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Plant–herbivore–parasitoid systems are poorly studied in the tropics. Enicospilus carmenae Campos and Palacio sp. nov. are described, originating from southern Mexico in the Yucatan Peninsula and establishing a new tri-trophic interaction. This species is a koinobiont larval endoparasitoid of the American silkworm moth caterpillar Zanola verago (Cramer) (Lepidoptera: Apatelodidae) feeding on the shrub Piper neesianum C.DC. (Piperaceae) in a semi-evergreen forest. The host plant P. neesianum had no herbivore records to date, and a single collection event yielded the rearing of a new species of Enicospilus (Ichneumonidae, Ophioninae). Morphological, molecular (COI), biological, ecological, and geographical data are integrated to delineate the new species. 

Abstract The construction of a better exchange-correlation potential in time-dependent density functional theory (TDDFT) can improve the accuracy of TDDFT calculations and provide more accurate predictions of the properties of many-electron systems. Here, we propose a machine learning method to develop the energy functional and the Kohn–Sham potential of a time-dependent Kohn–Sham (TDKS) system is proposed. The method is based on the dynamics of the Kohn–Sham system and does not require any data on the exact Kohn–Sham potential for training the model. We demonstrate the results of our method with a 1D harmonic oscillator example and a 1D two-electron example. We show that the machine-learned Kohn–Sham potential matches the exact Kohn–Sham potential in the absence of memory effect. Our method can still capture the dynamics of the Kohn–Sham system in the presence of memory effects. The machine learning method developed in this article provides insight into making better approximations of the energy functional and the Kohn–Sham potential in the TDKS system. 

A new species of the rarely collected neotropical microgastrine braconid wasp genusLarissimusNixon, represented previously by only a single described species,L. cassanderNixon, was recovered by the Caterpillars and Parasitoids of the Eastern Andes in Ecuador inventory project.Larissimus nigricanssp. nov.was reared from an unidentified species of arctiine Erebidae feeding on the common bamboo speciesChusquea scandensKunth at the Yanayacu Biological Station near Cosanga, Napo Province, Ecuador. The new species is described and diagnosed fromL. cassanderusing both morphological and DNA barcode data. 

Quantum algorithms are touted as a way around some classically intractable problems such as the simulation of quantum mechanics. At the end of all quantum algorithms is a quantum measurement whereby classical data is extracted and utilized. In fact, many of the modern hybrid-classical approaches are essentially quantum measurements of states with short quantum circuit descriptions. Here, we compare and examine three methods of extracting the time-dependent one-particle probability density from a quantum simulation: directZ-measurement, Bayesian phase estimation, and harmonic inversion. We have tested these methods in the context of the potential inversion problem of time-dependent density functional theory. Our test results suggest that direct measurement is the preferable method. We also highlight areas where the other two methods may be useful and report on tests using Rigetti's quantum virtual device. This study provides a starting point for imminent applications of quantum computing.