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1. Synthesis, crystal and electronic structure of BaLixCd13–x (x ≈ 2)

   https://doi.org/10.3389/fchem.2022.991625  

   Peng, Wanyue; Baranets, Sviatoslav; Bobev, Svilen (September 2022, Frontiers in Chemistry)

   A new ternary phase has been synthesized and structurally characterized. BaLi x Cd 13– x ( x ≈ 2) adopts the cubic NaZn 13 structure type (space group Fm 3 ¯ c , Pearson symbol cF 112) with unit cell parameter a = 13.5548 (10) Å. Structure refinements from single-crystal X-ray diffraction data demonstrate that the Li atoms are exclusively found at the centers of the Cd 12 -icosahedra. Since a cubic BaCd 13 phase does not exist, and the tetragonal BaCd 11 is the most Cd-rich phase in the Ba–Cd system, BaLi x Cd 13– x ( x ≈ 2) has to be considered as a true ternary compound. As opposed to the typical electron count of ca. 27 e -per formula unit for many known compounds with the NaZn 13 structure type, BaLi x Cd 13– x ( x ≈ 2) only has ca. 26 e -, suggesting that both electronic and geometric factors are at play. Finally, the bonding characteristics of the cubic BaLi x Cd 13– x ( x ≈ 2) and tetragonal BaCd 11 are investigated using the TB-LMTO-ASA method, showing metallic-like behavior. 

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2. New layered quaternary BaCu6Sn2As4−x and BaCu6Sn2P4−x phases: Crystal growth and physical properties

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   Wu, Hanlin; Li, Sheng; Wang, Xiqu; Kwon, Sunah; Liu, Wenhao; Ofenstein, Gareth A.; Kim, Moon J.; Lv, Bing (February 2022, Journal of Alloys and Compounds)
3. ALSO-X and ALSO-X+: Better Convex Approximations for Chance Constrained Programs

   https://doi.org/10.1287/opre.2021.2225  

   Jiang, Nan; Xie, Weijun (February 2022, Operations Research)

   In a chance constrained program (CCP), decision makers seek the best decision whose probability of violating the uncertainty constraints is within the prespecified risk level. As a CCP is often nonconvex and is difficult to solve to optimality, much effort has been devoted to developing convex inner approximations for a CCP, among which the conditional value-at-risk (CVaR) has been known to be the best for more than a decade. This paper studies and generalizes the ALSO-X, originally proposed by Ahmed, Luedtke, SOng, and Xie in 2017 , for solving a CCP. We first show that the ALSO-X resembles a bilevel optimization, where the upper-level problem is to find the best objective function value and enforce the feasibility of a CCP for a given decision from the lower-level problem, and the lower-level problem is to minimize the expectation of constraint violations subject to the upper bound of the objective function value provided by the upper-level problem. This interpretation motivates us to prove that when uncertain constraints are convex in the decision variables, ALSO-X always outperforms the CVaR approximation. We further show (i) sufficient conditions under which ALSO-X can recover an optimal solution to a CCP; (ii) an equivalent bilinear programming formulation of a CCP, inspiring us to enhance ALSO-X with a convergent alternating minimization method (ALSO-X+); and (iii) an extension of ALSO-X and ALSO-X+ to distributionally robust chance constrained programs (DRCCPs) under the ∞−Wasserstein ambiguity set. Our numerical study demonstrates the effectiveness of the proposed methods. 

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