The economical 3-3-1 model revisited

We show that the economical 3-3-1 model poses a very high new physics scale of the order of 1000~TeV due to the constraint on the flavor-changing neutral current. The implications of the model for neutrino masses, inflation, leptogenesis, and superheavy dark matter are newly recognized. Alternatively, we modify the model by rearranging the third quark generation differently from the first two quark generations, as well as changing the scalar sector. The resultant model now predicts a consistent new physics at TeV scale unlike the previous case and may be fully probed at the current colliders. Particularly, due to the minimal particle contents, the models under consideration manifestly accommodate dark matter candidates and neutrino masses, with novel and distinct production mechanisms. The large flavor-changing neutral currents that come from the ordinary and exotic quark mixings can be avoided due to the approximate $B-L$ symmetry.Comment: 21 pages; english writing improved, dark matter stability stated, and references added; matches journal versio

Dispersion Relations in String Theory

We analyze the analytic continuation of the formally divergent one-loop amplitude for scattering of the graviton multiplet in the Type II Superstring. In particular we obtain explicit double and single dispersion relations, formulas for all the successive branch cuts extending out to plus infinity, as well as for the decay rate of a massive string state of arbitrary mass 2N into two string states of lower mass. We compare our results with the box diagram in a superposition of $\phi^3$-like field theories. The stringy effects are traced to a convergence problem in this superposition.Comment: 17 pages, COLUMBIA-YITP-UCLA/93/TEP/45 (figures fixed up

Structures of the Neisseria meningitides methionine‐binding protein MetQ in substrate-free form and bound to L- and D-methionine isomers

The bacterial periplasmic methionine‐binding protein MetQ is involved in the import of methionine by the cognate MetNI methionine ABC transporter. The MetNIQ system is one of the few members of the ABC importer family that has been structurally characterized in multiple conformational states. Critical missing elements in the structural analysis of MetNIQ are the structure of the substrate‐free form of MetQ, and detailing how MetQ binds multiple methionine derivatives, including both L‐ and D‐methionine isomers. In this study, we report the structures of the Neisseria meningitides MetQ in substrate‐free form and in complexes with L‐methionine and with D‐methionine, along with the associated binding constants determined by isothermal titration calorimetry. Structures of the substrate‐free (N238A) and substrate‐bound N. meningitides MetQ are related by a “Venus‐fly trap” hinge‐type movement of the two domains accompanying methionine binding and dissociation. L‐methionine and D‐methionine bind to the same site on MetQ, and this study emphasizes the important role of asparagine 238 in ligand binding and affinity. A thermodynamic analysis demonstrates that ligand‐free MetQ associates with the ATP bound form of MetNI ~40 times more tightly than does liganded MetQ, consistent with the necessity of dissociating methionine from MetQ for transport to occur

Structures of the Neisseria meningitides methionine‐binding protein MetQ in substrate-free form and bound to L- and D-methionine isomers

The bacterial periplasmic methionine‐binding protein MetQ is involved in the import of methionine by the cognate MetNI methionine ABC transporter. The MetNIQ system is one of the few members of the ABC importer family that has been structurally characterized in multiple conformational states. Critical missing elements in the structural analysis of MetNIQ are the structure of the substrate‐free form of MetQ, and detailing how MetQ binds multiple methionine derivatives, including both L‐ and D‐methionine isomers. In this study, we report the structures of the Neisseria meningitides MetQ in substrate‐free form and in complexes with L‐methionine and with D‐methionine, along with the associated binding constants determined by isothermal titration calorimetry. Structures of the substrate‐free (N238A) and substrate‐bound N. meningitides MetQ are related by a “Venus‐fly trap” hinge‐type movement of the two domains accompanying methionine binding and dissociation. L‐methionine and D‐methionine bind to the same site on MetQ, and this study emphasizes the important role of asparagine 238 in ligand binding and affinity. A thermodynamic analysis demonstrates that ligand‐free MetQ associates with the ATP bound form of MetNI ~40 times more tightly than does liganded MetQ, consistent with the necessity of dissociating methionine from MetQ for transport to occur

Artificial intelligence approaches for materials-by-design of energetic materials: state-of-the-art, challenges, and future directions

Artificial intelligence (AI) is rapidly emerging as an enabling tool for solving various complex materials design problems. This paper aims to review recent advances in AI-driven materials-by-design and their applications to energetic materials (EM). Trained with data from numerical simulations and/or physical experiments, AI models can assimilate trends and patterns within the design parameter space, identify optimal material designs (micro-morphologies, combinations of materials in composites, etc.), and point to designs with superior/targeted property and performance metrics. We review approaches focusing on such capabilities with respect to the three main stages of materials-by-design, namely representation learning of microstructure morphology (i.e., shape descriptors), structure-property-performance (S-P-P) linkage estimation, and optimization/design exploration. We provide a perspective view of these methods in terms of their potential, practicality, and efficacy towards the realization of materials-by-design. Specifically, methods in the literature are evaluated in terms of their capacity to learn from a small/limited number of data, computational complexity, generalizability/scalability to other material species and operating conditions, interpretability of the model predictions, and the burden of supervision/data annotation. Finally, we suggest a few promising future research directions for EM materials-by-design, such as meta-learning, active learning, Bayesian learning, and semi-/weakly-supervised learning, to bridge the gap between machine learning research and EM research