this algorithm, control signals, which are the voltages applied on left and right motor of the vehicle, can be obtained, which make it possible to control the vehicle throughout the trajectory. The simulation shows that an increase in traveling time has a relatively bigger impact on energy consumption. If direction and wind velocity can be predicted, the algorithm can anticipate on a change in wind velocity, or wind direction. This way the vehicle will consume less energy. Lastly when the trajectory is free to choose by the algorithm, steep slopes will be avoided dependent on the defined weight factor.status: publishe

Evaluating conformance measures in process mining using conformance propositions

\u3cp\u3eProcess mining sheds new light on the relationship between process models and real-life processes. Process discovery can be used to learn process models from event logs. Conformance checking is concerned with quantifying the quality of a business process model in relation to event data that was logged during the execution of the business process. There exist different categories of conformance measures. Recall, also called fitness, is concerned with quantifying how much of the behavior that was observed in the event log fits the process model. Precision is concerned with quantifying how much behavior a process model allows for that was never observed in the event log. Generalization is concerned with quantifying how well a process model generalizes to behavior that is possible in the business process but was never observed in the event log. Many recall, precision, and generalization measures have been developed throughout the years, but they are often defined in an ad-hoc manner without formally defining the desired properties up front. To address these problems, we formulate 21 conformance propositions and we use these propositions to evaluate current and existing conformance measures. The goal is to trigger a discussion by clearly formulating the challenges and requirements (rather than proposing new measures). Additionally, this paper serves as an overview of the conformance checking measures that are available in the process mining area.\u3c/p\u3

Diversity and Evolution of Frog Visual Opsins: Spectral Tuning and Adaptation to Distinct Light Environments

\ua9 The Author(s) 2024. Published by Oxford University Press on behalf of Society for Molecular Biology and Evolution. Visual systems adapt to different light environments through several avenues including optical changes to the eye and neurological changes in how light signals are processed and interpreted. Spectral sensitivity can evolve via changes to visual pigments housed in the retinal photoreceptors through gene duplication and loss, differential and coexpression, and sequence evolution. Frogs provide an excellent, yet understudied, system for visual evolution research due to their diversity of ecologies (including biphasic aquatic-terrestrial life cycles) that we hypothesize imposed different selective pressures leading to adaptive evolution of the visual system, notably the opsins that encode the protein component of the visual pigments responsible for the first step in visual perception. Here, we analyze the diversity and evolution of visual opsin genes from 93 new eye transcriptomes plus published data for a combined dataset spanning 122 frog species and 34 families. We find that most species express the four visual opsins previously identified in frogs but show evidence for gene loss in two lineages. Further, we present evidence of positive selection in three opsins and shifts in selective pressures associated with differences in habitat and life history, but not activity pattern. We identify substantial novel variation in the visual opsins and, using microspectrophotometry, find highly variable spectral sensitivities, expanding known ranges for all frog visual pigments. Mutations at spectral-tuning sites only partially account for this variation, suggesting that frogs have used tuning pathways that are unique among vertebrates. These results support the hypothesis of adaptive evolution in photoreceptor physiology across the frog tree of life in response to varying environmental and ecological factors and further our growing understanding of vertebrate visual evolution